Substance Abuse and Mental Health Services Administration

Substance Abuse in States and

Metropolitan Areas:

Model Based Estimates from the 1991-1993

National Household Surveys on Drug Abuse

Summary Report

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

Substance Abuse and Mental Health Services Administration

Office of Applied Studies

SUBSTANCE ABUSE IN STATES AND METROPOLITAN AREAS: MODEL BASED

ESTIMATES FROM THE 1991-1993

NATIONAL HOUSEHOLD SURVEYS ON DRUG ABUSE

SUMMARY REPORT

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

ACKNOWLEDGMENTS

This publication was developed for the Substance Abuse and Mental Health Services Administration, Office of Applied Studies (SAMHSA/OAS), by the Research Triangle Institute (RTI), Research Triangle Park, North Carolina, under Contract No. 283-91-5402. Significant contributors at RTI include Ralph E. Folsom, Judith T. Lessler, and Michael B. Witt. Significant contributors at SAMHSA include Joseph Gfroerer, Douglas Wright, and Joseph Gustin (Project Officer).

Public Domain Notice

All material appearing in this report is in the pubic domain and may be reproduced or copied without permission from SAMHSA. Citation of the source is appreciated.

Obtaining Additional Copies

For additional copies, please write or call:

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Originating Office

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SEPTEMBER 1996

Table of Contents

Page

Executive Summary	1

1. Introduction	3

1.1	Drug Use Measures	4

1.2	Need for Small Area Estimates	7

1.3	Quality/Usefulness of the Estimates	7

2. Methodology Used	11

2.1	Potential Estimation Strategies for Small Areas	11

2.2	Small Area Estimation Method Used	12

2.3	Fitting the Small Area Estimation Model	13

2.3.1	Data Used	14

2.3.2	States and MSAs selected for estimation	18

2.3.3	Summary of methodology	21

2.3.4	Confidence Intervals	21

3. State And MSA Small Area Estimates	23

3.1	Cigarette And Alcohol Use	23

3.2	Illicit Drug Use	25

3.3	Dependence On Substances	26

3.4	Treatment For Substance Abuse	28

3.5	Arrested For Any Crime In Past Year	30

4. Method Evaluation	51

4.1	Evaluation Strategies	52

4.2	Results of the Evaluations	56

4.3	Summary of the Evaluations	64

Appendix A	Estimation of Drug and Alcohol Dependence	71

Appendix B	Definition of 25 MSAs Selected for Small Area Estimation	75

Appendix C	Confidence Intervals	83

List of Exhibits

Page

Exhibit 2.1	1990 Census Variables Used to Model Prevalence of Substance Abuse.	16

Exhibit 2.2	State and MSA Small Areas Selected for Inclusion in the Study.
Population Size and NHSDA Sample Characteristics.	19

Exhibit 3.1	Estimated Number of Persons Aged 12 and Older (In Thousands) Exhibiting Substance Abuse Behaviors and Problems for 26 States	31

Exhibit 3.2	Estimated Number of Persons Aged 12 and Older (In Thousands) Exhibiting Substance Abuse Behaviors and Problems for 25 MSAs	33

List of Exhibits (continued)

Exhibit 3.3	Estimated Percentage of Population Aged 12 and Older with Substance Abuse Behaviors and Problems for 26 States	35

Exhibit 3.4	Estimated Percentage of Population Aged 12 and Older with Substance Abuse Behaviors and Problems for 25 MSAs	37

Exhibit 3.5	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Cigarette Use Within 26 States.	39

Exhibit 3.6	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Alcohol Use Within 26 States.	40

Exhibit 3.7	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Any Illicit Drug Use Within 26 States.	41

Exhibit 3.8	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Use of Any Illicit Drug Except Marijuana Within 26 States.	42

Exhibit 3.9	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Cocaine Use Within 26 States.	43

Exhibit 3.10	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Year Dependency on Illicit Drugs Within 26 States.	44

Exhibit 3.11	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Year Dependency on Alcohol Within 26 States.	45

Exhibit 3.12	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Year Illicit Drug Use Treatment Within 26 States.	46

Exhibit 3.13	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Year Alcohol Treatment Within 26 States.	47

Exhibit 3.14	95% Confidence Bounds for Percent of Population Aged 12 and Older Needing Treatment in Past Year Within 26 States.	48

Exhibit 3.15	95% Confidence Bounds for Percent of Population Aged 12 and Older Arrested in Past Year Within 26 States.	49

Exhibit 4.1	Summary of evaluation of the final SAE model composite based on forming evaluation subgroups and comparing the model based estimates with the direct survey estimates. Correlations, Chi-squared probability and range ratios	57

Exhibit 4.2	Summary of Evaluations of Final SAE Model and Comparisons to Alternative Models Based on a Cross-validation Goodness-of-fit Tests. Correlations, 2- Probabilities, and Range Ratios.	59

Exhibit 4.3	Comparison of Alternative Small Area Estimators of Prevalence of Alcohol Use to Direct Survey Estimates from the Behavioral Risk Factor Surveillance System (BRFSS).	66

Exhibit 4.4	Comparison of Alternative Small Area Estimators of Past Month Prevalence of Cigarette Use to Direct Survey Estimates from the Behavioral Risk Factor Surveillance System (BRFSS).	67

Exhibit 4.5	Comparison of Alternative Small Area Estimators of Prevalence Past Year Drug Treatment to Estimates from the National Drug and Alcoholism Treatment Unit Survey (NDATUS).	68

Exhibit 4.6	Comparison of Alternative Small Area Direct Estimators of Prevalence Past Year Arrests to Estimates Based on the Uniform Crime Reports (UCR)	69

Executive Summary

This report presents estimates of substance abuse for 26 States and 25 metropolitan statistical areas (MSAs). These estimates were developed from data collected in the National Household Survey on Drug Abuse (NHSDA) combined with local area indicators from a variety of sources. They were produced by the Substance Abuse and Mental Health Services Administration (SAMHSA) to provide State and local area policy makers with information on the prevalence of substance abuse behaviors and problems in their local area. These estimates are an inexpensive alternative to the direct survey approach for describing substance abuse in State and local areas. They are based on a consistent methodology across areas and are constructed so that they sum to national estimates produced by the NHSDA.

These State and MSA estimates are the result of a comprehensive small area estimation (SAE) project that included the development of an innovative methodology based on the methods used by other federal agencies to meet needs for small area data. The methodology employs logistic regression models that combine NHSDA data with local area indicators, such as drug-
related arrests, alcohol-related death rates, and block group level characteristics from the 1990 Census, that were found to be associated with substance abuse.

An important feature of these models is that they produce estimates that are a weighted average of an indirect synthetic regression estimate and a direct survey estimate. Thus, the models require at least some NHSDA sample data for each small area under consideration. A total of 26 States met basic sample size criteria for estimation. All had at least 300 interviews and most had over 1,000. Each of the 25 metropolitan areas for which estimates were generated had at least 300 interviews.

Estimates are presented for eleven measures of substance abuse behaviors and problems including numbers and percent of people who use cigarettes, alcohol, any illicit substances, illicit substances other than marijuana, or cocaine during a month; numbers and percent of people who are dependent on alcohol or an illicit substance during a year; number and percent of people who need treatment for illicit drug use and receive treatment of illicit drug use in a year; and number and percent who need and receive treatment for alcohol abuse during a year. Yearly estimates of the numbers and percent of people arrested are also included. Estimates are based on data from the 1991 through 1993 NHSDA and represent the average across that time span.

Work was also carried out to evaluate the model used to produce these estimates. Considering a variety of evidence, it was concluded that:

•	The SAE model produced estimates of all measures that were much better than States could achieve by simply applying NHSDA national prevalence rates for demographic subgroups to the population distribution in their States.

•	A preponderance of evidence indicates that the estimates for alcohol, cigarette, and any illicit drug use are good in that they adequately reflect both levels of use and differences across States and MSAs in the level of use.

•	For past month use of any illicit but marijuana, past month use of cocaine, past year dependency on illicit drugs and dependency on alcohol, and need for treatment, the limited evidence that we have indicates that these estimates are also reasonably good.

•	For arrests, past year treatment for illicit drug use, and past year treatment for alcohol, the quality assessments resulted in mixed findings, and these small area data may not be good indicators of either differences between States or between MSAs, except in broad terms, or of the actual levels.

Finally, the estimates are subject to all of the limitations that the NHSDA national estimates are subject to, namely, reliance on the validity of self-reports of drug use and other behaviors and the exclusion of people who are homeless (not in shelters) or living in institutions such as jails and nursing homes at the time of the survey.

1. Introduction

This report presents estimates of substance abuse for 26 States and 25 metropolitan statistical areas (MSAs). These estimates were developed from data collected in the National Household Survey on Drug Abuse (NHSDA) combined with local area indicators from a variety of sources. They were produced by the Substance Abuse and Mental Health Services Administration (SAMHSA) to provide State and local area policymakers with information on prevalence of substance abuse behaviors and problems in their local area.

The National Household Survey on Drug Abuse (NHSDA), which is conducted by SAMHSA, provides a rich source of data on substance abuse at the national level. It is the federal government's chief source of information on the magnitude of substance use and abuse in the United States and has been used by policymakers to monitor trends in drug use and identify problem areas. In addition to the reports on prevalence and trends in drug use that are produced by SAMHSA, the agency uses the data to investigate many special topics that are important for understanding the nature of substance abuse, and numerous researchers from around the nation conduct special analyses using the public use files that are produced from the survey. In addition, the value of the NHSDA is enhanced by its use of a rigorous sampling and data collection methodology that has been developed and tested over a number of years.

In spite of the wealth of information that can be gleaned from the NHSDA, SAMHSA recognized that there was considerable need for information on drug use and abuse for States and other smaller geographic areas. In order to help States meet their needs for data on substance abuse, SAMHSA undertook a comprehensive small area estimation (SAE) project that included the development of a new methodology for producing estimates for small areas and an evaluation of that methodology. This report summarizes the results of this project. A more detailed report of the results and methodology is available including additional tabulations and cross-
tabulations.1

1.1	Drug Use Measures

Estimates for eleven measures of substance abuse behaviors and problems are presented in this report. They include:

Use of legal (licit) substances:

1.	Cigarette Use In Past Month - Smoked cigarettes at least once within the past month.

2.	Alcohol Use In Past Month - Had at least one drink of an alcoholic beverage, that is, beer, wine or liquor or a mixed alcohol drink within the past month.

Use of illicit substances:

Three measures of illicit drug use are presented including:

3.	Any Illicit Drug Use In Past Month - Use within the past month of hallucinogens, heroin, marijuana, cocaine, inhalants, or the nonmedical use of sedatives, tranquilizers, stimulants or analgesics.

4.	Any Illicit Drug Use Other Than Marijuana In Past Month - Past month use of any illicit drug excluding those whose only illicit drug use was marijuana.

5.	Cocaine Use In Past Month - Use within the past month of cocaine in any form, including crack.

Current use of any illicit drug provides a broad measure of illicit drug use; however it is dominated by marijuana use. Therefore estimates of the use of illicit drugs other than marijuana and of cocaine are presented since these latter two measures reflect different types of drug abuse.

Drug or alcohol dependence:

Substance dependence measures the most severely affected substance abusers who are likely to require some kind of treatment or intervention. The dependence measure developed for the NHSDA is based on an algorithm that approximates the DSM-III-R criteria.2 Two dependence measures are presented:

6.	Dependent On Illicit Drugs In Past Year - Dependent on marijuana, inhalants, cocaine, hallucinogens, heroin, opiates or nonmedical use of sedatives, tranquilizers, analgesics, or stimulants. Those who are dependent on both alcohol and another illicit substance are included, but those who are dependent on alcohol only are not.

7.	Dependent On Alcohol and Not Illicit Drugs In Past Year - Dependent on alcohol and not dependent on any illicit drugs.

Treatment for drug and alcohol problems:

Three measures related to drug and alcohol treatment are presented. Two are based on items in the NHSDA that asked about treatment use. A measure of drug abuse treatment need based on an algorithm developed by SAMHSA's Office of Applied Studies is also included. Comparing the estimated number of people receiving treatment to the estimated number needing treatment can demonstrate gaps in the provision of services for substance abusers:

8.	Received Treatment For Illicit Drugs In Past Year - Received treatment in the past 12 months at any location (including hospitals, clinics, self-help groups, doctors) for any illicit drug. These estimates include those who received treatment in the past 12 months for both drinking and illicit drugs.

9.	Received Treatment For Alcohol Use, But Not Illicit Drugs, In Past Year - Received treatment in the past 12 months for drinking (including hospitals, clinics, self-help groups, doctors). These estimates exclude those who received treatment in the past 12 months for both drinking and illicit drugs.

10.	Needed Treatment For Illicit Drug Use In Past Year - Persons who either: were dependent on illicit drugs in the past year; were a past year heroin user; received treatment in the past 12 months for any illicit drugs; were a needle user of heroin, stimulants or cocaine in the past 12 months; were a daily marijuana user during the past 12 months; or in the past 12 months were weekly users of hallucinogens, cocaine, inhalants or had weekly nonmedical use of stimulants, sedatives, tranquilizers or analgesics.

Past year arrest:

Estimates of past year arrest are also presented. 3 This attribute was included because it is correlated with substance abuse:

11.	Arrested For Any Crime In Past Year - Arrested and booked at least once for breaking a law in the past 12 months.

1.2	Need for Small Area Estimates

The State and Metropolitan Statistical Area (MSA) estimates in this report were developed to assist people who need information about substance abuse in their areas. The impetus for developing these local area statistics was based on the increasing demand for such estimates by State and Federal substance abuse planners. While there are existing data systems that provide small area indicators of drug abuse for selected MSAs, like the emergency room drug-related episode reports from the Drug Abuse Warning Network (DAWN) and the voluntary urine test reports among arrestees (DUF), these data systems do not provide estimates of the desired total population statistics on use, dependence, treatment and the need for treatment. Furthermore, national surveys like the NHSDA do not provide large enough samples for direct State level estimation, a limitation that persists even after pooling results over three years of NHSDA data. Local area surveys designed to provide reliable direct estimates tend to be costly and generally do not provide comparable estimates between areas due to different sampling and data collection methodologies. Thus, the model based approach to deriving the small area estimates has been used increasingly by government agencies as the demand for local area data has increased and the required statistical and computational tools have improved.

1.3	Quality/Usefulness of the Estimates

The estimates in this report will be useful to those States and local areas that need information on substance abuse behaviors in their areas. These estimates:

Take into account local area characteristics

Provide an inexpensive alternative to the direct survey approach for describing substance abuse in States or local areas

Use a consistent methodology across States and are constructed so that they sum to national estimates produced by the NHSDA.

Chapter 4 presents a summary of the work done to evaluate the model used to produce these estimates and other models which were constructed for comparative purposes. The evaluation revealed that: for most of the estimates presented in this report, we have evidence that they adequately reflect the prevalence of substance abuse characteristics for States and MSAs. Because there was no independently existing "gold-standard" to which to compare these estimates, a variety of methods were used in an attempt to evaluate their quality. These evaluations included some statistical tests that were appropriate for the methodology used and some comparisons to external data which were available for a few of the measures (alcohol and cigarette use, arrest, and drug treatment). Taken together, the preponderance of evidence indicates that:

The estimates appear to be good for the drug use characteristics - particularly, alcohol, cigarettes, and any illicit drug use in that they adequately reflect both level of use and differences across States and MSAs.

The pictures for the other measures is less clear.

For cocaine use, past year dependence on an illicit drug or alcohol, and need for treatment, we had only the statistical evaluation to use to consider the quality of the estimates. These statistical evaluations indicated that these estimates were of reasonable quality; however, we did not have any external figures to compare them with, and these are rare characteristics affecting less than about 3 percent of the population nationally.

For arrests, past year treatment for illicit drug use, and past year treatment for alcohol, the quality assessments resulted in mixed findings. Internal statistical evaluations showed that the estimates are reasonably good; however, correlations between these estimates and administrative data on treatment and arrest were low. This conflicting evidence is difficult to interpret because the administrative data that were available for comparison were collected using different definitions and methods than those that were used in the NHSDA. Also, there may be differences across states in these definitions and methods.

Finally, all the evaluations showed that the models used to produce these estimates are better than States could achieve by simply applying previously published NHSDA prevalence rates to the population distribution in their States.

In spite of these findings supporting the validity of these small area estimates, they should be used with caution. Chapter 3, which presents the estimates, includes a chart which shows the 95 percent confidence intervals for the estimates.4 Examining these, it can be seen that the estimates for some States are more precise than those for some others. In general:

Estimates for the larger States and MSAs are better than those for smaller places,

Estimates for extremely rare conditions like treatment for substance abuse are less accurate than those for more common characteristics.

Finally, the estimates are subject to all of the limitations that the NHSDA national estimates are subject to, namely, reliance on the validity of self-reports of drug use and other behaviors and the exclusion of people who are homeless (not in shelters) or living in institutions such as jails and nursing homes at the time of the survey. In particular, estimates of the number of people receiving treatment for drug abuse and the number of people arrested are believed to be substantially underestimated, based on comparisons with administrative data. Some heavy drug use measures, such as the number of people dependent on drugs or needing treatment, may also be underestimated.5 Estimates of current cigarette use shown in this report are also conservative, as they are based on interviewer-administered questions, which may be less conducive to complete reporting (particularly for youths) than the more private self-administered answer sheets used in the NHSDA beginning in 1994.

2. Methodology Used

The methodology that was used for these small area estimates employs logistic regression models that combine NHSDA data with local area indicators that were found to be associated with substance abuse. Several innovative strategies were used to produce these estimates.

2.1	Potential Estimation Strategies for Small Areas

Several procedures have been used by other researchers to produce various health and economic statistics for small areas in other fields. Schaible6 presents information describing three common indirect estimation methods that have been used by federal statistical agencies: synthetic estimators, regression estimators, and composite estimators.7 State level synthetic estimators are constructed by taking national estimates for demographic subgroups and applying them to the demographic composition of the particular States for which estimates are desired. Synthetic estimators, however, often fail to reflect the actual variation across local areas because demographic characteristics (age, gender, race/ethnicity, etc.) do not fully determine the phenomena being estimated. Regression estimators incorporate additional factors or predictors in the estimation procedure in an attempt to improve the estimates for the small area. Synthetic and regression estimators are indirect estimation methods and do not require any direct measures of the phenomea for the small area. In many cases, however, direct estimates are available for the small area but the sample size for the small area is not large enough to yield precise estimates. Composite estimators are constructed as a weighted combination of direct survey estimators and indirect estimators. They are used to take maximum advantage of any direct information from the survey for the small area, to reduce the bias associated with indirect estimators, and are likely to be more accurate than using solely either the direct or indirect estimator.8

In developing the small area estimates for the States and MSAs included in this report, we developed procedures that built on earlier small area estimation work, which focused on a variety of social, health and economic phenomena. The previously cited works by Schaible (1996) and Ghosh and Rao (1994) discuss these types of estimators and note that a variety of research has been carried out investigating the optimal weighting and estimation schemes for composite estimators. In this study, similar methods were developed to produce estimates of substance abuse.

2.2	Small Area Estimation Method Used

This study built on prior methodologies and improved them in several ways. The estimator used can be approximated mathematically as a composite estimator which is the weighted average of an indirect regression estimator and a direct survey estimator. The basic form of the composite estimator approximation is

where is the estimated prevalence rate for a given small area. In this equation, corresponds to the indirect regression estimator which is obtained as the population weighted average of block group level predictions of substance abuse prevalence and the term in the square brackets is the direct survey estimator which is a function of the actual NHSDA survey estimator for the local area.9 In the bracketed direct survey estimator, is the NHSDA survey weighted prevalence rate estimate and is the corresponding survey weighted mean of person level logistic model predicted probabilities of use.10 The weighting factors, in the composite estimator are a function of the sampling variance of the local area effects and the variances of the logistic regression estimators. They were constructed so that when the NHSDA sample size in a State was large, the estimates are very close to the direct survey estimate.

In fitting these models, three innovations were introduced:

•	First, logistic regression models for the relationship between substance abuse and a variety of predictors, including demographic characteristics and a number of social and economic characteristics, were fit at the block group level using Census block group and tract level predictors of substance abuse. These block level estimates were then summed to arrive at the estimates for the States and MSAs.

•	Second, additional county-level predictors were included in the model to account for still more variation across local areas.

•	Third, the survey design weights were used in the estimation of the logistic regression coefficients and the State and MSA local area effects. This innovation causes the State small area estimates to sum to the NHSDA estimates for US regions and the nation.11

2.3	Fitting the Small Area Estimation Model

The final estimates of the logistic regression coefficients and the local area effects for States and MSAs were estimated using an iteratively reweighted least squares algorithm patterned after Breslow and Clayton's (1993)12 prescription for analyzing generalized linear mixed models (GLMM).

2.3.1	Data Used

Four types of data were used in the estimation: NHSDA data, Census data, county level (social indicators) correlates of substance abuse, and block group level population projections.

The NHSDA data were used to fit models for each of the 11 outcome measures described in Chapter 1. Estimates were made for four age groups (12-17, 18-25, 26-34, and 35+). In addition, separate models were fit for two geographic subpopulations comprised of 1) the six large MSAs and 2) the remainder of the nation. Altogether, this resulted in the fitting of 88 models, (11 outcome measures x 4 age groups x 2 geographic subpopulations). The dependent variables in these models were the rates for the outcome measures. In fitting these models, two types of auxillary data were used: census data and county level indicators of drug use. After models were fit, the population projections were applied to the estimated rates at the block group level.

NHSDA data: The data that were used for the estimation came from the respondents to the NHSDA for 1991-1993. Essentially the same survey methodology was used in each of these three years: The sample was a deeply stratified, multistage national probability sample of civilian persons age 12 and older, living in households and certain group quarters, such as, college dormitories and homeless shelters. Civilians living on military installations are included in the target population. On the other hand, military personnel on active duty as well as most transient populations, such as homeless people not residing in shelters, were not included in the target population.

Each year, roughly 120 primary sampling units (PSUs) were selected at the first stage of sampling. These PSUs were generally individual counties or groups of adjacent counties constituting Metropolitan Statistical Areas (MSAs). At the second stage of selection, groups of Census blocks, called sample segments, were selected. Within each segment, dwelling units were selected; within each successfully screened dwelling unit, either zero, one or two occupants were selected for the NHSDA interview. Generally, the NHSDA yields dwelling unit screening response rates of approximately 94 percent and interview response rates of approximately 80 percent. The pooled 1991-1993 NHSDAs yielded a combined sample size of 87,915 people. In 1991-1993, the NHSDA included a special sample from six large MSAs. This sample was designed to provide independent estimates of prevalence of substance abuse in Chicago, Denver, Los Angeles, Miami, New York, and Washington, D.C.

Since nearly all of the substance abuse data that are collected in the survey are highly sensitive, rigorous methods are used in the NHSDA to protect the privacy and confidentiality of responses. The interviewer works with the respondent to find a private place for the interview. After the respondent answers some of the less sensitive questions by responding to interviewer queries, he or she is trained in the completion of a series of self-administered questionnaires. These self-administered questionnaires allow the respondent to conceal his or her answers from both the interviewer and any household members who may be nearby. These methods that are undertaken to protect the privacy and confidentiality of respondents have been shown to increase the reporting of substance abuse.13

Exhibit 2.1 1990 Census Variables Used to Model Prevalence of Substance Abuse.14

1. Race x Hispanic

Percent:

White nonhispanic

Black nonhispanic

Hispanic

Other

2. Education for persons 18 or older

Percent with:

0-8 years

9-12 years and no H.S. diploma

H.S. graduate

some college and no degree

associate degree

bachelors, graduate, or professional degree

3. Age

Percent aged:

0-18 years

19-24 years

25-34 years

35-44 years

45-54 years

55-64 years

65 and over

4. Poverty

Percent of:

families below poverty level

5. Public Assistance

Percent of:

households with public assistance income

6. Disability

Percent of:

persons 16-64 with a work disability

7. Household composition

Percent:

one-person households

of households with female heads (no

spouse present) with children under 18

8. Employment

Percent:

of men 16 years and older in the labor force

of women 16 years and older in the labor

force

9.Housing value - owner occupied units

Median value of owner occupied housing units

10. Housing rent - rental units

Median rents for rental units

11. Sex by marital status (persons 16 years and

older)

Percent of:

Females currently married and not

separated

Females separated, divorced, or widowed

Females never married

Males currently married and not separated

Males separated, divorced, or widowed

Males never married

12. Income

Median Household Income

13. Urbanicity

Percent:

of persons residing in an urban place

14. Urbanized Area

Percent:

of persons in an MSA urbanized area

15. Age of Housing

Percent:

of HUs built before 1939

of HUs built from 1940 to 1949

16. High School Dropout Rate (Tract level only)

Percent:

of high school age children who have

dropped out

17. Underclass Tract Indicator (Tract level only)

18. Hispanic Subpopulations

Percent:

of Hispanics that are Cuban

of Hispanics that are Puerto Rican

19. Other Race Subpopulations

Percent of:

Population that is Asian and Pacific Islander

Population that is Native American, Alaskan, or Aleut

Census data:	Exhibit 2.1 lists the Census data that were used in the modeling. Exhibit 2.1 lists nineteen groups of variables that were considered as potential predictors in each of the 88 models formulated for this study. All of the attributes come from the 1990 U.S. Census long form sample.15

County level (social indicator) correlates of substance abuse: In addition to the Census variables, recoded county level 'social indicators' of substance abuse were also considered. These county level variables were obtained from three sources: The first of these sources is the FBI's Uniform Crime Reports data base for 1991. This source yielded data on arrest rates per 10,000 persons for illegal drug possession, and drug sales/manufacture by several reported drug categories, and on total violent crime arrest rates. The second source combined data from the 1991 and 1992 National Drug and Alcoholism Treatment Unit Survey (NDATUS) conducted by the Substance Abuse and Mental Health Services Administration. From this source, data was obtained on the 1991 and 1992 average treatment rates per 1,000 county residents for (1) alcohol treatment alone and (2) for illicit drug treatment (includes treatment for both drug and alcohol use). Thirdly, 1990 alcohol related death rates per 10,000 county residents were obtained from the National Center for Health Statistics national death certificate registry. Two such rates were considered in this research: (a) the "any related rate" which includes all ICD-8 cause-of-death codes that are deemed to have a significant link to alcohol abuse, and (b) a more restrictive rate which requires explicit mention of alcohol on the death certificate.

Block group level population projections: Population projections for 1992 for each Census block group, were obtained from Claritas, Inc.16 These projections included counts by age group (12-17, 18-24, 25-29, 30-34, 35+ years old), by gender, by race (white, black, American Indian plus Asians & Pacific Islanders, and other races) and by Hispanic indicator. These block group level population projections were adjusted by gender category to conform to the four age groups and the four race ethnicity groups employed for data reporting by the NHSDA.

2.3.2	States and MSAs Selected for Estimation

Composite small area estimators require at least some direct information for the small areas under consideration. Thus, only those States and MSAs which had some NHSDA sample points were included in the study. The States and MSAs selected for small area estimation are presented in Exhibit 2.2. This exhibit shows the number of people who responded to the combined 1991-1993 NHSDA surveys plus information on the NHSDA sample including numbers of the sample MSA/County units, sample block groups, and the estimated 1992 population.

The States and MSAs presented in Exhibit 2.2 were chosen for small area estimation because:

•	The NHSDA sample size was large enough (~ 400 persons) to support model based, indirect estimation, and not necessarily large enough to support direct NHSDA estimation,

•	The number of distinct sample MSA/County units was greater than or equal to 4 units, and

•	The number of distinct sample segments was greater than 40 segments.

Four exceptions to the four or more MSA/County unit rule were allowed for States that met the sample person and area segment minimums.

Exhibit 2.2 State and MSA Small Areas Selected for Inclusion in the Study.

Population Size and NHSDA Sample Characteristics.

STATE	SAMPLE

MSA/

COUNTIES1	SAMPLE

BLOCK GROUPS2	SAMPLE

RESPONDING PERSONS3	1992

POPULATION

PROJECTION4

TOTAL UNITED STATES

213

8,942

84,974

205,945

NORTH EAST REGION

34

1,489

13,681

42,236
New Jersey	6	167	1,523	6,443
New York	7	843	8,505	14,892
Pennsylvania	11	269	2,133	9,945

SOUTH REGION

49

1,723

15,456

71,396
Florida	13	964	10,066	11,265
Georgia	5	118	1,061	5,442
Kentucky	6	112	1,081	3,053
Louisiana	6	133	1,099	3,387
North Carolina	12	222	1,863	5,641
Oklahoma	4	63	494	2,563
South Carolina	4	48	330	2,939
Tennessee	4	92	780	4,117
Texas	13	503	5,082	13,751
Virginia	9	362	3,538	5,227
West Virginia	3	44	394	1,497

NORTH CENTRAL REGION

85

3,271

32,346

48,968
Illinois	6	799	8,088	9,378
Indiana	6	114	978	4,581
Kansas	4	60	515	2,010
Michigan	5	175	1,187	7,615
Minnesota	3	76	684	3,576
Missouri	6	120	1,059	4,223
Ohio	12	264	2,021	8,946
Wisconsin	3	55	475	4,021

WEST REGION

45

2,459

23,491

43,346
California	22	1,320	12,364	24,342
New Mexico	5	74	676	1,199
Oregon	4	59	412	2,397
Washington	3	73	690	4,094

1MSA/Counties refers to geographic entities formed to estimate random effect terms in the logistic model and which are generally analogous to NHSDA primary sampling units (PSUs). The exceptions are the distinct MSA constituents of PSUs that crossed State boundaries or combined more than one MSA.

2Block groups refers to the sample segments which were selected at the second stage of selection in the 1991-1993 NHSDA.

387,915 people responded to the 1991-1993 NHSDA however 2,941 people were omitted from the small area estimation research because of missing local area indicator variables which were used as potential predictors in the models.

4Population projections presented in 1000's.

Exhibit 2.2 State and MSA Small Areas Selected for Inclusion in the Study.

Population Size and NHSDA Sample Characteristics (continued).

MSA	SAMPLE

BLOCK GROUPS2	SAMPLE

RESPONDING PERSONS3	1992

POPULATION

PROJECTION4
Anaheim-Santa Ana, CA	*	*	1,996
Atlanta, GA	*	*	2,425
Baltimore, MD	*	*	1,996
Boston, MA	*	*	3,145
Chicago, IL	735	7,537	4,981
Dallas, TX	*	*	2,120
Denver, CO	719	7,585	1,346
Detroit, MI	*	*	3,593
El Paso, TX	*	*	456
Houston, TX	*	*	2,661
Los Angeles, CA	768	7,533	7,127
Miami-Hialeah, FL	725	8,142	1,600
Minneapolis-St. Paul, MN	*	*	2,035
Nassau-Suffolk, NY	*	*	2,178
New York, NY	730	7,676	7,086
Newark, NJ	*	*	1,500
Oakland, CA	*	*	1,727
Philadelphia, PA-NJ	*	*	4,037
Phoenix, AZ	*	*	1,770
San Antonio, TX	*	*	1,040
San Bernardino, CA	*	*	2,122
San Diego, CA	*	*	2,089
St. Louis, MO-IL	*	*	2,006
Tampa-St. Petersburg, FL	*	*	1,822
Washington, DC	725	7,795	3,345

*Number of sample block groups ranged from 40 to 110; number of respondents ranged from 400 to 1200 in these MSAs.

2Block groups refers to the sample segments which were selected at the second stage of selection in the 1991-1993 NHSDA.

387,915 people responded to the 1991-1993 NHSDA however 2,941 people were omitted from the small area estimation research because of missing local area indicator variables which were used as potential predictors in the models.

4Population projections presented in 1000's.

See Appendix B for a list of counties included in each MSA.

2.3.3	Summary of Methodology

In summary, the estimates were produced by completing the following three basic steps:

•	Estimate regression parameters: Using NHSDA data, logistic regression models were developed which identified (and estimated the parameters associated with) local area indicators that were significant predictors of the eleven substance abuse measures in NHSDA sample locations. Separate models were run for each of the 11 outcome measures and for each of four age groups (12-17, 18-25, 26-34, and 35+). Because the 1991-1993 NHSDA included very large samples for six large US cities, separate models were fit collectively for these six large cities and for the remainder of the nation. Thus, a total of 88 separate models were used for this small area estimation. An important feature of these logistic regression models was the inclusion of random-effect parameters that adjust for the actual "direct" estimates obtained from NHSDA sample data in the States and MSAs for which small area estimates were to be generated.

•	Apply regression parameters to the entire U.S.: Predicted estimates of substance abuse rates were generated for every Census block group in the U.S. by applying the regression parameters to the local area indicators, which were known for every block group. Within each Census block	group, estimates were made for each of the 11 measures and for 32 demographic groups defined by four age groups, four race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, non-Hispanic other race) groups, and gender, (4x4x2 = 32 demographic subgroups).

•	Sum up block group estimates to the State and MSA levels: Estimated rates were multiplied by population estimates for each block group, resulting in estimated numbers of people for each of the eleven attributes. Data from all block groups in States and MSAs were then summed to give final State and MSA total estimates.

2.3.4	Confidence Intervals

Asymmetric 95 percent confidence intervals were constructed based on the logit transformation. This is the typical procedure that is used when estimating characteristics with small prevalence. The mean square errors that are used to construct the confidence intervals include a contribution that accounts for the variances and covariances of the logistic regression coefficients incorporated in the indirect component () and the sampling variance of the direct local area estimate. The weight put on these mean square error contributions depends on the values of the weights in Equation (1).

3. State And MSA Small Area Estimates

Small area estimates were developed for 26 States and 25 Metropolitan Statistical Areas (MSAs). In this chapter, we present estimates for the number of people affected by each of the eleven measures of substance abuse behaviors and problems, prevalence rate estimates, and 95 percent confidence bound charts for the State estimates. Appendix A presents the values for these confidence bounds. All estimates are annualized, that is, they represent rates and numbers of users for a single year based on the average over the three year, 1991-1993, period.

Exhibits 3.1 and 3.2 present the estimates of the numbers of people in each of the 26 States and 25 MSAs who had the particular characteristic in question. For example, overall, it is estimated that some 52.4 million people in the US will have smoked cigarettes in the prior month. For California, the estimate is approximately 5.6 million cigarette smokers.

Exhibits 3.3 and 3.4 present the corresponding estimates of the percentage of people with the characteristic, e.g., about 25.5 percent of the US population were estimated to have smoked cigarettes in the prior month, and the corresponding estimate for California is about 22.8.

Exhibits 3.5-3.15 shows the 95 percent confidence bounds on the prevalence estimates and orders the States by prevalence. Appendix C presents the bounds for the MSAs.

Examining these exhibits, the following can be noted.

3.1	Cigarette And Alcohol Use

The two most prevalent outcome measures which were considered for this project were past month use of cigarettes and alcohol. Unlike some of the illicit drug use and dependence measures which were constructed from a respondent's answers to several questions, these outcome measures were constructed from the NHSDA using the edited version of the corresponding single, drug recency-of-use questions.

Cigarette Use in Past Month. There were an estimated 52.4 million current smokers per year during 1991-1993, corresponding to a rate of 25.5 percent of Americans age 12 and older. Among the 26 States, rates ranged from 22.6 percent to 31.4 percent. States with the highest rates of smoking were Kentucky (31.4 percent), West Virginia (30.6 percent), Tennessee (29.5 percent), Ohio (29.2 percent), and South Carolina (29.0 percent). Minnesota (22.6 percent) and California (22.8 percent) had the lowest rates of cigarette use in the past month.

Among the 25 MSAs, rates ranged from 20.2 percent to 27.5 percent. MSAs with the highest rates of smoking were Boston (27.5 percent), Denver (27.1 percent), Detroit (26.5 percent), San Antonio (26.1 percent), and Dallas (26.1 percent). Nassau-Suffolk (20.2 percent), El Paso (20.4 percent), and Miami-Hialeah (20.9 percent) had the lowest rates of cigarette use in the past month.

Alcohol Use in Past Month. There were an estimated 102.8 million current drinkers per year during 1991-1993, corresponding to a rate of 49.9 percent of Americans age 12 and older. Among the 26 States, rates ranged from 36.4 percent to 58.7 percent. States with the highest rates of alcohol use were Minnesota (58.7 percent), New Jersey (56.4 percent), Wisconsin (55.1 percent), Washington (54.5 percent), and New York (53.4 percent). West Virginia (36.4 percent), Oklahoma (37.5 percent), Tennessee (38.2 percent) and Kentucky (38.5 percent) had the lowest rates of alcohol use in the past month.

Among the 25 MSAs, rates ranged from 41.2 percent to 65.9 percent. MSAs with the highest rates of alcohol use were Minneapolis-St. Paul (65.9 percent), Oakland (65.0 percent), Newark (62.2 percent), Boston (61.5 percent), and Nassau-Suffolk (59.6 percent). Tampa-St. Petersburg (41.2 percent), Miami-Hialeah (44.4 percent), and Baltimore (44.8 percent) had the lowest rates of alcohol use in the past month.

3.2	Illicit Drug Use

The three illicit drug use measures considered in this research included past month use of cocaine, any illicit drug and any illicit drug except marijuana. Like past month use of alcohol and cigarettes, past month use of cocaine was estimated from the NHSDA data using an edited version of the corresponding, single recency-of-use question. Any illicit drug use as well as the use of any illicit drug except marijuana were established using edited versions of several NHSDA recency-of-use responses, including most recent use of hallucinogens, heroin, cocaine, inhalants, marijuana and the nonmedical use of sedatives, tranquilizers, stimulants or analgesics.

Any Illicit Drug Use In Past Month. Estimates for any illicit drug use in past month are presented in the third columns of Exhibits 3.1-3.4 (located at the end of Section 3). There were an estimated 12.0 million past month illicit drug users during 1991-1993, corresponding to a rate of 5.8 percent of Americans age 12 and older. Among the 26 States, rates ranged from 4.1 percent to 8.2 percent. States with the highest rates of drug use were California (8.2 percent), New Mexico (7.8 percent), Oregon (7.1 percent), and Oklahoma (7.0 percent). Wisconsin (4.1 percent), West Virginia (4.2 percent) and Louisiana (4.4 percent) had the lowest rates of illicit drug use in the past month.

Among the 25 MSAs, rates ranged from 3.6 percent to 11.4 percent. MSAs with the highest rates of drug use were Oakland (11.4 percent, nearly twice as large as the national rate), Anaheim-Santa Ana (8.8 percent), Denver (8.3 percent), San Bernardino (7.4 percent), and San Diego (7.1 percent). El Paso (3.6 percent), Miami-Hialeah (3.8 percent), and Houston (4.0 percent) had the lowest rates of illicit drug use in the past month.

Use of Any Illicit Drug Other Than Marijuana In Past Month. Estimates for any illicit drug use other than marijuana in the past month showed that 1991-1993, there were an estimated 5.0 million people who used an illicit drug, other than marijuana, in the past month. This corresponds to a rate of 2.4 percent of Americans age 12 and older. Among the 26 States, rates ranged from 1.4 percent to 4.2 percent. States with the highest rates of drug use were Oklahoma (4.2 percent), California (3.9 percent), and New Mexico (3.5 percent). Minnesota (1.4 percent) and West Virginia (1.4 percent) had the lowest rates of illicit drug use other than marijuana in the past month.

Among the 25 MSAs, rates ranged from 1.4 percent to 5.1 percent. MSAs with the highest rates of drug use (other than marijuana) were Oakland (5.1 percent, again nearly twice as large as the national rate), Anaheim-Santa Ana (4.9 percent), San Bernardino (4.6 percent), Phoenix (4.0 percent), and San Diego (3.6 percent). Detroit (1.4 percent), Minneapolis-St. Paul (1.5 percent), and Baltimore (1.8 percent) had the lowest rates of illicit drug use other than marijuana in the past month.

Use of Cocaine In Past Month. Estimates for the use of cocaine in the past month are presented in the last column of the section on illicit substance use in Exhibits 3.1- 3.4. There were an estimated 1.6 million past month cocaine users during 1991-1993, corresponding to a rate of 0.8 percent of Americans age 12 and older. Among the 26 States, rates ranged from 0.3 percent to 1.5 percent. States with high rates were Virginia (1.3 percent), New Jersey (1.0), California (1.0 percent), and Tennessee (1.0 percent). Minnesota (0.3 percent), Oregon (0.4 percent) and Kentucky (0.4 percent) had the lowest rates of cocaine use in the past month.

Among the 25 MSAs, rates ranged from 0.4 percent to 1.5 percent. MSAs with the highest rates of cocaine use were Oakland (1.5 percent), Newark (1.4 percent), Anaheim-Santa Ana (1.2 percent), New York (1.1 percent), and Dallas (1.1 percent). Minneapolis-St. Paul (0.4 percent) and Nassau-Suffolk (0.4 percent) had the lowest rates of cocaine use in the past month.

3.3	Dependence On Substances

Two past year dependence measures were considered for this research: dependent on illicit drugs and dependent on alcohol only (and not dependent on illicit drugs) in the past year. These dependence measures were determined based on an algorithm developed for the NHSDA that approximates the DSM-III-R definition of dependence using the limited set of items included in the NHSDA that address the DSM-III-R criteria. (See Appendix A)

Dependent On Illicit Drugs In Past Year. Estimates for dependence on illicit drugs in the past year are presented in column six of Exhibits 3.1- 3.4. There were an estimated 2.5 million people per year dependent on illicit drugs during 1991-1993, corresponding to a rate of 1.2 percent of Americans age 12 and older. Among the 26 States, rates ranged from 0.8 percent to 2.0 percent. States with the highest rates of dependence on illicit drugs were Oregon (2.0 percent), Washington (2.0 percent) and California (1.9). West Virginia (0.8 percent), Pennsylvania (0.9 percent), Illinois (0.9 percent), Kentucky (0.9 percent), Tennessee (0.9 percent), and Florida (0.9 percent) had the lowest rates of dependence on illicit drugs in the past year.

Among the 25 MSAs, rates ranged from 0.7 percent to 3.0 percent. MSAs with the highest rates of dependence on illicit drugs were Oakland (3.0 percent), Anaheim-Santa Ana (2.2 percent), Houston (2.1 percent), and San Bernardino (2.1 percent). Miami-Hialeah (0.7 percent) and El Paso (0.8 percent) had the lowest rates of dependence on illicit drugs in the past year.

Dependent On Alcohol, Without Illicit Drugs, In Past Year. There were an estimated 6.4 million people per year dependent on alcohol only during 1991-1993, corresponding to a rate of 3.1 percent of Americans age 12 and older. Among the 26 States, rates ranged from 2.0 percent to 4.9 percent. States with the highest rates of dependence on alcohol were California (4.9 percent), Oklahoma (4.0 percent), and Louisiana (3.9 percent). New York (2.0 percent), Kentucky (2.1 percent), Tennessee (2.1 percent), and West Virginia (2.1 percent) had the lowest rates of dependence on alcohol, without illicit drugs, in the past year.

Among the 25 MSAs, rates ranged from 1.1 percent to 7.1 percent. MSAs with the highest rates of dependence on alcohol were Los Angeles (7.1 percent), Anaheim-Santa Ana (6.0 percent), Denver (4.6 percent), Atlanta (4.5 percent), and Boston (4.4 percent). Nassau-Suffolk (1.1 percent) and New York (1.8 percent) had the lowest rates of dependence on alcohol, without illicit drugs, in the past year.

3.4	Treatment For Substance Abuse

Three measures of treatment were considered for this research. Similar to the dependence measures above, two of the measures included receiving treatment for illicit drugs in the past year and receiving treatment for alcohol without illicit drugs, in the past year. Both of these outcome measures were constructed from edited versions of single questions from the NHSDA questionnaire. The third treatment, outcome measure considered was needing treatment for drug abuse in the past year. This needing-treatment outcome measure was constructed from edited responses to several NHSDA questions.

Had Treatment For Illicit Drugs In Past Year. There were an estimated 1.4 million people per year who had treatment for illicit drugs during 1991-1993, corresponding to a rate of 0.7 percent of Americans age 12 and older. Among the 26 States, rates ranged from 0.5 percent to 1.0 percent. The highest rates of treatment for illicit drugs were in California (1.0 percent), Oregon (0.9 percent) and Oklahoma (0.9 percent). West Virginia (0.5 percent), Indiana (0.5 percent), South Carolina (0.5 percent), and Illinois (0.5 percent) had the lowest rates of treatment for illicit drugs in the past year.

Among the 25 MSAs, rates ranged from 0.5 percent to 1.4 percent. MSAs with the highest rates of treatment for illicit drugs were Oakland (1.4 percent), San Bernardino (1.1 percent), Anaheim-Santa Ana (1.0 percent), and Boston (1.0 percent). Chicago (0.5 percent) and San Antonio (0.5 percent) had the lowest rates of treatment for illicit drugs in the past year.

Had Treatment For Alcohol, Without Illicit Drugs, In Past Year. There were an estimated 1.4 million people per year who had treatment for alcohol during 1991-1993, corresponding to a rate of 0.7 percent of Americans age 12 and older. Among the 26 States, rates ranged from 0.4 percent to 1.5 percent. The highest rates of treatment for alcohol were in Oklahoma (1.5 percent), New Mexico (0.9 percent) and Kansas (0.9 percent). New York (0.4 percent), Georgia (0.5 percent), and Kentucky (0.5 percent), North Carolina (0.5 percent), Washington (0.5 percent), and Tennessee (0.5 percent) had the lowest rates of treatment for alcohol, without illicit drugs, in the past year.

Among the 25 MSAs, rates ranged from 0.3 percent to 2.2 percent. MSAs with the highest rates of treatment for alcohol were Boston (2.2 percent), Oakland (2.1 percent), and San Bernardino (1.2 percent). New York (0.3 percent), Atlanta (0.4 percent), Tampa-St. Petersburg (0.4 percent), and San Antonio (0.4 percent) had the lowest rates of treatment for alcohol, without illicit drugs, in the past year.

Needed Treatment For Illicit Drugs In Past Year. There were an estimated 5.9 million people per year who needed treatment for illicit drugs during 1991-1993, corresponding to a rate of 2.9 percent of Americans age 12 and older. Among the 26 States, rates ranged from 2.0 percent to 4.2 percent. The highest rates of needing treatment for illicit drugs were in California (4.2 percent), Georgia (3.8 percent), and Oklahoma (3.8 percent). Indiana (2.0 percent), New Jersey (2.0 percent), Tennessee (2.1 percent), and South Carolina (2.1 percent) had the lowest rates of needed treatment for illicit drugs in the past year.

Among the 25 MSAs, rates ranged from 1.9 percent to 5.2 percent. MSAs with the highest rates of needed treatment for illicit drugs were Oakland (5.2 percent), Atlanta (4.8 percent), and Anaheim-Santa Ana (4.4 percent). Nassau-Suffolk (1.9 percent), Newark (1.9 percent), and Tampa-St. Petersburg (2.1 percent) had the lowest rates of needed treatment for illicit drugs in the past year.

3.5	Arrested For Any Crime In Past Year

Estimates for those people arrested for any crime in the past year are presented in the last column of Exhibits 3.1 - 3.4. This outcome measure refers to people who have been arrested and booked for breaking a law in the past 12 months, as was derived from an edited response to a single question in the NHSDA questionnaire. There were an estimated 3.7 million people arrested during 1991-1993, corresponding to a rate of 1.8 percent of Americans age 12 and older. Among the 26 States, rates ranged from 1.3 percent to 2.6 percent. States with the highest rates of arrests were New Mexico (2.6 percent), Georgia (2.5 percent), and Kansas (2.4 percent). Pennsylvania (1.3 percent), West Virginia (1.3 percent), New York (1.3 percent) and New Jersey (1.3 percent) had the lowest rates of arrests for any crime in the past year.

Among the 25 MSAs, rates ranged from 0.8 percent to 2.4 percent. MSAs with the highest rates of arrests were San Bernardino (2.4 percent), Denver (2.3 percent), San Diego (2.2 percent), and St. Louis (2.1 percent). New York (0.8 percent) and Nassau-Suffolk (1.1 percent) had the lowest rates of arrests for any crime in the past year.

Exhibit 3.1	Estimated Number of Persons Aged 12 and Older (In Thousands) Exhibiting Substance Abuse Behaviors and Problems for 26 States: 1991-93.*

State	Licit Substance Use In Past Month	Illicit Substance Use

In Past Month
Cigarette Use	Alcohol Use	Any Illicit Drug Use	Any Illicit

But Marijuana Use	Cocaine Use
Total United States	52,444	102,802	12,015	5,031	1,647
New Jersey	1,581	3,631	348	122	65
New York	3,534	7,948	959	352	122
Pennsylvania	2,672	5,203	505	203	59
Florida	2,744	5,164	557	225	78
Georgia	1,433	2,454	317	111	32
Kentucky	960	1,174	138	51	14
Louisiana	872	1,551	148	68	27
North Carolina	1,501	2,468	330	101	33
Oklahoma	690	962	178	106	14
South Carolina	851	1,280	147	49	19
Tennessee	1,215	1,573	187	79	39
Texas	3,601	6,724	767	308	108
Virginia	1,307	2,514	290	141	65
West Virginia	458	545	63	21	8
Illinois	2,434	4,749	435	182	65
Indiana	1,123	2,039	206	85	23
Kansas	485	1,057	101	64	15
Michigan	2,052	3,978	420	118	47
Minnesota	807	2,098	165	49	12
Missouri	1,065	2,134	210	119	38
Ohio	2,612	4,372	481	226	70
Wisconsin	945	2,215	164	78	34
California	5,559	12,877	2,004	951	237
New Mexico	341	597	93	43	10
Oregon	612	1,253	170	79	9
Washington	971	2,230	248	131	19

*All estimates are annualized and represent the number of users for a single year averaged over the three- year period.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model based estimates using 1991-1993 NHSDA data.

Exhibit 3.1	Estimated Number of Persons Aged 12 and Older (In Thousands) Exhibiting Substance Abuse Behaviors and Problems for 26 States: 1991-93.* (Continued)

State	Past Year Dependence	Past Year Treatment	Past Year Arrest
Dependent On Illicit Drugs	Dependent On Alcohol

But Not

Illicit Drugs	Received Treatment For Illicit Drug Use	Received Treatment For Alcohol Use

But Not

Illicit Drug Use	Needed Treatment For Illicit Drug Use
Total United States	2,549	6,350	1,447	1,429	5,877	3,689
New Jersey	81	160	44	41	131	87
New York	162	301	95	62	367	196
Pennsylvania	87	275	55	68	217	128
Florida	105	266	78	65	277	181
Georgia	66	197	39	25	206	136
Kentucky	28	63	17	15	69	51
Louisiana	34	133	22	20	94	79
North Carolina	64	144	36	28	136	95
Oklahoma	39	104	22	38	97	35
South Carolina	35	89	15	16	63	53
Tennessee	38	85	25	22	86	87
Texas	202	463	84	95	421	254
Virginia	58	158	34	32	152	80
West Virginia	13	32	7	11	32	19
Illinois	82	289	50	62	218	162
Indiana	45	98	24	30	91	105
Kansas	23	70	13	17	55	48
Michigan	82	256	58	53	232	151
Minnesota	36	112	30	29	78	60
Missouri	43	91	30	29	138	89
Ohio	86	244	63	72	229	202
Wisconsin	40	118	25	24	97	62
California	464	1,186	236	198	1,029	524
New Mexico	19	44	9	11	40	32
Oregon	48	66	22	15	53	45
Washington	80	144	35	21	145	78

*All estimates are annualized and represent the number of users for a single year averaged over the three-year period.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-1993 NHSDA data.
Exhibit 3.2	Estimated Number of Persons Aged 12 and Older (In Thousands) Exhibiting Substance Abuse Behaviors and Problems for 25 MSAs: 1991-93.*

MSA	Licit Substance Use

In Past Month	Illicit Substance Use

In Past Month
Cigarette

Use	Alcohol

Use	Any Illicit

Drug Use	Any Illicit

But Marijuana Use	Cocaine

Use
Anaheim-Santa Ana, CA	419	1,043	176	97	25
Atlanta, GA	575	1,236	142	47	14
Baltimore, MD	498	895	100	36	14
Boston, MA	865	1,934	212	81	30
Chicago, IL	1,232	2,681	275	117	47
Dallas, TX	550	1,065	121	51	22
Denver, CO	365	786	112	40	14
Detroit, MI	953	1,946	197	50	22
El Paso, TX	93	209	16	8	2
Houston, TX	667	1,355	108	65	16
Los Angeles, CA	1,555	3,517	476	194	65
Miami-Hialeah, FL	334	710	60	36	14
Minneapolis-St. Paul, MN	461	1,341	106	31	7
Nassau-Suffolk, NY	440	1,298	143	43	8
New York, NY	1,640	3,461	425	170	75
Newark, NJ	361	932	93	29	21
Oakland, CA	403	1,123	197	88	25
Philadelphia, PA-NJ	1,045	2,387	231	90	26
Phoenix, AZ	440	951	121	71	18
San Antonio, TX	271	566	45	23	8
San Bernardino, CA	495	1,042	158	98	14
San Diego, CA	465	1,025	148	75	19
St. Louis, MO-IL	507	1,104	104	51	19
Tampa-St. Petersburg, FL	466	751	96	37	10
Washington, DC	756	1,800	180	86	34

*All estimates are annualized and represent the number of users for a single year averaged over the three-year period.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-
based estimates using 1991-1993 NHSDA data.

Exhibit 3.2	Estimated Number of Persons Aged 12 and Older (In Thousands) Exhibiting Substance Abuse Behaviors and Problems for 25 MSAs: 1991-93.* (Continued)

MSA	Past Year Dependence	Past Year Treatment	Past Year Arrest
Dependent On Illicit Drugs	Dependent On Alcohol

But Not

Illicit Drugs	Received Treatment For Illicit Drug Use	Received Treatment For Alcohol Use But Not

Illicit Drug Use	Needed Treatment For Illicit Drug Use
Anaheim-Santa Ana, CA	43	120	20	11	89	31
Atlanta, GA	30	108	18	9	117	42
Baltimore, MD	18	43	12	9	45	26
Boston, MA	53	138	30	69	122	52
Chicago, IL	45	172	27	36	137	75
Dallas, TX	35	70	14	10	52	33
Denver, CO	17	61	9	13	52	30
Detroit, MI	42	129	28	18	129	63
El Paso, TX	4	17	3	2	11	7
Houston, TX	56	52	22	19	103	41
Los Angeles, CA	107	508	54	36	288	132
Miami-Hialeah, FL	12	33	9	8	35	20
Minneapolis-St. Paul, MN	23	70	19	19	49	36
Nassau-Suffolk, NY	22	24	14	11	41	25
New York, NY	66	125	47	18	177	56
Newark, NJ	21	38	13	12	28	21
Oakland, CA	51	67	24	36	90	33
Philadelphia, PA-NJ	40	114	33	25	121	55
Phoenix, AZ	27	64	12	12	56	26
San Antonio, TX	14	26	5	4	30	17
San Bernardino, CA	44	83	24	25	81	51
San Diego, CA	29	62	15	11	67	45
St. Louis, MO-IL	19	49	15	12	55	42
Tampa-St. Petersburg, FL	18	38	12	8	38	26
Washington, DC	36	120	19	29	89	45

*All estimates are annualized and represent the number of users for a single year averaged over the three-year period.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.3	Estimated Percentage of Population Aged 12 and Older with Substance Abuse Behaviors and Problems for 26 States: 1991-93.*

State	Licit Substance Use In Past Month	Illicit Substance Use

In Past Month
Cigarette Use	Alcohol

Use	Any Illicit Drug Use	Any Illicit

But Marijuana Use	Cocaine Use
Total United States	25.46	49.92	5.83	2.44	0.80
New Jersey	24.54	56.36	5.40	1.89	1.01
New York	23.73	53.37	6.44	2.37	0.82
Pennsylvania	26.87	52.32	5.07	2.04	0.59
Florida	24.36	45.84	4.95	1.99	0.69
Georgia	26.34	45.10	5.82	2.04	0.59
Kentucky	31.44	38.45	4.53	1.66	0.44
Louisiana	25.73	45.79	4.38	2.01	0.81
North Carolina	26.60	43.76	5.85	1.79	0.59
Oklahoma	26.93	37.52	6.96	4.15	0.56
South Carolina	28.95	43.56	4.99	1.66	0.66
Tennessee	29.51	38.20	4.53	1.91	0.96
Texas	26.19	48.90	5.57	2.24	0.79
Virginia	25.01	48.09	5.55	2.71	1.25
West Virginia	30.63	36.40	4.23	1.41	0.56
Illinois	25.96	50.64	4.64	1.94	0.69
Indiana	24.52	44.50	4.49	1.85	0.50
Kansas	24.15	52.56	5.01	3.19	0.77
Michigan	26.95	52.23	5.52	1.55	0.61
Minnesota	22.57	58.68	4.62	1.38	0.32
Missouri	25.22	50.52	4.98	2.82	0.89
Ohio	29.19	48.87	5.37	2.52	0.78
Wisconsin	23.51	55.07	4.07	1.93	0.84
California	22.84	52.90	8.23	3.91	0.97
New Mexico	28.42	49.82	7.76	3.54	0.85
Oregon	25.53	52.29	7.08	3.31	0.38
Washington	23.73	54.48	6.07	3.20	0.46

*All estimates are annualized and represent prevalence rates (percent) for a single year averaged over the three-year period.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-1993 NHSDA data.

Exhibit 3.3	Estimated Percentage of Population Aged 12 and Older with Substance Abuse Behaviors and Problems for 26 States: 1991-93.* (Continued)

State	Past Year Dependence	Past Year Treatment	Past Year Arrest
Dependent On Illicit Drugs	Dependent On Alcohol

But Not

Illicit Drugs	Received Treatment For Illicit Drug Use	Received Treatment For Alcohol Use

But Not

Illicit Drug Use	Needed Treatment For Illicit Drug Use
Total United States	1.24	3.08	0.70	0.69	2.85	1.79
New Jersey	1.26	2.48	0.68	0.64	2.03	1.35
New York	1.09	2.02	0.64	0.41	2.46	1.32
Pennsylvania	0.88	2.76	0.56	0.69	2.19	1.28
Florida	0.93	2.36	0.69	0.58	2.46	1.61
Georgia	1.21	3.61	0.71	0.45	3.78	2.49
Kentucky	0.90	2.06	0.57	0.48	2.27	1.67
Louisiana	0.99	3.93	0.64	0.59	2.77	2.32
North Carolina	1.14	2.55	0.64	0.50	2.40	1.68
Oklahoma	1.50	4.05	0.86	1.49	3.77	1.37
South Carolina	1.18	3.01	0.53	0.55	2.14	1.80
Tennessee	0.91	2.06	0.60	0.53	2.08	2.10
Texas	1.47	3.37	0.61	0.69	3.06	1.84
Virginia	1.11	3.03	0.65	0.61	2.91	1.54
West Virginia	0.84	2.12	0.49	0.73	2.15	1.29
Illinois	0.88	3.08	0.54	0.66	2.32	1.72
Indiana	0.97	2.15	0.52	0.66	1.98	2.30
Kansas	1.13	3.51	0.66	0.86	2.73	2.40
Michigan	1.08	3.36	0.76	0.70	3.05	1.99
Minnesota	1.00	3.12	0.84	0.80	2.19	1.67
Missouri	1.03	2.16	0.70	0.68	3.26	2.10
Ohio	0.96	2.73	0.70	0.81	2.56	2.25
Wisconsin	0.98	2.95	0.61	0.59	2.41	1.53
California	1.91	4.87	0.97	0.81	4.23	2.15
New Mexico	1.62	3.66	0.77	0.92	3.30	2.63
Oregon	1.99	2.75	0.90	0.61	2.22	1.88
Washington	1.96	3.51	0.85	0.51	3.55	1.91

*All estimates are annualized and represent prevalence rates (percent) for a single year averaged over the three-year period.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-1993 NHSDA data.

Exhibit 3.4	Estimated Percentage of Population Aged 12 and Older with Substance Abuse Behaviors and Problems for 25 MSAs: 1991-93*

MSA	Licit Substance Use

In Past Month	Illicit Substance Use

In Past Month
Cigarette

Use	Alcohol

Use	Any Illicit Drug Use	Any Illicit

But Marijuana Use	Cocaine Use
Anaheim-Santa Ana, CA	21 .01	52 .25	8 .82	4 .85	1 .23
Atlanta, GA	23 .70	50 .98	5 .86	1 .95	0 .56
Baltimore, MD	24 .96	44 .84	5 .03	1 .80	0 .70
Boston, MA	27 .51	61 .51	6 .73	2 .57	0 .94
Chicago, IL	24 .73	53 .82	5 .53	2 .34	0 .94
Dallas, TX	25 .95	50 .23	5 .69	2 .38	1 .05
Denver, CO	27 .13	58 .42	8 .30	2 .94	1 .01
Detroit, MI	26 .52	54 .17	5 .50	1 .38	0 .60
El Paso, TX	20 .41	45 .85	3 .58	1 .85	0 .53
Houston, TX	25 .07	50 .94	4 .05	2 .43	0 .60
Los Angeles, CA	21 .82	49 .34	6 .68	2 .72	0 .90
Miami-Hialeah, FL	20 .88	44 .40	3 .75	2 .26	0 .89
Minneapolis-St. Paul, MN	22 .64	65 .87	5 .19	1 .51	0 .36
Nassau-Suffolk, NY	20 .22	59 .60	6 .57	1 .99	0 .37
New York, NY	23 .15	48 .84	5 .99	2 .40	1 .06
Newark, NJ	24 .10	62 .17	6 .20	1 .91	1 .37
Oakland, CA	23 .33	65 .02	11 .39	5 .11	1 .47
Philadelphia, PA-NJ	25 .89	59 .13	5 .72	2 .22	0 .64
Phoenix, AZ	24 .83	53 .69	6 .82	3 .99	1 .01
San Antonio, TX	26 .09	54 .43	4 .35	2 .17	0 .78
San Bernardino, CA	23 .32	49 .12	7 .42	4 .60	0 .64
San Diego, CA	22 .25	49 .08	7 .07	3 .59	0 .93
St. Louis, MO-IL	25 .27	55 .02	5 .19	2 .53	0 .95
Tampa-St. Petersburg, FL	25 .55	41 .22	5 .27	2 .03	0 .57
Washington, DC	22 .61	53 .82	5 .38	2 .58	1 .03

*All estimates are annualized and represent prevalence rates (percent) for a single year averaged over the three-year period.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-1993 NHSDA data.

Exhibit 3.4	Estimated Percentage of Population Aged 12 and Older with Substance Abuse Behaviors and Problems for 25 MSAs 1991-93.* (Continued)

MSA	Past Year Dependence	Past Year Treatment	Past Year Arrest
Dependent On Illicit Drugs	Dependent On Alcohol

But Not

Illicit Drugs	Received Treatment For Illicit Drug Use	Received Treatment For Alcohol Use But Not

Illicit Drug Use	Needed Treatment For Illicit Drug Use
Anaheim-Santa Ana, CA	2 .17	6 .01	1 .01	0 .55	4 .45	1 .53
Atlanta, GA	1 .24	4 .47	0 .74	0 .38	4 .83	1 .74
Baltimore, MD	0 .89	2 .15	0 .58	0 .45	2 .28	1 .30
Boston, MA	1 .68	4 .38	0 .97	2 .18	3 .88	1 .66
Chicago, IL	0 .89	3 .46	0 .53	0 .71	2 .75	1 .51
Dallas, TX	1 .63	3 .31	0 .65	0 .45	2 .47	1 .55
Denver, CO	1 .26	4 .57	0 .63	0 .96	3 .83	2 .26
Detroit, MI	1 .18	3 .59	0 .78	0 .51	3 .58	1 .77
El Paso, TX	0 .79	3 .66	0 .55	0 .53	2 .50	1 .62
Houston, TX	2 .10	1 .96	0 .81	0 .71	3 .86	1 .56
Los Angeles, CA	1 .50	7 .13	0 .75	0 .50	4 .04	1 .85
Miami-Hialeah, FL	0 .74	2 .04	0 .56	0 .48	2 .16	1 .22
Minneapolis-St. Paul, MN	1 .15	3 .45	0 .93	0 .93	2 .38	1 .77
Nassau-Suffolk, NY	1 .00	1 .10	0 .64	0 .50	1 .87	1 .14
New York, NY	0 .94	1 .77	0 .66	0 .26	2 .50	0 .79
Newark, NJ	1 .43	2 .50	0 .89	0 .77	1 .90	1 .40
Oakland, CA	2 .98	3 .88	1 .37	2 .07	5 .22	1 .94
Philadelphia, PA-NJ	0 .98	2 .82	0 .82	0 .61	2 .99	1 .37
Phoenix, AZ	1 .55	3 .61	0 .65	0 .67	3 .14	1 .48
San Antonio, TX	1 .38	2 .53	0 .53	0 .42	2 .90	1 .60
San Bernardino, CA	2 .09	3 .92	1 .15	1 .19	3 .84	2 .41
San Diego, CA	1 .38	2 .99	0 .74	0 .52	3 .19	2 .18
St. Louis, MO-IL	0 .96	2 .46	0 .74	0 .62	2 .75	2 .09
Tampa-St. Petersburg, FL	0 .98	2 .10	0 .66	0 .41	2 .08	1 .44
Washington, DC	1 .07	3 .59	0 .56	0 .87	2 .66	1 .33

*All estimates are annualized and represent prevalence rates (percent) for a single year averaged over the three-year period.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.5	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Cigarette Use Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.6	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Alcohol Use Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.7	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Any Illicit Drug Use Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.8	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Use of Any Illicit Drug Except Marijuana Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.9	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Month Cocaine Use Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.10	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Year Dependency on Illicit Drugs Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.11	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Year Dependency on Alcohol Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.12	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Year Illicit Drug Use Treatment Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.13	95% Confidence Bounds for Percent of Population Aged 12 and Older with Past Year Alcohol Treatment Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.14	95% Confidence Bounds for Percent of Population Aged 12 and Older Needing Treatment in Past Year Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

Exhibit 3.15	95% Confidence Bounds for Percent of Population Aged 12 and Older Arrested in Past Year Within 26 States.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-
1993 NHSDA data.

4. Method Evaluation

In any small area estimation (SAE) project, questions arise as to how well the model describes the outcome variables, that is, in this case, how effective are the small area estimation models in describing substance abuse in the States and MSAs. When producing small area estimates, there usually are no "gold-standards," (i.e., direct estimates with known reliability and validity) that can be used to evaluate the overall methodology. In fact, if there were such estimates available, policy makers could use those and would not need small area estimates. Thus, in the absence of such gold-standards for use in an evaluation, one uses multiple evaluation procedures which, when considered jointly, give an overall indication of the quality of the small area estimates. In this small area estimation study, a large variety of evaluations were conducted. The following summarizes some of these evaluations.17

This SAE study developed a method that 1) reduced bias associated with synthetic/regression estimates which often fail to reflect the full range of variability across small areas, 2) made maximum use of the information that was collected in the NHSDA, and 3) produced estimates that summed to the NHSDA direct estimates for the nation. The evaluations discussed in this chapter found that:

The statistical evaluations based on examining the agreement between direct NHSDA estimates and the SAE estimates within evaluation subgroups (see below for an explanation of how this was done), indicated that the SAE estimates had moderate to high correlations with direct estimates, that there were few significant differences between the direct estimates and SAE estimates, and that the SAE estimates adequately reflected the range of prevalence in substance abuse.

The SAE national estimates closely track the NHSDA direct estimates.

Statistical evaluations based on comparisons to external data on cigarette and alcohol use indicated that the SAE estimates were at about the same level as the external data, that the correlations with the external data were moderate to high, and that the estimates were good to very good at reflecting the range of prevalence of cigarette and alcohol use across the States.

Statistical evaluations based on comparisons to external data from administrative data on arrest and past year treatment for substance abuse indicated that the SAE estimates for States were lower than those based on administrative data (as are the NHSDA estimates at the national level) and had only poor to fair correlations with the corresponding administrative data.

Substance abuse characteristics that have higher prevalence were more adequately reflected by the modeling than those characteristics with lower levels of prevalence.

4.1	Evaluation Strategies

Three strategies were used to evaluate the estimates:

1)	Goodness-of-fit tests

2)	Comparisons to external measures of substance abuse

3)	Comparisons of the final SAE model to other models

1)	Goodness-of-fit tests for the final SAE model

Statistical tests for how well models predict the outcome variables (substance abuse in this case) are termed goodness-of-fit tests. Different goodness-of-fit tests detect different types of problems in the estimates, i.e., different lack-of-fit attributes.18 Two sets of tests were completed, each of which used three goodness-of-fit measures ( the correlation, 2 probability, and range ratio). The first set evaluated the final SAE model by partitioning the

entire 1991-1993 NHSDA sample into evaluation subgroups,19 calculating both direct survey based and Model-based estimates for each evaluation subgroup, and comparing these using the three goodness-of-fit statistics. The evaluation subgroups are constructed so that the sample size in each evaluation subgroup is large enough to produce reliable direct estimates. This can be thought of as using the sample to create a set of artificial subpopulations that are predicted to have similar rates of substance abuse within each subgroup and different rates across subgroups. These predicted prevalence estimates are then compared to those that were actually observed in the NHSDA for the artificial populations.

The second set of goodness-of-fit tests were conducted using what is termed a cross-validation approach. Under the cross-validation approach, the models are first fit to part of the data and the resulting prediction equations are then used to make estimates using the remainder of the full data set; that is, the part not used initially to fit the model.20 This sample splitting is done multiple times and the associated remainder subsample estimates are used separately to form the evaluation subgroups and calculate the associated goodness-of-fit statistics. Finally, the multiple goodness-of-fit measures are averaged to form a single measure.21

2)	Comparisons to external measures of substance abuse

The second evaluation strategy that was used was to compare the SAE estimates to indicators of substance abuse from external sources. Three different comparisons were made:

•	Comparisons of the SAE estimates of use of alcohol and cigarettes to similar estimates from the Behavioral Risk Factor Surveillance System (BRFSS), which is a telephone survey conducted in all 50 States under cooperative agreements with the Centers for Disease Control and Prevention.

•	Comparison of the SAE estimates for drug treatment to data from the NDATUS study.

•	Comparison of the SAE estimates of arrest in the past year to UCR data on arrests.

Although external data were available for only a subset of the estimates from the SAE study, these comparisons were selected because they provided another vehicle for assessing the quality of the entire estimation methodology.

3)	Comparisons of the final SAE model to other models

Another approach that was used to evaluate the full SAE model was to compare it to other models using the cross-validation goodness-of-fit tests and the comparisons to external data. Six models were investigated including:

The final SAE model: The composite estimator which is approximated by a weighted combination of an indirect logistic regression estimator and a local area effect that is a function of the direct survey estimates. The indirect logistic regression estimator was constructed by fitting models which contained county and block group demographic characteristics which were hypothesized to have a relationship to the substance abuse outcome variable. These are listed in Exhibit 2.1. The block group demographic characteristics comprised two types: basic demographic variables (gender, age, and race/ethnicity) and the other demographic variables. The local area effect is a function of the actual NHSDA estimates for the local area. Because the 1991-1993 NHSDA included very large samples for six large US cities, separate models were fit for these six large cities and the remainder of the nation.

The big-city-subsample model is similar to the full SAE in that it is a composite estimator that includes both an indirect and direct component; however, in this case a subsample of the respondents from the six large cities was selected and pooled with the full sample from the remainder of the nation before fitting the models. This model was investigated because it reflects the composition of continuing NHSDA surveys which do not have large oversamples from these big cities.

The SAE indirect estimator drops the local area direct effects from the full SAE model. This model was fit to evaluate the usefulness of using the composite estimator. Differences between this model and the full SAE model give one an idea as to whether adding the local area effects to the model improves its ability to predict substance abuse. Also, comparing this model with the next simpler model, the county demographic model (below), measures the impact of the addition of the other demographic variables.

Three simpler models were used—a basic demographic model, a demographic model that used six large city/non-large city splits, and a model that included the basic demographics and county level predictions.

The basic demographic model uses only demographic predictors of gender, age and race/ethnicity, and applies the coefficients for the significant demographic predictors (main effects and their interactions) to population distributions in the block groups to estimate the prevalence in the block groups. Separate models were not fit for the six large cities and the remainder of the nation. This model is analogous to the simple synthetic estimators and was constructed to investigate the gains that come from using additional predictors (other than demographic characteristics) in the logistic regression models.

The six large city/non-large city demographic model is analogous to the basic demographic model in that it uses only the demographic predictors—gender, age, and race/ethnicity. However, in this model, the six-big-city/non-six-big city sample split was used to determine if it was possible to improve these estimates by reflecting the fact that there is a different association between substance abuse and demographic composition in the six-big-cities and the remainder of the nation. Comparing this model to the basic demographic model provides an indication of whether or not simple demographic models could be improved if they were fit within areas that were likely to have divergent associations between demographic characteristics and substance abuse.

The county demographic model adds county level predictors to the six-big-
city/non-big-city demographic model. This model was investigated to determine if it was possible to improve over simple demographic indirect estimators by including county level indicators of substance abuse. This type of model could be a good candidate for monitoring drug use over time because it is simpler than the full SAE model and because it may be possible to monitor changes over time by observing changes in the county level indicators of drug use.

Using these three strategies—goodness-of-fit tests, comparisons to external data, and comparisons to other models—we evaluated the SAE model by: 1) carrying out goodness-of-fit tests for the final SAE model using the evaluation subgroups, 2) conducting cross-validation goodness-of-fit tests on the final SAE and several other models, and 3) comparing estimates from the SAE model and the other types of models to external data by calculating rank correlations and range ratios for the various sets of estimates. The results are given in the next section.

4.2	Results of the Evaluations

Goodness-of-fit tests for the final SAE model using evaluation subgroups:

Exhibit 4.1 summarizes the evaluations of the final SAE model based on the Goodness-
of-fit tests that were constructed by dividing the sample into evaluation subgroups. Three statistics are presented:

•	The correlation between the sample mean of the predicted values and the associated NHSDA direct estimates for each of the evaluation subgroups. Values closer to 1 are better than smaller values.

•	The Chi-Square probabilities from a comparison of the observed (direct estimates) and predicted estimates for each of the evaluation

Exhibit 4.1	Summary of Evaluation of the Final SAE Model Composite Based on Forming Evaluation Subgroups and Comparing the Model Based Estimates with the Direct Survey Estimates. Correlations, Chi-square Probabilities*, and Range Ratios

Age Group
NHSDA SAE

Outcome Measure

Statistic

12-17

18-25

26-34

35 Plus	All

Ages
Licit Drugs
Past Month Cigarette Use	Correlation	0.917	0.952	0.952	0.946	0.978
2 probability	0.038	0.054	0.136	0.846	0.788
Range Ratio	0.870	0.934	1.016	1.027	0.985
Past Month Alcohol Use	Correlation	0.871	0.960	0.972	0.981	0.990
2 probability	0.056	0.066	0.049	0.984	0.759
Range Ratio	0.878	0.872	0.894	0.968	0.948
Illicit Drugs
Past Month Any Illicit Drug Use	Correlation	0.939	0.973	0.962	0.942	0.990
2 probability	0.340	0.639	0.384	0.474	0.450
Range Ratio	0.905	0.827	0.880	0.881	0.868
Past Month Any Illicit But Marijuana Use	Correlation	0.916	0.926	0.941	0.845	0.973
2 probability	0.611	0.089	0.774	0.566	0.609
Range Ratio	0.923	0.801	1.028	0.821	0.879
Past Month Cocaine Use	Correlation	0.824	0.878	0.920	0.903	0.970
2 probability	0.864	0.094	0.803	0.611	0.607
Range Ratio	0.856	0.740	0.880	0.889	0.849
Dependence
Past Year Dependence On Illicit Drugs	Correlation	0.912	0.868	0.827	0.723	0.966
2 probability	0.871	0.778	0.625	0.670	0.537
Range Ratio	1.031	0.882	0.894	1.147	1.000
Past Year Dependence On Alcohol	Correlation	0.787	0.898	0.943	0.927	0.978
2 probability	0.221	0.007	0.688	0.523	0.020
Range Ratio	0.892	0.716	0.899	0.878	0.807
Treatment
Past Year Treatment For Illicit Drugs	Correlation	0.908	0.772	0.918	0.884	0.962
2 probability	0.489	0.437	0.539	0.354	0.184
Range Ratio	0.879	0.920	0.923	0.984	0.944
Past Year Treatment For Alcohol	Correlation	0.776	0.842	0.763	0.878	0.948
2 probability	0.428	0.680	0.612	0.037	0.159
Range Ratio	0.978	0.748	0.899	0.814	0.826
Needing Treatment In Past Year	Correlation	0.899	0.910	0.923	0.934	0.980
2 probability	0.112	0.255	0.355	0.859	0.403
Range Ratio	0.892	0.851	0.937	0.823	0.872
Arrest
Past Year Arrested	Correlation	0.961	0.883	0.911	0.878	0.977
2 probability	0.623	0.225	0.902	0.589	0.416
Range Ratio	0.778	0.883	0.952	1.123	0.950
*Probability of observing the calculated difference in the predicted and direct estimates across evaluation subgroups given that there is no difference.

subgroups.22 Based on statistical theory, with a large number of tests as is shown in Exhibit 4.1, if the distribution of the observed and predicted is the same, about half of the 2 probabilities would be above 0.5 and half below.

•	The ratio of the range of the predicted values to the range of the direct estimates. Range ratios close to 1 indicate better agreement between the predicted and the direct estimates. Ratios larger than 1 indicate that the predicted values are more disperse than the actual values and probably means that the model predictions are too large for groups with highest prevalence and too small for the subgroups with lowest prevalence. Range ratios smaller than 1 indicate that the model fails to reflect the actual variability in prevalence across subgroups.

Summary test statistics for 55 estimates are presented--for 11 outcome measures by 4 age groups and overall for a total of 55 estimates. Examining Exhibit 4.1, it can be seen that all of the tests indicate that the full SAE model works quite well.

•	In all but three of the cases the correlation between the predicted and the direct estimates are above 0.8. In addition, it can be noted that these correlations are slightly higher for the more prevalent substance abuse measures.

•	The Chi-Square probabilities show a similar picture. The actual median of the values in the table is 0.467.

•	The range ratios are quite good. This shows that the final SAE model eliminates one of the major disadvantages of the prior methods in that they failed to reflect the full range of variation in the actual estimates.

Thus, the evaluations summarized in Exhibit 4.1 indicate that the final SAE model worked well. However, because the estimates within the evaluation of subgroups are not independent, this may present an overly optimistic picture of the quality of the estimates. The following cross validation approach corrects for this lack of independence.

Exhibit 4.2	Summary of Evaluations of Final SAE Model and Comparisons to Alternative Models Based on a Cross-validation Goodness-of-fit Tests. Correlations, 2- Probabilities*, and Range Ratios.

SAE Outcome Measure	Statistic	Final Composite SAE Model

Indirect estimators

Final SAE Model

County Demographic

Model

Large City/

NonLarge City

Demographic Model

Basic

Demographic

Model
Past Month Cigarette Use	Correlation	0.765	0.737	0.622	0.514	0.584
2 probability	0.015	0.007	0.008	0.000	0.000
Range Ratio	1.095	1.084	0.956	0.440	0.272
Past Month Alcohol Use	Correlation	0.866	0.858	0.832	0.824	0.839
2 probability	0.280	0.231	0.108	0.023	0.001
Range Ratio	0.969	0.841	0.966	0.685	0.524
Past Month Any Illicit Drug Use	Correlation	0.728	0.704	0.659	0.573	0.637
2 probability	0.043	0.036	0.132	0.080	0.109
Range Ratio	1.618	1.500	1.038	0.510	0.310
Past Month Any Illicit Drug Use But Marijuana	Correlation	0.636	0.615	0.392	0.212	0.297
2 probability	0.412	0.249	0.242	0.131	0.131
Range Ratio	1.450	1.451	1.213	0.256	0.153
Past Year Treatment For Illicit Drugs	Correlation	0.588	0.481	0.407	0.532	0.561
2 probability	0.287	0.275	0.386	0.294	0.297
Range Ratio	1.420	1.509	1.348	0.278	0.219
Past Year Arrested	Correlation	0.641	0.662	0.543	0.639	0.635
2 probability	0.418	0.399	0.324	0.278	0.165
Range Ratio	1.552	1.537	1.430	0.373	0.425
MEAN	Correlation	0.704	0.676	0.576	0.549	0.592
2 probability	0.243	0.199	0.200	0.134	0.117
Range Ratio	1.351	1.321	1.159	0.424	0.317

Note:	These tests were restricted to the 26- to 34-year-old age group due to the cost of computations.

*Probability of observing the calculated difference in the predicted and direct estimates across evaluation subgroups given that there is no difference.

Cross-validation goodness-of-fit tests for the final SAE model and comparison to other models:

Exhibit 4.2 summarizes the cross-validation analyses.23 In this table, we also introduce for the first time four of the other models that were examined. For example, looking at the first row of Exhibit 4.2, which shows the correlations between the predicted and the direct estimates in the evaluation subgroups, we note that the final SAE model has the highest correlation, the SAE model without the direct local area effects the next highest, and so on.

Exhibit 4.2 reveals several things about the various models, including the following:

•	The cross-validation approach presents a somewhat less optimistic view of the final SAE model than did the goodness-of-fit tests using evaluation subgroups. The correlations are lower and the range ratios are higher than those observed in Exhibit 4.1. This probably means that the final SAE model included some predictors that should not have been included. This is sometimes called "over-
fitting" in that some of the factors that were identified as being good predictors of substance abuse in one sample were actually not very good predictors when applied to a different sample.24

•	The models that used only the demographic characteristics are the poorest performers in almost all cases. The range ratios indicate that simply using demographic characteristics to predict drug use in a small area will probably not reflect the true range in prevalence estimates across the small areas. This indicates that improvements can be achieved by including more predictors in the models.

•	The county demographic model worked fairly well. Although the full SAE model was the overall best performer, both the county demographic model and the SAE model without the direct effects worked fairly well.25 The county demographic model estimates are simpler to calculate than the SAE model without the direct effects; therefore, it may be a good candidate for future SAE modeling activities.

Comparisons to other sources of substance abuse data:

For each of the six models examined, Exhibits 4.3 through 4.6 compare the estimated prevalences to those from other sources using both the rank correlation and the range ratios. Methodological and definitional differences between the NHSDA and the other sources are considered in making these comparisons. Adjustments were made to the external data in some cases, but it was not possible to fully account for the differences. The other data sources and the adjustments made for comparison with SAE estimates are described below:

Behavioral Risk Factor Surveillance System (BRFSS): Comparisons of the SAE estimates of the prevalence of past month alcohol and cigarette use are made to estimates from the BRFSS without making any adjustments. The BRFSS is a telephone survey conducted in all 50 States under cooperative agreements with the Centers for Disease Control and Prevention. Definitions used are comparable to NHSDA definitions, since the BRFSS estimates reflect past month use. Studies have shown that reporting of substance use behaviors may be lower in telephone surveys than in face-to-face surveys, particularly for illicit drugs. The BRFSS State estimates are simple averages over the three years 1991 through 1993.

National Drug and Alcoholism Treatment Unit Survey (NDATUS): SAE estimates of the number of persons receiving treatment for drug abuse are compared to estimates constructed from NDATUS. NDATUS is an inventory of all specialty substance abuse treatment facilities in the U.S. Based on reporting by State substance abuse agencies, it provides estimates (including adjustments for nonresponse) of the number of clients in treatment at a given point in time. To develop an estimate of persons treated during a year, the NDATUS client counts (including drug only and combined drug and alcohol clients) were multiplied by the reciprocals of average lengths of stay, and adjusted to account for multiple treatment episodes in a year by the same individual. Estimates of length of stay and multiple episodes were obtained from the Drug Services Research Survey, conducted in 1990. These calculations were done within categories of treatment modality and and applied separately to each state. No adjustment was made to account for the inclusion in the SAE estimates of persons reporting treatment through self-help groups, private physicians, or emergency rooms, none of which are counted in NDATUS. The State estimates are averages over only 1992 and 1993 since the 1991 estimates could not be adequately adjusted for nonresponse.

Uniform Crime Reports (UCR): The SAE estimates of the number of persons arrested in the past year are compared to estimates derived from the UCR. The UCR compiles data from local jurisdictions on the number of arrests. For comparison with the SAE estimates, an adjustment to the UCR data was made to account for persons arrested more than once during a year, so the adjusted UCR estimates reflect number of persons arrested at least once. This adjustment was made within the four Census regions, using data on multiple arrests reported by arrestees in the NHSDA sample. The State estimates are simple averages over the three years 1991 through 1993.

For each of the six models examined, Exhibits 4.3 through 4.6 compare the estimated prevalences to those from other sources using both the rank correlation26 and the range ratios.27 Of note in these exhibits is the following:

Exhibit 4.3 presents the results that compare the final SAE estimates for the BRFSS estimates for alcohol use. The rank correlations between the final SAE estimates and the BRFSS estimates are quite high (over 0.85) and the range ratio is good (nearly 0.6). This indicates that the SAE model produced estimates that are very consistent with what is found in the BRFSS. In addition, both sources estimate a similar prevalence of use at the national level with the NHSDA estimates being somewhat larger. This higher level of reporting is consistent with findings from methodology studies which have shown that telephone surveys yield lower reports of use than self-
administered surveys.28 In addition, we note that the rank correlations and range ratios are much better for the final SAE model than the corresponding statistics for the two demographic models. This indicates that the SAE model is performing much better than typical synthetic estimators which states might construct by applying the NHSDA rates of use to their population distribution.

The comparison of the final SAE estimates to the BRFSS smoking data (Exhibit 4.4) presents a similar picture. The range ratio is very good (over 0.9) and the rank correlations are moderate (about 0.5). The fact that the BRFSS uses a somewhat different question than the NHSDA may account for some of the lack of comparability. Again, we observe that the NHSDA estimates higher levels of cigarette use than the BRFSS, which is consistent with the difference in interview methodology. The demographic model does not do as well as the final SAE model, indicating that the SAE model is better than using a synthetic estimator.

Exhibits 4.5 and 4.6 present the results for the past year drug treatment and past year arrest. Although we again observe that the SAE models perform better than the demographic models, the rank correlations are still only 0.38 for treatment and 0.35 for arrest. These low correlations are probably due to the lack of correspondence in methodology between the divergent data sources, the low prevalence rates of these items, and the less restricted range across States.29

One of the characteristics that is desirable in a small area estimation procedure is that it produce estimates that adequately reflect the range of differences across areas. Because the estimated NHSDA prevalence rates for treatment and arrest are lower than the corresponding estimates from NDATUS and UCR, the calculated range ratios are going to appear artificially small. That is, the range ratios in Exhibits 4.5 and 4.6 are not a good indication of how well the models reflect the range of differences across areas because of the differences in overall prevalence levels. Therefore, as well as the range ratios that are shown, we calculated range ratios that were adjusted (scaled) for this overall difference in prevalence at the national level. When this was done, the adjusted range ratio was very high (0.93) for arrest, but only slightly larger (0.42 versus 0.35) for the treatment data. This indicates that the final SAE estimates for arrest, in particular, do reflect adequate variability.

4.3	Summary of the Evaluations

As was noted in the beginning of this section, a variety of approaches must be used to evaluate small area estimation methods. Considering all of the evidence presented,30 the following findings are noteworthy:

•	The full SAE model is generally the best model in that it tends to have the highest correlations with the direct estimates, to adequately reflect the range of prevalence, and to have exhibited few significant differences when compared to direct estimates in Goodness-of-fit tests.

•	The full SAE model is a better predictor for the more prevalent behaviors than for the less prevalent behaviors and better at reflecting the differences in prevalence rates across areas when there is a wider dispersion of rates across the areas.

•	The demographic models are poor predictors of substance abuse. The estimates presented in this report are much better than States could achieve by simply applying the NHSDA prevalence rates to the population distribution in their States.

•	Estimates of substance abuse are quite sensitive to method effects as evidenced by the fact that the correspondence between SAE models and external small area estimates was increasingly lower as the methodologies between the two information sources were increasingly divergent.

•	Using a NHSDA sample that does not oversample the largest cities and an associated global SAE model appears to work almost as well as the full SAE model. This result bodes well for any future NHSDA small area estimator projects based on data where the six cities were not oversampled.

•	The county demographic model is a very promising alternative to the full SAE model particularly if modeling resources are limited. The county level predictors are appealing since they can be updated from year to year reflecting temporal trends.

Exhibit 4.3	Comparison of Alternative Small Area Estimators of Prevalence of Alcohol Use to Direct Survey Estimates from the Behavioral Risk Factor Surveillance System (BRFSS).

State

BRFSS

Estimate	Composite

Estimators	Indirect Estimators

Direct

91-93 NHSDA Estimates
Final SAE

Model	Big City Sub-
Sampled Model	SAE Model	County Demog Model	Big City/

Remaind Demog Model	Basic

Demog Model
Total United States	50 .90	53 .46	53 .59	53 .43	53 .01	53 .41	53 .40	53 .01
North East Region
New Jersey	56 .60	59 .94	60 .25	62 .03	60 .32	52 .62	52 .91	61 .10
New York	53 .10	57 .04	57 .35	56 .60	56 .51	53 .60	52 .52	56 .96
Pennsylvania	57 .10	55 .82	56 .14	53 .75	55 .90	53 .39	53 .96	52 .70
South Region
Florida	53 .70	48 .45	48 .71	48 .52	49 .75	52 .43	52 .78	49 .67
Georgia	35 .90	48 .57	47 .04	48 .21	47 .48	52 .28	52 .78	48 .78
Kentucky	32 .40	41 .18	40 .48	44 .26	40 .97	54 .18	54 .79	32 .03
Louisiana	46 .00	49 .40	49 .77	44 .13	44 .66	51 .53	52 .02	56 .62
North Carolina	36 .10	46 .73	45 .18	48 .17	46 .44	52 .49	53 .01	43 .04
Oklahoma	35 .50	39 .81	39 .74	44 .02	44 .22	52 .84	53 .16	36 .50
South Carolina	36 .90	46 .84	44 .32	46 .67	41 .34	51 .83	52 .36	47 .03
Tennessee	26 .40	40 .70	38 .56	45 .10	39 .07	53 .06	53 .65	35 .76
Texas	52 .00	52 .88	53 .09	48 .80	50 .07	53 .10	53 .06	55 .23
Virginia	50 .20	51 .21	49 .88	52 .28	47 .34	54 .18	53 .42	48 .16
West Virginia	28 .30	38 .61	38 .61	39 .48	38 .58	53 .99	54 .63	38 .41
North Central Region
Illinois	50 .70	54 .43	55 .37	55 .26	55 .48	55 .18	53 .27	55 .73
Indiana	47 .40	47 .70	48 .73	52 .40	52 .36	54 .07	54 .65	44 .95
Kansas	45 .40	56 .51	57 .63	54 .33	56 .40	54 .10	54 .60	60 .82
Michigan	55 .50	56 .08	57 .53	55 .23	54 .44	53 .33	53 .86	58 .26
Minnesota	63 .50	63 .32	63 .32	57 .06	56 .56	54 .74	55 .29	64 .96
Missouri	46 .50	54 .04	53 .10	52 .22	54 .71	53 .49	54 .07	44 .10
Ohio	41 .20	52 .24	53 .06	53 .18	55 .30	53 .56	54 .14	50 .45
Wisconsin	67 .80	59 .15	59 .49	55 .95	56 .31	54 .38	54 .94	67 .92
West Region
California	58 .90	56 .67	56 .79	58 .07	56 .05	53 .08	52 .55	57 .69
New Mexico	48 .10	53 .98	53 .73	51 .57	57 .40	52 .41	51 .88	56 .21
Oregon	55 .40	55 .95	56 .83	54 .83	59 .81	53 .97	54 .45	59 .72
Washington	58 .40	58 .33	59 .46	57 .32	59 .97	54 .05	54 .43	59 .55
Rank Correlation1	.	0 .861	0 .854	0 .841	0 .765	0 .278	0 .097	0 .807
Range Ratio2	1 .000	0 .597	0 .598	0 .545	0 .525	0 .088	0 .082	0 .867

1 Correlation calculated by first ranking states and then calculating the correlation of the ranks.

2 Ratio of the range of the predicted values to the range of the BRFSS data.

Note: Estimates of prevalence rates have been multiplied by 100.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-1993 NHSDA data.

Exhibit 4.4	Comparison of Alternative Small Area Estimators of Past Month Prevalence of Cigarette Use to Direct Survey Estimates from the Behavioral Risk Factor Surveillance System (BRFSS).

State

BRFSS

Estimate	Composite

Estimators	Indirect Estimators

Direct

91-93 NHSDA Estimates
Final SAE Model	Big City Sub-
Sampled Model	Final SAE Model	County Demog Model	Big City/

Remaind Demog Model	Demog Model
Total United States	23 .10	27 .16	27 .27	27 .68	27 .73	27 .49	27 .43	27 .66
North East Region
New Jersey	20 .50	26 .08	24 .68	26 .24	26 .37	27 .75	27 .25	25 .43
New York	23 .50	25 .13	25 .26	26 .57	26 .06	26 .10	27 .24	24 .18
Pennsylvania	24 .30	28 .56	29 .19	28 .47	28 .24	27 .92	27 .53	30 .06
South Region
Florida	23 .10	25 .75	25 .15	27 .29	26 .62	27 .15	27 .03	26 .34
Georgia	21 .80	28 .55	28 .74	28 .81	29 .18	28 .59	28 .14	28 .41
Kentucky	29 .80	33 .74	31 .95	31 .17	29 .55	28 .15	27 .78	34 .97
Louisiana	24 .00	27 .90	30 .10	30 .75	29 .35	28 .55	28 .09	24 .21
North Carolina	25 .70	28 .26	30 .18	28 .69	28 .59	28 .43	27 .97	30 .49
Oklahoma	26 .20	29 .00	28 .14	31 .09	29 .36	27 .87	27 .20	25 .85
South Carolina	25 .20	31 .00	31 .51	30 .61	29 .76	28 .61	28 .17	30 .38
Tennessee	27 .20	31 .44	30 .37	30 .33	30 .44	28 .22	27 .82	31 .39
Texas	22 .70	28 .38	27 .88	27 .16	26 .74	27 .54	27 .11	27 .51
Virginia	23 .00	26 .49	27 .84	27 .16	27 .81	27 .47	27 .98	28 .14
West Virginia	25 .70	32 .67	32 .06	31 .58	29 .50	27 .75	27 .40	34 .39
North Central Region
Illinois	24 .10	27 .87	26 .58	27 .01	27 .15	26 .15	27 .54	27 .40
Indiana	26 .40	26 .04	28 .03	28 .20	28 .13	28 .14	27 .77	24 .65
Kansas	21 .60	25 .78	26 .00	27 .69	27 .17	27 .95	27 .54	24 .55
Michigan	26 .00	28 .78	27 .95	29 .89	29 .41	28 .26	27 .84	29 .45
Minnesota	22 .90	24 .16	27 .27	26 .30	28 .99	28 .11	27 .70	25 .42
Missouri	25 .20	26 .76	27 .21	28 .29	28 .25	28 .08	27 .68	27 .53
Ohio	24 .10	31 .18	29 .96	29 .26	29 .10	28 .16	27 .77	31 .80
Wisconsin	24 .50	24 .94	28 .86	27 .13	28 .21	28 .03	27 .63	27 .98
West Region
California	19 .50	24 .35	23 .97	24 .97	25 .81	26 .35	26 .70	25 .52
New Mexico	19 .60	30 .67	29 .08	27 .29	28 .02	26 .61	26 .02	28 .56
Oregon	21 .30	27 .11	27 .38	28 .59	27 .42	27 .65	27 .20	25 .20
Washington	22 .70	25 .16	26 .99	27 .33	28 .27	27 .92	27 .37	25 .00
Rank Correlation 1	.	0 .491	0 .610	0 .631	0 .670	0 .485	0 .472	0 .473
Range Ratio 2	1 .000	0 .930	0 .786	0 .642	0 .449	0 .244	0 .208	1 .048

1 Correlation calculated by first ranking states and then calculating the correlation of the ranks.

2 Ratio of the range of the predicted values to the range of the BRFSS data.

Note: Estimates of prevalence rates have been multiplied by 100.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-1993 NHSDA data.

Exhibit 4.5	Comparison of Alternative Small Area Estimators of Prevalence of Past Year Drug Treatment to Estimates from the National Drug and Alcoholism Treatment Unit Survey (NDATUS).

State

Adjusted

1992-93

NDATUS

Estimate	Composite

Estimators	Indirect Estimators

Direct

91-93 NHSDA Estimates
Final SAE Model	Big City Sub-
Sampled Model	Final SAE Model	County, Demog Model	Big City/

Remaind Demog Model	Demog Model
Total United States	0 .85	0 .70	0 .71	0 .70	0 .68	0 .65	0 .64	0 .62
North East Region
New Jersey	1 .08	0 .68	0 .68	0 .68	0 .67	0 .64	0 .63	0 .42
New York	1 .61	0 .64	0 .78	0 .60	0 .67	0 .62	0 .64	0 .68
Pennsylvania	0 .94	0 .56	0 .57	0 .60	0 .62	0 .62	0 .62	0 .45
South Region
Florida	0 .77	0 .69	0 .75	0 .73	0 .69	0 .60	0 .60	0 .47
Georgia	0 .48	0 .71	0 .64	0 .71	0 .70	0 .70	0 .70	0 .75
Kentucky	0 .61	0 .57	0 .58	0 .57	0 .66	0 .63	0 .63	0 .41
Louisiana	0 .88	0 .64	0 .65	0 .66	0 .61	0 .70	0 .70	0 .35
North Carolina	0 .54	0 .64	0 .78	0 .66	0 .85	0 .68	0 .68	0 .58
Oklahoma	0 .68	0 .86	0 .65	0 .88	0 .71	0 .66	0 .63	0 .60
South Carolina	0 .68	0 .53	0 .73	0 .54	0 .79	0 .69	0 .70	0 .39
Tennessee	0 .44	0 .60	0 .51	0 .60	0 .62	0 .65	0 .65	0 .27
Texas	0 .74	0 .61	0 .61	0 .66	0 .68	0 .66	0 .65	0 .65
Virginia	0 .61	0 .65	0 .66	0 .66	0 .63	0 .67	0 .69	0 .47
West Virginia	0 .29	0 .49	0 .46	0 .47	0 .42	0 .59	0 .59	0 .49
North Central Region
Illinois	0 .68	0 .54	0 .52	0 .59	0 .53	0 .63	0 .65	0 .46
Indiana	0 .62	0 .52	0 .53	0 .53	0 .63	0 .64	0 .64	0 .53
Kansas	0 .81	0 .66	0 .57	0 .66	0 .63	0 .63	0 .63	0 .48
Michigan	0 .92	0 .76	0 .89	0 .77	1 .01	0 .66	0 .65	0 .85
Minnesota	0 .45	0 .84	0 .61	0 .82	0 .54	0 .64	0 .63	0 .59
Missouri	0 .59	0 .70	0 .61	0 .64	0 .79	0 .63	0 .63	0 .78
Ohio	0 .75	0 .70	0 .73	0 .70	0 .66	0 .64	0 .64	0 .34
Wisconsin	0 .69	0 .61	0 .64	0 .63	0 .50	0 .63	0 .62	0 .19
West Region
California	0 .87	0 .97	0 .91	0 .92	0 .77	0 .65	0 .65	1 .04
New Mexico	0 .98	0 .77	0 .61	0 .80	0 .76	0 .64	0 .61	0 .25
Oregon	1 .20	0 .90	0 .79	0 .88	0 .80	0 .60	0 .59	0 .69
Washington	1 .67	0 .85	0 .79	0 .89	0 .73	0 .65	0 .63	0 .61
Rank Correlation1	.	0 .375	0 .523	0 .406	0 .289	-0 .160	-0 .240	0 .063
Range Ratio2	1 .000	0 .349	0 .326	0 .324	0 .432	0 .079	0 .080	0 .615

1 Correlation calculated by first ranking states and then calculating the correlation of the ranks.

2 Ratio of the range of the predicted values to the range of the NDATUS data.

Note: Estimates of prevalence rates have been multiplied by 100.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-1993 NHSDA data.

Exhibit 4.6	Comparison of Alternative Small Area Estimators of Prevalence of Past Year Arrest to Estimates Based on the Uniform Crime Reports (UCR).

Composite

Estimators

Indirect Estimators

State	Adjusted

1991-93

UCR

Estimate

Final

SAE

Model	Big City

Sub-

Sampled

Model

SAE

Model

County

Demog

Model	Big City/

Remaind

Demog

Model

Demog

Model	Direct

91-93

NHSDA

Estimates
Total United States	4.10	1.64	1.69	1.62	1.66	1.61	1.60	1.57

North East Region
New Jersey	3.24	1.18	1.14	1.23	1.26	1.63	1.55	1.18
New York	5.04	1.18	1.20	1.16	1.05	1.40	1.59	0.66
Pennsylvania	2.45	1.21	1.23	1.26	1.19	1.56	1.50	1.07

South Region
Florida	3.91	1.54	1.69	1.48	1.72	1.50	1.51	1.66
Georgia	5.35	2.57	2.14	2.04	1.99	1.91	1.80	2.50
Kentucky	6.17	1.59	1.66	1.69	1.77	1.63	1.57	1.31
Louisiana	5.37	2.26	2.10	2.14	2.06	1.89	1.78	1.47
North Carolina	5.92	1.70	1.92	1.95	2.21	1.82	1.73	1.46
Oklahoma	3.64	1.27	1.72	1.52	1.79	1.54	1.47	2.07
South Carolina	4.22	1.71	2.05	1.90	1.66	1.91	1.80	1.15
Tennessee	5.28	2.07	2.25	1.60	1.54	1.69	1.61	1.10
Texas	4.80	1.77	2.22	1.68	2.03	1.85	1.73	1.92
Virginia	5.07	1.40	1.40	1.60	1.77	1.67	1.73	1.31
West Virginia	2.89	1.17	1.43	1.35	1.74	1.47	1.42	1.75

North Central Region
Illinois	2.61	1.58	1.29	1.46	1.56	1.38	1.62	1.35
Indiana	2.78	2.29	2.24	1.88	1.73	1.64	1.57	2.68
Kansas	4.33	2.37	2.52	1.84	1.73	1.62	1.55	2.46
Michigan	3.18	1.89	1.73	1.97	1.76	1.69	1.62	1.41
Minnesota	2.27	1.50	1.74	1.70	2.01	1.58	1.52	1.75
Missouri	4.99	1.83	1.88	1.91	1.69	1.61	1.54	1.70
Ohio	3.47	2.07	1.96	1.96	1.81	1.63	1.56	2.03
Wisconsin	4.57	1.29	1.37	1.74	1.95	1.60	1.54	1.91

West Region
California	4.14	1.90	1.87	1.68	1.74	1.58	1.65	1.90
New Mexico	5.46	2.43	2.76	2.02	1.87	1.65	1.54	1.79
Oregon	3.36	1.70	1.79	1.66	1.55	1.46	1.41	1.67
Washington	4.15	1.51	1.66	1.77	1.60	1.56	1.50	1.21
Rank Correlation1	.	0.350	0.351	0.389	0.356	0.510	0.450	-0.066
Range Ratio2	1.000	0.361	0.415	0.252	0.298	0.136	0.100	0.518

1 Correlation calculated by first ranking states and then calculating the correlation of the ranks.

2 Ratio of the range of the predicted values to the range of the UCR data.

Note: Estimates of prevalence rates have been multiplied by 100.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse. Model-based estimates using 1991-1993 NHSDA data.

Appendix A:	Estimation of Drug and Alcohol Dependence

ESTIMATION OF DRUG AND ALCOHOL DEPENDENCE

The Diagnostic and Statistical Manual of Mental Disorders, Revised Third Addition (DSM-
III-R), published by the American Psychiatric Association is designed to be used by clinicians as well as researchers for making diagnosis of psychiatric disorders. Substance abuse and dependence are considered psychiatric disorders under DSM-III-R. DSM-III-R defines a person as dependent on a substance if they meet 3 out of 9 criteria for that substance. The method for estimating dependence from the NHSDA is based on the NHSDA questionnaire items that are assumed to approximate five of the nine DSM-III-R criteria. These five criteria include: unable to cut down on use; reduced social, occupational, or recreational activities; continued use despite knowledge of having a problem; tolerance; withdrawal. In the NHSDA algorithm, respondents are defined as dependent on a substance if they responded affirmatively to at least two of the five NHSDA criteria for that substance.

To evaluate this method for estimating dependence, NHSDA dependence estimates were compared to estimates from the National Comorbidity Survey (NCS).1,2 Conducted in 1991, the NCS was designed to provide nationally representative estimates of 14 psychiatric disorders, including substance abuse and dependence, using a structured diagnostic interview. It employed a multistage area probability sample of 8,098 respondents in the household population.

Based on this analysis it was concluded that the approximation to the DSM-III-R definition of drug dependence produces dependence estimates which are comparable to the NCS. The table below indicates estimates of dependence from the NHSDA from 1991 through 1993 and estimates of dependence from the NCS.

Twelve Month Estimates of Dependence from the NCS and NHSDA

(Old and New Methods) for Persons 15-54 Years of Age (1991-1994)

DRUG	NCS	NHSDA
1991	1991	1992	1993
Any Illicit	1.8%	1.8%	1.4%	1.5%
Marijuana	0.9	1.2	0.9	0.9
Cocaine	0.4	0.5	0.4	0.3
Alcohol	4.4	5.7	4.7	5.2

1This is included in a paper by Joan Epstein and Joe Gfroerer, A Method for Estimating Substance Abuse Treatment Need from a National Household Survey, that was presented at the 37th International Congress on Alcohol and Drug Dependence, August 20-25, 1995.

2Lifetime and 12-Month Prevalence of DSM-III-R Psychiatric Disorders in the United States. Results from the National Comorbidity Survey. R.C. Kessler, PhD; K.A.McGonagle, PhD; S. Zhao, PhD; C.B. Nelson, MPH; M. Hughes, PhD; S. Eshleman, MA; H.U. Wittchen, PhD; K.S.Kendler, MD. Arch Gen Psychiatry, Vol.51, P.P. 8-19, January 1994.

Appendix B:
Definition of 25 MSAs Selected for Small Area Estimation

Appendix B. Definition of 25 MSAs Selected for Small Area Estimation

1992

Population

Projection

1. Anaheim-Santa Ana, CA	1,996,343

California, Orange County	1,996,343

2. Atlanta, GA	2,424,882

Georgia, Barrow County	25,179

Georgia, Butts County	12,970

Georgia, Cherokee County	77,733

Georgia, Clayton County	151,319

Georgia, Cobb County	391,285

Georgia, Coweta County	45,782

Georgia, De Kalb County	463,515

Georgia, Douglas County	59,987

Georgia, Fayette County	55,556

Georgia, Forsyth County	38,659

Georgia, Fulton County	546,808

Georgia, Gwinnett County	310,368

Georgia, Henry County	51,116

Georgia, Newton County	35,030

Georgia, Paulding County	35,385

Georgia, Rockdale County	46,428

Georgia, Spalding County	45,367

Georgia, Walton County	32,395

3. Baltimore, MD	1,996,133

Maryland, Anne Arundel County	357,569

Maryland, Baltimore County	588,006

Maryland, Carroll County	104,516

Maryland, Harford County	153,125

Maryland, Howard County	163,767

Maryland, Queen Annes County	29,465

Maryland, Baltimore City	599,685

4. Boston, MA	3,144,871

Massachusetts, Essex County	551,793

Massachusetts, Middlesex County	1,170,799

Massachusetts, Norfolk County	516,160

Massachusetts, Plymouth County	355,370

Massachusetts, Suffolk County	550,750

5. Chicago, IL	4,981,375

Illinois, Cook County	4,175,359

Illinois, Du Page County	654,320

Illinois, Mchenry County	151,696

6. Dallas, TX	2,120,056

Texas, Collin County	227,616

Texas, Dallas County	1,518,374

Texas, Denton County	237,971

Texas, Ellis County	70,322

Texas, Kaufman County	43,729

Texas, Rockwall County	22,044

7. Denver, CO	1,345,668

Colorado, Adams County	211,897

Colorado, Arapahoe County	329,437

Colorado, Denver County	381,917

Colorado, Douglas County	53,022

Colorado, Jefferson County	369,394

8. Detroit, MI	3,592,682

Michigan, Lapeer County	60,050

Michigan, Livingston County	96,340

Michigan, Macomb County	603,448

Michigan, Monroe County	107,869

Michigan, Oakland County	909,637

Michigan, St Clair County	119,365

Michigan, Wayne County	1,695,974

9. El Paso, TX	456,359

Texas, El Paso County	456,359

10. Houston, TX	2,661,181

Texas, Fort Bend County	184,640

Texas, Harris County	2,261,297

Texas, Liberty County	42,903

Texas, Montgomery County	152,654

Texas, Waller County	19,687

11. Los Angeles, CA	7,127,293

California, Los Angeles County	7,127,293

12. Miami-Hialeah, FL	1,600,103

Florida, Dade County	1,600,103

13. Minneapolis-St. Paul	2,035,347

Minnesota, Anoka County	198,093

Minnesota, Carver County	39,020

Minnesota, Chisago County	24,780

Minnesota, Dakota County	228,159

Minnesota, Hennepin County	865,489

Minnesota, Isanti County	20,829

Minnesota, Ramsey County	398,328

Minnesota, Scott County	47,001

Minnesota, Washington County	118,214

Minnesota, Wright County	54,470

Wisconsin, St Croix County	40,963

14. Nassau-Suffolk, NY	2,177,956

New York, Nassau County	1,085,945

New York, Suffolk County	1,092,011

15. New York, NY	7,085,772

New York, Bronx County	938,595

New York, Kings County	1,873,534

New York, New York County	1,283,134

New York, Putnam County	69,880

New York, Queens County	1,648,200

New York, Richmond County	316,399

New York, Rockland County	218,687

New York, Westchester County	737,342

16. Newark, NJ	1,499,843

New Jersey, Essex County	628,943

New Jersey, Morris County	353,820

New Jersey, Sussex County	106,854

New Jersey, Union County	410,225

17. Oakland, CA	1,727,459

California, Alameda County	1,054,766

California, Contra Costa County	672,693

18. Philadelphia, PA-NJ	4,036,961

New Jersey, Burlington County	329,783

New Jersey, Camden County	407,164

New Jersey, Gloucester County	190,673

Pennsylvania, Bucks County	456,745

Pennsylvania, Chester County	320,513

Pennsylvania, Delaware County	454,906

Pennsylvania, Montgomery County	573,220

Pennsylvania, Philadelphia County	1,303,956

19. Phoenix, AZ	1,770,449

Arizona, Maricopa County	1,770,449

20. San Antonio, TX	1,040,318

Texas, Bexar County	941,916

Texas, Comal County	44,601

Texas, Guadalupe County	53,801

21. San Bernardino, CA	2,122,022

California, Riverside County	978,288

California, San Bernardino County	1,143,734

22. San Diego, CA	2,089,322

California, San Diego County	2,089,322

23. St. Louis, MO-IL	2,006,385

Illinois, Clinton County	28,060

Illinois, Jersey County	16,844

Illinois, Madison County	206,040

Illinois, Monroe County	18,700

Illinois, St Clair County	212,612

Missouri, Franklin County	66,062

Missouri, Jefferson County	139,312

Missouri, St Charles County	177,308

Missouri, St Louis County	826,259

Missouri, St Louis City	315,187

24. Tampa-St. Petersburg, FL	1,822,154

Florida, Hernando County	97,648

Florida, Hillsborough County	708,882

Florida, Pasco County	258,574

Florida, Pinellas County	757,050

25. Washington, DC	3,345,080

District of Columbia	513,842

Maryland, Calvert County	43,912

Maryland, Charles County	84,579

Maryland, Frederick County	127,118

Maryland, Montgomery County	651,746

Maryland, Prince Georges County	611,812

Virginia, Arlington County	151,317

Virginia, Fairfax County	706,226

Virginia, Loudoun County	73,193

Virginia, Prince William County	178,043

Virginia, Stafford County	51,577

Virginia, Alexandria City	98,061

Virginia, Fairfax City	16,699

Virginia, Falls Church City	8,157

Virginia, Manassas City	23,622

Virginia, Manassas Park City	5,176

Appendix C:	Confidence Intervals

Appendix C.	Estimated Percentage of Population with Substance Abuse Behaviors for 26 States. Estimates Based on Data From the 1991-1993 NHSDA. 95% Confidence Intervals

State	Licit Substance Use In Past Month	Illicit Substance Use In Past Month
Cigarette Use	Alcohol Use	Any Illicit Drug Use	Any Illicit But

Marijuana Use	Cocaine Use
Total United States	25.46	(25.29-25.64)	49.92	(49.32-50.51)	5.83	(5.69-5.98)	2.44	(2.37-2.52)	0.80	(0.75-0.85)
New Jersey	24.54	(23.72-25.37)	56.36	(54.43-58.26)	5.40	(4.85-6.02)	1.89	(1.64-2.19)	1.01	(0.80-1.26)
New York	23.73	(23.27-24.19)	53.37	(51.71-55.02)	6.44	(6.01-6.89)	2.37	(2.19-2.56)	0.82	(0.74-0.91)
Pennsylvania	26.87	(26.26-27.49)	52.32	(50.11-54.53)	5.07	(4.60-5.59)	2.04	(1.81-2.30)	0.59	(0.48-0.72)
Florida	24.36	(23.82-24.90)	45.84	(43.89-47.81)	4.95	(4.52-5.41)	1.99	(1.78-2.23)	0.69	(0.58-0.83)
Georgia	26.34	(25.43-27.27)	45.10	(42.39-47.83)	5.82	(5.02-6.73)	2.04	(1.72-2.41)	0.59	(0.42-0.83)
Kentucky	31.44	(30.48-32.41)	38.45	(35.44-41.55)	4.53	(3.93-5.21)	1.66	(1.38-2.00)	0.44	(0.32-0.61)
Louisiana	25.73	(24.67-26.83)	45.79	(43.19-48.42)	4.38	(3.78-5.08)	2.01	(1.68-2.40)	0.81	(0.59-1.12)
North Carolina	26.60	(25.91-27.31)	43.76	(41.14-46.42)	5.85	(5.27-6.50)	1.79	(1.55-2.07)	0.59	(0.46-0.75)
Oklahoma	26.93	(25.92-27.96)	37.52	(34.32-40.84)	6.96	(5.99-8.06)	4.15	(3.69-4.66)	0.56	(0.42-0.76)
South Carolina	28.95	(27.70-30.24)	43.56	(40.04-47.15)	4.99	(4.15-5.99)	1.66	(1.35-2.05)	0.66	(0.44-0.98)
Tennessee	29.51	(28.35-30.70)	38.20	(34.90-41.62)	4.53	(3.80-5.39)	1.91	(1.60-2.28)	0.96	(0.71-1.30)
Texas	26.19	(25.47-26.92)	48.90	(47.06-50.74)	5.57	(5.05-6.15)	2.24	(1.97-2.54)	0.79	(0.63-0.98)
Virginia	25.01	(24.29-25.73)	48.09	(45.74-50.45)	5.55	(4.95-6.23)	2.71	(2.46-2.98)	1.25	(1.00-1.57)
West Virginia	30.63	(29.48-31.79)	36.40	(32.84-40.12)	4.23	(3.61-4.95)	1.41	(1.15-1.73)	0.56	(0.41-0.75)
Illinois	25.96	(25.40-26.52)	50.64	(48.99-52.28)	4.64	(4.36-4.94)	1.94	(1.78-2.10)	0.69	(0.61-0.78)
Indiana	24.52	(23.71-25.34)	44.50	(41.37-47.68)	4.49	(3.95-5.11)	1.85	(1.56-2.19)	0.50	(0.38-0.67)
Kansas	24.15	(23.07-25.26)	52.56	(49.35-55.76)	5.01	(4.26-5.89)	3.19	(2.79-3.63)	0.77	(0.58-1.02)
Michigan	26.95	(26.12-27.79)	52.23	(49.38-55.07)	5.52	(4.89-6.23)	1.55	(1.29-1.85)	0.61	(0.48-0.79)
Minnesota	22.57	(21.70-23.47)	58.68	(55.77-61.54)	4.62	(4.02-5.31)	1.38	(1.14-1.68)	0.32	(0.23-0.45)
Missouri	25.22	(24.44-26.01)	50.52	(47.85-53.19)	4.98	(4.37-5.67)	2.82	(2.50-3.19)	0.89	(0.71-1.11)
Ohio	29.19	(28.53-29.86)	48.87	(46.49-51.25)	5.37	(4.87-5.92)	2.52	(2.27-2.80)	0.78	(0.64-0.94)
Wisconsin	23.51	(22.50-24.54)	55.07	(51.48-58.61)	4.07	(3.49-4.73)	1.93	(1.64-2.27)	0.84	(0.64-1.11)
California	22.84	(22.46-23.22)	52.90	(52.15-53.66)	8.23	(7.86-8.62)	3.91	(3.69-4.14)	0.97	(0.86-1.10)
New Mexico	28.42	(26.73-30.16)	49.82	(46.71-52.92)	7.76	(6.44-9.33)	3.54	(2.90-4.32)	0.85	(0.57-1.27)
Oregon	25.53	(24.61-26.47)	52.29	(48.87-55.69)	7.08	(6.28-7.99)	3.31	(2.89-3.78)	0.38	(0.28-0.51)
Washington	23.73	(22.80-24.67)	54.48	(51.64-57.29)	6.07	(5.32-6.91)	3.20	(2.74-3.74)	0.46	(0.34-0.63)
Appendix C.	Estimated Percentage of Population with Substance Abuse Behaviors for 26 States. Estimates Based on Data From the 1991-1993 NHSDA. 95% Confidence Intervals (Continued)

State	Past Year Dependence	Past Year Treatment	Past Year Arrest
Dependent On Illicit Drugs	Dependent On Alcohol	Received Treatment For Illicit Drug Use	Received Treatment For Alcohol Use	Needed Treatment For Illicit Drug Use
Total United States	1.24	(1.19-1.29)	3.08	(2.97-3.20)	0.70	(0.66-0.74)	0.69	(0.65-0.74)	2.85	(2.76-2.95)	1.79	(1.72-1.86)
New Jersey	1.26	(1.11-1.43)	2.48	(2.14-2.88)	0.68	(0.56-0.83)	0.64	(0.49-0.83)	2.03	(1.77-2.34)	1.35	(1.16-1.56)
New York	1.09	(1.00-1.18)	2.02	(1.82-2.24)	0.64	(0.55-0.75)	0.41	(0.33-0.51)	2.46	(2.28-2.66)	1.32	(1.21-1.43)
Pennsylvania	0.88	(0.78-0.99)	2.76	(2.41-3.16)	0.56	(0.48-0.65)	0.69	(0.56-0.85)	2.19	(1.95-2.45)	1.28	(1.16-1.42)
Florida	0.93	(0.84-1.04)	2.36	(2.07-2.69)	0.69	(0.60-0.80)	0.58	(0.47-0.70)	2.46	(2.21-2.74)	1.61	(1.47-1.75)
Georgia	1.21	(1.06-1.38)	3.61	(3.01-4.32)	0.71	(0.58-0.87)	0.45	(0.33-0.62)	3.78	(3.23-4.42)	2.49	(2.25-2.76)
Kentucky	0.90	(0.79-1.02)	2.06	(1.72-2.45)	0.57	(0.47-0.67)	0.48	(0.35-0.65)	2.27	(1.93-2.66)	1.67	(1.48-1.88)
Louisiana	0.99	(0.86-1.14)	3.93	(3.31-4.67)	0.64	(0.53-0.77)	0.59	(0.42-0.82)	2.77	(2.29-3.36)	2.32	(2.06-2.62)
North Carolina	1.14	(1.02-1.27)	2.55	(2.21-2.95)	0.64	(0.53-0.76)	0.50	(0.39-0.64)	2.40	(2.11-2.74)	1.68	(1.53-1.85)
Oklahoma	1.50	(1.34-1.69)	4.05	(3.32-4.93)	0.86	(0.71-1.04)	1.49	(0.97-2.26)	3.77	(3.07-4.63)	1.37	(1.19-1.58)
South Carolina	1.18	(1.02-1.36)	3.01	(2.41-3.77)	0.53	(0.42-0.66)	0.55	(0.38-0.81)	2.14	(1.72-2.66)	1.80	(1.55-2.08)
Tennessee	0.91	(0.80-1.04)	2.06	(1.69-2.51)	0.60	(0.50-0.70)	0.53	(0.38-0.76)	2.08	(1.72-2.52)	2.10	(1.83-2.41)
Texas	1.47	(1.33-1.63)	3.37	(2.95-3.84)	0.61	(0.51-0.73)	0.69	(0.55-0.87)	3.06	(2.73-3.43)	1.84	(1.66-2.05)
Virginia	1.11	(1.01-1.23)	3.03	(2.63-3.48)	0.65	(0.55-0.76)	0.61	(0.50-0.75)	2.91	(2.54-3.33)	1.54	(1.39-1.69)
West Virginia	0.84	(0.73-0.97)	2.12	(1.73-2.59)	0.49	(0.39-0.61)	0.73	(0.52-1.02)	2.15	(1.77-2.62)	1.29	(1.10-1.50)
Illinois	0.88	(0.81-0.94)	3.08	(2.83-3.36)	0.54	(0.48-0.59)	0.66	(0.56-0.78)	2.32	(2.15-2.51)	1.72	(1.57-1.90)
Indiana	0.97	(0.87-1.09)	2.15	(1.80-2.55)	0.52	(0.43-0.62)	0.66	(0.49-0.89)	1.98	(1.70-2.30)	2.30	(2.08-2.55)
Kansas	1.13	(1.00-1.27)	3.51	(2.88-4.26)	0.66	(0.55-0.79)	0.86	(0.59-1.24)	2.73	(2.25-3.31)	2.40	(2.10-2.74)
Michigan	1.08	(0.96-1.22)	3.36	(2.82-3.99)	0.76	(0.65-0.89)	0.70	(0.52-0.93)	3.05	(2.63-3.53)	1.99	(1.79-2.21)
Minnesota	1.00	(0.87-1.14)	3.12	(2.58-3.77)	0.84	(0.70-1.02)	0.80	(0.59-1.10)	2.19	(1.84-2.60)	1.67	(1.45-1.91)
Missouri	1.03	(0.92-1.15)	2.16	(1.83-2.55)	0.70	(0.59-0.84)	0.68	(0.50-0.91)	3.26	(2.82-3.76)	2.10	(1.85-2.38)
Ohio	0.96	(0.87-1.07)	2.73	(2.36-3.15)	0.70	(0.59-0.83)	0.81	(0.64-1.01)	2.56	(2.28-2.87)	2.25	(2.06-2.47)
Wisconsin	0.98	(0.88-1.11)	2.95	(2.41-3.60)	0.61	(0.51-0.73)	0.59	(0.44-0.79)	2.41	(1.99-2.92)	1.53	(1.30-1.80)
California	1.91	(1.78-2.04)	4.87	(4.62-5.14)	0.97	(0.87-1.08)	0.81	(0.71-0.94)	4.23	(3.99-4.48)	2.15	(1.99-2.33)
New Mexico	1.62	(1.41-1.87)	3.66	(2.93-4.57)	0.77	(0.61-0.96)	0.92	(0.55-1.56)	3.30	(2.61-4.17)	2.63	(2.07-3.32)
Oregon	1.99	(1.79-2.22)	2.75	(2.32-3.25)	0.90	(0.75-1.08)	0.61	(0.43-0.85)	2.22	(1.90-2.58)	1.88	(1.64-2.15)
Washington	1.96	(1.74-2.19)	3.51	(2.94-4.19)	0.85	(0.72-1.00)	0.51	(0.36-0.71)	3.55	(3.01-4.18)	1.91	(1.61-2.26)

Appendix C.	Estimated Percentage of Population with Substance Abuse Behaviors for 25 MSAs. Estimates Based on Data From the 1991-1993 NHSDA. 95% Confidence Intervals

MSA	Licit Substance Use In Past Month	Illicit Substance Use In Past Month
Cigarette Use	Alcohol Use	Any Illicit Drug Use	Any Illicit But Mrj Use	Cocaine Use
Anaheim-Santa Ana, CA	21.01	(20.19-21.84)	52.25	(50.58-53.91)	8.82	(7.78-9.98)	4.85	(4.25-5.53)	1.23	(0.93-1.61)
Atlanta, GA	23.70	(22.80-24.63)	50.98	(49.52-52.45)	5.86	(5.15-6.66)	1.95	(1.62-2.36)	0.56	(0.40-0.79)
Baltimore, MD	24.96	(24.00-25.95)	44.84	(43.48-46.20)	5.03	(4.37-5.80)	1.80	(1.48-2.19)	0.70	(0.53-0.93)
Boston, MA	27.51	(26.70-28.33)	61.51	(60.37-62.63)	6.73	(5.98-7.55)	2.57	(2.17-3.03)	0.94	(0.74-1.20)
Chicago, IL	24.73	(24.42-25.04)	53.82	(53.49-54.16)	5.53	(5.34-5.72)	2.34	(2.23-2.45)	0.94	(0.87-1.02)
Dallas, TX	25.95	(25.06-26.85)	50.23	(48.39-52.08)	5.69	(4.95-6.53)	2.38	(2.04-2.78)	1.05	(0.80-1.37)
Denver, CO	27.13	(26.83-27.44)	58.42	(58.09-58.75)	8.30	(8.07-8.54)	2.94	(2.80-3.07)	1.01	(0.93-1.08)
Detroit, MI	26.52	(25.61-27.46)	54.17	(52.79-55.54)	5.50	(4.83-6.25)	1.38	(1.12-1.71)	0.60	(0.45-0.81)
El Paso, TX	20.41	(18.86-22.05)	45.85	(42.18-49.56)	3.58	(2.62-4.89)	1.85	(1.35-2.51)	0.53	(0.33-0.85)
Houston, TX	25.07	(24.03-26.13)	50.94	(49.37-52.50)	4.05	(3.47-4.71)	2.43	(2.07-2.86)	0.60	(0.44-0.82)
Los Angeles, CA	21.82	(21.54-22.11)	49.34	(49.01-49.68)	6.68	(6.48-6.89)	2.72	(2.60-2.84)	0.90	(0.84-0.97)
Miami-Hialeah, FL	20.88	(20.60-21.17)	44.40	(44.05-44.75)	3.75	(3.59-3.92)	2.26	(2.15-2.37)	0.89	(0.82-0.96)
Minneapolis-St. Paul, MN	22.64	(21.73-23.57)	65.87	(64.62-67.10)	5.19	(4.54-5.92)	1.51	(1.22-1.87)	0.36	(0.26-0.50)
Nassau-Suffolk, NY	20.22	(19.38-21.08)	59.60	(58.36-60.84)	6.57	(5.84-7.38)	1.99	(1.65-2.41)	0.37	(0.28-0.49)
New York, NY	23.15	(22.85-23.45)	48.84	(48.50-49.19)	5.99	(5.80-6.19)	2.40	(2.29-2.51)	1.06	(0.99-1.15)
Newark, NJ	24.10	(23.10-25.13)	62.17	(60.45-63.85)	6.20	(5.39-7.11)	1.91	(1.60-2.28)	1.37	(0.98-1.92)
Oakland, CA	23.33	(22.61-24.07)	65.02	(63.55-66.46)	11.39	(10.31-12.57)	5.11	(4.59-5.69)	1.47	(1.17-1.85)
Philadelphia, PA-NJ	25.89	(25.20-26.59)	59.13	(58.06-60.19)	5.72	(5.11-6.40)	2.22	(1.94-2.55)	0.64	(0.51-0.81)
Phoenix, AZ	24.83	(23.85-25.84)	53.69	(52.12-55.25)	6.82	(5.95-7.81)	3.99	(3.45-4.62)	1.01	(0.77-1.31)
San Antonio, TX	26.09	(24.71-27.52)	54.43	(51.55-57.27)	4.35	(3.39-5.55)	2.17	(1.67-2.81)	0.78	(0.51-1.17)
San Bernardino, CA	23.32	(22.39-24.27)	49.12	(47.64-50.60)	7.42	(6.62-8.32)	4.60	(4.07-5.20)	0.64	(0.49-0.84)
San Diego, CA	22.25	(21.36-23.16)	49.08	(47.60-50.55)	7.07	(6.22-8.03)	3.59	(3.09-4.17)	0.93	(0.70-1.23)
St. Louis, MO-IL	25.27	(24.52-26.03)	55.02	(53.73-56.30)	5.19	(4.56-5.90)	2.53	(2.21-2.88)	0.95	(0.75-1.21)
Tampa-St. Petersburg, FL	25.55	(24.76-26.36)	41.22	(39.75-42.70)	5.27	(4.56-6.08)	2.03	(1.66-2.47)	0.57	(0.43-0.74)
Washington, DC	22.61	(22.40-22.81)	53.82	(53.59-54.05)	5.38	(5.26-5.51)	2.58	(2.50-2.67)	1.03	(0.98-1.08)

Appendix C.	Estimated Percentage of Population with Substance Abuse Behaviors for 25 MSAs. Estimates Based on Data From the 1991-1993 NHSDA. 95% Confidence Intervals (Continued)

MSA	Past Year Dependence	Past Year Treatment	Past Year Arrest
Dependent On Illicit Drugs	Dependent On Alcohol	Received Treatment For Illicit Drug Use	Received Treatment For Alcohol Use	Needed Treatment For Illicit Drug Use
Anaheim-Santa Ana, CA	2.17	(1.88-2.51)	6.01	(5.37-6.72)	1.01	(0.77-1.31)	0.55	(0.39-0.78)	4.45	(3.91-5.06)	1.53	(1.20-1.94)
Atlanta, GA	1.24	(1.06-1.45)	4.47	(3.84-5.20)	0.74	(0.60-0.91)	0.38	(0.27-0.55)	4.83	(4.21-5.53)	1.74	(1.52-1.98)
Baltimore, MD	0.89	(0.75-1.05)	2.15	(1.74-2.65)	0.58	(0.45-0.75)	0.45	(0.32-0.65)	2.28	(1.90-2.73)	1.30	(1.11-1.52)
Boston, MA	1.68	(1.46-1.92)	4.38	(3.83-5.01)	0.97	(0.77-1.22)	2.18	(1.69-2.81)	3.88	(3.41-4.41)	1.66	(1.39-1.98)
Chicago, IL	0.89	(0.82-0.97)	3.46	(3.33-3.60)	0.53	(0.49-0.59)	0.71	(0.66-0.77)	2.75	(2.63-2.88)	1.51	(1.41-1.62)
Dallas, TX	1.63	(1.45-1.84)	3.31	(2.80-3.91)	0.65	(0.53-0.80)	0.45	(0.31-0.66)	2.47	(2.10-2.90)	1.55	(1.31-1.82)
Denver, CO	1.26	(1.16-1.37)	4.57	(4.41-4.73)	0.63	(0.57-0.70)	0.96	(0.89-1.04)	3.83	(3.68-3.99)	2.26	(2.12-2.40)
Detroit, MI	1.18	(1.02-1.37)	3.59	(3.11-4.14)	0.78	(0.66-0.92)	0.51	(0.38-0.69)	3.58	(3.15-4.06)	1.77	(1.53-2.04)
El Paso, TX	0.79	(0.57-1.08)	3.66	(2.59-5.13)	0.55	(0.36-0.85)	0.53	(0.28-1.03)	2.50	(1.92-3.25)	1.62	(1.20-2.19)
Houston, TX	2.10	(1.87-2.37)	1.96	(1.53-2.50)	0.81	(0.68-0.98)	0.71	(0.53-0.94)	3.86	(3.40-4.37)	1.56	(1.32-1.84)
Los Angeles, CA	1.50	(1.39-1.61)	7.13	(6.96-7.31)	0.75	(0.65-0.88)	0.50	(0.46-0.55)	4.04	(3.89-4.20)	1.85	(1.74-1.97)
Miami-Hialeah, FL	0.74	(0.67-0.82)	2.04	(1.94-2.14)	0.56	(0.44-0.72)	0.48	(0.44-0.52)	2.16	(2.04-2.29)	1.22	(1.13-1.32)
Minneapolis-St. Paul, MN	1.15	(0.99-1.34)	3.45	(2.93-4.06)	0.93	(0.75-1.16)	0.93	(0.65-1.34)	2.38	(2.04-2.78)	1.77	(1.50-2.08)
Nassau-Suffolk, NY	1.00	(0.85-1.17)	1.10	(0.83-1.44)	0.64	(0.49-0.83)	0.50	(0.36-0.68)	1.87	(1.56-2.24)	1.14	(0.93-1.40)
New York, NY	0.94	(0.85-1.02)	1.77	(1.68-1.87)	0.66	(0.52-0.84)	0.26	(0.23-0.29)	2.50	(2.38-2.63)	0.79	(0.72-0.87)
Newark, NJ	1.43	(1.24-1.65)	2.50	(2.00-3.12)	0.89	(0.74-1.07)	0.77	(0.59-1.01)	1.90	(1.56-2.31)	1.40	(1.10-1.77)
Oakland, CA	2.98	(2.68-3.31)	3.88	(3.24-4.65)	1.37	(1.19-1.57)	2.07	(1.69-2.53)	5.22	(4.63-5.87)	1.94	(1.64-2.28)
Philadelphia, PA-NJ	0.98	(0.85-1.13)	2.82	(2.45-3.25)	0.82	(0.71-0.95)	0.61	(0.47-0.78)	2.99	(2.67-3.33)	1.37	(1.20-1.57)
Phoenix, AZ	1.55	(1.37-1.75)	3.61	(3.04-4.28)	0.65	(0.53-0.80)	0.67	(0.46-0.98)	3.14	(2.68-3.68)	1.48	(1.12-1.96)
San Antonio, TX	1.38	(1.19-1.61)	2.53	(1.69-3.76)	0.53	(0.37-0.74)	0.42	(0.22-0.79)	2.90	(2.20-3.81)	1.60	(1.30-1.96)
San Bernardino, CA	2.09	(1.82-2.39)	3.92	(3.42-4.50)	1.15	(0.96-1.37)	1.19	(0.89-1.59)	3.84	(3.35-4.39)	2.41	(2.01-2.89)
San Diego, CA	1.38	(1.15-1.65)	2.99	(2.51-3.56)	0.74	(0.56-0.98)	0.52	(0.34-0.79)	3.19	(2.75-3.71)	2.18	(1.82-2.60)
St. Louis, MO-IL	0.96	(0.85-1.09)	2.46	(2.09-2.90)	0.74	(0.62-0.87)	0.62	(0.45-0.85)	2.75	(2.37-3.19)	2.09	(1.77-2.45)
Tampa-St. Petersburg, FL	0.98	(0.84-1.13)	2.10	(1.67-2.63)	0.66	(0.55-0.80)	0.41	(0.29-0.60)	2.08	(1.76-2.45)	1.44	(1.22-1.70)
Washington, DC	1.07	(1.02-1.13)	3.59	(3.49-3.69)	0.56	(0.51-0.60)	0.87	(0.83-0.92)	2.66	(2.58-2.75)	1.33	(1.27-1.40)

Appendix C. Estimated Number of Persons with Substance Abuse Behaviors for 26 States. Estimates
Based on Data From the 1991-1993 NHSDA. 95% Confidence Intervals

State	Licit Substance Use In Past Month	Illicit Substance Use In Past Month
Cigarette Use	Alcohol Use	Any Illicit Drug Use	Any Illicit But Mrj Use	Cocaine Use
Total United States	52,444	(52,084-52,805)	102,802	(101,582-104,022)	12,01
5	(11,712-12,326)	5,031	(4,878-5,189)	1,647	(1,552-1,747)
New Jersey	1,581	(1,529-1,635)	3,631	(3,507-3,754)	348	(312-388)	122	(106-141)	65	(51-81)
New York	3,534	(3,466-3,602)	7,948	(7,701-8,193)	959	(896-1,026)	352	(326-381)	122	(110-136)
Pennsylvania	2,672	(2,612-2,733)	5,203	(4,983-5,423)	505	(458-556)	203	(180-229)	59	(48-72)
Florida	2,744	(2,683-2,805)	5,164	(4,944-5,386)	557	(509-610)	225	(201-252)	78	(65-93)
Georgia	1,433	(1,384-1,484)	2,454	(2,307-2,603)	317	(273-366)	111	(94-131)	32	(23-45)
Kentucky	960	(930-989)	1,174	(1,082-1,268)	138	(120-159)	51	(42-61)	14	(10-19)
Louisiana	872	(835-909)	1,551	(1,463-1,640)	148	(128-172)	68	(57-81)	27	(20-38)
North Carolina	1,501	(1,461-1,541)	2,468	(2,321-2,618)	330	(297-366)	101	(87-117)	33	(26-42)
Oklahoma	690	(664-717)	962	(880-1,047)	178	(154-207)	106	(95-119)	14	(11-19)
South Carolina	851	(814-889)	1,280	(1,177-1,386)	147	(122-176)	49	(40-60)	19	(13-29)
Tennessee	1,215	(1,167-1,264)	1,573	(1,437-1,713)	187	(157-222)	79	(66-94)	39	(29-53)
Texas	3,601	(3,502-3,702)	6,724	(6,472-6,977)	767	(695-845)	308	(271-349)	108	(87-135)
Virginia	1,307	(1,270-1,345)	2,514	(2,391-2,637)	290	(259-326)	141	(128-156)	65	(52-82)
West Virginia	458	(441-476)	545	(492-601)	63	(54-74)	21	(17-26)	8	(6-11)
Illinois	2,434	(2,382-2,488)	4,749	(4,594-4,903)	435	(409-463)	182	(167-197)	65	(57-73)
Indiana	1,123	(1,086-1,161)	2,039	(1,895-2,184)	206	(181-234)	85	(72-100)	23	(17-31)
Kansas	485	(464-508)	1,057	(992-1,121)	101	(86-118)	64	(56-73)	15	(12-20)
Michigan	2,052	(1,989-2,116)	3,978	(3,760-4,194)	420	(372-474)	118	(98-141)	47	(36-61)
Minnesota	807	(776-839)	2,098	(1,994-2,200)	165	(144-190)	49	(41-60)	12	(8-16)
Missouri	1,065	(1,032-1,098)	2,134	(2,021-2,246)	210	(185-239)	119	(106-135)	38	(30-47)
Ohio	2,612	(2,552-2,672)	4,372	(4,159-4,585)	481	(436-530)	226	(203-251)	70	(58-84)
Wisconsin	945	(905-987)	2,215	(2,070-2,357)	164	(141-190)	78	(66-91)	34	(26-45)
California	5,559	(5,466-5,652)	12,877	(12,693-13,061)	2,004	(1,913-2,099)	951	(897-1,007)	237	(210-268)
New Mexico	341	(321-362)	597	(560-635)	93	(77-112)	43	(35-52)	10	(7-15)
Oregon	612	(590-634)	1,253	(1,171-1,335)	170	(150-191)	79	(69-91)	9	(7-12)
Washington	971	(934-1,010)	2,230	(2,114-2,345)	248	(218-283)	131	(112-153)	19	(14-26)

MSA	Licit Substance Use In Past Month	Illicit Substance Use In Past Month
Cigarette Use	Alcohol Use	Any Illicit Drug Use	Any Illicit But Mrj Use	Cocaine Use
Anaheim-Santa Ana, CA	419	(403-436)	1,043	(1,010-1,076)	176	(155-199)	97	(85-110)	25	(19-32)
Atlanta, GA	575	(553-597)	1,236	(1,201-1,272)	142	(125-161)	47	(39-57)	14	(10-19)
Baltimore, MD	498	(479-518)	895	(868-922)	100	(87-116)	36	(29-44)	14	(11-18)
Boston, MA	865	(840-891)	1,934	(1,899-1,970)	212	(188-238)	81	(68-95)	30	(23-38)
Chicago, IL	1,232	(1,217-1,247)	2,681	(2,664-2,698)	275	(266-285)	117	(111-122)	47	(44-51)
Dallas, TX	550	(531-569)	1,065	(1,026-1,104)	121	(105-139)	51	(43-59)	22	(17-29)
Denver, CO	365	(361-369)	786	(782-791)	112	(109-115)	40	(38-41)	14	(13-15)
Detroit, MI	953	(920-987)	1,946	(1,897-1,995)	197	(173-225)	50	(40-61)	22	(16-29)
El Paso, TX	93	(86-101)	209	(192-226)	16	(12-22)	8	(6-11)	2	(2-4)
Houston, TX	667	(640-695)	1,355	(1,314-1,397)	108	(92-125)	65	(55-76)	16	(12-22)
Los Angeles, CA	1,555	(1,535-1,576)	3,517	(3,493-3,541)	476	(462-491)	194	(185-203)	65	(60-69)
Miami-Hialeah, FL	334	(330-339)	710	(705-716)	60	(57-63)	36	(34-38)	14	(13-15)
Minneapolis-St. Paul, MN	461	(442-480)	1,341	(1,315-1,366)	106	(92-120)	31	(25-38)	7	(5-10)
Nassau-Suffolk, NY	440	(422-459)	1,298	(1,271-1,325)	143	(127-161)	43	(36-52)	8	(6-11)
New York, NY	1,640	(1,619-1,662)	3,461	(3,437-3,485)	425	(411-439)	170	(163-178)	75	(70-81)
Newark, NJ	361	(346-377)	932	(907-958)	93	(81-107)	29	(24-34)	21	(15-29)
Oakland, CA	403	(391-416)	1,123	(1,098-1,148)	197	(178-217)	88	(79-98)	25	(20-32)
Philadelphia, PA-NJ	1,045	(1,017-1,073)	2,387	(2,344-2,430)	231	(206-258)	90	(78-103)	26	(21-33)
Phoenix, AZ	440	(422-457)	951	(923-978)	121	(105-138)	71	(61-82)	18	(14-23)
San Antonio, TX	271	(257-286)	566	(536-596)	45	(35-58)	23	(17-29)	8	(5-12)
San Bernardino, CA	495	(475-515)	1,042	(1,011-1,074)	158	(140-177)	98	(86-110)	14	(10-18)
San Diego, CA	465	(446-484)	1,025	(995-1,056)	148	(130-168)	75	(65-87)	19	(15-26)
St. Louis, MO-IL	507	(492-522)	1,104	(1,078-1,130)	104	(91-118)	51	(44-58)	19	(15-24)
Tampa-St. Petersburg, FL	466	(451-480)	751	(724-778)	96	(83-111)	37	(30-45)	10	(8-13)
Washington, DC	756	(749-763)	1,800	(1,793-1,808)	180	(176-184)	86	(84-89)	34	(33-36)

Appendix C.	Estimated Number of Persons with Substance Abuse Behaviors for 26 States. Estimates Based on Data From the 1991-1993 NHSDA. 95% Confidence Intervals

State	Past Year Dependence	Past Year Treatment	Past Year Arrest
Dependent On Illicit Drugs	Dependent On Alcohol	Received Treatment For Illicit Drug Use	Received Treatment For Alcohol Use	Needed Treatment For Illicit Drug Use
Total United States	2,549	(2,444-2,
658)	6,35
0	(6,126-6,5
82)	1,44
7	(1,365-1,5
33)	1,42
9	(1,336-1,
529)	5,87
7	(5,690-6,0
71)	3,689	(3,549-3,
835)
New Jersey	81	(72-92)	160	(138-185)	44	(36-53)	41	(32-53)	131	(114-151)	87	(75-100)
New York	162	(149-175
)	301	(270-334)	95	(82-111)	62	(50-76)	367	(340-395)	196	(180-213
)
Pennsylvania	87	(78-98)	275	(240-315)	55	(47-65)	68	(55-85)	217	(194-243)	128	(115-142
)
Florida	105	(94-117)	266	(233-303)	78	(67-90)	65	(53-79)	277	(248-309)	181	(166-197
)
Georgia	66	(58-75)	197	(164-235)	39	(32-47)	25	(18-34)	206	(176-240)	136	(123-150
)
Kentucky	28	(24-31)	63	(53-75)	17	(14-21)	15	(11-20)	69	(59-81)	51	(45-57)
Louisiana	34	(29-39)	133	(112-158)	22	(18-26)	20	(14-28)	94	(77-114)	79	(70-89)
North Carolina	64	(58-72)	144	(125-166)	36	(30-43)	28	(22-36)	136	(119-154)	95	(86-105)
Oklahoma	39	(34-43)	104	(85-126)	22	(18-27)	38	(25-58)	97	(79-119)	35	(30-41)
South Carolina	35	(30-40)	89	(71-111)	15	(12-19)	16	(11-24)	63	(50-78)	53	(46-61)
Tennessee	38	(33-43)	85	(70-103)	25	(21-29)	22	(15-31)	86	(71-104)	87	(75-99)
Texas	202	(182-225
)	463	(405-528)	84	(71-100)	95	(75-119)	421	(376-472)	254	(228-282
)
Virginia	58	(53-64)	158	(138-182)	34	(29-40)	32	(26-39)	152	(133-174)	80	(73-89)
West Virginia	13	(11-15)	32	(26-39)	7	(6-9)	11	(8-15)	32	(27-39)	19	(17-22)
Illinois	82	(76-89)	289	(265-315)	50	(45-55)	62	(52-73)	218	(201-235)	162	(147-178
)
Indiana	45	(40-50)	98	(83-117)	24	(20-28)	30	(23-41)	91	(78-105)	105	(95-117)
Kansas	23	(20-25)	70	(58-86)	13	(11-16)	17	(12-25)	55	(45-67)	48	(42-55)
Michigan	82	(73-93)	256	(215-304)	58	(50-68)	53	(40-71)	232	(200-269)	151	(136-168
)
Minnesota	36	(31-41)	112	(92-135)	30	(25-37)	29	(21-39)	78	(66-93)	60	(52-68)
Missouri	43	(39-48)	91	(77-108)	30	(25-35)	29	(21-39)	138	(119-159)	89	(78-100)
Ohio	86	(78-95)	244	(211-281)	63	(53-74)	72	(57-91)	229	(204-256)	202	(184-221
)
Wisconsin	40	(35-44)	118	(97-145)	25	(21-29)	24	(18-32)	97	(80-117)	62	(52-72)
California	464	(433-498
)	1,18
6	(1,124-1,2
51)	236	(213-262)	198	(172-228
)	1,02
9	(971-1,09
0)	524	(483-568
)
New Mexico	19	(17-22)	44	(35-55)	9	(7-12)	11	(7-19)	40	(31-50)	32	(25-40)
Oregon	48	(43-53)	66	(56-78)	22	(18-26)	15	(10-20)	53	(46-62)	45	(39-52)
Washington	80	(71-90)	144	(120-172)	35	(29-41)	21	(15-29)	145	(123-171)	78	(66-93)

MSAs	Past Year Dependence	Past Year Treatment	Past Year Arrest
Dependent On Illicit Drugs	Dependent On Alcohol	Received Treatment For Illicit Drug Use	Received Treatment For Alcohol Use	Needed Treatment For Illicit Drug Use
Anaheim-Santa Ana, CA	43	(38-50)	120	(107-134)	20	(15-26)	11	(8-16)	89	(78-101)	31	(24-39)
Atlanta, GA	30	(26-35)	108	(93-126)	18	(14-22)	9	(7-13)	117	(102-134)	42	(37-48)
Baltimore, MD	18	(15-21)	43	(35-53)	12	(9-15)	9	(6-13)	45	(38-54)	26	(22-30)
Boston, MA	53	(46-60)	138	(120-157)	30	(24-38)	69	(53-88)	122	(107-139)	52	(44-62)
Chicago, IL	45	(41-49)	172	(166-179)	27	(24-29)	36	(33-38)	137	(131-144)	75	(70-81)
Dallas, TX	35	(31-39)	70	(59-83)	14	(11-17)	10	(7-14)	52	(44-61)	33	(28-39)
Denver, CO	17	(16-18)	61	(59-64)	9	(8-9)	13	(12-14)	52	(50-54)	30	(29-32)
Detroit, MI	42	(37-49)	129	(112-149)	28	(24-33)	18	(14-25)	129	(113-146)	63	(55-73)
El Paso, TX	4	(3-5)	17	(12-23)	3	(2-4)	2	(1-5)	11	(9-15)	7	(5-10)
Houston, TX	56	(50-63)	52	(41-66)	22	(18-26)	19	(14-25)	103	(90-116)	41	(35-49)
Los Angeles, CA	107	(99-115)	508	(496-521)	54	(46-63)	36	(33-39)	288	(277-300)	132	(124-141 )
Miami-Hialeah, FL	12	(11-13)	33	(31-34)	9	(7-11)	8	(7-8)	35	(33-37)	20	(18-21)
Minneapolis-St. Paul, MN	23	(20-27)	70	(60-83)	19	(15-24)	19	(13-27)	49	(42-57)	36	(31-42)
Nassau-Suffolk, NY	22	(19-25)	24	(18-31)	14	(11-18)	11	(8-15)	41	(34-49)	25	(20-30)
New York, NY	66	(61-73)	125	(119-132)	47	(37-59)	18	(16-21)	177	(168-186)	56	(51-62)
Newark, NJ	21	(19-25)	38	(30-47)	13	(11-16)	12	(9-15)	28	(23-35)	21	(17-27)
Oakland, CA	51	(46-57)	67	(56-80)	24	(21-27)	36	(29-44)	90	(80-101)	33	(28-39)
Philadelphia, PA-NJ	40	(34-46)	114	(99-131)	33	(29-38)	25	(19-32)	121	(108-135)	55	(48-63)
Phoenix, AZ	27	(24-31)	64	(54-76)	12	(9-14)	12	(8-17)	56	(48-65)	26	(20-35)
San Antonio, TX	14	(12-17)	26	(18-39)	5	(4-8)	4	(2-8)	30	(23-40)	17	(14-20)
San Bernardino, CA	44	(39-51)	83	(72-95)	24	(20-29)	25	(19-34)	81	(71-93)	51	(43-61)
San Diego, CA	29	(24-35)	62	(52-74)	15	(12-20)	11	(7-16)	67	(57-78)	45	(38-54)
St. Louis, MO-IL	19	(17-22)	49	(42-58)	15	(12-18)	12	(9-17)	55	(47-64)	42	(36-49)
Tampa-St. Petersburg, FL	18	(15-21)	38	(30-48)	12	(10-15)	8	(5-11)	38	(32-45)	26	(22-31)
Washington, DC	36	(34-38)	120	(117-124)	19	(17-20)	29	(28-31)	89	(86-92)	45	(42-47)

DHHS Publication No. (SMA) 96-3095

Substance Abuse and Mental Health Services Administration

Printed 1996