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 https://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 https://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 https://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 https://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.
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
, D
where 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
, D
where 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
. D
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).
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 is a 95 percent symmetric confidence interval for the population proportion (p) and is an estimate of p obtained from the survey data, then the margin of error of is given by or . Because is a symmetric confidence interval, will be the same as . In this case, the probability is 0.95 that the true population value (p) is within ± or ± of the survey estimate ). 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 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.
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.
In addition to this methodology document, the following files are also available at https://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx:
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 https://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.
The model can be characterized as a complex mixed11 model (including both fixed and random effects) of the following form:
, D
where 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 denote a vector of auxiliary (predictor) variables associated with age group-a (12 to 17, 18 to 25, 26 to 34, and 35 or older) and 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 and grouped SSR-level random effects are assumed to be mutually independent with and where is the total number of individual age groups modeled (generally, ). For hierarchical Bayes (HB) estimation purposes, an improper uniform prior distribution is assumed for , and proper Wishart prior distributions are assumed for and . The HB solution for 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.
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:
Comparisons between the 2009-2010 and the 2010-2011 State estimates were produced for all of these measures as well.
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 |
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:
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):
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).
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 where is the design-based national prevalence estimate and is the population-weighted mean of the State small area estimates for age group-a. The exactly benchmarked State-s and age group-a small area estimates then are given by . 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 are defined below:
, D
where
, D
, and D
. 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.
Tables 1 to 26 of "NSDUH: 2010-2011 Model-Based Estimated Totals (in Thousands) (50 States and the District of Columbia)," available at https://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.
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:
, D
where is the number of marijuana initiates in the past 24 months and 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.
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 https://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.
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:
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:
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.
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).
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
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.
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.
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.
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.
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 can be estimated using the weighted logistic regression model. The final 2012 calibration model was determined as follows:
where refers to an estimate of the serious mental illness response probability . These covariates in equation (1) come from the main NSDUH interview data:
As with the 2008 model, a cut point probability was determined, so that if 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.
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 https://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.
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.
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 and denote the 2010-2011 age group-a specific prevalence rates for two different States, and , respectively. The null hypothesis of no difference, that is, is equivalent to the log-odds ratio equal to zero, that is, , where is defined as ,
where ln denotes the natural logarithm. An estimate of is given by ,
where and 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 https://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx. To compute the variance of , that is, , let and ,
then , where denotes the covariance between and . This covariance is defined in terms of the associated correlation as follows:
. D
The quantities and 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 https://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx. For this purpose, let and denote the 95 percent Bayesian confidence intervals for the two States, and , respectively. Then
where .
For all practical purposes, the correlation between and is assumed to be negligible; hence, can be approximated by . 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 . To calculate the p value for testing the null hypothesis of no difference (), it is assumed that the posterior distribution of is normal with and . With the null value of , the Bayes p value or posterior probability of no difference is , where Z is a standard normal random variate, , 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 https://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 . Then,
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 .
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% |
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% |
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% |
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% |
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% |
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% |
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% |
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% |
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% |
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% |
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% |
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% |
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% |
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% |
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 |
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.
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.
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 https://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 https://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 https://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.