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Appendix A: State Estimation Methodology

This report includes estimates of 25 substance use and mental health measures (see Section A.2) using the combined data from the 2008 and 2009 National Surveys on Drug Use and Health (NSDUHs). Also included in this report are comparisons between the 2007-2008 and the 2008-2009 State estimates and comparisons between the 2002-2003 and the 2008-2009 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 1999-2008 surveys also was used in the production of the 2008-2009 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 goals of small area estimation (SAE) modeling and the implementation of SAE modeling remain the same and are described in Appendix E of the 2001 State report (Wright, 2003b). A general model description is given in Section A.1. A list of outcomes for which small area estimates are produced in this report is given in Section A.2. The list of predictors used in the 2008-2009 SAE modeling is given in Section A.3. Information on the new population projections obtained from Claritas that were used for the first time in producing the 2007-2008 small area estimates and how they were used to create SAE model predictors is given in Section A.4. New variable selection was done for the mental health outcomes using the 2008-2009 data. For other outcomes, no new variable selection was done (as discussed in Section A.5).

Small area estimates obtained using the SWHB methodology are design consistent (i.e., the small area estimates for States with large sample sizes are close to the robust design-based estimates). The State small area estimates when aggregated 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.6.9 Tables of estimated numbers of persons associated with each outcome (in thousands) are available on the Web in the form of HTML tables (see http://www.samhsa.gov/data/2k9State/TOC.htm). An explanation of how these counts and their respective Bayesian confidence intervals10 are calculated can be found in Section A.7. The definition and explanation of the formula used in estimating the marijuana incidence rate is given in Section A.8.

For all outcomes except major depressive episode (i.e., depression), serious mental illness, any mental illness, and past year serious thoughts of suicide, the age groups for which estimates are provided in this report are 12 to 17, 18 to 25, and 26 or older. Estimates for those aged 12 or older also are provided in this report. Because it was determined that States may find estimates for persons aged 18 or older useful, estimates for that age group are available on the Web in the form of HTML tables (see http://www.samhsa.gov/data/2k9State/TOC.htm). Also included in this report are estimates of underage (aged 12 to 20) alcohol use and binge alcohol use. Alcohol consumption is expected to differ significantly across the 18 to 25 age group because of the legalization of alcohol at age 21. Therefore, 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 provided in Section A.9.

Section A.10 discusses the criteria used to define dependence on and abuse of illicit drugs and alcohol. Section A.11 discusses the production of estimates for major depressive episode (i.e., depression), serious mental illness, any mental illness, and suicidal thoughts. Note that for major depressive episode, there are no 12 or older estimates published; also, for serious mental illness, any mental illness, and serious thoughts of suicide, no 12 to 17 estimates are produced because youths are not asked these questions. Section A.12 discusses the method to compare prevalence rates of a particular outcome between two States. The methodology used to compare the 2007-2008 and the 2008-2009 State estimates and the 2002-2003 and the 2008-2009 State estimates is described in Section A.13.

At the end of this appendix, tables showing the 2007, 2008, 2009, pooled 2007-2008, and pooled 2008-2009 survey sample sizes, population estimates, and response rates are included (Tables A.1 to A.14). Table A.15 lists all outcomes and the years for which small area estimates were produced going back to the 2002 NSDUH.

A.1 General Model Description

The model can be characterized as a complex mixed model (including both fixed and random effects) of the following form:

Equation A1,     D

where pi sub a, i, j, k is the probability of engaging in the behavior of interest (e.g., using marijuana in the past month) for person-k belonging to age group-a in State sampling (SS) region-j of State-i. Let pi sub a, i, j, k denote a p sub a times 1 vector of auxiliary (predictor) variables associated with age group-a (12 to 17, 18 to 25, 26 to 34, and 35 or older) and beta sub a denote the associated vector of regression parameters. The age group-specific vectors of auxiliary variables are defined for every block group in the Nation and also include person-level demographic variables, such as race/ethnicity and gender. The vectors of State-level random effects An eta sub i is a transposed vector of values eta sub 1, i and so on until eta sub A, i. and SS region-level random effects A nu sub i, j is a vector of transposed values nu sub 1, i, j and so on until nu sub A, i, j. are assumed to be mutually independent with An eta sub i is normally distributed with mean 0 and variance denoted by matrix D sub eta. and A nu sub i, j is normally distributed with mean 0 and variance denoted by matrix D sub nu. where A is the total number of individual age groups modeled (generally, A=4). For hierarchical Bayes (HB) estimation purposes, an improper uniform prior distribution is assumed for beta sub a, and proper Wishart prior distributions are assumed for inverse of capital D sub eta and inverse of capital D sub nu . The HB solution for pi sub a, i, j, k involves a series of complex Markov Chain Monte Carlo (MCMC) steps to generate values of the desired fixed and random effects from the underlying joint posterior distribution. The basic process is described in Folsom et al. (1999), Shah, Barnwell, Folsom, and Vaish (2000), and Wright (2003a, 2003b).

Once the required number of MCMC samples for the parameters of interest are generated and tested for convergence properties (see Raftery & Lewis, 1992), the small area estimates for each age group × race/ethnicity × gender cell within a block group can be obtained. These block group-level small area estimates then can be aggregated using the appropriate population count projections to form State-level small area estimates for the desired age group(s). These State-level small area estimates are benchmarked to the national design-based estimates as described in Section A.6.

A.2 Variables Modeled

The 2009 NSDUH data were pooled with the 2008 NSDUH data, and age group-specific State prevalence estimates for 25 binary (0, 1) outcome variables were produced and presented in this report in Appendix B. Estimates were 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. serious mental illness in the past year,

  23. any mental illness in the past year,

  24. serious thoughts of suicide in the past year, and

  25. past year major depressive episode (i.e., depression).

Comparisons between the 2007-2008 and the 2008-2009 State estimates were produced for all of these outcomes except serious mental illness, any mental illness, and serious thoughts of suicide and are included in this report in Appendix C. In addition, tests of change between the 2002-2003 and the 2008-2009 State estimates were produced for all outcomes except major depressive disorder, serious mental illness, any mental illness, and past year serious thoughts of suicide and are included in this report in Appendix D. Note that the mental health outcomes included in this report are either being reported for the first time or are not comparable with estimates from prior years (except for the major depressive episode estimates for youths that are comparable with estimates from previous years).

A.3 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 Bureau of Labor Statistics, the Bureau of Economic Analysis, 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 values of these predictor variables are updated every year (when possible). 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) Claritas Data (Level)
% Population Aged 0 to 19 in Block Group Block Group
% Population Aged 20 to 24 in Block Group Block Group
% Population Aged 25 to 34 in Block Group Block Group
% Population Aged 35 to 44 in Block Group Block Group
% Population Aged 45 to 54 in Block Group Block Group
% Population Aged 55 to 64 in Block Group Block Group
% Population Aged 65 or Older in Block Group Block Group
% Non-Hispanic Blacks in Block Group Block Group
% Hispanics in Block Group Block Group
% Non-Hispanic Other Races in Block Group Block Group
% Non-Hispanic Whites in Block Group Block Group
% Males in Block Group Block Group
% Females in Block Group Block Group
% American Indians, Eskimos, Aleuts in Tract Tract
% Asians, Pacific Islanders in Tract Tract
% Population Aged 0 to 19 in Tract Tract
% Population Aged 20 to 24 in Tract Tract
% Population Aged 25 to 34 in Tract Tract
% Population Aged 35 to 44 in Tract Tract
% Population Aged 45 to 54 in Tract Tract
% Population Aged 55 to 64 in Tract Tract
% Population Aged 65 or Older in Tract Tract
% Non-Hispanic Blacks in Tract Tract
% Hispanics in Tract Tract
% Non-Hispanic Other Races in Tract Tract
% Non-Hispanic Whites in Tract Tract
% Males in Tract Tract
% Females in Tract Tract
% Population Aged 0 to 19 in County County
% Population Aged 20 to 24 in County County
% Population Aged 25 to 34 in County County
% Population Aged 35 to 44 in County County
% Population Aged 45 to 54 in County County
% Population Aged 55 to 64 in County County
% Population Aged 65 or Older in County County
% Non-Hispanic Blacks in County County
% Hispanics in County County
% Non-Hispanic Other Races in County County
% Non-Hispanic Whites in County County
% Males in County County
% Females in County County

2000 Census Data (Description) 2000 Census Data (Level)
% Population Who Dropped Out of High School Tract
% Housing Units Built in 1940 to 1949 Tract
% Persons Aged 16 to 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, or Other Tract
% One-Person Households Tract
% Female Heads of Household, No Spouse, Child #under 18 Tract
% Males 16 Years or Older in Labor Force Tract
% Males Never Married Tract
% Males Separated, Divorced, Widowed, or 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 to 12 Years of School, No High School Diploma Tract
% Population with 0 to 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) Uniform Crime Report Data (Level)
Drug Possession Arrest Rate County
Drug Sale or Manufacture Arrest Rate County
Drug Violations' Arrest Rate County
Marijuana Possession Arrest Rate County
Marijuana Sale or Manufacture Arrest Rate County
Opium or Cocaine Possession Arrest Rate County
Opium or Cocaine Sale or 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) Other Categorical Data (Source) Other Categorical Data (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) Miscellaneous Data (Source) Miscellaneous Data (Level)
Alcohol Death Rate, Underlying Cause NCHS-ICD-10 County
Cigarette 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 BLS County
Per Capita Income (in Thousands) BEA County
Average Suicide Rate (per 10,000) NCHS-ICD-10 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.4 Updated Claritas Data

For the State and substate reports published using the 2002 to 2007 NSDUH data, Claritas data obtained in 2002 were used to produce the small area estimates. In reports published using the 2008 and 2009 NSDUH data, Claritas data obtained in 2008 were used. The 2002 Claritas data had 2000 and 2002 population counts, as well as 2007 population projections. The 2008 Claritas data had 2008 population counts, as well as 2012 population projections. Claritas data were used for the following in the NSDUH SAE process:

  1. Creating demographic predictor variables (age group, race × ethnicity, and gender) at the block group, tract, and county levels (predictors such as percentage of the population aged 0 to 19 in a block group, percentage of population who are males in a tract). There are 13 such variables defined for each of the census geographies (block group, tract, and county). See Section A.3 for a complete list of these predictors.

  2. Creating census block group-level population projections at the age group × race/ethnicity × gender level (4 age groups, 4 races/ethnicities, and 2 genders = 32 cells) that are used in producing aggregate State-level small area estimates.11

  1. In the 2008 SAE process (and subsequent years), new Claritas data with 2008 population counts and 2012 population projections were used. The new Claritas data will be henceforth referred to as the 2008-2012 Claritas data, and the 2002 Claritas data will be referred to as the 2002-2007 Claritas data. After doing some data exploration on the 2008-2012 Claritas data and comparing them with the 2002-2007 Claritas data, some differences were observed when comparing the 2007 population counts (from the 2002-2007 Claritas data) with the 2008 population counts (from the 2008-2012 Claritas data). For example, the distributions of the population aged 20 to 24 in block groups were very different for the two datasets. Another difference was that there were more block groups that had a 0 population count for some of the 32 cells in 2008 as compared with the 32 cells in 2007.

  2. The format of the race/ethnicity data was also different for the two sets of Claritas data. To generate age group × race × Hispanicity × gender population counts at the block group level using the 2002-2007 Claritas data, two separate population distributions (age × gender × race and race × Hispanicity) at the block group level had to be used. The assumption that each of the age × gender cells within a race group had the same Hispanicity distribution was made. So, the data were manipulated to get the desired four-way cross of demographic domains. The 2008-2012 Claritas data had age group × race × Hispanicity × gender population distributions, so no assumptions or manipulations to the data had to be made.

  1. In State reports prior to 2008 when creating the 32 cells using the 2002-2007 Claritas data, the population from the 2 or more races category was distributed among the black, white, and other race categories. Starting in 2008 and subsequent years, a decision was made to merge the two or more races category with the other race category. This was based on a decision to discontinue creating a NSDUH sample variable that split the two or more races respondents into black, white, or other. Because NSDUH respondents with 2 or more races were now being grouped into the other category, the same technique was used to produce the 32 cell counts.

Some of the data differences can be attributed to reasons (2b) and (3), and the rest are most likely attributed to the fact that the 2008-2012 Claritas projections are based on updated population information. Because of these differences in the 2007 population projections based on 2002-2007 Claritas data and the 2008 population counts based on 2008-2012 Claritas data, it was decided that "new" 2007 population projections would be obtained by "projecting back" the 2008-2012 Claritas data. Population projections for 2006 also were obtained in the same manner, so that they could be used in the 2006-2008 SAE reports.

Based on the information above, the following steps were taken for the 2008-2009 SAE process (for more information on the steps taken for the 2007-2008 SAE process, see Appendix A of Hughes et al., 2010):

  1. Using the 2008-2012 Claritas data, 2008 and 2009 population counts were obtained (the 2008 counts were extracted directly from the file and the 2009 counts were obtained by using linear interpolation between the 2008 and 2012 counts) and used to create the predictors that were merged onto the 2008 and 2009 sample and universe files (the universe file is a census block-group level file containing SAE predictor variables and population counts).

  2. All block group, tract, and county-level continuous predictors were converted into 10-category, semicontinuous variables by using the corresponding 2007-2008 decile values created by pooling the 2007 and 2008 NSDUH data. The same 2007-2008 decile values will be used for future SAE analyses until new Claritas data containing the 2013 population counts and projections are obtained. Using the same decile values year after year makes it possible to keep track of any temporal changes occurring in the predictor variables, which may help in detecting any changes in State prevalence rates across years in an efficient manner. The 10-category predictor variables subsequently were used to form linear, quadratic, and cubic orthogonal polynomials eventually used in the SAE modeling process.

  3. The updated population counts for the 32 cells (age group × race/ethnicity × gender population counts) were used to create the universe files for both years (i.e., 2008 and 2009).

  4. The updated 2007 sample and universe files based on the 2008-2012 Claritas data were used in simultaneous modeling (see Section A.13) to produce the correlations required to estimate change between the 2007-2008 and 2008-2009 State prevalence rates.

  5. For any analysis using 2002 through 2005 NSDUH data, the old projections based on the 2002-2007 Claritas data were used (i.e., for the 2002-2003 vs. 2008-2009 comparisons, the 2002-2007 Claritas projections were used on the 2002-2003 sample and universe files, whereas the 2008-2012 Claritas projections were used on the 2008-2009 sample and universe files).

A.5 Selection of Independent Variables for the Models

New variable selection was done for serious mental illness, any mental illness, serious thoughts of suicide, and major depressive episode (i.e., depression) for persons aged 18 or older using the pooled 2008-2009 NSDUH data. Estimates for serious mental illness, any mental illness, and serious thoughts of suicide are being produced in this report for the first time; hence, no prior fixed-effect predictors were available. The serious mental illness estimates in prior State reports (e.g., 2001, 2002, and 2003 State reports) were based on a different definition. For major depressive episode, the variable selection for adults aged 18 or older is based on an adjusted major depressive episode variable (for details, see Section A.11). Fixed-effect predictors for the new outcome variables were selected using the method described by Wright and Sathe (2005).

For all of the other outcomes (including major depressive episode for youths aged 12 to 17), no new variable selection was done. The updated versions of fixed-effect predictors that were used in modeling the 2007-2008 data were used to model the 2008-2009 data. Because the interest was to estimate change between the 2007-2008 and 2008-2009 State estimates, the same set of fixed-effect predictors was used for producing both sets of estimates.

A.6 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. The national design-based estimates in NSDUH are based entirely on survey-weighted data using a direct estimation approach, whereas the State and census region estimates in this report are model-based. 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 point estimate, and the tail percentiles of the posterior distribution were used for the Bayesian confidence interval 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 2008-2009) is adjusted by adding the common factor Delta sub a is defined as the national design-based estimate, capital D sub a, minus the national model-based small area estimate, P sub a. where Captial D sub a is defined as the national design-based estimate. is the design-based national prevalence estimate and Capital P sub a is defined as the national model-based small area estimate. is the population-weighted mean of the State small area estimates Capital P sub s and a is the State-s and age group-a small area estimate. for age group-a. The exactly benchmarked State-s and age group-a small area estimates then are given by The benchmarked State-s and age group-a small area estimate, Theta sub s and a, is defined as the sum of P sub s and a and Delta sub a. 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 Bayesian confidence (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 Bayesian confidence intervals Lower sub s and a is the lower bound of the 95 percent Bayesian confidence interval of Theta sub s and a; upper sub s and a is the upper bound of the 95 confidence interval interval of Theta sub s and a. are defined below:

Equation A17,     D

where

Equation A18,     D

Equation A19, and     D

Equation A20.     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.

A.7 Calculation of Estimated Number of Persons Associated with Each Outcome

Tables 1 to 26, available at http://www.samhsa.gov/data/2k9State/TOC.htm, show the estimated numbers of persons (in thousands) associated with each of the 25 outcomes of interest. To calculate these estimated numbers of persons, the benchmarked small area estimates and the associated 95 percent Bayesian confidence intervals are multiplied by the average population across the 2 years (in this case, 2008 and 2009) of the State by age group of interest.

For example, past month use of alcohol among 18 to 25 year olds in Alabama was 54.51 percent (see Table B.9 in Appendix B). The corresponding Bayesian confidence intervals ranged from 50.59 to 58.37 percent. The population count for 18 to 25 year olds averaged across 2008-2009 in Alabama was 505,718 (see Table A.10). Hence, the estimated number of 18 to 25 year olds using alcohol in the past month in Alabama was 0.5451 * 505,718, which is 275,667 (see Table 9). The associated Bayesian confidence intervals ranged from 0.5059 * 505,718 (i.e., 255,843) to 0.5837 * 505,718 (i.e., 295,188). Note that when estimates of the number of persons are calculated for Tables 1 to 26, the unrounded prevalence estimates and population counts are used. Hence, the number obtained by multiplying the published prevalence rate with the published population estimate may not exactly match the counts that are published in these tables due to rounding differences.

A.8 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:

Equation A21,     D

where capital X sub 1 is the number of marijuana initiates in the past 24 months and capital X sub 2 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 2009 to indicate first use as early as the first part of 2007 or as late as the first part of 2009. Similarly, a subject interviewed in the last part of 2009 could indicate first use as early as the last part of 2007 or as late as the last part of 2009. Therefore, in the 2009 survey, the reported period of first use ranged from early 2007 to late 2009 and was "centered" in 2008. For example, about half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2008, while a quarter each reported first use in 2007 and 2009. Persons who responded in 2009 that they had never used marijuana were included in the count of "never used." Similarly, reports of first use in the past 24 months from the 2008 survey ranged from early 2006 to late 2008 and were centered in 2007. Half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2007, while a quarter each reported first use in 2006 and 2008. Note that only incidence rates for marijuana use are provided in this report.

A.9 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. For the 2008-2009 models, no new variable selection was done. Updated versions of the predictors were used to produce the small area estimates.

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.6. Comparisons between the 2007-2008 and the 2008-2009 small area estimates for underage drinking in the States also are presented in this report.

A.10 Illicit Drug and Alcohol Dependence and Abuse

The NSDUH computer-assisted interviewing (CAI) instrumentation includes questions that are designed to measure dependence on and abuse of illicit drugs and alcohol. For these substances,12 dependence and abuse questions were based on the criteria in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) (American Psychiatric Association [APA], 1994).

Specifically, for marijuana, hallucinogens, inhalants, and tranquilizers, a respondent was defined as having dependence if he or she met three or more of the following six dependence criteria:

  1. Spent a great deal of time over a period of a month getting, using, or getting over the effects of the substance.

  2. Used the substance more often than intended or was unable to keep set limits on the substance use.

  3. Needed to use the substance more than before to get desired effects or noticed that the same amount of substance use had less effect than before.

  4. Inability to cut down or stop using the substance every time tried or wanted to.

  5. Continued to use the substance even though it was causing problems with emotions, nerves, mental health, or physical problems.

  6. The substance use reduced or eliminated involvement or participation in important activities.

For alcohol, cocaine, heroin, pain relievers, sedatives, and stimulants, a seventh withdrawal criterion was added. A respondent was defined as having dependence if he or she met three or more of seven dependence criteria. The seventh withdrawal criterion is defined by a respondent reporting having experienced a certain number of withdrawal symptoms that vary by substance (e.g., having trouble sleeping, cramps, hands tremble).

For each illicit drug and alcohol, a respondent was defined as having abused that substance if he or she met one or more of the following four abuse criteria and was determined not to be dependent on the respective substance in the past year:

  1. Serious problems at home, work, or school caused by the substance, such as neglecting your children, missing work or school, doing a poor job at work or school, or losing a job or dropping out of school.

  2. Used the substance regularly and then did something that might have put you in physical danger.

  3. Use of the substance caused you to do things that repeatedly got you in trouble with the law.

  4. Had problems with family or friends that were probably caused by using the substance and continued to use the substance even though you thought the substance use caused these problems.

For additional details on how respondents were classified as being dependent on or having abused illicit drugs and alcohol, see Section B.4.3 in Appendix B of the 2009 NSDUH national findings report (OAS, 2010b, pp. 26-28).

A.11 Mental Health Measures

This section provides a summary of measurement issues associated with the four mental health outcome variables included in this report—serious mental illness, any mental illness, serious thoughts of suicide, and major depressive episode. Additional details can be found in Sections B.4.6 and B.4.7 of Appendix B in the 2008 NSDUH national findings report for serious mental illness and major depressive episode, respectively (OAS, 2009), and Sections B.4.2 to B.4.4 of Appendix B in the 2009 NSDUH mental health findings report for all four outcome variables (CBHSQ, 2010).

A.11.1 Serious Mental Illness

In the 2000-2001 and 2002-2003 NSDUH State reports, the Kessler-6 (K6) distress scale was used to measure serious mental illness (Kessler et al., 2003). However, SAMHSA discontinued producing State-level serious mental illness estimates beginning with the release of the 2003-2004 State report because of concerns about the validity of using only the K6 distress scale without an impairment scale; see Section B.4.4 of Appendix B in the 2004 NSDUH national findings report (OAS, 2005). The use of the K6 distress scale continued in the 2003-2004, 2004-2005, 2005-2006, and 2006-2007 State reports, but the outcome measure was changed from serious mental illness to serious psychological distress because it was determined that the K6 scale only measured serious psychological distress and only contributed to measuring serious mental illness (see details below).

In December 2006, a technical advisory group meeting of expert consultants was convened by SAMHSA's Center for Mental Health Services to solicit recommendations for mental health surveillance data collection strategies among the U.S. population. The panel recommended that NSDUH should be used to produce estimates of serious mental illness among adults using NSDUH's mental health measures and a gold-standard clinical psychiatric interview. In response, SAMHSA's CBHSQ initiated a Mental Health Surveillance Study (MHSS) under its NSDUH contract with RTI International to develop and implement methods to estimate serious mental illness. Based on recommendations from this panel, estimates of serious mental illness presented in this report for 2008 and 2009 are based on a revised methodology and, thus, are not comparable with serious mental illness estimates or serious psychological distress estimates shown in prior NSDUH State reports.

To develop methods for preparing the estimates of serious mental illness and any mental illness presented in this and other NSDUH reports, the MHSS was initiated as part of the 2008 NSDUH design and analysis. Because of constraints on the interview time in NSDUH and the need for trained mental health clinicians, it was not possible to administer a full structured diagnostic clinical interview to assess mental illness on approximately 45,000 adult respondents; therefore, the approach adopted by SAMHSA was to utilize short scales separately measuring psychological distress (K6) and functional impairment that could be used in a statistical model to accurately predict whether a respondent had a mental illness. Two impairment scales—the World Health Organization Disability Assessment Schedule (WHODAS) and the Sheehan Disability Scale (SDS)—were included in the 2008 survey for evaluation. The collection of clinical psychiatric interview data was achieved using a subsample of approximately 1,500 adult NSDUH participants in 2008. These participants were recruited for a follow-up clinical interview consisting of a gold-standard diagnostic assessment for mental disorders and functional impairment. In order to determine the optimal scale to measure functional impairment, a split-sample design was incorporated into the full 2008 NSDUH data collection in which half of the adult respondents received the WHODAS and half received the SDS. Statistical models using the data from the subsample of respondents collected as part of the MHSS then were developed for each half sample in which the short scales (the K6 in combination with the WHODAS or the K6 in combination with the SDS) were used as predictors in models of mental illness assessed via the clinical interviews. The model parameter estimates were then used to predict serious mental illness in the full 2008 NSDUH sample.

Kessler-6 Distress Scale

The K6 in NSDUH consists of two sets of six questions that asked adult respondents how frequently they experienced symptoms of psychological distress during two different time periods: (1) during the past 30 days, and (2) if applicable, the one month in the past year when they were at their worst emotionally. Respondents were asked about the second time period only if they indicated that there was a month in the past 12 months when they felt more depressed, anxious, or emotionally stressed than they felt during the past 30 days.

The six questions comprising the K6 scale for the past month are as follows:

NERVE30
During the past 30 days, how often did you feel nervous?

1   All of the time
2   Most of the time
3   Some of the time
4   A little of the time
5   None of the time
Don't know/Refused

Response categories are the same for the remaining questions shown below.

HOPE30
During the past 30 days, how often did you feel hopeless?
FIDG30
During the past 30 days, how often did you feel restless or fidgety?
NOCHR30
During the past 30 days, how often did you feel so sad or depressed that nothing could cheer you up?
EFFORT30
During the past 30 days, how often did you feel that everything was an effort?
DOWN30
During the past 30 days, how often did you feel down on yourself, no good or worthless?

To create a score, the six items (NERV30, HOPE30, FIDG30, NOCHR30, EFFORT30, and DOWN30) on the K6 scale were recoded from 0 to 4 so that "all of the time" was coded 4, "most of the time" 3, "some of the time" 2, "a little of the time" 1, and "none of the time" 0, with "don't know" and "refused" also coded as 0. Summing across the transformed responses in these six items resulted in a score with a range from 0 to 24.

If respondents were asked about a month in the past 12 months when they felt more depressed, anxious, or emotionally stressed than they felt during the past 30 days, they were asked comparable K6 items for that particular month in the past 12 months. The scoring procedures for these K6 items for the past 12 months were the same as those described above. The higher of the two K6 total scores for the past 30 days or past 12 months was used both for MHSS analysis purposes and in the adult respondents' final data.

An alternative K6 total score also was created in which K6 scores less than 8 were recoded as 0 and scores from 8 to 24 were recoded as 1 to 17. The rationale for creating the alternative past year K6 score was that serious mental illness prevalence was typically extremely low for respondents with past year K6 scores less than 8, and the prevalence rates started increasing only when scores were 8 or greater.

MHSS Clinical Interviews

As described previously, a subsample of approximately 1,500 adult NSDUH participants in 2008 completed follow-up clinical interviews to provide data for the statistical modeling of the NSDUH interview data of psychological distress and functional impairment on mental health status. The MHSS sample respondents were administered clinical interviews within 4 weeks of the NSDUH main interview to assess the presence of mental disorders and functional impairment. Specifically, each participant was assessed by a trained clinical interviewer (master's or doctoral-level clinician, counselor, or social worker) via paper-and-pencil interviewing (PAPI) over the telephone. The clinical interview used was an adapted version of the Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition (SCID-I/NP) (First, Spitzer, Gibbon, & Williams, 2002). Past year disorders that were assessed through the SCID included mood disorders (e.g., major depressive episode, manic episode), anxiety disorders (e.g., panic disorder, generalized anxiety disorder, posttraumatic stress disorder), eating disorders (e.g., anorexia nervosa), intermittent explosive disorder, and adjustment disorder. In addition, the presence of psychotic symptoms was assessed. Substance use disorders also were assessed, although these disorders were not included in the estimates of mental illness.

Functional impairment ratings were assigned by clinical interviewers using the Global Assessment of Functioning (GAF) scale (Endicott, Spitzer, Fleiss, & Cohen, 1976). Mental illness, measured using the SCID and differentiated by the level of functional impairment, was defined in the MHSS as follows:

The SCID and the GAF in combination were considered to be the gold standard for measuring mental illness.

Statistical modeling involved developing separate weighted logistic regression prediction models for the K6 and for each of the two impairment scales. With serious mental illness status based on having a SCID diagnosis plus a GAF less than or equal to 50, the response variable Y was defined so that

Y = 1 when a serious mental illness diagnosis is positive; otherwise, Y = 0.

If X is a vector of explanatory variables, then the response probability Pi equals the probability of capital Y given capital X, where capital X is the vector of explanatory variables. can be estimated using weighted logistic regression models for the WHODAS and SDS half samples. The final 2008 WHODAS and SDS calibration models, respectively, were determined as follows:

Equation A25     D     (1)

Equation A26,     D     (2)

where pi hat refers to an estimate of the serious mental illness response probability pi for the WHODAS and SDS models (indicated by the "w" subscript for the WHODAS and the "s" subscript for the SDS). The capital X sub k, capital X sub w, and capital X sub s terms refer to the alternative K6, WHODAS, and SDS scores:13

Rearranging terms of the two models provided a direct calculation of the predicted probability of serious mental illness:

Equation 32,     D

Equation 33.     D

Next, a cut point probability pi sub zero was determined, so that if Pi hat is greater than or equal to pi sub zero. for a particular respondent, then he or she was predicted to be serious mental illness positive; otherwise, he or she was predicted to be serious mental illness negative. Receiver operating characteristic (ROC) analyses were used to determine the cut point that resulted in the weighted number of false-positive and false-negative counts being (approximately) equal, thus ensuring unbiased estimates. The optimal cut points were determined to be 0.26972 and 0.26657 for the WHODAS and SDS models, respectively. See Aldworth et al. (2009) for further details.

Model fit statistics and various sensitivity analyses indicated that in combination with the K6, the WHODAS was a better predictor of serious mental illness than the SDS. Consequently, the decision was made to continue with the WHODAS as the measure of impairment for all adults in future NSDUHs. Nevertheless, for the final models, serious mental illness estimates based on the SDS in the 2008 full dataset were very similar to those based on the WHODAS, indicating that the estimates from the two half samples could be combined to form single estimates.

The 2008 prediction model parameters and cut points estimated using the 2008 WHODAS subsample were used to estimate serious mental illness in the 2009 NSDUH sample.

A.11.2 Any Mental Illness

Various methods to estimate any mental illness were investigated in the 2008 MHSS. These methods were subject to the constraint that they would have no effect on the serious mental illness estimates produced by the models discussed above. The methods investigated included logistic models based on any mental illness as the response variable, serious mental illness as the response variable, and multilogistic models based on a multilevel mental illness variable from which both serious mental illness and any mental illness could be derived. Analyses suggested that models based on serious mental illness as the response variable provided almost identical results to those of the other models, so this method was chosen to estimate any mental illness.

As noted previously, serious mental illness estimates for 2008 were based on both the WHODAS and SDS half samples because estimates of serious mental illness were comparable between half samples. Because estimates of any mental illness based on the SDS half sample were not comparable with those based on the WHODAS half sample, the decision was made to base estimates of any mental illness for 2008 only on the WHODAS half sample.

Estimates of any mental illness were obtained from the serious mental illness predicted probabilities calculated using the WHODAS model described above. Respondents with a predicted probability of serious mental illness greater than the cut point of 0.02400 were classified as having any mental illness.

A.11.3 Serious Thoughts of Suicide

Responding to a need for national data on the prevalence of suicidal thoughts and behavior, a set of questions was added beginning with the 2008 NSDUH questionnaire. These questions ask all adult respondents aged 18 or older if at any time during the past 12 months they had serious thoughts of suicide (suicidal ideation). State-level estimates of suicidal ideation are included in this report.

A.11.4 Major Depressive Episode (Depression)

According to the DSM-IV, a person is defined as having had major depressive episode 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. Respondents who have had a major depressive episode in their lifetime are asked if, during the past 12 months, they had a period of depression lasting 2 weeks or longer while also having some of the other symptoms mentioned. Those reporting that they have are defined as having had major depressive episode in the past year and then are asked questions from the SDS to measure the level of functional impairment in major life activities reported to be caused by the major depressive episode in the past 12 months (Leon, Olfson, Portera, Farber, & Sheehan, 1997).

Beginning in 2004, modules related to major depressive episode, derived from DSM-IV (APA, 1994) criteria for major depression, were included in the questionnaire. These questions permit prevalence estimates of major depressive episode to be calculated. Separate modules were administered to adults aged 18 or older and youths aged 12 to 17. The adult questions were adapted from the depression section of the National Comorbidity Survey Replication (NCS-R; Harvard School of Medicine, 2005), and the questions for youths were adapted from the depression section of the National Comorbidity Survey Adolescent (NCS-A; Harvard School of Medicine, 2005). To make the modules developmentally appropriate for youths, there are minor wording differences in a few questions between the adult and youth modules. Revisions to the questions in both modules were made primarily to reduce its length and to modify the NCS questions, which are interviewer-administered, to the audio computer-assisted self-interviewing (ACASI) format used in NSDUH. In addition, some revisions, based on cognitive testing, were made to improve comprehension.

Since 2004, the NSDUH questions that determine major depressive episode have remained unchanged. In the 2008 questionnaire, however, changes were made in other mental health items that precede the major depressive episode questions for adults (K6, suicide, and impairment). Questions also were retained in 2009 for the WHODAS impairment scale, and the questions for the SDS impairment scale were deleted; see Sections B.4.2 and B.4.3 in CBHSQ (2010) for further details about these questionnaire changes. These questionnaire changes in 2008 appear to have affected the reporting on major depressive episode questions among adults.

Because the WHODAS was selected to be used in the 2009 survey, model-based adjustments were applied to major depressive episode estimates from the SDS half sample in 2008 to remove the context effect differential between the two half samples. Additionally, model-based adjustments were made to the 2005, 2006, and 2007 adult major depressive episode estimates to make them comparable with the 2008 and 2009 major depressive episode estimates (for more information on these adjustments, see Aldworth, Kott, Yu, Mosquin, & Barnett-Walker, 2011). Thus, estimates of major depressive episode were produced for this report using the adjusted 2008 major depressive episode variable along with the unadjusted 2009 major depressive episode variable. Separate tables showing State-level 2007-2008 estimates along with 2005-2006 and 2006-2007 estimates, all based on the adjusted major depressive episode variables, are available at http://www.samhsa.gov/data/states.htm. A comparison of the 2007-2008 and 2008-2009 major depressive episode estimates are included in Table C.23. However, note that the major depressive episode estimates shown in this report for adults are not comparable with estimates shown in prior NSDUH State reports. For further discussion, see Sections B.4.4 and B.4.7 of the 2008 NSDUH national findings report (OAS, 2009).

In addition, changes to the youth mental health service utilization module questions in 2009 that preceded the questions about adolescent depression could have affected adolescents' responses to the adolescent depression questions and estimates of adolescent major depressive episode. However, these changes in 2009 did not appear to affect the estimates of adolescent major depressive episode. Therefore, data on trends in past year major depressive episode from 2004 to 2009 are available for adolescents aged 12 to 17.

A.12 Method for Determining Differences between Two State Estimates for 2008-2009

This section describes a method for determining whether differences between two 2008-2009 State estimates are statistically significant. This procedure can be used for any two State estimates representing the same age group (e.g., young adults aged 18 to 25) and time period (e.g., 2008-2009).

Let pi 1 sub a and pi 2 sub a denote the 2008-2009 age group-a specific prevalence rates for two different States, s1 and s2, respectively. The null hypothesis of no difference, that is, pi 1 sub a is equal to Pi 2 sub a, is equivalent to the log-odds ratio equal to zero, that is, Log-odds ratio lor sub a is equal to zero., where Log-odds ratio lor sub a. is defined as The log-odds ratio, lor sub a, is defined as the natural logarithm of the ratio of two quantities. The numerator of the ratio is Pi 2 sub a, divided by 1 minus Pi 2 sub a. The denominator of the ratio is Pi 1 sub a, divided by 1 minus Pi 1 sub a., where ln denotes the natural logarithm. An estimate of Log-odds ratio lor sub a. is given by The estimate of the log-odds ratio, lor hat sub a, is defined as the natural logarithm of the ratio of two quantities. The numerator of the ratio is p 2 sub a, divided by 1 minus p 2 sub a. The denominator of the ratio is p 1 sub a, divided by 1 minus p 1 sub a, where p 1 sub a is the 2008-2009 State estimate for State s1 and age group-a, and p 2 sub a is the 2008-2009 State estimate for State s2 and age group-a for a particular outcome of interest., where p 1 sub a and p 2 sub a are the 2008-2009 State estimates given in Appendix B. To compute the variance of estimate of the log-odds ratio, lor hat sub a that is, variance of the estimate of the log-odds ratio, lor hat sub a let Theta 1 hat is defined as the ratio of p 1 sub a and 1 minus p 1 sub a. and Theta 2 hat is defined as the ratio of p 2 sub a and 1 minus p 2 sub a.,

then Variance v of the estimate of the log-odds ratio, lor hat sub a, is a function of three quantities: q1, q2, and q3. It is expressed as the sum of q1 and q2 minus q3. Quantity q1 is the variance of the natural logarithm of Theta 1 hat, quantity q2 is the variance of the natural logarithm of Theta 2 hat, and quantity q3 is 2 times the covariance between the natural logarithm of Theta 1 hat and the natural logarithm of Theta 2 hat., where covariance between the natural logarithm of Theta 1 hat and the natural logarithm of Theta 2 hat denotes the covariance between natural logarithm of Theta 1 hat and natural logarithm of Theta 2 hat. This covariance is defined in

terms of the associated correlation as follows:

Equation A51.     D

The quantities variance of the natural logarithm of Theta 1 hat and variance of the natural logarithm of Theta 2 hat can be obtained by using the 95 percent Bayesian confidence intervals given in Appendix B. For this purpose, let Lower sub 1 and upper sub 1 represent the 95 percent confidence interval for State s1. and Lower sub 2 and upper sub 2 represent the 95 percent confidence interval for State s2. denote the 95 percent Bayesian confidence intervals for the two States, s1 and s2, respectively. Then

Equation A56,     D

where Equation A57.     D

For all practical purposes, the correlation between natural logarithm of Theta 1 hat and natural logarithm of Theta 2 hat is assumed to be negligible; hence, variance of the estimate of the log-odds ratio, lor hat sub a can be approximated by Variance v of the estimate of the log-odds ratio, lor hat sub a, is approximated by the sum of the variance of the natural logarithm of Theta 1 hat and the variance of the natural logarithm of Theta 2 hat. The correlation is assumed to be negligible because each State was a stratum in the first level of stratification; therefore, each State sample is selected independently. However, the correlation between the two State estimates is theoretically nonzero because State estimates share common fixed-effect parameters in the SAE models. Hence, the test statistic quantity z (defined below) might result in a different conclusion in a few cases when the correlation between the State estimates is incorporated in calculating variance of the estimate of the log-odds ratio, lor hat sub a To calculate the p value for testing the null hypothesis of no difference (Log-odds ratio lor sub a is equal to zero.), it is assumed that the posterior distribution of Log-odds ratio lor sub a. is normal with Mean is equal to the estimate of the log-odds ratio, lor hat sub a and Variance is equal to the variance v of the estimate of the log-odds ratio, lor hat sub a.. With the null value of Log-odds ratio lor sub a is equal to zero., the Bayes p value or posterior probability of no difference is The p value is equal to 2 times the probability of realizing a standard normal variate greater than or equal to the absolute value of a quantity z., where Z is a standard normal random variate, Quantity z is the estimate of the log-odds ratio, lor hat sub a, divided by the square root of the sum of the variance of the natural logarithm of Theta 1 hat and the variance of the natural logarithm of Theta 2 hat., and absolute value of quantity z denotes the absolute value of quantity z.


When comparing prevalence rates for two States, it is tempting and often convenient to look at their 95 percent Bayesian confidence intervals to decide whether the difference in the State prevalence rates is significant. If the two Bayesian confidence intervals overlap, one would conclude that the difference is not statistically significant. If the two Bayesian confidence intervals do not overlap, it implies that the State prevalence rates are significantly different from each other. However, the type-I error for the overlapping 95 percent Bayesian confidence intervals test is 0.6 percent (assuming that the two State estimates are uncorrelated and have the same variances) as compared with the 5 percent type-I error of the test based on the quantity z statistics defined above (Payton, Greenstone, & Schenker, 2003). Thus, using the overlap method with 95 percent Bayesian confidence intervals implies a type-I error that is much less than the 5 percent level that is typically prescribed for such tests.

As discussed in Schenker and Gentleman (2001), the method of overlapping Bayesian confidence intervals is more conservative (i.e., it rejects the null hypothesis of no difference less often) than the standard method based on quantity z statistics when the null hypothesis is true. Even if Bayesian confidence intervals for two States overlap, the two prevalence rates may be declared significantly different by the test based on quantity z statistics. Hence, the method of overlapping Bayesian confidence intervals is not recommended to test the equivalence of two State prevalence rates. A detailed description of the method of overlapping confidence intervals and its comparison with the standard methods for testing of a hypothesis is given in Schenker and Gentleman (2001) and Payton et al. (2003).

Example. The prevalence rates for past month alcohol use among 12 to 17 year olds in Arizona and North Dakota are shown in the exhibit below and also in Table B.9 in Appendix B. Looking at the two 95 percent Bayesian confidence intervals, it would appear that the Arizona and North Dakota prevalence rates for past month alcohol use are not statistically different at the 5 percent level of significance because the two Bayesian confidence intervals overlap:


State Point Estimate (%) 95% Bayesian Confidence Interval (%)
Arizona 14.72 (12.35, 17.45)
North Dakota 18.91 (16.31, 21.81)

However, in the following example, the test based on the quantity z statistic described earlier concludes that they are significantly different at the 5 percent level of significance.

Let Lowercase p 1 sub a equals 0.1472, lower sub1 equals 0.1235, upper sub1 equals 0.1745, p 2 sub a equals 0.1891, and lower sub 2 equals 0.1631. and Upper sub 2 equals 0.2181. Then,

Equation A66     D

Equation A67     D

Equation A68     D

Equation A69     D



Because the computed absolute value of quantity z is greater than or equal to 1.96 (the critical value of the quantity z statistic), then at the 5 percent level of significance, the hypothesis of no difference (Arizona prevalence rate = North Dakota prevalence rate) is rejected. Thus, the two State prevalence rates are statistically different. The Bayes p value or posterior probability of no difference is p value = The Bayes p value or posterior probability of no difference is calculated as 2 times the probability that capital Z is greater than or equal to 2.178. The p value is equal to 0.029..

A.13 Measuring Change in State Estimates

A.13.1 Change between 2007-2008 and 2008-2009 Small Area Estimates

Comparisons between State small area estimates displayed in Appendix C are based on the 2007 through 2009 NSDUHs. The State estimates for 2007-2008 are the previously published model-based small area estimates (Hughes et al., 2010). The State estimates for 2008-2009 are the small area estimates given in Appendix B. The moving average State prevalence estimates for the overlapping 2007-2008 and 2008-2009 time periods were obtained from independent applications of SWHB methodology; that is, the 2008-2009 models were fit independently of the previously fitted 2007-2008 models. This independent analysis approach was followed because there was no desire to revise the previously published 2007-2008 estimates. Moreover, the same fixed predictor variables were used in the 2007-2008 and 2008-2009 models, but annual updates were made when more current versions became available (see Section A.3 for details). The age group-specific fixed predictor variables were defined at five levels (namely, person-level, census block group-level, tract-level, county-level, and State-level). Also, each age group model had 51 State-level random effects and 300 "within-State" area-level random effects.

To estimate change in State estimates, let pi 1 sub s and a and pi 2 sub s and a denote 2007-2008 and 2008-2009 prevalence rates, respectively, for State-s and age group-a. The change between pi 1 sub s and a and pi 2 sub s and a is defined in terms of the log-odds ratio (log-odds ratio, lor sub s and a) as opposed to the simple difference because the posterior distribution of the log-odds ratio, lor sub s and a is closer to Gaussian than the posterior distribution of the simple difference (Pi 2 sub s and a minus pi 1 sub s and a represents the simple difference between the 2008-2009 and 2007-2008 prevalence rates.). The log-odds ratio, lor sub s and a is defined as

Equation A74,     D


where ln denotes the natural logarithm. The p value given in the Appendix C tables is computed to test the null hypothesis of no change (i.e., Pi 2 sub s and a is equal to pi 1 sub s and a. or equivalently log-odds ratio, lor sub s and a is equal to zero) An estimate of log-odds ratio, lor sub s and a is given by

Equation A75,     D


where the p 1 sub s and a are previously published 2007-2008 State estimates and the p 2 sub s and a are the 2008-2009 State estimates presented in this report (see Appendix B). To compute the variance of estimate of the log-odds ratio, lor hat sub s and a that is, variance 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 A82,     D


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

Equation A86.     D

Note that variance of the natural logarithm of Theta 1 hat and variance of the natural logarithm of Theta 2 hat used here to calculate variance of the estimate of the log-odds ratio, lor hat sub s and a are the same variances used in calculating the previously published 2007-2008 Bayesian confidence intervals and the 2008-2009 Bayesian confidence intervals given in this report, respectively.

The correlation between natural logarithm of Theta 1 hat and natural logarithm of Theta 2 hat was obtained by simultaneously modeling the 2007, 2008, and 2009 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 the 1999-2000 and 2000-2001 State estimates. For this simultaneous model, 4 age groups (12 to 17, 18 to 25, 26 to 34, and 35 or older) by 3 years (2007, 2008, and 2009), that is, 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 within-State random effects were 12 × 12 matrices corresponding to the 12 element (age group × year) vectors of random effects. Note that the survey-weighted, Bernoulli-type log likelihood employed in the SWHB methodology was appropriate for this simultaneous model because the 12 age group × year subpopulations were nonoverlapping. The correlation [natural logarithm of Theta 1 hat, natural logarithm of Theta 2 hat] was approximated by the correlation calculated using the posterior distributions of natural logarithm of pi 1 sub s and a divided by 1 minus pi 1 sub s and a and natural logarithm of pi 2 sub s and a divided by 1 minus pi 2 sub s and a from the simultaneous model.

To calculate the p value for testing the null hypothesis of no difference Log-odds ratio lor is equal to zero., it is assumed that the posterior distribution of log-odds ratio 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 Log-odds ratio lor is equal to zero., the Bayes p value or posterior probability of no difference is p value = The p value is equal to 2 times the probability of realizing a standard normal variate greater than or equal to the absolute value of a quantity 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 root of the variance v of the estimate of the log-odds ratio, lor hat sub s and a., and absolute value of quantity z denotes the absolute value of quantity z.

A.13.2 Change between 2002-2003 and 2008-2009 Small Area Estimates

The Bayes p values or posterior probabilities of no difference in prevalence rates for two nonoverlapping periods, 2002-2003 and 2008-2009, were calculated in a very similar manner to the method described in Section A.13.1. Borrowing from the notation above, let p 1 sub s and a refer to the previously published 2002-2003 State estimates (Wright & Sathe, 2005), and let p 2 sub s and a denote the 2008-2009 State estimates presented in this report (see Appendix B). The change between the two prevalence rates is defined in terms of the log-odds ratio as discussed in the prior section. The p value given in the Appendix D tables is computed to test the null hypothesis of no change, that is, to test Log-odds ratio lor sub s and a. = 0. An estimate of log-odds ratio, lor sub s and a is given by

Equation A104,     D


To compute the variance of estimate of the log-odds ratio, lor hat sub s and a that is, variance 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 A108,     D

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

Equation 112.     D

Note that variance of the natural logarithm of Theta 1 hat and variance of the natural logarithm of Theta 2 hat used here to calculate variance of the estimate of the log-odds ratio, lor hat sub s and a are the same posterior variances used in calculating the previously published 2002-2003 Bayesian confidence intervals and the 2008-2009 Bayesian confidence intervals given in this report, respectively.

The difference in the method discussed in Section A.13.1 and the method discussed here is in the model that was fit to find the correlation between natural logarithm of Theta 1 hat and natural logarithm of Theta 2 hat. Here, the correlation between natural logarithm of Theta 1 hat and natural logarithm of Theta 2 hat was obtained by simultaneously modeling the pooled 2002-2003 and pooled 2008-2009 NSDUH data. For this simultaneous model, four age groups (12 to 17, 18 to 25, 26 to 34, and 35 or older) by two time periods (2002-2003 and 2008-2009), that is, eight 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 8 × 8 matrices corresponding to the eight element (age group × time period) vectors of random effects.

The Bayes p value or posterior probability of no difference was calculated in a manner similar to that described in Section A.13.1.

101021
Table A.1 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2007
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, Center for Behavioral Health Statistics and Quality (formerly the Office of Applied Studies), National Survey on Drug Use and Health, 2007.
Total U.S. 192,092 158,411 141,487 89.45% 85,774 67,870 247,845,207 73.94% 66.14%
Northeast 42,071 35,148 29,843 83.68% 17,486 13,642 45,877,579 71.65% 59.96%
Midwest 52,386 44,279 39,697 90.07% 24,150 19,110 54,799,063 74.34% 66.96%
South 58,260 46,564 42,423 91.72% 25,737 20,683 89,939,563 75.75% 69.47%
West 39,375 32,420 29,524 90.01% 18,401 14,435 57,229,003 72.52% 65.27%
Alabama 2,375 1,914 1,794 93.71% 1,152 899 3,811,968 71.76% 67.25%
Alaska 2,419 1,682 1,520 90.41% 1,066 852 541,042 77.92% 70.45%
Arizona 2,745 2,059 1,828 88.61% 1,157 885 5,120,090 70.49% 62.46%
Arkansas 2,556 2,001 1,875 93.72% 1,115 912 2,316,670 79.93% 74.91%
California 8,737 7,799 6,888 88.34% 4,835 3,652 29,849,097 70.68% 62.44%
Colorado 2,648 2,176 1,989 91.29% 1,121 889 3,976,785 74.46% 67.97%
Connecticut 2,903 2,594 2,292 88.24% 1,166 920 2,917,789 76.99% 67.94%
Delaware 2,335 1,929 1,729 89.74% 1,102 883 714,649 77.05% 69.15%
District of Columbia 4,265 3,339 2,782 83.14% 1,044 824 501,857 75.29% 62.60%
Florida 10,898 8,452 7,543 89.21% 4,576 3,585 15,266,862 71.81% 64.06%
Georgia 2,201 1,720 1,608 93.55% 1,083 891 7,642,504 78.31% 73.26%
Hawaii 2,912 2,406 2,021 82.95% 1,179 849 1,053,117 64.34% 53.37%
Idaho 2,420 2,015 1,901 94.35% 1,160 943 1,200,903 78.11% 73.70%
Illinois 11,061 9,611 7,472 77.47% 4,984 3,634 10,545,802 67.57% 52.34%
Indiana 2,412 2,018 1,885 93.37% 1,160 921 5,201,443 74.01% 69.11%
Iowa 2,449 2,098 1,960 93.32% 1,110 920 2,475,077 77.20% 72.04%
Kansas 2,184 1,849 1,745 94.39% 1,107 890 2,255,504 79.65% 75.18%
Kentucky 2,335 1,970 1,855 94.13% 1,107 888 3,496,061 77.47% 72.92%
Louisiana 2,521 1,765 1,662 94.20% 1,094 901 3,484,871 74.17% 69.86%
Maine 3,196 2,350 2,144 91.28% 1,119 917 1,126,007 76.43% 69.77%
Maryland 2,346 2,017 1,681 83.23% 1,119 888 4,639,855 76.47% 63.65%
Massachusetts 2,818 2,382 2,078 87.07% 1,143 899 5,441,203 72.84% 63.42%
Michigan 9,220 7,622 6,826 89.55% 4,439 3,566 8,380,042 74.36% 66.59%
Minnesota 2,465 2,107 1,977 93.75% 1,132 925 4,305,593 78.89% 73.96%
Mississippi 2,279 1,692 1,599 94.19% 1,081 899 2,343,924 78.12% 73.58%
Missouri 2,490 2,072 1,953 94.26% 1,129 916 4,837,421 73.73% 69.49%
Montana 2,823 2,195 2,071 94.34% 1,080 891 801,167 78.25% 73.82%
Nebraska 2,391 2,013 1,899 94.34% 1,123 917 1,445,813 77.32% 72.94%
Nevada 2,413 1,996 1,883 94.54% 1,100 890 2,088,962 76.83% 72.64%
New Hampshire 2,626 2,067 1,866 90.08% 1,105 876 1,112,661 76.93% 69.30%
New Jersey 2,568 2,227 1,942 87.15% 1,153 898 7,227,870 74.93% 65.30%
New Mexico 2,701 2,037 1,923 94.43% 1,151 956 1,606,155 76.31% 72.06%
New York 12,392 10,631 8,106 75.87% 5,130 3,699 16,191,334 65.11% 49.40%
North Carolina 2,942 2,434 2,251 92.46% 1,206 974 7,381,205 74.56% 68.94%
North Dakota 2,649 2,145 2,022 94.27% 1,106 905 530,226 79.91% 75.33%
Ohio 10,168 8,632 8,124 94.10% 4,530 3,626 9,508,750 75.26% 70.82%
Oklahoma 2,802 2,279 2,070 90.77% 1,204 952 2,927,119 75.67% 68.69%
Oregon 2,482 2,130 1,968 92.17% 1,160 916 3,138,875 73.88% 68.10%
Pennsylvania 10,437 8,853 7,765 87.48% 4,525 3,649 10,433,605 75.65% 66.18%
Rhode Island 2,535 2,165 1,933 89.31% 1,118 914 892,599 75.72% 67.62%
South Carolina 2,792 2,188 2,053 93.83% 1,129 925 3,607,724 78.53% 73.69%
South Dakota 2,201 1,783 1,693 94.94% 1,122 922 649,052 79.35% 75.34%
Tennessee 2,306 1,887 1,765 93.57% 1,101 896 5,082,082 75.51% 70.66%
Texas 7,818 6,413 6,054 94.35% 4,324 3,557 18,904,425 77.52% 73.15%
Utah 1,924 1,611 1,531 95.04% 1,083 900 2,049,189 79.63% 75.67%
Vermont 2,596 1,879 1,717 91.39% 1,027 870 534,511 81.75% 74.71%
Virginia 2,579 2,134 1,864 87.24% 1,187 924 6,282,584 76.23% 66.51%
Washington 2,476 2,129 1,963 92.08% 1,150 909 5,372,199 75.66% 69.66%
West Virginia 2,910 2,430 2,238 91.99% 1,113 885 1,535,205 76.20% 70.10%
Wisconsin 2,696 2,329 2,141 92.14% 1,208 968 4,664,339 78.09% 71.96%
Wyoming 2,675 2,185 2,038 93.30% 1,159 903 431,423 74.79% 69.78%
101021
Table A.2 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2007
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly the Office of Applied Studies), National Survey on Drug Use and Health, 2007.
Total U.S. 26,191 22,475 25,241,088 85.35% 28,085 22,409 32,730,853 79.76% 31,498 22,986 189,873,266 71.42%
Northeast 5,317 4,496 4,458,471 82.32% 5,763 4,530 5,902,786 76.60% 6,406 4,616 35,516,321 69.50%
Midwest 7,415 6,364 5,614,954 85.94% 7,920 6,341 7,287,805 80.20% 8,815 6,405 41,896,304 71.79%
South 7,873 6,809 9,129,195 86.77% 8,322 6,765 11,686,936 81.75% 9,542 7,109 69,123,431 73.28%
West 5,586 4,806 6,038,467 84.85% 6,080 4,773 7,853,326 78.74% 6,735 4,856 43,337,209 69.62%
Alabama 333 276 385,087 82.86% 357 304 497,891 85.65% 462 319 2,928,991 68.12%
Alaska 366 318 64,158 88.19% 331 250 75,478 76.22% 369 284 401,406 76.40%
Arizona 332 288 535,271 85.60% 386 287 668,649 73.71% 439 310 3,916,169 67.83%
Arkansas 370 313 233,624 84.88% 325 259 294,086 81.27% 420 340 1,788,961 79.17%
California 1,461 1,221 3,239,651 83.14% 1,561 1,200 4,239,933 78.12% 1,813 1,231 22,369,512 67.39%
Colorado 364 315 388,527 88.00% 375 294 518,151 78.81% 382 280 3,070,107 71.99%
Connecticut 330 289 294,751 86.97% 411 310 354,623 75.78% 425 321 2,268,416 75.93%
Delaware 320 277 70,353 86.46% 404 324 91,857 82.64% 378 282 552,439 74.95%
District of Columbia 343 299 37,676 86.85% 324 256 84,330 78.90% 377 269 379,851 73.35%
Florida 1,285 1,101 1,383,657 85.40% 1,480 1,206 1,775,518 81.63% 1,811 1,278 12,107,687 68.84%
Georgia 328 290 825,764 88.73% 336 279 996,074 83.18% 419 322 5,820,666 75.88%
Hawaii 360 295 97,554 80.48% 375 272 127,524 73.01% 444 282 828,039 60.93%
Idaho 375 326 133,051 84.98% 379 305 163,370 80.51% 406 312 904,482 76.63%
Illinois 1,540 1,252 1,090,441 81.60% 1,591 1,172 1,430,266 73.58% 1,853 1,210 8,025,095 64.56%
Indiana 321 270 537,600 85.29% 439 361 680,319 81.32% 400 290 3,983,524 71.28%
Iowa 378 336 246,542 88.81% 327 279 342,411 85.30% 405 305 1,886,125 74.27%
Kansas 352 316 233,910 89.59% 339 254 322,756 74.27% 416 320 1,698,838 79.32%
Kentucky 337 286 341,080 85.06% 368 301 425,259 81.73% 402 301 2,729,722 75.71%
Louisiana 339 304 369,218 89.00% 351 299 508,682 85.81% 404 298 2,606,970 69.87%
Maine 342 301 104,509 87.63% 393 330 125,774 85.33% 384 286 895,724 73.67%
Maryland 316 271 475,278 85.07% 410 327 592,747 79.44% 393 290 3,571,831 75.05%
Massachusetts 364 303 511,379 79.34% 377 300 721,029 77.49% 402 296 4,208,794 71.11%
Michigan 1,317 1,132 882,825 85.67% 1,495 1,226 1,089,259 81.76% 1,627 1,208 6,407,959 71.52%
Minnesota 388 333 434,170 86.38% 344 282 579,707 82.49% 400 310 3,291,716 77.21%
Mississippi 325 288 258,825 88.59% 347 299 329,531 85.63% 409 312 1,755,568 75.06%
Missouri 348 305 492,534 87.89% 356 300 625,471 84.89% 425 311 3,719,416 70.31%
Montana 324 287 78,824 88.05% 357 292 105,687 80.10% 399 312 616,657 76.73%
Nebraska 378 330 148,560 88.07% 336 279 209,608 82.85% 409 308 1,087,646 74.48%
Nevada 301 267 213,775 90.01% 379 302 240,941 79.67% 420 321 1,634,245 74.87%
New Hampshire 339 282 110,622 82.17% 353 284 132,472 81.50% 413 310 869,567 75.67%
New Jersey 363 303 721,841 80.79% 358 276 855,683 75.53% 432 319 5,650,345 74.10%
New Mexico 373 340 169,013 91.93% 375 316 226,689 85.58% 403 300 1,210,452 72.06%
New York 1,541 1,240 1,569,950 79.78% 1,679 1,222 2,196,813 72.45% 1,910 1,237 12,424,572 62.09%
North Carolina 407 351 731,643 87.47% 385 312 916,505 82.26% 414 311 5,733,057 71.43%
North Dakota 372 313 50,461 84.21% 359 297 90,221 83.32% 375 295 389,544 78.44%
Ohio 1,343 1,173 965,669 87.49% 1,509 1,234 1,212,277 82.72% 1,678 1,219 7,330,804 72.44%
Oklahoma 429 360 298,069 84.23% 365 286 411,003 78.88% 410 306 2,218,047 73.84%
Oregon 319 274 297,399 85.62% 420 335 383,128 79.08% 421 307 2,458,349 71.81%
Pennsylvania 1,375 1,193 1,010,168 86.16% 1,521 1,231 1,322,592 80.95% 1,629 1,225 8,100,845 73.45%
Rhode Island 355 311 84,715 87.08% 336 288 126,010 87.70% 427 315 681,873 72.24%
South Carolina 319 281 362,012 88.01% 408 330 455,872 79.12% 402 314 2,789,840 77.27%
South Dakota 324 295 66,689 91.67% 355 294 91,410 84.11% 443 333 490,953 77.06%
Tennessee 360 316 498,268 88.15% 335 281 616,230 85.95% 406 299 3,967,584 72.28%
Texas 1,388 1,224 2,106,251 88.23% 1,398 1,151 2,698,089 82.21% 1,538 1,182 14,100,085 74.96%
Utah 349 311 245,792 90.52% 343 290 373,846 85.05% 391 299 1,429,552 76.08%
Vermont 308 274 50,535 89.07% 335 289 67,790 86.26% 384 307 416,186 80.25%
Virginia 347 294 617,259 84.79% 385 282 815,120 74.23% 455 348 4,850,205 75.53%
Washington 316 267 532,673 84.63% 401 324 670,845 82.14% 433 318 4,168,681 73.38%
West Virginia 327 278 135,134 83.98% 344 269 178,142 77.99% 442 338 1,221,929 75.12%
Wisconsin 354 309 465,555 87.02% 470 363 614,100 77.96% 384 296 3,584,684 76.95%
Wyoming 346 297 42,779 86.29% 398 306 59,086 77.75% 415 300 329,558 72.47%
101021
Table A.3 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2008
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, Center for Behavioral Health Statistics and Quality (formerly the Office of Applied Studies), National Survey on Drug Use and Health, 2008.
Total U.S. 194,815 160,133 142,938 89.04% 86,435 68,736 249,815,089 74.45% 66.29%
Northeast 41,088 34,150 29,235 84.54% 17,336 13,594 46,098,527 72.48% 61.28%
Midwest 52,794 44,490 39,977 90.15% 24,383 19,314 54,957,186 74.93% 67.55%
South 59,559 47,794 43,312 91.24% 25,641 20,877 90,962,960 76.59% 69.88%
West 41,374 33,699 30,414 88.10% 19,075 14,951 57,796,416 72.24% 63.64%
Alabama 2,946 2,329 2,140 92.06% 1,173 929 3,843,374 71.78% 66.09%
Alaska 2,628 1,763 1,597 90.64% 1,147 908 541,167 76.32% 69.18%
Arizona 2,899 2,071 1,820 88.20% 1,131 908 5,239,324 76.87% 67.79%
Arkansas 2,699 2,130 2,000 93.82% 1,122 933 2,332,677 77.25% 72.48%
California 9,128 8,079 6,843 84.56% 5,036 3,830 30,012,612 69.66% 58.90%
Colorado 2,963 2,366 2,149 90.78% 1,195 949 4,035,628 76.15% 69.13%
Connecticut 2,744 2,426 2,158 88.84% 1,162 938 2,919,630 75.10% 66.72%
Delaware 2,547 2,123 1,858 87.67% 1,166 943 721,693 78.71% 69.01%
District of Columbia 4,070 3,307 2,720 82.08% 1,078 900 505,593 78.87% 64.74%
Florida 11,058 8,486 7,704 90.84% 4,388 3,590 15,343,888 76.52% 69.51%
Georgia 2,610 2,026 1,836 90.56% 1,089 877 7,753,524 73.73% 66.77%
Hawaii 3,047 2,373 2,038 84.44% 1,277 897 1,052,720 65.04% 54.92%
Idaho 2,393 1,943 1,842 94.82% 1,147 942 1,219,776 78.15% 74.11%
Illinois 10,542 9,213 7,350 79.73% 5,045 3,743 10,598,573 68.66% 54.74%
Indiana 2,314 1,947 1,815 93.21% 1,147 914 5,225,927 77.75% 72.47%
Iowa 2,470 2,154 2,004 92.98% 1,152 945 2,484,297 80.80% 75.12%
Kansas 2,163 1,864 1,746 93.67% 1,100 884 2,269,597 76.83% 71.97%
Kentucky 2,644 2,163 2,040 94.34% 1,097 884 3,524,562 73.21% 69.06%
Louisiana 2,414 1,820 1,717 94.31% 1,082 881 3,581,692 78.79% 74.30%
Maine 3,212 2,374 2,196 92.46% 1,102 915 1,126,276 77.15% 71.33%
Maryland 2,526 2,212 1,858 83.86% 1,181 981 4,660,360 77.55% 65.03%
Massachusetts 2,562 2,159 1,908 88.09% 1,112 897 5,476,618 76.63% 67.50%
Michigan 10,246 8,222 7,299 88.81% 4,587 3,675 8,341,138 75.18% 66.77%
Minnesota 2,238 1,918 1,805 94.08% 1,073 881 4,323,170 78.86% 74.19%
Mississippi 2,109 1,677 1,587 94.69% 1,074 883 2,358,646 78.01% 73.87%
Missouri 2,613 2,186 2,045 93.58% 1,131 914 4,864,752 76.30% 71.40%
Montana 2,869 2,340 2,211 94.50% 1,139 919 808,201 77.02% 72.78%
Nebraska 2,316 1,915 1,805 94.26% 1,105 888 1,451,290 76.82% 72.41%
Nevada 2,778 2,256 2,121 94.20% 1,124 887 2,115,107 74.07% 69.77%
New Hampshire 2,585 2,006 1,761 87.82% 1,113 904 1,115,443 79.14% 69.50%
New Jersey 2,757 2,336 2,054 88.06% 1,247 974 7,225,089 73.12% 64.39%
New Mexico 2,591 1,946 1,835 94.30% 1,073 876 1,616,007 79.35% 74.83%
New York 11,715 9,885 7,693 77.90% 4,928 3,570 16,365,125 66.90% 52.12%
North Carolina 2,433 2,039 1,874 92.06% 1,084 890 7,496,430 78.16% 71.95%
North Dakota 2,818 2,293 2,158 94.19% 1,142 932 530,391 78.87% 74.29%
Ohio 10,373 8,808 8,239 93.53% 4,641 3,692 9,526,405 73.94% 69.15%
Oklahoma 2,192 1,775 1,602 90.43% 1,117 897 2,941,713 78.99% 71.43%
Oregon 2,756 2,353 2,170 92.31% 1,242 1,011 3,173,495 71.54% 66.04%
Pennsylvania 10,033 8,623 7,521 86.90% 4,441 3,601 10,448,312 75.76% 65.84%
Rhode Island 2,653 2,197 1,966 89.44% 1,080 881 887,019 77.68% 69.48%
South Carolina 2,806 2,167 1,977 91.00% 1,113 938 3,667,059 82.06% 74.68%
South Dakota 2,297 1,907 1,821 95.55% 1,143 963 653,933 78.42% 74.93%
Tennessee 2,418 1,978 1,822 92.15% 1,181 937 5,136,799 75.26% 69.35%
Texas 8,122 6,682 6,215 93.03% 4,367 3,556 19,229,370 76.81% 71.45%
Utah 1,730 1,521 1,440 94.74% 1,155 961 2,113,331 78.29% 74.17%
Vermont 2,827 2,144 1,978 92.26% 1,151 914 535,016 75.19% 69.37%
Virginia 2,592 2,142 1,878 87.62% 1,152 926 6,328,752 75.92% 66.52%
Washington 2,758 2,397 2,213 92.43% 1,197 920 5,431,264 73.35% 67.79%
West Virginia 3,373 2,738 2,484 90.51% 1,177 932 1,536,829 76.20% 68.97%
Wisconsin 2,404 2,063 1,890 91.53% 1,117 883 4,687,712 76.91% 70.39%
Wyoming 2,834 2,291 2,135 93.20% 1,212 943 437,785 72.21% 67.30%
101021
Table A.4 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2008
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly the Office of Applied Studies), National Survey on Drug Use and Health, 2008.
Total U.S. 26,501 22,559 24,892,326 84.73% 29,091 23,468 32,938,183 80.67% 30,843 22,709 191,984,580 72.00%
Northeast 5,245 4,437 4,374,575 83.22% 5,866 4,661 5,986,651 78.36% 6,225 4,496 35,737,300 70.21%
Midwest 7,439 6,305 5,508,681 84.56% 8,217 6,591 7,275,820 79.57% 8,727 6,418 42,172,686 72.85%
South 7,927 6,846 9,050,269 86.19% 8,663 7,169 11,764,906 83.27% 9,051 6,862 70,147,785 74.15%
West 5,890 4,971 5,958,801 83.78% 6,345 5,047 7,910,806 79.52% 6,840 4,933 43,926,809 69.31%
Alabama 340 292 380,937 86.23% 410 341 501,390 83.71% 423 296 2,961,047 67.68%
Alaska 370 300 61,212 80.19% 374 301 75,989 81.95% 403 307 403,966 74.55%
Arizona 352 307 538,925 87.29% 384 311 675,594 79.95% 395 290 4,024,805 74.78%
Arkansas 354 324 231,729 91.17% 398 328 293,143 84.25% 370 281 1,807,805 74.19%
California 1,471 1,223 3,178,553 82.21% 1,748 1,372 4,276,022 79.29% 1,817 1,235 22,558,037 66.03%
Colorado 398 341 385,509 85.87% 361 279 522,146 77.78% 436 329 3,127,973 74.54%
Connecticut 306 270 289,686 90.03% 443 359 358,342 79.84% 413 309 2,271,601 72.79%
Delaware 351 290 69,446 82.91% 437 354 92,890 81.55% 378 299 559,357 77.54%
District of Columbia 300 273 36,326 92.35% 398 336 84,963 84.30% 380 291 384,303 76.47%
Florida 1,383 1,197 1,353,763 86.91% 1,399 1,176 1,779,426 83.78% 1,606 1,217 12,210,699 74.30%
Georgia 364 313 823,565 85.83% 335 282 1,002,141 84.62% 390 282 5,927,818 69.89%
Hawaii 360 276 94,033 77.53% 431 317 130,031 72.11% 486 304 828,656 62.56%
Idaho 356 314 132,813 88.57% 360 301 163,669 82.85% 431 327 923,294 76.09%
Illinois 1,515 1,235 1,074,628 81.78% 1,689 1,272 1,455,604 74.87% 1,841 1,236 8,068,342 65.78%
Indiana 389 324 532,430 84.25% 370 289 675,007 78.93% 388 301 4,018,491 76.71%
Iowa 351 300 242,215 85.63% 372 304 339,024 82.29% 429 341 1,903,058 79.95%
Kansas 304 259 230,579 84.49% 395 317 320,106 82.00% 401 308 1,718,912 74.93%
Kentucky 361 314 338,183 85.31% 359 299 425,780 81.01% 377 271 2,760,600 70.28%
Louisiana 328 276 372,486 83.41% 361 301 519,209 84.62% 393 304 2,689,997 76.84%
Maine 321 286 101,011 88.64% 372 314 125,017 83.72% 409 315 900,248 75.00%
Maryland 380 332 463,837 86.83% 398 340 603,272 86.40% 403 309 3,593,251 74.53%
Massachusetts 352 301 501,071 85.22% 365 294 745,429 80.99% 395 302 4,230,117 74.93%
Michigan 1,381 1,192 855,511 86.10% 1,591 1,299 1,083,355 81.49% 1,615 1,184 6,402,273 72.60%
Minnesota 343 301 424,864 87.96% 360 290 572,788 80.73% 370 290 3,325,519 77.33%
Mississippi 330 289 254,843 87.98% 353 296 330,023 83.47% 391 298 1,773,779 75.80%
Missouri 358 315 484,594 85.58% 360 284 622,228 76.74% 413 315 3,757,931 74.90%
Montana 383 318 77,182 83.49% 371 312 105,186 84.56% 385 289 625,834 74.91%
Nebraska 346 299 145,878 86.01% 358 291 207,730 79.46% 401 298 1,097,683 75.01%
Nevada 367 320 213,611 87.72% 382 302 243,004 79.89% 375 265 1,658,492 71.42%
New Hampshire 336 285 107,937 84.98% 361 297 132,623 82.48% 416 322 874,884 78.02%
New Jersey 390 316 708,395 80.08% 488 394 861,235 80.20% 369 264 5,655,459 71.02%
New Mexico 316 281 165,144 87.61% 346 275 225,333 79.50% 411 320 1,225,529 78.27%
New York 1,418 1,155 1,548,677 80.19% 1,675 1,213 2,240,017 72.47% 1,835 1,202 12,576,431 64.26%
North Carolina 375 330 728,418 87.95% 312 256 936,723 83.17% 397 304 5,831,288 75.89%
North Dakota 346 296 49,073 85.02% 392 324 88,206 82.80% 404 312 393,112 77.23%
Ohio 1,498 1,262 948,248 84.14% 1,480 1,214 1,208,122 82.55% 1,663 1,216 7,370,036 71.18%
Oklahoma 324 276 293,748 84.67% 397 311 406,525 79.30% 396 310 2,241,440 78.22%
Oregon 369 312 293,880 84.04% 468 407 383,593 86.15% 405 292 2,496,022 67.47%
Pennsylvania 1,435 1,237 987,054 86.16% 1,440 1,203 1,329,112 83.43% 1,566 1,161 8,132,146 73.28%
Rhode Island 319 283 82,028 88.85% 354 289 126,487 82.24% 407 309 678,503 75.29%
South Carolina 350 302 357,713 86.20% 375 314 464,802 84.79% 388 322 2,844,544 81.14%
South Dakota 325 289 65,489 88.07% 399 351 90,410 87.27% 419 323 498,034 75.88%
Tennessee 316 263 495,488 83.78% 433 357 616,859 80.88% 432 317 4,024,452 73.37%
Texas 1,318 1,135 2,109,558 86.02% 1,475 1,232 2,706,388 83.77% 1,574 1,189 14,413,424 74.07%
Utah 378 337 251,154 86.62% 337 271 374,827 80.56% 440 353 1,487,351 76.52%
Vermont 368 304 48,716 81.63% 368 298 68,388 81.06% 415 312 417,912 73.53%
Virginia 360 307 607,065 85.38% 420 332 825,136 80.33% 372 287 4,896,552 73.98%
Washington 396 329 524,495 84.17% 383 290 675,978 77.04% 418 301 4,230,791 71.36%
West Virginia 393 333 133,164 85.61% 403 314 176,237 77.96% 381 285 1,227,428 74.83%
Wisconsin 283 233 455,175 83.70% 451 356 613,242 79.48% 383 294 3,619,295 75.54%
Wyoming 374 313 42,291 84.01% 400 309 59,434 76.96% 438 321 336,059 69.82%
101021
Table A.5 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2009
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, Center for Behavioral Health Statistics and Quality (formerly the Office of Applied Studies), National Survey on Drug Use and Health, 2009.
Total U.S. 195,132 161,321 143,565 88.77% 85,429 68,700 251,815,533 75.68% 67.19%
Northeast 42,197 34,950 29,573 83.72% 17,503 13,772 46,385,613 73.44% 61.48%
Midwest 53,123 44,770 40,360 90.34% 23,827 19,133 55,167,183 75.97% 68.64%
South 60,898 49,150 44,620 91.41% 25,560 20,976 92,048,862 77.39% 70.74%
West 38,914 32,451 29,012 87.11% 18,539 14,819 58,213,876 74.50% 64.90%
Alabama 2,831 2,286 2,128 92.98% 1,174 944 3,876,035 78.44% 72.93%
Alaska 2,303 1,768 1,631 92.08% 1,110 902 554,006 79.33% 73.05%
Arizona 2,723 2,050 1,778 82.93% 1,110 916 5,310,817 79.47% 65.91%
Arkansas 2,574 2,104 1,965 93.31% 1,133 914 2,358,363 77.30% 72.13%
California 8,934 7,761 6,499 83.86% 4,734 3,660 30,079,762 71.83% 60.24%
Colorado 2,727 2,272 2,088 92.12% 1,195 984 4,096,077 77.36% 71.26%
Connecticut 2,331 2,061 1,805 87.50% 1,147 915 2,937,125 76.43% 66.87%
Delaware 2,595 2,135 1,862 87.26% 1,129 920 731,769 73.59% 64.21%
District of Columbia 4,322 3,511 2,851 80.59% 1,042 886 510,289 83.69% 67.45%
Florida 11,388 8,721 8,040 91.93% 4,407 3,648 15,484,832 76.74% 70.54%
Georgia 2,295 1,864 1,716 91.79% 1,082 907 7,846,856 78.24% 71.82%
Hawaii 3,209 2,718 2,154 76.85% 1,321 960 1,052,232 67.00% 51.49%
Idaho 2,252 1,765 1,671 94.66% 1,119 916 1,235,558 77.15% 73.04%
Illinois 10,108 8,781 7,097 80.81% 4,786 3,655 10,592,235 71.70% 57.94%
Indiana 2,719 2,226 2,087 93.64% 1,119 904 5,261,391 79.31% 74.27%
Iowa 2,567 2,203 2,049 93.14% 1,099 924 2,486,476 81.80% 76.19%
Kansas 2,364 2,053 1,906 92.80% 1,132 909 2,279,789 76.12% 70.64%
Kentucky 2,411 1,946 1,828 93.94% 1,118 912 3,550,066 76.64% 72.00%
Louisiana 2,615 2,125 1,993 93.91% 1,143 923 3,640,052 78.89% 74.08%
Maine 3,209 2,339 2,150 92.05% 1,132 964 1,128,941 82.64% 76.07%
Maryland 2,231 1,911 1,581 82.74% 1,050 887 4,705,966 80.27% 66.42%
Massachusetts 3,277 2,813 2,385 84.82% 1,239 969 5,563,652 73.77% 62.57%
Michigan 10,360 8,303 7,345 88.44% 4,530 3,639 8,323,828 76.86% 67.98%
Minnesota 2,334 1,984 1,854 93.46% 1,132 925 4,356,171 77.67% 72.60%
Mississippi 2,084 1,619 1,527 94.27% 1,090 891 2,365,526 77.67% 73.22%
Missouri 2,529 2,077 1,933 93.09% 1,112 889 4,926,491 75.54% 70.32%
Montana 2,513 2,148 2,026 94.20% 1,119 909 814,381 75.98% 71.57%
Nebraska 2,274 1,940 1,830 94.35% 1,125 911 1,457,382 78.61% 74.16%
Nevada 2,605 2,063 1,941 94.25% 1,149 930 2,144,323 72.30% 68.14%
New Hampshire 2,786 2,255 2,004 88.82% 1,190 944 1,125,160 74.46% 66.14%
New Jersey 2,317 1,990 1,766 88.80% 1,172 906 7,241,791 72.36% 64.25%
New Mexico 2,548 2,032 1,916 94.26% 1,115 918 1,628,498 77.27% 72.83%
New York 13,014 10,782 8,289 76.73% 5,021 3,707 16,380,098 70.67% 54.23%
North Carolina 2,517 2,090 1,919 91.91% 1,112 929 7,612,327 79.41% 72.98%
North Dakota 2,919 2,427 2,290 94.35% 1,149 929 534,362 76.67% 72.33%
Ohio 9,800 8,405 7,847 93.27% 4,392 3,585 9,581,963 74.92% 69.88%
Oklahoma 2,648 2,142 1,964 91.82% 1,124 908 2,970,916 74.49% 68.40%
Oregon 2,802 2,379 2,184 91.95% 1,170 947 3,199,775 79.93% 73.49%
Pennsylvania 9,705 8,305 7,205 86.72% 4,391 3,557 10,583,566 75.72% 65.67%
Rhode Island 2,779 2,343 2,061 87.87% 1,155 913 889,360 76.51% 67.23%
South Carolina 3,097 2,362 2,145 90.20% 1,153 954 3,730,181 76.22% 68.75%
South Dakota 2,417 2,030 1,942 95.66% 1,088 920 659,093 81.15% 77.63%
Tennessee 3,023 2,465 2,298 93.13% 1,172 949 5,196,019 73.45% 68.40%
Texas 8,652 7,178 6,591 91.91% 4,388 3,596 19,519,442 77.65% 71.37%
Utah 1,539 1,376 1,306 94.90% 1,101 918 2,144,172 80.38% 76.28%
Vermont 2,779 2,062 1,908 92.57% 1,056 897 535,921 79.32% 73.43%
Virginia 2,499 2,171 1,924 88.59% 1,125 918 6,410,227 77.07% 68.28%
Washington 2,359 2,098 1,913 91.14% 1,158 936 5,509,332 77.01% 70.19%
West Virginia 3,116 2,520 2,288 90.81% 1,118 890 1,539,997 73.90% 67.11%
Wisconsin 2,732 2,341 2,180 93.19% 1,163 943 4,708,003 76.66% 71.44%
Wyoming 2,400 2,021 1,905 94.26% 1,138 923 444,942 78.67% 74.16%
101021
Table A.6 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2009
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly the Office of Applied Studies), National Survey on Drug Use and Health, 2009.
Total U.S. 26,377 22,644 24,608,987 85.73% 28,444 23,248 33,579,988 81.70% 30,608 22,808 193,626,558 73.34%
Northeast 5,372 4,557 4,305,676 83.66% 5,917 4,711 6,120,620 78.85% 6,214 4,504 35,959,317 71.25%
Midwest 7,451 6,398 5,410,447 85.51% 7,803 6,325 7,337,646 80.48% 8,573 6,410 42,419,090 73.94%
South 7,844 6,800 9,008,998 87.15% 8,574 7,218 12,022,931 84.47% 9,142 6,958 71,016,933 74.93%
West 5,710 4,889 5,883,867 85.24% 6,150 4,994 8,098,792 80.90% 6,679 4,936 44,231,217 71.90%
Alabama 390 326 377,817 84.54% 345 281 510,045 81.35% 439 337 2,988,173 77.25%
Alaska 348 302 60,144 87.18% 363 298 78,273 84.24% 399 302 415,589 77.37%
Arizona 343 300 538,805 87.04% 400 326 696,689 81.36% 367 290 4,075,324 78.18%
Arkansas 348 306 231,302 89.27% 376 302 298,338 82.71% 409 306 1,828,723 74.91%
California 1,379 1,169 3,117,227 84.22% 1,567 1,240 4,383,689 79.48% 1,788 1,251 22,578,845 68.69%
Colorado 404 365 383,909 88.69% 417 336 533,064 82.80% 374 283 3,179,105 74.49%
Connecticut 367 308 286,054 84.72% 381 312 368,953 82.06% 399 295 2,282,118 74.52%
Delaware 358 310 68,377 86.67% 419 350 94,723 83.22% 352 260 568,669 70.42%
District of Columbia 288 250 35,126 86.53% 402 344 88,250 85.28% 352 292 386,914 83.03%
Florida 1,312 1,126 1,343,518 84.99% 1,538 1,328 1,829,604 85.43% 1,557 1,194 12,311,710 74.51%
Georgia 344 306 821,827 89.76% 342 295 1,025,485 84.40% 396 306 5,999,543 75.62%
Hawaii 391 311 92,363 77.48% 397 285 131,979 70.91% 533 364 827,890 65.27%
Idaho 331 284 133,111 86.64% 351 305 165,070 87.02% 437 327 937,376 74.28%
Illinois 1,406 1,177 1,056,872 83.68% 1,555 1,187 1,467,611 75.72% 1,825 1,291 8,067,752 69.43%
Indiana 332 285 527,261 87.20% 356 287 683,131 80.52% 431 332 4,051,000 78.03%
Iowa 339 302 237,996 90.33% 376 308 340,764 82.02% 384 314 1,907,716 80.77%
Kansas 347 303 227,693 87.71% 415 322 323,487 76.46% 370 284 1,728,609 74.44%
Kentucky 307 267 335,609 88.29% 396 328 431,390 82.52% 415 317 2,783,068 74.53%
Louisiana 338 284 369,414 83.73% 366 308 528,427 83.76% 439 331 2,742,211 77.34%
Maine 379 337 98,248 88.85% 394 334 125,394 84.97% 359 293 905,299 81.60%
Maryland 348 310 456,071 88.51% 341 288 618,887 85.87% 361 289 3,631,008 78.03%
Massachusetts 351 288 496,369 82.35% 428 349 771,025 81.73% 460 332 4,296,258 71.38%
Michigan 1,463 1,243 829,913 84.40% 1,470 1,200 1,090,449 81.26% 1,597 1,196 6,403,467 75.07%
Minnesota 355 307 417,528 85.64% 396 320 575,857 80.13% 381 298 3,362,786 76.19%
Mississippi 300 255 250,210 85.65% 372 318 332,057 86.26% 418 318 1,783,258 75.16%
Missouri 374 306 480,290 81.76% 352 294 630,416 82.92% 386 289 3,815,785 73.41%
Montana 350 295 75,210 85.30% 403 334 105,702 82.72% 366 280 633,469 73.66%
Nebraska 338 290 143,848 87.86% 375 304 209,977 80.99% 412 317 1,103,557 76.98%
Nevada 363 312 214,441 85.95% 391 334 250,525 86.82% 395 284 1,679,357 68.38%
New Hampshire 387 327 105,079 84.57% 356 286 134,825 81.03% 447 331 885,256 72.36%
New Jersey 345 290 697,510 82.70% 408 317 881,986 77.63% 419 299 5,662,295 70.27%
New Mexico 346 305 161,883 89.05% 368 310 230,548 86.27% 401 303 1,236,068 74.09%
New York 1,460 1,203 1,521,667 81.80% 1,718 1,249 2,285,210 73.89% 1,843 1,255 12,573,221 68.68%
North Carolina 309 273 727,521 88.60% 416 358 958,312 87.15% 387 298 5,926,494 77.09%
North Dakota 370 325 48,044 88.03% 356 286 89,285 81.04% 423 318 397,033 74.33%
Ohio 1,393 1,211 931,091 86.54% 1,425 1,206 1,217,923 84.04% 1,574 1,168 7,432,949 71.92%
Oklahoma 365 309 292,731 85.44% 349 287 412,462 82.00% 410 312 2,265,722 71.56%
Oregon 419 336 290,722 80.11% 316 264 390,321 84.79% 435 347 2,518,733 79.14%
Pennsylvania 1,382 1,183 973,827 86.18% 1,501 1,269 1,356,120 84.70% 1,508 1,105 8,253,618 72.89%
Rhode Island 382 333 80,228 87.98% 366 275 128,448 75.86% 407 305 680,684 75.29%
South Carolina 406 351 354,659 85.95% 371 321 474,729 84.77% 376 282 2,900,794 73.28%
South Dakota 322 292 64,477 90.52% 385 329 91,186 85.79% 381 299 503,430 79.03%
Tennessee 394 351 492,599 89.05% 348 289 627,894 84.29% 430 309 4,075,526 69.38%
Texas 1,342 1,182 2,118,403 88.34% 1,439 1,196 2,768,449 83.76% 1,607 1,218 14,632,591 74.99%
Utah 357 318 253,766 89.75% 362 300 377,293 81.68% 382 300 1,513,113 78.55%
Vermont 319 288 46,695 90.40% 365 320 68,659 87.37% 372 289 420,568 76.79%
Virginia 348 297 602,602 84.65% 385 330 846,780 86.18% 392 291 4,960,846 74.49%
Washington 357 311 520,243 87.01% 397 326 694,724 81.30% 404 299 4,294,365 75.18%
West Virginia 347 297 131,213 86.38% 369 295 177,100 78.81% 402 298 1,231,684 71.85%
Wisconsin 412 357 445,433 86.36% 342 282 617,562 81.33% 409 304 3,645,007 74.69%
Wyoming 322 281 42,044 84.77% 418 336 60,915 80.40% 398 306 341,983 77.62%
101021
Table A.7 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2007 and 2008
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.
NOTE: To compute the pooled 2007-2008 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2007 and 2008 individual response rates. The 2007-2008 population estimate is the average of the 2007 and the 2008 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly Office of Applied Studies), National Survey on Drug Use and Health, 2007 and 2008.
Total U.S. 386,907 318,544 284,425 89.25% 172,209 136,606 248,830,148 74.19% 66.21%
Northeast 83,159 69,298 59,078 84.11% 34,822 27,236 45,988,053 72.07% 60.62%
Midwest 105,180 88,769 79,674 90.11% 48,533 38,424 54,878,124 74.64% 67.26%
South 117,819 94,358 85,735 91.48% 51,378 41,560 90,451,261 76.17% 69.67%
West 80,749 66,119 59,938 89.06% 37,476 29,386 57,512,709 72.38% 64.46%
Alabama 5,321 4,243 3,934 92.88% 2,325 1,828 3,827,671 71.77% 66.66%
Alaska 5,047 3,445 3,117 90.52% 2,213 1,760 541,104 77.11% 69.81%
Arizona 5,644 4,130 3,648 88.39% 2,288 1,793 5,179,707 73.62% 65.07%
Arkansas 5,255 4,131 3,875 93.77% 2,237 1,845 2,324,674 78.68% 73.78%
California 17,865 15,878 13,731 86.47% 9,871 7,482 29,930,854 70.17% 60.67%
Colorado 5,611 4,542 4,138 91.03% 2,316 1,838 4,006,207 75.29% 68.54%
Connecticut 5,647 5,020 4,450 88.55% 2,328 1,858 2,918,710 76.01% 67.31%
Delaware 4,882 4,052 3,587 88.73% 2,268 1,826 718,171 77.82% 69.05%
District of Columbia 8,335 6,646 5,502 82.59% 2,122 1,724 503,725 77.11% 63.69%
Florida 21,956 16,938 15,247 90.01% 8,964 7,175 15,305,375 74.13% 66.73%
Georgia 4,811 3,746 3,444 91.99% 2,172 1,768 7,698,014 76.04% 69.95%
Hawaii 5,959 4,779 4,059 83.67% 2,456 1,746 1,052,918 64.69% 54.13%
Idaho 4,813 3,958 3,743 94.60% 2,307 1,885 1,210,339 78.13% 73.91%
Illinois 21,603 18,824 14,822 78.60% 10,029 7,377 10,572,188 68.12% 53.54%
Indiana 4,726 3,965 3,700 93.29% 2,307 1,835 5,213,685 75.91% 70.81%
Iowa 4,919 4,252 3,964 93.15% 2,262 1,865 2,479,687 79.01% 73.60%
Kansas 4,347 3,713 3,491 94.01% 2,207 1,774 2,262,551 78.25% 73.57%
Kentucky 4,979 4,133 3,895 94.23% 2,204 1,772 3,510,312 75.33% 70.98%
Louisiana 4,935 3,585 3,379 94.26% 2,176 1,782 3,533,282 76.41% 72.02%
Maine 6,408 4,724 4,340 91.86% 2,221 1,832 1,126,141 76.80% 70.55%
Maryland 4,872 4,229 3,539 83.55% 2,300 1,869 4,650,108 76.96% 64.30%
Massachusetts 5,380 4,541 3,986 87.59% 2,255 1,796 5,458,910 74.84% 65.55%
Michigan 19,466 15,844 14,125 89.18% 9,026 7,241 8,360,590 74.77% 66.68%
Minnesota 4,703 4,025 3,782 93.92% 2,205 1,806 4,314,382 78.87% 74.07%
Mississippi 4,388 3,369 3,186 94.45% 2,155 1,782 2,351,285 78.06% 73.73%
Missouri 5,103 4,258 3,998 93.92% 2,260 1,830 4,851,087 74.94% 70.38%
Montana 5,692 4,535 4,282 94.42% 2,219 1,810 804,684 77.64% 73.31%
Nebraska 4,707 3,928 3,704 94.30% 2,228 1,805 1,448,552 77.06% 72.67%
Nevada 5,191 4,252 4,004 94.37% 2,224 1,777 2,102,034 75.51% 71.25%
New Hampshire 5,211 4,073 3,627 88.93% 2,218 1,780 1,114,052 78.04% 69.40%
New Jersey 5,325 4,563 3,996 87.60% 2,400 1,872 7,226,479 74.04% 64.85%
New Mexico 5,292 3,983 3,758 94.36% 2,224 1,832 1,611,081 77.88% 73.49%
New York 24,107 20,516 15,799 76.89% 10,058 7,269 16,278,230 66.00% 50.75%
North Carolina 5,375 4,473 4,125 92.26% 2,290 1,864 7,438,817 76.38% 70.47%
North Dakota 5,467 4,438 4,180 94.23% 2,248 1,837 530,308 79.38% 74.80%
Ohio 20,541 17,440 16,363 93.81% 9,171 7,318 9,517,578 74.60% 69.99%
Oklahoma 4,994 4,054 3,672 90.60% 2,321 1,849 2,934,416 77.39% 70.12%
Oregon 5,238 4,483 4,138 92.24% 2,402 1,927 3,156,185 72.78% 67.13%
Pennsylvania 20,470 17,476 15,286 87.18% 8,966 7,250 10,440,959 75.71% 66.00%
Rhode Island 5,188 4,362 3,899 89.38% 2,198 1,795 889,809 76.65% 68.51%
South Carolina 5,598 4,355 4,030 92.40% 2,242 1,863 3,637,391 80.33% 74.22%
South Dakota 4,498 3,690 3,514 95.24% 2,265 1,885 651,493 78.89% 75.14%
Tennessee 4,724 3,865 3,587 92.85% 2,282 1,833 5,109,440 75.38% 69.99%
Texas 15,940 13,095 12,269 93.69% 8,691 7,113 19,066,897 77.16% 72.29%
Utah 3,654 3,132 2,971 94.89% 2,238 1,861 2,081,260 78.91% 74.88%
Vermont 5,423 4,023 3,695 91.83% 2,178 1,784 534,763 78.54% 72.12%
Virginia 5,171 4,276 3,742 87.43% 2,339 1,850 6,305,668 76.08% 66.52%
Washington 5,234 4,526 4,176 92.25% 2,347 1,829 5,401,732 74.50% 68.72%
West Virginia 6,283 5,168 4,722 91.26% 2,290 1,817 1,536,017 76.20% 69.54%
Wisconsin 5,100 4,392 4,031 91.84% 2,325 1,851 4,676,025 77.50% 71.17%
Wyoming 5,509 4,476 4,173 93.25% 2,371 1,846 434,604 73.46% 68.50%
101021
Table A.8 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2007 and 2008
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
NOTE: To compute the pooled 2007-2008 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2007 and 2008 individual response rates. The 2007-2008 population estimate is the average of the 2007 and the 2008 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly Office of Applied Studies), National Survey on Drug Use and Health, 2007 and 2008.
Total U.S. 52,692 45,034 25,066,707 85.04% 57,176 45,877 32,834,518 80.21% 62,341 45,695 190,928,923 71.71%
Northeast 10,562 8,933 4,416,523 82.77% 11,629 9,191 5,944,719 77.48% 12,631 9,112 35,626,811 69.86%
Midwest 14,854 12,669 5,561,817 85.25% 16,137 12,932 7,281,812 79.88% 17,542 12,823 42,034,495 72.32%
South 15,800 13,655 9,089,732 86.48% 16,985 13,934 11,725,921 82.51% 18,593 13,971 69,635,608 73.71%
West 11,476 9,777 5,998,634 84.32% 12,425 9,820 7,882,066 79.13% 13,575 9,789 43,632,009 69.46%
Alabama 673 568 383,012 84.53% 767 645 499,641 84.67% 885 615 2,945,019 67.91%
Alaska 736 618 62,685 84.26% 705 551 75,733 79.10% 772 591 402,686 75.46%
Arizona 684 595 537,098 86.45% 770 598 672,122 76.77% 834 600 3,970,487 71.23%
Arkansas 724 637 232,677 87.98% 723 587 293,615 82.78% 790 621 1,798,383 76.90%
California 2,932 2,444 3,209,102 82.68% 3,309 2,572 4,257,978 78.71% 3,630 2,466 22,463,775 66.70%
Colorado 762 656 387,018 86.93% 736 573 520,149 78.28% 818 609 3,099,040 73.24%
Connecticut 636 559 292,219 88.48% 854 669 356,482 77.81% 838 630 2,270,009 74.29%
Delaware 671 567 69,899 84.71% 841 678 92,374 82.10% 756 581 555,898 76.13%
District of Columbia 643 572 37,001 89.58% 722 592 84,647 81.59% 757 560 382,077 74.94%
Florida 2,668 2,298 1,368,710 86.14% 2,879 2,382 1,777,472 82.69% 3,417 2,495 12,159,193 71.54%
Georgia 692 603 824,664 87.31% 671 561 999,107 83.92% 809 604 5,874,242 72.93%
Hawaii 720 571 95,794 79.05% 806 589 128,777 72.56% 930 586 828,347 61.77%
Idaho 731 640 132,932 86.74% 739 606 163,520 81.70% 837 639 913,888 76.34%
Illinois 3,055 2,487 1,082,534 81.69% 3,280 2,444 1,442,935 74.22% 3,694 2,446 8,046,718 65.17%
Indiana 710 594 535,015 84.76% 809 650 677,663 80.14% 788 591 4,001,008 74.04%
Iowa 729 636 244,378 87.23% 699 583 340,717 83.81% 834 646 1,894,591 77.15%
Kansas 656 575 232,244 87.07% 734 571 321,431 78.18% 817 628 1,708,875 77.15%
Kentucky 698 600 339,631 85.18% 727 600 425,520 81.37% 779 572 2,745,161 72.98%
Louisiana 667 580 370,852 86.21% 712 600 513,946 85.21% 797 602 2,648,484 73.20%
Maine 663 587 102,760 88.12% 765 644 125,395 84.52% 793 601 897,986 74.37%
Maryland 696 603 469,557 85.96% 808 667 598,009 82.93% 796 599 3,582,541 74.82%
Massachusetts 716 604 506,225 82.21% 742 594 733,229 79.30% 797 598 4,219,456 73.16%
Michigan 2,698 2,324 869,168 85.88% 3,086 2,525 1,086,307 81.62% 3,242 2,392 6,405,116 72.06%
Minnesota 731 634 429,517 87.16% 704 572 576,248 81.62% 770 600 3,308,617 77.27%
Mississippi 655 577 256,834 88.29% 700 595 329,777 84.58% 800 610 1,764,673 75.45%
Missouri 706 620 488,564 86.73% 716 584 623,849 80.82% 838 626 3,738,674 72.43%
Montana 707 605 78,003 85.78% 728 604 105,436 82.32% 784 601 621,245 75.83%
Nebraska 724 629 147,219 87.06% 694 570 208,669 81.17% 810 606 1,092,664 74.76%
Nevada 668 587 213,693 88.88% 761 604 241,972 79.78% 795 586 1,646,368 73.23%
New Hampshire 675 567 109,279 83.54% 714 581 132,548 81.99% 829 632 872,225 76.86%
New Jersey 753 619 715,118 80.43% 846 670 858,459 77.88% 801 583 5,652,902 72.59%
New Mexico 689 621 167,079 89.82% 721 591 226,011 82.65% 814 620 1,217,991 75.34%
New York 2,959 2,395 1,559,314 79.99% 3,354 2,435 2,218,415 72.46% 3,745 2,439 12,500,501 63.16%
North Carolina 782 681 730,031 87.71% 697 568 926,614 82.72% 811 615 5,782,173 73.68%
North Dakota 718 609 49,767 84.60% 751 621 89,213 83.07% 779 607 391,328 77.81%
Ohio 2,841 2,435 956,958 85.82% 2,989 2,448 1,210,200 82.63% 3,341 2,435 7,350,420 71.82%
Oklahoma 753 636 295,908 84.45% 762 597 408,764 79.09% 806 616 2,229,744 76.13%
Oregon 688 586 295,639 84.84% 888 742 383,360 82.66% 826 599 2,477,185 69.80%
Pennsylvania 2,810 2,430 998,611 86.16% 2,961 2,434 1,325,852 82.19% 3,195 2,386 8,116,495 73.36%
Rhode Island 674 594 83,372 87.96% 690 577 126,249 85.00% 834 624 680,188 73.67%
South Carolina 669 583 359,862 87.10% 783 644 460,337 81.98% 790 636 2,817,192 79.25%
South Dakota 649 584 66,089 89.89% 754 645 90,910 85.62% 862 656 494,494 76.48%
Tennessee 676 579 496,878 85.98% 768 638 616,545 83.44% 838 616 3,996,018 72.83%
Texas 2,706 2,359 2,107,904 87.12% 2,873 2,383 2,702,238 82.99% 3,112 2,371 14,256,754 74.51%
Utah 727 648 248,473 88.58% 680 561 374,336 82.77% 831 652 1,458,451 76.32%
Vermont 676 578 49,626 85.41% 703 587 68,089 83.67% 799 619 417,049 76.97%
Virginia 707 601 612,162 85.08% 805 614 820,128 77.30% 827 635 4,873,378 74.79%
Washington 712 596 528,584 84.40% 784 614 673,412 79.67% 851 619 4,199,736 72.36%
West Virginia 720 611 134,149 84.80% 747 583 177,189 77.97% 823 623 1,224,679 74.98%
Wisconsin 637 542 460,365 85.33% 921 719 613,671 78.70% 767 590 3,601,989 76.25%
Wyoming 720 610 42,535 85.19% 798 615 59,260 77.35% 853 621 332,809 71.08%
101021
Table A.9 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2008 and 2009
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
NOTE: To compute the pooled 2008-2009 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2008 and 2009 individual response rates. The 2008-2009 population estimate is the average of the 2008 and the 2009 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly Office of Applied Studies), National Survey on Drug Use and Health, 2008 and 2009.
Total U.S. 389,947 321,454 286,503 88.91% 171,864 137,436 250,815,311 75.07% 66.74%
Northeast 83,285 69,100 58,808 84.12% 34,839 27,366 46,242,070 72.96% 61.38%
Midwest 105,917 89,260 80,337 90.24% 48,210 38,447 55,062,184 75.45% 68.09%
South 120,457 96,944 87,932 91.32% 51,201 41,853 91,505,911 77.00% 70.32%
West 80,288 66,150 59,426 87.61% 37,614 29,770 58,005,146 73.38% 64.29%
Alabama 5,777 4,615 4,268 92.54% 2,347 1,873 3,859,705 75.28% 69.66%
Alaska 4,931 3,531 3,228 91.41% 2,257 1,810 547,587 77.90% 71.21%
Arizona 5,622 4,121 3,598 85.29% 2,241 1,824 5,275,070 78.21% 66.71%
Arkansas 5,273 4,234 3,965 93.56% 2,255 1,847 2,345,520 77.28% 72.30%
California 18,062 15,840 13,342 84.22% 9,770 7,490 30,046,187 70.76% 59.59%
Colorado 5,690 4,638 4,237 91.46% 2,390 1,933 4,065,853 76.73% 70.18%
Connecticut 5,075 4,487 3,963 88.18% 2,309 1,853 2,928,377 75.74% 66.79%
Delaware 5,142 4,258 3,720 87.47% 2,295 1,863 726,731 75.98% 66.46%
District of Columbia 8,392 6,818 5,571 81.33% 2,120 1,786 507,941 81.22% 66.05%
Florida 22,446 17,207 15,744 91.37% 8,795 7,238 15,414,360 76.63% 70.02%
Georgia 4,905 3,890 3,552 91.19% 2,171 1,784 7,800,190 76.11% 69.41%
Hawaii 6,256 5,091 4,192 80.76% 2,598 1,857 1,052,476 66.03% 53.33%
Idaho 4,645 3,708 3,513 94.74% 2,266 1,858 1,227,667 77.66% 73.58%
Illinois 20,650 17,994 14,447 80.27% 9,831 7,398 10,595,404 70.19% 56.34%
Indiana 5,033 4,173 3,902 93.43% 2,266 1,818 5,243,659 78.52% 73.36%
Iowa 5,037 4,357 4,053 93.06% 2,251 1,869 2,485,386 81.30% 75.66%
Kansas 4,527 3,917 3,652 93.24% 2,232 1,793 2,274,693 76.49% 71.32%
Kentucky 5,055 4,109 3,868 94.14% 2,215 1,796 3,537,314 75.04% 70.64%
Louisiana 5,029 3,945 3,710 94.10% 2,225 1,804 3,610,872 78.84% 74.19%
Maine 6,421 4,713 4,346 92.25% 2,234 1,879 1,127,608 79.87% 73.68%
Maryland 4,757 4,123 3,439 83.31% 2,231 1,868 4,683,163 78.91% 65.74%
Massachusetts 5,839 4,972 4,293 86.38% 2,351 1,866 5,520,135 75.20% 64.96%
Michigan 20,606 16,525 14,644 88.62% 9,117 7,314 8,332,483 76.02% 67.37%
Minnesota 4,572 3,902 3,659 93.78% 2,205 1,806 4,339,670 78.27% 73.40%
Mississippi 4,193 3,296 3,114 94.49% 2,164 1,774 2,362,086 77.84% 73.55%
Missouri 5,142 4,263 3,978 93.34% 2,243 1,803 4,895,621 75.91% 70.86%
Montana 5,382 4,488 4,237 94.35% 2,258 1,828 811,291 76.50% 72.17%
Nebraska 4,590 3,855 3,635 94.30% 2,230 1,799 1,454,336 77.73% 73.30%
Nevada 5,383 4,319 4,062 94.22% 2,273 1,817 2,129,715 73.16% 68.94%
New Hampshire 5,371 4,261 3,765 88.32% 2,303 1,848 1,120,302 76.83% 67.86%
New Jersey 5,074 4,326 3,820 88.43% 2,419 1,880 7,233,440 72.73% 64.32%
New Mexico 5,139 3,978 3,751 94.28% 2,188 1,794 1,622,253 78.31% 73.83%
New York 24,729 20,667 15,982 77.31% 9,949 7,277 16,372,612 68.77% 53.17%
North Carolina 4,950 4,129 3,793 91.98% 2,196 1,819 7,554,378 78.81% 72.50%
North Dakota 5,737 4,720 4,448 94.27% 2,291 1,861 532,376 77.76% 73.31%
Ohio 20,173 17,213 16,086 93.40% 9,033 7,277 9,554,184 74.43% 69.52%
Oklahoma 4,840 3,917 3,566 91.14% 2,241 1,805 2,956,314 76.81% 70.01%
Oregon 5,558 4,732 4,354 92.13% 2,412 1,958 3,186,635 75.86% 69.89%
Pennsylvania 19,738 16,928 14,726 86.81% 8,832 7,158 10,515,939 75.74% 65.75%
Rhode Island 5,432 4,540 4,027 88.68% 2,235 1,794 888,190 77.08% 68.35%
South Carolina 5,903 4,529 4,122 90.62% 2,266 1,892 3,698,620 79.29% 71.85%
South Dakota 4,714 3,937 3,763 95.61% 2,231 1,883 656,513 79.72% 76.22%
Tennessee 5,441 4,443 4,120 92.65% 2,353 1,886 5,166,409 74.40% 68.93%
Texas 16,774 13,860 12,806 92.46% 8,755 7,152 19,374,406 77.24% 71.41%
Utah 3,269 2,897 2,746 94.82% 2,256 1,879 2,128,752 79.32% 75.21%
Vermont 5,606 4,206 3,886 92.42% 2,207 1,811 535,469 77.24% 71.38%
Virginia 5,091 4,313 3,802 88.10% 2,277 1,844 6,369,490 76.49% 67.39%
Washington 5,117 4,495 4,126 91.79% 2,355 1,856 5,470,298 75.24% 69.06%
West Virginia 6,489 5,258 4,772 90.66% 2,295 1,822 1,538,413 75.03% 68.03%
Wisconsin 5,136 4,404 4,070 92.35% 2,280 1,826 4,697,857 76.78% 70.91%
Wyoming 5,234 4,312 4,040 93.73% 2,350 1,866 441,363 75.50% 70.77%
101021
Table A.10 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2008 and 2009
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
NOTE: To compute the pooled 2008-2009 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2008 and 2009 individual response rates. The 2008-2009 population estimate is the average of the 2008 and the 2009 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly Office of Applied Studies), National Survey on Drug Use and Health, 2008 and 2009.
Total U.S. 52,878 45,203 24,750,657 85.22% 57,535 46,716 33,259,086 81.19% 61,451 45,517 192,805,569 72.68%
Northeast 10,617 8,994 4,340,126 83.44% 11,783 9,372 6,053,636 78.61% 12,439 9,000 35,848,309 70.72%
Midwest 14,890 12,703 5,459,564 85.03% 16,020 12,916 7,306,733 80.03% 17,300 12,828 42,295,888 73.40%
South 15,771 13,646 9,029,633 86.67% 17,237 14,387 11,893,918 83.87% 18,193 13,820 70,582,359 74.55%
West 11,600 9,860 5,921,334 84.50% 12,495 10,041 8,004,799 80.23% 13,519 9,869 44,079,013 70.62%
Alabama 730 618 379,377 85.40% 755 622 505,718 82.51% 862 633 2,974,610 72.76%
Alaska 718 602 60,678 83.59% 737 599 77,131 83.11% 802 609 409,778 76.06%
Arizona 695 607 538,865 87.17% 784 637 686,141 80.67% 762 580 4,050,064 76.55%
Arkansas 702 630 231,516 90.22% 774 630 295,741 83.49% 779 587 1,818,264 74.55%
California 2,850 2,392 3,147,890 83.21% 3,315 2,612 4,329,856 79.39% 3,605 2,486 22,568,441 67.38%
Colorado 802 706 384,709 87.27% 778 615 527,605 80.34% 810 612 3,153,539 74.52%
Connecticut 673 578 287,870 87.35% 824 671 363,648 80.97% 812 604 2,276,860 73.61%
Delaware 709 600 68,911 84.77% 856 704 93,807 82.39% 730 559 564,013 73.67%
District of Columbia 588 523 35,726 89.49% 800 680 86,607 84.80% 732 583 385,608 79.62%
Florida 2,695 2,323 1,348,640 85.95% 2,937 2,504 1,804,515 84.62% 3,163 2,411 12,261,205 74.40%
Georgia 708 619 822,696 87.79% 677 577 1,013,813 84.51% 786 588 5,963,681 72.94%
Hawaii 751 587 93,198 77.51% 828 602 131,005 71.50% 1,019 668 828,273 63.93%
Idaho 687 598 132,962 87.60% 711 606 164,370 84.94% 868 654 930,335 75.20%
Illinois 2,921 2,412 1,065,750 82.73% 3,244 2,459 1,461,607 75.30% 3,666 2,527 8,068,047 67.62%
Indiana 721 609 529,845 85.72% 726 576 679,069 79.73% 819 633 4,034,745 77.35%
Iowa 690 602 240,105 87.93% 748 612 339,894 82.15% 813 655 1,905,387 80.37%
Kansas 651 562 229,136 86.11% 810 639 321,796 79.19% 771 592 1,723,761 74.70%
Kentucky 668 581 336,896 86.79% 755 627 428,585 81.76% 792 588 2,771,834 72.59%
Louisiana 666 560 370,950 83.57% 727 609 523,818 84.20% 832 635 2,716,104 77.11%
Maine 700 623 99,629 88.75% 766 648 125,205 84.36% 768 608 902,774 78.26%
Maryland 728 642 459,954 87.66% 739 628 611,079 86.13% 764 598 3,612,130 76.28%
Massachusetts 703 589 498,720 83.77% 793 643 758,227 81.36% 855 634 4,263,188 73.17%
Michigan 2,844 2,435 842,712 85.26% 3,061 2,499 1,086,902 81.38% 3,212 2,380 6,402,870 73.83%
Minnesota 698 608 421,196 86.79% 756 610 574,322 80.44% 751 588 3,344,152 76.76%
Mississippi 630 544 252,527 86.81% 725 614 331,040 84.88% 809 616 1,778,519 75.48%
Missouri 732 621 482,442 83.69% 712 578 626,322 79.87% 799 604 3,786,858 74.14%
Montana 733 613 76,196 84.40% 774 646 105,444 83.64% 751 569 629,651 74.29%
Nebraska 684 589 144,863 86.92% 733 595 208,854 80.23% 813 615 1,100,620 76.03%
Nevada 730 632 214,026 86.83% 773 636 246,764 83.45% 770 549 1,668,924 69.86%
New Hampshire 723 612 106,508 84.78% 717 583 133,724 81.75% 863 653 880,070 75.24%
New Jersey 735 606 702,953 81.36% 896 711 871,610 78.88% 788 563 5,658,877 70.63%
New Mexico 662 586 163,513 88.33% 714 585 227,941 82.94% 812 623 1,230,799 76.20%
New York 2,878 2,358 1,535,172 80.98% 3,393 2,462 2,262,614 73.21% 3,678 2,457 12,574,826 66.44%
North Carolina 684 603 727,969 88.27% 728 614 947,518 85.18% 784 602 5,878,891 76.53%
North Dakota 716 621 48,559 86.50% 748 610 88,745 81.90% 827 630 395,073 75.77%
Ohio 2,891 2,473 939,669 85.33% 2,905 2,420 1,213,022 83.30% 3,237 2,384 7,401,492 71.55%
Oklahoma 689 585 293,240 85.06% 746 598 409,494 80.65% 806 622 2,253,581 75.03%
Oregon 788 648 292,301 82.08% 784 671 386,957 85.47% 840 639 2,507,377 73.53%
Pennsylvania 2,817 2,420 980,441 86.17% 2,941 2,472 1,342,616 84.07% 3,074 2,266 8,192,882 73.09%
Rhode Island 701 616 81,128 88.42% 720 564 127,468 78.97% 814 614 679,594 75.29%
South Carolina 756 653 356,186 86.08% 746 635 469,765 84.78% 764 604 2,872,669 77.49%
South Dakota 647 581 64,983 89.29% 784 680 90,798 86.51% 800 622 500,732 77.36%
Tennessee 710 614 494,043 86.40% 781 646 622,376 82.60% 862 626 4,049,989 71.52%
Texas 2,660 2,317 2,113,980 87.19% 2,914 2,428 2,737,418 83.77% 3,181 2,407 14,523,007 74.54%
Utah 735 655 252,460 88.21% 699 571 376,060 81.12% 822 653 1,500,232 77.51%
Vermont 687 592 47,705 85.92% 733 618 68,523 84.23% 787 601 419,240 75.14%
Virginia 708 604 604,833 85.01% 805 662 835,958 83.27% 764 578 4,928,699 74.24%
Washington 753 640 522,369 85.55% 780 616 685,351 79.28% 822 600 4,262,578 73.34%
West Virginia 740 630 132,189 85.99% 772 609 176,668 78.37% 783 583 1,229,556 73.30%
Wisconsin 695 590 450,304 84.98% 793 638 615,402 80.41% 792 598 3,632,151 75.11%
Wyoming 696 594 42,168 84.39% 818 645 60,175 78.67% 836 627 339,021 73.82%
101021
Table A.11 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2007, 2008, and 2009
State 2007
Total
Selected
2007
Total
Responded
2007
Population
Estimate
2007
Weighted
Interview
Response
Rate
2008
Total
Selected
2008
Total
Responded
2008
Population
Estimate
2008
Weighted
Interview
Response
Rate
2009
Total
Selected
2009
Total
Responded
2009
Population
Estimate
2009
Weighted
Interview
Response
Rate
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly Office of Applied Studies), National Survey on Drug Use and Health, 2007, 2008, and 2009.
Total U.S. 36,653 31,132 38,475,786 84.40% 37,414 31,691 38,109,092 84.54% 37,372 31,946 38,254,344 85.38%
Northeast 7,615 6,380 6,927,594 81.31% 7,483 6,306 6,735,784 83.15% 7,656 6,448 6,798,944 83.03%
Midwest 10,332 8,793 8,530,144 85.00% 10,526 8,854 8,356,484 83.74% 10,586 9,034 8,472,393 84.90%
South 10,913 9,344 13,726,139 85.74% 11,156 9,609 13,857,311 86.31% 11,089 9,631 13,893,128 87.38%
West 7,793 6,615 9,291,909 84.13% 8,249 6,922 9,159,513 83.60% 8,041 6,833 9,089,879 84.55%
Alabama 464 396 592,470 85.95% 493 427 581,262 86.76% 530 447 599,028 85.17%
Alaska 471 402 94,412 86.48% 515 414 92,180 80.10% 488 416 91,611 85.87%
Arizona 485 402 831,600 81.32% 481 413 810,336 85.49% 514 437 824,443 84.93%
Arkansas 491 415 355,964 85.69% 513 454 350,656 88.83% 469 407 342,551 88.11%
California 2,027 1,690 4,954,430 83.32% 2,120 1,761 4,938,568 82.75% 1,946 1,634 4,775,356 83.65%
Colorado 488 415 573,755 85.11% 530 444 568,813 83.19% 564 494 585,652 86.73%
Connecticut 490 413 435,326 84.09% 453 396 422,896 88.56% 481 407 408,896 85.77%
Delaware 481 407 108,201 84.53% 495 410 104,894 83.20% 518 446 108,455 86.09%
District of Columbia 449 393 72,337 88.36% 410 368 58,497 90.11% 398 346 59,345 85.42%
Florida 1,798 1,535 2,079,077 85.28% 1,953 1,691 2,132,876 86.69% 1,921 1,662 2,133,592 86.01%
Georgia 457 401 1,220,703 88.30% 512 438 1,242,605 84.97% 474 420 1,182,593 88.73%
Hawaii 475 379 136,591 78.91% 525 408 141,555 77.46% 538 426 150,940 76.75%
Idaho 496 418 186,618 82.46% 477 418 193,450 87.15% 468 402 201,917 86.93%
Illinois 2,097 1,691 1,668,918 80.25% 2,113 1,719 1,626,682 81.62% 2,021 1,676 1,681,199 82.87%
Indiana 482 403 802,712 83.83% 540 447 818,888 83.63% 472 406 824,402 86.59%
Iowa 510 450 385,713 87.80% 484 413 367,032 86.07% 484 417 362,489 86.00%
Kansas 463 406 349,573 86.16% 441 367 343,518 83.67% 523 445 362,101 84.25%
Kentucky 475 403 510,132 84.81% 486 423 515,913 86.34% 465 411 533,285 89.41%
Louisiana 477 420 565,529 87.27% 466 404 615,972 86.94% 470 395 559,359 83.59%
Maine 506 437 159,826 86.86% 484 423 154,462 86.37% 515 448 141,437 87.04%
Maryland 482 411 729,464 85.16% 538 474 707,164 88.35% 481 427 679,118 88.47%
Massachusetts 507 416 790,129 78.14% 475 405 783,464 85.33% 503 407 756,845 80.42%
Michigan 1,873 1,596 1,322,124 84.60% 2,027 1,730 1,325,437 84.95% 2,054 1,735 1,286,421 84.14%
Minnesota 485 418 621,993 86.66% 452 390 620,418 86.35% 523 444 687,929 83.63%
Mississippi 457 405 397,984 88.94% 464 401 385,041 86.31% 464 396 401,760 86.27%
Missouri 508 446 779,438 88.13% 483 410 713,872 82.22% 496 410 701,234 83.00%
Montana 453 392 119,194 84.79% 525 442 121,269 85.39% 497 421 116,223 85.63%
Nebraska 510 441 232,321 87.70% 483 417 225,154 85.63% 498 429 240,816 87.71%
Nevada 431 374 305,708 87.49% 490 423 301,339 86.56% 500 438 312,307 88.06%
New Hampshire 480 396 165,706 82.26% 494 418 172,605 84.97% 525 437 162,423 83.24%
New Jersey 480 397 1,053,380 79.84% 579 474 1,042,888 81.60% 502 418 1,060,608 81.69%
New Mexico 512 460 258,604 90.87% 442 382 252,364 85.83% 476 418 255,788 89.54%
New York 2,204 1,749 2,512,654 78.46% 2,028 1,630 2,425,431 79.44% 2,121 1,721 2,486,753 80.92%
North Carolina 588 493 1,178,490 84.57% 480 420 1,102,143 88.11% 452 402 1,094,206 89.32%
North Dakota 510 431 85,278 84.71% 494 417 83,543 83.50% 494 431 82,565 87.52%
Ohio 1,932 1,670 1,476,889 86.70% 2,087 1,757 1,463,023 84.33% 2,014 1,745 1,474,740 86.21%
Oklahoma 553 449 433,136 80.15% 457 385 440,291 84.06% 502 427 460,633 85.55%
Oregon 528 452 502,263 85.88% 583 507 481,595 86.18% 534 429 445,204 81.09%
Pennsylvania 1,987 1,712 1,586,861 85.71% 2,002 1,729 1,525,711 86.46% 2,002 1,734 1,572,109 87.10%
Rhode Island 488 432 138,232 88.96% 448 398 130,107 88.98% 530 447 130,746 83.09%
South Carolina 444 385 532,386 86.10% 488 422 547,282 86.74% 540 471 539,722 86.26%
South Dakota 435 391 98,754 90.55% 497 447 106,030 89.70% 486 439 103,873 90.23%
Tennessee 478 421 721,190 88.71% 456 378 699,714 82.76% 534 473 778,937 88.58%
Texas 1,861 1,619 3,108,430 86.89% 1,906 1,645 3,243,147 86.32% 1,869 1,641 3,208,416 88.06%
Utah 471 415 399,647 88.79% 501 434 395,972 84.14% 483 426 400,057 88.25%
Vermont 473 428 85,480 90.65% 520 433 78,221 82.77% 477 429 79,128 89.98%
Virginia 485 394 906,519 80.33% 502 428 931,139 86.21% 507 441 1,007,100 87.39%
Washington 494 420 864,704 85.28% 538 446 795,765 84.12% 540 464 859,380 85.46%
West Virginia 473 397 214,128 83.09% 537 441 198,715 82.69% 495 419 205,027 84.86%
Wisconsin 527 450 706,430 85.91% 425 340 662,888 81.80% 521 457 664,624 87.47%
Wyoming 462 396 64,382 86.06% 522 430 66,307 81.99% 493 428 71,000 84.56%
101021
Table A.12 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2007-2008 and 2008-2009
State 2007-2008
Total
Selected
2007-2008
Total
Responded
2007-2008
Population
Estimate
2007-2008
Weighted
Interview
Response
Rate
2008-2009
Total
Selected
2008-2009
Total
Responded
2008-2009
Population
Estimate
2008-2009
Weighted
Interview
Response
Rate
NOTE: To compute the pooled weighted response rates, the two samples were combined, and the individual-year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the individual response rates. The population estimate is the average of the population across the 2 years.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly Office of Applied Studies), National Survey on Drug Use and Health, 2007, 2008, and 2009.
Total U.S. 74,067 62,823 38,292,439 84.47% 74,786 63,637 38,181,718 84.96%
Northeast 15,098 12,686 6,831,689 82.22% 15,139 12,754 6,767,364 83.09%
Midwest 20,858 17,647 8,443,314 84.37% 21,112 17,888 8,414,438 84.32%
South 22,069 18,953 13,791,725 86.03% 22,245 19,240 13,875,220 86.84%
West 16,042 13,537 9,225,711 83.87% 16,290 13,755 9,124,696 84.07%
Alabama 957 823 586,866 86.36% 1,023 874 590,145 85.97%
Alaska 986 816 93,296 83.28% 1,003 830 91,895 82.95%
Arizona 966 815 820,968 83.32% 995 850 817,390 85.20%
Arkansas 1,004 869 353,310 87.29% 982 861 346,604 88.49%
California 4,147 3,451 4,946,499 83.04% 4,066 3,395 4,856,962 83.20%
Colorado 1,018 859 571,284 84.12% 1,094 938 577,232 84.98%
Connecticut 943 809 429,111 86.26% 934 803 415,896 87.17%
Delaware 976 817 106,547 83.88% 1,013 856 106,674 84.66%
District of Columbia 859 761 65,417 89.17% 808 714 58,921 87.72%
Florida 3,751 3,226 2,105,976 85.99% 3,874 3,353 2,133,234 86.36%
Georgia 969 839 1,231,654 86.61% 986 858 1,212,599 86.81%
Hawaii 1,000 787 139,073 78.18% 1,063 834 146,248 77.10%
Idaho 973 836 190,034 84.80% 945 820 197,683 87.04%
Illinois 4,210 3,410 1,647,800 80.93% 4,134 3,395 1,653,940 82.26%
Indiana 1,022 850 810,800 83.73% 1,012 853 821,645 85.10%
Iowa 994 863 376,373 86.95% 968 830 364,761 86.04%
Kansas 904 773 346,546 84.90% 964 812 352,809 83.97%
Kentucky 961 826 513,022 85.56% 951 834 524,599 87.89%
Louisiana 943 824 590,750 87.10% 936 799 587,665 85.29%
Maine 990 860 157,144 86.62% 999 871 147,950 86.70%
Maryland 1,020 885 718,314 86.77% 1,019 901 693,141 88.41%
Massachusetts 982 821 786,797 81.69% 978 812 770,154 82.80%
Michigan 3,900 3,326 1,323,781 84.78% 4,081 3,465 1,305,929 84.55%
Minnesota 937 808 621,205 86.51% 975 834 654,173 84.92%
Mississippi 921 806 391,513 87.66% 928 797 393,401 86.29%
Missouri 991 856 746,655 85.28% 979 820 707,553 82.60%
Montana 978 834 120,231 85.09% 1,022 863 118,746 85.51%
Nebraska 993 858 228,738 86.69% 981 846 232,985 86.69%
Nevada 921 797 303,524 87.03% 990 861 306,823 87.33%
New Hampshire 974 814 169,156 83.62% 1,019 855 167,514 84.14%
New Jersey 1,059 871 1,048,134 80.74% 1,081 892 1,051,748 81.64%
New Mexico 954 842 255,484 88.43% 918 800 254,076 87.70%
New York 4,232 3,379 2,469,042 78.95% 4,149 3,351 2,456,092 80.19%
North Carolina 1,068 913 1,140,317 86.28% 932 822 1,098,175 88.72%
North Dakota 1,004 848 84,411 84.11% 988 848 83,054 85.45%
Ohio 4,019 3,427 1,469,956 85.51% 4,101 3,502 1,468,881 85.28%
Oklahoma 1,010 834 436,713 82.09% 959 812 450,462 84.83%
Oregon 1,111 959 491,929 86.03% 1,117 936 463,400 83.73%
Pennsylvania 3,989 3,441 1,556,286 86.08% 4,004 3,463 1,548,910 86.78%
Rhode Island 936 830 134,169 88.97% 978 845 130,426 85.99%
South Carolina 932 807 539,834 86.43% 1,028 893 543,502 86.50%
South Dakota 932 838 102,392 90.11% 983 886 104,952 89.97%
Tennessee 934 799 710,452 85.76% 990 851 739,326 85.78%
Texas 3,767 3,264 3,175,789 86.60% 3,775 3,286 3,225,782 87.18%
Utah 972 849 397,810 86.49% 984 860 398,014 86.22%
Vermont 993 861 81,850 86.82% 997 862 78,674 86.39%
Virginia 987 822 918,829 83.21% 1,009 869 969,120 86.84%
Washington 1,032 866 830,234 84.73% 1,078 910 827,573 84.82%
West Virginia 1,010 838 206,422 82.90% 1,032 860 201,871 83.78%
Wisconsin 952 790 684,659 83.84% 946 797 663,756 84.54%
Wyoming 984 826 65,344 84.01% 1,015 858 68,653 83.30%
101021
Table A.13 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2007, 2008, and 2009
State 2007
Total
Selected
2007
Total
Responded
2007
Population
Estimate
2007
Weighted
Interview
Response
Rate
2008
Total
Selected
2008
Total
Responded
2008
Population
Estimate
2008
Weighted
Interview
Response
Rate
2009
Total
Selected
2009
Total
Responded
2009
Population
Estimate
2009
Weighted
Interview
Response
Rate
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly Office of Applied Studies), National Survey on Drug Use and Health, 2007, 2008, and 2009.
Total U.S. 59,583 45,395 222,604,119 72.65% 59,934 46,177 224,922,763 73.29% 59,052 46,056 227,206,545 74.59%
Northeast 12,169 9,146 41,419,108 70.51% 12,091 9,157 41,723,952 71.36% 12,131 9,215 42,079,937 72.38%
Midwest 16,735 12,746 49,184,109 73.03% 16,944 13,009 49,448,506 73.84% 16,376 12,735 49,756,736 74.91%
South 17,864 13,874 80,810,368 74.51% 17,714 14,031 81,912,691 75.50% 17,716 14,176 83,039,864 76.32%
West 12,815 9,629 51,190,536 71.04% 13,185 9,980 51,837,615 70.89% 12,829 9,930 52,330,008 73.31%
Alabama 819 623 3,426,881 70.54% 833 637 3,462,438 70.09% 784 618 3,498,218 77.82%
Alaska 700 534 476,884 76.37% 777 608 479,955 75.78% 762 600 493,862 78.40%
Arizona 825 597 4,584,819 68.72% 779 601 4,700,399 75.56% 767 616 4,772,012 78.64%
Arkansas 745 599 2,083,047 79.44% 768 609 2,100,948 75.70% 785 608 2,127,061 76.02%
California 3,374 2,431 26,609,446 69.13% 3,565 2,607 26,834,059 68.17% 3,355 2,491 26,962,535 70.44%
Colorado 757 574 3,588,258 72.98% 797 608 3,650,120 75.05% 791 619 3,712,168 75.96%
Connecticut 836 631 2,623,038 75.91% 856 668 2,629,944 73.64% 780 607 2,651,071 75.54%
Delaware 782 606 644,296 76.04% 815 653 652,247 78.20% 771 610 663,392 72.25%
District of Columbia 701 525 464,182 74.35% 778 627 469,267 77.83% 754 636 475,164 83.47%
Florida 3,291 2,484 13,883,205 70.49% 3,005 2,393 13,990,125 75.51% 3,095 2,522 14,141,314 75.95%
Georgia 755 601 6,816,740 76.95% 725 564 6,929,959 72.18% 738 601 7,025,028 76.93%
Hawaii 819 554 955,563 62.59% 917 621 958,686 63.83% 930 649 959,869 66.03%
Idaho 785 617 1,067,852 77.22% 791 628 1,086,964 77.02% 788 632 1,102,446 76.10%
Illinois 3,444 2,382 9,455,361 65.94% 3,530 2,508 9,523,946 67.16% 3,380 2,478 9,535,363 70.37%
Indiana 839 651 4,663,843 72.75% 758 590 4,693,498 77.02% 787 619 4,734,130 78.39%
Iowa 732 584 2,228,536 75.93% 801 645 2,242,082 80.29% 760 622 2,248,480 80.95%
Kansas 755 574 2,021,594 78.58% 796 625 2,039,018 76.01% 785 606 2,052,096 74.78%
Kentucky 770 602 3,154,981 76.58% 736 570 3,186,380 71.84% 811 645 3,214,458 75.52%
Louisiana 755 597 3,115,653 72.47% 754 605 3,209,206 78.22% 805 639 3,270,638 78.34%
Maine 777 616 1,021,498 75.21% 781 629 1,025,265 76.07% 753 627 1,030,693 82.04%
Maryland 803 617 4,164,577 75.63% 801 649 4,196,523 76.41% 702 577 4,249,895 79.29%
Massachusetts 779 596 4,929,824 72.10% 760 596 4,975,546 75.82% 888 681 5,067,283 72.94%
Michigan 3,122 2,434 7,497,218 73.02% 3,206 2,483 7,485,628 73.93% 3,067 2,396 7,493,915 75.99%
Minnesota 744 592 3,871,423 78.03% 730 580 3,898,306 77.85% 777 618 3,938,642 76.78%
Mississippi 756 611 2,085,099 76.82% 744 594 2,103,803 76.92% 790 636 2,115,316 76.79%
Missouri 781 611 4,344,887 72.25% 773 599 4,380,159 75.18% 738 583 4,446,201 74.83%
Montana 756 604 722,344 77.21% 756 601 731,019 76.33% 769 614 739,171 74.99%
Nebraska 745 587 1,297,253 75.95% 759 589 1,305,413 75.74% 787 621 1,313,534 77.60%
Nevada 799 623 1,875,186 75.45% 757 567 1,901,495 72.53% 786 618 1,929,882 70.81%
New Hampshire 766 594 1,002,039 76.38% 777 619 1,007,507 78.56% 803 617 1,020,081 73.47%
New Jersey 790 595 6,506,029 74.29% 857 658 6,516,694 72.31% 827 616 6,544,280 71.29%
New Mexico 778 616 1,437,142 74.39% 757 595 1,450,863 78.45% 769 613 1,466,616 75.96%
New York 3,589 2,459 14,621,384 63.59% 3,510 2,415 14,816,448 65.48% 3,561 2,504 14,858,432 69.52%
North Carolina 799 623 6,649,562 73.04% 709 560 6,768,012 76.99% 803 656 6,884,806 78.46%
North Dakota 734 592 479,765 79.42% 796 636 481,318 78.24% 779 604 486,318 75.56%
Ohio 3,187 2,453 8,543,082 73.90% 3,143 2,430 8,578,157 72.79% 2,999 2,374 8,650,872 73.65%
Oklahoma 775 592 2,629,050 74.61% 793 621 2,647,965 78.37% 759 599 2,678,184 73.20%
Oregon 841 642 2,841,476 72.71% 873 699 2,879,615 70.16% 751 611 2,909,054 79.91%
Pennsylvania 3,150 2,456 9,423,437 74.51% 3,006 2,364 9,461,258 74.68% 3,009 2,374 9,609,739 74.60%
Rhode Island 763 603 807,883 74.58% 761 598 804,991 76.44% 773 580 809,132 75.38%
South Carolina 810 644 3,245,712 77.53% 763 636 3,309,346 81.64% 747 603 3,375,522 75.12%
South Dakota 798 627 582,364 78.09% 818 674 588,444 77.44% 766 628 594,616 80.10%
Tennessee 741 580 4,583,815 74.13% 865 674 4,641,311 74.35% 778 598 4,703,420 71.59%
Texas 2,936 2,333 16,798,174 76.16% 3,049 2,421 17,119,812 75.65% 3,046 2,414 17,401,039 76.35%
Utah 734 589 1,803,398 78.03% 777 624 1,862,178 77.28% 744 600 1,890,406 79.16%
Vermont 719 596 483,976 81.04% 783 610 486,299 74.56% 737 609 489,227 78.26%
Virginia 840 630 5,665,325 75.35% 792 619 5,721,688 74.92% 777 621 5,807,626 76.24%
Washington 834 642 4,839,526 74.66% 801 591 4,906,769 72.13% 801 625 4,989,090 76.03%
West Virginia 786 607 1,400,071 75.47% 784 599 1,403,665 75.25% 771 593 1,408,784 72.70%
Wisconsin 854 659 4,198,784 77.10% 834 650 4,232,537 76.11% 751 586 4,262,569 75.65%
Wyoming 813 606 388,644 73.34% 838 630 395,493 70.94% 816 642 402,898 78.03%
101021
Table A.14 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2007-2008 and 2008-2009
State 2007-2008
Total
Selected
2007-2008
Total
Responded
2007-2008
Population
Estimate
2007-2008
Weighted
Interview
Response
Rate
2008-2009
Total
Selected
2008-2009
Total
Responded
2008-2009
Population
Estimate
2008-2009
Weighted
Interview
Response
Rate
NOTE: To compute the pooled weighted response rates, the two samples were combined, and the individual-year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the individual response rates. The population estimate is the average of the population across the 2 years.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly Office of Applied Studies), National Survey on Drug Use and Health, 2007, 2008, and 2009.
Total U.S. 119,517 91,572 223,763,441 72.97% 118,986 92,233 226,064,654 73.94%
Northeast 24,260 18,303 41,571,530 70.94% 24,222 18,372 41,901,944 71.87%
Midwest 33,679 25,755 49,316,307 73.44% 33,320 25,744 49,602,621 74.38%
South 35,578 27,905 81,361,529 75.00% 35,430 28,207 82,476,278 75.92%
West 26,000 19,609 51,514,075 70.97% 26,014 19,910 52,083,811 72.12%
Alabama 1,652 1,260 3,444,660 70.32% 1,617 1,255 3,480,328 74.18%
Alaska 1,477 1,142 478,419 76.07% 1,539 1,208 486,909 77.17%
Arizona 1,604 1,198 4,642,609 72.07% 1,546 1,217 4,736,205 77.16%
Arkansas 1,513 1,208 2,091,997 77.71% 1,553 1,217 2,114,004 75.86%
California 6,939 5,038 26,721,752 68.65% 6,920 5,098 26,898,297 69.32%
Colorado 1,554 1,182 3,619,189 73.99% 1,588 1,227 3,681,144 75.48%
Connecticut 1,692 1,299 2,626,491 74.74% 1,636 1,275 2,640,507 74.56%
Delaware 1,597 1,259 648,271 77.04% 1,586 1,263 657,820 75.00%
District of Columbia 1,479 1,152 466,724 76.12% 1,532 1,263 472,215 80.57%
Florida 6,296 4,877 13,936,665 72.97% 6,100 4,915 14,065,719 75.73%
Georgia 1,480 1,165 6,873,349 74.58% 1,463 1,165 6,977,493 74.70%
Hawaii 1,736 1,175 957,125 63.22% 1,847 1,270 959,278 64.94%
Idaho 1,576 1,245 1,077,408 77.12% 1,579 1,260 1,094,705 76.56%
Illinois 6,974 4,890 9,489,653 66.55% 6,910 4,986 9,529,654 68.78%
Indiana 1,597 1,241 4,678,670 74.91% 1,545 1,209 4,713,814 77.69%
Iowa 1,533 1,229 2,235,309 78.13% 1,561 1,267 2,245,281 80.62%
Kansas 1,551 1,199 2,030,306 77.30% 1,581 1,231 2,045,557 75.42%
Kentucky 1,506 1,172 3,170,680 74.19% 1,547 1,215 3,200,419 73.81%
Louisiana 1,509 1,202 3,162,429 75.24% 1,559 1,244 3,239,922 78.28%
Maine 1,558 1,245 1,023,382 75.65% 1,534 1,256 1,027,979 79.02%
Maryland 1,604 1,266 4,180,550 75.98% 1,503 1,226 4,223,209 77.86%
Massachusetts 1,539 1,192 4,952,685 74.08% 1,648 1,277 5,021,415 74.39%
Michigan 6,328 4,917 7,491,423 73.47% 6,273 4,879 7,489,772 74.95%
Minnesota 1,474 1,172 3,884,865 77.94% 1,507 1,198 3,918,474 77.31%
Mississippi 1,500 1,205 2,094,451 76.87% 1,534 1,230 2,109,559 76.85%
Missouri 1,554 1,210 4,362,523 73.61% 1,511 1,182 4,413,180 75.00%
Montana 1,512 1,205 726,681 76.77% 1,525 1,215 735,095 75.66%
Nebraska 1,504 1,176 1,301,333 75.84% 1,546 1,210 1,309,473 76.70%
Nevada 1,556 1,190 1,888,341 74.05% 1,543 1,185 1,915,689 71.65%
New Hampshire 1,543 1,213 1,004,773 77.48% 1,580 1,236 1,013,794 76.05%
New Jersey 1,647 1,253 6,511,361 73.31% 1,684 1,274 6,530,487 71.78%
New Mexico 1,535 1,211 1,444,002 76.50% 1,526 1,208 1,458,739 77.21%
New York 7,099 4,874 14,718,916 64.53% 7,071 4,919 14,837,440 67.49%
North Carolina 1,508 1,183 6,708,787 75.04% 1,512 1,216 6,826,409 77.77%
North Dakota 1,530 1,228 480,541 78.82% 1,575 1,240 483,818 76.89%
Ohio 6,330 4,883 8,560,619 73.35% 6,142 4,804 8,614,515 73.22%
Oklahoma 1,568 1,213 2,638,508 76.57% 1,552 1,220 2,663,075 75.88%
Oregon 1,714 1,341 2,860,546 71.51% 1,624 1,310 2,894,334 75.20%
Pennsylvania 6,156 4,820 9,442,348 74.60% 6,015 4,738 9,535,498 74.64%
Rhode Island 1,524 1,201 806,437 75.45% 1,534 1,178 807,061 75.89%
South Carolina 1,573 1,280 3,277,529 79.62% 1,510 1,239 3,342,434 78.56%
South Dakota 1,616 1,301 585,404 77.77% 1,584 1,302 591,530 78.71%
Tennessee 1,606 1,254 4,612,563 74.24% 1,643 1,272 4,672,365 73.05%
Texas 5,985 4,754 16,958,993 75.90% 6,095 4,835 17,260,426 76.00%
Utah 1,511 1,213 1,832,788 77.62% 1,521 1,224 1,876,292 78.20%
Vermont 1,502 1,206 485,138 77.86% 1,520 1,219 487,763 76.40%
Virginia 1,632 1,249 5,693,506 75.14% 1,569 1,240 5,764,657 75.58%
Washington 1,635 1,233 4,873,148 73.39% 1,602 1,216 4,947,930 74.16%
West Virginia 1,570 1,206 1,401,868 75.36% 1,555 1,192 1,406,225 73.95%
Wisconsin 1,688 1,309 4,215,660 76.61% 1,585 1,236 4,247,553 75.88%
Wyoming 1,651 1,236 392,069 72.09% 1,654 1,272 399,196 74.56%
101021
Table A.15 Outcomes, by Survey Year, for Which Small Area Estimates Are Available
Measure 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008 2008-2009
1 Estimates for these outcomes were not included in the 2002-2003 State report (Wright & Sathe, 2005), but the 2002-2003 estimates are included in the 2003-2004 State report as part of the comparison tables (see Wright & Sathe, 2006). However, the Bayesian confidence intervals associated with these were not published.
2 Estimates for serious psychological distress (SPD) in the years 2002-2003 and 2003-2004 are not comparable with the 2004-2005 and subsequent SPD estimates. For more details, see Section A.8 in Appendix A of the 2005-2006 State report (Hughes et al., 2008). Note, in 2002-2003, SPD was referred to as "serious mental illness."
3 Questions used to determine a major depressive episode were added in 2004. Only estimates for youths aged 12 to 17 are shown in the 2007-2008 report. Estimates for adults aged 18 or older were produced later and are in a separate table; for more details, see Section A.11 in Appendix A of this report. Note that the adult major depressive episode estimates shown in the 2004-2005, 2005-2006, and 2006-2007 reports are not comparable with this report's adult major depressive episode estimates. However, the 2005-2006 and 2006-2007 adult adjusted major depressive episode estimates available at http://www.samhsa.gov/data/states.htm are comparable with this report's adult major depressive episode estimates.
Yes = available, No = not available.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality (formerly the Office of Applied Studies), National Survey on Drug Use and Health, 2002-2009.
Illicit Drug Use in Past Month Yes Yes Yes Yes Yes Yes Yes
Marijuana Use in Past Year Yes Yes Yes Yes Yes Yes Yes
Marijuana Use in Past Month Yes Yes Yes Yes Yes Yes Yes
Perceptions of Great Risk of Smoking Marijuana Once a Month Yes Yes Yes Yes Yes Yes Yes
First Use of Marijuana Yes Yes Yes Yes Yes Yes Yes
Illicit Drug Use Other Than Marijuana in Past Month Yes Yes Yes Yes Yes Yes Yes
Cocaine Use in Past Year Yes Yes Yes Yes Yes Yes Yes
Nonmedical Use of Pain Relievers in Past Year No1 Yes Yes Yes Yes Yes Yes
Alcohol Use in Past Month Yes Yes Yes Yes Yes Yes Yes
Underage Past Month Use of Alcohol No1 Yes Yes Yes Yes Yes Yes
Binge Alcohol Use in Past Month Yes Yes Yes Yes Yes Yes Yes
Underage Past Month Binge Alcohol Use No1 Yes Yes Yes Yes Yes Yes
Perceptions of Great Risk of Having Five or More Drinks of an Alcoholic
Beverage Once or Twice a Week
Yes Yes Yes Yes Yes Yes Yes
Tobacco Product Use in Past Month Yes Yes Yes Yes Yes Yes Yes
Cigarette Use in Past Month Yes Yes Yes Yes Yes Yes Yes
Perceptions of Great Risk of Smoking One or More Packs of Cigarettes Per Day Yes Yes Yes Yes Yes Yes Yes
Alcohol Dependence or Abuse in Past Year Yes Yes Yes Yes Yes Yes Yes
Alcohol Dependence in Past Year Yes Yes Yes Yes Yes Yes Yes
Illicit Drug Dependence or Abuse in Past Year Yes Yes Yes Yes Yes Yes Yes
Illicit Drug Dependence in Past Year Yes Yes Yes Yes Yes Yes Yes
Dependence on or Abuse of Illicit Drugs or Alcohol in Past Year Yes Yes Yes Yes Yes Yes Yes
Needing But Not Receiving Treatment for Illicit Drug Use in Past Year Yes Yes Yes Yes Yes Yes Yes
Needing But Not Receiving Treatment for Alcohol Use in Past Year Yes Yes Yes Yes Yes Yes Yes
Serious Psychological Distress in Past Year2 Yes Yes Yes Yes Yes No No
Had at Least One Major Depressive Episode in Past Year3 No No Yes Yes Yes Yes Yes
Serious Mental Illness in Past Year No No No No No No Yes
Any Mental Illness in Past Year No No No No No No Yes
Had Serious Thoughts of Suicide in Past Year No No No No No No Yes


End Notes

9 The census region-level estimates in the tables are population-weighted aggregates of the State estimates. The national estimates, however, are benchmarked to exactly match the design-based estimates.

10 Note that in past NSDUH State reports, the term "prediction interval" (PI) was used to represent uncertainty in the State and regional estimates. However, that term also is used in other applications to estimate future values of a parameter of interest. That interpretation does not apply to NSDUH State report estimates; thus, "prediction interval" was dropped and replaced with "Bayesian confidence interval."

11 The four age groups are 12 to 17, 18 to 25, 26 to 34, and 35 or older; the four race/ethnicity groups are non-Hispanic white, non-Hispanic black, non-Hispanic other, and Hispanic; and the two genders are male and female.

12 Substances include alcohol, marijuana, cocaine, heroin, hallucinogens, inhalants, pain relievers, tranquilizers, stimulants, and sedatives.

13 For more information on the WHODAS and SDS scores, see Section B.4.3 of the mental health findings report (CBHSQ, 2010).

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