2016-2017
National Survey on Drug Use and Health:
Guide to State Tables and Summary of Small Area Estimation Methodology

Section A: Overview of NSDUH and Model‑Based State Estimates

A.1 Introduction

This document provides information on the model-based small area estimates of substance use and mental disorders in states based on data from the combined 2016-2017 National Surveys on Drug Use and Health (NSDUHs). These estimates are available online along with other related information.1 NSDUH is an annual survey conducted from January through December of the civilian, noninstitutionalized population aged 12 or older and is sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA). The survey collects information from individuals residing in households, noninstitutionalized group quarters (e.g., shelters, rooming houses, dormitories), and civilians living on military bases. In 2016-2017, NSDUH collected data from 135,974 respondents aged 12 or older and was designed to obtain representative samples from the 50 states and the District of Columbia. NSDUH is planned and managed by SAMHSA's Center for Behavioral Health Statistics and Quality (CBHSQ). Data collection and analysis are conducted under contract with RTI International.2 A summary of NSDUH's methodology is given in Section A.2. Section A.3 lists all of the tables and files associated with the 2016‑2017 state small area estimates and when and where they can be found. Information is given in Section A.4 on the confidence intervals and margins of error and how to make interpretations with respect to the small area estimates. Section A.5 discusses related substance use measures and warns users about not drawing conclusions by subtracting small area estimates from two different measures. Section A.6 discusses NSDUH questionnaire changes from 2015 and how these changes affect the small area estimates.

The survey-weighted hierarchical Bayes (SWHB) estimation methodology used in the production of state estimates from the 1999 to 2016 surveys also was used in the production of the 2016-2017 state estimates. The SWHB methodology is described in Appendix E of the 2001 state report (Wright, 2003b) and in Folsom, Shah, and Vaish (1999). A general model description is given in Section B.1 of this document. A list of measures for which small area estimates are produced is given in Section B.2. Predictors used in the 2016-2017 small area estimation (SAE) modeling are listed and described in Section B.3. Variable selection is discussed in Section B.4.

Small area estimates obtained using the SWHB methodology are design consistent (i.e., the small area estimates for states with large sample sizes are close to the robust design-based estimates). The state small area estimates when aggregated using the appropriate population totals result in national small area estimates that are very close to the national design-based estimates. However, to ensure internal consistency, it is desirable to have national small area estimates3 exactly match the national design-based estimates. This process is called "benchmarking." The benchmarked state-level estimates are also potentially less biased than the unbenchmarked state-level estimates. Beginning in 2002, exact benchmarking was introduced, as described in Section B.5.4 Tables of the estimated numbers of individuals associated with each measure are available online,5 and an explanation of how these counts and their respective Bayesian confidence intervals6 are calculated can be found in Section B.6. Section B.7 discusses the method to compute aggregate estimates by combining two age groups. The definition and explanation of the formula used in estimating the marijuana initiation rate are given in Section B.8. Note that, unlike the other SAE outcomes discussed in this document, marijuana initiation is calculated as a ratio of two measures.

For all measures except major depressive episode (MDE, i.e., depression), serious mental illness (SMI), any mental illness (AMI), receipt of mental health services, and serious thoughts of suicide, the age groups for which estimates are provided are 12 to 17, 18 to 25, 26 or older, 18 or older, and 12 or older.7

Estimates of underage (aged 12 to 20) alcohol use and binge alcohol use were also produced.8 Alcohol consumption is expected to differ significantly across the 18 to 25 age group because of the legalization of alcohol at age 21. Therefore, it was decided that it would be useful to produce small area estimates for individuals aged 12 to 20. A short description of the methodology used to produce underage drinking estimates is provided in Section B.9.

The remainder of Section B covers two topics:

In Section C, the 2015, 2016, 2017, pooled 2015-2016, and pooled 2016-2017 survey sample sizes, population estimates, and response rates are included in Tables C.1 to C.14, respectively. Table C.15 lists all of the measures and the years for which small area estimates were produced going back to the 2002 NSDUH, and Table C.16 lists all of the measures by age groups for which small area estimates were produced. In addition, Table C.17 provides a summary of milestones implemented in the SAE production process from 2002 to 2017.

A.2 Summary of NSDUH Methodology

NSDUH is the primary source of statistical information on the use of illicit drugs, alcohol, and tobacco by the U.S. civilian, noninstitutionalized population aged 12 or older. The survey also includes several modules of questions that focus on mental health issues. Conducted by the federal government since 1971, the survey collects data by administering questionnaires to a representative sample of the population through face-to-face interviews at their place of residence.

The survey covers residents of households, noninstitutional group quarters (e.g., shelters, rooming houses, dormitories), and civilians living on military bases. Persons excluded from the survey include homeless people who do not use shelters, military personnel on active duty, and residents of institutional group quarters, such as jails or prisons and long-term hospitals. The 1999 survey marked the first year in which the national sample was interviewed using a computer-assisted interviewing (CAI) method. The survey used a combination of computer-assisted personal interviewing (CAPI) conducted by an interviewer and audio computer-assisted self-interviewing (ACASI). Use of ACASI is designed to provide the respondent with a highly private and confidential means of responding to questions and increases the level of honest reporting of illicit drug use and other sensitive behaviors. For further details on the development of the CAI procedures for the 1999 National Household Survey on Drug Abuse (NHSDA),9 see the Office of Applied Studies (OAS, 2001).

The 1999 through 2001 NHSDAs and the 2002 through 2013 NSDUHs employed an independent, multistage area probability sample design for each of the 50 states and the District of Columbia. For this design, eight states were designated as large sample states (California, Florida, Illinois, Michigan, New York, Ohio, Pennsylvania, and Texas) with target sample sizes of 3,600 per year. For the remaining 42 states and the District of Columbia, the target sample size was 900 per year. This approach ensured that there was sufficient sample in every state to support SAE while at the same time maintaining efficiency for national estimates. The design also oversampled youths and young adults, so that each state's sample was approximately equally distributed among three major age groups: 12 to 17 years, 18 to 25 years, and 26 years or older.

A coordinated design was developed for the 2014 through 2017 NSDUHs. Similar to the 1999 through 2013 surveys, the coordinated 4-year design is state-based with an independent, multistage area probability sample within each state and the District of Columbia. This design designates 12 states as large sample states. These 12 states have the following target sample sizes per year: 4,560 interviews in California; 3,300 interviews in Florida, New York, and Texas; 2,400 interviews in Illinois, Michigan, Ohio, and Pennsylvania; and 1,500 interviews in Georgia, New Jersey, North Carolina, and Virginia. Making the sample sizes more proportional to the state population sizes improves the precision of national NSDUH estimates. This change also allows for a more cost-efficient sample allocation to the largest states while slightly increasing the sample sizes in smaller states to improve the precision of state estimates (note that the target sample size per year in the small states is 960 interviews except for Hawaii where the target sample size is 967 interviews). The fielded sample sizes for each state in 2017 are provided in Table C.5, and the combined 2016-2017 sample sizes are provided in Table C.9.

Starting in 2014, the allocation of the NSDUH sample is 25 percent for adolescents aged 12 to 17, 25 percent for adults aged 18 to 25, and 50 percent for adults aged 26 or older. The sample of adults aged 26 or older is further divided into three subgroups: aged 26 to 34 (15 percent), aged 35 to 49 (20 percent), and aged 50 or older (15 percent). For more information on the 2014 through the 2017 NSDUH sample design and for differences between the 2013 and 2014 surveys, refer to the 2014 NSDUH sample design report (CBHSQ, 2015a).

Nationally in 2016-2017, 273,249 addresses were screened, and 135,974 individuals responded within the screened addresses (see Table C.9). The screening response rate (SRR) for 2016-2017 combined averaged 76.5 percent, and the interview response rate (IRR) averaged 67.8 percent, for an overall response rate (ORR) of 51.8 percent (Table C.9). The ORRs for 2016-2017 ranged from 38.4 percent in New York to 66.0 percent in New Mexico. Estimates have been adjusted to reflect the probability of selection, unit nonresponse, poststratification to known census population estimates, item imputation, and other aspects of the estimation process. These procedures are described in detail in the 2015, 2016, and 2017 NSDUHs' methodological resource books (MRBs) (CBHSQ, 2016a, 2017, in press).

The weighted SRR is defined as the weighted number of successfully screened households (or dwelling units)10 divided by the weighted number of eligible households, or

Equation 1,     D

where w sub h h is the inverse of the unconditional probability of selection for the household (hh) and excludes all adjustments for nonresponse and poststratification.

At the person level, the weighted IRR is defined as the weighted number of respondents divided by the weighted number of selected persons, or

Equation 2,     D

where w sub i is the inverse of the probability of selection for the ith person and includes household-level nonresponse and poststratification adjustments. To be considered a completed interview, a respondent must provide enough data to pass the usable case rule.11

The weighted ORR is defined as the product of the weighted SRR and the weighted IRR or

Equation 3.     D

A.3 Presentation of Data

In addition to this methodology document for the 2016-2017 state SAE results, the following files are available at https://www.samhsa.gov/data/:

A.4 Confidence Intervals and Margins of Error

At the top of each of the 31 tables showing state-level model-based estimates13 is the design-based national estimate along with a 95 percent design-based confidence interval, all of which are based on the survey design, the survey weights, and the reported data. The state estimates are model-based statistics (using SAE methodology) that have been adjusted (benchmarked) such that the population-weighted mean of the estimates across the 50 states and the District of Columbia equals the design-based national estimate. For more details on this benchmarking, see Section B.5. The region-level estimates are also benchmarked and are obtained by taking the population-weighted mean of the associated state-level benchmarked estimates. Associated with each state and regional estimate is a 95 percent Bayesian confidence interval. These intervals indicate the uncertainty in the estimate due to both sampling variability and model fit. For example, the state with the highest estimate of past month use of marijuana for young adults aged 18 to 25 was Vermont, with an estimate of 38.8 percent and a 95 percent confidence interval that ranged from 34.2 to 43.7 percent (see Table 3 of the state model‑based estimates' tables). Assuming that sampling and modeling conditions held, the Bayes posterior probability was 0.95 that the true percentage of past month marijuana use in Vermont for young adults aged 18 to 25 in 2016-2017 was between 34.2 and 43.7 percent. As noted earlier in a Section A.1 footnote, the term "prediction interval" (PI) was used in the 2004-2005 NSDUH state report (Wright et al., 2007) and prior reports to represent uncertainty in the state and regional estimates. However, that term also is used in other applications to estimate future values of a parameter of interest. That interpretation does not apply to NSDUH state model-based estimates, so PI was replaced with "Bayesian confidence interval."

Margin of error is another term used to describe uncertainty in the estimates. For example, if (lower interval l comma and upper interval u) is a 95 percent symmetric confidence interval for the population proportion (p) and p hat is an estimate of p obtained from the survey data, then the margin of error of p hat is given by (u minus p hat) or (p hat minus l). Because (lower interval l comma and upper interval u) is a symmetric confidence interval, (u minus p hat) will be the same as (p hat minus l). In this case, the probability is 0.95 that the interval ±(u minus p hat) or ±(p hat minus l) will contain the true population value (p). This defined margin of error will vary for each estimate and will be affected not only by the sample size (e.g., the larger the sample, the smaller the margin of error), but also by the sample design (e.g., telephone surveys using random digit dialing and surveys employing a stratified multistage cluster design will, more than likely, produce a different margin of error) (Scheuren, 2004).

The confidence intervals shown in NSDUH reports are asymmetric, meaning that the distance between the estimate and the lower confidence limit will not be the same as the distance between the upper confidence limit and the estimate. For example, Utah's past month marijuana use estimate is 14.7 percent for adults aged 18 to 25 years, with a 95 percent confidence interval equal to 12.0 to 17.9 percent (see Table 3 of the state model-based estimates' tables). Therefore, Utah's estimate is 2.7 (i.e., 14.7 − 12.0) percentage points from the lower 95 percent confidence limit and 3.2 (i.e., 17.9 − 14.7) percentage points from the upper limit. These asymmetric confidence intervals work well for small percentages often found in NSDUH tables and reports while still being appropriate for larger percentages. Some surveys or polls provide only one margin of error for all reported percentages. This single number is usually calculated by setting the sample percentage estimate p hat equal to 50 percent, which will produce an upper bound or maximum margin of error. Such an approach would not be feasible in NSDUH because the estimates vary from less than 1 percent to over 75 percent; hence, applying a single margin of error to these estimates could significantly overstate or understate the actual precision levels. Therefore, given the differences mentioned above, it is more useful and informative to report the confidence interval for each estimate instead of a margin of error.

When it is indicated that a state has the highest or lowest estimate, it does not imply that the state's estimate is significantly higher or lower than the next highest or lowest state's estimate. Additionally, two significantly different state estimates (at the 5 percent level of significance) may have overlapping 95 percent confidence intervals. For details on a more accurate test to compare state estimates, see the "2016-2017 National Survey on Drug Use and Health: Comparison of Population Percentages from the United States, Census Regions, States, and the District of Columbia" at https://www.samhsa.gov/data/.

A.5 Related Substance Use Measures

Small area estimates are produced for a number of related drug measures, such as marijuana use and illicit drug use. It might appear that one could draw conclusions by subtracting one from the other (e.g., subtracting the percentage who used illicit drugs other than marijuana in the past month from the percentage who used illicit drugs in the past month to find the percentage who used only marijuana in the past month). Because related measures have been estimated with different models (i.e., separate models by age group and outcome), subtracting one measure from another related measure at the state or census region level can give misleading results, perhaps even a "negative" estimate, and should be avoided. However, these comparisons can be made at the national level because these estimates are design-based estimates. For example, at the national level, subtracting cigarette use estimates from tobacco use estimates will give the estimate of individuals who did not use cigarettes, but used other forms of tobacco, such as cigars, pipes, or smokeless tobacco.

A.6 2015 NSDUH Changes and Their Effects on Small Area Estimates

In 2015, a number of changes were made to the NSDUH questionnaire and data collection procedures. These changes were intended to improve the quality of the data that were collected and to address the changing needs of substance use and mental health policy and research.14 For a more detailed discussion of the questionnaire redesign and its effect, see Section C of the 2015 NSDUH's methodological summary and definitions report (CBHSQ, 2016b) and a brief report summarizing the implications of the changes for data users (CBHSQ, 2016c). To specifically see the impact of the 2015 questionnaire redesign as it is related to the SAE outcomes,15 refer to Section A.6 of the "2015-2016 NSDUH: Guide to State Tables and Summary of Small Area Estimation Methodology" at https://www.samhsa.gov/data/. All SAE outcomes remained comparable between the 2015, 2016, and 2017 NSDUHs.

Section B: State Model-Based Estimation Methodology

B.1 General Model Description

The model can be characterized as a complex mixed16 model (including both fixed and random effects) of the following form:

Equation 4,     D

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

Once the required number of MCMC samples (1,250 in all) for the parameters of interest are generated and tested for convergence properties (see Raftery & Lewis, 1992), the small area estimates for each race/ethnicity × gender cell within a block group can be obtained for each age group. These block group-level small area estimates then can be aggregated using the appropriate population count projections for the desired age group(s) to form state-level small area estimates. These state-level small area estimates are benchmarked to the national design-based estimates as described in Section B.5.

B.2 Variables Modeled

The 2017 National Survey on Drug Use and Health (NSDUH) data were pooled with the 2016 NSDUH data, and age group-specific state estimates for 30 binary (0, 1) outcomes listed below were produced. Comparisons between the 2015-2016 and the 2016-2017 state estimates also were produced for all measures.

  1. past month use of illicit drugs,
  2. past year use of marijuana,
  3. past month use of marijuana,
  4. perceptions of great risk from smoking marijuana once a month,
  5. average annual rate of first use of marijuana,18
  6. past month use of illicit drugs other than marijuana,
  7. past year use of cocaine,
  8. perceptions of great risk from using cocaine once a month,
  9. past year use of heroin,
  10. perceptions of great risk from trying heroin once or twice,
  11. past year use of methamphetamine,19
  12. past year misuse of pain relievers,
  13. past month use of alcohol,20
  14. past month binge alcohol use, 21
  15. perceptions of great risk from having five or more drinks of an alcoholic beverage once or twice a week,
  16. past month use of tobacco products,
  17. past month use of cigarettes,
  18. perceptions of great risk from smoking one or more packs of cigarettes per day,
  19. past year illicit drug use disorder,
  20. past year pain reliever use disorder,
  21. past year alcohol use disorder,
  22. past year substance use disorder (SUD),
  23. past year needing but not receiving treatment for illicit drug use at a special facility,
  24. past year needing but not receiving treatment for alcohol use at a special facility,
  25. past year needing but not receiving treatment for substance use at a special facility,
  26. serious mental illness (SMI) in the past year,22
  27. any mental illness (AMI) in the past year,
  28. received mental health services in the past year,
  29. had serious thoughts of suicide in the past year, and
  30. past year major depressive episode (MDE, i.e., depression).

B.3 Predictors Used in Mixed Logistic Regression Models

Local area data used as potential predictor variables in the mixed logistic regression models were obtained from a number of sources, as noted in the following discussion. Variable selection was done using combined 2016 and 2017 data for all outcomes. Fixed-effect predictors were selected using the method described in Section B.4.

Sources and potential data items used in the 2016-2017 modeling are provided in the following text and lists.

The following lists provide the specific independent variables that were potential predictors in the models.

Claritas Data (Description) Claritas Data (Level)
% Population Aged 0 to 19 in Block Group Block Group
% Population Aged 20 to 24 in Block Group Block Group
% Population Aged 25 to 34 in Block Group Block Group
% Population Aged 35 to 44 in Block Group Block Group
% Population Aged 45 to 54 in Block Group Block Group
% Population Aged 55 to 64 in Block Group Block Group
% Population Aged 65 or Older in Block Group Block Group
% Non-Hispanic Blacks in Block Group Block Group
% Hispanics in Block Group Block Group
% Non-Hispanic Other Races in Block Group Block Group
% Non-Hispanic Whites in Block Group Block Group
% Males in Block Group Block Group
% American Indians, Eskimos, Aleuts in Tract Tract
% Asians, Pacific Islanders in Tract Tract
% Population Aged 0 to 19 in Tract Tract
% Population Aged 20 to 24 in Tract Tract
% Population Aged 25 to 34 in Tract Tract
% Population Aged 35 to 44 in Tract Tract
% Population Aged 45 to 54 in Tract Tract
% Population Aged 55 to 64 in Tract Tract
% Population Aged 65 or Older in Tract Tract
% Non-Hispanic Blacks in Tract Tract
% Hispanics in Tract Tract
% Non-Hispanic Other Races in Tract Tract
% Non-Hispanic Whites in Tract Tract
% Males in Tract Tract
% Population Aged 0 to 19 in County County
% Population Aged 20 to 24 in County County
% Population Aged 25 to 34 in County County
% Population Aged 35 to 44 in County County
% Population Aged 45 to 54 in County County
% Population Aged 55 to 64 in County County
% Population Aged 65 or Older in County County
% Non-Hispanic Blacks in County County
% Hispanics in County County
% Non-Hispanic Other Races in County County
% Non-Hispanic Whites in County County
% Males in County County
American Community Survey (ACS) (Description) ACS Data (Level)
% Population Who Dropped Out of High School Tract
% Housing Units Built in 1940 to 1949 Tract
% Females 16 Years or Older in Labor Force Tract
% Females Never Married Tract
% Females Separated, Divorced, Widowed, or Other Tract
% One-Person Households Tract
% Males 16 Years or Older in Labor Force Tract
% Males Never Married Tract
% Males Separated, Divorced, Widowed, or Other Tract
% Housing Units Built in 1939 or Earlier Tract
Average Number of Persons per Room Tract
% Families below Poverty Level Tract
% Households with Public Assistance Income Tract
% Housing Units Rented Tract
% Population with 9 to 12 Years of School, No High School Diploma Tract
% Population with 0 to 8 Years of School Tract
% Population with Associate's Degree Tract
% Population with Some College and No Degree Tract
% Population with Bachelor's, Graduate, Professional Degree Tract
% Housing Units with No Telephone Service Available Tract
% Households with No Vehicle Available Tract
% Population with No Health Insurance Tract
Median Rents for Rental Units Tract
Median Value of Owner-Occupied Housing Units Tract
Median Household Income Tract
% Families below the Poverty Level County
Uniform Crime Report (UCR) Data (Description) UCR Data (Level)
Drug Possession Arrest Rate County
Drug Sale or Manufacture Arrest Rate County
Drug Violations' Arrest Rate County
Marijuana Possession Arrest Rate County
Marijuana Sale or Manufacture Arrest Rate County
Opium or Cocaine Possession Arrest Rate County
Opium or Cocaine Sale or Manufacture Arrest Rate County
Other Drug Possession Arrest Rate County
Other Dangerous Non-Narcotics Arrest Rate County
Serious Crime Arrest Rate County
Violent Crime Arrest Rate County
Driving under Influence Arrest Rate County
Other Categorical Data (Description) Other Categorical Data (Source) Other Categorical Data (Level)
= 1 if Hispanic, = 0 Otherwise National Survey on Drug Use and
Health (NSDUH) Sample
Person
= 1 if Non-Hispanic Black, = 0 Otherwise NSDUH Sample Person
= 1 if Non-Hispanic Other, = 0 Otherwise NSDUH Sample Person
= 1 if Male, = 0 if Female NSDUH Sample Person
= 1 if Metropolitan Statistical Area (MSA) with
≥ 1 Million, = 0 Otherwise
2010 Census County
= 1 if MSA with < 1 Million, = 0 Otherwise 2010 Census County
= 1 if Non-MSA Urban, = 0 Otherwise 2010 Census Tract
= 1 if Urban Area, = 0 if Rural Area 2010 Census Tract
= 1 if No Cubans in Tract, = 0 Otherwise 2010 Census Tract
= 1 if No Arrests for Dangerous Non-Narcotics,
= 0 Otherwise
Uniform Crime Report (UCR) County
= 1 if No Arrests for Opium or Cocaine Possession
= 0 Otherwise
UCR County
= 1 if No Housing Units Built in 1939 or Earlier,
= 0 Otherwise
American Community Survey (ACS) Tract
=1 if No Housing Units Built in 1940 to 1949,
= 0 Otherwise
ACS Tract
= 1 if No Households with Public Assistance Income,
= 0 Otherwise
ACS Tract
Miscellaneous Data (Description) Miscellaneous Data (Source) Miscellaneous Data (Level)
Alcohol Death Rate, Underlying Cause National Center for Health Statistics' International
Classification of Diseases, 10th revision
(NCHS-ICD-10)
County
Cigarette Death Rate, Underlying Cause NCHS-ICD-10 County
Drug Death Rate, Underlying Cause NCHS-ICD-10 County
Alcohol Treatment Rate National Survey of Substance Abuse Treatment
Services (N-SSATS) (Formerly Called Uniform
Facility Data Set [UFDS])
County
Alcohol and Drug Treatment Rate N-SSATS (Formerly Called UFDS) County
Drug Treatment Rate N-SSATS (Formerly Called UFDS) County
Unemployment Rate Bureau of Labor Statistics (BLS) County
Per Capita Income (in Thousands) Bureau of Economic Analysis (BEA) County
Average Suicide Rate (per 10,000) NCHS-ICD-10 County
Food Stamp Participation Rate Census Bureau County
Single State Agency Maintenance of Effort National Association of State Alcohol and Drug
Abuse Directors (NASADAD)
State
Block Grant Awards Substance Abuse and Mental Health Services
Administration (SAMHSA)
State
Cost of Services Factor Index SAMHSA State
Total Taxable Resources per Capita
Index
U.S. Department of Treasury State
% Hispanics Who Are Cuban 2010 Census Tract

B.4 Selection of Independent Variables for the Models

State estimates for past year methamphetamine (METHAMYR) use were not produced in prior years. Hence, in order to be consistent with the other sets of outcomes, the fixed-effect predictors for this outcome were selected using the pooled 2015-2016 NSDUH data. These fixed-effect predictors were selected based on the steps detailed in Section B.4 of the "2015-2016 NSDUH: Guide to State Tables and Summary of Small Area Estimation Methodology" at https://www.samhsa.gov/data/, and their updated versions were used to produce 2016-2017 state estimates for METHAMYR. For all the other outcome variables, no new variable selection was done. The updated versions of fixed-effect predictors that were used in modeling the 2015-2016 data were used to model the 2016-2017 data. Because the interest was to estimate change between the 2015-2016 and 2016-2017 state estimates, the same set of fixed-effect predictors was used for producing both sets of estimates.

B.5 Benchmarking the Age Group-Specific Small Area Estimates

The self-calibration built into the survey-weighted hierarchical Bayes (SWHB) solution ensures that the population-weighted average of the state small area estimates will closely match the national design-based estimates. The national design-based estimates in NSDUH are based entirely on survey-weighted data using a direct estimation approach, whereas the state and census region estimates are model-based. Given the self-calibration ensured by the SWHB method, for state reports prior to 2002, the standard Bayes prescription was followed; specifically, the posterior mean was used for the point estimate, and the tail percentiles of the posterior distribution were used for the Bayesian confidence interval limits.

Singh and Folsom (2001) extended Ghosh's (1992) results on constrained Bayes estimation to include exact benchmarking to design-based national estimates. In the simplest version of this constrained Bayes solution where only the design-based mean is imposed as a benchmarking constraint, each of the 2016-2017 state-by-age group small area estimates is adjusted by adding the common factor Delta sub a is defined as the national design-based estimate, capital D sub a, minus the national model-based small area estimate, capital P sub a. where capital D sub a is the design-based national estimate and capital P sub a is the population-weighted mean of the state small area estimates (capital P sub s and a) for age group-a. The exactly benchmarked state-s and age group-a small area estimates then are given by The benchmarked state-s and age group-a small area estimate, Theta sub s and a, is defined as the sum of capital P sub s and a and Delta sub a. Experience with such additive adjustments suggests that the resulting exactly benchmarked state small area estimates will always be between 0 percent and 100 percent because the SWHB self-calibration ensures that the adjustment factor is small relative to the size of the state-level small area estimates.

Relative to the Bayes posterior mean, these benchmark-constrained state small area estimates are biased by the common additive adjustment factor. Therefore, the posterior mean squared error (MSE) for each benchmarked state small area estimate has the square of this adjustment factor added to its posterior variance. To achieve the desirable feature of exact benchmarking, this constrained Bayes adjustment factor was implemented for the state-by-age group small area estimates. The associated Bayesian confidence (credible) intervals can be recentered at the benchmarked small area estimates on the logit scale with the symmetric interval end points based on the posterior root mean squared errors (RMSEs). The adjusted 95 percent Bayesian confidence intervals (Lower sub s and a is the lower bound of the 95 percent Bayesian confidence interval of Theta sub s and a; upper sub s and a is the upper bound of the 95 confidence interval of Theta sub s and a) are defined as follows:

Equation 5     D

where

Equation 6     D

Equation 7     D     and

Equation 8     D

The associated posterior coverage probabilities for these benchmarked intervals are very close to the prescribed 0.95 value because the state small area estimates have posterior distributions that can be approximated exceptionally well by a Gaussian distribution after the logit transformation.

B.6 Calculation of Estimated Number of Individuals Associated with Each Outcome

Tables 1 to 31 of "2016-2017 NSDUHs: Model-Based Estimated Totals (in Thousands) (50 States and the District of Columbia)" show the estimated numbers of individuals associated with each of the 30 outcomes of interest.24 To calculate these numbers, the benchmarked small area estimates and the associated 95 percent Bayesian confidence intervals are multiplied by the average population across the 2 years (in this case, 2016 and 2017) of the state by the age group of interest.

For example, past month use of alcohol among 18 to 25 year olds in Alabama was 50.18 percent.25 The corresponding Bayesian confidence intervals ranged from 46.18 to 54.17 percent. The population count for 18 to 25 year olds averaged across 2015 and 2016 in Alabama was 515,697 (see Table C.10 in Section C of this methodology document). Hence, the estimated number of 18 to 25 year olds using alcohol in the past month in Alabama was 0.5018 × 515,697, which is 258,777.26 The associated Bayesian confidence intervals ranged from 0.4618 × 515,697 (i.e., 238,149) to 0.5417 × 515,697 (i.e., 279,353). Note that when estimates of the number of individuals are calculated for Tables 1 to 31 in "2016-2017 NSDUHs: Model-Based Estimated Totals (in Thousands) (50 States and the District of Columbia)" (follow the link in footnote 24), the unrounded percentages and population counts are used, then the numbers are reported to the nearest thousand. Hence, the number obtained by multiplying the published estimate with the published population estimate may not exactly match the counts that are published in these tables because of rounding differences.

The only exception to this calculation is the production of the estimated numbers of marijuana initiates. Those estimates cannot be directly calculated as the product of the percentage estimate of first use of marijuana and the population counts available in Section C. That is because the denominator of that percentage estimate is defined as the number of person years at risk for marijuana initiation, which is a combination of individuals who never used marijuana and one half of the individuals who initiated in the past 24 months (see Section B.8 for more details).

B.7 Calculation of Aggregate Age Group Estimates and Limitations

Tables 1 to 31 of "2016-2017 NSDUHs: Model-Based Prevalence Estimates (50 States and the District of Columbia)" show estimates for the following age groups: 12 to 17, 18 to 25, 26 or older, 18 or older, and 12 or older.27 If a user was interested in producing aggregated estimates, such as for those aged 12 to 25, the aggregated estimates could be calculated using prevalence estimates along with the population totals shown in Section C of this document. However, with the information that is provided in the tables, the confidence intervals cannot be calculated. Below is an example of this calculation for a given state.

For example, past month use of alcohol in Alabama among youths 12 to 17 was 8.88 percent, and among young adults 18 to 25 it was 50.18 percent.28 The population counts for 12 to 17 year olds and 18 to 25 year olds averaged across 2016 and 2017 in Alabama were 375,632 and 515,697, respectively (see Table C.10 in Section C of this methodology document). Hence, one would calculate the estimate for individuals aged 12 to 25 by first finding the number of users aged 12 to 25, which is 292,133 ([0.0888 × 375,632] + [0.5018 × 515,697]), then dividing that number by the population aged 12 to 25, which results in a rate of 32.77 percent (292,133 / [375,632 + 515,697]).

B.8 Calculation of Average Annual Initiation of Marijuana Use

Initiation29 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 initiation definition used here employs a simpler form of the at-risk population based on the model-based methodology. This model-based average annual initiation rate is defined as follows:

Equation 9     D

where capital X sub 1 is the number of marijuana initiates in the past 24 months capital X sub 2 and is the number of persons who never used marijuana.

The initiation rate is expressed as a percentage or rate per 100 person-years of exposure. Note that this estimate uses a 2-year time period to accumulate initiation cases from each annual survey. By assuming further that the distribution of first use for the initiation cases is uniform across the 2-year interval, the total number of person-years of exposure is 1 year on average for the initiation cases plus 2 years for all the "never users" at the end of the time period. This approximation to the person-years of exposure permits one to recast the initiation rate as a function of two population prevalence rates, namely, the fraction of persons who first used marijuana in the past 2 years and the fraction who had never used marijuana. Both of these prevalence estimates were estimated using the SWHB estimation approach.

The count of persons who first used marijuana in the past 2 years is based on a "moving" 2-year period that ranges over 3 calendar years. Subjects were asked when they first used marijuana. If a person indicated first use of marijuana between the day of the interview and 2 years prior, the person was included in the count. Thus, it is possible for a person interviewed in the first part of 2017 to indicate first use as early as the first part of 2015 or as late as the first part of 2017. Similarly, a subject interviewed in the last part of 2017 could indicate first use as early as the last part of 2015 or as late as the last part of 2017. Therefore, in the 2017 survey, the reported period of first use ranged from early 2015 to late 2017 and was "centered" in 2016. For example, about half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2016, while a quarter each reported first use in 2015 and 2017. Persons who responded in 2017 that they had never used marijuana were included in the count of "never used." Similarly, reports of first use in the past 24 months from the 2016 survey ranged from early 2014 to late 2016 and were centered in 2015. Half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2015, while a quarter each reported first use in 2014 and 2016. Note that only initiation rates for marijuana use are provided here.

B.9 Underage Drinking

To obtain small area estimates for individuals aged 12 to 20 for past month alcohol and binge alcohol use, a separate set of models was fit for these two outcomes for the 12 to 17 age group and the 18 to 20 age group. Model-based estimates for individuals aged 12 to 20 were produced by taking the population-weighted average of the individual age group (12 to 17 and 18 to 20) estimates. Estimates for underage drinking for past month alcohol and binge alcohol use were benchmarked to match national design-based estimates for that age group using the process described in Section B.5.

B.10 Substance Use Disorder / Needing But Not Receiving Treatment

The NSDUH computer-assisted interviewing (CAI) instrumentation includes questions that are designed to measure dependence or abuse of alcohol and illicit drugs (i.e., SUDs). For these substances,30 dependence and abuse questions were based on the criteria in the DSM-IV (APA, 1994).

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

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

For alcohol, cocaine, heroin, methamphetamine, pain relievers, sedatives, and prescription stimulants, a seventh withdrawal criterion was added. 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). A respondent was defined as having dependence if he or she met three or more of seven dependence criteria for these substances.

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 (i.e., because dependence takes precedence over abuse):

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

For additional details on how respondents were classified as having substance use disorder, see Section B.4.3 in Section B of the 2017 NSDUH methodological summary and definitions report (CBHSQ, 2018).

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 substance use treatment in the past year if they met either of the following criteria:

  1. presence of an SUD in the past year for alcohol or illicit drugs (i.e., dependence or abuse) (see Section B.4.3 in Section B of CBHSQ, 2018); or
  2. receipt of treatment at a specialty facility (i.e., drug and alcohol rehabilitation facility [inpatient or outpatient], hospital [inpatient only], or mental health center) in the past year for the use of alcohol or illicit drugs (or both).

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.

For additional details on how respondents were classified as needing substance use treatment, see Section B.4.4 in Section B of the 2017 NSDUH methodological summary and definitions report (CBHSQ, 2018).

B.11 Mental Health Measures

This section provides a summary of the measurement issues associated with three of the mental health outcome variables—SMI, AMI, and MDE. Additional details can be found in Sections B.4.6 through B.4.8 in Section B of the 2017 NSDUH methodological summary and definitions report (CBHSQ, 2018).

B.11.1 Mental Illness

In the 2000-2001 and 2002-2003 NSDUH state SAE reports (Wright, 2003a, 2003b; Wright & Sathe, 2005), the Kessler-6 (K6) distress scale was used to measure SMI (Kessler et al., 2003). However, SAMHSA discontinued producing state-level SMI estimates beginning with the release of the 2003-2004 state report (Wright & Sathe, 2006) because of concerns about the validity of using only the K6 distress scale without an impairment scale; see Section B.4.4 in Appendix B of the 2004 NSDUH national findings report (OAS, 2005). The use of the K6 distress scale continued in the 2003-2004 and the 2004-2005 state reports (Wright & Sathe, 2006; Wright et al., 2007), not as a measure of SMI, but as a measure of serious psychological distress (SPD) because it was determined that the K6 scale measured only SPD and merely contributed to measuring SMI and AMI (see the details that follow).

In December 2006, a new technical advisory group was convened by SAMHSA's OAS (which later became CBHSQ) and the Center for Mental Health Services (CMHS) to solicit recommendations for data collection strategies to address SAMHSA's legislative requirements. Although it was recognized that the ideal way to estimate SMI in NSDUH would be to administer a clinical diagnostic interview annually to all 45,000 adult respondents, this approach was not feasible because of constraints on the interview time and the need for trained mental health clinicians to conduct the interviews. Therefore, the approach recommended by the technical advisory group and adopted by SAMHSA for NSDUH was to utilize short scales in the NSDUH interview that separately measure psychological distress and functional impairment for use in a statistical model that predicts whether a respondent had mental illness.

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 SMI. Based on recommendations from this panel, estimates of SMI were presented based on a revised methodology and, thus, were not comparable with estimates for SMI or SPD shown in NSDUH state reports prior to 2009. However, in 2013, another revision to the methodology for creating SMI estimates was made, and the estimates presented for 2011 and 2012 are based on this revised methodology (and therefore are not comparable with previously published estimates of SMI). Thus, the 2008-2009, 2009-2010, and 2010-2011 SMI estimates were reproduced using the new 2013 methodology.

To develop methods for preparing the estimates of SMI and AMI presented in this and other NSDUH reports and documents, the MHSS was initiated as part of the 2008 NSDUH design and analysis. Because of constraints on the interview time in NSDUH and the need for trained mental health clinicians, it was not possible to administer a full structured diagnostic clinical interview to assess mental illness on approximately 45,000 adult respondents; therefore, the approach adopted by SAMHSA was to utilize short scales separately measuring psychological distress (K6) and functional impairment that could be used in a statistical model to accurately predict whether a respondent had a mental illness. Two impairment scales—the World Health Organization Disability Assessment Schedule (WHODAS) and the Sheehan Disability Scale (SDS)—were included in the 2008 survey for evaluation. The collection of clinical psychiatric interview data was achieved using a subsample of approximately 1,500 adult NSDUH participants in 2008. These participants were recruited for a follow-up clinical interview consisting of a gold-standard diagnostic assessment for mental disorders and functional impairment. In order to determine the optimal scale to measure functional impairment, a split-sample design was incorporated into the full 2008 NSDUH data collection in which half of the adult respondents received the WHODAS and half received the SDS (only the WHODAS scale was used starting in 2009). The 2008 statistical models (subsequently referred to as the "2008 model") using the data from the subsample of respondents collected as part of the MHSS then were developed for each half sample in which the short scales (the K6 in combination with the WHODAS or the K6 in combination with the SDS) were used as predictors in models of mental illness assessed via the clinical interviews. The model parameter estimates then were used to predict SMI in the full 2008 NSDUH sample. SMI probabilities and SMI predicted values (as well as for AMI) were computed for respondents in NSDUH samples from 2008 to 2011 using model parameter estimates from the 2008 model.

In 2010, SAMHSA began preliminary investigations to assess whether improvements to the model were warranted using all of the clinical data that had been collected since 2008. In 2011 and 2012, the clinical sample was augmented to include 1,500 respondents per year, leading to a combined sample of approximately 5,000 clinical interviews for 2008 to 2012. SAMHSA determined that the 2008 model had some important shortcomings that had not been detected in the original model fitting because of the small number of respondents in the 2008 clinical subsample. Specifically, the 2008 model substantially overestimated SMI and AMI among young adults aged 18 to 25 relative to the clinical interview data. In addition, improvements were needed in the weighting procedures for the MHSS sample data to account better for nonresponse and undercoverage. Therefore, SAMHSA decided to modify the model for the 2012 estimates using the combined 2008-2012 clinical data (subsequently referred to as the "2012 model"). To reduce bias and improve prediction, additional mental health-related variables and an age variable were added in the 2012 model. To provide consistent data for trend assessment, state mental illness estimates for 2008-2009, 2009-2010, and 2010-2011 were also recomputed using the new 2012 model. Note that tables or maps showing estimates of AMI and SMI based on these 2012 models include "Revised October 2013" in the source line for estimates using 2008 through 2011 data.

The next few paragraphs describe the instruments and items used to measure the variables employed in the 2012 model. Specifically, the instrument used to measure mental illness in the clinical interviews is described, followed by descriptions of the scales and items in the main NSDUH interviews that were used as predictor variables in the model (e.g., the K6 and WHODAS total scores, age, MDE, and suicidal thoughts).

Clinical Measurement of Mental Illness. Mental illness was measured in the MHSS clinical interviews using an adapted version of the Structured Clinical Interview for the DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition (SCID-I/NP) (First, Spitzer, Gibbon, & Williams, 2002) and was differentiated by the level of functional impairment based on the Global Assessment of Functioning (GAF) scale (Endicott, Spitzer, Fleiss, & Cohen, 1976). Past year disorders that were assessed through the SCID included mood disorders (e.g., MDE, manic episode), anxiety disorders (e.g., panic disorder, generalized anxiety disorder, posttraumatic stress disorder), eating disorders (e.g., anorexia nervosa), intermittent explosive disorder, and adjustment disorder. In addition, the presence of psychotic symptoms was assessed. SUDs also were assessed, although these disorders were not used to produce estimates of mental illness.

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

Kessler-6 (K6) Distress Scale. The K6 in the main 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:

NERVE30
During the past 30 days, how often did you feel nervous?

1 All of the time
2 Most of the time
3 Some of the time
4 A little of the time
5 None of the time
Don't know/Refused

Response categories are the same for the remaining questions shown below.

HOPE30
During the past 30 days, how often did you feel hopeless?

FIDG30
During the past 30 days, how often did you feel restless or fidgety?

NOCHR30
During the past 30 days, how often did you feel so sad or depressed that nothing could cheer you up?

EFFORT30
During the past 30 days, how often did you feel that everything was an effort?

DOWN30
During the past 30 days, how often did you feel down on yourself, no good or worthless?

To create a score, the six items (NERVE30, HOPE30, FIDG30, NOCHR30, EFFORT30, and DOWN30) on the K6 scale were recoded from 0 to 4 so that "all of the time" was coded 4, "most of the time" 3, "some of the time" 2, "a little of the time" 1, and "none of the time" 0, with "don't know" and "refused" also coded as 0. Summing across the transformed responses in these six items resulted in a score with a range from 0 to 24.

If respondents were asked about a month in the past 12 months when they felt more depressed, anxious, or emotionally stressed than they felt during the past 30 days, they were asked comparable K6 items for that particular month in the past 12 months. The scoring procedures for these K6 items for the past 12 months were the same as those described above. The higher of the two K6 total scores for the past 30 days or past 12 months was used both for MHSS analysis purposes and in the adult respondents' final data.

An alternative K6 total score also was created in which K6 scores of less than 8 were recoded as 0 and scores from 8 to 24 were recoded as 1 to 17. The rationale for creating the alternative past year K6 score was that SMI prevalence was typically extremely low for respondents with past year K6 scores of less than 8, and the prevalence rates started increasing only when scores were 8 or greater. The alternative K6 score was used in both the 2008 and 2012 SMI prediction models.

WHODAS. An initial step of the MHSS was to modify the WHODAS for use in a general population survey, including making minor changes to question wording and reducing its length (Novak, 2007). That is, a subset of 8 items was found to capture the information represented in the full 16-item scale with no significant loss of information.

These eight WHODAS items that were included in the main NSDUH interview were assessed on a 0 to 3 scale, with responses of "no difficulty," "don't know," and "refused" coded as 0; "mild difficulty" coded as 1; "moderate difficulty" coded as 2; and "severe difficulty" coded as 3. Some items had an additional category for respondents who did not engage in a particular activity (e.g., they did not leave the house on their own). Respondents who reported that they did not engage in an activity were asked a follow-up question to determine if they did not do so because of emotions, nerves, or mental health. Those who answered "yes" to this follow-up question were subsequently assigned to the "severe difficulty" category; otherwise (i.e., for responses of "no," "don't know," or "refused"), they were assigned to the "no difficulty" category. Summing across these codes for the eight responses resulted in a total score with a range from 0 to 24. More information about scoring of the WHODAS can be found in the 2015 NSDUH public use file codebook (CBHSQ, 2016d).

An alternative WHODAS total score was created in which individual WHODAS item scores of less than 2 were recoded as 0, and item scores of 2 to 3 were recoded as 1. The individual alternative item scores then were summed to yield a total alternative score ranging from 0 to 8. Creation of an alternative version of the WHODAS score was based on the assumption that a dichotomous measure dividing respondents into two groups (i.e., severely impaired vs. less severely impaired) might fit better than a linear continuous measure in models predicting SMI. This alternative WHODAS score was the variable used in both the 2008 and 2012 SMI prediction models.

Suicidal Thoughts, MDE, and Age. In addition to the K6 and WHODAS scales, the 2012 model included the following measures as predictors of SMI: (a) serious thoughts of suicide in the past year; (b) having a past year MDE; and (c) age. The first two variables were added to the model to decrease the error rate in the predictions (i.e., the sum of the false-negative and false-positive rates relative to the clinical interview results). A recoded age variable reduced the biases in estimates for particular age groups, especially 18 to 25 year olds.

Since 2008, all adult respondents in NSDUH have been asked the following question: "At any time in the past 12 months, that is from [DATEFILL] up to and including today, did you seriously think about killing yourself?"31 Definitions for MDE in the lifetime and past year periods are discussed in Section B.11.2 of this document. For respondents aged 18 to 30, an adjusted age was created by subtracting 18 from the respondent's current age, resulting in values ranging from 0 to 12. For a respondent aged 18, for example, the adjusted age was 0 (i.e., 18 minus 18), and for a respondent aged 30, the adjusted age was 12 (i.e., 30 minus 18). For respondents aged 31 or older, the adjusted age was assigned a value of 12.

The 2012 SMI Model. The 2012 SMI prediction model was fit with data from 4,912 WHODAS MHSS respondents from 2008 through 2012. The response variable Y equaled 1 when an SMI diagnosis was positive based on the clinical interview; otherwise, Y was 0. Letting X be a vector of characteristics attached to a NSDUH respondent and letting the probability that this respondent had SMI be Pi equals the probability of capital Y equals 1 given capital X, where capital X is the vector of explanatory variables,, the 2012 SMI prediction model was

Equation 10     D

where pi hat refers to an estimate of the SMI response probability pi.

These covariates in equation (1) came from the main NSDUH interview data:

As with the 2008 model, a cut point probability pi sub zero was determined, so that if Pi hat is greater than or equal to pi sub zero. for a particular respondent, then he or she was predicted to be SMI positive; otherwise, he or she was predicted to be SMI negative. The cut point (0.260573529) was chosen so that the weighted number of false positives and false negatives in the MHSS dataset were as close to equal as possible. The predicted SMI status for all adult NSDUH respondents was used to compute SMI small area estimates.

A second cut point probability (0.0192519810) was determined so that respondents with an SMI probability greater than or equal to the cut point were predicted to be positive for AMI, and the remainder were predicted to be negative for AMI. The second cut point was chosen so that the weighted numbers of AMI false positives and false negatives were as close to equal as possible.

B.11.2 Major Depressive Episode (Depression)

According to the DSM-5, 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 (except where noted) 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, 2013). These symptoms are as follows: (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 at a level that is observable by others; (6) fatigue or loss of energy; (7) feelings of worthlessness or excessive or inappropriate guilt; (8) diminished ability to think or concentrate or indecisiveness; and (9) recurrent thoughts of death or suicidal ideation (i.e., recurrent suicidal ideation without a specific plan, making a specific plan, or making an attempt). Unlike the other symptoms listed previously, recurrent thoughts of death or suicidality did not need to have occurred nearly every day. Respondents who have had an 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. Respondents reporting experiences consistent with them having had an MDE in the past year are asked questions from the SDS to measure the level of functional impairment in major life activities reported to be caused by the MDE in the past 12 months (Leon et al., 1997).

Beginning in 2004, sections related to MDE were included in the questionnaire. These sections, which were originally derived from DSM-IV (APA, 1994) criteria for MDE, contain questions that did not change for the 2017 NSDUH questionnaire. Consistent with the more recent DSM-5 criteria (APA, 2013), NSDUH does not exclude MDEs that occurred exclusively in the context of bereavement. 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 Replication Adolescent Supplement (NCS-A) (see https://www.hcp.med.harvard.edu/ncs/ exit icon). To make the sections developmentally appropriate for youths, there are minor wording differences in a few questions between the adult and youth modules. Revisions to the questions in both modules were made primarily to reduce the length and to modify the NCS questions, which are interviewer-administered, to the audio computer-assisted self-interviewing (ACASI) format used in NSDUH. In addition, some revisions, based on cognitive testing, were made to improve comprehension.

Since 2004, the NSDUH questions that determine MDE have remained unchanged for both adults and youths. In the 2008 questionnaire, however, changes were made in other mental health items that precede the MDE questions for adults (K6, suicide, and impairment). Questions also were retained in 2009 for the WHODAS impairment scale, and the questions for the SDS impairment scale were deleted; see Sections B.4.6 and B.4.7 in Section B of the 2017 NSDUH methodological summary and definitions (CBHSQ, 2018) for further details about these questionnaire changes. The questionnaire changes in 2008 appear to have affected the reporting on MDE questions among adults.

Because the WHODAS was selected to be used in the 2009 and subsequent surveys, model-based adjustments were applied to MDE estimates from the SDS half sample in 2008 to remove the context effect differential between the two half samples. Additionally, model-based adjustments were made to the 2005, 2006, and 2007 adult MDE estimates to make them comparable with the 2008 through 2012 MDE estimates (for more information on these adjustments, see CBHSQ, 2012). Thus, the 2008-2009 estimates of MDE were produced using the adjusted 2008 MDE variable along with the unadjusted 2009 MDE variable. Revised estimates for 2005-2006, 2006-2007, and 2007-2008 were produced using the adjusted MDE variable.

In addition, changes to the youth mental health service utilization section questions in 2009 that preceded the questions about adolescent depression could have affected adolescents' responses to the adolescent depression questions and estimates of adolescent MDE. However, these changes in 2009 did not appear to affect the estimates of adolescent MDE. Therefore, data on trends in past year MDE from 2004 to 2017 are available for adolescents aged 12 to 17.

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

Table C.1 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Individuals Aged 12 or Older: 2015
State Total
Selected DUs
Total
Eligible
DUs
Total
Completed
Screeners
Weighted DU
Screening
Response Rate
Total
Selected
Total
Responded
Population
Estimate
Weighted
Interview
Response
Rate
Weighted
Overall
Response
Rate
Total U.S. 197,962 165,328 132,210 79.69% 94,499 68,073 267,694,489 69.25% 55.19%
Northeast 44,157 37,292 28,065 73.23% 18,988 13,026 47,810,262 65.61% 48.04%
Midwest 46,269 38,853 32,108 81.52% 22,352 15,890 56,662,334 68.39% 55.75%
South 64,177 52,861 43,064 82.87% 30,920 22,768 100,182,409 70.93% 58.78%
West 43,359 36,322 28,973 77.73% 22,239 16,389 63,039,483 70.09% 54.48%
Alabama 2,797 2,185 1,831 83.26% 1,328 953 4,056,416 67.99% 56.61%
Alaska 3,289 2,381 1,892 79.18% 1,373 981 581,652 71.59% 56.68%
Arizona 3,022 2,314 1,949 84.15% 1,363 996 5,645,911 70.73% 59.52%
Arkansas 2,875 2,344 2,005 85.49% 1,343 981 2,457,367 68.96% 58.95%
California 11,282 10,153 7,564 73.80% 6,445 4,671 32,556,837 68.69% 50.69%
Colorado 2,637 2,240 1,795 80.03% 1,328 994 4,526,726 72.42% 57.96%
Connecticut 2,872 2,518 1,936 76.95% 1,411 964 3,058,139 66.21% 50.94%
Delaware 2,701 2,339 1,756 75.03% 1,323 945 795,351 71.21% 53.43%
District of Columbia 5,177 4,341 3,118 71.43% 1,231 924 574,552 74.47% 53.19%
Florida 10,530 8,387 6,793 80.63% 4,665 3,386 17,257,952 70.07% 56.50%
Georgia 4,015 3,307 2,603 78.78% 1,992 1,498 8,359,362 71.79% 56.56%
Hawaii 3,139 2,630 1,959 74.23% 1,389 1,020 1,158,550 70.76% 52.53%
Idaho 2,020 1,813 1,530 84.44% 1,277 949 1,347,084 72.78% 61.46%
Illinois 7,103 6,286 4,639 73.92% 3,592 2,365 10,737,272 63.14% 46.67%
Indiana 2,729 2,292 1,819 79.34% 1,376 973 5,486,199 68.00% 53.95%
Iowa 3,068 2,668 2,265 84.66% 1,357 962 2,597,548 68.53% 58.02%
Kansas 2,640 2,283 1,962 85.92% 1,351 986 2,367,256 71.42% 61.37%
Kentucky 2,469 2,000 1,695 84.66% 1,271 938 3,667,827 72.06% 61.01%
Louisiana 2,618 2,170 1,804 83.66% 1,282 957 3,819,762 73.03% 61.10%
Maine 4,277 3,140 2,643 84.00% 1,400 994 1,151,684 68.79% 57.78%
Maryland 2,308 2,018 1,513 75.20% 1,290 946 5,018,659 69.83% 52.52%
Massachusetts 3,366 2,960 2,131 72.27% 1,591 948 5,822,667 57.99% 41.91%
Michigan 7,166 5,787 4,853 83.66% 3,383 2,441 8,392,983 69.43% 58.08%
Minnesota 2,490 2,149 1,766 82.05% 1,286 951 4,575,592 73.16% 60.02%
Mississippi 2,554 2,060 1,741 84.80% 1,257 921 2,443,849 70.17% 59.51%
Missouri 2,582 2,094 1,846 88.22% 1,342 986 5,057,574 70.25% 61.98%
Montana 3,195 2,528 2,159 85.62% 1,329 977 866,257 69.44% 59.45%
Nebraska 2,510 2,156 1,794 82.82% 1,301 945 1,548,885 71.21% 58.97%
Nevada 2,676 2,287 1,746 76.61% 1,317 997 2,408,267 69.97% 53.60%
New Hampshire 3,324 2,763 2,191 79.00% 1,435 995 1,148,726 68.23% 53.90%
New Jersey 4,076 3,647 2,807 75.90% 2,247 1,517 7,552,211 65.39% 49.63%
New Mexico 2,568 1,853 1,644 88.94% 1,260 959 1,717,549 73.85% 65.68%
New York 12,117 10,496 6,863 64.83% 4,963 3,310 16,779,910 63.60% 41.23%
North Carolina 4,251 3,606 2,990 82.87% 2,125 1,576 8,320,518 69.99% 58.00%
North Dakota 3,425 2,758 2,484 89.86% 1,342 988 618,680 72.44% 65.09%
Ohio 7,032 5,899 4,773 80.86% 3,458 2,428 9,732,558 68.48% 55.38%
Oklahoma 2,857 2,285 1,918 84.37% 1,359 971 3,185,569 67.59% 57.02%
Oregon 2,526 2,195 1,803 82.11% 1,333 962 3,420,080 71.04% 58.33%
Pennsylvania 7,429 6,257 5,054 80.80% 3,232 2,374 10,849,493 71.72% 57.95%
Rhode Island 2,901 2,461 1,915 77.81% 1,354 964 903,886 69.45% 54.04%
South Carolina 2,944 2,436 2,040 83.70% 1,304 987 4,070,523 72.52% 60.70%
South Dakota 2,354 1,968 1,799 91.69% 1,199 904 695,959 74.77% 68.56%
Tennessee 2,670 2,172 1,846 84.96% 1,352 1,004 5,507,975 69.71% 59.22%
Texas 6,227 5,184 4,538 87.56% 4,358 3,308 22,151,524 73.28% 64.16%
Utah 1,506 1,316 1,176 89.31% 1,204 968 2,350,775 77.43% 69.16%
Vermont 3,795 3,050 2,525 82.82% 1,355 960 543,548 68.96% 57.11%
Virginia 3,934 3,410 2,754 80.78% 2,113 1,526 6,928,628 69.71% 56.32%
Washington 2,692 2,423 1,867 76.82% 1,306 944 5,978,195 69.98% 53.76%
West Virginia 3,250 2,617 2,119 80.92% 1,327 947 1,566,577 66.77% 54.03%
Wisconsin 3,170 2,513 2,108 84.08% 1,365 961 4,851,828 68.35% 57.47%
Wyoming 2,807 2,189 1,889 86.02% 1,315 971 481,602 72.26% 62.16%
DU = dwelling unit.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2015.
Table C.2 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2015
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
Total U.S. 21,859 16,955 24,893,417 77.66% 23,211 17,215 34,907,162 74.45% 49,429 33,903 207,893,910 67.36%
Northeast 4,308 3,228 4,124,414 72.98% 4,651 3,233 6,117,578 68.66% 10,029 6,565 37,568,270 64.28%
Midwest 5,296 3,955 5,351,313 73.95% 5,509 4,106 7,415,255 74.10% 11,547 7,829 43,895,766 66.73%
South 7,267 5,767 9,483,323 79.64% 7,496 5,676 12,959,382 76.41% 16,157 11,325 77,739,704 68.96%
West 4,988 4,005 5,934,367 81.09% 5,555 4,200 8,414,946 75.99% 11,696 8,184 48,690,170 67.73%
Alabama 289 229 380,027 78.20% 338 251 527,315 74.78% 701 473 3,149,075 65.56%
Alaska 322 227 58,808 69.67% 331 247 82,845 73.61% 720 507 439,999 71.46%
Arizona 296 239 547,813 80.67% 324 248 745,197 76.07% 743 509 4,352,901 68.60%
Arkansas 323 256 236,353 77.64% 329 245 318,810 74.57% 691 480 1,902,203 66.87%
California 1,411 1,148 3,044,310 80.84% 1,603 1,224 4,441,882 76.89% 3,431 2,299 25,070,645 65.77%
Colorado 320 269 419,211 84.39% 327 241 593,941 73.82% 681 484 3,513,574 70.56%
Connecticut 305 241 281,090 79.35% 347 227 387,506 64.40% 759 496 2,389,542 64.87%
Delaware 302 238 68,905 79.72% 325 221 98,641 67.69% 696 486 627,805 70.81%
District of Columbia 264 210 30,686 80.79% 257 190 94,114 73.72% 710 524 449,752 74.18%
Florida 1,072 844 1,406,795 78.55% 1,159 889 1,981,426 77.16% 2,434 1,653 13,869,730 68.21%
Georgia 524 420 851,391 80.68% 447 358 1,116,369 79.67% 1,021 720 6,391,602 69.17%
Hawaii 286 226 97,117 75.80% 360 275 139,707 76.77% 743 519 921,726 69.35%
Idaho 281 220 145,770 80.39% 346 260 174,661 76.34% 650 469 1,026,653 71.02%
Illinois 887 648 1,018,545 72.96% 809 561 1,382,295 68.56% 1,896 1,156 8,336,432 61.04%
Indiana 316 242 540,488 73.99% 352 256 743,142 73.45% 708 475 4,202,568 66.29%
Iowa 346 253 243,085 73.21% 346 249 358,657 72.25% 665 460 1,995,806 67.26%
Kansas 347 251 237,829 71.04% 296 242 329,951 83.24% 708 493 1,799,476 69.27%
Kentucky 296 232 339,561 77.14% 297 224 471,843 75.59% 678 482 2,856,423 70.90%
Louisiana 311 244 367,609 79.34% 319 233 509,882 73.11% 652 480 2,942,271 72.13%
Maine 382 293 91,980 75.70% 309 217 125,074 69.44% 709 484 934,630 67.99%
Maryland 307 238 453,696 78.67% 326 247 622,611 75.45% 657 461 3,942,353 68.06%
Massachusetts 337 228 487,806 67.52% 375 221 791,046 57.80% 879 499 4,543,815 56.96%
Michigan 798 601 784,266 74.15% 847 653 1,112,424 77.93% 1,738 1,187 6,496,293 67.36%
Minnesota 319 247 426,424 76.74% 304 230 571,849 77.88% 663 474 3,577,318 71.96%
Mississippi 287 231 244,034 81.89% 289 226 335,131 77.47% 681 464 1,864,684 67.41%
Missouri 308 244 470,294 77.78% 384 293 655,956 76.45% 650 449 3,931,325 68.27%
Montana 300 230 74,532 77.20% 302 229 111,838 73.93% 727 518 679,888 67.95%
Nebraska 289 220 152,144 76.73% 338 248 212,640 71.16% 674 477 1,184,101 70.52%
Nevada 324 271 223,603 84.13% 334 254 288,923 75.66% 659 472 1,895,740 67.17%
New Hampshire 322 238 97,633 75.02% 325 235 143,062 74.78% 788 522 908,031 66.49%
New Jersey 527 387 695,324 72.89% 588 411 894,807 69.65% 1,132 719 5,962,081 63.92%
New Mexico 255 215 164,982 84.38% 304 237 226,226 78.86% 701 507 1,326,341 71.89%
New York 1,065 766 1,421,217 69.93% 1,302 909 2,218,443 67.76% 2,596 1,635 13,140,250 62.15%
North Carolina 539 438 780,506 82.17% 515 397 1,065,839 77.39% 1,071 741 6,474,173 67.38%
North Dakota 318 231 52,164 71.69% 328 259 104,459 77.80% 696 498 462,057 71.27%
Ohio 803 589 914,823 72.84% 827 599 1,225,255 73.19% 1,828 1,240 7,592,481 67.22%
Oklahoma 349 260 313,866 75.40% 289 215 431,841 71.97% 721 496 2,439,862 65.76%
Oregon 281 214 291,606 77.27% 335 244 415,899 72.61% 717 504 2,712,575 70.12%
Pennsylvania 742 574 931,284 77.42% 794 596 1,354,815 76.16% 1,696 1,204 8,563,393 70.38%
Rhode Island 286 228 74,717 79.60% 332 235 128,339 71.08% 736 501 700,830 68.02%
South Carolina 344 282 366,745 82.77% 274 219 519,107 79.59% 686 486 3,184,672 70.29%
South Dakota 300 230 65,584 77.20% 297 233 93,003 77.41% 602 441 537,373 73.96%
Tennessee 295 230 508,351 77.48% 414 318 703,173 74.53% 643 456 4,296,451 67.99%
Texas 959 780 2,380,293 80.39% 1,085 849 3,080,905 78.32% 2,314 1,679 16,690,326 71.33%
Utah 299 262 292,037 88.19% 308 250 383,514 81.11% 597 456 1,675,224 74.73%
Vermont 342 273 43,364 79.72% 279 182 74,485 66.68% 734 505 425,699 68.21%
Virginia 490 392 625,315 79.95% 504 357 895,251 70.76% 1,119 777 5,408,062 68.32%
Washington 285 227 530,641 79.31% 350 250 747,302 71.32% 671 467 4,700,252 68.75%
West Virginia 316 243 129,191 78.60% 329 237 187,125 73.58% 682 467 1,250,260 64.34%
Wisconsin 265 199 445,668 72.18% 381 283 625,624 72.36% 719 479 3,780,537 67.14%
Wyoming 328 257 43,939 77.94% 331 241 63,010 74.06% 656 473 374,652 71.28%
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2015.
Table C.3 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Individuals Aged 12 or Older: 2016
State Total
Selected DUs
Total
Eligible
DUs
Total
Completed
Screeners
Weighted DU
Screening
Response Rate
Total
Selected
Total
Responded
Population
Estimate
Weighted
Interview
Response
Rate
Weighted
Overall
Response
Rate
Total U.S. 205,589 173,149 135,188 77.88% 95,607 67,942 269,430,135 68.44% 53.30%
Northeast 45,388 38,488 28,275 71.60% 18,782 12,711 47,797,488 64.63% 46.28%
Midwest 46,850 39,972 32,231 79.66% 22,649 16,023 56,744,903 68.00% 54.17%
South 67,261 56,067 44,353 80.59% 31,462 22,833 101,241,206 70.62% 56.91%
West 46,090 38,622 30,329 76.52% 22,714 16,375 63,646,539 68.21% 52.20%
Alabama 2,996 2,478 2,026 82.04% 1,392 983 4,064,691 66.70% 54.72%
Alaska 3,272 2,386 1,901 79.52% 1,325 960 585,025 69.03% 54.90%
Arizona 2,921 2,203 1,835 83.43% 1,313 982 5,742,769 74.79% 62.39%
Arkansas 3,036 2,503 2,041 81.73% 1,381 992 2,468,292 69.49% 56.80%
California 12,192 11,070 7,993 72.01% 6,720 4,619 32,689,876 65.40% 47.10%
Colorado 2,570 2,163 1,757 80.69% 1,324 920 4,612,005 67.04% 54.10%
Connecticut 2,980 2,559 1,931 75.41% 1,392 937 3,052,524 65.01% 49.03%
Delaware 2,953 2,459 1,880 76.98% 1,330 928 802,361 67.70% 52.12%
District of Columbia 5,940 5,119 3,401 65.20% 1,260 967 580,859 74.11% 48.32%
Florida 11,282 9,267 7,135 77.11% 4,794 3,435 17,554,248 68.22% 52.60%
Georgia 3,619 3,139 2,443 77.88% 1,998 1,508 8,462,591 71.10% 55.37%
Hawaii 3,949 3,329 2,478 73.74% 1,458 1,004 1,157,906 66.33% 48.91%
Idaho 2,653 2,151 1,842 85.77% 1,429 1,088 1,373,371 74.13% 63.59%
Illinois 7,222 6,310 4,501 71.35% 3,789 2,467 10,702,668 61.81% 44.10%
Indiana 2,560 2,149 1,665 77.38% 1,286 933 5,503,158 69.65% 53.90%
Iowa 2,893 2,461 2,076 84.27% 1,414 1,028 2,607,021 71.71% 60.43%
Kansas 2,522 2,204 1,848 83.82% 1,363 996 2,369,503 71.16% 59.64%
Kentucky 3,162 2,586 2,104 81.27% 1,445 953 3,684,220 62.76% 51.00%
Louisiana 2,946 2,381 1,934 81.24% 1,328 959 3,831,309 70.61% 57.37%
Maine 3,941 3,022 2,473 82.01% 1,394 992 1,154,268 71.53% 58.66%
Maryland 2,418 2,120 1,550 72.57% 1,317 990 5,027,075 73.23% 53.14%
Massachusetts 3,700 3,252 2,365 72.42% 1,596 988 5,849,205 61.77% 44.73%
Michigan 7,090 5,893 4,809 81.40% 3,311 2,420 8,406,442 70.59% 57.46%
Minnesota 2,596 2,278 1,855 81.33% 1,375 962 4,605,050 68.58% 55.78%
Mississippi 2,382 1,949 1,617 83.00% 1,283 934 2,447,209 71.09% 59.00%
Missouri 2,612 2,247 1,926 85.56% 1,334 938 5,069,324 66.20% 56.65%
Montana 3,217 2,602 2,247 86.51% 1,433 1,018 874,320 71.23% 61.62%
Nebraska 2,696 2,350 1,881 80.01% 1,364 964 1,557,938 68.95% 55.16%
Nevada 2,379 2,095 1,526 72.71% 1,268 966 2,448,780 72.48% 52.70%
New Hampshire 3,244 2,763 2,148 77.51% 1,355 936 1,153,236 67.19% 52.08%
New Jersey 4,370 3,866 2,791 71.09% 2,149 1,433 7,550,513 63.19% 44.92%
New Mexico 2,907 2,023 1,720 84.86% 1,215 980 1,719,897 79.43% 67.41%
New York 12,398 10,716 6,932 63.92% 4,934 3,232 16,748,367 61.44% 39.27%
North Carolina 4,122 3,470 2,832 81.56% 2,089 1,508 8,419,860 71.49% 58.31%
North Dakota 3,511 2,882 2,521 87.70% 1,344 960 617,001 69.08% 60.58%
Ohio 6,804 5,933 4,700 79.21% 3,363 2,377 9,738,448 67.60% 53.55%
Oklahoma 2,654 2,198 1,794 81.39% 1,374 965 3,198,970 68.24% 55.54%
Oregon 3,160 2,765 2,224 80.46% 1,391 1,004 3,478,192 71.05% 57.17%
Pennsylvania 7,825 6,665 5,277 79.17% 3,308 2,360 10,840,710 70.48% 55.80%
Rhode Island 3,072 2,653 2,043 77.12% 1,356 937 905,791 67.37% 51.96%
South Carolina 2,832 2,251 1,849 81.99% 1,326 970 4,133,914 72.46% 59.41%
South Dakota 2,813 2,338 2,037 86.96% 1,338 960 701,645 70.92% 61.67%
Tennessee 3,034 2,416 2,002 82.87% 1,373 993 5,556,863 70.57% 58.48%
Texas 6,793 5,725 4,877 84.53% 4,255 3,293 22,490,422 74.68% 63.13%
Utah 1,483 1,331 1,138 85.78% 1,215 936 2,403,330 74.82% 64.18%
Vermont 3,858 2,992 2,315 77.15% 1,298 896 542,875 71.09% 54.85%
Virginia 3,920 3,376 2,743 81.20% 2,077 1,493 6,961,461 68.86% 55.91%
Washington 2,779 2,421 1,911 78.99% 1,362 934 6,080,095 66.41% 52.45%
West Virginia 3,172 2,630 2,125 80.79% 1,440 962 1,556,861 63.87% 51.60%
Wisconsin 3,531 2,927 2,412 82.32% 1,368 1,018 4,866,705 73.22% 60.27%
Wyoming 2,608 2,083 1,757 84.46% 1,261 964 480,973 75.14% 63.46%
DU = dwelling unit.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2016.
Table C.4 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2016
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
Total U.S. 22,323 17,109 24,896,527 76.95% 22,836 16,573 34,570,728 72.66% 50,448 34,260 209,962,880 66.74%
Northeast 4,417 3,193 4,097,263 70.97% 4,459 3,059 6,052,258 67.96% 9,906 6,459 37,647,967 63.41%
Midwest 5,355 4,105 5,326,597 76.54% 5,444 3,896 7,367,324 71.62% 11,850 8,022 44,050,981 66.38%
South 7,219 5,625 9,530,368 78.55% 7,519 5,623 12,828,550 75.69% 16,724 11,585 78,882,288 68.85%
West 5,332 4,186 5,942,298 78.87% 5,414 3,995 8,322,597 72.31% 11,968 8,194 49,381,644 66.19%
Alabama 304 234 376,632 79.57% 313 243 518,185 76.56% 775 506 3,169,874 63.84%
Alaska 317 236 59,359 75.48% 362 276 77,379 76.69% 646 448 448,287 66.89%
Arizona 316 234 549,195 75.37% 317 237 747,345 74.16% 680 511 4,446,229 74.82%
Arkansas 307 235 236,955 78.47% 347 260 317,177 73.69% 727 497 1,914,160 67.68%
California 1,509 1,187 3,034,119 79.22% 1,517 1,092 4,358,028 71.70% 3,694 2,340 25,297,729 62.56%
Colorado 307 243 423,725 78.45% 303 212 599,128 68.58% 714 465 3,589,152 65.26%
Connecticut 303 224 278,000 75.81% 366 251 388,847 68.19% 723 462 2,385,677 63.36%
Delaware 288 217 69,423 77.17% 344 245 95,867 71.38% 698 466 637,071 66.16%
District of Columbia 292 240 30,940 82.15% 327 251 93,288 76.72% 641 476 456,632 72.98%
Florida 1,107 859 1,404,808 77.61% 1,031 793 1,961,863 76.96% 2,656 1,783 14,187,577 66.26%
Georgia 461 370 859,100 78.55% 432 352 1,107,792 80.49% 1,105 786 6,495,700 68.62%
Hawaii 388 282 96,028 71.79% 326 243 131,256 73.17% 744 479 930,622 64.71%
Idaho 334 270 147,812 79.99% 376 286 175,630 74.50% 719 532 1,049,928 73.19%
Illinois 884 641 1,012,090 72.69% 918 614 1,363,215 66.25% 1,987 1,212 8,327,363 59.80%
Indiana 283 222 538,647 78.86% 317 241 743,072 76.19% 686 470 4,221,440 67.20%
Iowa 349 272 243,421 78.47% 343 243 359,699 71.52% 722 513 2,003,901 70.90%
Kansas 337 258 237,465 75.77% 306 223 325,008 73.30% 720 515 1,807,031 70.19%
Kentucky 345 250 340,245 71.68% 359 233 470,276 65.18% 741 470 2,873,699 61.30%
Louisiana 325 249 367,320 75.79% 307 221 496,651 72.36% 696 489 2,967,339 69.64%
Maine 314 227 90,994 72.99% 312 225 124,447 73.55% 768 540 938,827 71.13%
Maryland 264 209 453,651 79.62% 309 231 612,960 74.02% 744 550 3,960,463 72.40%
Massachusetts 367 228 486,692 62.45% 347 212 793,386 62.16% 882 548 4,569,126 61.63%
Michigan 762 610 774,747 80.16% 800 598 1,104,650 75.06% 1,749 1,212 6,527,045 68.74%
Minnesota 314 239 428,949 76.11% 335 223 574,038 64.59% 726 500 3,602,063 68.38%
Mississippi 305 235 244,408 76.88% 305 235 326,958 78.37% 673 464 1,875,843 69.05%
Missouri 282 216 468,693 76.84% 309 232 649,195 75.02% 743 490 3,951,436 63.73%
Montana 333 258 74,323 76.38% 371 267 110,690 71.37% 729 493 689,307 70.64%
Nebraska 313 241 153,264 77.62% 350 236 213,572 67.37% 701 487 1,191,102 68.11%
Nevada 291 249 224,692 84.52% 296 230 285,894 77.28% 681 487 1,938,194 70.39%
New Hampshire 321 236 95,915 74.44% 298 203 142,331 68.39% 736 497 914,990 66.22%
New Jersey 483 369 693,040 76.68% 487 333 889,421 67.89% 1,179 731 5,968,052 60.92%
New Mexico 315 269 165,841 87.57% 273 220 221,098 82.25% 627 491 1,332,957 77.94%
New York 1,228 862 1,411,235 66.91% 1,142 779 2,176,812 66.82% 2,564 1,591 13,160,320 60.00%
North Carolina 463 350 787,252 75.62% 486 353 1,042,023 73.36% 1,140 805 6,590,585 70.67%
North Dakota 361 277 52,057 77.94% 326 236 99,863 70.30% 657 447 465,081 67.70%
Ohio 771 581 905,155 73.88% 809 582 1,215,046 72.06% 1,783 1,214 7,618,247 66.19%
Oklahoma 341 264 315,530 77.50% 347 237 425,978 67.58% 686 464 2,457,462 67.17%
Oregon 331 244 291,562 72.28% 310 215 420,001 70.39% 750 545 2,766,628 71.02%
Pennsylvania 814 614 925,024 74.86% 803 571 1,334,425 72.14% 1,691 1,175 8,581,261 69.74%
Rhode Island 295 224 73,856 76.68% 348 237 127,610 69.94% 713 476 704,325 65.94%
South Carolina 288 228 368,554 77.77% 324 240 511,293 75.12% 714 502 3,254,067 71.45%
South Dakota 332 255 66,650 76.73% 311 227 92,952 73.75% 695 478 542,043 69.60%
Tennessee 315 235 508,796 74.37% 315 230 698,244 73.51% 743 528 4,349,823 69.66%
Texas 1,001 826 2,410,422 82.34% 1,060 847 3,086,091 79.55% 2,194 1,620 16,993,908 72.64%
Utah 286 240 297,786 81.97% 266 206 390,726 79.39% 663 490 1,714,818 72.56%
Vermont 292 209 42,507 72.18% 356 248 74,978 72.38% 650 439 425,389 70.74%
Virginia 492 391 628,350 79.49% 539 394 880,842 72.90% 1,046 708 5,452,270 66.92%
Washington 324 253 533,613 79.36% 338 232 744,179 68.26% 700 449 4,802,304 64.65%
West Virginia 321 233 127,982 74.10% 374 258 183,063 66.48% 745 471 1,245,817 62.43%
Wisconsin 367 293 445,459 80.36% 320 241 627,016 74.85% 681 484 3,794,230 72.12%
Wyoming 281 221 44,244 76.40% 359 279 61,241 76.61% 621 464 375,489 74.74%
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2016.
Table C.5 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Individuals Aged 12 or Older: 2017
State Total
Selected DUs
Total
Eligible
DUs
Total
Completed
Screeners
Weighted DU
Screening
Response Rate
Total
Selected
Total
Responded
Population
Estimate
Weighted
Interview
Response
Rate
Weighted
Overall
Response
Rate
Total U.S. 217,756 184,266 138,061 75.08% 97,667 68,032 272,103,335 67.12% 50.39%
Northeast 48,883 41,502 29,428 69.30% 19,783 13,261 48,090,325 64.33% 44.58%
Midwest 48,605 41,607 32,247 76.92% 23,047 15,922 57,012,053 67.23% 51.71%
South 72,434 60,687 46,289 78.13% 31,954 22,839 102,562,560 69.48% 54.28%
West 47,834 40,470 30,097 72.65% 22,883 16,010 64,438,397 65.30% 47.44%
Alabama 3,168 2,545 2,071 81.43% 1,357 964 4,076,562 67.18% 54.71%
Alaska 3,433 2,665 2,015 75.19% 1,429 978 585,516 67.16% 50.50%
Arizona 2,719 1,990 1,609 80.35% 1,121 860 5,833,518 73.17% 58.79%
Arkansas 2,850 2,392 1,974 82.44% 1,366 990 2,482,628 68.24% 56.25%
California 13,486 12,260 8,250 67.30% 6,962 4,478 33,008,642 61.22% 41.20%
Colorado 2,707 2,310 1,837 80.02% 1,441 1,003 4,681,963 68.04% 54.45%
Connecticut 3,209 2,775 2,021 72.86% 1,483 987 3,069,866 66.95% 48.78%
Delaware 3,610 2,918 2,125 72.25% 1,415 950 812,528 66.35% 47.93%
District of Columbia 7,118 6,086 3,727 58.58% 1,304 975 590,677 73.42% 43.01%
Florida 11,910 9,835 7,339 74.76% 4,810 3,399 17,900,610 67.65% 50.57%
Georgia 4,231 3,648 2,722 74.48% 2,053 1,487 8,585,215 70.11% 52.22%
Hawaii 3,702 3,108 2,107 67.43% 1,408 971 1,159,804 63.70% 42.95%
Idaho 2,372 1,958 1,615 82.08% 1,291 980 1,404,781 74.77% 61.37%
Illinois 7,748 6,775 4,516 66.77% 3,769 2,332 10,721,867 59.76% 39.90%
Indiana 3,004 2,533 1,933 76.23% 1,378 942 5,537,990 67.56% 51.50%
Iowa 2,977 2,500 2,084 83.33% 1,431 971 2,617,650 67.20% 56.00%
Kansas 2,471 2,190 1,762 80.55% 1,365 992 2,377,160 70.97% 57.17%
Kentucky 2,748 2,290 1,810 78.94% 1,431 976 3,701,461 65.55% 51.74%
Louisiana 2,870 2,366 1,948 82.45% 1,371 966 3,836,082 69.04% 56.93%
Maine 3,630 2,804 2,332 83.44% 1,395 985 1,159,844 68.91% 57.50%
Maryland 3,119 2,778 1,964 70.69% 1,340 987 5,064,109 71.96% 50.87%
Massachusetts 3,844 3,424 2,340 67.90% 1,668 986 5,902,164 57.34% 38.93%
Michigan 7,383 6,231 4,956 79.55% 3,396 2,402 8,447,704 67.99% 54.09%
Minnesota 2,780 2,401 1,862 77.68% 1,358 968 4,656,860 71.41% 55.47%
Mississippi 2,490 2,124 1,737 81.66% 1,321 936 2,449,136 67.39% 55.03%
Missouri 2,934 2,539 2,075 82.03% 1,419 989 5,091,167 69.20% 56.77%
Montana 3,227 2,626 2,161 82.64% 1,324 971 882,133 74.16% 61.29%
Nebraska 2,760 2,422 1,850 76.49% 1,349 961 1,570,654 69.52% 53.18%
Nevada 2,562 2,343 1,559 64.42% 1,394 958 2,503,328 65.28% 42.05%
New Hampshire 3,579 3,008 2,280 74.74% 1,430 1,003 1,162,921 71.63% 53.53%
New Jersey 4,665 4,114 2,928 70.08% 2,364 1,559 7,616,050 64.12% 44.93%
New Mexico 2,910 2,056 1,673 81.46% 1,147 927 1,730,409 79.34% 64.62%
New York 14,111 12,155 7,364 60.31% 5,216 3,352 16,859,209 62.07% 37.44%
North Carolina 4,388 3,769 2,968 78.70% 2,075 1,491 8,557,556 70.14% 55.20%
North Dakota 3,289 2,585 2,210 85.38% 1,397 981 615,426 70.11% 59.86%
Ohio 7,392 6,544 4,974 76.04% 3,441 2,418 9,782,521 68.81% 52.32%
Oklahoma 2,897 2,469 1,899 76.80% 1,392 938 3,209,148 66.95% 51.42%
Oregon 3,438 3,008 2,340 77.80% 1,450 987 3,525,360 67.53% 52.54%
Pennsylvania 7,838 6,669 5,248 78.66% 3,341 2,392 10,866,811 69.17% 54.41%
Rhode Island 3,564 3,087 2,202 71.18% 1,457 995 910,587 67.51% 48.05%
South Carolina 2,736 2,221 1,747 78.77% 1,311 977 4,197,504 70.48% 55.52%
South Dakota 2,609 2,179 1,798 82.64% 1,339 977 705,267 71.94% 59.45%
Tennessee 2,915 2,408 1,933 80.13% 1,341 983 5,617,904 71.44% 57.24%
Texas 7,590 6,355 5,156 81.34% 4,474 3,335 22,910,762 72.14% 58.67%
Utah 1,586 1,392 1,167 83.58% 1,251 946 2,454,802 74.30% 62.09%
Vermont 4,443 3,466 2,713 77.81% 1,429 1,002 542,874 69.35% 53.97%
Virginia 4,377 3,738 2,967 79.40% 2,149 1,521 7,025,154 66.95% 53.16%
Washington 2,856 2,474 1,888 76.63% 1,445 973 6,190,537 64.98% 49.79%
West Virginia 3,417 2,745 2,202 80.11% 1,444 964 1,545,522 65.31% 52.33%
Wisconsin 3,258 2,708 2,227 82.42% 1,405 989 4,887,789 69.26% 57.09%
Wyoming 2,836 2,280 1,876 82.44% 1,220 978 477,603 78.32% 64.57%
DU = dwelling unit.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2017.
Table C.6 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2017
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
Total U.S. 22,750 17,033 24,942,794 75.07% 23,707 16,618 34,306,312 69.57% 51,210 34,381 212,854,229 65.78%
Northeast 4,621 3,304 4,062,028 70.60% 4,927 3,305 5,997,749 65.87% 10,235 6,652 38,030,549 63.39%
Midwest 5,355 3,976 5,307,422 74.05% 5,578 3,883 7,318,181 69.01% 12,114 8,063 44,386,451 66.11%
South 7,457 5,726 9,604,069 77.73% 7,715 5,548 12,774,000 72.39% 16,782 11,565 80,184,491 68.03%
West 5,317 4,027 5,969,275 74.74% 5,487 3,882 8,216,383 68.40% 12,079 8,101 50,252,739 63.64%
Alabama 317 244 374,631 77.59% 320 240 513,209 73.08% 720 480 3,188,722 64.92%
Alaska 392 292 58,282 75.76% 314 214 74,239 71.77% 723 472 452,995 65.19%
Arizona 309 245 552,984 78.92% 260 209 747,345 79.23% 552 406 4,533,189 71.35%
Arkansas 358 265 236,608 72.58% 312 242 315,393 76.59% 696 483 1,930,627 66.44%
California 1,553 1,135 3,033,709 72.96% 1,596 1,036 4,288,284 64.66% 3,813 2,307 25,686,650 59.22%
Colorado 335 247 428,263 75.43% 311 227 584,837 73.19% 795 529 3,668,863 66.50%
Connecticut 338 232 274,244 68.29% 399 262 389,556 65.66% 746 493 2,406,066 67.01%
Delaware 331 234 69,530 69.60% 310 208 95,131 69.78% 774 508 647,868 65.53%
District of Columbia 353 280 31,388 81.40% 286 216 87,973 78.10% 665 479 471,317 71.97%
Florida 1,145 894 1,426,526 78.31% 1,085 743 1,958,321 69.24% 2,580 1,762 14,515,763 66.37%
Georgia 441 330 865,968 76.79% 508 389 1,104,404 77.43% 1,104 768 6,614,843 68.02%
Hawaii 321 246 95,563 79.00% 324 227 125,577 68.82% 763 498 938,664 61.35%
Idaho 299 242 151,439 82.24% 300 232 178,468 76.57% 692 506 1,074,874 73.48%
Illinois 828 588 1,001,216 70.99% 843 528 1,342,655 63.02% 2,098 1,216 8,377,996 57.86%
Indiana 304 225 538,160 74.09% 298 211 740,720 71.27% 776 506 4,259,110 66.22%
Iowa 313 231 244,636 74.10% 388 263 359,287 65.36% 730 477 2,013,727 66.69%
Kansas 328 248 237,376 74.14% 342 252 323,999 72.23% 695 492 1,815,786 70.33%
Kentucky 331 247 340,219 74.55% 340 239 466,997 71.90% 760 490 2,894,246 63.66%
Louisiana 319 235 363,668 74.34% 340 229 485,824 65.19% 712 502 2,986,591 69.03%
Maine 381 280 90,045 74.34% 341 235 123,946 70.29% 673 470 945,853 68.22%
Maryland 289 228 454,007 78.97% 373 277 605,178 74.99% 678 482 4,004,924 70.64%
Massachusetts 392 272 483,097 71.49% 475 268 791,355 57.79% 801 446 4,627,712 55.75%
Michigan 780 595 766,463 75.47% 840 600 1,097,289 71.93% 1,776 1,207 6,583,952 66.41%
Minnesota 304 236 433,584 78.40% 377 263 574,994 69.78% 677 469 3,648,281 70.80%
Mississippi 301 238 242,287 78.12% 278 193 323,808 69.65% 742 505 1,883,042 65.73%
Missouri 342 235 466,944 66.93% 321 224 640,581 70.48% 756 530 3,983,641 69.26%
Montana 272 198 74,949 72.39% 327 247 110,136 76.10% 725 526 697,048 74.08%
Nebraska 336 251 155,372 76.05% 346 246 213,260 74.53% 667 464 1,202,023 67.80%
Nevada 308 236 228,207 76.65% 368 257 284,196 69.26% 718 465 1,990,925 63.29%
New Hampshire 361 264 95,120 71.53% 360 236 142,221 64.54% 709 503 925,580 72.72%
New Jersey 508 363 690,173 70.15% 582 402 891,735 68.25% 1,274 794 6,034,143 62.74%
New Mexico 254 214 166,008 84.42% 289 238 220,296 82.77% 604 475 1,344,105 78.10%
New York 1,232 830 1,394,803 65.96% 1,304 862 2,135,231 64.08% 2,680 1,660 13,329,176 61.31%
North Carolina 521 413 791,136 80.86% 524 361 1,053,588 67.77% 1,030 717 6,712,833 69.20%
North Dakota 359 253 52,695 70.80% 315 233 95,075 72.85% 723 495 467,655 69.44%
Ohio 807 607 899,095 74.90% 864 598 1,213,704 69.05% 1,770 1,213 7,669,722 68.07%
Oklahoma 314 222 316,734 68.52% 348 226 421,590 64.45% 730 490 2,470,824 67.20%
Oregon 350 243 293,722 70.71% 423 286 415,641 69.34% 677 458 2,815,997 66.92%
Pennsylvania 727 561 919,394 77.48% 817 583 1,322,903 71.02% 1,797 1,248 8,624,513 68.03%
Rhode Island 323 236 73,443 72.43% 328 232 126,842 72.32% 806 527 710,302 66.20%
South Carolina 295 242 372,484 76.92% 370 286 509,421 77.31% 646 449 3,315,599 68.76%
South Dakota 321 248 67,482 78.59% 326 247 91,812 75.27% 692 482 545,973 70.53%
Tennessee 335 262 511,129 75.44% 295 215 691,269 71.29% 711 506 4,415,506 71.04%
Texas 1,017 810 2,452,451 80.08% 1,105 853 3,087,771 76.19% 2,352 1,672 17,370,540 70.29%
Utah 282 218 303,235 78.26% 325 244 393,415 70.42% 644 484 1,758,153 74.50%
Vermont 359 266 41,710 73.39% 321 225 73,960 70.29% 749 511 427,203 68.78%
Virginia 446 348 628,884 78.90% 580 410 874,910 69.73% 1,123 763 5,521,360 65.01%
Washington 329 245 538,697 73.16% 361 237 735,718 65.09% 755 491 4,916,122 64.06%
West Virginia 344 234 126,421 67.37% 341 221 179,213 64.50% 759 509 1,239,888 65.22%
Wisconsin 333 259 444,400 78.91% 318 218 624,805 68.91% 754 512 3,818,584 68.11%
Wyoming 313 266 44,218 84.65% 289 228 58,232 78.54% 618 484 375,154 77.49%
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2017.
Table C.7 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Individuals Aged 12 or Older: 2015 and 2016
State Total
Selected DUs
Total
Eligible
DUs
Total
Completed
Screeners
Weighted DU
Screening
Response Rate
Total
Selected
Total
Responded
Population
Estimate
Weighted
Interview
Response
Rate
Weighted
Overall
Response
Rate
Total U.S. 403,551 338,477 267,398 78.78% 190,106 136,015 268,562,312 68.84% 54.24%
Northeast 89,545 75,780 56,340 72.42% 37,770 25,737 47,803,875 65.12% 47.15%
Midwest 93,119 78,825 64,339 80.59% 45,001 31,913 56,703,618 68.20% 54.96%
South 131,438 108,928 87,417 81.72% 62,382 45,601 100,711,808 70.78% 57.84%
West 89,449 74,944 59,302 77.13% 44,953 32,764 63,343,011 69.15% 53.34%
Alabama 5,793 4,663 3,857 82.65% 2,720 1,936 4,060,554 67.33% 55.65%
Alaska 6,561 4,767 3,793 79.36% 2,698 1,941 583,339 70.31% 55.80%
Arizona 5,943 4,517 3,784 83.81% 2,676 1,978 5,694,340 72.79% 61.01%
Arkansas 5,911 4,847 4,046 83.63% 2,724 1,973 2,462,829 69.23% 57.90%
California 23,474 21,223 15,557 72.90% 13,165 9,290 32,623,357 67.06% 48.89%
Colorado 5,207 4,403 3,552 80.36% 2,652 1,914 4,569,366 69.67% 55.99%
Connecticut 5,852 5,077 3,867 76.19% 2,803 1,901 3,055,331 65.59% 49.98%
Delaware 5,654 4,798 3,636 76.00% 2,653 1,873 798,856 69.45% 52.78%
District of Columbia 11,117 9,460 6,519 68.24% 2,491 1,891 577,705 74.29% 50.70%
Florida 21,812 17,654 13,928 78.86% 9,459 6,821 17,406,100 69.13% 54.51%
Georgia 7,634 6,446 5,046 78.32% 3,990 3,006 8,410,977 71.43% 55.94%
Hawaii 7,088 5,959 4,437 73.99% 2,847 2,024 1,158,228 68.58% 50.74%
Idaho 4,673 3,964 3,372 85.11% 2,706 2,037 1,360,227 73.45% 62.52%
Illinois 14,325 12,596 9,140 72.68% 7,381 4,832 10,719,970 62.47% 45.41%
Indiana 5,289 4,441 3,484 78.35% 2,662 1,906 5,494,678 68.81% 53.91%
Iowa 5,961 5,129 4,341 84.47% 2,771 1,990 2,602,285 70.11% 59.22%
Kansas 5,162 4,487 3,810 84.85% 2,714 1,982 2,368,380 71.29% 60.49%
Kentucky 5,631 4,586 3,799 82.96% 2,716 1,891 3,676,023 67.37% 55.89%
Louisiana 5,564 4,551 3,738 82.47% 2,610 1,916 3,825,536 71.78% 59.20%
Maine 8,218 6,162 5,116 82.99% 2,794 1,986 1,152,976 70.16% 58.23%
Maryland 4,726 4,138 3,063 73.86% 2,607 1,936 5,022,867 71.51% 52.81%
Massachusetts 7,066 6,212 4,496 72.35% 3,187 1,936 5,835,936 59.87% 43.32%
Michigan 14,256 11,680 9,662 82.52% 6,694 4,861 8,399,712 70.01% 57.78%
Minnesota 5,086 4,427 3,621 81.68% 2,661 1,913 4,590,321 70.84% 57.87%
Mississippi 4,936 4,009 3,358 83.89% 2,540 1,855 2,445,529 70.62% 59.25%
Missouri 5,194 4,341 3,772 86.85% 2,676 1,924 5,063,449 68.16% 59.19%
Montana 6,412 5,130 4,406 86.07% 2,762 1,995 870,289 70.32% 60.52%
Nebraska 5,206 4,506 3,675 81.41% 2,665 1,909 1,553,412 70.08% 57.05%
Nevada 5,055 4,382 3,272 74.68% 2,585 1,963 2,428,523 71.30% 53.24%
New Hampshire 6,568 5,526 4,339 78.24% 2,790 1,931 1,150,981 67.72% 52.98%
New Jersey 8,446 7,513 5,598 73.51% 4,396 2,950 7,551,362 64.31% 47.27%
New Mexico 5,475 3,876 3,364 86.94% 2,475 1,939 1,718,723 76.58% 66.58%
New York 24,515 21,212 13,795 64.38% 9,897 6,542 16,764,138 62.50% 40.24%
North Carolina 8,373 7,076 5,822 82.22% 4,214 3,084 8,370,189 70.72% 58.15%
North Dakota 6,936 5,640 5,005 88.76% 2,686 1,948 617,841 70.78% 62.83%
Ohio 13,836 11,832 9,473 80.02% 6,821 4,805 9,735,503 68.04% 54.44%
Oklahoma 5,511 4,483 3,712 82.82% 2,733 1,936 3,192,269 67.92% 56.25%
Oregon 5,686 4,960 4,027 81.27% 2,724 1,966 3,449,136 71.04% 57.74%
Pennsylvania 15,254 12,922 10,331 79.98% 6,540 4,734 10,845,101 71.10% 56.86%
Rhode Island 5,973 5,114 3,958 77.46% 2,710 1,901 904,838 68.42% 53.00%
South Carolina 5,776 4,687 3,889 82.87% 2,630 1,957 4,102,218 72.49% 60.07%
South Dakota 5,167 4,306 3,836 89.29% 2,537 1,864 698,802 72.83% 65.03%
Tennessee 5,704 4,588 3,848 83.93% 2,725 1,997 5,532,419 70.14% 58.87%
Texas 13,020 10,909 9,415 86.01% 8,613 6,601 22,320,973 73.98% 63.63%
Utah 2,989 2,647 2,314 87.51% 2,419 1,904 2,377,053 76.12% 66.62%
Vermont 7,653 6,042 4,840 79.94% 2,653 1,856 543,211 70.02% 55.97%
Virginia 7,854 6,786 5,497 80.99% 4,190 3,019 6,945,044 69.29% 56.12%
Washington 5,471 4,844 3,778 77.87% 2,668 1,878 6,029,145 68.19% 53.10%
West Virginia 6,422 5,247 4,244 80.85% 2,767 1,909 1,561,719 65.27% 52.78%
Wisconsin 6,701 5,440 4,520 83.20% 2,733 1,979 4,859,267 70.85% 58.95%
Wyoming 5,415 4,272 3,646 85.26% 2,576 1,935 481,287 73.68% 62.82%
DU = dwelling unit.
NOTE: To compute the pooled 2015-2016 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2015 and 2016 individual response rates. The 2015-2016 population estimate is the average of the 2015 and the 2016 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2015 and 2016.
Table C.8 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2015 and 2016
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
Total U.S. 44,182 34,064 24,894,972 77.31% 46,047 33,788 34,738,945 73.57% 99,877 68,163 208,928,395 67.05%
Northeast 8,725 6,421 4,110,839 71.98% 9,110 6,292 6,084,918 68.31% 19,935 13,024 37,608,119 63.84%
Midwest 10,651 8,060 5,338,955 75.24% 10,953 8,002 7,391,290 72.87% 23,397 15,851 43,973,374 66.56%
South 14,486 11,392 9,506,846 79.09% 15,015 11,299 12,893,966 76.05% 32,881 22,910 78,310,996 68.91%
West 10,320 8,191 5,938,333 79.97% 10,969 8,195 8,368,771 74.15% 23,664 16,378 49,035,907 66.96%
Alabama 593 463 378,330 78.87% 651 494 522,750 75.65% 1,476 979 3,159,474 64.66%
Alaska 639 463 59,083 72.56% 693 523 80,112 75.10% 1,366 955 444,143 69.16%
Arizona 612 473 548,504 78.00% 641 485 746,271 75.11% 1,423 1,020 4,399,565 71.77%
Arkansas 630 491 236,654 78.06% 676 505 317,993 74.15% 1,418 977 1,908,182 67.28%
California 2,920 2,335 3,039,214 80.03% 3,120 2,316 4,399,955 74.31% 7,125 4,639 25,184,187 64.18%
Colorado 627 512 421,468 81.37% 630 453 596,535 71.18% 1,395 949 3,551,363 67.84%
Connecticut 608 465 279,545 77.63% 713 478 388,177 66.27% 1,482 958 2,387,609 64.09%
Delaware 590 455 69,164 78.47% 669 466 97,254 69.48% 1,394 952 632,438 68.45%
District of Columbia 556 450 30,813 81.46% 584 441 93,701 75.27% 1,351 1,000 453,192 73.60%
Florida 2,179 1,703 1,405,801 78.08% 2,190 1,682 1,971,645 77.06% 5,090 3,436 14,028,654 67.21%
Georgia 985 790 855,245 79.58% 879 710 1,112,080 80.07% 2,126 1,506 6,443,651 68.89%
Hawaii 674 508 96,573 73.86% 686 518 135,482 74.95% 1,487 998 926,174 67.07%
Idaho 615 490 146,791 80.19% 722 546 175,145 75.43% 1,369 1,001 1,038,291 72.10%
Illinois 1,771 1,289 1,015,317 72.82% 1,727 1,175 1,372,755 67.41% 3,883 2,368 8,331,898 60.42%
Indiana 599 464 539,568 76.49% 669 497 743,107 74.81% 1,394 945 4,212,004 66.73%
Iowa 695 525 243,253 75.83% 689 492 359,178 71.88% 1,387 973 1,999,854 69.06%
Kansas 684 509 237,647 73.41% 602 465 327,480 78.33% 1,428 1,008 1,803,253 69.73%
Kentucky 641 482 339,903 74.44% 656 457 471,060 70.15% 1,419 952 2,865,061 66.07%
Louisiana 636 493 367,464 77.59% 626 454 503,266 72.73% 1,348 969 2,954,805 70.84%
Maine 696 520 91,487 74.37% 621 442 124,761 71.44% 1,477 1,024 936,729 69.58%
Maryland 571 447 453,674 79.16% 635 478 617,785 74.75% 1,401 1,011 3,951,408 70.20%
Massachusetts 704 456 487,249 65.06% 722 433 792,216 59.97% 1,761 1,047 4,556,470 59.29%
Michigan 1,560 1,211 779,507 77.10% 1,647 1,251 1,108,537 76.52% 3,487 2,399 6,511,669 68.05%
Minnesota 633 486 427,687 76.43% 639 453 572,944 71.18% 1,389 974 3,589,691 70.14%
Mississippi 592 466 244,221 79.24% 594 461 331,045 77.89% 1,354 928 1,870,263 68.22%
Missouri 590 460 469,494 77.31% 693 525 652,575 75.74% 1,393 939 3,941,380 65.89%
Montana 633 488 74,427 76.77% 673 496 111,264 72.71% 1,456 1,011 684,598 69.27%
Nebraska 602 461 152,704 77.18% 688 484 213,106 69.37% 1,375 964 1,187,602 69.31%
Nevada 615 520 224,147 84.33% 630 484 287,409 76.48% 1,340 959 1,916,967 68.90%
New Hampshire 643 474 96,774 74.72% 623 438 142,696 71.64% 1,524 1,019 911,511 66.35%
New Jersey 1,010 756 694,182 74.78% 1,075 744 892,114 68.78% 2,311 1,450 5,965,066 62.45%
New Mexico 570 484 165,412 86.00% 577 457 223,662 80.57% 1,328 998 1,329,649 74.82%
New York 2,293 1,628 1,416,226 68.42% 2,444 1,688 2,197,628 67.30% 5,160 3,226 13,150,285 61.05%
North Carolina 1,002 788 783,879 78.87% 1,001 750 1,053,931 75.40% 2,211 1,546 6,532,379 68.98%
North Dakota 679 508 52,111 74.85% 654 495 102,161 74.02% 1,353 945 463,569 69.53%
Ohio 1,574 1,170 909,989 73.36% 1,636 1,181 1,220,151 72.62% 3,611 2,454 7,605,364 66.70%
Oklahoma 690 524 314,698 76.44% 636 452 428,910 69.76% 1,407 960 2,448,662 66.47%
Oregon 612 458 291,584 74.83% 645 459 417,950 71.50% 1,467 1,049 2,739,602 70.57%
Pennsylvania 1,556 1,188 928,154 76.13% 1,597 1,167 1,344,620 74.15% 3,387 2,379 8,572,327 70.06%
Rhode Island 581 452 74,286 78.17% 680 472 127,975 70.53% 1,449 977 702,577 66.98%
South Carolina 632 510 367,649 80.27% 598 459 515,200 77.38% 1,400 988 3,219,369 70.86%
South Dakota 632 485 66,117 76.96% 608 460 92,977 75.58% 1,297 919 539,708 71.77%
Tennessee 610 465 508,573 75.94% 729 548 700,709 74.02% 1,386 984 4,323,137 68.83%
Texas 1,960 1,606 2,395,358 81.38% 2,145 1,696 3,083,498 78.94% 4,508 3,299 16,842,117 71.98%
Utah 585 502 294,912 85.09% 574 456 387,120 80.24% 1,260 946 1,695,021 73.64%
Vermont 634 482 42,936 76.00% 635 430 74,732 69.62% 1,384 944 425,544 69.47%
Virginia 982 783 626,833 79.72% 1,043 751 888,046 71.83% 2,165 1,485 5,430,166 67.62%
Washington 609 480 532,127 79.33% 688 482 745,740 69.76% 1,371 916 4,751,278 66.71%
West Virginia 637 476 128,586 76.36% 703 495 185,094 70.04% 1,427 938 1,248,039 63.35%
Wisconsin 632 492 445,564 76.30% 701 524 626,320 73.57% 1,400 963 3,787,384 69.73%
Wyoming 609 478 44,091 77.18% 690 520 62,125 75.33% 1,277 937 375,070 72.99%
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
NOTE: To compute the pooled 2015-2016 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2015 and 2016 individual response rates. The 2015-2016 population estimate is the average of the 2015 and the 2016 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2015 and 2016.
Table C.9 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Individuals Aged 12 or Older: 2016 and 2017
State Total
Selected DUs
Total
Eligible
DUs
Total
Completed
Screeners
Weighted DU
Screening
Response Rate
Total
Selected
Total
Responded
Population
Estimate
Weighted
Interview
Response
Rate
Weighted
Overall
Response
Rate
Total U.S. 423,345 357,415 273,249 76.47% 193,274 135,974 270,766,735 67.78% 51.83%
Northeast 94,271 79,990 57,703 70.45% 38,565 25,972 47,943,907 64.48% 45.43%
Midwest 95,455 81,579 64,478 78.29% 45,696 31,945 56,878,478 67.61% 52.94%
South 139,695 116,754 90,642 79.35% 63,416 45,672 101,901,883 70.05% 55.58%
West 93,924 79,092 60,426 74.55% 45,597 32,385 64,042,468 66.74% 49.76%
Alabama 6,164 5,023 4,097 81.73% 2,749 1,947 4,070,626 66.93% 54.71%
Alaska 6,705 5,051 3,916 77.36% 2,754 1,938 585,271 68.11% 52.69%
Arizona 5,640 4,193 3,444 81.91% 2,434 1,842 5,788,143 74.00% 60.61%
Arkansas 5,886 4,895 4,015 82.09% 2,747 1,982 2,475,460 68.84% 56.52%
California 25,678 23,330 16,243 69.62% 13,682 9,097 32,849,259 63.28% 44.06%
Colorado 5,277 4,473 3,594 80.33% 2,765 1,923 4,646,984 67.57% 54.28%
Connecticut 6,189 5,334 3,952 74.12% 2,875 1,924 3,061,195 65.93% 48.87%
Delaware 6,563 5,377 4,005 74.57% 2,745 1,878 807,445 67.01% 49.97%
District of Columbia 13,058 11,205 7,128 61.82% 2,564 1,942 585,768 73.76% 45.60%
Florida 23,192 19,102 14,474 75.93% 9,604 6,834 17,727,429 67.93% 51.58%
Georgia 7,850 6,787 5,165 76.16% 4,051 2,995 8,523,903 70.61% 53.78%
Hawaii 7,651 6,437 4,585 70.58% 2,866 1,975 1,158,855 65.01% 45.89%
Idaho 5,025 4,109 3,457 83.88% 2,720 2,068 1,389,076 74.47% 62.46%
Illinois 14,970 13,085 9,017 69.05% 7,558 4,799 10,712,268 60.78% 41.97%
Indiana 5,564 4,682 3,598 76.80% 2,664 1,875 5,520,574 68.56% 52.65%
Iowa 5,870 4,961 4,160 83.80% 2,845 1,999 2,612,335 69.40% 58.15%
Kansas 4,993 4,394 3,610 82.17% 2,728 1,988 2,373,332 71.07% 58.40%
Kentucky 5,910 4,876 3,914 80.10% 2,876 1,929 3,692,840 64.19% 51.42%
Louisiana 5,816 4,747 3,882 81.84% 2,699 1,925 3,833,696 69.83% 57.15%
Maine 7,571 5,826 4,805 82.72% 2,789 1,977 1,157,056 70.21% 58.08%
Maryland 5,537 4,898 3,514 71.62% 2,657 1,977 5,045,592 72.62% 52.01%
Massachusetts 7,544 6,676 4,705 70.14% 3,264 1,974 5,875,684 59.54% 41.76%
Michigan 14,473 12,124 9,765 80.46% 6,707 4,822 8,427,073 69.30% 55.76%
Minnesota 5,376 4,679 3,717 79.55% 2,733 1,930 4,630,955 70.00% 55.68%
Mississippi 4,872 4,073 3,354 82.31% 2,604 1,870 2,448,173 69.19% 56.95%
Missouri 5,546 4,786 4,001 83.81% 2,753 1,927 5,080,245 67.67% 56.71%
Montana 6,444 5,228 4,408 84.54% 2,757 1,989 878,226 72.72% 61.48%
Nebraska 5,456 4,772 3,731 78.27% 2,713 1,925 1,564,296 69.24% 54.19%
Nevada 4,941 4,438 3,085 68.37% 2,662 1,924 2,476,054 68.96% 47.15%
New Hampshire 6,823 5,771 4,428 76.13% 2,785 1,939 1,158,078 69.42% 52.85%
New Jersey 9,035 7,980 5,719 70.58% 4,513 2,992 7,583,282 63.66% 44.93%
New Mexico 5,817 4,079 3,393 83.19% 2,362 1,907 1,725,153 79.39% 66.04%
New York 26,509 22,871 14,296 62.13% 10,150 6,584 16,803,788 61.75% 38.37%
North Carolina 8,510 7,239 5,800 80.12% 4,164 2,999 8,488,708 70.80% 56.73%
North Dakota 6,800 5,467 4,731 86.56% 2,741 1,941 616,213 69.60% 60.25%
Ohio 14,196 12,477 9,674 77.62% 6,804 4,795 9,760,484 68.20% 52.94%
Oklahoma 5,551 4,667 3,693 79.08% 2,766 1,903 3,204,059 67.61% 53.46%
Oregon 6,598 5,773 4,564 79.16% 2,841 1,991 3,501,776 69.27% 54.84%
Pennsylvania 15,663 13,334 10,525 78.91% 6,649 4,752 10,853,760 69.82% 55.10%
Rhode Island 6,636 5,740 4,245 74.08% 2,813 1,932 908,189 67.44% 49.96%
South Carolina 5,568 4,472 3,596 80.38% 2,637 1,947 4,165,709 71.45% 57.44%
South Dakota 5,422 4,517 3,835 84.79% 2,677 1,937 703,456 71.45% 60.58%
Tennessee 5,949 4,824 3,935 81.51% 2,714 1,976 5,587,383 71.02% 57.89%
Texas 14,383 12,080 10,033 82.93% 8,729 6,628 22,700,592 73.39% 60.86%
Utah 3,069 2,723 2,305 84.68% 2,466 1,882 2,429,066 74.56% 63.13%
Vermont 8,301 6,458 5,028 77.48% 2,727 1,898 542,874 70.23% 54.41%
Virginia 8,297 7,114 5,710 80.28% 4,226 3,014 6,993,308 67.90% 54.51%
Washington 5,635 4,895 3,799 77.78% 2,807 1,907 6,135,316 65.68% 51.09%
West Virginia 6,589 5,375 4,327 80.46% 2,884 1,926 1,551,191 64.59% 51.97%
Wisconsin 6,789 5,635 4,639 82.37% 2,773 2,007 4,877,247 71.29% 58.72%
Wyoming 5,444 4,363 3,633 83.46% 2,481 1,942 479,288 76.69% 64.00%
DU = dwelling unit.
NOTE: To compute the pooled 2016-2017 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2016 and 2017 individual response rates. The 2016-2017 population estimate is the average of the 2016 and the 2017 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2016 and 2017.
Table C.10 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2016 and 2017
State 12-17
Total
Selected
12-17
Total
Responded
12-17
Population
Estimate
12-17
Weighted
Interview
Response
Rate
18-25
Total
Selected
18-25
Total
Responded
18-25
Population
Estimate
18-25
Weighted
Interview
Response
Rate
26+
Total
Selected
26+
Total
Responded
26+
Population
Estimate
26+
Weighted
Interview
Response
Rate
Total U.S. 45,073 34,142 24,919,660 76.01% 46,543 33,191 34,438,520 71.12% 101,658 68,641 211,408,555 66.26%
Northeast 9,038 6,497 4,079,645 70.79% 9,386 6,364 6,025,003 66.91% 20,141 13,111 37,839,258 63.40%
Midwest 10,710 8,081 5,317,010 75.29% 11,022 7,779 7,342,752 70.32% 23,964 16,085 44,218,716 66.25%
South 14,676 11,351 9,567,219 78.14% 15,234 11,171 12,801,275 74.04% 33,506 23,150 79,533,389 68.44%
West 10,649 8,213 5,955,787 76.80% 10,901 7,877 8,269,490 70.37% 24,047 16,295 49,817,192 64.89%
Alabama 621 478 375,632 78.56% 633 483 515,697 74.84% 1,495 986 3,179,298 64.35%
Alaska 709 528 58,821 75.62% 676 490 75,809 74.26% 1,369 920 450,641 66.06%
Arizona 625 479 551,089 77.16% 577 446 747,345 76.71% 1,232 917 4,489,709 73.14%
Arkansas 665 500 236,782 75.47% 659 502 316,285 75.16% 1,423 980 1,922,393 67.03%
California 3,062 2,322 3,033,914 76.08% 3,113 2,128 4,323,156 68.21% 7,507 4,647 25,492,189 60.86%
Colorado 642 490 425,994 76.98% 614 439 591,983 70.85% 1,509 994 3,629,008 65.93%
Connecticut 641 456 276,122 72.05% 765 513 389,201 66.88% 1,469 955 2,395,872 65.07%
Delaware 619 451 69,477 73.37% 654 453 95,499 70.58% 1,472 974 642,469 65.84%
District of Columbia 645 520 31,164 81.77% 613 467 90,630 77.37% 1,306 955 463,974 72.47%
Florida 2,252 1,753 1,415,667 77.97% 2,116 1,536 1,960,092 73.00% 5,236 3,545 14,351,670 66.31%
Georgia 902 700 862,534 77.69% 940 741 1,106,098 78.93% 2,209 1,554 6,555,271 68.32%
Hawaii 709 528 95,796 75.53% 650 470 128,416 71.11% 1,507 977 934,643 63.02%
Idaho 633 512 149,626 81.11% 676 518 177,049 75.55% 1,411 1,038 1,062,401 73.34%
Illinois 1,712 1,229 1,006,653 71.82% 1,761 1,142 1,352,935 64.66% 4,085 2,428 8,352,680 58.83%
Indiana 587 447 538,403 76.48% 615 452 741,896 73.80% 1,462 976 4,240,275 66.68%
Iowa 662 503 244,029 76.25% 731 506 359,493 68.50% 1,452 990 2,008,814 68.72%
Kansas 665 506 237,420 74.95% 648 475 324,503 72.78% 1,415 1,007 1,811,408 70.26%
Kentucky 676 497 340,232 73.09% 699 472 468,636 68.48% 1,501 960 2,883,972 62.53%
Louisiana 644 484 365,494 75.08% 647 450 491,237 68.96% 1,408 991 2,976,965 69.33%
Maine 695 507 90,519 73.66% 653 460 124,197 71.91% 1,441 1,010 942,340 69.66%
Maryland 553 437 453,829 79.30% 682 508 609,069 74.50% 1,422 1,032 3,982,694 71.56%
Massachusetts 759 500 484,894 67.05% 822 480 792,371 59.96% 1,683 994 4,598,419 58.67%
Michigan 1,542 1,205 770,605 77.79% 1,640 1,198 1,100,969 73.50% 3,525 2,419 6,555,498 67.59%
Minnesota 618 475 431,266 77.31% 712 486 574,516 67.17% 1,403 969 3,625,172 69.59%
Mississippi 606 473 243,347 77.48% 583 428 325,383 73.93% 1,415 969 1,879,442 67.33%
Missouri 624 451 467,819 71.84% 630 456 644,888 72.72% 1,499 1,020 3,967,539 66.41%
Montana 605 456 74,636 74.38% 698 514 110,413 73.70% 1,454 1,019 693,178 72.40%
Nebraska 649 492 154,318 76.83% 696 482 213,416 70.96% 1,368 951 1,196,562 67.96%
Nevada 599 485 226,449 80.55% 664 487 285,045 73.38% 1,399 952 1,964,559 66.93%
New Hampshire 682 500 95,517 72.97% 658 439 142,276 66.47% 1,445 1,000 920,285 69.48%
New Jersey 991 732 691,607 73.38% 1,069 735 890,578 68.07% 2,453 1,525 6,001,097 61.82%
New Mexico 569 483 165,925 86.04% 562 458 220,697 82.51% 1,231 966 1,338,531 78.02%
New York 2,460 1,692 1,403,019 66.44% 2,446 1,641 2,156,021 65.44% 5,244 3,251 13,244,748 60.63%
North Carolina 984 763 789,194 78.26% 1,010 714 1,047,806 70.55% 2,170 1,522 6,651,709 69.92%
North Dakota 720 530 52,376 74.31% 641 469 97,469 71.52% 1,380 942 466,368 68.59%
Ohio 1,578 1,188 902,125 74.38% 1,673 1,180 1,214,375 70.53% 3,553 2,427 7,643,985 67.12%
Oklahoma 655 486 316,132 72.98% 695 463 423,784 65.99% 1,416 954 2,464,143 67.18%
Oregon 681 487 292,642 71.49% 733 501 417,821 69.85% 1,427 1,003 2,791,313 68.95%
Pennsylvania 1,541 1,175 922,209 76.15% 1,620 1,154 1,328,664 71.59% 3,488 2,423 8,602,887 68.88%
Rhode Island 618 460 73,649 74.55% 676 469 127,226 71.12% 1,519 1,003 707,314 66.07%
South Carolina 583 470 370,519 77.34% 694 526 510,357 76.19% 1,360 951 3,284,833 70.07%
South Dakota 653 503 67,066 77.65% 637 474 92,382 74.53% 1,387 960 544,008 70.09%
Tennessee 650 497 509,963 74.91% 610 445 694,756 72.44% 1,454 1,034 4,382,664 70.37%
Texas 2,018 1,636 2,431,437 81.21% 2,165 1,700 3,086,931 77.86% 4,546 3,292 17,182,224 71.43%
Utah 568 458 300,510 80.08% 591 450 392,070 74.87% 1,307 974 1,736,486 73.52%
Vermont 651 475 42,109 72.77% 677 473 74,469 71.34% 1,399 950 426,296 69.76%
Virginia 938 739 628,617 79.18% 1,119 804 877,876 71.35% 2,169 1,471 5,486,815 65.96%
Washington 653 498 536,155 76.20% 699 469 739,948 66.72% 1,455 940 4,859,213 64.35%
West Virginia 665 467 127,201 70.80% 715 479 181,138 65.52% 1,504 980 1,242,852 63.83%
Wisconsin 700 552 444,929 79.63% 638 459 625,910 71.90% 1,435 996 3,806,407 70.18%
Wyoming 594 487 44,231 80.49% 648 507 59,736 77.55% 1,239 948 375,321 76.07%
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
NOTE: To compute the pooled 2016-2017 weighted response rates, two samples were combined, and the individual year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the 2016 and 2017 individual response rates. The 2016-2017 population estimate is the average of the 2016 and the 2017 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2016 and 2017.
Table C.11 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Individuals Aged 12 to 20, by State: 2015, 2016, and 2017
State 2015
Total
Selected
2015
Total
Responded
2015
Population
Estimate
2015
Weighted
Interview
Response
Rate
2016
Total
Selected
2016
Total
Responded
2016
Population
Estimate
2016
Weighted
Interview
Response
Rate
2017
Total
Selected
2017
Total
Responded
2017
Population
Estimate
2017
Weighted
Interview
Response
Rate
Total U.S. 29,838 23,169 37,885,089 78.03% 30,054 22,949 37,615,301 76.42% 30,946 23,001 37,826,079 74.21%
Northeast 5,906 4,435 6,451,797 73.79% 5,933 4,290 6,355,243 70.86% 6,326 4,504 6,196,921 70.45%
Midwest 7,212 5,457 8,034,193 75.02% 7,226 5,501 8,080,261 76.02% 7,312 5,412 8,101,681 73.59%
South 9,864 7,822 14,395,593 79.87% 9,697 7,541 14,134,174 78.21% 10,102 7,666 14,454,990 76.53%
West 6,856 5,455 9,003,507 80.76% 7,198 5,617 9,045,622 77.87% 7,206 5,419 9,072,487 73.65%
Alabama 432 339 614,743 78.20% 415 319 570,942 78.53% 454 347 623,472 76.38%
Alaska 442 316 89,171 71.95% 442 339 90,222 77.38% 491 362 85,526 75.51%
Arizona 392 314 760,931 79.70% 409 307 818,860 76.22% 405 322 851,823 78.72%
Arkansas 428 333 340,447 76.62% 421 329 338,779 79.12% 472 362 370,680 76.49%
California 1,988 1,612 4,728,513 81.28% 2,038 1,593 4,711,205 78.42% 2,118 1,537 4,736,697 72.00%
Colorado 422 351 635,534 83.50% 424 326 688,842 75.70% 427 314 609,264 74.03%
Connecticut 437 337 454,732 77.38% 422 319 428,681 77.41% 466 315 404,880 67.27%
Delaware 417 317 105,967 76.38% 413 311 107,994 76.98% 435 301 101,672 69.41%
District of Columbia 326 264 58,167 82.81% 369 303 55,479 81.29% 417 332 56,880 81.85%
Florida 1,473 1,171 2,168,609 79.65% 1,463 1,144 2,126,021 78.47% 1,512 1,154 2,179,015 76.31%
Georgia 672 542 1,239,168 81.30% 596 482 1,240,615 79.69% 608 464 1,271,553 78.13%
Hawaii 415 322 149,563 75.82% 509 374 145,477 73.57% 417 314 129,575 76.89%
Idaho 387 297 205,902 80.07% 461 372 218,580 79.50% 387 308 206,652 80.00%
Illinois 1,186 869 1,554,110 72.36% 1,203 860 1,537,523 72.44% 1,114 787 1,512,952 71.09%
Indiana 417 320 794,923 74.93% 406 319 876,721 79.17% 394 282 747,076 70.86%
Iowa 439 321 338,260 73.31% 461 354 366,248 77.14% 437 323 373,723 74.00%
Kansas 466 350 372,398 75.71% 466 358 384,433 76.71% 462 352 353,102 74.45%
Kentucky 392 303 491,135 76.70% 464 330 503,081 69.67% 458 346 548,045 76.16%
Louisiana 427 339 572,954 79.92% 423 330 551,525 78.20% 418 299 519,800 70.58%
Maine 504 383 144,861 74.75% 437 320 142,045 74.29% 508 370 136,642 74.34%
Maryland 417 325 697,838 79.23% 369 289 674,376 77.37% 405 306 645,964 75.41%
Massachusetts 451 302 762,945 66.67% 532 334 920,942 63.83% 586 393 846,090 69.42%
Michigan 1,085 831 1,181,367 76.19% 1,043 828 1,185,394 79.47% 1,076 821 1,208,711 75.78%
Minnesota 422 330 623,094 78.55% 419 311 633,924 72.50% 460 348 682,965 76.23%
Mississippi 394 317 369,439 81.70% 396 307 353,258 78.49% 402 310 360,474 76.81%
Missouri 440 347 707,841 78.13% 387 298 703,573 77.96% 465 327 742,690 69.79%
Montana 411 315 121,408 76.44% 470 351 111,958 73.38% 385 289 115,633 75.36%
Nebraska 432 340 243,776 79.28% 414 314 228,204 75.92% 458 338 231,189 75.89%
Nevada 429 352 328,354 82.07% 387 322 328,651 82.43% 441 335 334,801 75.53%
New Hampshire 449 336 162,150 76.55% 421 312 154,632 75.27% 485 343 141,957 68.68%
New Jersey 749 552 1,053,116 73.28% 644 479 984,942 74.19% 713 523 1,036,955 72.46%
New Mexico 355 299 249,393 85.28% 400 337 238,580 86.11% 346 291 239,351 84.66%
New York 1,472 1,086 2,225,741 72.22% 1,611 1,122 2,110,349 66.82% 1,648 1,118 2,077,765 66.53%
North Carolina 699 568 1,144,808 81.59% 616 463 1,144,882 74.81% 712 552 1,223,483 78.03%
North Dakota 456 343 97,216 74.74% 495 389 97,876 80.44% 461 331 86,113 72.85%
Ohio 1,086 806 1,380,951 73.71% 1,042 781 1,354,514 73.66% 1,094 812 1,339,799 73.50%
Oklahoma 455 339 482,049 75.21% 436 335 444,359 76.95% 418 282 432,163 65.12%
Oregon 383 286 428,705 75.19% 424 305 418,178 71.58% 517 353 454,274 69.59%
Pennsylvania 1,023 793 1,461,386 78.06% 1,090 822 1,436,509 75.56% 985 751 1,358,691 75.84%
Rhode Island 393 314 118,022 80.14% 384 294 111,874 77.37% 474 349 129,538 74.36%
South Carolina 430 357 556,176 84.04% 410 318 560,534 76.72% 434 357 585,789 78.35%
South Dakota 411 321 103,040 79.00% 434 327 96,080 75.11% 444 349 102,088 79.32%
Tennessee 455 356 801,826 76.98% 435 325 792,000 74.77% 440 338 741,197 73.37%
Texas 1,350 1,102 3,629,329 81.14% 1,370 1,123 3,549,674 81.47% 1,425 1,136 3,685,040 79.49%
Utah 392 337 407,524 85.58% 371 313 433,075 83.27% 390 303 461,460 76.30%
Vermont 428 332 68,842 75.76% 392 288 65,269 74.85% 461 342 64,405 73.91%
Virginia 644 508 909,340 78.71% 659 509 921,301 77.04% 625 469 921,487 75.22%
Washington 406 318 832,648 78.18% 430 330 773,901 75.14% 440 320 775,782 70.76%
West Virginia 453 342 213,596 76.74% 442 324 199,354 74.34% 467 311 188,276 65.45%
Wisconsin 372 279 637,216 73.65% 456 362 615,772 78.49% 447 342 721,273 77.56%
Wyoming 434 336 65,860 77.27% 433 348 68,094 77.82% 442 371 71,649 82.44%
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2015, 2016, and 2017.
Table C.12 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Individuals Aged 12 to 20, by State: 2015-2016 and 2016-2017
State 2015-2016
Total
Selected
2015-2016
Total
Responded
2015-2016
Population
Estimate
2015-2016
Weighted
Interview
Response
Rate
2016-2017
Total
Selected
2016-2017
Total
Responded
2016-2017
Population
Estimate
2016-2017
Weighted
Interview
Response
Rate
Total U.S. 59,892 46,118 37,750,195 77.23% 61,000 45,950 37,720,690 75.31%
Northeast 11,839 8,725 6,403,520 72.32% 12,259 8,794 6,276,082 70.66%
Midwest 14,438 10,958 8,057,227 75.52% 14,538 10,913 8,090,971 74.80%
South 19,561 15,363 14,264,883 79.04% 19,799 15,207 14,294,582 77.36%
West 14,054 11,072 9,024,564 79.32% 14,404 11,036 9,059,055 75.75%
Alabama 847 658 592,843 78.35% 869 666 597,207 77.42%
Alaska 884 655 89,697 74.65% 933 701 87,874 76.46%
Arizona 801 621 789,896 77.94% 814 629 835,341 77.54%
Arkansas 849 662 339,613 77.86% 893 691 354,729 77.76%
California 4,026 3,205 4,719,859 79.86% 4,156 3,130 4,723,951 75.21%
Colorado 846 677 662,188 79.40% 851 640 649,053 74.93%
Connecticut 859 656 441,707 77.39% 888 634 416,780 72.35%
Delaware 830 628 106,981 76.67% 848 612 104,833 73.20%
District of Columbia 695 567 56,823 82.06% 786 635 56,180 81.57%
Florida 2,936 2,315 2,147,315 79.07% 2,975 2,298 2,152,518 77.37%
Georgia 1,268 1,024 1,239,891 80.50% 1,204 946 1,256,084 78.90%
Hawaii 924 696 147,520 74.72% 926 688 137,526 75.17%
Idaho 848 669 212,241 79.78% 848 680 212,616 79.74%
Illinois 2,389 1,729 1,545,817 72.40% 2,317 1,647 1,525,238 71.76%
Indiana 823 639 835,822 77.19% 800 601 811,898 75.32%
Iowa 900 675 352,254 75.30% 898 677 369,985 75.59%
Kansas 932 708 378,416 76.21% 928 710 368,768 75.59%
Kentucky 856 633 497,108 73.15% 922 676 525,563 72.93%
Louisiana 850 669 562,239 79.07% 841 629 535,662 74.53%
Maine 941 703 143,453 74.53% 945 690 139,343 74.31%
Maryland 786 614 686,107 78.30% 774 595 660,170 76.42%
Massachusetts 983 636 841,944 65.13% 1,118 727 883,516 66.53%
Michigan 2,128 1,659 1,183,381 77.82% 2,119 1,649 1,197,053 77.62%
Minnesota 841 641 628,509 75.55% 879 659 658,444 74.49%
Mississippi 790 624 361,349 80.10% 798 617 356,866 77.64%
Missouri 827 645 705,707 78.04% 852 625 723,132 73.74%
Montana 881 666 116,683 74.91% 855 640 113,795 74.36%
Nebraska 846 654 235,990 77.69% 872 652 229,697 75.90%
Nevada 816 674 328,502 82.25% 828 657 331,726 78.95%
New Hampshire 870 648 158,391 75.93% 906 655 148,294 72.00%
New Jersey 1,393 1,031 1,019,029 73.72% 1,357 1,002 1,010,948 73.30%
New Mexico 755 636 243,987 85.70% 746 628 238,965 85.39%
New York 3,083 2,208 2,168,045 69.54% 3,259 2,240 2,094,057 66.68%
North Carolina 1,315 1,031 1,144,845 78.20% 1,328 1,015 1,184,182 76.46%
North Dakota 951 732 97,546 77.54% 956 720 91,995 76.77%
Ohio 2,128 1,587 1,367,732 73.69% 2,136 1,593 1,347,156 73.58%
Oklahoma 891 674 463,204 76.03% 854 617 438,261 70.93%
Oregon 807 591 423,441 73.42% 941 658 436,226 70.53%
Pennsylvania 2,113 1,615 1,448,948 76.81% 2,075 1,573 1,397,600 75.70%
Rhode Island 777 608 114,948 78.84% 858 643 120,706 75.70%
South Carolina 840 675 558,355 80.31% 844 675 573,161 77.55%
South Dakota 845 648 99,560 77.08% 878 676 99,084 77.28%
Tennessee 890 681 796,913 75.88% 875 663 766,599 74.09%
Texas 2,720 2,225 3,589,502 81.30% 2,795 2,259 3,617,357 80.47%
Utah 763 650 420,299 84.41% 761 616 447,267 79.71%
Vermont 820 620 67,055 75.32% 853 630 64,837 74.38%
Virginia 1,303 1,017 915,320 77.87% 1,284 978 921,394 76.12%
Washington 836 648 803,274 76.67% 870 650 774,841 72.94%
West Virginia 895 666 206,475 75.59% 909 635 193,815 69.90%
Wisconsin 828 641 626,494 76.04% 903 704 668,522 78.00%
Wyoming 867 684 66,977 77.55% 875 719 69,871 80.17%
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
NOTE: To compute the pooled weighted response rates, the two samples were combined, and the individual-year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the individual response rates. The population estimate is the average of the population across the 2 years.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2015, 2016, and 2017.
Table C.13 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Adults Aged 18 or Older, by State: 2015, 2016, and 2017
State 2015
Total
Selected
2015
Total
Responded
2015
Population
Estimate
2015
Weighted
Interview
Response
Rate
2016
Total
Selected
2016
Total
Responded
2016
Population
Estimate
2016
Weighted
Interview
Response
Rate
2017
Total
Selected
2017
Total
Responded
2017
Population
Estimate
2017
Weighted
Interview
Response
Rate
Total U.S. 72,640 51,118 242,801,072 68.39% 73,284 50,833 244,533,608 67.57% 74,917 50,999 247,160,541 66.31%
Northeast 14,680 9,798 43,685,848 64.90% 14,365 9,518 43,700,225 64.03% 15,162 9,957 44,028,298 63.74%
Midwest 17,056 11,935 51,311,021 67.82% 17,294 11,918 51,418,305 67.13% 17,692 11,946 51,704,631 66.52%
South 23,653 17,001 90,699,086 70.03% 24,243 17,208 91,710,838 69.80% 24,497 17,113 92,958,490 68.63%
West 17,251 12,384 57,105,116 68.94% 17,382 12,189 57,704,240 67.09% 17,566 11,983 58,469,122 64.31%
Alabama 1,039 724 3,676,390 66.92% 1,088 749 3,688,058 65.49% 1,040 720 3,701,931 66.05%
Alaska 1,051 754 522,844 71.80% 1,008 724 525,666 68.32% 1,037 686 527,234 66.17%
Arizona 1,067 757 5,098,098 69.66% 997 748 5,193,574 74.73% 812 615 5,280,534 72.53%
Arkansas 1,020 725 2,221,013 68.03% 1,074 757 2,231,337 68.52% 1,008 725 2,246,021 67.78%
California 5,034 3,523 29,512,527 67.44% 5,211 3,432 29,655,758 63.94% 5,409 3,343 29,974,934 60.00%
Colorado 1,008 725 4,107,515 71.05% 1,017 677 4,188,280 65.76% 1,106 756 4,253,700 67.35%
Connecticut 1,106 723 2,777,048 64.80% 1,089 713 2,774,524 63.98% 1,145 755 2,795,622 66.81%
Delaware 1,021 707 726,446 70.38% 1,042 711 732,938 66.82% 1,084 716 742,998 66.05%
District of Columbia 967 714 543,866 74.11% 968 727 549,919 73.64% 951 695 559,290 72.94%
Florida 3,593 2,542 15,851,157 69.33% 3,687 2,576 16,149,440 67.44% 3,665 2,505 16,474,084 66.71%
Georgia 1,468 1,078 7,507,971 70.76% 1,537 1,138 7,603,492 70.24% 1,612 1,157 7,719,247 69.36%
Hawaii 1,103 794 1,061,433 70.30% 1,070 722 1,061,878 65.85% 1,087 725 1,064,241 62.26%
Idaho 996 729 1,201,314 71.81% 1,095 818 1,225,558 73.38% 992 738 1,253,342 73.91%
Illinois 2,705 1,717 9,718,727 62.12% 2,905 1,826 9,690,578 60.72% 2,941 1,744 9,720,651 58.57%
Indiana 1,060 731 4,945,710 67.38% 1,003 711 4,964,511 68.62% 1,074 717 4,999,830 66.90%
Iowa 1,011 709 2,354,463 68.05% 1,065 756 2,363,600 71.00% 1,118 740 2,373,014 66.49%
Kansas 1,004 735 2,129,427 71.46% 1,026 738 2,132,038 70.66% 1,037 744 2,139,785 70.62%
Kentucky 975 706 3,328,266 71.53% 1,100 703 3,343,975 61.86% 1,100 729 3,361,242 64.73%
Louisiana 971 713 3,452,153 72.29% 1,003 710 3,463,990 70.05% 1,052 731 3,472,415 68.50%
Maine 1,018 701 1,059,704 68.17% 1,080 765 1,063,275 71.40% 1,014 705 1,069,799 68.46%
Maryland 983 708 4,564,964 69.04% 1,053 781 4,573,424 72.61% 1,051 759 4,610,102 71.25%
Massachusetts 1,254 720 5,334,861 57.09% 1,229 760 5,362,512 61.71% 1,276 714 5,419,068 56.05%
Michigan 2,585 1,840 7,608,717 68.93% 2,549 1,810 7,631,694 69.64% 2,616 1,807 7,681,241 67.21%
Minnesota 967 704 4,149,168 72.79% 1,061 723 4,176,101 67.85% 1,054 732 4,223,276 70.66%
Mississippi 970 690 2,199,815 69.02% 978 699 2,202,801 70.41% 1,020 698 2,206,850 66.29%
Missouri 1,034 742 4,587,280 69.47% 1,052 722 4,600,630 65.21% 1,077 754 4,624,223 69.43%
Montana 1,029 747 791,726 68.78% 1,100 760 799,997 70.74% 1,052 773 807,184 74.33%
Nebraska 1,012 725 1,396,741 70.63% 1,051 723 1,404,674 68.00% 1,013 710 1,415,282 68.81%
Nevada 993 726 2,184,663 68.41% 977 717 2,224,088 71.29% 1,086 722 2,275,121 64.07%
New Hampshire 1,113 757 1,051,093 67.61% 1,034 700 1,057,321 66.51% 1,069 739 1,067,801 71.64%
New Jersey 1,720 1,130 6,856,888 64.65% 1,666 1,064 6,857,473 61.81% 1,856 1,196 6,925,877 63.49%
New Mexico 1,005 744 1,552,567 72.80% 900 711 1,554,056 78.54% 893 713 1,564,401 78.79%
New York 3,898 2,544 15,358,693 63.00% 3,706 2,370 15,337,132 60.94% 3,984 2,522 15,464,407 61.71%
North Carolina 1,586 1,138 7,540,012 68.76% 1,626 1,158 7,632,608 71.04% 1,554 1,078 7,766,421 69.00%
North Dakota 1,024 757 566,516 72.51% 983 683 564,944 68.21% 1,038 728 562,731 70.04%
Ohio 2,655 1,839 8,817,736 68.04% 2,592 1,796 8,833,293 66.97% 2,634 1,811 8,883,426 68.21%
Oklahoma 1,010 711 2,871,703 66.69% 1,033 701 2,883,440 67.23% 1,078 716 2,892,414 66.78%
Oregon 1,052 748 3,128,475 70.44% 1,060 760 3,186,630 70.94% 1,100 744 3,231,638 67.24%
Pennsylvania 2,490 1,800 9,918,209 71.18% 2,494 1,746 9,915,686 70.07% 2,614 1,831 9,947,416 68.42%
Rhode Island 1,068 736 829,169 68.52% 1,061 713 831,935 66.55% 1,134 759 837,144 67.08%
South Carolina 960 705 3,703,779 71.56% 1,038 742 3,765,360 71.95% 1,016 735 3,825,020 69.85%
South Dakota 899 674 630,375 74.49% 1,006 705 634,995 70.24% 1,018 729 637,785 71.23%
Tennessee 1,057 774 4,999,624 68.92% 1,058 758 5,048,067 70.21% 1,006 721 5,106,775 71.07%
Texas 3,399 2,528 19,771,231 72.42% 3,254 2,467 20,080,000 73.74% 3,457 2,525 20,458,311 71.20%
Utah 905 706 2,058,738 75.91% 929 696 2,105,544 73.83% 969 728 2,151,568 73.72%
Vermont 1,013 687 500,184 67.99% 1,006 687 500,367 71.00% 1,070 736 501,164 69.01%
Virginia 1,623 1,134 6,303,312 68.67% 1,585 1,102 6,333,111 67.78% 1,703 1,173 6,396,270 65.66%
Washington 1,021 717 5,447,554 69.10% 1,038 681 5,546,482 65.17% 1,116 728 5,651,840 64.19%
West Virginia 1,011 704 1,437,385 65.62% 1,119 729 1,428,879 62.95% 1,100 730 1,419,101 65.14%
Wisconsin 1,100 762 4,406,160 67.95% 1,001 725 4,421,246 72.50% 1,072 730 4,443,389 68.23%
Wyoming 987 714 437,663 71.68% 980 743 436,729 75.01% 907 712 433,386 77.65%
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2015, 2016, and 2017.
Table C.14 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Adults Aged 18 or Older, by State: 2015-2016 and 2016-2017
State 2015-2016
Total
Selected
2015-2016
Total
Responded
2015-2016
Population
Estimate
2015-2016
Weighted
Interview
Response
Rate
2016-2017
Total
Selected
2016-2017
Total
Responded
2016-2017
Population
Estimate
2016-2017
Weighted
Interview
Response
Rate
Total U.S. 145,924 101,951 243,667,340 67.98% 148,201 101,832 245,847,075 66.94%
Northeast 29,045 19,316 43,693,037 64.47% 29,527 19,475 43,864,261 63.89%
Midwest 34,350 23,853 51,364,663 67.47% 34,986 23,864 51,561,468 66.83%
South 47,896 34,209 91,204,962 69.91% 48,740 34,321 92,334,664 69.21%
West 34,633 24,573 57,404,678 68.02% 34,948 24,172 58,086,681 65.69%
Alabama 2,127 1,473 3,682,224 66.18% 2,128 1,469 3,694,995 65.76%
Alaska 2,059 1,478 524,255 70.06% 2,045 1,410 526,450 67.26%
Arizona 2,064 1,505 5,145,836 72.24% 1,809 1,363 5,237,054 73.66%
Arkansas 2,094 1,482 2,226,175 68.27% 2,082 1,482 2,238,679 68.14%
California 10,245 6,955 29,584,142 65.71% 10,620 6,775 29,815,346 61.94%
Colorado 2,025 1,402 4,147,898 68.34% 2,123 1,433 4,220,990 66.60%
Connecticut 2,195 1,436 2,775,786 64.38% 2,234 1,468 2,785,073 65.32%
Delaware 2,063 1,418 729,692 68.59% 2,126 1,427 737,968 66.43%
District of Columbia 1,935 1,441 546,893 73.88% 1,919 1,422 554,604 73.29%
Florida 7,280 5,118 16,000,298 68.36% 7,352 5,081 16,311,762 67.08%
Georgia 3,005 2,216 7,555,731 70.49% 3,149 2,295 7,661,369 69.80%
Hawaii 2,173 1,516 1,061,655 68.11% 2,157 1,447 1,063,059 64.05%
Idaho 2,091 1,547 1,213,436 72.59% 2,087 1,556 1,239,450 73.66%
Illinois 5,610 3,543 9,704,653 61.42% 5,846 3,570 9,705,615 59.65%
Indiana 2,063 1,442 4,955,111 67.98% 2,077 1,428 4,982,171 67.71%
Iowa 2,076 1,465 2,359,031 69.51% 2,183 1,496 2,368,307 68.69%
Kansas 2,030 1,473 2,130,733 71.06% 2,063 1,482 2,135,911 70.64%
Kentucky 2,075 1,409 3,336,120 66.64% 2,200 1,432 3,352,608 63.35%
Louisiana 1,974 1,423 3,458,071 71.13% 2,055 1,441 3,468,202 69.28%
Maine 2,098 1,466 1,061,489 69.80% 2,094 1,470 1,066,537 69.92%
Maryland 2,036 1,489 4,569,194 70.80% 2,104 1,540 4,591,763 71.95%
Massachusetts 2,483 1,480 5,348,686 59.39% 2,505 1,474 5,390,790 58.86%
Michigan 5,134 3,650 7,620,206 69.29% 5,165 3,617 7,656,468 68.44%
Minnesota 2,028 1,427 4,162,634 70.29% 2,115 1,455 4,199,689 69.25%
Mississippi 1,948 1,389 2,201,308 69.70% 1,998 1,397 2,204,825 68.29%
Missouri 2,086 1,464 4,593,955 67.26% 2,129 1,476 4,612,426 67.27%
Montana 2,129 1,507 795,861 69.74% 2,152 1,533 803,591 72.57%
Nebraska 2,063 1,448 1,400,708 69.32% 2,064 1,433 1,409,978 68.41%
Nevada 1,970 1,443 2,204,376 69.95% 2,063 1,439 2,249,604 67.77%
New Hampshire 2,147 1,457 1,054,207 67.06% 2,103 1,439 1,062,561 69.08%
New Jersey 3,386 2,194 6,857,180 63.26% 3,522 2,260 6,891,675 62.65%
New Mexico 1,905 1,455 1,553,311 75.59% 1,793 1,424 1,559,228 78.66%
New York 7,604 4,914 15,347,913 61.95% 7,690 4,892 15,400,769 61.32%
North Carolina 3,212 2,296 7,586,310 69.87% 3,180 2,236 7,699,515 70.01%
North Dakota 2,007 1,440 565,730 70.40% 2,021 1,411 563,837 69.14%
Ohio 5,247 3,635 8,825,514 67.49% 5,226 3,607 8,858,359 67.58%
Oklahoma 2,043 1,412 2,877,571 66.96% 2,111 1,417 2,887,927 67.00%
Oregon 2,112 1,508 3,157,552 70.69% 2,160 1,504 3,209,134 69.07%
Pennsylvania 4,984 3,546 9,916,947 70.63% 5,108 3,577 9,931,551 69.24%
Rhode Island 2,129 1,449 830,552 67.54% 2,195 1,472 834,539 66.82%
South Carolina 1,998 1,447 3,734,569 71.75% 2,054 1,477 3,795,190 70.88%
South Dakota 1,905 1,379 632,685 72.36% 2,024 1,434 636,390 70.76%
Tennessee 2,115 1,532 5,023,845 69.57% 2,064 1,479 5,077,421 70.65%
Texas 6,653 4,995 19,925,615 73.08% 6,711 4,992 20,269,156 72.44%
Utah 1,834 1,402 2,082,141 74.86% 1,898 1,424 2,128,556 73.77%
Vermont 2,019 1,374 500,275 69.49% 2,076 1,423 500,766 70.01%
Virginia 3,208 2,236 6,318,212 68.23% 3,288 2,275 6,364,691 66.72%
Washington 2,059 1,398 5,497,018 67.14% 2,154 1,409 5,599,161 64.67%
West Virginia 2,130 1,433 1,433,132 64.24% 2,219 1,459 1,423,990 64.04%
Wisconsin 2,101 1,487 4,413,703 70.29% 2,073 1,455 4,432,318 70.43%
Wyoming 1,967 1,457 437,196 73.32% 1,887 1,455 435,057 76.29%
NOTE: Computations in this table are based on a respondent's age at screening. Thus, the data in the Total Responded column(s) could differ from data in other NSDUH tables that use the respondent's age recorded during the interview.
NOTE: To compute the pooled weighted response rates, the two samples were combined, and the individual-year weights were used for the pooled sample. Thus, the response rates presented here are weighted across 2 years of data rather than being a simple average of the individual response rates. The population estimate is the average of the population across the 2 years.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2015, 2016, and 2017.
Table C.15 – NSDUH Outcomes, by Survey Year, for Which Small Area Estimates Are Available
Measure 2002-
2003
2003-
2004
2004-
2005
2005-
2006
2006-
2007
2007-
2008
2008-
2009
2009-
2010
2010-
2011
2011-
2012
2012-
2013
2013-
2014
2014-
2015
2015-
2016
2016-
2017
Illicit Drug Use in the Past Month1 X X X X X X X X X X X X -- X X
Marijuana Use in the Past Year X X X X X X X X X X X X X X X
Marijuana Use in the Past Month X X X X X X X X X X X X X X X
Perceptions of Great Risk from Smoking Marijuana Once a Month1 X X X X X X X X X X X X -- X X
First Use of Marijuana (Marijuana Initiation) X X X X X X X X X X X X X X X
Illicit Drug Use Other Than Marijuana in the Past Month1 X X X X X X X X X X X X -- X X
Cocaine Use in the Past Year X X X X X X X X X X X X X X X
Perceptions of Great Risk from Using Cocaine Once a Month -- -- -- -- -- -- -- -- -- -- -- -- -- X X
Methamphetamine Use in the Past Year -- -- -- -- -- -- -- -- -- -- -- -- -- --2 X
Pain Reliever Misuse in the Past Year1 --3 X X X X X X X X X X X -- X X
Heroin Use in the Past Year -- -- -- -- -- -- -- -- -- -- -- --4 X X X
Perceptions of Great Risk from Trying Heroin
   Once or Twice
-- -- -- -- -- -- -- -- -- -- -- -- -- X X
Alcohol Use in the Past Month X X X X X X X X X X X X X X X
Underage Past Month Use of Alcohol --2 X X X X X X X X X X X X X X
Binge Alcohol Use in the Past Month1 X X X X X X X X X X X X -- X X
Underage Past Month Binge Alcohol Use1 --2 X X X X X X X X X X X -- X X
Perceptions of Great Risk from Having Five or More
   Drinks of an Alcoholic Beverage Once or Twice a Week1
X X X X X X X X X X X X -- X X
Tobacco Product Use in the Past Month X X X X X X X X X X X X X X X
Cigarette Use in the Past Month X X X X X X X X X X X X X X X
Perceptions of Great Risk from Smoking One or More
   Packs of Cigarettes per Day1
X X X X X X X X X X X X -- X X
Alcohol Use Disorder in the Past Year X X X X X X X X X X X X X X X
Alcohol Dependence in the Past Year X X X X X X X X X X X X X -- --
Illicit Drug Use Disorder in the Past Year1 X X X X X X X X X X X X -- X X
Illicit Drug Dependence in the Past Year X X X X X X X X X X X X -- -- --
Pain Reliever Use Disorder in the Past Year -- -- -- -- -- -- -- -- -- -- -- -- -- X X
Substance Use Disorder in the Past Year1 X X X X X X X X X X X X -- X X
Needing But Not Receiving Treatment at a Specialty
   Facility for Illicit Drug Use in the Past Year1
X X X X X X X X X X X X -- X X
Needing But Not Receiving Treatment at a Specialty
   Facility for Alcohol Use in the Past Year1
X X X X X X X X X X X X -- X X
Needing But Not Receiving Treatment at a Specialty
   Facility for Substance Use in the Past Year1,5
-- -- -- -- -- -- -- -- X X X X -- X X
Serious Psychological Distress (SPD) in the Past Year6 X X X -- -- -- -- -- -- -- -- -- -- -- --
Had at Least One Major Depressive Episode (MDE) in the
   Past Year7
-- -- X X X X X X X X X X X X X
Serious Mental Illness (SMI) in the Past Year -- -- -- -- -- -- X X X X X X X X X
Any Mental Illness (AMI) in the Past Year -- -- -- -- -- -- X X X X X X X X X
Had Serious Thoughts of Suicide in the Past Year -- -- -- -- -- -- X X X X X X X X X
Received Mental Health Services in the Past Year5 -- -- -- -- -- -- -- -- X X X X X X X
X = available; -- = not available.
1 For these outcomes, the 2015-2016 and 2016-2017 small area estimates are not comparable with the 2013-2014 estimates or the estimates from prior years. Because of comparability issues, 2014-2015 small area estimates were not produced for these outcomes. Prior to 2015-2016, "misuse of pain relievers" was referred to as "nonmedical use of pain relievers."
2 Estimates for this outcome were not included in the 2015-2016 state document at https://www.samhsa.gov/data/, but the 2015-2016 estimates were included in the 2016-2017 state documents as part of the comparison tables. However, the Bayesian confidence intervals associated with these estimates were not published.
3 Estimates for these outcomes were not included in the 2002-2003 state report (Wright & Sathe, 2005), but the 2002-2003 estimates were included in the 2003-2004 state report as part of the comparison tables (see Wright & Sathe, 2006). However, the Bayesian confidence intervals associated with these estimates were not published.
4 Estimates for this outcome were not included in the 2013-2014 state documents at https://www.samhsa.gov/data/, but the 2013-2014 estimates were included in the 2014-2015 state documents as part of the comparison tables. However, the Bayesian confidence intervals associated with these estimates were not published.
5 Estimates for these outcomes were produced for years prior to 2015-2016 and published separately from the main state documents. Starting in 2015-2016, these outcomes are included in the main state documents.
6 Estimates for SPD in the years 2002-2003 and 2003-2004 are not comparable with the 2004-2005 SPD estimates. For more details, see Section A.7 in Appendix A of the 2004-2005 state report (Wright, Sathe, & Spagnola, 2007). Note that, in 2002-2003, "SPD" was referred to as "serious mental illness."
7 Questions that were used to determine an MDE were added in 2004. Note that the adult MDE estimates shown in the 2004-2005 state report (Wright & Sathe, 2006) are not comparable with the adult MDE estimates for later years.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2002-2017.
Table C.16 – NSDUH Outcomes, by Age Groups, for Which Small Area Estimates Are Available
Measure Age Group
12+ 12-17 12-20 18-25 26+ 18+
Illicit Drug Use in the Past Month X X -- X X X
Marijuana Use in the Past Year X X -- X X X
Marijuana Use in the Past Month X X -- X X X
Perceptions of Great Risk from Smoking Marijuana Once a Month X X -- X X X
First Use of Marijuana (Marijuana Initiation) X X -- X X X
Illicit Drug Use Other Than Marijuana in the Past Month X X -- X X X
Cocaine Use in the Past Year X X -- X X X
Perceptions of Great Risk from Using Cocaine Once a Month X X -- X X X
Methamphetamine Use in the Past Year X X -- X X X
Pain Reliever Misuse in the Past Year X X -- X X X
Heroin Use in the Past Year X X -- X X X
Perceptions of Great Risk from Using Heroin Once or Twice X X -- X X X
Alcohol Use in the Past Month X X X X X X
Binge Alcohol Use in the Past Month X X X X X X
Perceptions of Great Risk from Having Five or More Drinks of an Alcoholic Beverage Once or
   Twice a Week
X X -- X X X
Tobacco Product Use in the Past Month X X -- X X X
Cigarette Use in the Past Month X X -- X X X
Perceptions of Great Risk from Smoking One or More Packs of Cigarettes per Day X X -- X X X
Alcohol Use Disorder in the Past Year X X -- X X X
Alcohol Dependence in the Past Year X X -- X X X
Illicit Drug Use Disorder in the Past Year X X -- X X X
Illicit Drug Dependence in the Past Year X X -- X X X
Pain Reliever Use Disorder in the Past Year X X -- X X X
Substance Use Disorder the Past Year X X -- X X X
Needing But Not Receiving Treatment at a Specialty Facility for Illicit Drug Use in the Past Year X X -- X X X
Needing But Not Receiving Treatment at a Specialty Facility for Alcohol Use in the Past Year X X -- X X X
Needing But Not Receiving Treatment at a Specialty Facility for Substance Use in the Past Year X X -- X X X
Serious Psychological Distress (SPD) in the Past Year -- -- -- X X X
Had at Least One Major Depressive Episode (MDE) in the Past Year1 -- X -- X X X
Serious Mental Illness (SMI) in the Past Year -- -- -- X X X
Any Mental Illness (AMI) in the Past Year -- -- -- X X X
Had Serious Thoughts of Suicide in the Past Year -- -- -- X X X
Received Mental Health Services in the Past Year -- -- -- X X X
X = available; -- = not available.
NOTE: For details on which years small area estimates are available for these outcomes, see Table C.15.
NOTE: Tables containing estimates for adults aged 18 or older were first presented with the 2005-2006 small area estimation tables.
NOTE: Estimates for those aged 18 to 25, 26 or older, and 18 or older are available for all outcomes.
1 There are minor wording differences in the questions for the adult and adolescent MDE modules. Therefore, data from youths aged 12 to 17 were not combined with data from adults aged 18 or older to get an overall MDE estimate (12 or older).
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2002-2017.
Table C.17 – Summary of Milestones Implemented in NSDUH's SAE Production Process, 2002-2017
SAE Production Milestone Years for Which Pooled 2-Year Small Area Estimates Were Published
2002-
2003
2003-
2004
2004-
2005
2005-
2006
2006-
2007
2007-
2008
2008-
2009
2009-
2010
2010-
2011
2011-
2012
2012-
2013
2013-
2014
2014-
2015
2015-
2016
2016-
2017
Weights Based on Projections from 2000 Census Control Totals checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark1 -- -- -- -- -- --
Weights Based on Projections from 2010 Census Control Totals -- -- -- -- -- -- -- -- checkmark1 checkmark checkmark checkmark checkmark checkmark checkmark
Small Area Estimates Produced Based on Variable Selection Done Using 2002-2003 Data2 checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark3 -- -- -- -- -- --
Small Area Estimates Produced Based on Variable Selection Done Using 2010-2011 Data4 -- -- -- -- -- -- -- -- checkmark3 checkmark checkmark checkmark checkmark -- --
Small Area Estimates Produced Based on Variable Selection Done Using 2015-2016 Data -- -- -- -- -- -- -- -- -- -- -- -- -- checkmark checkmark
Small Area Estimates Reproduced Using Data Omitting Falsified Data5 -- -- -- checkmark checkmark checkmark checkmark -- -- -- -- -- -- -- --
SMI and AMI Small Area Estimates Based on Updated 2013 Model6 -- -- -- -- -- -- checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark
MDE Small Area Estimates Based on Adjusted MDE Variable7 -- -- -- checkmark checkmark checkmark checkmark -- -- -- -- -- -- -- --
checkmark = SAE production milestone implemented; -- = SAE production milestone not implemented; AMI = any mental illness; MDE = major depressive episode; NSDUH = National Survey on Drug Use and Health; SAE = small area estimation; SMI = serious mental illness.
1 The weight used for 2010 was based on projections from the 2000 census control totals, and the 2011 weight was based on projections from the 2010 census control totals. For SMI and AMI, the weights used for both years were based on the 2010 census control totals.
2 Variable selection was done using 2002-2003 NSDUH data for all outcomes with the following exception: For SMI, AMI, suicidal thoughts in the past year, and MDE, variable selection was done using 2008-2009 NSDUH data. Note that the 2005-2006, 2006-2007, and 2007–2008 MDE small area estimates were based on the variable selection done in 2008-2009.
3 For all outcomes except SMI and AMI, the 2010-2011 small area estimates were produced based on 2002-2003 variable selection (see footnote 2 for an exception). For SMI and AMI, variable selection was done using 2010-2011 NSDUH data.
4 When new variable selection was done using 2010-2011 NSDUH data, one source of predictor data was revised: The American Community Survey (ACS) estimates were used in place of the 2000 long-form census estimates, which resulted in dropping several predictors and adding several new predictors. For past year heroin use, variable selection was done using 2014-2015 data.
5 The 2005-2006 through 2008-2009 small area estimates were revised and republished with falsified data removed. For more information, see Section A.7 of "2011-2012 NSDUH: Guide to State Tables and Summary of Small Area Estimation Methodology" at https://www.samhsa.gov/data/.
6 The 2008-2009, 2009-2010, and 2010-2011 small area estimates were revised and republished based on the new SMI and AMI variables. These new variables will continue to be used to produce SMI and AMI small area estimates. For more information, see Section B.11.1 of the document mentioned in this table's footnote 5.
7 An adjusted MDE variable was created for 2005-2008 that is comparable with the 2009-2013 MDE variables. Hence, MDE small area estimates were produced using the adjusted variable. For more information, see Section B.11.3 of the document mentioned in this table's footnote 5.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2002-2017.

Section D: References

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (DSM-IV) (4th ed.). Washington, DC: Author.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5) (5th ed.). Arlington, VA: Author.

Center for Behavioral Health Statistics and Quality. (2012). 2010 National Survey on Drug Use and Health: Methodological resource book (Section 16b, 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). Retrieved from https://www.samhsa.gov/data/

Center for Behavioral Health Statistics and Quality. (2015a). 2014 National Survey on Drug Use and Health: Methodological resource book (Section 2, Sample design report). Retrieved from https://www.samhsa.gov/data/

Center for Behavioral Health Statistics and Quality. (2015b, August). National Survey on Drug Use and Health: 2014 and 2015 redesign changes. Retrieved from https://www.samhsa.gov/data/

Center for Behavioral Health Statistics and Quality. (2016a). 2015 National Survey on Drug Use and Health: Methodological resource book. Retrieved from https://www.samhsa.gov/data/

Center for Behavioral Health Statistics and Quality. (2016b). 2015 National Survey on Drug Use and Health: Methodological summary and definitions. Retrieved from https://www.samhsa.gov/data/

Center for Behavioral Health Statistics and Quality. (2016c). 2015 National Survey on Drug Use and Health: Summary of the effects of the 2015 NSDUH questionnaire redesign: Implications for data users. Retrieved from https://www.samhsa.gov/data/

Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. (2016d). National Survey on Drug Use and Health: 2015 public use file and codebook. Retrieved from https://datafiles.samhsa.gov/

Center for Behavioral Health Statistics and Quality. (2017). 2016 National Survey on Drug Use and Health: Methodological summary and definitions. Retrieved from https://www.samhsa.gov/data/

Center for Behavioral Health Statistics and Quality. (2018). 2017 National Survey on Drug Use and Health: Methodological summary and definitions. Retrieved from https://www.samhsa.gov/data/

Center for Behavioral Health Statistics and Quality. (in press). 2017 National Survey on Drug Use and Health: Methodological resource book. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Endicott, J., Spitzer, R. L., Fleiss, J. L., & Cohen, J. (1976). The Global Assessment Scale: A procedure for measuring overall severity of psychiatric disturbance. Archives of General Psychiatry, 33, 766-771. https://doi.org/10.1001/archpsyc.1976.01770060086012 exit icon

First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (2002). Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition (SCID-I/NP). New York, NY: New York State Psychiatric Institute, Biometrics Research.

Folsom, R. E., Shah, B., & Vaish, A. (1999). Substance abuse in states: A methodological report on model based estimates from the 1994-1996 National Household Surveys on Drug Abuse. In Proceedings of the 1999 Joint Statistical Meetings, American Statistical Association, Survey Research Methods Section, Baltimore, MD (pp. 371-375). Alexandria, VA: American Statistical Association.

Ghosh, M. (1992). Constrained Bayes estimation with applications. Journal of the American Statistical Association, 87, 533-540. https://doi.org/10.2307/2290287 exit icon

Kessler, R. C., Barker, P. R., Colpe, L. J., Epstein, J. F., Gfroerer, J. C., Hiripi, E., Howes, M. J., Normand, S. L., Manderscheid, R. W., Walters, E. E., & Zaslavsky, A. M. (2003). Screening for serious mental illness in the general population. Archives of General Psychiatry, 60, 184-189. https://doi.org/10.1001/archpsyc.60.2.184 exit icon

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. https://doi.org/10.2190/t8em-c8yh-373n-1uwd exit icon

Novak, S. (2007, October). An item response analysis of the World Health Organization Disability Assessment Schedule (WHODAS) items in the 2002-2004 NSDUH (prepared for the Substance Abuse and Mental Health Services Administration under Contract No. 283-03-9028, RTI/8726). Research Triangle Park, NC: RTI International.

Office of Applied Studies. (2001). Development of computer-assisted interviewing procedures for the National Household Survey on Drug Abuse (HHS Publication No. SMA 01-3514, Methodology Series M-3). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2005). Results from the 2004 National Survey on Drug Use and Health: National findings (HHS Publication No. SMA 05-4062, NSDUH Series H-28). Rockville, MD: Substance Abuse and Mental Health Services Administration.

Raftery, A. E., & 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.

Rao, J. N. K. (2003). Small area estimation (Wiley Series in Survey Methodology). Hoboken, NJ: John Wiley & Sons.

Scheuren, F. (2004, June). What is a survey? (2nd ed.). Retrieved from https://www.whatisasurvey.info/overview.htm exit icon

Shah, B. V., Barnwell, B. G., Folsom, R., & Vaish, A. (2000). Design consistent small area estimates using Gibbs algorithm for logistic models. In Proceedings of the 2000 Joint Statistical Meetings, American Statistical Association, Survey Research Methods Section, Indianapolis, IN (pp. 105-111). Alexandria, VA: American Statistical Association.

Singh, A. C., & Folsom, R. E. (2001, April 11-14). Hierarchical Bayes calibrated domain estimation via Metropolis-Hastings Step in MCMC with application to small areas. Presented at the International Conference on Small Area Estimation and Related Topics, Potomac, MD.

Wright, D. (2003a). State estimates of substance use from the 2001 National Household Survey on Drug Abuse: Volume I. Findings (HHS Publication No. SMA 03-3775, NHSDA Series H-19). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D. (2003b). State estimates of substance use from the 2001 National Household Survey on Drug Abuse: Volume II. Individual state tables and technical appendices (HHS Publication No. SMA 03-3826, NHSDA Series H-20). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D., & Sathe, N. (2005). State estimates of substance use from the 2002-2003 National Surveys on Drug Use and Health (HHS Publication No. SMA 05-3989, NSDUH Series H-26). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D., & Sathe, N. (2006). State estimates of substance use from the 2003-2004 National Surveys on Drug Use and Health (HHS Publication No. SMA 06-4142, NSDUH Series H-29). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Wright, D., Sathe, N., & Spagnola, K. (2007). State estimates of substance use from the 2004-2005 National Surveys on Drug Use and Health (HHS Publication No. SMA 07-4235, NSDUH Series H-31). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

Section E: List of Contributors

This National Survey on Drug Use and Health (NSDUH) document was prepared by the Center for Behavioral Health Statistics and Quality (CBHSQ), Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services (HHS), and by RTI International (a registered trademark and a trade name of Research Triangle Institute), Research Triangle Park, North Carolina. Work by RTI was performed under Contract No. HHSS283201300001C.

At SAMHSA, Peter Tice and Arthur Hughes reviewed the document and provided substantive revisions. At RTI, Neeraja S. Sathe and Kathryn Spagnola were responsible for the writing of the document, and Akhil K. Vaish was responsible for the overall methodology and estimation for the model-based Bayes estimates and confidence intervals. One of the SAMHSA reviewers, Arthur Hughes, has retired from SAMHSA.

The following staff were responsible for generating the estimates and providing other support and analysis: Akhil K. Vaish, Neeraja S. Sathe, Kathryn Spagnola, and Brenda K. Porter. Ms. Spagnola provided oversight for production of the document. Richard S. Straw edited it; Debbie Bond formatted its text and tables; and Teresa F. Bass, Kimberly H. Cone, and Margaret A. Johnson prepared the web versions. Justine L. Allpress, Valerie Garner, and E. Andrew Jessup prepared and processed the maps used in the associated files.

End Notes

1 Use the NSDUH link on the following web page: https://www.samhsa.gov/data/.

2 RTI International is a registered trademark and a trade name of Research Triangle Institute, Research Triangle Park, North Carolina.

3 National small area estimates = Population-weighted averages of state-level small area estimates.

4 The census region-level estimates in the tables are population-weighted aggregates of the state estimates. The published national estimates, however, are benchmarked to exactly match the design-based estimates.

5 At https://www.samhsa.gov/data/, see Tables 1 to 31 in "2016-2017 NSDUHs: Model-Based Estimated Totals (in Thousands) (50 States and the District of Columbia)."

6 Note that in the 2004-2005 NSDUH state report (Wright, Sathe, & Spagnola, 2007) and prior reports, the term "prediction interval" (PI) was used to represent uncertainty in the state and regional estimates. However, that term also is used in other applications to estimate future values of a parameter of interest. That interpretation does not apply to NSDUH state report estimates; thus, "prediction interval" was dropped and replaced with "Bayesian confidence interval."

7 For MDE, estimates for individuals aged 12 or older are not included. For AMI, SMI, mental health services, and thoughts of suicide, estimates for youths aged 12 to 17 and individuals aged 12 or older are not included.

8 Binge drinking is defined as having five or more drinks (for males) or four or more drinks (for females) on the same occasion on at least 1 day in the 30 days prior to the survey. In 2015, the definition for females changed from five to four drinks.

9 In 2002, the survey's name changed from the National Household Survey on Drug Abuse (NHSDA) to the National Survey on Drug Use and Health (NSDUH).

10 A successfully screened household is one in which all screening questionnaire items were answered by an adult resident of the household and either zero, one, or two household members were selected for the NSDUH interview.

11 The usable case rule requires that a respondent answer "yes" or "no" to the question on lifetime use of cigarettes and "yes" or "no" to at least nine additional lifetime use questions.

12 The SAE expert panel, convened in April 2002, had six members: Dr. William Bell of the U.S. Bureau of the Census; Partha Lahiri, Professor of the Joint Program in Survey Methodology at the University of Maryland at College Park; Professor Balgobin Nandram of Worcester Polytechnic Institute; Wesley Schaible, formerly Associate Commissioner for Research and Evaluation at the Bureau of Labor Statistics; Professor J. N. K. Rao of Carleton University; and Professor Alan Zaslavsky of Harvard University.

13 At https://www.samhsa.gov/data/, see "2016-2017 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia)" (Tables 1 to 31, by Age Group).

14 The exact changes are documented in the 2015 NSDUH's Office of Management and Budget (OMB) clearance package and in a summary report (CBHSQ, 2015b). The summary report and the 2015 NSDUH questionnaire are available on the SAMHSA website at https://www.samhsa.gov/data/.

15 For a list of SAE outcomes, see Section B.2.

16 The use of mixed models (fixed and random effects) allows additional error components (random effects) to be included. These account for differences between states and within-state variations that are not taken into account by the predictor variables (fixed effects) alone. It is also difficult (if not impossible) to produce valid mean squared errors (MSEs) for small area estimates based solely on a fixed-effect national regression model (i.e., synthetic estimation) (Rao, 2003, p. 52). The mixed models produce estimates that are approximately represented by a weighted combination of the direct estimate from the state data and a regression estimate from the national model. The regression coefficients of the national model are estimated using data from all of the states (i.e., borrowing strength), and the regression estimate for a particular state is obtained by applying the national model to the state-specific predictor data. The regression estimate for the state is then combined with the direct estimate from the state data in a weighted combination where the weights are obtained by minimizing the MSE (variance + squared bias) of the small area estimate.

17 To increase the precision of the estimated random effects at the within-state level, three SSRs were grouped together. California had 12 grouped SSRs; Florida, New York, and Texas each had 10 grouped SSRs; Illinois, Michigan, Ohio, and Pennsylvania each had 8 grouped SSRs; Georgia, New Jersey, North Carolina, and Virginia each had 5 grouped SSRs; and the rest of the states and the District of Columbia each had 4 grouped SSRs. Note that these 250 grouped SSRs were used on both the 2016 and 2017 samples.

18 For details on how the average annual rate of marijuana (initiation of marijuana) is calculated, see Section B.8 of this document.

19 This is the first time that small area estimates of past year use of methamphetamine have been published by the Substance Abuse and Mental Health Services Administration (SAMHSA).

20 Estimates of underage (aged 12 to 20) alcohol use were also produced.

21 Estimates of underage (aged 12 to 20) binge alcohol use were also produced.

22 SMI reported here is defined as having a diagnosable mental, behavioral, or emotional disorder, other than a developmental disorder or SUD, assessed by the Mental Health Surveillance Study (MHSS) Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders—Fourth Edition—Research Version—Axis I Disorders (MHSS-SCID), which is based on the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) (American Psychiatric Association [APA], 1994). SMI includes individuals with diagnoses resulting in serious functional impairment. These mental illness estimates are based on a predictive model and are not direct measures of diagnostic status. For details on the methodology used in NSDUH to estimate SMI and other levels of mental illness, see Section B.11. In August 2016, SAMHSA updated the SMI definition for use in mental health block grants to include mental disorders as specified in the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM‑5) (APA, 2013); however, the estimates presented here are based on the DSM-IV.

23 Claritas is a market research firm headquartered in Ithaca, New York (see https://www.claritas.com/ exit icon). When the Claritas data were obtained for use in 2016-2017 NSDUH modeling, Claritas was affiliated with Nielsen Holdings, from which they became independent in January 2017.

24 This file is available at https://www.samhsa.gov/data/.

25 See Table 13 of the "2016-2017 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia)" at https://www.samhsa.gov/data/.

26 See Table 13 in the file described in footnote 25.

27 This file is available at https://www.samhsa.gov/data/.

28 See Table 13 of the "2016-2017 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia)" at https://www.samhsa.gov/data/.

29 In previous NSDUH SAE reports, the term "initiation" was referred to as "incidence."

30 Substances include alcohol, marijuana, cocaine, heroin, hallucinogens, inhalants, methamphetamine, and the misuse of prescription psychotherapeutics (i.e., pain relievers, tranquilizers, stimulants, and sedatives).

31 In the question about serious thoughts of suicide, [DATEFILL] refers to the date at the start of a respondent's 12-month reference period. The interview program sets the start of the 12-month reference period as the same month and day as the interview date but in the previous calendar year.

Long Descriptions—Equations

Long description, Equation 1. Capital S R R is equal to the ratio of two quantities. The numerator is the summation of the product of w sub h h and complete sub h h. The denominator is the summation of the product of w sub h h and eligible sub h h.

Long description end. Return to Equation 1.

Long description, Equation 2. Capital I R R is equal to the ratio of two quantities. The numerator is the summation of the product of w sub i and complete sub i. The denominator is the summation of the product of w sub i and selected sub i.

Long description end. Return to Equation 2.

Long description, Equation 3. Capital O R R is equal to the product of capital S R R and capital I R R.

Long description end. Return to Equation 3.

Long description, Equation 4. The model is given by the following equation: log of pi sub a, i, j, k divided by 1 minus pi sub a, i, j, k is equal to the sum of three terms. The first term is given by x transpose sub a, i, j, k times beta sub a. The second term is eta sub a, i. And the third term is nu sub a, i, j.

Long description end. Return to Equation 4.

Long description, Equation 5. Lower sub s and a is defined as the exponent of capital L sub s and a divided by the sum of 1 and the exponent of capital L sub s and a. And upper sub s and a is defined as the exponent of capital U sub s and a divided by the sum of 1 and the exponent of capital U sub s and a.

Long description end. Return to Equation 5.

Long description, Equation 6. Capital L sub s and a is defined as the difference of two quantities. The first quantity is the natural logarithm of the ratio of Theta sub s and a and 1 minus Theta sub s and a. The second quantity is the product of 1.96 and the square root of MSE sub s and a, which is the mean squared error for state-s and age group-a.

Long description end. Return to Equation 6.

Long description, Equation 7. Capital U sub s and a is defined as the sum of two quantities. The first quantity is the natural logarithm of the ratio of Theta sub s and a and 1 minus Theta sub s and a. The second quantity is the product of 1.96 and the square root of MSE sub s and a, which is the mean squared error for state-s and age group-a.

Long description end. Return to Equation 7.

Long description, Equation 8. The mean squared error, MSE sub s and a, is defined as the sum of two quantities. The first quantity is the square of the difference of two parts. Part 1 is defined as the natural logarithm of the ratio of capital P sub s and a and 1 minus capital P sub s and a. Part 2 is defined as the natural logarithm of the ratio of Theta sub s and a and 1 minus Theta sub s and a. The second quantity is the posterior variance of the natural logarithm of the ratio of capital P sub s and a and 1 minus capital P sub s and a.

Long description end. Return to Equation 8.

Long description, Equation 9. The average annual rate is defined as 100 times quantity q divided by 2. Quantity q is defined as capital X sub 1 divided by the sum of 0.5 times capital X sub 1 plus capital X sub 2.

Long description end. Return to Equation 9.

Long description, Equation 10. The logit of pi hat is equivalent to the logarithm of pi hat divided by the quantity 1 minus pi hat, which is equal to the sum of the following six quantities: negative 5.972664, the product of 0.0873416 and capital X sub k, the product of 0.3385193 and capital X sub w, the product of 1.9552664 and capital X sub s, the product of 1.1267330 and capital X sub m, and the product of 0.1059137 and capital X sub a
or
Pi hat is equal to the ratio of two quantities. The numerator is 1. The denominator is 1 plus e raised to the negative value of the sum of the following six quantities: negative 5.972664, the product of 0.0873416 and capital X sub k, the product of 0.3385193 and capital X sub w, the product of 1.9552664 and capital X sub s, the product of 1.1267330 and capital X sub m, and the product of 0.1059137 and capital X sub a.

Long description end. Return to Equation 10.

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