2018-2019
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 2018-2019 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 2018-2019, NSDUH collected data from 135,416 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 2018-2019 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 2018 surveys also was used in the production of the 2018-2019 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 2018-2019 small area estimation (SAE) modeling are listed and described in Section B.3.

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.4.4 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.5. Section B.6 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.7. 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 any mental illness (AMI), serious mental illness (SMI), receipt of mental health services, major depressive episode (MDE, i.e., depression), serious thoughts of suicide, suicide plans, and suicide attempts, 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.8.

The remainder of Section B covers two topics:

In Section C, the 2017, 2018, 2019, pooled 2017-2018, and pooled 2018-2019 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 2019.

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 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. A large reserve sample was selected at the time the 2014 through 2017 NSDUH sample was selected. This reserve sample was (or will be) used to field the 2018 through 2022 NSDUHs. Thus, the 2018 through 2022 NSDUHs simply continue the coordinated design. Similar to the 1999 through 2013 surveys, the coordinated sample 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 2019 are provided in Table C.5, and the combined 2018-2019 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 2018-2019, a total of 289,902 addresses were screened, and 135,416 individuals responded within the screened addresses (see Table C.9). The screening response rate (SRR) for 2018-2019 combined averaged 71.9 percent, and the interview response rate (IRR) averaged 65.7 percent, for an overall response rate (ORR) of 47.3 percent (Table C.9). The ORRs for 2018-2019 ranged from 33.5 percent in New York to 62.2 percent in Arkansas. 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 2017, 2018, and 2019 NSDUHs' methodological resource books (MRBs) (CBHSQ, 2018, in press a, in press b).

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 2018-2019 state SAE results, the following files are available at https://www.samhsa.gov/data/:

Note that because the estimates for past year heroin use for youths aged 12 to 17 and past year pain reliever use disorder for youths aged 12 to 17 were so low and had such an abbreviated range, no U.S. maps were included for these outcomes for youths.

A.4 Confidence Intervals and Margins of Error

At the top of each of the 33 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.4. 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 in 2018-2019 was Vermont, with an estimate of 39.0 percent and a 95 percent Bayesian confidence interval that ranged from 34.6 to 43.6 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 2018-2019 was between 34.6 and 43.6 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). When (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 2018-2019 past month marijuana use estimate is 14.5 percent for adults aged 18 to 25 years, with a 95 percent Bayesian confidence interval equal to 11.8 to 17.7 percent (see Table 3 of the state model-based estimates' tables). Therefore, Utah's estimate is 2.7 (i.e., 14.5 − 11.8) percentage points from the lower 95 percent confidence limit and 3.2 (i.e., 17.7 − 14.5) 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 "2018-2019 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 or alcohol use disorder and needing but not receiving treatment at a specialty facility for alcohol 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 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, 2016a) and a brief report summarizing the implications of the changes for data users (CBHSQ, 2016b). 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 from the 2015 through the 2019 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.4.

B.2 Variables Modeled

The 2019 National Survey on Drug Use and Health (NSDUH) data were pooled with the 2018 NSDUH data, and age group-specific state estimates for 32 binary (0, 1) outcomes listed below were produced. Comparisons between the 2017-2018 and the 2018-2019 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,
  12. past year misuse of pain relievers,
  13. past month use of alcohol,19
  14. past month binge alcohol use,20
  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. any mental illness (AMI) in the past year,
  27. serious mental illness (SMI) in the past year,21
  28. received mental health services in the past year,
  29. past year major depressive episode (MDE, i.e., depression),
  30. had serious thoughts of suicide in the past year,
  31. made suicide plans in the past year,22 and
  32. attempted suicide in the past year.23

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. Note that the predictors used to produce the 2018-2019 state small area estimates are the same as the predictors used to produce the 2017-2018 state small area estimates (however, values of the predictors were updated when possible). No new variable selection was done for 2018-2019, except for the two new past year suicide outcomes (made any suicide plans and attempted suicide). Variable selection was done using combined 2018 and 2019 data for these two outcomes. Fixed-effect predictors for these new outcome variables were selected using the method described 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/.

Sources and potential data items used in the 2018-2019 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 County
Drug Treatment Rate N-SSATS 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 Benchmarking the Age Group-Specific Small Area Estimates

The self-calibration built into the survey-weighted hierarchical Bayes (SWHB) solution ensures 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 2018-2019 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 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.5 Calculation of Estimated Number of Individuals Associated with Each Outcome

Tables 1 to 33 of "2018-2019 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 32 outcomes of interest.25 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, 2018 and 2019) 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 45.83 percent in 2018-2019.26 The corresponding Bayesian confidence intervals ranged from 41.67 to 50.05 percent. The population count for 18 to 25 year olds averaged across 2018 and 2019 in Alabama was 505,103 (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 0.4583 × 505,103, which is 231,489.27 The associated Bayesian confidence intervals ranged from 0.4167 × 505,103 (i.e., 210,476) to 0.5005 × 505,103 (i.e., 252,804). Note that when estimates of the number of individuals are calculated for Tables 1 to 33 in "2018-2019 NSDUHs: Model-Based Estimated Totals (in Thousands) (50 States and the District of Columbia)" (use the link in footnote 25), 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 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.7 for more details).

B.6 Calculation of Aggregate Age Group Estimates and Limitations

Tables 1 to 33 of "2018-2019 NSDUH: 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.28 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 provided in the tables, the confidence intervals cannot be calculated. Below is an example of this calculation for a given state.

In 2018-2019, past month use of alcohol in Alabama among youths 12 to 17 was 8.22 percent, and among young adults 18 to 25 it was 45.83 percent.29 The population counts for 12 to 17 year olds and 18 to 25 year olds averaged across 2018 and 2019 in Alabama were 371,033 and 505,103, 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 261,988 ([0.0822 × 371,033] + [0.4583 × 505,103]), then dividing that number by the population aged 12 to 25, which results in a rate of 29.90 percent (261,988 ÷ [371,033 + 505,103]).

B.7 Calculation of Average Annual Initiation of Marijuana Use

Initiation30 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 and capital X sub 2 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 2019 to indicate first use as early as the first part of 2017 or as late as the first part of 2019. Similarly, a subject interviewed in the last part of 2019 could indicate first use as early as the last part of 2017 or as late as the last part of 2019. Therefore, in the 2019 survey, the reported period of first use ranged from early 2017 to late 2019 and was "centered" in 2018. For example, about half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2018, while a quarter each reported first use in 2017 and 2019. Persons who responded in 2019 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 2018 survey ranged from early 2016 to late 2018 and were centered in 2017. Half of the 12 to 17 year olds who reported first use in the past 24 months reported first use in 2017, while a quarter each reported first use in 2016 and 2018. Note that only initiation rates for marijuana use are provided here.

B.8 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.4.

B.9 Substance Use Disorder and Needing But Not Receiving Treatment

The NSDUH computer-assisted interviewing (CAI) instrumentation includes questions designed to measure dependence or abuse of alcohol and illicit drugs (i.e., SUDs). For these substances,31 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, prescription pain relievers, prescription 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 3.4.3 in Chapter 3 of the 2019 NSDUH methodological summary and definitions report (CBHSQ, 2020).

Additionally, the NSDUH CAI instrument included a series of questions 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 3.4.3 in Chapter 3 of CBHSQ, 2020); 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 3.4.4 in Chapter 3 of the 2019 NSDUH methodological summary and definitions report (CBHSQ, 2020).

B.10 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 3.4.6 through 3.4.8 in Chapter 3 of the 2019 NSDUH methodological summary and definitions report (CBHSQ, 2020).

B.10.1 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 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, 2016c).

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?"32 Definitions for MDE in the lifetime and past year periods are discussed in Section B.10.2 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.10.2 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 2019 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 3.4.6 and 3.4.7 in Chapter 3 of the 2019 NSDUH methodological summary and definitions (CBHSQ, 2020) 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 2019 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 Persons 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.2 – 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.3 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2018
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. 227,252 193,456 141,879 73.30% 99,111 67,791 273,753,043 66.56% 48.79%
Northeast 50,900 43,775 30,172 66.91% 20,075 12,939 47,812,129 62.01% 41.49%
Midwest 51,981 44,319 33,386 74.61% 23,438 15,932 57,197,438 66.96% 49.96%
South 73,075 61,191 46,367 77.76% 31,929 22,817 103,666,960 69.90% 54.35%
West 51,296 44,171 31,954 69.76% 23,669 16,103 65,076,515 64.24% 44.81%
Alabama 2,857 2,288 1,934 84.30% 1,279 935 4,092,865 68.29% 57.57%
Alaska 3,293 2,483 1,841 73.06% 1,357 952 585,952 69.79% 50.99%
Arizona 2,952 2,342 1,597 67.92% 1,192 871 5,985,411 72.09% 48.96%
Arkansas 2,625 2,133 1,874 87.81% 1,313 999 2,494,811 72.96% 64.07%
California 14,501 13,463 8,605 63.83% 7,275 4,540 33,085,496 60.01% 38.31%
Colorado 2,940 2,476 1,894 76.09% 1,376 955 4,770,917 66.22% 50.39%
Connecticut 3,442 3,095 2,129 68.72% 1,639 1,006 3,060,394 58.45% 40.17%
Delaware 4,091 3,375 2,310 67.60% 1,498 985 818,343 64.29% 43.46%
District of Columbia 6,941 5,945 3,555 56.25% 1,301 975 596,107 71.25% 40.08%
Florida 11,601 9,609 6,989 71.78% 4,839 3,462 18,198,084 69.51% 49.89%
Georgia 4,337 3,695 2,825 76.42% 2,049 1,488 8,680,877 69.76% 53.31%
Hawaii 3,971 3,397 2,238 65.50% 1,564 1,045 1,156,640 66.18% 43.35%
Idaho 2,491 2,169 1,744 80.50% 1,300 944 1,441,575 72.87% 58.66%
Illinois 8,541 7,496 4,678 62.39% 3,846 2,372 10,691,591 60.38% 37.67%
Indiana 3,275 2,846 1,986 69.91% 1,401 996 5,565,964 69.94% 48.90%
Iowa 3,430 2,932 2,300 78.60% 1,450 959 2,629,456 66.79% 52.50%
Kansas 2,786 2,283 1,769 77.42% 1,355 960 2,380,437 69.24% 53.60%
Kentucky 2,707 2,225 1,806 81.13% 1,433 972 3,717,480 65.55% 53.18%
Louisiana 2,789 2,243 1,943 86.64% 1,338 1,006 3,821,937 72.27% 62.61%
Maine 3,668 2,800 2,280 81.43% 1,430 967 1,162,844 69.04% 56.22%
Maryland 3,265 2,937 2,003 68.43% 1,303 936 5,055,749 71.16% 48.70%
Massachusetts 3,324 3,053 2,175 71.18% 1,536 963 5,946,859 62.81% 44.71%
Michigan 7,909 6,674 5,152 77.15% 3,450 2,431 8,486,500 68.37% 52.75%
Minnesota 2,622 2,279 1,742 75.83% 1,313 928 4,689,671 69.94% 53.04%
Mississippi 2,493 2,043 1,767 86.42% 1,347 980 2,454,379 68.97% 59.60%
Missouri 3,057 2,494 2,079 83.33% 1,316 980 5,107,164 73.23% 61.03%
Montana 4,169 3,404 2,702 79.37% 1,468 972 894,272 66.79% 53.01%
Nebraska 2,605 2,310 1,818 79.04% 1,377 966 1,580,132 71.76% 56.72%
Nevada 2,802 2,527 1,713 67.09% 1,394 986 2,538,712 69.80% 46.83%
New Hampshire 3,590 2,965 2,275 76.64% 1,444 956 1,176,853 63.60% 48.74%
New Jersey 5,563 4,967 3,346 66.33% 2,442 1,511 7,533,240 59.89% 39.72%
New Mexico 3,025 2,232 1,871 84.11% 1,240 936 1,743,378 72.21% 60.74%
New York 14,345 12,675 7,485 57.98% 5,187 3,269 16,601,139 59.54% 34.53%
North Carolina 4,424 3,748 2,814 75.08% 2,076 1,451 8,672,691 67.96% 51.02%
North Dakota 3,664 2,954 2,442 82.88% 1,499 965 618,510 64.20% 53.21%
Ohio 7,993 6,914 5,247 75.88% 3,697 2,465 9,820,776 64.76% 49.14%
Oklahoma 3,186 2,627 2,015 76.27% 1,461 964 3,224,081 65.72% 50.12%
Oregon 3,605 3,176 2,425 76.13% 1,494 994 3,573,890 65.92% 50.19%
Pennsylvania 9,182 7,834 5,819 74.26% 3,521 2,383 10,875,795 66.28% 49.22%
Rhode Island 3,741 3,274 2,239 68.40% 1,417 937 909,061 66.92% 45.77%
South Carolina 2,779 2,336 1,764 75.57% 1,227 933 4,256,810 76.25% 57.62%
South Dakota 2,894 2,391 1,943 81.71% 1,336 941 716,559 71.30% 58.27%
Tennessee 2,575 2,185 1,829 83.76% 1,327 948 5,671,414 67.85% 56.83%
Texas 7,690 6,471 5,270 81.44% 4,459 3,307 23,305,572 71.72% 58.41%
Utah 1,876 1,679 1,445 86.06% 1,341 1,001 2,514,542 73.68% 63.41%
Vermont 4,045 3,112 2,424 78.01% 1,459 947 545,944 67.70% 52.81%
Virginia 4,940 4,279 3,328 77.74% 2,146 1,516 7,066,751 70.04% 54.45%
Washington 2,778 2,477 1,950 78.63% 1,431 957 6,307,741 65.26% 51.32%
West Virginia 3,775 3,052 2,341 76.78% 1,533 960 1,539,009 61.22% 47.01%
Wisconsin 3,205 2,746 2,230 81.20% 1,398 969 4,910,679 67.50% 54.81%
Wyoming 2,893 2,346 1,929 82.17% 1,237 950 477,989 71.84% 59.03%
DU = dwelling unit.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2018.
Table C.4 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2018
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. 22,962 16,852 24,895,613 73.85% 24,363 16,711 34,036,348 68.62% 51,786 34,228 214,821,082 65.39%
Northeast 4,783 3,315 3,996,315 68.70% 4,856 3,140 5,865,712 64.19% 10,436 6,484 37,950,102 60.97%
Midwest 5,415 3,979 5,284,214 73.60% 5,724 3,843 7,244,124 67.06% 12,299 8,110 44,669,101 66.17%
South 7,373 5,596 9,631,378 77.04% 8,017 5,787 12,759,656 72.59% 16,539 11,434 81,275,926 68.62%
West 5,391 3,962 5,983,706 72.37% 5,766 3,941 8,166,856 66.92% 12,512 8,200 50,925,953 62.85%
Alabama 303 233 371,499 74.67% 297 223 506,815 75.19% 679 479 3,214,550 66.40%
Alaska 341 245 57,930 71.89% 306 218 72,447 71.77% 710 489 455,576 69.17%
Arizona 267 214 559,972 81.99% 313 225 760,513 74.33% 612 432 4,664,926 70.40%
Arkansas 310 241 236,796 76.98% 320 262 313,342 80.72% 683 496 1,944,674 71.23%
California 1,562 1,107 3,019,147 70.41% 1,723 1,094 4,213,626 64.18% 3,990 2,339 25,852,722 58.16%
Colorado 306 226 432,601 72.86% 408 272 590,083 63.26% 662 457 3,748,233 65.86%
Connecticut 378 267 270,264 70.99% 414 254 385,633 61.47% 847 485 2,404,498 56.58%
Delaware 343 232 69,296 67.41% 406 263 93,302 65.26% 749 490 655,745 63.82%
District of Columbia 363 283 31,752 78.94% 257 202 87,077 77.46% 681 490 477,277 69.70%
Florida 1,087 840 1,437,069 76.93% 1,272 905 1,963,986 71.54% 2,480 1,717 14,797,029 68.55%
Georgia 472 367 866,515 76.61% 474 366 1,106,087 77.27% 1,103 755 6,708,276 67.61%
Hawaii 388 276 95,331 72.47% 368 254 122,304 69.36% 808 515 939,005 65.08%
Idaho 314 238 154,207 76.63% 331 242 182,631 72.58% 655 464 1,104,737 72.34%
Illinois 832 579 989,637 68.32% 971 587 1,319,767 58.22% 2,043 1,206 8,382,187 59.75%
Indiana 339 258 536,952 74.87% 327 225 735,308 69.75% 735 513 4,293,704 69.39%
Iowa 342 233 245,428 69.10% 324 213 356,422 63.94% 784 513 2,027,605 67.03%
Kansas 325 242 236,753 73.35% 329 242 320,953 73.68% 701 476 1,822,731 67.95%
Kentucky 302 214 340,220 73.97% 350 235 464,329 66.58% 781 523 2,912,930 64.39%
Louisiana 312 236 360,213 74.41% 374 287 472,645 75.20% 652 483 2,989,079 71.52%
Maine 314 211 89,366 67.03% 393 266 121,364 69.56% 723 490 952,114 69.16%
Maryland 332 256 450,904 79.28% 303 208 594,859 69.01% 668 472 4,009,986 70.51%
Massachusetts 327 221 480,574 67.06% 367 234 795,009 65.14% 842 508 4,671,276 61.99%
Michigan 810 622 758,240 76.34% 824 576 1,086,498 69.08% 1,816 1,233 6,641,762 67.36%
Minnesota 315 245 436,225 76.41% 296 202 570,868 65.99% 702 481 3,682,578 69.76%
Mississippi 330 254 241,249 77.58% 377 281 321,293 74.50% 640 445 1,891,837 66.90%
Missouri 326 256 465,639 79.13% 310 233 630,871 75.75% 680 491 4,010,654 72.17%
Montana 375 269 75,923 72.36% 315 191 110,175 60.71% 778 512 708,174 67.11%
Nebraska 309 218 156,615 69.45% 360 239 212,374 69.06% 708 509 1,211,143 72.52%
Nevada 278 211 230,080 76.72% 341 236 282,558 68.16% 775 539 2,026,074 69.31%
New Hampshire 365 267 94,845 73.52% 352 220 141,387 62.52% 727 469 940,620 62.78%
New Jersey 616 415 678,407 65.93% 558 332 868,314 59.69% 1,268 764 5,986,519 59.22%
New Mexico 308 258 165,747 81.68% 290 218 217,750 74.30% 642 460 1,359,881 70.69%
New York 1,224 860 1,356,212 68.99% 1,236 792 2,052,555 62.58% 2,727 1,617 13,192,372 58.12%
North Carolina 423 334 792,181 77.26% 553 386 1,064,005 68.80% 1,100 731 6,816,505 66.76%
North Dakota 352 240 53,907 68.12% 340 215 92,973 61.11% 807 510 471,630 64.36%
Ohio 806 591 893,036 73.35% 974 643 1,206,412 66.47% 1,917 1,231 7,721,328 63.54%
Oklahoma 373 260 317,493 71.61% 344 222 417,736 66.81% 744 482 2,488,852 64.84%
Oregon 374 257 294,599 68.70% 328 213 416,395 66.35% 792 524 2,862,896 65.60%
Pennsylvania 857 601 912,633 70.12% 860 590 1,303,202 68.93% 1,804 1,192 8,659,961 65.46%
Rhode Island 337 227 72,635 68.50% 330 236 123,705 75.52% 750 474 712,720 65.35%
South Carolina 274 222 374,940 81.50% 300 220 505,569 74.46% 653 491 3,376,301 75.95%
South Dakota 313 233 69,021 74.42% 304 214 92,383 70.93% 719 494 555,156 70.98%
Tennessee 331 254 511,583 76.19% 343 255 689,294 74.22% 653 439 4,470,536 65.86%
Texas 960 774 2,474,930 79.91% 1,196 892 3,110,009 73.48% 2,303 1,641 17,720,633 70.25%
Utah 294 232 309,836 77.46% 330 246 402,825 74.68% 717 523 1,801,880 72.89%
Vermont 365 246 41,379 68.99% 346 216 74,542 60.72% 748 485 430,023 68.65%
Virginia 501 370 629,687 73.57% 484 344 873,344 72.19% 1,161 802 5,563,720 69.27%
Washington 286 196 543,771 66.30% 406 275 738,106 67.76% 739 486 5,025,865 64.77%
West Virginia 357 226 125,051 62.59% 367 236 175,963 63.34% 809 498 1,237,995 60.79%
Wisconsin 346 262 442,761 76.17% 365 254 619,296 70.16% 687 453 3,848,622 65.95%
Wyoming 298 233 44,561 79.04% 307 257 57,445 83.84% 632 460 375,984 69.14%
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, 2018.
Table C.5 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2019
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. 245,763 210,244 148,023 70.50% 101,509 67,625 275,221,248 64.92% 45.77%
Northeast 53,398 46,262 30,751 64.52% 20,703 12,872 47,758,247 59.53% 38.41%
Midwest 57,356 49,005 35,597 72.32% 24,072 15,957 57,304,555 65.32% 47.24%
South 80,919 67,753 48,812 74.31% 32,555 22,666 104,586,263 68.09% 50.60%
West 54,090 47,224 32,863 67.15% 24,179 16,130 65,572,183 63.40% 42.58%
Alabama 3,287 2,616 2,213 84.87% 1,424 978 4,110,042 66.95% 56.82%
Alaska 3,622 2,858 2,066 72.18% 1,468 988 583,114 66.36% 47.90%
Arizona 3,474 2,943 1,978 67.41% 1,408 932 6,099,230 65.42% 44.09%
Arkansas 3,031 2,374 2,001 83.99% 1,273 936 2,503,376 71.78% 60.29%
California 15,105 14,044 8,774 61.66% 7,274 4,677 33,135,280 60.87% 37.53%
Colorado 3,417 2,905 1,995 68.81% 1,450 932 4,840,463 64.29% 44.24%
Connecticut 3,401 3,087 2,080 67.13% 1,580 985 3,060,096 59.36% 39.85%
Delaware 5,039 4,227 2,619 61.54% 1,560 935 825,113 59.35% 36.52%
District of Columbia 7,768 6,704 3,400 48.17% 1,244 961 599,449 74.87% 36.07%
Florida 11,805 9,736 7,032 70.76% 4,696 3,331 18,387,793 67.05% 47.44%
Georgia 4,370 3,759 2,947 78.40% 2,062 1,519 8,784,039 72.28% 56.67%
Hawaii 3,708 3,190 2,076 65.02% 1,540 1,007 1,155,277 65.07% 42.31%
Idaho 2,635 2,258 1,774 78.86% 1,350 960 1,474,464 70.54% 55.63%
Illinois 10,625 9,412 5,602 58.84% 4,157 2,361 10,652,731 54.31% 31.96%
Indiana 3,503 2,972 2,060 69.26% 1,445 974 5,607,015 66.00% 45.71%
Iowa 3,423 2,943 2,286 77.44% 1,422 968 2,633,615 68.30% 52.89%
Kansas 3,011 2,500 1,947 77.84% 1,403 951 2,388,520 67.44% 52.50%
Kentucky 3,070 2,570 2,005 78.07% 1,438 978 3,721,359 66.96% 52.27%
Louisiana 2,849 2,292 1,967 85.79% 1,329 936 3,819,636 67.54% 57.94%
Maine 4,465 3,455 2,723 78.51% 1,487 961 1,169,503 63.23% 49.64%
Maryland 3,328 2,991 2,000 67.01% 1,362 959 5,063,134 69.88% 46.83%
Massachusetts 4,191 3,825 2,434 64.04% 1,633 955 5,947,410 55.84% 35.76%
Michigan 7,940 6,736 5,231 77.62% 3,496 2,497 8,490,080 70.19% 54.49%
Minnesota 3,518 3,117 2,299 73.14% 1,410 962 4,721,395 68.88% 50.38%
Mississippi 2,884 2,393 1,976 83.25% 1,514 1,041 2,450,969 67.39% 56.10%
Missouri 3,357 2,768 2,184 78.81% 1,422 968 5,124,454 66.50% 52.41%
Montana 3,747 3,117 2,437 78.35% 1,501 963 902,279 65.51% 51.33%
Nebraska 3,086 2,654 2,013 75.75% 1,371 999 1,587,451 73.34% 55.55%
Nevada 3,376 3,094 1,967 63.68% 1,401 978 2,584,115 66.40% 42.28%
New Hampshire 3,977 3,324 2,422 72.61% 1,537 961 1,182,918 63.83% 46.34%
New Jersey 5,317 4,802 3,227 67.46% 2,461 1,490 7,518,524 59.57% 40.18%
New Mexico 2,954 2,380 1,758 73.95% 1,309 917 1,751,010 68.44% 50.61%
New York 14,394 12,697 7,419 57.76% 5,485 3,260 16,541,989 56.12% 32.42%
North Carolina 5,701 4,794 3,234 67.54% 2,181 1,572 8,780,240 70.71% 47.76%
North Dakota 3,911 3,200 2,391 75.04% 1,544 938 620,263 59.30% 44.50%
Ohio 8,295 7,188 5,206 72.53% 3,626 2,446 9,829,310 66.82% 48.47%
Oklahoma 2,729 2,325 1,801 77.40% 1,393 952 3,244,253 68.31% 52.87%
Oregon 3,250 2,943 2,286 77.34% 1,417 954 3,608,390 66.80% 51.67%
Pennsylvania 9,375 8,287 5,684 68.75% 3,627 2,386 10,880,971 65.37% 44.95%
Rhode Island 4,226 3,613 2,317 64.01% 1,432 930 911,627 62.09% 39.75%
South Carolina 3,475 2,796 1,996 71.48% 1,257 901 4,321,636 71.55% 51.15%
South Dakota 3,437 2,747 2,182 79.51% 1,376 935 720,183 69.51% 55.26%
Tennessee 2,915 2,451 1,915 78.21% 1,419 987 5,726,254 67.93% 53.13%
Texas 8,916 7,523 5,652 74.87% 4,666 3,238 23,624,747 66.61% 49.87%
Utah 1,972 1,710 1,271 74.58% 1,335 947 2,565,992 70.23% 52.38%
Vermont 4,052 3,172 2,445 77.01% 1,461 944 545,208 64.36% 49.56%
Virginia 5,295 4,531 3,269 71.44% 2,127 1,474 7,094,545 67.04% 47.90%
Washington 3,782 3,306 2,558 77.58% 1,475 945 6,391,782 63.17% 49.01%
West Virginia 4,457 3,671 2,785 75.87% 1,610 968 1,529,678 58.99% 44.76%
Wisconsin 3,250 2,768 2,196 79.19% 1,400 958 4,929,540 68.06% 53.90%
Wyoming 3,048 2,476 1,923 78.11% 1,251 930 480,786 76.14% 59.48%
DU = dwelling unit.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2019.
Table C.6 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2019
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. 23,630 16,894 24,905,038 72.10% 25,169 16,665 33,732,492 66.40% 52,710 34,066 216,583,718 63.87%
Northeast 4,850 3,311 3,958,778 67.62% 5,004 3,042 5,764,931 58.65% 10,849 6,519 38,034,538 58.82%
Midwest 5,603 3,893 5,267,639 69.26% 5,966 3,952 7,163,761 66.45% 12,503 8,112 44,873,156 64.67%
South 7,640 5,644 9,675,192 74.97% 8,025 5,630 12,710,850 71.05% 16,890 11,392 82,200,221 66.83%
West 5,537 4,046 6,003,430 72.95% 6,174 4,041 8,092,950 64.62% 12,468 8,043 51,475,803 62.08%
Alabama 324 235 370,567 73.69% 351 252 503,390 71.96% 749 491 3,236,085 65.36%
Alaska 359 251 56,959 69.26% 348 233 69,918 67.17% 761 504 456,237 65.86%
Arizona 311 216 565,121 72.20% 394 252 767,322 64.44% 703 464 4,766,788 64.74%
Arkansas 324 255 237,969 79.07% 254 174 311,355 70.56% 695 507 1,954,052 71.05%
California 1,625 1,181 3,016,255 71.87% 1,805 1,175 4,136,298 64.61% 3,844 2,321 25,982,726 59.02%
Colorado 305 221 434,837 74.18% 427 254 588,550 58.98% 718 457 3,817,076 63.97%
Connecticut 294 200 267,351 65.81% 417 276 380,961 64.48% 869 509 2,411,785 57.84%
Delaware 405 250 69,355 58.74% 404 230 91,748 57.89% 751 455 664,010 59.60%
District of Columbia 353 291 32,068 79.83% 276 214 85,763 75.69% 615 456 481,619 74.37%
Florida 1,088 828 1,441,990 74.62% 1,221 890 1,948,866 72.27% 2,387 1,613 14,996,938 65.63%
Georgia 544 428 871,169 78.75% 449 334 1,108,561 74.50% 1,069 757 6,804,309 71.06%
Hawaii 331 241 94,975 72.24% 389 267 119,336 67.98% 820 499 940,966 63.94%
Idaho 302 231 155,795 75.51% 362 246 184,392 67.97% 686 483 1,134,277 70.30%
Illinois 884 557 980,223 62.31% 1,018 582 1,297,477 56.30% 2,255 1,222 8,375,031 53.10%
Indiana 344 243 537,808 69.96% 338 228 738,146 65.98% 763 503 4,331,060 65.52%
Iowa 393 262 246,264 66.90% 346 244 350,923 69.91% 683 462 2,036,428 68.19%
Kansas 316 227 237,371 74.69% 309 209 319,625 71.53% 778 515 1,831,524 65.75%
Kentucky 313 225 339,302 71.56% 351 236 459,407 68.34% 774 517 2,922,651 66.22%
Louisiana 332 244 360,544 74.00% 313 235 464,078 73.60% 684 457 2,995,015 65.95%
Maine 366 246 89,075 67.47% 356 228 120,997 64.07% 765 487 959,430 62.73%
Maryland 320 239 450,438 74.28% 325 221 586,965 64.37% 717 499 4,025,731 70.17%
Massachusetts 392 266 474,535 67.68% 362 198 782,914 52.28% 879 491 4,689,961 55.22%
Michigan 821 587 748,985 71.34% 834 624 1,067,946 75.99% 1,841 1,286 6,673,149 69.12%
Minnesota 309 214 439,866 67.95% 366 245 566,538 68.96% 735 503 3,714,991 68.97%
Mississippi 318 217 241,997 69.94% 425 314 310,924 74.68% 771 510 1,898,049 65.82%
Missouri 312 218 465,893 70.39% 344 235 623,753 68.11% 766 515 4,034,808 65.86%
Montana 355 233 76,595 64.85% 424 260 109,033 62.20% 722 470 716,651 66.12%
Nebraska 352 268 157,904 77.62% 407 292 210,672 73.76% 612 439 1,218,875 72.71%
Nevada 324 262 233,449 82.05% 378 253 283,997 65.69% 699 463 2,066,669 64.59%
New Hampshire 368 240 93,711 62.31% 379 221 139,066 58.77% 790 500 950,141 64.78%
New Jersey 547 363 672,884 65.73% 630 363 853,383 57.79% 1,284 764 5,992,257 59.14%
New Mexico 291 231 165,666 81.24% 330 217 215,352 64.64% 688 469 1,369,992 67.40%
New York 1,245 850 1,340,514 67.41% 1,320 790 2,009,990 58.03% 2,920 1,620 13,191,485 54.66%
North Carolina 436 334 795,278 77.34% 635 452 1,070,088 71.68% 1,110 786 6,914,874 69.79%
North Dakota 368 243 55,075 66.57% 387 225 90,720 58.14% 789 470 474,467 58.70%
Ohio 848 584 886,363 68.95% 937 617 1,194,395 65.59% 1,841 1,245 7,748,552 66.78%
Oklahoma 284 211 319,059 73.04% 427 277 418,177 66.32% 682 464 2,507,017 68.02%
Oregon 376 272 295,505 71.93% 317 206 414,186 67.75% 724 476 2,898,700 66.13%
Pennsylvania 935 656 907,627 70.18% 814 507 1,282,534 61.12% 1,878 1,223 8,690,810 65.51%
Rhode Island 361 253 72,327 68.38% 328 213 122,075 63.69% 743 464 717,226 61.16%
South Carolina 312 234 380,001 76.00% 270 193 503,621 71.01% 675 474 3,438,013 71.14%
South Dakota 332 243 69,628 74.82% 347 231 91,002 66.48% 697 461 559,553 69.30%
Tennessee 288 216 514,439 73.15% 350 230 686,583 66.09% 781 541 4,525,231 67.60%
Texas 1,082 819 2,496,989 76.25% 1,083 778 3,123,590 71.59% 2,501 1,641 18,004,169 64.54%
Utah 283 223 314,853 79.43% 364 242 409,221 68.55% 688 482 1,841,918 69.13%
Vermont 342 237 40,754 70.55% 398 246 73,012 61.67% 721 461 431,443 64.20%
Virginia 541 392 629,762 72.88% 480 349 865,225 73.45% 1,106 733 5,599,558 65.45%
Washington 380 268 548,165 69.82% 340 208 737,998 62.76% 755 469 5,105,619 62.44%
West Virginia 376 226 124,268 60.63% 411 251 172,509 59.82% 823 491 1,232,900 58.71%
Wisconsin 324 247 442,258 76.11% 333 220 612,565 64.25% 743 491 3,874,718 67.74%
Wyoming 295 216 45,255 74.52% 296 228 57,346 77.62% 660 486 378,184 76.11%
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, 2019.
Table C.7 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2017 and 2018
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. 445,008 377,722 279,940 74.18% 196,778 135,823 272,928,189 66.84% 49.59%
Northeast 99,783 85,277 59,600 68.09% 39,858 26,200 47,951,227 63.17% 43.01%
Midwest 100,586 85,926 65,633 75.77% 46,485 31,854 57,104,746 67.10% 50.84%
South 145,509 121,878 92,656 77.95% 63,883 45,656 103,114,760 69.69% 54.32%
West 99,130 84,641 62,051 71.17% 46,552 32,113 64,757,456 64.76% 46.09%
Alabama 6,025 4,833 4,005 82.89% 2,636 1,899 4,084,713 67.74% 56.16%
Alaska 6,726 5,148 3,856 74.17% 2,786 1,930 585,734 68.45% 50.76%
Arizona 5,671 4,332 3,206 73.41% 2,313 1,731 5,909,464 72.62% 53.31%
Arkansas 5,475 4,525 3,848 85.13% 2,679 1,989 2,488,720 70.55% 60.06%
California 27,987 25,723 16,855 65.52% 14,237 9,018 33,047,069 60.61% 39.71%
Colorado 5,647 4,786 3,731 78.07% 2,817 1,958 4,726,440 67.17% 52.44%
Connecticut 6,651 5,870 4,150 70.80% 3,122 1,993 3,065,130 62.55% 44.29%
Delaware 7,701 6,293 4,435 69.95% 2,913 1,935 815,436 65.34% 45.70%
District of Columbia 14,059 12,031 7,282 57.40% 2,605 1,950 593,392 72.33% 41.51%
Florida 23,511 19,444 14,328 73.26% 9,649 6,861 18,049,347 68.59% 50.25%
Georgia 8,568 7,343 5,547 75.43% 4,102 2,975 8,633,046 69.93% 52.75%
Hawaii 7,673 6,505 4,345 66.48% 2,972 2,016 1,158,222 64.94% 43.17%
Idaho 4,863 4,127 3,359 81.31% 2,591 1,924 1,423,178 73.83% 60.03%
Illinois 16,289 14,271 9,194 64.56% 7,615 4,704 10,706,729 60.07% 38.78%
Indiana 6,279 5,379 3,919 73.13% 2,779 1,938 5,551,977 68.74% 50.27%
Iowa 6,407 5,432 4,384 80.94% 2,881 1,930 2,623,553 67.00% 54.23%
Kansas 5,257 4,473 3,531 79.00% 2,720 1,952 2,378,798 70.09% 55.38%
Kentucky 5,455 4,515 3,616 80.04% 2,864 1,948 3,709,470 65.55% 52.47%
Louisiana 5,659 4,609 3,891 84.51% 2,709 1,972 3,829,009 70.65% 59.71%
Maine 7,298 5,604 4,612 82.44% 2,825 1,952 1,161,344 68.97% 56.86%
Maryland 6,384 5,715 3,967 69.54% 2,643 1,923 5,059,929 71.56% 49.76%
Massachusetts 7,168 6,477 4,515 69.58% 3,204 1,949 5,924,511 60.09% 41.81%
Michigan 15,292 12,905 10,108 78.35% 6,846 4,833 8,467,102 68.18% 53.42%
Minnesota 5,402 4,680 3,604 76.71% 2,671 1,896 4,673,265 70.67% 54.21%
Mississippi 4,983 4,167 3,504 84.00% 2,668 1,916 2,451,758 68.16% 57.26%
Missouri 5,991 5,033 4,154 82.67% 2,735 1,969 5,099,165 71.21% 58.87%
Montana 7,396 6,030 4,863 80.98% 2,792 1,943 888,203 70.41% 57.02%
Nebraska 5,365 4,732 3,668 77.77% 2,726 1,927 1,575,393 70.67% 54.96%
Nevada 5,364 4,870 3,272 65.78% 2,788 1,944 2,521,020 67.65% 44.50%
New Hampshire 7,169 5,973 4,555 75.66% 2,874 1,959 1,169,887 67.54% 51.10%
New Jersey 10,228 9,081 6,274 68.15% 4,806 3,070 7,574,645 62.03% 42.27%
New Mexico 5,935 4,288 3,544 82.79% 2,387 1,863 1,736,893 75.77% 62.73%
New York 28,456 24,830 14,849 59.14% 10,403 6,621 16,730,174 60.79% 35.95%
North Carolina 8,812 7,517 5,782 76.80% 4,151 2,942 8,615,124 69.03% 53.02%
North Dakota 6,953 5,539 4,652 84.14% 2,896 1,946 616,968 67.10% 56.46%
Ohio 15,385 13,458 10,221 75.96% 7,138 4,883 9,801,648 66.77% 50.72%
Oklahoma 6,083 5,096 3,914 76.55% 2,853 1,902 3,216,614 66.32% 50.76%
Oregon 7,043 6,184 4,765 76.97% 2,944 1,981 3,549,625 66.71% 51.34%
Pennsylvania 17,020 14,503 11,067 76.47% 6,862 4,775 10,871,303 67.75% 51.81%
Rhode Island 7,305 6,361 4,441 69.81% 2,874 1,932 909,824 67.21% 46.92%
South Carolina 5,515 4,557 3,511 77.17% 2,538 1,910 4,227,157 73.33% 56.59%
South Dakota 5,503 4,570 3,741 82.18% 2,675 1,918 710,913 71.62% 58.86%
Tennessee 5,490 4,593 3,762 81.97% 2,668 1,931 5,644,659 69.69% 57.12%
Texas 15,280 12,826 10,426 81.39% 8,933 6,642 23,108,167 71.93% 58.54%
Utah 3,462 3,071 2,612 84.84% 2,592 1,947 2,484,672 73.98% 62.76%
Vermont 8,488 6,578 5,137 77.91% 2,888 1,949 544,409 68.49% 53.36%
Virginia 9,317 8,017 6,295 78.55% 4,295 3,037 7,045,953 68.49% 53.80%
Washington 5,634 4,951 3,838 77.66% 2,876 1,930 6,249,139 65.12% 50.58%
West Virginia 7,192 5,797 4,543 78.45% 2,977 1,924 1,542,265 63.27% 49.64%
Wisconsin 6,463 5,454 4,457 81.83% 2,803 1,958 4,899,234 68.39% 55.97%
Wyoming 5,729 4,626 3,805 82.30% 2,457 1,928 477,796 75.01% 61.73%
DU = dwelling unit.
NOTE: To compute the pooled 2017-2018 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 2017 and 2018 individual response rates. The 2017-2018 population estimate is the average of the 2017 and the 2018 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2017 and 2018.
Table C.8 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2017 and 2018
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. 45,712 33,885 24,919,203 74.46% 48,070 33,329 34,171,330 69.10% 102,996 68,609 213,837,656 65.58%
Northeast 9,404 6,619 4,029,171 69.66% 9,783 6,445 5,931,730 65.05% 20,671 13,136 37,990,326 62.17%
Midwest 10,770 7,955 5,295,818 73.83% 11,302 7,726 7,281,152 68.04% 24,413 16,173 44,527,776 66.14%
South 14,830 11,322 9,617,723 77.38% 15,732 11,335 12,766,828 72.49% 33,321 22,999 80,730,208 68.32%
West 10,708 7,989 5,976,491 73.56% 11,253 7,823 8,191,619 67.67% 24,591 16,301 50,589,346 63.24%
Alabama 620 477 373,065 76.13% 617 463 510,012 74.16% 1,399 959 3,201,636 65.67%
Alaska 733 537 58,106 73.89% 620 432 73,343 71.77% 1,433 961 454,286 67.14%
Arizona 576 459 556,478 80.46% 573 434 753,929 76.70% 1,164 838 4,599,057 70.87%
Arkansas 668 506 236,702 74.77% 632 504 314,368 78.64% 1,379 979 1,937,651 68.77%
California 3,115 2,242 3,026,428 71.69% 3,319 2,130 4,250,955 64.43% 7,803 4,646 25,769,686 58.68%
Colorado 641 473 430,432 74.12% 719 499 587,460 68.24% 1,457 986 3,708,548 66.19%
Connecticut 716 499 272,254 69.62% 813 516 387,595 63.56% 1,593 978 2,405,282 61.55%
Delaware 674 466 69,413 68.53% 716 471 94,216 67.51% 1,523 998 651,806 64.70%
District of Columbia 716 563 31,570 80.18% 543 418 87,525 77.79% 1,346 969 474,297 70.81%
Florida 2,232 1,734 1,431,798 77.62% 2,357 1,648 1,961,154 70.39% 5,060 3,479 14,656,396 67.48%
Georgia 913 697 866,242 76.70% 982 755 1,105,245 77.35% 2,207 1,523 6,661,559 67.81%
Hawaii 709 522 95,447 75.76% 692 481 123,940 69.09% 1,571 1,013 938,835 63.22%
Idaho 613 480 152,823 79.30% 631 474 180,549 74.53% 1,347 970 1,089,805 72.92%
Illinois 1,660 1,167 995,426 69.67% 1,814 1,115 1,331,211 60.66% 4,141 2,422 8,380,092 58.80%
Indiana 643 483 537,556 74.48% 625 436 738,014 70.50% 1,511 1,019 4,276,407 67.79%
Iowa 655 464 245,032 71.64% 712 476 357,855 64.65% 1,514 990 2,020,666 66.86%
Kansas 653 490 237,064 73.74% 671 494 322,476 72.96% 1,396 968 1,819,258 69.12%
Kentucky 633 461 340,220 74.25% 690 474 465,663 69.25% 1,541 1,013 2,903,588 64.01%
Louisiana 631 471 361,940 74.38% 714 516 479,234 70.29% 1,364 985 2,987,835 70.27%
Maine 695 491 89,705 70.75% 734 501 122,655 69.93% 1,396 960 948,984 68.68%
Maryland 621 484 452,456 79.13% 676 485 600,019 72.02% 1,346 954 4,007,455 70.58%
Massachusetts 719 493 481,835 69.30% 842 502 793,182 61.42% 1,643 954 4,649,494 58.90%
Michigan 1,590 1,217 762,352 75.89% 1,664 1,176 1,091,893 70.49% 3,592 2,440 6,612,857 66.89%
Minnesota 619 481 434,904 77.41% 673 465 572,931 67.92% 1,379 950 3,665,430 70.27%
Mississippi 631 492 241,768 77.85% 655 474 322,550 72.06% 1,382 950 1,887,440 66.30%
Missouri 668 491 466,292 73.01% 631 457 635,726 73.00% 1,436 1,021 3,997,148 70.72%
Montana 647 467 75,436 72.38% 642 438 110,155 68.14% 1,503 1,038 702,611 70.54%
Nebraska 645 469 155,993 72.69% 706 485 212,817 71.75% 1,375 973 1,206,583 70.22%
Nevada 586 447 229,143 76.68% 709 493 283,377 68.70% 1,493 1,004 2,008,499 66.48%
New Hampshire 726 531 94,983 72.50% 712 456 141,804 63.51% 1,436 972 933,100 67.64%
New Jersey 1,124 778 684,290 68.10% 1,140 734 880,024 64.02% 2,542 1,558 6,010,331 61.00%
New Mexico 562 472 165,877 83.05% 579 456 219,023 78.61% 1,246 935 1,351,993 74.37%
New York 2,456 1,690 1,375,507 67.46% 2,540 1,654 2,093,893 63.36% 5,407 3,277 13,260,774 59.67%
North Carolina 944 747 791,658 79.08% 1,077 747 1,058,797 68.29% 2,130 1,448 6,764,669 67.96%
North Dakota 711 493 53,301 69.47% 655 448 94,024 67.14% 1,530 1,005 469,643 66.82%
Ohio 1,613 1,198 896,066 74.13% 1,838 1,241 1,210,058 67.77% 3,687 2,444 7,695,525 65.78%
Oklahoma 687 482 317,113 70.03% 692 448 419,663 65.60% 1,474 972 2,479,838 65.97%
Oregon 724 500 294,160 69.71% 751 499 416,018 67.88% 1,469 982 2,839,447 66.23%
Pennsylvania 1,584 1,162 916,014 73.80% 1,677 1,173 1,313,052 69.99% 3,601 2,440 8,642,237 66.77%
Rhode Island 660 463 73,039 70.45% 658 468 125,273 73.94% 1,556 1,001 711,511 65.76%
South Carolina 569 464 373,712 79.15% 670 506 507,495 75.85% 1,299 940 3,345,950 72.30%
South Dakota 634 481 68,252 76.52% 630 461 92,097 73.15% 1,411 976 550,564 70.76%
Tennessee 666 516 511,356 75.82% 638 470 690,281 72.77% 1,364 945 4,443,021 68.54%
Texas 1,977 1,584 2,463,690 79.99% 2,301 1,745 3,098,890 74.84% 4,655 3,313 17,545,587 70.27%
Utah 576 450 306,536 77.88% 655 490 398,120 72.60% 1,361 1,007 1,780,017 73.65%
Vermont 724 512 41,544 71.19% 667 441 74,251 65.64% 1,497 996 428,613 68.71%
Virginia 947 718 629,286 76.32% 1,064 754 874,127 70.99% 2,284 1,565 5,542,540 67.14%
Washington 615 441 541,234 69.68% 767 512 736,912 66.45% 1,494 977 4,970,994 64.41%
West Virginia 701 460 125,736 64.96% 708 457 177,588 63.92% 1,568 1,007 1,238,942 63.02%
Wisconsin 679 521 443,580 77.60% 683 472 622,050 69.55% 1,441 965 3,833,603 67.06%
Wyoming 611 499 44,389 81.78% 596 485 57,838 81.11% 1,250 944 375,569 73.18%
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 2017-2018 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 2017 and 2018 individual response rates. The 2017-2018 population estimate is the average of the 2017 and the 2018 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2017 and 2018.
Table C.9 – Sample Sizes, Weighted Screening and Interview Response Rates, and Population Estimates, by State, for Persons Aged 12 or Older: 2018 and 2019
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. 473,015 403,700 289,902 71.89% 200,620 135,416 274,487,145 65.74% 47.26%
Northeast 104,298 90,037 60,923 65.71% 40,778 25,811 47,785,188 60.76% 39.93%
Midwest 109,337 93,324 68,983 73.46% 47,510 31,889 57,250,997 66.14% 48.58%
South 153,994 128,944 95,179 76.02% 64,484 45,483 104,126,612 68.99% 52.44%
West 105,386 91,395 64,817 68.45% 47,848 32,233 65,324,349 63.82% 43.68%
Alabama 6,144 4,904 4,147 84.58% 2,703 1,913 4,101,453 67.61% 57.19%
Alaska 6,915 5,341 3,907 72.60% 2,825 1,940 584,533 68.05% 49.40%
Arizona 6,426 5,285 3,575 67.64% 2,600 1,803 6,042,320 68.69% 46.46%
Arkansas 5,656 4,507 3,875 85.93% 2,586 1,935 2,499,094 72.38% 62.19%
California 29,606 27,507 17,379 62.75% 14,549 9,217 33,110,388 60.44% 37.93%
Colorado 6,357 5,381 3,889 72.51% 2,826 1,887 4,805,690 65.24% 47.30%
Connecticut 6,843 6,182 4,209 67.91% 3,219 1,991 3,060,245 58.90% 40.00%
Delaware 9,130 7,602 4,929 64.50% 3,058 1,920 821,728 61.79% 39.86%
District of Columbia 14,709 12,649 6,955 52.05% 2,545 1,936 597,778 73.05% 38.03%
Florida 23,406 19,345 14,021 71.27% 9,535 6,793 18,292,939 68.29% 48.67%
Georgia 8,707 7,454 5,772 77.40% 4,111 3,007 8,732,458 71.02% 54.97%
Hawaii 7,679 6,587 4,314 65.26% 3,104 2,052 1,155,959 65.63% 42.83%
Idaho 5,126 4,427 3,518 79.68% 2,650 1,904 1,458,020 71.69% 57.12%
Illinois 19,166 16,908 10,280 60.59% 8,003 4,733 10,672,161 57.29% 34.71%
Indiana 6,778 5,818 4,046 69.59% 2,846 1,970 5,586,490 67.98% 47.31%
Iowa 6,853 5,875 4,586 78.03% 2,872 1,927 2,631,535 67.51% 52.68%
Kansas 5,797 4,783 3,716 77.63% 2,758 1,911 2,384,478 68.36% 53.07%
Kentucky 5,777 4,795 3,811 79.61% 2,871 1,950 3,719,420 66.26% 52.75%
Louisiana 5,638 4,535 3,910 86.19% 2,667 1,942 3,820,786 69.85% 60.21%
Maine 8,133 6,255 5,003 79.94% 2,917 1,928 1,166,174 66.10% 52.84%
Maryland 6,593 5,928 4,003 67.71% 2,665 1,895 5,059,442 70.51% 47.74%
Massachusetts 7,515 6,878 4,609 67.74% 3,169 1,918 5,947,135 59.29% 40.16%
Michigan 15,849 13,410 10,383 77.38% 6,946 4,928 8,488,290 69.28% 53.61%
Minnesota 6,140 5,396 4,041 74.47% 2,723 1,890 4,705,533 69.40% 51.68%
Mississippi 5,377 4,436 3,743 84.86% 2,861 2,021 2,452,674 68.17% 57.86%
Missouri 6,414 5,262 4,263 81.03% 2,738 1,948 5,115,809 69.78% 56.54%
Montana 7,916 6,521 5,139 78.85% 2,969 1,935 898,276 66.17% 52.18%
Nebraska 5,691 4,964 3,831 77.39% 2,748 1,965 1,583,792 72.54% 56.13%
Nevada 6,178 5,621 3,680 65.37% 2,795 1,964 2,561,414 68.16% 44.56%
New Hampshire 7,567 6,289 4,697 74.58% 2,981 1,917 1,179,885 63.71% 47.51%
New Jersey 10,880 9,769 6,573 66.88% 4,903 3,001 7,525,882 59.73% 39.95%
New Mexico 5,979 4,612 3,629 78.75% 2,549 1,853 1,747,194 70.31% 55.37%
New York 28,739 25,372 14,904 57.87% 10,672 6,529 16,571,564 57.85% 33.48%
North Carolina 10,125 8,542 6,048 71.23% 4,257 3,023 8,726,466 69.35% 49.40%
North Dakota 7,575 6,154 4,833 78.85% 3,043 1,903 619,386 61.74% 48.68%
Ohio 16,288 14,102 10,453 74.19% 7,323 4,911 9,825,043 65.79% 48.82%
Oklahoma 5,915 4,952 3,816 76.84% 2,854 1,916 3,234,167 67.01% 51.49%
Oregon 6,855 6,119 4,711 76.75% 2,911 1,948 3,591,140 66.36% 50.93%
Pennsylvania 18,557 16,121 11,503 71.43% 7,148 4,769 10,878,383 65.82% 47.02%
Rhode Island 7,967 6,887 4,556 66.27% 2,849 1,867 910,344 64.60% 42.81%
South Carolina 6,254 5,132 3,760 73.52% 2,484 1,834 4,289,223 73.90% 54.33%
South Dakota 6,331 5,138 4,125 80.60% 2,712 1,876 718,371 70.42% 56.76%
Tennessee 5,490 4,636 3,744 80.89% 2,746 1,935 5,698,834 67.89% 54.92%
Texas 16,606 13,994 10,922 78.10% 9,125 6,545 23,465,160 69.12% 53.98%
Utah 3,848 3,389 2,716 80.39% 2,676 1,948 2,540,267 71.89% 57.79%
Vermont 8,097 6,284 4,869 77.50% 2,920 1,891 545,576 66.09% 51.22%
Virginia 10,235 8,810 6,597 74.52% 4,273 2,990 7,080,648 68.51% 51.05%
Washington 6,560 5,783 4,508 78.11% 2,906 1,902 6,349,762 64.24% 50.18%
West Virginia 8,232 6,723 5,126 76.32% 3,143 1,928 1,534,343 60.12% 45.88%
Wisconsin 6,455 5,514 4,426 80.17% 2,798 1,927 4,920,109 67.79% 54.35%
Wyoming 5,941 4,822 3,852 80.08% 2,488 1,880 479,388 73.99% 59.25%
DU = dwelling unit.
NOTE: To compute the pooled 2018-2019 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 2018 and 2019 individual response rates. The 2018-2019 population estimate is the average of the 2018 and the 2019 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2018 and 2019.
Table C.10 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates, by State and Three Age Groups: 2018 and 2019
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. 46,592 33,746 24,900,325 72.98% 49,532 33,376 33,884,420 67.52% 104,496 68,294 215,702,400 64.62%
Northeast 9,633 6,626 3,977,546 68.16% 9,860 6,182 5,815,321 61.42% 21,285 13,003 37,992,320 59.89%
Midwest 11,018 7,872 5,275,926 71.42% 11,690 7,795 7,203,942 66.75% 24,802 16,222 44,771,128 65.42%
South 15,013 11,240 9,653,285 76.00% 16,042 11,417 12,735,253 71.83% 33,429 22,826 81,738,074 67.71%
West 10,928 8,008 5,993,568 72.66% 11,940 7,982 8,129,903 65.78% 24,980 16,243 51,200,878 62.46%
Alabama 627 468 371,033 74.19% 648 475 505,103 73.57% 1,428 970 3,225,318 65.87%
Alaska 700 496 57,444 70.55% 654 451 71,182 69.58% 1,471 993 455,906 67.47%
Arizona 578 430 562,546 77.04% 707 477 763,917 69.37% 1,315 896 4,715,857 67.51%
Arkansas 634 496 237,382 78.02% 574 436 312,348 75.73% 1,378 1,003 1,949,363 71.14%
California 3,187 2,288 3,017,701 71.13% 3,528 2,269 4,174,962 64.40% 7,834 4,660 25,917,724 58.59%
Colorado 611 447 433,719 73.51% 835 526 589,317 61.10% 1,380 914 3,782,655 64.89%
Connecticut 672 467 268,807 68.43% 831 530 383,297 62.97% 1,716 994 2,408,141 57.21%
Delaware 748 482 69,325 62.99% 810 493 92,525 61.73% 1,500 945 659,878 61.68%
District of Columbia 716 574 31,910 79.38% 533 416 86,420 76.52% 1,296 946 479,448 72.00%
Florida 2,175 1,668 1,439,529 75.77% 2,493 1,795 1,956,426 71.90% 4,867 3,330 14,896,983 67.10%
Georgia 1,016 795 868,842 77.67% 923 700 1,107,324 75.86% 2,172 1,512 6,756,292 69.34%
Hawaii 719 517 95,153 72.36% 757 521 120,820 68.69% 1,628 1,014 939,985 64.51%
Idaho 616 469 155,001 76.08% 693 488 183,511 70.22% 1,341 947 1,119,507 71.30%
Illinois 1,716 1,136 984,930 65.34% 1,989 1,169 1,308,622 57.26% 4,298 2,428 8,378,609 56.35%
Indiana 683 501 537,380 72.40% 665 453 736,727 67.82% 1,498 1,016 4,312,382 67.48%
Iowa 735 495 245,846 67.98% 670 457 353,673 66.83% 1,467 975 2,032,017 67.58%
Kansas 641 469 237,062 74.01% 638 451 320,289 72.61% 1,479 991 1,827,127 66.88%
Kentucky 615 439 339,761 72.76% 701 471 461,868 67.46% 1,555 1,040 2,917,791 65.33%
Louisiana 644 480 360,378 74.20% 687 522 468,361 74.44% 1,336 940 2,992,047 68.64%
Maine 680 457 89,221 67.25% 749 494 121,181 66.84% 1,488 977 955,772 65.89%
Maryland 652 495 450,671 76.79% 628 429 590,912 66.73% 1,385 971 4,017,859 70.34%
Massachusetts 719 487 477,554 67.37% 729 432 788,962 58.67% 1,721 999 4,680,619 58.57%
Michigan 1,631 1,209 753,613 73.78% 1,658 1,200 1,077,222 72.44% 3,657 2,519 6,657,456 68.24%
Minnesota 624 459 438,045 72.19% 662 447 568,703 67.47% 1,437 984 3,698,785 69.36%
Mississippi 648 471 241,623 73.69% 802 595 316,108 74.59% 1,411 955 1,894,943 66.36%
Missouri 638 474 465,766 74.78% 654 468 627,312 71.92% 1,446 1,006 4,022,731 68.91%
Montana 730 502 76,259 68.53% 739 451 109,604 61.46% 1,500 982 712,413 66.64%
Nebraska 661 486 157,259 73.51% 767 531 211,523 71.35% 1,320 948 1,215,009 72.62%
Nevada 602 473 231,765 79.47% 719 489 283,278 66.93% 1,474 1,002 2,046,371 67.06%
New Hampshire 733 507 94,278 67.92% 731 441 140,227 60.66% 1,517 969 945,381 63.76%
New Jersey 1,163 778 675,646 65.83% 1,188 695 860,848 58.76% 2,552 1,528 5,989,388 59.18%
New Mexico 599 489 165,707 81.45% 620 435 216,551 69.49% 1,330 929 1,364,936 69.03%
New York 2,469 1,710 1,348,363 68.20% 2,556 1,582 2,031,273 60.30% 5,647 3,237 13,191,928 56.41%
North Carolina 859 668 793,729 77.30% 1,188 838 1,067,047 70.27% 2,210 1,517 6,865,689 68.29%
North Dakota 720 483 54,491 67.33% 727 440 91,846 59.63% 1,596 980 473,049 61.52%
Ohio 1,654 1,175 889,700 71.15% 1,911 1,260 1,200,403 66.02% 3,758 2,476 7,734,940 65.15%
Oklahoma 657 471 318,276 72.35% 771 499 417,957 66.56% 1,426 946 2,497,934 66.41%
Oregon 750 529 295,052 70.36% 645 419 415,291 67.05% 1,516 1,000 2,880,798 65.86%
Pennsylvania 1,792 1,257 910,130 70.15% 1,674 1,097 1,292,868 64.98% 3,682 2,415 8,675,385 65.49%
Rhode Island 698 480 72,481 68.44% 658 449 122,890 69.84% 1,493 938 714,973 63.34%
South Carolina 586 456 377,471 78.72% 570 413 504,595 72.75% 1,328 965 3,407,157 73.54%
South Dakota 645 476 69,325 74.62% 651 445 91,692 68.72% 1,416 955 557,354 70.16%
Tennessee 619 470 513,011 74.63% 693 485 687,939 70.20% 1,434 980 4,497,884 66.74%
Texas 2,042 1,593 2,485,959 78.09% 2,279 1,670 3,116,800 72.55% 4,804 3,282 17,862,401 67.31%
Utah 577 455 312,345 78.51% 694 488 406,023 71.66% 1,405 1,005 1,821,899 70.92%
Vermont 707 483 41,066 69.78% 744 462 73,777 61.20% 1,469 946 430,733 66.53%
Virginia 1,042 762 629,725 73.22% 964 693 869,285 72.81% 2,267 1,535 5,581,639 67.29%
Washington 666 464 545,968 68.07% 746 483 738,052 65.32% 1,494 955 5,065,742 63.64%
West Virginia 733 452 124,659 61.62% 778 487 174,236 61.58% 1,632 989 1,235,448 59.76%
Wisconsin 670 509 442,510 76.14% 698 474 615,930 67.31% 1,430 944 3,861,670 66.89%
Wyoming 593 449 44,908 76.79% 603 485 57,395 80.72% 1,292 946 377,084 72.63%
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 2018-2019 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 2018 and 2019 individual response rates. The 2018-2019 population estimate is the average of the 2018 and the 2019 population.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2018 and 2019.
Table C.11 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2017, 2018, and 2019
State 2017
Total
Selected		 2017
Total
Responded		 2017
Population
Estimate		 2017
Weighted
Interview
Response
Rate		 2018
Total
Selected		 2018
Total
Responded		 2018
Population
Estimate		 2018
Weighted
Interview
Response
Rate		 2019
Total
Selected		 2019
Total
Responded		 2019
Population
Estimate		 2019
Weighted
Interview
Response
Rate
Total U.S. 30,946 23,001 37,826,079 74.21% 31,510 23,081 37,959,335 73.50% 32,624 23,134 38,073,463 71.47%
Northeast 6,326 4,504 6,196,921 70.45% 6,510 4,505 6,168,619 68.64% 6,659 4,480 6,129,045 66.16%
Midwest 7,312 5,412 8,101,681 73.59% 7,483 5,468 8,138,371 72.97% 7,772 5,393 8,116,569 69.51%
South 10,102 7,666 14,454,990 76.53% 10,185 7,742 14,631,769 76.84% 10,598 7,817 14,833,048 74.79%
West 7,206 5,419 9,072,487 73.65% 7,332 5,366 9,020,576 71.82% 7,595 5,444 8,994,801 71.37%
Alabama 454 347 623,472 76.38% 421 329 593,183 76.69% 452 334 580,254 75.60%
Alaska 491 362 85,526 75.51% 452 326 86,539 72.32% 480 338 83,145 69.46%
Arizona 405 322 851,823 78.72% 386 304 886,300 80.88% 434 293 822,677 69.63%
Arkansas 472 362 370,680 76.49% 426 343 366,547 80.45% 418 320 350,642 76.51%
California 2,118 1,537 4,736,697 72.00% 2,139 1,496 4,517,745 69.22% 2,230 1,598 4,550,043 70.89%
Colorado 427 314 609,264 74.03% 432 319 636,300 72.52% 422 305 647,284 73.40%
Connecticut 466 315 404,880 67.27% 544 380 440,945 69.66% 475 329 444,616 67.89%
Delaware 435 301 101,672 69.41% 473 326 102,325 69.00% 558 343 105,966 58.96%
District of Columbia 417 332 56,880 81.85% 406 321 48,746 82.24% 414 339 47,596 78.15%
Florida 1,512 1,154 2,179,015 76.31% 1,562 1,199 2,295,548 76.60% 1,538 1,167 2,285,322 75.10%
Georgia 608 464 1,271,553 78.13% 650 516 1,294,795 78.77% 719 560 1,345,152 77.74%
Hawaii 417 314 129,575 76.89% 504 356 139,374 70.48% 471 337 139,373 70.25%
Idaho 387 308 206,652 80.00% 410 310 218,088 76.10% 413 307 217,046 72.99%
Illinois 1,114 787 1,512,952 71.09% 1,146 796 1,485,037 68.13% 1,262 798 1,491,403 62.92%
Indiana 394 282 747,076 70.86% 447 338 780,516 74.04% 467 324 798,916 67.42%
Iowa 437 323 373,723 74.00% 453 310 391,421 68.92% 541 373 412,327 70.06%
Kansas 462 352 353,102 74.45% 446 336 368,737 74.58% 444 311 385,180 74.17%
Kentucky 458 346 548,045 76.16% 426 306 527,935 74.03% 457 330 546,387 72.76%
Louisiana 418 299 519,800 70.58% 457 348 549,049 74.22% 465 346 564,282 74.63%
Maine 508 370 136,642 74.34% 459 320 139,778 70.62% 523 353 147,048 67.59%
Maryland 405 306 645,964 75.41% 429 326 650,144 76.74% 422 308 620,331 72.30%
Massachusetts 586 393 846,090 69.42% 466 319 808,973 69.56% 495 323 700,235 63.28%
Michigan 1,076 821 1,208,711 75.78% 1,125 856 1,214,547 75.18% 1,110 812 1,151,072 73.82%
Minnesota 460 348 682,965 76.23% 410 310 626,959 73.25% 442 308 673,142 69.01%
Mississippi 402 310 360,474 76.81% 477 372 375,010 78.46% 532 385 400,895 73.95%
Missouri 465 327 742,690 69.79% 440 345 727,969 79.45% 448 321 775,205 72.90%
Montana 385 289 115,633 75.36% 474 334 118,695 70.54% 493 314 112,805 63.22%
Nebraska 458 338 231,189 75.89% 443 313 250,200 71.06% 490 376 244,526 78.90%
Nevada 441 335 334,801 75.53% 393 295 347,789 75.01% 458 354 347,387 78.34%
New Hampshire 485 343 141,957 68.68% 495 344 140,212 67.98% 480 305 138,607 61.32%
New Jersey 713 523 1,036,955 72.46% 816 544 1,029,549 65.44% 769 494 976,133 63.14%
New Mexico 346 291 239,351 84.66% 416 344 254,901 80.16% 415 315 256,370 76.20%
New York 1,648 1,118 2,077,765 66.53% 1,606 1,118 2,026,990 68.13% 1,700 1,145 2,118,450 66.41%
North Carolina 712 552 1,223,483 78.03% 622 475 1,169,473 74.47% 664 508 1,217,723 77.67%
North Dakota 461 331 86,113 72.85% 476 331 93,664 69.70% 506 324 87,650 63.19%
Ohio 1,094 812 1,339,799 73.50% 1,167 844 1,405,306 72.72% 1,155 792 1,323,093 68.76%
Oklahoma 418 282 432,163 65.12% 485 337 463,779 71.11% 435 312 485,283 71.48%
Oregon 517 353 454,274 69.59% 498 342 454,817 68.14% 493 348 460,280 70.60%
Pennsylvania 985 751 1,358,691 75.84% 1,158 814 1,382,875 70.22% 1,240 860 1,412,034 68.89%
Rhode Island 474 349 129,538 74.36% 467 327 126,570 73.50% 492 340 116,570 67.12%
South Carolina 434 357 585,789 78.35% 390 311 595,839 80.88% 399 302 588,407 77.02%
South Dakota 444 349 102,088 79.32% 443 329 106,383 74.73% 457 325 105,519 71.64%
Tennessee 440 338 741,197 73.37% 459 350 777,077 75.09% 395 293 764,688 72.27%
Texas 1,425 1,136 3,685,040 79.49% 1,369 1,097 3,711,139 79.45% 1,505 1,132 3,815,202 74.89%
Utah 390 303 461,460 76.30% 379 295 420,730 76.67% 397 302 468,072 78.10%
Vermont 461 342 64,405 73.91% 499 339 72,728 68.55% 485 331 75,352 69.49%
Virginia 625 469 921,487 75.22% 648 476 916,749 73.88% 705 519 923,854 74.34%
Washington 440 320 775,782 70.76% 427 306 869,913 70.30% 495 337 823,678 67.12%
West Virginia 467 311 188,276 65.45% 485 310 194,429 63.55% 520 319 191,065 61.83%
Wisconsin 447 342 721,273 77.56% 487 360 687,633 73.73% 450 329 668,537 71.58%
Wyoming 442 371 71,649 82.44% 422 339 69,384 81.69% 394 296 66,640 77.43%
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, 2018, and 2019.
Table C.12 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 12 to 20, by State: 2017-2018 and 2018-2019
State 2017-2018
Total
Selected		 2017-2018
Total
Responded		 2017-2018
Population
Estimate		 2017-2018
Weighted
Interview
Response
Rate		 2018-2019
Total
Selected		 2018-2019
Total
Responded		 2018-2019
Population
Estimate		 2018-2019
Weighted
Interview
Response
Rate
Total U.S. 62,456 46,082 37,892,707 73.86% 64,134 46,215 38,016,399 72.48%
Northeast 12,836 9,009 6,182,770 69.55% 13,169 8,985 6,148,832 67.40%
Midwest 14,795 10,880 8,120,026 73.28% 15,255 10,861 8,127,470 71.23%
South 20,287 15,408 14,543,379 76.69% 20,783 15,559 14,732,409 75.81%
West 14,538 10,785 9,046,532 72.74% 14,927 10,810 9,007,688 71.60%
Alabama 875 676 608,327 76.53% 873 663 586,719 76.16%
Alaska 943 688 86,033 73.93% 932 664 84,842 70.91%
Arizona 791 626 869,061 79.79% 820 597 854,488 75.24%
Arkansas 898 705 368,614 78.44% 844 663 358,595 78.48%
California 4,257 3,033 4,627,221 70.64% 4,369 3,094 4,533,894 70.05%
Colorado 859 633 622,782 73.27% 854 624 641,792 72.95%
Connecticut 1,010 695 422,912 68.52% 1,019 709 442,780 68.78%
Delaware 908 627 101,999 69.21% 1,031 669 104,146 63.89%
District of Columbia 823 653 52,813 82.03% 820 660 48,171 80.12%
Florida 3,074 2,353 2,237,281 76.45% 3,100 2,366 2,290,435 75.85%
Georgia 1,258 980 1,283,174 78.46% 1,369 1,076 1,319,973 78.25%
Hawaii 921 670 134,475 73.59% 975 693 139,374 70.37%
Idaho 797 618 212,370 77.93% 823 617 217,567 74.56%
Illinois 2,260 1,583 1,498,994 69.65% 2,408 1,594 1,488,220 65.47%
Indiana 841 620 763,796 72.48% 914 662 789,716 70.67%
Iowa 890 633 382,572 71.45% 994 683 401,874 69.52%
Kansas 908 688 360,920 74.51% 890 647 376,958 74.37%
Kentucky 884 652 537,990 75.09% 883 636 537,161 73.38%
Louisiana 875 647 534,424 72.49% 922 694 556,665 74.42%
Maine 967 690 138,210 72.49% 982 673 143,413 69.06%
Maryland 834 632 648,054 76.08% 851 634 635,238 74.57%
Massachusetts 1,052 712 827,531 69.49% 961 642 754,604 66.60%
Michigan 2,201 1,677 1,211,629 75.48% 2,235 1,668 1,182,809 74.51%
Minnesota 870 658 654,962 74.82% 852 618 650,050 71.08%
Mississippi 879 682 367,742 77.65% 1,009 757 387,952 76.11%
Missouri 905 672 735,330 74.51% 888 666 751,587 76.14%
Montana 859 623 117,164 72.96% 967 648 115,750 66.80%
Nebraska 901 651 240,694 73.38% 933 689 247,363 74.85%
Nevada 834 630 341,295 75.27% 851 649 347,588 76.71%
New Hampshire 980 687 141,084 68.33% 975 649 139,409 64.76%
New Jersey 1,529 1,067 1,033,252 69.05% 1,585 1,038 1,002,841 64.28%
New Mexico 762 635 247,126 82.37% 831 659 255,636 78.14%
New York 3,254 2,236 2,052,378 67.32% 3,306 2,263 2,072,720 67.26%
North Carolina 1,334 1,027 1,196,478 76.28% 1,286 983 1,193,598 76.12%
North Dakota 937 662 89,889 71.26% 982 655 90,657 66.44%
Ohio 2,261 1,656 1,372,552 73.10% 2,322 1,636 1,364,199 70.77%
Oklahoma 903 619 447,971 68.13% 920 649 474,531 71.30%
Oregon 1,015 695 454,546 68.88% 991 690 457,549 69.40%
Pennsylvania 2,143 1,565 1,370,783 73.01% 2,398 1,674 1,397,454 69.55%
Rhode Island 941 676 128,054 73.94% 959 667 121,570 70.37%
South Carolina 824 668 590,814 79.61% 789 613 592,123 79.00%
South Dakota 887 678 104,236 77.02% 900 654 105,951 73.18%
Tennessee 899 688 759,137 74.25% 854 643 770,883 73.69%
Texas 2,794 2,233 3,698,089 79.47% 2,874 2,229 3,763,171 77.14%
Utah 769 598 441,095 76.48% 776 597 444,401 77.43%
Vermont 960 681 68,567 71.07% 984 670 74,040 69.02%
Virginia 1,273 945 919,118 74.56% 1,353 995 920,301 74.11%
Washington 867 626 822,848 70.52% 922 643 846,796 68.73%
West Virginia 952 621 191,353 64.50% 1,005 629 192,747 62.70%
Wisconsin 934 702 704,453 75.67% 937 689 678,085 72.66%
Wyoming 864 710 70,516 82.07% 816 635 68,012 79.63%
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, 2017, 2018, and 2019.
Table C.13 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2017, 2018, and 2019
State 2017
Total
Selected		 2017
Total
Responded		 2017
Population
Estimate		 2017
Weighted
Interview
Response
Rate		 2018
Total
Selected		 2018
Total
Responded		 2018
Population
Estimate		 2018
Weighted
Interview
Response
Rate		 2019
Total
Selected		 2019
Total
Responded		 2019
Population
Estimate		 2019
Weighted
Interview
Response
Rate
Total U.S. 74,917 50,999 247,160,541 66.31% 76,149 50,939 248,857,430 65.83% 77,879 50,731 250,316,210 64.21%
Northeast 15,162 9,957 44,028,298 63.74% 15,292 9,624 43,815,814 61.40% 15,853 9,561 43,799,469 58.80%
Midwest 17,692 11,946 51,704,631 66.52% 18,023 11,953 51,913,224 66.29% 18,469 12,064 52,036,916 64.92%
South 24,497 17,113 92,958,490 68.63% 24,556 17,221 94,035,582 69.16% 24,915 17,022 94,911,072 67.39%
West 17,566 11,983 58,469,122 64.31% 18,278 12,141 59,092,809 63.41% 18,642 12,084 59,568,753 62.42%
Alabama 1,040 720 3,701,931 66.05% 976 702 3,721,365 67.62% 1,100 743 3,739,475 66.27%
Alaska 1,037 686 527,234 66.17% 1,016 707 528,022 69.55% 1,109 737 526,155 66.03%
Arizona 812 615 5,280,534 72.53% 925 657 5,425,439 71.01% 1,097 716 5,534,109 64.69%
Arkansas 1,008 725 2,246,021 67.78% 1,003 758 2,258,016 72.53% 949 681 2,265,407 70.98%
California 5,409 3,343 29,974,934 60.00% 5,713 3,433 30,066,348 58.97% 5,649 3,496 30,119,024 59.78%
Colorado 1,106 756 4,253,700 67.35% 1,070 729 4,338,316 65.50% 1,145 711 4,405,626 63.30%
Connecticut 1,145 755 2,795,622 66.81% 1,261 739 2,790,131 57.26% 1,286 785 2,792,745 58.76%
Delaware 1,084 716 742,998 66.05% 1,155 753 749,047 64.01% 1,155 685 755,759 59.40%
District of Columbia 951 695 559,290 72.94% 938 692 564,354 70.81% 891 670 567,382 74.58%
Florida 3,665 2,505 16,474,084 66.71% 3,752 2,622 16,761,015 68.89% 3,608 2,503 16,945,803 66.41%
Georgia 1,612 1,157 7,719,247 69.36% 1,577 1,121 7,814,362 68.99% 1,518 1,091 7,912,870 71.57%
Hawaii 1,087 725 1,064,241 62.26% 1,176 769 1,061,309 65.60% 1,209 766 1,060,302 64.41%
Idaho 992 738 1,253,342 73.91% 986 706 1,287,368 72.37% 1,048 729 1,318,669 69.95%
Illinois 2,941 1,744 9,720,651 58.57% 3,014 1,793 9,701,955 59.54% 3,273 1,804 9,672,508 53.52%
Indiana 1,074 717 4,999,830 66.90% 1,062 738 5,029,012 69.44% 1,101 731 5,069,207 65.58%
Iowa 1,118 740 2,373,014 66.49% 1,108 726 2,384,027 66.56% 1,029 706 2,387,351 68.46%
Kansas 1,037 744 2,139,785 70.62% 1,030 718 2,143,684 68.78% 1,087 724 2,151,149 66.63%
Kentucky 1,100 729 3,361,242 64.73% 1,131 758 3,377,260 64.70% 1,125 753 3,382,058 66.51%
Louisiana 1,052 731 3,472,415 68.50% 1,026 770 3,461,724 72.05% 997 692 3,459,093 66.88%
Maine 1,014 705 1,069,799 68.46% 1,116 756 1,073,479 69.21% 1,121 715 1,080,428 62.88%
Maryland 1,051 759 4,610,102 71.25% 971 680 4,604,846 70.31% 1,042 720 4,612,696 69.44%
Massachusetts 1,276 714 5,419,068 56.05% 1,209 742 5,466,285 62.43% 1,241 689 5,472,875 54.81%
Michigan 2,616 1,807 7,681,241 67.21% 2,640 1,809 7,728,259 67.61% 2,675 1,910 7,741,095 70.07%
Minnesota 1,054 732 4,223,276 70.66% 998 683 4,253,446 69.27% 1,101 748 4,281,529 68.97%
Mississippi 1,020 698 2,206,850 66.29% 1,017 726 2,213,130 68.03% 1,196 824 2,208,972 67.10%
Missouri 1,077 754 4,624,223 69.43% 990 724 4,641,525 72.64% 1,110 750 4,658,561 66.14%
Montana 1,052 773 807,184 74.33% 1,093 703 818,349 66.30% 1,146 730 825,684 65.57%
Nebraska 1,013 710 1,415,282 68.81% 1,068 748 1,423,518 72.01% 1,019 731 1,429,547 72.87%
Nevada 1,086 722 2,275,121 64.07% 1,116 775 2,308,632 69.17% 1,077 716 2,350,666 64.73%
New Hampshire 1,069 739 1,067,801 71.64% 1,079 689 1,082,007 62.74% 1,169 721 1,089,207 63.96%
New Jersey 1,856 1,196 6,925,877 63.49% 1,826 1,096 6,854,833 59.29% 1,914 1,127 6,845,640 58.97%
New Mexico 893 713 1,564,401 78.79% 932 678 1,577,631 71.19% 1,018 686 1,585,344 67.02%
New York 3,984 2,522 15,464,407 61.71% 3,963 2,409 15,244,927 58.70% 4,240 2,410 15,201,475 55.11%
North Carolina 1,554 1,078 7,766,421 69.00% 1,653 1,117 7,880,510 67.04% 1,745 1,238 7,984,962 70.05%
North Dakota 1,038 728 562,731 70.04% 1,147 725 564,603 63.84% 1,176 695 565,187 58.61%
Ohio 2,634 1,811 8,883,426 68.21% 2,891 1,874 8,927,740 63.93% 2,778 1,862 8,942,947 66.62%
Oklahoma 1,078 716 2,892,414 66.78% 1,088 704 2,906,588 65.11% 1,109 741 2,925,194 67.78%
Oregon 1,100 744 3,231,638 67.24% 1,120 737 3,279,291 65.68% 1,041 682 3,312,886 66.33%
Pennsylvania 2,614 1,831 9,947,416 68.42% 2,664 1,782 9,963,162 65.92% 2,692 1,730 9,973,344 64.94%
Rhode Island 1,134 759 837,144 67.08% 1,080 710 836,426 66.78% 1,071 677 839,301 61.52%
South Carolina 1,016 735 3,825,020 69.85% 953 711 3,881,870 75.75% 945 667 3,941,635 71.12%
South Dakota 1,018 729 637,785 71.23% 1,023 708 647,538 70.97% 1,044 692 650,555 68.87%
Tennessee 1,006 721 5,106,775 71.07% 996 694 5,159,830 67.02% 1,131 771 5,211,815 67.40%
Texas 3,457 2,525 20,458,311 71.20% 3,499 2,533 20,830,642 70.74% 3,584 2,419 21,127,759 65.52%
Utah 969 728 2,151,568 73.72% 1,047 769 2,204,705 73.22% 1,052 724 2,251,140 69.03%
Vermont 1,070 736 501,164 69.01% 1,094 701 504,565 67.60% 1,119 707 504,455 63.83%
Virginia 1,703 1,173 6,396,270 65.66% 1,645 1,146 6,437,064 69.69% 1,586 1,082 6,464,783 66.48%
Washington 1,116 728 5,651,840 64.19% 1,145 761 5,763,970 65.16% 1,095 677 5,843,617 62.49%
West Virginia 1,100 730 1,419,101 65.14% 1,176 734 1,413,959 61.10% 1,234 742 1,405,410 58.85%
Wisconsin 1,072 730 4,443,389 68.23% 1,052 707 4,467,918 66.62% 1,076 711 4,487,282 67.26%
Wyoming 907 712 433,386 77.65% 939 717 433,429 71.07% 956 714 435,530 76.31%
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, 2018, and 2019.
Table C.14 – Sample Sizes, Weighted Interview Response Rates, and Population Estimates among Persons Aged 18 or Older, by State: 2017-2018 and 2018-2019
State 2017-2018
Total
Selected		 2017-2018
Total
Responded		 2017-2018
Population
Estimate		 2017-2018
Weighted
Interview
Response
Rate		 2018-2019
Total
Selected		 2018-2019
Total
Responded		 2018-2019
Population
Estimate		 2018-2019
Weighted
Interview
Response
Rate
Total U.S. 151,066 101,938 248,008,986 66.07% 154,028 101,670 249,586,820 65.02%
Northeast 30,454 19,581 43,922,056 62.56% 31,145 19,185 43,807,642 60.09%
Midwest 35,715 23,899 51,808,928 66.41% 36,492 24,017 51,975,070 65.60%
South 49,053 34,334 93,497,036 68.89% 49,471 34,243 94,473,327 68.27%
West 35,844 24,124 58,780,966 63.85% 36,920 24,225 59,330,781 62.92%
Alabama 2,016 1,422 3,711,648 66.85% 2,076 1,445 3,730,420 66.94%
Alaska 2,053 1,393 527,628 67.83% 2,125 1,444 527,089 67.76%
Arizona 1,737 1,272 5,352,987 71.75% 2,022 1,373 5,479,774 67.79%
Arkansas 2,011 1,483 2,252,018 70.10% 1,952 1,439 2,261,711 71.76%
California 11,122 6,776 30,020,641 59.48% 11,362 6,929 30,092,686 59.37%
Colorado 2,176 1,485 4,296,008 66.46% 2,215 1,440 4,371,971 64.37%
Connecticut 2,406 1,494 2,792,876 61.84% 2,547 1,524 2,791,438 58.01%
Delaware 2,239 1,469 746,023 65.06% 2,310 1,438 752,403 61.68%
District of Columbia 1,889 1,387 561,822 71.87% 1,829 1,362 565,868 72.69%
Florida 7,417 5,127 16,617,550 67.82% 7,360 5,125 16,853,409 67.65%
Georgia 3,189 2,278 7,766,805 69.17% 3,095 2,212 7,863,616 70.28%
Hawaii 2,263 1,494 1,062,775 63.93% 2,385 1,535 1,060,805 65.01%
Idaho 1,978 1,444 1,270,355 73.15% 2,034 1,435 1,303,018 71.14%
Illinois 5,955 3,537 9,711,303 59.05% 6,287 3,597 9,687,231 56.47%
Indiana 2,136 1,455 5,014,421 68.16% 2,163 1,469 5,049,109 67.53%
Iowa 2,226 1,466 2,378,520 66.53% 2,137 1,432 2,385,689 67.46%
Kansas 2,067 1,462 2,141,734 69.69% 2,117 1,442 2,147,416 67.73%
Kentucky 2,231 1,487 3,369,251 64.72% 2,256 1,511 3,379,659 65.62%
Louisiana 2,078 1,501 3,467,069 70.27% 2,023 1,462 3,460,408 69.41%
Maine 2,130 1,461 1,071,639 68.83% 2,237 1,471 1,076,953 66.00%
Maryland 2,022 1,439 4,607,474 70.78% 2,013 1,400 4,608,771 69.87%
Massachusetts 2,485 1,456 5,442,676 59.26% 2,450 1,431 5,469,580 58.58%
Michigan 5,256 3,616 7,704,750 67.41% 5,315 3,719 7,734,677 68.84%
Minnesota 2,052 1,415 4,238,361 69.96% 2,099 1,431 4,267,487 69.12%
Mississippi 2,037 1,424 2,209,990 67.14% 2,213 1,550 2,211,051 67.56%
Missouri 2,067 1,478 4,632,874 71.03% 2,100 1,474 4,650,043 69.30%
Montana 2,145 1,476 812,766 70.24% 2,239 1,433 822,017 65.95%
Nebraska 2,081 1,458 1,419,400 70.45% 2,087 1,479 1,426,532 72.43%
Nevada 2,202 1,497 2,291,877 66.76% 2,193 1,491 2,329,649 67.04%
New Hampshire 2,148 1,428 1,074,904 67.09% 2,248 1,410 1,085,607 63.34%
New Jersey 3,682 2,292 6,890,355 61.41% 3,740 2,223 6,850,236 59.13%
New Mexico 1,825 1,391 1,571,016 74.98% 1,950 1,364 1,581,487 69.09%
New York 7,947 4,931 15,354,667 60.19% 8,203 4,819 15,223,201 56.93%
North Carolina 3,207 2,195 7,823,465 68.00% 3,398 2,355 7,932,736 68.56%
North Dakota 2,185 1,453 563,667 66.87% 2,323 1,420 564,895 61.22%
Ohio 5,525 3,685 8,905,583 66.05% 5,669 3,736 8,935,343 65.27%
Oklahoma 2,166 1,420 2,899,501 65.91% 2,197 1,445 2,915,891 66.43%
Oregon 2,220 1,481 3,255,465 66.44% 2,161 1,419 3,296,088 66.00%
Pennsylvania 5,278 3,613 9,955,289 67.19% 5,356 3,512 9,968,253 65.42%
Rhode Island 2,214 1,469 836,785 66.93% 2,151 1,387 837,863 64.26%
South Carolina 1,969 1,446 3,853,445 72.77% 1,898 1,378 3,911,752 73.44%
South Dakota 2,041 1,437 642,662 71.11% 2,067 1,400 649,047 69.95%
Tennessee 2,002 1,415 5,133,303 69.10% 2,127 1,465 5,185,822 67.21%
Texas 6,956 5,058 20,644,477 70.97% 7,083 4,952 20,979,200 68.07%
Utah 2,016 1,497 2,178,137 73.46% 2,099 1,493 2,227,923 71.05%
Vermont 2,164 1,437 502,864 68.27% 2,213 1,408 504,510 65.79%
Virginia 3,348 2,319 6,416,667 67.68% 3,231 2,228 6,450,924 68.04%
Washington 2,261 1,489 5,707,905 64.68% 2,240 1,438 5,803,794 63.86%
West Virginia 2,276 1,464 1,416,530 63.13% 2,410 1,476 1,409,684 59.98%
Wisconsin 2,124 1,437 4,455,653 67.44% 2,128 1,418 4,477,600 66.96%
Wyoming 1,846 1,429 433,407 74.28% 1,895 1,431 434,479 73.70%
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, 2017, 2018, and 2019.
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		 2017-
2018		 2018-
2019
Illicit Drug Use in the Past Month1 X X 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 X X
Marijuana Use in the Past Month X X 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 X X
First Use of Marijuana (Marijuana Initiation) X X 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 X X
Cocaine Use in the Past Year X X 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 X X
Heroin Use in the Past Year -- -- -- -- -- -- -- -- -- -- -- --2 X X X X X
Perceptions of Great Risk from Trying Heroin
   Once or Twice		 -- -- -- -- -- -- -- -- -- -- -- -- -- X X X X
Methamphetamine Use in the Past Year -- -- -- -- -- -- -- -- -- -- -- -- -- --3 X X X
Pain Reliever Misuse in the Past Year1 --4 X X X X X X X X X X X -- X X X X
Alcohol Use in the Past Month X X X X X X X X X X X X X X X X X
Underage Past Month Use of Alcohol --4 X X 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 X X
Underage Past Month Binge Alcohol Use1 --4 X X 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 X X
Tobacco Product Use in the Past Month X X 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 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 X X
Illicit Drug Use Disorder in the Past Year1 X X 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 X X
Alcohol Use Disorder in the Past Year X X 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 -- -- -- --
Substance Use Disorder in the Past Year1 X X 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 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 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 X X
Serious Psychological Distress (SPD) in the Past Year6 X X X -- -- -- -- -- -- -- -- -- -- -- -- -- --
Any Mental Illness (AMI) in the Past Year -- -- -- -- -- -- 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 X X
Received Mental Health Services in the Past Year5 -- -- -- -- -- -- -- -- X X X X X X 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 X X
Had Serious Thoughts of Suicide in the Past Year -- -- -- -- -- -- X X X X X X X X X X X
Made Any Suicide Plans in the Past Year -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --8 X
Attempted Suicide in the Past Year -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --8 X
X = available; -- = not available.
1 For these outcomes, the 2015-2016, 2016-2017, 2017-2018, and 2018-2019 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 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.
3 Estimates for this outcome were not included in the 2015-2016 state documents 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.
4 Estimates for this outcome 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.
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 have been 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.
8 Estimates for this outcome were not included in the 2017-2018 state document at https://www.samhsa.gov/data/, but the 2017-2018 estimates were included in the 2018-2019 state documents as part of the comparison tables. However, the Bayesian confidence intervals associated with these estimates were not published.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2002-2019.
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
Heroin Use in the Past Year X X -- X X X
Perceptions of Great Risk from Trying Heroin
   Once or Twice		 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
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
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
Alcohol Use Disorder in the Past Year X X -- X X X
Alcohol Dependence 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
Any Mental Illness (AMI) in the Past Year -- -- -- X X X
Serious Mental Illness (SMI) in the Past Year -- -- -- X X X
Received Mental Health Services in the Past Year -- -- -- X X X
Had at Least One Major Depressive Episode (MDE)
   in the Past Year1		 -- X -- X X X
Had Serious Thoughts of Suicide in the Past Year -- -- -- X X X
Made Any Suicide Plans in the Past Year -- -- -- X X X
Attempted Suicide 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. Tables containing estimates for adults aged 18 or older were first presented with the 2005-2006 small area estimation tables. 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, 2002-2019.
Table C.17 – Summary of Milestones Implemented in NSDUH's SAE Production Process, 2002-2019
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		 2017-
2018		 2018-
2019
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 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 Data5		 -- -- -- -- -- -- -- -- -- -- -- -- -- checkmark checkmark checkmark checkmark
Small Area Estimates Reproduced Using Data Omitting
   Falsified Data6		 -- -- -- checkmark checkmark checkmark checkmark -- -- -- -- -- -- -- -- -- --
SMI and AMI Small Area Estimates Based on Updated
   2013 Model7		 -- -- -- -- -- -- checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark checkmark
MDE Small Area Estimates Based on Adjusted MDE
   Variable8		 -- -- -- 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 For past year suicide plans and attempted suicide, variable selection was done using 2018-2019 data.
6 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/.
7 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 6.
8 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 6.
Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2002-2019.

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 summary and definitions. Retrieved from https://www.samhsa.gov/data/

Center for Behavioral Health Statistics and Quality. (2016b). 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. (2016c). 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. (2018). 2017 National Survey on Drug Use and Health: Methodological resource book. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Center for Behavioral Health Statistics and Quality. (2019). 2018 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. (2020). 2019 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 a). 2018 National Survey on Drug Use and Health: Methodological resource book. Rockville, MD: Substance Abuse and Mental Health Services Administration.

Center for Behavioral Health Statistics and Quality. (in press b). 2019 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.unh.edu/institutional-research/sites/default/files/pamphlet.pdf 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, Research Triangle Park, North Carolina. Work by RTI was performed under Contract No. HHSS283201700002C.

At SAMHSA, Jennifer Hoenig and Shiromani Gyawali 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.

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, with formatting assistance from Debbie Bond. Teresa F. Bass, Kimberly H. Cone, Danny Occoquan, and Pamela G. Tuck 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 webpage: https://www.samhsa.gov/data/.

2 RTI International is a trade name of Research Triangle Institute. RTI and the RTI logo are U.S. registered trademarks of Research Triangle Institute.

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 33 in "2018-2019 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, receipt of mental health services, thoughts of suicide, suicide plans, and suicide attempts, estimates for youths aged 12 to 17 and individuals aged 12 or older are not included because youths are not asked these questions.

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.

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 1999 and 2000, 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 "2018-2019 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia)" (Tables 1 to 33, by Age Group).

14 The exact changes are documented in the 2015 NSDUH's Office of Management and Budget 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 2018 and 2019 samples.

18 For details on how the average annual rate of marijuana (initiation of marijuana) is calculated, see Section B.7 of this document.

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

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

21 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.10. In August 2016, the Substance Abuse and Mental Health Services Administration (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.

22 This is the first time small area estimates of suicide plans made in the past year have been published by SAMHSA.

23 This is the first time small area estimates of attempted suicide in the past year have been published by SAMHSA.

24 Claritas is a market research firm headquartered in Cincinnati, Ohio (see https://claritas.com/ exit icon).

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

26 See Table 13 of the "2018-2019 NSDUH: Model-Based Prevalence Estimates (50 States and the District of Columbia)" at https://www.samhsa.gov/data/.

27 See Table 13 in the file with model-based estimated totals (in thousands) using the link in footnote 25.

28 The file with model-based prevalence estimates is available using the link in footnote 26.

29 See Table 13's model-based prevalence estimates in the file that is available using the link in footnote 26.

30 In NSDUH SAE documents prior to 2016-2017, the term "initiation" was referred to as "incidence."

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

32 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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