2010-2011 National Survey on Drug Use and Health:
Guide to State Tables and Summary of Small Area Estimation Methodology
Section A: Overview of NSDUH and Model-Based State Estimates
A.1 Introduction
This document provides information on the model-based small area estimates of substance use and mental disorders in States based on data from the combined 2010-2011 National Surveys on Drug Use and Health (NSDUHs). The estimates are available at 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.
A.2 Summary of NSDUH Methodology
NSDUH is the primary source of statistical information on the use of illicit drugs, alcohol, and tobacco by the U.S. civilian, noninstitutionalized population aged 12 or older. The survey also includes several modules of questions that focus on mental health issues. Conducted by the Federal Government since 1971, the survey collects data by administering questionnaires to a representative sample of the population through face-to-face interviews at their place of residence.
The survey covers residents of households, noninstitutional group quarters (e.g., shelters, rooming houses, dormitories), and civilians living on military bases. Persons excluded from the survey include homeless people who do not use shelters, military personnel on active duty, and residents of institutional group quarters, such as jails or prisons and long-term hospitals.
The 1999 survey marked the first year in which the national sample was interviewed using a computer-assisted interviewing (CAI) method. The survey used a combination of computer-assisted personal interviewing (CAPI) conducted by an interviewer and audio computer-assisted self-interviewing (ACASI). Use of ACASI is designed to provide the respondent with a highly private and confidential means of responding to questions and increases the level of honest reporting of illicit drug use and other sensitive behaviors. For further details on the development of the CAI procedures for the 1999 National Household Survey on Drug Abuse (NHSDA, the former name of NSDUH), see OAS (2001).
The 1999 through 2001 NHSDAs and the 2002 through 2011 NSDUHs employed a 50‑State design with an independent, multistage area probability sample for each of the 50 States and the District of Columbia. For the 50-State design, 8 States were designated as large sample States (California, Florida, Illinois, Michigan, New York, Ohio, Pennsylvania, and Texas) with target sample sizes of 3,600 per year or 7,200 over a 2-year period. In 2010-2011, sample sizes in these States ranged from 6,059 in Pennsylvania to 7,684 in Florida (Table C.9). For the remaining 42 States and the District of Columbia, the target sample size was 900 per year or 1,800 over a 2-year period. Sample sizes in these States ranged from 1,773 in Alaska to 2,652 in Louisiana in 2010-2011. This approach ensures there is sufficient sample in every State to support SAE while at the same time maintaining efficiency for national estimates. The design also oversampled youths and young adults, so that each State's sample was approximately equally distributed among three major age groups: 12 to 17 years, 18 to 25 years, and 26 years or older.
In 2002, several changes were introduced to the survey. Incentive payments of $30 were given to respondents for the first time in order to address concerns about the national and State response rates. Other changes included a change in the survey name (i.e., from NHSDA to NSDUH), new data collection quality control procedures, and a shift from the 1990 decennial census to the 2000 census as a basis for population count totals and to calculate any census-related predictor variables that are used in small area estimation.
An unanticipated result of these changes was that the prevalence rates for 2002 were in general substantially higher than those for 2001—higher than could be attributable to the usual year-to-year trend—and thus are not comparable with estimates for 2001 and prior years.4 Therefore, the 2002 NSDUH was established as a new baseline for both the national and the State estimates. Given the varying effects of the incentive and other changes, not only are the estimates for 2002 and later years not comparable with prior years, but the relative rankings of States also may have been affected. Therefore, the rankings of States for 2002-2003 or later should not be compared with those for prior years. By combining data across 2 years, the precision of the small area estimates for the small sample States, and thus their rankings, have been improved significantly. In addition, by combining 2 years of data, the impact of the national model on those States has been reduced significantly relative to estimates based on a single year's data.5
Nationally in 2010-2011, 303,058 addresses were screened, and 137,913 persons responded within the screened addresses (see Table C.9). The survey is conducted from January through December each year. The screening response rate (SRR) for 2010-2011 combined averaged 87.7 percent, and the interview response rate (IRR) averaged 74.5 percent, for an overall response rate (ORR) of 65.3 percent (Table C.9). The ORRs for 2010-2011 ranged from 48.3 percent in New York to 75.0 percent in South Dakota. Estimates have been adjusted to reflect the probability of selection, unit nonresponse, poststratification to known census population estimates, item imputation, and other aspects of the estimation process. These procedures are described in NSDUH's methodological resource books (MRBs) (RTI International, 2012, 2013).
The weighted SRR is defined as the weighted number of successfully screened households (or dwelling units)6 divided by the weighted number of eligible households, or
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
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
To address SAMHSA's need for estimates of serious mental illness, any mental illness, and suicidal thoughts (i.e., suicidal ideation), several important changes were made to the adult mental health items in the 2008 NSDUH questionnaire. Items were added that assessed functional impairment due to mental health problems (abbreviated World Health Organization Disability Assessment Schedule [WHODAS]; Novak, 2007) and that assessed suicidal thoughts and behavior among adults. In 2008, CBHSQ also expanded the Kessler-6 (K6) questions to ask about the past 30 days (the time frame for which the K6 was originally designed).
In addition, as part of the Mental Health Surveillance Study (MHSS), a clinical follow-up study was initiated in which a randomly selected subsample of adults (about 1,500 in 2008, 2011, and 2012, and 500 in 2009 and 2010) who had completed the NSDUH interview was administered a standard clinical interview by mental health clinicians via paper and pencil over the telephone to determine their mental illness status; the clinical interview was used as a "gold standard" for measuring mental illness among adults. Using both the clinical interview and the NSDUH CAI data for the respondents who completed the clinical interview (using only 2008 data), statistical models were developed that then were applied to data from all adult respondents who had completed the NSDUH CAI interviews (regardless of whether they had clinical interview data) to produce estimates of mental illness among the adult civilian, noninstitutionalized population. Subsequently, using the entire clinical interview sample of approximately 5,000 interviews that were collected in 2008 to 2012, CBHSQ developed a more accurate statistical model for adults. This revised model incorporated the NSDUH respondent's age, past year suicidal thoughts, past year major depressive episode, and the variables that were specified in the 2008 model (i.e., the K6 and the WHODAS). Results for serious mental illness and any mental illness from this revised model were closer to the direct estimates of serious mental illness and any mental illness from the clinical interviews in the MHSS than the previous model's results produced, especially for young adults aged 18 to 25. See Section B.11 for a more complete discussion of the revised 2012 model and estimates.
Estimates of serious mental illness and any mental illness for 2009-2010 and 2010-2011 were produced using this new model and are shown in the tables listed in Section A.6. These tables and maps with revised estimates include a source note with the text (i.e., "Revised October 2013") to indicate that the estimates are based on the updated 2012 model.
The questionnaire changes caused discontinuities in trends for major depressive episode (i.e., depression) and serious psychological distress among adults aged 18 or older. For youths aged 12 to 17, no questionnaire changes were made in 2008 that affected the estimation of youth depression items; so, estimates of youth depression are available for all years beginning with the 2004-2005 report. An analysis was performed to better understand the nature of the changes in the reporting of adult depression associated with the questionnaire changes in 2008. This led to the development of statistical adjustments for the adult depression estimates for the years from 2005 to 2008; thus, comparable adult depression data are now available for the years 2005 and beyond. For more information about these changes, see Section B.11 in Appendix B of the 2008 NSDUH national findings report (OAS, 2009) and Appendix B of the 2010 NSDUH mental health findings report (CBHSQ, 2012b).
A.4 Confidence Intervals and Margins of Error
At the top of each of the 26 State model-based estimate tables8 is the design-based national estimate along with a 95 percent design-based confidence interval, all of which are based on survey weights and the reported data. The State and regional estimates are model-based statistics (using SAE methodology) that have been adjusted such that the population-weighted mean of the estimates across the 50 States and the District of Columbia equal the design-based national estimate. For more details on this benchmarking, see Section B.6. Associated with each State and regional estimate is a 95 percent Bayesian confidence interval. These intervals indicate the uncertainty in the estimate due to both sampling variability and model bias. For example, the State with the highest estimated rate of past month use of marijuana for young adults aged 18 to 25 was Vermont, with a rate of 33.2 percent and a 95 percent confidence interval that ranged from 29.4 to 37.2 percent (Table 3 of the State model-based estimates' tables). Therefore, the probability is 0.95 that the true prevalence of past month marijuana use in Vermont for persons aged 18 to 25 is between 29.4 and 37.2 percent. As noted earlier in a Section A.1 footnote, the term "prediction interval" (PI) was used in the 2004-2005 NSDUH State report and prior reports to represent uncertainty in the State and regional estimates. However, that term also is used in other applications to estimate future values of a parameter of interest. That interpretation does not apply to NSDUH State model-based estimates, so PI was replaced with "Bayesian confidence interval."
Margin of error is another term used to describe uncertainty in the estimates. For example, if 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.
A.5 Related Drug Measures
Small area estimates are produced for a number of related drug measures, such as marijuana use and illicit drug use. It might appear that one could draw conclusions by subtracting one from the other (e.g., subtracting the percentage who used illicit drugs other than marijuana in the past month from the percentage who used illicit drugs in the past month to find the percentage who only used marijuana in the past month). Because related measures have been estimated with different models, subtracting one measure from another related measure at the State or census region level can give misleading results, perhaps even a "negative" estimate, and should be avoided. However, these comparisons can be made at the national level because these estimates are design-based estimates. For example, at the national level, subtracting cigarette use rates from tobacco use rates will give the rate of persons who did not use cigarettes, but used other forms of tobacco.
2010-2011 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia) (Tables 1 to 26, by Age Group): Tables of prevalence estimates and associated 95 percent Bayesian confidence intervals are included for youths aged 12 to 17, young adults aged 18 to 25, adults aged 26 or older, and all persons 12 or older. Also included are tables for underage (12 to 20) use of alcohol and underage binge alcohol use. These tables are available in Excel and PDF format.
2010-2011 NSDUH National Maps of Prevalence Estimates, by State (Figures 1.a to 26.d): The color of each State on the U.S. maps indicates how the State ranks relative to other States for each measure. States could fall into one of five groups according to their ranking by quintiles. Because there are 51 areas to be ranked for each measure, the middle quintile was assigned 11 areas and the remaining groups 10 each. In some cases, a "quintile" could have more or fewer States than desired because two (or more) States have the same estimate (to two decimal places). When such ties occurred at the "boundary" between two quintiles, all States with the same estimate were assigned to the lower quintile. Those States with the highest rates for a given measure are in red, with the exception of the perceptions of risk measures, for which the lowest perceptions of great risk are in red. Those States with the lowest estimates are in white, with the exception of the perceptions of risk measures, for which the highest perceptions of great risk are in white.
Note that because the average annual incidence of marijuana was so low for the 26 or older age group and had such an abbreviated range, no map has been included for it.
NSDUH: Comparison of 2009-2010 and 2010-2011 Model-Based Prevalence Estimates for Adults 18 or Older (50 States and the District of Columbia) (Tables 1 to 25): Tables include 2009-2010 and 2010-2011 prevalence estimates and associated confidence intervals and an indication of the statistical significance of the difference (p value).
NSDUH: 2010-2011 Model-Based Estimated Totals (in Thousands) (50 States and the District of Columbia) (Tables 1 to 26): Tables showing estimated numbers (counts in thousands) and confidence intervals are included for youths aged 12 to 17, young adults aged 18 to 25, adults aged 26 or older, persons aged 18 or older, and all persons aged 12 or older. Also included are tables for underage (12 to 20) alcohol use and underage binge alcohol use.
2010-2011 NSDUH State-Specific Tables (Tables 1 to 112): Tables are provided for each individual State and the District of Columbia, as well as for the total United States and the four census regions (i.e., Northeast, Midwest, South, and West). The tables (two per area) show prevalence estimates and the number of persons (counts in thousands).
NSDUH: Comparison of 2009-2010 and 2010-2011 Model-Based Prevalence Estimates (50 States and the District of Columbia) (Tables 1 to 26): Tables are presented that show the 2009-2010 and 2010-2011 NSDUH State estimates and an indication of the statistical significance of the difference (p value). Estimates are shown for youths aged 12 to 17, young adults aged 18 to 25, adults aged 26 or older, and all persons aged 12 or older. Also included are tables for underage (12 to 20) alcohol use and underage binge alcohol use.
NSDUH: Comparison of 2002-2003 and 2010-2011 Model-Based Prevalence Estimates (50 States and the District of Columbia) (Tables 1 to 22): Tables are presented that show the 2002-2003 and 2010-2011 NSDUH State estimates and an indication of the statistical significance of the difference (p value). Estimates are shown for youths aged 12 to 17, young adults aged 18 to 25, adults aged 26 or older, and all persons aged 12 or older. Also included are tables for underage (12 to 20) alcohol use and underage binge alcohol use.
2010-2011 NSDUH: Other Sources of Data: This document compares two outcomes (cigarette and alcohol use) from NSDUH with data from the Behavioral Risk Factor Surveillance System (BRFSS).
A.7 Revised 2006 to 2010 Estimates
During regular data collection and processing checks for the 2011 NSDUH, data errors were identified. These errors affected the data for Pennsylvania (2006 to 2010) and Maryland (2008 and 2009). Cases with erroneous data were removed from the data files, and the remaining cases were reweighted to provide representative estimates. Therefore, some estimates using 2006 to 2010 NSDUH data in the 2011 national findings report and detailed tables, as well as other reports (including the 2009-2010 SAE report), will contain estimates that differ from corresponding estimates found in some previous reports. All of the tables and maps available at 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.
Section B: State Model-Based Estimation Methodology
B.1 General Model Description
The model can be characterized as a complex mixed11 model (including both fixed and random effects) of the following form:
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.
B.2 Variables Modeled
The 2011 NSDUH data were pooled with the 2010 NSDUH data, and age group-specific State prevalence estimates for 25 binary (0, 1) measures were produced for the following outcomes:
past month use of illicit drugs,
past year use of marijuana,
past month use of marijuana,
perception of great risk of smoking marijuana once a month,
past month use of illicit drugs other than marijuana,
past year use of cocaine,
past year nonmedical use of pain relievers,
past month use of alcohol,
past month binge alcohol use,
perception of great risk of having five or more drinks of an alcoholic beverage once or twice a week,
past month use of tobacco products,
past month use of cigarettes,
perception of great risk of smoking one or more packs of cigarettes per day,
past year alcohol dependence or abuse,
past year alcohol dependence,
past year illicit drug dependence or abuse,
past year illicit drug dependence,
past year dependence or abuse of illicit drugs or alcohol,
needing but not receiving treatment for illicit drug use in the past year,
needing but not receiving treatment for alcohol use in the past year,
serious mental illness in the past year,
any mental illness in the past year,
serious thoughts of suicide in the past year, and
past year major depressive episode (i.e., depression).
Comparisons between the 2009-2010 and the 2010-2011 State estimates were produced for all of these measures as well.
B.3 Predictors Used in Mixed Logistic Regression Models
Local area data used as potential predictor variables in the mixed logistic regression models were obtained from several sources, including Claritas Inc., the U.S. Census Bureau, the Federal Bureau of Investigation (FBI) (Uniform Crime Reports), Health Resources and Services Administration (Area Resource File), the Bureau of Labor Statistics, the Bureau of Economic Analysis, the Substance Abuse and Mental Health Services Administration (SAMHSA) (National Survey of Substance Abuse Treatment Services [N-SSATS]), and the National Center for Health Statistics (mortality data). The values of these predictor variables are updated every year (when possible). Sources and potential data items used in the modeling are provided in the following text and lists.
Claritas. This demographic data package contains data for 2008 with projections to 2012. The population projections are used to update these predictor variables each year. The 2010 and 2011 projections were used for producing the 2010-2011 State estimates. For more information on these data, see Section B.4.
U.S. Census Bureau. The 2000 census (demographic and socioeconomic variables) and 2009 food stamp participation rates were used (http://www.census.gov/did/www/saipe/inputdata/cntysnap.xls). The Census Bureau's small area income and poverty estimates division obtains Food Stamp Program (now known as the Supplemental Nutrition Assistance Program [SNAP]) participation rates from the U.S. Department of Agriculture, Food and Nutrition Service.
Federal Bureau of Investigation. Uniform Crime Report (UCR) arrest totals were obtained from http://www.icpsr.umich.edu/icpsrweb/NACJD/archive.jsp. The most current data used are from 2009 for most counties, with previous years' data substituted in a few cases.
Health Resources and Services Administration. Families below the Federal poverty-level rates from the 2000 census data were obtained from the Area Resource File (ARF) February 2005 release from the Bureau of Health Professions, Office of Research and Planning.
Bureau of Labor Statistics (BLS). The 2011 county-level unemployment rates were used (http://www.bls.gov/lau/#cntyaa). The BLS uses results from the Current Population Survey (CPS) to provide county-level unemployment rates. The CPS is a monthly survey of households conducted by the Census Bureau for the BLS.
Bureau of Economic Analysis (BEA). The 2010 county-level per capita income rates were used (http://www.bea.gov/regional/index.htm). These county-level per capita income rates are produced by the Regional Income Division of the BEA.
National Center for Health Statistics. Mortality data using International Classification of Diseases, 10th revision (ICD-10), 2003-2008, were used. The ICD-10 death rate data are from the National Center for Health Statistics at the Centers for Disease Control and Prevention.
SAMHSA, Center for Behavioral Health Statistics and Quality (CBHSQ), formerly the Office of Applied Studies (OAS). Data were used from the National Survey of Substance Abuse Treatment Services (N-SSATS), formerly known as the Uniform Facility Data Set (UFDS). The 2010-2011 data on drug and alcohol treatment rates were obtained. Maintenance of effort expenditures, block grant awards, cost of services, and total taxable resources data were also used.
The following lists provide the specific independent variables that were potential predictors in the models.
Claritas Data (Description)
Claritas Data (Level)
% Population Aged 0 to 19 in Block Group
Block Group
% Population Aged 20 to 24 in Block Group
Block Group
% Population Aged 25 to 34 in Block Group
Block Group
% Population Aged 35 to 44 in Block Group
Block Group
% Population Aged 45 to 54 in Block Group
Block Group
% Population Aged 55 to 64 in Block Group
Block Group
% Population Aged 65 or Older in Block Group
Block Group
% Non-Hispanic Blacks in Block Group
Block Group
% Hispanics in Block Group
Block Group
% Non-Hispanic Other Races in Block Group
Block Group
% Non-Hispanic Whites in Block Group
Block Group
% Males in Block Group
Block Group
% Females in Block Group
Block Group
% American Indians, Eskimos, Aleuts in Tract
Tract
% Asians, Pacific Islanders in Tract
Tract
% Population Aged 0 to 19 in Tract
Tract
% Population Aged 20 to 24 in Tract
Tract
% Population Aged 25 to 34 in Tract
Tract
% Population Aged 35 to 44 in Tract
Tract
% Population Aged 45 to 54 in Tract
Tract
% Population Aged 55 to 64 in Tract
Tract
% Population Aged 65 or Older in Tract
Tract
% Non-Hispanic Blacks in Tract
Tract
% Hispanics in Tract
Tract
% Non-Hispanic Other Races in Tract
Tract
% Non-Hispanic Whites in Tract
Tract
% Males in Tract
Tract
% Females in Tract
Tract
% Population Aged 0 to 19 in County
County
% Population Aged 20 to 24 in County
County
% Population Aged 25 to 34 in County
County
% Population Aged 35 to 44 in County
County
% Population Aged 45 to 54 in County
County
% Population Aged 55 to 64 in County
County
% Population Aged 65 or Older in County
County
% Non-Hispanic Blacks in County
County
% Hispanics in County
County
% Non-Hispanic Other Races in County
County
% Non-Hispanic Whites in County
County
% Males in County
County
% Females in County
County
2000 Census Data (Description)
2000 Census Data (Level)
% Population Who Dropped Out of High School
Tract
% Housing Units Built in 1940 to 1949
Tract
% Persons Aged 16 to 64 with a Work Disability
Tract
% Hispanics Who Are Cuban
Tract
% Females 16 Years or Older in Labor Force
Tract
% Females Never Married
Tract
% Females Separated, Divorced, Widowed, or Other
Tract
% One-Person Households
Tract
% Female Heads of Household, No Spouse, Child under 18
Tract
% Males 16 Years or Older in Labor Force
Tract
% Males Never Married
Tract
% Males Separated, Divorced, Widowed, or Other
Tract
% Housing Units Built in 1939 or Earlier
Tract
Average Persons per Room
Tract
% Families below Poverty Level
Tract
% Households with Public Assistance Income
Tract
% Housing Units Rented
Tract
% Population with 9 to 12 Years of School, No High School Diploma
Tract
% Population with 0 to 8 Years of School
Tract
% Population with Associate's Degree
Tract
% Population with Some College and No Degree
Tract
% Population with Bachelor's, Graduate, Professional Degree
Tract
Median Rents for Rental Units
Tract
Median Value of Owner-Occupied Housing Units
Tract
Median Household Income
Tract
Uniform Crime Report Data (Description)
Uniform Crime Report Data (Level)
Drug Possession Arrest Rate
County
Drug Sale or Manufacture Arrest Rate
County
Drug Violations' Arrest Rate
County
Marijuana Possession Arrest Rate
County
Marijuana Sale or Manufacture Arrest Rate
County
Opium or Cocaine Possession Arrest Rate
County
Opium or Cocaine Sale or Manufacture Arrest Rate
County
Other Drug Possession Arrest Rate
County
Other Dangerous Non-Narcotics Arrest Rate
County
Serious Crime Arrest Rate
County
Violent Crime Arrest Rate
County
Driving under Influence Arrest Rate
County
Other Categorical Data (Description)
Other Categorical Data (Source)
Other Categorical Data (Level)
= 1 if Hispanic, = 0 Otherwise
NSDUH Sample
Person
= 1 if Non-Hispanic Black, = 0 Otherwise
NSDUH Sample
Person
= 1 if Non-Hispanic Other, = 0 Otherwise
NSDUH Sample
Person
= 1 if Male, = 0 if Female
NSDUH Sample
Person
= 1 if metropolitan statistical area (MSA) with ≥ 1 Million, = 0 Otherwise
2000 Census
County
= 1 if MSA with < 1 Million, = 0 Otherwise
2000 Census
County
= 1 if Non-MSA Urban, = 0 Otherwise
2000 Census
Tract
= 1 if Urban Area, = 0 if Rural Area
2000 Census
Tract
= 1 if No Cubans in Tract, = 0 Otherwise
2000 Census
Tract
= 1 if No Arrests for Dangerous Non-Narcotics, = 0 Otherwise
UCR
County
Miscellaneous Data (Description)
Miscellaneous Data (Source)
Miscellaneous Data (Level)
Alcohol Death Rate, Underlying Cause
NCHS-ICD-10
County
Cigarette Death Rate, Underlying Cause
NCHS-ICD-10
County
Drug Death Rate, Underlying Cause
NCHS-ICD-10
County
Alcohol Treatment Rate
N-SSATS (Formerly Called UFDS)
County
Alcohol and Drug Treatment Rate
N-SSATS (Formerly Called UFDS)
County
Drug Treatment Rate
N-SSATS (Formerly Called UFDS)
County
% Families below Poverty Level
ARF
County
Unemployment Rate
BLS
County
Per Capita Income (in Thousands)
BEA
County
Average Suicide Rate (per 10,000)
NCHS-ICD-10
County
Food Stamp Participation Rate
Census Bureau
County
Single State Agency Maintenance of Effort
National Association of State Alcohol and
Drug Abuse Directors (NASADAD)
State
Block Grant Awards
SAMHSA
State
Cost of Services Factor Index
SAMHSA
State
Total Taxable Resources per Capita Index
U.S. Department of Treasury
State
B.4 Updated Claritas Data
For the NSDUH State and substate estimates published using the 2002 to 2006 NSDUH data, Claritas data obtained in 2002 were used to produce the small area estimates. For State estimates published using the 2007 to 2011 NSDUH data, Claritas data obtained in 2008 were used. The 2002 Claritas data had 2000 and 2002 population counts, as well as 2007 population projections. The 2008 Claritas data had 2008 population counts, as well as 2012 population projections. Both sets of Claritas data were based on 2000 census geography. Claritas data were used for the following in the NSDUH SAE process:
Creating demographic predictor variables (age group, race × ethnicity, and gender) at the block group, tract, and county levels (predictors such as percentage of the population aged 0 to 19 in a block group, percentage of population who are males in a tract). There are 13 such variables defined for each of the census geographies (block group, tract, and county). See Section B.3 for a complete list of these predictors.
Creating census block group-level population projections at the age group × race/ethnicity × gender level (4 age groups, 4 races/ethnicities, and 2 genders = 32 cells) that are used in aggregating the block group-level small area estimates to produce State and census region-level small area estimates.14
In the 2007 SAE process (and subsequent years), new Claritas data with 2008 population counts and 2012 population projections were used. The new Claritas data will be henceforth referred to as the 2008-2012 Claritas data, and the 2002 Claritas data will be referred to as the 2002-2007 Claritas data. After exploring the 2008-2012 Claritas data and comparing them with the 2002-2007 Claritas data, some differences were observed when comparing the 2007 population counts (from the 2002-2007 Claritas data) with the 2008 population counts (from the 2008-2012 Claritas data). For example, the distributions of the population aged 20 to 24 in block groups were very different for the two datasets. Another difference was that there were more block groups that had a 0 population count for some of the 32 cells in 2008 as compared with the 32 cells in 2007.
The format of the race/ethnicity data was also different for the two sets of Claritas data. To generate age group × race × Hispanicity × gender population counts at the block group level using the 2002-2007 Claritas data, two separate population distributions (age × gender × race and race × Hispanicity) at the block group level had to be used. The assumption was made that each of the age × gender cells within a race group had the same Hispanicity distribution. So, the data were manipulated to get the desired four-way cross of demographic domains. The 2008-2012 Claritas data had age group × race × Hispanicity × gender population distributions, so no assumptions or manipulations to the data had to be made.
When creating the 32 cells using the 2002-2007 Claritas data, the population from the two or more races category was distributed among the black, white, and other race categories. With the switch to 2008-2012 Claritas, a decision was made to merge the two or more races category with the other race category. This was based on a decision to discontinue creating a NSDUH sample variable that split the two or more races' respondents into black, white, or other. Because NSDUH respondents with two or more races were now being grouped into the other category, the same technique was used to produce the 32 cell counts.
Some of the data differences can be attributed to reasons (2b) and (3), and the rest are most likely attributed to the fact that the 2008-2012 Claritas projections are based on updated population information. Because of these differences in the 2007 population projections based on 2002-2007 Claritas data and the 2008 population counts based on 2008-2012 Claritas data, it was decided that "new" 2007 population projections would be obtained by "projecting back" the 2008-2012 Claritas data. Population projections for 2006 also were obtained in the same manner, so that they could be used in the 2006-2008 SAE estimates.
Based on the information above, the following steps were taken for the 2010-2011 SAE process (for more information on the steps taken for the 2009-2010 SAE processes, see Appendix A of Hughes et al., 2012):
Using the 2008-2012 Claritas data, 2010 and 2011 population counts were obtained (the 2010 and 2011 counts were obtained by using linear interpolation between the 2008 and 2012 counts) and used to create the predictors that were merged onto the 2010 and 2011 sample and universe files (the universe file is a census block-group level file containing SAE predictor variables and population counts).
All block group, tract, and county-level continuous predictors were converted into 10-category, semicontinuous variables by using the corresponding 2007-2008 decile values created by pooling the 2007 and 2008 NSDUH data. The same 2007-2008 decile values will be used for subsequent SAE analyses until new Claritas data containing the 2013 population counts and projections are obtained. Using the same decile values year after year makes it possible to keep track of any temporal changes occurring in the predictor variables, which may help in detecting any changes in State prevalence rates across years in an efficient manner. The 10-category predictor variables subsequently were used to form linear, quadratic, and cubic orthogonal polynomials eventually used in the SAE modeling process. For all predictors other than the unemployment rate, the same 2007-2008 decile values were used in the 2010-2011 SAE process. Because of the recent large jumps in the unemployment rate, the decile values for the unemployment rate needed to be re-created using the 2009 and 2010 NSDUH data. Using the older set of decile values resulted in the distribution of the unemployment deciles to be very skewed. Hence, a decision was made to update the unemployment rate deciles based on 2009 and 2010 data. This updated decile was used in 2010-2011 as well. The predictor based on the unemployment deciles was used in the SAE model for the 35 or older age group for producing the small area estimates for the measure on needing but not receiving treatment for illicit drug use. Using this updated data is not expected to cause any inconsistencies in the estimation of trends for this measure.
The updated population counts for the 32 cells (age group × race/ethnicity × gender population counts) were used to create the universe files for both years (i.e., 2010 and 2011).
The 2009 sample and universe files based on the 2008-2012 Claritas data were used in simultaneous modeling to produce the correlations required to estimate change between the 2009-2010 and 2010-2011 State prevalence rates. See the methodology discussion in "NSDUH: Comparison of 2009-2010 and 2010-2011 Model-Based Prevalence Estimates (50 States and the District of Columbia)" at https://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.
The 2002-2007 Claritas projections were used on the 2002-2003 sample and universe files, whereas the 2008-2012 Claritas projections were used on the 2010-2011 sample and universe files to produce the 2002-2003 versus 2010-2011 comparisons. See the methodology discussion in "NSDUH: Comparison of 2002-2003 and 2010-2011 Model-Based Prevalence Estimates (50 States and the District of Columbia)" at https://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.
B.5 Selection of Independent Variables for the Models
No new variable selection was done for any measure in 2010-2011 except for serious mental illness and any mental illness. The updated versions of fixed-effect predictors that were used in modeling the 2009-2010 data were used to model the 2010-2011 data. Because the interest was to estimate change between the 2009-2010 and 2010-2011 State estimates, the same set of fixed-effect predictors was used for producing both sets of estimates.
The 2010-2011 small area estimates for serious mental illness and any mental illness were produced using updated SAE model predictors for both 2010 and 2011 based on a new variable selection process. In order to produce the 2011-2012 small area estimates, variable selection was done using 2010-2011 NSDUH data and an updated set of predictors derived from the 2010 census (for more information on the updated predictors and variable selection, refer to the "2011-2012 NSDUH: Guide to State Tables and Summary of Small Area Estimation Methodology" that will be available in late 2013). Because the 2010-2011 mental illness and any mental illness estimates based on the 2012 model (see Section B.11 for more information on the revised mental illness models) were produced after that variable selection was done, a decision was made by SAMHSA to produce the revised 2010-2011 mental illness and any mental illness estimates using the new set of predictors (based on variable selection done using 2010-2011 data). Also, for the 2010 sample for mental illness and any mental illness, weights based on control totals poststratified to 2010 census-based population counts were used (unlike weights used for all other outcomes where the 2010 sample weights were based on control totals poststratified to 2010 projections from 2000 census-based population counts).
B.6 Benchmarking the Age Group-Specific Small Area Estimates
The self-calibration built into the survey-weighted hierarchical Bayes (SWHB) solution ensures that the population-weighted average of the State small area estimates will closely match the national design-based estimates. The national design-based estimates in NSDUH are based entirely on survey-weighted data using a direct estimation approach, whereas the State and census region estimates are model-based. Given the self-calibration ensured by the SWHB solution, for State reports prior to 2002, the standard Bayes prescription was followed; specifically, the posterior mean was used for the point estimate, and the tail percentiles of the posterior distribution were used for the Bayesian confidence interval limits.
Singh and Folsom (2001) extended Ghosh's (1992) results on constrained Bayes estimation to include exact benchmarking to design-based national estimates. In the simplest version of this constrained Bayes solution where only the design-based mean is imposed as a benchmarking constraint, each of the 2010-2011 State-by-age group small area estimates is adjusted by adding the common factor 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:
The associated posterior coverage probabilities for these benchmarked intervals are very close to the prescribed 0.95 value because the State small area estimates have posterior distributions that can be approximated exceptionally well by a Gaussian distribution.
B.7 Calculation of Estimated Number of Persons Associated with Each Outcome
Tables 1 to 26 of "NSDUH: 2010-2011 Model-Based Estimated Totals (in Thousands) (50 States and the District of Columbia)," available at 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.
B.8 Calculation of Average Annual Incidence of Marijuana Use
Incidence rates typically are calculated as the number of new initiates of a substance during a period of time (such as in the past year) divided by an estimate of the number of person‑years of exposure (in thousands). The incidence definition used here employs a simpler form of the at-risk population based on the model-based methodology. This model-based average annual incidence rate is defined as follows:
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.
B.9 Underage Drinking
To obtain small area estimates for persons aged 12 to 20 for past month alcohol and binge alcohol use, a separate set of models was fit for these two outcomes for the 12 to 17 age group and the 18 to 20 age group. For the 2010-2011 models, no new variable selection was done. Updated versions of the predictors were used to produce the small area estimates.
Model-based estimates for persons aged 12 to 20 were produced by taking the population-weighted average of the individual age group (12 to 17 and 18 to 20) estimates. Estimates for underage drinking for past month alcohol and binge alcohol use were benchmarked to match national design-based estimates for that age group using the process described in Section B.6. Comparisons between the 2009-2010 and the 2010-2011 small area estimates for underage drinking in the States are presented at https://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.
B.10 Illicit Drug and Alcohol Dependence or Abuse / Needing But Not Receiving Treatment
The NSDUH computer-assisted interviewing (CAI) instrumentation includes questions that are designed to measure illicit drug and alcohol dependence and abuse. For these substances,17 dependence and abuse questions were based on the criteria in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) (American Psychiatric Association [APA], 1994).
Specifically, for marijuana, hallucinogens, inhalants, and tranquilizers, a respondent was defined as having dependence if he or she met three or more of the following six dependence criteria:
Spent a great deal of time over a period of a month getting, using, or getting over the effects of the substance.
Used the substance more often than intended or was unable to keep set limits on the substance use.
Needed to use the substance more than before to get desired effects or noticed that the same amount of substance use had less effect than before.
Inability to cut down or stop using the substance every time tried or wanted to.
Continued to use the substance even though it was causing problems with emotions, nerves, mental health, or physical problems.
The substance use reduced or eliminated involvement or participation in important activities.
For alcohol, cocaine, heroin, pain relievers, sedatives, and stimulants, a seventh withdrawal criterion was added. A respondent was defined as having dependence if he or she met three or more of seven dependence criteria. The seventh withdrawal criterion is defined by a respondent reporting having experienced a certain number of withdrawal symptoms that vary by substance (e.g., having trouble sleeping, cramps, hands tremble).
For each illicit drug and alcohol, a respondent was defined as having abused that substance if he or she met one or more of the following four abuse criteria and was determined not to be dependent on the respective substance in the past year:
Serious problems at home, work, or school caused by the substance, such as neglecting your children, missing work or school, doing a poor job at work or school, or losing a job or dropping out of school.
Used the substance regularly and then did something that might have put you in physical danger.
Use of the substance caused you to do things that repeatedly got you in trouble with the law.
Had problems with family or friends that were probably caused by using the substance and continued to use the substance even though you thought the substance use caused these problems.
For additional details on how respondents were classified as having dependence or abuse of illicit drugs and alcohol, see Section B.4.2 in Appendix B of the 2011 NSDUH national findings report (CBHSQ, 2012d, pp. 127-130).
Additionally, the NSDUH CAI instrument included a series of questions that are designed to measure treatment need for an alcohol or illicit drug use problem and to determine persons needing but not receiving treatment.
Respondents were classified as needing treatment for an alcohol use problem in the past year if they met at least one of three criteria during the past year: (1) dependence on alcohol; (2) abuse of alcohol; or (3) received treatment for alcohol use at a specialty facility (i.e., drug and alcohol rehabilitation facility [inpatient or outpatient], hospital [inpatient only], or mental health center). A respondent was classified as needing but not receiving treatment for an alcohol problem if he or she met the criteria for alcohol dependence or abuse in the past year, but did not receive treatment at a specialty facility for an alcohol problem in the past year.
Respondents were classified as needing treatment for an illicit drug use problem in the past year if they met at least one of three criteria during the past year: (1) dependence on illicit drugs; (2) abuse of illicit drugs; or (3) received treatment for illicit drug use at a specialty facility (i.e., drug and alcohol rehabilitation facility [inpatient or outpatient], hospital [inpatient only], or mental health center). A respondent was classified as needing but not receiving treatment for an illicit drug problem if he or she met the criteria for illicit drug dependence or abuse in the past year, but did not receive treatment at a specialty facility for an illicit drug problem in the past year.
B.11 Mental Health Measures
This section provides a summary of measurement issues associated with the four mental health outcome variables included in this document—serious mental illness, any mental illness, serious thoughts of suicide, and major depressive episode. Additional details can be found in Section B.4.7 of Appendix B in the 2008 NSDUH national findings report for major depressive episode (OAS, 2009) and in Sections B.4.2 through B.4.4 of Appendix B in the 2012 NSDUH mental health findings report for all four outcome variables (CBHSQ, in press).
B.11.1 Mental Illness
In the 2000-2001 and 2002-2003 NSDUH State reports, the Kessler-6 (K6) distress scale was used to measure serious mental illness (Kessler et al., 2003). However, SAMHSA discontinued producing State-level serious mental illness estimates beginning with the release of the 2003-2004 State report because of concerns about the validity of using only the K6 distress scale without an impairment scale; see Section B.4.4 of Appendix B in the 2004 NSDUH national findings report (OAS, 2005). The use of the K6 distress scale continued in the 2003-2004, 2004-2005, 2005-2006, and 2006-2007 State reports, not as a measure of serious mental illness, but as a measure of serious psychological distress because it was determined that the K6 scale only measured serious psychological distress and only contributed to measuring serious mental illness (see details below).
In December 2006, a technical advisory group meeting of expert consultants was convened by SAMHSA's Center for Mental Health Services to solicit recommendations for mental health surveillance data collection strategies among the U.S. population. The panel recommended that NSDUH should be used to produce estimates of serious mental illness among adults using NSDUH's mental health measures and a gold-standard clinical psychiatric interview.
In response, SAMHSA's CBHSQ initiated in 2008 a Mental Health Surveillance Study (MHSS) under its NSDUH contract with RTI International to develop and implement methods to estimate serious mental illness. Based on recommendations from this panel, estimates of serious mental illness were presented based on this revised methodology and, thus, are not comparable with estimates for serious mental illness or serious psychological distress shown in NSDUH State reports prior to 2009. However, in 2013, another revision to the methodology for creating serious mental illness estimates was made, and the estimates presented for 2010-2011 are based on this revised methodology (and are therefore not comparable with previously published estimates of serious mental illness). Thus, the 2008-2009 and 2009-2010 serious mental illness estimates were reproduced using the new 2013 methodology. To develop methods for preparing the estimates of serious mental illness and any mental illness presented in this and other NSDUH reports and documents, the MHSS was initiated as part of the 2008 NSDUH design and analysis. Because of constraints on the interview time in NSDUH and the need for trained mental health clinicians, it was not possible to administer a full structured diagnostic clinical interview to assess mental illness on approximately 45,000 adult respondents; therefore, the approach adopted by SAMHSA was to utilize short scales separately measuring psychological distress (K6) and functional impairment that could be used in a statistical model to accurately predict whether a respondent had a mental illness. Two impairment scales—the World Health Organization Disability Assessment Schedule (WHODAS) and the Sheehan Disability Scale (SDS)—were included in the 2008 survey for evaluation. The collection of clinical psychiatric interview data was achieved using a subsample of approximately 1,500 adult NSDUH participants in 2008. These participants were recruited for a follow-up clinical interview consisting of a gold-standard diagnostic assessment for mental disorders and functional impairment. In order to determine the optimal scale to measure functional impairment, a split-sample design was incorporated into the full 2008 NSDUH data collection in which half of the adult respondents received the WHODAS and half received the SDS. The 2008 statistical models (subsequently referred to as the "2008 model") using the data from the subsample of respondents collected as part of the MHSS then were developed for each half sample in which the short scales (the K6 in combination with the WHODAS or the K6 in combination with the SDS) were used as predictors in models of mental illness assessed via the clinical interviews. The model parameter estimates then were used to predict serious mental illness in the full 2008 NSDUH sample. Serious mental illness probabilities and predicted values (as well as any mental illness values) were computed for respondents in the NSDUH sample from 2008 to 2010 using model parameter estimates from the 2008 sample.
In 2010, SAMHSA began preliminary investigations to assess whether improvements to the model were warranted using all of the clinical data that been collected since 2008. In 2011 and 2012, the clinical sample was augmented to include 1,500 respondents per year, leading to a combined sample of approximately 5,000 clinical interviews for 2008 to 2012. SAMHSA determined that the 2008 model has some important shortcomings that had not been detected in the original model fitting because of the small number of respondents in the 2008 clinical subsample. Specifically, the 2008 model substantially overestimated serious mental illness and any mental illness among young adults aged 18 to 25 relative to the clinical interview data. In addition, improvements were needed in the weighting procedures for the MHSS sample data to account better for nonresponse and undercoverage. Therefore, SAMHSA decided to modify the model for the 2012 estimates using the combined 2008-2012 clinical data (subsequently referred to as the "2012 model"). To reduce bias and improve prediction, additional mental health-related variables and an age variable were added in the 2012 model. To provide consistent data for trend assessment, State mental illness estimates for 2008-2009, 2009-2010, and 2010-2011 were also recomputed using the new 2012 model (including the 2009-2010 and 2010-2011 small area estimates shown in these 2010-2011 files). Note that tables or maps showing estimates of serious mental illness and any mental illness based on these 2012 models include "Revised October 2013" in the source line for estimates using 2008 through 2011 data.
The next subsection describes the instruments and items used to measure the variables employed in the 2012 model. Specifically, the instrument used to measure mental illness in the clinical interviews is described, followed by descriptions of the scales and items in the main NSDUH interviews that were used as predictor variables in the model (e.g., the K6 and WHODAS total scores, age, and suicidal thoughts).18
MHSS Clinical Interviews
As described previously, a subsample of NSDUH participants completed follow-up clinical interviews to provide data for the statistical modeling of the NSDUH interview data of psychological distress and functional impairment on mental health status. The MHSS sample respondents were administered clinical interviews within 4 weeks of the NSDUH main interview to assess the presence of mental disorders and functional impairment. Specifically, each participant was assessed by a trained clinical interviewer (master's or doctoral-level clinician, counselor, or social worker) via paper-and-pencil interviewing (PAPI) over the telephone. The clinical interview used was an adapted version of the Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition (SCID-I/NP) (First, Spitzer, Gibbon, & Williams, 2002). Past year disorders that were assessed through the SCID included mood disorders (e.g., major depressive episode, manic episode), anxiety disorders (e.g., panic disorder, generalized anxiety disorder, posttraumatic stress disorder), eating disorders (e.g., anorexia nervosa), intermittent explosive disorder, and adjustment disorder. In addition, the presence of psychotic symptoms was assessed. Substance use disorders also were assessed, although these disorders were not included in the estimates of mental illness.
Functional impairment ratings were assigned by clinical interviewers using the Global Assessment of Functioning (GAF) scale (Endicott, Spitzer, Fleiss, & Cohen, 1976). Mental illness, measured using the SCID and differentiated by the level of functional impairment, was defined in the MHSS as follows:
Respondents were defined as having any mental illness if they were determined to have any of the mental disorders assessed in the SCID (not including substance use disorders), regardless of the level of functional impairment.
Respondents were defined as having serious mental illness if they had any of the mental disorders assessed in the SCID (not including substance use disorders), and these disorders resulted in substantial impairment in carrying out major life activities, based on GAF scores of 50 or below. The serious mental illness diagnosis was used as the response variable in both the 2008 and 2012 prediction models.
The SCID and the GAF in combination were considered to be the gold standard for measuring mental illness.
Kessler-6 Distress Scale
The K6 in the main NSDUH interview consists of two sets of six questions that asked adult respondents how frequently they experienced symptoms of psychological distress during two different time periods: (1) during the past 30 days, and (2) if applicable, the one month in the past year when they were at their worst emotionally. Respondents were asked about the second time period only if they indicated that there was a month in the past 12 months when they felt more depressed, anxious, or emotionally stressed than they felt during the past 30 days.
The six questions comprising the K6 scale for the past month are as follows:
NERVE30 During the past 30 days, how often did you feel nervous?
1 All of the time
2 Most of the time
3 Some of the time
4 A little of the time
5 None of the time
Don't know/Refused
Response categories are the same for the remaining questions shown below.
HOPE30 During the past 30 days, how often did you feel hopeless?
FIDG30 During the past 30 days, how often did you feel restless or fidgety?
NOCHR30 During the past 30 days, how often did you feel so sad or depressed that nothing could cheer you up?
EFFORT30 During the past 30 days, how often did you feel that everything was an effort?
DOWN30 During the past 30 days, how often did you feel down on yourself, no good or worthless?
To create a score, the six items (NERVE30, HOPE30, FIDG30, NOCHR30, EFFORT30, and DOWN30) on the K6 scale were recoded from 0 to 4 so that "all of the time" was coded 4, "most of the time" 3, "some of the time" 2, "a little of the time" 1, and "none of the time" 0, with "don't know" and "refused" also coded as 0. Summing across the transformed responses in these six items resulted in a score with a range from 0 to 24.
If respondents were asked about a month in the past 12 months when they felt more depressed, anxious, or emotionally stressed than they felt during the past 30 days, they were asked comparable K6 items for that particular month in the past 12 months. The scoring procedures for these K6 items for the past 12 months were the same as those described above. The higher of the two K6 total scores for the past 30 days or past 12 months was used both for MHSS analysis purposes and in the adult respondents' final data.
An alternative K6 total score also was created in which K6 scores less than 8 were recoded as 0 and scores from 8 to 24 were recoded as 1 to 17. The rationale for creating the alternative past year K6 score was that serious mental illness prevalence was typically extremely low for respondents with past year K6 scores less than 8, and the prevalence rates started increasing only when scores were 8 or greater. This alternative K6 score was used in both the 2008 and 2012 serious mental illness prediction models.
WHODAS
An initial step of the MHSS was to modify the WHODAS for use in a general population survey, including making minor changes to question wording and reducing its length (Novak, 2007). That is, a subset of 8 items was found to capture the information represented in the full 16-item scale with no significant loss of information.
These eight WHODAS items that were included in the main NSDUH interview were assessed on a 0 to 3 scale, with responses of "no difficulty," "don't know," and "refused" coded as 0; "mild difficulty" coded as 1; "moderate difficulty" coded as 2; and "severe difficulty" coded as 3. Some items had an additional category for respondents who did not engage in a particular activity (e.g., they did not leave the house on their own). Respondents who reported that they did not engage in an activity were asked a follow-up question to determine if they did not do so because of emotions, nerves, or mental health. Those who answered "yes" to this follow-up question were subsequently assigned to the "severe difficulty" category; otherwise (i.e., for responses of "no," "don't know," or "refused"), they were assigned to the "no difficulty" category. Summing across these codes for the eight responses resulted in a total score with a range from 0 to 24. More information about scoring of the WHODAS can be found in the 2011 NSDUH public use file codebook (CBHSQ, 2012a).
An alternative WHODAS total score was created in which individual WHODAS item scores of less than 2 were recoded as 0, and item scores of 2 to 3 were recoded as 1. The individual alternative item scores then were summed to yield a total alternative score ranging from 0 to 8. Creation of an alternative version of the WHODAS score was based on the assumption that a dichotomous measure dividing respondents into two groups (i.e., severely impaired vs. less severely impaired) might fit better than a linear continuous measure in models predicting serious mental illness. This alternative WHODAS score was the variable used in both the 2008 and 2012 serious mental illness prediction models.
Suicidal Thoughts, Major Depressive Episode, and Age
In addition to the K6 and WHODAS scales, the 2012 model included the following measures as predictors of serious mental illness: (a) serious thoughts of suicide in the past year; (b) having a past year major depressive episode; and (c) age. The first two variables were added to the model to decrease the error rate in the predictions (i.e., the sum of the false-negative and false-positive rates relative to the clinical interview results). A recoded age variable reduced the biases in estimates for particular age groups, especially 18 to 25 year olds.
Since 2008, all adult respondents in NSDUH have been asked the following question: "At any time in the past 12 months, that is from [DATEFILL] up to and including today, did you seriously think about killing yourself?"19 Definitions for major depressive episode in the lifetime and past year periods are discussed in Section B.4.4 of Appendix B in the 2012 mental health findings report (CBHSQ, in press). For respondents aged 18 to 30, an adjusted age was created by subtracting 18 from the respondent's current age, resulting in values ranging from 0 to 12. For a respondent aged 18, for example, the adjusted age was 0 (i.e., 18 minus 18), and for a respondent aged 30, the adjusted age was 12 (i.e., 30 minus 18). For respondents aged 31 or older, the adjusted age was assigned a value of 12.
2012 SMI Model
Statistical modeling involved developing separate weighted logistic regression prediction models for the K6 and for each of the two impairment scales. With serious mental illness status based on having a SCID diagnosis plus a GAF less than or equal to 50, the response variable Y was defined so that
Y = 1 when a serious mental illness diagnosis is positive; otherwise, Y = 0.
If X is a vector of explanatory variables, then the response probability 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:
= Alternative Past Year K6 Score: Past year K6 score less than 8 recoded as 0; past year K6 score 8 to 24 recoded as 1 to 17.
= Alternative WHODAS Score: WHODAS item scores less than 2 recoded as 0; WHODAS item scores 2 to 3 recoded as 1, then summed for a score ranging from 0 to 8.
= Serious Thoughts of Suicide in the Past Year: Coded as 1 if "yes"; coded as 0 otherwise.
= Past Year MDE: Coded as 1 if criteria for past year MDE were met (see Section B.11.3); coded as 0 otherwise.
= Adjusted Age: Coded as age minus 18 if aged 18 to 30; coded as 12 otherwise.
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.
B.11.2 Serious Thoughts of Suicide
Responding to a need for national data on the prevalence of suicidal thoughts and behavior, a set of questions was added beginning with the 2008 NSDUH questionnaire (and the questions were continued to be asked in 2009, 2010, and 2011). These questions asked all adult respondents aged 18 or older if at any time during the past 12 months they had serious thoughts of suicide (suicidal ideation). State-level estimates of suicidal ideation are included at https://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/Index.aspx.
B.11.3 Major Depressive Episode (Depression)
According to the DSM-IV, a person is defined as having had major depressive episode in his or her lifetime if he or she has had at least five or more of the following nine symptoms nearly every day in the same 2-week period, where at least one of the symptoms is a depressed mood or loss of interest or pleasure in daily activities (APA, 1994): (1) depressed mood most of the day; (2) markedly diminished interest or pleasure in all or almost all activities most of the day; (3) significant weight loss when not sick or dieting, or weight gain when not pregnant or growing, or decrease or increase in appetite; (4) insomnia or hypersomnia; (5) psychomotor agitation or retardation; (6) fatigue or loss of energy; (7) feelings of worthlessness; (8) diminished ability to think or concentrate or indecisiveness; and (9) recurrent thoughts of death or suicidal ideation. Respondents who have had a major depressive episode in their lifetime are asked if, during the past 12 months, they had a period of depression lasting 2 weeks or longer while also having some of the other symptoms mentioned. Those reporting that they have are defined as having had major depressive episode in the past year and then are asked questions from the SDS to measure the level of functional impairment in major life activities reported to be caused by the major depressive episode in the past 12 months (Leon, Olfson, Portera, Farber, & Sheehan, 1997).
Beginning in 2004, modules related to major depressive episode, derived from DSM-IV (APA, 1994) criteria for major depression, were included in the questionnaire. These questions permit prevalence estimates of major depressive episode to be calculated. Separate modules were administered to adults aged 18 or older and youths aged 12 to 17. The adult questions were adapted from the depression section of the National Comorbidity Survey Replication (NCS-R), and the questions for youths were adapted from the depression section of the National Comorbidity Survey Adolescent (NCS-A) (see http://www.hcp.med.harvard.edu/ncs/). To make the modules developmentally appropriate for youths, there are minor wording differences in a few questions between the adult and youth modules. Revisions to the questions in both modules were made primarily to reduce the length and to modify the NCS questions, which are interviewer-administered, to the audio computer-assisted self-interviewing (ACASI) format used in NSDUH. In addition, some revisions, based on cognitive testing, were made to improve comprehension.
Since 2004, the NSDUH questions that determine major depressive episode have remained unchanged. In the 2008 questionnaire, however, changes were made in other mental health items that precede the major depressive episode questions for adults (K6, suicide, and impairment). Questions also were retained in 2009, 2010, and 2011 for the WHODAS impairment scale, and the questions for the SDS impairment scale were deleted; see Sections B.4.2 and B.4.4 in Appendix B of the 2011 NSDUH mental health findings report (CBHSQ, 2012b) for further details about these questionnaire changes. These questionnaire changes in 2008 appear to have affected the reporting on major depressive episode questions among adults.
Because the WHODAS was selected to be used in the 2009 and subsequent surveys, model-based adjustments were applied to major depressive episode estimates from the SDS half sample in 2008 to remove the context effect differential between the two half samples. Additionally, model-based adjustments were made to the 2005, 2006, and 2007 adult major depressive episode estimates to make them comparable with the 2008 through 2011 major depressive episode estimates (for more information on these adjustments, see Aldworth, Kott, Yu, Mosquin, & Barnett-Walker, 2012). Thus, the 2008-2009 estimates of major depressive episode were produced using the adjusted 2008 major depressive episode variable along with the unadjusted 2009 major depressive episode variable. Revised estimates for 2005-2006, 2006-2007, and 2007-2008 were produced using the adjusted major depressive episode variable.
In addition, changes to the youth mental health service utilization module questions in 2009 that preceded the questions about adolescent depression could have affected adolescents' responses to the adolescent depression questions and estimates of adolescent major depressive episode. However, these changes in 2009 did not appear to affect the estimates of adolescent major depressive episode. Therefore, data on trends in past year major depressive episode from 2004 to 2011 are available for adolescents aged 12 to 17.
B.12 Comparison of Two 2010-2011 Small Area Estimates
This section describes a method for determining whether differences between two 2010-2011 State estimates are statistically significant. This procedure can be used for any two State estimates representing the same age group (e.g., young adults aged 18 to 25) and time period (e.g., 2010-2011).
Let 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:
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
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.
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 .
Section C: Sample Sizes, Response Rates, and Population Estimates
Table C.1 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2009
State
Total Selected DUs
Total Eligible DUs
Total Completed Screeners
Weighted DU Screening Response Rate
Total Selected
Total Responded
Population Estimate
Weighted Interview Response Rate
Weighted Overall Response Rate
DU = dwelling unit.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009 (Revised March 2012).
Total U.S.
195,132
161,377
142,933
88.40%
84,785
68,007
251,815,533
75.56%
66.79%
Northeast
42,197
35,012
28,978
81.91%
16,907
13,130
46,385,613
72.81%
59.63%
Midwest
53,123
44,770
40,360
90.34%
23,827
19,133
55,167,183
75.97%
68.64%
South
60,898
49,144
44,583
91.33%
25,512
20,925
92,048,862
77.36%
70.65%
West
38,914
32,451
29,012
87.11%
18,539
14,819
58,213,876
74.50%
64.90%
Alabama
2,831
2,286
2,128
92.98%
1,174
944
3,876,035
78.44%
72.93%
Alaska
2,303
1,768
1,631
92.08%
1,110
902
554,006
79.33%
73.05%
Arizona
2,723
2,050
1,778
82.93%
1,110
916
5,310,817
79.47%
65.91%
Arkansas
2,574
2,104
1,965
93.31%
1,133
914
2,358,363
77.30%
72.13%
California
8,934
7,761
6,499
83.86%
4,734
3,660
30,079,762
71.83%
60.24%
Colorado
2,727
2,272
2,088
92.12%
1,195
984
4,096,077
77.36%
71.26%
Connecticut
2,331
2,061
1,805
87.50%
1,147
915
2,937,125
76.43%
66.87%
Delaware
2,595
2,135
1,862
87.26%
1,129
920
731,769
73.59%
64.21%
District of Columbia
4,322
3,511
2,851
80.59%
1,042
886
510,289
83.69%
67.45%
Florida
11,388
8,721
8,040
91.93%
4,407
3,648
15,484,832
76.74%
70.54%
Georgia
2,295
1,864
1,716
91.79%
1,082
907
7,846,856
78.24%
71.82%
Hawaii
3,209
2,718
2,154
76.85%
1,321
960
1,052,232
67.00%
51.49%
Idaho
2,252
1,765
1,671
94.66%
1,119
916
1,235,558
77.15%
73.04%
Illinois
10,108
8,781
7,097
80.81%
4,786
3,655
10,592,236
71.70%
57.94%
Indiana
2,719
2,226
2,087
93.64%
1,119
904
5,261,391
79.31%
74.27%
Iowa
2,567
2,203
2,049
93.14%
1,099
924
2,486,476
81.80%
76.19%
Kansas
2,364
2,053
1,906
92.80%
1,132
909
2,279,789
76.12%
70.64%
Kentucky
2,411
1,946
1,828
93.94%
1,118
912
3,550,066
76.64%
72.00%
Louisiana
2,615
2,125
1,993
93.91%
1,143
923
3,640,052
78.89%
74.08%
Maine
3,209
2,339
2,150
92.05%
1,132
964
1,128,941
82.64%
76.07%
Maryland
2,231
1,905
1,544
81.00%
1,002
836
4,705,966
79.72%
64.57%
Massachusetts
3,277
2,813
2,385
84.82%
1,239
969
5,563,652
73.77%
62.57%
Michigan
10,360
8,303
7,345
88.44%
4,530
3,639
8,323,828
76.86%
67.98%
Minnesota
2,334
1,984
1,854
93.46%
1,132
925
4,356,171
77.67%
72.60%
Mississippi
2,084
1,619
1,527
94.27%
1,090
891
2,365,526
77.67%
73.22%
Missouri
2,529
2,077
1,933
93.09%
1,112
889
4,926,491
75.54%
70.32%
Montana
2,513
2,148
2,026
94.20%
1,119
909
814,381
75.98%
71.57%
Nebraska
2,274
1,940
1,830
94.35%
1,125
911
1,457,382
78.61%
74.16%
Nevada
2,605
2,063
1,941
94.25%
1,149
930
2,144,323
72.30%
68.14%
New Hampshire
2,786
2,255
2,004
88.82%
1,190
944
1,125,160
74.46%
66.14%
New Jersey
2,317
1,990
1,766
88.80%
1,172
906
7,241,791
72.36%
64.25%
New Mexico
2,548
2,032
1,916
94.26%
1,115
918
1,628,498
77.27%
72.83%
New York
13,014
10,782
8,289
76.73%
5,021
3,707
16,380,098
70.67%
54.23%
North Carolina
2,517
2,090
1,919
91.91%
1,112
929
7,612,327
79.41%
72.98%
North Dakota
2,919
2,427
2,290
94.35%
1,149
929
534,362
76.67%
72.33%
Ohio
9,800
8,405
7,847
93.27%
4,392
3,585
9,581,963
74.92%
69.88%
Oklahoma
2,648
2,142
1,964
91.82%
1,124
908
2,970,916
74.49%
68.40%
Oregon
2,802
2,379
2,184
91.95%
1,170
947
3,199,775
79.93%
73.49%
Pennsylvania
9,705
8,367
6,610
79.12%
3,795
2,915
10,583,566
72.92%
57.69%
Rhode Island
2,779
2,343
2,061
87.87%
1,155
913
889,360
76.51%
67.23%
South Carolina
3,097
2,362
2,145
90.20%
1,153
954
3,730,181
76.22%
68.75%
South Dakota
2,417
2,030
1,942
95.66%
1,088
920
659,093
81.15%
77.63%
Tennessee
3,023
2,465
2,298
93.13%
1,172
949
5,196,019
73.45%
68.40%
Texas
8,652
7,178
6,591
91.91%
4,388
3,596
19,519,442
77.65%
71.37%
Utah
1,539
1,376
1,306
94.90%
1,101
918
2,144,172
80.38%
76.28%
Vermont
2,779
2,062
1,908
92.57%
1,056
897
535,921
79.32%
73.43%
Virginia
2,499
2,171
1,924
88.59%
1,125
918
6,410,227
77.07%
68.28%
Washington
2,359
2,098
1,913
91.14%
1,158
936
5,509,332
77.01%
70.19%
West Virginia
3,116
2,520
2,288
90.81%
1,118
890
1,539,997
73.90%
67.11%
Wisconsin
2,732
2,341
2,180
93.19%
1,163
943
4,708,003
76.66%
71.44%
Wyoming
2,400
2,021
1,905
94.26%
1,138
923
444,942
78.67%
74.16%
Table C.2 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2009
State
12-17 Total Selected
12-17 Total Responded
12-17 Population Estimate
12-17 Weighted Interview Response Rate
18-25 Total Selected
18-25 Total Responded
18-25 Population Estimate
18-25 Weighted Interview Response Rate
26+ Total Selected
26+ Total Responded
26+ Population Estimate
26+ Weighted Interview Response Rate
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009 (Revised March 2012).
Total U.S.
26,157
22,416
24,608,987
85.60%
28,158
22,930
33,579,988
81.48%
30,470
22,661
193,626,558
73.23%
Northeast
5,185
4,362
4,305,676
83.00%
5,638
4,401
6,120,620
77.62%
6,084
4,367
35,959,317
70.73%
Midwest
7,451
6,398
5,410,447
85.51%
7,803
6,325
7,337,646
80.48%
8,573
6,410
42,419,090
73.94%
South
7,811
6,767
9,008,998
87.12%
8,567
7,210
12,022,931
84.47%
9,134
6,948
71,016,933
74.89%
West
5,710
4,889
5,883,867
85.24%
6,150
4,994
8,098,792
80.90%
6,679
4,936
44,231,217
71.90%
Alabama
390
326
377,817
84.54%
345
281
510,045
81.35%
439
337
2,988,173
77.25%
Alaska
348
302
60,144
87.18%
363
298
78,273
84.24%
399
302
415,589
77.37%
Arizona
343
300
538,805
87.04%
400
326
696,689
81.36%
367
290
4,075,324
78.18%
Arkansas
348
306
231,302
89.27%
376
302
298,338
82.71%
409
306
1,828,723
74.91%
California
1,379
1,169
3,117,227
84.22%
1,567
1,240
4,383,689
79.48%
1,788
1,251
22,578,845
68.69%
Colorado
404
365
383,909
88.69%
417
336
533,064
82.80%
374
283
3,179,105
74.49%
Connecticut
367
308
286,054
84.72%
381
312
368,953
82.06%
399
295
2,282,118
74.52%
Delaware
358
310
68,377
86.67%
419
350
94,723
83.22%
352
260
568,669
70.42%
District of Columbia
288
250
35,126
86.53%
402
344
88,250
85.28%
352
292
386,914
83.03%
Florida
1,312
1,126
1,343,518
84.99%
1,538
1,328
1,829,604
85.43%
1,557
1,194
12,311,710
74.51%
Georgia
344
306
821,827
89.76%
342
295
1,025,485
84.40%
396
306
5,999,543
75.62%
Hawaii
391
311
92,363
77.48%
397
285
131,979
70.91%
533
364
827,890
65.27%
Idaho
331
284
133,111
86.64%
351
305
165,070
87.02%
437
327
937,376
74.28%
Illinois
1,406
1,177
1,056,872
83.68%
1,555
1,187
1,467,611
75.72%
1,825
1,291
8,067,752
69.43%
Indiana
332
285
527,261
87.20%
356
287
683,131
80.52%
431
332
4,051,000
78.03%
Iowa
339
302
237,996
90.33%
376
308
340,764
82.02%
384
314
1,907,716
80.77%
Kansas
347
303
227,693
87.71%
415
322
323,487
76.46%
370
284
1,728,609
74.44%
Kentucky
307
267
335,609
88.29%
396
328
431,390
82.52%
415
317
2,783,068
74.53%
Louisiana
338
284
369,414
83.73%
366
308
528,427
83.76%
439
331
2,742,211
77.34%
Maine
379
337
98,248
88.85%
394
334
125,394
84.97%
359
293
905,299
81.60%
Maryland
315
277
456,071
87.92%
334
280
618,887
85.95%
353
279
3,631,008
77.34%
Massachusetts
351
288
496,369
82.35%
428
349
771,025
81.73%
460
332
4,296,258
71.38%
Michigan
1,463
1,243
829,913
84.40%
1,470
1,200
1,090,449
81.26%
1,597
1,196
6,403,467
75.07%
Minnesota
355
307
417,528
85.64%
396
320
575,857
80.13%
381
298
3,362,786
76.19%
Mississippi
300
255
250,210
85.65%
372
318
332,057
86.26%
418
318
1,783,258
75.16%
Missouri
374
306
480,290
81.76%
352
294
630,416
82.92%
386
289
3,815,785
73.41%
Montana
350
295
75,210
85.30%
403
334
105,702
82.72%
366
280
633,469
73.66%
Nebraska
338
290
143,848
87.86%
375
304
209,977
80.99%
412
317
1,103,557
76.98%
Nevada
363
312
214,441
85.95%
391
334
250,525
86.82%
395
284
1,679,357
68.38%
New Hampshire
387
327
105,079
84.57%
356
286
134,825
81.03%
447
331
885,256
72.36%
New Jersey
345
290
697,510
82.70%
408
317
881,986
77.63%
419
299
5,662,295
70.27%
New Mexico
346
305
161,883
89.05%
368
310
230,548
86.27%
401
303
1,236,068
74.09%
New York
1,460
1,203
1,521,667
81.80%
1,718
1,249
2,285,210
73.89%
1,843
1,255
12,573,221
68.68%
North Carolina
309
273
727,521
88.60%
416
358
958,312
87.15%
387
298
5,926,494
77.09%
North Dakota
370
325
48,044
88.03%
356
286
89,285
81.04%
423
318
397,033
74.33%
Ohio
1,393
1,211
931,091
86.54%
1,425
1,206
1,217,923
84.04%
1,574
1,168
7,432,949
71.92%
Oklahoma
365
309
292,731
85.44%
349
287
412,462
82.00%
410
312
2,265,722
71.56%
Oregon
419
336
290,722
80.11%
316
264
390,321
84.79%
435
347
2,518,733
79.14%
Pennsylvania
1,195
988
973,827
83.33%
1,222
959
1,356,120
79.03%
1,378
968
8,253,618
70.58%
Rhode Island
382
333
80,228
87.98%
366
275
128,448
75.86%
407
305
680,684
75.29%
South Carolina
406
351
354,659
85.95%
371
321
474,729
84.77%
376
282
2,900,794
73.28%
South Dakota
322
292
64,477
90.52%
385
329
91,186
85.79%
381
299
503,430
79.03%
Tennessee
394
351
492,599
89.05%
348
289
627,894
84.29%
430
309
4,075,526
69.38%
Texas
1,342
1,182
2,118,403
88.34%
1,439
1,196
2,768,449
83.76%
1,607
1,218
14,632,591
74.99%
Utah
357
318
253,766
89.75%
362
300
377,293
81.68%
382
300
1,513,113
78.55%
Vermont
319
288
46,695
90.40%
365
320
68,659
87.37%
372
289
420,568
76.79%
Virginia
348
297
602,602
84.65%
385
330
846,780
86.18%
392
291
4,960,846
74.49%
Washington
357
311
520,243
87.01%
397
326
694,724
81.30%
404
299
4,294,365
75.18%
West Virginia
347
297
131,213
86.38%
369
295
177,100
78.81%
402
298
1,231,684
71.85%
Wisconsin
412
357
445,433
86.36%
342
282
617,562
81.33%
409
304
3,645,007
74.69%
Wyoming
322
281
42,044
84.77%
418
336
60,915
80.40%
398
306
341,983
77.62%
Table C.3 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2010
State
Total Selected DUs
Total Eligible DUs
Total Completed Screeners
Weighted DU Screening Response Rate
Total Selected
Total Responded
Population Estimate
Weighted Interview Response Rate
Weighted Overall Response Rate
DU = dwelling unit.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2010 (Revised March 2012).
Total U.S.
201,865
166,532
147,010
88.42%
84,997
67,804
253,619,107
74.57%
65.94%
Northeast
43,420
36,033
29,645
81.63%
16,782
13,017
46,535,320
72.81%
59.44%
Midwest
54,767
45,892
41,118
89.86%
24,139
19,301
55,345,459
74.81%
67.22%
South
63,813
51,533
46,241
90.54%
25,597
20,769
92,961,895
76.24%
69.03%
West
39,865
33,074
30,006
89.17%
18,479
14,717
58,776,433
73.17%
65.24%
Alabama
2,879
2,284
2,099
91.94%
1,121
878
3,893,688
71.86%
66.07%
Alaska
2,226
1,719
1,583
92.02%
1,057
868
555,964
77.75%
71.55%
Arizona
2,655
2,059
1,861
90.14%
1,149
925
5,386,782
72.97%
65.77%
Arkansas
2,595
2,108
1,948
92.51%
1,123
899
2,375,992
75.16%
69.53%
California
9,282
8,087
6,910
85.48%
4,739
3,715
30,322,142
71.96%
61.52%
Colorado
2,529
2,084
1,912
92.20%
1,117
904
4,151,930
79.29%
73.11%
Connecticut
2,474
2,158
1,812
83.73%
1,151
926
2,951,217
75.17%
62.94%
Delaware
2,621
2,118
1,857
87.67%
1,099
889
737,571
77.52%
67.96%
District of Columbia
5,113
4,192
3,403
79.88%
1,110
935
517,942
81.34%
64.97%
Florida
13,206
9,961
8,891
89.01%
4,460
3,655
15,611,774
77.37%
68.87%
Georgia
2,385
1,978
1,804
91.21%
1,131
910
7,940,651
75.51%
68.88%
Hawaii
2,861
2,443
2,098
85.56%
1,296
974
1,047,745
66.88%
57.22%
Idaho
2,624
2,046
1,932
94.43%
1,113
912
1,250,238
78.24%
73.88%
Illinois
10,614
9,121
7,392
80.95%
4,762
3,609
10,629,517
70.77%
57.29%
Indiana
2,743
2,281
2,104
91.97%
1,142
916
5,286,018
73.88%
67.95%
Iowa
2,574
2,187
2,069
94.61%
1,113
925
2,502,115
78.90%
74.65%
Kansas
2,340
1,988
1,824
91.75%
1,101
885
2,296,286
74.78%
68.61%
Kentucky
2,583
2,147
1,991
92.73%
1,109
900
3,574,784
76.88%
71.29%
Louisiana
2,605
2,092
1,955
93.42%
1,112
906
3,661,821
77.97%
72.84%
Maine
3,327
2,404
2,197
90.98%
1,100
924
1,127,285
80.65%
73.37%
Maryland
2,415
2,061
1,692
82.13%
1,096
883
4,737,806
77.66%
63.78%
Massachusetts
3,116
2,716
2,365
87.32%
1,149
930
5,605,641
78.23%
68.31%
Michigan
10,828
8,669
7,623
87.81%
4,561
3,690
8,313,433
75.65%
66.43%
Minnesota
2,532
2,087
1,949
93.42%
1,149
946
4,382,130
78.32%
73.17%
Mississippi
2,485
1,976
1,839
93.07%
1,087
893
2,373,593
76.50%
71.20%
Missouri
2,642
2,170
2,031
93.58%
1,142
921
4,952,896
75.89%
71.01%
Montana
2,713
2,255
2,128
94.34%
1,137
919
820,115
76.91%
72.56%
Nebraska
2,336
1,996
1,883
94.30%
1,120
906
1,469,129
73.19%
69.02%
Nevada
2,674
2,063
1,935
94.68%
1,183
958
2,155,405
71.81%
67.99%
New Hampshire
3,232
2,558
2,219
86.80%
1,160
918
1,128,997
74.48%
64.65%
New Jersey
2,382
2,061
1,831
88.85%
1,157
923
7,269,834
78.46%
69.72%
New Mexico
2,610
2,078
1,959
94.26%
1,117
912
1,641,892
77.09%
72.66%
New York
13,218
11,170
8,452
75.25%
5,061
3,626
16,410,083
66.82%
50.28%
North Carolina
2,674
2,303
2,118
92.18%
1,103
904
7,679,126
76.53%
70.54%
North Dakota
3,053
2,567
2,420
94.30%
1,188
954
540,202
76.32%
71.97%
Ohio
10,268
8,717
7,947
91.17%
4,633
3,731
9,580,362
74.81%
68.20%
Oklahoma
2,626
2,122
1,903
89.71%
1,173
923
2,995,565
73.17%
65.64%
Oregon
2,603
2,293
2,146
93.61%
1,134
907
3,229,211
74.87%
70.09%
Pennsylvania
10,193
8,715
6,952
79.79%
3,853
2,985
10,607,311
73.24%
58.44%
Rhode Island
2,574
2,094
1,866
89.19%
1,117
915
896,384
74.52%
66.46%
South Carolina
2,616
2,152
1,927
89.56%
1,138
927
3,760,624
75.68%
67.78%
South Dakota
2,399
2,048
1,945
95.06%
1,115
929
666,589
80.45%
76.47%
Tennessee
2,588
2,149
1,968
91.41%
1,117
901
5,238,574
73.38%
67.08%
Texas
8,885
7,290
6,697
91.78%
4,431
3,590
19,847,501
76.61%
70.31%
Utah
1,507
1,324
1,252
94.58%
1,105
919
2,180,889
79.81%
75.48%
Vermont
2,904
2,157
1,951
90.39%
1,034
870
538,568
82.45%
74.53%
Virginia
2,609
2,284
2,037
89.17%
1,096
888
6,471,190
76.48%
68.20%
Washington
2,636
2,288
2,103
91.87%
1,194
897
5,585,609
70.16%
64.45%
West Virginia
2,928
2,316
2,112
91.30%
1,091
888
1,543,694
78.37%
71.55%
Wisconsin
2,438
2,061
1,931
93.62%
1,113
889
4,726,785
76.78%
71.88%
Wyoming
2,945
2,335
2,187
93.74%
1,138
907
448,513
73.07%
68.50%
Table C.4 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2010
State
12-17 Total Selected
12-17 Total Responded
12-17 Population Estimate
12-17 Weighted Interview Response Rate
18-25 Total Selected
18-25 Total Responded
18-25 Population Estimate
18-25 Weighted Interview Response Rate
26+ Total Selected
26+ Total Responded
26+ Population Estimate
26+ Weighted Interview Response Rate
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2010 (Revised March 2012).
Total U.S.
25,908
21,992
24,346,528
84.65%
28,164
23,026
34,072,349
81.20%
30,925
22,786
195,200,229
72.14%
Northeast
4,966
4,105
4,234,758
81.31%
5,612
4,412
6,045,018
77.44%
6,204
4,500
36,255,544
71.05%
Midwest
7,357
6,264
5,334,556
85.18%
8,035
6,589
7,496,530
81.44%
8,747
6,448
42,514,373
72.34%
South
8,029
6,858
8,956,559
85.57%
8,407
7,027
12,418,811
83.30%
9,161
6,884
71,586,525
73.79%
West
5,556
4,765
5,820,656
85.19%
6,110
4,998
8,111,990
80.57%
6,813
4,954
44,843,787
70.31%
Alabama
369
307
374,067
82.98%
345
286
520,974
81.37%
407
285
2,998,646
68.80%
Alaska
312
266
57,362
85.56%
362
310
85,086
85.36%
383
292
413,516
75.09%
Arizona
333
292
538,540
87.31%
428
351
701,269
79.13%
388
282
4,146,973
70.30%
Arkansas
334
284
232,460
84.94%
362
296
305,518
82.20%
427
319
1,838,015
72.68%
California
1,526
1,303
3,086,730
84.79%
1,416
1,151
4,268,110
80.94%
1,797
1,261
22,967,302
68.62%
Colorado
273
231
379,157
82.92%
424
345
566,389
81.41%
420
328
3,206,384
78.51%
Connecticut
331
288
281,757
88.09%
400
326
381,359
81.42%
420
312
2,288,101
72.46%
Delaware
319
268
67,234
83.34%
340
288
93,677
85.05%
440
333
576,660
75.64%
District of Columbia
356
324
34,240
91.90%
384
320
84,993
82.39%
370
291
398,709
80.13%
Florida
1,424
1,215
1,329,956
85.86%
1,419
1,212
1,870,501
85.10%
1,617
1,228
12,411,317
75.23%
Georgia
371
313
818,462
84.43%
355
301
1,076,087
84.73%
405
296
6,046,102
72.53%
Hawaii
400
338
89,846
83.34%
439
335
130,340
78.06%
457
301
827,559
63.29%
Idaho
353
294
130,819
83.21%
356
305
177,534
85.11%
404
313
941,886
76.19%
Illinois
1,357
1,122
1,049,679
82.64%
1,615
1,232
1,453,014
76.32%
1,790
1,255
8,126,824
68.31%
Indiana
389
341
523,789
88.17%
343
280
719,041
81.57%
410
295
4,043,187
70.81%
Iowa
336
287
234,049
85.14%
385
321
359,379
81.94%
392
317
1,908,687
77.57%
Kansas
331
296
225,398
89.33%
357
285
338,453
81.23%
413
304
1,732,436
71.72%
Kentucky
352
299
333,232
85.21%
370
304
461,899
82.16%
387
297
2,779,654
75.08%
Louisiana
382
328
365,624
86.45%
345
285
526,082
82.60%
385
293
2,770,114
75.99%
Maine
325
284
94,501
87.86%
356
302
130,971
85.20%
419
338
901,813
79.28%
Maryland
315
268
448,006
86.14%
367
300
613,529
79.93%
414
315
3,676,271
76.21%
Massachusetts
360
296
491,663
80.42%
392
324
761,003
81.71%
397
310
4,352,974
77.40%
Michigan
1,432
1,212
814,296
84.10%
1,453
1,220
1,105,211
84.44%
1,676
1,258
6,393,926
73.01%
Minnesota
337
296
409,292
87.51%
410
340
590,704
82.82%
402
310
3,382,134
76.41%
Mississippi
333
290
247,423
87.78%
368
316
340,138
85.71%
386
287
1,786,033
73.21%
Missouri
341
288
472,583
85.51%
386
320
656,859
82.72%
415
313
3,823,454
73.55%
Montana
348
302
72,261
86.91%
343
280
114,819
81.70%
446
337
633,035
75.07%
Nebraska
335
300
141,249
88.32%
372
306
218,880
82.98%
413
300
1,108,999
69.37%
Nevada
298
264
210,434
90.50%
405
339
263,872
83.13%
480
355
1,681,099
67.89%
New Hampshire
300
250
101,483
84.76%
467
387
145,527
82.81%
393
281
881,988
71.74%
New Jersey
387
324
692,595
83.33%
334
264
865,591
81.47%
436
335
5,711,649
77.39%
New Mexico
364
327
161,227
89.38%
370
303
226,963
83.54%
383
282
1,253,702
74.21%
New York
1,457
1,141
1,498,050
77.55%
1,709
1,234
2,188,721
71.54%
1,895
1,251
12,723,312
64.80%
North Carolina
346
311
719,819
89.83%
375
304
1,014,496
82.27%
382
289
5,944,811
73.67%
North Dakota
357
300
46,378
83.63%
393
340
96,560
86.92%
438
314
397,264
72.96%
Ohio
1,395
1,191
918,549
85.27%
1,634
1,371
1,210,150
83.56%
1,604
1,169
7,451,663
72.00%
Oklahoma
394
337
291,436
84.66%
355
278
425,691
76.96%
424
308
2,278,438
71.09%
Oregon
376
318
285,470
83.17%
361
296
412,163
82.85%
397
293
2,531,579
72.45%
Pennsylvania
1,165
955
951,061
82.17%
1,203
946
1,365,550
78.58%
1,485
1,084
8,290,700
71.31%
Rhode Island
322
292
79,082
90.34%
418
350
129,842
83.67%
377
273
687,461
70.69%
South Carolina
351
292
349,533
83.84%
376
325
487,235
85.47%
411
310
2,923,856
73.12%
South Dakota
365
309
62,886
85.00%
338
296
96,018
88.02%
412
324
507,684
78.44%
Tennessee
370
319
489,539
86.92%
364
302
664,620
83.99%
383
280
4,084,416
69.67%
Texas
1,329
1,125
2,131,714
84.76%
1,532
1,288
2,858,101
83.62%
1,570
1,177
14,857,686
74.07%
Utah
283
250
255,595
88.81%
420
357
381,486
85.32%
402
312
1,543,809
77.17%
Vermont
319
275
44,568
87.55%
333
279
76,455
82.92%
382
316
417,546
81.80%
Virginia
349
295
594,024
85.00%
360
301
884,909
83.26%
387
292
4,992,257
74.20%
Washington
365
301
512,686
83.53%
377
280
719,040
71.68%
452
316
4,353,883
68.27%
West Virginia
335
283
129,792
83.49%
390
321
190,362
81.95%
366
284
1,223,540
77.18%
Wisconsin
382
322
436,408
84.04%
349
278
652,261
78.40%
382
289
3,638,115
75.54%
Wyoming
325
279
40,531
85.48%
409
346
64,920
83.34%
404
282
343,061
69.61%
Table C.5 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2011
State
Total Selected DUs
Total Eligible DUs
Total Completed Screeners
Weighted DU Screening Response Rate
Total Selected
Total Responded
Population Estimate
Weighted Interview Response Rate
Weighted Overall Response Rate
DU = dwelling unit.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2011.
Total U.S.
216,521
179,293
156,048
86.98%
88,536
70,109
257,598,945
74.38%
64.69%
Northeast
46,446
38,803
31,569
80.08%
17,251
13,090
46,891,412
69.86%
55.94%
Midwest
58,190
48,817
42,805
88.19%
24,570
19,258
55,687,448
73.92%
65.18%
South
70,821
57,462
51,276
89.47%
28,122
22,980
95,181,797
76.88%
68.78%
West
41,064
34,211
30,398
87.20%
18,593
14,781
59,838,287
74.41%
64.88%
Alabama
4,338
3,360
3,032
89.89%
1,708
1,383
3,985,593
74.64%
67.09%
Alaska
2,459
1,911
1,700
88.87%
1,121
905
569,155
79.52%
70.67%
Arizona
2,731
2,149
1,915
89.43%
1,126
928
5,285,358
82.24%
73.55%
Arkansas
2,687
2,180
2,008
92.12%
1,160
919
2,411,125
72.47%
66.76%
California
9,464
8,223
6,869
83.58%
4,692
3,640
31,060,033
72.25%
60.39%
Colorado
3,127
2,571
2,300
88.95%
1,153
921
4,187,811
76.05%
67.64%
Connecticut
2,805
2,398
2,025
84.35%
1,200
951
3,015,283
72.47%
61.13%
Delaware
2,845
2,334
2,054
87.89%
1,109
900
756,390
76.51%
67.24%
District of Columbia
4,627
3,808
3,119
80.97%
1,067
900
534,393
83.28%
67.43%
Florida
13,954
10,951
9,602
86.92%
4,941
4,029
16,131,977
74.96%
65.16%
Georgia
2,255
1,909
1,745
91.50%
1,082
878
7,928,493
77.49%
70.91%
Hawaii
2,835
2,470
2,015
81.14%
1,260
950
1,116,660
72.08%
58.49%
Idaho
2,237
1,842
1,735
94.05%
1,124
916
1,274,823
76.97%
72.39%
Illinois
11,772
10,195
7,912
77.53%
4,929
3,655
10,652,220
68.90%
53.41%
Indiana
2,475
2,015
1,875
93.20%
1,104
896
5,365,682
73.89%
68.86%
Iowa
2,659
2,295
2,137
93.15%
1,137
933
2,537,918
78.95%
73.54%
Kansas
2,579
2,243
2,043
91.08%
1,164
915
2,323,751
75.45%
68.71%
Kentucky
2,619
2,188
2,048
93.62%
1,113
899
3,597,429
76.19%
71.33%
Louisiana
5,114
4,039
3,768
93.48%
2,126
1,746
3,719,351
77.92%
72.83%
Maine
3,568
2,517
2,313
91.74%
1,039
865
1,142,856
79.50%
72.93%
Maryland
2,587
2,290
1,842
80.47%
1,121
924
4,849,618
77.62%
62.47%
Massachusetts
3,419
2,941
2,518
85.24%
1,230
975
5,601,752
74.44%
63.45%
Michigan
11,276
9,000
7,698
85.60%
4,667
3,685
8,291,125
74.32%
63.62%
Minnesota
2,723
2,369
2,135
90.09%
1,160
940
4,434,303
79.23%
71.38%
Mississippi
3,478
2,708
2,504
92.66%
1,462
1,226
2,408,918
77.57%
71.88%
Missouri
2,501
2,073
1,925
92.84%
1,127
912
4,967,492
73.10%
67.86%
Montana
3,075
2,483
2,340
94.29%
1,194
956
835,577
76.54%
72.17%
Nebraska
2,547
2,123
1,956
91.82%
1,178
908
1,500,994
71.98%
66.10%
Nevada
2,125
1,680
1,584
95.22%
1,125
907
2,241,024
74.26%
70.71%
New Hampshire
3,003
2,402
2,099
87.19%
1,228
945
1,127,509
72.59%
63.29%
New Jersey
2,534
2,163
1,898
87.73%
1,129
894
7,385,619
71.57%
62.79%
New Mexico
2,478
1,876
1,769
94.23%
1,134
938
1,695,728
79.87%
75.26%
New York
14,528
12,454
9,093
72.46%
5,123
3,531
16,423,062
63.90%
46.31%
North Carolina
2,843
2,319
2,112
90.63%
1,103
935
7,910,951
80.92%
73.34%
North Dakota
3,321
2,629
2,476
94.18%
1,133
904
565,372
74.23%
69.91%
Ohio
11,134
9,463
8,496
89.29%
4,697
3,695
9,616,044
74.43%
66.45%
Oklahoma
2,614
2,068
1,895
91.72%
1,128
890
3,073,328
76.09%
69.79%
Oregon
2,729
2,389
2,171
90.89%
1,190
951
3,261,406
76.65%
69.66%
Pennsylvania
10,738
9,207
7,401
79.86%
4,011
3,074
10,760,673
72.87%
58.19%
Rhode Island
2,634
2,140
1,896
88.56%
1,155
930
893,903
73.56%
65.14%
South Carolina
2,978
2,441
2,205
90.33%
1,143
927
3,853,142
74.53%
67.32%
South Dakota
2,495
2,128
2,027
95.23%
1,107
913
667,896
77.20%
73.52%
Tennessee
2,590
2,149
1,914
89.19%
1,110
911
5,312,944
77.92%
69.50%
Texas
9,328
7,741
7,096
91.51%
4,478
3,636
20,486,703
75.86%
69.43%
Utah
1,797
1,590
1,505
94.62%
1,125
918
2,176,506
77.23%
73.08%
Vermont
3,217
2,581
2,326
90.14%
1,136
925
540,755
78.83%
71.06%
Virginia
2,726
2,431
2,074
85.29%
1,105
939
6,647,559
81.71%
69.69%
Washington
2,950
2,586
2,298
88.23%
1,254
959
5,668,143
72.78%
64.22%
West Virginia
3,238
2,546
2,258
87.80%
1,166
938
1,573,884
75.61%
66.39%
Wisconsin
2,708
2,284
2,125
92.73%
1,167
902
4,764,652
75.45%
69.97%
Wyoming
3,057
2,441
2,197
89.85%
1,095
892
466,065
78.14%
70.21%
Table C.6 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2011
State
12-17 Total Selected
12-17 Total Responded
12-17 Population Estimate
12-17 Weighted Interview Response Rate
18-25 Total Selected
18-25 Total Responded
18-25 Population Estimate
18-25 Weighted Interview Response Rate
26+ Total Selected
26+ Total Responded
26+ Population Estimate
26+ Weighted Interview Response Rate
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2011.
Total U.S.
27,911
23,549
24,973,646
84.95%
28,589
23,083
34,301,730
80.48%
32,036
23,477
198,323,568
71.96%
Northeast
5,443
4,425
4,277,870
82.07%
5,465
4,270
6,120,583
77.18%
6,343
4,395
36,492,959
67.15%
Midwest
7,649
6,388
5,445,784
83.26%
7,982
6,373
7,340,274
80.46%
8,939
6,497
42,901,391
71.62%
South
9,087
7,870
9,256,114
87.02%
9,028
7,542
12,610,321
83.06%
10,007
7,568
73,315,362
74.47%
West
5,732
4,866
5,993,878
85.37%
6,114
4,898
8,230,553
78.93%
6,747
5,017
45,613,857
72.13%
Alabama
529
452
385,875
85.66%
577
486
536,911
83.41%
602
445
3,062,807
71.72%
Alaska
392
333
60,921
85.33%
368
284
79,374
77.63%
361
288
428,860
79.00%
Arizona
363
308
535,373
86.03%
375
308
705,171
83.29%
388
312
4,044,814
81.51%
Arkansas
351
296
234,612
84.34%
431
352
316,930
81.16%
378
271
1,859,582
69.15%
California
1,403
1,181
3,173,750
84.94%
1,562
1,230
4,401,989
78.04%
1,727
1,229
23,484,294
69.41%
Colorado
376
326
395,811
84.87%
361
290
552,881
80.31%
416
305
3,239,119
74.43%
Connecticut
361
309
292,050
86.67%
389
320
366,697
83.62%
450
322
2,356,536
68.68%
Delaware
347
292
69,137
84.31%
349
295
100,448
82.88%
413
313
586,805
74.47%
District of Columbia
343
304
31,407
88.80%
408
339
97,511
82.66%
316
257
405,475
83.00%
Florida
1,649
1,440
1,380,074
87.03%
1,466
1,222
1,947,535
82.91%
1,826
1,367
12,804,369
72.50%
Georgia
360
312
821,078
87.30%
309
254
1,073,944
81.77%
413
312
6,033,471
75.45%
Hawaii
395
303
98,668
74.86%
412
329
135,970
82.72%
453
318
882,022
70.07%
Idaho
382
331
138,364
87.43%
326
269
173,071
83.08%
416
316
963,388
74.47%
Illinois
1,547
1,254
1,063,049
81.28%
1,630
1,207
1,394,519
73.93%
1,752
1,194
8,194,652
66.32%
Indiana
336
292
540,048
86.96%
374
315
728,277
84.58%
394
289
4,097,357
70.25%
Iowa
395
332
241,080
85.04%
320
273
344,974
84.99%
422
328
1,951,863
77.28%
Kansas
338
279
235,652
82.61%
394
321
320,124
82.19%
432
315
1,767,975
73.31%
Kentucky
359
297
339,927
83.56%
355
300
457,966
84.54%
399
302
2,799,536
73.80%
Louisiana
671
588
367,017
88.27%
666
567
525,065
87.75%
789
591
2,827,268
74.55%
Maine
350
300
97,195
85.41%
348
296
129,785
84.83%
341
269
915,876
77.99%
Maryland
370
324
460,905
87.15%
368
303
624,724
82.56%
383
297
3,763,989
75.67%
Massachusetts
461
384
495,429
83.49%
410
330
765,174
79.20%
359
261
4,341,149
72.35%
Michigan
1,420
1,195
819,033
84.29%
1,569
1,261
1,094,805
80.72%
1,678
1,229
6,377,287
71.97%
Minnesota
370
315
425,134
85.39%
339
274
570,169
81.72%
451
351
3,439,001
78.13%
Mississippi
452
410
248,626
91.19%
453
390
335,084
85.87%
557
426
1,825,208
74.15%
Missouri
338
293
476,256
82.39%
359
304
654,304
84.44%
430
315
3,836,932
70.24%
Montana
352
299
74,309
83.99%
396
326
106,543
82.17%
446
331
654,725
74.87%
Nebraska
342
298
146,677
87.64%
418
315
205,271
76.00%
418
295
1,149,047
69.10%
Nevada
239
204
218,674
89.40%
446
381
280,630
88.39%
440
322
1,741,720
70.36%
New Hampshire
407
324
103,573
79.53%
404
327
138,419
81.88%
417
294
885,517
70.19%
New Jersey
350
301
712,565
87.81%
360
295
870,975
84.31%
419
298
5,802,078
67.72%
New Mexico
319
280
169,846
87.11%
393
326
226,296
80.21%
422
332
1,299,586
78.88%
New York
1,537
1,180
1,482,881
76.97%
1,702
1,176
2,238,168
68.70%
1,884
1,175
12,702,014
61.53%
North Carolina
379
339
754,179
89.13%
339
282
1,016,089
81.19%
385
314
6,140,683
79.89%
North Dakota
334
291
48,835
87.85%
398
325
89,850
81.27%
401
288
426,688
71.23%
Ohio
1,491
1,220
932,467
81.91%
1,462
1,184
1,228,851
80.53%
1,744
1,291
7,454,725
72.47%
Oklahoma
322
264
302,691
82.91%
389
311
421,806
81.30%
417
315
2,348,831
74.21%
Oregon
414
355
291,549
86.35%
373
286
409,460
76.97%
403
310
2,560,397
75.46%
Pennsylvania
1,252
1,023
969,456
83.05%
1,105
889
1,406,406
81.30%
1,654
1,162
8,384,811
70.33%
Rhode Island
356
301
78,432
84.88%
372
324
132,407
87.65%
427
305
683,065
69.48%
South Carolina
348
302
356,131
86.42%
392
331
511,928
84.82%
403
294
2,985,082
71.06%
South Dakota
363
317
64,382
86.27%
340
295
90,856
85.84%
404
301
512,659
74.58%
Tennessee
336
293
503,104
88.26%
358
297
679,027
82.54%
416
321
4,130,814
75.89%
Texas
1,516
1,314
2,251,878
87.02%
1,426
1,180
2,896,598
82.35%
1,536
1,142
15,338,228
72.77%
Utah
350
317
264,830
90.99%
350
278
362,847
77.60%
425
323
1,548,828
74.74%
Vermont
369
303
46,290
83.39%
375
313
72,552
84.62%
392
309
421,913
77.36%
Virginia
378
332
618,074
87.87%
354
307
879,583
85.65%
373
300
5,149,902
80.14%
Washington
367
309
529,144
83.87%
447
339
733,670
74.35%
440
311
4,405,329
71.11%
West Virginia
377
311
131,399
82.69%
388
326
189,172
84.72%
401
301
1,253,313
73.59%
Wisconsin
375
302
453,172
80.52%
379
299
618,275
81.47%
413
301
3,693,206
73.70%
Wyoming
380
320
42,640
84.62%
305
252
62,649
83.42%
410
320
360,775
76.42%
Table C.7 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2009 and 2010
State
Total Selected DUs
Total Eligible DUs
Total Completed Screeners
Weighted DU Screening Response Rate
Total Selected
Total Responded
Population Estimate
Weighted Interview Response Rate
Weighted Overall Response Rate
DU = dwelling unit.
NOTE: To compute the pooled 2009-2010 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2009 and 2010 individual response rates. The 2009-2010 population estimate is the average of the 2009 and the 2010 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009 and 2010 (Revised March 2012).
Total U.S.
396,997
327,909
289,943
88.41%
169,782
135,811
252,717,320
75.06%
66.36%
Northeast
85,617
71,045
58,623
81.77%
33,689
26,147
46,460,466
72.81%
59.54%
Midwest
107,890
90,662
81,478
90.10%
47,966
38,434
55,256,321
75.39%
67.92%
South
124,711
100,677
90,824
90.92%
51,109
41,694
92,505,378
76.80%
69.83%
West
78,779
65,525
59,018
88.16%
37,018
29,536
58,495,154
73.83%
65.08%
Alabama
5,710
4,570
4,227
92.46%
2,295
1,822
3,884,861
75.20%
69.53%
Alaska
4,529
3,487
3,214
92.05%
2,167
1,770
554,985
78.55%
72.31%
Arizona
5,378
4,109
3,639
86.23%
2,259
1,841
5,348,799
76.07%
65.60%
Arkansas
5,169
4,212
3,913
92.90%
2,256
1,813
2,367,178
76.23%
70.82%
California
18,216
15,848
13,409
84.68%
9,473
7,375
30,200,952
71.90%
60.88%
Colorado
5,256
4,356
4,000
92.16%
2,312
1,888
4,124,003
78.41%
72.27%
Connecticut
4,805
4,219
3,617
85.61%
2,298
1,841
2,944,171
75.81%
64.90%
Delaware
5,216
4,253
3,719
87.48%
2,228
1,809
734,670
75.59%
66.13%
District of Columbia
9,435
7,703
6,254
80.24%
2,152
1,821
514,116
82.50%
66.19%
Florida
24,594
18,682
16,931
90.42%
8,867
7,303
15,548,303
77.05%
69.67%
Georgia
4,680
3,842
3,520
91.50%
2,213
1,817
7,893,753
76.92%
70.38%
Hawaii
6,070
5,161
4,252
81.28%
2,617
1,934
1,049,989
66.94%
54.41%
Idaho
4,876
3,811
3,603
94.55%
2,232
1,828
1,242,898
77.67%
73.44%
Illinois
20,722
17,902
14,489
80.88%
9,548
7,264
10,610,876
71.24%
57.62%
Indiana
5,462
4,507
4,191
92.80%
2,261
1,820
5,273,705
76.51%
71.00%
Iowa
5,141
4,390
4,118
93.87%
2,212
1,849
2,494,295
80.38%
75.46%
Kansas
4,704
4,041
3,730
92.28%
2,233
1,794
2,288,038
75.43%
69.60%
Kentucky
4,994
4,093
3,819
93.34%
2,227
1,812
3,562,425
76.76%
71.64%
Louisiana
5,220
4,217
3,948
93.67%
2,255
1,829
3,650,936
78.43%
73.47%
Maine
6,536
4,743
4,347
91.50%
2,232
1,888
1,128,113
81.63%
74.69%
Maryland
4,646
3,966
3,236
81.58%
2,098
1,719
4,721,886
78.64%
64.15%
Massachusetts
6,393
5,529
4,750
86.07%
2,388
1,899
5,584,646
76.02%
65.42%
Michigan
21,188
16,972
14,968
88.12%
9,091
7,329
8,318,631
76.25%
67.20%
Minnesota
4,866
4,071
3,803
93.44%
2,281
1,871
4,369,150
78.00%
72.88%
Mississippi
4,569
3,595
3,366
93.67%
2,177
1,784
2,369,559
77.09%
72.21%
Missouri
5,171
4,247
3,964
93.34%
2,254
1,810
4,939,693
75.72%
70.67%
Montana
5,226
4,403
4,154
94.27%
2,256
1,828
817,248
76.46%
72.08%
Nebraska
4,610
3,936
3,713
94.32%
2,245
1,817
1,463,255
75.92%
71.61%
Nevada
5,279
4,126
3,876
94.53%
2,332
1,888
2,149,864
72.06%
68.11%
New Hampshire
6,018
4,813
4,223
87.84%
2,350
1,862
1,127,079
74.47%
65.41%
New Jersey
4,699
4,051
3,597
88.82%
2,329
1,829
7,255,812
75.41%
66.99%
New Mexico
5,158
4,110
3,875
94.26%
2,232
1,830
1,635,195
77.18%
72.75%
New York
26,232
21,952
16,741
75.99%
10,082
7,333
16,395,090
68.69%
52.20%
North Carolina
5,191
4,393
4,037
92.05%
2,215
1,833
7,645,726
78.04%
71.83%
North Dakota
5,972
4,994
4,710
94.32%
2,337
1,883
537,282
76.49%
72.15%
Ohio
20,068
17,122
15,794
92.21%
9,025
7,316
9,581,162
74.86%
69.03%
Oklahoma
5,274
4,264
3,867
90.79%
2,297
1,831
2,983,240
73.81%
67.01%
Oregon
5,405
4,672
4,330
92.80%
2,304
1,854
3,214,493
77.46%
71.88%
Pennsylvania
19,898
17,082
13,562
79.45%
7,648
5,900
10,595,438
73.08%
58.06%
Rhode Island
5,353
4,437
3,927
88.51%
2,272
1,828
892,872
75.54%
66.85%
South Carolina
5,713
4,514
4,072
89.88%
2,291
1,881
3,745,403
75.94%
68.25%
South Dakota
4,816
4,078
3,887
95.36%
2,203
1,849
662,841
80.79%
77.04%
Tennessee
5,611
4,614
4,266
92.24%
2,289
1,850
5,217,297
73.41%
67.72%
Texas
17,537
14,468
13,288
91.84%
8,819
7,186
19,683,472
77.13%
70.84%
Utah
3,046
2,700
2,558
94.73%
2,206
1,837
2,162,531
80.09%
75.87%
Vermont
5,683
4,219
3,859
91.46%
2,090
1,767
537,245
80.87%
73.96%
Virginia
5,108
4,455
3,961
88.89%
2,221
1,806
6,440,709
76.78%
68.24%
Washington
4,995
4,386
4,016
91.51%
2,352
1,833
5,547,471
73.61%
67.35%
West Virginia
6,044
4,836
4,400
91.05%
2,209
1,778
1,541,846
76.08%
69.27%
Wisconsin
5,170
4,402
4,111
93.41%
2,276
1,832
4,717,394
76.72%
71.66%
Wyoming
5,345
4,356
4,092
94.00%
2,276
1,830
446,727
75.87%
71.31%
Table C.8 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2009 and 2010
State
12-17 Total Selected
12-17 Total Responded
12-17 Population Estimate
12-17 Weighted Interview Response Rate
18-25 Total Selected
18-25 Total Responded
18-25 Population Estimate
18-25 Weighted Interview Response Rate
26+ Total Selected
26+ Total Responded
26+ Population Estimate
26+ Weighted Interview Response Rate
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
NOTE: To compute the pooled 2009-2010 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2009 and 2010 individual response rates. The 2009-2010 population estimate is the average of the 2009 and the 2010 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009 and 2010 (Revised March 2012).
Total U.S.
52,065
44,408
24,477,758
85.13%
56,322
45,956
33,826,169
81.34%
61,395
45,447
194,413,393
72.68%
Northeast
10,151
8,467
4,270,217
82.16%
11,250
8,813
6,082,819
77.53%
12,288
8,867
36,107,431
70.89%
Midwest
14,808
12,662
5,372,501
85.35%
15,838
12,914
7,417,088
80.97%
17,320
12,858
42,466,732
73.14%
South
15,840
13,625
8,982,778
86.35%
16,974
14,237
12,220,871
83.88%
18,295
13,832
71,301,729
74.34%
West
11,266
9,654
5,852,262
85.21%
12,260
9,992
8,105,391
80.74%
13,492
9,890
44,537,502
71.09%
Alabama
759
633
375,942
83.76%
690
567
515,510
81.36%
846
622
2,993,410
73.13%
Alaska
660
568
58,753
86.39%
725
608
81,679
84.83%
782
594
414,553
76.26%
Arizona
676
592
538,672
87.18%
828
677
698,979
80.25%
755
572
4,111,148
74.02%
Arkansas
682
590
231,881
87.09%
738
598
301,928
82.45%
836
625
1,833,369
73.79%
California
2,905
2,472
3,101,979
84.51%
2,983
2,391
4,325,900
80.19%
3,585
2,512
22,773,074
68.65%
Colorado
677
596
381,533
85.87%
841
681
549,726
82.10%
794
611
3,192,744
76.74%
Connecticut
698
596
283,906
86.38%
781
638
375,156
81.73%
819
607
2,285,109
73.51%
Delaware
677
578
67,805
85.01%
759
638
94,200
84.15%
792
593
572,665
73.09%
District of Columbia
644
574
34,683
89.17%
786
664
86,622
83.83%
722
583
392,811
81.55%
Florida
2,736
2,341
1,336,737
85.42%
2,957
2,540
1,850,052
85.26%
3,174
2,422
12,361,513
74.87%
Georgia
715
619
820,145
87.07%
697
596
1,050,786
84.56%
801
602
6,022,823
74.13%
Hawaii
791
649
91,104
80.44%
836
620
131,160
74.46%
990
665
827,725
64.29%
Idaho
684
578
131,965
84.90%
707
610
171,302
86.06%
841
640
939,631
75.18%
Illinois
2,763
2,299
1,053,276
83.16%
3,170
2,419
1,460,312
76.02%
3,615
2,546
8,097,288
68.87%
Indiana
721
626
525,525
87.68%
699
567
701,086
81.07%
841
627
4,047,093
74.29%
Iowa
675
589
236,022
87.73%
761
629
350,071
81.98%
776
631
1,908,202
79.22%
Kansas
678
599
226,545
88.52%
772
607
330,970
78.85%
783
588
1,730,522
73.02%
Kentucky
659
566
334,420
86.76%
766
632
446,645
82.33%
802
614
2,781,361
74.80%
Louisiana
720
612
367,519
85.09%
711
593
527,254
83.19%
824
624
2,756,163
76.67%
Maine
704
621
96,374
88.37%
750
636
128,182
85.09%
778
631
903,556
80.42%
Maryland
630
545
452,038
87.03%
701
580
616,208
82.95%
767
594
3,653,639
76.74%
Massachusetts
711
584
494,016
81.38%
820
673
766,014
81.72%
857
642
4,324,616
74.42%
Michigan
2,895
2,455
822,105
84.26%
2,923
2,420
1,097,830
82.89%
3,273
2,454
6,398,696
74.03%
Minnesota
692
603
413,410
86.54%
806
660
583,280
81.49%
783
608
3,372,460
76.30%
Mississippi
633
545
248,816
86.72%
740
634
336,097
85.98%
804
605
1,784,646
74.21%
Missouri
715
594
476,436
83.59%
738
614
643,637
82.82%
801
602
3,819,620
73.48%
Montana
698
597
73,735
86.08%
746
614
110,260
82.19%
812
617
633,252
74.40%
Nebraska
673
590
142,549
88.08%
747
610
214,428
82.01%
825
617
1,106,278
73.22%
Nevada
661
576
212,438
88.15%
796
673
257,198
84.96%
875
639
1,680,228
68.13%
New Hampshire
687
577
103,281
84.66%
823
673
140,176
81.96%
840
612
883,622
72.07%
New Jersey
732
614
695,052
83.02%
742
581
873,788
79.50%
855
634
5,686,972
73.83%
New Mexico
710
632
161,555
89.22%
738
613
228,756
84.91%
784
585
1,244,885
74.15%
New York
2,917
2,344
1,509,858
79.67%
3,427
2,483
2,236,966
72.75%
3,738
2,506
12,648,267
66.66%
North Carolina
655
584
723,670
89.21%
791
662
986,404
84.66%
769
587
5,935,653
75.49%
North Dakota
727
625
47,211
85.91%
749
626
92,922
84.11%
861
632
397,148
73.64%
Ohio
2,788
2,402
924,820
85.90%
3,059
2,577
1,214,036
83.80%
3,178
2,337
7,442,306
71.96%
Oklahoma
759
646
292,084
85.05%
704
565
419,077
79.42%
834
620
2,272,080
71.31%
Oregon
795
654
288,096
81.63%
677
560
401,242
83.81%
832
640
2,525,156
75.91%
Pennsylvania
2,360
1,943
962,444
82.77%
2,425
1,905
1,360,835
78.80%
2,863
2,052
8,272,159
70.95%
Rhode Island
704
625
79,655
89.14%
784
625
129,145
79.80%
784
578
684,072
73.06%
South Carolina
757
643
352,096
84.90%
747
646
480,982
85.11%
787
592
2,912,325
73.20%
South Dakota
687
601
63,682
87.84%
723
625
93,602
86.97%
793
623
505,557
78.72%
Tennessee
764
670
491,069
87.97%
712
591
646,257
84.14%
813
589
4,079,971
69.53%
Texas
2,671
2,307
2,125,058
86.57%
2,971
2,484
2,813,275
83.69%
3,177
2,395
14,745,138
74.53%
Utah
640
568
254,680
89.28%
782
657
379,390
83.54%
784
612
1,528,461
77.83%
Vermont
638
563
45,631
89.01%
698
599
72,557
85.03%
754
605
419,057
79.23%
Virginia
697
592
598,313
84.83%
745
631
865,844
84.69%
779
583
4,976,551
74.34%
Washington
722
612
516,464
85.23%
774
606
706,882
76.50%
856
615
4,324,124
71.76%
West Virginia
682
580
130,503
84.95%
759
616
183,731
80.47%
768
582
1,227,612
74.42%
Wisconsin
794
679
440,921
85.20%
691
560
634,912
79.83%
791
593
3,641,561
75.10%
Wyoming
647
560
41,288
85.11%
827
682
62,918
81.96%
802
588
342,522
73.63%
Table C.9 Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2010 and 2011
State
Total Selected DUs
Total Eligible DUs
Total Completed Screeners
Weighted DU Screening Response Rate
Total Selected
Total Responded
Population Estimate
Weighted Interview Response Rate
Weighted Overall Response Rate
DU = dwelling unit.
NOTE: To compute the pooled 2010-2011 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2010 and 2011 individual response rates. The 2010-2011 population estimate is the average of the 2010 and the 2011 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2010 and 2011 (2010 Data – Revised March 2012).
Total U.S.
418,386
345,825
303,058
87.69%
173,533
137,913
255,609,026
74.47%
65.31%
Northeast
89,866
74,836
61,214
80.86%
34,033
26,107
46,713,366
71.34%
57.69%
Midwest
112,957
94,709
83,923
89.01%
48,709
38,559
55,516,454
74.36%
66.19%
South
134,634
108,995
97,517
90.00%
53,719
43,749
94,071,846
76.56%
68.90%
West
80,929
67,285
60,404
88.16%
37,072
29,498
59,307,360
73.79%
65.05%
Alabama
7,217
5,644
5,131
90.93%
2,829
2,261
3,939,640
73.25%
66.61%
Alaska
4,685
3,630
3,283
90.47%
2,178
1,773
562,560
78.63%
71.13%
Arizona
5,386
4,208
3,776
89.79%
2,275
1,853
5,336,070
77.24%
69.36%
Arkansas
5,282
4,288
3,956
92.32%
2,283
1,818
2,393,558
73.83%
68.16%
California
18,746
16,310
13,779
84.49%
9,431
7,355
30,691,087
72.11%
60.93%
Colorado
5,656
4,655
4,212
90.44%
2,270
1,825
4,169,870
77.62%
70.20%
Connecticut
5,279
4,556
3,837
84.04%
2,351
1,877
2,983,250
73.84%
62.05%
Delaware
5,466
4,452
3,911
87.78%
2,208
1,789
746,980
77.02%
67.60%
District of Columbia
9,740
8,000
6,522
80.39%
2,177
1,835
526,168
82.34%
66.19%
Florida
27,160
20,912
18,493
87.99%
9,401
7,684
15,871,875
76.14%
66.99%
Georgia
4,640
3,887
3,549
91.36%
2,213
1,788
7,934,572
76.54%
69.92%
Hawaii
5,696
4,913
4,113
83.26%
2,556
1,924
1,082,202
69.54%
57.90%
Idaho
4,861
3,888
3,667
94.23%
2,237
1,828
1,262,531
77.58%
73.10%
Illinois
22,386
19,316
15,304
79.23%
9,691
7,264
10,640,868
69.85%
55.34%
Indiana
5,218
4,296
3,979
92.58%
2,246
1,812
5,325,850
73.89%
68.41%
Iowa
5,233
4,482
4,206
93.84%
2,250
1,858
2,520,016
78.93%
74.07%
Kansas
4,919
4,231
3,867
91.40%
2,265
1,800
2,310,019
75.11%
68.65%
Kentucky
5,202
4,335
4,039
93.20%
2,222
1,799
3,586,107
76.54%
71.33%
Louisiana
7,719
6,131
5,723
93.45%
3,238
2,652
3,690,586
77.94%
72.84%
Maine
6,895
4,921
4,510
91.36%
2,139
1,789
1,135,070
80.10%
73.18%
Maryland
5,002
4,351
3,534
81.26%
2,217
1,807
4,793,712
77.64%
63.09%
Massachusetts
6,535
5,657
4,883
86.33%
2,379
1,905
5,603,697
76.41%
65.97%
Michigan
22,104
17,669
15,321
86.70%
9,228
7,375
8,302,279
74.98%
65.01%
Minnesota
5,255
4,456
4,084
91.65%
2,309
1,886
4,408,217
78.79%
72.22%
Mississippi
5,963
4,684
4,343
92.86%
2,549
2,119
2,391,255
77.03%
71.53%
Missouri
5,143
4,243
3,956
93.20%
2,269
1,833
4,960,194
74.46%
69.39%
Montana
5,788
4,738
4,468
94.32%
2,331
1,875
827,846
76.72%
72.36%
Nebraska
4,883
4,119
3,839
93.08%
2,298
1,814
1,485,062
72.58%
67.56%
Nevada
4,799
3,743
3,519
94.95%
2,308
1,865
2,198,214
73.08%
69.39%
New Hampshire
6,235
4,960
4,318
86.99%
2,388
1,863
1,128,253
73.52%
63.96%
New Jersey
4,916
4,224
3,729
88.31%
2,286
1,817
7,327,726
74.96%
66.20%
New Mexico
5,088
3,954
3,728
94.25%
2,251
1,850
1,668,810
78.53%
74.01%
New York
27,746
23,624
17,545
73.83%
10,184
7,157
16,416,573
65.37%
48.27%
North Carolina
5,517
4,622
4,230
91.40%
2,206
1,839
7,795,039
78.85%
72.07%
North Dakota
6,374
5,196
4,896
94.24%
2,321
1,858
552,787
75.28%
70.94%
Ohio
21,402
18,180
16,443
90.23%
9,330
7,426
9,598,203
74.61%
67.32%
Oklahoma
5,240
4,190
3,798
90.71%
2,301
1,813
3,034,446
74.62%
67.68%
Oregon
5,332
4,682
4,317
92.22%
2,324
1,858
3,245,308
75.79%
69.89%
Pennsylvania
20,931
17,922
14,353
79.82%
7,864
6,059
10,683,992
73.05%
58.31%
Rhode Island
5,208
4,234
3,762
88.87%
2,272
1,845
895,144
74.02%
65.78%
South Carolina
5,594
4,593
4,132
89.95%
2,281
1,854
3,806,883
75.12%
67.57%
South Dakota
4,894
4,176
3,972
95.14%
2,222
1,842
667,242
78.84%
75.01%
Tennessee
5,178
4,298
3,882
90.31%
2,227
1,812
5,275,759
75.73%
68.39%
Texas
18,213
15,031
13,793
91.64%
8,909
7,226
20,167,102
76.24%
69.87%
Utah
3,304
2,914
2,757
94.60%
2,230
1,837
2,178,698
78.58%
74.34%
Vermont
6,121
4,738
4,277
90.26%
2,170
1,795
539,662
80.61%
72.76%
Virginia
5,335
4,715
4,111
87.11%
2,201
1,827
6,559,374
79.09%
68.89%
Washington
5,586
4,874
4,401
89.98%
2,448
1,856
5,626,876
71.47%
64.30%
West Virginia
6,166
4,862
4,370
89.51%
2,257
1,826
1,558,789
76.90%
68.83%
Wisconsin
5,146
4,345
4,056
93.17%
2,280
1,791
4,745,719
76.12%
70.92%
Wyoming
6,002
4,776
4,384
91.76%
2,233
1,799
457,289
75.64%
69.41%
Table C.10 Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2010 and 2011
State
12-17 Total Selected
12-17 Total Responded
12-17 Population Estimate
12-17 Weighted Interview Response Rate
18-25 Total Selected
18-25 Total Responded
18-25 Population Estimate
18-25 Weighted Interview Response Rate
26+ Total Selected
26+ Total Responded
26+ Population Estimate
26+ Weighted Interview Response Rate
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
NOTE: To compute the pooled 2010-2011 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2010 and 2011 individual response rates. The 2010-2011 population estimate is the average of the 2010 and the 2011 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2010 and 2011 (2010 Data – Revised March 2012).
Total U.S.
53,819
45,541
24,660,087
84.80%
56,753
46,109
34,187,040
80.84%
62,961
46,263
196,761,899
72.05%
Northeast
10,409
8,530
4,256,314
81.69%
11,077
8,682
6,082,801
77.31%
12,547
8,895
36,374,251
69.12%
Midwest
15,006
12,652
5,390,170
84.20%
16,017
12,962
7,418,402
80.96%
17,686
12,945
42,707,882
71.98%
South
17,116
14,728
9,106,336
86.31%
17,435
14,569
12,514,566
83.18%
19,168
14,452
72,450,944
74.13%
West
11,288
9,631
5,907,267
85.28%
12,224
9,896
8,171,271
79.75%
13,560
9,971
45,228,822
71.22%
Alabama
898
759
379,971
84.34%
922
772
528,943
82.38%
1,009
730
3,030,727
70.26%
Alaska
704
599
59,141
85.44%
730
594
82,230
81.63%
744
580
421,188
77.02%
Arizona
696
600
536,957
86.69%
803
659
703,220
81.23%
776
594
4,095,893
75.34%
Arkansas
685
580
233,536
84.63%
793
648
311,224
81.67%
805
590
1,848,798
70.96%
California
2,929
2,484
3,130,240
84.87%
2,978
2,381
4,335,049
79.46%
3,524
2,490
23,225,798
69.01%
Colorado
649
557
387,484
83.93%
785
635
559,635
80.89%
836
633
3,222,752
76.39%
Connecticut
692
597
286,904
87.37%
789
646
374,028
82.49%
870
634
2,322,318
70.59%
Delaware
666
560
68,185
83.83%
689
583
97,063
83.96%
853
646
581,733
75.06%
District of Columbia
699
628
32,823
90.41%
792
659
91,252
82.53%
686
548
402,092
81.60%
Florida
3,073
2,655
1,355,015
86.45%
2,885
2,434
1,909,018
84.00%
3,443
2,595
12,607,843
73.83%
Georgia
731
625
819,770
85.85%
664
555
1,075,015
83.21%
818
608
6,039,787
74.05%
Hawaii
795
641
94,257
78.95%
851
664
133,155
80.46%
910
619
854,791
66.75%
Idaho
735
625
134,591
85.35%
682
574
175,303
84.12%
820
629
952,637
75.29%
Illinois
2,904
2,376
1,056,364
81.95%
3,245
2,439
1,423,767
75.16%
3,542
2,449
8,160,738
67.34%
Indiana
725
633
531,919
87.56%
717
595
723,659
83.07%
804
584
4,070,272
70.54%
Iowa
731
619
237,564
85.09%
705
594
352,176
83.43%
814
645
1,930,275
77.42%
Kansas
669
575
230,525
85.92%
751
606
329,289
81.69%
845
619
1,750,205
72.51%
Kentucky
711
596
336,580
84.36%
725
604
459,932
83.37%
786
599
2,789,595
74.46%
Louisiana
1,053
916
366,321
87.36%
1,011
852
525,574
85.26%
1,174
884
2,798,691
75.28%
Maine
675
584
95,848
86.60%
704
598
130,378
85.02%
760
607
908,845
78.67%
Maryland
685
592
454,455
86.65%
735
603
619,127
81.25%
797
612
3,720,130
75.94%
Massachusetts
821
680
493,546
81.94%
802
654
763,089
80.41%
756
571
4,347,062
75.03%
Michigan
2,852
2,407
816,665
84.20%
3,022
2,481
1,100,008
82.61%
3,354
2,487
6,385,606
72.49%
Minnesota
707
611
417,213
86.42%
749
614
580,436
82.28%
853
661
3,410,568
77.31%
Mississippi
785
700
248,024
89.49%
821
706
337,611
85.79%
943
713
1,805,620
73.68%
Missouri
679
581
474,419
83.92%
745
624
655,582
83.56%
845
628
3,830,193
71.84%
Montana
700
601
73,285
85.43%
739
606
110,681
81.92%
892
668
643,880
74.97%
Nebraska
677
598
143,963
87.96%
790
621
212,075
79.60%
831
595
1,129,023
69.24%
Nevada
537
468
214,554
89.94%
851
720
272,251
85.86%
920
677
1,711,409
69.17%
New Hampshire
707
574
102,528
82.05%
871
714
141,973
82.36%
810
575
883,752
70.95%
New Jersey
737
625
702,580
85.59%
694
559
868,283
82.88%
855
633
5,756,864
72.46%
New Mexico
683
607
165,536
88.22%
763
629
226,630
81.87%
805
614
1,276,644
76.65%
New York
2,994
2,321
1,490,465
77.26%
3,411
2,410
2,213,444
70.09%
3,779
2,426
12,712,663
63.19%
North Carolina
725
650
736,999
89.48%
714
586
1,015,292
81.72%
767
603
6,042,747
76.99%
North Dakota
691
591
47,606
85.83%
791
665
93,205
84.30%
839
602
411,976
72.09%
Ohio
2,886
2,411
925,508
83.58%
3,096
2,555
1,219,501
82.05%
3,348
2,460
7,453,194
72.24%
Oklahoma
716
601
297,063
83.77%
744
589
423,749
79.16%
841
623
2,313,634
72.62%
Oregon
790
673
288,509
84.77%
734
582
410,811
79.85%
800
603
2,545,988
74.01%
Pennsylvania
2,417
1,978
960,258
82.61%
2,308
1,835
1,385,978
79.94%
3,139
2,246
8,337,756
70.81%
Rhode Island
678
593
78,757
87.53%
790
674
131,124
85.69%
804
578
685,263
70.06%
South Carolina
699
594
352,832
85.13%
768
656
499,582
85.13%
814
604
2,954,469
72.13%
South Dakota
728
626
63,634
85.65%
678
591
93,437
87.00%
816
625
510,171
76.52%
Tennessee
706
612
496,321
87.59%
722
599
671,823
83.26%
799
601
4,107,615
72.93%
Texas
2,845
2,439
2,191,796
85.93%
2,958
2,468
2,877,349
82.98%
3,106
2,319
15,097,957
73.43%
Utah
633
567
260,212
89.94%
770
635
372,167
81.78%
827
635
1,546,318
76.02%
Vermont
688
578
45,429
85.42%
708
592
74,503
83.74%
774
625
419,730
79.52%
Virginia
727
627
606,049
86.46%
714
608
882,246
84.48%
760
592
5,071,079
77.15%
Washington
732
610
520,915
83.71%
824
619
726,355
73.02%
892
627
4,379,606
69.69%
West Virginia
712
594
130,595
83.09%
778
647
189,767
83.35%
767
585
1,238,427
75.24%
Wisconsin
757
624
444,790
82.25%
728
577
635,268
79.88%
795
590
3,665,660
74.63%
Wyoming
705
599
41,586
85.02%
714
598
63,785
83.38%
814
602
351,918
73.08%
Table C.11 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2009, 2010, and 2011
State
2009 Total Selected
2009 Total Responded
2009 Population Estimate
2009 Weighted Interview Response Rate
2010 Total Selected
2010 Total Responded
2010 Population Estimate
2010 Weighted Interview Response Rate
2011 Total Selected
2011 Total Responded
2011 Population Estimate
2011 Weighted Interview Response Rate
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009, 2010, and 2011 (2009 and 2010 Data – Revised March 2012).
Total U.S.
37,009
31,560
38,241,242
85.21%
36,493
30,926
37,977,621
84.37%
38,505
32,349
38,497,742
84.37%
Northeast
7,329
6,098
6,785,606
82.08%
7,068
5,812
6,622,750
80.74%
7,493
6,098
6,824,455
82.10%
Midwest
10,586
9,034
8,472,393
84.90%
10,415
8,841
8,359,258
84.73%
10,686
8,872
8,368,112
82.81%
South
11,053
9,595
13,893,364
87.36%
11,208
9,600
14,070,149
85.73%
12,390
10,682
14,024,266
86.28%
West
8,041
6,833
9,089,879
84.55%
7,802
6,673
8,925,464
84.62%
7,936
6,697
9,280,909
84.53%
Alabama
530
447
599,028
85.17%
510
425
587,014
82.65%
744
631
604,574
84.49%
Alaska
488
416
91,611
85.87%
433
370
86,119
86.09%
515
431
89,332
83.83%
Arizona
514
437
824,443
84.93%
509
441
834,235
85.27%
511
433
798,580
85.99%
Arkansas
469
407
342,551
88.11%
458
380
334,786
82.16%
528
442
374,992
83.30%
California
1,946
1,634
4,775,356
83.65%
2,058
1,755
4,745,134
84.60%
2,003
1,685
5,066,496
84.30%
Colorado
564
494
585,652
86.73%
406
337
551,247
81.84%
480
411
564,436
84.33%
Connecticut
481
407
408,896
85.77%
494
422
446,654
85.63%
516
441
436,152
86.19%
Delaware
518
446
108,455
86.09%
439
375
105,183
85.49%
465
393
105,240
84.25%
District of Columbia
398
346
59,345
85.42%
442
401
56,178
91.06%
487
422
65,173
83.44%
Florida
1,921
1,662
2,133,592
86.01%
2,038
1,759
2,165,742
86.98%
2,250
1,949
2,211,773
86.30%
Georgia
474
420
1,182,593
88.73%
508
430
1,222,236
84.71%
480
413
1,207,618
86.51%
Hawaii
538
426
150,940
76.75%
561
468
142,051
82.88%
541
424
149,682
78.74%
Idaho
468
402
201,917
86.93%
479
409
202,052
85.98%
493
422
205,495
85.84%
Illinois
2,021
1,676
1,681,199
82.87%
1,948
1,593
1,638,431
81.51%
2,144
1,711
1,619,137
79.79%
Indiana
472
406
824,402
86.59%
511
445
808,335
87.62%
489
424
852,672
85.97%
Iowa
484
417
362,489
86.00%
464
400
369,554
85.30%
523
443
382,062
85.81%
Kansas
523
445
362,101
84.25%
452
397
349,540
87.74%
484
398
344,035
82.51%
Kentucky
465
411
533,285
89.41%
508
430
515,140
84.34%
481
400
501,556
83.75%
Louisiana
470
395
559,359
83.59%
507
431
567,474
85.18%
918
804
573,374
88.93%
Maine
515
448
141,437
87.04%
458
405
152,571
89.02%
495
424
153,910
85.35%
Maryland
445
391
679,353
88.22%
428
367
671,790
86.29%
487
422
657,919
85.91%
Massachusetts
503
407
756,845
80.42%
474
387
730,933
80.15%
620
520
822,796
83.78%
Michigan
2,054
1,735
1,286,421
84.14%
1,998
1,690
1,266,567
84.36%
2,034
1,702
1,293,907
83.70%
Minnesota
523
444
687,929
83.63%
496
425
635,101
85.43%
488
411
622,236
84.21%
Mississippi
464
396
401,760
86.27%
483
422
393,379
87.64%
597
539
365,463
90.08%
Missouri
496
410
701,234
83.00%
474
400
741,708
85.70%
465
398
714,937
82.47%
Montana
497
421
116,223
85.63%
480
416
118,731
86.51%
491
411
112,790
82.79%
Nebraska
498
429
240,816
87.71%
469
415
227,519
87.62%
514
427
225,527
83.87%
Nevada
500
438
312,307
88.06%
467
410
329,077
89.28%
440
385
370,767
90.91%
New Hampshire
525
437
162,423
83.24%
485
406
163,192
85.20%
589
479
177,762
82.39%
New Jersey
502
418
1,060,608
81.69%
518
429
1,033,688
82.93%
494
424
1,119,943
88.15%
New Mexico
476
418
255,788
89.54%
502
447
255,942
88.87%
469
404
258,176
84.99%
New York
2,121
1,721
2,486,753
80.92%
2,091
1,629
2,367,030
76.92%
2,120
1,607
2,330,810
76.15%
North Carolina
452
402
1,094,206
89.32%
487
434
1,158,894
89.02%
487
433
1,114,423
88.06%
North Dakota
494
431
82,565
87.52%
516
438
84,291
85.53%
476
414
80,431
86.41%
Ohio
2,014
1,745
1,474,740
86.21%
2,085
1,791
1,450,314
85.68%
2,081
1,715
1,474,645
82.49%
Oklahoma
502
427
460,633
85.55%
510
430
445,994
83.55%
454
373
462,928
83.45%
Oregon
534
429
445,204
81.09%
510
425
435,243
82.04%
534
450
424,881
83.95%
Pennsylvania
1,675
1,384
1,558,771
82.99%
1,641
1,344
1,529,660
81.90%
1,677
1,377
1,583,008
83.76%
Rhode Island
530
447
130,746
83.09%
472
419
127,717
88.13%
483
413
126,155
85.65%
South Carolina
540
471
539,722
86.26%
498
421
554,128
84.56%
482
414
521,289
85.95%
South Dakota
486
439
103,873
90.23%
500
429
98,463
86.31%
470
411
87,535
86.40%
Tennessee
534
473
778,937
88.58%
521
448
783,233
87.28%
462
401
768,020
86.68%
Texas
1,869
1,641
3,208,416
88.06%
1,918
1,638
3,337,978
85.37%
2,010
1,738
3,303,733
86.40%
Utah
483
426
400,057
88.25%
412
364
375,829
88.48%
463
406
364,611
85.21%
Vermont
477
429
79,128
89.98%
435
371
71,307
85.97%
499
413
73,919
84.39%
Virginia
507
441
1,007,100
87.39%
489
415
974,776
85.04%
516
452
966,316
86.34%
Washington
540
464
859,380
85.46%
501
410
783,258
81.66%
510
424
809,041
82.70%
West Virginia
495
419
205,027
84.86%
464
394
196,221
84.13%
542
456
219,874
85.17%
Wisconsin
521
457
664,624
87.47%
502
418
689,435
82.92%
518
418
670,989
81.33%
Wyoming
493
428
71,000
84.56%
484
421
66,546
86.75%
486
411
66,621
85.07%
Table C.12 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2009-2010 and 2010-2011
State
2009-2010 Total Selected
2009-2010 Total Responded
2009-2010 Population Estimate
2009-2010 Weighted Interview Response Rate
2010-2011 Total Selected
2010-2011 Total Responded
2010-2011 Population Estimate
2010-2011 Weighted Interview Response Rate
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
NOTE: To compute the pooled weighted response rates, the two samples were combined, and the individual-year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the individual response rates. The population estimate is the average of the population across the 2 years.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009, 2010, and 2011 (2009 and 2010 Data – Revised March 2012).
Total U.S.
73,502
62,486
38,109,432
84.79%
74,998
63,275
38,237,681
84.37%
Northeast
14,397
11,910
6,704,178
81.42%
14,561
11,910
6,723,602
81.43%
Midwest
21,001
17,875
8,415,825
84.82%
21,101
17,713
8,363,685
83.77%
South
22,261
19,195
13,981,756
86.54%
23,598
20,282
14,047,208
86.01%
West
15,843
13,506
9,007,672
84.58%
15,738
13,370
9,103,187
84.57%
Alabama
1,040
872
593,021
83.90%
1,254
1,056
595,794
83.58%
Alaska
921
786
88,865
85.97%
948
801
87,725
84.92%
Arizona
1,023
878
829,339
85.10%
1,020
874
816,408
85.62%
Arkansas
927
787
338,668
85.08%
986
822
354,889
82.76%
California
4,004
3,389
4,760,245
84.12%
4,061
3,440
4,905,815
84.44%
Colorado
970
831
568,449
84.43%
886
748
557,842
83.09%
Connecticut
975
829
427,775
85.69%
1,010
863
441,403
85.91%
Delaware
957
821
106,819
85.79%
904
768
105,212
84.87%
District of Columbia
840
747
57,762
88.14%
929
823
60,676
86.89%
Florida
3,959
3,421
2,149,667
86.51%
4,288
3,708
2,188,757
86.64%
Georgia
982
850
1,202,415
86.71%
988
843
1,214,927
85.62%
Hawaii
1,099
894
146,495
79.79%
1,102
892
145,867
80.76%
Idaho
947
811
201,984
86.46%
972
831
203,773
85.91%
Illinois
3,969
3,269
1,659,815
82.20%
4,092
3,304
1,628,784
80.65%
Indiana
983
851
816,369
87.10%
1,000
869
830,504
86.77%
Iowa
948
817
366,022
85.65%
987
843
375,808
85.56%
Kansas
975
842
355,820
85.93%
936
795
346,788
85.09%
Kentucky
973
841
524,213
86.87%
989
830
508,348
84.05%
Louisiana
977
826
563,417
84.38%
1,425
1,235
570,424
87.09%
Maine
973
853
147,004
88.02%
953
829
153,241
87.12%
Maryland
873
758
675,572
87.28%
915
789
664,855
86.10%
Massachusetts
977
794
743,889
80.29%
1,094
907
776,864
82.07%
Michigan
4,052
3,425
1,276,494
84.25%
4,032
3,392
1,280,237
84.03%
Minnesota
1,019
869
661,515
84.49%
984
836
628,669
84.82%
Mississippi
947
818
397,570
86.95%
1,080
961
379,421
88.81%
Missouri
970
810
721,471
84.36%
939
798
728,322
84.09%
Montana
977
837
117,477
86.07%
971
827
115,761
84.69%
Nebraska
967
844
234,167
87.67%
983
842
226,523
85.63%
Nevada
967
848
320,692
88.67%
907
795
349,922
90.14%
New Hampshire
1,010
843
162,808
84.20%
1,074
885
170,477
83.73%
New Jersey
1,020
847
1,047,148
82.32%
1,012
853
1,076,815
85.58%
New Mexico
978
865
255,865
89.20%
971
851
257,059
86.88%
New York
4,212
3,350
2,426,892
78.94%
4,211
3,236
2,348,920
76.54%
North Carolina
939
836
1,126,550
89.17%
974
867
1,136,658
88.55%
North Dakota
1,010
869
83,428
86.49%
992
852
82,361
85.95%
Ohio
4,099
3,536
1,462,527
85.95%
4,166
3,506
1,462,479
84.09%
Oklahoma
1,012
857
453,313
84.57%
964
803
454,461
83.50%
Oregon
1,044
854
440,223
81.56%
1,044
875
430,062
82.98%
Pennsylvania
3,316
2,728
1,544,215
82.45%
3,318
2,721
1,556,334
82.84%
Rhode Island
1,002
866
129,231
85.53%
955
832
126,936
86.87%
South Carolina
1,038
892
546,925
85.40%
980
835
537,709
85.23%
South Dakota
986
868
101,168
88.32%
970
840
92,999
86.35%
Tennessee
1,055
921
781,085
87.92%
983
849
775,627
86.99%
Texas
3,787
3,279
3,273,197
86.70%
3,928
3,376
3,320,856
85.89%
Utah
895
790
387,943
88.36%
875
770
370,220
86.84%
Vermont
912
800
75,217
88.07%
934
784
72,613
85.17%
Virginia
996
856
990,938
86.24%
1,005
867
970,546
85.70%
Washington
1,041
874
821,319
83.64%
1,011
834
796,149
82.18%
West Virginia
959
813
200,624
84.50%
1,006
850
208,048
84.69%
Wisconsin
1,023
875
677,030
85.16%
1,020
836
680,212
82.12%
Wyoming
977
849
68,773
85.64%
970
832
66,584
85.91%
Table C.13 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2009, 2010, and 2011
State
2009 Total Selected
2009 Total Responded
2009 Population Estimate
2009 Weighted Interview Response Rate
2010 Total Selected
2010 Total Responded
2010 Population Estimate
2010 Weighted Interview Response Rate
2011 Total Selected
2011 Total Responded
2011 Population Estimate
2011 Weighted Interview Response Rate
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009, 2010, and 2011 (2009 and 2010 Data – Revised March 2012).
Total U.S.
58,628
45,591
227,206,546
74.46%
59,089
45,812
229,272,579
73.49%
60,625
46,560
232,625,299
73.22%
Northeast
11,722
8,768
42,079,937
71.75%
11,816
8,912
42,300,562
71.96%
11,808
8,665
42,613,542
68.62%
Midwest
16,376
12,735
49,756,736
74.91%
16,782
13,037
50,010,904
73.71%
16,921
12,870
50,241,664
72.89%
South
17,701
14,158
83,039,864
76.29%
17,568
13,911
84,005,336
75.22%
19,035
15,110
85,925,683
75.76%
West
12,829
9,930
52,330,008
73.31%
12,923
9,952
52,955,777
71.84%
12,861
9,915
53,844,410
73.17%
Alabama
784
618
3,498,218
77.82%
752
571
3,519,621
70.68%
1,179
931
3,599,718
73.44%
Alaska
762
600
493,862
78.40%
745
602
498,602
76.86%
729
572
508,235
78.77%
Arizona
767
616
4,772,012
78.64%
816
633
4,848,242
71.45%
763
620
4,749,984
81.79%
Arkansas
785
608
2,127,061
76.02%
789
615
2,143,532
74.10%
809
623
2,176,513
71.07%
California
3,355
2,491
26,962,535
70.44%
3,213
2,412
27,235,412
70.49%
3,289
2,459
27,886,283
70.78%
Colorado
791
619
3,712,168
75.96%
844
673
3,772,773
78.94%
777
595
3,792,000
75.18%
Connecticut
780
607
2,651,071
75.54%
820
638
2,669,460
73.79%
839
642
2,723,233
70.84%
Delaware
771
610
663,392
72.25%
780
621
670,337
76.96%
762
608
687,253
75.70%
District of Columbia
754
636
475,164
83.47%
754
611
483,703
80.56%
724
596
502,986
82.93%
Florida
3,095
2,522
14,141,314
75.95%
3,036
2,440
14,281,818
76.56%
3,292
2,589
14,751,904
73.85%
Georgia
738
601
7,025,028
76.93%
760
597
7,122,189
74.41%
722
566
7,107,414
76.39%
Hawaii
930
649
959,869
66.03%
896
636
957,900
65.29%
865
647
1,017,992
71.81%
Idaho
788
632
1,102,446
76.10%
760
618
1,119,419
77.60%
742
585
1,136,459
75.69%
Illinois
3,380
2,478
9,535,363
70.37%
3,405
2,487
9,579,838
69.50%
3,382
2,401
9,589,171
67.45%
Indiana
787
619
4,734,130
78.39%
753
575
4,762,228
72.40%
768
604
4,825,634
72.44%
Iowa
760
622
2,248,480
80.95%
777
638
2,268,066
78.26%
742
601
2,296,838
78.35%
Kansas
785
606
2,052,096
74.78%
770
589
2,070,889
73.21%
826
636
2,088,098
74.63%
Kentucky
811
645
3,214,458
75.52%
757
601
3,241,553
76.04%
754
602
3,257,502
75.37%
Louisiana
805
639
3,270,638
78.34%
730
578
3,296,197
77.01%
1,455
1,158
3,352,333
76.72%
Maine
753
627
1,030,693
82.04%
775
640
1,032,784
80.01%
689
565
1,045,661
78.89%
Maryland
687
559
4,249,895
78.75%
781
615
4,289,800
76.76%
751
600
4,388,713
76.64%
Massachusetts
888
681
5,067,283
72.94%
789
634
5,113,977
78.02%
769
591
5,106,323
73.51%
Michigan
3,067
2,396
7,493,915
75.99%
3,129
2,478
7,499,137
74.75%
3,247
2,490
7,472,092
73.25%
Minnesota
777
618
3,938,642
76.78%
812
650
3,972,838
77.37%
790
625
4,009,170
78.60%
Mississippi
790
636
2,115,316
76.79%
754
603
2,126,170
75.20%
1,010
816
2,160,292
75.97%
Missouri
738
583
4,446,201
74.83%
801
633
4,480,314
74.91%
789
619
4,491,236
72.16%
Montana
769
614
739,171
74.99%
789
617
747,854
76.00%
842
657
761,268
75.83%
Nebraska
787
621
1,313,534
77.60%
785
606
1,327,879
71.62%
836
610
1,354,318
70.17%
Nevada
786
618
1,929,882
70.81%
885
694
1,944,971
69.92%
886
703
2,022,350
72.78%
New Hampshire
803
617
1,020,081
73.47%
860
668
1,027,514
73.45%
821
621
1,023,936
71.86%
New Jersey
827
616
6,544,280
71.29%
770
599
6,577,240
77.93%
779
593
6,673,054
69.81%
New Mexico
769
613
1,466,616
75.96%
753
585
1,480,665
75.70%
815
658
1,525,882
79.08%
New York
3,561
2,504
14,858,432
69.52%
3,604
2,485
14,912,033
65.77%
3,586
2,351
14,940,181
62.61%
North Carolina
803
656
6,884,806
78.46%
757
593
6,959,307
75.02%
724
596
7,156,772
80.07%
North Dakota
779
604
486,318
75.56%
831
654
493,824
75.68%
799
613
516,537
72.93%
Ohio
2,999
2,374
8,650,872
73.65%
3,238
2,540
8,661,813
73.64%
3,206
2,475
8,683,577
73.60%
Oklahoma
759
599
2,678,184
73.20%
779
586
2,704,129
71.96%
806
626
2,770,637
75.32%
Oregon
751
611
2,909,054
79.91%
758
589
2,943,741
73.99%
776
596
2,969,857
75.67%
Pennsylvania
2,600
1,927
9,609,739
71.80%
2,688
2,030
9,656,250
72.35%
2,759
2,051
9,791,217
71.86%
Rhode Island
773
580
809,132
75.38%
795
623
817,303
72.92%
799
629
815,472
72.44%
South Carolina
747
603
3,375,522
75.12%
787
635
3,411,091
74.84%
795
625
3,497,010
73.23%
South Dakota
766
628
594,616
80.10%
750
620
603,702
80.01%
744
596
603,514
76.24%
Tennessee
778
598
4,703,420
71.59%
747
582
4,749,036
71.83%
774
618
4,809,840
76.82%
Texas
3,046
2,414
17,401,039
76.35%
3,102
2,465
17,715,787
75.65%
2,962
2,322
18,234,826
74.41%
Utah
744
600
1,890,406
79.16%
822
669
1,925,295
78.73%
775
601
1,911,676
75.26%
Vermont
737
609
489,227
78.26%
715
595
494,001
81.98%
767
622
494,466
78.40%
Virginia
777
621
5,807,626
76.24%
747
593
5,877,166
75.57%
727
607
6,029,485
81.02%
Washington
801
625
4,989,090
76.03%
829
596
5,072,923
68.75%
887
650
5,138,999
71.57%
West Virginia
771
593
1,408,784
72.70%
756
605
1,413,902
77.86%
789
627
1,442,485
75.00%
Wisconsin
751
586
4,262,569
75.65%
731
567
4,290,376
75.98%
792
600
4,311,481
74.87%
Wyoming
816
642
402,898
78.03%
813
628
407,981
71.88%
715
572
423,425
77.45%
Table C.14 Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2009-2010 and 2010-2011
State
2009-2010 Total Selected
2009-2010 Total Responded
2009-2010 Population Estimate
2009-2010 Weighted Interview Response Rate
2010-2011 Total Selected
2010-2011 Total Responded
2010-2011 Population Estimate
2010-2011 Weighted Interview Response Rate
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
NOTE: To compute the pooled weighted response rates, the two samples were combined, and the individual-year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the individual response rates. The population estimate is the average of the population across the 2 years.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2009, 2010, and 2011 (2009 and 2010 Data – Revised March 2012).
Total U.S.
117,717
91,403
228,239,562
73.97%
119,714
92,372
230,948,939
73.36%
Northeast
23,538
17,680
42,190,250
71.86%
23,624
17,577
42,457,052
70.30%
Midwest
33,158
25,772
49,883,820
74.31%
33,703
25,907
50,126,284
73.30%
South
35,269
28,069
83,522,600
75.76%
36,603
29,021
84,965,509
75.50%
West
25,752
19,882
52,642,893
72.57%
25,784
19,867
53,400,093
72.50%
Alabama
1,536
1,189
3,508,919
74.32%
1,931
1,502
3,559,669
72.06%
Alaska
1,507
1,202
496,232
77.64%
1,474
1,174
503,418
77.80%
Arizona
1,583
1,249
4,810,127
74.88%
1,579
1,253
4,799,113
76.18%
Arkansas
1,574
1,223
2,135,297
75.05%
1,598
1,238
2,160,022
72.61%
California
6,568
4,903
27,098,973
70.46%
6,502
4,871
27,560,847
70.63%
Colorado
1,635
1,292
3,742,471
77.60%
1,621
1,268
3,782,387
77.01%
Connecticut
1,600
1,245
2,660,265
74.68%
1,659
1,280
2,696,347
72.33%
Delaware
1,551
1,231
666,864
74.66%
1,542
1,229
678,795
76.33%
District of Columbia
1,508
1,247
479,433
81.99%
1,478
1,207
493,344
81.78%
Florida
6,131
4,962
14,211,566
76.26%
6,328
5,029
14,516,861
75.18%
Georgia
1,498
1,198
7,073,609
75.72%
1,482
1,163
7,114,802
75.44%
Hawaii
1,826
1,285
958,884
65.66%
1,761
1,283
987,946
68.62%
Idaho
1,548
1,250
1,110,933
76.82%
1,502
1,203
1,127,939
76.62%
Illinois
6,785
4,965
9,557,601
69.94%
6,787
4,888
9,584,505
68.50%
Indiana
1,540
1,194
4,748,179
75.29%
1,521
1,179
4,793,931
72.42%
Iowa
1,537
1,260
2,258,273
79.64%
1,519
1,239
2,282,452
78.31%
Kansas
1,555
1,195
2,061,492
73.97%
1,596
1,225
2,079,494
73.91%
Kentucky
1,568
1,246
3,228,005
75.77%
1,511
1,203
3,249,527
75.71%
Louisiana
1,535
1,217
3,283,417
77.68%
2,185
1,736
3,324,265
76.86%
Maine
1,528
1,267
1,031,739
81.01%
1,464
1,205
1,039,223
79.47%
Maryland
1,468
1,174
4,269,848
77.70%
1,532
1,215
4,339,257
76.70%
Massachusetts
1,677
1,315
5,090,630
75.50%
1,558
1,225
5,110,150
75.87%
Michigan
6,196
4,874
7,496,526
75.36%
6,376
4,968
7,485,614
73.99%
Minnesota
1,589
1,268
3,955,740
77.08%
1,602
1,275
3,991,004
78.01%
Mississippi
1,544
1,239
2,120,743
76.01%
1,764
1,419
2,143,231
75.58%
Missouri
1,539
1,216
4,463,257
74.87%
1,590
1,252
4,485,775
73.50%
Montana
1,558
1,231
743,513
75.52%
1,631
1,274
754,561
75.92%
Nebraska
1,572
1,227
1,320,707
74.62%
1,621
1,216
1,341,099
70.89%
Nevada
1,671
1,312
1,937,426
70.36%
1,771
1,397
1,983,660
71.40%
New Hampshire
1,663
1,285
1,023,798
73.46%
1,681
1,289
1,025,725
72.65%
New Jersey
1,597
1,215
6,560,760
74.60%
1,549
1,192
6,625,147
73.80%
New Mexico
1,522
1,198
1,473,640
75.83%
1,568
1,243
1,503,274
77.45%
New York
7,165
4,989
14,885,232
67.58%
7,190
4,836
14,926,107
64.21%
North Carolina
1,560
1,249
6,922,057
76.83%
1,481
1,189
7,058,040
77.70%
North Dakota
1,610
1,258
490,071
75.62%
1,630
1,267
505,181
74.31%
Ohio
6,237
4,914
8,656,342
73.65%
6,444
5,015
8,672,695
73.62%
Oklahoma
1,538
1,185
2,691,157
72.55%
1,585
1,212
2,737,383
73.62%
Oregon
1,509
1,200
2,926,397
77.03%
1,534
1,185
2,956,799
74.86%
Pennsylvania
5,288
3,957
9,632,994
72.08%
5,447
4,081
9,723,733
72.10%
Rhode Island
1,568
1,203
813,218
74.18%
1,594
1,252
816,387
72.67%
South Carolina
1,534
1,238
3,393,307
74.98%
1,582
1,260
3,454,051
74.06%
South Dakota
1,516
1,248
599,159
80.05%
1,494
1,216
603,608
78.15%
Tennessee
1,525
1,180
4,726,228
71.71%
1,521
1,200
4,779,438
74.42%
Texas
6,148
4,879
17,558,413
76.00%
6,064
4,787
17,975,306
75.03%
Utah
1,566
1,269
1,907,850
78.93%
1,597
1,270
1,918,485
77.09%
Vermont
1,452
1,204
491,614
80.10%
1,482
1,217
494,233
80.16%
Virginia
1,524
1,214
5,842,396
75.90%
1,474
1,200
5,953,325
78.29%
Washington
1,630
1,221
5,031,007
72.42%
1,716
1,246
5,105,961
70.16%
West Virginia
1,527
1,198
1,411,343
75.22%
1,545
1,232
1,428,194
76.33%
Wisconsin
1,482
1,153
4,276,473
75.81%
1,523
1,167
4,300,929
75.43%
Wyoming
1,629
1,270
405,440
74.94%
1,528
1,200
415,703
74.69%
Table C.15 Outcomes, by Survey Year, for Which Small Area Estimates Are Available
Measure
2002- 2003
2003- 2004
2004- 2005
2005- 2006
2006- 2007
2007- 2008
2008- 2009
2009- 2010
2010- 2011
1 Estimates for these outcomes were not included in the 2002-2003 State report (Wright & Sathe, 2005), but the 2002-2003 estimates are included in the 2003-2004 State report as part of the comparison tables (see Wright & Sathe, 2006). However, the Bayesian confidence intervals associated with these were not published. 2 Estimates for serious psychological distress (SPD) in the years 2002-2003 and 2003-2004 are not comparable with the 2004-2005 SPD estimates. For more details, see Section A.7 in Appendix A of the 2004-2005 State report (Wright et al., 2007). Note that, in 2002-2003, SPD was referred to as "serious mental illness." 3 Questions used to determine a major depressive episode (MDE) were added in 2004. Note that the adult MDE estimates shown in the 2004-2005 report are not comparable with the adult MDE estimates for later years.
Yes = available, No = not available.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2002-2011.
Illicit Drug Use in Past Month
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Marijuana Use in Past Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Marijuana Use in Past Month
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Perceptions of Great Risk of Smoking Marijuana Once a Month
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
First Use of Marijuana
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Illicit Drug Use Other Than Marijuana in Past Month
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Cocaine Use in Past Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Nonmedical Use of Pain Relievers in Past Year
No1
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Alcohol Use in Past Month
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Underage Past Month Use of Alcohol
No1
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Binge Alcohol Use in Past Month
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Underage Past Month Binge Alcohol Use
No1
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Perceptions of Great Risk of Having Five or More Drinks of an Alcoholic Beverage Once or Twice a Week
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Tobacco Product Use in Past Month
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Cigarette Use in Past Month
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Perceptions of Great Risk of Smoking One or More Packs of Cigarettes per Day
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Alcohol Dependence or Abuse in Past Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Alcohol Dependence in Past Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Illicit Drug Dependence or Abuse in Past Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Illicit Drug Dependence in Past Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Dependence or Abuse of Illicit Drugs or Alcohol in Past Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Needing But Not Receiving Treatment for Illicit Drug Use in Past Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Needing But Not Receiving Treatment for Alcohol Use in Past Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Serious Psychological Distress in Past Year2
Yes
Yes
Yes
No
No
No
No
No
No
Had at Least One Major Depressive Episode in Past Year3
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Serious Mental Illness in Past Year
No
No
No
No
No
No
Yes
Yes
Yes
Any Mental Illness in Past Year
No
No
No
No
No
No
Yes
Yes
Yes
Had Serious Thoughts of Suicide in Past Year
No
No
No
No
No
No
Yes
Yes
Yes
Section D: References
Aldworth, J., Kott, P., Yu, F., Mosquin, P., & Barnett-Walker, K. (2012). Analysis of effects of 2008 NSDUH questionnaire changes: Methods to adjust adult MDE and SPD estimates and to estimate SMI in the 2005-2009 surveys. In 2010 National Survey on Drug Use and Health: Methodological resource book (Section 16b, prepared for the Substance Abuse and Mental Health Services Administration under Contract No. HHSS283200800004C, Deliverable No. 39, RTI/0211838.108.005). Research Triangle Park, NC: RTI International.
American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (DSM-IV) (4th ed.). Washington, DC: Author.
Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. (2012a). National Survey on Drug Use and Health: 2011 public use file and codebook. Retrieved from http://dx.doi.org/10.3886/ICPSR34481.v1
Center for Behavioral Health Statistics and Quality. (2012b). Results from the 2010 National Survey on Drug Use and Health: Mental health findings (HHS Publication No. SMA 11-4667, NSDUH Series H-42). Rockville, MD: Substance Abuse and Mental Health Services Administration.
Center for Behavioral Health Statistics and Quality. (2012c). Results from the 2011 National Survey on Drug Use and Health: Mental health findings (HHS Publication No. SMA 12-4725, NSDUH Series H-45). Rockville, MD: Substance Abuse and Mental Health Services Administration.
Center for Behavioral Health Statistics and Quality. (2012d). Results from the 2011 National Survey on Drug Use and Health: Summary of national findings (HHS Publication No. SMA 12-4713, NSDUH Series H-44). Rockville, MD: Substance Abuse and Mental Health Services Administration.
Center for Behavioral Health Statistics and Quality. (in press). Results from the 2012 National Survey on Drug Use and Health: Mental health findings. Rockville, MD: Substance Abuse and Mental Health Services Administration.
Endicott, J., Spitzer, R. L., Fleiss, J. L., & Cohen, J. (1976). The Global Assessment Scale: A procedure for measuring overall severity of psychiatric disturbance. Archives of General Psychiatry, 33, 766-771.
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (2002). Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition (SCID-I/NP). New York, NY: New York State Psychiatric Institute, Biometrics Research.
Folsom, R. E., Shah, B., & Vaish, A. (1999). Substance abuse in states: A methodological report on model based estimates from the 1994-1996 National Household Surveys on Drug Abuse. In Proceedings of the 1999 Joint Statistical Meetings, American Statistical Association, Survey Research Methods Section, Baltimore, MD (pp. 371-375). Alexandria, VA: American Statistical Association.
Ghosh, M. (1992). Constrained Bayes estimation with applications. Journal of the American Statistical Association, 87, 533-540.
Hughes, A., Muhuri, P., Sathe, N., & Spagnola, K. (2012). State estimates of substance use and mental disorders from the 2009-2010 National Surveys on Drug Use and Health (HHS Publication No. SMA 12-4703, NSDUH Series H-43). Rockville, MD: Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality.
Kessler, R. C., Barker, P. R., Colpe, L. J., Epstein, J. F., Gfroerer, J. C., Hiripi, E., Howes, M. J., Normand, S. L., Manderscheid, R. W., Walters, E. E., & Zaslavsky, A. M. (2003). Screening for serious mental illness in the general population. Archives of General Psychiatry, 60, 184-189.
Leon, A. C., Olfson, M., Portera, L., Farber, L., & Sheehan, D. V. (1997). Assessing psychiatric impairment in primary care with the Sheehan Disability Scale. International Journal of Psychiatry in Medicine, 27(2), 93-105.
Novak, S. (2007, October). An item response analysis of the World Health Organization Disability Assessment Schedule (WHODAS) items in the 2002-2004 NSDUH (prepared for the Substance Abuse and Mental Health Services Administration under Contract No. 283-03-9028, RTI/8726). Research Triangle Park, NC: RTI International.
Office of Applied Studies. (2000). Summary of findings from the 1999 National Household Survey on Drug Abuse (HHS Publication No. SMA 00-3466, NHSDA Series H-12). Rockville, MD: Substance Abuse and Mental Health Services Administration.
Office of Applied Studies. (2001). Development of computer-assisted interviewing procedures for the National Household Survey on Drug Abuse (HHS Publication No. SMA 01-3514, Methodology Series M-3). Rockville, MD: Substance Abuse and Mental Health Services Administration.
Office of Applied Studies. (2005). Results from the 2004 National Survey on Drug Use and Health: National findings (HHS Publication No. SMA 05-4062, NSDUH Series H-28). Rockville, MD: Substance Abuse and Mental Health Services Administration.
Office of Applied Studies. (2009). Results from the 2008 National Survey on Drug Use and Health: National findings (HHS Publication No. SMA 09-4434, NSDUH Series H-36). Rockville, MD: Substance Abuse and Mental Health Services Administration.
Payton, M. E., Greenstone, M. H., & Schenker, N. (2003). Overlapping confidence intervals or standard error intervals: What do they mean in terms of statistical significance? Journal of Insect Science, 3, 34.
Raftery, A. L., & Lewis, S. (1992). How many iterations in the Gibbs sampler? In J. M. Bernardo, J. O. Berger, A. P. Dawid, & A. F. M. Smith (Eds.), Bayesian statistics 4 (pp. 763-774). London, England: Oxford University Press.
RTI International. (2012). 2010 National Survey on Drug Use and Health: Methodological resource book (prepared for the Substance Abuse and Mental Health Services Administration under Contract No. HHSS283200800004C, Deliverable No. 39, RTI 0211838). Research Triangle Park, NC: Author.
RTI International. (2013). 2011 National Survey on Drug Use and Health: Methodological resource book (prepared for the Substance Abuse and Mental Health Services Administration under Contract No. HHSS283200800004C, Deliverable No. 39, Report No. RTI 0211838). Research Triangle Park, NC: Author.
Schenker, N., & Gentleman, J. F. (2001). On judging the significance of differences by examining the overlap between confidence intervals. American Statistician, 55(3), 182-186.
Shah, B. V., Barnwell, B. G., Folsom, R., & Vaish, A. (2000). Design consistent small area estimates using Gibbs algorithm for logistic models. In Proceedings of the 2000 Joint Statistical Meetings, American Statistical Association, Survey Research Methods Section, Indianapolis, IN (pp. 105-111). Alexandria, VA: American Statistical Association.
Singh, A. C., & Folsom, R. E. (2001, April 11-14). Hierarchical Bayes calibrated domain estimation via Metropolis-Hastings Step in MCMC with application to small areas. Presented at the International Conference on Small Area Estimation and Related Topics, Potomac, MD.
Wright, D. (2002a). State estimates of substance use from the 2000 National Household Survey on Drug Abuse: Volume I. Findings (HHS Publication No. SMA 02-3731, NHSDA Series H-15). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.
Wright, D. (2002b). State estimates of substance use from the 2000 National Household Survey on Drug Abuse: Volume II. Supplementary technical appendices (HHS Publication No. SMA 02-3732, NHSDA Series H-16). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.
Wright, D. (2003a). State estimates of substance use from the 2001 National Household Survey on Drug Abuse: Volume I. Findings (HHS Publication No. SMA 03-3775, NHSDA Series H-19). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.
Wright, D. (2003b). State estimates of substance use from the 2001 National Household Survey on Drug Abuse: Volume II. Individual state tables and technical appendices (HHS Publication No. SMA 03-3826, NHSDA Series H-20). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.
Wright, D. (2004). State estimates of substance use from the 2002 National Survey on Drug Use and Health (HHS Publication No. SMA 04-3907, NSDUH Series H-23). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.
Wright, D., & Sathe, N. (2005). State estimates of substance use from the 2002-2003 National Surveys on Drug Use and Health (HHS Publication No. SMA 05-3989, NSDUH Series H-26). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.
Wright, D., & Sathe, N. (2006). State estimates of substance use from the 2003-2004 National Surveys on Drug Use and Health (HHS Publication No. SMA 06-4142, NSDUH Series H-29). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.
Wright, D., Sathe, N., & Spagnola, K. (2007). State estimates of substance use from the 2004-2005 National Surveys on Drug Use and Health (HHS Publication No. SMA 07-4235, NSDUH Series H-31). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.
Section E: List of Contributors
This National Survey on Drug Use and Health (NSDUH) document was prepared by the Center for Behavioral Health Statistics and Quality (CBHSQ), Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services (HHS), and by RTI International (a trade name of Research Triangle Institute), Research Triangle Park, North Carolina. Work by RTI was performed under Contract Nos. HHSS283200800004C and HHSS283201000003C.
At SAMHSA, Arthur Hughes reviewed the document and provided substantive revisions. At RTI, Neeraja S. Sathe and Kathryn Spagnola were responsible for the writing of the document, and Ralph E. Folsom and Akhil K. Vaish were responsible for the overall methodology and estimation for the model-based Bayes estimates and confidence intervals.
The following staff were responsible for generating the estimates and providing other support and analysis: Akhil K. Vaish, Neeraja S. Sathe, Kathryn Spagnola, and Brenda K. Porter. Ms. Spagnola provided oversight for production of the document. Richard S. Straw edited it; Debbie Bond, Valerie Garner, and Roxanne Snaauw formatted its text and tables; and Teresa F. Bass, Kimberly Cone, Danny Occoquan, Margaret Smith, Marissa R. Straw, Pamela Tuck, and Cheryl Velez prepared the Web versions. Justine L. Allpress and E. Andrew Jessup prepared and processed the maps used in the associated files.
End Notes
1 RTI International is a trade name of Research Triangle Institute, Research Triangle Park, North Carolina.
2 The census region-level estimates in the tables are population-weighted aggregates of the State estimates. The national estimates, however, are benchmarked to exactly match the design-based estimates.
3 Note that in the 2004-2005 NSDUH State report and prior reports, the term "prediction interval" (PI) was used to represent uncertainty in the State and regional estimates. However, that term also is used in other applications to estimate future values of a parameter of interest. That interpretation does not apply to NSDUH State report estimates; thus, "prediction interval" was dropped and replaced with "Bayesian confidence interval."
4 For an overview of the impact of these changes, see Section C.2 of Appendix C in OAS (2005a).
5 Combining data across 2 years permits the estimation of change at the State level by expressing it as the difference of two consecutive 2-year SAE moving averages. Comparisons between the combined 2009-2010 data and the combined 2010-2011 data are presented in this report. This method is similar to the one used in the 2009-2010 State report (Hughes et al., 2012).
6 A successfully screened household is one in which all screening questionnaire items were answered by an adult resident of the household and either zero, one, or two household members were selected for the NSDUH interview.
7 The usable case rule requires that a respondent answer "yes" or "no" to the question on lifetime use of cigarettes and "yes" or "no" to at least nine additional lifetime use questions.
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.
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.
, D
is the inverse of the unconditional probability of selection for the household (hh) and excludes all adjustments for nonresponse and poststratification.
, D
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
. D
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 
or 
. Because 
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.
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 
. The HB solution for 
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.
are defined below:
is the number of marijuana initiates in the past 24 months and 
is the number of persons who never used marijuana.
can be estimated using the weighted logistic regression model. The final 2012 calibration model was determined as follows:
refers to an estimate of the serious mental illness response probability 
.
These covariates in equation (1) come from the main NSDUH interview data:
= Alternative Past Year K6 Score: Past year K6 score less than 8 recoded as 0; past year K6 score 8 to 24 recoded as 1 to 17.
= Alternative WHODAS Score: WHODAS item scores less than 2 recoded as 0; WHODAS item scores 2 to 3 recoded as 1, then summed for a score ranging from 0 to 8.
= Serious Thoughts of Suicide in the Past Year: Coded as 1 if "yes"; coded as 0 otherwise.
= Past Year MDE: Coded as 1 if criteria for past year MDE were met (see 
= Adjusted Age: Coded as age minus 18 if aged 18 to 30; coded as 12 otherwise.
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.
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 
,
,
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 
, that is, 
, let 
and 
,
, where 
denotes the covariance between 
and 
. This covariance is defined in terms of the associated correlation as follows:
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 
and 
denote the 95 percent Bayesian confidence intervals for the two States, 
.
. 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 
and 
. With the null value of 
, where Z is a standard normal random variate, 
, and abs(z) denotes the absolute value of z.
. Then,
.