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2005 State Estimates of Substance Use & Mental Health

Appendix A: State Estimation Methodology

This report includes estimates of 23 substance use measures (see Section A.1) using the combined data from the 2004 and 2005 National Surveys on Drug Use and Health (NSDUHs). In addition to the 22 substance use measures for which age group–specific State estimates were produced and documented in the 2004 State report (Wright & Sathe, 2006), a new measure, "having at least one major depressive episode (MDE) in the past year" is introduced in this report. Also included in this report are estimates of change between 2003-2004 and 2004-2005 State estimates. As discussed in Chapter 1 (Section 1.1), several changes were introduced to the survey in 2002; thus, estimates for 2001 and prior years are not comparable with estimates from 2002 and later years.

The survey-weighted hierarchical Bayes (SWHB) methodology used in the production of State estimates from the 1999-2004 surveys also was used in the production of the 2004-2005 State estimates. The SWHB methodology is described in Appendix E of the 2001 State report (Wright, 2003b) and by Folsom, Shah, and Vaish (1999). The list of predictors used in the 2004-2005 small area estimation (SAE) modeling is given in Section A.2. The methodology used to select relevant predictors remains similar to the one used in prior years and is described in brief in Section A.3. The goals of SAE modeling, general model description, and the implementation of SAE modeling remain the same and are described in Appendix E of the 2001 State report (Wright, 2003b). At the end of this appendix, tables showing the 2003, 2004, 2005, pooled 2003-2004, and pooled 2004-2005 survey response rates are included (Table s A.1 to A.12).

Small area estimates obtained using the SWHB methodology are design consistent (i.e., for States with large sample sizes, the small area estimates are close to the robust design-based estimates). The State small area estimates when aggregated by using the appropriate population totals result in national small area estimates that are very close to the national design-based estimates. However, for numerous reasons (including internal consistency), it is desirable to have national small area estimates exactly match the national design-based estimates. Beginning in 2002, exact benchmarking was introduced as described in Section A.4. The definition and explanation of the formula used in estimating the marijuana incidence rate is given in Section A.5.

Included in this report are estimates of underage (aged 12 to 20) alcohol use and binge alcohol use. For all other outcomes, the age groups of interest were 12 to 17, 18 to 25, and 26 or older. As alcohol consumption is expected to differ significantly across the 18 to 25 age group due to the legalization of alcohol at age 21, it was decided that it would be useful to produce small area estimates for persons aged 12 to 20. A short description of the methodology used to produce underage drinking estimates is described in Section A.6.

Section A.7 discusses how the serious psychological distress (SPD) estimates were produced. Section A.8 discusses production of MDE estimates. The methodology used to produce estimates of change between the 2003-2004 and the 2004-2005 State estimates is described in Section A.9.

A.1 Variables Modeled

The 2005 NSDUH data were pooled with the 2004 NSDUH data, and age group–specific State estimates for 23 binary (0,1) outcome variables were produced and presented in this report. These estimates are produced for the following outcomes:

  1. past month use of illicit drugs,

  2. past year use of marijuana,

  3. past month use of marijuana,

  4. perception of great risk of smoking marijuana once a month,

  5. average annual rate of first use of marijuana,

  6. past month use of illicit drugs other than marijuana,

  7. past year use of cocaine,

  8. past year nonmedical use of pain relievers,

  9. past month use of alcohol,

  10. past month binge alcohol use,

  11. perception of great risk of having five or more drinks of an alcoholic beverage once or twice a week,

  12. past month use of tobacco products,

  13. past month use of cigarettes,

  14. perception of great risk of smoking one or more packs of cigarettes per day,

  15. past year alcohol dependence or abuse,

  16. past year alcohol dependence,

  17. past year illicit drug dependence or abuse,

  18. past year illicit drug dependence,

  19. past year dependence on or abuse of illicit drugs or alcohol,

  20. needing but not receiving treatment for illicit drug use in the past year,

  21. needing but not receiving treatment for alcohol use in the past year,

  22. past year serious psychological distress (SPD), and

  23. past year major depressive episode (MDE).

The estimates of change between the 2003-2004 and 2004-2005 State estimates were produced for all these outcomes except for SPD and MDE.

A.2 Predictors Used in Mixed Logistic Regression Models

Local area data used as potential predictor variables in the mixed logistic regression models were obtained from several sources, including Claritas Inc., the U.S. Census Bureau, the Federal Bureau of Investigation (FBI) (Uniform Crime Reports), Health Resources and Services Administration (Area Resource File), the Substance Abuse and Mental Health Services Administration (SAMHSA) (National Survey of Substance Abuse Treatment Services [N-SSATS]), and the National Center for Health Statistics (mortality data). The list of major sources and potential data items used in the modeling are provided in the following text and lists.

The following lists provide the specific independent variables that were potential predictors in the models.

Claritas Data
Description Level
% Population aged 0-19 in block group Block group
% Population aged 20-24 in block group Block group
% Population aged 25-34 in block group Block group
% Population aged 35-44 in block group Block group
% Population aged 45-54 in block group Block group
% Population aged 55-64 in block group Block group
% Population aged 65+ in block group Block group
% Blacks in block group Block group
% Hispanics in block group Block group
% Other race in block group Block group
% Whites in block group Block group
% Males in block group Block group
% Females in block group Block group
% American Indian, Eskimo, Aleut in tract Tract
% Asian, Pacific Islander in tract Tract
% Population aged 0-19 in tract Tract
% Population aged 20-24 in tract Tract
% Population aged 25-34 in tract Tract
% Population aged 35-44 in tract Tract
% Population aged 45-54 in tract Tract
% Population aged 55-64 in tract Tract
% Population aged 65+ in tract Tract
% Blacks in tract Tract
% Hispanics in tract Tract
% Other race in tract Tract
% Whites in tract Tract
% Males in tract Tract
% Females in tract Tract
% Population aged 0-19 in county County
% Population aged 20-24 in county County
% Population aged 25-34 in county County
% Population aged 35-44 in county County
% Population aged 45-54 in county County
% Population aged 55-64 in county County
% Population aged 65+ in county County
% Blacks in county County
% Hispanics in county County
% Other race in county County
% Whites in county County
% Males in county County
% Females in county County

2000 Census Data
Description Level
% Population who dropped out of high school Tract
% Housing units built in 1940-1949 Tract
% Persons aged 16-64 with a work disability Tract
% Hispanics who are Cuban Tract
% Females 16 years or older in labor force Tract
% Females never married Tract
% Females separated/divorced/widowed/other Tract
% One-person households Tract
% Female head of household, no spouse, child #18 Tract
% Males 16 years or older in labor force Tract
% Males never married Tract
% Males separated/divorced/widowed/other Tract
% Housing units built in 1939 or earlier Tract
Average persons per room Tract
% Families below poverty level Tract
% Households with public assistance income Tract
% Housing units rented Tract
% Population with 9-12 years of school, no high school diploma Tract
% Population with 0-8 years of school Tract
% Population with associate's degree Tract
% Population with some college and no degree Tract
% Population with bachelor's, graduate, professional degree Tract
Median rents for rental units Tract
Median value of owner-occupied housing units Tract
Median household income Tract

Uniform Crime Report Data
Description Level
Drug possession arrest rate County
Drug sale/manufacture arrest rate County
Drug violations' arrest rate County
Marijuana possession arrest rate County
Marijuana sale/manufacture arrest rate County
Opium cocaine possession arrest rate County
Opium cocaine sale/manufacture arrest rate County
Other drug possession arrest rate County
Other dangerous non-narcotics arrest rate County
Serious crime arrest rate County
Violent crime arrest rate County
Driving under influence arrest rate County

Other Categorical Data
Description Source Level
=1 if Hispanic, =0 otherwise NSDUH sample Person
=1 if non-Hispanic black, =0 otherwise NSDUH sample Person
=1 if non-Hispanic other, =0 otherwise NSDUH sample Person
=1 if male, =0 if female NSDUH sample Person
=1 if MSA with 1 million +, =0 otherwise 2000 Census County
=1 if MSA with <1 million, =0 otherwise 2000 Census County
=1 if non-MSA urban, =0 otherwise 2000 Census Tract
=1 if urban area, =0 if rural area 2000 Census Tract
=1 if no Cubans in tract, =0 otherwise 2000 Census Tract
=1 if no arrests for dangerous non-narcotics,
=0 otherwise		 UCR County

Miscellaneous Data
Variable Description Source Level
Alcohol death rate, underlying cause NCHS-ICD-10 County
Cigarettes death rate, underlying cause NCHS-ICD-10 County
Drug death rate, underlying cause NCHS-ICD-10 County
Alcohol treatment rate N-SSATS (formerly called UFDS) County
Alcohol and drug treatment rate N-SSATS (formerly called UFDS) County
Drug treatment rate N-SSATS (formerly called UFDS) County
% Families below poverty level ARF County
Unemployment rate ARF County
Per capita income (in thousands) ARF County
Average suicide rate (per 10,000) ARF County
Food stamp participation rate Census Bureau County
Single state agency maintenance of effort National Association of State Alcohol
and Drug Abuse Directors
(NASADAD)		 State
Block grant awards SAMHSA State
Cost of Services Factor Index SAMHSA State
Total Taxable Resources Per Capita Index U.S. Department of Treasury State

A.3 Selection of Independent Variables for the Models

New variable selection was done for SPD and MDE using the pooled 2004-2005 NSDUH data. Estimates for MDE are being produced in this report for the first time; hence, no prior fixed-effect predictors were available. For SPD (as discussed in Section A.7) because we were producing estimates for the short-form version of SPD for the first time, new variable selection was conducted. Variable selection was conducted in a manner consistent with prior years (Wright & Sathe, 2005).

For all the other outcome variables, no new variable selection was done. The updated versions of fixed-effect predictors that were used in modeling the 2002-2003 and 2003-2004 data were used to model the 2004-2005 data. Because the interest was to estimate change between the 2003-2004 and 2004-2005 State estimates, the same set of fixed-effect predictors was used for producing both sets of estimates.

A.4 Benchmarking the Age Group–Specific Small Area Estimates

The self-calibration built into the SWHB solution ensures that the population-weighted average of the State small area estimates will closely match the national design-based estimates. Given the self-calibration ensured by the SWHB solution, for State reports prior to 2002, the standard Bayes prescription was followed; specifically, the posterior mean was used for the SAE point estimate, and the tail percentiles of the posterior distribution were used for the prediction interval (PI) limits.

Singh and Folsom (2001) extended Ghosh's (1992) results on constrained Bayes estimation to include exact benchmarking to design-based national estimates. In the simplest version of this constrained Bayes solution where only the design-based mean is imposed as a benchmarking constraint, each of the State-by-age group small area estimates (for 2004-2005) is adjusted by adding the common factor image representing deltaa = (Da - Pa), where Da is the design-based national prevalence estimate and Pa is the population-weighted mean of the State small area estimates (Psa) for age group-a. The exactly benchmarked State-s and age group-a small area estimates then are given by image representing thetasa = Psa + image representing deltaa. Experience with such additive adjustments suggests that the resulting exactly benchmarked State small area estimates will always be between 0 and 100 percent because the SWHB self-calibration ensures that the adjustment factor is small relative to the size of the State-level small area estimates.

Relative to the Bayes posterior mean, these benchmark-constrained State small area estimates are biased by the common additive adjustment factor. Therefore, the posterior mean-squared error for each benchmarked State small area estimate has the square of this adjustment factor added to its posterior variance. To achieve the desirable feature of exact benchmarking, this constrained Bayes adjustment factor was implemented for the State-by-age group small area estimates. The associated credible intervals can be recentered at the benchmarked small area estimates on the logit scale with the symmetric interval end points based on the posterior root mean-squared errors. The adjusted 95 percent PIs (Lowersa, Uppersa) are defined below:

Lowersa = exp(Lsa)/[1 + exp(Lsa)] and Uppersa = exp(Usa)/[1 + exp(Usa)],

where

Lsa = log[image representing thetasa/(1 - image representing thetasa)] - 1.96 * image representing the square root of MSE sub s and a,

Usa = log[image representing thetasa/(1 - image representing thetasa)] + 1.96 * image representing the square root of MSE sub s and a, and

MSEsa = (log[Psa/(1 - Psa)]- log[image representing thetasa/(1 - image representing thetasa)])2 + posterior variance of log[Psa/(1 - Psa)].

The associated posterior coverage probabilities for these benchmarked intervals are very close to the prescribed 0.95 value because the State small area estimates have posterior distributions that can be approximated exceptionally well by a Gaussian distribution.

A.5 Calculation of Average Annual Incidence of Marijuana Use

Incidence rates typically are calculated as the number of new initiates of a substance during a period of time (such as in the past year) divided by an estimate of the number of person years of exposure (in thousands). The incidence definition used in this report employs a simpler form of the at-risk population based on the model-based methodology. This model-based average annual incidence rate is defined as follows:

Average annual rate = 100*{[X1 ÷ (0.5 * X1 + X2) ] ÷ 2},

where X1 is the number of marijuana initiates in the past 24 months and X2 is the number of persons who never used marijuana.

In this report, the incidence rate is expressed as a percentage or rate per 100 person years of exposure. Note that this estimate uses a 2-year time period to accumulate incidence cases from each annual survey. By assuming further that the distribution of first use for the incidence cases is uniform across the 2-year interval, the total number of person years of exposure is 1 year on average for the incidence cases plus 2 years for all the "never users" at the end of the time period. This approximation to the person years of exposure permits one to recast the incidence rate as a function of two population prevalence rates, namely, the fraction of persons who first used marijuana in the past 2 years and the fraction who had never used marijuana. Both of these prevalence estimates were estimated using the SWHB estimation approach.

The count of persons who first used marijuana in the past 2 years is based on a "moving" 2-year period that ranges over 3 calendar years. Subjects were asked when they first used marijuana. If a person indicated first use of marijuana between the day of the interview and 2 years prior, the person was included in the count. Thus, it is possible for a person interviewed in the first part of 2005 to indicate first use as early as the first part of 2003 or as late as the first part of 2005. Similarly, a subject interviewed in the last part of 2005 could indicate first use as early as the last part of 2003 or as late as the last part of 2005. Therefore, in the 2005 survey, the reported period of first use ranged from early 2003 to late 2005 and was "centered" in 2004. About half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2004, while a quarter each reported first use in 2003 and 2005. Persons who responded in 2005 that they had never used marijuana were included in the count of "never used." Similarly, reports of first use in past 24 months from the 2004 survey ranged from early 2002 to late 2004 and were centered in 2003. Half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2003, while a quarter each reported first use in 2002 and 2004. Note that only incidence rates for marijuana use are provided in this report.

A.6 Underage Drinking

To obtain small area estimates for persons aged 12 to 20 for past month alcohol and binge alcohol use, a separate set of models was fit for these two outcomes for the 12 to 17 age group and the 18 to 20 age group. New variable selection (using the same methodology as described in Section A.3) was done for the 18 to 20 age group. Even though separate models were fit for the 12 to 17 age group along with the 18 to 20 year olds, no new variable selection was done for the 12 to 17 age group. Model-based estimates for persons aged 12 to 20 were produced by taking the population-weighted average of the individual age group (12 to 17 and 18 to 20) estimates. Estimates for underage drinking for past month alcohol and binge alcohol use were benchmarked to match national design-based estimates for that age group using the process described in Section A.4. Estimates of change between the 2003-2004 and 2004-2005 estimates for underage drinking in the States also are presented in this report.

A.7 Serious Psychological Distress

In 2005, serious psychological distress (SPD) was measured using the "short-form" module consisting only of the K6 screening instrument for nonspecific psychological distress (Kessler et al., 2003). In the 2004 NSDUH, the sample of respondents aged 18 or older was split evenly between the "long-form" module, which included all items in the mental health module used in the 2003 NSDUH (sample A) and a "short-form" module consisting only of the K6 items (sample B). For more details on how SPD was measured in the 2004 or earlier NSDUHs and how State estimates for SPD were produced using the pooled 2003-2004 NSDUH data, see Wright and Sathe (2006). The small area estimates presented in prior reports are not comparable because they were based on the "long-form" version of SPD. Hence, measures of change between 2003-2004 and 2004-2005 are not presented in this report.

To produce the pooled 2004-2005 SPD estimates, the 2004 sample A "long-form" scores were transformed to match the distributional characteristics of the 2004 sample B "short-form" scores using the cumulative distribution function (CDF) adjustment method described by Wright and Sathe (2006). These adjusted 2004 sample A scores were used in conjunction with the 2004 sample B "short-form" scores and the 2005 "short-form" SPD scores to produce the 2004-2005 SPD estimates.

A.8 Major Depressive Episode

Beginning in 2004, a module was included in the questionnaire that was related to having a major depressive episode (MDE); it was derived from the criteria specified for major depression in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) (American Psychiatric Association [APA], 1994). These questions permit estimates to be calculated for lifetime and past year prevalence of MDE, treatment for MDE, and role impairment resulting from MDE. In this report, estimates of having at least one MDE in the past year were the only ones reported.

In 2004, a split-sample design was implemented where adults aged 18 or older in half of the sample (sample B) received the depression module, while adult respondents in the other half (sample A) did not. All adolescents aged 12 to 17 were administered the adolescent depression module. In 2005, however, all adult and adolescent respondents were administered their respective depression modules. Due to minor wording differences in the questions in the adult and adolescent MDE modules, data from youths aged 12 to 17 were not combined with data from persons aged 18 or older to get an overall estimate for those aged 12 or older. Instead, an estimate for those aged 18 or older was produced. To produce the pooled 2004-2005 MDE estimates, the 2005 MDE data were pooled with the 2004 sample B MDE data. Because the 2004 sample A was not used in the estimation process, the 2004 sample B weights were properly adjusted to account for the missing 2004 sample A MDE data.

According to DSM-IV, a person is defined as having had MDE in his or her lifetime if he or she has had at least five or more of the following nine symptoms nearly every day in the same 2-week period, where at least one of the symptoms is a depressed mood or loss of interest or pleasure in daily activities (APA, 1994): (1) depressed mood most of the day; (2) markedly diminished interest or pleasure in all or almost all activities most of the day; (3) significant weight loss when not sick or dieting, or weight gain when not pregnant or growing, or decrease or increase in appetite; (4) insomnia or hypersomnia; (5) psychomotor agitation or retardation; (6) fatigue or loss of energy; (7) feelings of worthlessness; (8) diminished ability to think or concentrate or indecisiveness; and (9) recurrent thoughts of death or suicidal ideation.

For details on the adult and adolescent modules for MDE, see Section B.4.5 in Appendix B of the 2005 NSDUH's national results report (Office of Applied Studies [OAS], 2006b, pp. 125-129). Because MDE was not asked about in 2003 and prior years, no estimates of change are available for it.

A.9 Measuring Change in State Estimates between 2003-2004 and 2004-2005

The estimates of change between State estimates displayed in Appendix C are based on the 2003 through 2005 NSDUHs. The State estimates for 2003-2004 are the previously published model-based small area estimates (see Wright & Sathe, 2006). The State estimates for 2004-2005 are the small area estimates given in Appendix B. The moving average State prevalence estimates for the overlapping 2003-2004 and 2004-2005 time periods were obtained from independent applications of RTI's SWHB methodology; that is, the 2004-2005 models were fit independently of the previously fitted 2003-2004 models. This independent analysis approach was followed because there was no desire to revise the previously published estimates. Moreover, the same fixed predictor variables were used in the 2003-2004 and 2004-2005 models, but annual updates were made when more current versions became available. The age group–specific fixed predictor variables were defined at five levels (namely, person-level, 2000 decennial census block group-level, tract-level, county-level, and State-level). Also, each age group model had 51 State-level random effects and 300 substate region–level random effects.

To estimate change in State estimates, let image representing pisa(1) and image representing pisa(2) denote 2003-2004 and 2004-2005 prevalence rates, respectively, for State-s and age group-a. The change between image representing pisa(1) and image representing pi sa(2) is defined in terms of the log-odds ratio (lorsa) as opposed to the simple difference because the posterior distribution of the lorsa is closer to Gaussian than the posterior distribution of the simple difference image representing pisa(2) – image representing pisa(1)). The lorsa is defined as

Equation A-2.     D

The p value given in the Appendix C tables is computed to test the null hypothesis of no change (i.e., image representing pisa(2) = image representing pisa(1) or equivalently lorsa = 0). An estimate of lorsa is given by

Equation A-3     D

where the psa(1) are previously published 2003-2004 State estimates and the psa(2) are the 2004-2005 State estimates presented in this report (see Appendix B). To compute the variance of The estimate of the log-odds ratio, lor hat, sub s and a, i.e., Variance v of the estimate of the log-odds ratio, lor hat, sub s and a let Theta 1 hat is defined as the ratio of p 1 sub s and a and 1 minus p 1 sub s and a and Theta 2 hat is defined as the ratio of p 2 sub s and a and 1 minus p 2 sub s and a then

Equation A-8,     D

where covariance between the logarithm of Theta 1 hat and the logarithm of Theta 2 hat denotes the covariance between image represents the logarithm of Theta 1 hat and image represents the logarithm of Theta 2 hat. This covariance is defined in terms of the associated correlation as follows:

Equation A-10,     D

Note that the variance of the logarithm of Theta 1 hat and variance of the logarithm of Theta 2 hat used here to calculate Variance v of the estimate of the log-odds ratio, lor hat, sub s and a are the same variances used in calculating the previously published 2003-2004 prediction intervals (PIs) and the 2004-2005 PIs given in this report, respectively.

The correlation between image represents the logarithm of Theta 1 hat and image represents the logarithm of Theta 2 hat was obtained by simultaneously modeling the 2003, 2004, and 2005 NSDUH data. This simultaneous modeling approach was adopted based on the results of the validation study (see Appendix E, Section E.2, of Wright, 2003b) conducted for measuring change in 1999-2000 and 2000-2001 State estimates. For this simultaneous model, four age groups by 3 years (i.e., 12 subpopulation-specific models) were fitted, each with its own set of fixed and random effects. In this case, the general covariance matrices for the State and substate random effects were 12 by 12 matrices corresponding to the 12 element (age group by year) vectors of random effects. Note that the survey-weighted Bernoulli-type log likelihood employed in SWHB methodology was appropriate for this simultaneous model because the 12 age group by year subpopulations were nonoverlapping. The correlation [image represents the logarithm of Theta 1 hat, image represents the logarithm of Theta 2 hat] was approximated by the correlation calculated using the posterior distributions of logarithm of Pi 1 sub s and a, divided by 1 minus Pi 1 sub s and a and logarithm of Pi 1 sub s and a, divided by 1 minus Pi 1 sub s and a from the simultaneous model.

To calculate the p value for testing the null hypothesis of no difference (lor = 0), it is assumed that the posterior distribution of lor is normal with Mean is equal to estimate of the log-odds ratio, lor hat, sub s and a and Variance is equal to variance v of the estimate of the log-odds ratio, lor hat, sub s and a. With the null value of lor = 0, the Bayes p value or posterior probability of no difference is p value = 2*P [Z ≥ abs(z)], where Z is a standard normal random variate, Quantity z is the estimate of the log-odds ratio, lor hat, sub s and a, divided by the square room of the variance v of the estimate of the log-odds ratio, lor hat, sub s and a, and abs(z) denotes the absolute value of z.

Table A.1 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2003
State Total Selected DUs Total Eligible DUs Total Completed Screeners Weighted DU Screening Response Rate Total Selected Total Responded Population Estimate Weighted Interview Response Rate Weighted Overall Response Rate
DU = dwelling unit.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2003.
Overall 170,762 143,485 130,605 90.72% 81,631 67,784 237,682,009 77.39% 70.21%
Alabama 2,071 1,712 1,558 91.14% 1,029 879 3,699,723 79.60% 72.55%
Alaska 2,314 1,814 1,666 91.97% 1,098 883 505,278 75.00% 68.98%
Arizona 2,159 1,757 1,662 94.64% 1,057 897 4,473,518 81.20% 76.85%
Arkansas 2,258 1,850 1,767 95.53% 1,092 922 2,228,670 79.84% 76.27%
California 7,687 6,858 6,015 86.86% 4,471 3,600 28,673,990 73.76% 64.07%
Colorado 2,225 1,855 1,709 92.06% 1,103 911 3,701,560 78.79% 72.53%
Connecticut 2,623 2,288 2,073 90.56% 1,128 933 2,880,493 76.25% 69.06%
Delaware 2,419 1,936 1,774 91.59% 1,105 911 671,922 75.12% 68.80%
District of Columbia 3,692 3,078 2,576 83.69% 1,116 949 476,873 80.38% 67.27%
Florida 10,451 8,453 7,575 89.77% 4,414 3,541 14,145,707 73.68% 66.14%
Georgia 2,112 1,734 1,612 92.81% 1,088 902 6,951,437 79.46% 73.74%
Hawaii 2,259 1,953 1,767 90.25% 1,142 928 1,013,259 73.21% 66.07%
Idaho 1,998 1,596 1,509 94.45% 1,112 912 1,099,895 77.63% 73.32%
Illinois 9,163 8,128 6,803 83.45% 4,652 3,711 10,319,948 74.36% 62.05%
Indiana 2,046 1,741 1,637 94.11% 1,082 903 5,049,910 79.37% 74.69%
Iowa 2,035 1,829 1,721 94.16% 993 884 2,448,928 85.81% 80.79%
Kansas 2,042 1,744 1,638 93.94% 1,041 875 2,209,221 81.11% 76.20%
Kentucky 2,266 1,991 1,878 94.25% 1,102 908 3,381,254 75.69% 71.34%
Louisiana 2,084 1,757 1,637 93.12% 1,095 943 3,618,197 81.80% 76.17%
Maine 2,827 2,240 2,045 91.21% 1,094 928 1,113,100 82.07% 74.86%
Maryland 1,899 1,673 1,475 88.04% 1,000 863 4,510,290 82.58% 72.70%
Massachusetts 2,413 2,129 1,878 88.16% 1,220 964 5,377,359 75.04% 66.16%
Michigan 9,000 7,447 6,709 90.14% 4,353 3,667 8,316,442 79.06% 71.26%
Minnesota 2,029 1,801 1,673 92.73% 1,052 909 4,193,331 82.14% 76.17%
Mississippi 2,196 1,732 1,650 95.33% 1,078 899 2,311,859 78.81% 75.13%
Missouri 2,495 2,042 1,912 93.64% 1,105 932 4,683,914 81.99% 76.77%
Montana 2,384 1,871 1,766 94.40% 1,068 911 767,946 79.57% 75.12%
Nebraska 1,996 1,716 1,622 94.51% 1,071 918 1,418,952 79.62% 75.25%
Nevada 2,071 1,751 1,663 94.91% 1,072 902 1,818,116 79.78% 75.71%
New Hampshire 2,015 1,688 1,568 92.94% 1,112 910 1,082,138 76.29% 70.90%
New Jersey 2,564 2,287 1,981 86.56% 1,126 883 7,118,305 72.97% 63.17%
New Mexico 2,260 1,822 1,740 95.42% 1,132 944 1,520,180 77.03% 73.50%
New York 9,973 8,575 7,205 83.97% 4,609 3,634 15,948,708 71.96% 60.42%
North Carolina 2,239 1,852 1,753 94.65% 1,086 904 6,805,722 79.21% 74.98%
North Dakota 2,072 1,714 1,619 94.57% 977 867 525,140 87.43% 82.69%
Ohio 8,874 7,690 7,246 94.17% 4,313 3,559 9,433,820 75.91% 71.49%
Oklahoma 2,455 1,972 1,812 91.80% 1,042 871 2,846,785 78.62% 72.17%
Oregon 2,102 1,853 1,760 94.94% 1,095 912 2,970,969 79.79% 75.75%
Pennsylvania 9,866 8,252 7,482 90.76% 4,214 3,572 10,356,055 80.56% 73.12%
Rhode Island 2,255 1,991 1,772 88.58% 1,141 914 903,348 75.20% 66.61%
South Carolina 2,205 1,807 1,723 95.45% 1,109 920 3,384,520 79.64% 76.02%
South Dakota 2,154 1,749 1,660 94.78% 980 881 621,498 86.26% 81.76%
Tennessee 2,290 1,978 1,864 94.27% 1,004 856 4,823,157 79.89% 75.32%
Texas 7,901 6,466 6,079 94.03% 4,231 3,566 17,432,369 79.14% 74.42%
Utah 1,623 1,392 1,325 95.14% 995 898 1,816,737 87.98% 83.71%
Vermont 2,638 2,047 1,909 93.19% 1,092 917 530,133 79.87% 74.43%
Virginia 2,168 1,908 1,667 87.33% 1,076 907 5,951,031 78.61% 68.65%
Washington 2,475 2,033 1,920 94.43% 1,128 941 5,053,331 78.65% 74.28%
West Virginia 2,923 2,384 2,236 93.83% 1,058 871 1,534,650 78.86% 74.00%
Wisconsin 2,282 1,793 1,655 92.28% 1,046 887 4,546,217 77.76% 71.76%
Wyoming 2,214 1,756 1,659 94.48% 1,032 885 416,105 84.33% 79.67%

Table A.2 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2003
State 12-17 18-25 26+
Total Selected Total Responded Population Estimate Weighted Interview Response Rate Total Selected Total Responded Population Estimate Weighted Interview Response Rate Total Selected Total Responded Population Estimate Weighted Interview Response Rate
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2003.
Overall 25,387 22,696 24,995,357 89.57% 27,259 22,941 31,728,286 83.47% 28,985 22,147 180,958,366 74.63%
Alabama 324 297 382,688 92.61% 394 340 501,543 86.10% 311 242 2,815,492 76.33%
Alaska 348 298 68,750 86.80% 378 314 67,522 82.66% 372 271 369,006 71.30%
Arizona 346 314 493,252 91.48% 377 317 611,163 84.15% 334 266 3,369,104 78.82%
Arkansas 352 320 233,744 91.18% 356 301 304,728 85.42% 384 301 1,690,198 77.24%
California 1,381 1,236 3,161,827 89.71% 1,463 1,195 3,928,708 81.65% 1,627 1,169 21,583,456 69.91%
Colorado 327 292 385,020 88.53% 379 305 499,513 79.29% 397 314 2,817,027 77.43%
Connecticut 313 279 292,982 88.47% 423 353 331,774 83.64% 392 301 2,255,738 73.62%
Delaware 344 305 68,298 88.69% 373 315 89,106 84.55% 388 291 514,518 71.54%
District of Columbia 370 326 32,832 88.64% 373 326 73,453 87.28% 373 297 370,589 78.33%
Florida 1,377 1,203 1,360,537 87.23% 1,418 1,171 1,626,149 81.73% 1,619 1,167 11,159,021 71.02%
Georgia 342 308 756,648 88.43% 323 267 959,782 84.93% 423 327 5,235,007 77.32%
Hawaii 388 353 100,981 90.91% 329 275 121,594 83.63% 425 300 790,684 69.33%
Idaho 331 299 128,037 90.50% 348 287 166,977 81.40% 433 326 804,881 74.87%
Illinois 1,423 1,238 1,083,365 86.69% 1,537 1,242 1,395,959 81.48% 1,692 1,231 7,840,623 71.43%
Indiana 338 308 545,217 90.65% 365 292 710,330 79.87% 379 303 3,794,364 77.73%
Iowa 329 304 245,539 89.91% 333 292 353,759 87.71% 331 288 1,849,631 84.81%
Kansas 317 280 240,109 87.93% 363 309 322,145 84.48% 361 286 1,646,967 79.40%
Kentucky 349 306 337,609 86.98% 349 293 451,685 83.75% 404 309 2,591,960 72.97%
Louisiana 353 321 405,066 92.36% 382 335 541,507 86.50% 360 287 2,671,623 79.32%
Maine 345 304 110,584 87.73% 388 330 132,168 86.27% 361 294 870,349 80.84%
Maryland 318 292 481,268 90.86% 280 237 547,577 83.87% 402 334 3,481,445 81.21%
Massachusetts 344 303 514,569 88.08% 414 324 674,611 76.98% 462 337 4,188,180 73.23%
Michigan 1,336 1,196 898,823 89.25% 1,536 1,323 1,104,530 86.20% 1,481 1,148 6,313,089 76.36%
Minnesota 393 357 445,182 91.19% 311 270 581,147 85.52% 348 282 3,167,002 80.08%
Mississippi 310 284 257,972 93.11% 347 293 348,335 85.15% 421 322 1,705,552 75.67%
Missouri 363 312 493,755 86.13% 385 329 635,283 85.62% 357 291 3,554,877 80.74%
Montana 308 272 81,338 88.05% 395 350 105,014 88.66% 365 289 581,594 76.60%
Nebraska 325 295 152,127 91.02% 404 351 207,187 86.79% 342 272 1,059,638 76.51%
Nevada 306 278 187,341 90.35% 364 312 222,655 86.49% 402 312 1,408,120 77.26%
New Hampshire 328 288 114,288 88.06% 399 332 132,490 83.61% 385 290 835,361 73.63%
New Jersey 326 288 726,704 88.67% 373 287 807,111 75.67% 427 308 5,584,490 70.62%
New Mexico 354 319 177,001 90.44% 365 316 213,899 87.67% 413 309 1,129,280 73.13%
New York 1,392 1,232 1,559,994 88.11% 1,534 1,227 2,046,657 80.51% 1,683 1,175 12,342,057 68.43%
North Carolina 324 285 693,740 88.12% 420 352 884,534 84.21% 342 267 5,227,448 77.02%
North Dakota 285 259 54,050 91.09% 309 276 82,629 89.55% 383 332 388,461 86.51%
Ohio 1,356 1,199 984,255 88.08% 1,435 1,229 1,244,999 85.43% 1,522 1,131 7,204,566 72.56%
Oklahoma 374 329 300,218 88.45% 316 272 413,370 84.45% 352 270 2,133,197 75.75%
Oregon 345 313 296,519 90.45% 377 309 390,879 82.15% 373 290 2,283,571 78.02%
Pennsylvania 1,367 1,232 1,030,859 90.72% 1,350 1,160 1,309,752 85.92% 1,497 1,180 8,015,444 78.25%
Rhode Island 361 308 86,777 85.36% 375 313 127,775 84.68% 405 293 688,797 71.97%
South Carolina 343 307 354,988 89.36% 373 311 458,297 82.69% 393 302 2,571,235 77.80%
South Dakota 301 281 69,339 94.03% 344 315 92,111 92.37% 335 285 460,048 83.73%
Tennessee 346 324 474,491 93.33% 270 223 632,850 80.82% 388 309 3,715,817 77.93%
Texas 1,279 1,153 2,033,118 90.38% 1,414 1,222 2,546,961 86.63% 1,538 1,191 12,852,291 75.82%
Utah 304 286 231,320 94.61% 321 301 357,456 94.31% 370 311 1,227,961 85.08%
Vermont 351 306 53,957 87.12% 355 306 71,119 85.94% 386 305 405,058 77.88%
Virginia 324 298 614,433 91.96% 368 311 749,393 82.44% 384 298 4,587,205 76.33%
Washington 369 344 527,057 93.61% 390 321 666,923 82.04% 369 276 3,859,351 75.89%
West Virginia 324 281 139,083 86.58% 371 306 195,671 82.42% 363 284 1,199,896 77.34%
Wisconsin 291 271 482,916 92.43% 405 349 627,502 85.36% 350 267 3,435,798 74.33%
Wyoming 343 313 44,796 92.11% 308 255 60,007 84.13% 381 317 311,302 83.18%

Table A.3 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2004
State Total Selected DUs Total Eligible DUs Total Completed Screeners Weighted DU Screening Response Rate Total Selected Total Responded Population Estimate Weighted Interview Response Rate Weighted Overall Response Rate
DU = dwelling unit.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2004.
Overall 169,514 142,612 130,130 90.92% 81,973 67,760 240,514,815 77.00% 70.01%
Alabama 1,991 1,611 1,477 91.72% 1,055 880 3,740,924 74.76% 68.57%
Alaska 1,902 1,525 1,399 91.61% 1,078 894 511,059 79.21% 72.57%
Arizona 2,226 1,858 1,750 94.21% 1,119 903 4,616,821 77.92% 73.41%
Arkansas 2,369 1,933 1,833 94.83% 1,062 900 2,259,150 80.09% 75.95%
California 7,911 6,957 6,192 88.60% 4,631 3,725 29,016,735 72.88% 64.57%
Colorado 2,207 1,822 1,712 93.92% 1,135 934 3,735,710 77.90% 73.17%
Connecticut 2,493 2,209 2,013 90.99% 1,098 897 2,901,872 75.85% 69.02%
Delaware 2,253 1,954 1,794 91.90% 1,144 932 688,666 77.70% 71.41%
District of Columbia 3,155 2,606 2,242 86.24% 1,041 903 466,433 82.55% 71.19%
Florida 10,456 8,488 7,581 88.99% 4,526 3,662 14,478,448 73.89% 65.75%
Georgia 2,141 1,752 1,597 91.32% 1,054 890 7,063,198 80.38% 73.41%
Hawaii 1,959 1,715 1,575 91.94% 1,088 903 1,014,184 77.42% 71.18%
Idaho 2,015 1,704 1,607 94.31% 1,051 902 1,125,089 82.42% 77.74%
Illinois 8,457 7,458 6,342 85.01% 4,444 3,575 10,387,581 75.12% 63.86%
Indiana 2,176 1,833 1,742 95.05% 1,085 891 5,098,367 77.64% 73.79%
Iowa 1,990 1,745 1,641 94.14% 1,039 890 2,468,073 81.10% 76.35%
Kansas 2,294 1,953 1,841 94.22% 993 828 2,226,734 78.58% 74.04%
Kentucky 2,372 2,059 1,949 94.67% 1,144 933 3,421,489 73.82% 69.88%
Louisiana 2,106 1,713 1,614 94.17% 1,082 933 3,646,863 81.16% 76.43%
Maine 2,731 2,168 2,025 93.40% 1,064 896 1,127,062 81.46% 76.08%
Maryland 2,122 1,855 1,617 86.77% 1,039 901 4,557,984 81.39% 70.63%
Massachusetts 2,218 1,895 1,686 89.13% 1,087 877 5,380,703 76.92% 68.56%
Michigan 9,530 7,969 7,155 89.78% 4,490 3,670 8,364,197 75.61% 67.88%
Minnesota 2,001 1,714 1,578 91.98% 1,066 907 4,237,627 83.72% 77.00%
Mississippi 1,931 1,549 1,482 95.71% 1,053 914 2,341,802 80.45% 77.00%
Missouri 2,190 1,872 1,764 94.23% 1,104 897 4,751,346 77.96% 73.46%
Montana 2,511 1,990 1,874 94.18% 1,080 907 781,536 79.58% 74.95%
Nebraska 2,044 1,729 1,629 94.21% 1,072 897 1,430,465 80.70% 76.03%
Nevada 1,903 1,641 1,552 93.71% 1,053 888 1,898,843 78.32% 73.39%
New Hampshire 2,348 1,908 1,765 92.38% 1,114 904 1,095,589 76.40% 70.58%
New Jersey 2,764 2,359 2,033 85.50% 1,153 886 7,172,774 72.04% 61.60%
New Mexico 2,190 1,799 1,719 95.54% 1,072 922 1,552,672 80.98% 77.37%
New York 10,475 8,940 7,372 82.28% 4,585 3,638 15,978,304 73.79% 60.72%
North Carolina 2,185 1,733 1,635 94.33% 1,029 869 6,927,805 79.39% 74.89%
North Dakota 2,576 2,128 2,020 94.95% 1,071 911 530,030 81.21% 77.11%
Ohio 8,599 7,463 7,026 94.14% 4,404 3,613 9,489,788 76.91% 72.40%
Oklahoma 2,382 1,889 1,769 93.71% 1,054 867 2,867,524 76.21% 71.42%
Oregon 2,234 1,931 1,825 94.50% 1,108 910 3,001,872 76.30% 72.10%
Pennsylvania 9,599 8,236 7,448 90.44% 4,360 3,590 10,399,693 77.05% 69.68%
Rhode Island 2,030 1,785 1,588 89.11% 1,126 911 907,154 76.31% 68.00%
South Carolina 2,392 1,946 1,844 94.73% 1,042 885 3,437,860 81.78% 77.47%
South Dakota 2,024 1,674 1,594 95.24% 1,034 893 630,156 82.20% 78.30%
Tennessee 2,387 2,049 1,933 94.37% 1,023 896 4,888,070 85.51% 80.70%
Texas 7,923 6,599 6,254 94.72% 4,334 3,631 17,783,855 79.21% 75.03%
Utah 1,718 1,464 1,389 94.70% 1,040 910 1,851,896 83.73% 79.28%
Vermont 2,689 1,954 1,820 93.02% 1,087 924 534,195 81.75% 76.04%
Virginia 2,060 1,773 1,587 89.40% 1,080 902 6,027,395 79.88% 71.41%
Washington 1,998 1,769 1,677 94.81% 1,086 886 5,134,850 75.97% 72.03%
West Virginia 2,721 2,173 2,049 94.31% 1,058 909 1,543,726 79.17% 74.67%
Wisconsin 2,338 1,944 1,805 92.86% 1,118 917 4,597,266 77.89% 72.33%
Wyoming 2,228 1,819 1,715 94.28% 1,018 857 423,382 81.54% 76.88%

Table A.4 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2004
State 12-17 18-25 26+
Total Selected Total Responded Population Estimate Weighted Interview Response Rate Total Selected Total Responded Population Estimate Weighted Interview Response Rate Total Selected Total Responded Population Estimate Weighted Interview Response Rate
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2004.
Overall 25,141 22,309 25,214,390 88.56% 27,408 23,075 32,193,946 83.87% 29,424 22,376 183,106,479 74.22%
Alabama 335 300 380,438 88.15% 317 277 506,024 87.42% 403 303 2,854,462 70.97%
Alaska 343 301 68,234 87.37% 376 308 71,635 80.38% 359 285 371,190 77.66%
Arizona 355 307 504,134 86.71% 356 280 632,441 79.72% 408 316 3,480,247 76.36%
Arkansas 336 301 232,624 89.62% 372 312 309,270 83.12% 354 287 1,717,256 78.00%
California 1,408 1,251 3,256,862 88.81% 1,523 1,259 3,971,071 82.89% 1,700 1,215 21,788,802 68.82%
Colorado 339 309 392,567 92.63% 435 358 502,509 81.78% 361 267 2,840,634 75.05%
Connecticut 351 310 297,475 88.74% 341 290 340,627 82.95% 406 297 2,263,770 72.90%
Delaware 344 296 67,017 86.70% 402 330 91,920 81.90% 398 306 529,729 75.84%
District of Columbia 324 291 33,936 90.50% 369 328 67,513 88.13% 348 284 364,984 80.63%
Florida 1,422 1,248 1,392,381 88.13% 1,426 1,197 1,690,586 83.29% 1,678 1,217 11,395,480 70.76%
Georgia 310 281 770,391 90.24% 384 325 974,428 85.42% 360 284 5,318,379 77.85%
Hawaii 314 290 100,117 92.32% 374 313 121,874 84.55% 400 300 792,193 74.27%
Idaho 310 279 127,641 90.53% 362 318 170,720 87.99% 379 305 826,729 80.03%
Illinois 1,316 1,166 1,096,436 89.10% 1,483 1,214 1,405,081 81.40% 1,645 1,195 7,886,063 72.15%
Indiana 339 284 547,820 80.66% 370 321 712,431 87.14% 376 286 3,838,117 75.51%
Iowa 354 319 241,677 90.80% 322 283 354,834 89.24% 363 288 1,871,562 78.38%
Kansas 309 279 235,602 90.08% 331 278 326,635 84.04% 353 271 1,664,497 75.58%
Kentucky 338 297 336,208 88.01% 379 324 454,337 85.35% 427 312 2,630,944 70.36%
Louisiana 315 288 401,563 91.61% 384 345 546,374 89.71% 383 300 2,698,926 77.88%
Maine 325 292 109,324 88.79% 378 310 136,314 82.23% 361 294 881,424 80.39%
Maryland 331 311 490,535 94.06% 350 299 564,517 86.07% 358 291 3,502,932 78.60%
Massachusetts 320 280 511,108 87.59% 372 304 678,194 81.46% 395 293 4,191,401 74.97%
Michigan 1,441 1,273 906,283 88.40% 1,503 1,266 1,113,043 83.80% 1,546 1,131 6,344,871 72.25%
Minnesota 346 305 440,475 87.61% 333 280 594,051 85.11% 387 322 3,203,101 82.96%
Mississippi 292 276 255,992 94.84% 415 367 350,329 88.32% 346 271 1,735,480 76.64%
Missouri 349 296 488,189 84.08% 355 293 650,694 81.04% 400 308 3,612,463 76.59%
Montana 320 277 78,581 87.14% 373 324 108,216 85.88% 387 306 594,739 77.30%
Nebraska 266 236 149,210 88.31% 413 342 210,327 82.97% 393 319 1,070,927 79.27%
Nevada 307 281 197,330 89.52% 356 307 234,194 87.69% 390 300 1,467,319 75.18%
New Hampshire 340 292 115,175 86.06% 335 285 136,081 83.41% 439 327 844,334 74.06%
New Jersey 308 265 741,001 83.21% 393 297 825,494 76.88% 452 324 5,606,279 70.03%
New Mexico 341 315 173,978 91.56% 333 296 222,316 88.48% 398 311 1,156,379 77.93%
New York 1,345 1,144 1,583,424 85.11% 1,564 1,275 2,048,409 81.31% 1,676 1,219 12,346,471 71.15%
North Carolina 336 307 710,225 91.75% 338 285 893,651 84.47% 355 277 5,323,929 76.67%
North Dakota 350 314 51,236 89.71% 368 315 83,256 84.18% 353 282 395,539 79.50%
Ohio 1,418 1,243 982,106 87.60% 1,428 1,186 1,258,053 83.17% 1,558 1,184 7,249,629 74.37%
Oklahoma 325 288 293,667 89.22% 386 324 417,990 85.22% 343 255 2,155,867 72.18%
Oregon 349 311 297,975 88.86% 365 309 394,016 85.49% 394 290 2,309,881 72.97%
Pennsylvania 1,314 1,177 1,037,595 89.81% 1,433 1,197 1,321,982 84.56% 1,613 1,216 8,040,116 74.30%
Rhode Island 342 285 87,882 85.57% 377 326 127,105 86.38% 407 300 692,166 73.19%
South Carolina 349 307 357,948 87.80% 292 258 463,134 89.41% 401 320 2,616,779 79.59%
South Dakota 277 257 67,385 91.02% 387 346 94,182 89.49% 370 290 468,590 79.79%
Tennessee 295 273 476,738 91.61% 341 298 640,352 88.72% 387 325 3,770,980 84.21%
Texas 1,350 1,205 2,044,166 89.33% 1,444 1,236 2,607,359 85.92% 1,540 1,190 13,132,330 76.31%
Utah 348 324 227,860 93.80% 343 301 354,811 86.85% 349 285 1,269,225 80.91%
Vermont 354 318 53,165 89.86% 350 295 70,039 87.11% 383 311 410,991 79.80%
Virginia 296 268 619,572 89.10% 374 310 765,684 80.74% 410 324 4,642,140 78.68%
Washington 345 301 527,781 86.17% 378 311 685,109 80.79% 363 274 3,921,960 73.76%
West Virginia 313 285 137,455 91.56% 355 319 194,513 90.54% 390 305 1,211,758 76.03%
Wisconsin 382 339 474,936 89.82% 342 273 638,512 80.49% 394 305 3,483,818 75.77%
Wyoming