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.
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:
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.
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.
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.
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:
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 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.
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):
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.
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.
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 2006-2008 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 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.
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. 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,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:
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.
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).
|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|
|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%|
|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%|
|Region 3R (Sacramento)||1,101||991||894||90.32%||537||413||1,117,439||68.21%||61.60%|
|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 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%|
|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 18R (San Bernardino)||1,374||1,187||1,081||91.32%||893||718||1,600,079||76.27%||69.65%|
|Regions 2 and 7||4,305||3,642||3,243||89.00%||1,876||1,453||2,212,469||74.46%||66.27%|
|Regions 5 and 6||1,003||651||606||92.66%||337||271||441,590||75.61%||70.06%|
|New Castle (excluding Wilmington City)||3,379||3,047||2,643||87.01%||1,794||1,407||372,307||75.46%||65.65%|
|District of Columbia||12,139||9,807||8,237||83.93%||3,205||2,604||500,465||77.18%||64.78%|
|Region A - Northwest||2,404||1,853||1,697||91.45%||1,048||855||1,114,243||75.76%||69.28%|
|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%|
|Region C - Central||7,618||5,886||5,353||90.97%||3,243||2,611||3,773,571||73.87%||67.20%|
|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 13 (Hillsborough)||2,356||1,953||1,794||92.00%||1,025||799||950,178||74.34%||68.39%|
|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%|
|Kauai and Maui||1,555||1,095||983||84.82%||553||420||167,428||69.87%||59.26%|
|Region I (Cook)||12,548||11,014||7,778||70.56%||5,628||3,912||4,281,733||62.89%||44.38%|
|Kansas City Metro||2,325||2,070||1,920||92.77%||1,276||1,009||747,336||76.51%||70.98%|
|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%|
|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%|
|Pathways and Western||1,059||686||634||92.51%||373||321||268,472||82.56%||76.37%|
|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%|
|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 (excluding Jackson)||892||760||708||92.93%||373||296||625,656||71.86%||66.78%|
|Regions 1 and 2||1,014||729||684||93.84%||380||313||155,714||78.82%||73.96%|
|Southern 1 (Rockingham)||1,712||1,465||1,293||88.07%||768||592||250,333||76.44%||67.32%|
|Region 3 (Bernalillo)||2,337||2,007||1,880||93.70%||1,100||897||512,406||76.11%||71.31%|
|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%|
|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%|
|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%|
|Northwest and Southwest||1,256||951||870||91.17%||501||383||419,687||71.41%||65.10%|
|Region 1 (Multnomah)||1,569||1,319||1,175||88.89%||675||516||594,185||68.51%||60.90%|
|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%|
|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%|
|Bristol and Newport||918||738||664||89.92%||364||282||114,715||67.42%||60.63%|
|Region 4 (Davidson)||580||470||419||89.31%||244||191||480,713||78.64%||70.23%|
|Region 7 (Shelby)||857||703||636||90.28%||436||350||743,589||73.43%||66.29%|
|Region 11c (Hidalgo)||575||471||445||94.62%||425||344||527,969||76.57%||72.45%|
|Bear River, Northeastern, Summit,
Tooele, and Wasatch
|Central, Four Corners, San Juan, and
|Salt Lake County||2,042||1,834||1,711||93.20%||1,261||1,055||788,947||80.22%||74.77%|
|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%|
|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%|
|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%|
|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%|
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.
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.
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.