2006-2008
National Survey on Drug Use and Health:
Guide to Substate Tables and Summary of Small Area Estimation Methodology


Section A: Overview

A.1. Introduction

This document provides information on the model-based estimates used to produce estimates of substance use and depression in substate regions based on data from the combined 2006-2008 National Surveys on Drug Use and Health (NSDUHs). The estimates are available at http://www.samhsa.gov/data/NSDUH/substate2k08/toc.aspx along with other related information. An annual survey of the civilian, noninstitutionalized population aged 12 or older, NSDUH is sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA). It collects information from persons residing in households, noninstitutionalized group quarters (e.g., shelters, rooming houses, dormitories), and civilians living on military bases. In 2006-2008, NSDUH collected data from 202,796 respondents aged 12 or older and was designed to obtain representative samples from the 50 States and the District of Columbia. The survey is planned and managed by SAMHSA's Center for Behavioral Health Statistics and Quality (CBHSQ). Data collection and analysis were conducted under contract with RTI International.1

The substate region estimates for 2006-2008 in all States were recalculated after removing erroneous (falsified) data for Pennsylvania and Maryland (for more details, see Section A.4). These 2006-2008 substate small area estimates were produced using the 2008-2010 substate region definitions. Hence, the 2006-2008 substate region estimates provided in the tables at http://www.samhsa.gov/data/NSDUH/substate2k08/toc.aspx may not match the small area estimates that were initially published (see Office of Applied Studies [OAS], 2010). The revision of the data files due to the falsification issue presented an opportunity to revise the 2006-2008 definitions so that the 2006-2008 and 2008-2010 estimates can be compared for all substate areas. If they were not revised, then comparisons would not have been possible in seven States that changed their substate region definitions since the release of the original 2006-2008 substate data. The updated 2006-2008 substate region definitions consist of the original 2006-2008 definitions for 43 States and the District of Columbia and revisions in the following seven States: Alaska, Arkansas, California, Georgia, North Carolina, Pennsylvania, and West Virginia.

The 2006-2008 substate region estimates were produced for 22 measures and are available at http://www.samhsa.gov/data/NSDUH/substate2k08/toc.aspx. 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. Presentation of Data

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 22 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 population estimates for persons aged 12 or older and the combined 2006, 2007, and 2008 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 (41,666 persons) can be obtained by multiplying the 3.9 percent prevalence rate from the 2006-2008 NSDUH Substate Regions: Excel Tables (shown as 3.87 percent in Table 3 in the Excel tables at http://www.samhsa.gov/data/NSDUH/substate2k08/toc.aspx) and the 1,068,369 population estimate from Table C1 in Section C of this document. Section D lists the references, and Section E provides a list of contributors to the production of the 2006-2008 substate small area estimates. In addition to the 2006-2008 NSDUH substate region estimates presented in the Excel tables, the following files are available 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. Note that these region definitions were first developed for the 2008-2010 NSDUH substate estimates. They were then used for the revised 2006-2008 substate small area estimates. These substate region definitions are available in a document titled 2006-2008 NSDUH Substate Region Definitions (see http://www.samhsa.gov/data/NSDUH/substate2k08/toc.aspx as listed in Section A.2). Revised maps were not created with the updated 2006-2008 substate small area estimates. However, maps can be created using the 2006-2008 Substate Region Shapefile also provided at http://www.samhsa.gov/data/NSDUH/substate2k08/toc.aspx. The shapefile has a map group defined for each map region for persons aged 12 or older. Among the States with aggregate regions, a few wanted the map groups 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 map groups. Hence, for each measure, map groups (having values 1 through 7) were produced for 362 planning regions and not for 383 regions.

These 362 substate regions used in the map groups were ranked from lowest to highest for each measure and were divided into 7 categories designed to represent distributions that are somewhat symmetric, like a normal distribution. Numbers were assigned to all substate regions, as follows:

The only exceptions were the three perception-of-risk outcomes for marijuana, alcohol, and cigarettes, which have the highest estimates represented with values 1, 2, and 3 and the lowest represented with values 5, 6, 7. 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. The shapefile with the map groups is available at http://www.samhsa.gov/data/NSDUH/substate2k08/toc.aspx as listed in Section A.2.

The 2006-2008 substate estimates and corresponding Bayesian CIs are available in the 2006-2008 NSDUH Substate Regions: Excel Tables (as mentioned in Section A.2, see http://www.samhsa.gov/data/NSDUH/substate2k08/toc.aspx). 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 in the shapefile).

Estimates presented in the tables (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 (2006-2008) to improve the precision of substate region estimates. The estimate for each region is accompanied by a 95 percent Bayesian CI (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 NSDUH State estimates 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 small substate regions 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 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 (2005).

Because of the changes in the survey that took place in 2002, estimates for 2006-2008 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 2006-2008 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 interviewing staff and affected the data for Pennsylvania (2006-2010) and Maryland (2008-2009). 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 2006-2008 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 2004-2006 and 2006-2008 substate region estimates, model-based substate and State estimates for 2006-2008 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. It was decided that the erroneous data in 2006 would have minimal impact on the 2004-2006 substate estimates; thus, these estimates were not revised.

Section B: Substate Region Estimation Methodology

Substate region-level estimates of 22 binary (0,1) substance use and mental health measures using combined data from the 2006, 2007, and 2008 National Surveys on Drug Use and Health (NSDUHs) for persons aged 12 or older are presented in the 2006-2008 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 (12 to 20) use of alcohol and binge alcohol use also are presented in the same tables.

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 2006-2008 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 22 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 2006-2008 substate-level small area estimation (SAE) modeling is given in Section B.5. Information is given in Section B.6 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. 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 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 2006-2008 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 2006-2008 substate small area estimates is similar to the logistic mixed hierarchical Bayes (HB) model that was used to produce the 2004-2006 substate small area estimates (Office of Applied Studies [OAS], 2008). The following model was used:

The model is given by the equation: log of pi sub a, i, j, k divided by 1 minus pi sub a, i, j, k and is equal to the sum of three terms. The first term is given by x transpose sub a, i, j, k times beta sub a. The second term is eta sub a, i. And the third term is nu sub a, i, j.,

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 of random effects An eta sub i is a transposed vector of values eta sub 1, i and so on until eta sub capital 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 capital A, i, j. are assumed to be mutually independent with An eta sub i is normally distributed with mean 0 and variance denoted by matrix 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, Capital A equals 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 3.9 percent, and the 95 percent CI ranges from 2.9 to 5.2 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.3 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 2006-2008), the average relative standard error (RSE)4 was about 5.4 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 4.6 percent; for small sample sizes, the average RSE was 6.0 percent.

For past month use of marijuana (with a national prevalence of 6.6 percent), the average RSE was 10.1 percent for substate regions with large samples. For medium sample sizes, the average RSE was 13.2 percent, and for small samples, the RSE was 16.2 percent, whereas the overall national average RSE was 14.8 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 15.0 percent. For those with medium sample sizes, the average RSE was 18.0 percent, and for those with small sample sizes, the average RSE was 20.0 percent.

The SAE methods used for producing the 2006-2008 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 22 binary (0, 1) substance use measures, using combined data from the 2006-2008 NSDUHs for persons aged 12 or older (or persons 18 or older for depression):

  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, and

  22. past year major depressive episode (MDE) (i.e., depression).

In addition to the 22 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 and 2004-2006 substate estimates were used to produce the 2006-2008 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 2009-2010 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 reports 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 reports.

In summary, based on the information above, the following steps were taken for the current 2006-2008 substate SAE analysis:

  1. Using the 2008-2012 Claritas data, 2006, 2007, and 2008 population counts were obtained (the 2006 and 2007 counts were obtained by projecting backwards using the 2008 and 2012 counts) and used to create the predictors that were merged onto the 2006, 2007, and 2008 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, the same 2007-2008 decile values were used in the 2006-2008 substate SAE process. The updated population estimates for the 32 cells (age group × race/ethnicity × gender population estimates) were used to create the updated universe files for all 3 years (2006, 2007, and 2008). The 2004, 2005, and 2006 sample and universe files based on the 2008-2012 Claritas data for 2006 and the 2002-2007 Claritas data for 2004 and 2005 were used in simultaneous modeling to produce the correlations required to estimate change between the 2004-2006 and 2006-2008 substate prevalence rates.

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 2006-2008. 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 collectively over 3 years (2006, 2007, and 2008) of data 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.

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:

The average annual rate is calculated as capital X sub 1 times 0.5 divided by capital X sub 1 times 0.5 plus capital X sub 2. This rate is then multiplied by 100 to get a percentage.

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. 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, pp. 118-120).

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

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

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

B.10 Major Depressive Episode (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 are defined as having had MDE in the past year and then are asked questions from the Sheehan Disability Scale (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, Olfson, Portera, Farber, & Sheehan, 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 prevalence estimates of MDE to be calculated. Separate modules were administered to adults aged 18 or older and youths aged 12 to 17. The adult questions were adapted from the depression section of the National Comorbidity Survey Replication (NCS-R), and the questions for youths were adapted from the depression section of the National Comorbidity Survey Adolescent (NCS-A) (see http://www.hcp.med.harvard.edu/ncs/). To make the modules developmentally appropriate for youths, there are minor wording differences in a few questions between the adult and youth modules. Revisions to the questions in both modules were made primarily to reduce its length and to modify the NCS questions, which are interviewer-administered, to the audio computer-assisted self-interviewing (ACASI) format used in NSDUH. In addition, some revisions, based on cognitive testing, were made to improve comprehension.

Since 2004, the NSDUH questions that determine MDE have remained unchanged. In the 2008 questionnaire, however, changes were made in other mental health items that precede the MDE questions for adults (Kessler-6 or K6, suicide, and impairment). See Sections B.4.2 and B.4.3 in Appendix B of the 2010 NSDUH mental health findings report (CBHSQ, 2012) for further details about these questionnaire changes. These questionnaire changes in 2008 appear to have affected the reporting on MDE questions among adults.

Because the World Health Organization Disability Assessment Schedule (WHODAS) scale was selected to be used in the 2009 NSDUH 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 2006-2008 substate estimates of MDE were produced using the adjusted 2006, 2007, and 2008 MDE variable. Hence, no comparisons between these and the 2004-2006 substate small area estimates of MDE can be made (i.e., there is no equivalent depression variable for 2004).

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. The 2004-2006 MDE estimates are not comparable with the 2006-2008 and 2008-2010 MDE estimates. 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: 2006, 2007, and 2008 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 "2006-2008 National Survey on Drug Use and Health Substate Region Definitions" at http://www.samhsa.gov/data/NSDUH/substate2k08/toc.aspx.
NOTE: To compute the pooled 2006-2008 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 2006, 2007, and 2008 individual response rates.
NOTE: The total responded column represents the combined sample size from the 2006, 2007, and 2008 NSDUHs.
NOTE: The population estimate is the simple average of the 2006, 2007, and 2008 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, 2006, 2007, and 2008 (Revised March 2012).
Total United States 569,366 469,752 419,233 89.30% 255,668 202,796 248,247,257 74.10% 66.18%
Northeast 122,895 102,460 85,763 82.77% 50,542 39,260 45,963,480 71.62% 59.28%
Midwest 154,290 130,317 117,379 90.32% 72,299 57,412 54,915,882 74.89% 67.64%
South 175,465 140,823 128,329 91.64% 77,132 62,264 90,124,533 75.75% 69.42%
West 116,716 96,152 87,762 90.01% 55,695 43,860 57,243,363 72.78% 65.51%
Alabama 7,567 6,027 5,567 92.42% 3,455 2,740 3,831,570 72.50% 67.01%
Region 1 1,943 1,633 1,492 91.35% 916 727 1,068,369 71.24% 65.07%
Region 2 2,589 2,040 1,894 93.15% 1,232 986 1,228,434 73.94% 68.88%
Region 3 1,281 935 851 90.80% 540 412 683,317 70.99% 64.46%
Region 4 1,754 1,419 1,330 93.63% 767 615 851,450 72.71% 68.08%
Alaska 7,295 5,141 4,639 90.23% 3,344 2,670 537,129 76.79% 69.29%
Anchorage 2,716 2,249 2,022 89.77% 1,459 1,189 225,013 78.22% 70.22%
Northern 1,658 1,071 980 91.66% 742 599 114,963 78.16% 71.64%
South Central 2,260 1,320 1,176 89.06% 839 649 138,817 72.86% 64.89%
Southeast 661 501 461 92.37% 304 233 58,336 78.70% 72.70%
Arizona 7,944 5,891 5,311 90.26% 3,393 2,673 5,120,038 73.52% 66.36%
Maricopa 4,480 3,542 3,192 90.10% 2,154 1,668 3,077,034 71.43% 64.36%
Pima 1,380 1,111 986 89.09% 570 445 807,266 75.21% 67.01%
Rural North 1,162 650 592 91.75% 364 304 595,349 78.36% 71.89%
Rural South 922 588 541 91.74% 305 256 640,389 78.76% 72.25%
Arkansas 7,633 6,035 5,668 93.86% 3,279 2,707 2,319,691 79.15% 74.29%
Catchment Area 1 1,025 818 762 93.13% 452 361 348,628 76.35% 71.10%
Catchment Area 2 919 684 656 95.79% 329 281 288,039 80.94% 77.53%
Catchment Area 3 1,117 844 791 93.51% 474 409 320,869 84.99% 79.48%
Catchment Area 4 655 525 499 95.31% 310 246 206,227 74.10% 70.63%
Catchment Area 5 1,275 1,058 1,000 94.35% 621 525 339,425 79.03% 74.57%
Catchment Area 6 574 438 419 95.70% 245 205 182,573 86.10% 82.40%
Catchment Area 7 595 426 415 97.24% 212 179 195,763 79.53% 77.33%
Catchment Area 8 1,473 1,242 1,126 90.68% 636 501 438,167 76.19% 69.09%
California 26,104 23,263 20,401 87.77% 14,624 11,139 29,832,841 70.74% 62.09%
Region 1R 808 664 608 91.77% 374 303 808,416 78.43% 71.98%
Region 2R 865 744 683 91.89% 491 397 801,151 79.20% 72.78%
Region 3R (Sacramento) 1,101 991 894 90.32% 537 413 1,117,439 68.21% 61.60%
Region 4R 911 777 686 88.36% 443 352 1,058,805 77.31% 68.31%
Region 5R (San Francisco) 731 649 525 81.51% 259 164 669,643 57.32% 46.72%
Region 6 (Santa Clara) 1,058 974 891 91.56% 620 462 1,398,582 70.61% 64.65%
Region 7R (Contra Costa) 913 822 713 86.68% 433 306 842,958 67.24% 58.29%
Region 8R (Alameda) 1,061 983 853 86.99% 644 465 1,200,209 65.50% 56.99%
Region 9R (San Mateo) 580 526 481 91.69% 290 209 579,928 69.77% 63.98%
Region 10 869 766 644 84.31% 468 379 993,703 77.37% 65.23%
Region 11 (Los Angeles) 6,783 6,136 5,308 86.69% 3,996 2,941 8,118,470 66.33% 57.51%
LA SPA 1 and 5 907 798 646 81.27% 368 261 836,262 64.20% 52.17%
LA SPA 2 1,395 1,241 1,062 85.77% 788 598 1,726,562 68.73% 58.95%
LA SPA 3 1,209 1,119 999 89.36% 826 568 1,471,954 63.04% 56.33%
LA SPA 4 950 840 719 85.93% 481 343 984,583 64.05% 55.04%
LA SPA 6 527 474 409 86.40% 359 285 784,402 74.08% 64.01%
LA SPA 7 756 706 638 90.67% 553 432 1,052,333 69.68% 63.18%
LA SPA 8 1,039 958 835 87.14% 621 454 1,262,375 63.79% 55.59%
Region 12R 437 369 328 89.51% 245 197 672,468 72.19% 64.62%
Regions 13 and 19R 1,335 1,148 1,009 88.57% 825 644 1,759,896 74.95% 66.38%
Region 13 (Riverside) 1,234 1,054 919 87.91% 743 571 1,628,710 74.07% 65.11%
Region 19R (Imperial) 101 94 90 95.78% 82 73 131,186 84.82% 81.24%
Region 14 (Orange) 1,941 1,837 1,592 86.55% 1,105 825 2,433,355 68.56% 59.34%
Region 15R (Fresno) 584 534 460 83.90% 378 292 706,439 74.29% 62.33%
Region 16R (San Diego) 2,337 2,044 1,715 83.86% 1,162 897 2,435,447 72.64% 60.92%
Region 17R 972 871 787 90.41% 634 525 1,084,004 78.18% 70.68%
Region 18R (San Bernardino) 1,374 1,187 1,081 91.32% 893 718 1,600,079 76.27% 69.65%
Region 20R 681 590 548 93.07% 416 330 738,467 73.90% 68.78%
Region 21R 763 651 595 91.40% 411 320 813,382 73.58% 67.25%
Colorado 8,119 6,612 6,032 91.18% 3,417 2,737 3,980,138 76.42% 69.69%
Region 1 1,042 932 872 93.68% 481 402 520,023 78.26% 73.32%
Regions 2 and 7 4,305 3,642 3,243 89.00% 1,876 1,453 2,212,469 74.46% 66.27%
Region 3 1,174 955 898 94.01% 500 428 576,040 81.61% 76.72%
Region 4 595 432 413 95.69% 223 183 230,015 80.01% 76.56%
Regions 5 and 6 1,003 651 606 92.66% 337 271 441,590 75.61% 70.06%
Connecticut 7,994 7,024 6,241 88.77% 3,461 2,749 2,921,356 75.28% 66.82%
Eastern 888 753 701 93.50% 395 308 354,697 73.77% 68.97%
North Central 2,273 2,057 1,795 87.18% 945 772 825,198 78.89% 68.78%
Northwestern 1,566 1,336 1,175 87.58% 624 508 508,864 75.31% 65.95%
South Central 1,988 1,743 1,564 89.69% 883 688 688,944 72.66% 65.17%
Southwest 1,279 1,135 1,006 88.46% 614 473 543,654 74.76% 66.13%
Delaware 7,295 5,982 5,303 88.81% 3,377 2,723 714,096 77.64% 68.95%
Kent 1,282 1,082 976 90.20% 598 511 123,426 80.77% 72.86%
New Castle (excluding Wilmington City) 3,379 3,047 2,643 87.01% 1,794 1,407 372,307 75.46% 65.65%
Sussex 2,062 1,371 1,273 92.90% 710 576 154,565 79.50% 73.85%
Wilmington City 572 482 411 85.28% 275 229 63,798 81.44% 69.46%
District of Columbia 12,139 9,807 8,237 83.93% 3,205 2,604 500,465 77.18% 64.78%
Ward 1 1,561 1,101 923 83.95% 312 248 62,455 76.90% 64.55%
Ward 2 1,567 1,222 1,068 87.47% 406 338 71,470 78.35% 68.54%
Ward 3 1,716 1,393 1,159 83.05% 426 344 68,716 76.43% 63.47%
Ward 4 1,616 1,415 1,208 85.36% 477 369 65,588 70.19% 59.91%
Ward 5 1,448 1,178 984 83.61% 376 298 61,403 78.30% 65.46%
Ward 6 1,300 1,089 874 80.07% 273 210 59,251 74.93% 60.00%
Ward 7 1,456 1,219 1,023 83.98% 434 375 58,062 82.98% 69.69%
Ward 8 1,475 1,190 998 83.62% 501 422 53,520 82.58% 69.05%
Florida 32,494 25,289 22,791 90.10% 13,591 10,846 15,254,172 73.67% 66.38%
Region A - Northwest 2,404 1,853 1,697 91.45% 1,048 855 1,114,243 75.76% 69.28%
Circuit 1 1,193 932 835 89.53% 450 367 564,247 76.13% 68.16%
Circuit 2 576 426 401 94.26% 289 246 311,247 80.85% 76.20%
Circuit 14 635 495 461 92.69% 309 242 238,749 71.51% 66.28%
Region B - Northeast 4,440 3,393 3,112 91.64% 1,829 1,465 2,066,773 73.51% 67.37%
Circuits 3 and 8 1,004 719 674 93.62% 417 342 457,072 78.94% 73.90%
Circuit 4 1,742 1,489 1,357 91.27% 851 682 897,704 72.13% 65.83%
Circuit 7 1,694 1,185 1,081 90.95% 561 441 711,997 72.11% 65.58%
Region C - Central 7,618 5,886 5,353 90.97% 3,243 2,611 3,773,571 73.87% 67.20%
Circuit 5 1,673 1,328 1,184 89.31% 627 489 846,611 72.83% 65.05%
Circuit 9 2,142 1,641 1,531 92.97% 1,054 891 1,064,191 79.16% 73.60%
Circuit 10 1,008 729 654 89.94% 414 331 572,787 71.27% 64.10%
Circuit 18 1,594 1,306 1,179 90.45% 697 544 802,687 71.90% 65.04%
Circuit 19 1,201 882 805 91.37% 451 356 487,295 71.52% 65.35%
Region D - Southeast 5,662 4,672 4,200 89.79% 2,504 2,029 2,529,677 75.38% 67.68%
Circuit 15 (Palm Beach) 2,245 1,831 1,668 91.21% 919 701 1,076,480 70.03% 63.87%
Circuit 17 (Broward) 3,417 2,841 2,532 88.94% 1,585 1,328 1,453,197 78.56% 69.87%
Region E - Sun Coast 8,445 6,400 5,744 89.93% 3,163 2,455 3,682,605 72.13% 64.86%
Circuit 6 2,685 2,015 1,803 89.51% 1,006 772 1,166,689 71.53% 64.02%
Circuit 12 1,320 988 878 89.02% 420 328 618,926 68.91% 61.34%
Circuit 13 (Hillsborough) 2,356 1,953 1,794 92.00% 1,025 799 950,178 74.34% 68.39%
Circuit 20 2,084 1,444 1,269 88.17% 712 556 946,813 71.94% 63.43%
Region F - Southern (Circuits 11 and 16) 3,925 3,085 2,685 86.70% 1,804 1,431 2,087,302 72.58% 62.93%
Georgia 7,088 5,576 5,130 92.06% 3,318 2,693 7,668,720 75.01% 69.05%
Region 1 1,813 1,379 1,246 90.40% 835 658 1,932,917 73.53% 66.47%
Region 2 949 727 672 92.41% 451 382 1,007,378 80.82% 74.68%
Region 3 2,082 1,717 1,566 91.37% 1,020 834 2,356,210 75.09% 68.61%
Region 4 525 417 394 94.40% 258 201 493,482 73.95% 69.81%
Region 5 784 599 572 95.65% 347 309 827,198 83.13% 79.51%
Region 6 935 737 680 92.29% 407 309 1,051,536 66.17% 61.07%
Hawaii 8,358 6,803 5,904 86.06% 3,646 2,635 1,057,289 65.91% 56.72%
Hawaii Island 1,178 871 803 92.29% 507 403 141,876 77.07% 71.13%
Honolulu 5,625 4,837 4,118 85.21% 2,586 1,812 747,985 63.03% 53.71%
Kauai and Maui 1,555 1,095 983 84.82% 553 420 167,428 69.87% 59.26%
Kauai 481 367 347 94.51% 152 116 52,044 71.07% 67.18%
Maui 1,074 728 636 80.62% 401 304 115,384 69.42% 55.97%
Idaho 7,065 5,841 5,520 94.53% 3,420 2,786 1,205,273 77.91% 73.64%
Region 1 1,174 952 897 94.09% 467 372 175,102 73.19% 68.86%
Region 2 452 366 354 96.94% 192 165 86,514 84.13% 81.56%
Region 3 993 895 843 94.11% 516 426 192,041 78.77% 74.14%
Region 4 1,896 1,567 1,483 94.87% 922 753 336,012 76.39% 72.47%
Region 5 879 643 586 91.03% 359 295 141,254 80.46% 73.24%
Region 6 640 529 500 94.39% 299 238 126,875 78.84% 74.42%
Region 7 1,031 889 857 96.44% 665 537 147,476 78.92% 76.12%
Illinois 31,372 27,338 21,824 79.78% 14,708 10,889 10,559,138 68.22% 54.43%
Region I (Cook) 12,548 11,014 7,778 70.56% 5,628 3,912 4,281,733 62.89% 44.38%
Region II 9,087 8,140 6,739 82.43% 4,682 3,512 3,318,532 70.19% 57.86%
Region III 4,069 3,424 3,064 89.68% 1,859 1,489 1,195,432 78.45% 70.36%
Region IV 2,537 2,108 1,877 89.08% 1,059 800 760,194 68.00% 60.58%
Region V 3,131 2,652 2,366 89.43% 1,480 1,176 1,003,247 73.29% 65.54%
Indiana 7,063 5,916 5,475 92.51% 3,504 2,805 5,211,643 76.93% 71.17%
Central 1,976 1,646 1,509 91.64% 901 696 1,315,998 74.69% 68.44%
East 523 413 384 93.05% 247 197 453,546 69.81% 64.96%
North Central 986 843 771 91.41% 525 419 762,005 76.34% 69.78%
Northeast 675 531 486 91.61% 309 262 520,327 83.39% 76.40%
Northwest 791 685 615 89.60% 441 358 614,500 77.84% 69.75%
Southeast 658 576 556 96.46% 370 314 561,748 79.45% 76.63%
Southwest 707 589 560 95.03% 290 217 413,900 76.16% 72.38%
West 747 633 594 93.80% 421 342 569,618 78.82% 73.94%
Iowa 7,207 6,227 5,789 93.00% 3,353 2,758 2,498,449 79.22% 73.68%
Central 1,210 1,081 1,002 92.48% 578 464 434,716 76.70% 70.93%
North Central 999 864 785 90.87% 459 383 280,261 81.40% 73.97%
Northeast 1,645 1,389 1,290 93.06% 764 638 606,697 77.51% 72.13%
Northwest 1,290 1,128 1,064 94.32% 592 475 400,806 77.95% 73.52%
Southeast 1,415 1,200 1,108 92.30% 648 531 528,008 80.77% 74.55%
Southwest 648 565 540 95.72% 312 267 247,961 83.80% 80.22%
Kansas 6,549 5,585 5,256 94.09% 3,336 2,674 2,257,085 78.35% 73.72%
Kansas City Metro 2,325 2,070 1,920 92.77% 1,276 1,009 747,336 76.51% 70.98%
Northeast 1,186 980 932 94.87% 587 485 428,555 82.95% 78.70%
South Central 717 609 574 94.46% 334 270 292,593 77.95% 73.64%
Southeast 477 386 364 94.29% 216 180 159,129 78.33% 73.86%
West 729 598 564 94.19% 378 295 252,691 73.27% 69.01%
Wichita (Sedgwick) 1,115 942 902 95.75% 545 435 376,781 80.94% 77.50%
Kentucky 7,420 6,194 5,834 94.17% 3,345 2,685 3,522,707 74.65% 70.29%
Adanta, Cumberland River, and Lifeskills 968 753 714 94.73% 387 326 599,542 79.74% 75.54%
Bluegrass, Comprehend, and North Key 2,127 1,771 1,657 93.56% 969 762 1,014,498 71.75% 67.14%
Communicare and River Valley 823 708 649 91.85% 382 306 387,914 72.86% 66.93%
Four Rivers and Pennyroyal 790 629 590 93.60% 331 257 338,046 72.99% 68.32%
Kentucky River, Mountain, and Pathways 1,071 866 834 96.33% 485 406 415,829 75.07% 72.31%
Seven Counties 1,641 1,467 1,390 94.68% 791 628 766,878 76.97% 72.87%
Louisiana 7,373 5,276 4,978 94.40% 3,262 2,651 3,536,784 75.25% 71.04%
Regions 1 and 3 1,524 754 712 94.26% 490 371 583,594 70.79% 66.72%
Regions 2 and 9 1,753 1,391 1,307 94.12% 923 778 935,708 78.74% 74.11%
Regions 4, 5, and 6 1,925 1,399 1,343 95.95% 857 693 931,192 75.34% 72.29%
Regions 7 and 8 1,458 1,151 1,082 94.09% 649 552 713,993 80.84% 76.07%
Region 10 (Jefferson) 713 581 534 92.04% 343 257 372,296 63.58% 58.52%
Maine 9,612 6,958 6,399 91.96% 3,308 2,735 1,135,461 77.95% 71.68%
Aroostook/Downeast 1,166 864 809 93.27% 387 334 138,600 80.18% 74.78%
Central 1,190 857 775 90.49% 370 317 148,455 80.64% 72.97%
Cumberland 2,049 1,521 1,382 91.04% 757 617 236,355 77.43% 70.49%
Midcoast 1,290 862 789 91.53% 359 321 130,693 84.21% 77.08%
Penquis 1,049 816 757 92.69% 450 386 141,643 81.34% 75.39%
Western 1,342 949 874 92.14% 442 361 167,206 78.90% 72.70%
York 1,526 1,089 1,013 92.99% 543 399 172,508 68.34% 63.55%
Maryland 7,198 6,256 5,200 83.10% 3,360 2,659 4,646,186 75.61% 62.84%
Anne Arundel 778 682 542 79.19% 343 278 419,564 75.28% 59.62%
Baltimore City 994 728 595 81.82% 358 302 527,307 81.18% 66.42%
Baltimore County 895 811 673 82.88% 447 364 660,120 76.54% 63.44%
Montgomery 1,087 992 809 81.41% 530 396 756,214 73.30% 59.67%
North Central 500 478 417 87.41% 299 231 364,160 77.87% 68.07%
Northeast 506 447 399 89.06% 241 184 398,104 68.84% 61.31%
Prince George's 1,079 944 737 77.87% 508 394 687,906 71.95% 56.03%
South 807 666 594 89.45% 355 298 440,561 81.19% 72.63%
West 552 508 434 85.63% 279 212 392,248 74.62% 63.90%
Massachusetts 7,985 6,789 5,930 87.23% 3,424 2,706 5,451,958 75.08% 65.49%
Boston 824 669 560 83.46% 336 284 641,804 81.96% 68.40%
Central 865 765 661 86.25% 412 321 708,579 70.90% 61.16%
Metrowest 2,112 1,913 1,648 85.86% 971 751 1,251,659 74.05% 63.57%
Northeast 1,549 1,378 1,199 87.13% 668 523 1,060,112 76.43% 66.60%
Southeast 1,668 1,209 1,095 90.56% 567 437 1,076,360 71.87% 65.09%
Western 967 855 767 89.55% 470 390 713,443 79.13% 70.86%
Michigan 28,131 23,118 20,705 89.62% 13,489 10,866 8,370,090 75.39% 67.56%
Detroit City 2,200 1,642 1,466 89.31% 1,040 888 685,912 82.32% 73.52%
Genesee 1,299 1,081 969 89.85% 646 528 361,785 76.28% 68.54%
Kalamazoo 1,945 1,529 1,362 89.23% 856 653 563,206 72.91% 65.06%
Kent 1,414 1,226 1,067 87.32% 747 590 485,338 72.22% 63.06%
Lakeshore 1,778 1,513 1,386 91.66% 889 708 585,320 75.09% 68.83%
Macomb 2,081 1,878 1,691 90.22% 1,138 891 697,234 71.12% 64.16%
Mid South 2,646 2,221 1,994 89.76% 1,420 1,194 772,867 81.33% 73.00%
Northern 2,890 1,959 1,709 87.20% 1,019 816 732,822 77.00% 67.14%
Oakland 3,422 3,003 2,685 89.31% 1,696 1,335 1,007,433 72.48% 64.73%
Pathways and Western 1,059 686 634 92.51% 373 321 268,472 82.56% 76.37%
Riverhaven 893 773 699 90.58% 393 337 297,809 81.63% 73.94%
Saginaw 785 641 578 90.02% 380 306 171,348 76.09% 68.50%
Southeast 3,694 3,302 2,948 89.55% 1,954 1,521 1,035,567 70.14% 62.81%
St. Clair 701 595 544 91.00% 342 274 260,475 75.18% 68.42%
Washtenaw 1,324 1,069 973 90.84% 596 504 444,499 81.48% 74.01%
Minnesota 6,945 5,946 5,533 93.00% 3,262 2,678 4,317,788 79.32% 73.77%
Regions 1 and 2 825 681 635 93.09% 339 261 440,651 69.05% 64.28%
Regions 3 and 4 1,385 1,096 1,037 94.54% 615 517 745,055 79.91% 75.54%
Regions 5 and 6 1,374 1,182 1,144 96.76% 651 543 831,789 82.63% 79.95%
Region 7A (Hennepin) 1,236 1,083 978 90.26% 558 453 941,009 78.71% 71.05%
Region 7B (Ramsey) 610 524 481 91.62% 303 245 410,962 74.29% 68.06%
Region 7C 1,515 1,380 1,258 91.19% 796 659 948,322 82.68% 75.40%
Mississippi 6,779 5,164 4,900 94.77% 3,241 2,669 2,374,868 76.89% 72.87%
Region 1 1,528 1,147 1,091 94.48% 681 534 524,183 68.76% 64.97%
Region 2 856 574 549 95.44% 362 279 325,222 73.03% 69.70%
Region 3 935 718 683 95.35% 438 359 339,635 82.36% 78.53%
Region 4 1,179 960 890 92.55% 628 527 430,215 79.22% 73.32%
Region 5 583 442 430 97.48% 241 202 153,955 78.76% 76.77%
Region 6 597 510 489 95.94% 344 302 240,833 83.71% 80.31%
Region 7 1,101 813 768 94.56% 547 466 360,825 78.94% 74.64%
Missouri 7,368 6,113 5,749 94.08% 3,393 2,754 4,864,563 75.03% 70.58%
Central 1,181 952 919 96.39% 559 467 645,739 77.30% 74.51%
Eastern 2,397 1,997 1,868 93.57% 1,098 875 1,731,759 73.70% 68.96%
Eastern (St. Louis City and County) 1,671 1,407 1,306 92.90% 756 594 1,121,131 72.07% 66.95%
Eastern (excluding St. Louis) 726 590 562 95.18% 342 281 610,628 77.73% 73.98%
Northwest 1,752 1,493 1,394 93.35% 794 645 1,170,565 77.30% 72.15%
Northwest (Jackson) 860 733 686 93.80% 421 349 544,908 81.97% 76.90%
Northwest (excluding Jackson) 892 760 708 92.93% 373 296 625,656 71.86% 66.78%
Southeast 938 783 749 95.76% 427 362 579,223 76.74% 73.49%
Southwest 1,100 888 819 92.46% 515 405 737,278 70.67% 65.34%
Montana 8,166 6,588 6,217 94.37% 3,341 2,719 806,266 77.62% 73.25%
Region 1 611 455 435 95.32% 200 173 64,466 83.74% 79.82%
Region 2 1,150 962 918 95.25% 471 391 116,468 77.28% 73.61%
Region 3 1,801 1,458 1,367 93.93% 729 588 166,121 78.64% 73.87%
Region 4 2,157 1,704 1,628 95.54% 933 775 209,294 80.27% 76.69%
Region 5 2,447 2,009 1,869 93.05% 1,008 792 249,917 73.54% 68.43%
Nebraska 7,074 5,982 5,637 94.24% 3,324 2,695 1,460,777 77.43% 72.97%
Regions 1 and 2 1,014 729 684 93.84% 380 313 155,714 78.82% 73.96%
Region 1 509 367 339 92.37% 174 145 72,116 81.38% 75.17%
Region 2 505 362 345 95.39% 206 168 83,598 75.73% 72.24%
Region 3 770 679 645 95.03% 352 278 186,241 75.82% 72.05%
Region 4 859 751 710 94.43% 444 365 172,474 79.15% 74.74%
Region 5 1,862 1,574 1,487 94.45% 879 746 357,442 81.47% 76.95%
Region 6 2,569 2,249 2,111 93.92% 1,269 993 588,907 74.29% 69.77%
Nevada 7,471 6,114 5,760 94.35% 3,324 2,653 2,081,192 75.10% 70.86%
Clark 4,709 3,872 3,635 93.86% 2,228 1,789 1,475,198 75.72% 71.07%
Rural 1,283 980 926 94.48% 481 372 275,973 73.53% 69.48%
Washoe 1,479 1,262 1,199 95.52% 615 492 330,021 74.36% 71.03%
New Hampshire 7,941 6,297 5,635 89.32% 3,322 2,683 1,117,416 78.01% 69.68%
Central 2,323 1,785 1,631 91.30% 967 808 318,397 81.52% 74.42%
Central 1 1,080 828 768 92.74% 467 372 155,071 80.43% 74.59%
Central 2 1,243 957 863 90.08% 500 436 163,326 82.55% 74.36%
Northern 1,299 813 751 92.00% 374 302 144,538 74.45% 68.49%
Southern 4,319 3,699 3,253 87.76% 1,981 1,573 654,480 76.99% 67.56%
Southern 1 (Rockingham) 1,712 1,465 1,293 88.07% 768 592 250,333 76.44% 67.32%
Southern 2 2,607 2,234 1,960 87.55% 1,213 981 404,147 77.39% 67.76%
New Jersey 8,017 6,899 6,005 87.06% 3,651 2,771 7,235,874 71.65% 62.38%
Central 1,838 1,513 1,282 84.91% 748 566 1,685,026 71.99% 61.13%
Metropolitan 1,640 1,439 1,264 87.78% 883 704 1,721,166 75.32% 66.12%
Northern 2,763 2,480 2,151 86.83% 1,220 919 2,283,814 70.36% 61.10%
Southern 1,776 1,467 1,308 88.94% 800 582 1,545,868 69.63% 61.93%
New Mexico 7,500 5,801 5,474 94.37% 3,289 2,716 1,614,897 77.61% 73.24%
Region 1 1,561 1,231 1,180 95.58% 825 689 332,053 81.91% 78.29%
Region 2 1,138 788 716 91.13% 341 267 246,371 73.28% 66.78%
Region 3 (Bernalillo) 2,337 2,007 1,880 93.70% 1,100 897 512,406 76.11% 71.31%
Region 4 1,083 815 765 94.13% 440 356 201,595 74.23% 69.88%
Region 5 1,381 960 933 97.13% 583 507 322,472 80.45% 78.14%
New York 35,519 30,212 23,624 78.19% 14,929 10,853 16,226,216 66.86% 52.28%
Region A 14,618 12,579 8,609 68.32% 5,728 3,908 6,810,690 61.52% 42.03%
Region 1 2,145 1,893 1,404 73.98% 1,055 797 1,106,417 70.86% 52.43%
Region 2 5,225 4,443 3,243 72.85% 2,170 1,452 2,458,170 59.08% 43.04%
Region 3 3,639 3,045 1,790 58.30% 1,019 700 1,390,286 64.50% 37.60%
Region 4 3,609 3,198 2,172 68.13% 1,484 959 1,855,816 57.43% 39.12%
Region B 7,798 6,976 5,539 79.64% 3,599 2,520 4,176,786 64.89% 51.68%
Region 5 4,394 3,974 3,132 79.19% 1,968 1,400 2,337,200 65.58% 51.93%
Region 6 2,047 1,850 1,447 78.26% 965 661 1,115,648 65.57% 51.32%
Region 7 1,357 1,152 960 83.34% 666 459 723,939 61.41% 51.18%
Region C 9,821 8,249 7,302 88.50% 4,313 3,400 3,891,709 74.47% 65.90%
Region 8 2,325 1,822 1,537 84.53% 877 648 845,627 69.13% 58.44%
Region 9 1,970 1,759 1,515 86.06% 903 702 808,441 71.34% 61.40%
Region 10 962 820 749 91.32% 494 424 379,061 79.59% 72.68%
Region 11 2,079 1,829 1,665 91.00% 979 786 883,849 77.48% 70.50%
Region 12 2,485 2,019 1,836 90.78% 1,060 840 974,731 76.41% 69.37%
Region D 3,282 2,408 2,174 90.26% 1,289 1,025 1,347,032 76.31% 68.88%
Region 13 1,288 911 821 90.19% 487 378 417,974 74.09% 66.82%
Region 14 1,089 759 676 88.84% 410 316 459,779 74.69% 66.36%
Region 15 905 738 677 91.81% 392 331 469,278 81.26% 74.61%
North Carolina 8,379 6,886 6,400 92.92% 3,508 2,864 7,365,392 77.13% 71.67%
CenterPoint/Guilford 984 848 793 93.55% 422 328 805,450 69.25% 64.78%
CenterPoint 652 555 524 94.45% 266 209 427,248 70.02% 66.13%
Guilford 332 293 269 91.79% 156 119 378,202 67.87% 62.30%
Durham 1,627 1,380 1,296 93.94% 787 655 1,208,562 79.36% 74.55%
East Carolina 594 422 403 95.51% 188 159 474,813 83.67% 79.91%
Eastpointe 651 533 500 93.96% 240 198 641,833 76.67% 72.04%
ECCS 426 368 334 90.55% 173 144 457,281 72.31% 65.48%
Mecklenburg 836 716 655 91.21% 458 372 678,580 76.62% 69.88%
Pathways 829 683 638 93.12% 333 263 728,134 74.82% 69.67%
PBH 1,090 871 827 94.99% 419 332 1,084,402 75.27% 71.50%
Sandhills 349 297 278 93.47% 174 148 437,368 85.38% 79.80%
Smoky Mountain 463 350 323 92.31% 174 141 432,090 76.44% 70.56%
Western Highlands 530 418 353 84.63% 140 124 416,877 89.16% 75.46%
North Dakota 8,039 6,512 6,142 94.34% 3,371 2,771 530,545 79.42% 74.92%
Badlands and West Central 2,020 1,731 1,643 94.93% 845 703 143,870 77.81% 73.87%
Lake Region and South Central 1,159 896 852 94.97% 455 357 80,958 74.89% 71.12%
North Central and Northwest 1,608 1,190 1,147 96.41% 553 464 89,792 81.72% 78.78%
Northeast 1,112 905 848 93.75% 521 422 74,209 78.82% 73.89%
Southeast 2,140 1,790 1,652 92.36% 997 825 141,716 82.02% 75.76%
Ohio 30,148 25,618 24,074 93.96% 13,720 10,945 9,518,034 74.62% 70.11%
Boards 2, 46, 55, and 68 1,526 1,285 1,266 98.52% 692 554 427,424 77.44% 76.29%
Boards 3, 52, and 85 748 687 640 93.20% 390 328 312,088 78.08% 72.77%
Boards 4 and 78 865 727 701 96.40% 403 320 268,021 75.22% 72.51%
Boards 5 and 60 1,130 944 891 94.30% 558 454 278,448 77.68% 73.25%
Boards 7, 15, 41, 79, and 84 1,171 1,006 967 96.17% 550 449 390,755 76.88% 73.94%
Boards 8, 13, and 83 1,065 904 849 93.92% 482 364 396,640 71.97% 67.60%
Board 9 (Butler) 904 775 736 95.12% 380 283 294,229 67.84% 64.53%
Board 12 937 819 771 94.22% 474 366 281,472 68.68% 64.71%
Boards 18 and 47 4,321 3,680 3,337 90.65% 1,808 1,482 1,340,173 76.18% 69.06%
Boards 20, 32, 54, and 69 693 604 592 98.03% 377 323 289,408 84.22% 82.56%
Boards 21, 39, 51, 70, and 80 1,411 1,242 1,178 94.91% 691 535 443,962 71.05% 67.43%
Boards 22, 74, and 87 907 723 670 92.63% 398 289 324,816 63.98% 59.26%
Boards 23 and 45 898 804 749 93.12% 474 377 295,993 78.87% 73.44%
Board 25 (Franklin) 3,185 2,622 2,470 94.06% 1,434 1,168 888,903 76.05% 71.53%
Boards 27, 71, and 73 1,290 1,051 1,014 96.47% 612 501 403,455 79.93% 77.11%
Boards 28, 43, and 67 1,409 1,283 1,223 95.32% 700 559 408,409 75.24% 71.71%
Board 31 (Hamilton) 1,988 1,680 1,466 87.24% 802 621 674,047 73.07% 63.75%
Board 48 (Lucas) 1,198 987 934 94.53% 558 429 365,718 68.43% 64.69%
Boards 50 and 76 1,563 1,342 1,284 95.78% 702 558 532,563 73.83% 70.71%
Board 57 (Montgomery) 1,452 1,163 1,111 95.56% 562 438 447,643 70.83% 67.68%
Board 77 (Summit) 1,487 1,290 1,225 94.86% 673 547 453,867 74.91% 71.06%
Oklahoma 7,909 6,359 5,760 90.47% 3,481 2,774 2,928,711 76.98% 69.65%
Central 934 798 714 88.65% 462 368 345,000 77.64% 68.83%
East Central 781 626 588 93.78% 356 285 342,695 73.17% 68.62%
Northeast 942 722 674 93.41% 370 312 384,933 81.03% 75.69%
Northwest and Southwest 1,256 951 870 91.17% 501 383 419,687 71.41% 65.10%
Oklahoma County 1,350 1,054 945 89.59% 604 476 557,487 77.07% 69.05%
Southeast 1,089 872 817 93.87% 501 410 412,537 83.16% 78.07%
Tulsa County 1,557 1,336 1,152 86.39% 687 540 466,372 76.19% 65.82%
Oregon 7,783 6,605 6,123 92.70% 3,503 2,809 3,150,540 73.02% 67.69%
Region 1 (Multnomah) 1,569 1,319 1,175 88.89% 675 516 594,185 68.51% 60.90%
Region 2 1,737 1,498 1,350 90.22% 691 545 735,942 73.19% 66.03%
Region 3 2,434 2,112 2,007 95.13% 1,307 1,097 988,788 75.99% 72.29%
Region 4 1,211 969 906 93.56% 448 347 464,125 71.78% 67.16%
Region 5 (Central) 350 299 290 96.81% 181 143 168,801 71.97% 69.67%
Region 6 (Eastern) 482 408 395 96.82% 201 161 198,698 75.43% 73.03%
Pennsylvania 30,416 25,931 20,784 80.08% 11,859 9,349 10,444,618 73.87% 59.16%
Region 1 (Allegheny) 3,651 3,171 2,662 83.89% 1,425 1,100 1,028,638 70.13% 58.84%
Regions 3, 8, 9, and 51 1,585 1,332 1,140 85.73% 594 481 599,276 75.89% 65.06%
Regions 4, 11, 37, and 49 2,078 1,630 1,451 89.17% 827 662 753,692 72.65% 64.78%
Regions 5, 18, 23, 24, and 46 1,789 1,556 729 46.81% 398 322 607,925 75.38% 35.29%
Regions 6, 12, 16, 31, 35, 45, and 47 1,849 1,460 1,312 89.80% 786 638 581,006 78.85% 70.81%
Regions 7, 13, 20, and 33 5,297 4,849 4,009 82.28% 2,399 1,841 2,027,293 73.24% 60.26%
Regions 10, 15, 27, 32, 43,and 44 1,136 982 911 92.89% 495 410 446,033 80.23% 74.53%
Regions 17 and 21 1,039 894 811 90.73% 507 404 307,964 75.01% 68.06%
Regions 19, 26, 28, and 42 3,190 2,814 1,509 53.53% 910 715 1,162,326 74.33% 39.79%
Regions 22, 38, 40, 41, and 48 2,202 1,877 1,688 89.86% 830 627 717,157 68.83% 61.85%
Regions 29 and 34 1,392 1,108 1,016 91.79% 503 369 531,172 65.05% 59.71%
Regions 30 and 50 1,567 1,278 1,178 92.08% 629 528 506,394 81.50% 75.04%
Region 36 (Philadelphia) 3,641 2,980 2,368 79.33% 1,556 1,252 1,175,741 75.46% 59.86%
Rhode Island 7,605 6,457 5,736 88.78% 3,328 2,714 892,881 77.03% 68.39%
Bristol and Newport 918 738 664 89.92% 364 282 114,715 67.42% 60.63%
Kent 1,205 1,073 950 88.32% 479 405 143,485 84.39% 74.53%
Providence 4,336 3,703 3,261 88.04% 2,022 1,656 526,507 77.72% 68.42%
Washington 1,146 943 861 91.28% 463 371 108,174 74.20% 67.73%
South Carolina 8,251 6,431 5,998 93.21% 3,369 2,784 3,611,554 79.03% 73.66%
Region 1 1,847 1,499 1,411 94.18% 757 585 949,539 72.26% 68.05%
Region 2 2,663 2,159 2,042 94.39% 1,115 972 1,075,893 85.56% 80.75%
Region 3 1,503 1,151 1,049 91.06% 610 491 642,403 77.52% 70.59%
Region 4 2,238 1,622 1,496 92.32% 887 736 943,720 78.55% 72.52%
South Dakota 6,865 5,592 5,316 95.07% 3,369 2,811 651,002 79.46% 75.55%
Region 1 1,030 759 732 96.47% 498 430 102,837 82.21% 79.32%
Region 2 1,701 1,525 1,452 95.31% 998 835 166,489 79.00% 75.29%
Region 3 1,095 910 875 96.15% 567 478 105,617 81.21% 78.08%
Region 4 582 477 457 95.91% 245 201 60,915 79.93% 76.66%
Region 5 852 626 597 95.55% 323 278 64,873 82.40% 78.73%
Region 6 613 483 469 97.16% 261 216 48,723 73.85% 71.75%
Region 7 992 812 734 90.02% 477 373 101,548 76.93% 69.26%
Tennessee 6,935 5,740 5,333 92.88% 3,353 2,737 5,103,574 76.90% 71.42%
Region 1 628 558 526 94.27% 312 255 426,835 77.31% 72.89%
Region 2 1,341 1,073 1,008 93.95% 627 503 969,598 74.34% 69.84%
Region 3 1,145 916 871 95.07% 502 404 812,218 76.80% 73.01%
Region 4 (Davidson) 580 470 419 89.31% 244 191 480,713 78.64% 70.23%
Region 5 1,661 1,412 1,298 92.15% 876 718 1,144,038 77.11% 71.05%
Region 6 723 608 575 93.88% 356 316 526,583 84.12% 78.97%
Region 7 (Shelby) 857 703 636 90.28% 436 350 743,589 73.43% 66.29%
Texas 24,231 19,856 18,636 93.83% 13,074 10,650 18,926,024 76.60% 71.88%
Region 1 944 762 738 96.81% 453 376 648,109 77.46% 74.98%
Region 2 653 496 483 97.42% 284 245 446,301 84.30% 82.13%
Region 3 6,350 5,365 5,096 95.02% 3,547 3,007 5,100,546 79.57% 75.61%
Region 3a 3,859 3,242 3,059 94.42% 2,171 1,801 3,244,112 78.01% 73.66%
Region 3bc 2,491 2,123 2,037 95.94% 1,376 1,206 1,856,434 82.29% 78.95%
Region 4 1,463 1,169 1,049 89.47% 629 495 892,779 75.39% 67.45%
Region 5 680 544 534 98.08% 379 336 620,013 90.00% 88.27%
Region 6 5,549 4,543 4,129 90.86% 3,092 2,402 4,530,381 72.51% 65.89%
Region 6a 4,952 4,031 3,659 90.72% 2,767 2,147 4,034,181 72.49% 65.76%
Region 6bc 597 512 470 92.00% 325 255 496,200 72.73% 66.92%
Region 7 3,004 2,473 2,371 95.92% 1,650 1,366 2,177,287 77.62% 74.46%
Region 7a 1,950 1,604 1,525 95.19% 1,046 838 1,340,376 75.52% 71.89%
Region 7bcd 1,054 869 846 97.35% 604 528 836,911 82.11% 79.94%
Region 8 2,317 1,949 1,796 92.01% 1,167 939 1,947,362 75.81% 69.75%
Region 9 623 515 497 96.42% 302 232 432,979 69.17% 66.69%
Region 10 628 556 543 97.63% 431 338 600,228 72.06% 70.35%
Region 11 2,020 1,484 1,400 94.34% 1,140 914 1,530,038 74.86% 70.63%
Region 11abd 1,445 1,013 955 94.22% 715 570 1,002,070 73.96% 69.68%
Region 11c (Hidalgo) 575 471 445 94.62% 425 344 527,969 76.57% 72.45%
Utah 5,213 4,472 4,243 94.89% 3,312 2,773 2,051,022 79.60% 75.53%
Bear River, Northeastern, Summit,
   Tooele, and Wasatch
690 554 526 94.96% 440 363 242,825 76.52% 72.67%
Central, Four Corners, San Juan, and
   Southwest
648 513 500 97.51% 384 311 254,626 75.34% 73.46%
Davis County 519 490 469 95.78% 355 304 221,482 83.91% 80.37%
Salt Lake County 2,042 1,834 1,711 93.20% 1,261 1,055 788,947 80.22% 74.77%
Utah County 842 654 628 95.99% 567 481 364,460 79.54% 76.35%
Weber, Morgan 472 427 409 96.02% 305 259 178,682 81.73% 78.48%
Vermont 7,806 5,893 5,409 91.75% 3,260 2,700 537,701 80.08% 73.47%
Champlain Valley 2,758 2,344 2,151 91.63% 1,507 1,261 208,527 81.39% 74.58%
Rural Northeast 1,761 1,350 1,212 89.77% 658 521 128,609 72.71% 65.27%
Rural Southeast 1,896 1,326 1,240 93.57% 660 568 113,468 84.48% 79.06%
Rural Southwest 1,391 873 806 92.30% 435 350 87,097 79.66% 73.53%
Virginia 7,801 6,499 5,714 87.82% 3,496 2,756 6,276,014 75.22% 66.06%
Region 1 1,512 1,201 1,044 86.71% 709 556 974,885 77.98% 67.61%
Region 2 1,745 1,559 1,322 84.71% 874 673 1,652,323 72.45% 61.38%
Region 3 1,485 1,205 1,093 90.73% 570 487 1,112,600 81.81% 74.23%
Region 4 1,471 1,299 1,146 88.02% 651 490 1,075,302 69.16% 60.87%
Region 5 1,588 1,235 1,109 89.80% 692 550 1,460,904 75.89% 68.16%
Washington 7,666 6,537 6,068 92.86% 3,530 2,758 5,372,494 74.68% 69.35%
Region 1 1,609 1,385 1,315 94.87% 859 709 1,164,440 79.53% 75.45%
East 1 (previously Region 1) 925 788 739 93.61% 493 404 674,658 78.65% 73.63%
East 2 (previously Region 2) 684 597 576 96.43% 366 305 489,781 80.63% 77.76%
Region 2 3,520 3,027 2,775 91.80% 1,489 1,109 2,451,260 70.48% 64.70%
North 1 (previously Region 3) 1,333 1,081 1,001 92.73% 515 382 896,909 68.35% 63.38%
North 2 (previously Region 4) 2,187 1,946 1,774 91.29% 974 727 1,554,351 71.74% 65.49%
Region 3 2,537 2,125 1,978 93.05% 1,182 940 1,756,794 76.89% 71.55%
West 1 (previously Region 5) 1,179 1,027 934 90.74% 576 431 846,476 73.59% 66.78%
West 2 (previously Region 6) 1,358 1,098 1,044 95.17% 606 509 910,318 79.82% 75.96%
West Virginia 8,973 7,446 6,880 92.41% 3,418 2,722 1,544,005 75.55% 69.81%
Region I 650 553 501 90.35% 250 187 127,904 69.26% 62.58%
Region II 1,297 1,107 1,059 95.86% 576 473 210,476 79.51% 76.21%
Region III 877 758 652 86.18% 338 273 144,920 75.03% 64.66%
Region IV 1,788 1,449 1,324 91.33% 704 567 323,303 79.80% 72.88%
Region V 2,419 2,054 1,904 92.64% 912 717 446,741 74.38% 68.90%
Region VI 1,942 1,525 1,440 94.37% 638 505 290,660 71.97% 67.92%
Wisconsin 7,529 6,370 5,879 92.28% 3,470 2,766 4,676,767 77.15% 71.19%
Milwaukee 1,159 1,024 916 89.38% 619 479 751,065 74.32% 66.43%
Northeastern 1,475 1,202 1,106 91.87% 628 501 1,018,184 77.52% 71.22%
Northern 746 589 548 93.12% 349 275 419,743 76.96% 71.66%
Southeastern 1,615 1,396 1,271 90.91% 783 610 948,956 72.88% 66.26%
Southern 1,626 1,409 1,337 94.93% 689 573 898,450 81.57% 77.43%
Western 908 750 701 93.61% 402 328 640,369 80.47% 75.33%
Wyoming 8,032 6,484 6,070 93.64% 3,552 2,792 434,243 74.70% 69.96%
Judicial District 1 (Laramie) 1,201 1,033 942 91.18% 509 402 71,953 74.72% 68.13%
Judicial District 2 710 542 510 94.05% 375 317 39,056 78.49% 73.82%
Judicial District 3 1,236 1,013 955 94.36% 577 452 62,502 75.73% 71.45%
Judicial District 4 445 381 366 96.08% 183 137 30,453 71.08% 68.29%
Judicial District 5 1,008 756 702 93.03% 371 298 43,478 74.59% 69.40%
Judicial District 6 828 687 657 95.78% 447 353 43,438 74.38% 71.24%
Judicial District 7 (Natrona) 1,174 1,015 950 93.55% 533 408 59,066 76.23% 71.31%
Judicial District 8 362 321 305 95.04% 174 145 30,529 76.44% 72.65%
Judicial District 9 1,068 736 683 92.71% 383 280 53,767 69.93% 64.83%

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. (2012). 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.

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). 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. (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.

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.

Office of Applied Studies. (2005). 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. (2008). 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. (2010). 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). 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). 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). 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) report 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 No. HHSS283200800004C.

At RTI, Neeraja S. Sathe and Kathryn Spagnola were responsible for the writing of the report, 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 report 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 report. 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, Pamela Tuck, and Cheryl Velez prepared its Web versions. Justine L. Allpress, E. Andrew Jessup, and Shari B. Lambert prepared and processed the maps.


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 eight large sample States are California, Florida, Illinois, Michigan, New York, Ohio, Pennsylvania, and Texas.

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.

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