2010-2011 National Survey on Drug Use and Health:
Guide to State Tables and Summary of Small Area Estimation Methodology

Section A: Overview of NSDUH and Model-Based State Estimates

A.1 Introduction

This document provides information on the model-based small area estimates of substance use and mental disorders in States based on data from the combined 2010-2011 National Surveys on Drug Use and Health (NSDUHs). The estimates are available at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx along with other related information. An annual survey of the civilian, noninstitutionalized population aged 12 or older, NSDUH is sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA). It collects information from persons residing in households, noninstitutionalized group quarters (e.g., shelters, rooming houses, dormitories), and civilians living on military bases. In 2010-2011, NSDUH collected data from 137,913 respondents aged 12 or older and was designed to obtain representative samples from the 50 States and the District of Columbia. The survey is planned and managed by SAMHSA's Center for Behavioral Health Statistics and Quality (CBHSQ). Data collection and analysis were conducted under contract with RTI International.1

A summary of NSDUH's methodology is given in Section A.2, followed in Section A.3 by a summary of issues related to the mental disorder measures. Information is given in Section A.4 on the confidence intervals and margin of error and how to make interpretations with respect to the small area estimates. Several related drug measures for which small area estimates are produced are discussed in Section A.5. Section A.6 lists all of the tables and documents associated with the 2010-2011 small area estimates and when and where they can be found. During regular data collection and processing checks for the 2011 NSDUH, data errors were identified that affected the data for Pennsylvania (2006 to 2010) and Maryland (2008 and 2009). Section A.7 discusses the revisions to the 2006 to 2010 NSDUH data and corresponding estimates.

The survey-weighted hierarchical Bayes (SWHB) estimation methodology used in the production of State estimates from the 1999 to 2010 surveys also was used in the production of the 2010-2011 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 B.1. A list of measures for which small area estimates are produced is given in Section B.2. The list of predictors used in the 2010-2011 SAE modeling is given in Section B.3. Information is given in Section B.4 on the updated population projections obtained from Claritas Inc. that were used for the first time in producing the 2006-2007 small area estimates and how they were used to create SAE model predictors. No new variable selection was done for any measure except for any mental illness and serious mental illness (as discussed in Section B.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 B.6.2 Tables of estimated numbers of persons associated with each measure are available (see Tables 1 to 26 in "NSDUH: 2010‑2011 Model-Based Estimated Totals [in Thousands], [50 States and the District of Columbia]" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx). An explanation of how these counts and their respective Bayesian confidence intervals3 are calculated can be found in Section B.7. The definition and explanation of the formula used in estimating the marijuana incidence rate are given in Section B.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 are 12 to 17, 18 to 25, and 26 or older. Estimates for those aged 12 or older also are provided here. Because it was determined that States may find it useful to have estimates for persons aged 18 or older, estimates for that age group also are available (see Tables 1 to 25 in "NSDUH: Comparison of 2009-2010 and 2010-2011 Model-Based Prevalence Estimates for Adults 18 or Older [50 States and the District of Columbia]" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx).

Estimates of underage (aged 12 to 20) alcohol use and binge alcohol use were also produced. 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 B.9.

Section B.10 discusses the criteria used to define illicit drug and alcohol dependence and abuse and needing but not receiving treatment. Section B.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 B.12 discusses the method to compare prevalence rates of a particular measure between two States.

At the end of this document, tables showing the 2009, 2010, 2011, pooled 2009-2010, and pooled 2010-2011 survey sample sizes, population estimates, and response rates are included (Tables C.1 to C.14). Table C.15 lists all of the measures and the years for which small area estimates were produced going back to the 2002 NSDUH.

Increases or decreases that occurred between 2009-2010 and 2010-2011 for these measures also are presented (see Tables 1 to 26 in "NSDUH: Comparison of 2009-2010 and 2010-2011 Model-Based Prevalence Estimates [50 States and the District of Columbia]" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx).

Interview data from 137,913 persons were collected in 2010-2011 (see Table C.9). State estimates have been developed using an SAE procedure in which State-level NSDUH data are combined with county and census block group/tract-level data from the State. Aggregates of these State estimates are presented as regional and national estimates. Note that these estimates are benchmarked to the national design-based estimates (for details, see Section B.6). This model-based methodology provides more precise estimates of substance use and mental disorders at the State level than those based solely on the sample, particularly for States with smaller samples.

Starting in 1999, the NSDUH sample was expanded to produce State-level estimates. The first report with State estimates was published in 2000 (Office of Applied Studies [OAS], 2000). It utilized the 1999 survey data and the SAE procedure. Because the SAE procedure requires significant preparatory steps for the modeling and extensive computation to generate results, the number of measures estimated has been limited to ones with high policy value. The first report included only seven measures. Subsequent State reports have been published annually, gradually extending the capabilities of the SAE procedure and increasing the number of measures estimated (Hughes, Muhuri, Sathe, & Spagnola, 2012; Wright, 2002a, 2002b, 2003a, 2003b, 2004; Wright & Sathe, 2005, 2006; Wright, Sathe, & Spagnola, 2007). The current practice is to base annual estimates on a 2-year moving average of NSDUH data in order to enhance the precision for States with smaller samples.

State estimates also have been produced for additional measures by combining multiple years of NSDUH data and using sampling weights and direct estimation. The advantage of this approach is that it can be used on any variable in the NSDUH dataset; however, these direct estimates typically are not as accurate as the estimates based on the SAE methods. Direct State estimates have been included in some reports and tables on the SAMHSA Web site.

A.2 Summary of NSDUH Methodology

NSDUH is the primary source of statistical information on the use of illicit drugs, alcohol, and tobacco by the U.S. civilian, noninstitutionalized population aged 12 or older. The survey also includes several modules of questions that focus on mental health issues. Conducted by the Federal Government since 1971, the survey collects data by administering questionnaires to a representative sample of the population through face-to-face interviews at their place of residence.

The survey covers residents of households, noninstitutional group quarters (e.g., shelters, rooming houses, dormitories), and civilians living on military bases. Persons excluded from the survey include homeless people who do not use shelters, military personnel on active duty, and residents of institutional group quarters, such as jails or prisons and long-term hospitals.

The 1999 survey marked the first year in which the national sample was interviewed using a computer-assisted interviewing (CAI) method. The survey used a combination of computer-assisted personal interviewing (CAPI) conducted by an interviewer and audio computer-assisted self-interviewing (ACASI). Use of ACASI is designed to provide the respondent with a highly private and confidential means of responding to questions and increases the level of honest reporting of illicit drug use and other sensitive behaviors. For further details on the development of the CAI procedures for the 1999 National Household Survey on Drug Abuse (NHSDA, the former name of NSDUH), see OAS (2001).

The 1999 through 2001 NHSDAs and the 2002 through 2011 NSDUHs employed a 50‑State design with an independent, multistage area probability sample for each of the 50 States and the District of Columbia. For the 50-State design, 8 States were designated as large sample States (California, Florida, Illinois, Michigan, New York, Ohio, Pennsylvania, and Texas) with target sample sizes of 3,600 per year or 7,200 over a 2-year period. In 2010-2011, sample sizes in these States ranged from 6,059 in Pennsylvania to 7,684 in Florida (Table C.9). For the remaining 42 States and the District of Columbia, the target sample size was 900 per year or 1,800 over a 2-year period. Sample sizes in these States ranged from 1,773 in Alaska to 2,652 in Louisiana in 2010-2011. This approach ensures there is sufficient sample in every State to support SAE while at the same time maintaining efficiency for national estimates. The design also oversampled youths and young adults, so that each State's sample was approximately equally distributed among three major age groups: 12 to 17 years, 18 to 25 years, and 26 years or older.

In 2002, several changes were introduced to the survey. Incentive payments of $30 were given to respondents for the first time in order to address concerns about the national and State response rates. Other changes included a change in the survey name (i.e., from NHSDA to NSDUH), new data collection quality control procedures, and a shift from the 1990 decennial census to the 2000 census as a basis for population count totals and to calculate any census-related predictor variables that are used in small area estimation.

An unanticipated result of these changes was that the prevalence rates for 2002 were in general substantially higher than those for 2001—higher than could be attributable to the usual year-to-year trend—and thus are not comparable with estimates for 2001 and prior years.4 Therefore, the 2002 NSDUH was established as a new baseline for both the national and the State estimates. Given the varying effects of the incentive and other changes, not only are the estimates for 2002 and later years not comparable with prior years, but the relative rankings of States also may have been affected. Therefore, the rankings of States for 2002-2003 or later should not be compared with those for prior years. By combining data across 2 years, the precision of the small area estimates for the small sample States, and thus their rankings, have been improved significantly. In addition, by combining 2 years of data, the impact of the national model on those States has been reduced significantly relative to estimates based on a single year's data.5

Nationally in 2010-2011, 303,058 addresses were screened, and 137,913 persons responded within the screened addresses (see Table C.9). The survey is conducted from January through December each year. The screening response rate (SRR) for 2010-2011 combined averaged 87.7 percent, and the interview response rate (IRR) averaged 74.5 percent, for an overall response rate (ORR) of 65.3 percent (Table C.9). The ORRs for 2010-2011 ranged from 48.3 percent in New York to 75.0 percent in South Dakota. Estimates have been adjusted to reflect the probability of selection, unit nonresponse, poststratification to known census population estimates, item imputation, and other aspects of the estimation process. These procedures are described in NSDUH's methodological resource books (MRBs) (RTI International, 2012, 2013).

The weighted SRR is defined as the weighted number of successfully screened households (or dwelling units)6 divided by the weighted number of eligible households, or

Equation A.2-1 ,     D

where w sub h h is the inverse of the unconditional probability of selection for the household (hh) and excludes all adjustments for nonresponse and poststratification.

At the person level, the weighted IRR is defined as the weighted number of respondents divided by the weighted number of selected persons, or

Equation A.2-2 ,     D

where w sub i is the inverse of the probability of selection for ith the person and includes household-level nonresponse and poststratification adjustments. To be considered a completed interview, a respondent must provide enough data to pass the usable case rule.7

The weighted ORR is defined as the product of the weighted SRR and the weighted IRR or

Equation A.2-3 .     D

A.3 Mental Disorders

To address SAMHSA's need for estimates of serious mental illness, any mental illness, and suicidal thoughts (i.e., suicidal ideation), several important changes were made to the adult mental health items in the 2008 NSDUH questionnaire. Items were added that assessed functional impairment due to mental health problems (abbreviated World Health Organization Disability Assessment Schedule [WHODAS]; Novak, 2007) and that assessed suicidal thoughts and behavior among adults. In 2008, CBHSQ also expanded the Kessler-6 (K6) questions to ask about the past 30 days (the time frame for which the K6 was originally designed).

In addition, as part of the Mental Health Surveillance Study (MHSS), a clinical follow-up study was initiated in which a randomly selected subsample of adults (about 1,500 in 2008, 2011, and 2012, and 500 in 2009 and 2010) who had completed the NSDUH interview was administered a standard clinical interview by mental health clinicians via paper and pencil over the telephone to determine their mental illness status; the clinical interview was used as a "gold standard" for measuring mental illness among adults. Using both the clinical interview and the NSDUH CAI data for the respondents who completed the clinical interview (using only 2008 data), statistical models were developed that then were applied to data from all adult respondents who had completed the NSDUH CAI interviews (regardless of whether they had clinical interview data) to produce estimates of mental illness among the adult civilian, noninstitutionalized population. Subsequently, using the entire clinical interview sample of approximately 5,000 interviews that were collected in 2008 to 2012, CBHSQ developed a more accurate statistical model for adults. This revised model incorporated the NSDUH respondent's age, past year suicidal thoughts, past year major depressive episode, and the variables that were specified in the 2008 model (i.e., the K6 and the WHODAS). Results for serious mental illness and any mental illness from this revised model were closer to the direct estimates of serious mental illness and any mental illness from the clinical interviews in the MHSS than the previous model's results produced, especially for young adults aged 18 to 25. See Section B.11 for a more complete discussion of the revised 2012 model and estimates.

Estimates of serious mental illness and any mental illness for 2009-2010 and 2010-2011 were produced using this new model and are shown in the tables listed in Section A.6. These tables and maps with revised estimates include a source note with the text (i.e., "Revised October 2013") to indicate that the estimates are based on the updated 2012 model.

The questionnaire changes caused discontinuities in trends for major depressive episode (i.e., depression) and serious psychological distress among adults aged 18 or older. For youths aged 12 to 17, no questionnaire changes were made in 2008 that affected the estimation of youth depression items; so, estimates of youth depression are available for all years beginning with the 2004-2005 report. An analysis was performed to better understand the nature of the changes in the reporting of adult depression associated with the questionnaire changes in 2008. This led to the development of statistical adjustments for the adult depression estimates for the years from 2005 to 2008; thus, comparable adult depression data are now available for the years 2005 and beyond. For more information about these changes, see Section B.11 in Appendix B of the 2008 NSDUH national findings report (OAS, 2009) and Appendix B of the 2010 NSDUH mental health findings report (CBHSQ, 2012b).

A.4 Confidence Intervals and Margins of Error

At the top of each of the 26 State model-based estimate tables8 is the design-based national estimate along with a 95 percent design-based confidence interval, all of which are based on survey weights and the reported data. The State and regional estimates are model-based statistics (using SAE methodology) that have been adjusted such that the population-weighted mean of the estimates across the 50 States and the District of Columbia equal the design-based national estimate. For more details on this benchmarking, see Section B.6. Associated with each State and regional estimate is a 95 percent Bayesian confidence interval. These intervals indicate the uncertainty in the estimate due to both sampling variability and model bias. For example, the State with the highest estimated rate of past month use of marijuana for young adults aged 18 to 25 was Vermont, with a rate of 33.2 percent and a 95 percent confidence interval that ranged from 29.4 to 37.2 percent (Table 3 of the State model-based estimates' tables). Therefore, the probability is 0.95 that the true prevalence of past month marijuana use in Vermont for persons aged 18 to 25 is between 29.4 and 37.2 percent. As noted earlier in a Section A.1 footnote, the term "prediction interval" (PI) was used in the 2004-2005 NSDUH State report and prior reports 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 model-based estimates, so PI was replaced with "Bayesian confidence interval."

Margin of error is another term used to describe uncertainty in the estimates. For example, if lower interval l comma and upper interval u is a 95 percent symmetric confidence interval for the population proportion (p) and p hat is an estimate of p obtained from the survey data, then the margin of error of p hat is given by u minus p hat or p hat minus l. Because lower interval l comma and upper interval u is a symmetric confidence interval, u minus p hat will be the same as p hat minus l. In this case, the probability is 0.95 that the true population value (p) is within ± u minus p hat or ± p hat minus l of the survey estimate p hat). The margin of error defined above will vary for each estimate and will be affected not only by the sample size (e.g., the larger the sample, the smaller the margin of error), but also by the sample design (e.g., telephone surveys using random digit dialing and surveys employing a stratified multistage cluster design will, more than likely, produce a different margin of error) (Scheuren, 2004).

The confidence intervals shown in NSDUH reports are asymmetric, meaning that the distance between the estimate and the lower confidence limit will not be the same as the distance between the upper confidence limit and the estimate. For example, Utah's past month marijuana use rate of 8.3 percent for persons aged 18 to 25 years with a 95 percent confidence interval equal to (6.4, 10.8) (see Table 3 of the State model-based estimates' tables).9 Therefore Utah's rate is 1.9 (i.e., 8.3 – 6.4) percentage points from the lower 95 percent confidence limit and 2.5 (i.e., 10.8 – 8.3) percentage points from the upper limit. These asymmetric confidence intervals work well for small percentages often found in NSDUH tables and reports while still being appropriate for larger percentages. Some surveys or polls provide only one margin of error for all reported percentages. This single number is usually calculated by setting the sample percentage estimate p hat equal to 50 percent, which will produce an upper bound or maximum margin of error. Such an approach would not be feasible in NSDUH because the estimates vary from less than 1 percent to over 75 percent; hence, applying a single margin of error to these estimates could significantly overstate or understate the actual precision levels. Therefore, given the differences mentioned above, it is more useful and informative to report the confidence interval for each estimate instead of a margin of error.

When it is indicated that a State has the highest or lowest rate, it does not imply that the State's rate is significantly higher or lower than the next highest or lowest State. When comparing two State prevalence rates, two overlapping 95 percent confidence intervals do not imply that their State prevalence rates are statistically equivalent at the 5 percent level of significance. For details on a more accurate test to compare State prevalence rates, see Section B.12.

A.5 Related Drug Measures

Small area estimates are produced for a number of related drug measures, such as marijuana use and illicit drug use. It might appear that one could draw conclusions by subtracting one from the other (e.g., subtracting the percentage who used illicit drugs other than marijuana in the past month from the percentage who used illicit drugs in the past month to find the percentage who only used marijuana in the past month). Because related measures have been estimated with different models, subtracting one measure from another related measure at the State or census region level can give misleading results, perhaps even a "negative" estimate, and should be avoided. However, these comparisons can be made at the national level because these estimates are design-based estimates. For example, at the national level, subtracting cigarette use rates from tobacco use rates will give the rate of persons who did not use cigarettes, but used other forms of tobacco.

A.6 Presentation of Data

In addition to this methodology document, the following files are also available at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx:

A.7 Revised 2006 to 2010 Estimates

During regular data collection and processing checks for the 2011 NSDUH, data errors were identified. These errors affected the data for Pennsylvania (2006 to 2010) and Maryland (2008 and 2009). Cases with erroneous data were removed from the data files, and the remaining cases were reweighted to provide representative estimates. Therefore, some estimates using 2006 to 2010 NSDUH data in the 2011 national findings report and detailed tables, as well as other reports (including the 2009-2010 SAE report), will contain estimates that differ from corresponding estimates found in some previous reports. All of the tables and maps available at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx that are related to the 2010-2011 State estimates have a source note with the text (i.e., "Revised March 2012") included to indicate that the estimates with 2009 and 2010 data are based on updated NSDUH data (excluding the erroneous data for Pennsylvania and Maryland).10

The errors had minimal impact on the national estimates and no effect on direct estimates for the other 48 States and the District of Columbia. The direct estimates for an area (e.g., a State or substate) are only based on its data. However, in reports where model-based SAE techniques are used, estimates for all States may be affected, even though the errors were concentrated in only two States. This is because the model-based estimate for a given State is a combination of the direct estimate for that State and the State estimate obtained from a national model. The national model, which has estimated parameter coefficients based on data from all States, changed when the erroneous Pennsylvania and Maryland data were removed and the remaining cases were reweighted. As a result, the model-based estimates in all States changed, although the most notable changes occurred in Pennsylvania and Maryland because the direct estimates in those States changed, as did their estimates based on the national model. In reports that do not use model-based estimates, the only estimates appreciably affected were estimates for Pennsylvania, Maryland, the mid-Atlantic division, and the Northeast region.

Section B: State Model-Based Estimation Methodology

B.1 General Model Description

The model can be characterized as a complex mixed11 model (including both fixed and random effects) of the following form:

Equation B.1-1 ,     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.12 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 capital A is the total number of individual age groups modeled (generally, Capital A equals 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 B.6.

B.2 Variables Modeled

The 2011 NSDUH data were pooled with the 2010 NSDUH data, and age group-specific State prevalence estimates for 25 binary (0, 1) measures 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,13
  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 2009-2010 and the 2010-2011 State estimates were produced for all of these measures as well.

B.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 metropolitan statistical area (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

B.4 Updated Claritas Data

For the NSDUH State and substate estimates published using the 2002 to 2006 NSDUH data, Claritas data obtained in 2002 were used to produce the small area estimates. For State estimates published using the 2007 to 2011 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. Both sets of Claritas data were based on 2000 census geography. 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 B.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 2007 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.
  1. 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. With the switch to 2008-2012 Claritas, 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 estimates.

Based on the information above, the following steps were taken for the 2010-2011 SAE process (for more information on the steps taken for the 2009-2010 SAE processes, see Appendix A of Hughes et al., 2012):

  1. Using the 2008-2012 Claritas data, 2010 and 2011 population counts were obtained (the 2010 and 2011 counts were obtained by using linear interpolation between the 2008 and 2012 counts) and used to create the predictors that were merged onto the 2010 and 2011 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 subsequent 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 2010-2011 SAE process. Because of the recent large jumps in the unemployment rate, the decile values for the unemployment rate needed to be re-created 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. This updated decile was used in 2010-2011 as well. 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., 2010 and 2011).
  4. The 2009 sample and universe files based on the 2008-2012 Claritas data were used in simultaneous modeling to produce the correlations required to estimate change between the 2009-2010 and 2010-2011 State prevalence rates. See the methodology discussion in "NSDUH: Comparison of 2009-2010 and 2010-2011 Model-Based Prevalence Estimates (50 States and the District of Columbia)" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.
  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 2010-2011 sample and universe files to produce the 2002-2003 versus 2010-2011 comparisons. See the methodology discussion in "NSDUH: Comparison of 2002-2003 and 2010-2011 Model-Based Prevalence Estimates (50 States and the District of Columbia)" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.

B.5 Selection of Independent Variables for the Models

No new variable selection was done for any measure in 2010-2011 except for serious mental illness and any mental illness. The updated versions of fixed-effect predictors that were used in modeling the 2009-2010 data were used to model the 2010-2011 data. Because the interest was to estimate change between the 2009-2010 and 2010-2011 State estimates, the same set of fixed-effect predictors was used for producing both sets of estimates.

The 2010-2011 small area estimates for serious mental illness and any mental illness were produced using updated SAE model predictors for both 2010 and 2011 based on a new variable selection process. In order to produce the 2011-2012 small area estimates, variable selection was done using 2010-2011 NSDUH data and an updated set of predictors derived from the 2010 census (for more information on the updated predictors and variable selection, refer to the "2011-2012 NSDUH: Guide to State Tables and Summary of Small Area Estimation Methodology" that will be available in late 2013). Because the 2010-2011 mental illness and any mental illness estimates based on the 2012 model (see Section B.11 for more information on the revised mental illness models) were produced after that variable selection was done, a decision was made by SAMHSA to produce the revised 2010-2011 mental illness and any mental illness estimates using the new set of predictors (based on variable selection done using 2010-2011 data). Also, for the 2010 sample for mental illness and any mental illness, weights based on control totals poststratified to 2010 census-based population counts were used (unlike weights used for all other outcomes where the 2010 sample weights were based on control totals poststratified to 2010 projections from 2000 census-based population counts).

B.6 Benchmarking the Age Group-Specific Small Area Estimates

The self-calibration built into the survey-weighted hierarchical Bayes (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 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 2010-2011 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 re-centered 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 B.6-1 ,     D

where

Equation B.6-2 ,     D

Equation B.6-3 , and     D

Equation B.6-4 .     D

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

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

Tables 1 to 26 of "NSDUH: 2010-2011 Model-Based Estimated Totals (in Thousands) (50 States and the District of Columbia)," available at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/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, 2010 and 2011) of the State by age group of interest.

For example, past month use of alcohol among 18 to 25 year olds in Alabama was 51.29 percent.15 The corresponding Bayesian confidence intervals ranged from 47.46 to 55.10 percent. The population count for 18 to 25 year olds averaged across 2010-2011 in Alabama was 528,943 (see Table C.10 in Section C of this methodology document). Hence, the estimated number of 18 to 25 year olds using alcohol in the past month in Alabama was 0.5129 * 528,943, which is 271,295.16 The associated Bayesian confidence intervals ranged from 0.4746 * 528,943 (i.e., 251,036) to 0.5510 * 528,943 (i.e., 291,448). Note that when estimates of the number of persons are calculated for Tables 1 to 26 in 2010-2011 NSDUH: Model-Based Estimated Totals (follow the link in footnote 16), 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 because of rounding differences.

B.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 here 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 B.8-1 ,     D

where capital X sub 1 is the number of marijuana initiates in the past 24 months and capital X sub 2  is the number of persons who never used marijuana.

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 2011 to indicate first use as early as the first part of 2009 or as late as the first part of 2011. Similarly, a subject interviewed in the last part of 2011 could indicate first use as early as the last part of 2009 or as late as the last part of 2011. Therefore, in the 2011 survey, the reported period of first use ranged from early 2009 to late 2011 and was "centered" in 2010. For example, about half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2010, while a quarter each reported first use in 2009 and 2011. Persons who responded in 2011 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 2010 survey ranged from early 2008 to late 2010 and were centered in 2009. 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. Note that only incidence rates for marijuana use are provided here.

B.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 2010-2011 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 B.6. Comparisons between the 2009-2010 and the 2010-2011 small area estimates for underage drinking in the States are presented at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.

B.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,17 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 2011 NSDUH national findings report (CBHSQ, 2012d, pp. 127-130).

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.

B.11 Mental Health Measures

This section provides a summary of measurement issues associated with the four mental health outcome variables included in this document—serious mental illness, any mental illness, serious thoughts of suicide, and major depressive episode. Additional details can be found in Section B.4.7 of Appendix B in the 2008 NSDUH national findings report for major depressive episode (OAS, 2009) and in Sections B.4.2 through B.4.4 of Appendix B in the 2012 NSDUH mental health findings report for all four outcome variables (CBHSQ, in press).

B.11.1 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 in 2008 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 were presented 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. However, in 2013, another revision to the methodology for creating serious mental illness estimates was made, and the estimates presented for 2010-2011 are based on this revised methodology (and are therefore not comparable with previously published estimates of serious mental illness). Thus, the 2008-2009 and 2009-2010 serious mental illness estimates were reproduced using the new 2013 methodology. To develop methods for preparing the estimates of serious mental illness and any mental illness presented in this and other NSDUH reports and documents, 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. The 2008 statistical models (subsequently referred to as the "2008 model") 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. Serious mental illness probabilities and predicted values (as well as any mental illness values) were computed for respondents in the NSDUH sample from 2008 to 2010 using model parameter estimates from the 2008 sample.

In 2010, SAMHSA began preliminary investigations to assess whether improvements to the model were warranted using all of the clinical data that been collected since 2008. In 2011 and 2012, the clinical sample was augmented to include 1,500 respondents per year, leading to a combined sample of approximately 5,000 clinical interviews for 2008 to 2012. SAMHSA determined that the 2008 model has some important shortcomings that had not been detected in the original model fitting because of the small number of respondents in the 2008 clinical subsample. Specifically, the 2008 model substantially overestimated serious mental illness and any mental illness among young adults aged 18 to 25 relative to the clinical interview data. In addition, improvements were needed in the weighting procedures for the MHSS sample data to account better for nonresponse and undercoverage. Therefore, SAMHSA decided to modify the model for the 2012 estimates using the combined 2008-2012 clinical data (subsequently referred to as the "2012 model"). To reduce bias and improve prediction, additional mental health-related variables and an age variable were added in the 2012 model. To provide consistent data for trend assessment, State mental illness estimates for 2008-2009, 2009-2010, and 2010-2011 were also recomputed using the new 2012 model (including the 2009-2010 and 2010-2011 small area estimates shown in these 2010-2011 files). Note that tables or maps showing estimates of serious mental illness and any mental illness based on these 2012 models include "Revised October 2013" in the source line for estimates using 2008 through 2011 data.

The next subsection describes the instruments and items used to measure the variables employed in the 2012 model. Specifically, the instrument used to measure mental illness in the clinical interviews is described, followed by descriptions of the scales and items in the main NSDUH interviews that were used as predictor variables in the model (e.g., the K6 and WHODAS total scores, age, and suicidal thoughts).18

MHSS Clinical Interviews

As described previously, a subsample of NSDUH participants 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.

Kessler-6 Distress Scale

The K6 in the main NSDUH interview 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 (NERVE30, HOPE30, FIDG30, NOCHR30, EFFORT30, and DOWN30) on the K6 scale were recoded from 0 to 4 so that "all of the time" was coded 4, "most of the time" 3, "some of the time" 2, "a little of the time" 1, and "none of the time" 0, with "don't know" and "refused" also coded as 0. Summing across the transformed responses in these six items resulted in a score with a range from 0 to 24.

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

An alternative K6 total score also was created in which K6 scores less than 8 were recoded as 0 and scores from 8 to 24 were recoded as 1 to 17. The rationale for creating the alternative past year K6 score was that serious mental illness prevalence was typically extremely low for respondents with past year K6 scores less than 8, and the prevalence rates started increasing only when scores were 8 or greater. This alternative K6 score was used in both the 2008 and 2012 serious mental illness prediction models.

WHODAS

An initial step of the MHSS was to modify the WHODAS for use in a general population survey, including making minor changes to question wording and reducing its length (Novak, 2007). That is, a subset of 8 items was found to capture the information represented in the full 16-item scale with no significant loss of information.

These eight WHODAS items that were included in the main NSDUH interview were assessed on a 0 to 3 scale, with responses of "no difficulty," "don't know," and "refused" coded as 0; "mild difficulty" coded as 1; "moderate difficulty" coded as 2; and "severe difficulty" coded as 3. Some items had an additional category for respondents who did not engage in a particular activity (e.g., they did not leave the house on their own). Respondents who reported that they did not engage in an activity were asked a follow-up question to determine if they did not do so because of emotions, nerves, or mental health. Those who answered "yes" to this follow-up question were subsequently assigned to the "severe difficulty" category; otherwise (i.e., for responses of "no," "don't know," or "refused"), they were assigned to the "no difficulty" category. Summing across these codes for the eight responses resulted in a total score with a range from 0 to 24. More information about scoring of the WHODAS can be found in the 2011 NSDUH public use file codebook (CBHSQ, 2012a).

An alternative WHODAS total score was created in which individual WHODAS item scores of less than 2 were recoded as 0, and item scores of 2 to 3 were recoded as 1. The individual alternative item scores then were summed to yield a total alternative score ranging from 0 to 8. Creation of an alternative version of the WHODAS score was based on the assumption that a dichotomous measure dividing respondents into two groups (i.e., severely impaired vs. less severely impaired) might fit better than a linear continuous measure in models predicting serious mental illness. This alternative WHODAS score was the variable used in both the 2008 and 2012 serious mental illness prediction models.

Suicidal Thoughts, Major Depressive Episode, and Age

In addition to the K6 and WHODAS scales, the 2012 model included the following measures as predictors of serious mental illness: (a) serious thoughts of suicide in the past year; (b) having a past year major depressive episode; and (c) age. The first two variables were added to the model to decrease the error rate in the predictions (i.e., the sum of the false-negative and false-positive rates relative to the clinical interview results). A recoded age variable reduced the biases in estimates for particular age groups, especially 18 to 25 year olds.

Since 2008, all adult respondents in NSDUH have been asked the following question: "At any time in the past 12 months, that is from [DATEFILL] up to and including today, did you seriously think about killing yourself?"19 Definitions for major depressive episode in the lifetime and past year periods are discussed in Section B.4.4 of Appendix B in the 2012 mental health findings report (CBHSQ, in press). For respondents aged 18 to 30, an adjusted age was created by subtracting 18 from the respondent's current age, resulting in values ranging from 0 to 12. For a respondent aged 18, for example, the adjusted age was 0 (i.e., 18 minus 18), and for a respondent aged 30, the adjusted age was 12 (i.e., 30 minus 18). For respondents aged 31 or older, the adjusted age was assigned a value of 12.

2012 SMI Model

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 the weighted logistic regression model. The final 2012 calibration model was determined as follows:

Equation (1)     D

where pi hat refers to an estimate of the serious mental illness response probability pi. These covariates in equation (1) come from the main NSDUH interview data:

As with the 2008 model, 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. The cut point (0.260573529) was chosen so that the weighted number of false positives and false negatives in the MHSS dataset were as close to equal as possible. The predicted serious mental illness status for all adult NSDUH respondents was used to compute serious mental illness small area estimates. A second cut point probability (0.0192519810) was determined so that respondents with a serious mental illness probability greater than or equal to the cut point was predicted to be positive for any mental illness, and the remainder was predicted to be negative for any mental illness. The second cut point was chosen so that the weighted numbers of any mental illness false positives and false negatives were as close to equal as possible.

B.11.2 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 the questions were continued to be asked in 2009, 2010, and 2011). 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 at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.

B.11.3 Major Depressive Episode (Depression)

According to the DSM-IV, a person is defined as having had major depressive episode in his or her lifetime if he or she has had at least five or more of the following nine symptoms nearly every day in the same 2-week period, where at least one of the symptoms is a depressed mood or loss of interest or pleasure in daily activities (APA, 1994): (1) depressed mood most of the day; (2) markedly diminished interest or pleasure in all or almost all activities most of the day; (3) significant weight loss when not sick or dieting, or weight gain when not pregnant or growing, or decrease or increase in appetite; (4) insomnia or hypersomnia; (5) psychomotor agitation or retardation; (6) fatigue or loss of energy; (7) feelings of worthlessness; (8) diminished ability to think or concentrate or indecisiveness; and (9) recurrent thoughts of death or suicidal ideation. Respondents who have had a major depressive episode in their lifetime are asked if, during the past 12 months, they had a period of depression lasting 2 weeks or longer while also having some of the other symptoms mentioned. Those reporting that they have are defined as having had major depressive episode in the past year and then are asked questions from the SDS to measure the level of functional impairment in major life activities reported to be caused by the major depressive episode in the past 12 months (Leon, Olfson, Portera, Farber, & Sheehan, 1997).

Beginning in 2004, modules related to major depressive episode, derived from DSM-IV (APA, 1994) criteria for major depression, were included in the questionnaire. These questions permit prevalence estimates of major depressive episode to be calculated. Separate modules were administered to adults aged 18 or older and youths aged 12 to 17. The adult questions were adapted from the depression section of the National Comorbidity Survey Replication (NCS-R), 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 the 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, 2010, and 2011 for the WHODAS impairment scale, and the questions for the SDS impairment scale were deleted; see Sections B.4.2 and B.4.4 in Appendix B of the 2011 NSDUH mental health findings report (CBHSQ, 2012b) 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 2011 major depressive episode estimates (for more information on these adjustments, see Aldworth, Kott, Yu, Mosquin, & Barnett-Walker, 2012). Thus, the 2008-2009 estimates of major depressive episode were produced using the adjusted 2008 major depressive episode variable along with the unadjusted 2009 major depressive episode variable. Revised estimates for 2005-2006, 2006-2007, and 2007-2008 were produced using the adjusted 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 2011 are available for adolescents aged 12 to 17.

B.12 Comparison of Two 2010-2011 Small Area Estimates

This section describes a method for determining whether differences between two 2010-2011 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., 2010-2011).

Let pi 1 sub a and pi 2 sub a denote the 2010-2011 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 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 and p 2 sub a are the 2010-2011 State estimates given in the "2010-2011 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia) (Tables 1 to 26, by Age Group)" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx. 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 B.12-1 .     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 the "2010-2011 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia) (Tables 1 to 26, by Age Group)" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx. For this purpose, let lower sub 1 and upper sub 1 and lower sub 2 and upper sub 2 denote the 95 percent Bayesian confidence intervals for the two States, State 1 and State 2, respectively. Then

Equation B.12-2     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 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 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 hat sub a . To calculate the p value for testing the null hypothesis of no difference (Log-odds ratio lor sub a is equal to zero.), it is assumed that the posterior distribution of log-odds ratio, lor sub a is normal with Mean is equal to the estimate of the log-odds ratio, lor hat sub a. and Variance is equal to the variance v of the estimate of the log-odds ratio, lor hat sub a. . With the null value of Log-odds ratio, lor sub a, is equal to zero., the Bayes p value or posterior probability of no difference is The p value is equal to 2 times the probability of realizing a standard normal variate greater than or equal to the absolute value of a quantity z., where Z is a standard normal random variate, Quantity z is the estimate of the log-odds ratio, lor hat sub a, divided by the square root of the sum of the variance v of the natural logarithm of Theta 1 hat and the variance v of the natural logarithm of Theta 2 hat. , and abs(z) denotes the absolute value of 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 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 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 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 New Jersey are shown in the following exhibit and also in Table 9 of the "2010-2011 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia)" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx. Looking at the two 95 percent Bayesian confidence intervals, it would appear that the Minnesota and New Jersey 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 13.14 (11.00, 15.62)
New Jersey 16.84 (14.36, 19.64)

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

Let p 1 sub a equal 0.1314, lower sub 1 equal 0.1100, upper sub 1 equal 0.1562, p 2 sub a equal 0.1684, lower sub 2 equal 0.1436 upper sub 2 equal 0.1964 . Then,

Equation B.12-3     D

Equation B.12-4     D

Equation B.12-5     D

Equation B.12-6     D

Because the computed absolute value of z is greater than or equal to 1.96 (the critical value of the z statistic), then at the 5 percent level of significance, the hypothesis of no difference (Minnesota prevalence rate = New Jersey 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 2.0695. The p value is equal to 0.0385. .

Section C: Sample Sizes, Response Rates, and Population Estimates

Table C.1 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2009
State Total
Selected DUs
Total
Eligible
DUs
Total
Completed
Screeners
Weighted DU
Screening
Response Rate
Total
Selected
Total
Responded
Population
Estimate
Weighted
Interview
Response
Rate
Weighted
Overall
Response
Rate
DU = dwelling unit.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, 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 10,592,236 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 C.2 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: 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 C.3 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 C.4 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 C.5 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2011
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, 2011.
Total U.S. 216,521 179,293 156,048 86.98% 88,536 70,109 257,598,945 74.38% 64.69%
Northeast 46,446 38,803 31,569 80.08% 17,251 13,090 46,891,412 69.86% 55.94%
Midwest 58,190 48,817 42,805 88.19% 24,570 19,258 55,687,448 73.92% 65.18%
South 70,821 57,462 51,276 89.47% 28,122 22,980 95,181,797 76.88% 68.78%
West 41,064 34,211 30,398 87.20% 18,593 14,781 59,838,287 74.41% 64.88%
Alabama 4,338 3,360 3,032 89.89% 1,708 1,383 3,985,593 74.64% 67.09%
Alaska 2,459 1,911 1,700 88.87% 1,121 905 569,155 79.52% 70.67%
Arizona 2,731 2,149 1,915 89.43% 1,126 928 5,285,358 82.24% 73.55%
Arkansas 2,687 2,180 2,008 92.12% 1,160 919 2,411,125 72.47% 66.76%
California 9,464 8,223 6,869 83.58% 4,692 3,640 31,060,033 72.25% 60.39%
Colorado 3,127 2,571 2,300 88.95% 1,153 921 4,187,811 76.05% 67.64%
Connecticut 2,805 2,398 2,025 84.35% 1,200 951 3,015,283 72.47% 61.13%
Delaware 2,845 2,334 2,054 87.89% 1,109 900 756,390 76.51% 67.24%
District of Columbia 4,627 3,808 3,119 80.97% 1,067 900 534,393 83.28% 67.43%
Florida 13,954 10,951 9,602 86.92% 4,941 4,029 16,131,977 74.96% 65.16%
Georgia 2,255 1,909 1,745 91.50% 1,082 878 7,928,493 77.49% 70.91%
Hawaii 2,835 2,470 2,015 81.14% 1,260 950 1,116,660 72.08% 58.49%
Idaho 2,237 1,842 1,735 94.05% 1,124 916 1,274,823 76.97% 72.39%
Illinois 11,772 10,195 7,912 77.53% 4,929 3,655 10,652,220 68.90% 53.41%
Indiana 2,475 2,015 1,875 93.20% 1,104 896 5,365,682 73.89% 68.86%
Iowa 2,659 2,295 2,137 93.15% 1,137 933 2,537,918 78.95% 73.54%
Kansas 2,579 2,243 2,043 91.08% 1,164 915 2,323,751 75.45% 68.71%
Kentucky 2,619 2,188 2,048 93.62% 1,113 899 3,597,429 76.19% 71.33%
Louisiana 5,114 4,039 3,768 93.48% 2,126 1,746 3,719,351 77.92% 72.83%
Maine 3,568 2,517 2,313 91.74% 1,039 865 1,142,856 79.50% 72.93%
Maryland 2,587 2,290 1,842 80.47% 1,121 924 4,849,618 77.62% 62.47%
Massachusetts 3,419 2,941 2,518 85.24% 1,230 975 5,601,752 74.44% 63.45%
Michigan 11,276 9,000 7,698 85.60% 4,667 3,685 8,291,125 74.32% 63.62%
Minnesota 2,723 2,369 2,135 90.09% 1,160 940 4,434,303 79.23% 71.38%
Mississippi 3,478 2,708 2,504 92.66% 1,462 1,226 2,408,918 77.57% 71.88%
Missouri 2,501 2,073 1,925 92.84% 1,127 912 4,967,492 73.10% 67.86%
Montana 3,075 2,483 2,340 94.29% 1,194 956 835,577 76.54% 72.17%
Nebraska 2,547 2,123 1,956 91.82% 1,178 908 1,500,994 71.98% 66.10%
Nevada 2,125 1,680 1,584 95.22% 1,125 907 2,241,024 74.26% 70.71%
New Hampshire 3,003 2,402 2,099 87.19% 1,228 945 1,127,509 72.59% 63.29%
New Jersey 2,534 2,163 1,898 87.73% 1,129 894 7,385,619 71.57% 62.79%
New Mexico 2,478 1,876 1,769 94.23% 1,134 938 1,695,728 79.87% 75.26%
New York 14,528 12,454 9,093 72.46% 5,123 3,531 16,423,062 63.90% 46.31%
North Carolina 2,843 2,319 2,112 90.63% 1,103 935 7,910,951 80.92% 73.34%
North Dakota 3,321 2,629 2,476 94.18% 1,133 904 565,372 74.23% 69.91%
Ohio 11,134 9,463 8,496 89.29% 4,697 3,695 9,616,044 74.43% 66.45%
Oklahoma 2,614 2,068 1,895 91.72% 1,128 890 3,073,328 76.09% 69.79%
Oregon 2,729 2,389 2,171 90.89% 1,190 951 3,261,406 76.65% 69.66%
Pennsylvania 10,738 9,207 7,401 79.86% 4,011 3,074 10,760,673 72.87% 58.19%
Rhode Island 2,634 2,140 1,896 88.56% 1,155 930 893,903 73.56% 65.14%
South Carolina 2,978 2,441 2,205 90.33% 1,143 927 3,853,142 74.53% 67.32%
South Dakota 2,495 2,128 2,027 95.23% 1,107 913 667,896 77.20% 73.52%
Tennessee 2,590 2,149 1,914 89.19% 1,110 911 5,312,944 77.92% 69.50%
Texas 9,328 7,741 7,096 91.51% 4,478 3,636 20,486,703 75.86% 69.43%
Utah 1,797 1,590 1,505 94.62% 1,125 918 2,176,506 77.23% 73.08%
Vermont 3,217 2,581 2,326 90.14% 1,136 925 540,755 78.83% 71.06%
Virginia 2,726 2,431 2,074 85.29% 1,105 939 6,647,559 81.71% 69.69%
Washington 2,950 2,586 2,298 88.23% 1,254 959 5,668,143 72.78% 64.22%
West Virginia 3,238 2,546 2,258 87.80% 1,166 938 1,573,884 75.61% 66.39%
Wisconsin 2,708 2,284 2,125 92.73% 1,167 902 4,764,652 75.45% 69.97%
Wyoming 3,057 2,441 2,197 89.85% 1,095 892 466,065 78.14% 70.21%

Table C.6 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2011
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, 2011.
Total U.S. 27,911 23,549 24,973,646 84.95% 28,589 23,083 34,301,730 80.48% 32,036 23,477 198,323,568 71.96%
Northeast 5,443 4,425 4,277,870 82.07% 5,465 4,270 6,120,583 77.18% 6,343 4,395 36,492,959 67.15%
Midwest 7,649 6,388 5,445,784 83.26% 7,982 6,373 7,340,274 80.46% 8,939 6,497 42,901,391 71.62%
South 9,087 7,870 9,256,114 87.02% 9,028 7,542 12,610,321 83.06% 10,007 7,568 73,315,362 74.47%
West 5,732 4,866 5,993,878 85.37% 6,114 4,898 8,230,553 78.93% 6,747 5,017 45,613,857 72.13%
Alabama 529 452 385,875 85.66% 577 486 536,911 83.41% 602 445 3,062,807 71.72%
Alaska 392 333 60,921 85.33% 368 284 79,374 77.63% 361 288 428,860 79.00%
Arizona 363 308 535,373 86.03% 375 308 705,171 83.29% 388 312 4,044,814 81.51%
Arkansas 351 296 234,612 84.34% 431 352 316,930 81.16% 378 271 1,859,582 69.15%
California 1,403 1,181 3,173,750 84.94% 1,562 1,230 4,401,989 78.04% 1,727 1,229 23,484,294 69.41%
Colorado 376 326 395,811 84.87% 361 290 552,881 80.31% 416 305 3,239,119 74.43%
Connecticut 361 309 292,050 86.67% 389 320 366,697 83.62% 450 322 2,356,536 68.68%
Delaware 347 292 69,137 84.31% 349 295 100,448 82.88% 413 313 586,805 74.47%
District of Columbia 343 304 31,407 88.80% 408 339 97,511 82.66% 316 257 405,475 83.00%
Florida 1,649 1,440 1,380,074 87.03% 1,466 1,222 1,947,535 82.91% 1,826 1,367 12,804,369 72.50%
Georgia 360 312 821,078 87.30% 309 254 1,073,944 81.77% 413 312 6,033,471 75.45%
Hawaii 395 303 98,668 74.86% 412 329 135,970 82.72% 453 318 882,022 70.07%
Idaho 382 331 138,364 87.43% 326 269 173,071 83.08% 416 316 963,388 74.47%
Illinois 1,547 1,254 1,063,049 81.28% 1,630 1,207 1,394,519 73.93% 1,752 1,194 8,194,652 66.32%
Indiana 336 292 540,048 86.96% 374 315 728,277 84.58% 394 289 4,097,357 70.25%
Iowa 395 332 241,080 85.04% 320 273 344,974 84.99% 422 328 1,951,863 77.28%
Kansas 338 279 235,652 82.61% 394 321 320,124 82.19% 432 315 1,767,975 73.31%
Kentucky 359 297 339,927 83.56% 355 300 457,966 84.54% 399 302 2,799,536 73.80%
Louisiana 671 588 367,017 88.27% 666 567 525,065 87.75% 789 591 2,827,268 74.55%
Maine 350 300 97,195 85.41% 348 296 129,785 84.83% 341 269 915,876 77.99%
Maryland 370 324 460,905 87.15% 368 303 624,724 82.56% 383 297 3,763,989 75.67%
Massachusetts 461 384 495,429 83.49% 410 330 765,174 79.20% 359 261 4,341,149 72.35%
Michigan 1,420 1,195 819,033 84.29% 1,569 1,261 1,094,805 80.72% 1,678 1,229 6,377,287 71.97%
Minnesota 370 315 425,134 85.39% 339 274 570,169 81.72% 451 351 3,439,001 78.13%
Mississippi 452 410 248,626 91.19% 453 390 335,084 85.87% 557 426 1,825,208 74.15%
Missouri 338 293 476,256 82.39% 359 304 654,304 84.44% 430 315 3,836,932 70.24%
Montana 352 299 74,309 83.99% 396 326 106,543 82.17% 446 331 654,725 74.87%
Nebraska 342 298 146,677 87.64% 418 315 205,271 76.00% 418 295 1,149,047 69.10%
Nevada 239 204 218,674 89.40% 446 381 280,630 88.39% 440 322 1,741,720 70.36%
New Hampshire 407 324 103,573 79.53% 404 327 138,419 81.88% 417 294 885,517 70.19%
New Jersey 350 301 712,565 87.81% 360 295 870,975 84.31% 419 298 5,802,078 67.72%
New Mexico 319 280 169,846 87.11% 393 326 226,296 80.21% 422 332 1,299,586 78.88%
New York 1,537 1,180 1,482,881 76.97% 1,702 1,176 2,238,168 68.70% 1,884 1,175 12,702,014 61.53%
North Carolina 379 339 754,179 89.13% 339 282 1,016,089 81.19% 385 314 6,140,683 79.89%
North Dakota 334 291 48,835 87.85% 398 325 89,850 81.27% 401 288 426,688 71.23%
Ohio 1,491 1,220 932,467 81.91% 1,462 1,184 1,228,851 80.53% 1,744 1,291 7,454,725 72.47%
Oklahoma 322 264 302,691 82.91% 389 311 421,806 81.30% 417 315 2,348,831 74.21%
Oregon 414 355 291,549 86.35% 373 286 409,460 76.97% 403 310 2,560,397 75.46%
Pennsylvania 1,252 1,023 969,456 83.05% 1,105 889 1,406,406 81.30% 1,654 1,162 8,384,811 70.33%
Rhode Island 356 301 78,432 84.88% 372 324 132,407 87.65% 427 305 683,065 69.48%
South Carolina 348 302 356,131 86.42% 392 331 511,928 84.82% 403 294 2,985,082 71.06%
South Dakota 363 317 64,382 86.27% 340 295 90,856 85.84% 404 301 512,659 74.58%
Tennessee 336 293 503,104 88.26% 358 297 679,027 82.54% 416 321 4,130,814 75.89%
Texas 1,516 1,314 2,251,878 87.02% 1,426 1,180 2,896,598 82.35% 1,536 1,142 15,338,228 72.77%
Utah 350 317 264,830 90.99% 350 278 362,847 77.60% 425 323 1,548,828 74.74%
Vermont 369 303 46,290 83.39% 375 313 72,552 84.62% 392 309 421,913 77.36%
Virginia 378 332 618,074 87.87% 354 307 879,583 85.65% 373 300 5,149,902 80.14%
Washington 367 309 529,144 83.87% 447 339 733,670 74.35% 440 311 4,405,329 71.11%
West Virginia 377 311 131,399 82.69% 388 326 189,172 84.72% 401 301 1,253,313 73.59%
Wisconsin 375 302 453,172 80.52% 379 299 618,275 81.47% 413 301 3,693,206 73.70%
Wyoming 380 320 42,640 84.62% 305 252 62,649 83.42% 410 320 360,775 76.42%

Table C.7   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 C.8 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 C.9 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2010 and 2011
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 2010-2011 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 2010 and 2011 individual response rates. The 2010-2011 population estimate is the average of the 2010 and the 2011 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2010 and 2011 (2010 Data – Revised March 2012).
Total U.S. 418,386 345,825 303,058 87.69% 173,533 137,913 255,609,026 74.47% 65.31%
Northeast 89,866 74,836 61,214 80.86% 34,033 26,107 46,713,366 71.34% 57.69%
Midwest 112,957 94,709 83,923 89.01% 48,709 38,559 55,516,454 74.36% 66.19%
South 134,634 108,995 97,517 90.00% 53,719 43,749 94,071,846 76.56% 68.90%
West 80,929 67,285 60,404 88.16% 37,072 29,498 59,307,360 73.79% 65.05%
Alabama 7,217 5,644 5,131 90.93% 2,829 2,261 3,939,640 73.25% 66.61%
Alaska 4,685 3,630 3,283 90.47% 2,178 1,773 562,560 78.63% 71.13%
Arizona 5,386 4,208 3,776 89.79% 2,275 1,853 5,336,070 77.24% 69.36%
Arkansas 5,282 4,288 3,956 92.32% 2,283 1,818 2,393,558 73.83% 68.16%
California 18,746 16,310 13,779 84.49% 9,431 7,355 30,691,087 72.11% 60.93%
Colorado 5,656 4,655 4,212 90.44% 2,270 1,825 4,169,870 77.62% 70.20%
Connecticut 5,279 4,556 3,837 84.04% 2,351 1,877 2,983,250 73.84% 62.05%
Delaware 5,466 4,452 3,911 87.78% 2,208 1,789 746,980 77.02% 67.60%
District of Columbia 9,740 8,000 6,522 80.39% 2,177 1,835 526,168 82.34% 66.19%
Florida 27,160 20,912 18,493 87.99% 9,401 7,684 15,871,875 76.14% 66.99%
Georgia 4,640 3,887 3,549 91.36% 2,213 1,788 7,934,572 76.54% 69.92%
Hawaii 5,696 4,913 4,113 83.26% 2,556 1,924 1,082,202 69.54% 57.90%
Idaho 4,861 3,888 3,667 94.23% 2,237 1,828 1,262,531 77.58% 73.10%
Illinois 22,386 19,316 15,304 79.23% 9,691 7,264 10,640,868 69.85% 55.34%
Indiana 5,218 4,296 3,979 92.58% 2,246 1,812 5,325,850 73.89% 68.41%
Iowa 5,233 4,482 4,206 93.84% 2,250 1,858 2,520,016 78.93% 74.07%
Kansas 4,919 4,231 3,867 91.40% 2,265 1,800 2,310,019 75.11% 68.65%
Kentucky 5,202 4,335 4,039 93.20% 2,222 1,799 3,586,107 76.54% 71.33%
Louisiana 7,719 6,131 5,723 93.45% 3,238 2,652 3,690,586 77.94% 72.84%
Maine 6,895 4,921 4,510 91.36% 2,139 1,789 1,135,070 80.10% 73.18%
Maryland 5,002 4,351 3,534 81.26% 2,217 1,807 4,793,712 77.64% 63.09%
Massachusetts 6,535 5,657 4,883 86.33% 2,379 1,905 5,603,697 76.41% 65.97%
Michigan 22,104 17,669 15,321 86.70% 9,228 7,375 8,302,279 74.98% 65.01%
Minnesota 5,255 4,456 4,084 91.65% 2,309 1,886 4,408,217 78.79% 72.22%
Mississippi 5,963 4,684 4,343 92.86% 2,549 2,119 2,391,255 77.03% 71.53%
Missouri 5,143 4,243 3,956 93.20% 2,269 1,833 4,960,194 74.46% 69.39%
Montana 5,788 4,738 4,468 94.32% 2,331 1,875 827,846 76.72% 72.36%
Nebraska 4,883 4,119 3,839 93.08% 2,298 1,814 1,485,062 72.58% 67.56%
Nevada 4,799 3,743 3,519 94.95% 2,308 1,865 2,198,214 73.08% 69.39%
New Hampshire 6,235 4,960 4,318 86.99% 2,388 1,863 1,128,253 73.52% 63.96%
New Jersey 4,916 4,224 3,729 88.31% 2,286 1,817 7,327,726 74.96% 66.20%
New Mexico 5,088 3,954 3,728 94.25% 2,251 1,850 1,668,810 78.53% 74.01%
New York 27,746 23,624 17,545 73.83% 10,184 7,157 16,416,573 65.37% 48.27%
North Carolina 5,517 4,622 4,230 91.40% 2,206 1,839 7,795,039 78.85% 72.07%
North Dakota 6,374 5,196 4,896 94.24% 2,321 1,858 552,787 75.28% 70.94%
Ohio 21,402 18,180 16,443 90.23% 9,330 7,426 9,598,203 74.61% 67.32%
Oklahoma 5,240 4,190 3,798 90.71% 2,301 1,813 3,034,446 74.62% 67.68%
Oregon 5,332 4,682 4,317 92.22% 2,324 1,858 3,245,308 75.79% 69.89%
Pennsylvania 20,931 17,922 14,353 79.82% 7,864 6,059 10,683,992 73.05% 58.31%
Rhode Island 5,208 4,234 3,762 88.87% 2,272 1,845 895,144 74.02% 65.78%
South Carolina 5,594 4,593 4,132 89.95% 2,281 1,854 3,806,883 75.12% 67.57%
South Dakota 4,894 4,176 3,972 95.14% 2,222 1,842 667,242 78.84% 75.01%
Tennessee 5,178 4,298 3,882 90.31% 2,227 1,812 5,275,759 75.73% 68.39%
Texas 18,213 15,031 13,793 91.64% 8,909 7,226 20,167,102 76.24% 69.87%
Utah 3,304 2,914 2,757 94.60% 2,230 1,837 2,178,698 78.58% 74.34%
Vermont 6,121 4,738 4,277 90.26% 2,170 1,795 539,662 80.61% 72.76%
Virginia 5,335 4,715 4,111 87.11% 2,201 1,827 6,559,374 79.09% 68.89%
Washington 5,586 4,874 4,401 89.98% 2,448 1,856 5,626,876 71.47% 64.30%
West Virginia 6,166 4,862 4,370 89.51% 2,257 1,826 1,558,789 76.90% 68.83%
Wisconsin 5,146 4,345 4,056 93.17% 2,280 1,791 4,745,719 76.12% 70.92%
Wyoming 6,002 4,776 4,384 91.76% 2,233 1,799 457,289 75.64% 69.41%

Table C.10 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2010 and 2011
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 2010-2011 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 2010 and 2011 individual response rates. The 2010-2011 population estimate is the average of the 2010 and the 2011 population.
Source:  SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2010 and 2011 (2010 Data – Revised March 2012).
Total U.S. 53,819 45,541 24,660,087 84.80% 56,753 46,109 34,187,040 80.84% 62,961 46,263 196,761,899 72.05%
Northeast 10,409 8,530 4,256,314 81.69% 11,077 8,682 6,082,801 77.31% 12,547 8,895 36,374,251 69.12%
Midwest 15,006 12,652 5,390,170 84.20% 16,017 12,962 7,418,402 80.96% 17,686 12,945 42,707,882 71.98%
South 17,116 14,728 9,106,336 86.31% 17,435 14,569 12,514,566 83.18% 19,168 14,452 72,450,944 74.13%
West 11,288 9,631 5,907,267 85.28% 12,224 9,896 8,171,271 79.75% 13,560 9,971 45,228,822 71.22%
Alabama 898 759 379,971 84.34% 922 772 528,943 82.38% 1,009 730 3,030,727 70.26%
Alaska 704 599 59,141 85.44% 730 594 82,230 81.63% 744 580 421,188 77.02%
Arizona 696 600 536,957 86.69% 803 659 703,220 81.23% 776 594 4,095,893 75.34%
Arkansas 685 580 233,536 84.63% 793 648 311,224 81.67% 805 590 1,848,798 70.96%
California 2,929 2,484 3,130,240 84.87% 2,978 2,381 4,335,049 79.46% 3,524 2,490 23,225,798 69.01%
Colorado 649 557 387,484 83.93% 785 635 559,635 80.89% 836 633 3,222,752 76.39%
Connecticut 692 597 286,904 87.37% 789 646 374,028 82.49% 870 634 2,322,318 70.59%
Delaware 666 560 68,185 83.83% 689 583 97,063 83.96% 853 646 581,733 75.06%
District of Columbia 699 628 32,823 90.41% 792 659 91,252 82.53% 686 548 402,092 81.60%
Florida 3,073 2,655 1,355,015 86.45% 2,885 2,434 1,909,018 84.00% 3,443 2,595 12,607,843 73.83%
Georgia 731 625 819,770 85.85% 664 555 1,075,015 83.21% 818 608 6,039,787 74.05%
Hawaii 795 641 94,257 78.95% 851 664 133,155 80.46% 910 619 854,791 66.75%
Idaho 735 625 134,591 85.35% 682 574 175,303 84.12% 820 629 952,637 75.29%
Illinois 2,904 2,376 1,056,364 81.95% 3,245 2,439 1,423,767 75.16% 3,542 2,449 8,160,738 67.34%
Indiana 725 633 531,919 87.56% 717 595 723,659 83.07% 804 584 4,070,272 70.54%
Iowa 731 619 237,564 85.09% 705 594 352,176 83.43% 814 645 1,930,275 77.42%
Kansas 669 575 230,525 85.92% 751 606 329,289 81.69% 845 619 1,750,205 72.51%
Kentucky 711 596 336,580 84.36% 725 604 459,932 83.37% 786 599 2,789,595 74.46%
Louisiana 1,053 916 366,321 87.36% 1,011 852 525,574 85.26% 1,174 884 2,798,691 75.28%
Maine 675 584 95,848 86.60% 704 598 130,378 85.02% 760 607 908,845 78.67%
Maryland 685 592 454,455 86.65% 735 603 619,127 81.25% 797 612 3,720,130 75.94%
Massachusetts 821 680 493,546 81.94% 802 654 763,089 80.41% 756 571 4,347,062 75.03%
Michigan 2,852 2,407 816,665 84.20% 3,022 2,481 1,100,008 82.61% 3,354 2,487 6,385,606 72.49%
Minnesota 707 611 417,213 86.42% 749 614 580,436 82.28% 853 661 3,410,568 77.31%
Mississippi 785 700 248,024 89.49% 821 706 337,611 85.79% 943 713 1,805,620 73.68%
Missouri 679 581 474,419 83.92% 745 624 655,582 83.56% 845 628 3,830,193 71.84%
Montana 700 601 73,285 85.43% 739 606 110,681 81.92% 892 668 643,880 74.97%
Nebraska 677 598 143,963 87.96% 790 621 212,075 79.60% 831 595 1,129,023 69.24%
Nevada 537 468 214,554 89.94% 851 720 272,251 85.86% 920 677 1,711,409 69.17%
New Hampshire 707 574 102,528 82.05% 871 714 141,973 82.36% 810 575 883,752 70.95%
New Jersey 737 625 702,580 85.59% 694 559 868,283 82.88% 855 633 5,756,864 72.46%
New Mexico 683 607 165,536 88.22% 763 629 226,630 81.87% 805 614 1,276,644 76.65%
New York 2,994 2,321 1,490,465 77.26% 3,411 2,410 2,213,444 70.09% 3,779 2,426 12,712,663 63.19%
North Carolina 725 650 736,999 89.48% 714 586 1,015,292 81.72% 767 603 6,042,747 76.99%
North Dakota 691 591 47,606 85.83% 791 665 93,205 84.30% 839 602 411,976 72.09%
Ohio 2,886 2,411 925,508 83.58% 3,096 2,555 1,219,501 82.05% 3,348 2,460 7,453,194 72.24%
Oklahoma 716 601 297,063 83.77% 744 589 423,749 79.16% 841 623 2,313,634 72.62%
Oregon 790 673 288,509 84.77% 734 582 410,811 79.85% 800 603 2,545,988 74.01%
Pennsylvania 2,417 1,978 960,258 82.61% 2,308 1,835 1,385,978 79.94% 3,139 2,246 8,337,756 70.81%
Rhode Island 678 593 78,757 87.53% 790 674 131,124 85.69% 804 578 685,263 70.06%
South Carolina 699 594 352,832 85.13% 768 656 499,582 85.13% 814 604 2,954,469 72.13%
South Dakota 728 626 63,634 85.65% 678 591 93,437 87.00% 816 625 510,171 76.52%
Tennessee 706 612 496,321 87.59% 722 599 671,823 83.26% 799 601 4,107,615 72.93%
Texas 2,845 2,439 2,191,796 85.93% 2,958 2,468 2,877,349 82.98% 3,106 2,319 15,097,957 73.43%
Utah 633 567 260,212 89.94% 770 635 372,167 81.78% 827 635 1,546,318 76.02%
Vermont 688 578 45,429 85.42% 708 592 74,503 83.74% 774 625 419,730 79.52%
Virginia 727 627 606,049 86.46% 714 608 882,246 84.48% 760 592 5,071,079 77.15%
Washington 732 610 520,915 83.71% 824 619 726,355 73.02% 892 627 4,379,606 69.69%
West Virginia 712 594 130,595 83.09% 778 647 189,767 83.35% 767 585 1,238,427 75.24%
Wisconsin 757 624 444,790 82.25% 728 577 635,268 79.88% 795 590 3,665,660 74.63%
Wyoming 705 599 41,586 85.02% 714 598 63,785 83.38% 814 602 351,918 73.08%

Table C.11 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2009, 2010, and 2011
State 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
2011
Total
Selected
2011
Total
Responded
2011
Population
Estimate
2011
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, 2009, 2010, and 2011 (2009 and 2010 Data – Revised March 2012).
Total U.S. 37,009 31,560 38,241,242 85.21% 36,493 30,926 37,977,621 84.37% 38,505 32,349 38,497,742 84.37%
Northeast 7,329 6,098 6,785,606 82.08% 7,068 5,812 6,622,750 80.74% 7,493 6,098 6,824,455 82.10%
Midwest 10,586 9,034 8,472,393 84.90% 10,415 8,841 8,359,258 84.73% 10,686 8,872 8,368,112 82.81%
South 11,053 9,595 13,893,364 87.36% 11,208 9,600 14,070,149 85.73% 12,390 10,682 14,024,266 86.28%
West 8,041 6,833 9,089,879 84.55% 7,802 6,673 8,925,464 84.62% 7,936 6,697 9,280,909 84.53%
Alabama 530 447 599,028 85.17% 510 425 587,014 82.65% 744 631 604,574 84.49%
Alaska 488 416 91,611 85.87% 433 370 86,119 86.09% 515 431 89,332 83.83%
Arizona 514 437 824,443 84.93% 509 441 834,235 85.27% 511 433 798,580 85.99%
Arkansas 469 407 342,551 88.11% 458 380 334,786 82.16% 528 442 374,992 83.30%
California 1,946 1,634 4,775,356 83.65% 2,058 1,755 4,745,134 84.60% 2,003 1,685 5,066,496 84.30%
Colorado 564 494 585,652 86.73% 406 337 551,247 81.84% 480 411 564,436 84.33%
Connecticut 481 407 408,896 85.77% 494 422 446,654 85.63% 516 441 436,152 86.19%
Delaware 518 446 108,455 86.09% 439 375 105,183 85.49% 465 393 105,240 84.25%
District of Columbia 398 346 59,345 85.42% 442 401 56,178 91.06% 487 422 65,173 83.44%
Florida 1,921 1,662 2,133,592 86.01% 2,038 1,759 2,165,742 86.98% 2,250 1,949 2,211,773 86.30%
Georgia 474 420 1,182,593 88.73% 508 430 1,222,236 84.71% 480 413 1,207,618 86.51%
Hawaii 538 426 150,940 76.75% 561 468 142,051 82.88% 541 424 149,682 78.74%
Idaho 468 402 201,917 86.93% 479 409 202,052 85.98% 493 422 205,495 85.84%
Illinois 2,021 1,676 1,681,199 82.87% 1,948 1,593 1,638,431 81.51% 2,144 1,711 1,619,137 79.79%
Indiana 472 406 824,402 86.59% 511 445 808,335 87.62% 489 424 852,672 85.97%
Iowa 484 417 362,489 86.00% 464 400 369,554 85.30% 523 443 382,062 85.81%
Kansas 523 445 362,101 84.25% 452 397 349,540 87.74% 484 398 344,035 82.51%
Kentucky 465 411 533,285 89.41% 508 430 515,140 84.34% 481 400 501,556 83.75%
Louisiana 470 395 559,359 83.59% 507 431 567,474 85.18% 918 804 573,374 88.93%
Maine 515 448 141,437 87.04% 458 405 152,571 89.02% 495 424 153,910 85.35%
Maryland 445 391 679,353 88.22% 428 367 671,790 86.29% 487 422 657,919 85.91%
Massachusetts 503 407 756,845 80.42% 474 387 730,933 80.15% 620 520 822,796 83.78%
Michigan 2,054 1,735 1,286,421 84.14% 1,998 1,690 1,266,567 84.36% 2,034 1,702 1,293,907 83.70%
Minnesota 523 444 687,929 83.63% 496 425 635,101 85.43% 488 411 622,236 84.21%
Mississippi 464 396 401,760 86.27% 483 422 393,379 87.64% 597 539 365,463 90.08%
Missouri 496 410 701,234 83.00% 474 400 741,708 85.70% 465 398 714,937 82.47%
Montana 497 421 116,223 85.63% 480 416 118,731 86.51% 491 411 112,790 82.79%
Nebraska 498 429 240,816 87.71% 469 415 227,519 87.62% 514 427 225,527 83.87%
Nevada 500 438 312,307 88.06% 467 410 329,077 89.28% 440 385 370,767 90.91%
New Hampshire 525 437 162,423 83.24% 485 406 163,192 85.20% 589 479 177,762 82.39%
New Jersey 502 418 1,060,608 81.69% 518 429 1,033,688 82.93% 494 424 1,119,943 88.15%
New Mexico 476 418 255,788 89.54% 502 447 255,942 88.87% 469 404 258,176 84.99%
New York 2,121 1,721 2,486,753 80.92% 2,091 1,629 2,367,030 76.92% 2,120 1,607 2,330,810 76.15%
North Carolina 452 402 1,094,206 89.32% 487 434 1,158,894 89.02% 487 433 1,114,423 88.06%
North Dakota 494 431 82,565 87.52% 516 438 84,291 85.53% 476 414 80,431 86.41%
Ohio 2,014 1,745 1,474,740 86.21% 2,085 1,791 1,450,314 85.68% 2,081 1,715 1,474,645 82.49%
Oklahoma 502 427 460,633 85.55% 510 430 445,994 83.55% 454 373 462,928 83.45%
Oregon 534 429 445,204 81.09% 510 425 435,243 82.04% 534 450 424,881 83.95%
Pennsylvania 1,675 1,384 1,558,771 82.99% 1,641 1,344 1,529,660 81.90% 1,677 1,377 1,583,008 83.76%
Rhode Island 530 447 130,746 83.09% 472 419 127,717 88.13% 483 413 126,155 85.65%
South Carolina 540 471 539,722 86.26% 498 421 554,128 84.56% 482 414 521,289 85.95%
South Dakota 486 439 103,873 90.23% 500 429 98,463 86.31% 470 411 87,535 86.40%
Tennessee 534 473 778,937 88.58% 521 448 783,233 87.28% 462 401 768,020 86.68%
Texas 1,869 1,641 3,208,416 88.06% 1,918 1,638 3,337,978 85.37% 2,010 1,738 3,303,733 86.40%
Utah 483 426 400,057 88.25% 412 364 375,829 88.48% 463 406 364,611 85.21%
Vermont 477 429 79,128 89.98% 435 371 71,307 85.97% 499 413 73,919 84.39%
Virginia 507 441 1,007,100 87.39% 489 415 974,776 85.04% 516 452 966,316 86.34%
Washington 540 464 859,380 85.46% 501 410 783,258 81.66% 510 424 809,041 82.70%
West Virginia 495 419 205,027 84.86% 464 394 196,221 84.13% 542 456 219,874 85.17%
Wisconsin 521 457 664,624 87.47% 502 418 689,435 82.92% 518 418 670,989 81.33%
Wyoming 493 428 71,000 84.56% 484 421 66,546 86.75% 486 411 66,621 85.07%

Table C.12  Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2009-2010 and 2010-2011
State 2009-2010
Total
Selected
2009-2010
Total
Responded
2009-2010
Population
Estimate
2009-2010
Weighted
Interview
Response
Rate
2010-2011
Total
Selected
2010-2011
Total
Responded
2010-2011
Population
Estimate
2010-2011
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 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, 2009, 2010, and 2011 (2009 and 2010 Data – Revised March 2012).
Total U.S. 73,502 62,486 38,109,432 84.79% 74,998 63,275 38,237,681 84.37%
Northeast 14,397 11,910 6,704,178 81.42% 14,561 11,910 6,723,602 81.43%
Midwest 21,001 17,875 8,415,825 84.82% 21,101 17,713 8,363,685 83.77%
South 22,261 19,195 13,981,756 86.54% 23,598 20,282 14,047,208 86.01%
West 15,843 13,506 9,007,672 84.58% 15,738 13,370 9,103,187 84.57%
Alabama 1,040 872 593,021 83.90% 1,254 1,056 595,794 83.58%
Alaska 921 786 88,865 85.97% 948 801 87,725 84.92%
Arizona 1,023 878 829,339 85.10% 1,020 874 816,408 85.62%
Arkansas 927 787 338,668 85.08% 986 822 354,889 82.76%
California 4,004 3,389 4,760,245 84.12% 4,061 3,440 4,905,815 84.44%
Colorado 970 831 568,449 84.43% 886 748 557,842 83.09%
Connecticut 975 829 427,775 85.69% 1,010 863 441,403 85.91%
Delaware 957 821 106,819 85.79% 904 768 105,212 84.87%
District of Columbia 840 747 57,762 88.14% 929 823 60,676 86.89%
Florida 3,959 3,421 2,149,667 86.51% 4,288 3,708 2,188,757 86.64%
Georgia 982 850 1,202,415 86.71% 988 843 1,214,927 85.62%
Hawaii 1,099 894 146,495 79.79% 1,102 892 145,867 80.76%
Idaho 947 811 201,984 86.46% 972 831 203,773 85.91%
Illinois 3,969 3,269 1,659,815 82.20% 4,092 3,304 1,628,784 80.65%
Indiana 983 851 816,369 87.10% 1,000 869 830,504 86.77%
Iowa 948 817 366,022 85.65% 987 843 375,808 85.56%
Kansas 975 842 355,820 85.93% 936 795 346,788 85.09%
Kentucky 973 841 524,213 86.87% 989 830 508,348 84.05%
Louisiana 977 826 563,417 84.38% 1,425 1,235 570,424 87.09%
Maine 973 853 147,004 88.02% 953 829 153,241 87.12%
Maryland 873 758 675,572 87.28% 915 789 664,855 86.10%
Massachusetts 977 794 743,889 80.29% 1,094 907 776,864 82.07%
Michigan 4,052 3,425 1,276,494 84.25% 4,032 3,392 1,280,237 84.03%
Minnesota 1,019 869 661,515 84.49% 984 836 628,669 84.82%
Mississippi 947 818 397,570 86.95% 1,080 961 379,421 88.81%
Missouri 970 810 721,471 84.36% 939 798 728,322 84.09%
Montana 977 837 117,477 86.07% 971 827 115,761 84.69%
Nebraska 967 844 234,167 87.67% 983 842 226,523 85.63%
Nevada 967 848 320,692 88.67% 907 795 349,922 90.14%
New Hampshire 1,010 843 162,808 84.20% 1,074 885 170,477 83.73%
New Jersey 1,020 847 1,047,148 82.32% 1,012 853 1,076,815 85.58%
New Mexico 978 865 255,865 89.20% 971 851 257,059 86.88%
New York 4,212 3,350 2,426,892 78.94% 4,211 3,236 2,348,920 76.54%
North Carolina 939 836 1,126,550 89.17% 974 867 1,136,658 88.55%
North Dakota 1,010 869 83,428 86.49% 992 852 82,361 85.95%
Ohio 4,099 3,536 1,462,527 85.95% 4,166 3,506 1,462,479 84.09%
Oklahoma 1,012 857 453,313 84.57% 964 803 454,461 83.50%
Oregon 1,044 854 440,223 81.56% 1,044 875 430,062 82.98%
Pennsylvania 3,316 2,728 1,544,215 82.45% 3,318 2,721 1,556,334 82.84%
Rhode Island 1,002 866 129,231 85.53% 955 832 126,936 86.87%
South Carolina 1,038 892 546,925 85.40% 980 835 537,709 85.23%
South Dakota 986 868 101,168 88.32% 970 840 92,999 86.35%
Tennessee 1,055 921 781,085 87.92% 983 849 775,627 86.99%
Texas 3,787 3,279 3,273,197 86.70% 3,928 3,376 3,320,856 85.89%
Utah 895 790 387,943 88.36% 875 770 370,220 86.84%
Vermont 912 800 75,217 88.07% 934 784 72,613 85.17%
Virginia 996 856 990,938 86.24% 1,005 867 970,546 85.70%
Washington 1,041 874 821,319 83.64% 1,011 834 796,149 82.18%
West Virginia 959 813 200,624 84.50% 1,006 850 208,048 84.69%
Wisconsin 1,023 875 677,030 85.16% 1,020 836 680,212 82.12%
Wyoming 977 849 68,773 85.64% 970 832 66,584 85.91%

Table C.13 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2009, 2010, and 2011
State 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
2011
Total
Selected
2011
Total
Responded
2011
Population
Estimate
2011
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, 2009, 2010, and 2011 (2009 and 2010 Data – Revised March 2012).
Total U.S. 58,628 45,591 227,206,546 74.46% 59,089 45,812 229,272,579 73.49% 60,625 46,560 232,625,299 73.22%
Northeast 11,722 8,768 42,079,937 71.75% 11,816 8,912 42,300,562 71.96% 11,808 8,665 42,613,542 68.62%
Midwest 16,376 12,735 49,756,736 74.91% 16,782 13,037 50,010,904 73.71% 16,921 12,870 50,241,664 72.89%
South 17,701 14,158 83,039,864 76.29% 17,568 13,911 84,005,336 75.22% 19,035 15,110 85,925,683 75.76%
West 12,829 9,930 52,330,008 73.31% 12,923 9,952 52,955,777 71.84% 12,861 9,915 53,844,410 73.17%
Alabama 784 618 3,498,218 77.82% 752 571 3,519,621 70.68% 1,179 931 3,599,718 73.44%
Alaska 762 600 493,862 78.40% 745 602 498,602 76.86% 729 572 508,235 78.77%
Arizona 767 616 4,772,012 78.64% 816 633 4,848,242 71.45% 763 620 4,749,984 81.79%
Arkansas 785 608 2,127,061 76.02% 789 615 2,143,532 74.10% 809 623 2,176,513 71.07%
California 3,355 2,491 26,962,535 70.44% 3,213 2,412 27,235,412 70.49% 3,289 2,459 27,886,283 70.78%
Colorado 791 619 3,712,168 75.96% 844 673 3,772,773 78.94% 777 595 3,792,000 75.18%
Connecticut 780 607 2,651,071 75.54% 820 638 2,669,460 73.79% 839 642 2,723,233 70.84%
Delaware 771 610 663,392 72.25% 780 621 670,337 76.96% 762 608 687,253 75.70%
District of Columbia 754 636 475,164 83.47% 754 611 483,703 80.56% 724 596 502,986 82.93%
Florida 3,095 2,522 14,141,314 75.95% 3,036 2,440 14,281,818 76.56% 3,292 2,589 14,751,904 73.85%
Georgia 738 601 7,025,028 76.93% 760 597 7,122,189 74.41% 722 566 7,107,414 76.39%
Hawaii 930 649 959,869 66.03% 896 636 957,900 65.29% 865 647 1,017,992 71.81%
Idaho 788 632 1,102,446 76.10% 760 618 1,119,419 77.60% 742 585 1,136,459 75.69%
Illinois 3,380 2,478 9,535,363 70.37% 3,405 2,487 9,579,838 69.50% 3,382 2,401 9,589,171 67.45%
Indiana 787 619 4,734,130 78.39% 753 575 4,762,228 72.40% 768 604 4,825,634 72.44%
Iowa 760 622 2,248,480 80.95% 777 638 2,268,066 78.26% 742 601 2,296,838 78.35%
Kansas 785 606 2,052,096 74.78% 770 589 2,070,889 73.21% 826 636 2,088,098 74.63%
Kentucky 811 645 3,214,458 75.52% 757 601 3,241,553 76.04% 754 602 3,257,502 75.37%
Louisiana 805 639 3,270,638 78.34% 730 578 3,296,197 77.01% 1,455 1,158 3,352,333 76.72%
Maine 753 627 1,030,693 82.04% 775 640 1,032,784 80.01% 689 565 1,045,661 78.89%
Maryland 687 559 4,249,895 78.75% 781 615 4,289,800 76.76% 751 600 4,388,713 76.64%
Massachusetts 888 681 5,067,283 72.94% 789 634 5,113,977 78.02% 769 591 5,106,323 73.51%
Michigan 3,067 2,396 7,493,915 75.99% 3,129 2,478 7,499,137 74.75% 3,247 2,490 7,472,092 73.25%
Minnesota 777 618 3,938,642 76.78% 812 650 3,972,838 77.37% 790 625 4,009,170 78.60%
Mississippi 790 636 2,115,316 76.79% 754 603 2,126,170 75.20% 1,010 816 2,160,292 75.97%
Missouri 738 583 4,446,201 74.83% 801 633 4,480,314 74.91% 789 619 4,491,236 72.16%
Montana 769 614 739,171 74.99% 789 617 747,854 76.00% 842 657 761,268 75.83%
Nebraska 787 621 1,313,534 77.60% 785 606 1,327,879 71.62% 836 610 1,354,318 70.17%
Nevada 786 618 1,929,882 70.81% 885 694 1,944,971 69.92% 886 703 2,022,350 72.78%
New Hampshire 803 617 1,020,081 73.47% 860 668 1,027,514 73.45% 821 621 1,023,936 71.86%
New Jersey 827 616 6,544,280 71.29% 770 599 6,577,240 77.93% 779 593 6,673,054 69.81%
New Mexico 769 613 1,466,616 75.96% 753 585 1,480,665 75.70% 815 658 1,525,882 79.08%
New York 3,561 2,504 14,858,432 69.52% 3,604 2,485 14,912,033 65.77% 3,586 2,351 14,940,181 62.61%
North Carolina 803 656 6,884,806 78.46% 757 593 6,959,307 75.02% 724 596 7,156,772 80.07%
North Dakota 779 604 486,318 75.56% 831 654 493,824 75.68% 799 613 516,537 72.93%
Ohio 2,999 2,374 8,650,872 73.65% 3,238 2,540 8,661,813 73.64% 3,206 2,475 8,683,577 73.60%
Oklahoma 759 599 2,678,184 73.20% 779 586 2,704,129 71.96% 806 626 2,770,637 75.32%
Oregon 751 611 2,909,054 79.91% 758 589 2,943,741 73.99% 776 596 2,969,857 75.67%
Pennsylvania 2,600 1,927 9,609,739 71.80% 2,688 2,030 9,656,250 72.35% 2,759 2,051 9,791,217 71.86%
Rhode Island 773 580 809,132 75.38% 795 623 817,303 72.92% 799 629 815,472 72.44%
South Carolina 747 603 3,375,522 75.12% 787 635 3,411,091 74.84% 795 625 3,497,010 73.23%
South Dakota 766 628 594,616 80.10% 750 620 603,702 80.01% 744 596 603,514 76.24%
Tennessee 778 598 4,703,420 71.59% 747 582 4,749,036 71.83% 774 618 4,809,840 76.82%
Texas 3,046 2,414 17,401,039 76.35% 3,102 2,465 17,715,787 75.65% 2,962 2,322 18,234,826 74.41%
Utah 744 600 1,890,406 79.16% 822 669 1,925,295 78.73% 775 601 1,911,676 75.26%
Vermont 737 609 489,227 78.26% 715 595 494,001 81.98% 767 622 494,466 78.40%
Virginia 777 621 5,807,626 76.24% 747 593 5,877,166 75.57% 727 607 6,029,485 81.02%
Washington 801 625 4,989,090 76.03% 829 596 5,072,923 68.75% 887 650 5,138,999 71.57%
West Virginia 771 593 1,408,784 72.70% 756 605 1,413,902 77.86% 789 627 1,442,485 75.00%
Wisconsin 751 586 4,262,569 75.65% 731 567 4,290,376 75.98% 792 600 4,311,481 74.87%
Wyoming 816 642 402,898 78.03% 813 628 407,981 71.88% 715 572 423,425 77.45%

Table C.14 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2009-2010 and 2010-2011
State 2009-2010
Total
Selected
2009-2010
Total
Responded
2009-2010
Population
Estimate
2009-2010
Weighted
Interview
Response
Rate
2010-2011
Total
Selected
2010-2011
Total
Responded
2010-2011
Population
Estimate
2010-2011
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 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, 2009, 2010, and 2011 (2009 and 2010 Data – Revised March 2012).
Total U.S. 117,717 91,403 228,239,562 73.97% 119,714 92,372 230,948,939 73.36%
Northeast 23,538 17,680 42,190,250 71.86% 23,624 17,577 42,457,052 70.30%
Midwest 33,158 25,772 49,883,820 74.31% 33,703 25,907 50,126,284 73.30%
South 35,269 28,069 83,522,600 75.76% 36,603 29,021 84,965,509 75.50%
West 25,752 19,882 52,642,893 72.57% 25,784 19,867 53,400,093 72.50%
Alabama 1,536 1,189 3,508,919 74.32% 1,931 1,502 3,559,669 72.06%
Alaska 1,507 1,202 496,232 77.64% 1,474 1,174 503,418 77.80%
Arizona 1,583 1,249 4,810,127 74.88% 1,579 1,253 4,799,113 76.18%
Arkansas 1,574 1,223 2,135,297 75.05% 1,598 1,238 2,160,022 72.61%
California 6,568 4,903 27,098,973 70.46% 6,502 4,871 27,560,847 70.63%
Colorado 1,635 1,292 3,742,471 77.60% 1,621 1,268 3,782,387 77.01%
Connecticut 1,600 1,245 2,660,265 74.68% 1,659 1,280 2,696,347 72.33%
Delaware 1,551 1,231 666,864 74.66% 1,542 1,229 678,795 76.33%
District of Columbia 1,508 1,247 479,433 81.99% 1,478 1,207 493,344 81.78%
Florida 6,131 4,962 14,211,566 76.26% 6,328 5,029 14,516,861 75.18%
Georgia 1,498 1,198 7,073,609 75.72% 1,482 1,163 7,114,802 75.44%
Hawaii 1,826 1,285 958,884 65.66% 1,761 1,283 987,946 68.62%
Idaho 1,548 1,250 1,110,933 76.82% 1,502 1,203 1,127,939 76.62%
Illinois 6,785 4,965 9,557,601 69.94% 6,787 4,888 9,584,505 68.50%
Indiana 1,540 1,194 4,748,179 75.29% 1,521 1,179 4,793,931 72.42%
Iowa 1,537 1,260 2,258,273 79.64% 1,519 1,239 2,282,452 78.31%
Kansas 1,555 1,195 2,061,492 73.97% 1,596 1,225 2,079,494 73.91%
Kentucky 1,568 1,246 3,228,005 75.77% 1,511 1,203 3,249,527 75.71%
Louisiana 1,535 1,217 3,283,417 77.68% 2,185 1,736 3,324,265 76.86%
Maine 1,528 1,267 1,031,739 81.01% 1,464 1,205 1,039,223 79.47%
Maryland 1,468 1,174 4,269,848 77.70% 1,532 1,215 4,339,257 76.70%
Massachusetts 1,677 1,315 5,090,630 75.50% 1,558 1,225 5,110,150 75.87%
Michigan 6,196 4,874 7,496,526 75.36% 6,376 4,968 7,485,614 73.99%
Minnesota 1,589 1,268 3,955,740 77.08% 1,602 1,275 3,991,004 78.01%
Mississippi 1,544 1,239 2,120,743 76.01% 1,764 1,419 2,143,231 75.58%
Missouri 1,539 1,216 4,463,257 74.87% 1,590 1,252 4,485,775 73.50%
Montana 1,558 1,231 743,513 75.52% 1,631 1,274 754,561 75.92%
Nebraska 1,572 1,227 1,320,707 74.62% 1,621 1,216 1,341,099 70.89%
Nevada 1,671 1,312 1,937,426 70.36% 1,771 1,397 1,983,660 71.40%
New Hampshire 1,663 1,285 1,023,798 73.46% 1,681 1,289 1,025,725 72.65%
New Jersey 1,597 1,215 6,560,760 74.60% 1,549 1,192 6,625,147 73.80%
New Mexico 1,522 1,198 1,473,640 75.83% 1,568 1,243 1,503,274 77.45%
New York 7,165 4,989 14,885,232 67.58% 7,190 4,836 14,926,107 64.21%
North Carolina 1,560 1,249 6,922,057 76.83% 1,481 1,189 7,058,040 77.70%
North Dakota 1,610 1,258 490,071 75.62% 1,630 1,267 505,181 74.31%
Ohio 6,237 4,914 8,656,342 73.65% 6,444 5,015 8,672,695 73.62%
Oklahoma 1,538 1,185 2,691,157 72.55% 1,585 1,212 2,737,383 73.62%
Oregon 1,509 1,200 2,926,397 77.03% 1,534 1,185 2,956,799 74.86%
Pennsylvania 5,288 3,957 9,632,994 72.08% 5,447 4,081 9,723,733 72.10%
Rhode Island 1,568 1,203 813,218 74.18% 1,594 1,252 816,387 72.67%
South Carolina 1,534 1,238 3,393,307 74.98% 1,582 1,260 3,454,051 74.06%
South Dakota 1,516 1,248 599,159 80.05% 1,494 1,216 603,608 78.15%
Tennessee 1,525 1,180 4,726,228 71.71% 1,521 1,200 4,779,438 74.42%
Texas 6,148 4,879 17,558,413 76.00% 6,064 4,787 17,975,306 75.03%
Utah 1,566 1,269 1,907,850 78.93% 1,597 1,270 1,918,485 77.09%
Vermont 1,452 1,204 491,614 80.10% 1,482 1,217 494,233 80.16%
Virginia 1,524 1,214 5,842,396 75.90% 1,474 1,200 5,953,325 78.29%
Washington 1,630 1,221 5,031,007 72.42% 1,716 1,246 5,105,961 70.16%
West Virginia 1,527 1,198 1,411,343 75.22% 1,545 1,232 1,428,194 76.33%
Wisconsin 1,482 1,153 4,276,473 75.81% 1,523 1,167 4,300,929 75.43%
Wyoming 1,629 1,270 405,440 74.94% 1,528 1,200 415,703 74.69%

Table C.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
2010-
2011
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 SPD estimates. For more details, see Section A.7 in Appendix A of the 2004-2005 State report (Wright et al., 2007). Note that, 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. Note that the adult MDE estimates shown in the 2004-2005 report are not comparable with the adult MDE estimates for later years.
Yes = available, No = not available.
Source:  SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2002-2011.
Illicit Drug Use in Past Month Yes Yes Yes Yes Yes Yes Yes Yes Yes
Marijuana Use in Past Year Yes Yes Yes Yes Yes Yes Yes Yes Yes
Marijuana Use in Past Month Yes 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 Yes
First Use of Marijuana Yes 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 Yes
Cocaine Use in Past Year Yes Yes Yes Yes Yes Yes Yes Yes Yes
Nonmedical Use of Pain Relievers in Past Year No1 Yes Yes Yes Yes Yes Yes Yes Yes
Alcohol Use in Past Month Yes Yes Yes Yes Yes Yes Yes Yes Yes
Underage Past Month Use of Alcohol No1 Yes Yes Yes Yes Yes Yes Yes Yes
Binge Alcohol Use in Past Month Yes Yes Yes Yes Yes Yes Yes Yes Yes
Underage Past Month Binge Alcohol Use No1 Yes 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 Yes
Tobacco Product Use in Past Month Yes Yes Yes Yes Yes Yes Yes Yes Yes
Cigarette Use in Past Month Yes 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 Yes
Alcohol Dependence or Abuse in Past Year Yes Yes Yes Yes Yes Yes Yes Yes Yes
Alcohol Dependence in Past Year Yes Yes Yes Yes Yes Yes Yes Yes Yes
Illicit Drug Dependence or Abuse in Past Year Yes Yes Yes Yes Yes Yes Yes Yes Yes
Illicit Drug Dependence in Past Year Yes 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 Yes
Needing But Not Receiving Treatment for Illicit Drug Use in Past Year Yes 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 Yes
Serious Psychological Distress in Past Year2 Yes Yes Yes No No No No No No
Had at Least One Major Depressive Episode in Past Year3 No No Yes Yes Yes Yes Yes Yes Yes
Serious Mental Illness in Past Year No No No No No No Yes Yes Yes
Any Mental Illness in Past Year No No No No No No Yes Yes Yes
Had Serious Thoughts of Suicide in Past Year No No No No No No Yes Yes Yes

Section D: References

Aldworth, J., Kott, P., Yu, F., Mosquin, P., & Barnett-Walker, K. (2012). Analysis of effects of 2008 NSDUH questionnaire changes: Methods to adjust adult MDE and SPD estimates and to estimate SMI in the 2005-2009 surveys. In 2010 National Survey on Drug Use and Health: Methodological resource book (Section 16b, prepared for the Substance Abuse and Mental Health Services Administration under Contract No. HHSS283200800004C, Deliverable No. 39, RTI/0211838.108.005). Research Triangle Park, NC: RTI International.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (DSM-IV) (4th ed.). Washington, DC: Author.

Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. (2012a). National Survey on Drug Use and Health: 2011 public use file and codebook. Retrieved from http://dx.doi.org/10.3886/ICPSR34481.v1

Center for Behavioral Health Statistics and Quality. (2012b). Results from the 2010 National Survey on Drug Use and Health: Mental health findings (HHS Publication No. SMA 11-4667, NSDUH Series H-42). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Center for Behavioral Health Statistics and Quality. (2012c). Results from the 2011 National Survey on Drug Use and Health: Mental health findings (HHS Publication No. SMA 12-4725, NSDUH Series H-45). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Center for Behavioral Health Statistics and Quality. (2012d). Results from the 2011 National Survey on Drug Use and Health: Summary of national findings (HHS Publication No. SMA 12-4713, NSDUH Series H-44). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Center for Behavioral Health Statistics and Quality. (in press). Results from the 2012 National Survey on Drug Use and Health: Mental health findings. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Endicott, J., Spitzer, R. L., Fleiss, J. L., & Cohen, J. (1976). The Global Assessment Scale: A procedure for measuring overall severity of psychiatric disturbance. Archives of General Psychiatry, 33, 766-771.

First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (2002). Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition (SCID-I/NP). New York, NY: New York State Psychiatric Institute, Biometrics Research.

Folsom, R. E., Shah, B., & Vaish, A. (1999). Substance abuse in states: A methodological report on model based estimates from the 1994-1996 National Household Surveys on Drug Abuse. In Proceedings of the 1999 Joint Statistical Meetings, American Statistical Association, Survey Research Methods Section, Baltimore, MD (pp. 371-375). Alexandria, VA: American Statistical Association.

Ghosh, M. (1992). Constrained Bayes estimation with applications. Journal of the American Statistical Association, 87, 533-540.

Hughes, A., Muhuri, P., Sathe, N., & Spagnola, K. (2012). State estimates of substance use and mental disorders from the 2009-2010 National Surveys on Drug Use and Health (HHS Publication No. SMA 12-4703, NSDUH Series H-43). Rockville, MD: Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality.

Kessler, R. C., Barker, P. R., Colpe, L. J., Epstein, J. F., Gfroerer, J. C., Hiripi, E., Howes, M. J., Normand, S. L., Manderscheid, R. W., Walters, E. E., & Zaslavsky, A. M. (2003). Screening for serious mental illness in the general population. Archives of General Psychiatry, 60, 184-189.

Leon, A. C., Olfson, M., Portera, L., Farber, L., & Sheehan, D. V. (1997). Assessing psychiatric impairment in primary care with the Sheehan Disability Scale. International Journal of Psychiatry in Medicine, 27(2), 93-105.

Novak, S. (2007, October). An item response analysis of the World Health Organization Disability Assessment Schedule (WHODAS) items in the 2002-2004 NSDUH (prepared for the Substance Abuse and Mental Health Services Administration under Contract No. 283-03-9028, RTI/8726). Research Triangle Park, NC: RTI International.

Office of Applied Studies. (2000). Summary of findings from the 1999 National Household Survey on Drug Abuse (HHS Publication No. SMA 00-3466, NHSDA Series H-12). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2001). Development of computer-assisted interviewing procedures for the National Household Survey on Drug Abuse (HHS Publication No. SMA 01-3514, Methodology Series M-3). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2005). Results from the 2004 National Survey on Drug Use and Health: National findings (HHS Publication No. SMA 05-4062, NSDUH Series H-28). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2009). Results from the 2008 National Survey on Drug Use and Health: National findings (HHS Publication No. SMA 09-4434, NSDUH Series H-36). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Payton, M. E., Greenstone, M. H., & Schenker, N. (2003). Overlapping confidence intervals or standard error intervals: What do they mean in terms of statistical significance? Journal of Insect Science, 3, 34.

Raftery, A. L., & Lewis, S. (1992). How many iterations in the Gibbs sampler? In J. M. Bernardo, J. O. Berger, A. P. Dawid, & A. F. M. Smith (Eds.), Bayesian statistics 4 (pp. 763-774). London, England: Oxford University Press.

RTI International. (2012). 2010 National Survey on Drug Use and Health: Methodological resource book (prepared for the Substance Abuse and Mental Health Services Administration under Contract No. HHSS283200800004C, Deliverable No. 39, RTI 0211838). Research Triangle Park, NC: Author.

RTI International. (2013). 2011 National Survey on Drug Use and Health: Methodological resource book (prepared for the Substance Abuse and Mental Health Services Administration under Contract No. HHSS283200800004C, Deliverable No. 39, Report No. RTI 0211838). Research Triangle Park, NC: Author.

Schenker, N., & Gentleman, J. F. (2001). On judging the significance of differences by examining the overlap between confidence intervals. American Statistician, 55(3), 182-186.

Scheuren, F. (2004, June). What is a survey (2nd ed.). Retrieved January 15, 2013, from https://www.whatisasurvey.info/overview.htm

Shah, B. V., Barnwell, B. G., Folsom, R., & Vaish, A. (2000). Design consistent small area estimates using Gibbs algorithm for logistic models. In Proceedings of the 2000 Joint Statistical Meetings, American Statistical Association, Survey Research Methods Section, Indianapolis, IN (pp. 105-111). Alexandria, VA: American Statistical Association.

Singh, A. C., & Folsom, R. E. (2001, April 11-14). Hierarchical Bayes calibrated domain estimation via Metropolis-Hastings Step in MCMC with application to small areas. Presented at the International Conference on Small Area Estimation and Related Topics, Potomac, MD.

Wright, D. (2002a). State estimates of substance use from the 2000 National Household Survey on Drug Abuse: Volume I. Findings (HHS Publication No. SMA 02-3731, NHSDA Series H-15). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D. (2002b). State estimates of substance use from the 2000 National Household Survey on Drug Abuse: Volume II. Supplementary technical appendices (HHS Publication No. SMA 02-3732, NHSDA Series H-16). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D. (2003a). State estimates of substance use from the 2001 National Household Survey on Drug Abuse: Volume I. Findings (HHS Publication No. SMA 03-3775, NHSDA Series H-19). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D. (2003b). State estimates of substance use from the 2001 National Household Survey on Drug Abuse: Volume II. Individual state tables and technical appendices (HHS Publication No. SMA 03-3826, NHSDA Series H-20). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D. (2004). State estimates of substance use from the 2002 National Survey on Drug Use and Health (HHS Publication No. SMA 04-3907, NSDUH Series H-23). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D., & Sathe, N. (2005). State estimates of substance use from the 2002-2003 National Surveys on Drug Use and Health (HHS Publication No. SMA 05-3989, NSDUH Series H-26). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D., & Sathe, N. (2006). State estimates of substance use from the 2003-2004 National Surveys on Drug Use and Health (HHS Publication No. SMA 06-4142, NSDUH Series H-29). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D., Sathe, N., & Spagnola, K. (2007). State estimates of substance use from the 2004-2005 National Surveys on Drug Use and Health (HHS Publication No. SMA 07-4235, NSDUH Series H-31). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Section E: List of Contributors

This National Survey on Drug Use and Health (NSDUH) document was prepared by the Center for Behavioral Health Statistics and Quality (CBHSQ), Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services (HHS), and by RTI International (a trade name of Research Triangle Institute), Research Triangle Park, North Carolina. Work by RTI was performed under Contract Nos. HHSS283200800004C and HHSS283201000003C.

At SAMHSA, Arthur Hughes reviewed the document and provided substantive revisions. At RTI, Neeraja S. Sathe and Kathryn Spagnola were responsible for the writing of the document, and Ralph E. Folsom and Akhil K. Vaish were responsible for the overall methodology and estimation for the model-based Bayes estimates and confidence intervals.

The following staff were responsible for generating the estimates and providing other support and analysis: Akhil K. Vaish, Neeraja S. Sathe, Kathryn Spagnola, and Brenda K. Porter. Ms. Spagnola provided oversight for production of the document. Richard S. Straw edited it; Debbie Bond, Valerie Garner, and Roxanne Snaauw formatted its text and tables; and Teresa F. Bass, Kimberly Cone, Danny Occoquan, Margaret Smith, Marissa R. Straw, Pamela Tuck, and Cheryl Velez prepared the Web versions. Justine L. Allpress and E. Andrew Jessup prepared and processed the maps used in the associated files.

End Notes

1 RTI International is a trade name of Research Triangle Institute, Research Triangle Park, North Carolina.

2 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.

3 Note that in the 2004-2005 NSDUH State report and prior reports, the term "prediction interval" (PI) was used to represent uncertainty in the State and regional estimates. However, that term also is used in other applications to estimate future values of a parameter of interest. That interpretation does not apply to NSDUH State report estimates; thus, "prediction interval" was dropped and replaced with "Bayesian confidence interval."

4 For an overview of the impact of these changes, see Section C.2 of Appendix C in OAS (2005a).

5 Combining data across 2 years permits the estimation of change at the State level by expressing it as the difference of two consecutive 2-year SAE moving averages. Comparisons between the combined 2009-2010 data and the combined 2010-2011 data are presented in this report. This method is similar to the one used in the 2009-2010 State report (Hughes et al., 2012).

6 A successfully screened household is one in which all screening questionnaire items were answered by an adult resident of the household and either zero, one, or two household members were selected for the NSDUH interview.

7 The usable case rule requires that a respondent answer "yes" or "no" to the question on lifetime use of cigarettes and "yes" or "no" to at least nine additional lifetime use questions.

8 See "2010-2011 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia) (Tables 1 to 26, by Age Group)" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.

9 See footnote 8.

10 Note that no 2009-2010 or 2010-2011 model-based estimates were published using the erroneous data.

11 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.

12 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.

13 For details on how the average annual rate of marijuana (incidence of marijuana) is calculated, see Section B.8.

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 See Table 9 of the "2010-2011 NSDUH: Model-Based Estimates (50 States and the District of Columbia)" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.

16 See Table 9 of "2010-2011 NSDUH: Model-Based Estimated Totals (in Thousands) (50 States and the District of Columbia)" at http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.

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

18 Major depressive episode also was included in the 2012 model and is discussed in more detail in Section B.4.4 of Appendix B in the 2012 NSDUH mental health findings report (CBHSQ, in press).

19 In the question about serious thoughts of suicide, [DATEFILL] refers to the date at the start of a respondent's 12-month reference period. The interview program sets the start of the 12-month reference period as the same month and day as the interview date but in the previous calendar year.

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