This report provides an overview and summary of the methodology used to produce 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 (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.
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:
The following sets of tables and files will appear on the substate Web site later in 2012:
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 7 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:
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.
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 (2006-2010) 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 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/substate region is a combination of the direct estimate for that State/substate region and the estimate obtained from a national model. The national model, which has estimated parameter coefficients based on data from all States/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 (to be available on the SAMHSA Web site later in 2012), 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.
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. 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. Substate-level small area estimates of serious mental illness, any mental illness, 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 serious mental illness, any mental illness, serious thoughts of suicide, and major depressive episode (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.
The model described here to produce the 2008-2010 substate small area estimates is similar to the logistic mixed 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:
where is the probability of engaging in the behavior of interest (e.g., using marijuana in the past month) for person-k belonging to age group-a in substate region-j of State-i. Let denote a vector of auxiliary variables associated with age group-a (12 to 17, 18 to 25, 26 to 34, and 35 or older) and denote the associated vector of regression parameters. The age group-specific vectors of auxiliary variables are defined for every block group in the Nation and also include person-level demographic variables, such as race/ethnicity and gender. The vectors of random effects and are assumed to be mutually independent with and , where A is the total number of individual age groups modeled (generally, ). For HB estimation purposes, an improper uniform prior distribution is assumed for , and proper Wishart prior distributions are assumed for and . The HB solution for involves a series of complex Markov Chain Monte Carlo (MCMC) steps to generate values of the desired fixed and random effects from the underlying joint 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).
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)3 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.
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):
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 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.
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 2009-2010 report.
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).
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:
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 2008-2010 substate SAE analysis:
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.
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:
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).
The NSDUH computer-assisted interviewing (CAI) instrumentation includes questions that are designed to measure illicit drug and alcohol dependence and abuse. For these substances,5 dependence and abuse questions were based on the criteria in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) (American Psychiatric Association [APA], 1994).
Specifically, for marijuana, hallucinogens, inhalants, and tranquilizers, a respondent was defined as having dependence if he or she met three or more of the following six dependence criteria:
For alcohol, cocaine, heroin, pain relievers, sedatives, and stimulants, a seventh withdrawal criterion was added. A respondent was defined as having dependence if he or she met three or more of seven dependence criteria. The seventh withdrawal criterion is defined by a respondent reporting having experienced a certain number of withdrawal symptoms that vary by substance (e.g., having trouble sleeping, cramps, hands tremble).
For each illicit drug and alcohol, a respondent was defined as having abused that substance if he or she met one or more of the following four abuse criteria and was determined not to be dependent on the respective substance in the past year:
For additional details on how respondents were classified as having dependence or abuse of illicit drugs and alcohol, see Section B.4.2 in Appendix B of the 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.
This section provides a summary of measurement issues associated with the four mental health outcome variables for which 2008-2010 substate small area estimates were produced—serious mental illness, any mental illness, serious thoughts of suicide, and major depressive episode. Additional details can be found in Sections B.4.6 and B.4.7 of Appendix B in the 2008 NSDUH national findings report for serious mental illness and major depressive episode, respectively (OAS, 2009), and in Sections B.4.2 to B.4.4 of Appendix B in the 2010 NSDUH mental health findings report for all four outcome variables (CBHSQ, 2012).
In the 2000-2001 and 2002-2003 NSDUH State reports, the Kessler-6 (K6) distress scale was used to measure serious mental illness (Kessler et al., 2003). However, SAMHSA discontinued producing State-level serious mental illness estimates beginning with the release of the 2003-2004 State report because of concerns about the validity of using only the K6 distress scale without an impairment scale; see Section B.4.4 of Appendix B in the 2004 NSDUH national findings report (OAS, 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 serious mental illness, but as a measure of serious psychological distress because it was determined that the K6 scale only measured serious psychological distress and just contributed to measuring serious mental illness (see details below).
In December 2006, a technical advisory group meeting of expert consultants was convened by SAMHSA's Center for Mental Health Services to solicit recommendations for mental health surveillance data collection strategies among the U.S. population. The panel recommended that NSDUH should be used to produce estimates of serious mental illness among adults using NSDUH's mental health measures and a gold-standard clinical psychiatric interview. In response, SAMHSA's CBHSQ initiated a Mental Health Surveillance Study (MHSS) under its NSDUH contract with RTI International to develop and implement methods to estimate serious mental illness. Using recommendations from this panel, substate estimates of serious mental illness using 2008, 2009, and 2010 NSDUH data were based on this revised methodology and, thus, are not comparable with estimates for serious mental illness or serious psychological distress shown in NSDUH State reports prior to 2009.
To develop methods for preparing the estimates of serious mental illness and any mental illness presented here and in other NSDUH reports, the MHSS was initiated as part of the 2008 NSDUH design and analysis. Because of constraints on the interview time in NSDUH and the need for trained mental health clinicians, it was not possible to administer a full structured diagnostic clinical interview to assess mental illness on approximately 45,000 adult respondents; therefore, the approach adopted by SAMHSA was to utilize short scales separately measuring psychological distress (K6) and functional impairment that could be used in a statistical model to accurately predict whether a respondent had a mental illness. Two impairment scales—the World Health Organization Disability Assessment Schedule (WHODAS) and the Sheehan Disability Scale (SDS)—were included in the 2008 survey for evaluation. The collection of clinical psychiatric interview data was achieved using a subsample of approximately 1,500 adult NSDUH participants in 2008. These participants were recruited for a follow-up clinical interview consisting of a gold-standard diagnostic assessment for mental disorders and functional impairment. In order to determine the optimal scale to measure functional impairment, a split-sample design was incorporated into the full 2008 NSDUH data collection in which half of the adult respondents received the WHODAS and half received the SDS. Statistical models using the data from the subsample of respondents collected as part of the MHSS then were developed for each half sample in which the short scales (the K6 in combination with the WHODAS or the K6 in combination with the SDS) were used as predictors in models of mental illness assessed via the clinical interviews. The model parameter estimates then were used to predict serious mental illness in the full 2008 NSDUH sample.
The K6 in NSDUH 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:
Response categories are the same for the remaining questions shown below.
To create a score, the six items (NERVE30, HOPE30, FIDG30, NOCHR30, EFFORT30, and DOWN30) on the K6 scale were recoded from 0 to 4 so that "all of the time" was coded 4, "most of the time" 3, "some of the time" 2, "a little of the time" 1, and "none of the time" 0, with "don't know" and "refused" also coded as 0. Summing across the transformed responses in these six items resulted in a score with a range from 0 to 24.
If respondents were asked about a month in the past 12 months when they felt more depressed, anxious, or emotionally stressed than they felt during the past 30 days, they were asked comparable K6 items for that particular month in the past 12 months. The scoring procedures for these K6 items for the past 12 months were the same as those described above. The higher of the two K6 total scores for the past 30 days or past 12 months was used both for MHSS analysis purposes and in the adult respondents' final data.
An alternative K6 total score also was created in which K6 scores less than 8 were recoded as 0 and scores from 8 to 24 were recoded as 1 to 17. The rationale for creating the alternative past year K6 score was that serious mental illness prevalence was typically extremely low for respondents with past year K6 scores less than 8, and the prevalence rates started increasing only when scores were 8 or greater.
As described previously, a subsample of approximately 1,500 adult NSDUH participants in 2008 completed follow-up clinical interviews to provide data for the statistical modeling of the NSDUH interview data of psychological distress and functional impairment on mental health status. The MHSS sample respondents were administered clinical interviews within 4 weeks of the NSDUH main interview to assess the presence of mental disorders and functional impairment. Specifically, each participant was assessed by a trained clinical interviewer (master's or doctoral-level clinician, counselor, or social worker) via paper-and-pencil interviewing (PAPI) over the telephone. The clinical interview used was an adapted version of the Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition (SCID-I/NP) (First, Spitzer, Gibbon, & Williams, 2002). Past year disorders that were assessed through the SCID included mood disorders (e.g., major depressive episode, manic episode), anxiety disorders (e.g., panic disorder, generalized anxiety disorder, posttraumatic stress disorder), eating disorders (e.g., anorexia nervosa), intermittent explosive disorder, and adjustment disorder. In addition, the presence of psychotic symptoms was assessed. Substance use disorders also were assessed, although these disorders were not included in the estimates of mental illness.
Functional impairment ratings were assigned by clinical interviewers using the Global Assessment of Functioning (GAF) scale (Endicott, Spitzer, Fleiss, & Cohen, 1976). Mental illness, measured using the SCID and differentiated by the level of functional impairment, was defined in the MHSS as follows:
The SCID and the GAF in combination were considered to be the gold standard for measuring mental illness.
Statistical modeling involved developing separate weighted logistic regression prediction models for the K6 and for each of the two impairment scales. With serious mental illness status based on having a SCID diagnosis plus a GAF less than or equal to 50, the response variable Y was defined so that
Y = 1 when a serious mental illness diagnosis is positive; otherwise, Y = 0.
If X is a vector of explanatory variables, then the response probability can be estimated using weighted logistic regression models for the WHODAS and SDS half samples. The final 2008 WHODAS and SDS calibration models, respectively, were determined as follows:
, D (2)
where refers to an estimate of the serious mental illness response probability for the WHODAS and SDS models (indicated by the "w" subscript for the WHODAS and the "s" subscript for the SDS). The , , and terms refer to the alternative K6, WHODAS, and SDS scores, respectively:6
= Alternative Past Year K6 Score: Past year K6 score less than 8 recoded as 0; past year K6 score 8 to 24 recoded as 1 to 17.
= Alternative WHODAS Score: WHODAS item scores less than 2 recoded as 0; WHODAS item scores 2 to 3 recoded as 1, then summed for a score ranging from 0 to 8.
= Alternative SDS Score: SDS item scores less than 7 recoded as 0; SDS item scores 7 to 10 recoded as 1, then summed for a score ranging from 0 to 4.
Rearranging terms of the two models provided a direct calculation of the predicted probability of serious mental illness:
Next, a cut point probability was determined, so that if for a particular respondent, then he or she was predicted to be serious mental illness positive; otherwise, he or she was predicted to be serious mental illness negative. Receiver operating characteristic (ROC) analyses were used to determine the cut point that resulted in the weighted number of false-positive and false-negative counts being (approximately) equal, thus ensuring unbiased estimates. The optimal cut points were determined to be 0.26972 and 0.26657 for the WHODAS and SDS models, respectively. See Aldworth et al. (2009) for further details.
Model fit statistics and various sensitivity analyses indicated that in combination with the K6, the WHODAS was a better predictor of serious mental illness than the SDS. Consequently, the decision was made to continue with the WHODAS as the measure of impairment for all adults in future NSDUHs. Nevertheless, for the final models, serious mental illness estimates based on the SDS in the 2008 full dataset were very similar to those based on the WHODAS, indicating that the estimates from the two half samples could be combined to form single estimates.
The 2008 prediction model parameters and cut points estimated using the 2008 WHODAS subsample were used to estimate serious mental illness in the 2009 and 2010 NSDUH samples.
Various methods to estimate any mental illness were investigated in the 2008 MHSS. These methods were subject to the constraint that they would have no effect on the serious mental illness estimates produced by the models discussed above. The methods investigated included logistic models based on any mental illness as the response variable, serious mental illness as the response variable, and multilogistic models based on a multilevel mental illness variable from which both serious mental illness and any mental illness could be derived. Analyses suggested that models based on serious mental illness as the response variable provided almost identical results to those of the other models, so this method was chosen to estimate any mental illness.
As noted previously, serious mental illness estimates for 2008 were based on both the WHODAS and SDS half samples because estimates of serious mental illness were comparable between half samples. Because estimates of any mental illness based on the SDS half sample were not comparable with those based on the WHODAS half sample, the decision was made to base estimates of any mental illness for 2008 only on the WHODAS half sample.
Estimates of any mental illness were obtained from the serious mental illness predicted probabilities calculated using the WHODAS model described above. Respondents with a predicted probability of serious mental illness greater than the cut point of 0.02400 were classified as having any mental illness. The same models were implemented for 2009 and 2010.
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.
According to the DSM-IV, a person is defined as having had major depressive episode in his or her lifetime if he or she has had at least five or more of the following nine symptoms nearly every day in the same 2-week period, where at least one of the symptoms is a depressed mood or loss of interest or pleasure in daily activities (APA, 1994): (1) depressed mood most of the day; (2) markedly diminished interest or pleasure in all or almost all activities most of the day; (3) significant weight loss when not sick or dieting, or weight gain when not pregnant or growing, or decrease or increase in appetite; (4) insomnia or hypersomnia; (5) psychomotor agitation or retardation; (6) fatigue or loss of energy; (7) feelings of worthlessness; (8) diminished ability to think or concentrate or indecisiveness; and (9) recurrent thoughts of death or suicidal ideation. Respondents who have had a major depressive episode in their lifetime are asked if, during the past 12 months, they had a period of depression lasting 2 weeks or longer while also having some of the other symptoms mentioned. Those reporting that they have are defined as having had major depressive episode in the past year and then are asked questions from the SDS to measure the level of functional impairment in major life activities reported to be caused by the major depressive episode in the past 12 months (Leon, Olfson, Portera, Farber, & Sheehan, 1997).
Beginning in 2004, modules related to major depressive episode, derived from DSM-IV (APA, 1994) criteria for major depression, were included in the questionnaire. These questions permit prevalence estimates of major depressive episode to be calculated. Separate modules were administered to adults aged 18 or older and youths aged 12 to 17. The adult questions were adapted from the depression section of the National Comorbidity Survey Replication (NCS-R), and the questions for youths were adapted from the depression section of the National Comorbidity Survey Adolescent (NCS-A) (see http://www.hcp.med.harvard.edu/ncs/). To make the modules developmentally appropriate for youths, there are minor wording differences in a few questions between the adult and youth modules. Revisions to the questions in both modules were made primarily to reduce 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 major depressive episode have remained unchanged. In the 2008 questionnaire, however, changes were made in other mental health items that precede the major depressive episode questions for adults (K6, suicide, and impairment). Questions also were retained in 2009 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, 2012) for further details about these questionnaire changes. These questionnaire changes in 2008 appear to have affected the reporting on major depressive episode questions among adults.
Because the WHODAS was selected to be used in the 2009 and subsequent surveys, model-based adjustments were applied to major depressive episode estimates from the SDS half sample in 2008 to remove the context effect differential between the two half samples. Additionally, model-based adjustments were made to the 2005, 2006, and 2007 adult major depressive episode estimates to make them comparable with the 2008 through 2010 major depressive episode estimates (for more information on these adjustments, see Aldworth, Kott, Yu, Mosquin, & Barnett-Walker, 2012). Thus, the 2008-2010 substate estimates of major depressive episode were produced using the adjusted 2008 major depressive episode variable along with the unadjusted 2009 and 2010 major depressive episode variable. Additionally, the 2006-2008 substate small area estimates of major depressive episode were re-created using the adjusted major depressive episode variable.
In addition, changes to the youth mental health service utilization module questions in 2009 that preceded the questions about adolescent depression could have affected adolescents' responses to the adolescent depression questions and estimates of adolescent major depressive episode. However, these changes in 2009 did not appear to affect the estimates of adolescent major depressive episode. Therefore, data on trends in past year major depressive episode from 2004 to 2010 are available for adolescents aged 12 to 17.
|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 will be produced for 2006-2008 and will be shown in 2008-2010 NSDUH Substate Comparison Tables. The 2004-2006 MDE estimate is not comparable with the 2006-2008 and 2008-2010 MDE estimates that will be 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|
|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%|
|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%|
|Region 3R (Sacramento)||977||855||722||84.78%||443||343||1,144,189||69.10%||58.59%|
|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 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%|
|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 18R (San Bernardino)||1,339||1,171||1,071||91.58%||926||741||1,650,606||70.72%||64.77%|
|Regions 2 and 7||4,136||3,605||3,239||89.97%||1,865||1,492||2,276,050||76.56%||68.88%|
|Regions 5 and 6||1,234||756||718||95.00%||373||284||457,747||72.06%||68.46%|
|New Castle (excluding
|District of Columbia||13,505||11,010||8,974||80.84%||3,230||2,721||511,275||81.26%||65.69%|
|Region A - Northwest||2,910||2,219||2,036||91.92%||1,126||906||1,121,473||75.02%||68.96%|
|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%|
|Region C - Central||8,480||6,571||6,024||91.73%||3,384||2,793||3,887,055||76.19%||69.89%|
|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 13 (Hillsborough)||2,444||2,100||1,928||92.05%||1,143||944||969,022||77.43%||71.27%|
|Region F - Southern (Circuits
11 and 16)
|Kauai and Maui||1,607||1,219||1,087||85.56%||600||464||169,995||73.28%||62.70%|
|Region I (Cook)||12,491||10,968||7,816||71.21%||5,502||3,915||4,228,949||65.16%||46.40%|
|Kansas City Metro||2,502||2,279||2,087||91.59%||1,311||1,038||771,541||74.20%||67.96%|
|Adanta, Cumberland River, and
|Bluegrass, Comprehend, and
|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
|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%|
|Pathways and Western||1,175||884||807||91.47%||458||396||264,361||80.58%||73.71%|
|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%|
|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 (excluding Jackson)||935||796||744||93.31%||448||361||637,026||73.21%||68.32%|
|Regions 1 and 2||900||650||620||95.47%||337||290||153,765||82.47%||78.74%|
|Southern 1 (Rockingham)||1,675||1,370||1,207||88.39%||701||544||253,793||75.00%||66.29%|
|Region 3 (Bernalillo)||2,242||1,953||1,826||93.50%||1,040||837||523,402||77.38%||72.35%|
|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%|
|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%|
|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%|
|Northwest and Southwest||1,200||905||812||89.82%||482||366||417,975||68.69%||61.70%|
|Region 1 (Multnomah)||1,489||1,326||1,195||90.42%||621||475||600,209||71.25%||64.42%|
|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%|
|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%|
|Bristol and Newport||945||799||709||88.73%||388||297||113,593||71.18%||63.15%|
|Region 4 (Davidson)||1,004||782||688||87.93%||398||322||490,582||76.43%||67.20%|
|Region 7 (Shelby)||961||743||684||91.55%||418||336||755,442||74.98%||68.64%|
|Region 11c (Hidalgo)||529||439||411||93.86%||381||332||552,033||83.19%||78.08%|
|Bear River, Northeastern, Summit,
Tooele, and Wasatch
|Central, Four Corners, San Juan, and
|Salt Lake County||1,906||1,673||1,583||94.63%||1,258||1,064||816,732||82.72%||78.28%|
|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%|
|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%|
|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%|
|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%|
Aldworth, J., Barnett-Walker, K., Chromy, J., Karg, R., Morton, K., Novak, S., & Spagnola, K. (2009, June). Measuring serious mental illness with the NSDUH: Results of 2008 12-month analysis. In 2008 National Survey on Drug Use and Health: Methodological resource book (Section 16, prepared for the Substance Abuse and Mental Health Services Administration under Contract No. 283-2004-00022, Mental Health Surveillance Survey Deliverable 5, RTI/0209009.423.006.008). Research Triangle Park, NC: RTI International.
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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.
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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: 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.
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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, Marissa R. Straw, Pamela Tuck, and Cheryl Velez prepared its print and Web versions. Justine L. Allpress, E. Andrew Jessup, and Shari B. Lambert prepared and processed the maps.
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 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.
4 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.
5 Substances include alcohol, marijuana, cocaine, heroin, hallucinogens, inhalants, pain relievers, tranquilizers, stimulants, and sedatives.
6 For more information on the WHODAS and SDS scores, see Section B.4.3 of Appendix B in the 2009 mental health findings report (CBHSQ, 2010).