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2002 State Estimates of Substance Use

Appendix B: State Estimation Methodology

This report includes estimates of 20 substance use measures (see Section B.1). In addition to the 19 substance use measures for which age group–specific State estimates were produced and documented in the 2001 State report (Wright, 2003a, 2003b), there was 1 new measure (needing but not receiving treatment for alcohol problems in past year) introduced in 2002. 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. Due to improvements in the data collection procedures implemented for the 2002 National Survey on Drug Use and Health (NSDUH) and the associated effects on prevalence rates, it was deemed inadvisable to compare 2002 State estimates with the earlier published State estimates from the 1999–2001 surveys. Hence, this report is based on single-year (2002) State estimates. The broad increase in prevalence rates at the national level between 2001 and 2002 that was caused by the methodological differences in those years was reflected in increased prevalence rates across most States. Due to the uneven effects of the incentive and other methodological changes on the State response rates and prevalence rates, a State's ranking for 2002 is not comparable with its rankings in prior years.

The survey-weighted hierarchical Bayes (SWHB) methodology used in the production of State estimates from the 1999–2001 surveys also was used in the production of the 2002 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 2002 small area estimation (SAE) modeling is given in Section B.2. The improved methodology used to select relevant predictors is described in Section B.3. The goals of SAE modeling, general model description, and the implementation of SAE modeling remain the same and are described in the Appendix E of the 2001 State report (Wright, 2003b). The only difference is that the model used in the 2001 State report used the pooled 2000–2001 NHSDA data, whereas the model in 2002 used a single year of data. At the end of this appendix, tables showing the 2000–2001 and 2002 survey response rates are included (Tables B.1 to B.4). It should be noted that 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.

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. In the 2002 State estimation, exact benchmarking was introduced as described in Section B.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 B.5. Section B.6 includes the definition and explanation of the formula used in estimating the marijuana incidence rate.

B.1. Variables Modeled

In the 2002 NSDUH, age group–specific State estimates were produced for the following set of 20 binary (0, 1) substance use measures:

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

B.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 #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 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 no arrests for dangerous non-narcotics,
=0 otherwise
UCR County
=1 if no Cubans in tract, =0 otherwise 2000 Census Tract

Miscellaneous Data
Variable Description Source Level
Alcohol death rate, underlying cause ICD-10 County
Cigarettes death rate, underlying cause ICD-10 County
Drug death rate, underlying cause 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

B.3. Selection of Independent Variables for the Models

The variable selection process consists of multiple steps:

  1. Individual SAS® stepwise logistic regression models were fit for all outcomes by 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 B.2. All predictors that were significant at 3 percent (except in a few cases) then were input to the 2nd step of variable selection.

  2. Almost all significant predictors from step 1 then were input to AnswerTree® to identify significant higher order (at most three-way) interaction terms. AnswerTree® is an SPSS® software 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.

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

  4. All significant variables from step 3 were input to SUDAAN® logistic regression models, and predictors that remained significant at the 1 percent level were input to PROC GIBBS and PROC GSTAT software. In all mixed logistic models, race and gender were forced.

B.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 previous State reports 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 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)],     D

where

Lsa = log[image representing thetasa/(1 - image representing thetasa)] - 1.96 * image representing square root(MSEsa),     D

Usa = log[image representing thetasa/(1 - image representing thetasa)] + 1.96 * image representing square root(MSEsa), and     D

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

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.

B.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.

B.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, this 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 2002 to indicate first use as early as the first part of 2000 or as late as the first part of 2002. Similarly, a subject interviewed in the last part of 2002 could indicate first use as early as the last part of 2000 or as late as the last part of 2002. Therefore, in the 2002 survey, the reported period of first use ranged from early 2000 to late 2002 and was "centered" in 2001. About 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. Persons who responded in 2002 that they had never used marijuana were included in the count of "never used." Note that only incidence rates for marijuana use are provided in this report.

 

Table B.1 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2000–2001
State Total Selected DUs Total Eligible DUs Total Completed Screeners Weighted DU Screening Response Rate Total Selected Total Responded Average Population Estimate Weighted Interview Response Rate Weighted Overall Response Rate
Overall 419,404 354,095 327,240 92.35% 181,706 140,693 224,457,694 73.61% 67.98%
Alabama 5,459 4,469 4,203 93.84% 2,263 1,821 3,636,290 75.56% 70.91%
Alaska 5,126 3,851 3,687 95.73% 2,195 1,784 485,734 79.93% 76.52%
Arizona 5,178 4,320 4,028 93.26% 2,416 1,891 3,918,104 75.12% 70.06%
Arkansas 5,832 4,758 4,615 96.96% 2,290 1,871 2,139,458 78.28% 75.89%
California 27,388 24,364 22,297 91.73% 11,490 8,751 26,467,764 70.71% 64.86%
Colorado 4,950 4,317 4,094 94.81% 2,391 1,797 3,417,096 72.95% 69.16%
Connecticut 6,601 5,980 5,446 90.97% 2,719 1,946 2,713,483 70.52% 64.15%
Delaware 4,866 4,173 3,863 92.47% 2,449 1,821 631,683 68.67% 63.50%
District of Columbia 8,866 7,339 6,611 89.93% 2,125 1,795 424,291 81.91% 73.66%
Florida 22,131 17,464 16,219 92.88% 8,839 6,980 12,786,693 74.00% 68.73%
Georgia 6,753 5,769 5,341 92.24% 2,694 2,085 6,413,682 70.31% 64.86%
Hawaii 4,936 4,106 3,782 91.54% 2,354 1,832 952,374 73.48% 67.26%
Idaho 4,595 3,770 3,532 93.88% 2,384 1,830 1,061,440 75.57% 70.94%
Illinois 22,083 19,263 16,803 87.27% 10,470 7,218 9,823,269 64.99% 56.71%
Indiana 6,793 5,902 5,456 92.46% 2,688 1,976 4,929,021 71.71% 66.30%
Iowa 4,965 4,346 4,103 94.40% 2,332 1,882 2,380,326 78.72% 74.31%
Kansas 4,472 3,858 3,600 93.33% 2,388 1,819 2,161,048 75.40% 70.37%
Kentucky 5,498 4,677 4,456 95.27% 2,338 1,929 3,289,493 80.26% 76.46%
Louisiana 4,821 3,951 3,753 94.77% 2,273 1,848 3,538,614 77.48% 73.43%
Maine 6,417 5,014 4,589 91.54% 2,206 1,797 1,060,023 81.41% 74.52%
Maryland 4,617 4,095 3,839 93.63% 2,318 1,928 4,293,682 78.04% 73.07%
Massachusetts 6,602 5,822 5,237 89.88% 2,737 1,935 5,143,813 67.01% 60.23%
Michigan 22,316 18,569 17,116 92.24% 9,700 7,344 8,051,414 73.44% 67.75%
Minnesota 4,518 3,937 3,702 93.85% 2,217 1,776 3,974,541 80.22% 75.29%
Mississippi 5,019 4,028 3,812 94.57% 2,226 1,802 2,258,079 76.42% 72.27%
Missouri 6,149 5,182 4,806 92.69% 2,349 1,775 483,400 74.72% 69.26%
Montana 5,246 4,069 3,866 94.99% 2,246 1,810 83,740 78.89% 74.94%
Nebraska 4,519 3,900 3,646 93.63% 2,363 1,826 154,329 75.51% 70.69%
Nevada 4,590 3,902 3,652 93.61% 2,357 1,869 158,222 74.92% 70.13%
New Hampshire 5,858 4,863 4,497 92.38% 2,376 1,796 108,635 75.54% 69.78%
New Jersey 7,840 6,907 6,229 89.76% 3,073 2,269 653,125 68.34% 61.34%
New Mexico 4,770 3,725 3,622 97.24% 2,121 1,746 170,647 80.81% 78.58%
New York 25,293 21,719 18,707 86.56% 10,276 7,612 1,460,595 71.18% 61.61%
North Carolina 6,586 5,601 5,252 93.65% 2,486 1,895 655,188 72.64% 68.03%
North Dakota 5,074 4,229 3,988 94.40% 2,244 1,779 59,474 78.56% 74.16%
Ohio 20,640 18,013 16,961 94.18% 9,341 7,384 965,378 76.16% 71.73%
Oklahoma 4,963 4,225 3,926 93.06% 2,399 1,835 303,116 74.77% 69.58%
Oregon 4,926 4,139 3,836 92.65% 2,190 1,744 281,452 75.64% 70.09%
Pennsylvania 23,577 20,019 18,850 94.01% 9,924 7,731 995,397 74.24% 69.79%
Rhode Island 5,593 4,853 4,421 91.11% 2,506 1,845 84,173 71.92% 65.52%
South Carolina 5,390 4,387 4,150 94.58% 2,267 1,746 322,996 74.68% 70.64%
South Dakota 4,615 3,839 3,630 94.64% 2,264 1,786 71,696 78.57% 74.36%
Tennessee 5,842 4,977 4,572 92.54% 2,338 1,868 458,460 73.44% 67.97%
Texas 18,369 15,481 14,547 93.85% 9,329 7,624 1,866,305 77.95% 73.15%
Utah 3,138 2,737 2,613 95.65% 2,329 1,926 236,561 81.85% 78.29%
Vermont 6,053 4,581 4,256 92.81% 2,322 1,907 53,804 80.54% 74.75%
Virginia 6,495 5,615 5,136 91.47% 2,522 1,976 562,035 75.19% 68.78%
Washington 5,811 4,809 4,508 93.63% 2,422 1,917 500,872 74.75% 69.99%
West Virginia 6,351 5,237 4,960 94.76% 2,339 1,826 140,291 74.19% 70.31%
Wisconsin 6,439 5,456 5,113 93.62% 2,632 2,002 482,323 73.04% 68.38%
Wyoming 4,475 3,488 3,312 94.92% 2,189 1,741 47,276 76.67% 72.77%
DU = dwelling unit.
Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000–2001.

 

Table B.2 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2000–2001
State 12–17 18–25 26+
Total Selected Total Responded Average Population Estimate Weighted Interview Response Rate Total Selected Total Responded Average Population Estimate Weighted Interview Response Rate Total Selected Total Responded Average Population Estimate Weighted Interview Response Rate
Overall 59,430 48,934 23,483,867 82.38% 59,728 45,780 29,234,745 76.42% 62,548 45,979 171,739,082 71.96%
Alabama 746 635 362,648 85.60% 742 600 478,332 80.06% 775 586 2,795,311 73.51%
Alaska 739 612 64,215 83.77% 693 568 73,850 83.47% 763 604 347,669 78.54%
Arizona 740 607 440,311 81.44% 827 644 526,762 77.73% 849 640 2,951,030 73.74%
Arkansas 787 662 223,378 83.66% 752 628 281,178 83.06% 751 581 1,634,902 76.73%
California 4,441 3,625 2,824,532 82.25% 3,456 2,588 3,676,233 74.67% 3,593 2,538 19,966,999 68.39%
Colorado 763 605 364,225 80.22% 779 573 458,468 74.91% 849 619 2,594,402 71.64%
Connecticut 893 692 267,871 77.68% 881 592 301,035 66.28% 945 662 2,144,577 70.19%
Delaware 734 599 62,670 81.78% 838 641 78,898 76.20% 877 581 490,115 65.62%
District of Columbia 688 583 36,173 84.69% 745 647 54,573 85.56% 692 565 333,545 80.98%
Florida 2,731 2,354 1,210,123 85.28% 2,963 2,327 1,394,995 78.38% 3,145 2,299 10,181,575 72.04%
Georgia 1,017 833 687,484 81.18% 852 679 867,932 78.18% 825 573 4,858,265 67.36%
Hawaii 736 626 91,227 86.38% 790 619 116,004 77.39% 828 587 745,142 71.11%
Idaho 755 611 126,928 81.60% 829 616 163,461 74.42% 800 603 771,050 74.81%
Illinois 3,101 2,400 1,018,242 77.33% 3,509 2,345 1,297,703 66.67% 3,860 2,473 7,507,324 63.10%
Indiana 960 756 514,479 77.67% 830 588 659,394 70.11% 898 632 3,755,148 71.21%
Iowa 734 615 253,076 84.03% 835 666 319,132 79.83% 763 601 1,808,118 77.82%
Kansas 723 574 240,959 79.61% 855 639 299,792 73.37% 810 606 1,620,297 75.16%
Kentucky 748 644 328,197 85.83% 799 652 438,991 81.14% 791 633 2,522,305 79.38%
Louisiana 727 633 406,867 86.13% 759 619 524,229 81.31% 787 596 2,607,517 75.41%
Maine 731 608 106,140 82.97% 656 527 123,368 80.33% 819 662 830,516 81.38%
Maryland 735 638 432,980 86.97% 812 695 506,651 85.26% 771 595 3,354,052 75.73%
Massachusetts 883 685 498,933 78.45% 902 609 604,059 67.33% 952 641 4,040,822 65.58%
Michigan 3,101 2,481 862,656 80.35% 3,196 2,372 1,040,480 74.53% 3,403 2,491 6,148,278 72.28%
Minnesota 707 588 444,499 82.22% 759 597 538,339 79.11% 751 591 2,991,703 80.13%
Mississippi 735 626 255,565 84.49% 719 595 325,891 81.17% 772 581 1,676,623 74.29%
Missouri 795 603 483,400 77.29% 776 599 594,725 76.56% 778 573 3,449,927 74.03%
Montana 693 564 83,740 82.91% 802 656 100,228 81.13% 751 590 574,792 77.89%
Nebraska 756 626 154,329 82.80% 759 572 191,072 75.22% 848 628 1,022,375 74.46%
Nevada 696 607 158,222 87.05% 767 604 188,252 78.50% 894 658 1,209,036 72.83%
New Hampshire 774 645 108,635 81.52% 791 539 119,121 68.16% 811 612 790,887 75.85%
New Jersey 1,173 951 653,125 80.62% 854 604 769,358 70.49% 1,046 714 5,325,215 66.65%
New Mexico 716 622 170,647 87.43% 661 532 204,963 81.72% 744 592 1,076,633 79.63%
New York 3,173 2,594 1,460,595 82.33% 3,490 2,501 1,817,968 72.54% 3,613 2,517 11,599,032 69.53%
North Carolina 918 724 655,188 78.34% 718 561 774,650 77.44% 850 610 4,905,938 71.20%
North Dakota 703 570 59,474 80.38% 802 627 75,818 77.63% 739 582 389,200 78.46%
Ohio 3,031 2,519 965,378 83.05% 3,076 2,410 1,207,300 78.04% 3,234 2,455 7,118,110 74.91%
Oklahoma 728 576 303,116 79.16% 832 643 374,302 77.91% 839 616 2,064,304 73.59%
Oregon 688 566 281,452 82.66% 737 587 358,170 79.69% 765 591 2,170,244 74.00%
Pennsylvania 3,240 2,674 995,397 82.19% 3,212 2,504 1,174,458 77.66% 3,472 2,553 7,891,763 72.73%
Rhode Island 765 593 84,173 78.22% 899 648 95,297 72.23% 842 604 643,357 71.00%
South Carolina 712 576 322,996 81.19% 775 602 406,990 77.38% 780 568 2,456,947 73.34%
South Dakota 720 582 71,696 80.34% 748 579 87,993 77.52% 796 625 450,065 78.49%
Tennessee 786 680 458,460 86.70% 731 585 592,617 79.41% 821 603 3,574,443 70.59%
Texas 3,135 2,692 1,866,305 85.94% 3,040 2,505 2,387,720 82.31% 3,154 2,427 11,944,788 75.87%
Utah 700 619 236,561 89.62% 925 737 333,876 80.27% 704 570 1,129,777 80.74%
Vermont 737 635 53,804 85.56% 833 674 62,752 80.78% 752 598 394,846 79.77%
Virginia 947 761 562,035 80.73% 693 546 705,466 77.20% 882 669 4,403,438 74.25%
Washington 846 718 500,872 85.20% 750 586 620,227 78.68% 826 613 3,660,295 72.78%
West Virginia 739 623 140,291 84.91% 744 586 193,991 78.81% 856 617 1,202,125 72.19%
Wisconsin 933 757 482,323 81.18% 811 590 587,492 71.35% 888 655 3,310,305 72.18%
Wyoming 671 565 47,276 83.71% 724 577 60,186 79.91% 794 599 303,954 75.06%
Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000–2001.

 

Table B.3 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 B.4 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2002
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
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.

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