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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 2009 and 2010 National Surveys on Drug Use and Health (NSDUHs). Also included in this report are comparisons between the 2008-2009 and the 2009-2010 combined-year 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 to 2009 surveys also was used in the production of the 2009-2010 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 measures for which small area estimates are produced in this report is given in Section A.2. The list of predictors used in the 2009-2010 SAE modeling is given in Section A.3. Information on the updated 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. No new variable selection was done for any measure (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.6.10 Tables of estimated numbers of persons associated with each measure (in thousands) are available on the Web (see http://www.samhsa.gov/data/NSDUH/2k10State/NSDUHsae2010/Index.aspx). An explanation of how these counts and their respective Bayesian confidence intervals11 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 measures 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 (see http://www.samhsa.gov/data/NSDUH/2k10State/NSDUHsae2010/Index.aspx). 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 illicit drug and alcohol dependence and abuse and needing but not receiving treatment. 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 measure between two States. The methodology used to compare the 2008-2009 and the 2009-2010 State estimates is described in Section A.13.

At the end of this appendix, tables showing the 2008, 2009, 2010, pooled 2008-2009, and pooled 2009-2010 survey sample sizes, population estimates, and response rates are included (Tables A.1 to A.14). Table A.15 lists all measures 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 mixed12 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 grouped State sampling region (SSR)-j of State-i.13 Let x 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 grouped SSR-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 2010 NSDUH data were pooled with the 2009 NSDUH data, and age group-specific State prevalence estimates for 25 binary (0, 1) measures 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 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 2008-2009 and the 2009-2010 State estimates were produced for all of these measures and are included in this report in Appendix C.

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 (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, 2009, and 2010 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 aggregating the block group-level small area estimates to produce State and census region-level small area estimates.14

    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 exploring 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 was made that each of the age × gender cells within a race group had the same Hispanicity distribution. 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.
  3. In State reports prior to 2008 when creating the 32 cells using the 2002-2007 Claritas data, the population from the two 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 two 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 2009-2010 SAE process (for more information on the steps taken for the 2007-2008 and 2008-2009 SAE processes, see Appendix A of Hughes et al., 2010, and Appendix A of Hughes et al., 2011, respectively):

  1. Using the 2008-2012 Claritas data, 2009 and 2010 population counts were obtained (the 2009 and 2010 counts were obtained by using linear interpolation between the 2008 and 2012 counts) and used to create the predictors that were merged onto the 2009 and 2010 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. For all predictors other than the unemployment rate, the same 2007-2008 decile values were used in the 2009-2010 SAE process. Because of the recent large jumps in the unemployment rate, the decile values for the unemployment rate needed to be recreated using the 2009 and 2010 NSDUH data. Using the older set of decile values resulted in the distribution of the unemployment deciles to be very skewed. Hence, a decision was made to update the unemployment rate deciles based on 2009 and 2010 data. The predictor based on the unemployment deciles was used in the SAE model for the 35 or older age group for producing the small area estimates for the measure on needing but not receiving treatment for illicit drug use. Using this updated data is not expected to cause any inconsistencies in the estimation of trends for this measure.
  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., 2009 and 2010).
  4. The 2008 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 2008-2009 and 2009-2010 State prevalence rates.
  5. 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 2009-2010 sample and universe files to produce the 2002-2003 versus 2009-2010 comparisons that will be available at http://www.samhsa.gov/data/NSDUH/2k10State/NSDUHsae2010/Index.aspx.

A.5  Selection of Independent Variables for the Models

No new variable selection was done for any measure in 2009-2010. The updated versions of fixed-effect predictors that were used in modeling the 2008-2009 data were used to model the 2009-2010 data. Because the interest was to estimate change between the 2008-2009 and 2009-2010 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 2009-2010 State-by-age group small area estimates 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 capital D sub a is the design-based national prevalence estimate and capital P sub a is the population-weighted mean of the State small area estimates (capital P sub s and a) 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 capital 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 of Theta sub s and a. are defined below:

Equation A2,     D

where

Equation A3,     D

Equation A4, and     D

Equation A5.     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/NSDUH/2k10State/NSDUHsae2010/Index.aspx, 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, 2009 and 2010) of the State by age group of interest.

For example, past month use of alcohol among 18 to 25 year olds in Alabama was 52.31 percent (see Table B.9 in Appendix B). The corresponding Bayesian confidence intervals ranged from 48.01 to 56.57 percent. The population count for 18 to 25 year olds averaged across 2009-2010 in Alabama was 515,510 (see Table A.10). Hence, the estimated number of 18 to 25 year olds using alcohol in the past month in Alabama was 0.5231 × 515,510, which is 269,663 (see Table 9). The associated Bayesian confidence intervals ranged from 0.4801 × 515,510 (i.e., 247,496) to 0.5657 × 515,510 (i.e., 291,624). 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, then the numbers are reported to the nearest thousand. 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 A6,     D

where Description: capital X sub 1 is the number of marijuana initiates in the past 24 months and Description: 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 2010 to indicate first use as early as the first part of 2008 or as late as the first part of 2010. Similarly, a subject interviewed in the last part of 2010 could indicate first use as early as the last part of 2008 or as late as the last part of 2010. Therefore, in the 2010 survey, the reported period of first use ranged from early 2008 to late 2010 and was "centered" in 2009. For example, about half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2009, while a quarter each reported first use in 2008 and 2010. Persons who responded in 2010 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 2009 survey ranged from early 2007 to late 2009 and were centered in 2008. 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. 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 2009-2010 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 2008-2009 and the 2009-2010 small area estimates for underage drinking in the States also are presented in this report.

A.10  Illicit Drug and Alcohol Dependence or Abuse / Needing But Not Receiving Treatment

The NSDUH computer-assisted interviewing (CAI) instrumentation includes questions that are designed to measure illicit drug and alcohol dependence and abuse. For these substances,15 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 having dependence or abuse of illicit drugs and alcohol, see Section B.4.2 in Appendix B of the 2010 NSDUH national findings report (CBHSQ, 2011, pp. 118-120).

Additionally, the NSDUH CAI instrument included a series of questions that are designed to measure treatment need for an alcohol or illicit drug use problem and to determine persons needing but not receiving treatment.

Respondents were classified as needing treatment for an alcohol use problem in the past year if they met at least one of three criteria during the past year: (1) dependence on alcohol; (2) abuse of alcohol; or (3) received treatment for alcohol use at a specialty facility (i.e., drug and alcohol rehabilitation facility [inpatient or outpatient], hospital [inpatient only], or mental health center). A respondent was classified as needing but not receiving treatment for an alcohol problem if he or she met the criteria for alcohol dependence or abuse in the past year, but did not receive treatment at a specialty facility for an alcohol problem in the past year.

Respondents were classified as needing treatment for an illicit drug use problem in the past year if they met at least one of three criteria during the past year: (1) dependence on illicit drugs; (2) abuse of illicit drugs; or (3) received treatment for illicit drug use at a specialty facility (i.e., drug and alcohol rehabilitation facility [inpatient or outpatient], hospital [inpatient only], or mental health center). A respondent was classified as needing but not receiving treatment for an illicit drug problem if he or she met the criteria for illicit drug dependence or abuse in the past year, but did not receive treatment at a specialty facility for an illicit drug problem in the past year.

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 in Sections B.4.2 to B.4.4 of Appendix B in the 2010 NSDUH mental health findings report for all four outcome variables (CBHSQ, 2012).

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, not as a measure of serious mental illness, but as a measure of 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 2009 and 2010 are based on this revised methodology and, thus, are not comparable with estimates for serious mental illness or serious psychological distress shown in NSDUH State reports prior to 2009. They are, however, comparable with the 2008-2009 serious mental illness estimates; hence, comparisons between those estimates are provided in this report.

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 then were 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 thetime" 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 being 1 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 A7 and     D     (1)

Equation A8.     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, respectively:16

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

Equation A9,     D

Equation A10.     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 and 2010 NSDUH samples.

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. The same models were implemented for 2009 and 2010.

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 (and those sets of questions were continued to be asked in 2009 and 2010). These questions asked 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

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), and the questions for youths were adapted from the depression section of the National Comorbidity Survey Adolescent (NCS-A) (see http://www.hcp.med.harvard.edu/ncs/). 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 and 2010 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 Appendix B of the 2010 NSDUH mental health findings report (CBHSQ, 2012) 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 and subsequent surveys, 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 through 2010 major depressive episode estimates (for more information on these adjustments, see Aldworth, Kott, Yu, Mosquin, & Barnett-Walker, 2012). Thus, estimates of major depressive episode were produced for the 2008-2009 SAE report using the adjusted 2008 major depressive episode variable along with the unadjusted 2009 major depressive episode variable.

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 2010 are available for adolescents aged 12 to 17.

A.12  Comparison of Two 2009-2010 Small Area Estimates

This section describes a method for determining whether differences between two 2009-2010 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., 2009-2010).

Let pi 1 sub a and pi 2 sub a denote the 2009-2010 age group-a specific prevalence rates for two different States, State 1 and State 2, 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 lor sub a is defined as lor sub a = 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 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 2009-2010 State estimate for State s1 and age group-a, and p 2 sub a is the 2009-2010 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 2009-2010 State estimates given in Appendix B. To compute the variance of estimate of the log-odds ratio, lor hat sub a, that is, variance v 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 v of the natural logarithm of Theta 1 hat, quantity q2 is the variance v 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 A11.     D

The quantities variance v of the natural logarithm of Theta 1 hat and variance v 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, s sub 1, representing State 1 and s sub 2, representing State 2, respectively. Then

Equation A12,     D

where Capital U sub i is the natural logarithm of upper sub i divided by 1 minus upper sub i, and capital L sub i is the natural logarithm of lower sub i divided by 1 minus lower sub i..


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 v 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 v of the natural logarithm of Theta 1 hat and the variance v 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 v of the estimate of the log-odds ratio, lor 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 capital 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 v of the natural logarithm of Theta 1 hat and the variance v 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 Minnesota 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 Minnesota 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 (%)
Minnesota 13.16 (11.10, 15.55)
North Dakota 16.58 (14.25, 19.20)

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 p 1 sub a equal 0.1316, lower sub 1 equal 0.1110, upper sub 1 equal 0.1555, p 2 sub a equal 0.1658, lower sub 2 equal 0.1425., upper sub 2 equals 0.1920. Then,

Equation A14,     D

Equation A15,     D

Equation A16,     D

Equation A17,     D

Equation A18, and     D

Equation A18.     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 (Minnesota 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 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 2.0134. The p value is equal to 0.044.

A.13  Comparison of 2008-2009 and 2009-2010 Small Area Estimates

Comparisons between State small area estimates displayed in Appendix C are based on the 2008 through 2010 NSDUHs. The State estimates for 2008-2009 were recalculated after removing erroneous data (for more details, see Section 1.5 in the Introduction). Hence, the 2008-2009 small area estimates listed here may not match previously published 2008-2009 model-based small area estimates (Hughes et al., 2011). The State estimates for 2009-2010 are the small area estimates given in Appendix B. The moving average State prevalence estimates for the overlapping 2008-2009 and 2009-2010 time periods were obtained from independent applications of SWHB methodology; that is, the 2009-2010 models were fit independently of the previously fitted 2008-2009 models. This independent analysis approach was followed because there was no desire to revise the previously published 2008-2009 estimates. Moreover, the same fixed predictor variables were used in the 2008-2009 and 2009-2010 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 2008-2009 and 2009-2010 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 (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 2009-2010 and 2008-2009 prevalence rates.). The log-odds ratio, lor sub s and a is defined as

Equation A20,     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 A21,     D


where the p 1 sub s and a are previously published 2008-2009 State estimates and the p 2 sub s and a are the 2009-2010 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 v of the estimate of the log-odds ratio, lor hat sub s and a, let Theta 1 hat equals the ratio of p 1 sub s and a and 1 minus p 1 sub s and a. and Theta 2 hat equals the ratio of p 2 sub s and a and 1 minus p 2 sub s and a., then

Equation A22,     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 A23.     D

Note that variance v of the natural logarithm of Theta 1 hat and variance v of the natural logarithm of Theta 2 hat used here to calculate variance v of the estimate of the log-odds ratio, lor hat sub s and a are the same variances used in calculating the previously published 2008-2009 Bayesian confidence intervals and the 2009-2010 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 2008, 2009, and 2010 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 (2008, 2009, and 2010), 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 the correlation between the natural logarithm of Theta 1 hat and the 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 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 capital 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.

Table A.1 – 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.
NOTE: The 2008 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2008 (Revised March 2012).
Total U.S. 194,815 160,114 142,159 88.62% 85,711 67,928 249,815,089 74.24% 65.79%
Northeast 41,088 34,126 28,544 82.73% 16,706 12,923 46,098,527 71.79% 59.40%
Midwest 52,794 44,490 39,977 90.15% 24,383 19,314 54,957,186 74.93% 67.55%
South 59,559 47,799 43,224 91.01% 25,547 20,740 90,962,960 76.37% 69.50%
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 2,217 1,770 79.33% 1,087 844 4,660,360 72.92% 57.84%
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 8,599 6,830 79.31% 3,811 2,930 10,448,312 72.73% 57.68%
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%
Table A.2 – 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
NOTE: The 2008 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2008 (Revised March 2012).
Total U.S. 26,228 22,261 24,892,326 84.51% 28,793 23,140 32,938,183 80.38% 30,690 22,527 191,984,580 71.81%
Northeast 5,009 4,184 4,374,575 82.21% 5,608 4,389 5,986,651 77.34% 6,089 4,350 35,737,300 69.61%
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 7,890 6,801 9,050,269 86.06% 8,623 7,113 11,764,906 82.98% 9,034 6,826 70,147,785 73.92%
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 343 287 463,837 84.32% 358 284 603,272 80.92% 386 273 3,593,251 69.76%
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 1,199 984 987,054 81.74% 1,182 931 1,329,112 78.89% 1,430 1,015 8,132,146 70.67%
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%
Table A.3 – 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.
NOTE: The 2009 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009 (Revised March 2012).
Total U.S. 195,132 161,377 142,933 88.40% 84,785 68,007 251,815,533 75.56% 66.79%
Northeast 42,197 35,012 28,978 81.91% 16,907 13,130 46,385,613 72.81% 59.63%
Midwest 53,123 44,770 40,360 90.34% 23,827 19,133 55,167,183 75.97% 68.64%
South 60,898 49,144 44,583 91.33% 25,512 20,925 92,048,862 77.36% 70.65%
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 1,905 1,544 81.00% 1,002 836 4,705,966 79.72% 64.57%
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 8,367 6,610 79.12% 3,795 2,915 10,583,566 72.92% 57.69%
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%
Table A.4 – 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
NOTE: The 2009 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009 (Revised March 2012).
Total U.S. 26,157 22,416 24,608,987 85.60% 28,158 22,930 33,579,988 81.48% 30,470 22,661 193,626,558 73.23%
Northeast 5,185 4,362 4,305,676 83.00% 5,638 4,401 6,120,620 77.62% 6,084 4,367 35,959,317 70.73%
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 7,811 6,767 9,008,998 87.12% 8,567 7,210 12,022,931 84.47% 9,134 6,948 71,016,933 74.89%
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 315 277 456,071 87.92% 334 280 618,887 85.95% 353 279 3,631,008 77.34%
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 1,195 988 973,827 83.33% 1,222 959 1,356,120 79.03% 1,378 968 8,253,618 70.58%
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%
Table A.5 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2010
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, National Survey on Drug Use and Health, 2010 (Revised March 2012).
Total U.S. 201,865 166,532 147,010 88.42% 84,997 67,804 253,619,107 74.57% 65.94%
Northeast 43,420 36,033 29,645 81.63% 16,782 13,017 46,535,320 72.81% 59.44%
Midwest 54,767 45,892 41,118 89.86% 24,139 19,301 55,345,459 74.81% 67.22%
South 63,813 51,533 46,241 90.54% 25,597 20,769 92,961,895 76.24% 69.03%
West 39,865 33,074 30,006 89.17% 18,479 14,717 58,776,433 73.17% 65.24%
Alabama 2,879 2,284 2,099 91.94% 1,121 878 3,893,688 71.86% 66.07%
Alaska 2,226 1,719 1,583 92.02% 1,057 868 555,964 77.75% 71.55%
Arizona 2,655 2,059 1,861 90.14% 1,149 925 5,386,782 72.97% 65.77%
Arkansas 2,595 2,108 1,948 92.51% 1,123 899 2,375,992 75.16% 69.53%
California 9,282 8,087 6,910 85.48% 4,739 3,715 30,322,142 71.96% 61.52%
Colorado 2,529 2,084 1,912 92.20% 1,117 904 4,151,930 79.29% 73.11%
Connecticut 2,474 2,158 1,812 83.73% 1,151 926 2,951,217 75.17% 62.94%
Delaware 2,621 2,118 1,857 87.67% 1,099 889 737,571 77.52% 67.96%
District of Columbia 5,113 4,192 3,403 79.88% 1,110 935 517,942 81.34% 64.97%
Florida 13,206 9,961 8,891 89.01% 4,460 3,655 15,611,774 77.37% 68.87%
Georgia 2,385 1,978 1,804 91.21% 1,131 910 7,940,651 75.51% 68.88%
Hawaii 2,861 2,443 2,098 85.56% 1,296 974 1,047,745 66.88% 57.22%
Idaho 2,624 2,046 1,932 94.43% 1,113 912 1,250,238 78.24% 73.88%
Illinois 10,614 9,121 7,392 80.95% 4,762 3,609 10,629,517 70.77% 57.29%
Indiana 2,743 2,281 2,104 91.97% 1,142 916 5,286,018 73.88% 67.95%
Iowa 2,574 2,187 2,069 94.61% 1,113 925 2,502,115 78.90% 74.65%
Kansas 2,340 1,988 1,824 91.75% 1,101 885 2,296,286 74.78% 68.61%
Kentucky 2,583 2,147 1,991 92.73% 1,109 900 3,574,784 76.88% 71.29%
Louisiana 2,605 2,092 1,955 93.42% 1,112 906 3,661,821 77.97% 72.84%
Maine 3,327 2,404 2,197 90.98% 1,100 924 1,127,285 80.65% 73.37%
Maryland 2,415 2,061 1,692 82.13% 1,096 883 4,737,806 77.66% 63.78%
Massachusetts 3,116 2,716 2,365 87.32% 1,149 930 5,605,641 78.23% 68.31%
Michigan 10,828 8,669 7,623 87.81% 4,561 3,690 8,313,433 75.65% 66.43%
Minnesota 2,532 2,087 1,949 93.42% 1,149 946 4,382,130 78.32% 73.17%
Mississippi 2,485 1,976 1,839 93.07% 1,087 893 2,373,593 76.50% 71.20%
Missouri 2,642 2,170 2,031 93.58% 1,142 921 4,952,896 75.89% 71.01%
Montana 2,713 2,255 2,128 94.34% 1,137 919 820,115 76.91% 72.56%
Nebraska 2,336 1,996 1,883 94.30% 1,120 906 1,469,129 73.19% 69.02%
Nevada 2,674 2,063 1,935 94.68% 1,183 958 2,155,405 71.81% 67.99%
New Hampshire 3,232 2,558 2,219 86.80% 1,160 918 1,128,997 74.48% 64.65%
New Jersey 2,382 2,061 1,831 88.85% 1,157 923 7,269,834 78.46% 69.72%
New Mexico 2,610 2,078 1,959 94.26% 1,117 912 1,641,892 77.09% 72.66%
New York 13,218 11,170 8,452 75.25% 5,061 3,626 16,410,083 66.82% 50.28%
North Carolina 2,674 2,303 2,118 92.18% 1,103 904 7,679,126 76.53% 70.54%
North Dakota 3,053 2,567 2,420 94.30% 1,188 954 540,202 76.32% 71.97%
Ohio 10,268 8,717 7,947 91.17% 4,633 3,731 9,580,362 74.81% 68.20%
Oklahoma 2,626 2,122 1,903 89.71% 1,173 923 2,995,565 73.17% 65.64%
Oregon 2,603 2,293 2,146 93.61% 1,134 907 3,229,211 74.87% 70.09%
Pennsylvania 10,193 8,715 6,952 79.79% 3,853 2,985 10,607,311 73.24% 58.44%
Rhode Island 2,574 2,094 1,866 89.19% 1,117 915 896,384 74.52% 66.46%
South Carolina 2,616 2,152 1,927 89.56% 1,138 927 3,760,624 75.68% 67.78%
South Dakota 2,399 2,048 1,945 95.06% 1,115 929 666,589 80.45% 76.47%
Tennessee 2,588 2,149 1,968 91.41% 1,117 901 5,238,574 73.38% 67.08%
Texas 8,885 7,290 6,697 91.78% 4,431 3,590 19,847,501 76.61% 70.31%
Utah 1,507 1,324 1,252 94.58% 1,105 919 2,180,889 79.81% 75.48%
Vermont 2,904 2,157 1,951 90.39% 1,034 870 538,568 82.45% 74.53%
Virginia 2,609 2,284 2,037 89.17% 1,096 888 6,471,190 76.48% 68.20%
Washington 2,636 2,288 2,103 91.87% 1,194 897 5,585,609 70.16% 64.45%
West Virginia 2,928 2,316 2,112 91.30% 1,091 888 1,543,694 78.37% 71.55%
Wisconsin 2,438 2,061 1,931 93.62% 1,113 889 4,726,785 76.78% 71.88%
Wyoming 2,945 2,335 2,187 93.74% 1,138 907 448,513 73.07% 68.50%
Table A.6 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2010
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: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2010 (Revised March 2012).
Total U.S. 25,908 21,992 24,346,528 84.65% 28,164 23,026 34,072,349 81.20% 30,925 22,786 195,200,229 72.14%
Northeast 4,966 4,105 4,234,758 81.31% 5,612 4,412 6,045,018 77.44% 6,204 4,500 36,255,544 71.05%
Midwest 7,357 6,264 5,334,556 85.18% 8,035 6,589 7,496,530 81.44% 8,747 6,448 42,514,373 72.34%
South 8,029 6,858 8,956,559 85.57% 8,407 7,027 12,418,811 83.30% 9,161 6,884 71,586,525 73.79%
West 5,556 4,765 5,820,656 85.19% 6,110 4,998 8,111,990 80.57% 6,813 4,954 44,843,787 70.31%
Alabama 369 307 374,067 82.98% 345 286 520,974 81.37% 407 285 2,998,646 68.80%
Alaska 312 266 57,362 85.56% 362 310 85,086 85.36% 383 292 413,516 75.09%
Arizona 333 292 538,540 87.31% 428 351 701,269 79.13% 388 282 4,146,973 70.30%
Arkansas 334 284 232,460 84.94% 362 296 305,518 82.20% 427 319 1,838,015 72.68%
California 1,526 1,303 3,086,730 84.79% 1,416 1,151 4,268,110 80.94% 1,797 1,261 22,967,302 68.62%
Colorado 273 231 379,157 82.92% 424 345 566,389 81.41% 420 328 3,206,384 78.51%
Connecticut 331 288 281,757 88.09% 400 326 381,359 81.42% 420 312 2,288,101 72.46%
Delaware 319 268 67,234 83.34% 340 288 93,677 85.05% 440 333 576,660 75.64%
District of Columbia 356 324 34,240 91.90% 384 320 84,993 82.39% 370 291 398,709 80.13%
Florida 1,424 1,215 1,329,956 85.86% 1,419 1,212 1,870,501 85.10% 1,617 1,228 12,411,317 75.23%
Georgia 371 313 818,462 84.43% 355 301 1,076,087 84.73% 405 296 6,046,102 72.53%
Hawaii 400 338 89,846 83.34% 439 335 130,340 78.06% 457 301 827,559 63.29%
Idaho 353 294 130,819 83.21% 356 305 177,534 85.11% 404 313 941,886 76.19%
Illinois 1,357 1,122 1,049,679 82.64% 1,615 1,232 1,453,014 76.32% 1,790 1,255 8,126,824 68.31%
Indiana 389 341 523,789 88.17% 343 280 719,041 81.57% 410 295 4,043,187 70.81%
Iowa 336 287 234,049 85.14% 385 321 359,379 81.94% 392 317 1,908,687 77.57%
Kansas 331 296 225,398 89.33% 357 285 338,453 81.23% 413 304 1,732,436 71.72%
Kentucky 352 299 333,232 85.21% 370 304 461,899 82.16% 387 297 2,779,654 75.08%
Louisiana 382 328 365,624 86.45% 345 285 526,082 82.60% 385 293 2,770,114 75.99%
Maine 325 284 94,501 87.86% 356 302 130,971 85.20% 419 338 901,813 79.28%
Maryland 315 268 448,006 86.14% 367 300 613,529 79.93% 414 315 3,676,271 76.21%
Massachusetts 360 296 491,663 80.42% 392 324 761,003 81.71% 397 310 4,352,974 77.40%
Michigan 1,432 1,212 814,296 84.10% 1,453 1,220 1,105,211 84.44% 1,676 1,258 6,393,926 73.01%
Minnesota 337 296 409,292 87.51% 410 340 590,704 82.82% 402 310 3,382,134 76.41%
Mississippi 333 290 247,423 87.78% 368 316 340,138 85.71% 386 287 1,786,033 73.21%
Missouri 341 288 472,583 85.51% 386 320 656,859 82.72% 415 313 3,823,454 73.55%
Montana 348 302 72,261 86.91% 343 280 114,819 81.70% 446 337 633,035 75.07%
Nebraska 335 300 141,249 88.32% 372 306 218,880 82.98% 413 300 1,108,999 69.37%
Nevada 298 264 210,434 90.50% 405 339 263,872 83.13% 480 355 1,681,099 67.89%
New Hampshire 300 250 101,483 84.76% 467 387 145,527 82.81% 393 281 881,988 71.74%
New Jersey 387 324 692,595 83.33% 334 264 865,591 81.47% 436 335 5,711,649 77.39%
New Mexico 364 327 161,227 89.38% 370 303 226,963 83.54% 383 282 1,253,702 74.21%
New York 1,457 1,141 1,498,050 77.55% 1,709 1,234 2,188,721 71.54% 1,895 1,251 12,723,312 64.80%
North Carolina 346 311 719,819 89.83% 375 304 1,014,496 82.27% 382 289 5,944,811 73.67%
North Dakota 357 300 46,378 83.63% 393 340 96,560 86.92% 438 314 397,264 72.96%
Ohio 1,395 1,191 918,549 85.27% 1,634 1,371 1,210,150 83.56% 1,604 1,169 7,451,663 72.00%
Oklahoma 394 337 291,436 84.66% 355 278 425,691 76.96% 424 308 2,278,438 71.09%
Oregon 376 318 285,470 83.17% 361 296 412,163 82.85% 397 293 2,531,579 72.45%
Pennsylvania 1,165 955 951,061 82.17% 1,203 946 1,365,550 78.58% 1,485 1,084 8,290,700 71.31%
Rhode Island 322 292 79,082 90.34% 418 350 129,842 83.67% 377 273 687,461 70.69%
South Carolina 351 292 349,533 83.84% 376 325 487,235 85.47% 411 310 2,923,856 73.12%
South Dakota 365 309 62,886 85.00% 338 296 96,018 88.02% 412 324 507,684 78.44%
Tennessee 370 319 489,539 86.92% 364 302 664,620 83.99% 383 280 4,084,416 69.67%
Texas 1,329 1,125 2,131,714 84.76% 1,532 1,288 2,858,101 83.62% 1,570 1,177 14,857,686 74.07%
Utah 283 250 255,595 88.81% 420 357 381,486 85.32% 402 312 1,543,809 77.17%
Vermont 319 275 44,568 87.55% 333 279 76,455 82.92% 382 316 417,546 81.80%
Virginia 349 295 594,024 85.00% 360 301 884,909 83.26% 387 292 4,992,257 74.20%
Washington 365 301 512,686 83.53% 377 280 719,040 71.68% 452 316 4,353,883 68.27%
West Virginia 335 283 129,792 83.49% 390 321 190,362 81.95% 366 284 1,223,540 77.18%
Wisconsin 382 322 436,408 84.04% 349 278 652,261 78.40% 382 289 3,638,115 75.54%
Wyoming 325 279 40,531 85.48% 409 346 64,920 83.34% 404 282 343,061 69.61%
Table A.7 – 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
DU = dwelling unit.
NOTE: The 2008-2009 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
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, National Survey on Drug Use and Health, 2008 and 2009 (Revised March 2012).
Total U.S. 389,947 321,491 285,092 88.51% 170,496 135,935 250,815,311 74.90% 66.29%
Northeast 83,285 69,138 57,522 82.32% 33,613 26,053 46,242,070 72.30% 59.51%
Midwest 105,917 89,260 80,337 90.24% 48,210 38,447 55,062,184 75.45% 68.09%
South 120,457 96,943 87,807 91.17% 51,059 41,665 91,505,911 76.87% 70.08%
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 4,122 3,314 80.14% 2,089 1,680 4,683,163 76.30% 61.15%
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 16,966 13,440 79.21% 7,606 5,845 10,515,939 72.82% 57.68%
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%
Table A.8 – 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: The 2008-2009 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
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, National Survey on Drug Use and Health, 2008 and 2009 (Revised March 2012).
Total U.S. 52,385 44,677 24,750,657 85.05% 56,951 46,070 33,259,086 80.94% 61,160 45,188 192,805,569 72.52%
Northeast 10,194 8,546 4,340,126 82.60% 11,246 8,790 6,053,636 77.48% 12,173 8,717 35,848,309 70.16%
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 15,701 13,568 9,029,633 86.59% 17,190 14,323 11,893,918 83.74% 18,168 13,774 70,582,359 74.42%
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 658 564 459,954 86.09% 692 564 611,079 83.48% 739 552 3,612,130 73.52%
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 2,394 1,972 980,441 82.54% 2,404 1,890 1,342,616 78.96% 2,808 1,983 8,192,882 70.63%
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%
Table A.9 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2009 and 2010
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 2009-2010 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 2009 and 2010 individual response rates. The 2009-2010 population estimate is the average of the 2009 and the 2010 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009 and 2010 (Revised March 2012).
Total U.S. 396,997 327,909 289,943 88.41% 169,782 135,811 252,717,320 75.06% 66.36%
Northeast 85,617 71,045 58,623 81.77% 33,689 26,147 46,460,466 72.81% 59.54%
Midwest 107,890 90,662 81,478 90.10% 47,966 38,434 55,256,321 75.39% 67.92%
South 124,711 100,677 90,824 90.92% 51,109 41,694 92,505,378 76.80% 69.83%
West 78,779 65,525 59,018 88.16% 37,018 29,536 58,495,154 73.83% 65.08%
Alabama 5,710 4,570 4,227 92.46% 2,295 1,822 3,884,861 75.20% 69.53%
Alaska 4,529 3,487 3,214 92.05% 2,167 1,770 554,985 78.55% 72.31%
Arizona 5,378 4,109 3,639 86.23% 2,259 1,841 5,348,799 76.07% 65.60%
Arkansas 5,169 4,212 3,913 92.90% 2,256 1,813 2,367,178 76.23% 70.82%
California 18,216 15,848 13,409 84.68% 9,473 7,375 30,200,952 71.90% 60.88%
Colorado 5,256 4,356 4,000 92.16% 2,312 1,888 4,124,003 78.41% 72.27%
Connecticut 4,805 4,219 3,617 85.61% 2,298 1,841 2,944,171 75.81% 64.90%
Delaware 5,216 4,253 3,719 87.48% 2,228 1,809 734,670 75.59% 66.13%
District of Columbia 9,435 7,703 6,254 80.24% 2,152 1,821 514,116 82.50% 66.19%
Florida 24,594 18,682 16,931 90.42% 8,867 7,303 15,548,303 77.05% 69.67%
Georgia 4,680 3,842 3,520 91.50% 2,213 1,817 7,893,753 76.92% 70.38%
Hawaii 6,070 5,161 4,252 81.28% 2,617 1,934 1,049,989 66.94% 54.41%
Idaho 4,876 3,811 3,603 94.55% 2,232 1,828 1,242,898 77.67% 73.44%
Illinois 20,722 17,902 14,489 80.88% 9,548 7,264 10,610,876 71.24% 57.62%
Indiana 5,462 4,507 4,191 92.80% 2,261 1,820 5,273,705 76.51% 71.00%
Iowa 5,141 4,390 4,118 93.87% 2,212 1,849 2,494,295 80.38% 75.46%
Kansas 4,704 4,041 3,730 92.28% 2,233 1,794 2,288,038 75.43% 69.60%
Kentucky 4,994 4,093 3,819 93.34% 2,227 1,812 3,562,425 76.76% 71.64%
Louisiana 5,220 4,217 3,948 93.67% 2,255 1,829 3,650,936 78.43% 73.47%
Maine 6,536 4,743 4,347 91.50% 2,232 1,888 1,128,113 81.63% 74.69%
Maryland 4,646 3,966 3,236 81.58% 2,098 1,719 4,721,886 78.64% 64.15%
Massachusetts 6,393 5,529 4,750 86.07% 2,388 1,899 5,584,646 76.02% 65.42%
Michigan 21,188 16,972 14,968 88.12% 9,091 7,329 8,318,631 76.25% 67.20%
Minnesota 4,866 4,071 3,803 93.44% 2,281 1,871 4,369,150 78.00% 72.88%
Mississippi 4,569 3,595 3,366 93.67% 2,177 1,784 2,369,559 77.09% 72.21%
Missouri 5,171 4,247 3,964 93.34% 2,254 1,810 4,939,693 75.72% 70.67%
Montana 5,226 4,403 4,154 94.27% 2,256 1,828 817,248 76.46% 72.08%
Nebraska 4,610 3,936 3,713 94.32% 2,245 1,817 1,463,255 75.92% 71.61%
Nevada 5,279 4,126 3,876 94.53% 2,332 1,888 2,149,864 72.06% 68.11%
New Hampshire 6,018 4,813 4,223 87.84% 2,350 1,862 1,127,079 74.47% 65.41%
New Jersey 4,699 4,051 3,597 88.82% 2,329 1,829 7,255,812 75.41% 66.99%
New Mexico 5,158 4,110 3,875 94.26% 2,232 1,830 1,635,195 77.18% 72.75%
New York 26,232 21,952 16,741 75.99% 10,082 7,333 16,395,090 68.69% 52.20%
North Carolina 5,191 4,393 4,037 92.05% 2,215 1,833 7,645,726 78.04% 71.83%
North Dakota 5,972 4,994 4,710 94.32% 2,337 1,883 537,282 76.49% 72.15%
Ohio 20,068 17,122 15,794 92.21% 9,025 7,316 9,581,162 74.86% 69.03%
Oklahoma 5,274 4,264 3,867 90.79% 2,297 1,831 2,983,240 73.81% 67.01%
Oregon 5,405 4,672 4,330 92.80% 2,304 1,854 3,214,493 77.46% 71.88%
Pennsylvania 19,898 17,082 13,562 79.45% 7,648 5,900 10,595,438 73.08% 58.06%
Rhode Island 5,353 4,437 3,927 88.51% 2,272 1,828 892,872 75.54% 66.85%
South Carolina 5,713 4,514 4,072 89.88% 2,291 1,881 3,745,403 75.94% 68.25%
South Dakota 4,816 4,078 3,887 95.36% 2,203 1,849 662,841 80.79% 77.04%
Tennessee 5,611 4,614 4,266 92.24% 2,289 1,850 5,217,297 73.41% 67.72%
Texas 17,537 14,468 13,288 91.84% 8,819 7,186 19,683,472 77.13% 70.84%
Utah 3,046 2,700 2,558 94.73% 2,206 1,837 2,162,531 80.09% 75.87%
Vermont 5,683 4,219 3,859 91.46% 2,090 1,767 537,245 80.87% 73.96%
Virginia 5,108 4,455 3,961 88.89% 2,221 1,806 6,440,709 76.78% 68.24%
Washington 4,995 4,386 4,016 91.51% 2,352 1,833 5,547,471 73.61% 67.35%
West Virginia 6,044 4,836 4,400 91.05% 2,209 1,778 1,541,846 76.08% 69.27%
Wisconsin 5,170 4,402 4,111 93.41% 2,276 1,832 4,717,394 76.72% 71.66%
Wyoming 5,345 4,356 4,092 94.00% 2,276 1,830 446,727 75.87% 71.31%
Table A.10 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2009 and 2010
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: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
NOTE: To compute the pooled 2009-2010 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 2009 and 2010 individual response rates. The 2009-2010 population estimate is the average of the 2009 and the 2010 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009 and 2010 (Revised March 2012).
Total U.S. 52,065 44,408 24,477,758 85.13% 56,322 45,956 33,826,169 81.34% 61,395 45,447 194,413,393 72.68%
Northeast 10,151 8,467 4,270,217 82.16% 11,250 8,813 6,082,819 77.53% 12,288 8,867 36,107,431 70.89%
Midwest 14,808 12,662 5,372,501 85.35% 15,838 12,914 7,417,088 80.97% 17,320 12,858 42,466,732 73.14%
South 15,840 13,625 8,982,778 86.35% 16,974 14,237 12,220,871 83.88% 18,295 13,832 71,301,729 74.34%
West 11,266 9,654 5,852,262 85.21% 12,260 9,992 8,105,391 80.74% 13,492 9,890 44,537,502 71.09%
Alabama 759 633 375,942 83.76% 690 567 515,510 81.36% 846 622 2,993,410 73.13%
Alaska 660 568 58,753 86.39% 725 608 81,679 84.83% 782 594 414,553 76.26%
Arizona 676 592 538,672 87.18% 828 677 698,979 80.25% 755 572 4,111,148 74.02%
Arkansas 682 590 231,881 87.09% 738 598 301,928 82.45% 836 625 1,833,369 73.79%
California 2,905 2,472 3,101,979 84.51% 2,983 2,391 4,325,900 80.19% 3,585 2,512 22,773,074 68.65%
Colorado 677 596 381,533 85.87% 841 681 549,726 82.10% 794 611 3,192,744 76.74%
Connecticut 698 596 283,906 86.38% 781 638 375,156 81.73% 819 607 2,285,109 73.51%
Delaware 677 578 67,805 85.01% 759 638 94,200 84.15% 792 593 572,665 73.09%
District of Columbia 644 574 34,683 89.17% 786 664 86,622 83.83% 722 583 392,811 81.55%
Florida 2,736 2,341 1,336,737 85.42% 2,957 2,540 1,850,052 85.26% 3,174 2,422 12,361,513 74.87%
Georgia 715 619 820,145 87.07% 697 596 1,050,786 84.56% 801 602 6,022,823 74.13%
Hawaii 791 649 91,104 80.44% 836 620 131,160 74.46% 990 665 827,725 64.29%
Idaho 684 578 131,965 84.90% 707 610 171,302 86.06% 841 640 939,631 75.18%
Illinois 2,763 2,299 1,053,276 83.16% 3,170 2,419 1,460,312 76.02% 3,615 2,546 8,097,288 68.87%
Indiana 721 626 525,525 87.68% 699 567 701,086 81.07% 841 627 4,047,093 74.29%
Iowa 675 589 236,022 87.73% 761 629 350,071 81.98% 776 631 1,908,202 79.22%
Kansas 678 599 226,545 88.52% 772 607 330,970 78.85% 783 588 1,730,522 73.02%
Kentucky 659 566 334,420 86.76% 766 632 446,645 82.33% 802 614 2,781,361 74.80%
Louisiana 720 612 367,519 85.09% 711 593 527,254 83.19% 824 624 2,756,163 76.67%
Maine 704 621 96,374 88.37% 750 636 128,182 85.09% 778 631 903,556 80.42%
Maryland 630 545 452,038 87.03% 701 580 616,208 82.95% 767 594 3,653,639 76.74%
Massachusetts 711 584 494,016 81.38% 820 673 766,014 81.72% 857 642 4,324,616 74.42%
Michigan 2,895 2,455 822,105 84.26% 2,923 2,420 1,097,830 82.89% 3,273 2,454 6,398,696 74.03%
Minnesota 692 603 413,410 86.54% 806 660 583,280 81.49% 783 608 3,372,460 76.30%
Mississippi 633 545 248,816 86.72% 740 634 336,097 85.98% 804 605 1,784,646 74.21%
Missouri 715 594 476,436 83.59% 738 614 643,637 82.82% 801 602 3,819,620 73.48%
Montana 698 597 73,735 86.08% 746 614 110,260 82.19% 812 617 633,252 74.40%
Nebraska 673 590 142,549 88.08% 747 610 214,428 82.01% 825 617 1,106,278 73.22%
Nevada 661 576 212,438 88.15% 796 673 257,198 84.96% 875 639 1,680,228 68.13%
New Hampshire 687 577 103,281 84.66% 823 673 140,176 81.96% 840 612 883,622 72.07%
New Jersey 732 614 695,052 83.02% 742 581 873,788 79.50% 855 634 5,686,972 73.83%
New Mexico 710 632 161,555 89.22% 738 613 228,756 84.91% 784 585 1,244,885 74.15%
New York 2,917 2,344 1,509,858 79.67% 3,427 2,483 2,236,966 72.75% 3,738 2,506 12,648,267 66.66%
North Carolina 655 584 723,670 89.21% 791 662 986,404 84.66% 769 587 5,935,653 75.49%
North Dakota 727 625 47,211 85.91% 749 626 92,922 84.11% 861 632 397,148 73.64%
Ohio 2,788 2,402 924,820 85.90% 3,059 2,577 1,214,036 83.80% 3,178 2,337 7,442,306 71.96%
Oklahoma 759 646 292,084 85.05% 704 565 419,077 79.42% 834 620 2,272,080 71.31%
Oregon 795 654 288,096 81.63% 677 560 401,242 83.81% 832 640 2,525,156 75.91%
Pennsylvania 2,360 1,943 962,444 82.77% 2,425 1,905 1,360,835 78.80% 2,863 2,052 8,272,159 70.95%
Rhode Island 704 625 79,655 89.14% 784 625 129,145 79.80% 784 578 684,072 73.06%
South Carolina 757 643 352,096 84.90% 747 646 480,982 85.11% 787 592 2,912,325 73.20%
South Dakota 687 601 63,682 87.84% 723 625 93,602 86.97% 793 623 505,557 78.72%
Tennessee 764 670 491,069 87.97% 712 591 646,257 84.14% 813 589 4,079,971 69.53%
Texas 2,671 2,307 2,125,058 86.57% 2,971 2,484 2,813,275 83.69% 3,177 2,395 14,745,138 74.53%
Utah 640 568 254,680 89.28% 782 657 379,390 83.54% 784 612 1,528,461 77.83%
Vermont 638 563 45,631 89.01% 698 599 72,557 85.03% 754 605 419,057 79.23%
Virginia 697 592 598,313 84.83% 745 631 865,844 84.69% 779 583 4,976,551 74.34%
Washington 722 612 516,464 85.23% 774 606 706,882 76.50% 856 615 4,324,124 71.76%
West Virginia 682 580 130,503 84.95% 759 616 183,731 80.47% 768 582 1,227,612 74.42%
Wisconsin 794 679 440,921 85.20% 691 560 634,912 79.83% 791 593 3,641,561 75.10%
Wyoming 647 560 41,288 85.11% 827 682 62,918 81.96% 802 588 342,522 73.63%
Table A.11 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2008, 2009, and 2010
State 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
2010
Total
Selected
2010
Total
Responded
2010
Population
Estimate
2010
Weighted
Interview
Response
Rate
NOTE: The 2008 and 2009 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2008, 2009, and 2010 (Revised March 2012).
Total U.S. 36,999 31,238 38,081,341 84.29% 37,009 31,560 38,241,242 85.21% 36,493 30,926 37,977,621 84.37%
Northeast 7,122 5,920 6,707,117 82.07% 7,329 6,098 6,785,606 82.08% 7,068 5,812 6,622,750 80.74%
Midwest 10,526 8,854 8,356,484 83.74% 10,586 9,034 8,472,393 84.90% 10,415 8,841 8,359,258 84.73%
South 11,102 9,542 13,858,227 86.15% 11,053 9,595 13,893,364 87.36% 11,208 9,600 14,070,149 85.73%
West 8,249 6,922 9,159,513 83.60% 8,041 6,833 9,089,879 84.55% 7,802 6,673 8,925,464 84.62%
Alabama 493 427 581,262 86.76% 530 447 599,028 85.17% 510 425 587,014 82.65%
Alaska 515 414 92,180 80.10% 488 416 91,611 85.87% 433 370 86,119 86.09%
Arizona 481 413 810,336 85.49% 514 437 824,443 84.93% 509 441 834,235 85.27%
Arkansas 513 454 350,656 88.83% 469 407 342,551 88.11% 458 380 334,786 82.16%
California 2,120 1,761 4,938,568 82.75% 1,946 1,634 4,775,356 83.65% 2,058 1,755 4,745,134 84.60%
Colorado 530 444 568,813 83.19% 564 494 585,652 86.73% 406 337 551,247 81.84%
Connecticut 453 396 422,896 88.56% 481 407 408,896 85.77% 494 422 446,654 85.63%
Delaware 495 410 104,894 83.20% 518 446 108,455 86.09% 439 375 105,183 85.49%
District of Columbia 410 368 58,497 90.11% 398 346 59,345 85.42% 442 401 56,178 91.06%
Florida 1,953 1,691 2,132,876 86.69% 1,921 1,662 2,133,592 86.01% 2,038 1,759 2,165,742 86.98%
Georgia 512 438 1,242,605 84.97% 474 420 1,182,593 88.73% 508 430 1,222,236 84.71%
Hawaii 525 408 141,555 77.46% 538 426 150,940 76.75% 561 468 142,051 82.88%
Idaho 477 418 193,450 87.15% 468 402 201,917 86.93% 479 409 202,052 85.98%
Illinois 2,113 1,719 1,626,682 81.62% 2,021 1,676 1,681,199 82.87% 1,948 1,593 1,638,431 81.51%
Indiana 540 447 818,888 83.63% 472 406 824,402 86.59% 511 445 808,335 87.62%
Iowa 484 413 367,032 86.07% 484 417 362,489 86.00% 464 400 369,554 85.30%
Kansas 441 367 343,518 83.67% 523 445 362,101 84.25% 452 397 349,540 87.74%
Kentucky 486 423 515,913 86.34% 465 411 533,285 89.41% 508 430 515,140 84.34%
Louisiana 466 404 615,972 86.94% 470 395 559,359 83.59% 507 431 567,474 85.18%
Maine 484 423 154,462 86.37% 515 448 141,437 87.04% 458 405 152,571 89.02%
Maryland 484 407 708,080 85.30% 445 391 679,353 88.22% 428 367 671,790 86.29%
Massachusetts 475 405 783,464 85.33% 503 407 756,845 80.42% 474 387 730,933 80.15%
Michigan 2,027 1,730 1,325,437 84.95% 2,054 1,735 1,286,421 84.14% 1,998 1,690 1,266,567 84.36%
Minnesota 452 390 620,418 86.35% 523 444 687,929 83.63% 496 425 635,101 85.43%
Mississippi 464 401 385,041 86.31% 464 396 401,760 86.27% 483 422 393,379 87.64%
Missouri 483 410 713,872 82.22% 496 410 701,234 83.00% 474 400 741,708 85.70%
Montana 525 442 121,269 85.39% 497 421 116,223 85.63% 480 416 118,731 86.51%
Nebraska 483 417 225,154 85.63% 498 429 240,816 87.71% 469 415 227,519 87.62%
Nevada 490 423 301,339 86.56% 500 438 312,307 88.06% 467 410 329,077 89.28%
New Hampshire 494 418 172,605 84.97% 525 437 162,423 83.24% 485 406 163,192 85.20%
New Jersey 579 474 1,042,888 81.60% 502 418 1,060,608 81.69% 518 429 1,033,688 82.93%
New Mexico 442 382 252,364 85.83% 476 418 255,788 89.54% 502 447 255,942 88.87%
New York 2,028 1,630 2,425,431 79.44% 2,121 1,721 2,486,753 80.92% 2,091 1,629 2,367,030 76.92%
North Carolina 480 420 1,102,143 88.11% 452 402 1,094,206 89.32% 487 434 1,158,894 89.02%
North Dakota 494 417 83,543 83.50% 494 431 82,565 87.52% 516 438 84,291 85.53%
Ohio 2,087 1,757 1,463,023 84.33% 2,014 1,745 1,474,740 86.21% 2,085 1,791 1,450,314 85.68%
Oklahoma 457 385 440,291 84.06% 502 427 460,633 85.55% 510 430 445,994 83.55%
Oregon 583 507 481,595 86.18% 534 429 445,204 81.09% 510 425 435,243 82.04%
Pennsylvania 1,641 1,343 1,497,043 81.72% 1,675 1,384 1,558,771 82.99% 1,641 1,344 1,529,660 81.90%
Rhode Island 448 398 130,107 88.98% 530 447 130,746 83.09% 472 419 127,717 88.13%
South Carolina 488 422 547,282 86.74% 540 471 539,722 86.26% 498 421 554,128 84.56%
South Dakota 497 447 106,030 89.70% 486 439 103,873 90.23% 500 429 98,463 86.31%
Tennessee 456 378 699,714 82.76% 534 473 778,937 88.58% 521 448 783,233 87.28%
Texas 1,906 1,645 3,243,147 86.32% 1,869 1,641 3,208,416 88.06% 1,918 1,638 3,337,978 85.37%
Utah 501 434 395,972 84.14% 483 426 400,057 88.25% 412 364 375,829 88.48%
Vermont 520 433 78,221 82.77% 477 429 79,128 89.98% 435 371 71,307 85.97%
Virginia 502 428 931,139 86.21% 507 441 1,007,100 87.39% 489 415 974,776 85.04%
Washington 538 446 795,765 84.12% 540 464 859,380 85.46% 501 410 783,258 81.66%
West Virginia 537 441 198,715 82.69% 495 419 205,027 84.86% 464 394 196,221 84.13%
Wisconsin 425 340 662,888 81.80% 521 457 664,624 87.47% 502 418 689,435 82.92%
Wyoming 522 430 66,307 81.99% 493 428 71,000 84.56% 484 421 66,546 86.75%
Table A.12 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2008-2009 and 2009-2010
State 2008-2009
Total
Selected
2008-2009
Total
Responded
2008-2009
Population
Estimate
2008-2009
Weighted
Interview
Response
Rate
2009-2010
Total
Selected
2009-2010
Total
Responded
2009-2010
Population
Estimate
2009-2010
Weighted
Interview
Response
Rate
NOTE: The 2008-2009 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
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, National Survey on Drug Use and Health, 2008, 2009, and 2010 (Revised March 2012).
Total U.S. 74,008 62,798 38,161,292 84.75% 73,502 62,486 38,109,432 84.79%
Northeast 14,451 12,018 6,746,362 82.08% 14,397 11,910 6,704,178 81.42%
Midwest 21,112 17,888 8,414,438 84.32% 21,001 17,875 8,415,825 84.82%
South 22,155 19,137 13,875,795 86.76% 22,261 19,195 13,981,756 86.54%
West 16,290 13,755 9,124,696 84.07% 15,843 13,506 9,007,672 84.58%
Alabama 1,023 874 590,145 85.97% 1,040 872 593,021 83.90%
Alaska 1,003 830 91,895 82.95% 921 786 88,865 85.97%
Arizona 995 850 817,390 85.20% 1,023 878 829,339 85.10%
Arkansas 982 861 346,604 88.49% 927 787 338,668 85.08%
California 4,066 3,395 4,856,962 83.20% 4,004 3,389 4,760,245 84.12%
Colorado 1,094 938 577,232 84.98% 970 831 568,449 84.43%
Connecticut 934 803 415,896 87.17% 975 829 427,775 85.69%
Delaware 1,013 856 106,674 84.66% 957 821 106,819 85.79%
District of Columbia 808 714 58,921 87.72% 840 747 57,762 88.14%
Florida 3,874 3,353 2,133,234 86.36% 3,959 3,421 2,149,667 86.51%
Georgia 986 858 1,212,599 86.81% 982 850 1,202,415 86.71%
Hawaii 1,063 834 146,248 77.10% 1,099 894 146,495 79.79%
Idaho 945 820 197,683 87.04% 947 811 201,984 86.46%
Illinois 4,134 3,395 1,653,940 82.26% 3,969 3,269 1,659,815 82.20%
Indiana 1,012 853 821,645 85.10% 983 851 816,369 87.10%
Iowa 968 830 364,761 86.04% 948 817 366,022 85.65%
Kansas 964 812 352,809 83.97% 975 842 355,820 85.93%
Kentucky 951 834 524,599 87.89% 973 841 524,213 86.87%
Louisiana 936 799 587,665 85.29% 977 826 563,417 84.38%
Maine 999 871 147,950 86.70% 973 853 147,004 88.02%
Maryland 929 798 693,717 86.74% 873 758 675,572 87.28%
Massachusetts 978 812 770,154 82.80% 977 794 743,889 80.29%
Michigan 4,081 3,465 1,305,929 84.55% 4,052 3,425 1,276,494 84.25%
Minnesota 975 834 654,173 84.92% 1,019 869 661,515 84.49%
Mississippi 928 797 393,401 86.29% 947 818 397,570 86.95%
Missouri 979 820 707,553 82.60% 970 810 721,471 84.36%
Montana 1,022 863 118,746 85.51% 977 837 117,477 86.07%
Nebraska 981 846 232,985 86.69% 967 844 234,167 87.67%
Nevada 990 861 306,823 87.33% 967 848 320,692 88.67%
New Hampshire 1,019 855 167,514 84.14% 1,010 843 162,808 84.20%
New Jersey 1,081 892 1,051,748 81.64% 1,020 847 1,047,148 82.32%
New Mexico 918 800 254,076 87.70% 978 865 255,865 89.20%
New York 4,149 3,351 2,456,092 80.19% 4,212 3,350 2,426,892 78.94%
North Carolina 932 822 1,098,175 88.72% 939 836 1,126,550 89.17%
North Dakota 988 848 83,054 85.45% 1,010 869 83,428 86.49%
Ohio 4,101 3,502 1,468,881 85.28% 4,099 3,536 1,462,527 85.95%
Oklahoma 959 812 450,462 84.83% 1,012 857 453,313 84.57%
Oregon 1,117 936 463,400 83.73% 1,044 854 440,223 81.56%
Pennsylvania 3,316 2,727 1,527,907 82.37% 3,316 2,728 1,544,215 82.45%
Rhode Island 978 845 130,426 85.99% 1,002 866 129,231 85.53%
South Carolina 1,028 893 543,502 86.50% 1,038 892 546,925 85.40%
South Dakota 983 886 104,952 89.97% 986 868 101,168 88.32%
Tennessee 990 851 739,326 85.78% 1,055 921 781,085 87.92%
Texas 3,775 3,286 3,225,782 87.18% 3,787 3,279 3,273,197 86.70%
Utah 984 860 398,014 86.22% 895 790 387,943 88.36%
Vermont 997 862 78,674 86.39% 912 800 75,217 88.07%
Virginia 1,009 869 969,120 86.84% 996 856 990,938 86.24%
Washington 1,078 910 827,573 84.82% 1,041 874 821,319 83.64%
West Virginia 1,032 860 201,871 83.78% 959 813 200,624 84.50%
Wisconsin 946 797 663,756 84.54% 1,023 875 677,030 85.16%
Wyoming 1,015 858 68,653 83.30% 977 849 68,773 85.64%
Table A.13 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2008, 2009, and 2010
State 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
2010
Total
Selected
2010
Total
Responded
2010
Population
Estimate
2010
Weighted
Interview
Response
Rate
NOTE: The 2008 and 2009 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2008, 2009, and 2010 (Revised March 2012).
Total U.S. 59,483 45,667 224,922,763 73.08% 58,628 45,591 227,206,545 74.46% 59,089 45,812 229,272,578 73.49%
Northeast 11,697 8,739 41,723,952 70.70% 11,722 8,768 42,079,937 71.75% 11,816 8,912 42,300,562 71.96%
Midwest 16,944 13,009 49,448,506 73.84% 16,376 12,735 49,756,736 74.91% 16,782 13,037 50,010,904 73.71%
South 17,657 13,939 81,912,691 75.27% 17,701 14,158 83,039,864 76.29% 17,568 13,911 84,005,336 75.22%
West 13,185 9,980 51,837,615 70.89% 12,829 9,930 52,330,008 73.31% 12,923 9,952 52,955,777 71.84%
Alabama 833 637 3,462,438 70.09% 784 618 3,498,218 77.82% 752 571 3,519,621 70.68%
Alaska 777 608 479,955 75.78% 762 600 493,862 78.40% 745 602 498,602 76.86%
Arizona 779 601 4,700,399 75.56% 767 616 4,772,012 78.64% 816 633 4,848,242 71.45%
Arkansas 768 609 2,100,948 75.70% 785 608 2,127,061 76.02% 789 615 2,143,532 74.10%
California 3,565 2,607 26,834,059 68.17% 3,355 2,491 26,962,535 70.44% 3,213 2,412 27,235,412 70.49%
Colorado 797 608 3,650,120 75.05% 791 619 3,712,168 75.96% 844 673 3,772,773 78.94%
Connecticut 856 668 2,629,944 73.64% 780 607 2,651,071 75.54% 820 638 2,669,460 73.79%
Delaware 815 653 652,247 78.20% 771 610 663,392 72.25% 780 621 670,337 76.96%
District of Columbia 778 627 469,267 77.83% 754 636 475,164 83.47% 754 611 483,703 80.56%
Florida 3,005 2,393 13,990,125 75.51% 3,095 2,522 14,141,314 75.95% 3,036 2,440 14,281,818 76.56%
Georgia 725 564 6,929,959 72.18% 738 601 7,025,028 76.93% 760 597 7,122,189 74.41%
Hawaii 917 621 958,686 63.83% 930 649 959,869 66.03% 896 636 957,899 65.29%
Idaho 791 628 1,086,964 77.02% 788 632 1,102,446 76.10% 760 618 1,119,419 77.60%
Illinois 3,530 2,508 9,523,946 67.16% 3,380 2,478 9,535,363 70.37% 3,405 2,487 9,579,838 69.50%
Indiana 758 590 4,693,498 77.02% 787 619 4,734,130 78.39% 753 575 4,762,228 72.40%
Iowa 801 645 2,242,082 80.29% 760 622 2,248,480 80.95% 777 638 2,268,066 78.26%
Kansas 796 625 2,039,018 76.01% 785 606 2,052,096 74.78% 770 589 2,070,889 73.21%
Kentucky 736 570 3,186,380 71.84% 811 645 3,214,458 75.52% 757 601 3,241,553 76.04%
Louisiana 754 605 3,209,206 78.22% 805 639 3,270,638 78.34% 730 578 3,296,197 77.01%
Maine 781 629 1,025,265 76.07% 753 627 1,030,693 82.04% 775 640 1,032,784 80.01%
Maryland 744 557 4,196,523 71.53% 687 559 4,249,895 78.75% 781 615 4,289,800 76.76%
Massachusetts 760 596 4,975,546 75.82% 888 681 5,067,283 72.94% 789 634 5,113,977 78.02%
Michigan 3,206 2,483 7,485,628 73.93% 3,067 2,396 7,493,915 75.99% 3,129 2,478 7,499,137 74.75%
Minnesota 730 580 3,898,306 77.85% 777 618 3,938,642 76.78% 812 650 3,972,838 77.37%
Mississippi 744 594 2,103,803 76.92% 790 636 2,115,316 76.79% 754 603 2,126,170 75.20%
Missouri 773 599 4,380,159 75.18% 738 583 4,446,201 74.83% 801 633 4,480,314 74.91%
Montana 756 601 731,019 76.33% 769 614 739,171 74.99% 789 617 747,854 76.00%
Nebraska 759 589 1,305,413 75.74% 787 621 1,313,534 77.60% 785 606 1,327,879 71.62%
Nevada 757 567 1,901,495 72.53% 786 618 1,929,882 70.81% 885 694 1,944,971 69.92%
New Hampshire 777 619 1,007,507 78.56% 803 617 1,020,081 73.47% 860 668 1,027,514 73.45%
New Jersey 857 658 6,516,694 72.31% 827 616 6,544,280 71.29% 770 599 6,577,240 77.93%
New Mexico 757 595 1,450,863 78.45% 769 613 1,466,616 75.96% 753 585 1,480,665 75.70%
New York 3,510 2,415 14,816,448 65.48% 3,561 2,504 14,858,432 69.52% 3,604 2,485 14,912,033 65.77%
North Carolina 709 560 6,768,012 76.99% 803 656 6,884,806 78.46% 757 593 6,959,307 75.02%
North Dakota 796 636 481,318 78.24% 779 604 486,318 75.56% 831 654 493,824 75.68%
Ohio 3,143 2,430 8,578,157 72.79% 2,999 2,374 8,650,872 73.65% 3,238 2,540 8,661,813 73.64%
Oklahoma 793 621 2,647,965 78.37% 759 599 2,678,184 73.20% 779 586 2,704,129 71.96%
Oregon 873 699 2,879,615 70.16% 751 611 2,909,054 79.91% 758 589 2,943,741 73.99%
Pennsylvania 2,612 1,946 9,461,258 71.79% 2,600 1,927 9,609,739 71.80% 2,688 2,030 9,656,250 72.35%
Rhode Island 761 598 804,991 76.44% 773 580 809,132 75.38% 795 623 817,303 72.92%
South Carolina 763 636 3,309,346 81.64% 747 603 3,375,522 75.12% 787 635 3,411,091 74.84%
South Dakota 818 674 588,444 77.44% 766 628 594,616 80.10% 750 620 603,702 80.01%
Tennessee 865 674 4,641,311 74.35% 778 598 4,703,420 71.59% 747 582 4,749,036 71.83%
Texas 3,049 2,421 17,119,812 75.65% 3,046 2,414 17,401,039 76.35% 3,102 2,465 17,715,787 75.65%
Utah 777 624 1,862,178 77.28% 744 600 1,890,406 79.16% 822 669 1,925,295 78.73%
Vermont 783 610 486,299 74.56% 737 609 489,227 78.26% 715 595 494,001 81.98%
Virginia 792 619 5,721,688 74.92% 777 621 5,807,626 76.24% 747 593 5,877,166 75.57%
Washington 801 591 4,906,769 72.13% 801 625 4,989,090 76.03% 829 596 5,072,923 68.75%
West Virginia 784 599 1,403,665 75.25% 771 593 1,408,784 72.70% 756 605 1,413,902 77.86%
Wisconsin 834 650 4,232,537 76.11% 751 586 4,262,569 75.65% 731 567 4,290,376 75.98%
Wyoming 838 630 395,493 70.94% 816 642 402,898 78.03% 813 628 407,981 71.88%
Table A.14 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2008-2009 and 2009-2010
State 2008-2009
Total
Selected
2008-2009
Total
Responded
2008-2009
Population
Estimate
2008-2009
Weighted
Interview
Response
Rate
2009-2010
Total
Selected
2009-2010
Total
Responded
2009-2010
Population
Estimate
2009-2010
Weighted
Interview
Response
Rate
NOTE: The 2008-2009 numbers may differ from the previously published numbers due to updates (see Section 1.5 of the report).
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
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, National Survey on Drug Use and Health, 2008, 2009, and 2010 (Revised March 2012).
Total U.S. 118,111 91,258 226,064,654 73.78% 117,717 91,403 228,239,562 73.97%
Northeast 23,419 17,507 41,901,944 71.23% 23,538 17,680 42,190,250 71.86%
Midwest 33,320 25,744 49,602,621 74.38% 33,158 25,772 49,883,820 74.31%
South 35,358 28,097 82,476,278 75.79% 35,269 28,069 83,522,600 75.76%
West 26,014 19,910 52,083,811 72.12% 25,752 19,882 52,642,893 72.57%
Alabama 1,617 1,255 3,480,328 74.18% 1,536 1,189 3,508,919 74.32%
Alaska 1,539 1,208 486,909 77.17% 1,507 1,202 496,232 77.64%
Arizona 1,546 1,217 4,736,205 77.16% 1,583 1,249 4,810,127 74.88%
Arkansas 1,553 1,217 2,114,004 75.86% 1,574 1,223 2,135,297 75.05%
California 6,920 5,098 26,898,297 69.32% 6,568 4,903 27,098,973 70.46%
Colorado 1,588 1,227 3,681,144 75.48% 1,635 1,292 3,742,471 77.60%
Connecticut 1,636 1,275 2,640,507 74.56% 1,600 1,245 2,660,265 74.68%
Delaware 1,586 1,263 657,820 75.00% 1,551 1,231 666,864 74.66%
District of Columbia 1,532 1,263 472,215 80.57% 1,508 1,247 479,433 81.99%
Florida 6,100 4,915 14,065,719 75.73% 6,131 4,962 14,211,566 76.26%
Georgia 1,463 1,165 6,977,493 74.70% 1,498 1,198 7,073,609 75.72%
Hawaii 1,847 1,270 959,278 64.94% 1,826 1,285 958,884 65.66%
Idaho 1,579 1,260 1,094,705 76.56% 1,548 1,250 1,110,933 76.82%
Illinois 6,910 4,986 9,529,654 68.78% 6,785 4,965 9,557,601 69.94%
Indiana 1,545 1,209 4,713,814 77.69% 1,540 1,194 4,748,179 75.29%
Iowa 1,561 1,267 2,245,281 80.62% 1,537 1,260 2,258,273 79.64%
Kansas 1,581 1,231 2,045,557 75.42% 1,555 1,195 2,061,492 73.97%
Kentucky 1,547 1,215 3,200,419 73.81% 1,568 1,246 3,228,005 75.77%
Louisiana 1,559 1,244 3,239,922 78.28% 1,535 1,217 3,283,417 77.68%
Maine 1,534 1,256 1,027,979 79.02% 1,528 1,267 1,031,739 81.01%
Maryland 1,431 1,116 4,223,209 75.13% 1,468 1,174 4,269,848 77.70%
Massachusetts 1,648 1,277 5,021,415 74.39% 1,677 1,315 5,090,630 75.50%
Michigan 6,273 4,879 7,489,772 74.95% 6,196 4,874 7,496,526 75.36%
Minnesota 1,507 1,198 3,918,474 77.31% 1,589 1,268 3,955,740 77.08%
Mississippi 1,534 1,230 2,109,559 76.85% 1,544 1,239 2,120,743 76.01%
Missouri 1,511 1,182 4,413,180 75.00% 1,539 1,216 4,463,257 74.87%
Montana 1,525 1,215 735,095 75.66% 1,558 1,231 743,513 75.52%
Nebraska 1,546 1,210 1,309,473 76.70% 1,572 1,227 1,320,707 74.62%
Nevada 1,543 1,185 1,915,689 71.65% 1,671 1,312 1,937,426 70.36%
New Hampshire 1,580 1,236 1,013,794 76.05% 1,663 1,285 1,023,798 73.46%
New Jersey 1,684 1,274 6,530,487 71.78% 1,597 1,215 6,560,760 74.60%
New Mexico 1,526 1,208 1,458,739 77.21% 1,522 1,198 1,473,640 75.83%
New York 7,071 4,919 14,837,440 67.49% 7,165 4,989 14,885,232 67.58%
North Carolina 1,512 1,216 6,826,409 77.77% 1,560 1,249 6,922,057 76.83%
North Dakota 1,575 1,240 483,818 76.89% 1,610 1,258 490,071 75.62%
Ohio 6,142 4,804 8,614,515 73.22% 6,237 4,914 8,656,342 73.65%
Oklahoma 1,552 1,220 2,663,075 75.88% 1,538 1,185 2,691,157 72.55%
Oregon 1,624 1,310 2,894,334 75.20% 1,509 1,200 2,926,397 77.03%
Pennsylvania 5,212 3,873 9,535,498 71.80% 5,288 3,957 9,632,994 72.08%
Rhode Island 1,534 1,178 807,061 75.89% 1,568 1,203 813,218 74.18%
South Carolina 1,510 1,239 3,342,434 78.56% 1,534 1,238 3,393,307 74.98%
South Dakota 1,584 1,302 591,530 78.71% 1,516 1,248 599,159 80.05%
Tennessee 1,643 1,272 4,672,365 73.05% 1,525 1,180 4,726,228 71.71%
Texas 6,095 4,835 17,260,426 76.00% 6,148 4,879 17,558,413 76.00%
Utah 1,521 1,224 1,876,292 78.20% 1,566 1,269 1,907,850 78.93%
Vermont 1,520 1,219 487,763 76.40% 1,452 1,204 491,614 80.10%
Virginia 1,569 1,240 5,764,657 75.58% 1,524 1,214 5,842,396 75.90%
Washington 1,602 1,216 4,947,930 74.16% 1,630 1,221 5,031,007 72.42%
West Virginia 1,555 1,192 1,406,225 73.95% 1,527 1,198 1,411,343 75.22%
Wisconsin 1,585 1,236 4,247,553 75.88% 1,482 1,153 4,276,473 75.81%
Wyoming 1,654 1,272 399,196 74.56% 1,629 1,270 405,440 74.94%
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
2009-
2010
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 (MDE) 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 MDE estimates shown in the 2004-2005, 2005-2006, and 2006-2007 reports are not comparable with this report's adult MDE estimates. However, the 2005-2006 and 2006-2007 adult-adjusted MDE estimates available at http://www.samhsa.gov/data/NSDUH/states.htm are comparable with this report's adult MDE estimates.
Yes = available, No = not available.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2002-2010.
Illicit Drug Use in Past Month Yes Yes Yes Yes Yes Yes Yes Yes
Marijuana Use in Past Year Yes Yes Yes Yes Yes Yes Yes Yes
Marijuana Use in Past Month Yes Yes Yes Yes Yes Yes Yes Yes
Perceptions of Great Risk of Smoking Marijuana Once a Month Yes Yes Yes Yes Yes Yes Yes Yes
First Use of Marijuana Yes Yes Yes Yes Yes Yes Yes Yes
Illicit Drug Use Other Than Marijuana in Past Month Yes Yes Yes Yes Yes Yes Yes Yes
Cocaine Use in Past Year Yes Yes Yes Yes Yes Yes Yes Yes
Nonmedical Use of Pain Relievers in Past Year No1 Yes Yes Yes Yes Yes Yes Yes
Alcohol Use in Past Month Yes Yes Yes Yes Yes Yes Yes Yes
Underage Past Month Use of Alcohol No1 Yes Yes Yes Yes Yes Yes Yes
Binge Alcohol Use in Past Month Yes Yes Yes Yes Yes Yes Yes Yes
Underage Past Month Binge Alcohol Use No1 Yes 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 Yes
Tobacco Product Use in Past Month Yes Yes Yes Yes Yes Yes Yes Yes
Cigarette Use in Past Month Yes 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 Yes
Alcohol Dependence or Abuse in Past Year Yes Yes Yes Yes Yes Yes Yes Yes
Alcohol Dependence in Past Year Yes Yes Yes Yes Yes Yes Yes Yes
Illicit Drug Dependence or Abuse in Past Year Yes Yes Yes Yes Yes Yes Yes Yes
Illicit Drug Dependence in Past Year Yes Yes Yes Yes Yes Yes Yes Yes
Dependence or Abuse of Illicit Drugs or Alcohol in Past Year Yes 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 Yes
Needing But Not Receiving Treatment for Alcohol Use in Past Year Yes Yes Yes Yes Yes Yes Yes Yes
Serious Psychological Distress in Past Year2 Yes Yes Yes Yes Yes No No No
Had at Least One Major Depressive Episode in Past Year3 No No Yes Yes Yes Yes Yes Yes
Serious Mental Illness in Past Year No No No No No No Yes Yes
Any Mental Illness in Past Year No No No No No No Yes Yes
Had Serious Thoughts of Suicide in Past Year No No No No No No Yes Yes

End Notes

10 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.

11 Note that in NSDUH State reports prior to 2009, 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."

12 The use of mixed models (fixed and random effects) allows additional error components (random effects) to be included. These account for differences between States and within-State variations that are not taken into account by the predictor variables (fixed effects) alone. These models produce estimates that are approximately represented by a weighted combination of the direct estimate from the State data and a regression estimate from the national model, where the weights are obtained by minimizing the mean squared error of the small area estimate. It is also difficult if not impossible to produce valid mean squared errors for small area estimates based solely on a fixed-effect national regression model.

13 To increase the precision of estimated random effects at the within-State level, three SSRs were grouped together. Each of the 8 large sample States consists of 16 grouped SSRs, and the rest of the States and the District of Columbia each has 4 grouped SSRs.

14 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.

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

16 For more information on the WHODAS and SDS scores, see Section B.4.3 of the 2009 mental health findings report (CBHSQ, 2010).

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