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2003 State Estimates of Substance Use

Appendix A: State Estimation Methodology

This report includes estimates of 21 substance use measures (see Section A.1) using the combined data from the 2002 and 2003 National Surveys on Drug Use and Health (NSDUHs). In addition to the 20 substance use measures for which age group-specific State estimates were produced and documented in the 2002 State report (Wright, 2004), there was a new measure (past year marijuana use) introduced in 2003. The 2000 and 2001 State reports (Wright, 2002a, 2002b, 2003a, 2003b) contained age group-specific State estimates obtained by pooling 1999–2000 and 2000–2001 National Household Survey on Drug Abuse (NHSDA) data, respectively. The 2001 State report also contained estimates of change between the 1999–2000 and 2000–2001 data for the 12 common substance use measures.

In 2002, several changes were introduced to the survey. Incentive payments of $30 were given to respondents for the first time in order to address concerns about the national and State response rates. Other changes included a change in the survey name, new data collection quality control procedures, and a shift from the 1990 decennial census to the 2000 census as a basis for population count totals and to calculate any census-related predictor variables that are used in the estimation. These changes and others improved the quality of the data provided by the survey, with the most notable result being the increase in the weighted interview response rate from 73.3 percent in 2001 (Table E.20, Wright, 2003b) to 78.6 percent in 2002 (see Table A.1 in this report).

An unanticipated result of these changes was that the prevalence rates for 2002 were in general substantially higher than those for 2001—substantially higher than could be attributable to the usual year-to-year trend—and thus are not comparable with estimates for 2001 and prior years.1 Therefore, the 2002 NSDUH was established as a new baseline for the State, as well as national, estimates. Given the varying effects of the incentive and other changes on the States, not only are the estimates for 2002 and later years not comparable with prior years, but also the relative rankings of States may have been affected. Therefore, the rankings of States for 2002–2003 should not be compared with those for prior years.

The survey-weighted hierarchical Bayes (SWHB) methodology used in the production of State estimates from the 1999–2002 surveys also was used in the production of the 2002–2003 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 2003 small area estimation (SAE) modeling is given in Section A.2. The improved methodology used to select relevant predictors is described 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 2002, 2003, and 2002–2003 survey response rates are included (Tables A.1 to A.6). It should be noted that smaller sample sizes and response rates were attained in Mississippi, Nevada, and New Mexico in 2002 because the review of completed records determined a number of those interviews to be fraudulent. These interviews were consequently dropped from the 2002 NSDUH data.

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, due to many reasons, such as 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 year 2002 was the first year in which most of the predictors used in the SAE modeling were based on the 2000 census rather than the 1990 census. The impact on the estimates is described in Section A.5. Section A.6 includes the definition and explanation of the formula used in estimating the marijuana incidence rate.

A.1. Variables Modeled

In the 2002–2003 NSDUHs, age group-specific State estimates were produced for the following set of 21 binary (0, 1) substance use measures:

  1. past month use of any illicit drug,
  2. past year use of marijuana,
  3. past month use of marijuana,
  4. perceptions of great risk of smoking marijuana once a month,
  5. average annual rates of first use of marijuana,
  6. past month use of any illicit drug other than marijuana,
  7. past year use of cocaine,
  8. past month use of alcohol,
  9. past month binge alcohol use,
  10. perceptions of great risk of having five or more drinks of an alcoholic beverage once or twice a week,
  11. past month use of any tobacco product,
  12. past month use of cigarettes,
  13. perceptions of great risk of smoking one or more packs of cigarettes per day,
  14. past year alcohol dependence or abuse,
  15. past year alcohol dependence,
  16. past year any illicit drug dependence or abuse,
  17. past year any illicit drug dependence,
  18. past year dependence on or abuse of any illicit drug or alcohol,
  19. needing but not receiving treatment for illicit drug problems in the past year,
  20. needing but not receiving treatment for alcohol problems in the past year, and
  21. past year serious mental illness (SMI).

A.2. Predictors Used in Logistic Regression Models

Local area data used as potential predictor variables in the mixed logistic regression models were obtained from several sources, including Claritas, the U.S. Bureau of the Census, 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 major list of sources and potential data items used in the modeling are provided below.

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 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 less than equal to symbol18 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 9–12 years of school, no high school diploma Tract
% Population 0–8 years of school Tract
% Population with associate's degree Tract
% Population 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 Sample Person
=1 if non-Hispanic Black, =0 otherwise Sample Person
=1 if non-Hispanic Other, =0 otherwise Sample Person
=1 if male, =0 if female 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

To produce small area estimates based on the pooled 2002 and 2003 NSDUH data, the fixed effect predictors were selected using the following methodology:

  1. There were 135,910 respondents in the pooled 2002 and 2003 NSDUH data. Any variable selection performed on such a large dataset would result in an excessive number of predictors in the final model. To avoid this and build parsimonious models, the pooled data were partitioned into modeling and validation samples. The modeling sample was first used to get a preliminary list of significant predictors using the variable selection methodology described below. These predictors were further reduced by using SUDAAN® logistic regression on the validation dataset resulting in parsimonious models.

    2. According to the 1999–2003 NSDUH sample design, there were 12 field interviewer (FI) regions in each of the 42 small States and the District of Columbia. Each of the eight large-sample States had 48 FI regions. Also, each FI region was expected to have four quarterly samples, each comprising two area segments (group of blocks). A 50 percent overlap in segments within each successive 2–year period from 1999 through 2003 was maintained. Let S1 denote the set of all segments present in the 2002 NSDUH sample and S2 denote the set of all segments present in 2003 NSDUH sample. Also, let S be the set of common segments between the 2002 and 2003 NSDUH samples. Let U1(S) and U2(S) denote groups of survey respondents belonging to the common segments in the 2002 and 2003 NSDUH samples, respectively. Then the modeling sample is created as the union of U1(S) and U2 (S2–S) and the validation sample is created by taking the union of U2(S) and U1 (S1–S) where U1 (S1–S) and U2 (S2-S) represent groups of survey respondents who belonged to the uncommon segments in 2002 and 2003 NSDUH samples. The modeling sample (hence referred to as sample 1) had 68,540 respondents, whereas the validation sample (hence referred to as sample 2) had 67,370 respondents. Also, both of the samples contained respondents from both of the survey years, which minimized the chance of selecting year-specific predictors at the first stage of modeling. Both the samples mimicked the annual NSDUH design by having two selected area segments per quarter for each FI region.

  2. Separate SAS® stepwise logistic regression models were fit to sample 1 for all outcomes by four age group domains. The input list to these models included all linear polynomials (constructed from continuous predictor variables) and other categorical or indicator variables given in Section A.2. All predictors that were significant at 5 percent (except in a few cases, where the 10 percent level was chosen) then were input to the 3rd step of variable selection.

  3. Using sample 1, almost all significant predictors from step 2 then were input to AnswerTree® to identify significant higher order (at most three-way) interaction terms. AnswerTree® is an SPSS® software package that uses decision-tree algorithms to build classification systems. The exhaustive chi-squared automatic interaction detector algorithm (CHAID) was used to create the trees. The constraints for making a tree were maximum depth = 3; minimum number of records in parent node = 1,000; minimum number of records in child node = 300; and splitting criterion = 3 percent.

  4. All the significant variables from step 2 along with their corresponding higher order polynomials (quadratic and cubic), interaction of gender and race, and the significant interactions detected by AnswerTree® in step 3 then were input to SAS® stepwise logistic regression models, run on sample 1. All predictors that remained significant at 5 percent (except in a few cases, where the 10 percent level was chosen) then were input to the 5th step of variable selection.

  5. All significant variables from step 4 were input to SUDAAN® logistic regression models fit to the validation sample 2, and predictors that remained significant at the 5 percent level (except in a few cases, where the 10 percent level was chosen) were input to PROC GIBBS and PROC GSTAT software. In all mixed logistic models, race and gender were forced.

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 credible interval limits.

Exploring this issue further, 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 2002–2003) 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 prediction intervals (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 M S E sub s a

Usa = log[image representing thetasa/(1 - image representing thetasa)] + 1.96 * image representing the square root of M S E sub s 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. Change to the 2000 Census

In 2002, all census variables used in the national prediction models were updated from the 1990 census to the 2000 census. To compare the updated prediction results with the 1990 prediction estimates, small area estimates were estimated for five substances (past month alcohol, past month cigarettes, past month marijuana, past month any illicit drug, and past year cocaine) by four age groups (12 to 17, 18 to 25, 26 to 34, 35 or older), first based upon the 1990 census and then the 2000 census, using the identical set of predictors in both cases. Comparing residual variances (random effects) for the models fit using the two census' data; the 2000 census-based models had a smaller residual (a better fit) in all but 3 of the 20 substance-by-age groups. The 18 to 25 age group and the 26 to 34 age group had a better fit for all five substances, the 35 or older age group was better for four out of five substances, and the 12 to 17 age group was better for three out of five substances.

A.6. Calculation of Average Annual Incidence of Marijuana Use

Incidence rates are typically calculated as the number of new initiates of a substance during a period of time (such as in the past year) divided by the estimate of the number of person years of exposure (in thousands). The incidence definition in this report is the result of a simpler definition based on the model-based methodology and is as follows:

Average annual incidence rate = {(Number of marijuana initiates in past 24 months) /
[(Number of marijuana initiates in past 24 months * 0.5) +
Number of persons who never used marijuana]} / 2.

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 survey-weighted hierarchical Bayes 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 2003 to indicate first use as early as the first part of 2001 or as late as the first part of 2003. Similarly, a subject interviewed in the last part of 2003 could indicate first use as early as the last part of 2001 or as late as the last part of 2003. Therefore, in the 2003 survey, the reported period of first use ranged from early 2001 to late 2003 and was "centered" in 2002. About half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2002, while a quarter each reported first use in 2001 and 2003. Persons who responded in 2003 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 2002 survey ranged from early 2000 to late 2002 and were centered in 2001. Half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2001, while a quarter each reported first use in 2000 and 2002. Note that only incidence rates for marijuana use are provided in this report.

 

Table A.1 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2002
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
Overall 178,013 150,162 136,349 90.72% 80,581 68,126 235,143,245 78.56% 71.27%
Alabama 2,403 2,028 1,852 91.31% 1,103 960 3,686,602 81.85% 74.74%
Alaska 2,408 1,898 1,751 92.13% 1,067 915 496,025 82.05% 75.59%
Arizona 2,346 1,908 1,770 92.66% 1,078 924 4,361,020 79.66% 73.81%
Arkansas 2,540 2,102 2,005 95.28% 1,054 877 2,216,033 76.09% 72.50%
California 8,425 7,601 6,816 89.60% 4,363 3,599 28,231,483 74.93% 67.14%
Colorado 2,099 1,827 1,664 91.01% 1,087 914 3,655,496 81.67% 74.32%
Connecticut 2,718 2,440 2,227 91.44% 1,188 977 2,827,588 76.73% 70.16%
Delaware 2,585 2,116 1,908 89.64% 1,159 964 665,926 78.55% 70.42%
District of Columbia 3,701 3,100 2,608 84.08% 979 864 482,635 84.79% 71.29%
Florida 10,742 8,622 7,723 89.47% 4,340 3,653 13,832,088 77.23% 69.10%
Georgia 2,206 1,896 1,660 87.50% 1,066 897 6,842,168 77.76% 68.04%
Hawaii 2,276 1,942 1,759 90.38% 1,111 925 962,485 76.50% 69.14%
Idaho 2,033 1,634 1,515 92.80% 1,052 907 1,074,515 82.81% 76.86%
Illinois 9,263 8,181 6,986 85.45% 4,613 3,729 10,258,735 75.32% 64.36%
Indiana 2,261 1,961 1,856 94.61% 1,123 945 5,019,711 77.60% 73.42%
Iowa 2,252 1,939 1,835 94.68% 1,028 894 2,440,614 84.42% 79.93%
Kansas 1,933 1,683 1,579 93.86% 1,041 898 2,202,285 81.96% 76.92%
Kentucky 2,641 2,273 2,155 94.79% 1,098 909 3,395,143 79.55% 75.41%
Louisiana 2,189 1,816 1,701 93.64% 1,070 930 3,607,669 84.44% 79.07%
Maine 2,828 2,290 2,082 90.85% 1,017 906 1,104,764 87.35% 79.36%
Maryland 1,984 1,801 1,610 89.42% 1,039 919 4,449,299 81.71% 73.07%
Massachusetts 2,567 2,216 1,930 86.95% 1,142 916 5,387,071 71.93% 62.55%
Michigan 9,820 8,073 7,414 91.75% 4,432 3,792 8,255,399 81.82% 75.06%
Minnesota 2,173 1,895 1,765 93.09% 996 873 4,154,504 83.23% 77.48%
Mississippi1 2,261 1,750 1,508 86.58% 988 839 2,307,320 77.37% 66.99%
Missouri 2,725 2,236 2,098 93.87% 1,039 890 4,656,459 82.05% 77.02%
Montana 2,772 2,174 2,057 94.64% 1,075 914 759,543 81.98% 77.58%
Nebraska 1,954 1,746 1,652 94.59% 1,042 891 1,411,983 82.01% 77.57%
Nevada1 2,534 2,069 1,956 94.67% 1,147 954 1,742,004 73.54% 69.62%
New Hampshire 2,597 2,154 1,966 91.27% 1,092 910 1,065,165 78.10% 71.28%
New Jersey 2,554 2,290 2,042 89.28% 1,065 854 7,075,581 74.61% 66.61%
New Mexico1 1,950 1,586 1,236 77.38% 794 674 1,500,281 81.83% 63.32%
New York 10,480 9,032 7,516 83.31% 4,615 3,716 15,882,822 73.14% 60.94%
North Carolina 2,289 1,940 1,792 92.57% 1,046 902 6,726,205 80.99% 74.98%
North Dakota 2,307 1,873 1,770 94.52% 1,011 913 527,574 84.91% 80.26%
Ohio 9,194 7,970 7,476 93.76% 4,221 3,554 9,369,125 78.58% 73.68%
Oklahoma 2,300 1,932 1,791 92.64% 1,100 922 2,822,615 78.63% 72.84%
Oregon 2,456 2,158 2,019 93.43% 1,071 917 2,916,974 80.74% 75.44%
Pennsylvania 10,104 8,482 7,710 90.86% 4,251 3,606 10,298,942 79.56% 72.29%
Rhode Island 2,458 2,117 1,883 89.14% 1,107 925 896,699 74.12% 66.07%
South Carolina 2,332 1,824 1,729 94.77% 1,091 913 3,371,646 80.90% 76.67%
South Dakota 2,053 1,717 1,632 95.03% 1,013 914 619,768 86.83% 82.52%
Tennessee 2,732 2,357 2,212 92.82% 1,057 920 4,766,688 83.26% 77.28%
Texas 7,730 6,408 5,960 93.05% 4,212 3,649 17,207,615 82.73% 76.98%
Utah 1,487 1,336 1,264 94.52% 990 889 1,807,003 84.94% 80.29%
Vermont 2,410 1,914 1,803 94.36% 1,013 896 525,061 88.02% 83.06%
Virginia 2,426 2,104 1,873 89.03% 1,069 884 5,862,299 75.20% 66.95%
Washington 2,454 2,002 1,832 91.35% 1,079 901 4,962,300 78.20% 71.44%
West Virginia 2,763 2,299 2,169 94.33% 1,059 898 1,527,885 79.91% 75.38%
Wisconsin 2,152 1,709 1,587 92.87% 1,029 887 4,511,335 82.44% 76.56%
Wyoming 2,146 1,741 1,645 94.49% 1,059 907 413,099 79.40% 75.02%
1 Smaller sample sizes and response rates were attained in Mississippi, Nevada, and New Mexico because the review of completed records determined a number of those interviews to be fraudulent. These interviews were consequently dropped.
DU = dwelling unit.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2002.

 

Table A.2 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2002
State 12–17 18–25 26 or older
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
Overall 26,230 23,659 24,753,586 89.99% 27,216 23,271 31,024,280 85.16% 27,135 21,196 179,365,379 75.81%
Alabama 361 331 378,922 92.11% 370 324 497,362 86.86% 372 305 2,810,318 79.54%
Alaska 393 353 70,050 90.00% 353 305 58,061 85.24% 321 257 367,914 79.65%
Arizona 360 330 477,791 91.87% 346 303 593,368 86.21% 372 291 3,289,861 76.81%
Arkansas 385 340 232,228 88.68% 287 256 299,329 89.70% 382 281 1,684,476 71.97%
California 1,439 1,304 3,119,651 90.54% 1,459 1,224 3,910,445 83.32% 1,465 1,071 21,201,387 70.93%
Colorado 349 309 386,275 88.67% 380 317 488,328 82.92% 358 288 2,780,893 80.55%
Connecticut 369 335 297,332 90.70% 423 341 314,467 82.08% 396 301 2,215,789 74.39%
Delaware 392 350 64,655 88.74% 344 285 87,670 83.05% 423 329 513,601 76.54%
District of Columbia 354 326 33,553 91.52% 284 256 73,858 89.63% 341 282 375,224 83.16%
Florida 1,335 1,213 1,332,058 91.10% 1,523 1,317 1,526,407 86.35% 1,482 1,123 10,973,623 74.40%
Georgia 339 309 740,287 91.81% 332 281 931,197 85.79% 395 307 5,170,684 74.28%
Hawaii 337 306 106,624 92.14% 351 300 123,983 85.94% 423 319 731,877 72.94%
Idaho 346 314 128,019 89.27% 348 302 162,155 87.73% 358 291 784,341 80.82%
Illinois 1,475 1,304 1,081,426 88.16% 1,620 1,301 1,366,021 79.82% 1,518 1,124 7,811,288 72.73%
Indiana 351 323 537,937 90.92% 415 346 699,137 84.53% 357 276 3,782,636 74.38%
Iowa 343 312 247,154 91.07% 315 278 348,675 89.36% 370 304 1,844,784 82.50%
Kansas 324 301 242,248 93.27% 374 321 316,706 86.26% 343 276 1,643,332 79.59%
Kentucky 376 325 317,845 84.53% 342 288 457,462 84.10% 380 296 2,619,836 78.11%
Louisiana 344 311 408,864 91.56% 359 310 533,943 86.92% 367 309 2,664,863 82.83%
Maine 337 310 107,138 92.04% 336 295 128,854 88.23% 344 301 868,772 86.65%
Maryland 376 346 472,125 91.83% 331 302 525,127 90.68% 332 271 3,452,047 78.58%
Massachusetts 402 353 502,081 87.86% 350 285 670,475 84.04% 390 278 4,214,516 68.13%
Michigan 1,458 1,301 892,683 89.81% 1,570 1,371 1,078,221 87.65% 1,404 1,120 6,284,494 79.57%
Minnesota 318 289 447,909 90.45% 352 317 564,444 90.66% 326 267 3,142,151 80.71%
Mississippi1 342 312 257,043 91.28% 314 274 346,485 87.36% 332 253 1,703,792 72.96%
Missouri 364 328 489,034 90.34% 335 289 621,802 85.99% 340 273 3,545,624 80.20%
Montana 383 348 82,057 91.77% 309 262 101,662 85.48% 383 304 575,825 80.05%
Nebraska 353 317 152,803 90.07% 327 280 202,014 86.69% 362 294 1,057,166 79.90%
Nevada1 396 359 182,000 91.12% 356 308 208,607 86.18% 395 287 1,351,398 69.19%
New Hampshire 344 300 112,627 88.19% 405 343 126,521 84.89% 343 267 826,017 75.60%
New Jersey 324 290 712,611 89.35% 383 308 775,060 79.98% 358 256 5,587,910 71.75%
New Mexico1 235 213 176,221 89.25% 296 250 207,372 85.15% 263 211 1,116,688 80.02%
New York 1,426 1,241 1,564,858 86.12% 1,649 1,344 2,026,299 80.59% 1,540 1,131 12,291,665 70.20%
North Carolina 354 325 677,525 89.91% 341 292 866,820 84.88% 351 285 5,181,860 79.25%
North Dakota 357 337 54,725 94.54% 332 307 81,994 92.38% 322 269 390,856 81.86%
Ohio 1,358 1,221 991,716 89.83% 1,429 1,224 1,217,589 85.83% 1,434 1,109 7,159,820 75.66%
Oklahoma 362 308 305,129 84.00% 385 333 408,904 85.11% 353 281 2,108,583 76.37%
Oregon 354 322 297,634 90.31% 361 308 379,401 85.13% 356 287 2,239,939 78.69%
Pennsylvania 1,395 1,243 1,025,357 89.15% 1,489 1,293 1,270,338 86.58% 1,367 1,070 8,003,247 77.15%
Rhode Island 365 334 83,814 91.12% 357 306 124,681 84.64% 385 285 688,204 70.20%
South Carolina 339 304 336,271 90.47% 412 343 458,511 82.93% 340 266 2,576,865 79.24%
South Dakota 359 343 70,145 95.94% 320 286 89,870 89.15% 334 285 459,753 85.02%
Tennessee 381 352 472,625 91.52% 260 228 610,807 87.69% 416 340 3,683,257 81.42%
Texas 1,347 1,224 2,004,787 90.81% 1,427 1,251 2,477,451 87.79% 1,438 1,174 12,725,377 80.50%
Utah 316 309 227,575 97.46% 324 289 363,300 88.95% 350 291 1,216,128 81.15%
Vermont 339 312 53,892 92.84% 367 314 68,583 86.88% 307 270 402,586 87.51%
Virginia 297 278 600,443 93.43% 412 341 728,869 83.24% 360 265 4,532,987 71.75%
Washington 298 264 530,187 86.66% 361 304 640,479 84.62% 420 333 3,791,634 76.00%
West Virginia 339 305 139,243 89.85% 336 292 193,439 87.55% 384 301 1,195,204 77.58%
Wisconsin 317 280 482,456 87.97% 380 338 613,508 87.26% 332 269 3,415,371 80.85%
Wyoming 323 295 45,958 91.71% 385 339 58,222 88.37% 351 273 308,919 75.91%
1 Smaller sample sizes and response rates were attained in Mississippi, Nevada, and New Mexico because the review of completed records determined a number of those interviews to be fraudulent. These interviews were consequently dropped.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2002.

 

Table A.3 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
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%
DU = dwelling unit.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2003.

 

Table A.4 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2003
State 12–17 18–25 26 or older
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
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%
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2003.

 

Table A.5 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2002 and 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
Overall 348,775 293,647 266,954 90.72% 162,212 135,910 236,412,627 77.97% 70.74%
Alabama 4,474 3,740 3,410 91.23% 2,132 1,839 3,693,162 80.75% 73.66%
Alaska 4,722 3,712 3,417 92.05% 2,165 1,798 500,651 78.39% 72.16%
Arizona 4,505 3,665 3,432 93.64% 2,135 1,821 4,417,269 80.40% 75.28%
Arkansas 4,798 3,952 3,772 95.40% 2,146 1,799 2,222,351 77.97% 74.38%
California 16,112 14,459 12,831 88.24% 8,834 7,199 28,452,737 74.34% 65.59%
Colorado 4,324 3,682 3,373 91.53% 2,190 1,825 3,678,528 80.24% 73.44%
Connecticut 5,341 4,728 4,300 90.97% 2,316 1,910 2,854,040 76.50% 69.60%
Delaware 5,004 4,052 3,682 90.65% 2,264 1,875 668,924 76.87% 69.69%
District of Columbia 7,393 6,178 5,184 83.89% 2,095 1,813 479,754 82.57% 69.27%
Florida 21,193 17,075 15,298 89.62% 8,754 7,194 13,988,898 75.42% 67.59%