2008-2010 National Surveys on
Drug Use and Health: Guide to Substate
Tables and Summary of Small Area
Estimation Methodology

Section A: Overview

A.1. Introduction

This report provides a guide on the development and presentation of model-based small area estimates of the prevalence of substance use and mental disorders in substate regions based on data from the combined 2008-2010 National Surveys on Drug Use and Health (NSDUHs). The estimates along with this report and other related information are available at http://www.samhsa.gov/data/NSDUH/substate2k10/toc.aspx. 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 2008-2010, NSDUH collected data from 203,739 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 of Behavioral Health Statistics and Quality (CBHSQ). Data collection and analysis were conducted under contract with RTI International.1

This marks the fifth time that detailed estimates for substate regions (also referred to as planning regions, substate areas, or regions) in all 50 States and the District of Columbia have been presented by SAMHSA. The first report provided estimates for 12 measures or outcomes based on data from the 1999-2001 surveys (Office of Applied Studies [OAS], 2005c). The second report presented estimates for 22 measures based on the 2002-2004 NSDUHs (OAS, 2006). The third report presented estimates for 23 measures based on the 2004-2006 NSDUHs (OAS, 2008). The fourth report presented estimates for 21 measures based on the 2006-2008 NSDUHs (OAS, 2010). Additionally, after 2002, these reports included estimates for underage (aged 12 to 20) alcohol use and underage binge alcohol use. These substate reports provide a more detailed perspective on the variations in substance use rates both within and across States than is possible with the State reports (e.g., Hughes, Muhuri, Sathe, & Spagnola, 2011, 2012). The 2008-2010 substate region estimates were produced for 25 measures and are available at http://www.samhsa.gov/data/NSDUH/substate2k10/toc.aspx. Unlike prior years, a full report presenting the findings of the 2008-2010 substate estimates will not be produced. More information about what will be available is provided in Section A.2.

Estimates were generated for 383 substate regions representing collectively the 50 States and the District of Columbia (hereafter referred to as States). These regions were defined by officials from each State and were typically based on the substance abuse treatment planning regions specified by the States in their applications for the Substance Abuse Prevention and Treatment (SAPT) Block Grant administered by SAMHSA.

A.2. Format of the Report

Section A of this methodology document provides a brief background on the survey, how substate regions were formed, and the general methodological approach. A complete list of the 25 substance use measures presented is given in Section B, which also provides further information on the small area estimation (SAE) methodology used to produce substate estimates. Section C includes the population estimates for persons aged 12 or older and the combined 2008, 2009, and 2010 NSDUH sample sizes and response rates for each substate region. Users may find the population estimates helpful in calculating the weighted average prevalence estimate for any combination of substate regions or to determine the number of people using a particular substance in a substate region. For example, the number of persons aged 12 or older who used marijuana in the past month in Alabama's Region 1 (49,204 persons) can be obtained by multiplying the prevalence rate from Table 3 in the "2008-2010 NSDUH Substate Regions: Excel Tables" (see http://www.samhsa.gov/data/NSDUH/substate2k10/toc.aspx) (4.5 percent—shown as 4.54 percent in the table) and the population estimate from Table C1 (1,083,792). Section D lists the references, and Section E provides a list of contributors to the production of the 2008-2010 substate small area estimates. In addition to the 2008-2010 NSDUH substate region estimates presented in Excel tables, the following files are available now at the above Web site:

A.3. Substate Regions, Ranking of Regions, and Small Area Estimation Methods

The substate regions for each State were developed in a series of communications during the fall of 2011 between SAMHSA staff and State officials responsible for the SAPT Block Grant application. The goal of the project was to provide substate-level estimates showing the geographic distribution of substance use prevalence for regions that States would find useful for treatment planning purposes.2 The final substate region boundaries were based on the State's recommendations, assuming that the NSDUH sample sizes were large enough to provide estimates with adequate precision. Most States defined regions in terms of counties or groups of counties. A few States defined the regions in terms of census tracts. Several States also requested estimates for aggregate planning regions along with the estimates for their substate planning regions. An aggregate planning region is made up of two or more substate planning regions. These substate region definitions are available in a document titled "2008-2010 NSDUH Substate Region Definitions" (see http://www.samhsa.gov/data/NSDUH/substate2k10/toc.aspx as listed above in Section A.2). A few of these States wanted the maps to be produced only for the aggregate regions instead of for their substate regions. For example, New York has 15 substate regions, and those 15 regions were combined to create 4 aggregate regions that are used in the maps. Hence, for each measure, maps were produced for 362 planning regions and not for 383 regions.

These 362 substate regions used in the maps were ranked from lowest to highest for each measure and were divided into seven categories designed to represent distributions that are somewhat symmetric, like a normal distribution. Colors were assigned to all substate regions such that the third having the lowest prevalence are in blue (121 substate regions), the middle third are in white (120 substate regions), and the third with the highest prevalence are in red (121 substate regions). The only exceptions were the three perception-of-risk outcomes shown in Figure 4 (marijuana), Figure 11 (alcohol), and Figure 16 (cigarettes) of the national maps, which have the highest estimates represented in blue and the lowest represented in red. To further distinguish among the substate regions that display relatively higher prevalence, the "highest" third in red has been further subdivided into (a) dark red for the 16 substate regions with the highest estimates, (b) medium red for the 33 substate regions with the next highest estimates, and (c) light red for the 72 substate regions in the third highest group. The "lowest" third is categorized in a similar way using three distinct shades of blue. In some cases, a group (or category) could have more or fewer substate regions because two (or more) substate regions have the same estimate (to two decimal places). When such ties occurred at the "boundary" between two groups, all substate regions with the same estimate were assigned to the lower group. These national maps are available at http://www.samhsa.gov/data/NSDUH/substate2k10/toc.aspx as listed above in Section A.2.

The 2008-2010 substate estimates and corresponding Bayesian confidence intervals are available in the "2008-2010 NSDUH Substate Regions: Excel Tables" (see http://www.samhsa.gov/data/NSDUH/substate2k10/toc.aspx as mentioned in Section A.2). These tables also contain a sort order number and a map-group indicator (= 1 for the Nation, = 2 for States, = 3 for census regions, = 4 if a region is part of the 362 mapping regions, and = 5 for all other substate/aggregate regions not included on the maps).

Estimates presented in the tables and maps (listed above) are based on hierarchical Bayes estimation methods that combine survey data with a national model. Applying this methodology to the State substance use measures has been shown to result in more precise estimates than using the sample-based results alone (Wright, 2002). The methodology used to produce estimates in these tables is the same as that used to produce State estimates from the NSDUH data since 1999 and has been used for prior substate reports (see Hughes et al., 2010; OAS, 2008). Sample data have been combined across 3 years (2008-2010) to improve the precision of substate region estimates. The estimate for each region is accompanied by a 95 percent Bayesian confidence interval (for more details, see Section B).

In addition to the substate region estimates, comparable estimates are provided for the 50 States and the District of Columbia using the same methodology. Because these estimates are based on 3 consecutive years of data, they are not directly comparable with the State estimates in earlier reports that are based on only 2 consecutive years. Estimates for the Nation and the four census regions also are presented. These regions, defined by the U.S. Census Bureau, are defined as follows:

Northeast Region - Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont.
Midwest Region - Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin.
South Region - Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia.
West Region - Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

Because the SAE methods used here tend to borrow strength from both the national model and the State-level random effects, estimates for substate regions with sample sizes that were closer to the minimum (150) tend to be shrunk more toward the corresponding State prevalence estimate than substate regions with large sample sizes. This methodology tends to cluster the small sample substate estimates around their State means. Thus, relatively high estimates for substate regions with small sample sizes tend to shrink toward the State mean, while relatively low estimates tend to increase toward the State mean. On the other hand, for substate regions with large sample sizes, the methodology produces estimates that are close to the weighted average of the sample data in that substate region. In addition, these estimates are design consistent so that, as the sample size for a substate region increases, the estimate approaches the true population value.

A.4. Comparability with Past Estimates

For the 2002 NSDUH, a number of methodological changes were introduced, including a $30 incentive for participating in the survey, additional training for interviewers to encourage adherence to survey protocols, a change in the survey name, and a shift to the 2000 decennial census (from the 1990 census) as a basis for population counts used in estimation. An unanticipated result of these changes was that the prevalence rates for 2002 were in general substantially higher than those for 2001. These rates were substantially higher than could be attributable to the usual year-to-year trend. Additional information on these methodological changes is available in OAS (2005a).

Because of the changes in the survey that took place in 2002, estimates for 2008-2010 are not comparable with estimates for 1999-2001, and it is not possible to separate the effect of the methodological changes from the true trends in substance use. Therefore, one should not conclude that any differences between estimates from 1999-2001 and 2008-2010 represent true changes. However, estimates from 2002-2004, 2004-2006, 2006-2008, and 2008-2010 are comparable for outcomes that were defined in a similar manner and for substate regions defined consistently across these time periods.

During regular data collection and processing checks for the 2011 NSDUH, data errors were identified. These errors were falsified cases submitted by field interviewers and 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.

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, when model-based SAE techniques are used, as is the case for the 2008-2010 substate small area estimates, estimates for all substate regions and 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 or substate region is a combination of the direct estimate for that State or substate region and the estimate obtained from a national model. The national model, which has estimated parameter coefficients based on data from all States and substate regions, 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 affected were estimates for Pennsylvania, Maryland, the mid-Atlantic division, and the Northeast region.

In the tables that show the comparison of 2006-2008 and 2008-2010 substate region estimates (available at http://www.samhsa.gov/data/NSDUH/substate2k10/toc.aspx), model-based substate and State estimates are based on the corrected data. As mentioned in Section A.2, the 2006-2008 substate small area estimates were revised after removing erroneous data for Pennsylvania and Maryland and using the updated substate region definitions used in producing 2008-2010 substate small area estimates. Hence, these 2006-2008 small area estimates may not match the previously published model-based estimates.

Section B: Substate Region Estimation Methodology

Substate region-level estimates of 25 binary (0, 1) substance use and mental health measures using combined data from the 2008, 2009, and 2010 National Surveys on Drug Use and Health (NSDUHs) for persons aged 12 or older are presented in the "2008-2010 NSDUH Substate Regions: Excel Tables" (see Section A.2). Binary measures correspond to questions where a "yes" or "no" response is provided (in this case, "no" = 0 and "yes" = 1). Additionally, two binary (0, 1) estimates for underage (aged 12 to 20) use of alcohol and binge alcohol use also are presented in the same tables. Substate-level small area estimates of serious mental illness (SMI), any mental illness (AMI), and having serious thoughts of suicide in the past year for adults aged 18 or older are presented for the first time.

The survey-weighted hierarchical Bayes (SWHB) methodology used in the production of State estimates from the 1999-2010 surveys also was used in the production of the 2008-2010 substate estimates. The SWHB methodology is described by Folsom, Shah, and Vaish (1999). A general model description is given in Section B.1. A brief discussion of the precision of the estimates and interpretation of the Bayesian confidence intervals (CIs) is given in Section B.2. Section B.3 lists the 25 substance use measures for which substate-level small area estimates were produced. The methodology used to select relevant predictors is described in Section B.4. The list of predictors used in the 2008-2010 substate-level small area estimation (SAE) modeling is given in Section B.5. Information on the updated population projections (obtained from Claritas) that were used for the first time in producing the 2007-2008 State small area estimates and the 2006-2008 substate small area estimates and how they were used to create SAE model predictors is given in Section B.6. Procedures used to implement the adjustment of NSDUH weights for the purpose of obtaining substate small area estimates is described briefly in Section B.7. The goals of the SAE modeling, the general model description, and the implementation of SAE modeling remain the same and are described in Appendix E of the 2001 State report (Wright, 2003). A short description of the calculation of the rate of first use of marijuana and underage drinking is included in Section B.8. Section B.9 discusses the criteria used to define illicit drug and alcohol dependence and abuse and needing but not receiving treatment. Section B.10 discusses the production of estimates for SMI, AMI, serious thoughts of suicide, and major depressive episode (MDE, i.e., depression).

Small area estimates obtained using the SWHB methodology are design consistent (i.e., for States or substate areas with large sample sizes, the small area estimates are close to the corresponding robust design-based estimates). The substate small area estimates when aggregated by using the appropriate population totals result in national small area estimates that are very close to the national design-based estimates. However, for many 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 (see Appendix A, Section A.4, in Wright & Sathe, 2005). The 2008-2010 substate small area estimates have been benchmarked to the national design-based estimates.

B.1. General Model Description

The model described here to produce the 2008-2010 substate small area estimates is similar to the logistic mixed3 hierarchical Bayes (HB) model that was used to produce the 2006‑2008 substate small area estimates (Office of Applied Studies [OAS], 2010). The following model was used:

Equation B.1-1     D

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

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 estimate projections to form substate- and State-level small area estimates for the desired age group(s). These small area estimates then are benchmarked to the national design-based estimates (see Hughes et al., 2012).

B.2. Precision and Validation of the Estimates

The primary purpose of producing substate estimates is to give policy officials and data users a better perspective on the range of prevalence estimates within and across States. Because the data were collected in a consistent manner by field interviewers who adhered to the same procedures and administered the same questions across all States and substate regions, the results are comparable within and across the 50 States and the District of Columbia.

The 95 percent Bayesian CI associated with each estimate provides a measure of the accuracy of the estimate. It defines the range within which the true value can be expected to fall 95 percent of the time. For example, the estimated prevalence of past month use of marijuana in Region 1 in Alabama is 4.5 percent, and the 95 percent CI ranges from 3.4 to 6.1 percent. Therefore, the probability is 0.95 that the true value is within that range. The CI indicates the uncertainty due to both sampling variability and model bias. The key assumption underlying the validity of the CIs is that the State- and substate-level error (or bias correction) terms in the models behave like random effects with zero means and common variance components.

A comparison of the standard errors (SEs) among substate regions with small (n ≤ 500), medium (500 < n ≤ 1,000), and large (n > 1,000) sample sizes for certain measures shows that the small area estimates behave in predictable ways. Regardless of whether the substate region is from 1 of the 8 States with a large annual sample size (3,000 to 4,000) or 1 of the 43 other States (n = 900 annually), the sizes of the CIs are very similar and are primarily a function of the sample size of the substate region and the prevalence estimate of the measure. Substate regions with large sample sizes had the smallest SEs.

For past month use of alcohol, where the national prevalence for all persons aged 12 or older was 51.7 percent (for 2008-2010), the average relative standard error (RSE)4 was about 5.1 percent, and the RSE for substate regions with a large sample size was about 3.3 percent. For substate regions with a medium sample size, the average RSE was 4.4 percent; for small sample sizes, the average RSE was 5.7 percent.

For past month use of marijuana (with a national prevalence of 6.6 percent), the average RSE was 10.0 percent for substate regions with large samples. For medium sample sizes, the average RSE was 13.1 percent, and for small samples, the RSE was 15.9 percent, whereas the overall national average RSE was 14.6 percent. Substance use measures with lower prevalences, such as past year use of cocaine (1.9 percent nationally), displayed larger average RSEs. For substate regions with large sample sizes, the average RSE was 16.9 percent. For those with medium sample sizes, the average RSE was 20.4 percent, and for those with small sample sizes, the average RSE was 22.9 percent.

The SAE methods used for producing the 2008-2010 substate region estimates were previously validated for the NSDUH State-by-age group small area estimates (Wright, 2002). This validation exercise used direct estimates from pairs of large sample States (n = 7,200) as internal benchmarks. These internal benchmarks were compared with small area estimates based on random subsamples (n = 900) that mimicked a single year small State sample. The associated age group-specific small area estimates were based on sample sizes targeted at n = 300. Therefore, validation of the State-by-age group small area estimates should lend some validity to the small sample size substate small area estimates reported here.

B.3. Variables Modeled

Substate-level small area estimates were produced for the following set of 25 binary (0, 1) substance use measures, using combined data from the 2008-2010 NSDUHs for persons aged 12 or older (or persons 18 or older for the four mental disorders):

  1. past month use of illicit drugs,
  2. past year use of marijuana,
  3. past month use of marijuana,
  4. perception of great risk of smoking marijuana once a month,
  5. average annual rate of first use of marijuana,
  6. past month use of illicit drugs other than marijuana,
  7. past year use of cocaine,
  8. past year nonmedical use of pain relievers,
  9. past month use of alcohol,
  10. past month binge alcohol use,
  11. perception of great risk of having five or more drinks of an alcoholic beverage once or twice a week,
  12. past month use of tobacco products,
  13. past month use of cigarettes,
  14. perception of great risk of smoking one or more packs of cigarettes per day,
  15. past year alcohol dependence or abuse,
  16. past year alcohol dependence,
  17. past year illicit drug dependence or abuse,
  18. past year illicit drug dependence,
  19. past year dependence or abuse of illicit drugs or alcohol,
  20. needing but not receiving treatment for illicit drug use in the past year,
  21. needing but not receiving treatment for alcohol use in the past year,
  22. serious mental illness (SMI) in the past year,
  23. any mental illness (AMI) in the past year,
  24. serious thoughts of suicide in the past year, and
  25. past year major depressive episode (MDE, i.e., depression).

In addition to the 25 measures listed above, estimates also have been produced for underage (aged 12 to 20) past month use of alcohol and underage past month binge alcohol use. Table B1 at the end of this section lists all of the outcomes and the years (2002-2004, 2004-2006, 2006-2008, and 2008-2010) for which substate-level small area estimates were produced going back to the 2002 NSDUH.

B.4. Selection of Independent Variables for the Models

No new variable selection was done. The same fixed-effect predictors that were used in producing the 2002-2004, 2004-2006, and 2006-2008 substate estimates were used to produce the 2008-2010 substate estimates. These are also the same predictors used to produce estimates for State SAE reports beginning with the 2002-2003 report up to and including the 2010-2011 report.

B.5. Predictors Used in Logistic Regression Models

Local area data used as potential predictor variables in the mixed logistic regression models were obtained from several sources, including Claritas, the U.S. Census Bureau, the Federal Bureau of Investigation (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 sources of data used in the modeling are provided in the following list.

For more information about the predictors defined from the above sources, see Appendix A, Section A.3, of the 2009-2010 State estimates report (Hughes et al., 2012).

B.6. Updated Claritas Data

For the State and substate reports published using the 2002 to 2007 NSDUH data, Claritas data obtained in 2002 were used to produce the small area estimates. In reports published using the 2008, 2009, and 2010 NSDUH data, Claritas data obtained in 2008 were used. The 2002 Claritas data had 2000 and 2002 population counts, as well as 2007 population projections. The 2008 Claritas data had 2008 population counts, as well as 2012 population projections. Claritas data are used for the following in the NSDUH SAE process:

In the 2008 SAE process (and subsequent years), new Claritas data with 2008 population counts and 2012 population projections were used. The new Claritas data will be henceforth referred to as the 2008-2012 Claritas data, and the 2002 Claritas data will be referred to as the 2002-2007 Claritas data. The following main differences were observed between the two Claritas datasets:

  1. The format of the race/ethnicity data was different for the two sets of Claritas data. To generate age group × race × Hispanicity × gender population estimates 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. Hence, 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.
  2. The 2007 (from the 2002-2007 Claritas data) and 2008 (from the 2008-2012 Claritas data) 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 estimate for some of the 32 cells in 2008 as compared with the 32 cells in 2007.
  3. In prior State and substate publications when creating the 32 cells using the 2002-2007 Claritas data, the population from the two or more races' category was distributed among the black, white, and other race categories. Starting in 2008 and subsequent years, a decision was made to merge the two or more races' category with the other race category. This was based on a decision to discontinue creating a sample variable that split the two or more races' respondents into black, white, or other. Because the two or more races' respondents on the NSDUH sample were now all being grouped into the other category, the same technique was used to produce the 32 cell population estimates.

Some of the differences in the 2007 and 2008 population estimates can be attributed to reasons (1) 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" 2006 and 2007 population projections would be obtained by "projecting back" the 2008-2012 Claritas data. These new population projections were obtained so that they could be used in the 2006-2008 SAE publications.

In summary, based on the information above, the following steps were taken for the current 2008-2010 substate SAE analysis:

  1. Using the 2008-2012 Claritas data, 2008, 2009, and 2010 population counts were obtained (the 2009 and 2010 counts were obtained by using linear interpolation between the 2008 and 2012 counts) and used to create the predictors that were merged onto the 2008, 2009, and 2010 sample and universe files (the universe file is a census block-group level file containing SAE predictor variables and population counts).
  2. All block group, tract, and county-level continuous predictors were converted into 10‑category, semicontinuous variables by using the corresponding 2007-2008 decile values created by pooling the 2007 and 2008 NSDUH data. The same 2007-2008 decile values will be used for future SAE analyses until new Claritas data containing the 2013 population counts and projections are obtained. Using the same decile values year after year makes it possible to keep track of any temporal changes occurring in the predictor variables, which may help in detecting any changes in State prevalence rates across years in an efficient manner. The 10-category predictor variables subsequently were used to form linear, quadratic, and cubic orthogonal polynomials eventually used in the SAE modeling process.
  3. For all predictors other than the unemployment rate, the same 2007-2008 decile values were used in the 2008-2010 substate 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. 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 estimates for the 32 cells (age group × race/ethnicity × gender population estimates) and the new deciles were used to create the updated universe files for all 3 years (2008, 2009, and 2010). The 2006, 2007, and 2008 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 2006-2008 and 2008-2010 substate prevalence rates. The 2006-2008 substate small area estimates were created using 2006-2008 population projections that were obtained from the new 2008-2012 Claritas data.

B.7. Adjustment of Weights

The person-level NSDUH weights are poststratified (adjusted) to match census population estimates at the State level. These population estimates were based on the 2000 decennial census and updated by Claritas to projections for the years 2008-2010. Because the objective here was to produce small area estimates for substate regions, it was decided to ratio adjust the person-level sampling weights to population projections (available from Claritas as shown in Table C1 in Section C) at the substate × age group × gender level. The advantage to doing this ratio adjustment is to ensure that the adjusted sampling weights better reflect the demography of the substate regions. The downside to this adjustment is that the design-based estimates based on the unadjusted sampling weights may be slightly different (at the national level) from the design-based estimates obtained from the adjusted weights. However, because the aim was to be able to produce reliable substate region-level small area estimates, this ratio adjustment to the weights seemed more appropriate. Note that this ratio adjustment was done at the substate region (383 regions) × age group (12 to 17, 18 to 25, 26 to 34, and 35 or older) × gender (male and female) level collectively over 3 years (2008, 2009, and 2010) of data.

B.8. Calculation of Average Annual Rate (Incidence) of First Use of Marijuana, and Underage Drinking

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 for first use of marijuana is defined as follows:

Equation B.8-1 ,     D

where X1 is the number of marijuana initiates in the past 24 months and X2 is the number of persons who never used marijuana. Both X1 and X2 are based on binary measures that correspond to questions with a "yes" or "no" response option. For details on calculating the average annual rate of first use of marijuana from NSDUH data, see Appendix A, Section A.8, of the 2009-2010 State estimates report (Hughes et al., 2012).

To obtain small area estimates for persons aged 12 to 20 for past month alcohol use 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 (similar to what was done for producing substate estimates using the 2006-2008 NSDUH data). For details on underage drinking, see Section A.9, Appendix A, of the 2009-2010 State estimates report (Hughes et al., 2012).

B.9. 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,6 dependence and abuse questions were based on the criteria in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) (American Psychiatric Association [APA], 1994).

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

  1. Spent a great deal of time over a period of a month getting, using, or getting over the effects of the substance.
  2. Used the substance more often than intended or was unable to keep set limits on the substance use.
  3. Needed to use the substance more than before to get desired effects or noticed that the same amount of substance use had less effect than before.
  4. Inability to cut down or stop using the substance every time tried or wanted to.
  5. Continued to use the substance even though it was causing problems with emotions, nerves, mental health, or physical problems.
  6. The substance use reduced or eliminated involvement or participation in important activities.

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

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

  1. Serious problems at home, work, or school caused by the substance, such as neglecting your children, missing work or school, doing a poor job at work or school, or losing a job or dropping out of school.
  2. Used the substance regularly and then did something that might have put you in physical danger.
  3. Use of the substance caused you to do things that repeatedly got you in trouble with the law.
  4. Had problems with family or friends that were probably caused by using the substance and continued to use the substance even though you thought the substance use caused these problems.

For additional details on how respondents were classified as having dependence or abuse of illicit drugs and alcohol, see Section B.4.2 in Appendix B of the 2010 NSDUH national findings report (CBHSQ, 2011).

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.10. Mental Health Measures

This section provides a summary of the measurement issues associated with the four mental health outcome variables for which 2008-2010 substate small area estimates were produced—serious mental illness (SMI), any mental illness (AMI), serious thoughts of suicide, and major depressive episode (MDE). Additional details can be found in Section B.4.7 of Appendix B in the 2008 NSDUH national findings report for MDE (OAS, 2009), and in Sections B.4.2 to B.4.4 of Appendix B in the 2012 NSDUH mental health findings report for all four outcome variables (CBHSQ, 2013).

B.10.1 Mental Illness

In the 2000-2001 and 2002-2003 NSDUH State reports, the Kessler-6 (K6) psychological distress scale was used to measure SMI (Kessler et al., 2003). However, SAMHSA discontinued producing State-level SMI 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 including a functional impairment scale; for a discussion, see Section B.4.4 of Appendix B in the 2004 NSDUH national findings report (OAS, 2005b). The use of the K6 distress scale continued in the 2003-2004, 2004-2005, 2005-2006, and 2006-2007 State reports and in the 2002-2004 and 2004-2006 substate reports, not as a measure of SMI, but as a measure of serious psychological distress (SPD) because it was determined that the K6 scale only measured SPD and just contributed to measuring SMI (see details below).

In December 2006, a technical advisory group meeting of expert consultants was convened by SAMHSA's OAS (which later became CBHSQ) and the Center for Mental Health Services (CMHS) 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 SMI 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 SMI. The estimation methodology was implemented in the 2008 NSDUH. Using recommendations from this panel, substate estimates of SMI using 2008, 2009, and 2010 NSDUH data were based on this revised methodology and, thus, are not comparable with estimates for SMI or SPD shown in NSDUH State reports prior to 2009. However, in 2013, another revision to the methodology for producing mental illness estimates (for SMI and AMI) was made, and the substate estimates presented for 2008-2010 are based on this revised methodology (and therefore are not comparable with previously published estimates of SMI and AMI).

To develop methods for preparing the estimates of SMI and AMI presented here and in other NSDUH 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 to conduct the interviews, 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 in the NSDUH interview that separately measure psychological distress and functional impairment for use in a statistical model that predicts whether a respondent had 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 were assigned to receive an abbreviated eight-item version of the WHODAS (Novak, Colpe, Barker, & Gfroerer, 2010) and half were assigned to receive the SDS (Leon, Olfson, Portera, Farber, & Sheean, 1997). 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 SMI in the full 2008 NSDUH sample. SMI probabilities and predicted values (as well as AMI 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 SMI and AMI among young adults relative to the clinical interview data. In addition, improvements were needed in the weighting procedures for the MHSS sample data to account better for undercoverage and nonresponse (i.e., because NSDUH respondents who competed their surveys in Spanish were not eligible for the clinical follow-up7 and because persons without mental illness appeared to be less likely to participate in the follow-up). 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, substate mental illness estimates for 2008-2010 were recomputed using the new 2012 model. Note that tables or maps showing estimates of SMI and AMI based on these 2012 models include "Revised October 2013" in the source line.

The next subsections describe 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).8

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., MDE, 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 used to produce estimates of mental illness.

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

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

K6 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 of 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 SMI prevalence was typically extremely low for respondents with past year K6 scores of 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 SMI 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 SMI. This alternative WHODAS score was the variable used in both the 2008 and 2012 SMI prediction models.

Suicidal Thoughts, MDE, and Age. In addition to the K6 and WHODAS scales, the 2012 model included the following measures as predictors of SMI: (a) serious thoughts of suicide in the past year; (b) having a past year NDE; 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?"9 Definitions for MDE 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, 2013). 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 SMI 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 an SMI diagnosis is positive; otherwise, Y = 0.

If X is a vector of explanatory variables, then the response probability Pi equals the probability of capital Y given capital X, where capital X is the vector of explanatory variables. can be estimated using weighted logistic regression models for the WHODAS and SDS half samples. The final 2012 WHODAS calibration model was determined as follows:

Equation (1)     D

where pi hat refers to an estimate of the SMI response probability pi for the WHODAS models. These covariates in equation (1) come from the main NSDUH interview data:

As with the 2008 model, a cut point probability pi sub zero was determined, so that if Pi hat is greater than or equal to pi sub zero. for a particular respondent, then he or she was predicted to be SMI positive; otherwise, he or she was predicted to be SMI 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 SMI status for all adult NSDUH respondents was used to compute SMI small area estimates. A second cut point probability (0.0192519810) was determined so that any respondent with an SMI probability greater than or equal to the cut point was predicted to be positive for AMI, and the remainder were predicted to be negative for AMI. The second cut point was chosen so that the weighted numbers of AMI false positives and false negatives were as close to equal as possible.

Alternative 2012 Model for the SDS Half Sample. In 2008, approximately half of the respondents in the adult NSDUH sample were assigned to receive questions about impairment based on the WHODAS, and the other half were assigned to receive questions based on the SDS. As noted previously, the purpose of this split sample was to determine whether the SDS or WHODAS impairment scale was a better predictor of SMI. The WHODAS scale was identified as the better predictor.

For the clinical interview respondents who had been administered the SDS in the main survey, an alternative SMI model was fit using the complete MHSS dataset of clinical interviews from 2008 through 2012. SMI and AMI estimates were obtained using the same cut point methodology described previously but applied to the alternative model.

The modified 2012 SMI prediction model for the SDS half sample was

Equation (2)     D

All of the covariates in equation (2) appeared in equation (1) as well.

As noted previously, SMI estimates for 2008 were based on both the WHODAS and SDS half samples because estimates of SMI were comparable between the half samples. Using the 2012 model allows AMI estimates for 2008 to be created based on both the half samples as well. Previous 2008 AMI estimates using the 2008 model, on the other hand, were based on only the WHODAS half sample.

Serious Thoughts of Suicide. Responding to a need for national data on the prevalence of suicidal thoughts and behavior, a set of questions was added beginning with the 2008 NSDUH questionnaire (and those sets of questions were continued to be asked in 2009 and 2010). These questions asked all adult respondents aged 18 or older if at any time during the past 12 months they had serious thoughts of suicide (suicidal ideation). Substate-level estimates of suicidal ideation were produced using 2008, 2009, and 2010 data.

MDE (i.e., Depression). According to the DSM-IV, a person is defined as having had MDE 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 MDE 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 had MDE in the past year are asked questions from the SDS to measure the level of functional impairment in major life activities reported to be caused by the MDE in the past 12 months (Leon et al., 1997).

Beginning in 2004, modules related to MDE derived from DSM-IV (APA, 1994) criteria for major depression were included in the questionnaire. These questions permit estimates to be calculated for the prevalence of MDE and treatment for MDE. 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 Replication Adolescent Supplement (NCS-A).10 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 their 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 MDE have remained unchanged. In the 2008 questionnaire, however, changes were made in other mental health items that precede the MDE questions for adults (K6, suicide, and impairment). Questions also were retained in 2009 and 2010 for the WHODAS impairment scale, and the questions for the SDS impairment scale were deleted; see Sections B.4.2 and B.4.3 in Appendix B of the 2010 NSDUH mental health findings report (CBHSQ, 2012b) for further details about these questionnaire changes. These questionnaire changes in 2008 appear to have affected the reporting on MDE questions among adults.

Because the WHODAS was selected to be used in the 2009 and subsequent surveys, model-based adjustments were applied to MDE 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 MDE estimates to make them comparable with the 2008 through 2010 MDE estimates (for more information on these adjustments, see Aldworth, Kott, Yu, Mosquin, & Barnett-Walker, 2012). Thus, the 2008-2010 substate estimates of MDE were produced using the adjusted 2008 MDE variable along with the unadjusted 2009 and 2010 MDE variable. Additionally, the 2006-2008 substate small area estimates of MDE were re-created using the adjusted MDE 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 MDE. However, these changes in 2009 did not appear to affect the estimates of adolescent MDE. Therefore, data on trends in past year MDE from 2004 to 2010 are available for adolescents aged 12 to 17.

Table B1. – Outcomes, by Survey Year, for Which Substate Small Area Estimates Are Available
Measure 2002-2004 2004-2006 2006-2008 2008-2010
Yes = available, No = not available.
1 Because of questionnaire changes, estimates for serious psychological distress (SPD) in the years 2002-2004 are not comparable with the 2004-2006 SPD estimates. For more details, see Section B.7 of the report on Substate Estimates from the 2004-2006 National Surveys on Drug Use and Health (Office of Applied Studies [OAS], 2008). Additional questionnaire changes were made in 2008 that affected past year SPD trends. However, revised past year SPD measures were created for 2005 through 2007 that are comparable with the 2008 through 2010 past year SPD measure. Substate small area estimates for 2006-2008 and 2008-2010 were not created for this measure.
2 Questions used to determine a major depressive episode (MDE) were added in 2004. Estimates for adults aged 18 or older are not available in the 2006-2008 substate report. However, MDE substate estimates for youths aged 12 to 17 were produced for 2006-2008 and were included in a set of age group tables separate from the main report. Estimates for 18 or older were produced for 2006-2008 and shown in the "2008-2010 NSDUH Substate Comparison Tables." The 2004-2006 MDE estimate is not comparable with the 2006-2008 and 2008-2010 MDE estimates that are shown in the "2008-2010 NSDUH Substate Comparison Tables." For more details, see Section B.10.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2002-2010.
Illicit Drug Use in Past Month Yes Yes Yes Yes
Marijuana Use in Past Year Yes Yes Yes Yes
Marijuana Use in Past Month Yes Yes Yes Yes
Perceptions of Great Risk of Smoking Marijuana Once a Month Yes Yes Yes Yes
First Use of Marijuana Yes Yes Yes Yes
Illicit Drug Use Other Than Marijuana in Past Month Yes Yes Yes Yes
Cocaine Use in Past Year Yes Yes Yes Yes
Nonmedical Use of Pain Relievers in Past Year Yes Yes Yes Yes
Alcohol Use in Past Month Yes Yes Yes Yes
Underage Past Month Use of Alcohol Yes Yes Yes Yes
Binge Alcohol Use in Past Month Yes Yes Yes Yes
Underage Past Month Binge Alcohol Use 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
Tobacco Product Use in Past Month Yes Yes Yes Yes
Cigarette Use in Past Month Yes Yes Yes Yes
Perceptions of Great Risk of Smoking One or More Packs of Cigarettes
   Per Day
Yes Yes Yes Yes
Alcohol Dependence or Abuse in Past Year Yes Yes Yes Yes
Alcohol Dependence in Past Year Yes Yes Yes Yes
Illicit Drug Dependence or Abuse in Past Year Yes Yes Yes Yes
Illicit Drug Dependence in Past Year Yes Yes Yes Yes
Dependence or Abuse of Illicit Drugs or Alcohol in Past Year Yes Yes Yes Yes
Needing But Not Receiving Treatment for Illicit Drug Use in Past Year Yes Yes Yes Yes
Needing But Not Receiving Treatment for Alcohol Use in Past Year Yes Yes Yes Yes
Serious Psychological Distress in Past Year1 Yes Yes No No
Had at Least One Major Depressive Episode in Past Year2 No Yes Yes Yes
Serious Mental Illness in Past Year No No No Yes
Any Mental Illness in Past Year No No No Yes
Had Serious Thoughts of Suicide in Past Year No No No Yes

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

120409
Table C1. – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by Substate Region, for Persons Aged 12 or Older: 2008, 2009, and 2010 NSDUHs
State/Substate Region Total
Selected DUs
Total Eligible
DUs
Total
Completed
Screeners
Weighted DU
Screening
Response
Rate
(Percentage)
Total
Selected
Total
Responded
Population
Estimate
Weighted
Interview
Response
Rate
(Percentage)
Weighted
Overall
Response
Rate
(Percentage)
DU = dwelling unit; ECCS = Eastern Coastal Care System; PBH = Piedmont Behavioral Health; SPA = service planning area.
NOTE: For substate region definitions, see the "2008-2010 National Survey on Drug Use and Health Substate Region Definitions" at http://www.samhsa.gov/data/NSDUH/substate2k10/toc.aspx.
NOTE: To compute the pooled 2008-2010 weighted response rates, the three samples were combined, and the individual-year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 3 years of data rather than being a simple average of the 2008, 2009, and 2010 individual response rates.
NOTE: The total responded column represents the combined sample size from the 2008, 2009, and 2010 NSDUHs.
NOTE: The population estimate is the simple average of the 2008, 2009, and 2010 population counts for persons aged 12 or older. Because of rounding, the sum of the substate region population counts within a State may not exactly match the State population count listed in the table.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2008, 2009, and 2010 (Revised March 2012).
Total United States 591,812 488,023 432,102 88.48% 255,493 203,739 252,093,001 74.79% 66.17%
Northeast 126,705 105,171 87,167 82.09% 50,395 39,070 46,380,374 72.47% 59.49%
Midwest 160,684 135,152 121,455 90.11% 72,349 57,748 55,263,136 75.24% 67.80%
South 184,270 148,476 134,048 90.95% 76,656 62,434 92,099,064 76.66% 69.72%
West 120,153 99,224 89,432 88.14% 56,093 44,487 58,350,427 73.31% 64.61%
Alabama 8,656 6,899 6,367 92.34% 3,468 2,751 3,881,067 74.13% 68.44%
Region 1 2,269 1,898 1,746 92.03% 982 772 1,083,792 74.29% 68.36%
Region 2 2,958 2,342 2,145 91.68% 1,142 886 1,246,030 69.23% 63.47%
Region 3 1,469 1,139 1,062 93.30% 579 476 689,036 77.89% 72.67%
Region 4 1,960 1,520 1,414 92.98% 765 617 862,209 78.55% 73.03%
Alaska 7,157 5,250 4,811 91.63% 3,314 2,678 550,880 77.85% 71.33%
Anchorage 2,792 2,365 2,171 91.84% 1,483 1,211 231,658 77.77% 71.43%
Northern 1,581 1,114 1,021 91.38% 755 602 117,322 78.47% 71.71%
South Central 1,898 1,141 1,026 90.06% 701 557 144,244 76.60% 68.99%
Southeast 886 630 593 94.18% 375 308 57,656 79.31% 74.70%
Arizona 8,277 6,180 5,459 86.83% 3,390 2,749 5,333,984 76.32% 66.27%
Maricopa 4,860 3,859 3,365 85.25% 2,157 1,744 3,217,104 74.95% 63.90%
Pima 1,180 987 885 89.83% 474 384 826,110 81.22% 72.96%
Rural North 1,310 745 669 90.01% 457 371 617,189 78.81% 70.94%
Rural South 927 589 540 91.13% 302 250 673,580 77.06% 70.23%
Arkansas 7,868 6,342 5,913 93.19% 3,378 2,746 2,357,158 76.56% 71.35%
Catchment Area 1 1,142 929 831 89.50% 525 426 366,185 76.00% 68.02%
Catchment Area 2 879 724 679 93.64% 310 254 291,964 77.43% 72.50%
Catchment Area 3 1,295 975 928 95.13% 540 413 320,661 71.58% 68.10%
Catchment Area 4 652 548 500 91.01% 341 267 208,918 70.13% 63.83%
Catchment Area 5 1,383 1,105 1,047 94.74% 596 505 346,907 79.30% 75.13%
Catchment Area 6 647 515 495 95.94% 250 215 181,044 85.32% 81.86%
Catchment Area 7 528 420 409 97.32% 240 196 195,006 73.15% 71.19%
Catchment Area 8 1,342 1,126 1,024 91.06% 576 470 446,472 79.77% 72.64%
California 27,344 23,927 20,252 84.64% 14,509 11,205 30,138,172 71.16% 60.23%
Region 1R 725 605 520 86.34% 305 248 816,993 77.77% 67.15%
Region 2R 898 763 648 84.89% 476 374 832,296 75.60% 64.18%
Region 3R (Sacramento) 977 855 722 84.78% 443 343 1,144,189 69.10% 58.59%
Region 4R 1,032 894 763 85.59% 467 349 1,058,929 67.48% 57.76%
Region 5R (San Francisco) 893 808 584 72.63% 270 195 661,449 65.65% 47.68%
Region 6 (Santa Clara) 1,243 1,143 977 85.22% 619 438 1,390,996 66.73% 56.86%
Region 7R (Contra Costa) 734 650 543 83.72% 326 249 853,197 72.16% 60.41%
Region 8R (Alameda) 1,133 1,018 834 82.31% 575 403 1,191,649 59.54% 49.01%
Region 9R (San Mateo) 571 522 445 85.43% 288 211 571,487 68.59% 58.59%
Region 10 866 747 650 87.20% 470 353 995,090 70.10% 61.13%
Region 11 (Los Angeles) 6,972 6,258 5,270 84.31% 3,911 2,979 8,126,588 69.80% 58.85%
LA SPA 1 and 5 841 715 556 77.60% 323 239 846,525 68.54% 53.18%
LA SPA 2 1,590 1,441 1,178 81.69% 763 590 1,735,407 70.22% 57.36%
LA SPA 3 1,206 1,113 978 88.26% 743 532 1,467,884 63.62% 56.15%
LA SPA 4 840 726 585 81.07% 419 336 983,681 76.00% 61.61%
LA SPA 6 686 606 537 88.30% 508 398 786,028 72.95% 64.42%
LA SPA 7 801 738 644 87.43% 581 439 1,043,744 65.05% 56.88%
LA SPA 8 1,008 919 792 86.19% 574 445 1,263,319 76.25% 65.72%
Region 12R 550 474 404 85.39% 316 258 692,984 71.81% 61.32%
Regions 13 and 19R 1,737 1,271 1,137 89.38% 899 733 1,855,281 74.80% 66.86%
Region 13 (Riverside) 1,441 1,012 884 87.45% 651 510 1,721,650 72.01% 62.97%
Region 19R (Imperial) 296 259 253 97.60% 248 223 133,631 86.43% 84.35%
Region 14 (Orange) 2,015 1,876 1,571 83.79% 1,233 963 2,443,343 70.13% 58.76%
Region 15R (Fresno) 682 610 499 79.03% 419 352 719,014 80.31% 63.47%
Region 16R (San Diego) 2,478 2,147 1,808 84.13% 1,218 909 2,448,679 71.37% 60.04%
Region 17R 1,131 999 809 80.24% 641 535 1,116,646 81.23% 65.17%
Region 18R (San Bernardino) 1,339 1,171 1,071 91.58% 926 741 1,650,606 70.72% 64.77%
Region 20R 665 552 493 89.42% 392 318 758,885 77.78% 69.56%
Region 21R 703 564 504 89.70% 315 254 809,870 80.03% 71.79%
Colorado 8,219 6,722 6,149 91.72% 3,507 2,837 4,102,784 77.66% 71.23%
Region 1 880 747 681 91.05% 391 327 542,307 79.64% 72.51%
Regions 2 and 7 4,136 3,605 3,239 89.97% 1,865 1,492 2,276,050 76.56% 68.88%
Region 3 1,412 1,189 1,112 93.97% 674 558 595,606 82.75% 77.76%
Region 4 557 425 399 93.80% 204 176 231,074 78.47% 73.61%
Regions 5 and 6 1,234 756 718 95.00% 373 284 457,747 72.06% 68.46%
Connecticut 7,549 6,645 5,775 86.71% 3,460 2,779 2,952,037 75.56% 65.52%
Eastern 785 689 616 89.17% 349 276 359,822 75.47% 67.29%
North Central 2,223 2,033 1,761 86.58% 1,070 863 833,207 73.67% 63.78%
Northwestern 1,441 1,236 1,016 81.76% 612 512 516,198 75.18% 61.46%
South Central 1,841 1,582 1,433 90.47% 816 656 696,180 78.18% 70.73%
Southwest 1,259 1,105 949 85.66% 613 472 546,629 75.67% 64.82%
Delaware 7,763 6,376 5,577 87.54% 3,394 2,752 730,344 76.53% 66.99%
Kent 1,307 1,064 933 87.77% 592 491 129,068 79.11% 69.44%
New Castle (excluding Wilmington City) 3,587 3,114 2,666 85.72% 1,708 1,365 377,301 76.07% 65.20%
Sussex 1,946 1,446 1,316 90.94% 693 552 160,441 73.22% 66.59%
Wilmington City 923 752 662 88.14% 401 344 63,535 83.14% 73.28%
District of Columbia 13,505 11,010 8,974 80.84% 3,230 2,721 511,275 81.26% 65.69%
Ward 1 1,772 1,417 1,215 85.71% 395 332 64,979 84.83% 72.71%
Ward 2 1,777 1,449 1,132 76.97% 357 295 74,341 80.21% 61.74%
Ward 3 1,985 1,639 1,305 77.53% 443 367 68,990 81.60% 63.27%
Ward 4 1,510 1,312 1,098 83.61% 413 331 66,276 72.06% 60.25%
Ward 5 1,189 966 758 78.92% 317 275 62,263 82.41% 65.04%
Ward 6 2,116 1,645 1,328 80.02% 412 336 60,350 80.45% 64.38%
Ward 7 1,836 1,546 1,275 82.40% 499 436 59,012 84.85% 69.91%
Ward 8 1,320 1,036 863 83.25% 394 349 55,064 86.72% 72.19%
Florida 35,652 27,168 24,635 90.56% 13,255 10,893 15,480,164 76.87% 69.62%
Region A - Northwest 2,910 2,219 2,036 91.92% 1,126 906 1,121,473 75.02% 68.96%
Circuit 1 1,432 1,121 1,013 90.67% 559 461 568,424 79.80% 72.35%
Circuit 2 800 576 530 92.12% 315 245 313,187 71.18% 65.57%
Circuit 14 678 522 493 94.35% 252 200 239,863 69.85% 65.91%
Region B - Northeast 4,743 3,803 3,500 91.34% 1,765 1,410 2,110,410 74.29% 67.85%
Circuits 3 and 8 1,139 916 871 95.13% 433 351 462,058 74.44% 70.81%
Circuit 4 2,128 1,725 1,556 89.27% 868 687 912,907 73.85% 65.93%
Circuit 7 1,476 1,162 1,073 92.26% 464 372 735,444 74.73% 68.95%
Region C - Central 8,480 6,571 6,024 91.73% 3,384 2,793 3,887,055 76.19% 69.89%
Circuit 5 1,985 1,598 1,472 92.35% 703 551 883,400 74.31% 68.62%
Circuit 9 2,164 1,756 1,648 93.83% 1,105 938 1,100,423 78.96% 74.08%
Circuit 10 1,652 1,175 1,070 90.92% 566 484 583,666 76.14% 69.23%
Circuit 18 1,651 1,301 1,183 91.04% 655 525 816,409 74.09% 67.45%
Circuit 19 1,028 741 651 88.09% 355 295 503,157 77.52% 68.29%
Region D - Southeast 6,201 4,367 3,860 88.41% 2,098 1,750 2,544,841 77.85% 68.82%
Circuit 15 (Palm Beach) 2,663 1,775 1,509 85.29% 767 584 1,089,052 69.42% 59.20%
Circuit 17 (Broward) 3,538 2,592 2,351 90.39% 1,331 1,166 1,455,788 82.79% 74.84%
Region E - Sun Coast 9,128 6,873 6,213 90.29% 3,062 2,484 3,754,990 76.28% 68.87%
Circuit 6 2,794 2,087 1,891 90.78% 885 701 1,171,601 75.98% 68.97%
Circuit 12 1,392 1,030 908 88.06% 394 324 631,253 75.48% 66.47%
Circuit 13 (Hillsborough) 2,444 2,100 1,928 92.05% 1,143 944 969,022 77.43% 71.27%
Circuit 20 2,498 1,656 1,486 88.92% 640 515 983,114 75.64% 67.26%
Region F - Southern (Circuits 11 and 16) 4,190 3,335 3,002 89.84% 1,820 1,550 2,061,396 82.35% 73.98%
Georgia 7,290 5,868 5,356 91.20% 3,302 2,694 7,847,010 75.91% 69.23%
Region 1 1,863 1,440 1,291 89.55% 816 653 2,002,117 73.92% 66.20%
Region 2 967 776 710 91.62% 396 326 1,019,758 77.93% 71.40%
Region 3 2,056 1,731 1,559 89.81% 1,010 853 2,415,319 80.61% 72.40%
Region 4 510 420 398 94.87% 200 155 493,136 74.70% 70.86%
Region 5 840 632 594 94.04% 353 299 835,046 78.67% 73.98%
Region 6 1,054 869 804 92.41% 527 408 1,081,634 66.96% 61.88%
Hawaii 9,117 7,534 6,290 82.36% 3,894 2,831 1,069,970 66.31% 54.62%
Hawaii Island 1,334 989 834 84.16% 513 390 146,741 70.92% 59.69%
Honolulu 6,176 5,326 4,369 81.26% 2,781 1,977 753,234 63.93% 51.95%
Kauai and Maui 1,607 1,219 1,087 85.56% 600 464 169,995 73.28% 62.70%
Kauai 517 421 396 94.10% 196 159 52,513 73.36% 69.04%
Maui 1,090 798 691 81.32% 404 305 117,482 73.24% 59.56%
Idaho 7,269 5,754 5,445 94.64% 3,379 2,770 1,240,669 77.84% 73.67%
Region 1 1,126 780 735 94.25% 413 326 181,323 72.04% 67.90%
Region 2 449 326 313 96.19% 177 153 86,052 90.17% 86.73%
Region 3 1,007 914 869 94.92% 573 480 202,263 79.54% 75.49%
Region 4 2,244 1,777 1,675 94.29% 980 820 349,384 77.64% 73.21%
Region 5 878 651 602 92.27% 361 294 143,028 78.64% 72.56%
Region 6 757 638 601 94.33% 373 300 127,216 79.45% 74.94%
Region 7 808 668 650 97.53% 502 397 151,402 75.34% 73.48%
Illinois 31,264 27,115 21,839 80.49% 14,593 11,007 10,606,775 70.39% 56.66%
Region I (Cook) 12,491 10,968 7,816 71.21% 5,502 3,915 4,228,949 65.16% 46.40%
Region II 8,817 7,775 6,492 83.40% 4,533 3,496 3,425,673 72.72% 60.65%
Region III 4,272 3,476 3,122 89.82% 1,939 1,533 1,193,555 76.81% 68.99%
Region IV 2,353 2,032 1,821 89.49% 1,029 802 755,520 72.79% 65.14%
Region V 3,331 2,864 2,588 90.29% 1,590 1,261 1,003,078 74.41% 67.19%
Indiana 7,776 6,454 6,006 92.93% 3,408 2,734 5,263,445 76.93% 71.49%
Central 1,972 1,648 1,512 91.59% 828 627 1,350,747 74.53% 68.26%
East 645 494 472 95.54% 277 243 448,124 77.92% 74.44%
North Central 1,054 875 818 93.31% 472 360 765,290 73.58% 68.66%
Northeast 782 629 580 92.36% 328 264 525,384 75.80% 70.01%
Northwest 834 699 633 90.15% 386 328 618,589 82.78% 74.63%
Southeast 870 755 718 94.93% 392 330 568,692 78.57% 74.58%
Southwest 607 530 498 93.76% 258 198 415,153 77.18% 72.36%
West 1,012 824 775 94.04% 467 384 571,466 78.74% 74.05%
Iowa 7,611 6,544 6,122 93.57% 3,364 2,794 2,520,427 80.52% 75.34%
Central 1,277 1,166 1,091 93.52% 597 486 446,866 74.67% 69.83%
North Central 1,097 933 861 92.38% 427 340 279,161 74.90% 69.20%
Northeast 1,700 1,449 1,349 93.11% 761 645 610,504 82.77% 77.06%
Northwest 1,219 982 933 94.90% 548 461 398,566 83.69% 79.42%
Southeast 1,641 1,432 1,341 93.74% 771 645 532,185 82.96% 77.76%
Southwest 677 582 547 94.03% 260 217 253,146 83.65% 78.66%
Kansas 6,867 5,905 5,476 92.76% 3,333 2,678 2,281,891 75.90% 70.41%
Kansas City Metro 2,502 2,279 2,087 91.59% 1,311 1,038 771,541 74.20% 67.96%
Northeast 1,104 877 820 93.55% 504 414 428,861 83.45% 78.07%
South Central 721 587 553 94.32% 296 237 292,088 69.32% 65.38%
Southeast 461 387 359 93.07% 215 166 158,246 73.53% 68.44%
West 929 774 712 92.05% 423 335 249,472 73.43% 67.59%
Wichita (Sedgwick) 1,150 1,001 945 94.29% 584 488 381,684 79.64% 75.08%
Kentucky 7,638 6,256 5,859 93.67% 3,324 2,696 3,579,401 75.66% 70.87%
Adanta, Cumberland River, and Lifeskills 1,412 1,071 1,012 94.50% 569 459 610,807 77.18% 72.93%
Bluegrass, Comprehend, and North Key 2,174 1,815 1,674 92.30% 984 795 1,039,966 74.59% 68.85%
Communicare and River Valley 675 569 528 92.85% 324 260 393,876 71.30% 66.20%
Four Rivers and Pennyroyal 892 693 658 95.06% 355 314 338,341 88.24% 83.89%
Kentucky River, Mountain, and Pathways 951 736 706 95.93% 364 302 419,022 74.73% 71.70%
Seven Counties 1,534 1,372 1,281 93.28% 728 566 777,389 72.89% 67.99%
Louisiana 7,634 6,037 5,665 93.87% 3,337 2,710 3,632,758 78.54% 73.73%
Regions 1 and 3 1,396 919 846 91.94% 590 472 631,645 76.88% 70.69%
Regions 2 and 9 1,785 1,523 1,458 95.72% 970 826 960,603 82.91% 79.36%
Regions 4, 5, and 6 2,071 1,627 1,539 94.76% 788 622 943,149 76.85% 72.82%
Regions 7 and 8 1,594 1,289 1,206 93.52% 673 553 718,733 80.46% 75.25%
Region 10 (Jefferson) 788 679 616 90.78% 316 237 378,628 69.90% 63.46%
Maine 9,748 7,117 6,543 91.82% 3,334 2,803 1,134,608 80.13% 73.58%
Aroostook/Downeast 1,279 894 853 94.98% 457 414 137,389 83.82% 79.60%
Central 1,220 893 818 91.73% 441 368 148,494 78.48% 71.99%
Cumberland 1,985 1,577 1,399 88.70% 707 584 235,871 78.43% 69.57%
Midcoast 1,266 821 768 93.70% 309 277 130,898 84.15% 78.86%
Penquis 1,089 791 726 91.67% 408 349 140,685 84.36% 77.34%
Western 1,634 1,197 1,108 91.94% 565 471 167,004 82.96% 76.28%
York 1,275 944 871 92.25% 447 340 174,267 70.54% 65.08%
Maryland 7,172 6,183 5,006 80.82% 3,185 2,563 4,701,377 76.78% 62.05%
Anne Arundel 732 671 523 77.10% 299 252 421,933 77.50% 59.75%
Baltimore City 840 654 495 75.39% 309 266 520,595 79.71% 60.09%
Baltimore County 886 786 615 78.31% 381 305 665,697 77.75% 60.89%
Montgomery 1,099 1,028 818 79.25% 548 417 762,906 73.92% 58.59%
North Central 543 502 428 85.38% 305 254 373,353 81.08% 69.22%
Northeast 580 518 446 85.42% 267 220 409,291 81.59% 69.69%
Prince George's 980 817 641 78.31% 437 337 693,889 72.60% 56.85%
South 955 702 594 84.82% 327 272 453,959 77.84% 66.02%
West 557 505 446 88.58% 312 240 399,754 74.28% 65.79%
Massachusetts 8,955 7,688 6,658 86.70% 3,500 2,796 5,556,250 76.22% 66.08%
Boston 1,026 851 719 84.64% 361 302 647,793 80.08% 67.78%
Central 1,034 910 761 83.35% 422 336 729,748 72.73% 60.62%
Metrowest 2,139 1,908 1,616 84.93% 878 702 1,272,100 77.54% 65.85%
Northeast 1,681 1,510 1,318 87.11% 682 538 1,080,907 76.40% 66.55%
Southeast 1,890 1,468 1,323 90.37% 628 482 1,099,740 72.89% 65.88%
Western 1,185 1,041 921 88.41% 529 436 725,962 79.22% 70.04%
Michigan 31,434 25,194 22,267 88.35% 13,678 11,004 8,326,133 75.90% 67.06%
Detroit City 2,526 1,805 1,601 88.91% 1,067 883 663,193 78.91% 70.16%
Genesee 1,193 970 842 87.07% 536 445 359,424 78.04% 67.94%
Kalamazoo 2,262 1,709 1,572 91.94% 961 748 558,898 73.68% 67.74%
Kent 1,651 1,452 1,253 86.41% 810 640 486,251 73.82% 63.79%
Lakeshore 2,187 1,814 1,640 90.25% 998 782 587,186 74.70% 67.42%
Macomb 2,439 2,200 1,856 84.14% 1,097 855 701,444 73.95% 62.22%
Mid South 2,895 2,339 2,054 87.34% 1,231 1,042 769,199 81.09% 70.83%
Northern 3,071 1,860 1,703 91.59% 922 769 732,014 80.35% 73.60%
Oakland 3,582 3,185 2,779 87.13% 1,694 1,320 1,003,970 73.55% 64.08%
Pathways and Western 1,175 884 807 91.47% 458 396 264,361 80.58% 73.71%
Riverhaven 1,178 973 888 91.13% 511 431 294,438 77.36% 70.50%
Saginaw 871 734 657 89.19% 429 350 168,530 74.59% 66.53%
Southeast 3,634 3,153 2,748 87.49% 1,760 1,369 1,023,356 71.04% 62.15%
St. Clair 1,025 897 803 88.90% 516 419 261,419 78.72% 69.98%
Washtenaw 1,745 1,219 1,064 87.43% 688 555 452,451 79.09% 69.15%
Minnesota 7,104 5,989 5,608 93.66% 3,354 2,752 4,384,564 78.28% 73.32%
Regions 1 and 2 1,077 804 740 92.17% 370 274 440,485 65.52% 60.39%
Regions 3 and 4 1,313 1,047 1,000 95.55% 618 515 768,639 82.70% 79.01%
Regions 5 and 6 1,476 1,212 1,167 96.18% 691 585 836,739 83.46% 80.27%
Region 7A (Hennepin) 1,284 1,158 1,060 91.66% 632 527 944,494 78.47% 71.93%
Region 7B (Ramsey) 725 631 574 90.82% 387 316 407,755 74.99% 68.10%
Region 7C 1,229 1,137 1,067 93.86% 656 535 986,453 78.35% 73.54%
Mississippi 6,678 5,272 4,953 94.03% 3,251 2,667 2,380,781 77.40% 72.78%
Region 1 1,524 1,239 1,146 92.54% 827 644 533,101 70.54% 65.28%
Region 2 807 530 510 96.20% 290 234 317,444 79.86% 76.82%
Region 3 956 744 701 94.26% 482 395 334,845 79.75% 75.17%
Region 4 1,204 1,046 967 92.48% 644 550 434,654 81.06% 74.96%
Region 5 462 369 359 97.19% 222 187 152,050 78.49% 76.29%
Region 6 605 480 464 96.66% 295 255 241,131 82.35% 79.60%
Region 7 1,120 864 806 93.72% 491 402 367,557 78.06% 73.16%
Missouri 7,784 6,433 6,009 93.42% 3,385 2,724 4,929,201 75.90% 70.91%
Central 989 754 725 96.13% 386 318 652,325 76.56% 73.60%
Eastern 2,594 2,167 2,035 93.92% 1,140 920 1,757,850 76.47% 71.82%
Eastern (St. Louis City and County) 1,906 1,587 1,482 93.46% 782 629 1,121,478 77.01% 71.97%
Eastern (excluding St. Louis) 688 580 553 95.23% 358 291 636,372 75.12% 71.54%
Northwest 2,013 1,682 1,552 92.31% 930 759 1,183,313 77.48% 71.52%
Northwest (Jackson) 1,078 886 808 91.37% 482 398 546,287 81.59% 74.56%
Northwest (excluding Jackson) 935 796 744 93.31% 448 361 637,026 73.21% 68.32%
Southeast 984 841 797 94.75% 421 329 583,337 72.10% 68.31%
Southwest 1,204 989 900 91.11% 508 398 752,376 74.73% 68.08%
Montana 8,095 6,743 6,365 94.35% 3,395 2,747 818,561 76.64% 72.31%
Region 1 624 500 482 96.32% 251 216 63,353 82.45% 79.42%
Region 2 1,164 998 960 96.13% 507 418 115,576 76.72% 73.75%
Region 3 1,709 1,444 1,330 92.11% 701 576 168,669 79.10% 72.86%
Region 4 2,114 1,707 1,612 94.22% 927 754 214,266 75.37% 71.01%
Region 5 2,484 2,094 1,981 94.66% 1,009 783 256,697 74.55% 70.57%
Nebraska 6,926 5,851 5,518 94.30% 3,350 2,705 1,462,435 76.21% 71.87%
Regions 1 and 2 900 650 620 95.47% 337 290 153,765 82.47% 78.74%
Region 1 399 310 293 94.47% 166 140 70,737 83.47% 78.86%
Region 2 501 340 327 96.32% 171 150 83,028 81.09% 78.10%
Region 3 964 846 803 94.75% 481 393 185,906 77.96% 73.87%
Region 4 731 574 545 94.93% 297 232 169,721 75.48% 71.65%
Region 5 1,684 1,429 1,333 93.42% 871 724 360,459 80.44% 75.15%
Region 6 2,647 2,352 2,217 94.20% 1,364 1,066 592,584 71.90% 67.73%
Nevada 8,057 6,382 5,997 94.44% 3,456 2,775 2,138,278 72.71% 68.66%
Clark 5,414 4,250 3,978 94.28% 2,404 1,942 1,524,755 72.05% 67.92%
Rural 1,073 832 791 95.18% 360 291 279,616 78.13% 74.36%
Washoe 1,570 1,300 1,228 94.68% 692 542 333,907 72.19% 68.35%
New Hampshire 8,603 6,819 5,984 87.83% 3,463 2,766 1,130,191 76.11% 66.85%
Central 2,652 2,060 1,852 89.76% 1,079 873 322,203 77.72% 69.77%
Central 1 1,273 1,002 906 90.21% 520 401 156,735 73.98% 66.73%
Central 2 1,379 1,058 946 89.35% 559 472 165,468 81.10% 72.47%
Northern 1,418 851 761 89.55% 366 301 145,205 77.23% 69.16%
Southern 4,533 3,908 3,371 86.41% 2,018 1,592 662,783 75.06% 64.85%
Southern 1 (Rockingham) 1,675 1,370 1,207 88.39% 701 544 253,793 75.00% 66.29%
Southern 2 2,858 2,538 2,164 85.33% 1,317 1,048 408,990 75.08% 64.07%
New Jersey 7,456 6,387 5,651 88.58% 3,576 2,803 7,245,571 74.68% 66.15%
Central 1,751 1,363 1,190 87.81% 721 558 1,703,097 74.33% 65.27%
Metropolitan 1,698 1,511 1,351 89.49% 911 743 1,708,009 78.58% 70.33%
Northern 2,342 2,104 1,840 87.51% 1,166 923 2,276,564 75.77% 66.31%
Southern 1,665 1,409 1,270 89.95% 778 579 1,557,902 69.17% 62.22%
New Mexico 7,749 6,056 5,710 94.27% 3,305 2,706 1,637,309 77.91% 73.45%
Region 1 1,658 1,352 1,289 95.51% 868 701 340,441 74.40% 71.06%
Region 2 1,323 943 875 92.67% 408 339 247,265 82.90% 76.83%
Region 3 (Bernalillo) 2,242 1,953 1,826 93.50% 1,040 837 523,402 77.38% 72.35%
Region 4 947 733 687 93.64% 382 313 200,222 81.19% 76.03%
Region 5 1,579 1,075 1,033 95.98% 607 516 325,979 78.73% 75.57%
New York 37,947 31,837 24,434 76.62% 15,010 10,903 16,385,102 68.10% 52.18%
Region A 15,866 13,540 9,127 67.22% 5,867 4,050 6,873,429 63.63% 42.77%
Region 1 2,319 2,050 1,568 76.23% 1,073 808 1,112,439 72.35% 55.16%
Region 2 5,483 4,606 3,328 72.29% 2,221 1,529 2,480,170 63.44% 45.86%
Region 3 3,832 3,104 1,775 56.53% 950 694 1,409,808 68.46% 38.70%
Region 4 4,232 3,780 2,456 65.15% 1,623 1,019 1,871,011 56.70% 36.94%
Region B 8,784 7,774 6,060 77.85% 4,014 2,810 4,232,149 66.23% 51.56%
Region 5 4,460 4,031 3,156 78.18% 2,079 1,451 2,358,897 66.63% 52.09%
Region 6 2,435 2,187 1,681 76.89% 1,086 747 1,130,035 63.84% 49.09%
Region 7 1,889 1,556 1,223 78.35% 849 612 743,217 68.60% 53.75%
Region C 9,699 8,028 7,011 87.30% 3,913 3,045 3,919,524 74.12% 64.71%
Region 8 2,282 1,819 1,576 86.51% 858 641 861,251 69.60% 60.21%
Region 9 2,098 1,658 1,413 85.36% 769 618 814,919 74.44% 63.54%
Region 10 938 797 713 89.49% 394 336 380,859 80.57% 72.11%
Region 11 1,952 1,716 1,520 88.79% 913 687 890,030 76.63% 68.03%
Region 12 2,429 2,038 1,789 87.47% 979 763 972,465 73.49% 64.28%
Region D 3,598 2,495 2,236 89.20% 1,216 998 1,360,000 80.26% 71.59%
Region 13 1,299 696 632 90.43% 357 268 423,082 72.25% 65.33%
Region 14 1,330 997 886 88.53% 489 407 465,439 83.10% 73.57%
Region 15 969 802 718 88.91% 370 323 471,480 84.37% 75.02%
North Carolina 7,624 6,432 5,911 92.06% 3,299 2,723 7,595,961 78.07% 71.87%
CenterPoint/Guilford 760 640 587 91.79% 343 271 824,256 76.07% 69.82%
CenterPoint 310 268 245 91.46% 130 100 437,015 71.26% 65.17%
Guilford 450 372 342 92.06% 213 171 387,241 79.32% 73.02%
Durham 1,514 1,350 1,255 92.82% 749 608 1,267,570 73.39% 68.13%
East Carolina 513 383 372 97.11% 193 151 485,123 79.92% 77.61%
Eastpointe 563 453 434 95.76% 227 196 646,693 78.80% 75.46%
ECCS 420 359 325 90.96% 176 143 475,675 79.92% 72.70%
Mecklenburg 861 728 653 89.32% 392 314 712,396 75.30% 67.25%
Pathways 864 742 697 93.84% 396 342 746,034 82.52% 77.43%
PBH 877 720 663 93.11% 349 288 1,122,782 78.35% 72.94%
Sandhills 429 349 314 90.32% 160 130 451,080 75.65% 68.33%
Smoky Mountain 324 284 252 89.17% 152 142 438,863 93.19% 83.10%
Western Highlands 499 424 359 84.94% 162 138 425,488 83.04% 70.53%
North Dakota 8,790 7,287 6,868 94.28% 3,479 2,815 537,026 77.27% 72.85%
Badlands and West Central 2,249 1,873 1,792 95.85% 843 687 146,443 76.34% 73.17%
Lake Region and South Central 1,296 1,025 978 95.29% 449 357 80,376 77.16% 73.53%
North Central and Northwest 1,470 1,188 1,128 95.17% 544 428 89,456 72.54% 69.04%
Northeast 1,087 887 823 92.75% 463 383 75,001 76.75% 71.19%
Southeast 2,688 2,314 2,147 92.74% 1,180 960 145,750 80.64% 74.79%
Ohio 30,441 25,930 24,033 92.63% 13,666 11,008 9,562,910 74.55% 69.06%
Boards 2, 46, 55, and 68 1,597 1,383 1,335 96.31% 706 595 428,709 80.56% 77.59%
Boards 3, 52, and 85 815 726 680 93.75% 424 346 319,177 70.55% 66.14%
Boards 4 and 78 779 663 626 94.24% 325 256 266,461 72.36% 68.19%
Boards 5 and 60 1,055 882 842 95.56% 592 502 279,467 75.09% 71.75%
Boards 7, 15, 41, 79, and 84 1,259 1,066 986 92.39% 570 466 389,291 74.36% 68.70%
Boards 8, 13, and 83 1,061 935 845 90.42% 491 385 413,875 72.91% 65.93%
Board 9 (Butler) 927 791 719 90.52% 387 292 301,261 71.98% 65.15%
Board 12 1,060 938 870 92.70% 550 452 282,864 74.54% 69.10%
Boards 18 and 47 4,546 3,831 3,505 91.53% 1,862 1,524 1,326,792 75.76% 69.34%
Boards 20, 32, 54, and 69 824 724 714 98.71% 419 350 290,861 80.38% 79.34%
Boards 21, 39, 51, 70, and 80 1,116 988 895 90.61% 496 403 457,588 77.94% 70.62%
Boards 22, 74, and 87 1,221 1,031 962 93.15% 511 372 325,308 66.33% 61.78%
Boards 23 and 45 887 776 730 94.10% 423 346 305,100 76.55% 72.03%
Board 25 (Franklin) 3,102 2,626 2,380 90.50% 1,435 1,138 895,510 73.37% 66.40%
Boards 27, 71, and 73 1,298 1,066 1,001 93.96% 540 417 406,100 72.67% 68.28%
Boards 28, 43, and 67 1,440 1,281 1,212 94.59% 710 586 412,824 79.57% 75.27%
Board 31 (Hamilton) 2,031 1,658 1,401 84.51% 752 583 666,883 73.08% 61.76%
Board 48 (Lucas) 1,029 871 833 95.67% 536 422 363,495 68.05% 65.11%
Boards 50 and 76 1,602 1,374 1,311 95.34% 671 560 532,665 75.81% 72.27%
Board 57 (Montgomery) 1,320 1,060 1,002 94.56% 597 491 443,454 74.15% 70.12%
Board 77 (Summit) 1,472 1,260 1,184 93.85% 669 522 455,223 73.62% 69.09%
Oklahoma 7,466 6,039 5,469 90.67% 3,414 2,728 2,969,398 75.56% 68.51%
Central 830 702 633 90.19% 443 354 358,460 78.04% 70.38%
East Central 665 569 527 92.79% 329 272 348,800 77.18% 71.61%
Northeast 1,100 825 752 91.17% 442 345 391,855 72.86% 66.43%
Northwest and Southwest 1,200 905 812 89.82% 482 366 417,975 68.69% 61.70%
Oklahoma County 1,362 1,099 990 90.15% 634 506 565,537 77.67% 70.02%
Southeast 1,036 875 817 93.59% 514 420 416,367 71.38% 66.81%
Tulsa County 1,273 1,064 938 88.35% 570 465 470,405 81.99% 72.44%
Oregon 8,161 7,025 6,500 92.64% 3,546 2,865 3,210,349 75.54% 69.98%
Region 1 (Multnomah) 1,489 1,326 1,195 90.42% 621 475 600,209 71.25% 64.42%
Region 2 1,863 1,654 1,508 91.28% 801 606 757,648 69.70% 63.63%
Region 3 2,572 2,154 2,019 93.69% 1,222 1,056 1,004,226 81.01% 75.90%
Region 4 1,157 999 915 91.70% 438 343 470,461 75.09% 68.86%
Region 5 (Central) 408 337 331 98.24% 166 148 179,635 83.08% 81.62%
Region 6 (Eastern) 672 555 532 95.90% 298 237 198,169 77.78% 74.59%
Pennsylvania 29,931 25,681 20,392 79.40% 11,459 8,830 10,546,396 72.96% 57.93%
Region 1 (Allegheny) 3,422 2,981 2,509 84.11% 1,382 1,047 1,016,566 70.08% 58.94%
Regions 3, 8, 9, and 51 1,757 1,467 1,249 85.70% 586 452 600,039 74.99% 64.27%
Regions 4, 11, 37, and 49 2,210 1,776 1,522 85.95% 814 624 779,498 72.38% 62.21%
Regions 5, 18, 23, 24, and 46 1,752 1,516 580 37.29% 320 257 617,919 75.60% 28.19%
Regions 6, 12, 16, 31, 35, 45, and 47 1,669 1,342 1,174 87.59% 665 549 582,897 80.35% 70.37%
Regions 7, 13, 20, and 33 4,998 4,536 3,673 81.03% 2,271 1,736 2,066,328 74.54% 60.40%
Regions 10, 15, 27, 32, 43,and 44 1,218 1,017 958 94.18% 514 433 444,868 79.69% 75.05%
Regions 17 and 21 946 811 744 91.62% 419 333 308,708 74.50% 68.26%
Regions 19, 26, 28, and 42 3,295 2,976 1,871 62.92% 1,095 776 1,190,062 67.65% 42.57%
Regions 22, 38, 40, 41, and 48 2,133 1,844 1,626 88.14% 873 654 719,874 68.81% 60.65%
Regions 29 and 34 1,447 1,261 1,114 88.26% 525 377 548,936 65.75% 58.03%
Regions 30 and 50 1,460 1,178 1,064 90.31% 560 460 506,900 77.67% 70.14%
Region 36 (Philadelphia) 3,624 2,976 2,308 77.32% 1,435 1,132 1,163,803 72.77% 56.27%
Rhode Island 8,006 6,634 5,893 88.84% 3,352 2,709 890,921 76.23% 67.72%
Bristol and Newport 945 799 709 88.73% 388 297 113,593 71.18% 63.15%
Kent 1,100 977 868 88.67% 458 357 143,475 74.47% 66.03%
Providence 4,842 4,068 3,601 88.52% 2,109 1,725 524,982 77.74% 68.81%
Washington 1,119 790 715 90.73% 397 330 108,871 76.37% 69.29%
South Carolina 8,519 6,681 6,049 90.27% 3,404 2,819 3,722,158 78.07% 70.47%
Region 1 2,162 1,826 1,651 90.73% 904 730 977,963 75.80% 68.77%
Region 2 2,523 2,022 1,837 90.25% 1,072 918 1,111,180 82.96% 74.87%
Region 3 1,636 1,137 1,044 91.59% 592 472 660,477 71.26% 65.27%
Region 4 2,198 1,696 1,517 88.93% 836 699 972,538 79.20% 70.43%
South Dakota 7,113 5,985 5,708 95.42% 3,346 2,812 666,440 79.97% 76.31%
Region 1 1,169 936 901 96.25% 551 488 104,109 84.72% 81.54%
Region 2 1,817 1,599 1,526 95.50% 926 757 174,441 75.25% 71.87%
Region 3 1,203 1,031 990 96.14% 628 543 105,752 85.10% 81.82%
Region 4 577 484 460 95.17% 228 190 62,315 77.32% 73.59%
Region 5 772 647 630 97.50% 329 275 64,675 81.30% 79.27%
Region 6 473 370 356 96.24% 160 131 49,982 78.04% 75.11%
Region 7 1,102 918 845 91.95% 524 428 105,166 78.15% 71.85%
Tennessee 8,029 6,592 6,088 92.21% 3,470 2,787 5,229,574 74.06% 68.29%
Region 1 700 600 566 94.30% 299 255 431,391 82.75% 78.03%
Region 2 1,494 1,247 1,162 93.23% 635 508 994,343 72.42% 67.52%
Region 3 1,488 1,217 1,132 92.87% 599 471 824,718 73.37% 68.14%
Region 4 (Davidson) 1,004 782 688 87.93% 398 322 490,582 76.43% 67.20%
Region 5 1,512 1,260 1,134 89.73% 731 570 1,199,721 72.23% 64.81%
Region 6 870 743 722 97.19% 390 325 533,377 70.55% 68.56%
Region 7 (Shelby) 961 743 684 91.55% 418 336 755,442 74.98% 68.64%
Texas 25,659 21,150 19,503 92.23% 13,186 10,742 19,532,104 77.03% 71.04%
Region 1 1,137 937 895 95.49% 589 461 651,357 74.75% 71.38%
Region 2 648 458 442 96.24% 260 236 443,878 89.70% 86.33%
Region 3 6,599 5,631 5,348 95.04% 3,611 2,988 5,315,556 77.94% 74.08%
Region 3a 4,098 3,494 3,319 95.10% 2,269 1,838 3,383,390 75.18% 71.49%
Region 3bc 2,501 2,137 2,029 94.95% 1,342 1,150 1,932,166 83.06% 78.86%
Region 4 1,378 1,111 973 87.05% 579 478 911,120 77.29% 67.28%
Region 5 986 705 679 96.42% 448 389 623,690 82.12% 79.18%
Region 6 5,703 4,805 4,135 86.03% 2,976 2,312 4,719,423 73.87% 63.55%
Region 6a 5,072 4,263 3,668 85.99% 2,649 2,057 4,203,911 73.42% 63.13%
Region 6bc 631 542 467 86.34% 327 255 515,512 77.59% 66.99%
Region 7 3,383 2,751 2,602 94.60% 1,616 1,343 2,265,243 78.08% 73.87%
Region 7a 2,102 1,780 1,689 94.76% 1,049 853 1,411,776 76.52% 72.50%
Region 7bcd 1,281 971 913 94.24% 567 490 853,467 82.10% 77.38%
Region 8 2,554 2,026 1,844 91.08% 1,143 940 1,994,684 79.04% 71.98%
Region 9 706 610 592 96.96% 398 308 432,141 69.59% 67.48%
Region 10 681 619 603 97.42% 463 355 606,041 66.65% 64.93%
Region 11 1,884 1,497 1,390 92.96% 1,103 932 1,568,971 81.64% 75.90%
Region 11abd 1,355 1,058 979 92.60% 722 600 1,016,938 80.83% 74.85%
Region 11c (Hidalgo) 529 439 411 93.86% 381 332 552,033 83.19% 78.08%
Utah 4,776 4,221 3,998 94.73% 3,361 2,798 2,151,413 79.49% 75.30%
Bear River, Northeastern, Summit, Tooele, and Wasatch 557 484 456 94.45% 372 315 255,324 79.16% 74.77%
Central, Four Corners, San Juan, and Southwest 605 499 471 94.24% 390 308 269,437 71.06% 66.97%
Davis County 529 487 462 94.94% 382 331 234,167 85.14% 80.83%
Salt Lake County 1,906 1,673 1,583 94.63% 1,258 1,064 816,732 82.72% 78.28%
Utah County 853 788 751 95.23% 705 570 391,203 76.97% 73.30%
Weber, Morgan 326 290 275 94.86% 254 210 184,551 75.90% 72.00%
Vermont 8,510 6,363 5,837 91.71% 3,241 2,681 539,298 78.94% 72.39%
Champlain Valley 2,929 2,481 2,302 92.72% 1,466 1,207 210,276 79.35% 73.57%
Rural Northeast 2,187 1,588 1,375 86.57% 702 561 129,408 73.95% 64.02%
Rural Southeast 1,918 1,281 1,190 92.76% 569 485 112,946 81.58% 75.67%
Rural Southwest 1,476 1,013 970 96.03% 504 428 86,668 81.86% 78.61%
Virginia 7,700 6,597 5,839 88.47% 3,373 2,732 6,403,390 76.49% 67.67%
Region 1 1,452 1,223 1,082 88.57% 652 529 1,014,701 79.65% 70.55%
Region 2 1,689 1,523 1,353 88.80% 829 655 1,699,869 72.42% 64.31%
Region 3 1,319 1,090 985 90.29% 572 482 1,115,898 77.66% 70.12%
Region 4 1,399 1,252 1,080 86.11% 594 464 1,094,859 75.18% 64.73%
Region 5 1,841 1,509 1,339 88.75% 726 602 1,478,062 78.31% 69.50%
Washington 7,753 6,783 6,229 91.82% 3,549 2,753 5,513,047 73.52% 67.50%
Region 1 1,585 1,338 1,290 96.40% 710 564 1,192,621 77.19% 74.41%
East 1 (previously Region 1) 999 832 800 96.10% 452 358 687,824 77.03% 74.03%
East 2 (previously Region 2) 586 506 490 96.90% 258 206 504,797 77.46% 75.06%
Region 2 3,695 3,231 2,906 89.96% 1,610 1,220 2,507,907 71.36% 64.19%
North 1 (previously Region 3) 1,388 1,155 1,054 91.31% 576 439 925,860 71.43% 65.22%
North 2 (previously Region 4) 2,307 2,076 1,852 89.21% 1,034 781 1,582,047 71.32% 63.63%
Region 3 2,473 2,214 2,033 91.72% 1,229 969 1,812,518 74.42% 68.26%
West 1 (previously Region 5) 1,112 1,001 924 92.16% 608 474 870,026 76.14% 70.17%
West 2 (previously Region 6) 1,361 1,213 1,109 91.38% 621 495 942,492 73.00% 66.71%
West Virginia 9,417 7,574 6,884 90.87% 3,386 2,710 1,545,143 76.12% 69.17%
Region I 844 692 586 84.49% 274 204 125,406 63.81% 53.92%
Region II 1,146 909 842 92.68% 444 373 220,044 82.67% 76.61%
Region III 883 743 651 87.69% 323 270 144,273 78.81% 69.10%
Region IV 1,837 1,458 1,318 90.39% 711 561 324,156 77.02% 69.62%
Region V 2,812 2,335 2,163 92.59% 1,067 844 443,016 75.51% 69.91%
Region VI 1,895 1,437 1,324 92.11% 567 458 288,247 76.12% 70.11%
Wisconsin 7,574 6,465 6,001 92.77% 3,393 2,715 4,721,889 76.78% 71.23%
Milwaukee 1,316 1,177 1,079 91.46% 640 503 745,910 70.38% 64.37%
Northeastern 1,766 1,531 1,403 91.73% 797 639 1,028,299 78.18% 71.72%
Northern 766 551 521 94.31% 252 206 422,164 76.56% 72.21%
Southeastern 1,227 1,075 982 91.18% 638 476 962,612 71.25% 64.96%
Southern 1,368 1,166 1,090 93.54% 551 457 912,909 81.89% 76.60%
Western 1,131 965 926 96.00% 515 434 649,995 83.70% 80.35%
Wyoming 8,179 6,647 6,227 93.74% 3,488 2,773 445,011 74.68% 70.00%
Judicial District 1 (Laramie) 1,401 1,174 1,080 92.09% 572 439 74,342 73.86% 68.02%
Judicial District 2 696 507 473 93.05% 304 269 39,005 82.77% 77.02%
Judicial District 3 1,153 922 878 95.33% 589 474 64,503 75.59% 72.06%
Judicial District 4 522 464 439 94.65% 197 152 31,161 76.95% 72.84%
Judicial District 5 1,101 830 772 93.09% 349 292 44,092 78.76% 73.32%
Judicial District 6 858 727 696 95.95% 446 333 45,431 68.12% 65.36%
Judicial District 7 (Natrona) 1,066 914 852 93.42% 471 356 60,693 72.41% 67.64%
Judicial District 8 438 373 346 92.63% 188 163 30,834 79.57% 73.70%
Judicial District 9 944 736 691 93.74% 372 295 54,950 72.51% 67.97%

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. (2011). Results from the 2010 National Survey on Drug Use and Health: Summary of national findings (HHS Publication No. SMA 11-4658, NSDUH Series H-41). Rockville, MD: Substance Abuse and Mental Health Services Administration.

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. (2013). Results from the 2012 National Survey on Drug Use and Health: Mental health findings (HHS Publication No. SMA 13-4805, NSDUH Series H-47). 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.

Hughes, A., Muhuri, P., Sathe, N., & Spagnola, K. (2010, June). State estimates of substance use from the 2007-2008 National Surveys on Drug Use and Health (HHS Publication No. SMA 10-4472, NSDUH Series H-37). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Hughes, A., Muhuri, P., Sathe, N., & Spagnola, K. (2011). State estimates of substance use from the 2008-2009 National Surveys on Drug Use and Health (HHS Publication No. SMA 11-4641, NSDUH Series H-40). Rockville, MD: Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality.

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.

Novak, S. P., Colpe, L. J., Barker, P. R., & Gfroerer, J. C. (2010). Development of a brief mental health impairment scale using a nationally representative sample in the USA. International Journal of Methods in Psychiatric Research, 19(Suppl. 1), 49-60. doi:10.1002/mpr.313

Office of Applied Studies. (2005a, September). Appendix C: Research on the impact of changes in NSDUH methods. In Results from the 2004 National Survey on Drug Use and Health: National findings (HHS Publication No. SMA 05-4062, NSDUH Series H-28, pp. 145-154). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2005b). 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. (2005c, May). Substate estimates from the 1999-2001 National Surveys on Drug Use and Health. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2006, August). Substate estimates from the 2002-2004 National Surveys on Drug Use and Health. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2008, June). Substate estimates from the 2004-2006 National Surveys on Drug Use and Health. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2009, September). 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.

Office of Applied Studies. (2010, July). Substate estimates from the 2006-2008 National Surveys on Drug Use and Health. Rockville, MD: Substance Abuse and Mental Health Services Administration.

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.

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.

Wright, D. (2002, October). 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. (2003, July). 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., & Sathe, N. (2005, January). 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.

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 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. At SAMHSA, Arthur Hughes reviewed the document and provided substantive revisions.

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, Marissa R. Straw, Pamela Tuck, and Cheryl Velez prepared the Web versions. Justine L. Allpress, E. Andrew Jessup, and Shari B. Lambert 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 These substate regions were defined by officials from each State, typically based on the substance abuse treatment planning regions specified by States in their applications for an SAPT Block Grant administered by SAMHSA. There is extensive variation in treatment planning regions across States. In some States, the planning regions are used more for administrative purposes rather than for planning purposes. Because the estimation method required a minimum NSDUH sample size of approximately 150 to provide adequate precision, planning regions with sample sizes that were much smaller than that were collapsed with adjacent regions until an adequate sample size was obtained.

3 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.

4 The RSE of an estimate is the posterior SE divided by the estimate itself. Note that the RSEs have been calculated based on the unbenchmarked small area estimates.

5 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.

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

7 Clinical interviewers were trained to conduct these interviews only in English.

8 MDE 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, 2013).

9 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.

10 For details, see http://www.hcp.med.harvard.edu/ncs/.

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