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An Integrated Substance Abuse Treatment
Needs Assessment for North Dakota

Final Report

Prepared by
William McAuliffe, Ph.D.
Ryan P. Dunn, B.A.
Caroline Zhang, M.A.

North Charles Research and Planning Group
North Charles, Inc.
875 Massachusetts Avenue
Cambridge, MA 02139
September 2, 2002
Phone: (617) 864-9115
wmcauliffe@ntc.org

North Dakota Department of Human Services (NDDHS)
Carol K. Olson, Executive Director
Division of Mental Health and Substance Abuse Services (DMHSAS)
Karen Romig Larson
600 South Second Street, Suite 1E
Bismark, ND 58504
Phone: (701) 328-8921
Toll Free: (800) 755-2719
TTY: (710) 328-8969
dhsmhsas@nd.gov

CSAT CONTRACT # 270-98-7064

Acknowledgements

The authors wish to acknowledge the contributions of others that made this study possible. The Center for Substance Abuse Treatment (CSAT) provided funding, administrative, and technical support for this study. Debra Fulcher was the CSAT project officer. Without CSAT's support for the State Treatment Needs Assessment Program (STNAP) this study and the studies upon which it drew would not have been possible. The entire field has advanced greatly as a result of the STNAP initiative.

The study's North Dakota project officer, Sue Tohm, helped out in more ways than we can mention here and showed great patience as the authors worked on the study. Karen Larson and Lauren Sauer of the Division of Mental Health and Substance Abuse Services offered indispensable insights on the treatment system. Kerry Wicks of the State Hospital generously shared his knowledge of the treatment needs of the homeless. Al Lick of the Department of Juvenile Services enthusiastically provided information on the juvenile corrections population and their treatment needs. Mike Froemke lent his knowledge of the needs of prisoners and their treatment system. Girish Budhwar was closely involved in implementation of the social indicator model.

The Gallup Organization conducted the telephone survey that was critical to completion of this integrated study. Many former North Charles staffers worked on the assessment of the treatment needs of recently incarcerated prisoners and the homeless, and the social indicator study; they included Richard LaBrie, Eric Sevigny, Ryan Woodworth, Jamie Mellitt, Stephen Haddad and Timothy Stablein. Athena Kazantzas provided administrative support while the Integration Study was being conducted. Earlier work by Stephanie Geller of the National Technical Center for Substance Abuse Needs Assessment created the foundation for the integrated analysis. Gary Houle, North Charles's Executive Director, has assisted in numerous ways over the years.

Authors

William E. McAuliffe is an associate professor in the Department of Psychiatry, Harvard Medical School at Cambridge Hospital. He has been a professor at Harvard since he received his doctorate in sociology from the Johns Hopkins University in 1972. His research has focused on drug abuse research, quality of medical care, and services planning. One of his studies earned him the Socio-Psychological Prize awarded by the American Association for the Advancement of Science in 1974. He developed a relapse prevention program for heroin and cocaine addiction, which is described in Recovery Training and Self Help: Relapse Prevention and Aftercare for Drug Addicts (Rockville, MD: National Institute on Drug Abuse, 1993). Dr. McAuliffe is Director of the National Technical Center for Substance Abuse Needs Assessment and the North Charles Research and Planning Group.

Caroline (Hui) Zhang is a Research Associate/Programmer at North Charles Research and Planning Group. She received her master's degree in economics from Tufts University in 2001. Before joining North Charles , she was a Research Assistant at Tufts where she worked on the Chinese Household Health & Nutrition Survey analysis and the Tobacco Use Survey analysis.

Ryan Dunn is a research assistant at North Charles Research and Planning Group. He received his bachelor's degree in economics from Vassar College in 2001.

Table of Contents

Executive Summary

Executive Summary

This final report describes the integrated results of a family of studies of the substance use disorder treatment needs of North Dakota's citizens, especially those who are most in need of services. Employing funds from the Center for Substance Abuse Treatment (CSAT), State officials contracted with the National Technical Center (NTC) of the North Charles Research and Planning Group (NCRPG) to conduct this study. The problems and issues that were addressed included the answers to three basic planning questions:

  • How many people are in need of treatment in the State? The goal was to have an adequate supply of services to meet the absolute level of demand that these cases would produce.
  • Where should services be located? The goal was to locate where services are needed most.
  • What mix of treatment modalities do these clients need and want? The goal is to match additional treatment services to the needs and desires of those who need and want them in order to achieve maximal effectiveness and efficiency.

North Dakota's Needs Assessment Studies

North Dakota conducted two rounds of needs assessment studies. The first round of studies included a household telephone survey, a survey of American Indians on reservations, an integrated study, and a social indicator study. The second round of studies included a social indicator study and the present integrated needs assessment.

Integrated Analysis

The integrated analysis presented in this report employed a series of methodologies to estimate the overall level of treatment needs in the State. First, the study examined trends in the past decade with regard to the need for treatment and the supply of services nationwide. Second, the study compared the State with other states to assess the comparative level of needs and services. Third, the analysis developed estimates of the past-year treatment needs of components of the State's population. The study integrated estimates of treatment need and services received for residents aged 12 and older of households with and without telephones, the homeless, and recently incarcerated prisoners. The sum of these estimates was a statewide estimate of the number of people who had a substance use disorder in the past year, how many of them have not received treatment, and how many would seek treatment if it were readily available. Analysis of the survey data assessed the levels of care needed to fill unmet demand and obstacles preventing those in need from obtaining treatment. Finally, the analysis used the State's social indicator data to determine where additional services were needed most.

National Trends. Analysis of a series of indicators of need and treatment services revealed that over the past decade the gap between the number of people in need and the amount of treatment services provided to them appeared to have been widening. Indicator trends were somewhat mixed. While alcohol use, alcohol mortality, DUI arrests, and alcohol treatment clients and admissions have declined, survey estimates of alcohol dependence and liquor law violation arrests have increased in the last decade. Some drug need indicators (e.g., positive drug tests among employees) suggested a long-term decline, other indicators (e.g., drug dependence rates, treatment measures) have been mixed or relatively stable, while yet other indicators (e.g., mortality, emergency room episodes, arrests, and survey reports of use) suggested increases, especially among young people, in the second half of the last decade. Cocaine use has declined, but use of stimulants and club drugs (e.g., Ecstasy) has increased.

To measure the relative gaps between the measures of treatment need and services, the authors divided the service rates by need indicators. Regardless of which measures of need (dependence, mortality or arrests) or services (survey, UFDS or TEDS) were considered, the gap between alcohol need and treatment increased over the decade of the 1990s. The alcohol treatment gap appeared to widen because there was a sharper decline in the number of persons receiving treatment than in the indicators of need that declined, and some of the indicators of need increased. Depending on which drug need and service indicators were used, the analysis suggested that the drug treatment gap widened or at least stabilized. Thus, over the past decade the amount of treatment per need for drugs and alcohol combined decreased.

Interstate Comparisons. To measure the adequacy of the State's treatment services relative to other states, the authors created a series of composite treatment need indexes. The Drug Need Index (DNI) consisted of the sum of standardized mean rates per 100,000 of explicit-mention drug mortality and drug possession/sale arrests. Similarly structured, the Alcohol Need Index (ANI) consisted of the sum of explicit-mention alcohol mortality rates and arrests rates for driving under the influence (DUI) and liquor law violations. The Substance Need Index (SNI) combined standardized explicit-mention drug and alcohol mortality rates and the sum of the drug and alcohol arrest rates.

North Dakota's biggest substance use problem is alcoholism. Its alcohol treatment need as measured by the ANI (55) ranked 14th highest in the country in 1994-1996 . North Dakota's alcohol mortality rate was the 18th highest in the country, and its alcohol arrest rate ranked 13th highest. North Dakota ranked 3rd on the BRFSS's measure of driving after drinking too much, and 23 rd on the alcohol-related traffic fatality rate . The State's alcohol treatment services were ranked slightly lower than one would expect based on the need indicators . That is, while the State's alcohol treatment needs were in the second highest quintile in the country according to the index, its treatment services were in the middle quintile according to the UFDS alcohol-only treatment client rate.

North Dakota's controlled drug treatment needs were the lowest in the nation. The State's DNI score of 9 was half that of the next lowest score (West Virginia and Vermont both scored 18) , but North Dakota ranked 35th in the nation according to the NHSDA's 1999 household survey estimates of drug dependence. Unlike the DNI, the NHSDA's dependence measure consists mostly of cases of marijuana dependence. Consistent with North Dakota's NHSDA dependence measure, North Dakota had the highest percentages of marijuana arrests and marijuana treatment admissions. Compared to other states, North Dakota had the lowest drug mortality mean rate between 1994 and 1996 (0.31 per 100,000) and the second lowest mean drug arrest rate between 1994 and 1996 (119 per 100,000) . North Dakota had the lowest rate of drug-only treatment clients (15 per 100,000) in the UFDS between 1994 and 1996 and the second lowest rate of primary drug admissions (47 per 100,000) among the 41 states that reported to TEDS. The State's low treatment rate matches its low level of need for drug treatment.

North Dakota's Substance Abuse Need Index (SNI) ranking was 24 th in the country, clearly attributable to its high level of alcohol treatment needs. The State's combined UFDS substance abuse client rate (alcohol-only, drug-only, and drug plus alcohol) ranked 32 nd in the country for 1994-1996. By this measure, North Dakota's treatment services were again one quintile below its moderate overall treatment needs.

Trends in North Dakota. North Dakota's alcohol arrest (DUI, disorderly conduct, and liquor law violations) rates and explicit-mention alcohol mortality rates increased from 1993 to 1998. The other alcohol need indicators were stable over that period. Alcohol treatment admissions (TEDS data) declined slightly over the years between 1994 to 1998. It appears that the rate of admissions compared to rates of arrests and deaths, a proxy for the proportion in need who received treatment, has decreased slightly over time, suggesting a widening of the treatment gap. Whereas the alcohol indicators increased only slightly, the controlled drug indicators in North Dakota increased more sharply during the period from 1993 to 1998 . While drug admissions and drug clients increased as well, the increases in drug mortality and arrests appeared to be sharper. These trends suggest a widening of the drug treatment gap.

Statewide Treatment Need Estimate. To estimate the absolute number of persons in North Dakota who had a past-year substance use disorder, the study combined prevalence and population estimates of treatment need for adults (18 and over) in households with telephones, adolescents in households with telephones, persons 12 and older living in households without telephones, recently incarcerated state prisoners and training school inmates, and homeless people. Applying these estimates to population statistics from the 2000 Census count resulted in an estimated total of 30,880 people with a substance use disorder in North Dakota during the past year.

Whenever possible, the authors made conservative assumptions. It would be reasonable therefore to assume that there were at least 30,880 people with current substance use disorders in the State. If these individuals sought treatment, they would meet the minimum medical necessity criteria employed by treatment programs and managed care organizations.

Although residents of households with telephones account for the largest proportion of cases in the total, generalizing the prevalence rate for that group (5.2%) to the rest of the population would have produced an underestimate of the total number of people in need. In its report of the household survey, the Gallup Organization (1998) applied the telephone survey estimate to the entire population aged 18 and older rather than just the population of adults in households with telephones. After the present authors took the prevalence estimates for the other groups, the estimated total state prevalence rate for persons 12 and older increased to 5.9%. Each of the population subgroups not covered by the telephone survey (residents of households without telephones, recently incarcerated prisoners, and homeless, adolescents in households with telephones, and training school inmates) had a higher estimated prevalence of substance use disorders than the adults in households with telephones (11.1%, 62.5%, 47%, 8.5%, and 62.5% respectively). Although the prisoners and homeless had the highest estimated prevalence rates, they were small populations and therefore contributed relatively few cases to the total population in need. Persons 12 and older in households without telephones had a prevalence rate that was a little more than twice as high the prevalence rate of the adults in households with telephones. Because adolescents in households with telephones was a relatively large subpopulation, they contributed the most (4,820 cases or 15.6%) to the overall increase in the estimate of the total need for treatment. By estimating the rates for the groups other than those covered by the telephone survey rather than generalizing the prevalence rate from the telephone survey, the present integrated analysis arrived at an estimated number of people in need that was higher by 3,707 people.

Treatment Gap. There were clearly many people in North Dakota with a substance use disorder who did not obtain treatment in the past year. In 2000, an estimated 2,826 North Dakota residents received treatment for a substance use disorder. This number equals 9.2% of the estimated 30,880 people in need of treatment that year. These figures are probably the most reliable measure of the treatment gap in North Dakota.

Unmet Demand for Treatment. Even if treatment were readily available to all who needed it, only a portion of those in need would seek care in a given year. The study's surveys asked respondents who had a substance use disorder but who had not obtained treatment whether they thought they needed treatment and would have sought it had it been readily available. The integrated analysis estimated that 4.3% of the persons with a current disorder that did not obtain treatment in the past year said they thought they needed treatment and would have sought it if it were more readily available. Compared to several other states, that percentage was relatively low. Applied to the state's population, the study estimated that 1,204 North Dakota residents needed and wanted treatment in the past year but did not obtain it. This number would be a reasonable target for providing additional services, if the State sought to provide treatment on demand. Experience in other states suggests that survey estimates of unmet demand have successfully predicted the utilization of new substance abuse treatment services. This success appeared to depend on the type of treatment and location of the services in areas that clearly had relatively high levels of unmet need. If the State increased the number of people in need who obtained treatment by 1,204, the number who received treatment would increase by 43%. The total number who would receive care (4,030) would be 13.1% of the 30,880 who needed it.

Analysis of the telephone survey data showed that about one in five of the subjects who needed treatment and had not obtained it but wanted it should receive residential or hospital care in accordance with the patient placement criteria of the American Society for Addiction Medicine (ASAM). The remaining subjects should receive intensive outpatient treatment.

When asked what prevented them from obtaining treatment, North Dakota telephone survey respondents were most likely to cite lack of insurance, facilities being located too far away, programs being full, and lack of ancillary services such as child care or medical care.

Location of Treatment Needs

The authors compared the average annual treatment admissions rate for 1994-1998 (State data) with the SNI to determine how well the observed regional treatment admissions rates compared to the rates predicted by the Substance Abuse Need Index. In general, the existing distribution of treatment resources in North Dakota reflects relative need among regions reasonably well. Forty-seven percent of the variation in client rates among regions was explained by the SNI scores. Region V, the most populous region in North Dakota, had the largest gap (the observed average annual admissions minus the average admissions expected on the basis of need).

To allocate services geographically to meet the needs of the 1,204 persons with unmet demand, the authors used the SNI to ensure that all regions would have a treatment service rate consistent with its level of need . Because the current regional admissions rates already matched need reasonably well and because serving 1,204 more persons represents a substantial increase in the number of people served statewide, the analysis allocated some additional services to all regions.

The authors recommend that the State consider using the results of this analysis as one part of its decision making process for allocating services if additional funds become available. Although the authors found that the indicator data at the regional level to were reliable and valid, no single measure should be relied on in isolation. Accordingly, the estimates should be used along with other qualitative and quantitative information (e.g., knowledge of waiting lists in specific areas or concerns by other medical personnel or social agencies regarding the availability of specific services). Responses of local providers to the reasonableness of the estimates should also be considered. The social indicator methodology has been developed over a period of years, and has been used in other states. Whenever it is employed in a new state for the first time, there is always the possibility that modification must be made to refine the indexes.

Conclusions

The results of the needs assessment suggest that North Dakota would be justified in expanding its treatment services. The analysis of national, interstate, longitudinal data, and crossectional survey data produced evidence that a substantial number of the State's residents had an active addictive disease in the past year, but only a small percentage of them received treatment in the past year. While many of those individuals would probably not seek treatment immediately if the supply of services were increased, an estimated 1,204 people indicated that they wanted treatment even though they did not obtain it. Only experience will show how many of even that group will seek care, but the number is sufficiently large to suggest that an increase in the number of facilities would be reasonable. Recent statistics suggested that the treatment gap, especially regarding drugs, has been widening, and a reversal of that trend appears to be in order.

The analysis suggested that the State may wish to consider programming (e.g., outreach) directed towards increasing the proportion of persons in need who actually seek treatment. The persons who said that they wanted treatment was relatively small, and this group, especially in high-risk groups such as prisoners-to-be and homeless people, appeared to need relatively high levels of care, mostly residential and hospital treatment at the onset of treatment. Many of the household residents who wanted treatment appear to need intensive outpatient treatment to initiate treatment. Research suggests that location of future services in accordance with the indicators of unmet need, especially in rural areas, may be a key step for increasing the demand for treatment. Several administrative changes, such as reducing red tape, could make a difference. To increase access to treatment in rural areas, especially for youth, the State may wish to investigate the feasibility and efficacy of online counseling, assessment, and referral. Analysis of survey data from other states indicated that adolescents obtain a large proportion of treatment services from nonspecialty providers (e.g., clergy, school health counselors, general psychological counselors, and social workers). An important consideration for youth and residents of small towns and rural areas is the stigma attached to obtaining treatment services from specialty providers. A recent report by the National Center for Addiction and Substance Abuse suggests that relatively few of some nonspecialty providers such as clergy have received substance abuse training. Of course, attention to cultural issues and identification is important for American Indians.

The integrated analysis indicated several areas for which additional needs assessment research should be considered. The need indexes developed for the study should be kept up to date and refined. A commitment to ongoing data collection and updating of the social indicator data each year could provide the State with timely data for future planning. The study had to estimate the treatment needs of homeless and adolescents from studies conducted in other states. Those are two groups the state may consider studying in future rounds of the State Treatment Needs Assessment Program.

Introduction

This final report describes the integrated results of a family of studies of the substance use disorder treatment needs of North Dakota's citizens, especially those who are most in need of services. Employing funds from the Center for Substance Abuse Treatment (CSAT), State officials contracted with the National Technical Center (NTC) of the North Charles Research and Planning Group (NCRPG) to conduct this study.

Purpose of the Study

A primary objective of the study was to provide the State with the data it needs for its planning process. To assist the State in obtaining essential needs assessment data in a form that is most useful for the planning process, the integrated analysis made use of the needs assessment and resource data collected by the State in two rounds of needs assessment studies. The analysis can serve as a model for a systematic assessment of the adequacy of the current population treatment needs and services.

Problems and Issues

When conducting the comprehensive needs assessment for treatment of substance abuse, the authors sought to answer three basic planning questions:

  • a. How many people were in need of treatment in the State during the past year? The goal was to have an adequate supply of services to meet the absolute level of demand that these cases would produce.
  • b. Where should services be located? The goal is to locate the services where they are needed most.
  • c. What mix of treatment modalities do these clients need and want? The goal is to have the optimal mix of treatment services to achieve maximal effectiveness and efficiency.

The study team examined data on treatment needs. It compared those needs with current resources in amount, type and location. It also examined special service delivery issues, such as the barriers to treatment service delivery in the large rural areas of the State and the service needs of such special populations such as women, American Indians, prisoners, and the homeless. The analysis used this information as a basis for recommendations regarding the gaps between treatment need and utilization.

Background

The roots of this comprehensive population-based study can be found in the recommendations of the Institute of Medicine's landmark study, Treating Drug Problems (Gerstein and Harwood 1990). The study recommended that each state conduct studies that produce objective estimates of need and use the resulting data to prepare a plan that should be the basis of the Substance Abuse Prevention and Treatment Block Grant application. This recommendation stemmed from a growing body of literature on substance abuse and mental health planning (Ford 1997; Frank 1985; Ingram 1988; Kimmel 1993; McKillip 1992; Maddock et al. 1988; NIAAA 1981; Goldsmith et al. 1992; Richards 1985; Ryan 1984-85; Schlesinger et al. 1994; Shapiro et al. 1985; Simeone et al. 1993; Soriano 1995; Wallack 1994; Warheit et al. 1977; Wilson and Hearne 1986; Cochran et al. 1997; Lo and Stephens 2000; SAMHSA 1997; CSAT 1999). Moreover, there is increasing recognition in the social sciences of the importance of needs assessment in education, health, and social services (Witkin and Altshuld 1995; Soriano 1995). These important works describe the basic epidemiological needs assessment methodologies to be used in this comprehensive study.

The basic needs assessment research model was described by the NTC in its telephone survey monograph (McAuliffe et al. 1995). New York pioneered this approach to treatment needs assessment (Frank 1985; Simeone et al. 1993; Welte and Barnes 1995). North Charles developed a similar statewide treatment services plan for Rhode Island a decade ago (McAuliffe et al. 1991). Rhode Island continues to use the plan for a broad range of policy and planning purposes, and Rhode Island currently has one of the most adequate supplies of treatment services in the country (McAuliffe et al. 1999a).

North Dakota's Needs Assessment Studies

North Dakota conducted two rounds of needs assessment studies. The first round of studies included a household telephone survey, a social indicator study, a survey of American Indians on reservations, and an integrated analysis. The Adult Household Survey (Gallup 1998a) interviewed 6,814 North Dakota residents aged 18 and older. The survey revealed that North Dakota adults had a low rate of substance use disorders relative to other states. Only 5.2% of the sample had a current substance use disorder (abuse or dependence). While 7.8% of those with a current treatment need received some in the past year, only 2% of those with a need that did not receive treatment in the past year expressed a desire for treatment. North Dakota's second Round One study was a social indicator study (Kraft 2000). The study used common indicators of substance abuse and treatment data to identify which service areas were in greatest need. The third Round One study was a face-to-face survey of American Indian adults on reservations that used the same instrument as the household survey (Gallup 1998b). The Round One studies also included an integrated analysis, however, the only portion of the need estimate from that study that had a diagnostic assessment was the household portion (Johnson, Bassin, and Shaw 1999).

The Round Two studies included a social indicator study, and the present integrated needs assessment. The social indicator study showed that North Dakotans suffered the lowest rate of social problems due to drug abuse in the United States (McAuliffe et al. 1999b). North Dakota ranked 14th nationally in an index of alcohol abuse indicators. In a combined substance treatment need index, North Dakota ranked 24th nationally. Analysis of the indicators within the state revealed that Human Service Regions I and III had the highest scores in an alcohol treatment need index.

The second Round Two study is this integrated treatment needs assessment. This final report seeks to combine previous estimates of treatment need for mutually exclusive population subgroups and to develop estimates for any groups not covered by one of the State's needs assessment studies in order to obtain a comprehensive, statewide estimate of need. This report will describe the estimates of need for each population subgroup including adults in households with telephones, people aged 12 and older in households without telephones, prisoners and juvenile detainees, the homeless, and adolescents. The report combines the needs of these groups to estimate total need and assesses the performance of the State's treatment system based on an analysis of the gap between need for treatment and treatment delivered (see chapter on gap analysis).

Organization of the Final Report

The next part of the report includes a review of the study's overall methodology, a chapter on historical trends in substance abuse needs and treatment services at the national level, and an analysis of how North Dakota's treatment needs compare to the treatment needs of other states. Those chapters are followed by a series of chapters that focus on the treatment needs of non-overlapping segments of the State's population. The first chapter presents the results for people in households with telephones, and the next chapter estimates the needs of residents of households without telephones. Another chapter addresses the needs of North Dakota's American Indian population (this population is not a component of the overall need estimate). The remaining chapters in the series address the needs of prisoners and the homeless. The next chapter combines the results for all subpopulations. The analysis examines the integrated statewide past-year need for treatment, the percentage of people in need who received treatment, and the unmet demand for treatment. The integrated estimate of treatment need for the entire state population will be used to identify the gap between treatment need and services provided at the statewide level. The North Dakota substance abuse indicator model is then used to distribute the needs across the State's regions. The penultimate chapter describes the results for the levels of care of those who need and want treatment, and it describes their own preferences for additional services. The last chapter of the report summarizes the results and presents a series of recommendations based on them.

References

Center for Substance Abuse Treatment (CSAT) and Substance Abuse and Mental Health Services Administration (SAMHSA). (1999). Proceedings of the 1999 Annual State Needs Assessment Program. Rockville, MD: U.S. Department of Health and Human Services.

Cochran, Deborah C., Sonia A. Alemagno, Thomas E. Feucht, Richard C. Stephens, Stephanie A. Wolfe, and John M. Butts. (1997). Ohio Needs Assessment Study Special Population Study: Homeless Shelter Pilot Study Report. Cleveland, OH: NOVA Research Company.

Ford, William E. (1997). "Perspective on the Integration of Substance User Needs Assessment and Treatment Planning." Substance Use and Misuse. 32(3): 343-349.

Frank, Blanche. (1985). "Telephone Surveying for Drug Abuse: Methodological Issues and An Application." Self-Report Methods of Estimating Drug Use: Meeting Current Challenges to Validity. Eds., Beatrice A. Rouse, Nicholas J. Kozel, and Louise G. Richards. Rockville, MD: National Institute on Drug Abuse. 71-82.

The Gallup Organization. (1998a). North Dakota Adult Household Survey. North Dakota Division of Mental Health and Substance Abuse Services.

The Gallup Organization. (1998b). Demand and Needs Assessment Study of Alcohol and Other Drugs Among Native American Indians Living on Reservations in North Dakota. North Dakota Department of Human Services, Division of Mental Health and Substance Abuse Services.

Gerstein, Dean R. and Henrick J. Harwood, Eds. (1990). Treating Drug Problems: A Study of the Evolution, Effectiveness, and Financing of Public and Private Drug Treatment Systems. Washington, D.C.: National Academy Press.

Goldsmith, Harold F., Roger A. Bell, and George J. Warheit. (1992). "Indirect Needs Assessment for Mental Health Services Planning: Introduction to This Special Issue." Evaluation and Program Planning. 15(2): 111-113.

Ingram, Jerry J. (1988). "Alcoholism Treatment Demand Estimation." Health Marketing Quarterly. 6(1-3): 195-205.

Johnson, Bassin, and Shaw, Inc. (1999). North Dakota Integrated Analysis Report. North Dakota Department of human Services, Division of Mental Health and Substance Abuse Services.

Kimmel, Wayne A. (1993). Need, Demand, and Problem Assessment for Substance Abuse Services. Rockville, MD: U.S. Department of Health and Human Services. 93-1741.

Kraft, Kathy, Robert Woodle, and Sue Tohm. (2000). North Dakota Substance Abuse Indicator Study. North Dakota Department of Human Services, Division of Mental Health and Substance Abuse Services.

Lo, Cecilia C. and Richard C. Stephens. (2000). "Drugs and Prisoners: Treatment Needs on Entering Prison." American Journal of Drug and Alcohol Abuse. 26(2): 229-245.

Maddock, John M., Dennis C. Daley, and Howard B. Moss. (1988). "A Practical Approach to Needs Assessment for Chemical Dependency Programs." Journal of Substance Abuse Treatment. 5(2): 105-111.

McAuliffe, William E., Paul Breer, Nancy White Ahmadifar, and Cathie Spino. (1991). "Assessment of Drug Abuser Treatment Needs in Rhode Island." American Journal of Public Health. 81(3): 365-371.

McAuliffe, William E., Richard A. LaBrie, Norah Mulvaney, Howard J. Shaffer, Stephanie Geller, Elizabeth A. Fournier, Eliot B. Levine, Qiuyun Wang, Susan M. Wortman, and Kathleen A. Miller. (1995). Assessment of Substance Dependence Treatment Needs A Telephone Survey Manual and Questionnaire, Revised Edition. Cambridge, MA: National Technical Center on Substance Abuse Needs Assessment.

McAuliffe, William E., Richard A. LaBrie, Nicoletta Lomuto, Rebecca Betjemann, and Elizabeth A. Fournier. (1999a). " Measuring Interstate Variations in Drug Problems." Drug and Alcohol Dependence. 53(2): 125-145.
McAuliffe, William E., Richard LaBrie, and Ryan Woodworth. (1999b). An Interstate Substance Abuse Indicator Chartbook. Cambridge, MA: North Charles Research and Planning Group.

McKillip, J. (1992). "On Defining Need." Needs Assessment Notes. 4(1): 2-3.

National Institute on Alcohol Abuse and Alcoholism. (1981). Current Practices in Alcoholism Treatment Needs Estimation. Rockville, MD: National Institute on Alcohol Abuse and Alcoholism.

Richards, Louise G. (1985). Drug Abuse Epidemiologic and Needs Assessment Approaches by States and Small Areas: A State of the Art Review. Rockville, MD: National Institute on Drug Abuse.

Ryan, Keith. (1984-1985). "Assessment of Need for Alcoholism Treatment Services: Planning Procedures." Alcohol Health and Research World. 9(2): 37-44.
Schlesinger, Mark, Robert A. Dorwart, Sherrie Epstein, and Jonathan Sablone. (1994). "The Mismeasure of Need: The Social Costs of Substance Abuse." Unpublished manuscript.
Shapiro, Sam, E. A. Skinner, Morton Kramer, D. M. Steinwachs, Darrel A. Regier. (1985). "Measuring Need for Mental Health Services in a General Population." Medical Care. 9: 1033-1043.
Simeone, Ronald S., Blanche Frank, and Zahra Aryan. (1993). "Needs Assessment in Substance Misuse: A Comparison of Approaches and Case Study." International Journal of the Addictions. 28(8): 767-792.

Soriano, Fernando I. (1995). Conducting Needs Assessments: A Multidisciplinary Approach. London: Sage Publications.

Substance Abuse and Mental Health Services Administration (SAMHSA), Office of Applied Studies and Research Triangle Institute. (1997). National Household Survey on Drug Abuse: Population Estimates 1996. Rockville, MD: U.S. Department of Health and Human Services.

Wallack, Stanley S. (1994). Resource Materials for State Needs Assessment Studies. Rockville, MD: U. S. Department of Health and Human Services.

Warheit, George J., Roger A. Bell, and John J. Schwab. (1977). Needs Assessment Approaches: Concepts and Methods. Washington, D.C.: Department of Health, Education, and Welfare.

Welte, John W. and Grace M. Barnes. (1995). Alcohol and Other Drug Use Among Hispanics in New York State. Alcoholism Clinical and Experimental Research. 19 (4):1061-1066.

Wilson, Robert A. and Barbara E. Hearne. (1986). "An Assessment of the State of the Art in Drug Abuse and Alcoholism Treatment Needs Estimation Methods." Treating the Drug User: Selected Planning Models, Issues, Parameters, and Programs. Ed. Stanley Einstein. Danbury, CT: Sandoz Publications. 186-221.

Witkin, Belle R. and James W. Altschuld. (1995). Planning and Conducting Needs Assessments: A Practical Guide. London: Sage Publications.

Methods

This chapter describes the study's research methodology. Development of an integrated needs assessment and plan requires a broad understanding of the treatment needs of the population (results of the family of studies), the resources currently available (from the utilization studies), and the policy and technical contexts in which the treatment system will be functioning in the coming years. Bringing together these three elements was the object of data collection and analysis for this study. To obtain this information, the study team used a series of methodologies, each selected to address a specific component in the process. The steps described below include: a review of the recent developments in substance abuse epidemiology, analysis of how North Dakota's services per unit of need compare to other states, background interviews with State substance abuse officials, a review of other relevant North Dakota studies and substance abuse literature, analyses of needs assessment data and findings from treatment utilization statistics, analyses of census data and prevalence statistics on special populations from the first round of studies and the literature, gap analyses comparing need and resource data, and analysis of the State's treatment system. In applying each of these methodologies, the study team attempted to determine the overall statewide level of need, substate area needs, and the appropriate treatment allocation. The specific questions addressed by each of the methods are described in Table 2.1. In fashioning plans to address the needs that have developed, the study team sought a clear picture of North Dakota's substance abuse treatment goals, what has been tried in the past, and how the treatment system will change in the future to address existing needs.

Description of Specific Methods

This comprehensive study will include a series of substudies described in Table 2.1. This section describes the methods employed in each of these study components:

A conference call with key officials

Review of prior related studies

Review of relevant literature and reports to analyze the substance abuse context: epidemics, long-term trends in treatment, and financing

Analysis of social indicator data

Analysis of interstate data on needs and services comparing North Dakota to other states

Integration of estimates from non-overlapping segments of the population

Analysis of needs and resources to identify gaps in amount of services, types of services, and location of services.

Any research, including a treatment needs assessment, should begin with hypotheses about what the study is likely to find or what the key questions are. Only then can one be sure that all of the essential data will be collected in the required form and all irrelevant data, no matter how "interesting" from an academic perspective, will be excluded. The hypotheses also inform the analysts of precisely what questions to ask of the data once it has been collected. The "Background Studies" in Table 2.1 are designed to be hypothesis generating.

Background Interviews

The study team conducted telephone interviews with agency officials who are knowledgeable about North Dakota's substance abuse treatment system and about groups that utilize the system (e.g. prisoners, homeless). Their perspectives and day-to-day experiences working within or with the treatment system were crucial to understanding how the system functions. Learning their ideas for change was critical for formulating recommendations. The interviews were semi-structured, with a series of questions devised by the study team prior to the telephone calls.

The calls covered the officials' roles in the system, perceptions of the system's performance in meeting the needs of North Dakotans, perspectives and recommendations for change, coordination issues, and service gaps. The objective of the calls was to develop specific recommendations about the treatment system's functioning.

Review of Prior Studies and Documents

North Dakota has conducted a number of previous studies and produced planning reports that served as background for the study. NCRPG obtained these studies and used them for this report.

Literature Review

Although every state is unique to some degree, the substance abuse problem is national in scope. Identifying relevant trends can help organize a range of facts. This understanding is critical when using the resulting data to develop a treatment needs assessment and plan for the State. Current trends that were important include the up-tick in use of drugs and the decline in alcohol use, increases in substance use disorders among prisoners, and widening of the treatment gap.

Table 2.1 Needs Assessment and Planning Process

Study Objectives

Study

Statewide Need

Substate Area Needs

Modality/Service Mix

Background Studies

Interviews with State officials.

Is overall level of services perceived to be adequate? Are there new policy initiatives? What are budget constraints?

Are there specific areas that are thought to be underserved? Are all groups adequately served?

Is the mix of modalities adequate to meet client needs? Is the mix cost-efficient?



Prior Studies Review including earlier planning studies; time-series indicator and census data

Has problem been increasing? Have services kept pace?

Have there been major demographic changes in North Dakota's high-risk populations due to migration or birth rate?

Has nature of State's alcoholism and addiction problem changed significantly (e.g., alcoholics taken up marijuana)? Have treatment services kept up with the changes?



Literature Review including relevant local and national studies of trends that affect treatment services

Is there a national substance abuse trend that is likely to affect State service system (e.g., AIDS or crack epidemics)?

Is there a mis-allocation of services (e.g., services tend to be in urban areas)?

Are there new modalities such as intensive outpatient detox services that could be more cost-effective than current residential modalities?

Preliminary Analysis

Interstate Comparisons of need indicators and treatment service statistics

Compare North Dakota with other states regarding need indicators, service statistics, and services per unit of need.

Are services in North Dakota more concentrated than they are in other rural states?

Comparison of treatment modality mix for opiate addicts in State with mix in other States: % in each modality



Social Indicator Study of substance-abuse related variables

Develop validated estimates of need for alcohol- and drug-related treatment

Which substate areas have higher alcohol-related problems? Which have higher drug-related problems?

Placement of alcohol-related services (e.g., detox) vs. placement of drug-related services (e.g., methadone maintenance)

Preparatory Studies

Uncovered Population Estimates using census data and prevalence rates in literature to estimate the substance abuser population not covered by the household telephone survey and the supplementary studies.

Determine statewide population size and number of substance abusers living in households without phones, who are homeless, or who were institutionalized in last year in prison facilities. Find out how many were in long-term residential drug treatment.

Identify where these uncovered populations lived based on analyses of census data, arrest, and prison statistics.

Review literature on program/service needs of special population groups, such as criminal justice populations, homeless, and people in households without telephones.

Gap Analysis

Analysis of Survey and Social Indicators of Need

Estimate the overall services needs. Validate accuracy of combined estimates of the telephone and supplementary surveys.

Create index of survey data and social indicators to estimate needs in substate areas.

Analyze treatment mix data to determine optimal treatment mix.



Analysis of Service Gaps

Compare total estimates of need and demand from surveys with services to determine how many need and want service but are not obtaining treatment. Recommend increases to fill gaps.

Compare proportion of need in each area compared to the proportion of services. Recommend service allocation changes if needed.

Examine need and demand for individual service mix statewide and compare with services provided or available. Make recommendations to establish program types to fill gaps and improve service mix.

Report Creation

Specific Recommend-ations

Steps to achieve goals and structure

Substate profiles and plans, reallocation plans

Facilities to be added, personnel training, credentials, etc.

Social Indicator Study

NCRPG conducted a social indicator analysis to guide allocation of resources over substate regions for which the State's survey estimates are too imprecise (see McAuliffe et al. 1987, 1991 and Folsom et al. 1996 for a description of this use). Social indicator studies complement other studies using different methodologies (thus increasing validity) and thereby serve to strengthen a state's credibility when attempting to allocate substance abuse treatment resources equitably. Social indicator analyses are especially effective when used in conjunction with survey data. Because social indicator analyses provide relative differences, rather than absolute counts, household survey data can be used to provide a baseline from which to calibrate estimates for actual counts of need. Due to the small number of cases in substate areas, the combined telephone and supplementary surveys which comprise most of the family of studies are less reliable at the substate level than they are at the State level. As a result, the survey data may not be as effective for distributing resources over subareas as they are for estimating statewide need. Consequently, the social indicator study will be employed to supplement survey estimates and to estimate substate level estimates more reliably.

The study began with the selection of a small number of measures of need from different data sources or systems (i.e., substance-abuse related deaths and arrests). Data on substance-abuse related treatment admissions were also collected. Indicator selection is based on the measures' theoretical relationships with substance abuse treatment need, the results of previous validation studies, and data availability. County-level data were obtained for the selected variables. The data were cleaned, entered into a database, and then subjected to rigorous empirical reliability and validity testing.

The rationale for the initial selection of a small number of variables was that they are more manageable (e.g., can be cleaned and validated individually), explain most of the relevant variance in treatment service needs, and are more easily interpreted by both investigators as well as State officials and the public (Dembling et al. 1993; McAuliffe et al.1999). Experience in Rhode Island clearly indicates that the public will be interested in the validity of the index when the resulting resource allocations affect the relative availability of services in the State's substate areas (Breer et al. 1996).

Empirical research by the NTC (McAuliffe et al.1999) and by other states (Aktan, Calkins et al. 1997) has shown that the geographic location of alcohol problems often differs from the location of other drug problems. Analyzing individual indicators of both alcohol and other drugs together can obscure important information for planning purposes (e.g., where to locate drug-specific programs versus where to locate alcohol detox facilities).

Interstate Comparisons

It is useful to compare the State's treatment needs with those of other states in order to have a basis for evaluating the adequacy of its treatment services. Recent examples of interstate alcohol and drug treatment comparisons include McAuliffe et al.(1999; 2000). Following the analysis reported by Dayhoff et al. (1994) with regard to alcohol treatment services for all states, this study analyzed interstate differences with regard to both alcohol and drug treatment.

Uncovered Population Estimates

NCRPG has estimated that 90% of all people with substance use disorders nationwide live in households with telephones (Geller 1995). As a result, the backbone of the NCRPG model family of studies is the specially designed household telephone survey conducted by North Dakota in its first round of studies. Nevertheless, there are important treatment populations in which the percentage living in households with telephones is smaller. A recent face-to-face survey of household and nonhousehold residents of the Washington, DC metropolitan area revealed that failing to include nonhousehold populations would have little impact on the overall rates of illicit drug use, but it would result in a 20% underestimation of past year heroin and cocaine users (Gfroerer 1996).The Epidemiologic Catchment Area Survey (ECA) studies had previously included studies of nonhousehold populations to respond to this problem.

North Dakota surveyed American Indians living on reservations in order to obtain estimates for that group that were not biased due to low telephone coverage. One objective of the integration study is to employ data from such studies in order to reinforce the more comprehensive estimates of treatment need outlined below. Because all relevant groups were not covered in the North Dakota family of studies, the current study team developed estimates of the State's need estimates by obtaining statistics on the prevalence rates for the omitted groups and obtaining estimates of the size of the groups from census data (Geller et al. 1997; McAuliffe et al. 1998). The data to be used for this estimation process include estimates of numbers of adults in households without phones, adolescents, and the homeless. In addition to its own surveys of special populations such as the homeless (recently completed in Rhode Island), the study team conducted a series of literature reviews to obtain estimates of the prevalence of substance abuse treatment need for these groups from a literature search of such major databases as MEDLINE, PSYCHINFO, and HEALTHSTAR, adjusting the estimates so that they capture the correct age range and both past-year and lifetime prevalence. The number of persons in need of substance abuse treatment in each uncovered population was estimated by multiplying the adjusted prevalence rates for each of these populations by the number of members of these populations in North Dakota.

Gap Analysis

A central feature of this study was to bring together the State's data on treatment needs and resources. Many important questions about subgroups, substate areas, and the State as a whole will be addressed concerning the amount, type, and location of treatment needs and services. For example, what percentage of the population needed treatment, and what proportion of the people in need wanted to obtain services? What reasons did they cite that prevented them from obtaining treatment? Finally, the analysis identified the gaps in services by comparing the need and demand for services with the services that were available.

Conclusions and Recommendations

The report includes proposals regarding how the treatment system should respond to fill the gaps in treatment and meet the challenges of the coming years. The recommendations stemmed from analysis of the estimates of treatment gaps, the State's service mix, and the perspectives of State officials.

References

Aktan, Georgia B., Richard F. Calkins, Rafa M. Kasim, Sandra Kimball, and Karen Schrock. (1997). Social Indicators Modeling for Substance Abuse Treatment Needs Assessment Substance Abuse Need Index (SANI). Michigan Department of Community Health.

Breer, Paul, William E. McAuliffe, and Eliot B. Levine. (1996). "Statewide substance abuse prevention planning." Evaluation Review. 20(5): 596-618.

Dayhoff, D.A., G.C. Pope, and J.H. Huber. (1994). "State Variations in Public and Private Alcoholism Treatment at Specialty Substance Abuse Treatment Facilities." Journal of Studies on Alcohol. 55(5): 549-560.

Dembling, Bruce. (1993). Distribution of Consumer Demand for a Public Psychiatric Hospital: Application of a Geographic Information System. National Conference on Mental Health Statistics.

Folsom, Ralph E., Judith T. Lessler, Michael B. Witt, Joseph C. Gfroerer, Douglas A. Wright, and Joseph Gustin. (1996). Substance Abuse in States and Metropolitan Areas: Model Based Estimates from the 1991-1993 National Household Surveys on Drug Abuse, Summary Report. Substance Abuse and Mental Health Services Administration (SAMHSA), Office of Applied Studies (OAS). Rockville, MD: U.S. Department of Health and Human Services.

Geller, Stephanie. (1995). "Supplementary Studies: Introduction" Proceedings of the 1995 Newly Funded States Workshop: Measuring Need for Substance Abuse Treatment and the Implications of Managed Care. Cambridge, MA: National Technical Center (NTC).

Geller, Stephanie, W.E. McAuliffe, and P.A. Minugh. (1997). Bulletin #14: Combining Subpopulation Estimates to Develop Comprehensive Estimates of Treatment Need. Cambridge, MA: National Technical Center (NTC).

Gfroerer, J.C. (1996). "Special Populations, Sensitive Issues, and The Use of Computer-assisted Interviewing in Surveys." Health Survey Research Methods: Conference Proceedings. Hyattsville, MD: U.S. Department of Health and Human Services. 177-180.

McAuliffe, W.E., P. Breer, N. White, C. Spino, L. Goldsmith, S. Robel, and L. Byam. (1987). A Drug Abuse Treatment and Intervention Plan for Rhode Island: Review Copy. Cranston, Rhode Island: Rhode Island Department of Mental Health, Retardation and Hospitals, Division of Substance Abuse.

McAuliffe, W.E., P. Breer, N.W. Ahmadifar, and C. Spino. (1991). "Assessment of Drug Abuser Treatment Needs in Rhode Island." American Journal of Public Health. 81(3): 365-371.

McAuliffe, W.E., S. Geller, R.A. LaBrie, S.B.F. Paletz, and E.A. Fournier. (1998). "Are Telephone Surveys Suitable for Studying Substance Abuse Epidemiology? Cost, Administration, Coverage and Response Rate Issues." Journal of Drug Issues. 28(2): 455-482.

McAuliffe, W.E., R.A. LaBrie, N. Pollock, N. Lomuto, E.A. Fournier, R. Betjemann. (1999). "Measuring Interstate Variations in Drug Abuse." Drug and Alcohol Dependence. 53(2): 125-45.

McAuliffe, William E., Richard A. LaBrie, Nicoletta A. Lomuto, Nancy E. Pollock, Rebecca Betjemann, and Elizabeth Fournier. (2000). "Measuring Interstate Variations in Problems Related to Alcohol Use Disorders." Eds. Robert A. Wilson and Mary C. Dufour. The Epidemiology of Alcohol Problems in Small Geographic Areas. First ed. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism (NIAAA). 213-244.

National Trends

This chapter examines epidemiological evidence regarding national trends that affect individual states. As the next chapter will show, every state has its unique history and profile with regard to alcohol and drug abuse. However, history reveals convincingly that most substance abuse problems have a national or regional basis (Hunt 1974). The national heroin addiction epidemic of the 1950s got its start in Harlem (Brown 1965), and the psychedelic epidemic of the 1960s and 1970s began at Harvard and in California. Cocaine gained in popularity as a national media phenomena. The AIDS epidemic among injection drug users was first detected in New York, New Jersey, and Maryland (McAuliffe and Ackerman 1991). Only several years later did the prevalence of HIV become painfully evident in other states away from those epicenters. In the past decade, similar national trends in adolescent drug use and use of "club drugs," especially "ecstasy," have spread across the country (Community Epidemiology Work Group 2001). While regions and states may vary in their vulnerability to specific epidemiological trends, states can ill afford to ignore these developments. In the present context, these national trends help explain the current treatment needs in individual states. The remaining sections will describe national trends in alcohol and drug abuse and services over the past decade. The Data Sources appendix presents the citations for the sources of the statistics presented in this chapter.

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Alcohol Treatment Needs

Indicators of alcohol use, dependence, and adverse consequences over the last decade suggest that the need for alcohol treatment services has not increased, except possibly among adolescents. Measures of national consumption of alcohol peaked at the beginning of the 1980s, and then diminished thereafter (Office of Applied Studies 2000c; Greenfield et al. 2000). Per capita alcohol consumption (gallons of ethanol per person 14 and older) declined from 1990 to 1995, and it remained more or less stable thereafter (Figure 3.1; Nephew et al. 2000). As shown in Figure 3.2, the percentage of National Household Survey on Drug Abuse (NHSDA) respondents who drank alcohol in the past year also tapered off throughout the late 1980s to 1998. An even steeper decline occurred among the survey's adolescent respondents.

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Starting in 1985, survey estimates of heavy drinking in the past month (Figure 3.3) declined gradually, although that rate increased somewhat in 1998 (Office of Applied Studies 1999a, p. 34; 2000c, p. 31). Heavy drinking by the NHSDA's adolescent respondents increased slightly between 1994 and 1995, after a sharp decline in the late 1980s and early 1990s (Office of Applied Studies 2000c).

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The rate of alcohol dependence symptoms in the 1984, 1990, and 1995 National Alcoholism Surveys increased slightly over time but there were no significant differences between it and the NHSDA (Figure 3.4; Midanik & Greenfield 2000). Because the NHSDA Main Findings include only rates of alcohol dependence symptoms, the authors analyzed NHSDA public-use data sets in order to find a dependence diagnosis rate. The rates of persons having three symptoms of dependence were higher than the diagnosis rates, but both held to the same upward trend between 1995 and 1998, the years for which the public-use data sets are available. Because the survey questions on symptoms changed between 1994 and 1995 and because the 1999 Main Findings focused on the dependence diagnosis rate rather than symptoms, Figure 3.4 does not include NHSDA estimates for years earlier than 1995 or later than 1998. The apparent differences between the National Alcoholism Survey and the NHSDA estimates, which is not unlike differences in the prevalence rates reported by the Epidemiological Catchment Area study and the National Comorbidity Survey, raises questions about how useful these survey estimates of alcohol dependence are for trend analysis and policy making (Regier et al. 1998).

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Among the arrest categories that may be referred to as "alcohol-defined," the rates of arrests for driving under the influence (DUI) and for drunkenness showed the sharpest decreases (24%) between 1991 and 1998 (Figure 3.5). Disorderly conduct arrest rates, which often include arrests for drunkenness ( "drunk and disorderly"), were also down (9%) in that period. The one exception to this trend was the rate of arrests for liquor law violations, which increased 10% between 1991 and 1998. Between 1993 and 1996 liquor law violation arrests increased 32%. Liquor law arrests in part reflect underage drinking, which surveys suggested began increasing in 1995.

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Figure 3.6 shows that alcohol mortality rates fell between 1989 and 1999. Three different measures displayed this pattern. A clear reduction is evident in the Centers for Disease Control's (CDC) "alcohol-induced" underlying cause mortality rates. Similarly, the National Institute on Alcohol Abuse and Alcoholism's (NIAAA) age-adjusted mortality rates with explicit-mention of alcohol as an underlying cause, which employs the same set of diagnoses as the CDC measure, sunk from 7.3 per 100,000 to 6.4 per 100,000 over the same period. While the age-adjusted rates are consistently lower than the crude rates, they follow virtually the same trend, suggesting that age alone cannot account for the drop. Finally, alcohol-related motor vehicle deaths (defined as alcohol-related by the investigating officer's judgment or a positive reading from any one of several blood tests), reported by the NIAAA from Fatal Accident Reporting System (FARS) data, dropped even more sharply than the other two indicators (Yi et al. 2000). Accidental deaths are not included in the explicit-mention indicators. These results are consistent with the sharp drop in DUI arrests just described.

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Alcohol Treatment

Private spending for alcohol treatment went up 250% in the decade between 1979 and 1989 (Dayhoff et al. 2000). Across the country, the percentage of employees at large and medium-sized firms who had coverage for hospital detoxification increased two and one-half times from 37% in 1982 to 96% in 1989 (Dayhoff et al. 2000). Dayhoff et al. (2000) reported that national public spending for alcohol services increased 60% in the decade between 1979 and 1989. In the early 1990s, total public spending for alcohol and drug specialty treatment services continued increasing by 5%, largely due to the broadening of Medicaid coverage of substance abuse treatment and a 25% increase in the federal Alcohol, Drug and Mental Health Services Block Grant. However, individual state and local government spending for substance abuse services declined (16% and 31% respectively) in the first four years of the 1990s, thus dampening the continuing trend towards greater availability of public treatment services.

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Three measures of the amount of alcohol treatment services from the OAS indicated that national alcohol treatment rates descended steadily from 1992 to 1999 (Figure 3.7). The three measures were primary alcohol treatment admissions (public specialty) from the Treatment Episode Data Set (TEDS), alcohol-only treatment clients (public and private specialty) from the National Drug and Alcohol Treatment Unit Survey (NDATUS)/Uniform Facilities Data Set (UFDS), and the percentage of NHSDA respondents that reported receiving treatment for alcohol use in the past year. The context in which the NHSDA's treatment question is asked in the national survey's interview suggests that "treatment" may include the specialty treatment measured in the UFDS and TEDS estimates as well as treatment provided in an emergency room, private doctor's office, prison, or self help group.

While the TEDS admission rate and the NDATUS/UFDS alcohol-only client rate fell steadily throughout the period, the NHSDA treatment statistics described a much steeper decline. It is also noteworthy that the estimated number of clients in the NHSDA series was much higher than the NDATUS/UFDS series that includes both private and public services.

Alcohol Treatment Gap

Although the alcohol treatment need and service indicators were not always perfectly in step, they mostly indicated stabilization or decline in alcohol use, problems, and services. To measure the relative changes between the measures of treatment need and treatment services, the authors divided the service rates by the need indicators. The ratio of treatment to the percent alcohol dependent in the NHSDA declined from 1995 to 1998 regardless of the measure of services, although the extent of the widening of the treatment gap depended greatly on which measure of services the analysis employed (Figure 3.8). The ratio of the percentage of NHSDA respondents who received alcohol treatment in the past year relative to the number who had a current alcohol dependence diagnosis dropped from a high of .383 in 1996 to .216 two years later. The last number suggested that less than one respondent received treatment in the past year for every four respondents who had a current alcohol dependence diagnosis. Note that because of the way these gap ratios were designed - the treatment rates are in the denominator while the need indicators are in the denominator - a smaller ratio represents a wider treatment gap. So, in the gap analysis graphs (for example, Figure 3.8), a line that goes down over time shows a widening in the treatment gap. The present analysis does not indicate whether the respondents who received treatment were the same ones that had an alcohol dependence diagnosis. It is also noteworthy that the NHSDA's dependence measure is a point prevalence referring to the percentage who had a diagnosis at the time of the interview. It does not indicate how many people had a diagnosis at any time during the year. By contrast, the treatment measure describes the percentage of respondents who received treatment for alcohol at any time during the past year. Thus, this ratio is conservative and may underestimate the true alcohol treatment gap, but it seems likely that the trend is reasonably accurate.

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When the measure of treatment was the rate of admissions for publicly-funded specialty alcohol treatment according to the TEDS database, the decline was from .103 in 1995 to .074 in 1998. The variation between these three measures suggests that part of the task in measuring the national gap in alcohol treatment is to determine how best to measure the amount of treatment services that were provided each year.

When the measure of treatment in the numerator of the ratio was the UFDS/NDATUS alcohol-only rate, the decline relative to the percent dependent was more gradual, going from a peak of .037 in 1995 to .024 in 1998. The alcohol-only NDATUS/UFDS statistics describe a point prevalence of both public and private specialty treatment, but this measure excludes people who received treatment for both alcohol and drug problems. In some years, the excluded persons with both drug and alcohol treatment needs were nearly equal to the number of alcohol-only clients, while in other years they were nearly twice as numerous as the alcohol-only clients.

When assessing the gap between treatment and dependence, it is important to consider the amount of treatment services delivered to those in need of treatment for alcohol dependence. One of the ratios of services to dependence cited above measures all services reported by NHSDA respondents relative to all respondents that had a dependence. That is why the ratio for NHSDA treatment services to NHSDA dependence is the highest. The other treatment measures, the UFDS and the TEDS, capture only clients at specialty treatment facilities where all clients are likely to have a diagnosis, whereas the NHSDA data capture people in non-specialty treatment that may or may not have had a diagnosis in the past year.

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As shown in Figure 3.9, the percentage of those with a past year diagnosis for alcohol dependence that received treatment in the past year declined steadily between 1995 and 1997, then dropped by more than three percentage points over the next year. The percentage of respondents with a past year alcohol dependence that received treatment in the past year in 1998 (6.58%) was less than two-thirds the rate in 1995 (10.72%). That trend was consistent with the trend shown in Figure 3.8 that compared treatment rates with the dependence rate regardless of whether those who received treatment were dependent.

Comparison of the ratio between the NHSDA past-year alcohol treatment and NHSDA diagnosed past-year alcohol dependence with the rate of treatment among those with a dependence diagnosis revealed that there were many respondents in each year that did not have a dependence diagnosis, but received treatment for alcohol use. Consider that the gap ratio indicated that the number that received treatment was about 37% of the number with a dependence diagnosis in 1995, while the treatment rate among NHSDA respondents with a diagnosis was less than 11% in that year. There are several reasons for this disparity. First, about 71% of those who said they received treatment for alcohol in 1995 did not have a diagnosed alcohol dependence. The NHSDA data do not permit diagnoses of non-dependent abuse. Those people are considered to be in need of treatment and it is likely that many of those without a diagnosis for dependence that received treatment would have met the criteria for abuse. Second, the treatment question in the survey is designed to capture a broad range of treatment types including non-specialty modes such as, participation in self-help groups, counseling, and consultation with a private doctor. As such, the treatment group may include respondents that had a lifetime diagnosis of abuse or dependence, but were in full remission at the time of the survey. Third, a diagnosis is for the whole year, but the survey only counts from the time of the survey itself. For example, a respondent might have had a diagnosis 13 months prior to the survey and still manifested symptoms during the time of the survey, but had only one or two symptoms and would therefore, not be diagnosed as dependent when he should have been.

For those reasons, it is difficult to state with certainty whether treatment among those with a diagnosis is the estimate most relevant to needs assessment or if a comparison of treatment against need is the correct measure. The correct estimate probably lies somewhere in between. A correct treatment need estimate measures both dependence and abuse and evaluates respondents for a diagnosis over the whole period, rather than gauging only symptoms over the past year. To confirm the inferences derived from using the NHSDA measure of dependence in the denominator, the authors replaced it with arrest and mortality indicators. The first analysis combined alcohol arrest rates (DUI, drunkenness, and liquor law violation arrests) and divided the measure into treatment as measured by the NHSDA, the NDATUS/UFDS client survey, and the TEDS admissions data (Figure 3.10). As before, the extent of the increase in the treatment gap depended on which measure the analysis used. The gap grew sharply when treatment was measured by the NHSDA estimate of the number of persons who received treatment in the past year. When the numerator was the NDATUS/UFDS estimate of people in treatment on a given day and the denominator was the number of people who were arrested in the past year, the gap widened only gradually. When the TEDS treatment measure was used as the numerator of the gap ratio, the gap increased gradually starting in 1994 and stabilized in 1997.

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When the CDC's alcohol induced crude mortality rate in a year was the denominator, the trends remained basically the same as for when the survey dependence estimates or arrest rates were in the denominator of the ratio of treatment to need (Figure 3.11). The major determinant appears to be how treatment is measured rather than how need is measured. With NHSDA estimates of the number of persons who received treatment in the past year in the numerator, the treatment gap widened sharply over the period. With the NDATUS/UFDS survey estimates of static capacity in the numerator and alcohol-induced mortality in the denominator, the treatment gap widened more gradually over the period. The ratio of the TEDS treatment admissions rate to the alcohol-related mortality rate indicated a trend in the treatment gap very similar to the trend indicated by the gap measure that employed the NDATUS/UFDS treatment estimate.

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Drug Abuse Treatment Needs

National surveys found that use of most illicit drugs decreased sharply during the 1980s, but then drug use rates (including marijuana, cocaine, and heroin) increased during the early 1990s, especially among youths (OAS 2000c, p. 30). The consumption of cocaine in particular declined to much lower levels since the mid 1980s (OAS 1999a, p. 32). For example, the percentage of persons aged 18 to 25 who reported using cocaine in the past year declined from 14% in 1985 to 4% in 1997. That age cohort routinely had the highest rates of controlled drug use. Past-month use of marijuana by this age group declined from 22% in 1985 to 11% in 1992. Beginning in 1993, past-month use of marijuana increased slightly until 1997, when it declined slightly to a rate (13%) that was still well below the peak year of 1979 (37%) and below the 1985 rate (22%). Statistics from the Monitoring the Future survey of youth also found that marijuana use began increasing between 1993 and 1997 before tapering off in 1998 and 1999 (Office of National Drug Control Policy [ONDCP] 2000). Cocaine initiations also turned upward in early 1990s. Statistics from the NHSDA indicated that past-month heroin use increased from 1990 to 1997 to levels that exceeded 1985 before dropping back down to 1985 levels in 1998 (Epstein and Gfroerer 2001; ONDCP 2000). With some exceptions, current levels of nonmedical controlled drug use are lower than 1985 levels despite increases since 1992 (ONDCP 2000).

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The survey self-reports of dropping drug use were confirmed by urine test results from employee testing. Figure 3.13 shows that the percentage of positive urine testing conducted by employers around the country dropped steadily from 13.6% in 1988 to 4.6% in 1999 (Quest Diagnostics, Inc. 2000). The increases in drug use, most evident among adolescents, has apparently not yet begun to affect employee testing data significantly, although some flattening of the rate of decline is apparent in 1997-1999.

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Drug Dependence

The authors analyzed NHSDA public-use data sets to obtain rates of drug dependence for the years 1995 through 1998. In order to illustrate the extent to which that dependence measure is overwhelmingly composed of marijuana and cocaine users, the drug dependence measure is graphed alongside NHSDA estimates of the rate of the population 12 and older that experienced at least three problems due to use that are components of a dependence diagnosis (Figure 3.14). The questions on problems stemming from use changed between 1994 and 1995 and, as such, fluctuations between those years may be exaggerated. Clearly, the overall drug dependence rate rose between 1995 and 1996, then remained constant at 1,770 per 100,000 in the population thereafter. The rate of having three problems due to cocaine or marijuana use remained reasonably steady, as well.

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The total number of drug-related emergency room episodes recorded in the Drug Abuse Warning Network (DAWN) has increased from 1990 (Figure 3.15; Caulkins 2001; OAS 2001; ONDCP 2000). The number of emergency room visits for drug-related episodes declined briefly in 1995, but then increased steadily from 1996 to 1999. The most common drug mentions continued to be cocaine-related problems (Caulkins 2001), apparently resulting from long-term consequences of chronic use according to the ONDCP (2000). In the mid 1990s, the consequences of growing use of "club drugs" (MDMA or "ecstasy" and GHB) as well as prescription opiates (hydrocodone and oxycodone) began to show up in DAWN emergency room statistics (Substance Abuse and Mental Health Services Administration [SAMHSA] 2001). The sharpest increases in mentions of the club drugs occurred in the period between 1998 and 2000.

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Arrests for drug abuse violations rose nationally in the past 15 years. From 1985 to 1995, drug abuse violation arrests rose 82% (Federal Bureau of Investigation [FBI] 1996, p.280), and by 1999 drug arrests were 7% higher than the same statistics in 1995. In 1999, drug arrests were 36% higher than they were in 1990. By contrast, arrests were down 1% for all offenses, 9% for violent crimes, and 26% for property crimes during the same period (FBI 2000, p.211). As shown in Figure 3.16, Uniform Crime Reports (UCR) drug arrest rates increased steadily from 1991 to 1998, with a noticeable increase in 1994, which may be a methodological artifact resulting from the change over of many states to the FBI's new reporting system that permits tabulation of drug arrests that were not the most serious crime in the episode.

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Drug Mortality

Drug mortality rates from three data sources suggest the same trend: up (Figure 3.17). The rate of explicit-mention drug deaths using the present authors' (North Charles Research and Planning Group's [NCRPG]) set of diagnoses from multiple cause data supplied by the National Center for Health Statistics (NCHS) of the CDC closely parallels the "drug-induced" mortality underlying cause rate published by the CDC. The primary difference between the two rates is the use of multiple causes in the NCRPG measure and inclusion of overdoses due to suicide, assault, and undetermined external causes for some drugs in the drug-induced measure ( CDC 1993). The authors also included a measure based on the DAWN medical examiner statistics and the total population. Readers are cautioned not to interpret the absolute level of the DAWN rates as comparable to the other rates, since the DAWN deaths come mainly from urban areas. Lacking the relevant population base for the DAWN statistics, the authors used the total population as a proxy for indicating changes in population over time. The DAWN medical examiner data stem from the investigations of a panel of medical examiners (138 up to 1995, and 134 thereafter) (see Data Sources). In 1996, estimates were available for both sets of panels, and the results did not differ when rounded to one decimal. The medical examiner data include deaths due to suicide (ONDCP 2000). The obvious correlation between the three series suggests the basic validity of all three measures, which is important given the limited geographic coverage of the DAWN medical examiners data.

It is noteworthy that the average purity of retail heroin increased in 1992 and 1993 and has remained at high levels ever since, and the price per gram declined steadily after 1991 (ONDCP 2000). It is likely that increases in purity and decreases in price contribute to the increase in the prevalence of use and to the prevalence of drug overdoses by users.

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Summary of Drug Treatment Need Indicators. The drug indicators revealed some inconsistency, but the overall trends were reasonably clear. Employee-testing and self-reported overall drug use by persons 12 and older went down steadily during the 1980s and the first few years of the 1990s, but the past month drug use by adolescents, drug mortality, emergency room episodes, and arrest statistics for the entire population increased in the decade of the 1990s; especially during the middle of the decade.

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Drug Abuse Treatment

Contrasting trends were apparent in the measures of treatment services received during the 1990s. All three measures are maintained by the OAS and are shown in Figure 3.18. Whereas the TEDS primary drug treatment admissions reported by states and the NDATU/UFDS drug-only treatment clients increased over the period, the NHSDA's estimates of the number of persons who received treatment in the past year declined after a spike upward in 1996. Federal funding of treatment increased by 35% between FY1996 and FY2001 (ONDCP 2000).

It is noteworthy that the absolute rates among the NDATUS/UFDS clients and the NHSDA's estimates of the number of clients differed by between seven and ten-fold in the period. This difference contrasts with the assertion by the NHSDA's top analysts that the "NHSDA undercounts . . . drug clients treated" (Woodward et al. 1997, p. 8). In previous research, the present authors found that the interstate NDATUS rates for 1991-1993 did not correlate with the NHSDA's estimates of treatment received in the same years (McAuliffe et al. 1999). The lack of correlation among the indicators raises questions about the planned use of the NHSDA to measure the drug treatment gap (Woodward et al. 1997).

Drug Treatment Gap

Due to the inconsistency between the measures of treatment services, the measures of treatment relative to indicators of treatment need were also inconsistent. Because the authors were unable to obtain published NHSDA estimates of dependence on any controlled drug for more than two years, this study uses measures created from the NHSDA's public-use databases for 1995-1998.

The ratio of the NHSDA's estimates of the percentage of respondents who received treatment in the past year to the NHSDA's estimate of the percentage of respondents who had a drug dependence diagnosis at the time of the interview indicated an increasing drug treatment gap. That is, the proportion of persons in need that received treatment declined. Recall that in the alcohol treatment gap analysis, a smaller ratio of treatment to need indicators indicated a larger treatment gap. Hence, a graph line that shows a treatment to need indicator ratio decreasing over time shows a widening of the treatment gap. The greatest dip was in 1998 for which there was a published drug dependence estimate. By contrast, the ratio of the NDATUS/UFDS drug-only client rate to the percent with a drug dependence between 1995 and 1998 and the TEDS primary drug treatment admission rate to the percent who were drug dependent between 1996 and 1998 indicated a slightly decreasing gap in treatment relative to need. The TEDS-based measure declined only slightly between 1997 and 1998 and the NDATUS/UFDS-based measure increased slightly between 1997 and 1998, but the NHSDA-based series declined sharply from a peak in 1996 (Figure 3.19). In most years the NHSDA-based series moved in the opposite direction from the TEDS measure. Because Woodward et al. (1997) indicated their own concerns about the NHSDA's estimates of treatment, readers may prefer to place greater reliance on the other two measures.

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It is important to examine the extent to which those with a current dependence on illicit drugs receive treatment. In the treatment need literature this is referred to as "met demand" for treatment. The treatment data from the NHSDA used above do not discriminate between those that received treatment for a dependence in the past year and those that received treatment, but had no past-year dependence. The UFDS and TEDS treatment data are assumed to capture mostly people with a substance abuse problem because those data sets are collected from specialty treatment facilities. The question on past-year treatment in the NHSDA is designed to capture a variety of treatment modes including self-help groups, all types of counseling, and consultation with a private physician. As shown in Figure 3.20, the percentage of those with a drug dependence that received some treatment in the past year peaked in 1997 at almost 19%. That was up by over five percentage points from 1995. The rate tumbled by almost six and a half percentage points the next year. The proportion of those with a drug dependence that got treatment fell by a little over 1 percentage point over the period 1995 to 1998 and the rate fell by about a third between 1997 and 1998. This decline in services among those with a diagnosed dependence is similar to the decline in services among those with a diagnosed alcohol dependence, but it started several years later.

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Comparison of the gap ratio of reported drug treatment to drug dependence with the treatment rate among those with a diagnosed dependence, prompts similar concerns as was the case with the measures of the alcohol treatment gap. The reasons for the difference are similar, as well. The treatment measure used in the gap ratio measures all treatment while the met demand measure captures only treatment among those in need. In 1995, 73% of those that reported receiving drug treatment in the past year had no past-year dependence diagnosis. The survey question on treatment is designed to encompass a variety of treatment modes, so the measure may capture people in full sustained remission. It is also likely that the treatment measure includes people who would meet the criteria for a drug abuse diagnosis, which the NHSDA does not measure. Further, the survey only measures dependence at the time of the survey. Therefore, the dependence measure fails to capture those who had a dependence or abuse diagnosis over the past year, but had less than three dependence symptoms at the time of the survey. It is impossible to determine from the NHSDA data how many of those that received treatment in the past year should have been diagnosed as dependent or as an abuser but were missed by the survey. Further, it is impossible to determine how many were in full sustained remission and received treatment. Finally, people in long term outpatient treatment, such as methadone maintenance, would be counted as having received treatment, but were in partial remission and not counted as dependent. Because the rate of treatment need includes more than just those with a dependence and because the NHSDA fails to identify some of those with a treatment need, the gap ratio is included here.

Replacing the NHSDA drug dependence estimates with drug arrests as the measure of need eliminated some of the differences between the treatment need and supply measures, but inconsistency between the NHSDA's measure of treatment and the other two indicators of treatment persisted. The NHSDA-based treatment measure suggests a substantial increase in the treatment gap over time, while the other measures suggest a more stable relationship between services and need over time (Figure 3.21). While both the NDATUS/UFDS-based and TEDS-based measures indicated a broadening of the treatment gap, the amount was small in both cases.

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Using the drug mortality rate as the measure of drug treatment need, the authors found that the basic findings remained unchanged. For this analysis, the authors used the crude drug-induced underlying cause mortality rate from the Vital Statistics program of the CDC (1999) because the variable had more data points than the other measures of drug mortality had. As shown in Figure 3.22, all three ratios indicated a broadening drug treatment gap, but the amount was much greater for the ratio using the NHSDA treatment measure than the ratios using the other two measures (TEDS and UFDS/NDATUS).

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Summary

Analysis of survey, arrest, mortality, emergency room, and treatment statistics revealed several national trends. Alcohol treatment needs declined during the 1990s as part of a long-term trend, but the alcohol treatment gap appeared to widen somewhat because the number of persons receiving treatment declined more rapidly than the indicators of need declined. Drug use declined sharply in the 1980s and the very beginning of the 1990s, but increased thereafter, especially in the middle of the decade. Adolescent drug use increased notably, just as heavy alcohol use by adolescents increased. Emergency room episodes followed a similar pattern. Notable increases occurred as a result of use of club drugs. Drug arrests and mortality increased steadily during the decade. The percentage of persons who were dependent on drugs was stable from 1994 to 1998. The drug treatment gap appeared to be widening in the late 1990s, substantially according to NHSDA treatment statistics, but much more moderately according to treatment admission and client statistics. Thus, there was some indication of a widening of the treatment gap for both drugs and alcohol in the late 1990s, even though alcohol treatment needs were declining while drug treatment needs were growing. The total number of clients in treatment for both drug and alcohol according to UFDS statistics remained essentially constant between 1992 and 1997 (ONDCP 2000). A substantial increase occurred in 1998.

Appendix: Data Sources

Alcohol and Drug Mortality Rates

There are four measures of mortality used in this chapter. For the multiple cause of death data, the NCHS of the CDC provided the authors with disks containing death certificate data for 1991-1996. NCHS nosologists, using special software, coded each cause of death from death certificates according to the International Classification of Diseases, Ninth Edition (ICD-9) (NCHS, 1998; Hopkins et al. 1989).

Each death record contained information on the decedent's demographics, residence, a code for the underlying cause of death, and the contributing conditions, including as many as 20 "entity-axis" and 20 "record-axis" codes (NCHS, 1998). The authors extracted all records that included at least one cause-of- death code with explicit mention of alcohol or controlled drugs in any of the 41 cause-of-death fields. It was possible for a death that was counted as having a drug diagnosis also to have one or more explicit-mention-of-alcohol diagnoses as well; similarly, a death counted as having an alcohol diagnosis could also have a explicit-mention drug-related diagnosis.

The explicit-mention drug codes used in this measure included drug psychoses (292.0, 292.1, 292.2, 292.8, 292.9), drug dependence (304.0 to 304.9), nondependent abuse of drugs (305.2 to 305.7, 305.9), and accidental poisoning. The ICD-9 codes for accidental poisoning are supposed to include combinations of poisoning nature ("N") codes for controlled drugs and external ("E") codes which indicated that the poisoning was accidental. As explained in the next paragraph, the authors also included deaths that had E-codes indicating undetermined external causes for some drugs of abuse, but did not include deaths due to suicide, therapeutic accidents, or assault. The N codes included deaths associated with ingestion of opiates (965.0), other specified analgesics (965.8), sedatives (967.0 to 967.9), other gaseous, intravenous, and surface anesthetics (968.2, 968.3, 968.5), benzodiazepines (969.4), other tranquilizers (, 969.5), hallucinogens (969.6), psychostimulants (N969.7), parasympatholytics (971.1), and dietetics (977.0). The relevant accidental or undetermined intent E codes included E850.0-E850.2, E850.8, E851, E852, E853.2, E853.8, E854.1, E854.2, E855.1, E855.2, E855.4, E858.8, E980.0, and E980.4.

The combinations of N and E codes varied depending on the drug. All deaths with the relevant N and accidental E codes were considered cases. In some instances, the records did not have both an N code for poisoning and an E code. Deaths that lacked an N code but had an accidental poisoning E code for a drug of abuse were counted as relevant cases. Deaths due to undetermined external causes for opiates, cocaine, and dietetics were also considered cases because the large majority of deaths associated with these substances had accidental E codes in national 1994-1996 mortality data. Deaths with an N code for opiates or cocaine (surface and infiltration anesthetics), but no E code, were treated the same as cases that had an undetermined E code. A full count of all drug deaths was divided by the Census Bureau's total population estimate (Census 2000), then multiplied by 100,000 to obtain a rate of drug deaths for each year, 1991-96.

Annually, the CDC publishes alcohol-induced and drug-induced death rates in its "Vital Statistics" series (CDC 1999, 2001). These rates reflect the underlying cause of death. The age-adjusted rates are published alongside the crude rates. These drug data differ from the explicit-mention rates developed by the authors from mortality files in that the CDC measure includes E codes for suicides, assaults and undetermined causes for some drugs not included in explicit-mention counts. With the possible exception of combinations of N and E codes for alcohol poisoning deaths, the CDC's alcohol-induced mortality rate uses the same diagnostic categories as the present authors used in developing their rates. The 1999 CDC deaths were coded using ICD-10 codes, whereas the previous years reflect ICD-9 coding. The CDC provided a crosswalk in the appendix of the 1999 preliminary report that makes the differences in coding clear (CDC 2001). While the CDC cautioned against comparisons across ICD-9 and ICD-10 codes, review of the trends presented in this chapter did not reveal any obvious discontinuity in the trends from previous years.

Alcohol mortality data from the NIAAA (Stinson et al., 1996) included any case with the underlying cause of death listed as one of the widely used explicit-mention alcohol diagnoses (e.g., McAuliffe et al., 2000; Stinson et al., 1994, 1996). The explicit-mention alcohol ICD-9 codes were 291 (alcoholic psychoses), 303 (alcohol dependence), 305.0 (alcohol abuse), 357.5 (Alcohol Polyneuropathy), 425.5 (Alcohol Cardiomyopathy), 535.3 (Alcohol Gastritis), 571.0 (Alcoholic fatty liver), 571.1(Acute Alcoholic Hepatitis), 571.2 (Alcoholic Cirrhosis of the Liver), 571.3 (Alcoholic Liver Damage, Unspecified), 790.3 (Excessive Blood Level of Alcohol), E860.0 (Accidental Poisoning by Ethyl Alcohol Beverages), and E860.1(Accidental Poisoning by Ethyl Alcohol). The differences between the NIAAA rates and the present authors' explicit-mention rates were the use of multiple-cause versus underlying cause data and the combination of N and E codes for poisoning. Whereas the present authors use specific combinations of codes, the description of the methods used in the NIAAA rates suggests that any case with a relevant code was included.

The OAS publishes annual DAWN Medical Examiner (ME) reports (OAS 1998, 2000a). These reports contain data submitted by medical examiners in metropolitan areas nationwide. The medical examiners report on the incidence of drug use among decedents. A "consistent panel" of examiners who report most consistently is employed to facilitate comparisons among different years. That is, the data are compiled from the same group of medical examiners in order to discern differences among years. This chapter drew on two such consistent panels; one from 1993-96 and the other from 1996-99. The former had 138 medical examiners, and the latter 134 (OAS 1998, 2000a). When rounded to one decimal and graphed, these separate lines appear to form a single continuous line.

For purely methodological purposes, the authors included these ME statistics because of the assumed accuracy of drug-related diagnoses in medical examiner data when compared to mortality statistics for all deaths, which have been questioned by some authors (see McAuliffe et al. 2001 for a review). In order to compare the trends in the ME data to the other drug mortality rates, the authors created rates per 100,000 using total US population estimates from the Census Bureau (2000). The authors assumed that the population changes over time in the DAWN areas would closely reflect changes in the national population. Because the DAWN data reflect urban populations rather than the total national population, readers should not interpret the absolute level of the rates as estimates of the national mortality rates.

Treatment Client Rates

With the exception of 1994, the OAS (1995a,b, 1996a, 1997b,c, 1999b, 2000g) has conducted an annual survey of treatment facilities to assess the number of persons in specialty substance abuse treatment at one point in time. The study has been known by two names over the relevant years: NDATUS from 1987 to 1994 and the UFDS thereafter. The survey was not conducted in 1994; instead the values for that year were estimates from an econometric model incorporating prior years, changes in state treatment funding levels, unemployment, population changes, and changes in food stamp costs (OAS 1996a). In the years used in this chapter (1992-1998), the sampling frame was a listing called the National Facility Register that OAS updates continuously. The total number of facilities ranged from 11,316 in 1992 to 19,174 in 1998. The facilities included specialty providers of substance abuse treatment, including public and private free-standing units and units in multi-purpose institutions. The facilities were owned by private for-profit and non-profits, as well as federal, state, local, and tribal governments. The federal facilities included the Veterans Administration, Defense Department, Bureau of Prisons, and the Indian Health Service. Identified mostly by state and federal agencies, these providers completed a questionnaire about all active clients in treatment on a specific reference day in the previous year (e.g., September 30, 1992, and October 1 or in the first week in October in 1993 to 1998).

The NDATUS/UFDS clients are categorized into three groups: Alcohol only (35% nationally), alcohol and drugs (40%), and drug only (25%) in 1991-1993. On the basis of theoretical and empirical analysis, the authors selected the drug-only and alcohol-only clients as measures rather than the combined drug-only plus drug-and-alcohol-treatment clients or the combined alcohol-only plus drug-and-alcohol treatment clients. Of course, the primary reason for not including the drug-and-alcohol treatment clients in both measures was to avoid counting the same clients in both measures, and the authors believed that the selected measures would be more valid indicators of the supply of the two different types of services. The authors' review of NDATUS/UFDS statistics revealed that providers in many states (especially Massachusetts, Alaska, New Hampshire, Nebraska and Texas) were more likely to utilize the drug-and-alcohol-treatment clients measure than the drug-only measure, but providers in other states (Alabama, Arizona, New Mexico, New York, Rhode Island, and California) were more likely to use the drug-only category. Years of clinical experience working in several of these states caused us to hypothesize that many of the clients in the drug-and-alcohol category may have had alcohol use disorders but were only users of illicit drugs rather than persons who met standard criteria for drug abuse or dependence. Although NDATUS/UFDS defines this category adequately in the glossary that accompanies its survey questionnaire, even experts sometimes use the term "drug abuse" to refer to the use of illicit drugs, whereas they reserve the terms "alcohol abuse" for excessive use that results in symptoms and is likely to require treatment (see McAuliffe et al. 1999).

The empirical behavior of the drug-only client measure and the combined client measure for 1991-1993 data mostly supported the authors' hypothesis. Although the two measures correlated substantially with each other (.79, p<.05), the correlation of drug arrest rates with drug-only client rates (.63) was significantly greater than the correlation of drug arrest rates with the combined client rates (.42). Drug mortality rates correlated significantly more with the drug-only client rate (.89) than with the combined client rates (.70), and IV-AIDS statistics correlated significantly more with the drug-only client rate than with the combined client rate (.76 versus .57). The drug-only client rate correlated slightly more strongly with NHSDA model estimates of past year drug treatment than did the combined measure (.28 versus .23), but the difference was not significant. The drug-only client rates correlated significantly less than the combined client measure with National Association of State Alcohol and Drug Abuse Directors (NASADAD) drug-related admissions (.47 versus .62).

Treatment Admission Rates

The OAS collects administrative data on treatment admissions from all states. The data include admissions from only those programs that are licensed by the state substance abuse agency and required to report data to it. Inclusion of private, hospital, and prison treatment varies from state to state depending on the state substance abuse agency's licensing and reporting regulations. Mostly, the admissions represent admissions to publicly-funded treatment. OAS publishes reports called the Treatment Episode Data Set (TEDS)using those data (OAS and Synectics for Management Decisions, Inc., 2000e). The report includes rates per 100,000 in the total population that are involved in substance abuse treatment at a facility of the type described above. Because the data are published on an admissions rather than a client basis, an individual may be counted more than once if he/she was admitted for treatment more than once within the same year. Rates are reported for total admissions as well as for specific categories of substances (such as alcohol, cocaine, marijuana or hashish, etc.).

Survey-Reported Treatment

The NHSDA (OAS 1996b, 1997a, 1999a, 2000c, d, f) reports the percentage of respondents who said they received substance abuse treatment in the previous 12 months. The data break down into two categories: primary alcohol treatment, and primary drug treatment. For the purposes of this chapter, the authors expressed the percentages as rates per 100,000 in the population in order to compare those statistics with the rates per 100,000 used for the other treatment measures.

The NHSDA treatment rates are higher than the rates indicated by the other treatment measures used in this chapter. There are several reasons for this. First, the questions on past-year treatment in the NHSDA are designed to capture a broader group than the other data sets. Whereas, the UFDS and the TEDS data are collected at specialty treatment facilities, the NHSDA captures participation in self-help groups, consultation with a doctor, and all types of counseling for substance-related problems. Second, the NHSDA and the TEDS data set capture the whole year, whereas the UFDS is a one-day census of treatment facilities. Third, the NHSDA does not require a respondent to have had a dependence in the past-year to have received treatment for substance use-related issues in the past year. Although the UFDS and the TEDS data sets do not require a diagnosis for a client to be counted, if an individual did not have a current, diagnosable substance-related condition, it is unlikely they would be at a treatment facility.

Some of the treatment gap variables used in this chapter express all treatment reported in the NHSDA as a proportion of dependence. The current authors attempted to create a measure of the treatment gap that would more accurately reflect the gap between need for treatment as represented by the past-year substance use disorder rate and treatment received among those in need as represented by the NHSDA treatment estimate using NHSDA public-use data sets. Unfortunately, limitations posed by the NHSDA data prevented creation of a gap estimate that was clearly better than the one used in this chapter. The authors' initial concern was that including all reported cases of treatment overestimated the proportion of those with a past-year dependence that received treatment. One possible solution was to use only those treatment cases where there was also a dependence diagnosis. However, dependence alone may not be an adequate measure of treatment need. For example, the NHSDA does not permit diagnoses of abuse. Therefore, excluding those treatment cases without a diagnosis might understate the amount of treatment delivered to those with a current treatment need. Another possible solution was to include all cases where there was a need for treatment, but no past-year dependence diagnosis in the denominator of the treatment gap ratio. Because the NHSDA does not permit lifetime diagnoses of dependence or abuse, the authors examined the perceived need for treatment in the past 12 months as a possible proxy for a lifetime diagnosis rate. Because the perceived need variable had a small number of cases and correlated with the past-year dependence variable significantly for both alcohol and drugs (.173 p<.01 for alcohol and .229 p<.01 for drugs), it was decided that it was not an adequate proxy for lifetime treatment need, nor did it necessarily capture past-year abusers without a diagnosis.

As such, the authors decided to use the ratio of past-year treatment to past-year dependence despite its imperfections. The authors corroborated the trends in the treatment gap using social indicators such as substance-related arrests and substance-related mortalities as the denominators in ratio estimates of the treatment gap. Further, the authors included trends in treatment among those with a past-year dependence diagnosis, the "met demand" for treatment, in order to confirm the trend in the gap between diagnosed treatment need and treatment delivered.

Alcohol- and Drug-Defined Arrest Rates

The FBI's Uniform Crime Reporting (UCR)(FBI 1996, 1997, 1998, 1999) system reports statistics on arrests for violations of state and local laws that are associated with alcohol abuse, including DUI arrests, drunkenness, disorderly conduct, and liquor-law violations. An arrest occurs when police detain a person and a record is made of the detention. The arrest statistics in the early part of the decade counted only those cases in which the drug or alcohol charge was the most serious (General Accounting Office, 1990), but states that have converted to the UCR's new National Incident-Based Reporting System (NIBRS) coding system count any substance-defined arrest (FBI 1992).

The authors downloaded the UCR "Persons Arrested" data from www.FBI.gov . The reports include rates of arrest for specific crimes per 100,000 in the population based on their estimate of the population that is covered by agencies reportingcomplete data for a given year. The FBI also publishes estimates of nationwide total arrests for each crime, the rates used in this chapter were based on the actual number of offences reported and the covered population estimate.

Population Estimates

The population bases for specific indicators vary depending on the decisions of the agencies that supplied the rates. The rates created by the present authors used denominators based on annual Census estimates of the entire population. The 1993 through 1999 estimates were obtained on-line at census.gov/population/estimates/nation/popclockest.txt. (Census 2000). The authors did not use age-adjustments because the crude rates are more relevant for determining the amount of services that are needed. Age-adjustments are relevant only when the analyst wishes to determine when changes in rates occur independently of the effects of aging, but aging is a relevant variable in determining the need for treatment.

Alcohol-Related Traffic Fatalities

The annual rates per 100,000 in the population that died in an alcohol-related traffic accident were drawn from "Surveillance Report #53: Trends in Alcohol-Related Fatal Traffic Crashes, United States, 1977-98"(Yi et al. 2000). The authors of that report drew the data from the Fatal Accident Reporting System (FARS) of the National Highway Traffic Safety Administration (NHTSA). The authors of the report defined "alcohol-related" fatalities somewhat differently than they are defined in the FARS reports. Yi et al. (2000) defined a fatality as alcohol related when either the blood alcohol content of the driver involved in a crash was known to be above zero or when the reporting officer indicated that alcohol was involved in the accident.

Alcohol and Drug Dependence

The NHSDA Main Findings (OAS 1996b, 1997a, 1999a, 2000c, d, f) reports the percentage of respondents with three symptoms of dependence on alcohol, marijuana, and cocaine for persons 12 and older. Because those estimates do not reflect a rate of dependence diagnoses, the authors analyzed NHSDA public-use data sets to determine dependence rates for alcohol and drugs. The authors expressed the figures as the rate per 100,000 for purposes of comparability.

Several factors affected the utility of NHSDA for trend analysis. In 1999, the NHSDA switched from a paper and pencil interview (PAPI) method of administration to a computer-assisted method, which combined a computer-assisted personal interview conducted by the interviewer (CAPI) and a computer-assisted self interview (audio computer assisted self interview [ACASI]). In a effort to assess the effect of the new interview mode, the NHSDA also included a supplemental sample of 13,809 cases using the same PAPI methodology employed in the previous year. However, analysis of the resulting data suggested that the 1999 PAPI data were not comparable to the previous year. There were "unusually large and unexpected increases in various drug measures " (OAS 2000b, p. D-1). Because the NHSDA had also expanded the size of its sample to include interviews from all states, the survey contractor had to hire a large number of new interviewers. The analysis showed that the prevalence of drug use declined as the new interviewers became more experienced during the year, and interviewers from prior years obtained lower levels of prevalence than the new interviewers obtained. As a result, the NHSDA developed adjusted estimates for some of its indicators in 1999 (OAS 2000b, p. D-11). The only 1999 NHSDA estimates this report employed were treatment rates.

The NHSDA Main Findings for years prior to 1999 did not report dependence rates for alcohol or drugs, but instead reported rates of respondents that had three symptoms of dependence. Those rates are higher than actual rates of dependence. Further, Main Findings reports prior to 1998 reported rates of respondents that had three symptoms for cocaine and marijuana, but not other drugs. If a respondent had three symptoms for both cocaine and marijuana dependence, he was counted for both substances. The 1998 data includes variables for drug dependence and alcohol dependence. The authors replicated those variables for 1995 through 1997 using public-use data sets. The authors were able to replicate the results for the alcohol dependence variable in 1998, but the rate of drug dependence obtained by the authors was lower than the result published in the 1998 Main Findings. As such, the rates drug dependence rates for the previous years are likely lower as well. Although the rates of respondents that had three problems due to use of substances probably captured some non-dependent abusers that were also in need of treatment, the authors decided to use the dependence rates for this report because the group captured by that variable is well defined.

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Greenfield, Thomas K., Lorraine T. Midanik, and John D. Rogers. (2000). "A ten-year national trend study of alcohol consumption, 1984-1995: Is the Period of Declining Drinking Over?" American Journal of Public Health. 90(1): 47-52.

Hopkins, David D., Joyce A. Grant-Worley, and Terrie L. Bollinger. (1989). "Survey of Cause-of-Death Query Criteria Used by State Vital Statistics Programs in the US and the Efficacy of the Criteria Used by the Oregon Vital Statistics Program." American Journal of Public Health. 79(5): 570-574.

Hunt, Leon Gibson. (1974). Recent Spread of Heroin Use in the United States: Unanswered Questions. Washington, DC.: Drug Abuse Council, Inc.

McAuliffe, William E. and Kathy Ackerman. (1991). Health Care Policy Issues in the Drug Abuser Treatment Field. In Health Policy and the Disadvantaged. Brown, L. D., Editor. Durham, NC: Duke University Press. 105-142.

McAuliffe, William E., Richard A. LaBrie, Nicoletta Lomuto, Rebecca Betjemann, and Elizabeth A. Fournier. (1999). "Measuring Interstate Variations in Drug Problems." Drug and Alcohol Dependence. 53(2): 125-145.

McAuliffe, William E., Richard A. LaBrie, Nicoletta A. Lomuto, Nancy E. Pollock, Rebecca Betjemann, and Elizabeth Fournier. (2000). "Measuring Interstate Variations in Problems Related to Alcohol Use Disorders." In The Epidemiology of Alcohol Problems in Small Geographic Areas, First ed. vol. 36. Eds. Robert A. Wilson and Mary C. Dufour. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism (NIAAA). 213-44.

McAuliffe, William E., Richard A. LaBrie, and Ryan Woodworth. (2001). Measuring Interstate Variations in Substance Abuse Treatment Needs. Unpublished manuscript.

Midanik, Lorraine T. and Thomas K. Greenfield. (2000). "Trends in Social Consequences and Dependence Symptoms in the United States: The National Alcohol Surveys." American Journal of Public Health. 90(1): 53-56.

National Center for Health Statistics (NCHS). (1998). 1994 Multiple Cause-of-Death File. Hyattsville, MD: Centers for Disease Control and Prevention.

National Institute on Alcohol Abuse and Alcoholism (NIAAA). (1999). Number of deaths and age-adjusted rates per 100,000 population for categories of alcohol-related (A-R) mortality, United States and States, 1979-96. Downloaded from http://www.niaaa.nih.gov/databases/qf.htm on 8-13-01. Table based on F.S. Stinson, T.M. Nephew, M.C. Dufour, and B.F. Grant. (1996). State Trends in Alcohol-Related Mortality, 1979-92. U.S. Alcohol Epidemiologic Data Reference Manual, Volume 5, First Edition. Bethesda, MD: NIAAA, National Institutes of Health, and unpublished data from the Alcohol Epidemiologic Data System (AEDS).

Nephew, T.M., G.D. Williams, F.S. Stinson, K. Nguyen, and M.C. Dufour. (2000). Apparent Per Capita Alcohol Consumption: National, State and Regional Trends, 1970-98. Surveillance Report #55. Rockville, MD: National Institute on Alcohol Abuse and Alcoholism, Division of Biometry and Epidemiology.

Office of Applied Studies (OAS). (1995a). Overview of the National Drug and Alcoholism Treatment Unit Survey 1992 and 1980-1992. Rockville, MD: Substance Abuse and Mental Health Services Administration (SAMHSA).

Office of Applied Studies. (1995b). Overview of the FY94 National Drug and Alcoholism Treatment Unit Survey Data from 1993 and 1980-1993. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies and Macro International Inc. (1996a). National Drug and Alcoholism Treatment Unit Survey Data for 1994 and 1980-1994. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies and National Opinion Research Center. (1996b). National Household Survey on Drug Abuse Main Findings 1994. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies and National Opinion Research Center. (1997a). National Household Survey on Drug Abuse Main Findings 1995. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (1997b). Uniform Facility Data Set Data for 1995 and 1980-1995. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (1997c). Uniform Facility Data Set Data for 1996 and 1980-1996. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (1998). Drug Abuse Warning Network (DAWN) Annual Medical Examiner Data 1996. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies and National Opinion Research Center. (1999a). National Household Survey on Drug Abuse: Main Findings 1997. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies and Synectics for Management Decisions, Inc. (1999b). Uniform Facility Data Set 1997 Data on Substance Abuse Treatment Facilities. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2000a). Drug Abuse Warning Network Annual Medical Examiner Data, (DAWN) 1999. Rockville, MD: U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2000b). Drug Abuse Warning Network: Advance Report #17. Downloaded 8/22/01 from www.samhsa.gov/oas/dawn/ar17_toc.htm.

Office of Applied Studies and Research Triangle Institute. (2000c). National Household Survey on Drug Abuse: Main Findings, 1998. Rockville, MD: Substance Abuse and Mental Health Administration.

Office of Applied Studies. (2000d). Summary of Findings from the 1999 National Household Survey on Drug Abuse. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2000e). Treatment Episode Data Set (TEDS)1993-1998, National Admissions to Substance Abuse Treatment Services. Rockville, MD: National Clearinghouse for Alcohol and Drug Information (NCADI).

Office of Applied Studies and National Opinion Research Center. (2000f). National Household Survey on Drug Abuse Main Findings 1996. Rockville, MD: U.S. Department of Health and Human Services.

Office of Applied Studies. (2000g). Uniform Facility Data Set 1998. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2001). Mid-Year 2000 Preliminary Emergency Department Data from the Drug Abuse Warning Network. Rockville, MD: Substance Abuse and Mental Health Services Administration .

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Quest Diagnostics, Incorporated. June 20, 2000. Positive Drug Test Results in 1999 Decline to Record Low in Quest Diagnostics' Workplace Drug Testing Index. Teterboro, New Jersey. www.questdiagnostics.com/corporatehealth/news/dti.htm. Downloaded on 11/30/2000.

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Stinson, F.S.,T.M. Nephew, M.C. Dufour, and B.F. Grant. (1996). State Trends in Alcohol-Related Mortality, 1979-92. U.S. Alcohol Epidemiologic Data Reference Manual, Volume 5, First Edition. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health.

Substance Abuse and Mental Health Services Administration (SAMHSA). (2001). Year-end 2000 Emergency Department Data from the Drug Abuse Warning Network. Adapted by the CESAR FAX. 10(33):1.

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Yi, Hsiao-ye, Frederick S. Stinson, Gerald D. Williams, and Mary C. Dufour. (2000). Trends in Alcohol-Related Fatal Traffic Crashes, United States, 1977-98. Surveillance Report #53. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism.

Interstate Comparisons

This chapter describes how the State compares to other states with regard to its need for treatment and its supply of treatment services. The analysis employs existing indicators of drug, alcohol, and combined substance abuse to describe the distribution of differences among states in the need and supply of treatment services. The indicators were average annual rates per 100,000 in the population in the three years between 1994 and 1996. The analysis identifies treatment service gaps by comparing state treatment needs and services. The ultimate objective of the study is to determine whether the State's provision of treatment services is on par with other states.

Methods

The authors created a series of composite treatment need indexes. The Drug Need Index (DNI) consists of the sum of standard (z) scores of mean rates of explicit-mention drug mortality and drug possession or sale arrests. The denominators for each annual rate were state population projections for the relevant years. The combined z-scores were scaled to range from 0 to 100. The state with the most severe drug or alcohol abuse problem received the highest score and was ranked 1st, and the state with the least severe problem received the lowest score and was ranked 50 th . The drug mortality indicator counted only deaths with codes that explicitly mentioned drugs of abuse as one of the causes listed on the death certificate. The diagnostic codes included accidental drug overdoses, drug dependence, nondependent drug abuse, and drug psychoses (including drug withdrawal syndrome). An index score of 0 reflects no arrests or deaths in the period, while a score of 100 reflects the sum of the highest observed mean rates of both components. Similarly structured, the Alcohol Need Index (ANI) consists of the sum of explicit-mention alcohol mortality rates and arrests rates for driving under the influence (DUI) and liquor law violations. The alcohol mortality measure employed 12 explicit-mention diagnoses widely used as measures of alcohol abuse. Examples were alcohol dependence, non-dependent alcohol abuse, alcohol psychoses, alcoholic cirrhosis of the liver, and alcohol cardiomyopathy. The Substance Abuse Need Index (SNI) combines all explicit-mention drug and alcohol mortality rates and the sum of the drug and alcohol arrest rates. The authors conducted analyses that established that the three indexes had a reasonably high degree of reliability and validity (McAuliffe et al. 1999, 2000, 2001).

The Office of Applied Studies (OAS) conducts a national survey of substance abuse treatment facilities annually. The name of the survey changed between 1994 and 1995. Formerly, it was called the National Drug and Alcohol Treatment Unit Survey (NDATUS), and it is now called the Uniform Facility Data Set (UFDS). The survey counts treatment clients on a single day (usually September 30), and the clients are categorized as drug-only, alcohol-only, and drug and alcohol. The present study used the drug-only client rates for the DNI and alcohol-only client rates for the ANI in order to avoid double counting clients. The SNI used all clients.

A second measure of interstate treatment services is the Treatment Episode Data Set (TEDS), also managed by the Office of Applied Studies. TEDS collects admissions statistics from the states. It includes only state-funded treatment. The present study categorized admissions by primary substance: alcohol or drugs.

Because of the differences between the TEDS primary alcohol admission estimates and UFDS alcohol-only client estimates, the two available national measures of treatment for drugs and alcohol are imperfectly correlated (.57 for drugs and .49 for alcohol). The authors tried other combinations (e.g., alcohol-only and alcohol-and-drug client rate and any mention of alcohol rate), but the correlation did not increase. Based on experience with several states, the authors suspect that the composition of the alcohol-and-drug clients and any-mention-of-alcohol admissions measures depends upon the state. In states where alcohol use disorders are the predominate substance abuse problem, the combined categories consist mostly of people with primary alcohol problems who also use marijuana. In states where drug abuse problems predominate, the category consists of primary drug abusers (mostly opiate and cocaine) who also drink. Because of the imperfect correlation among the two measures, the analysis uses both client and admissions measures and notes differences between them. The client measure was complete while the admissions measures has nine missing states. Therefore, the client measure is used in the graphs that compare need and treatment.

Data Sources

This study employs existing substance abuse indicator data that the study team gathered from national sources (see Appendix). Before using them, the analysts examined each data set for the presence of outliers and other sources of error. An example of an outlier would be an annual count that is many times higher (lower) than the previous (subsequent) year for the state, especially when the annual change is not consistent with the usual annual variations in the data for that state and other states. In such cases, the study team alerted State officials who contacted the responsible officials or agency about the outlying values. If corrected values or comparable figures were available, the authors used them. As a general protection against undetected or uncorrected random errors, the study combined multiple years of data, 1994-1996, to obtain more reliable composite indicators.Experience with the 1991-1993 data indicated the three-year mean rates were reliable.

Analysis

The analysis begins with the drug indicators, and then turns to the controlled alcohol indicators. The analysis then describes the combined substance abuse indicators. In the presentation of results for each substance, the report begins with the composite index, and then it describes the components of the index and other supplementary indicators.

RESULTS

Drug Treatment Needs

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Drug Need Index. The map of the DNI (Figure 4.1) reveals the wide variation among states in problems related to drug use disorders. The states with more severe drug treatment needs were New York (100), California (81.4), New Jersey (73.8),and Maryland (72.8)"“all states long known to have acute drug problems. They were the same four states with the highest scores on a similar 1991-1993 index described by McAuliffe et al. (1999).

The most severe drug problems were found in two clusters: the Northeastern urban states and the West Coast and Southwest. A member of the Northeast cluster, Rhode Island ranked 5th in the country in 1991-1993 (McAuliffe et al. 1999), but its rank dropped tothe 18th most severe drug abuse treatment needs in the nation during 1994 to 1996. Neighboring Connecticut (7th) and Massachusetts (15 th ) also had high DNI scores in that period.

Colorado ranked 13th in the country, just at the cutoff for the states in the most severe quartile. Colorado's DNI (49.8) was substantially higher than the scores of other plains and mountain states that border it, although it was lower than New Mexico's (67.7). Colorado appears to be in a transitional area with neighboring states having both higher and lower rankings. Among the states with moderately severe controlled drug treatment needs were Alaska with a DNI of 36 (ranked 31 st ) and Montana with a DNI of 34.4 (34th).

North Dakota, Vermont, West Virginia, Iowa, and South Dakota had the lowest drug index scores in the country.All are rural states. North Dakota's DNI (8.5) reflected their rural nature. North Dakota is surrounded by states in the lowest quartile on this composite measure of drug treatment need.

Drug Mortality. A key component of the DNI counted deaths for which at least one of the multiple causes included drug dependence, nondependent drug abuse, accidental poisoning or drug psychoses (e.g., withdrawal) associated with controlled drugs. As Figure 4.2 shows, New York's average annual drug-mortality in 1991-1993 (12 per 100,000) was the highest in the country and was 40 times as great as North Dakota's (0.3 per 100,000), which was the lowest. The mortality rates and ranks for those two states in 1994-1996 were virtually unchanged from the earlier period. North Dakota's rate was one half as large as the next highest state (Vermont). Rhode Island's 1991-1993 rate (4.4 per 100,000) ranked 7 th most severe in the country. Three years later the State's drug mortality rate increased to 5.3 per 100,000, but its rank dropped to 15 th .The 1991-1993 rate was 3.2 for Colorado (15th), but in 1994-1996 its rate increased to 5.6 per 100,000 (12 th in the country). Montana's drug mortality rate increased from 1.5 per 100,000 (ranking 29th) to 1.9 (ranking 36 th). Alaska's rate increased from 1.4 to 4.3 per 100,000, while its rank increased from 31 st in 1991-1993 to 18 th in 1994-1996.

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Drug-Defined Arrests. In 1994-1996, New York also had the highest drug arrest rate, while Vermont had the lowest (Figure 4.3). Montana had the lowest rate in 1991-1993, while North Dakota had the third lowest in the country (57 and 77 per 100,000, respectively). North Dakota's rank dropped to 49th in the nation in the 1994-1996 period. Alaska's 1991-1993 rate (157 per 100,000) ranked 41 st in the nation and rose to 38th (313 per 100,000) in the 1994-1996 period.

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In contrast with Rhode Island's high drug mortality rate, its 1991-1993 average annual arrest rate of 293 per 100,000 was in the middle of the national distribution (25th most serious in the country). Three years later Rhode Island's average annual drug arrest rate had increased to 409 per 100,000, but its rank dropped to 30 th in the country. Rhode Island's neighboring states, such as New York (845) and Connecticut (635) had substantially higher drug-defined arrest rates, but Massachusetts' rate (428) was similar to Rhode Island's.

In contrast to drug mortality, the composition of the drug arrests rates represents somewhat different aspects of drug treatment need, which may vary from one region of the country to another. Nationally, 41 % of all drug-defined arrests were associated with marijuana possession and sales, which is the same percentage associated with cocaine and heroin arrests combined (41%). Much smaller percentages of arrests are associated with use of synthetic narcotics (2%) and non-narcotic drugs other than marijuana (16%). Figure 4.4 shows that in rural states a majority of the arrests involved marijuana arrests, whereas in urban states a majority of the arrests involved cocaine and opiates. The percentage of arrests due to marijuana varied from 82% in North Dakota to 19% in California. Marijuana arrests were predominate in the rural and mountain states of the West (including North and South Dakota, Alaska, and Colorado) and northern New England (Maine, Vermont). Comparable arrest data were not available for Montana. Arrests for possession and sale of hard drugs (combined arrests for cocaine/opiates and synthetic narcotics) ranged from 60% in New York to six percent in Idaho. Hard drug arrests were predominate in the urban East Coast states of Connecticut, Pennsylvania, Maryland, New York, New Jersey, Massachusetts, and Delaware. Rhode Island was in the middle of the distribution with regard to soft versus hard drugs. Rhode Island's arrests for drugs other than marijuana, cocaine, and opiates were among the lowest in the country. High arrest rates for drugs such as amphetamines were evident in the West and Midwest.

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Drug Incarceration Rates. Rhode Island ranked 14th in the country (40 states had data) with regard to the rate of incarcerations related to drug offenses in 1994 (Figure 4.5). Colorado and Montana ranked 37 th and 38 th respectively. Drug incarceration data were not available for North Dakota or Alaska.

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Surveys of Drug Abuse. There was surprisingly little survey data available on drug dependence among the 50 states. The National Household Survey on Drug Abuse (NHSDA) expanded its sample to include all states in 1999. Because small states had samples of only 750 interviews, the NHSDA estimates are supplemented by census and social indicator data (deaths, arrests, and treatment statistics). Although based on some of the same indicator data, the DNI and the NHSDA drug dependence estimates were not significantly correlated with each other in 1991-1993 versions (McAuliffe et al. 1999) and were correlated only moderately (.46) in 1994-1996. One key difference between the DNI and NHSDA estimates is that the NHSDA's drug dependence survey measurements overwhelmingly (78% in 1995-1998) reflect marijuana use disorders rather than other drugs of abuse such as cocaine and opiate use disorders. Thus, the NHSDA estimates are likely to be good predictors of treatment needs only in rural states, where treatment for marijuana abuse is predominant. Thus, Alaska (ranked 1 st in the country), Montana (15th ), and North Dakota (35 th ) have much higher ranks on the NHSDA's estimate of drug dependence than on the other measures of drug treatment need (Figure 4.6). No doubt because of the high rate of marijuana use and marijuana arrests in Rhode Island, it ranked 8th in the country on the NHSDA's past-year drug dependence measure.

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Drug Treatment Clients and Admissions. In 1991-1993, New York had the country's highest mean NDATUS drug-only client rate (269 per 100,000), and the state retained the same position in the 1994-1996 UFDS drug-only client rates with a rate of 271 per 100,000 (Figure 4.7). Rhode Island ranked second in the nation behind New York in 1991-1993 (192 per 100,000) as well as in 1994-1996 (229 per 100,000). Colorado's 1991-1993 rate was 91 (11th), and its 1994-1996 rate declined to 79 (16th). Alaska's 1991-1993 rate was 30 (40th), and its 1994-1996 rate was 44 (34 th ). Montana's rates were 14 in 1991-1991 and 20 in 1994-1996; both ranked 47th in the country. North Dakota had the lowest drug-only client mean rate in the country for both three-year periods: 9 per 100,000 in 1991-1993 and 15 per 100,000 in 1994-1996.

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The TEDS admissions rates shown in Figure 4.8 revealed similar patterns. Rhode Island ranked 6th with regard to its drug admissions rate, and Colorado's drug admissions rate was next (12th). Alaska and Montana have very similar drug admissions rates (Montana was ranked 18th and Alaska 19 th). North Dakota's rate was next to the lowest in the country.

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As was true for the arrest statistics, rural states had higher rates of persons receiving treatment for a marijuana use disorder, whereas urban states had higher rates of persons receiving treatment for heroin use disorders (Figure 4.9). As a proportion of treatment, North Dakota had the highest percentage of marijuana admissions in the country, while California had the lowest percentage of marijuana admissions. Montana (8th), Alaska (11th), and Colorado (13th; it ranked 12th in marijuana arrests) also had high percentages of admissions for marijuana use disorders; in some cases more admissions for marijuana use disorders than for any other drug use disorder in 1994-1996. Alaska treated more cocaine dependent people, but marijuana was a close second. Cocaine treatment was a close second in Colorado. Despite the large number of marijuana arrests in Rhode Island and its high ranking on the marijuana-dominated NHSDA drug dependence measure, the state had relatively few admissions for marijuana dependence. It is noteworthy that a large percentage of marijuana treatment admissions are for adolescent abusers.

The states with the highest percentage of their admissions for heroin and other opiates were California, and the eastern states of Rhode Island, Massachusetts, Connecticut, New Jersey, and Maryland. Rhode Island provided treatment to a higher percentage of people with opiate use disorders than any other state in the country. New Mexico, Washington, Oregon, Nevada, and Colorado were western states that treated a moderately high proportion of opiate addicts (about half as many as marijuana and cocaine). Montana, Alaska, and North Dakota had relatively few admissions of people with primary opiate use disorders.

Comparison of Drug Treatment Need Index and Treatment Client Rate

The DNI was compared to the UFDS drug-only client rate to determine how states compared with regard to the supply of services relative to need as illustrated in Figure 4.10. For this analysis the authors divided the two measures into quintiles. Rhode Island had a relatively high level of need (18th nationally) and was in the highest quintile of the client treatment rate (2nd, also 6th in drug treatment admissions). Alaska (31st need, 34 th treatment client), Colorado (13th, 16 th ) and North Dakota (50 th , 50th) matched the levels of drug need and drug-only treatment client rates. Montana had a low level of need (34 th ), but its treatment client rate was in the lowest quintile (47 th ). Montana's drug treatment admissions ranked much higher (18 th out of 41 states). Consequently, Montana's position in the matrix may reflect a measurement artifact of the UFDS survey.

It is important to recognize that survey results suggest that there is a lot of unmet need for treatment, and therefore relative differences among states may say little regarding their absolute ability to meet demand for treatment services.

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Alcohol Treatment Needs

Alcohol Need Index (ANI). According to the Alcohol Need Index (based on deaths with explicit mention of alcohol, driving under the influence arrests, and liquor law violation arrests), New Mexico had the nation's highest alcohol treatment needs, while Hawaii had the lowest alcohol treatment needs (Figure 4.11). In contrast to the states with the highest drug treatment needs, the states in the highest quartile were rural and in the West, except for North Carolina. Alaska'salcohol needs ranked second in the country, while Colorado's ranked 5th, Montana's ranked 7th, and North Dakota ranked 14th. Rhode Island ranked 34th.

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Causes of Death with Explicit Mention of Alcohol.A key component of the ANI, alcohol mortality has long been used as an indicator of the severity of an area's alcohol problems. The authors' own research suggests that deaths with explicit mention of alcohol is the best available indicator of alcohol abuse (McAuliffe et al. 1999, 2000). Inspection of the distribution of this indicator reveals that Alaska had the highest rate in the country, and Colorado (ranked 3rd ) and Montana (7 th )were closely behind it (Figure 4.12). Rhode Island ranked 13th with regard to the alcohol mortality indicator, and North Dakota was also high in the distribution of deaths with explicit mention of alcohol (18th). Hawaii had the lowest explicit alcohol mortality rate in the country. Alaska's alcohol mortality rate was nearly four times higher than Hawaii's.

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Alcohol Defined Arrests. As shown in Figure 4.13, there was wide variation among states in the rate of alcohol-defined arrests, including drunk driving and liquor law violation arrests.For the alcohol need index, the authors wanted to include a measure of alcohol-defined arrests and decided to focus on drunk-driving arrests and liquor law violations. Drunk driving is the most common cause of arrest in the United States, and a large percentage of drunk drivers have been found to have alcohol use disorders according to a variety of standardized measurements (see review in McAuliffe et al. 1999). Liquor law violation arrests are often arrests for underage drinking (in the median state, 25% of arrestees for liquor law violations were adolescents). Other alcohol-related arrest statistics, such as drunkenness, were not used because of the inconsistency among states with regard to these offenses (e.g., 21 states report drunkenness arrests, the rest do not). South Dakota had the highest rate of alcohol-defined arrests, while Rhode Island had the lowest rate. Colorado (5th ), Alaska (7 th), Montana (12th ), and North Dakota (13 th ) had high rates of alcohol arrests.

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DUI's and Driving After Drinking Too Much. Survey data and arrest rates on drinking and driving were inconsistent indicators in some states (Figure 4.14). With regard to the Centers for Disease Control's Behavioral Risk Factor Surveillance Survey (BRFSS) statistics on the percentage of the population that reported driving after drinking too much, the highest percentage was reported in Nevada, and the lowest percentage was in Maine. The highest rate of Uniform Crime Report driving under the influence (DUI) arrests in 1994-1996 was in North Carolina, and the next highest rate was in New Mexico. North Dakota ranked 3rd nationally in the BRFSS for driving after drinking to much, but 30th on the DUI rate . Colorado ranked 10th in driving after drinking too much and 4 th on DUIs per 100,000. Alaska ranked 31st in the BRFSS driving and drinking measure but 6 th in the DUI arrest rate . Montana ranked 12th in the BRFSS measure and 16th in the DUI rate . Even though Rhode Island ranked 21st nationally according to the BRFSS measure, its drunk-driving arrest statistics ranked dead last in 1994-1996 and in 1991-1993. Because of the wording of the BRFSS question, it is possible that the survey and arrest statistics focus on different parts of the drinking and driving picture.

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Another explicit-mention alcohol mortality measure is the state rates of motor vehicle deaths in which the driver or a non-occupant had a blood alcohol concentration equaling or exceeding .10, a level exceeding the official drunk driving standard. New Mexico's rate was the highest in the country, which comes as no surprise since its ANI was also the highest (Figure 4.15). Montana's alcohol-related motor vehicle fatality rate was 5th highest in the country, substantially higher than its DUI rate. Alaska, Colorado, and North Dakota had rates that were above average. Rhode Island's relatively low DUI statistics were confirmed by the State's ranking of 49th lowest with regard to motor vehicle fatalities in which the blood alcohol content was at least .10 percent. Rhode Island's overall motor vehicle fatality rate was 50 th in the country for 1993.

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Summary: Alcohol Treatment Needs. Analysis of the Alcohol Need Index and related measures showed that Alaska, Colorado, Montana, and North Dakota had relatively severe rates of alcohol problems. Rhode Island's level of need was slightly below average (34th nationally). These index scores were generally confirmed by other alcohol-related indicators.

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Alcohol Treatment Clients and Admissions

Colorado had the highest rate of TEDS alcohol treatment admissions in 1994-1996, and it ranked second with regard to the estimated rate of alcohol-only treatment clients reported by the UFDS survey (Figures 4.16 and 4.17). Colorado ranked 4th in the country in the NDATUS alcohol-only measure in 1991-1993. Alaska ranked 2nd in the nation in the TEDS measure, but 11th in the UFDS measure. A similar divergence is apparent for Montana. There the alcohol-only client rate ranked 32nd nationally, and the TEDS primary alcohol treatment rate ranked 9th nationally. North Dakota's values for both measures ranked nearly the same: 22 nd in alcohol-only treatment clients and 23rd in TEDS alcohol treatment admissions. In 1991-1993, North Dakota ranked 21 st in the NDATUS alcohol-only client measure. Rhode Island ranked the same (17th) in both the 1994-1996 TEDS and UFDS measures. It ranked 13th in the 1991-1993 NDATUS measure.

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Comparison of Alcohol Treatment Need and Services

Figure 4.18 compares states with regard to the Alcohol Treatment Need Index and the number of people per 100,000 who were in treatment for alcohol only on a single day. For ease of inspection, the measures are in quintiles. The analysis describes how states compare to each other in meeting their residents' alcohol treatment needs, but it does not describe the absolute level at which states are meeting those needs. Research suggests that all states have been unable to meet the need and demand for alcohol treatment.

Even when persons who are being treated for both alcohol and drugs are included, Nevada stood out as the state that did not have as many alcohol treatment clients as one might expect based on its alcohol mortality and arrests. Nevada's alcohol-only client rate ranked 43 rd in the nation despite the State's relatively high alcohol mortality rate, maternal drinking, motor vehicle fatalities associated with drunkenness, chronic and binge drinking, and DUI rates. Nevada ranked 10 th in the country with regard to alcohol treatment need. New York (35 th in need, 11th in treatment admissions) and Kentucky (38 th in need, 1 st in treatment clients) had the highest levels of clients compared to the states' levels of need.

Rhode Island appeared to be exceeding most states in its treatment of alcohol problems when need is taken into account. Rhode Island ranked 34th with regard to alcohol needs and 17 th with regard to alcohol-only treatment clients per 100,000 residents. As with drug problems, Rhode Island has done a relatively good job providing treatment services to its residents.

Colorado's has also done a good job relative to other states. Colorado ranked 5th with regard to the Alcohol Need Index and second with regard to treatment clients. It was first with regard to alcohol treatment admissions.

Alaska's relative performance depends on which measure of treatment supply one uses. Alaska ranked second in the country with regard to treatment needs. The State also ranked second with regard to the primary alcohol treatment admissions per 100,000, but it ranked 11 th with regard to alcohol-only treatment clients. Even when one includes treatment clients that were treated for both alcohol and drugs, the State's client rate is slightly below its treatment need rank. North Dakota's services were slightly below what would be expected on the basis of its residents' treatment needs. The State ranked 14 th in need and 22 nd with regard to clients in treatment.

Montana ranked 7th in the Alcohol Need Index, but it ranked 32nd in the alcohol-treatment client rate. However, the State ranked 9th in the primary alcohol treatment admissions rate. Consequently, the State's position on the grid may overstate the gap between alcohol need and treatment services relative to other states.

Summary

Overall, the analysis indicated that Rhode Island provided services relative to need at an above-average level among the states for both alcohol and drugs. Colorado's services matched its rank on drug needs and was above average with regard to alcohol. Alaska's drug services were on par with its level of drug treatment need. Alaska's performance regarding alcohol treatment depended on the treatment measure that was used. The State's needs and alcohol treatment admissions matched perfectly, but the alcohol-only treatment client rate was below its treatment needs. North Dakota provided services that matched it low drug need, but its alcohol services were below average relative to its citizens' needs. Montana's success in meeting its residents' needs also depended on which measure was used. For both drugs and alcohol, Montana's treatment admissions rate was much more favorable than its treatment client rate.

Substance Abuse Treatment Needs and Services

Substance Abuse Need Index. Because this index combines the previously presented drug and alcohol need indexes, there should be few surprises. Most notable is the extent to which alcohol needs dominate drug needs because the number of deaths and arrests associated with alcohol is greater than the number of deaths and arrests associated with controlled drugs. As Figure 4.19 shows, New Mexico (99) had the highest SNI score, while Pennsylvania had the lowest (35). While New Mexico's score indicates that it had among the highest rates of both combined substance related mortality and arrests, Pennsylvania's score indicates that no state is even close to being free of substance abuse deaths and arrests.

Colorado and Alaska ranked 2 nd and 3 rd in the country respectively. Both had very high alcohol need scores (87 and 86 respectively). Montana's SNI ranking was 11 th, most clearly attributable to its high alcohol treatment needs. North Dakota ranked 24 th in the country, entirely a function of its severe alcohol treatment needs. Finally, Rhode Island ranked 35 th in the country, despite its significant drug treatment needs.

legend-1.gif

Substance Abuse Treatment Clients. New York State had the highest total treatment client rate in the country for 1994-1996, and Washington had the second highest treatment client rate (Figure 4.20). Minnesota and Arkansas had the lowest rates in the country. Rhode Island ranked 4th and Colorado ranked 5 th among states. Alaska ranked 14th. North Dakota ranked 32 nd , while Montana ranked 42nd.

[ole16.gif]

Figure 4.21 Substance Abuse Treatment Needs and Services

Substance Abuse Treatment Client Rate (Ranks)

Substance Abuse Need (SNI) Ranks

Lowest

(41-50)

Low

(31-40)

Moderate (21-30)

High

(11-20)

Highest

(1-10)

Highest

(1-10)



RI, KY

MI, ME

NY, MD

WA, DE

OR,CO

High

(11-20)

OH, MA

CT, IL

UT, FL



NM, AK, WY, CA

Moderate (21-30)

VA, VT, IN, PA

LA

NE, KS, NJ

SC, WI



Low

(31-40)

WV

MO, NH

OK, ND

ID

SD, NC, AZ, NV

Lowest

(41-50)

TX, AL, HI

TN, AR, IA

GA

MT, MN, MS





Comparison of Substance Abuse Treatment Needs and Treatment Clients

As Figure 4.21 shows there was wide variation among states in the extent to which the total treatment client rate was consistent with the SNI scores. Alaska's overall need for substance abuse services was among the highest in the country, while its services measured by the UFDS treatment clients ranked in the second quintile (11th ). That placed the State slightly below average when compared to other states. This position was due to the State's very high alcohol treatment needs and above average treatment clients. The State's drug need and clients were in the same quintiles. Also, Alaska's treatment admissions rate was second in the country, so by that measure its needs and services matched closely. Colorado ranked in the top ten states with regard to both substance abuse treatment needs and treatment clients. North Dakota was ranked in the middle on both need and supply of services. Montana's treatment services were several quintiles below its high treatment needs. As noted earlier, Montana's TEDS treatment admissions rates ranked higher than its UFDS treatment client rates, and therefore Montana's apparent underperformance may be an artifact of the measurement method. Rhode Island's treatment client rate was among the highest in the country, while its treatment needs were below average, due mainly to a relatively low rank on the alcohol measures.

The analysis of Alaska's and Montana's relative rankings is a clear example of why measuring the adequacy of a state's treatment needs requires valid measurement of both treatment need and supply. If the treatment admissions rather than the treatment clients had been the focus of analysis, the states' performance would have improved substantially. The authors have previously conducted research on the TEDS and UFDS measures, and the findings indicated a slight edge in favor of the UFDS measure. While the two measures correlate with each other reasonably, they are not identical. One measures only public treatment paid by the states but covers all admissions in a year, while the other covers both public and other treatment (e.g., private, tribal, Indian Health Service, etc.) clients on a single day. As explained in the methods section, the use of the UFDS measure in the comparisons with need was dictated by completeness rather than obvious superiority. Accordingly, the authors believe that concern regarding a state's performance in this analysis is warranted only if both measures clearly indicate below average performance.

Appendix

Table 1. Sources of Substance Abuse Indicators

Substance

Indicators

Source

Alcohol

Arrests for DUI, Liquor Law Violations

Uniform Crime Reports; Interuniversity Consortium for Political and Social Research (ICPSR)

Alcohol

Motor Vehicle Fatalities, with BAC> or = .10

Fatal Accident Reporting System, National Highway Traffic Safety Administration.

Alcohol

Explicit-Mention Alcohol Deaths

National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention

Drug

Explicit-Mention Drug Deaths

National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention

Drug

Arrests for Drug Offenses

Uniform Crime Reports; Interuniversity Consortium for Political and Social Research (ICPSR)

Drug, Alcohol

Annual Population Estimates for 1990-2000

U.S. Bureau of the Census

Drug, Alcohol

Clients in Treatment

Uniform Facilities Data Set (UFDS), Office of Applied Studies (OAS), Center for Substance Abuse Treatment (CSAT)

Drug, Alcohol

Treatment Admissions

Treatment Episode Data Set (TEDS), Office of Applied Studies (OAS), Center for Substance Abuse Treatment (CSAT)

Alcohol

Drunk Driving Survey Estimates

Behavioral Risk Factor Surveillance System (BRFSS), Centers for Disease Control and Prevention

References

McAuliffe, W.E., R.A. LaBrie, N. Pollock, N. Lomuto, E.A. Fournier, and R. Betjemann. (1999). "Measuring Interstate Variations in Drug Problems." Drug and Alcohol Dependence. 53 (2): 125-145.

McAuliffe, William E., Richard A. LaBrie, Nicoletta A. Lomuto, Nancy E. Pollock, Rebecca Betjemann, and Elizabeth Fournier. (2000). "Measuring Interstate Variations in Problems Related to Alcohol Use Disorders." The Epidemiology of Alcohol Problems in Small Geographic Areas . vol. 36. Eds. Robert A. Wilson and Mary C. Dufour. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism. 213-44.

McAuliffe, William E., Richard A. LaBrie , and Ryan Woodworth. (2001).Measuring Interstate Variations in Substance Abuse Treatment Needs . Cambridge, MA: National Technical Center for Substance Abuse Needs Assessment.

Households With Telephones

Introduction

This chapter summarizes the results of the household telephone survey conducted in North Dakota by The Gallup Organization in 1996 and 1997. The estimate of treatment need from the household survey is by far the most important component of the integration study because it captures the largest share of the overall population. In 2000, 95.8% of households in North Dakota had telephones (Belinfante 2001), and only a small percentage of the population does not live in a household. The treatment need estimate from this survey, combined with treatment need estimates from subsequent studies of smaller population subgroups will provide an overall estimate of the substance use disorder treatment needs of North Dakotans. The telephone survey also provided an estimate how many of those in need of treatment would like to receive treatment. Comparing that figure with an estimate of how many people received treatment in North Dakota in the past year will make clear the extent of the gap between treatment demand and services.

Methods

The Adult Household Telephone Survey was administered by The Gallup Organization in partnership with the Division of Mental Health and Substance Abuse Services of the North Dakota Department of Human Services. The survey was implemented using a computer-aided telephone interview (CATI) system and a random-digit-dialing sampling procedure designed to avoid unproductive calls to non-household telephone lines (Casady & Lepkowski 1993). The sample consisted of 6,814 North Dakotans ages 18 and over that lived in households with at least one telephone at the time of the survey. The sample was designed to provide substance use disorder prevalence estimates for each of eight planning regions; referred to as Regions 1 through 8. The survey employed the National Technical Center's (NTC) procedure for optimal allocation of the sample. The 18-45 age bracket was oversampled as well in order to obtain estimates for illicit drug use. The survey data provided prevalence estimates of substance use disorders and demand for treatment among North Dakota's American Indians, along with women with dependent children and injection drug users. Response rates for this study were computed using the Council of American Survey Research Organizations' (CASRO) (1982) method.

The telephone survey used the National Technical Center's Needs Assessment Questionnaire (McAuliffe, LaBrie, et al.1995) which measured demographics, alcohol and drug use, DSM-III-R diagnoses of substance use disorders, treatment history, unmet demand for services, treatment service mix, and obstacles to obtaining treatment services. Designed for telephone administration, the instrument included measures drawn from the questionnaires of the Behavioral Risk-factor Surveillance System (BRFSS) (Remington, Smith, et al. 1988; Gentry & CDC, 1989; Bradstock, Forman, et al.1988) and the National Household Survey on Drug Abuse (NHSDA) (SAMHSA & Office of Applied Studies, 1997), as well as the Diagnostic Interview Survey (DIS) (Robins, Cottler, et al. 1990, 1995; Robins, Helzer, et al. 1981, 1989).

The instrument enabled the survey researchers to assess the DSM-III-R's clinical criteria for a substance use disorder for each case (American Psychiatric Association 1994). Several investigators have published results indicating that the diagnoses have reliability and validity (Aktan, Calkins, et al. 1997;Alemagno, Cochran, et al. 1996). Using the data, the authors calculated the overall percentage of North Dakota adults that had a substance use disorder (dependence or abuse) in the past year. Following the recommendations of the National Technical Center, the study assumed that a person with a current substance use disorder was in need of some form of treatment. Further, the study estimated the four levels of care (hospital inpatient, residential, day treatment, outpatient) required by those in need. The level of care estimates were based on the patient placement criteria of the American Society of Addiction Medicine (ASAM) (Hoffmann et al. 1991). This chapter employed the prevalence rates produced by the household telephone survey and population estimates from the 2000 Census (2001). In order to provide perspective for North Dakota's data, the chapter will present household survey data from similar telephone surveys in Montana and Alaska (Table 5.1) (Haynes, et al. 2001; The Gallup Org. 1998b).

Table 5.1 Sampling and Field Results

State

Survey Contractor

Eligibility Ages

Total SampleSize

Statewide CASRO Response Rate

Substate Response Rates

Dates of Survey

North Dakota

The Gallup Organization

18+

6,814

74%

68%-76%

11/96-8/97

Alaska

The Gallup Organization

18+

8,167

71%

68%, 69%, 69%, 76%

5/97-12/97

Montana

Survey Research Unit, Colorado Department of Public Health

18-65

5,501

79%

75%, 77%, 80%

3/96-6/97

Results

The North Dakota Adult Household Survey was conducted between November of 1996 and August of 1997. It interviewed 6,814 non-institutionalized individuals living in households with telephones in North Dakota. Interviewers achieved a 74% percent response rate for the general statewide sample and more than 68% in each of the substate planning regions. That statewide response rate was greater than the rate obtained in Alaska, but less than the rate in Montana. All of the surveys met the NTC's recommended 70% response rate (Table 5.1).

Demographics

The survey respondents from the three states had similar demographic characteristics in most respects (Table 5.2). The average adult respondents were in their early 40's, and slightly more than half were females. While whites comprised the largest racial group in the samples (95% in North Dakota), the Alaska and Montana samples were more than one-fifth American Indians or Alaska Natives. There were few African Americans, Hispanics, or Asian Americans in any of the three states. The adult samples had similar educational levels and employment rates, although North Dakota's employment rate was slightly lower than the rates in Alaska and Montana. In subsequent analyses, the data have been weighted to adjust for oversampling.

Table 5.2 Demographic Characteristics (unweighted)

Demographic Characteristics

Alaska

Montana

North Dakota

Age (mean years)

40.2

40.8

43.9

Sex: % female

54

57

58

Race:







% White

71

77

95

% African American

2

0

1

% American Indian, Alaska Native

23

21

4

% Asian, Pacific Islander

3

0

0

% Other

1

2

0

Education: % with some college

61.6

59.6

62.3

Employed:







% full time

64

64

59

% part time

14

14

14

Substance Use Rates

Among the three states, Montana had slightly higher levels of alcohol use, while Alaska had the highest drug use rates for most time periods (Table 5.3). North Dakota had the lowest drug use rates; Montana's drug use rates were consistently between the other two. As is typical for such statistics, the patterns for marijuana use closely followed estimates of any illicit drug use. It is noteworthy that the state studies estimated past month alcohol use at the same level as the BRFSS telephone survey. Both surveys used the same item to obtain those estimates.

Table 5.3 Substance Use Among State Residents Aged 18 to 65 (%)

Substance

Period of Use

Alaska

(n=7,671)

Montana (n=5,501)

North Dakota (n=5,762)

Alcohol

Lifetime

95

98

95

Last 18 months

72

78

76

Last 30 Days

53

58

46

1995 BRFSS Alcohol

Last 30 Days

59

56

52

Illicit Drugs

Lifetime

51

44

23

Last Year

9

6

3

Last 30 Days

5

3

1

Marijuana

Lifetime

47

44

20

Last Year

9

6

2

Last 30 Days

5

3

1

Illicit Drugs Except Marijuana

Lifetime

27

18

8

Last Year

2.4

1.3

0.6

Last 30 Days

0.8

0.5

0.2

Cocaine

Lifetime

18

12

4

Last Year

1

0.4

0.2

Last 30 days

0.4

0.1

0

Hallucinogens

Lifetime

17

13

4

Last Year

1

1

0.3

Last 30 Days

0.5

0.2

0

Stimulants

Lifetime

12

9

4

Last Year

0.3

0.4

0.3

Last 30 Days

0.1

0.2

0.1

Inhalants

Lifetime

4

na

1

Last Year

0.1

na

0

Last 30 Days

0

na

0

Heroin and other Opiates

Lifetime

3

2

1

Last Year

0.2

0.1

0

Last 30 days

0

0

0

Lifetime Substance Use Disorder Rate

The North Dakota household telephone survey estimated that 9.6% of North Dakota adults aged 18 and older had a lifetime substance use disorder diagnosis. The percentage for respondents aged 18 to 65 was 10.9% (Table 5.4). Alcohol use disorders were most common: 13.2% of the men and 4.0 % of the women surveyed had a lifetime alcohol use disorder (Gallup 1998). In the current study, having a substance use disorder (either abuse or dependence) implies a need for treatment, but only a past year substance use disorder implies a current treatment need. According to the report, North Dakotan men experienced a lifetime need for alcohol treatment that was three times the needs of women, and a need for drug treatment that was four times the women's rate. Dependence was more common than abuse in both the case of drugs and alcohol (Gallup 1998). North Dakota's lifetime substance use disorder rate for 18 to 65-year-olds was lower that Alaska and Montana's.

Current Substance Use Disorders: Need for Treatment

Based on data from the household telephone survey, NCRPG estimated that 5.2% of North Dakotans over the age of 18 living in households with telephones had a current substance use disorder. In 2000, that estimate represented over 23,000 people (Table 5.5). North Dakota's rate was less than Montana's and Alaska's for the 18 to 65 age group. The lion's share of the residents in need of treatment in all three states consisted of people with alcohol use disorders. Of those between the ages of 18 and 65 surveyed in North Dakota, 5.9% had a past year alcohol use disorder while less than 1% had a past year drug use disorder. Among all persons ages 18 and older, there was a 0.1% rate of marijuana dependence (Gallup 1998a). Statewide estimates of dependence and abuse for all other drugs were 0%. There were some non-zero estimates for drugs other than marijuana at the regional level, but only one where zero was not within the standard error of the point estimate (a 0.2% rate of stimulant dependence in the Northwest region with a standard error of 0.1)(Gallup 1998) . The preponderance of marijuana problems over other drug use disorders is consistent with the high proportions of marijuana arrests and treatment admissions demonstrated in the previous chapter (the interstate chapter).

Table 5.4 Use Disorders: Abuse and Dependence on Alcohol, Marijuana, Other Drugs, All Substances: Weighted Percent (Unweighted Number of Respondents in Sample 18-65)

Substance

Recency

Alaska

Montana

North Dakota

Alcohol

Lifetime

14.4% (1,161)

15.8% (922)

10.6% (623)



Last Year

7.6% (612)

8.6% (443)

5.9% (335)

Marijuana

Lifetime

1.7% (140)

2.2% (134)

0.5% (26)



Last Year

0.6% (53)

0.9% (47)

0.2% (15)

Other Drugs*

Lifetime

0.5% (29)

1.7% (109)

0.3% (18)



Last Year

0.3% (17)

0.3% (23)

0.1% (8)

Alcohol or Drugs

Lifetime

14.9% (1,214)

16.8% (973)

10.9% (639)



Last Year

7.8% (631)

8.8% (460)

5.9% (338)

Alcohol or Drugs

Last Year Dependence (1999 NHSDA)

7.3%

4.8%

5.7%

*Includes hallucinogens, cocaine, and opiates in all states.

Comparison with 1999 National Household Survey on Drug Abuse

Interestingly, the 1999 National Household Survey on Drug Abuse (NHSDA) produced estimates of substance dependence for North Dakota and Alaskathat were nearly as high as the total substance use disorder estimates from the telephone surveys in those states (Table 5.4) (OAS 2000). The past year substance dependence estimate from the NHSDA for Montana was four percentage points lower than the substance use disorder estimate from the telephone survey. Consequently, the NHSDA would rank the states differently than the telephone surveys in terms of relative need for treatment. According to the NHSDA, Alaska had the highest past year substance dependence rate among the three (7.3%), while Montana had the lowest rate (OAS 2000). The NHSDA uses the 12 and older population as its base, whereas, the estimates cited above use the 18 to 65 population as a base. Therefore, it is difficult to know how to interpret these differences. In most studies, adolescents have higher dependence rates than adults, especially when older adults are included. It is reasonable, therefore, that the NHSDA dependence estimates are somewhat higher than the telephone survey dependence estimates. The NHSDA indicated that adults in Alaska continued to experience relatively high rates of substance dependence after the age of 26 (6.3%), and because that is the largest population group in the NHSDA sample, the rate for the State was higher. The NHSDA's sample also included residents of households without telephones, whereas the telephone survey does not. As shown in the next chapter, that difference alone could account for the relatively high NHSDA estimate.

Table 5.5 Need for Treatment, Met and Unmet Demand Among North Dakota Adults Living in Households with Telephones (18 and older)



Current Substance Use Disorder

Lifetime Substance Use Disorder



%

Number

%

Number

Need for Treatment

7.4

29,739

14.3

57,468

Met Treatment Demand Among Those In Need of Treatment

14.7

4,372

-

-

Unmet Treatment Demand Among Those In Need of Treatment

1.8

457

-

-

The lifetime and current numbers in need of treatment were estimated using the 18 and older population count from the 2000 Census.

Met Demand for Treatment

Those who received treatment for a substance use disorder in the past year may be thought of as having a "met demand" for treatment, though some may not have received the amount or type of treatment they wanted and some may have been ordered to obtain to treatment by the courts. The North Dakota Adult Household Survey revealed that 7.8% of those with a current diagnosis for a substance use disorder received treatment in the past year (Table 5.5). That was 1,796 North Dakotans in 2000. That estimate included both specialty and non-specialty treatment, as reported by respondents in the telephone survey. Men with a past-year need were more likely to receive treatment than women.

Unmet Demand for Treatment

Those who needed treatment in the past year and wanted it, but did not receive any can be considered to have an "unmet demand" for treatment . Among the respondents in need of treatment who did not receive it during the past year, less than 2% reported that they would have sought treatment if it had been readily available. That estimate implied that only 425 North Dakota adults who needed treatment in the past year but did not get any, perceived they had a need for treatment and wanted it. However, 15.2% of those with a current diagnosis who did receive treatment in the past year reported that they would have liked additional or more intensive treatment than they received. That group's needs must be taken into account when assessing the need for new services. Women with a past-year need were far more likely than men to express an unmet demand for treatment.

The Treatment Gap

Because the telephone survey estimate of need comprises the largest part of total statewide estimated need for treatment, it is informative to examine the gap between reported need and reported treatment services from the telephone survey. Whereas 23,031 of those surveyed needed treatment in the past year, only 1,796 received treatment for a substance use disorder. Hence, 7.8% of total need for treatment among adults in households with telephones was accommodated to at least some degree. If the State were to provide treatment services to all of the persons estimated to need it, services would have to increase by almost 13 times the current level. Of course, the fact that a person was diagnosed as having an addictive disease does not imply that the individual would seek treatment if it were readily available. Given the rate of unmet demand for treatment in North Dakota, treatment services would have to be increased by 23.7% of their current level in order to serve everyone with a demand.

Summary

There is a moderate level of need for treatment for substance use disorders in North Dakota. Over 5% of the adult household population had a need for treatment in the past year. Less than 8% of those with a need for treatment received some form of treatment in the past year. Among those in need of treatment who did not receive treatment in the past year, only 2% expressed a desire to get treatment. Because the percentage of those that expressed an unmet demand was low, the State may wish to encourage demand for treatment among those in need by employing outreach and other techniques designed to remove obstacles to obtaining treatment. Potential target groups for such an effort include American Indians and people between the ages of 18 and 25.

References

Aktan, Georgia B., Richard F. Calkins, Kurt M. Ribisl, Alice Kroliczak, and Rafa M. Kasim. (1997). "Test-Retest Reliability of Psychoactive Substance Abuse and Dependence Diagnoses in Telephone Interviews Using a Modified Diagnostic Interview Schedule - Substance Abuse Module." American Journal of Drug and Alcohol Abuse. 23(2):229-48.

Alemagno, Sonia A., Deborah C. Cochran, Thomas E. Feucht, Richard C. Stephens, John M. Butts, and Stephanie A. Wolfe. (1996). "Assessing Substance Abuse Treatment Needs Among the Homeless: a Telephone-Based Interactive Voice Response System." American Journal of Public Health. 86(11):1626-28.

American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition): DSM-IV. Washington, DC: American Psychiatric Association.

Belinfante, Alexander. (2001).Telephone Subscribership in the United States (Data Through November 2000). Washington, D.C.: Federal Communications Comission.

Bradstock, M. K., Michele R. Forman, Nancy J. Binkin, Eileen M. Gentry, Gary C. Hogelin, David F. Williamson, and Frederick L. Trowbridge. (1988). "Alcohol Use and Health Behavior Lifestyles Among U.S. Women: the Behavioral Risk Factor Surveys." Addictive Behaviors. 13(1):61-71.

Casady, Robert J. and James M. Lepkowski. (1993). "Stratified Telephone Survey Designs." Survey Methodology. 19(1):103-13.

Council of American Survey Research Organizations (CASRO). (1982).On the Definition of Response Rates: A Special Report of the CASRO Task Force on Completion Rates. Port Jefferson, NY: Council of American Survey Research Organizations.

The Gallup Organization. North Dakota Adult Household Needs Assessment Survey, North Dakota Adult Household Telephone Survey Statewide and Substate Planning Regions. (1998a). North Dakota Department of Health and Social Services, Division of Alcoholism and Drug Abuse.

The Gallup Organization.North Dakota Adult Household Telephone Survey . 1998b. North Dakota Division of Mental Health and Substance Abuse Services.

Gentry, Eileen M. and CDC. 1989. Behavioral Risk Factor Surveillance System: Operations Manual . Atlanta, GA: Office of Surveillance and Analysis, CDC.

Hoffmann, Norman G., James A. Halikas, David Mee-Lee, and Richard D. Weedman. (1991).Patient Placement Criteria for the Treatment of Psychoactive Substance Use Disorders. Washington, DC: American Society of Addiction Medicine.

Kish, Leslie. (1965).Survey Sampling. New York, NY: John Wiley & Sons.

McAuliffe, William E., Richard A. LaBrie, Norah Mulvaney, Howard J. Shaffer, Stephanie Geller, Elizabeth A. Fournier, Eliot B. Levine, Qiuyun Wang, Susan M. Wortman, and Kathleen A. Miller. (1995). Assessment of Substance Dependence Treatment Needs: A Telephone Survey Manual and Questionnaire, Revised Edition . Cambridge, MA: National Technical Center on Substance Abuse Needs Assessment.

Office of Applied Studies and Research Triangle Institute. (2000). Summary of Findings from the 1999 National Household Survey on Drug Abuse. Substance Abuse and Mental Health Services Administration.

Remington, Patrick L., Meredith Y. Smith, David F. Williamson, Robert F. Anda, Eileen M. Gentry, and Gary C. Hogelin. (1988). "Design, Characteristics, and Usefulness of State-Based Behavioral Risk Factor Surveillance: 1981-87." Public Health Reports. 103(4): 366-75.

Riley, William, George Haynes, and Vincent Smith. (1997). Montana Adult Household Substance Abuse Treatment Analysis. Montana Department of Public Health and Human Services.

Robins, Lee N., Linda B. Cottler, and Thomas F. Babor. (1990). Diagnostic Interview Schedule-- Substance Abuse Module (DIS-SAM). St. Louis: Department of Psychiatry, Washington University School of Medicine.

Robins, Lee N., Linda B. Cottler, Kathleen K. Bucholz, and Wilson M. Compton III. (1995). Diagnostic Interview Schedule for DSM-IV (DIS-IV). St. Louis, MO: National Institute of Mental Health.

Robins, Lee N., John E. Helzer, Linda B. Cottler, and E. Goldring. (1989). NIMH Diagnostic Interview Schedule Version III Revised (DIS-III-R). St. Louis: Department of Psychiatry, Washington University School of Medicine.


Robins, Lee N., John E. Helzer, J. L. Croughan, and K. S. Ratcliff. (1981). "The NIMH Diagnostic Interview Schedule: Its History, Characteristics and Validity." Archives of General Psychiatry . 38:381-89.

Substance Abuse and Mental Health Services Administration (SAMHSA), Office of Applied Studies and Research Triangle Institute. (1997). National Household Survey on Drug Abuse: Population Estimates 1996. Rockville, MD: U.S. Department of Health and Human Services.

United States Census Bureau. (2001). State and County QuickFacts : Population Estimates, 2000 Census of Population andHousing. www.quickfacts.census.gov/qfd/states/30000.html.

Households Without Telephones

This chapter estimates the amount of potential noncoverage bias in estimates of the need for treatment that may result because telephone substance abuse surveys fail to include households without telephones. Of the populations missed by a telephone survey, the largest group consists of residents of households without telephones (McAuliffe et al. 1998).If the substance use disorder rate of residents of households without telephones differs from the rate of residents of all households, failing to adjust for this difference would result in bias if a telephone survey's estimates were generalized to the entire household population. This shortcoming of telephone surveys is of course widely recognized by scientists and lay persons, but few researchers can afford to eliminate it by conducting a face-to-face survey or a multi-mode survey that covers households without telephones (McAuliffe et al. 1998). To address the issue, authors routinely caution readers in publications of the results of telephone substance abuse surveys.

These caveats provide readers with little guidance to interpret the survey's results. The amount of bias depends upon the proportion of households without telephones and the size of the difference between rates for households with and without telephones. In the last decade, the national rate of households without telephones was between 5% and 6%. The median state rate was 5% in 1990 and 5.4% in 2000 (Bureau of the Census 1994; Belinfante 2001). The Census Bureau's most recent national estimate of the percentage of households without telephone coverage was 5.6% in 2000(Belinfante 2001). The range for individual states was narrow: only two states have rates in excess of 10% (Mississippi 10.2% and Arkansas 11.4%). Consequently, the size of the difference between households with and wit hout telephones would have to be substantial to cause a meaningful amount of bias for most purposes. For example, if a state's telephone noncoverage rate were 5% and the substance use disorder rate for the residents of households with telephones were 10%, the rate for residents of households with telephones would have to be 30%, or three times greater, in order to increase the estimated rate for residents of all households by one percent, that is, to 11%. If the difference in the substance abuse rate between households with and without telephones were smaller than 30%, even though the difference was statistically significant, the amount of bias may be no more than a few tenths of a percent. Removing bias of this magnitude may not be important enough for many substantive questions to increase the survey cost significantly. Many publications report results rounded to whole percentage points or to one decimal place (e.g., National Houshold Survey on Drug Abuse). However, many readers do not know that the bias is likely to be that size. It is therefore important to estimate the likely impact of excluding households without telephones so that investigators may adjust their results or provide readers with more precise guidance regarding the accuracy of their results (McAuliffe et al. 1998).

Telephone Noncoverage Bias in Prior Research

Unfortunately, published research on this issue is too limited and the results are too inconsistent to provide the estimates needed for this purpose from existing statistics. Several studies have found that failure to include households without telephones had only modest effects on most health indicators (Anderson et al. 1998; Ford 1998 ), but some authors have argued that substance use may be an exception (Aquilino 1992;Gfroerer & Hughes 1991; Pearson et al. 1994). Using data from the 1994 National Health Interview Survey (n=12,035 to 83,719), Anderson et al. (1998) found that one of the largest differences between all households and households with telephones concerned current smoking (25.4% vs 24.4%, a difference of 1%), but the difference was greater for households in poverty (36.5% vs 33.3%). Using data from the National Health and Nutrition and Examination Survey conducted between 1988 and 1991 (n=10,120), Ford (1998) found that participants with and without telephones did not differ significantly with regard to alcohol consumption, but the trend suggested more drinking by people in households with telephones. Aquilino (1992) reported that a face-to-face survey of 1,002 New Jersey household residents conducted in 1986 found that a higher percentage of people with telephones than people without telephones reported use of alcohol in the last month and in the last year. Aquilino (1992, Table 4) also found the same effect for ever use of marijuana, but an opposite effect for the amount of alcohol use in the last month, marijuana use in the last month, and cocaine use in the last month, year, and ever. Only one of these differences was statistically significant. Moreover, Aquilino reported his results as whole percentages, and therefore the impact of these latter differences on the percentages for all households versus households with telephones were 1% for past month marijuana and cocaine use and 0% for ever use and past month use of marijuana and cocaine.

Gfroerer & Hughes (1991, Table 3)reported the potential for telephone noncoverage bias in analysis of the 1985 and 1988 National Household Survey on Drug Abuse (n= 5,751 in 1985 and 8,814 in 1988). They found that people in households without telephones reported greater lifetime and past month use of marijuana and cocaine than people in households with telephones. The percentage differences between all households and households with telephones ranged from 0.3% for past year cocaine use and 1.3% for lifetime marijuana use in 1988; the differences were smaller in 1985, although in the same direction. Pearson et al. (1994) reported significant differences between households with and without telephones in a face-to-face survey of 410 young adult American Indians; the odds ratio for past month marijuana use was 3.5. The present authors could find no study that reported data on rates of substance use disorders or treatment utilization. No studies reported the effect for Hispanic populations. Overall, the number of studies is small, the findings were incomplete and somewhat inconsistent, the geographical coverage and sample sizes for several of the studies were limited, and most of the results for drugs are now somewhat out of date.

The present study used a large sample of recent data to test the hypothesis that excluding households without phones results in noncoverage bias in estimates of substance abuse and treatment needs if the results are generalized to all households, but the size of the bias is relatively small at the national level. The effects vary depending on the substance and the demographic composition of the target population.

Methods

The study employed public use data collected in the 1995, 1996, 1997, and 1998 versions of the National Household Survey on Drug Abuse (NHSDA). The accompanying public use codebooks downloaded from the Office of Applied Studies' web page provided the variable names and labels, value codes and labels, and composite variable definitions. The authors used the NHSDA's composite definitions as much as possible in order to produce results that are comparable to the findings reported in the codebooks and in published NHSDA documents. The NHSDA's target sample is the civilian, noninstitutionalized, 12 and older residents of households and group quarters. Interviews were conducted in person, with a mix of interviewer- and respondent-recorded (answer sheets) questions. The sampling design is a multi-stage, area probability sample with an in-household selection procedure to ensure that desired race/ethnicity and age subsample sizes are achieved (see SAMHSA & Office of Applied Studies 1999for details). The overall (screening x interview) response rates ranged from 72% to 76% for the four years (Office of Applied Studies 1996,1997a,b, 1999, 2000). Individual responses were weighted to adjust for probability of selection, household and person-level nonresponse, and to replicate census demographic distributions.

The present study analyzed the NHSDA interview data from the 84,796residents living in households and for whom the NHSDA were able to determine telephone subscribership. Beginning in 1991, the National Household Survey on Drug Abuse added a small, heterogenous subsample of persons living in noninstitutional group quarters (college dorms, shelters, etc.). Because persons in group quarters are often excluded from telephone household surveys, the study excluded them and persons lacking data (refusals, item nonresponse) on telephone subscribership from the analysis. The percentage of subjects living in households without at least one telephone for the combined four years of survey data was 6.8% for the unweighted NHSDA sample of respondents.

Results

Compared to respondents living in households with telephones, those without telephones reported higher rates of drug use, dependence, need for treatment, and having received treatment (Table 6.1). All of the differences between residents of houses with and without telephones were statistically significant at the .01 level. The median relative risk for residents of households without telephones was 2.13, with most risk ratios between 1.00 and 2.00 (6) and between 2.00 and 3.00 (10). Only one exceeded 3.00. Consistent with previous studies, persons in households without telephones reported lower rates of alcohol use in the past month and year than persons in households with phones.

Table 6.1 Effect of Excluding Households Without Telephones on Substance Use, Dependence, and Treatment Estimates

Substance

Measure

A. Households without Telephones (n=5,822)

B.

Households with Telephones (n=78,974)

C.

Relative Risk (A/B)

D.

All Households (n=84,796)

% Difference Between All Households and Households with Telephones (D-B)

Ratio of All Households and Households with Telephones (D/B)

Alcohol or Illicit Drugs

% Dependent Past Year

10.16

4.78

2.13

4.99

0.21

1.04



% Received Treatment in Past Year*

2.14

0.99

2.16

1.04

0.05

1.05



% Received or Perceived Need for Treatment in Past Year*

4.6

1.6

2.87

1.7

0.1

1.07



% Received Treatment Lifetime*

7.5

3.3

2.27

3.5

0.2

1.05

Alcohol

% Used Past Month

46.5

51.7

0.90

51.5

-0.2

.996



% Used Past Year

56.9

64.8

0.88

64.5

-0.3

.995



% DependentPast Year

8.3

4.0

2.09

4.2

0.2

1.04



% Received TreatmentPast Year

1.34

0.78

1.72

0.81

0.03

1.04

Any Illicit Drug

% Used Ever

39.47

34.80

1.13

34.98

0.18

1.005



% Used Past Year

17.95

10.37

1.73

10.67

0.30

1.03



% Dependent Past Year

4.44

1.57

2.83

1.68

0.11

1.07



% Received Treatment in Past Year

1.01

0.40

2.53

0.43

.03

1.08

Marijuana

% Used Ever

35.8

32.0

1.12

32.2

0.2

1.005



% Used Past Year

14.5

8.3

1.76

8.5

0.2

1.03



% Dependent Past Year

3.15

1.28

2.46

1.35

0.07

1.05

Cocaine

% Used Ever

13.4

10.2

1.31

10.4

0.1

1.01



% Used Past Year

4.3

1.7

2.57

1.8

0.1

1.06



% Dependent Past Year

1.25

0.25

5.00

0.29

0.04

1.16

Heroin

/opiates

% Used Ever*

2.7

1.0

2.79

1.1

0.1

1.1

Note: The table presents the unweighted sample size and the weighted percentages.

* The sample sizes are slightly smaller for these variables because of missing data, blanks, and item refusals.

Comparing the prevalence rates for the entire household sample with the rates of persons in households with telephones, the analysis showed that the impact of omitting households without telephones was generally small. The percentage difference between all households and households with telephones ranged from between -0.3% and 0.3%, with a mean of 0.09, median of 0.1%, and mode of 0.1%. The ratio of the rate for all households to the rate for households with telephones ranged from .995 to 1.16, with a mean and median of 1.04. In other words, the percentages for all households were 4% higher on average than the percentages for households with telephones alone.

There was lessbias for the alcohol than for illicit drugs, and there was less bias for ever use than past year dependence. The smallest indication of noncoverage bias for a specific drug was for ever use of marijuana, while the largest was for past-year cocaine dependence.

Bias and Demographic Characteristics

The amount of bias resulting from telephone noncoverage depends on the percentage of people without telephones and the relative risk of not having a telephone. As expected from previous research (e.g., Aquilino, 1992; Belinfante, 2001), the highest proportions of respondents living in households without telephones were among American Indians/Alaskan Natives (19.2% ) and African Americans (9.2%), p oor people (12.3%), Hispanics (8.6%), males (4.3%), and adolescents (4.1%) (Table 6.2). Significant variations in the amount of telephone noncoverage bias in dependence or treatment estimates were evident across each of these major demographic variables. There was a significant interaction between race and phone subscribership on past-year drug dependence. The greatest amount of bias (2.18 ratio of all households to households with telephones) was a mong American Indians/Alaska Natives (19.2% were without phones and the relative risk of drug dependence for people without a telephone was 6.8).

Lower income persons had more telephone noncoverage bias than people with higher incomeswith respect to the dependence measures, even though the relative risk of not having a telephone was significantly greater for the higher income group. Few respondents with incomes over $20,000 lacked telephones, although those who reported no phone had significantly higher rates of drug, alcohol, and substance use disorders than those with telephones. Adolescents had significantly lower rates of telephone noncoverage bias than adults had with regard to the dependence measures, an effect due entirely to the adolescents' lower relative risks. Hispanics had higher proportions of households without phones than non-Hispanics had, but Hispanics had much lower relative risks. As a result, Hispanics had relatively smaller differences than non-Hispanics between all households and households with telephones for all four of the measures in Table 6.2. Females had slightly more evidence of telephone noncoverage bias than males had for drug dependence and treatment, but females were less likely than males to live in households without telephones. The males and females had the same relative risks for alcohol dependence and substance dependence (i.e., the interaction between gender and telephone subscribership was not significant for those two variables).

Table 6.2 Relative Risk and Ratios of All Households to Households with Telephones by Demographic Characteristic

Demographic

Characteristic

Group

% House-holds Without Tele-phones

Drug Dependence in the Past Year

Alcohol Dependence in the Past Year

Alcohol or Drug Dependence in the Past Year

Received Treatment in the Past Year

Relative Risk of No Tele-phone

Ratio of All Households to Households with Telephones

Relative Risk of No Tele-phone

Ratio of All Households to Households with Telephones

Relative Risk of No Tele-phone

Ratio of All Households to Households with Telephones

Relative Risk of No Tele-phone

Ratio of All Households to Households with Telephones

Race

American Indian/

Alaska Native (n=1,889)

19.2

6.86

2.13

2.18

1.23

2.46

1.28

2.68

1.32



African American (n=19,650)

9.2

1.98

1.09

2.01

1.09

1.98

1.09

1.38

1.03



White (n=60,734)

3.1

2.75

1.05

1.93

1.03

1.99

1.03

2.33

1.04



Asian/Pacific Islander (n=2,523)

1.2

0.76

.997

3.83

1.03

3.12

1.03

0.00

0.99



Race Interaction



p<.05



ns



ns



ns



Income

Less than $20,000 (n=19,313)

12.3

1.92

1.11

1.71

1.09

1.67

1.08

1.58

1.07



$20,000 or more (n=51,232)

1.5

3.99

1.04

2.52

1.02

2.59

1.02

2.04

1.02



Income Interaction



p<.05



p<.05



p<.05



ns



Age

Adult (18+) (n=61,210)

3.9

3.40

1.09

2.19

1.05

2.27

1.05

2.23

1.05



Adolescents (12-17) (n=23,586)

4.1

1.29

1.01

1.21

1.01

1.21

1.01

1.66

1.03



Age Interaction



p<.05



p<.05



p<.05



ns



Hispanic Origin

No (n=62,524)

3.4

3.29

1.08

2.30

1.04

2.37

1.05

2.41

1.05



Yes (n=22,272)

8.6

1.20

1.02

1.28

1.02

1.22

1.02

1.20

1.02



Hispanic Interaction



p<.05



p<.05



p<.05



p<.05



Gender

Female

5.4

3.53

1.09

2.01

1.04

2.32

1.05

2.75

1.06



Male

4.3

2.36

1.06

2.01

1.04

1.94

1.04

1.82

1.03



Gender Interaction



p<.05



ns



ns



p<.05



Discussion

Analyzing data from four years of the National Household Survey on Drug Abuse, the study found that residents of households with telephones consistently reported less drug use, less dependence on drugs and alcohol, and less substance treatment, but more alcohol use than residents of households without telephones. The percentage differences between respondents with telephones and all respondents averaged one tenth of a percent, and ratios of all households to households with telephones averaged 1.04. Thus, adjusting the rates for households with telephones to estimate noncoverage bias would likely result in an estimate that is about 4% larger for the entire household population.

The amount of bias appeared to vary by substance, timeframe, use versus dependence, and demographic characteristics. Whereas alcohol use was higher in households with telephones, drug use was higher in households without telephones. Ever use of marijuana had less bias than past year cocaine dependence. Race and income had the greatest variations in the amount of potential bias that was evident.

Comparison With Previous Studies. The present study found smaller differences between all households and households with telephones in use of marijuana and cocaine than reported by Gfroerer and Hughes (1991). As Anderson et al. (1998) found with other health indicators, household drug use estimates from a telephone survey would not generally be increased substantially by noncoverage of households without telephones.

The present study was the first to report estimates of the effect of noncoverage of households without telephones on dependence and treatment estimates. Most likely due to their small samples, the previously published studies examined use of alcohol, marijuana and cocaine, but none of them considered dependence or treatment. The percentage difference between all household and households with telephones was 0.2% for dependence on alcohol or controlled drugs. Adding in households without telephones increased the estimated prevalence by a small proportion (0.04). Estimates of bias were larger for drug dependence than for alcohol dependence and were larger for cocaine dependence than for marijuana dependence.

Race and Ethnicity.In the present study, American Indians/Alaskan Natives had the greatest amount of telephone noncoverage bias, and the amount for African Americans was second. Asian and Pacific Islander respondents had the lowest evidence of noncoverage bias. Pearson et al. (1994) found significant odds ratios among Native Americans between residents with and without telephones, but his small study presented no information that permitted an investigator to estimate the amount of bias that these differences might produce. Because Native Americans have the highest rates of households without telephones of any racial group, the evidence in the present study should be especially useful for interpreting telephone survey results for that racial group. Aquilino (1992) found higher rates of noncoverage effects among African Americans than among the full sample, but his sample of African Americans was small (21 without telephones and 139 with). Findings from a much larger study reported by Gfroerer and Hughes (1991, Table 2) for African Americans and whites were inconsistent with Aquilino's findings: the differences in percentages who used a substance between households with and without telephones were smaller for African Americans than for whites in every comparison. The present study found that Hispanics had lower evidence of noncoverage bias than persons who were not Hispanics.

Age and Income. In the present study, adolescents had lower rates of telephone noncoverage bias than adults.In the only previous study that addressed the issue, Gfroerer & Hughes (1991) found that lifetime and past year rates of marijuana and cocaine use were higher for adults in nontelephone households than in telephone households in 1985 and 1988, but for adolescents in the telephone households respondents reported higher rates of drug use in 1985 and the reverse in 1988. Consequently, telephone surveys of adolescents appear to be less vulnerable to noncoverage bias. The present study found that low-income persons had more telephone noncoverage bias than people with higher incomes. This result was consistent with Anderson et al.'s (1998) finding with regard to tobacco.

Use of the Results. How much concern researchers and policy makers should have regarding these levels of potential bias depends upon the nature of their study or their intended use of the data. The relevance of these differences between subsamples of face-to-face survey respondents may generalize imperfectly to the differences between telephone and face-to-face surveys or the parts of a multi-mode study that used telephone interviewing for residents of households with telephones and face to face interviews for residents of households without telephones, but it is likely that the present results provide an indication of what is likely to be found.

In some research situations even very small systematic errors of estimation translate into large numbers of people. However, a review of many reports of substance abuse surveys in scholarly journals and state needs assessment study reports reveals that rates of substance use and use disorders were generally reported as whole percentages or at most to one decimal place. In those cases, a potential bias of 0.1% to .3% is likely to have relatively little or no effect on the study's results or conclusions.

From a scientific standpoint, it is also reasonable to argue that the NHSDA, the nation's premier substance abuse survey, should be as accurate as possible and should include groups, such as people in households without telephones and group quarters, that may be ignored by most studies that have limited budgets. Otherwise, important subgroups would be systematically missing from the nation's primary data on substance abuse, and of course there would be no data available for studies such as the current one.

For many state and local studies of the general population, however, it is often only financially feasible to conduct telephone surveys or perhaps mixed-mode surveys. The cost savings of a telephone survey is substantial compared to a face-to-face survey (McAuliffe et al. 1998), and the bias reported in the present study is within the level of accuracy required by many state and local survey users. For the same amount of money, a face-to-face survey would have a fraction of the number of interviews and would therefore have much more random error of estimation than a telephone survey would have. The tradeoff is therefore between systematic error and random error. For studies that require a high level of precision but lack the resources to conduct face-to-face interviews, estimates from the current study can be used with estimates of the number of people in households with telephones to adjust the results for noncoverage bias. In any case, researchers can use the present results to provide readers with more informed guidance regarding their study's accuracy.

State Estimates

Using the information from the analysis, the authors estimated the percentage of residents in all households who had a current substance use disorder (treatment need). The estimate was imputed by constructing a weighted sum of the treatment need prevalence of people living in households with telephones from the state's telephone survey plus the treatment need rate of residents in need living in households without telephones. For example, in North Dakota's the calculation first obtained the percent of the household population who were people in need in households with telephones (telephone survey estimate of the percentage with a substance use disorder diagnosis x the proportion of people in households with telephones= 5.2% x .958= 4.982%) and then imputes the estimate of the prevalence rate for people in households without telephones by multiplying the telephone prevalence rate times the relative risk estimated in Table 1 above (telephone rate x relative risk= 5.2 x 2.13= 11.076). That imputed estimate is then weighted by the proportion of people in households without telephones (11.076 x .042= .465). In this instance, the authors assumed that the percentage of people living in households without telephones was equal to the percentage of households without telephones. The two weighted prevalences were then added together (4.982 + .465= 5.4). The result is an imputed estimate of the percentage of people in need who live in all households. These estimates will be incorporated into the integration study to adjust for noncoverage of households without telephones.

Table 6.3 State Estimates of Noncoverage Bias on Estimates of Treatment Need from Telephone Surveys

State

Proportion of Residents of Households With Telephones

Telephone Estimate of the % of Population in Need of Treatment

Proportion of Residents in Households without Telephones 2000*

Adjusted Estimate of % Population in Need of Treatment Living in Households Without Telephones

Estimate of the % of Population in Need of Treatment After Adjustment for Households without Telephones

Alaska

.943

7.4

5.7

15.762

7.9

Colorado

.963

8.0

3.7

17.040

8.3

Montana

.946

8.8

5.4

18.744

9.3

North Dakota

.958

5.2

4.2

11.076

5.4

Rhode Island

.949

8.0

5.1

17.040

8.5

* Source: Belinfante 2001

Table Appendix 6.1 NHSDA's response rates

Year

Sample Size

Screening

Interviewing

Overall Response Rate

Source

1995

17,747

94.2%

80.6%

75.9%

Office of Applied Studies 1996, 1997a

1996

18,269

92.7%

78.6%

72.9%

Office of Applied Studies 1997b}

1997

24,505

92.7%

78.3%

72.5%

Office of Applied Studies 1999

1998

25,500

93%

77%

71.6%

Office of Applied Studies 2000

Total

103,830









Table Appendix 6.2 Survey Access Groups

Persons Living In:

1995

1996

1997

1998

Total

%

Households with telephones

16,226

16,765

22,571

23,412

78,974

91.8

Households without telephones

1,300

1,273

1,605

1,644

5,822

6.8

Household Sample

17,526

18,038

24,176

25,056

84,796

98.6

Group Quarters

141

135

203

267

746

0.9

NHSDA's Analyzed sample

17,667

18,173

24,379

25,323

85,542

99.4

Missing, Don't Know, or Refused for Telephone Coverage

80

96

126

177

479

0.6

Total NHSDA sample

17,747

18,269

24,505

25,500

86,021

100

Note: These sample counts are the raw, unweighted number of subjects that were interviewed.

References

Anderson, John E., David E. Nelson, and Ronald W . Wilson. (1998). "Telephone Coverage and Measurement of Health Risk Indicators Data from the National Health Interview Survey." American Journal of Public Health. 88(9): 1392-1395.

Aquilino, William S. (1992). "Telephone Versus Face-to-Face Interviewing for Household Drug Use Surveys." International Journal of the Addictions. 27(1): 71-91.

Belinfante, Alexander. (1998). FCC Releases New Telphone Subscribership Report. Washington, D.C.: Federal Communications Commission.

Belinfante, Alexander. (2001). Telephone Subscribership in the United States. Washington, D.C.: Federal Communications Commission.

Ford, Earl S. (1998). "Characteristics of Survey Participants With and Without a Telephone: Findings from the Third National Health and Nutrition Examination Survey." Journal of Clinical Epidemiology. 51(1): 55-60.

Gfroerer, Joseph C. and Arthur L. Hughes. (1991). " The Feasibility of Collecting Drug Abuse Data by Telephone." Public Health Reports. 106(4): 384-393.

McAuliffe, William E., Stephanie Geller, Richard A. LaBrie, Susannah B. F. Paletz, and Elizabeth A. Fournier. (1998). "Are Telephone Surveys Suitable for Studying Substance Abuse Epidemiology? Cost, Administration, Coverage and Response Rate Issues." Journal of Drug Issues. 28(2): 455-482.

Office of Applied Studies and Research Triangle Institute. (1996). National Household Survey on Drug Abuse: Population Estimates 1995. Rockville, MD: U.S. Department of Health and Human Services.

Office of Applied Studies and National Opinion Research Center. (1997a). National Household Survey on Drug Abuse: Main Findings 1995. Rockville, MD: U.S. Department of Health and Human Services.

Office of Applied Studies, Substance Abuse and Mental Health Services Administration (SAMHSA), and Research Triangle Institute. (1997b). National Household Survey on Drug Abuse Population Estimates 1996. Rockville, MD: U.S. Department of Health and Human Services.

Office of Applied Studies (OAS), Substance Abuse and Mental Health Services Administration (SAMHSA) and Research Triangle Institute. (1999). National Household Survey on Drug Abuse Main Findings 1997. Rockville, MD: U.S. Department of Health and Human Services.

Office of Applied Studies and Research Triangle Institute. National Household Survey on Drug Abuse Main Findings, 1998. (2000). Rockville, MD: U.S. Department of Health and Human Services.

Pearson, David, Allen Cheadle, Edward Wagner, Robert Tonsberg, and Bruce M. Psaty. (1994). "Differences in Sociodemographic, Health Status, and Lifestyle Characteristics Among American Indians by Telephone Coverage." Preventive Medicine. 23: 461-464.

U.S. Bureau of the Census. (1994). Phoneless in America . Washington, DC: U.S. Department of Commerce.

American Indians

This chapter describes several diagnostic studies of the treatment needs of American Indians. It includes a summary of the studies conducted on American Indian reservations in North Dakota, Montana, and South Dakota. Although American Indians on reservations will not comprise a separate component of the total treatment need estimate developed in the chapter on gaps in treatment, this chapter is meant to bring attention to this group's specific needs. Further, American Indians have the lowest rates of telephone coverage among all of the Census Bureau's race categories (Census 1994). Given the high rates of substance use disorders found in North Dakota's face-to-face survey of American Indian adults on reservations described below, the telephone survey probably underestimated need. In other reports, the authors have estimated the needs of recently incarcerated prisoners, households without telephones, and homeless people. These subgroups typically face a high risk of substance abuse and do not intersect with each other or the population represented in the telephone survey. Because some of those captured by the estimate from the face-to-face survey were also captured by the telephone survey, it would be incorrect to integrate the two.

While there have been few diagnostic studies of American Indians, there is a substantial body of literature focusing on other measures of substance abuse problems affecting the American Indian population. Those studies describe behavior such as binge drinking (Beauvais 1998), women drinking while pregnant (May 1989), and drunk driving (Tippetts and Voas 1999). This literature has brought much attention to the adverse consequences of those behaviors, such as a high rate of alcohol-related motor vehicle fatalities (Tippetts and Voas 1999), a high rate of substance abuse related deaths (Beauvais 1985, May 1989), and a high rate of fetal alcohol syndrome among some segments of the American Indian population (May et al. 1983). There are also indications that American Indians are less likely to want treatment for their problems (Bhardwaj et al.1998). While estimates from the National Household Survey of Drug Abuse (NHSDA) show American Indians were the most likely to have received treatment in the prior year, the gap between past year treatment and past year dependence was also the widest for that group (OAS 2000).

Review Criteria and Methods

In an effort to gather all of the materials with substance abuse prevalence estimates for American Indians, the authors conducted a general literature search. Those materials provided background for this report, especially in regards to the nature of substance use among American Indians. The team used basic internet search methods to find references including MedLine, the National Clearinghouse for Alcohol and Drug Information (NCADI), and the NIAAA's Alcohol and Alcohol Problems Science Database (ETOH). The team also searched North Charles Research and Planning Group's own library of literature. These materials were compiled in a file on substance abuse and treatment among American Indians developed throughout the course of the State Treatment Needs Assessment Program. Based on knowledge that several states did surveys on reservations with CSAT funding, the study team contacted officials in those states to obtain copies of their reports. The authors obtained three state studies (North Dakota, Montana, and South Dakota), which are the focus of the present review. Each of these reports contained substance use disorder prevalence rates based on the National Technical Center's adaptation of the Diagnostic Interview Schedule's (DIS) Substance Abuse Module (Robins et al. 1990). Besides the three State Treatment Needs Assessment Program studies, the team did not find any population-based diagnostic studies of American Indians. Manson et al. (1992) reported encouraging results regarding the use of the DIS to measure alcohol use disorders in limited samples of three tribes in the Southwest, the Pacific Northwest, and the Norther Midwest Plains.

Each of the three studies included in this review were conducted on reservations within the respective states. Therefore, the studies refer to generally similar populations, though there were a number of different tribes involved in each study. The size of the samples ranged from 998 to 2,249, and each sample was restricted to adults 18 and older. The Montana study had an upper age limit of 65. The interviews were conducted in person, and the North and South Dakota studies used a computer-assisted format.

Results

Overview

Each of these studies found high substance abuse rates on reservations (Table 7.1). Further, each found that alcohol was the primary problem. The studies found that men on the reservation experienced higher rates of substance use disorders than women, often by a wide margin. The reports also suggested that treatment utilization was low. The study done in North Dakota showed that few of those with an untreated need wanted treatment. The Montana study measured obstacles to treatment and showed many with a need that did not get treatment cited family pressures, transportation issues, and administrative barriers as reasons for not receiving any. The surveys also found that many American Indian women of childbearing age were heavy drinkers, putting children at a higher risk for Fetal Alcohol Syndrome (FAS).

Table 7.1 Substance Use Disorders Among American Indians on Reservations

Reference

Data Collection Location and Date

Study Population

Diagnostic Measurement

% Alcohol or Drug Use Disorder

Current

Lifetime

Bhardwaj et al. 1998

North Dakota and Montana 1997

998 adults on 5 reservations; ages 18 and over

NTC version of DIS-III-R modified for face-to-face computer-assisted interviewing

Past Year

Alcohol:

19

Drugs:

2.6

27.1

Bray et al. 1999

South Dakota 1996-1997

Random sample of 2,449 adults on 9 reservations; ages 18 and over

NTC telephone survey based on DIS-III-R modified for face-to-face computer-assisted interviewing

Past Year

30

-

Haynes et al. 2001

Montana

2000

1,821 adults on 6 reservations; ages 18 to 65

NTC survey based on DIS-III-R modified for paper and pencil interviewing

Past Year

28.4

-

Generalizing the Results

In order for a study to be included in this literature review, it had to include a population-based diagnostic estimate of substance use disorder prevalence among American Indian on reservations. Because applying diagnostic criteria within a survey is a relatively new practice (dating back to 1980-1984), there have been only a few such studies of the reservation population. Because the studies included here were done in states with a distinct geographic proximity to each other and each uses the same diagnostic scale, the results of each may comfortably be compared. Previous studies of substance use and substance use-related problems show that these issues can be similar across American Indian groups in different regions (Manson 1992). Therefore, the South Dakota and Montana studies are used to corroborate the results of the North Dakota study.

Other studies that did not include substance abuse prevalence estimates often looked at rates within narrow subsets of the American Indian population, such as youths or clinical cases. Some studies included American Indians living off the reservations. Many of the studies focused only on alcohol because the problems American Indians face with alcohol abuse are more widely known. Beauvais and LaBoueff addressed the problem of inference by noting, "Other studies which purport to measure Indian drug and alcohol use have such a skewed sample of Indians as to make their conclusions suspect"(Beauvais 1985). This criticism points not only to the problem of attempting to generalize from a narrowly defined sample, but also to the problem of interpolating results across American Indian tribes that can have dramatically different cultures and problems. Because the present study used only population-based diagnostic estimates, the authors assumed that some broader similarities do exist. Fortunately, North Dakota did a study of adults on the reservations. Therefore, an estimate exists for that specific population and the findings of other studies can be used to corroborate the findings of the North Dakota study.

The North Dakota study reported current diagnoses for drugs and alcohol separately, while it offered more complete findings of lifetime diagnoses including demographic breakdowns. It is not possible to simply add up the current rates for drugs and alcohol, as it is likely that some people are included in both. The Montana and South Dakota reports contained estimates of the current substance use disorder rate of American Indian adults on the reservations.

North Dakota's Study on American Indian Adults on Reservations

Bhardwaj et al.(1998) surveyed a sample of 998 adult American Indians on 5 reservations in North Dakota. Just over 54% of the respondents were women. The study used a modified version of the National Technical Center's DIS-III-R instrument for the survey. This study found that 27.1% of those over the age of 18 on reservations had a lifetime substance use disorder diagnosis (Bhardwaj et al. 1998). The larger part of those with a diagnosis were alcohol abusers: 19% were dependent and 7.2% were abusers. Further, 19% of adults on the reservation had a current alcohol use disorder: 14.2% were dependent and 4.8% were abusers. Although alcohol was the most common problem, 3.5% had a lifetime dependence or abuse diagnosis for marijuana while 1.2% had a lifetime methamphetamine dependence. There were some who had a diagnosis for both marijuana and alcohol (Bhardwaj et al. 1998). Less than 1% had a lifetime diagnosis for drugs only, while 3.1% had a lifetime diagnosis for both drugs and alcohol. Less than 2% had a current diagnosis of dependence or abuse for marijuana while 0.6% had a current methamphetamine dependence.

Despite a high need for treatment among American Indians on reservations in North Dakota, the survey found that few of those surveyed wanted treatment. Only 1.4% of those with a diagnosis who did not receive treatment in the past year said they wanted some (Bhardwaj 1998). However, many of those who wanted treatment in the past year, but did not get it complained of physical handicaps and not getting the type of treatment they wanted as obstacles to getting treatment. However, the sample size was low for those estimates, so the results should be treated as indicative of obstacles at best. Reservations are typically rural and convenient access to treatment facilities is a problem. The report from the Gallup Organization estimated the survey population to be 13,163. That implies that 2,501 American Indian adults on reservations had an alcohol treatment need. There were 224 in need of treatment for marijuana, but it is likely that many of those were counted as being in need of treatment for alcohol as well. Those totals are not unsubstantial when compared with the statewide need total estimated in the chapter on gaps in treatment. As noted previously, though, some cases were captured by both surveys.

To get a notion of the magnitude of potential underestimation due to low telephone coverage on the reservations, consider results from North Dakota's household telephone survey. That survey indicated that American Indian adults had a current treatment need rate of 9.3%. Although that estimate includes American Indians living off the reservations, the difference of almost 10 percentage points between it and the alcohol-only estimate (19%) from the face-to-face survey is indicative of the large difference that arises when those in households without telephones are included in the sample.

Other Studies of American Indians on Reservations

Montana

Haynes et al.(2001) surveyed 1,821 adults between the ages of 18 and 65 living on reservations in Montana using a paper and pencil interview. Along with the necessity of using a face-to-face interview to evade telephone coverage bias, the authors emphasized the use of interviewers recruited from the reservations. The authors argued that doing so was critical to elicit optimum honesty from respondents. This survey found that 28.4% of American Indian adults on reservations in Montana had a past-year substance use disorder. A large proportion of those surveyed were dependent on drugs or alcohol (15.3%); men were almost twice as likely as women to be dependent on a substance. The reservation's population had a greater need for alcohol treatment than for drug treatment according to this study, but the authors emphasized in their report that the rate of drug dependence was nonetheless far greater than that of the general United States population (Haynes et al. 2001). Frequently, subjects demonstrated a need for both drug and alcohol treatment (6.6%).
Almost 13% of those diagnosed with a current substance use disorder had a demand for treatment for their disorder. Compared to the rate of unmet demand in North Dakota, that rate is high. Those who expressed a desire for treatment, but did not get it in the past year cited a variety of obstacles to treatment. Many specified administrative factors such as, red tape and waiting lists while lack of insurance and adequate transportation also presented barriers.

The difference between the household survey results for American Indians and the face-to-face interview results in Montana is also illustrative of potential underestimation due to low telephone coverage. Riley at al. (1997) reported specifically on the substance use disorder rates of American Indians based on the Montana Adult Household Survey. That telephone survey was designed to procure statistically significant estimates for American Indians by oversampling in 8 census tracts on reservations. However, the sample they reported on did not distinguish between American Indians on and off the reservations, though it was largely comprised of those from reservations. The report on the household telephone survey showed that 13.7% of American Indian adults in Montana were in need of treatment for a substance use disorder (Riley et al. 1997). Whereas the rate for the adult population from the telephone survey was 8.8%, it is clear that American Indians had a substantially higher rate of current substance use disorders. Lifetime substance use disorder estimates for American Indian adults from the telephone survey were high. For the whole American Indian population, 26% had a lifetime alcohol use disorder while 8.9% had a lifetime diagnosis of a drug use disorder.

However, the rate of current substance use disorders for the adult American Indian population from the survey did not approach the estimated rate for adult American Indians on reservations in Montana from the more recent survey of that population. As noted above, that population had a current treatment need rate of over 28%. This difference is primarily attributable to bias from low telephone coverage on reservations (Riley et al. 1997, Haynes et al. 2001).

South Dakota

Bray et al. (1999) surveyed the largest sample among the studies reviewed here. They interviewed 2,449 adult Native Americans aged 18 and over on nine reservations in South Dakota. The study found that about 30% of adult American Indians living on reservations needed treatment for a current substance use disorder. Consistent with estimates from the other surveys covered here, this study found that a greater proportion of men were in need of treatment and that alcohol was the predominant substance of abuse. The report stated, " Nearly one-quarter of adults in this population were in need of treatment specifically for alcohol (with or without the need for treatment for problems related to use of other drugs). In comparison, about 1 in 10 Native Americans was in need of treatment for the other drugs covered in the survey" (Bray et al. 1999). Like those surveyed in the studies described above, American Indians on reservations in South Dakota experienced high rates of alcohol use disorders accompanied by a greater incidence of drug problems than in the general population. Furthermore, some individuals demonstrated both drug and alcohol use disorders. This survey found that less than one-third of those in need of treatment in the sample received some in the past year. The report emphasized lack of insurance coverage as a significant barrier to obtaining treatment (Bray et al. 1999).

Methodological Problems of Integrating the Estimates

Unfortunately, there is no way to integrate an estimate of need for those American Indian adults on reservations into the overall estimate. The American Indian population is implicitly included in all of the population subgroups covered in the gap analysis done in this study. Each adult component of the overall need estimate overlaps with the target population of the reservation survey. The current analysis is meant only to bring attention to the high rate of treatment need found in this group.

Conclusions

Though there are few studies available that offer estimates of substance use disorder rates for American Indians, those that do exist have consistent results. Alcohol use disorders are common among adults on the reservations, as previously inferred form high rates of alcohol-related problems such as, high motor-vehicle fatality rates, high FAS rates, and high alcohol-related mortality rates. In North Dakota, the problem is apparent in the rates of substance use disorders reported in the face-to-face survey. Over 27% of the adults surveyed had a lifetime diagnosis for substance abuse or dependence; the overwhelming majority of those had alcohol problems. The group had a low rate of unmet demand for treatment (1.4%) and among those that did receive treatment in the past year there was little desire for additional services (0.8%). The State may consider ways to get those individuals interested in treatment including more convenient access to services, increasing awareness of the dangers of substance abuse, developing programs specifically targeted at American Indians, and increasing the variety of services available.

References

Beauvais, Fred, and Steve LaBoueff. (1985). "Drug and Alcohol Abuse Intervention in American Indian Communities." The International Journal of the Addictions. 20(1).

Beauvais, Fred. (1998). "American Indians and Alcohol." Alcohol Health and Research World. 22(4).

Bhardwaj, Ajay, David Moore, and Max D. Larsen. (1998). Demand and Needs Assesment Study: Alcohol and Other Drugs of Native American Indians on Reservations in North Dakota. North Dakota Department of Human Services, Division of Mental Health and Substance Abuse Services.

Bray, Robert M., Barbara T. Dalberth, Mindy Herman-Stahl, June A. Walker, and Rebecca P. Sanchez. (1999). Substance Use and Need for Treatment: Findings from the 1996-97 South Dakota Native American Survey. South Dakota Department of Human Services, Division of Alcohol and Drug Abuse.

Haynes, George, Vincent Smith, and Nathaniel R. St. Pierre. (2001). Need for Substance Abuse Treatment on Montana's Native American Reservations. Montana Department of Public Health and Human Services, Addictive and Mental Disorders Division.

Manson, Spero M., james H. Shore, Anna E. Baron, Lynn Ackerson, and Gordon Neligh. (1992). "Alcohol Abuse and Dependence Among American Indians." in Alcoholism in North America, Europe, and Asia. (1992). John E. Helzer and Glorisa J. Canino.

May, Philip A., Karen J. Hymbaugh, Jon M. Aase, and Jonathan M. Samet. (1983). "Epidemiology of fetal Alcohol Syndrome Among American Indians of the Southwest." Social Biology. 30(4).

May, Philip A. (1989). "Alcohol Abuse and Alcoholism Among American Indians: An Overview." in Alcoholism in Minority Populations. (1989). Thomas D. Watts and Roosevelt Wright, Jr.

Office of Applied Studies(OAS), Substance Abuse and Mental Health Services Administration, and the National Opinion Research Center. (1998). Prevalence Substance Use Among Racial and Ethnic Subgroups in the United States 1991-1993. Substance Abuse and Mental Health Services Administration .

Office of Applied Studies(OAS), Substance Abuse and Mental Health Services Administration, and Research Triangle Institute. (2000). Summary of Findings from the 1999 National Household Survey on Drug Abuse. National Clearinghouse for Alcohol and Drug Information .

Riley, William, George Haynes, and Vincent Smith. (1997). Montana Adult Household Substance Abuse Treatment Analysis. Montana Department of Public Health and Human Services, Addictive and Mental Disorders Division.

Robins, Lee N.; Cottler, Linda B., and Babor, Thomas F. (1990) "Diagnostic Interview Schedule-- Substance Abuse Module (DIS-SAM)." St. Louis Department of Psychiatry, Washington University School of Medicine.

Tippetts, Scott, and Robert B. Voas. (1999). Ethnicity and Alcohol-Related Fatalities: 1990 to 1994. Center for Alcohol Policy Analysis, Pacific Institute of Research and Evaluation.

U.S. Bureau of the Census. (1994). Phoneless in America. Washington, DC. U.S. Department of Commerce.

Prisoners

This chapter estimates the treatment needs of prisoners in North Dakota. Because the State did not conduct a survey of recently incarcerated prisoners as part of the State Treatment Needs Assessment Program (STNAP) , the authors obtained an estimate through examination of other prisoner studies. This substudy estimated the number of persons who were on the outside and needed treatment during the past year but whom the household survey would have missed because they were incarcerated when the household survey was underway. Prisoners represent the largest and most high-risk institutionalized population who are missed by the household survey (McAuliffe et al. 1998; Anthony 1991).

Other Prisoner Studies

In order to gather materials that included population-based diagnostic substance use disorder estimates for prisoners, the study team reviewed the existing literature on the treatment needs of incarcerated populations dating back to the early 1980s. The study team searched the NIAAA's Alcohol and Alcohol Problems Science Database (ETOH), the National Library on Medicine, the National Criminal Justice Reference Service's abstract database, the Addiction Research Foundation's bibliographic database, and a series of internet search engines (e.g., Google) for unpublished papers. The team made use of the North Charles Research and Planning Group's own extensive needs assessment library and database that included reports collected from states that conducted prison studies as part of the STNAP. The authors examined the bibliographies of all of the articles for any studies not found by computer search routines.

The search focused on prisoner studies conducted in the United States and Canada. Relevant studies included efforts to estimate the percentage of recently incarcerated prisoners who had a substance use disorder diagnosis that met the clinical criteria of the American Psychiatric Association's (1994) Diagnostic and Statistical Manual of Mental Disorders (DSM). The telephone survey estimated the DSM-III-R or DSM-IV. Although the primary objective was to obtain prevalence estimates for the prior year when the prisoners were on the outside, estimates that approximate that time period were included because of the paucity of estimates available in the literature. The study team also focused on studies that used structured instruments, especially investigations that used the Diagnostic Interview Schedule. Because changes in mandatory sentencing laws have led to a substantial increase in the number of persons in prison for drug-related crimes (Schiraldi et al. 2000), the study limited its review to the 1980s and more recently.

Table 8.1 Published Studies of Substance Use Disorders among Prisoners


Authors

Data Collection Location/ Date


Study Population


Diagnostic Measurement

% Alcohol or Drug Use Disorder

CurrentTime Frame


Current Estimate

Lifetime Estimate

Regier et al. 1990

Baltimore,Durham,New Haven, St. Louis, and Los Angeles, 1980-84

715 combined ECA prisoner sample

DIS-III





72

Teplin 1994

Chicago, 1983-84

728 males at Cook County Department of Corrections

DIS-III

Past 2 weeks

29

61

Bland et al. 1990

Edmonton,

1986-87

222 males,18-44 years of age in two correctional centers

DIS-III

Past six months

62

87

Teplin et al. 1996

Cook County, 1991-1993

1,272 female arrestees awaiting trial at Cook County Department of Corrections

DIS-III-R

Past six months

60

70

Hudik et al. 1994

Iowa, 1993

242 males and 132 females newly admitted to Iowa's prisons. Estimates reweighted to prison gender proportions (8% female)

Substance abuse counselors using local assessment scale

Time of interview

80

85

Farabee 1994, 1995

Texas 1993, 1994

1,030 entering adult males (except gang members and known homosexuals); 500 females

DIS-III-R items. Asked if had 10+ drinks or used illicit drugs in past year. Dependence=3+ Sx, abuse=1 or 2 Sx.

Past year

63



Illinois Dept of Corrections 1995

Illinois, 1994

630 (105 females)new inmates in four intake sites. Estimates reweighted to prison gender proportions(7% female)

DIS-III-R

Past year

35

57

Peters et al. 1998

Huntsville, TX 1996

400 male inmates in Holliday Transfer Facility where they are processed during the first 14-60 days

DSM-IV (SCID-IV)

Past month

56

74

Farabee et al. 1997

Kentucky, 1997

600 randomly selected inmates (567 male/ 33 female) from 15 prisons

Items based on DSM-IV. Qualify for items if ever used alcohol or ever felt addicted to drugs.





59

Kerber 2000

Texas, 1998

792 males newly admitted to 4 intake facilities

DIS-III-R

Past year

64



Kerber and Harris 2001

Texas, 1998

658 females newly admitted to two intake facilities

DIS-III-R

Past year

60



Kerber 2001

Texas, 1998

498 males newly admitted to six jail intake facilities

DIS-III-R

Past year

63



Studies from the State Treatment Needs Assessment Program (STNAP)

Riley et al. 1997

Montana, 1997

150 recently incarcerated adult male inmates at Montana State Prison and 44 female inmates at Women's Correctional Facility. Estimates reweighted to prison gender proportions.

DIS-III-R

Past Year

58

77

McAuliffe et al. 2000

Rhode Island, 1999

198 adult and 118 adolescent inmates recently incarcerated in Adult Correctional Institution or Intake Service Center. Estimates reweighted to prison gender proportions

DIS-IV

During Past Year When on Outside

adult: 82

adol: 81

adult: 85

adol: 81

McAuliffe et al. 2001

Alaska, 2000

208 adult inmates recently incarcerated in three Alaskan prisons. Estimates reweighted to prison gender proportions (9%).

DIS-IV

During the Past Year When on Outside

79

91

The study team found twelve published studies that fit its criteria (Table 8.1). Most (7 of 12) of the studies were not found in journals. The study periods range from the early 1980s (Regier et al. 1990) to the very recent studies in Montana, Texas, Alaska and Rhode Island (Kerber 2000, McAuliffe et al. 2000; McAuliffe et al. 2001; Riley et al. 1997). Consequently, the summary described in Table 1 is the most up-to-date and complete compilation of data on this issue that is currently available.

The pioneering Epidemiological Catchment Area (ECA) Study used the Diagnostic Interview Schedule (DIS-III) in 1980-1984 to assess the prevalence of substance use disorders of a combined sample of 715 prisoners from five sites across the country (Baltimore, New Haven, St. Louis, Los Angeles, and Durham) (Regier et al. 1990; Robins & Regier 1991). The DIS-III permitted lay survey interviewers to determine whether respondents met clinical criteria for alcohol and drug use disorders according to the Diagnostic and Statistical Manual, Third Edition (DSM-III) of the American Psychiatric Association (1980). Seventy-two percent of the inmates in that study had a history of substance abuse or dependence (Regier et al. 1990). This rate was the highest of any of the major population groups (household residents, patients in psychiatric hospitals, homeless, and nursing home residents) surveyed in the classic epidemiologic study (Robins and Regier 1991, p.109). The prisoners had a one-year alcohol use disorder prevalence of 26.4%. Furthermore, 55% of the males and 70% of the females had a lifetime diagnosis of drug abuse or dependence (Anthony 1991, p. 67).

In a 1983-84 study, Teplin (1994) evaluated a sample of 728 male jail detainees in Cook County using the DIS-III. She found that 29% had a current substance use disorder (19% alcohol and 15% drugs), and 61% had a lifetime substance use disorder. Similar results were obtained ten years later in another study of Illinois inmates using the revised version of the NIMH instrument, the DIS-III-R (Illinois Department of Corrections 1995). It found that 56.6% of 526 male prisoners and 62.5% of 104 female prisoners (57% in the overall weighted sample) had a diagnosis of abuse or dependence on one or more drugs during their lifetime. The Illinois study used the National Technical Center's DSM-III-R instrument that all states used in their household surveys to measure the revised criteria of the American Psychiatric Association (American Psychiatric Association 1987).

Subsequent studies conducted elsewhere using similar methods and measurement scales have found similar rates of substance use disorders among prisoners in most cases. Using the DIS-III, Bland et al. (1990) found that nearly nine out of ten male inmates (87%), 18 to 44 years of age, in Canada had a lifetime substance use disorder. In an unpublished study conducted in Iowa, experienced substance abuse counselors conducted clinical assessments of newly incarcerated prisoners and found that 81% of the males and 71% of the females had symptoms indicating a current diagnosis of substance abuse or dependence (Hudik et al. 1994). It was the only study in Table 1 that did not use a structured instrument. Two unpublished Texas studies, one conducted with male inmates in 1993 and one conducted with female inmates in 1994 found that 63% of the subjects (both males and females) had a current substance use disorder diagnosis (Farabee 1994, 1995). An unpublished survey of males entering prison in Kentucky by the same author found that 59% had a lifetime diagnosis of a substance use disorder according to the American Psychiatric Association's DSM-IV criteria (Farabee 1997). Peters et al. (1998) found that 56% of a sample of Texas inmates had a current substance use disorder, and an unpublished study (Kerber 2000) found that 64% of a Texas prisoner sample had a current substance use disorder. Additional studies done by Kerber at women's intake facilities and men's state jail intake facilities (Kerber and Harris 2001; Kerber 2001) yielded very similar results for current substance use disorder rates.

State Needs Assessment Program (STNAP) Prisoner Studies

Montana, Alaska, and Rhode Island recently conducted prisoner studies as part of the STNAP program. A review of findings from the Montana prisoner study may simulate a North Dakota study most closely because of the geographic proximity and similar population characteristics of the two states. The Montana study employed a survey instrument that provided for a diagnostic assessment based on the DIS as adapted by the present authors. Riley et al. (1997) conducted a treatment need study of incoming male inmates of the Montana State Prison and female inmates of the Women's Correctional Facility. Using a modified version of the National Technical Center's telephone survey questionnaire, the study team conducted face-to-face interviews with 150 male inmates and 44 female inmates. The resulting information provided estimates of the prevalence of substance use disorders and levels of care estimates.

The study found that 77% of in-coming male inmates and 89% of female inmates had a lifetime substance use disorder (Riley et al. 1997, Table 1). For males, the breakdown by substance was: 64% had an alcohol use disorder, and 49% had a drug use diso rder. Among women, 73% had an alcohol use disorder while 75% had a lifetime drug use disorder. In both cases, a large percentage had both drug and alcohol use disorders. As for current substance use disorders, Riley et al. found that 58% of in-coming male inmates had a drug or alcohol disorder in the past year and 64% of inmates of the Women's Correctional Facility had a current use disorder diagnosis. Among male inmates, 53% had a current alcohol use disorder, and 20% had a current drug use disorder. Among female inmates, 52% had a current alcohol use disorder, and 36% had a current drug use disorder. The estimates for lifetime and current substance use disorder prevalence for men and women prisoners can be combined as a weighted average, but b ecause there are nearly 20 times as many male inmates as female inmates the rates are no different from the male rates when rounded to the nearest whole number.

The authors concluded that alcohol and drug use prevalence rates were significantly higher for prison inmates than for the general population. These inmates, especially the females, were also far more likely to demand treatment than the general population. The prisoners mostly wanted in-patient services. Fifty-nine percent of women inmates had a current demand for treatment while 35% of men at the Montana State Prison had a current demand for treatment (Riley at al. 1997).

In a study conducted between July and October of 2000, the current authors interviewed 208 recently incarcerated male and female inmates of three correctional facilities in Alaska: Hiland Mountain Correctional Center, Palmer Correctional Center, and Wildwood Correctional Center. The study was designed to capture those who were on the outside prisons for at least a month during the past year but were incarcerated. It was assumed that those people would not be captured by the telephone survey. The study team achieved a 77% overall response rate. The sample was weighted to accurately reflect the gender proportions of the prison population, and the racial composition of the sample was similar to that of the general prison population in Alaska. Although the study sampled only three prisons, analysis suggested that the results may be generalized to the population of inmates that were incarcerated in State prisoners during the previous 11 months. The study estimated that number to be 610 inmates.

The study found that substance use disorders were widespread among prisoners in Alaska. Among recently incarcerated Alaska prisoners, 79% needed treatment for a substance use disorder during the past year when they were on the outside. Over half were dependent on alcohol, while 39% were dependent on drugs. More than a quarter (27%) were dependent on cocaine. Among those with a treatment need, dependence was more common than abuse for all categories of drugs and alcohol except hallucinogens. Two percent of the sample were dependent on hallucinogens, and 2% were abusers of hallucinogens. Dependence was over five times more common than abuse when all substances were considered. More than nine out of ten prisoners had a lifetime diagnosis for a substance use disorder. More than half had a lifetime drug dependence, while almost 60% had a lifetime alcohol dependence. Prisoners had a 38% lifetime rate of cocaine use disorders, and the same percentage had a lifetime marijuana use disorder. More than eight out of ten had a lifetime alcohol use disorder.

Male prisoners had a slightly higher lifetime rate of substance use disorders than female prisoners; 91% versus 85%. However, the rate of past year treatment need was essentially the same; 79% for men and 78% for women. Alaska Natives/American Indians had higher lifetime rates and past year rates of substance use disorders than Whites and African Americans. Alaska Native and American Indian prisoners had a 97% lifetime substance use disorder rate, whereas whites and African Americans had 90% and 83% lifetime substance abuse rates, respectively. Rates varied somewhat from facility to facility, but the differences were small. The rates of drug treatment need at Hiland Mountain were higher than the other facilities because that is the women's facility. Female prisoners in this study had higher rates of drug use disorders than men, especially for cocaine and stimulants. A recent study of the suitability for women of substance abuse treatment programs in prisons that were originally designed for men showed that female prisoners used drugs more frequently and used harder drugs than male prisoners (Langan et al. 2001). Male prisoners had higher rates of alcohol use disorders, and accordingly the rates of alcohol treatment need reported at Wildwood and Palmer were higher than the rates at Hiland Mountain.

Of interviewed prisoners, 53% reported receiving some type of treatment for their substance use disorder in the past year while outside prison. Overall, 79% reported receiving some type of treatment in their lifetime. A little more than a quarter of those who received treatment in the past year on the outside received specialty treatment, while the largest portion of the sample (37%) received "self help" treatment, which included Alcoholics Anonymous (AA) and Narcotics Anonymous (NA). A third of the subjects received treatment from a nonspecialty source, which included a range of sources such as counseling with clergy or a health advisor, consulting a family doctor, or drunk-driving classes. Most of the study respondents received some combination of specialty and non-specialty services. A majority of these prisoners received specialty treatment and self help treatment in their lifetime. More than half reported receiving treatment for an alcohol use disorder while in prison at some point in their lives, though only a third of those who reported receiving alcohol treatment were women. Among those who reported receiving alcohol treatment while in prison, 48% received some during their current incarceration.

Among those with a need for treatment (that is, had a substance use disorder) in the past year, 57% received treatment while outside prison in the past year. Among those diagnosed with a current treatment need that did not receive it in the past year, 37% reported that they wanted treatment. This percentage is referred to as the amount of "unmet demand" for treatment. A slight majority (54%) of the prisoners with an unmet demand for treatment said that they took steps to obtain treatment, but failed. These individuals sought a variety of treatment types including detox, residential care, outpatient treatment, and self-help groups. No specific type of treatment was the most common response. Among those that did receive treatment, 49% reported that they wanted additional treatment. These individuals reported demand for multiple types of service. More than ninety percent wanted treatment outside of a formal program and outpatient care. Eighty-eight percent wanted to attend self-help groups such as AA or NA. More than three quarters wanted residential or inpatient rehabilitation and the almost as many wanted treatment at a halfway house or group home. Over half wanted additional detox services. Clearly, the additional treatment demanded by prisoners that received some in the past year were extensive.

Alaskan prisoners with an unmet demand for treatment cited a variety of obstacles to obtaining it. Among those with an unmet demand for treatment and those who had a demand for additional treatment, a majority reported lack of insurance as a barrier to obtaining treatment. A majority of all prisoners with an unmet demand reported that bureaucracy, or "red tape", was an impediment. A majority (51%) of those who wanted more treatment said that treatment facilities were full, and 51% of those who pursued treatment but did not receive it cited a lack of adequate transportation. The other reasons that were cited, albeit less frequently, included not wanting their problem to be public, distance from facilities, and failure to address cultural and gender issues such as language barriers, and programs' inattentiveness to the special needs of women.

Finally, the Rhode Island prisoner study used the research design and instrument as employed in the Alaska prisoner study, and the Rhode Island study included a sample of 198 adults and 118 adolescents. The Rhode Island survey used the same assessment instrument as the authors used in the Alaska prisoner study. The results showed that 82% of the adults and 81% of the adolescents had a DSM-IV substance use disorder diagnosis in the past year when they were on the outside.

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Lifetime and Past Year Estimates of Treatment Need

Although nearly all of the published prisoner studies used a version of the DIS and sampled recently incarcerated inmates, the studies reported a wide range of results. The surveys occurred at different times, used three different versions of the DSM and DIS, used different sampling strategies (e.g., persons incarcerated in the last year versus persons just entering prison), and defined "current" differently (time of interview, last month, last year). The variation in the percentages of lifetime diagnoses was not as great as the variation in the percentages with current diagnoses. Several of the studies disproportionately sampled female inmates, but the results were reweighted to reflect the proportion of female inmates in the prisoner population.

Alaska's study found that 91% of the sample had a lifetime substance use disorder, the highest of the nine published studies with lifetime estimates of the substance use disorder prevalence among prisoners. Those 10 studies had an unweighted mean rate of 72.9%. The weighted mean rate was 69%. The median rate among those nine studies was 74% (Figure 1). The lifetime estimates ranged from 57% to 87%.

The unweighted mean rate for the current disorder rates among the 10 studies with a current diagnosis was 58.8%, the weighted mean rate was 56%, and the median rate was 62% (Figure 2). The estimates of current use disorders ranged from 29% to 84%. The lowest rates were the two Illinois studies. The authors decided to use the median rate as the estimate for North Dakota's newly incarcerated prisoners.

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References

American Psychiatric Association. (1980). Diagnostic and Statistical Manual of Mental Disorders. Washington, D.C.: American Psychiatric Association.

American Psychiatric Association. (1987). Diagnostic and Statistical Manual of Mental Disorders (Third Edition- Revised): DSM-III-R. Washington, DC: American Psychiatric Association.

American Psychiatric Association . (1994). Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition): DSM-IV. Washington, DC: American Psychiatric Association.

Anthony, James C. (1991). "The Epidemiology of Drug Addiction." in Comprehensive Handbook of Drug and Alcohol Addiction. Ed. Norman S. Miller. New York: Marcel Dekker, Inc. 55-86.

Bland, Roger C., Stephen C. Newman, Ronald J. Dyck, and Helene T. Orn. (1990). "Prevalence of Psychiatric Disorders and Suicide Attempts in a Prison Population." Canadian Journal of Psychiatry. 35:407-13.

Farabee, David. (1994). Substance Use Among Male Inmates Entering the Texas Department of Criminal Justice--Institutional Division: 1993. Austin, Texas: Texas Commission on Alcohol and Drug Abuse.

Farabee, David. (1995). Substance Use Among Female Inmates Entering the Texas Department of Criminal Justice--Institutional Division: 1994. Austin, TX: Texas Commission on Alcohol and Drug Abuse.

Farabee, David, Carl G. Leukefeld, Deena D. Watson, Michael Townsend, Hugh Spalding, and Richard Purvis. (1997). Substance Abuse Treatment Needs Among Kentucky Prison Inmates. Kentucky: University of Kentucky Center on Drug and Alcohol Research, Kentucky Division of Substance Abuse, and Kentucky Department of Corrections.

Hudik, Terry L., David Huff, Lettie Prell, Laura Roeder, Becky Bell, and Kim Porter. (1994). An Assessment of the Substance Abuse Treatment Needs of the Inmates of Iowa's Correctional Institutions. Iowa: Iowa Department of Human Rights.

Illinois Department of Corrections. (1995). Adult Prison Study: Draft Version. Illinois Department of Alcoholism and Substance Abuse.

Kerber, Lisa. (2000). Substance Use Among Male Inmates: Texas Department of Criminal Justice--Institutional Division: 1998. Austin, Texas: Texas Commission on Alcohol and Drug Abuse.

Kerber, Lisa and Rand Harris. (2001). Substance Use Among Female Inmates: Texas Department of Criminal Justice, Institutional Division: 1998. Texas Commission on Alcohol and Drug Abuse.

Kerber, Lisa. (2001). Substance Use Among Male State Jail Inmates: Texas Department of Criminal Justice, State Jail Division: 1998. Texas Commission on Alcohol and Drug Abuse.

Langan, Neal P. and Bernadette M.M. Pelissier. (2001). "Gender differences among prisoners in drug treatment." Journal of Substance Abuse. 13 (3): 291-301.

McAuliffe, William E., Stephanie Geller, Richard A. LaBrie, Susannah B. F. Paletz, and Elizabeth A. Fournier. (1998). "Are Telephone Surveys Suitable for Studying Substance Abuse Epidemiology? Cost, Administration, Coverage and Response Rate Issues." Journal of Drug Issues. 28(2):455-82.

McAuliffe, William E., Richard LaBrie, Stephen Haddad, Eric Sevigny, Scott Ronis, Ryan Woodworth, Sarah Williamson, JoAnn Scherer-Gonzales, and Jocelyn Leary. (2000). The Substance Abuse Treatment Needs of Rhode Island's Prisoner and Training School Populations . Cambridge, MA: North Charles Research and Planning Group.

McAuliffe, William E., Richard LaBrie, Eric Sevigny, Ryan Woodworth, Jaime Mellitt, Timothy Stablein. (2001).The Substance Abuse Treatment Needs of Alaska's Newly Incarcerated Prisoner Population: Final Report . Cambridge, MA: North Charles Research and Planning Group.

Peters, Roger H., Paul E. Greenbaum, and John F. Edens. (1998). "Prevalence of DSM-IV Substance Abuse and Dependence Disorders among Prison Inmates." American Journal of Drug and Alcohol Abuse. 24(4): 573-87.

Regier, Darrel A., Mary E. Farmer, Donald S. Rae, Ben Z. Locke, Samuel J. Keith, Lewis L. Judd, and Frederick K. Goodwin. (1990). "Comorbidity of Mental Disorders With Alcohol and Other Drug Abuse: Results From the Epidemiologic Catchment Area (ECA) Study." Journal of the American Medical Association. 264(19): 2511-18.

Riley, William, George Haynes, and Vincent Smith. (1997).Montana State Prison and Women's Correctional Facility Substance Abuse Treatment Demand Analysis. Montana Department of Public Health and Human Services, Addictive and Mental Disorders Division.

Robins, Lee N. and Darrel A. Regier, Eds. (1991). Psychiatric Disorders in America: The Epidemiologic Catchment Area Study. New York: The Free Press.

Schiraldi, Vincent, Barry Holman, and Philip Beatty. (2000). Poor Prescription: The Costs of Imprisoning Drug Offenders in the United States. Washington, DC: Justice Policy Institute.

Teplin, Linda A. (1994). "Psychiatric and Substance Abuse Disorders among Male Urban Jail Detainees." American Journal of Public Health. 84(2): 290-293.

Teplin, Linda A., Karen M. Abram, and Gary M. McClelland. (1996 ). Prevalence of Psychiatric Disorders Among Incarcerated Women. pretrial jail detainees. Archives of General Psychiatry . 53(6): 505-512.

Homeless

In a recently released report on the adequacy of substance abuse epidemiology, the National Academy of Sciences (2001) criticized the National Household Survey on Drug Abuse for failing to include estimates of drug use by nonhousehold high-risk populations. The report stated, "The committee recommends that methods be developed to supplement the data collected in the National Household Survey on Drug Abuse . . . in order to obtain adequate coverage of subpopulations with high rates of drug use" (p. 3-8). A large number of studies have documented the high rates of drug and alcohol use among the homeless (e.g., Research Triangle Institute 1994; see Table 1 below). This chapter describes the results of a literature review designed to estimate the need for treatment (i.e., the prevalence of current and lifetime substance use disorders) in homeless populations. The overall estimate of the need for treatment in the State combines estimates of need for non-overlapping populations of households with telephones, households without telephones, and non-household high-risk groups: homeless people and prisoners incarcerated during the past year. Using the National Technical Center's (NTC) needs assessment questionnaire, the State previously obtained a statewide estimate of the number of people who needed substance abuse treatment by interviewing the population living in households with telephones. In other chapters, the authors estimated the needs of recently incarcerated prisoners and households without telephones . The present substudy will add to that statewide needs assessment by estimating the number of persons who were homeless that needed treatment when the household survey was underway.

The Homeless in North Dakota

Because the authors could not identify any diagnostic studies of the substance use disorder prevalence rate of homeless North Dakotans, they drew upon the composite rate from the literature review described below to develop a synthetic estimate of treatment needs of the homeless. However, the homeless population in each state has nuances that can affect the needs of the homeless and their treatment system. In order to gain a clearer notion of homelessness in North Dakota, the authors had a conference call with state officials in the Division of Mental Health and Substance Abuse Services and the State Hospital who were knowledgeable about the characteristics of the homeless and how the treatment system handles those individuals. The state officials addressed a variety of issues including a description of the homeless population in general as well as their treatment options and needs. Although the population at any one point in time is estimated to be small (see chapter on gap analysis) (North Dakota Division of Community Services and North Dakota Coalition for Homeless People, Inc. 2000) relative to other states, one official stated that there are many "couch homeless" who are essentially transients that divide their time between staying with friends and family and being homeless. Although the household survey was designed to capture those individuals, they use the same treatment system as the shelter and street homeless. The July 2000 survey of the homeless done through service providers confirmed that there were nearly as many couch homeless as shelter homeless (211 versus 237). The state officials stated that the treatment system does not serve nearly all the needs of the homeless population and that the number of available slots in residential programs were inadequate. They explained that because North Dakota is a rural state, it is difficult to have a full continuum of treatment available in all areas. Treatment is provided at the Human Service Centers which are located in urban areas. As such, it is difficult for those outside of the cities to access treatment. Further, because the population is so spread out, it is difficult economically to provide a full complement of specialty services where there are fewer potential clients. When asked what types of additional services are needed for the homeless, there was a strong consensus that wrap-around services including mental health care, habilitation services, and housing are necessary to effectively treat homeless clients.

Homeless Population Estimate. Because they could not identify an estimate of the number of North Dakota residents who were homeless, the authors developed an estimate based on the 2000 Census count of the sheltered homeless using an adjustment described in the literature for estimating the number of nonsheltered homeless (Smith et al. 2001, Jencks 1994). The Census count captures only the sheltered population, and it was necessary to use an estimate of the ratio of the number of homeless on the street to the number in shelters in order to obtain a more accurate estimate of North Dakota's homeless population. That procedure yielded an estimate just shy of 315 homeless. The ratio estimation procedure is described in more detail in the chapter on gap analysis. The authors considered using the count from the July 2000 survey of service providers, but determined that it was not possible to separate out children accompanied by adults, who are not part of the target population (North Dakota Division of Community Services and North Dakota Coalition for Homeless People, Inc. 2000). Further, the count from service providers includes the couch homeless who are part of the household telephone survey population.

Literature Review

Because virtually every study done elsewhere found that the homeless have higher rates of substance use disorders than the general population, generalizing from the literature is better than either leaving the homeless out altogether or using the rate observed in the general population (which is done implicitly when telephone survey results are generalized to the entire target population).

Review Criteria and Methods

In order to collect the results of published estimates of the treatment needs of incarcerated populations, the study team reviewed the existing literature. The study team searched the NIAAA's Alcohol and Alcohol Problems Science Database (ETOH), the National Library on Medicine, the National Criminal Justice Reference Service's abstract database, the Addiction Research Foundation's bibiographic database, and a series of internet search engines for unpublished reports. The team made use of the North Charles Research and Planning Group's own extensive needs assessment library and database that included reports collected from states that conducted prison studies as part of the State Treatment Needs Assessment Program (STNAP). The authors examined the bibliographies of all of the studies for any publications not found by computer search routines. Earlier reviews of the literature on substance use and abuse by the homeless (Fischer 1991; Johnson and Barrett 1995) were examined as well.

Table 9.1 Substance Use Disorders in Homeless Samples


Reference

Data Collection Location and Date

Study Population

Definition of Homeless

Diagnostic Measurement

% Alcohol or Drug Use Disorder

Current

Lifetime

Breakey et al. 1989

Baltimore, Maryland

1986-1987

203 adults interviewed in missions, shelters, and jails. Oversampled females: 57% male.

Prionsers were homeless at time of arrest.

Psychiatrists Evaluated DSM-III Criteria





Alc:

Males: 68

Females: 32

Drugs: Males: 22

females: 17

North and Smith 1993

St. Louis, Missouri

1989-1990

600 males and 300 females randomly selected from all night shelters and day shelters in St. Louis and sampled from street corners. Age 18 and over.

Stayed prior night in a shelter or on street, car, abandoned building; no permanent home.

DIS-III-R

Past year


Alc:

37

drugs: 16



Koegel et al.

1988

Los Angeles, California

Date Not Reported

328 adults interviewed in missions, skid row hotels, shelters, soup kitchens, and drop-in centers in Los Angeles. Ages range from 18 to 69. 95 % male.

Not reported

DIS-III

Past six months

31

69

Robertson et al.

1997

Alameda County, California

1991

564 adults (age 18 and over) living in shelters, soup kitchens, and drop-in shelters in Alameda County. 68% male.

Spent previous night in shelter or on street

DIS-III-R

Past year

52

69

Alemagno et al.

1996

Ohio

1994

207 adults (age 18 and over) were interviewed in shelters , soup kitchens, and a health care clinic. 76% male.

Not reported

DIS-III-R





Alc:

57;

Cocaine:

45

OASAS, RIA.

1995

New York

1994

354 adults (age 18 and over) were interviewed in NYC and upstate shelters (n = 333) and in single room occupancies (n =21) in New York City. 64% male.

Not reported

DIS-III-R

Past year

48

51

Peuquet et al. 1997

Delawaare

1994-1995

147 adults (age 18 and over) were interviewed by telephone from 16 shelters. % male not reported.

Residing in an emergency shelter

DIS-III-R

Past year

46




Kline et al. 1997

New Jersey 1996

540 residents of 32 shelters. Aged 18 and older. 66% male.

Not reported

DIS-III-R

Past year

46



Caton et al.

2000

New York

1996 and 1997

200 adults with no history of a psychiatric disorder were interviewed in drop-in shelters in New York City. Ages ranged from 18 to 65. 50% male.

Literally homeless: no fixed abode, sleeps in shelter, a public place or shelter

Structured Clinical Interview for DSM-IV






Alc:

Males: 51

Females: 40

Drugs:

Males: 55

Females: 59

McAuliffe et al.1999

Rhode Island 1999

198 adults from shelters, drop-in center, soup kitchen, and transitional housing. Aged 18 and over. 70% male.

Resided in shelter, living on the street, or resident of transitional housing with history of homelessness

DIS-IV

Past year

66

78

Women Only

Bassuk et al.

1998

Worcester, Massachusetts

1992-1995

220 single mothers were interviewed in shelters in Worcester. Aged 15 to 58.

Not reported

Structured Clinical Interview for DSM-III-R-Non-Patient (SCID-NP)

Past month

5

41

Smith et al.

1993 ; Same as North and Smith 1993 above

St. Louis, Missouri

1989-1990

300 females randomly selected from all night shelters and day shelters in St. Louis and sampled from street corners. Aged 18 and over.

Stayed prior night in a shelter or on street, car, abandoned building; no permanent home.

DIS-III-R

Past six months


18

31

Adolescents and Young Adults

Warheit and Biafora 1991

Miami, Florida, date unknown

100 homeless (74 males, 26 females) in a short-term shelter, Aged 18-21.

Shelter residents; 60% considered themselves homeless

DIS-III

Past year

Drugs :

45

Alc: 39

58

Robertson

1989

California

1987

93 adolescents were interviewed in shelters, drop-in shelters, and on the streets of Hollywood. Ages range from 13 to 17. 61% male.

Stayed prior night in a shelter or on street, car, abandoned building; no permanent home.

DIS-III





61

Kipke et al.

1997

Hollywood, California

1994-1995

432 youth (age 13 to 23) were interviewed in shelters and on the streets. Ages range from 13 to 23. 66% male.

Resided in shelter, in an institution providing temporary residence (e.g. treatment facility, motel, prison), living in street, or inadequately housed at friend's residence.

DIS-III





71

Because the objective of the study was to add the estimate for homeless persons to the estimates from the telephone survey, the authors sought studies that closely approximated the diagnostic methods (the Diagnostic Interview Schedule [DIS]) used in the state treatment needs assessment telephone survey. The search focused on homeless studies conducted in the United States and Canada. The studies varied in the recruitment sites used to access homeless populations. Shelters, soup kitchens, jails, rooming houses, transitional housing, and drop-in centers were the most common sources of homeless persons. Relevant studies included efforts to estimate the percentage of homeless persons who had a substance use disorder diagnosis that met the clinical criteria of the American Psychiatric Association's (1994) Diagnostic and Statistical Manual of Mental Disorders (DSM). The objective of the review was to obtain prevalence estimates for both genders and combined alcohol and drug use disorders during the year prior to incarceration. Because of the paucity of past-year estimates of combined substance use disorders available in the literature, the analysis includes estimates that approximate that time period (e.g., six months), that apply to males or females separately, and that are for alcohol and drugs separately. The authors limited the review to the 1980s and more recently.

Results

The literature review yielded fourteen studies that met the search criteria (Table 9.1). However, comparing the studies was difficult because the studies differed in many ways. The studies used different diagnostic measures (psychiatrists, DIS-III, DIS-III-R, and DIS-IV, and the SCID). Some authors reported current diagnoses, while others reported only lifetime diagnoses. Several investigators defined "current" as the last year, while two others defined it as the past six months and one defined it as the past month. Some studies reported combined substance use disorders, while others reported drug use disorders and alcohol use disorders separately. Five of the studies covered only women or only adolescents. Some studies covered both men and women, but the studies reported the genders' rates separately. The geographic coverage was limited because the studies were primarily from urban areas, and several were conducted in California. In a large national study of the homeless at 700 sites in 1996, Burt et al. (1999, p. 13-20) found that urban homeless were more likely to have a drug problem than were suburban homeless and rural homeless (41% versus 35% versus 21% respectively), but that the rural homeless were most likely to have alcohol problems (48%, 36%, 55%). Similar percentages of urban and rural homeless had a problem in the last year with either drugs, alcohol, or both (62% and 59% respectively). The rate for suburban homeless was smaller (51%). These results are important for justification of generalizing the results of the present review. Unfortunately, the study did not collect diagnostic data. For the present purpose, the primary objective is to estimate the percentage of combined males and females who had a substance use disorder diagnosis in the last year, and therefore the studies that report that information will be the primary focus of the review. Other studies will be used to corroborate the studies that are the main focus.

Adult Males and Females. Four studies reported the percentage of current substance use disorders among a sample of both males and females, and four studies reported lifetime substance use disorder diagnoses. Breakey et al. (1989) employed trained psychiatrists to assess DSM-III criteria for substance use disorders in a random sample of homeless people from all shelters, missions and jail in Baltimore during 1986 and 1987. The study oversampled females, but it did not report the proportion of males and females in the target population. Sixty eight percent of the male respondents and 32% of the females either had an active alcohol use disorder or were in remission. Twenty-two percent of the males and 17% of the females had lifetime drug use disorders. The study did not report the percentage who had either an alcohol or drug use disorder. The investigators conducted a supplementary survey of homeless people on the street, in drop-in centers, and soup kitchens. It revealed that 75% of these respondents could also have been recruited from shelters, missions, or jails.

Koegel et al. (1988) interviewed 328 adults in missions, skid row hotels, shelters, soup kitchens, and drop-in centers in Los Angeles. Using the DIS-III, the study found that 31% had a substance use disorder during the last six months, and 69% had a lifetime substance use disorder diagnosis. The highest lifetime prevalence rate was among the small sample of American Indians (89%, overwhelmingly alcohol use disorders). Koegel et al. (1988) found that alcoholics who were homeless reported many more symptoms and adverse effects of alcoholism than alcoholics who lived in households reported.

Robertson et al. (1997) interviewed 564 homeless adults in Alameda County, California in 1991. Fifty-two percent had a DSM-III-R substance use disorder in the past year, and 69% had a lifetime disorder. Using the National Technical Center's questionnaire that incorporated the DIS-III-R (McAuliffe et al., 1995), Alemagno et al.(1996) found that 57% of homeless adults in Cleveland shelters, soup kitchens, and a health clinic had lifetime DSM-III-R diagnoses of alcohol abuse or dependence, and 45% had lifetime drug abuse and dependence diagnoses.

The Office of Alcoholism and Substance Abuse Services of New York State and the Research Institute on Addictions (1995) found that 51% of a sample of shelter and transitional single room occupancy residents in New York City and upstate New York had a lifetime history of having a substance use disorder, and 48% had a current substance use disorder. The study also used the National Technical Center's instrument and conducted the interviews by cellular telephone. In a similar study, Kline et al. (1997) found that 46% of 540 homeless shelter residents in New Jersey had a substance use disorder in the last year.

McAuliffe et al. (1999) interviewed a sample of homeless people recruited from shelters, a soup kitchen, a drop-in center, and two transitional housing units. Using a version of the National Technical Center's needs assessment questionnaire that had been updated to measure DSM-IV criteria, the study found that 66% had a current substance use disorder, and 78% had a lifetime substance use disorder.

Studies that reported their results separately for alcohol and drugs indicated that more homeless had alcohol use disorders than drug use disorders. In most of these studies, there were more males than females.

Women Only. Smith et al. (1993) surveyed a random sample of 300 homeless women in St. Louis during 1989 and 1990 to determine their alcohol, drug, and psychiatric comorbidity. The lifetime substance use disorder rate was 31%, while the six-month rate was 18%. From August, 1992, to July, 1995, Bassuk et al. (1998 ) interviewed 220 single mothers (aged 15 to 58 years) who were recruited from all nine emergency shelters in Worcester, Massachusetts. Using the SCID, the authors found that 41% had a lifetime substance use disorder diagnosis and five percent had a past-month diagnosis. Studies that reported their results for males and females separately indicated that males tended to have higher rates of substance use disorders, especially alcoholism (Breakey et al. 1989; Caton et al. 2000) .

Adolescents and Young Adults. Robertson et al. (1989) found that 45 (48%)of 93 homeless adolescents in Hollywood in 1987 had an alcohol use disorder. Of the remaining 48 adolescents, 25% had drug use disorder. Sixty-one percent of the total sample had a drug or alcohol use disorder. Kipke et al.(1997 ) interviewed 432 runaway and homeless youth from shelters and streets of Hollywood, California. Using items drawn from the DIS-III, the investigators found that 71% had a "history of alcohol or other "˜drug abuse' or dependence based on DSM-III criteria." Although this description of the results suggests that they describe lifetime diagnoses, the present authors' research on adolescent populations suggests that virtually all of these cases were probably currently dependent as well.

Warheit and Biafora (1991) interviewed 100 young adults (aged 18 to 21 years) in a short-term shelter in South Florida. Using the DIS-III, the study found that 58% had a lifetime substance use disorder diagnosis. Forty-nine percent had a lifetime drug-use disorder, and 43% had a lifetime alcohol use disorder. In the past year, 45% had a drug use disorderand 39% had an alcohol use disorder.

Lifetime and Past-Year Estimates of Treatment Need

The authors used the results of these studies to develop estimates of the substance abuse treatment needs of the homeless. The four studies with lifetime estimates of the substance use disorder prevalence among homeless men and women had an unweighted mean rate of 67%. The median rate was also 69% (Figure 1). The lifetime estimates from adult samples ranged from 49% to 78%. The two Hollywood adolescent studies suggest that the substance use disorder rates among adolescents were similar to the rates for California adults.

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The unweighted mean rate for the current disorder rates was 48%, and the median rate was 47% (Figure 2). The current estimates ranged from 31% to 66%. The authors decided to use the median rates as the study's estimates because the median is less sensitive to extreme values. These median rates should be multiplied by the number of recently homeless persons. Because statistics on the static (point in time) size of the state's homeless population will not be reported for the 2000 Census, states must obtain estimates from state agencies concerned with homelessness.

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Unmet Need for Treatment

Despite the high prevalence and severity of substance use disorders, many homeless people have not received treatment for these problems. T he homeless with substance use disorders frequently have coexisting psychological and physical conditions that necessitate the most intensive levels of substance abuse treatment services (Drake, Osher, & Wallach 1991 ; Gelberg, Linn, & Leake 1988 ; Susser, Struening, & Conover 1989), but there is evidence that the homeless respond to treatment as well as other groups respond (Warheit & Biafora 1991; Milby et al., 1996) . Seventy percent of the 481 homeless persons surveyed by Johnson and Barrett (1995) were identified as needing treatment for the use of alcohol, other drugs, or both. Twenty-five percent of the subjects reported ever having received treatment for substance use. Koegel et al. (1995) found that 26% of homeless persons with a current drug use disorder obtained treatment in the last year. I n a New York City homeless study, Padgett, Struening, and Andrews (1990), found that 35% of the people rated as needing alcohol treatment actually received help (mostly from Alcoholics Anonymous or medical clinics). The percent receiving treatment among people who experience problems with other drugs was nearly the same, 32%, and a methadone program was the most often cited source of services. North and Smith (1993) found that approximately half of the males and two thirds of the females with lifetime substance use disorders had ever received inpatient substance abuse treatment. About half of them had received treatment in the past year. Males had been admitted to treatment about twice as often as females. In a 1994-1995 Delaware study, 17.5% of adult homeless people with a current substance use disorder received treatment in the past year (Peuquet et al. 1997).

The current authors' study of the homeless in Rhode Island found that treatment was commonplace among respondents with substance use disorders. In that study, 77% of those with a current need for treatment received some in the past year. More than half (56%) of homeless people with a current treatment need received specialty treatment for their disorder in the past year. Among those homeless individuals that were in need but did not receive treatment in the past year, the Rhode Island study found that 42% wanted treatment for their disorder. Among those that did receive treatment in the past year, 63% reported they wanted additional treatment. Thus, the homeless have high levels of need and demand.

Homeless adolescents appeared to have higher rates of substance use disorders than the adult homeless have, and there is evidence that the percent of homeless adolescents seeking and receiving treatment is less than the percent of homeless adults who receive treatment for a substance abuse disorder. Among homeless youth living in Hollywood with substance abuse disorders, 15% utilized treatment services (Kipke, et al., 1997);14% of homeless youth with alcohol abuse problems received either in-patient or out-patient treatment (Robertson et al.,1989). From these results, i t is evident that the homeless use treatment services more commonly than most other population groups. Despite their small size, the homeless are a group whose treatment needs require public attention and inclusion in a comprehensive statewide treatment needs assessment.

Conclusions

The study identified 15 studies that reported lifetime or current rates of substance use disorders in homeless populations. Because many of the diagnostic studies used a version of the Diagnostic Interview Schedule (DIS) to measure the presence of clinical symptoms of abuse or dependence in samples of the homeless, their results were comparable to the North Dakota household survey that also used a version of the DIS. The median rate of current substance abuse disorders reported by six studies of adult males and females was 47%. The median lifetime rate was 67%. In the absence of a study conducted in the State, these estimates were used to develop an integrated estimate of the level of treatment need in the state.

References

American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) DSM-IV. American Psychiatric Association. Washington, DC.

Alemagno, Sonia A. , Deborah C. Cochran , Thomas E. Feucht , Richard C. Stephens , John M. Butts , and Stephanie A.Wolfe. (1996). "Assessing Substance Abuse Treatment Needs Among the Homeless: a Telephone-Based Interactive Voice Response System." American Journal of Public Health. 86(11): 1626-1628.

Bassuk, Ellen L., John C. Buckner , Jennifer N. Perloff , and Shari S.Bassuk,. (1998). "Prevalence of Mental Health and Substance Use Disorders Among Homeless and Low-Income Housed Mothers." The American Journal of Psychiatry. 155(11): 1561-1564.

Breakey, William R., Pamela J. Fischer, Morton Kramer, Gerald R Nestadt, Alan J. Romanoski, Alan Ross, Richard M. Royall, and Oscar C. Stine. (1989)." Health and Mental Health Problems of Homeless Men and Women in Baltimore." Journal of the American Medical Association. 262(10):1352-1357.

Burt, Martha R., Laudan Y. Aron, Toby Douglas, Jesse Valente, Edgar Lee, and Britta Iwen. (1999). Homelessness: Programs and the People They Serve. Findings of the National Survey of Homeless Assistance Providers and Clients. The Urban Institute. Downloaded from www.huduser.org/publications/homeless/homelesstech.html

Caton, C. L. M., D. Hasin, P. E. Shrout, L. A. Opler, S. Hirshfield, B. Dominguez, and A. Felix. (2000). "Risk Factors for Homelessness Among Indigent Urban Adults With No History of Psychotic Illness: A Case-Control Study." American Journal of Public Health. 90(2).

Drake, Robert E., Fred C. Osher, and Michael A. Wallach. (1991). "Homelessness and Dual Diagnosis." American Psychologist. 46(11):1149-1158.

Fischer, Pamela J., Alcohol, Drug Abuse and Mental Health Administration, and NIAAA. (1991). Alcohol, Drug Abuse and Mental Health Problems Among Homeless Persons: A Review of the Literature, 1980-1990, Executive Summary. Rockville, MD: National Institute on Alcohol Abuse and Alcoholism.

Gelberg, Lillian, Lawrence S. Linn, and Barbara Leake. (1988). "Mental Health, Alcohol and Drug Use, and Criminal History Among Homeless Adults." American Journal of Psychiatry. 145(2): 191-196.

Jencks, Christopher. (1994). The Homeless. Cambridge, MA. Harvard University Press.

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McAuliffe, William E., Richard A. LaBrie, Norah Mulvaney, Howard J. Shaffer, Stephanie Geller, Elizabeth A. Fournier, Eliot B. Levine, Qiuyun Wang, Susan M. Wortman, and Kathleen A. Miller. (1995). Assessment of Substance Dependence Treatment Needs: A Telephone Survey Manual and Questionnaire, Revised Edition. Cambridge, MA: National Technical Center on Substance Abuse Needs Assessment.

McAuliffe, William E., Richard A. LaBrie, Eric Sevigny, Kenneth Nielsen, Scott Ronis, Tristan Robinson, and Caroline Sunshine. (1999). The Substance Abuse Treatment Needs of Rhode Island's Homeless . Cambridge, MA: North Charles Research and Planning Group.

Milby, Jesse B., Joseph E. Schumacher, James M. Raczynski, Ellen Caldwell, Molly Engle, Max Michael, and James Carr. (1996). "Sufficient Conditions for Effective Treatment of Substance Abusing Homeless Persons." Drug and Alcohol Dependence. 43(1 & 2): 39-47.

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Gap Analysis

As described in the introductory chapter, the first goal of the integrated analysis was to develop a statewide estimate of the substance use disorder treatment needs of North Dakota residents. The previous chapters described prevalence estimates for a series of nonoverlapping subgroups of the population. The North Dakota family of needs assessment studies included a survey of adult residents of households with telephones and a study of American Indian adults on reservations. To round out the coverage of all relevant adult populations, the present authors developed synthetic estimates of the treatment needs of adult residents of households without telephones, the homeless, and recently incarcerated prisoners. This analysis refers to the past year treatment needs of these prisoners when they were on the outside. "Synthetic estimates" generalize national estimates to the State, and they generalize results from other states to the State. Because the North Dakota family of studies did not include adolescents, this chapter supplemented the adult estimates with synthetic estimates of the treatment needs of adolescents who were homeless, lived in households with or without telephones, or were recently incarcerated. These population subgroups constitute the major at-risk groups which receive services funded by the Substance Abuse Prevention and Treatment (SAPT) Block Grant and state funds. The analysis did not attempt separate estimates for several other groups (e.g., nursing home patients, psychiatric inpatients, those in military quarters, and college students in dormitories) because either they were small in size, had low risk of substance use disorders due to being institutionalized part of the year, there were no prevalence estimates available in the literature, or sources other than state-funded programs provide the group's substance abuse treatment services. The analysis assumed that these groups were not members of the target population.

The process of integrating the separate subgroup estimates of treatment need into an overall state estimate of the need for treatment services in the past year was conceptually straightforward. For each population subgroup, the analysis multiplied the prevalence rate times the number of people within that population subgroup, and then the analysis summed the resulting estimated number of people to obtain the total need for the State. The prevalence rates, population estimates, statewide total number of people in need, and the proportion of the total population in need represented by the subgroup are summarized in Table 10.1. Subsequent sections of this chapter describe the assumptions that the authors made when developing these estimates.

In all cases, the study sought to estimate the number of people in each group on a single, hypothetical day, and to estimate the number of people on that day who needed and received substance abuse treatment during the previous year. The difference between the number in need and the number in need that received treatment was the gap in treatment services in the past year. Because the prevalence of substance use disorders and the amount of treatment services change only gradually over time, the study assumed that the best estimate of the treatment gap in future years was the gap in the past year. State planning will presumably focus on closing those gaps in the near future.

Table 10.1 Substance Use Disorder Prevalence Estimates



Survey Group

Ages

Prevalence Estimate (%)

Population Subgroup Total

Number in Need of Treatment

Proportion of Total Treatment Need (%)

Adults in Households with Telephones

18 and older

5.2

442,901

23,031

74.5

Adolescents in Households with Telephones

12 to 17

8.5

56,711

4,821

15.6

Residents of Households Without Telephones

12 and older

11.1

21,903

2,431

7.9

Recently Incarcerated Prisoners

18 and older

62.5

679

424

1.4

Adolescents in Juvenile Corrections Facilities

12 to 17

62.5

40

25

0.1

The Homeless

All excluding children accompanied by an adult

47

315

148

0.5

Total



5.9

522,549

30,880

100.0

Statewide Treatment Need

The estimated total number of North Dakotans in need of treatment for a substance use disorder during the past year was 30,880 (Table 10.1). The State's telephone survey of adults identified the largest number of people in need of treatment: 74.5% of the total people in need. The estimated number of adolescents in households with telephones (15.6%) and people 12 and over in households without telephones (7.9%) constituted the next largest groups. Altogether, people in households accounted for 98% of the population in need. The homeless, recently incarcerated prison inmates, and adolescents at correctional facilities accounted for the rest (597 people). While homeless people and recently incarcerated adult and adolescent inmates had much higher rates of substance use disorders than the other groups had, the small population size of those groups limited their overall contribution to the total population in need. If the study did not estimate the past-year needs of the homeless and recently incarcerated prisoners, the final estimate of past-year treatment need would be biased downward by 2%.

The Treatment Gap

The gap between the number of people in need of treatment for a substance use disorder in the past year and the number that were actually treated was large (Table 10.2). Of the estimated 30,880 people in need, an estimated total of 2,826 (9.2%) received treatment for a substance use disorder in the past year. In other words, 90.8% of people in need of treatment during the past year did not receive treatment. Only 7.8% of those surveyed in households with telephones that were in need received treatment in the past year. Analysis revealed that surprisingly few of those with a need that did not receive treatment said that they would have sought treatment if it had been readily available. The present integrated analysis estimated that there were 1,204 people with an unmet demand for treatment. Providing additional treatment services to eliminate unmet demand is probably a more defensible long-term planning goal. To provide sufficient treatment services to eliminate unmet demand, the State would have to increase the current level of services by almost 43%.

Impacts of Incorporating Estimates of the Needs of the Special Populations. The study estimated that adolescents utilize treatment at a lower rate than the household survey population. Therefore, including adolescents in the analysis increased the gap between need for treatment and treatment delivered. However, adolescents in need were estimated to have a higher rate of unmet demand than adults in need and including adolescents in the total estimate raised the overall rate of unmet demand. Because the current analysis generalized the household telephone survey unmet demand rate (2%) to cover households without telephones as well, the estimated total rate of unmet demand in North Dakota was relatively low compared to the rate in other states.

Although inclusion of the recently incarcerated and homeless populations did not contribute greatly to the total treatment need estimate, their inclusion did impact demand. Those groups have high estimated rates of met and unmet demand. The actual number of people in those groups in need of treatment was small compared to the number in need from the household population, but the rates of substance use disorders among the homeless and prisoners were higher than the rate for the household population. Further, the rates of treatment utilization and unmet demand for the high-risk groups were higher than the rates in the household population. As such, these groups warrant attention. Because their needs are relatively severe, an additional prisoner or homeless person may require more extensive treatment resources or a higher level of treatment than an additional person from the household population requires.

Table 10.2 Estimates of Met and Unmet Demand for Treatment

Group

Number In Need of Treatment

% in Need Who Received Treatment

Met Demand: Number in Need that Received Treatment

% Who Wanted Treatment Among Those in Need Who Did Not Receive It

Unmet Demand: Number Who Wanted Treatment Among Those in Need Who Did Not Receive It

Adults in Households with Telephones

23,031

7.8

1,796

2

425

Adolescents in Households with Telephones

4,821

10.0

482

15

651

Residents of Households Without Telephones

2,431

7.8

190

2

45

Recently Incarcerated Prisoners

424

54.5

231

35

68

Adolescents in Correctional Facilities

25

50

13

11

1

The Homeless

148

77

114

42

14

Total

30,880

9.2

2,826

4.3

1,204

Note: Need for treatment was defined as having a substance use disorder in the past year. Unmet demand for treatment was defined as the percentage of the population with a past-year substance use disorder that did not receive treatment but wanted treatment. Met demand for treatment was defined as the percentage in need that received treatment for a past-year substance use disorder.

Arriving at Specific Estimates of Treatment Need and Supply

Readers should bear in mind that this estimate of the treatment gap in North Dakota depends on a series of estimates, any of which could be imperfect. The authors have provided explanations of the estimation procedures and descriptions of potential sources of error in the following sections to alert readers to these issues and to clarify any adjustments the authors made in the process of estimating need in each of the subpopulations. Readers who do not wish to examine the methodological details of each estimate at this time may skip to the next chapter.

Census Estimates of Target Population

Most of the population estimates used in this analysis were from the U. S. Census Bureau. In order to calculate specific segments of the age distribution in North Dakota, for example, adolescents aged 12 to 17 as a percentage of the total population, the authors used a single-year-of-age file from the Census. Because the relevant file from the 2000 Census was not available when this analysis was in process, the authors estimated the age distribution using single-year-of-age estimates from 1999 (Census 2000). The authors then applied that age distribution to the 2000 Census total population count in order to account for growth and exploit the likely greater accuracy of the decennial count over the 1999 projection (Census 2001). Using the 1999 age distribution, the authors subtracted the 12 and under population and split the remainder into adolescents (12 to 17 years of age) and adults (18 years and older). North Dakota's population of adults in households with telephones is the 18 and older household population less 4.2% to account for those households without telephones. The population of households without telephones is 4.2% of the 12 and older population, which is based on the household telephone noncoverage rate reported by the Federal Communications Commission for 2000 (Belinfante 2001). It was necessary to use the 12 and older population because the data used to do the households without telephones study were for the 12 and older population (the National Household Survey on Drug Abuse [NHSDA]). Therefore, the adolescent population of households with telephones is simply the 12 to 17 year old population minus 4.2% to account for adolescents in households without telephones.

The estimation procedure for those population subgroups assumes that the age distribution is the same regardless of whether or not a household has a telephone (Geller 1997). In order to assure that only the household population is used for those estimates, the authors used the census estimate of the household population for their calculations. The authors obtained the population of recently incarcerated prisoners from the North Dakota Department of Corrections and Rehabilitation (personal communication with Kathy Jensen on 1/14/02). The Division of Juvenile Services provided the population estimate for incarcerated adolescents (personal communication with Al Lick on 1/17/02). The section on the homeless below describes the estimation procedure for the homeless population.

The North Dakota Adult Household Survey

The results of the household telephone survey largely determined the statewide estimates for treatment need and demand because residents of households with telephones comprise by far the largest component of North Dakota's population. The point is best exemplified by the similarity between the survey estimates and the total estimates. However, a quarter of those with a need were from the other subgroups. This section briefly summarizes the survey and discusses its potential shortcomings.

In 1997, the Gallup Organization (1998) conducted a telephone survey of 6,814 adults aged 18 and older living in North Dakota. The study employed the National Technical Center's adaptation of the Diagnostic Interview Schedule (DIS) in order to assess whether the respondents met DSM-III-R criteria for a substance use disorder in the past year (Robins et al. 1981; McAuliffe et al. 1995). The survey found that 5.2% of the respondents had a substance use disorder in the past year. Applying that percentage to the 2000 Census estimate of the State's adult population living in households with one or more telephones (442,901; Census 2000, 1999) resulted in an estimate of 23,031 (95% Confidence interval: 20,162; 25,906), representing the number that were in need of treatment for a substance use disorder in the past year. Because the vast majority (98%) of people live in households, 95.8% of which have at least one telephone, the adult household survey population is the largest population subgroup in the State (Belinfante 2001).

In contrast to this estimate of treatment need, the Gallup Organization (1998, p. 13) used the lifetime substance use disorder rate to develop its estimate of treatment need. Gallup also generalized these percentages to the entire state population rather than to residents of households with telephones only. As a result, Gallup (1998, p. 13) estimated the number of people in need to be 38,480 due to alcohol dependence or abuse, 541 people in need of drug treatment and 1,504 people in need of treatment for both alcohol and drugs. The Gallup report does not provide an overall, unduplicated estimate of current need for alcohol or drug use disorders. The present authors concluded that Gallup's estimates of treatment need were too large because many of the people with lifetime substance use disorders were in full remission and therefore did not need treatment in the past year.

It is important to recognize that many people who were members of the household population at the time of the survey may have also been members of other populations, such as prisoners and the homeless, at some time during the past year. In theory, survey estimates such as the U. S. Census population counts are treated as if they referred to one day, even though the survey data collection actually occurred over a period of weeks or months. The estimated sizes of the homeless and prisoner populations on one day from the Census are therefore much smaller than the number of people who have been in prison or who have been in a shelter during the past year. Many readers will be surprised how small these populations appear to be in the present study. The explanation is that most of the people who were homeless or in prison in the past year were covered by the household survey rather than by the prisoner survey or by the surveys reviewed to create a synthetic estimate of the homeless. The people in the present study's estimates were only those people who were homeless or in prison at the time of the study. Inclusion of those populations was still important for obtaining an unbiased estimate of the population in need.

Potential Sources of Error. As is the case with any estimate from a sample survey, the telephone survey's results were subject to both sampling and non-sampling error. Sampling error arises from imperfections in the sampling design and implementation. If an element of the population was left out of the sampled population, the sample estimates would be subject to noncoverage error. The results of a telephone survey, for example, cannot be generalized to the entire population because not everyone lives in a household with a telephone. The presence of that form of bias in telephone surveys is the reason for the present study's efforts to include populations missed by the telephone survey. As noted above, there were also small groups that were not estimated directly (e.g., nursing home patients, psychiatric inpatients, persons in military barracks, and college students in dormitories). Because these groups were generally small and their members in need received most of their treatment from sources other than the State, the authors assumed that a minimal amount of bias resulted from their exclusion from the sampled and target populations.

Random sampling error is another potential source of error of estimation. Gallup developed the telephone survey's sampling design to obtain reliable estimates for the state as a whole and for each of eight substate planning regions. Drawn from a stratified (regions) random digit-dialing sample of 6,814 members of the State's adult population, the survey's estimates of substance use disorders were relatively reliable. The 95% confidence limits for the 5.16% estimate were 4.55% and 5.85% (our calculation using SUDAAN, Design =STRWR, using REGION as the nest variable). That is, the amount of error was likely to be less than one percent in either direction.

Non-sampling error is systematic and random error that arises from the process of data collection. Nonresponse bias is one potential source of this type of error (Caspar 1992; Cottler et al. 1987; Caetano 2001). The response rate as calculated by the CASRO method was 74% for the state and 68% or better in each of the four planning regions (Council of American Survey Research Organizations 1982). While the evidence that non-response results in substantial bias is mixed, most observers assume that the direction of the bias is downwards, that is, nonresponse bias causes lower prevalence rates. In addition, there is evidence that people may under-report their use of alcohol and illicit drugs in telephone surveys (Alquilino 1992) and in face-to-face surveys (Fendich et al. 1999). Therefore, although they are reasonably reliable, the survey prevalence estimates are likely to be conservative. Decision makers should probably assume that the true number of people in households with telephones who met DSM-III-R criteria for having a substance use disorder in the last year was at least as large as the telephone survey's estimate.

The North Dakota telephone survey found that 7.8% of the adults that needed treatment in the past year received some form of treatment. This estimate translated into 1,796 adults from households with telephones received treatment in the past year (CI: 1,144; 2,768). The survey question on treatment was designed to capture a broad range of treatment, including non-specialty treatment from psychiatrists, primary care medical doctors, clergy, general therapists, and so on (McAuliffe et al. 1995).

Households Without Telephones

Failing to survey the estimated 4.2% of North Dakotans who live in households without telephones can lead to bias. This observation is especially relevant to the American Indian population. Nationally, American Indians and Alaska Natives have the lowest telephone coverage rate among all ethnic and racial groups (Census 1994). That group comprises about 5% of the North Dakota population (Census 2001). To address this form of bias, the authors developed a synthetic estimate of the substance use disorder prevalence rates of people who live in households without telephones (for full details of the study see the chapter on Households Without Telephones). The authors analyzed four years (1995-1998) of NHSDA data. The study found that nationally, 10.16% of people 12 and older in households without telephones and 4.78% of those 12 and older in households with telephones had a dependence on drugs or alcohol in the past year. Therefore, a person in a household without at least one telephone was 2.13 times more likely to have been dependent in the past year than a person in a household with at least one telephone. The authors applied this relative risk from the NHSDA to North Dakota's telephone survey substance use disorder prevalence rate in order to obtain a synthetic estimate of the prevalence rate for North Dakotans who live in households without telephones. Applying the ratio of 2.13 to the telephone survey estimate of the substance use disorder prevalence (5.2%), the study inferred that the substance use disorder prevalence estimate for North Dakotans 12 and older in households without telephones was 11.1%, which was equal to 2,431people in 2000.

The authors also analyzed the four years of NHSDA data to ascertain the relationship between treatment received by those that live in households with telephones and those that live in households without telephones. The weighted results showed a slightly lower rate of treatment among those with a need living in households without telephones (the risk ratio of receiving treatment of a substance dependence due to living in a household with a telephone was 1.06). However, the difference between the rates of treatment among those in need in households with and without telephones was not significant. Therefore, the authors deemed it prudent to apply the household survey rate of met demand for treatment to the population of those in households without telephones. In the absence of relevant data on unmet demand for residents of households without telephones, the authors also generalized the rate of unmet demand for treatment from the household telephone survey to the residents of households without telephones.

The authors estimated the total population of households without telephones by applying the telephone coverage rate and the relevant age distribution (12 and older) to the household population of North Dakota. This method assumes that the age distributions of households with and without telephones are the same (Geller et al. 1997).

Adolescents in Households With Telephones

To develop a synthetic estimate of the treatment needs of adolescents in the State, the authors developed estimates for several subgroups of youth 12-17. Following CSAT's guidelines, North Dakota's needs assessment studies focused on the treatment needs of persons 18 and older, even though the State provides treatment to adolescents. Analysis of the Treatment Episode Data Set (TEDS) from 1998 showed that 359 North Dakotans between the ages of 12 and 17 received treatment that year (Inter-university Consortium for Political and Social Research [ICPSR] 2001). That was 13.5% of all treatment admissions in North Dakota recorded in the 1998 TEDS data (see Table 10.4 below). Given that persons between the ages of 12-17 (60,664) comprise about 13.7% of North Dakota's at-risk population aged 12 to 64 (441,855), the inclusion of the adolescent cohort in the overall treatment need estimate might increase the total number of people in need of treatment who have not obtained it because that group demonstrated a higher rate of need than the adult population (Census 2001). The present section focuses on estimating the treatment needs of adolescents in households with telephones. The previous section on residents in households without telephones included adolescents, and following sections estimate the needs of the State's homeless and incarcerated adolescents.

To fill the gap in prevalence estimates for North Dakota's adolescents living in households with telephones, the authors analyzed prevalence estimates from several other data sources (Table 10.3). The 1999 National Household Survey on Drug Abuse (NHSDA) estimated that North Dakota adolescents (12-17 years of age) had a rate of substance dependence (8.7%) that was 65% higher than the estimated adult rate (5.26%, our calculation) (Office of Applied Studies 2000, p. 94). It is not unusual for adolescents to have a higher substance use disorder rate than adults. A study conducted by the authors for Rhode Island showed that adolescents had a rate of substance use disorders (7.2%) that was 1.11 times higher than the rate for adults 18 and over (6.5%) (LaBrie et al. 2000). There were two issues that the authors considered when deciding whether and how to use the NHSDA estimates. First, the NHSDA's sample size was small: 951 for the State's 12 and older population, of which 331 were between 12 and 17. The small sample size raised questions of the reliability of the results. However, no other samples of North Dakota adolescents were available. The authors decided that it was better to use a small state-specific sample than a large sample from a different state. Second, the NHSDA estimates only dependence, not abuse. The current study defined having a treatment need as having a diagnosis for dependence or abuse. The authors hypothesized that the adolescents' substance use disorder rate could be inferred based on the relationship between the adult substance use disorder rate from the telephone survey and the adult NHSDA dependence rate.

Determining the potential bias that resulted from failing to include cases with abuse diagnoses was difficult because there was little consistency in the literature on this point. Rhode Island adolescents with a diagnosis were more likely to have a diagnosis of abuse rather than dependence (Table 10.3), while the opposite was true for Rhode Island adults. Holding the relationship between dependence and abuse from the North Dakota household telephone survey constant assumes that adolescents and adults have the same ratio of dependence to abuse. However, North Dakota adults had a slightly higher rate of dependence than abuse in the household telephone survey, so the Rhode Island results may be irrelevant. A questionnaire study of high school students in Minnesota also found that adolescents were more likely to have a drug abuse diagnosis than a drug dependence diagnosis (13.8% abuse versus 8.2% dependence)(see Martin and Winters 1998, p. 97). However, it is unlikely that drugs are the primary problem among North Dakota youths. In a telephone survey of adolescents in Virginia, McAuliffe et al. (2000) found that 1.8% had a diagnosis of substance abuse and 5.0% had a diagnosis of substance dependence. In a Michigan study that employed the same instrument and telephone survey methods as the Rhode Island and Virginia studies, 5.9% of the adolescents had a dependence diagnosis, while 4.5% had an abuse diagnosis (Aktan and Calkins 2000, p. 59). Finally, an adolescent high school study conducted in Oregon also found a preponderance of alcohol dependence (4.3%) diagnoses over alcohol abuse diagnoses (1.9%) (see Martin and Winters 1998). Thus, while the evidence on the matter is mixed, the majority of evidence shows adolescents have higher rates of dependence than abuse. It is unclear, though, how uniform the ratio of dependence to abuse is between adults and adolescents. The current study employs an estimate that assumes the ratio of substance dependence versus abuse is equal across adult and adolescent populations. The estimate used here is lower than the estimated adolescent dependence rate from the NHSDA and, therefore, may be conservative.

Based on the NHSDA estimate, there were 4,821 North Dakota adolescents that were dependent on a substance in 1999 (Table 10.2; OAS 2000; Census 2000). The authors' analysis of Treatment Episode Data Set (TEDS) for 1998 showed that 359 people between the ages of 12 and 17 were admitted to treatment that year (ICPSR 2001). That rate implies that 7.4% of those adolescents with a need received specialty treatment. The Rhode Island study estimated that 10% of adolescents that needed treatment received it (McAuliffe et al. 1988). Due to evidence that adolescents are prone to use nonspecialty treatment resources (for example, counseling at school), the authors chose to use the Rhode Island survey estimate here. In North Dakota, that estimate indicates there were 482 adolescents in need that received treatment in the past year. The Rhode Island study indicated that 15% of adolescents in need had an unmet demand for treatment. In North Dakota, that is 651 adolescents with an unmet demand for treatment.

Table 10.3 Inferring the Substance Use Disorder Prevalence of Adolescents From Existing Estimates


Rate

North Dakota Household Survey a

National Household Survey on Drug Abuseb

Rhode Island Household Survey c

Adolescent

Adult

Adolescent

Adult

Adolescent

Adult

Abuse

-

2.54

-

-

4.5

2.6

Dependence

-

2.62

8.7

5.3*

2.7

3.9

Substance Use Disorder

8.5

5.16

-

-

7.2

6.5

The bolded figure, the substance use disorder rate of North Dakota adolescents, is the one being estimated.

*The authors estimated this figure by combining the rates for adults 18-25 (13.4%) and 26 or older (3.7%).

a Gallup 1998, b Office of Applied Studies 2000a,c McAuliffe et al. 2001b .

Using a ratio estimation procedure that employed the 1999 NHSDA dependence estimates for adolescents and adults along with the findings of the North Dakota Adult Household Survey, the authors estimated that 8.5% of North Dakota adolescents had a substance use disorder in the past year (Gallup 1998; OAS 2000). The estimate in Table 10.3 implies that about 4,820 youths between the ages of 12 and 17 in North Dakota have a drug or alcohol use disorder. The ratio estimation procedure involved creating two ratios, setting them equal, and solving for the unknown substance use disorder prevalence rate of North Dakota youths. First, the authors created the ratio of the adult substance use disorder rate from the North Dakota Adult Household Survey to the North Dakota adult dependence rate from the NHSDA (5.16% / 5.26%). Then, the authors created the ratio of the unknown substance use disorder rate of North Dakota adolescents to the dependence rate of North Dakota adolescents from the NHSDA (8.7%). The authors then set the two ratios equal to each other and obtained the unknown North Dakota adolescent substance use disorder rate by cross multiplication. Because the estimates used to derive this rate were from the household telephone survey, the adolescent population from the NHSDA was adjusted to reflect the population of adolescents in households with telephones by subtracting the number of adolescents living in households without telephones. Adolescents in households without telephones were already captured by the need estimate for households without telephones presented in the previous chapter. The final outcome was that an estimated 8.5% of the adolescents residing in households with telephones had a substance use disorder diagnosis.

The Homeless

Because the North Dakota family of studies did not include a survey of the homeless and no previous homeless studies were done in North Dakota, the authors referred to previous studies conducted elsewhere in order to obtain estimates of the prevalence of treatment need, the percentage who obtained treatment, and the percentage in need who wanted treatment but did not obtain it in the past year among the homeless. The authors estimated the past-year substance use disorder prevalence rate for the homeless to be 47%. That estimate was a composite of prevalence estimates from a series of diagnostic studies of homeless samples summarized in the chapter on the homeless. The inclusion of the homeless in the total need estimate adds 148 people to the total in need. Though this is not a substantial change in the statewide total and does not greatly affect the overall substance use disorder rate, the homeless are an underserved population that typically displays an intense need for treatment.

Research done by the authors in Rhode Island suggests that about 42% of homeless adults who needed treatment but did not receive it would have sought it if it had been available. That estimate is used in this study, and it indicates that approximately 14 homeless people at the time of the survey wanted treatment for their disorder, but did not receive treatment in the past year. The estimate for the treatment rate among the homeless is borrowed from the Rhode Island study as well (McAuliffe et al. 1999). It is estimated that more than three-quarters of homeless adults with a substance use disorder (77%) received some form of substance abuse treatment for their disorder in the past year, or 114 people.

After developing the prevalence estimates, the authors had to develop an estimate of the size of North Dakota's homeless population. There is no generally agreed upon definition of homelessness or method of estimating the number of homeless people in the literature. In the current analysis, the authors used the 2000 Census count of people in emergency shelters as a basis for estimating the homeless population of North Dakota. The authors felt that the Census count (178) was the best available information upon which to base an estimate (Smith et al. 2001). The Census Bureau cautioned the user, however, that the count of people in emergency shelters is not a complete count of the homeless (Smith et al. 2001). There are many other places a homeless person might spend the night. Many are couch homeless who are able to spend the night at the home of a friend or relative. A recent study of the homeless in Rhode Island found that among those surveyed at shelters 29% had spent at least one night during the previous month at a friend's place, and 24% had stayed with family at least one night during the previous month. Of those surveyed at soup kitchens and drop-in centers, 61% had spent at least one night with friends, and 53% had spent at least one night with family (McAuliffe et al. 1999). The results from the July 2000 survey of North Dakota's homeless (respondents were recruited through homeless service providers) corroborate those findings ( North Dakota Division of Community Services and North Dakota Coalition for Homeless People, Inc. 2000). In addition, homeless people sleep in cars, abandoned buildings, and bus stations (Jencks 1994). These people may be referred to as the "street homeless." Because the street homeless population can be difficult to identify, the Census Bureau has announced that it will not publish an estimate of the street homeless as was done in previous years. As a result, the authors had to adjust the census count of clients in emergency shelters to account for non-shelter homeless.

The method used in the current analysis follows the estimation procedure described in Christopher Jencks's 1994 book, The Homeless. He adapted his methodology from research done by Martha Burt in 1987. His procedure allows the user to derive the homeless population based on shelter counts using a ratio of the homeless on the streets to the homeless in shelters. The user can also eliminate children accompanied by at least one adult from the shelter count. Although it appears to be the best available approach, this method has obvious limitations. First, the method was developed using data from a week long survey, whereas the 2000 Census reflects a point-in-time count of those using emergency and transitional shelters.

The second major limitation of this method is that the estimated ratio of street homeless to shelter homeless may be out of date. That ratio expresses only a point-in-time estimate of the number of homeless on the street relative to the number in shelters. Jencks offered several such estimates and the estimate used in the current analysis was from March 1990 (1.224). It is the most recent among those he estimated, and it is the lowest. The highest ratio of street homeless to shelter homeless that he estimated was 2.464. Furthermore, he stated that the ratio for the whole year 1990 would be higher than the March-only ratio, presumably due to weather considerations. As such, the ratio used in the current analysis is probably conservative.

The calculation itself was simple. The Census count of North Dakotans in transitional and emergency shelters was 178 (Smith 2001). The point-in-time estimate from the survey of North Dakota's homeless done through service providers did not allow the user to separate out children accompanied by adults. In order to subtract off children escorted by adults, who are presumably not part of the target population, the authors reduced the shelter count by Jencks's estimate of 20.4% (36.312), leaving less than 142 adults. The final step was to apply the ratio of street homeless to shelter homeless. Applying the March 1990 ratio of 1.224 to the less than 142 adult shelter residents, the authors estimated that there were 173 street homeless in North Dakota. The total of shelter and street homeless, then, was 315. The point-in-time survey done in North Dakota included the couch homeless who were already captured in the household telephone survey. Further, some of the individuals may have been double counted at separate service provider locations. Therefore, the count from that survey was not used as the homeless population in the current analysis. However, subtracting all clients under the age of 18 in that survey results in a total client count of 373. Given the two estimates differ by only 16% and that point-in-time study had errors that biased its count upwards, the two population counts can be considered similar.

Prisoners

The authors determined the past-year substance use disorder prevalence estimate for North Dakota's recently incarcerated prisoners by examining a number of similar estimates from other states. Three of the studies examined were done as part of the State Treatment Needs Assessment Program (Alaska, Rhode Island, and Montana). Those studies were described in greater depth in the chapter on prisoners. The estimated prevalence for recently incarcerated prisoners was 62.5%. The North Dakota Department of Correction and Rehabilitation provided the population estimate. There were 679 inmates who were part of the target population (i.e. on the outside in the past year) but were missed by the survey because they were incarcerated. Applying the estimated prevalence to the 679 prisoners resulted in a total of 424 in need. The estimates of met and unmet demand were based on findings from the Alaska and Rhode Island studies (McAuliffe et al. 2000; 2001a). The authors decided to take the mean of the two estimates in each case because the difference between the two was minimal in both cases. The authors estimated met demand for the recently incarcerated to be 54.5% and unmet demand to be 35%.

Adolescents in Correctional Facilities

North Dakota's Division of Juvenile Services maintains a detention facility, the North Dakota Youth Correctional Center, that is similar to what is commonly called a training school. The Youth Corrections Center accommodates youthful offenders and provides substance treatment services on site. There were an estimated 40 adolescents in the facility who were on the outside for at least one month during the past year. One official estimated that 75% of youths in the entire system (the training school population comprises a small portion of the 500 youths involved in the juvenile services program) had a substance abuse problem. Those youths not detained at the corrections facility were covered by the estimates for adolescents in households with and without telephones and the homeless estimate. Based on evidence from a study of training school detainees in Rhode Island, the authors assumed that adolescent inmates had the same rate of past-year substance use disorders as their adult counterparts; in this case, 62.5%. That rate implies there were 25 incarcerated adolescents in need of treatment in the past-year in North Dakota. The estimated rates of met and unmet demand came from the Rhode Island study. Given that 50% of those adolescent inmates in need received treatment for their disorder, there were 13 treated in the past year. Given that only 11% of those in need that did not receive treatment in the past year wanted it, the authors estimated that only 1 training school inmate had an unmet demand for treatment when on the outside during the past year.

Treatment During the Past Year

A significant problem when measuring the treatment gap is the absence of an established, widely-accepted measure of treatment received. There are several candidate measures: 1) the number of admissions reported by the State treatment agency, 2) the estimates of the number of treatment admissions reported to the Treatment Episode Data Set (TEDS) program that were obtained from the state and analyzed by the Office of Applied Studies, 3) the estimates of the number of clients in treatment on a single day in North Dakota as obtained from the Uniform Facility Data Set (UFDS) survey conducted annually by the Office of Applied Studies, and 4) the estimates of the number of persons in need who received some form of treatment in the past year as measured by North Dakota's needs assessment survey and the non-telephone sample estimates presented in Table 10.2. As shown in Table 10.4, there are substantial differences among the measures. The Treatment Episode Data Set (TEDS) statistics are consistently the highest, while the UFDS treatment clients are consistently the lowest. Treatment admissions data from the State do not include state-funded services provided at the State Hospital; they are therefore lower than the TEDS statistics. The State data are unduplicated and do not include transfers or services provided to families of those in treatment. The State data indicated there were 2,003 clients that received services in the year prior to the survey while the survey-based estimate counted 2,826 clients. The survey-based estimate captured those who received services at the State Hospital and more closely resembles the TEDS estimate (2,468).

Table 10.4 Comparison of Treatment Measures for North Dakota, 1993-1999

Data Source

1991

1992

1993

1994

1995

1996

1997

1998

1999

State

2,375

2,412

2,174

2,182

2,156

2,077

2,003

-

-

Treatment Episode Data Set**

-

-

2,233

2,410

2,322

2,465

2,468

2,659

2,108

Estimate From Gap Analysis

-

-

-

-

-

-

2,826

-

-

Uniform Facility Data Set/

NDATUS

-

-

1,955

1,950

1,184

1,686

2,086

3,011

-

**The number of admissions reported in this table differ slightly from those reported in Chapter 11 which eliminated cases that lacked county identifiers.

Sources: Office of Applied Studies 1995, 1996, 1997a,b, 1999, 2000c,d.

There are several reasons why estimates of past-year treatment may differ. The estimate depends both on how treatment is defined and the source of the data. Of course, because some clients have multiple episodes of treatment within a year, admissions rates such as the TEDS statistics are higher than client rates such as UFDS and the State figures in Table 10.2. The TEDS data, are gathered mainly from publicly-funded specialty treatment providers, whereas household survey data, such as those from the North Dakota telephone needs assessment survey and the National Household Survey on Drug Abuse (NHSDA), capture a wider range of specialty and nonspecialty treatment types. The North Dakota telephone survey, as well as the NHSDA, also included self help treatment, pastoral counseling, and care from other nonspecialty providers. The UFDS estimates varied widely from year to year, suggesting that they are less reliable than the other measures. The current analysis used the survey-based estimate of treatment to estimate the treatment gap because that figure is most comparable with the survey estimate of need, especially in terms of the specific population they cover. The survey estimate of treatment captured a variety of specialty and nonspecialty services, whereas the TEDS and State admissions data cover specialty care only.

Treatment Received by Respondents with a Substance Use Disorder. Unmet need for treatment was defined as the percentage of respondents with a past-year substance use disorder who did not receive treatment in the past year. This definition is the most reasonable at the moment, even though there are reasons to suspect that the definition may both underestimate and overestimate the true amount. There is reason to believe that not everyone with a substance use disorder needs treatment (Regier et al 1984, 1998; Spitzer 1998; Shapiro et al. 1985) Research shows that many people with an addictive disease eventually remit without the benefit of treatment (Bischof et al. 2000, 2001; Blomquist 1999; Sobell et al. 1992, 1996; Russell et al. 2001; Toneatto et al. 1999). Some of these people may have chronic substance use disorders that wax and wane without reaching a point at which the afflicted individuals feel the need for, seek, or accept help.

As explained in Chapter 3 on national trends, a survey respondent that reported having received treatment may or may not have had a diagnosable substance use disorder in the past year. This apparent anomaly occurred in the four years of NHSDA data that were analyzed in the present study as well as in the North Dakota telephone survey. In the North Dakota telephone survey, 7.8% of the respondents with a past year substance use disorder diagnosis received treatment in the last year, but 1% of people without a current diagnosis received treatment in the past year, as well. In the entire sample, 1.4% received treatment in the past year. Despite this difference, there were actually more respondents without a diagnosis than with a diagnosis who reported receiving treatment in the past year (70 versus 28). It is possible that the survey questions were not sufficiently sensitive to detect the addictive disorders in these respondents. Despite the discrepancy between measures of diagnosis and treatment need, the authors concluded diagnosis is the best available measure of treatment need currently available and focused on the respondents who clearly needed treatment.

The authors focused their recommendations regarding unmet need and demand on people who have a diagnosis and would be eligible for treatment should they decide to seek it. While many people seek treatment despite not having a diagnosis, receiving treatment is strongly predicted by having a diagnosis. Moreover, clinicians employ diagnostic criteria when determining the medical necessity of providing treatment to people who seek it.

Summary

This chapter integrated multiple subpopulation rates of treatment need and services received to obtain a statewide, comprehensive estimate of the treatment gap. The analysis covers all relevant populations in order to assure maximum coverage of the North Dakota population. Several small institutionalized populations (e.g., college dorm residents, nursing home residents) were excluded because the institutions, not the State, provide treatment services in most cases involving those groups. The telephone survey estimate establishes the range within which the final estimate will settle because the survey covers the largest part of the population in need; almost 96% of those covered in this study live in households with telephones. However, the other non-household populations covered here, along with household residents that do not have telephones, have high levels of risk, and those in need often suffer from severe substance use disorders. As such, these additional estimates are an essential component of any comprehensive treatment need study. If the telephone survey prevalence rate had been applied to these groups, the estimated number in need would have been 27,173. By obtaining more precise estimates from other sources, the study observed an additional 3,707 people in need of treatment. Those people are among the State's residents most in need of treatment services.

If the State increased services by 43% of their current level, all unmet demand would be filled. If all those with an unmet demand were treated, 13% of those in need would receive some services. At 2%, the rate of unmet demand from the household survey is low. The State may wish to focus its efforts to encourage more of those in need to seek treatment. Each component of the treatment need total is subject to imperfection. The estimates from the adult household telephone survey are the most reliable because they are from population-based diagnostic surveys conducted in North Dakota. The other population subgroup estimates (adolescents, prisoners, homeless, and households without telephones) were derived from diagnostic analyses, but do not specifically address the relevant North Dakotan populations. In its future needs assessment studies, the State may wish to fill one or more of these information gaps.

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Intrastate Variations

This chapter describes the results of a study of how North Dakota's 53 counties and eight human service regions compared to each other regarding major substance abuse indicators. The chapter on the treatment gap assessed the statewide gap between treatment needs and services. The present analysis focuses on learning which areas have the greatest relative need for treatment services.

The intrastate indicator analysis is important for the integration study because substance abuse and the substance abuse treatment system are partially statewide in scope and partially unique to each county or region. As previous chapters in this report have shown, alcohol is the primary substance of abuse in North Dakota . The present chapter will show that the highest alcoholism rates are in rural counties with low population densities. Each area's response to its mix of substance abuse problems depends partly on its own history, population, and policies. The region's response also depends on clinical developments, regulations, and funding that may be available for substance abuse services from a range of sources, including local, state, and federal programs. By examining how an area differs from its counterparts regarding both its substance abuse problems and treatment services, the study will help reveal the area's relative unmet need for substance abuse services.

Readers should bear in mind that a county or region's relative status may say surprisingly little about the absolute severity of its substance abuse problems or the area's absolute success in meeting its substance abuse service goals. The interstate comparison chapter showed that North Dakota ranked in the middle among states in the severity of its substance abuse problems, and ranked similarly in its treatment provision. However, previous needs assessment studies have shown that even states which have provided relatively high levels of treatment services compared to other states may nevertheless have a substantial amount of unmet demand for services.The chapter on North Dakota's treatment gap was devoted to assessing the absolute size of the gap for the state as a whole. Using survey data and synthetic estimates, that chapter showed that there was a substantial gap between the absolute number of people in need of treatment and the number that had received it. In the present chapter, the intrastate measures of need for services are compared statistically to the delivery of services to determine the relative extent of the gap between treatment need and services in each region. That is, overall there is a gap between need and services in the state, but some areas may have a larger gap than others have.

Methods

The study employed existing substance abuse indicator data available from state and national sources. Indicators varied with regard to the number of years that were available for analysis. The study used all available data between 1991 and 1998. Drug and alcohol arrest statistics covered the period from 1994-1998, while county-level treatment client information from the State covered 1991 to 1997. The mortality data covered the period 1993 to 1996. The rates are average annual ("mean") rates per 100,000 residents. The authors selected this base rate in order to avoid having rates that were hard-to-interpret decimal fractions of a death, arrest, or treatment admission. The denominators for each annual rate were state population projections for the relevant years. After reviewing each data set for indications of clerical, coding, or programming errors and making any needed corrections, the study team created a series of annual count and rate variables at the county and regional levels, and combined them in a data base. The data and methodology are described in detail in North Dakota's Substance Abuse Indicator Manual (McAuliffe et al. 2001).

Measurements and Index Construction

To summarize the information from multiple indicators, the authors created a composite index of alcohol treatment needs: the Alcohol Need Index (ANI). The primary purpose of the index is to assess a county's or region's alcohol treatment needs.The composite index includes measures of alcohol-related rates of death and arrest per 100,000. The ANI is a sum of standardized mean alcohol death rates and alcohol-defined arrest rates. The alcohol mortality measure employs 12 explicit-mention diagnoses widely employed as a measure of alcoholism. Examples are diagnoses of alcohol dependence, non-dependent alcohol abuse, alcohol psychoses, alcoholic cirrhosis of the liver, and alcohol cardiomyopathy. The alcohol-defined arrest measure includes liquor law violations (e.g., underage drinking) and disorderly conduct arrests. The authors collected and cleaned parallel data in hopes of creating a similar Drug Need Index, but the data did not have reliability or validity as indicators of drug abuse. Therefore, the authors recommended that the State collect additional years of data in order to increase the reliability and validity of the drug need indicators to an acceptable level before creating a separate drug need index in the future.

The ANI's scale score of 100 equals the combined highest observed alcohol arrest and mortality rates during the study period. A score of zero on the index indicates that there is no evidence of treatment need, as shown by there being no arrests or deaths in the county or region during the study period. There were no scores of 100 on the alcohol scale because no area had the highest rate on all the index components, though Human Service Center Region III (Lake) had a score of 97. Similarly, no area received a zero score because it lacked evidence of any need for treatment.

The Substance Abuse Need Index (SNI) combines the indicators for drugs and alcohol, with any overlap removedfrom the drug and alcohol mortality variables (due to people who died as a result of both alcohol an drug consumption). North Dakota's SNI reflects the alcohol data overwhelmingly, but it is critical to take both alcohol and drug needs into account for purposes of calculating gaps in treatment services by region that reflect all substances (later in this chapter).

Analysis and Presentation

When describing these indicators, the chapter focuses on the comparative nature of the analysis by reporting the county's or region's average annual rate per 100,000 and in some cases, its rank in the State. In all cases, the county or region with the most severe alcohol abuse problem is ranked 1st, and the area with the least severe problem is ranked 53rd for counties or 8 th for regions. The chapter seeks to make the results of the indicator analyses readily accessible to local officials and citizens, state officials, and other interested individuals. By use of maps and charts, the authors sought to minimize the technical requirements for understanding and utilizing the analyses. The bar charts present the index scores, counts, or rates per 100,000, and in some cases, the county/region rankings.

Readers should exercise substantial caution when interpreting the results for individual indicators, especially the mortality rates and the traffic fatality rates at the county level. Many of the counties are small. One county (Slope) has less than 1,000 residents. As a result, even rates based on four or five years of data can be very volatile. As a result, some of the very high or very low rates may be poor estimates of long-term rates in the area. For those variables, the charts include the actual number of cases that occurred during the study period. The treatment indexes are based on several indicators and are therefore more reliable predictors of long-term need. Also, the maps provide a context for interpreting the rates. When there are clusters of small areas with similar rates, it is likely that the rates are more stable estimates. The regions were used in order to minimize the impact of this methodological problem and to help formulate recommendations at a more practical planning level.

RESULTS

The study found that there is substantial variation in substance abuse problems across counties and regions in North Dakota. The results confirmed that alcohol problems are far more common than drug problems in the State.

ALCOHOL INDICATORS

Alcohol-Related Indicators in North Dakota

For the most part, rates of major alcohol indicators were stable in North Dakota over the years 1993 through 1998 (Figure 11.1). The rate of alcohol-related arrests rose sharply between 1997 and 1998 and was up about 40% over the whole period. The graph above is on a log scale.

[ole.gif]

Alcohol Treatment Need Index

The highest rate of alcohol treatment need (as measured by the alcohol indicators) was registered in Human Service Center Region III, with a score of 97 (Figures 11.2 and 11.3). The lowest score (45) was in Region II. A large part of Region III is covered by American Indian reservations and the area is rural. Although Region II contains part of a reservation, it has a large population center in Ward County. Regions I (72) and VI (66) had high scores, and both are relatively rural areas.

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[ole2.gif]

Alcohol Mortality

As shown in Figure 11.4, Sioux County had the highest explicit-mention alcohol mortality rate in North Dakota over the period 1993 through 1996. Sioux County is in Region VII, which had a low ANI score because of low mortality rates in the more populous urban areas of Bismark and Mandan. There were a number of counties that had no explicit-mention alcohol deaths during the four years in question.

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The highest alcohol mortality rates occurred in counties that have American Indian reservations (Rolette, Benson, Mountrail, McKenzie, and Sioux) (Figure 11.5). Some counties, including Adams, Griggs, and Logan, had five or fewer alcohol deaths over the four years, but had high rates because they have small populations.

Alcohol-related Arrests

Disorderly Conduct and Liquor Law Violations

Ramsey County had the highest rate of alcohol-related arrests by far (Figure 11.6). Ramsey County is adjacent to the Fort Totten reservation in Benson County, and it has a small population. Those factors that may contribute to its high rate.

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The map below (Figure 11.7) shows no obvious geographic pattern of arrest rates aside from the concentration of low and medium rates around urban areas (Ward, Cass, Burleigh, and Morton Counties). Ramsey County had a rate that was more than double the next highest. That may be partly attributable to the fact that the adjacent county, Benson, is largely covered by the Fort Totten Reservation which does not allow sale or possession of alcohol. Therefore, some of that activity may be forced off of the reservation and into Ramsey County.

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Driving Under the Influence

Ramsey County, which had the highest rate for the combined disorderly conduct and liquor law violations measure, had the second highest rate of DUI arrests (Figure 11.8). Other counties with high DUI rates were, for the most part, those that contained urban areas, including Cass, Morton, Burleigh, Stutsman, and Grand Forks Counties.

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Alcohol-related Hospital Reimbursement Claims

The third component of the ANI was alcohol-related hospital claims. As shown in Figure 11.9, Benson County, Rolette County, and Ramsey County had three of the four highest rates in North Dakota. They are all in Human Service Center Region III, which had the highest ANI score (97).

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The alcohol-related hospital claims rates do not have the defined differences between urban and rural counties that the other alcohol indicators showed (Figure 11.10). However, counties with American Indian reservation including Mountrail, Dunn, Sioux, Rolette, and Benson had high rates.

alchos-1.gif

Alcohol-related Motor Vehicle Fatalities

Note that the lowest alcohol-related motor vehicle fatality rates were recorded in urban counties. For example, Cass, Ward, Burleigh, and Grand Forks Counties are all in the lowest category (Figure 11.11). The highest rates were recorded in the rural northwest counties and in Rolette, Benson, and Sioux Counties, all of which are rural and have American Indian reservations.

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Because the alcohol-related motor vehicle fatality rate was not a component of the need index, these results are meant to confirm the findings of the index. Indeed, Figure 11.12 reveals that the highest alcohol-related motor vehicle fatality rates were found in Regions I and III, which also had the highest ANI scores, while the lowest rates were in the more urbanized counties, which had the lowest ANI scores.

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Alcohol Treatment

Treatment client data from the State (by county of residence) show that the highest alcohol treatment rates are in those counties with the greatest need. Sioux, Mountrail, Rolette, Benson, and Ramsey Counties had high rates for the indicators and also have high treatment rates. The more populous counties with low mortality but moderate arrest rates (Cass, Burleigh, Grand Forks, and Ward) had somewhat lower treatment rates, while rural areas with no reservations and low rates for the indicators had low treatment rates.

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The map below (Figure 11.14) illustrates the general concordance between need and treatment rates in North Dakota. Those counties that had the greatest need for alcohol treatment according to the indicators described above also had the highest treatment rates. Quantitative analysis later in this chapter confirms that relationship.

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CONTROLLED DRUG INDICATORS

In contrast to the stable trends observed for the alcohol indicators, the controlled drug indicators increased steadily during the period from 1993 through 1998 (Figure 11.15). Because the rates differ so greatly in scale, this graph is on a log scale.

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Because low rates of drug deaths and arrests prevented the construction of reliable indicators when analyzed by themselves, the authors did not create a Drug Need Index. The drug indicators were used instead along with the alcohol indicators as components of the Substance Need Index. Drug arrests and drug mortality have been validated as indicators in other states.

Drug Mortality

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Drug-related deaths were spread across the state, in or around urban areas (Figure 11.17).

Drug Mortality

Drug Arrests

Except for Burke County, the highest rates for drug arrests were in urban counties (Figure 11.18). That is a common pattern in other states.

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Figure 11.19 shows that the more urban counties such as Ward, Grand Forks, Cass, Burleigh, and Morton all had relatively high drug arrest (sale and possession) rates. Some other less populous areas had high rates as well, including Ramsey, Burke, Golden Valley, and Sargent Counties.

Drug Arrests

Drug-related Hospital Reimbursement Claims

Cases of drug-related hospital claims are not as concentrated around urban centers as the other drug indicators. Benson and Rolette Counties are rural areas with American Indian reservations.

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There is no easily discernable geographic pattern in these data other than the fact that most of the highest rates are in the eastern part of the state (Figures 11.20 and 11.21). Benson and Rolette Counties had the highest rates and both have American Indian reservations. Although Slope County is in the second highest category, it has fewer than 1,000 people, and its rate therefore reflects few cases.

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Drug Treatment

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Sioux County had the highest rate of treatment clients over the years 1991 through 1997, while eight counties had no clients admitted to treatment for drugs over the entire period (Figures 11.22 and 11.23). The highest treatment rates were in areas with relatively large populations or American Indian reservations.

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Substance Treatment Need

In order to obtain maximum reliability and validity for the index and in order to develop recommendations at the level most meaningful for planning purposes, the authors calculated the Substance Treatment Need Index (SNI) at the regional level. The eight regions used here correspond with North Dakota's Human Service Regions, each of which has a Regional Human Service Center (RHSC) where most state-funded treatment is delivered.

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As the map above (Figure 11.24) clearly shows the highest SNI score was observed in Region III (Lake Region). That region includes Ramsey and Rolette Counties, and it has a relatively small population. Regions II and VII had the lowest scores. They include some of the more populous counties in North Dakota, including Burleigh and Ward. The most populous county, Cass, is in Region V, and that region's SNI score (56) was in the second lowest category. Although urban areas had higher rates for the drug indicators, the alcohol indicators influenced the SNI score more heavily because occurrences of those events (deaths and arrest) were more frequent. Hence, the more rural regions (Regions I, III, and VI) had higher SNI scores than the more urban regions.

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Comparison of the SNI scores and treatment rates by region suggests that treatment is allocated reasonably closely to need. However, that relationship indicates nothing about the absolute level of need that is served. It indicates only that services provided are delivered where they are most needed. The gap analysis in the previous chapter indicated that the overall level of services was lower than total demand by 1,204 persons. The authors recommend allocating those services so that all regions would have a rate consistent with its level of need (Table 11.1).

Table 11.1 Recommended Allocation of Additional Treatment Services*

Region

Substance Need Index Score

Observed Treatment Rate

Treatment Rate Predicted by Regression

Difference Between the Observed and Predicted Treatment Rates

Recom-mended Number of New Clients

Recomme-nded Total Rate per 10,000 in the Population

III-Lake RHSC

96

470

507

-37

94

68.1

I-Northwest RHSC

74

453

423

30

42

59.8

VI-South Central RHSC

65

485

387

99

49

56.3

VIII-Badlands RHSC

56

376

351

25

60

52.6

V-Southeast RHSC

56

240

349

-109

424

52.4

IV-Northeast RHSC

53

276

338

-62

231

51.3

VII-West Central RHSC

51

398

331

67

137

50.6

II-North Central RHSC

47

303

315

-12

167

49.0

* The model was specified as Treatment = 128.4 + 3.95 SNI. Treatment is the dependent variable and need is the independent variable. The R-squared statistic was .458. Minor discrepancies may occur due to rounding error.

In order to formulate recommendations on how new services should be allocated throughout the State, the authors developed a regression model designed to take both the current levels of treatment services and need into account. The model used the need index scores from the social indicator study as a determinant and used the State's treatment client data as the dependent variable. The authors then used the predicted and residual values from regression to allocate the additional services recommended in the gap analysis in the most equitable manner. There were 1,204 additional persons recommended in the gap analysis based on the estimated amount of unmet demand for treatment. Table 11.1 shows the results from regression and the recommendations the authors determined based on the methodology described in greater depth in the Appendix.

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The map above (Figure 11.26) shows the geographic distribution of treatment service needs relative to demand as determined by regression of need on treatment. A negative residual implies that a region is underserved given a certain level of treatment statewide. A positive residual does not imply, however, that a region has too much treatment relative to its level of need. It only means that a region has a higher rate of treatment services than predicted by its level of need given a certain level of services statewide. There is unmet demand for treatment in all counties across the State. Consequently, this analysis suggests that each region should receive additional resources.

Allocation of Recommended New Admissions to Maximize Equitable Access to Treatment

The model the authors developed measured how well the existing distribution of treatment services among regions matches relative levels of need among regions. The observed values for treatment (see Table 11.1) are the mean treatment client rates in each region between 1991 and 1997. The predicted values are the treatment client rates that each region was expected to have based on its need (SNI score) vis a vis other regions. The residual values are the differences between the observed and predicted values in each region. Starting with a statistical comparison (regression) of the SNI with the average annual treatment admissions rate for 1991-1997, the authors developed a recommended allocation by region that would as much as possible bring the rate in all regions in line with the statistical relationship between need and treatment while increasing the overall level of services for all regions. The first goal of this allocation process was to assign new resources to the residents of the most relatively underserved areas (e.g., Regions IV and V ) to bring their treatment rates in line with the rates predicted by regression. The second goal was to allocate any additional recommended new resources to all regions on the basis of relative need for treatment.

In order to achieve those goals, all regions with negative residuals from the linear regression line received enough new clients to match the predicted treatment rate. The rates recommended by the authors are depicted in Figure 11.27. Raising the treatment rate in areas that fell below the regression line did not allocate all of the recommended new services. The remainder of new services were then allocated up to the point where each region had a relationship between need and treatment similar to that of the region with the highest positive residual from regression, Region VI. In order to take both need and the existing treatment rate into account, the authors sought to maintain the relationship between need and treatment by fixing the slope of the regression line and shifting the line up. This process was continued until all of the new services were allocated. According to this methodology, each region would have rates that fell on a new allocation line that was parallel to the original regression line but had a higher intercept. Because the existing allocation of services in North Dakota was equitable, the recommendations cited above are driven by population to a considerable degree. Each region needs additional resources in part because the existing distribution of resources apparently took need into account and in part because the existing level of services is low.

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APPENDIX

Table A11.1 Sources of Substance Abuse Indicators

Substance

Indicators

Source

Alcohol

Arrests for DUI, Disorderly Conduct, Liquor Law Violations Burglary, Robbery, Prostitution Arrests

Uniform Crime Reports; Interuniversity Consortium for Political and Social Research (ICPSR); North Dakota Bureau of Criminal Investigation.

Alcohol

Motor Vehicle Fatalities, with BAC>.10

Fatal Accident Reporting System, National Highway Traffic Safety Administration.

Alcohol

Alcohol-Related Deaths

National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention

Drug

Drug-Related Deaths

National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention

Drug

Arrests for Drug Offenses

Uniform Crime Reports; Interuniversity Consortium for Political and Social Research (ICPSR); North Dakota Bureau of Criminal Investigation.

Drug, Alcohol

Population 1990-2000

Bureau of the Census

Drug, Alcohol

Clients in Treatment

State Client Data System, North Dakota Department of Human Services; Uniform Facilities Data Set (UFDS), Office of Applied Studies (OAS), Center for Substance Abuse Treatment (CSAT)

Drug, Alcohol

Treatment Admissions

Treatment Episode Data Set (TEDS), Office of Applied Studies (OAS), Center for Substance Abuse Treatment (CSAT)

Drug,

Alcohol

Hospital Claims

Division of Health Statistics, North Dakota Department of Health

Table A11.2 North Dakota Human Service Regions and County Crosswalk

Human Service Region

Facility Location

Counties Served

I - Northwest RHSC

Williston

Divide, Williams, McKenzie

II - North Central RHSC

Minot

Burke, Bottineau, Renville, Pierce, McHenry, Ward, Mountrail

III - Lake RHSC

Devil's Lake

Rollete, Towner, Cavalier, Ramsey, Benson, Eddy

IV - Northeast RHSC

Grand Forks

Pembina, Walsh, Nelson, Grand Forks

V - Southeast RHSC

Fargo

Steele, Cass, Trail, Richland, Ransom, Sargent

VI - West Central RHSC

Jamestown

Wells, Foster, Griggs, Stutsman, Barnes, Logan, LaMoure, Dickey, McIntosh

VII - Southwest RHSC

Bismark

McLean, Mercer, Sheridan, Oliver, Morton, Burleigh, Kidder, Emmons, Sioux, Grant

VIII - Badlands RHSC

Dickinson

Dunn, Billings, Golden Valley, Stark, Hettinger, Slope, Adams, Bowman

References

McAuliffe, William E., Richard LaBrie, Stephen Haddad, Ryan Woodworth, Tim Stablein, and Jamie Mellitt. (2001).North Dakota's Substance Abuse Indicator Manual . North Dakota Department of Human Services, Division of Mental health and Substance Abuse Services.

Levels Of Care, Obstacles and Desired Treatment

This chapter addresses two key issues in planning substance abuse treatment services. First, what mix of services should the State provide for citizens that needed and wanted treatment but did not receive it? To assess the desirable treatment mix, the needs assessment surveys estimated the levels of care for the respondents with a diagnosis who did not obtain treatment but said that they would have sought treatment if it had been available. Second, what were the obstacles that prevented respondents who needed and wanted treatment from obtaining it?

Levels of Care

In the North Dakota telephone survey, interviewers asked subjects who had a current diagnosis but who did not receive treatment in the past year if they would have sought treatment if it had been available. The interviewers asked respondents who would have sought treatment what kind of treatment the respondents wanted. In addition, the National Technical Center's questionnaire included a series of questions designed to assess the patient placement criteria of the American Society for Addiction Medicine (Hoffmann et al. 1991). The ASAM criteria assign an appropriate level of care based on the following factors: severity of withdrawal symptoms, biomedical complications, emotional/behavioral complications, treatment resistance, relapse potential, and recovery environment. The ASAM scoring algorithm assigned respondents with a current substance use disorder to one of four levels of care: outpatient, intensive outpatient (partial hospitalization, day treatment or evening care), residential ("medically monitored"), and hospital inpatient ("medically managed"). Note that the State levels of care guidelines do not strictly follow the ASAM conventions (see appendix A.1). The household telephone survey questionnaire used in North Dakota reflects the original ASAM levels of care (PPC1) whereas the levels of care used by the State more closely reflect the revised ASAM levels (PPC2). PPP2 includes detox treatment and assigns different categories within each level, whereas PPC1 did not distinguish between types with a given level. Aside from those differences, the only obvious difference between the State's convention and the ASAM levels is that halfway house care would typically be considered Level II ASAM. Because the majority of people in need from the survey required intensive outpatient services and that ASAM level corresponds with intensive outpatient according to the State's conventions, the distinction is not problematic in this case.

Among North Dakotans living in a household with a telephone who had a current diagnosis for a substance use disorder, 89% required intensive outpatient care (Table 12.1), a level which typically involves treatment for two or more hours for two to five days a week for at least the first six weeks of recovery (Riley et al. 1997). This level of care does not involve residential care or medical supervision. Most of the remaining people in need required more intense treatment, either medically monitored residential (6.6%) or medically managed hospital inpatient care (3.4%).

Among the small number of subjects (8) in need that did not received treatment but said that they wanted it, the ASAM levels of care were slightly higher. Almost one in five of them (18.1%) required residential treatment (15.2%) or hospital care (2.9%). All of those who did not require inpatient (residential or hospital) treatment needed intensive outpatient care. These patient placements refer to the beginning of the episode of treatment for respondents who did not receive treatment. In most instances, it is reasonable that clients would need more than low intensity outpatient care when detoxifying and making a break from alcohol or drugs. The survey results suggest that while overall demand in North Dakota was low, there was a substantial amount of unmet demand for intensive outpatient services.

Table 12.1 ASAM Levels of Care Required By Adults in Need of Treatment Who Wanted Treatment but Did Not Receive It , AK Household Telephone Survey

Group

State (unweighted sample)

ASAM Level of Care (Weighted Percent)

Outpatient Treatment

Intensive Outpatient

Residential: Medically Monitored

Hospital: Medically Managed

Those With a Need For Treatment

Alaska (641 )

1.4

75.7

12.6

10.3

Montana (460)

1.6

84.3

7.7

6.4

North Dakota (341)

1.0

89.0

6.6

3.4


Those With a Need and a Demand For Treatment

Alaska (13 )

8

41

45

6

Montana (9)

1

66

27

6

North Dakota (8)

0

82

15

3

Because the sample sizes for these analyses are so small, the authors compared North Dakota's telephone survey results with those from Montana and Alaska. The three surveys had remarkably similar findings. Just as was true for North Dakota, most of those in need of treatment for a substance use disorder in Montana and Alaska required intensive outpatient care (Table 12.1). As for those who were in need of treatment and expressed a desire to obtain it, the estimated level of care required was also more intensive in Montana and Alaska. In Montana, 33% of that group needed inpatient treatment, either residential (27%) or hospital care (6%), while in Alaska more than half required residential or hospital treatment. The consistency over the three states lends credibility to the overall findings.

Required Levels of Care Among American Indians on Reservations

In their report on the study of American Indian adults on reservations in North Dakota, the Gallup Organization analyzed the ASAM required levels of treatment for those with a lifetime diagnosis for a substance use disorder. The study found that those individuals had more intense requirements than the household survey population. For those with a lifetime diagnosis for an alcohol use disorder, 62.8% required intensive outpatient care, while 26% required medically monitored residential care and almost 10% needed hospital care. These figures indicate that many (36%) would require inpatient care at the onset of recovery. The State prefers to treat its clients using social detoxification, which corresponds to level III-2-D of the revised ASAM patient placement criteria (Mee-Lee et al. 1996). Because the survey questionnaire incorporated the first version of the ASAM criteria that lacked specific criteria for detoxification services, it is unclear whether it would be possible to use social detox for all of those who met Level III criteria in the survey. It is important to note, though, that these American Indian survey estimates apply to the whole population in need and not only those with a demand.

Those in need of treatment for drugs had relatively severe disorders as well. Among those with a marijuana treatment need, almost 57% required some sort of inpatient care at the onset of recovery while 42.3 % required intensive outpatient care. The standard errors for these estimates are larger than they were for the alcohol levels of care. That is due to the small sample size (n=41). So, the results should be interpreted with caution (Gallup 1998, p. 51). Among those with an amphetamine treatment need, more than half required hospital care at the onset of recovery. Again, because the sample size was small (n=16), the standard errors for the estimates were large (Gallup 1998, p. 51).

Obstacles to Obtaining Treatment

Clinicians have long recognized that many people in need are in denial or lack sufficient motivation to obtain treatment. There are also legitimate obstacles that prevent people seeking treatment from obtaining it. The North Dakota household survey inquired about these obstacles. Unfortunately, the number of these respondents in the survey that qualified for these questions (those with an unmet demand in the past year) may have been too small to yield reliable results (The Gallup Organization 1998, Table 26). Because the rankings of the reasons were consistent across the household telephone surveys of North Dakota, Montana, and Alaska, the authors decided to present the combined results from the three states along with the results for North Dakota only (Table 12.2) .

Because the sample size for North Dakota was small (4), the reader should view the results as suggestive, but not definitive.There were no obstacles cited by more than one respondent, and therefore none emerged as prevailing problems of access to substance abuse treatment. The number of cases was small because the respondents were asked questions on obstacles only if they said they needed treatment in the past 12 months.

Table 12.2 Obstacles to Obtaining Treatment in the Past Year, North Dakota Telephone Survey, Combined Telephone Survey Results for Alaska, Montana, and North Dakota, and Survey of American Indians on Reservations

Obstacle

North Dakota American Indians on Reservations Unmet Demand (n=23)*

North Dakota in Need, But Did Not Want Treatment (n=6)

North Dakota with an Unmet Demand

(n=4)

Combined States (n=28)

No insurance or way to pay for treatment

3

42

23

64

Too much red tape

4

11

0

49

Facilities too far away

38

0

26

49

Couldn't get the type of treatment desired

47

-

23

42

Programs were full

47

0

21

42

Lacked transportation

23

0

0

31

Facility only had hours during work time

29

42

0

37

No counselors from ethnic or language group

31

0

23

31

Program lacked special services such as medical care or child care

19

42

23

23

Not accessible due to disability

54

0

0

7

* All of those with a lifetime diagnosis that had an unmet demand for additional or any treatment in the past year are included in this group.

Note: The percentages are weighted results, but the sample sizes are unweighted.

The combined results for the three states are more reliable because there were more cases. It is reasonable to generalize problems across these states because they are all rural and have somewhat similar population characteristics. Ability to pay was the most commonly cited obstacle among the three states, while red tape, program capacity, and available treatment types were frequently cited as well. As one would expect in the case of rural states, the respondents' distance from the nearest treatment facilities was an issue.

In an effort to confirm the findings on obstacles in North Dakota, the analysts examined obstacles cited by those who were in need, but did not want treatment. The numbers of cases for this group (6) was small, as well. Those respondents indicated that treatment was available only during hours they had to work, they lacked insurance or any way to pay for treatment, the programs did not have staff from their language groups, and there were no ancillary services such as child care available through the programs. Again, these results should be interpreted as suggestive because there were few cases.

Obstacles to Treatment in the Survey of American Indian Adults on Reservations

The survey of American Indian adults on reservations asked questions about obstacles to treatment among those in need. Because the sample sizes were small, the Gallup organization elected to combine those that wanted additional treatment and those that did not get any treatment in the past year (n=23). The only questions likely to be affected by that distinction are questions about insurance, cultural or gender sensitivity, and possibly types of treatment desired. More than half of those with an unmet demand for additional or any treatment referred to a physical handicap as an impediment to obtaining treatment, but the standard error was very high for that estimate. Respondents frequently cited programs being full (47.3%), not being able to obtain the type of treatment they wanted (46.6%), changing their mind after being on a waiting list (35.5%), and the nearest facility being too far away from where they lived (37.6%) as obstacles to obtaining treatment they desired. More than a quarter said that there were no counselors from their ethnic/language group at the facilities and that the programs were not sensitive to the cultural needs of Native Americans. Those are clearly important issues given the nature of the survey and the standard errors for those estimates were lower than those for some other higher estimates.

Types of Treatment Desired and Received

In assessing the performance of the treatment system from a levels of care and obstacles to treatment perspective, it is instructive to examine what types of care the system delivers in comparison with what types of care are desired by those in need. By far, the largest share of those that received treatment in the past year got it through self-help groups like Alcoholics Anonymous (Table 12.3). Other modes such as consultation with a medical professional or clergy were common as well. Among specialty modes of treatment, outpatient care was most common. Residential modes were also common among those that had a diagnosis in the past year. The number that expressed a desire for any particular mode of treatment in that group was small. Like those that did receive treatment, those with a need that did not receive treatment appear to prefer less formal types of treatment. They also preferred outpatient services. Given that the majority of those with a need could be treated with intensive outpatient care, the State may consider expanding those services.

Table 12.3 Types of Treatment Wanted and Received



Received Treatment in the Past Year

With a Past-year Treatment Need, but Did Not Receive (n=313)

Treatment Type

All

(n=98)

With a Past-year Substance Use Disorder (n=28)

Detox

3.9

10.6

0.3

Hospital Detox

0.7

2.4

0.3

Residential Detox

0.6

2.1

0.3

Outpatient Detox

3.2

8.2

0.3

Residential Rehab

7.8

21.7

0.7

Hospital Rehab

4.0

13.8

0.7

Long-term Rehab

3.2

11.1

0.4

Short-term Rehab

4.8

11.5

0.7

Halfway House

2.6

8.9

0

Outpatient

16.1

39.1

Rehab: 1.0

Day Treatment: 1.0

Intensive

11.0

34.2

0.3

Less Intensive

6.5

9.8

0.7

Self-help

83.7

83.0

0.7

Physician

22.2

27.8

1.1

Religious

22.2

23.4

0.8

Appendix A.1 Crosswalk, ASAM, ND Household Survey, ND Alcohol and Drug Treatment Services, TEDS



ND Household

Survey

Outpatient Services

Intensive Outpatient/

Partial Hospitalization

Medically Monitored Inpatient

Medically Managed Inpatient

Medically Managed Inpatient

"¦"¦"¦"¦"¦"¦.

ASAM (2001)

Level I ASAM

Level .5 Early Intervention

Outpatient

Level II ASAM

Intensive Outpatient/Partial Hospitalization

Level III ASAM

Residential/Inpatient

Level IV ASAM

Medically Managed Intensive Inpatient

Level IV-D ASAM

Detox

Level III-2-D ASAM

Clinically Managed Residential Detox

ND Treatment Services and Codes

Client-Parent Education (07)

Prevention Public Information (34)

Low Intensity Outpatient (15)

Family Therapy (09)

Group Therapy (10)

Individual Therapy (11)

Marital/Couple Therapy (16)

Medication Admin.(Center) (17)

Medication Admin. (Client) (18)

Medication Review (19)

Nursing Assessment/Meds (47)

Addiction Evaluation (01, 06)

ND Treatment Services and Codes

Day Treatment (40)

Day Treatment (State Hospital)(code?)

Intensive Outpatient (39)

Intensive Outpatient (State Hosp.)(code?)

ND Treatment Services and Codes

CD Halfway House (73)

Long Term Residential > 30 days (51)

Short Term Residential < =30 days (50)

ND Treatment Services and Codes

State Hospital Inpatient (T)

Emergency Medical (52)

Inpatient Care (54)

ND Treatment Services and Codes

State Hospital Inpatient Services (D)

ND Treatment Services and Codes

Social Detoxification (49)

TEDS

Ambulatory

Outpatient (07)

Ambulatory

Intensive Outpatient (06)

Rehabilitation/Residential

Long Term > 30 Days (05)

Long Term > 30 Days (05)

Short Term <=30 Days (04)

Rehabilitation/Residential

Hospital Inpatient (03)

Hospital Inpatient (01)

Hospital Inpatient (01)

Detoxification

Hospital Inpatient (01)

Free-Standing Residential (02)

References

Hoffmann, Norman G., James A. Halikas, David Mee-Lee, and Richard D. Weedman. (1991).Patient Placement Criteria for the Treatment of Psychoactive Substance Use Disorders. Washington, DC: American Society of Addiction Medicine.

Mee-Lee, David, Lee Gartner, Michael M. Miller, Gerald D. Shulman, Bonnie B. Wilford, and Working Group on the ASAM PPC-2. (1996). Patient Placement Criteria for the Treatment of Substance-Related Disorders . American Society of Addiction Medicine, Inc. Chevy Chase, MD.

Riley, William, George Haynes and Vincent Smith. (1997). Montana Adult Household Substance Abuse Treatment Analysis. Montana Department of Public Health and Human Services, Addictive and Mental Disorders Division.

The Gallup Organization. (1998). Alaska Adult Household Needs Assessment Survey, Alaska Adult Household Telephone Survey Statewide and Substate Planning Regions. Alaska Department of Health and Social Services, Division of Alcoholism and Drug Abuse.

Summary and Conclusions

This final chapter summarizes the integrated results of a family of studies of the substance use disorder treatment needs of North Dakota's citizens. To accomplish this, the chapter returns to the questions asked in the introduction:

How many people are in need of treatment in the State? The planning goal was to have an adequate supply of services to meet the absolute level of demand for substance abuse treatment services.

Where are they located? The planning goal was to locate the services where they are needed most.

What types of treatment modalities do these clients need and want? The goal is to have the optimal mix of treatment services to achieve maximal effectiveness and efficiency.

Need for Treatment

The study employed a series of methodologies to estimate the overall level of treatment needs in the State. First, the study examined national trends in the past decade with regard to the need for treatment and the supply of services nationwide. Second, the study compared the State with other states to assess the comparative level of needs and services. Third, the analysis developed estimates of the past-year treatment needs of components of the State's population. The study integrated estimates of treatment need and services received for residents aged 12 and older of households with and without telephones, the homeless, and recently incarcerated prisoners. The sum of these estimates was a statewide estimate of the number of people who had a substance use disorder in the past year, how many of them have not received treatment, and how many would seek treatment if it were readily available.

National Trends. Analysis of a series of indicators of need and treatment services revealed that over the past decade the gap between the number of people in need and the amount of treatment services provided to them appeared to have been widening. Alcohol indicators were somewhat mixed. While alcohol use, alcohol mortality, DUI arrests, and alcohol treatment clients and admissions have declined, survey estimates of alcohol dependence and liquor law violation arrests have increased in the last decade.

Some drug need indicators (e.g., positive drug tests among employees) suggested a long-term decline, other indicators (e.g., drug dependence rates, treatment measures) have been mixed or relatively stable, while yet other indicators (e.g., mortality, emergency room episodes, arrests, and survey reports of use) suggested increases, especially among young people, in the second half of the last decade. Cocaine use has declined, but use of stimulants and club drugs (e.g., Ecstasy) has increased.

To measure the relative gaps between the measures of treatment need and services, the authors divided the service rates by need indicators. Regardless of which measures of need (dependence, mortality or arrests) or services (survey, UFDS or TEDS) were considered, the gap between alcohol need and treatment increased over the decade of the 1990s. The alcohol treatment gap appeared to widen because there was a sharper decline in the number of persons receiving treatment than in the indications of need that declined, and some of the indications of need increased. Depending on which drug need and service indicators were used, the analysis suggested that the drug treatment gap widened or at least stabilized. Thus, over the past decade the overall amount of substance abuse treatment per unit of need decreased nationally.

Interstate Comparisons. To measure the adequacy of the State's treatment services relative to other states, the authors created a series of composite treatment need indexes. The Drug Need Index (DNI) consisted of the sum of standardized mean rates per 100,000 of explicit-mention drug mortality and drug possession/sale arrests. The index values ranged from 0 to 100. An index score of 0 reflected no arrests or deaths in the period, while a score of 100 reflected the sum of the highest observed mean rates of both components . The state with the greatest drug treatment need was ranked 1st , and the state with the lowest drug treatment need received the lowest score and was ranked 50th. Similarly structured, the Alcohol Need Index (ANI) consisted of the sum of explicit-mention alcohol mortality rates and arrests rates for driving under the influence (DUI) and liquor law violations. An average of 25% of liquor law violation arrestees are juveniles, presumably arrested for underage drinking. The Substance Need Index (SNI) combined standardized explicit-mention drug and alcohol mortality rates and the sum of the drug and alcohol arrest rates. The authors conducted analyses that established that the three indexes had a substantial degree of reliability and evidence of validity (McAuliffe et al. 1999, 2000, 2001).

North Dakota's biggest substance use problem is alcoholism. Its alcohol treatment need as measured by the ANI (55) ranked 14th highest in the country in 1994-1996 . Like North Dakota, the states in the highest quartile on the ANI were rural and in the West . North Dakota's alcohol mortality rate was the 18th highest in the country, and its alcohol arrest rate ranked 13th highest. North Dakota ranked 3rd on the BRFSS's measure of driving after drinking too much, and 23rd on the alcohol-related traffic fatality rate . On both of the two available interstate measures of alcohol treatment services, North Dakota ranked in the middle . The State's alcohol-only UFDS client rate ranked 22nd nationally, while its TEDS primary alcohol treatment rate ranked 23 rd highest out of 41 states. Given its high rankings in need indicators, the State's treatment level appears to be somewhat low. That is, while the State scored in the second highest quintile in the alcohol treatment need index, both treatment measures were in the third quintile.

North Dakota's controlled drug treatment needs were the lowest in the country according to the Drug Need Index. The State's DNI score of 9 was half that of the next lowest score (West Virginia and Vermont tied with 18) , but North Dakota ranked higher according to the NHSDA's 1999 household survey estimates of drug dependence. Unlike the DNI, the NHSDA's dependence measure consists mostly of cases of marijuana dependence. Consistent with North Dakota's NHSDA dependence measure, North Dakota had the highest percentages of marijuana arrests and marijuana treatment admissions in the nation. North Dakota's drug mortality rate (0.3 per 100,000) and its drug-only treatment client rate (15 per 100,000) were the lowest in the county . North Dakota's Treatment Episode Data Set (TEDS) drug admission rate in 1994-1996 ranked 40th out of 41 states in the country. TEDS 1994-1996 data were missing for nine states. North Dakota's treatment admissions were overwhelmingly for marijuana use disorders. North Dakota's low drug treatment rates correspond with its low level of need as expressed by the DNI.

North Dakota's Substance Abuse Need Index (SNI) ranking was 24 th highest in the country, clearly attributable to its high level of alcohol treatment needs. The State's combined UFDS substance abuse client rate (alcohol-only, drug-only, and drug plus alcohol) ranked 32 nd in the country for 1994-1996. By this measure, North Dakota's treatment services were one quartile below its treatment needs. As previously noted, North Dakota's TEDS treatment admissions rank was similar to its UFDS treatment client rank for both drugs and alcohol. The authors conducted linear regression analysis that used treatment need (SNI) as a predictor of treatment (UFDS substance treatment rate) for all states. The residual from regression for North Dakota (the difference between its treatment rate and the rate predicted by the model) was negative, indicating that the State's treatment rate was less than predicted by its level of need when compared to other states. Given the level of treatment nationwide and North Dakota's need vis a vis other states, the State had 57 fewer clients than predicted per 100,000 in the population . Overall, it appears North Dakota's treatment rate was slightly below average when the level of need is considered.

Trends in North Dakota. Aside from a substantial increase in alcohol-related arrests, alcohol indicator rates were reasonably stable from 1993 to 1998. Alcohol-related deaths were up slightly over the period, but there was no consistent trend. Treatment admissions for alcohol were down over the period, but there was no steady trend for that indicator either. It appears that the ratio of admissions to arrests and deaths has decreased slightly over time, suggesting an increase in the treatment gap. The ordinate of the graph is on a log scale because the rate levels for the indicators vary so greatly.

ole.gif

In contrast to the stable trends observed for the alcohol indicators, each of the controlled drug indicators increased during the period from 1993 to 1998. Like the previous graph on alcohol indicator trends, this graph is on a log scale because of the great difference in the overall rates of the mortality indicator compared to the rates of arrests and treatment.Drug-related arrests nearly doubled between 1994 and 1998, while drug deaths nearly quadrupled between 1993 and 1996. The drug treatment client rate doubled between 1993 and 1997. Each of the indicators rose more or less steadily, and it appears the drug treatment gap may have increased.

ole1.gif

Statewide Treatment Need Estimate. To estimate the absolute number of persons in North Dakota who had a past-year substance use disorder, the study combined prevalence and population estimates of treatment need for adults (18 and over) in households with telephones, adolescents (persons 12-17) in households with telephones, persons 12 and older living in households without telephones, recently incarcerated state prisoners and training school inmates, and homeless people. Applying these estimates to population statistics from the 2000 Census count resulted in an estimated total of 30,880 people with a substance use disorder in North Dakota during the past year.

Although residents of households with telephones account for the largest proportion of cases in the total, generalizing the prevalence rate for that group (5.2%) to the rest of the population would have clearly produced an underestimate of the total number of people in need. In its report of the household survey, the Gallup Organization (1998a) applied the telephone survey estimate to the entire population aged 18 and older rather than just the population of adults in households with telephones. After the present authors took the prevalence estimates for the other groups, the estimated total state prevalence rate for persons 12 and older increased to 5.9%. Each group not covered by the adult household telephone survey (residents of households without telephones, recently incarcerated prisoners, and homeless) had substantially higher estimated rates of current substance use disorders (11.1%, 62.5%, and 47% respectively) than the telephone survey population. Although the prisoners and homeless had the highest estimated prevalence rates, they were small populations and therefore contributed relatively few cases to the total population in need. Persons 12 and older in households without telephones had a prevalence rate that was a little more than twice as high as the prevalence rate of the adults in households with telephones. Because residents of households without telephones comprise a relatively large subpopulation, they contributed the most (2,431 cases or 7.9%) to the overall increase in the estimate of the total need for treatment. By estimating the rates for the groups other than those covered by the telephone survey rather than generalizing the prevalence rate from the telephone survey, the present integrated analysis arrived at an estimated number of people in need that was higher by 3,707 people, a figure that exceeds the number of people who actually received treatment annually during the recent past.

Of course, the treatment need estimates for all of the subpopulations besides adults in households with telephones were based on prevalence and population estimates from other sources rather than surveys conducted in North Dakota. The authors described the assumptions that were required to make these estimates. Whenever possible, the authors made conservative assumptions. It would be reasonable therefore to assume that there were at least 30,880people with current substance use disorders in the State. If these individuals sought treatment, they would meet the minimum medical necessity criteria employed by treatment programs and managed care organizations.

Treatment Gap. There were clearly many people in North Dakota with a substance use disorder who did not obtain treatment in the past year. In 2000, an estimated 2,826North Dakota residents received treatment for a substance use disorder. That was 9.2% of people in need of treatment that year; fewer than one in ten. An estimated total of 28,054 people who needed treatment in the past year did not receive it. This figure is the study's estimate of the treatment gap in North Dakota.

Comparison of the estimated treatment gap for residents of households with telephones in North Dakota with similar estimates in Montana and Alaska showed North Dakota lags somewhat behind those two states in the provision of services to those in need. In North Dakota's household telephone survey 7.8% of respondents with a current substance use disorder reported having received treatment in the previous year. In Alaska's household telephone survey, 14.7% of the respondents who had a current substance use disorder reported that they had received some form of treatment for alcohol or drugs in the past year. The Montana household survey yielded more favorable results than North Dakota as well; 11.9 % of those between the ages of 18 and 65 that were in need received treatment in the past year. In the years between 1995 and 1998, between 6.6% and 10.7% of NHSDA respondents with a past-year alcohol dependence diagnosis received treatment in the past year (our calculations).

The telephone survey also asked about lifetime treatment history. Of the respondents who had a lifetime substance use disorder, 24%had received treatment at some time in their lives. The percentage was lower than the comparable figures for Montana (30%) and Alaska (31%).

When the high-risk groups missed by the telephone survey were also included in the analysis, even more people would be estimated to have received treatment. A study of recently incarcerated prisoners in Alaskaand t he studies of prisoners and homeless persons in Rhode Island revealed that these high-risk populations have more severe substance use disorders and were much more likely to obtain treatment than were household residents. In the Rhode Island recently incarcerated prisoner study, 56% of adult prisoners and 50% of the adolescent training school residents with an active disorder had obtained some form of treatment during the past year in the months when they were on the outside, while in the Alaska study the comparable figure for adult prisoners was 58%. When only specialty treatment was considered, the percentage of met need for Rhode Island adult prisoners was 37%, for Rhode Island training school inmates it was 14%, and for the Alaska prisoners it was 29% . In both household and nonhousehold populations, adolescents are more likely than adults to have obtained care from nonspecialty providers. Among homeless persons in Rhode Island, 77% with a current substance use disorder obtained treatment in the past year. Considering only treatment provided by specialists, 56% of the homeless obtained treatment in the past year. Thus, while these high risk populations are small in size, they are much more likely to need treatment and to utilize treatment services when they need them.

It is important to bear in mind that the number of people who received treatment based on the telephone survey was higher than the number of State and TEDS admissions statistics for North Dakota (see the chapter on the gap analysis).There are several reasons why the survey estimates might be higher than the State's count of the number of treatment admissions. The treatment covered in the survey questionnaires included specialty treatment services as well as nonspecialty treatment such as professionally-delivered psychotherapy, seeing a family doctor, and attending self-help groups, whereas the State admission statistics covered only specialty care . Of course, the survey estimates are subject to sampling error. Whichever measure of met demand is used, it is clear that only a relatively small percentage of North Dakota's residents with an active addictive disorder received treatment in the past year.

Unmet Demand for Treatment. Even if treatment were readily available to all who needed it, only a portion of those in need would seek care in a given year. The surveys from which the study drew information asked respondents who had a substance use disorder but who had not obtained treatment, whether they thought they needed treatment and if they would have sought it had it been readily available. The integrated analysis estimated that 4.3% of the persons with a current disorder that did not obtain treatment in the past year said they thought they needed treatment and would have sought it if it were more readily available. The telephone survey showed that adults in households with telephones had a low rate of unmet demand for treatment as compared to other state surveys (e.g., Montana). In the Rhode Island household telephone survey, adolescents in need who had not obtained treatment were nearly twice as likely as Rhode Island adults to want treatment (15% verus 8%). Using the Rhode Island estimate, the authors estimated that there were 651 North Dakota adolescents living in households with telephones who needed treatment, had not received it, but wanted it.

Evidence from surveys of nonhousehold populations conducted in other states indicated that the populations not covered by the telephone survey experienced much higher levels of unmet demand for treatment. In the Montana prisoner study, over 50% of the males and 80% of the females in need said that they wanted treatment but had not received it. Of the Alaska prisoners, 40% said that they would have sought treatment. The parallel figure for Rhode Island adult prisoners was 33%, but it was only 11% for the adolescent training school inmates. The adolescent inmates' primary problems stemmed from marijuana use, whereas the adult prisoners' problems stemmed from alcohol, heroin, and cocaine use.

Combining estimates for all of these groups plus the homeless and those in households without telephones that have an unmet demand, the authors found that 1,204 North Dakotans needed and wanted treatment but had not obtained it. This number is the study's recommended target for providing additional services, if the State maintained a policy of providing treatment to all that wanted and needed it. Experience in other states suggests that survey estimates of unmet demand have successfully predicted the utilization of new substance abuse treatment services (McAuliffe et al. 1991). This success appeared to depend on the type of treatment and location of the services in areas that clearly had relatively high levels of unmet need. Other aspects of the services may also affect their utilization. If the State increased the number of people in need who obtained treatment by 1,204, the number in need who received treatment would increase by 43%. The total number who would receive care (4,030) would be 13.1% of the 30,880 who needed it.

Levels of Care. Analysis of the telephone survey data showed that almost one in five of the subjects who needed treatment and had not obtained it but wanted it should receive residential or hospital care in accordance with the patient placement criteria of the American Society for Addiction Medicine (ASAM). The remaining subjects should receive intensive outpatient treatment. When compared to similar statistics from Montana and Alaska, these figures suggest that fewer of those likely to utilize new services in North Dakota needed the highest levels of care.

Special Populations

American Indians. The Gallup organization's report on the North Dakota household telephone survey includedlifetime prevalence estimates for American Indians in North Dakota. Those estimates showed that American Indians had high rates of alcohol abuse and dependence as compared with the rest of the North Dakota residents in households with telephones (15.8% versus 8.8%). Those rates were especially high for American Indians in rural areas of the State (Human Service Regions II [26.8%] and III [14.7%]) (Gallup 1998a). That pattern matches evidence from the face-to-face survey of American Indian adults on reservations in North Dakota which yielded even higher rates of substance use disorders than the telephone survey because it included those highly at-risk adults who live in households without telephones (Gallup 1998b). American Indians also had a higher ratio of alcohol dependence to abuse and their problems were more severe than were those of the other telephone survey respondents . American Indians in the household telephone survey had a higher rate of lifetime marijuana use disorders than the other respondents (0.9% for American Indians versus 0.1% for the whole sample). American Indians in the household telephone survey did not have considerably higher rates of use disorders for other drugs than the population at large. The face-to-face survey of American Indian adults on reservations showed higher lifetime rates of amphetamine and cocaine use disorders than the American Indian sample in the household survey. In the face-to-face survey, 3.5% had a lifetime diagnosis for a marijuana use disorder while 1.2% had a lifetime amphetamine dependence (Gallup 1998b). The household telephone survey produced no cases of amphetamine abuse among American Indians.

Adolescents. The authors estimated the substance use disorder prevalence rate of North Dakota adolescents that live in households with telephones (ages 12-17) to be 8.5% (4,820 persons). That estimate was based on evidence from the telephone survey and the National Household Survey on Drug Abuse (NHSDA) (Office of Applied Studies [OAS] 2000). The NHSDA estimate for adolescents was higher than the estimate for adults. However, the sample size for that age group was small (n=331) and the sampling variance was likely large (OAS 2000). For an in depth examination of the substance use disorder prevalence rate of adolescents and how the estimate used here was obtained see the chapter on gap analysis. Because the dependence estimate for those between the ages of 12 and 17 in North Dakota from the NHSDA was higher than the substance use disorder estimate for adults from the household survey, the number of youths estimated to have a substance use disorder here is relatively large. Adolescents in households without telephones were included in the separate estimate for households without telephones and homeless adolescents were captured by the estimate for the homeless.

The current analysis estimated that 25 recently incarcerated adolescents in juvenile corrections facilities had a treatment need in the past year. That estimate was based on the authors' research in Rhode Island. The Rhode Island study showed juvenile offenders have the same prevalence as recently incarcerated adult offenders. David Brenna, a researcher from the Division of Alcohol and Substance Abuse of the State of Washington, confirmed that substance abuse is common among juvenile offenders (1992). Because there are no state-level diagnostic surveys of substance use disorder prevalence for North Dakota adolescents, the State may wish to target these populations in future rounds of the State Treatment Needs Assessment Program.

Women. Women had a lower rate of current substance use disorders than men (2.4% versus 8.1%). The Gallup Organization's report on the North Dakota Adult Household Survey provided estimates of substance use disorder prevalence among women (Gallup 1998). Among North Dakota's adult females, an estimated 4.2% had a lifetime diagnosis for an alcohol use disorder. That is less than one-third of the corresponding rate for men (13.7%). Women had a lifetime rate of marijuana use disorders (0.2%) that was one-third the rate of men (0.6%) as well.

Women in need of treatment were less likely to receive treatment than men with a need. Only 4.9% of women with a past-year treatment need received treatment, while 8.7% of men with a need received treatment. Over 85% of those with a need that received treatment were men. Moreover, women in need that did not receive treatment in the past year were more likely to have an unmet demand for treatment than their male counterparts.

The report also provided estimates of lifetime treatment need for women of childbearing age, women with dependent children, and women that were pregnant at the time of the survey or within the 18 months prior to the survey. Women of child bearing age had a lifetime alcohol use disorder rate of 6.4%. Because that group simply excludes older women, it is not surprising that the rate is a little higher than the rate for all women. The difference is not considerable. Women of childbearing age had low rates of drug use disorders. Women who were caring for children had a high lifetime rate of alcohol use disorders; 23.8%. Women who had children did not have a substantial rate of drug abuse. The rates of unmet demand for treatment among women of childbearing age and women with children were high (36.8% and 20.1%, respectively). Meeting some of that demand may substantially improve the treatment rate for women in need.

It is important to bear in mind that the definition of treatment need used in the Gallup report differs from the definition used in the gap analysis chapter of this report. Gallup considered anyone with a lifetime diagnosis for a substance use disorder to be in need of treatment, while the current study considers only those with a past-year diagnosis to be in need of treatment. Therefore, the population relevant to unmet demand estimates differs as well. While the current diagnostic estimate may miss some cases that are genuinely in need of treatment (for example, those in remission, but in need of long-term aftercare), the authors believe it is the most defensible. The definition of treatment need used in the current study errs on the conservative side.

Location of Treatment Needs

The study sought to recommend an allocation of new services by combining social indicator data with the survey-based estimate of unmet demand. The authors took both relative need for services and the goal of equity into account when developing the allocation procedure. The analysis examined the treatment need for all substances, and the authors compared the measures of need with treatment admissions at the regional level (see Table A11.2 for a crosswalk between the counties and regions). The Substance Need Index (SNI) consisted of the combined rates of alcohol and drug related deaths and arrests. The authors used linear regression analysis to compare the average annual treatment admissions rate for 1991-1997 (State data) and the SNI to determine how well the observed regional treatment admissions rates matched the rates predicted by the Substance Need Index.

The analysis revealed that the existing allocation of treatment services among the human service regions in North Dakota is reasonably equitable. The R-squared statistic from regression was .458, which means that almost 46% of variance in treatment rates across regions is explained by differences in need when need is the only factor taken into account (simple regression). Region V, the most populous region, was the most underserved based on the analysis. Regions III and IV were also underserved. However, the authors recommend that each region be given additional treatment resources. That recommendation is partly a product of the fact that the existing allocation fits need closely and partly due to the fact that the existing level of services is low relative to need and demand.

Table 13.1 Recommended Allocation of Additional Treatment Services*

Region

Substance Need Index Score

Observed Treatment Rate

Treatment Rate Predicted by Regression

Difference Between the Observed and Predicted Treatment Rates

Recom-mended Number of New Clients

Recomme-nded Total Rate per 10,000 in the Population

III-Lake RHSC

96

470

507

-37

94

68.1

I-Northwest RHSC

74

453

423

30

42

59.8

VI-South Central RHSC

65

485

387

99

49

56.3

VIII-Badlands RHSC

56

376

351

25

60

52.6

V-Southeast RHSC

56

240

349

-109

424

52.4

IV-Northeast RHSC

53

276

338

-62

231

51.3

VII-West Central RHSC

51

398

331

67

137

50.6

II-North Central RHSC

47

303

315

-12

167

49.0

* The model was specified as Treatment = 128.4 + 3.95 SNI. Treatment is the dependent variable and need is the independent variable. The R-squared statistic was .458. Minor discrepancies may occur due to rounding error.

It is important to bear in mind that these gap estimates are relative. The statewide estimate of the treatment gap described above indicated that a large proportion of the people in need throughout the state have not received treatment services in the past year. Consequently, the positive residuals in the analysis (Table 13.1) may say surprisingly little about the absolute degree to which services met the needs of residents of a region .

The authors recommend that the State consider using the results of this analysis as one part of its decision making process for allocating services if additional funds become available. The authors analyzed the indicator data at the regional level to maximize the reliability and validity of the need index, but the component rates of the indexes and treatment rates may be influenced by random distortions in small population areas. Accordingly, the estimates should be used along with other qualitative and quantitative information (e.g., knowledge of waiting lists in specific areas or concerns by other medical personnel or social agencies regarding the availability of specific services). Responses of local providers to the reasonableness of the estimates should also be considered. The social indicator methodology has been developed over a period of years, and has been used in other states. Whenever it is employed in a new state for the first time, there is always the possibility that modification must be made to refine the indexes.

Conclusions

The results of the needs assessment suggest that North Dakota would be justified in expanding its treatment services. The analysis of national, interstate, longitudinal data, and crossectional survey data produced evidence that a substantial number of State residents had an active addictive disease in the past year, but only a small percentage of them received treatment in the past year. While many of those individuals would probably not seek treatment immediately if the supply of services were increased, an estimated 1,204 people indicated that they wanted treatment even though they did not obtain it. Only experience will show how many of even that group will obtain care, but the number is sufficiently large to suggest that an increase in the number of facilities would be reasonable. Recent statistics suggested that the treatment gap, especially regarding drugs, has been widening, and a reversal of that trend appears to be in order.

The analysis suggested that the State may wish to consider programming directed towards increasing the proportion of persons in need who actually seek treatment. The persons who said that they wanted treatment was relatively small, and this group, especially in high-risk groups such as prisoners-to-be and homeless people, appeared to need relatively high levels of care, mostly residential and hospital treatment at the onset of treatment. It is evident that a moderate level of care (intensive outpatient) would be appropriate for a large majority of household residents at the onset of treatment. Improved location of future services in accordance with the indicators of unmet need, especially in rural areas, may be a key step for increasing the demand for treatment. Several administrative changes, such as reducing red tape, could make a difference. To increase access to treatment in rural areas, especially for youth, the State may wish to investigate the feasibility and efficacy of online counseling, assessment, and referral (Stofle 2000; Cunningham et al. 2000; Simpson 2000). Analysis of survey data from other states indicated that adolescents obtain a large proportion of treatment services from nonspecialty providers (e.g., clergy, school health counselors, general psychological counselors, and social workers). An important consideration for youth and residents of small towns and rural areas is the stigma attached to obtaining treatment services from specialty providers. Of course, attention to cultural issues and ethnic identification is important for American Indians.

The integrated analysis indicated several areas for which additional research should be considered. The need indexes developed for the study should be updated and refined. Additional years of data and improvement in the arrest data (e.g., coding by residence of the arrestees rather than location of the crime) are obvious ways in ways in which the indexes could be enhanced. A commitment to ongoing data collection and updating of the social indicator data each year could provide the State with timely data for future planning. The study had to estimate the treatment needs of homeless and adolescents from other studies conducted in other states. These data gaps could be filled by future applications for needs assessment studies funded by STNAP program.

References
Brenna, David. (1992). "Substance Abuse Services in Juvenile Justice." Research Monograph Series: Drug Abuse Treatment in Prisons and Jails. National Institute on Drug Abuse (NIDA). 118: 99-109.
Cunningham, John A., Keith Humphreys, and Anja Koski-Jannes. (2000). "Providing Personalized Assessment Feedback for Problem Drinking on the Internet: A Pilot Project." Journal of Study of Alcohol. 61(November): 794-798.
The Gallup Organization. (1998a). North Dakota Adult Household Needs Assessment Survey. North Dakota Department of Human Services, Division of Mental Health and Substance Abuse Services.
The Gallup Organization. (1998b). Demand and Needs Assessment Study of Alcohol and Other Drugs Among Native American Indians Living on Reservations in North Dakota. North Dakota Department of Human Services, Division of Mental Health and Substance Abuse Services.
Hoffmann, Norman G., James A. Halikas, David Mee-Lee, and Richard D. Weedman. (1991). Patient Placement Criteria for the Treatment of Psychoactive Substance Use Disorders. Washington, DC: American Society of Addiction Medicine.
Hser, Y-I., V. Hoffman, C. E. Grella, M. D. Anglin. "A 33-year Follow-up of Narcotic Addicts." Archives of General Psychiatry. 58(5): 503-508.
McAuliffe, William E., Paul Breer, Nancy White Ahmadifar, and Cathie Spino. (1991). "Assessment of Drug Abuser Treatment Needs in Rhode Island." American Journal of Public Health. 81(3): 365-371.
McAuliffe, William E., Richard A. LaBrie, Nicoletta Lomuto, Rebecca Betjemann, and Elizabeth A. Fournier. (1999). "Measuring Interstate Variations in Drug Problems." Drug and Alcohol Dependence. 53(2):125-45.

McAuliffe, William E., Richard A. LaBrie, Nicoletta Lomuto, Nancy Pollock, Rebecca Betjemann, and Elizabeth A. Fournier. (2000). "Measuring Interstate Variations in Alcohol Abuse Problems." The Epidemiology of Alcohol Problems in Small Geographic Areas, Eds. Robert A. Wilson and Mary C. Dufour. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism.

Office of Applied Studies (OAS). (1999). Treatment Episode Data Set (TEDS): 1992-1997. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies, Substance Abuse and Mental Health Services Administration (SAMHSA) and Research Triangle Institute. Summary of Findings from the 1999 National Household Survey on Drug Abuse. Rockville, MD.

Office of Applied Studies. (2001). Quick Statistics from the Drug and Alcohol Services Information. Substance Abuse and Mental Health Services Administration. Downloaded from http://synx1.smdi.com/webt/quicklink/AK99.htm. on 10/24/2001.

Riley, William, George Haynes and Vincent Smith. (1997). Montana Adult Household Substance Abuse Treatment Analysis. Montana Department of Public Health and Human Services, Addictive and Mental Disorders Division.

Simpson, Dwaye. (2000). Online Client Assessments. Research Roundup, Institute of Behavioral Research at Texas Christian University. 10(3): 2, 5.
Stofle, Gary S. (2000). "Thoughts on Online Psychotherapy: Ethical and Practical Considerations." Downloaded from http://members.aol.com/stofle/onlinepsych.htm.

 

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