A variety of surveys and data systems other than the National Survey on Drug Use and Health (NSDUH) collect data on substance use and mental health. This document briefly describes one of these other data systems that publish state estimates and presents selected comparisons with NSDUH results. The state-level survey that collects data on substance use discussed in this document is the Behavioral Risk Factor Surveillance System (BRFSS), sponsored by the Centers for Disease Control and Prevention (CDC).
Another CDC data system that provides state-level substance use estimates for most but not all states is the Youth Risk Behavior Survey (YRBS). Differences between the YRBS and NSDUH sampling designs, as well as the wider range of age groups used in NSDUH state small area estimates mean that comparisons of estimates are not straightforward. However, ignoring these differences and examining estimates at the national level, the YRBS has generally been shown to have higher estimates than NSDUH (Center for Behavioral Health Statistics and Quality [CBHSQ], 2023).1 Note that comparisons between the state YRBS estimates and the NSDUH state small area estimates are not presented because of some of the differences discussed above.
When considering the information presented in this document, it is important to understand the methodological differences between the BRFSS and NSDUH and the impact that these differences could have on estimates of substance use and mental health. Several studies have compared NSDUH estimates with estimates from other studies and have evaluated how differences may have been affected by differences in survey methodology (Brener et al., 2006; CBHSQ, 2012; Gfroerer et al., 1997; Grucza et al., 2007; Hennessy & Ginsberg, 2001; Miller et al., 2004). These studies suggest that the goals and approaches of surveys are often different, making comparisons among them difficult. Some methodological differences that have been identified as affecting comparisons include populations covered, sampling methods, mode of data collection, survey setting, questionnaires, and estimation methods.
Because of the coronavirus disease 2019 (COVID-19) pandemic, an additional web data collection mode was introduced to the 2020 NSDUH. The 2022 and 2023 surveys continued the use of multimode data collection procedures that were first implemented in October 2020 for the 2020 NSDUH. For 2022, 42.4 percent of interviews were completed via the web, and 57.6 percent were completed in person. In 2023, 36.1 percent of the interviews were completed via the web, and 63.9 percent were completed in person.2
BRFSS is an annual state-based system of health surveys that collects information using landline and cellular telephones on health-related risk behaviors (including cigarette and alcohol use), chronic health conditions, healthcare access, and use of preventive services among the noninstitutionalized adult population aged 18 or older. During 2022 and 2023, all 50 states, the District of Columbia, Guam, Puerto Rico, and the U.S. Virgin Islands collected BRFSS data using computer-assisted telephone interviewing (CATI). Note that, in 2023, Kentucky and Pennsylvania were unable to collect enough BRFSS data to meet the minimum requirements for inclusion in the 2023 public dataset. Thus, the estimates shown in this report for Kentucky and Pennsylvania are based only on 2022 data. The 2022‑2023 BRFSS state estimates and confidence intervals presented here are design-based (direct) estimates (i.e., each respondent is weighted in a way that accounts for the survey design).3
In both BRFSS and NSDUH, data are collected on the following three substance use measures in each of the 50 states and the District of Columbia:4
Note that estimates for these measures are compared in this document. The BRFSS and NSDUH questions that were used for the three measures are shown in the next section.
Past month alcohol use is defined consistently in both BRFSS and NSDUH as having an alcoholic beverage in the past month. Similarly, past month binge alcohol use is defined consistently in the two surveys as drinking five or more drinks (for males) or four or more drinks (for females) on the same occasion (i.e., at the same time or within a couple of hours of each other) on at least 1 day in the past 30 days.
In NSDUH, past month cigarette use is defined as having smoked part or all of a cigarette during the past 30 days (i.e., the 30 days prior to the interview). In BRFSS, the cigarette use measure reported is current cigarette use, which is defined as having smoked at least 100 cigarettes during the lifetime and indicating smoking every day or some days at the time of the survey. Because of these subtle but present differences in definitions, NSDUH’s cigarette use estimates tend to be higher in that they cover two groups of people that the BRFSS estimates would not: (1) respondents who have not smoked 100 cigarettes in their lifetime but had smoked in the past month, and (2) respondents who had smoked a cigarette earlier in the month but were not smoking at the time of the survey.
This document presents findings comparing 2022‑2023 BRFSS state design-based estimates with corresponding 2022‑2023 NSDUH state small area estimates5 for past month alcohol use, past month binge alcohol use, and cigarette use (“past month” use for NSDUH and “current” use for BRFSS). In Tables 1, 2, and 3 (shown after this text discussion), the 2022‑2023 BRFSS state design-based estimates for adults aged 18 or older are shown alongside the 2022‑2023 NSDUH small area estimates for the same age group. The BRFSS estimates were calculated using SUDAAN® Software for Statistical Analysis of Correlated Data (RTI International, 2013) and the publicly available BRFSS SAS® (SAS Institute Inc., 2017) datasets. Tables 1 and 2 also include p values that indicate whether the BRFSS and NSDUH alcohol use and binge alcohol use population percentages are significantly different from each other for a given state. The statistical test used for calculating the p values is described in the Methodology for Comparing BRFSS and NSDUH Estimates section. Users are advised to use caution when interpreting these significant differences due to the methodological differences in the two surveys.
Due to definitional differences in the cigarette use measure, no formal statistical tests of differences between NSDUH and BRFSS estimates were produced.
The 2022 and 2023 NSDUH questions that were used to determine past month alcohol use and past month binge alcohol use were as follows:6
The 2022 and 2023 BRFSS questions that were used to determine past month alcohol use and past month binge alcohol use were as follows:8
The 2002 and 2023 NSDUH questions that were used to determine past month cigarette use were as follows:
The 2002 and 2023 BRFSS questions that were used to determine current cigarette use were as follows:
The methodology used to compare BRFSS and NSDUH estimates is similar to what is described in Section B.7 of 2014-2015 National Survey on Drug Use and Health: Guide to State Tables and Summary of Small Area Estimation Methodology.9 Here, the null hypothesis of no difference is tested, that is,
(where
is the expected value10 of the BRFSS estimate and
is the expected value of the NSDUH estimate), or equivalently the log-odds ratio is zero, that is,
, where
is defined as
, and ln denotes the natural logarithm. An estimate of
is given by
, where
and
are the 2022‑2023 BRFSS state-level design-based estimates and the 2022‑2023 NSDUH state model-based estimates, respectively (as given in Tables 1 and 2). To compute the variance of
, that is,
, let
and
, then
. D
The covariance term can be assumed to be zero because the BRFSS and NSDUH samples are independent.
The quantity
can be obtained by using the 95 percent Bayesian confidence intervals in Tables 1 and 2. For this purpose, let
denote the 95 percent Bayesian confidence interval11 for a given state:
, D
where
.
The quantity
can be obtained by using the 95 percent confidence intervals in Tables 1 and 2. For this purpose, let
denote the 95 percent BRFSS confidence interval for a given state, then
is given by
. D
Now, using the first-order Taylor series approximation,12
can be calculated from
as follows:
. D
The p value that is given in Tables 1 and 2 for testing the null hypothesis of no difference (
) is provided by
, where
is a standard normal random variate,
, and
denotes the absolute value of
.
As seen in Table 1, for past month alcohol use, the 2022‑2023 NSDUH estimates and the 2022‑2023 BRFSS estimates were statistically significantly different (i.e., at the 5 percent level of significance) for seven states (Arizona, Delaware, Hawaii, Kentucky, Pennsylvania, Texas, and Wyoming).13 Also, these two sets of estimates were highly correlated (correlation coefficient = 0.92).
The NSDUH estimates of past month binge alcohol use were significantly larger than the BRFSS estimates for all states (see Table 2). As noted previously, NSDUH and BRFSS used the same thresholds for binge alcohol use among males and females in the 2022 and 2023 surveys; therefore, these differences can be partly attributed to differences in data collection methodologies of BRFSS and NSDUH. First, the 2022‑2023 NSDUHs used audio computer-assisted self-interviewing (ACASI) for in-person data collection and self-administration for web data collection, whereas BRFSS used CATI. Self-administration (including ACASI for in-person data collection) can increase respondent privacy for reporting of sensitive behaviors (such as binge drinking) and therefore may yield higher prevalence estimates than interviewer-administered modes such as CATI (Kreuter et al., 2008; Lind et al., 2013; Tourangeau & Smith, 1996; Turner et al., 1998). Although the NSDUH estimates were larger, these two sets of estimates are moderately correlated (correlation coefficient = 0.75).
Table 3 shows the NSDUH estimates of past month cigarette use and the BRFSS estimates of current cigarette use. Statistical tests to examine significant differences between the NSDUH and BRFSS cigarette use population percentages are not included because the definitions are different, as discussed earlier in this document. Although the NSDUH estimates tended to be larger, these two sets of estimates were highly correlated (correlation coefficient = 0.91).
The BRFSS estimates are design based, whereas the NSDUH estimates are model based. Both sets of estimates are based on 2 years of pooled data (2022‑2023). The BRFSS sample sizes for a given state were, in general, much larger than the sample sizes for NSDUH. In the 2022‑2023 NSDUHs, the 18 or older sample sizes in the states ranged from approximately 1,130 to 6,580 respondents, with a median sample size of 1,680.14 For the 2022‑2023 BRFSSs, states had larger sample sizes as compared with their counterparts in NSDUH. Overall, the BRFSS sample sizes for the states varied from a low of 4,023 respondents in Kentucky15 to a high of 52,596 in Washington, with a median sample size of 15,579.16 Sample size differences of this magnitude explain why the NSDUH Bayesian confidence intervals were generally wider than the corresponding BRFSS design-based confidence intervals.
Brener, N. D., Eaton, D. K., Kann, L., Grunbaum, J. A., Gross, L. A., Kyle, T. M., & Ross, J. G. (2006). The association of survey setting and mode with self-reported health risk behaviors among high school students. Public Opinion Quarterly, 70(3), 354-374. https://doi.org/10.1093/poq/nfl003
Center for Behavioral Health Statistics and Quality. (2012). Comparing and evaluating youth substance use estimates from the National Survey on Drug Use and Health and other surveys (HHS Publication No. SMA 12-4727, Methodology Series M-9). Substance Abuse and Mental Health Services Administration. https://www.samhsa.gov/data/sites/default/files/NSDUH-M9-Youth-2012/NSDUH-M9-Youth-2012.pdf
Center for Behavioral Health Statistics and Quality. (2023). Chapter 5: Other sources of data. In 2022 National Survey on Drug Use and Health (NSDUH): Methodological summary and definitions. https://www.samhsa.gov/data/sites/default/files/reports/rpt42729/2022-nsduh-method-summary-defs/2022-nsduh-method-summary-defs-110123.pdf
Gfroerer, J., Wright, D., & Kopstein, A. (1997). Prevalence of youth substance use: The impact of methodological differences between two national surveys. Drug and Alcohol Dependence, 47(1), 19-30. https://doi.org/10.1016/s0376-8716(97)00063-x
Grucza, R. A., Abbacchi, A. M., Przybeck, T. R., & Gfroerer, J. C. (2007). Discrepancies in estimates of prevalence and correlates of substance use and disorders between two national surveys. Addiction, 102(4), 623-629. https://doi.org/10.1111/j.1360-0443.2007.01745.x
Hennessy, K. H., & Ginsberg, C. (2001). Substance use survey data collection methodologies: Introduction. Journal of Drug Issues, 31(3), 595-597. https://doi.org/10.1177/002204260103100301
Kreuter, F., Presser, S., & Tourangeau, R. (2008). Social desirability bias in CAI, IVR, and web surveys: The effects of mode and question sensitivity. Public Opinion Quarterly, 72(5), 847-865. https://doi.org/10.1093/poq/nfn063
Lind, L., Schober, M., Conrad, F., & Reichert, H. (2013). Why do survey respondents disclose more when computers ask the questions? Public Opinion Quarterly, 77(4), 888-935. https://doi.org/10.1093/poq/nft038
Miller, J. W., Gfroerer, J. C., Brewer, R. D., Naimi, T. S., Mokdad, A., & Giles, W. H. (2004). Prevalence of adult binge drinking: A comparison of two national surveys. American Journal of Preventive Medicine, 27(3), 197-204. https://doi.org/10.1016/s0749-3797(04)00121-7
RTI International. (2013). SUDAAN® language manual, release 11.0.1.
SAS Institute Inc. (2017). SAS/STAT software: Release 14.1.
Tourangeau, R., & Smith, T. W. (1996). Asking sensitive questions: The impact of data collection mode, question format, and question context. Public Opinion Quarterly, 60(2), 275-304. https://doi.org/10.1086/297751
Turner, C. F., Ku, L., Rogers, S. M., Lindberg, L. D., Pleck, J. H., & Sonenstein, F. L. (1998). Adolescent sexual behavior, drug use, and violence: Increased reporting with computer survey technology. Science, 280(5365), 867-873. https://doi.org/10.1126/science.280.5365.867
| State | BRFSS (Estimate) |
BRFSS (95% Confidence Interval) |
NSDUH (Estimate) |
NSDUH (95% Confidence Interval) |
P Value |
|---|---|---|---|---|---|
| Alabama | 43.82 | (42.37 - 45.28) | 45.42 | (42.42 - 48.45) | 0.351 |
| Alaska | 53.13 | (51.79 - 54.46) | 51.50 | (47.97 - 55.02) | 0.399 |
| Arizona | 51.95 | (50.84 - 53.06) | 47.20 | (43.42 - 51.02) | 0.019 |
| Arkansas | 43.58 | (42.25 - 44.90) | 46.26 | (42.60 - 49.95) | 0.178 |
| California | 51.40 | (50.37 - 52.43) | 52.25 | (50.38 - 54.11) | 0.434 |
| Colorado | 61.18 | (60.22 - 62.15) | 58.64 | (55.53 - 61.68) | 0.118 |
| Connecticut | 58.22 | (57.04 - 59.40) | 57.64 | (54.01 - 61.19) | 0.762 |
| Delaware | 53.35 | (51.71 - 54.99) | 57.35 | (54.10 - 60.54) | 0.031 |
| District of Columbia | 67.45 | (65.69 - 69.21) | 65.99 | (62.55 - 69.27) | 0.449 |
| Florida | 54.03 | (52.69 - 55.36) | 52.62 | (50.57 - 54.65) | 0.257 |
| Georgia | 49.56 | (48.36 - 50.76) | 50.00 | (47.19 - 52.81) | 0.779 |
| Hawaii | 49.94 | (48.75 - 51.13) | 45.30 | (41.63 - 49.03) | 0.020 |
| Idaho | 47.63 | (46.50 - 48.75) | 48.51 | (44.82 - 52.21) | 0.655 |
| Illinois | 55.99 | (54.58 - 57.39) | 57.49 | (54.98 - 59.96) | 0.305 |
| Indiana | 49.50 | (48.59 - 50.40) | 50.58 | (47.56 - 53.61) | 0.499 |
| Iowa | 56.60 | (55.63 - 57.57) | 56.14 | (52.38 - 59.84) | 0.817 |
| Kansas | 53.65 | (52.68 - 54.61) | 57.19 | (53.66 - 60.65) | 0.058 |
| Kentucky1 | 35.16 | (33.04 - 37.29) | 46.26 | (43.18 - 49.36) | 0.000 |
| Louisiana | 50.24 | (48.94 - 51.54) | 51.54 | (48.38 - 54.68) | 0.457 |
| Maine | 54.62 | (53.64 - 55.60) | 56.42 | (52.71 - 60.06) | 0.356 |
| Maryland | 52.08 | (51.14 - 53.02) | 52.72 | (49.12 - 56.30) | 0.735 |
| Massachusetts | 58.74 | (57.71 - 59.77) | 59.03 | (55.78 - 62.20) | 0.865 |
| Michigan | 53.65 | (52.66 - 54.64) | 53.75 | (51.55 - 55.94) | 0.935 |
| Minnesota | 58.84 | (58.03 - 59.64) | 56.73 | (52.69 - 60.68) | 0.309 |
| Mississippi | 42.78 | (41.34 - 44.21) | 42.19 | (39.01 - 45.44) | 0.747 |
| Missouri | 50.95 | (49.81 - 52.08) | 52.75 | (49.06 - 56.40) | 0.360 |
| Montana | 59.58 | (58.53 - 60.63) | 55.94 | (52.09 - 59.72) | 0.070 |
| Nebraska | 57.22 | (56.25 - 58.18) | 55.84 | (52.48 - 59.15) | 0.437 |
| Nevada | 54.64 | (52.54 - 56.75) | 51.32 | (47.96 - 54.66) | 0.099 |
| New Hampshire | 61.58 | (60.28 - 62.88) | 62.99 | (59.80 - 66.07) | 0.417 |
| New Jersey | 55.49 | (54.34 - 56.63) | 54.35 | (51.40 - 57.28) | 0.481 |
| New Mexico | 47.38 | (45.72 - 49.05) | 48.66 | (45.23 - 52.11) | 0.512 |
| New York | 54.54 | (53.71 - 55.37) | 53.41 | (51.37 - 55.44) | 0.313 |
| North Carolina | 48.76 | (47.25 - 50.26) | 49.22 | (46.21 - 52.24) | 0.786 |
| North Dakota | 58.45 | (57.16 - 59.73) | 59.70 | (55.71 - 63.57) | 0.555 |
| Ohio | 52.21 | (51.33 - 53.10) | 52.72 | (50.57 - 54.87) | 0.667 |
| Oklahoma | 43.96 | (42.86 - 45.07) | 44.53 | (41.32 - 47.79) | 0.744 |
| Oregon | 56.54 | (55.34 - 57.75) | 56.05 | (52.35 - 59.67) | 0.800 |
| Pennsylvania1 | 51.81 | (49.63 - 53.99) | 55.71 | (53.34 - 58.06) | 0.017 |
| Rhode Island | 56.89 | (55.47 - 58.30) | 58.76 | (54.97 - 62.45) | 0.360 |
| South Carolina | 50.66 | (49.61 - 51.70) | 51.89 | (48.06 - 55.71) | 0.541 |
| South Dakota | 57.70 | (55.32 - 60.08) | 54.70 | (50.92 - 58.43) | 0.186 |
| Tennessee | 47.92 | (46.59 - 49.24) | 48.50 | (44.97 - 52.05) | 0.761 |
| Texas | 50.80 | (49.57 - 52.03) | 47.21 | (45.37 - 49.06) | 0.002 |
| Utah | 33.12 | (32.23 - 34.01) | 31.14 | (28.56 - 33.83) | 0.169 |
| Vermont | 60.75 | (59.54 - 61.95) | 63.04 | (59.44 - 66.51) | 0.233 |
| Virginia | 53.38 | (52.23 - 54.53) | 54.20 | (51.72 - 56.66) | 0.556 |
| Washington | 57.37 | (56.78 - 57.96) | 56.55 | (53.16 - 59.89) | 0.639 |
| West Virginia | 39.78 | (38.54 - 41.03) | 40.79 | (37.05 - 44.64) | 0.621 |
| Wisconsin | 59.54 | (58.62 - 60.45) | 61.84 | (58.47 - 65.10) | 0.194 |
| Wyoming | 51.80 | (50.40 - 53.21) | 56.45 | (52.45 - 60.36) | 0.032 |
| BRFSS = Behavioral Risk Factor Surveillance System; NSDUH = National Survey on Drug Use and Health. NOTE: NSDUH estimates along with 95 percent Bayesian confidence (credible) intervals are based on the survey-weighted hierarchical Bayes estimation approach and are generated by Markov Chain Monte Carlo techniques. BRFSS estimates are based on a survey-weighted direct estimation approach. NOTE: The p value is the probability of more extreme values than the observed difference between the BRFSS and NSDUH estimates under the null hypothesis of no difference. 1 In 2023, Kentucky and Pennsylvania were unable to collect enough BRFSS data to meet the minimum requirements for inclusion in the 2023 annual aggregate dataset. Thus, the BRFSS estimates for Kentucky and Pennsylvania are based on only the 2022 BRFSS data. Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Surveys on Drug Use and Health, 2022 and 2023; Centers for Disease Control and Prevention (CDC), Behavioral Risk Factor Surveillance Systems, 2022 and 2023. |
|||||
| State | BRFSS (Estimate) |
BRFSS (95% Confidence Interval) |
NSDUH (Estimate) |
NSDUH (95% Confidence Interval) |
P Value |
|---|---|---|---|---|---|
| Alabama | 12.81 | (11.77 - 13.84) | 22.38 | (20.05 - 24.91) | 0.000 |
| Alaska | 17.42 | (16.38 - 18.46) | 21.06 | (18.41 - 23.98) | 0.012 |
| Arizona | 15.69 | (14.84 - 16.54) | 23.38 | (20.47 - 26.56) | 0.000 |
| Arkansas | 14.63 | (13.63 - 15.62) | 21.11 | (18.54 - 23.92) | 0.000 |
| California | 15.69 | (14.96 - 16.42) | 22.88 | (21.42 - 24.40) | 0.000 |
| Colorado | 18.60 | (17.86 - 19.34) | 24.79 | (22.10 - 27.69) | 0.000 |
| Connecticut | 15.28 | (14.44 - 16.11) | 25.30 | (22.37 - 28.47) | 0.000 |
| Delaware | 13.77 | (12.60 - 14.95) | 24.71 | (22.15 - 27.47) | 0.000 |
| District of Columbia | 26.54 | (24.89 - 28.19) | 33.05 | (29.64 - 36.65) | 0.001 |
| Florida | 14.50 | (13.53 - 15.46) | 22.57 | (20.99 - 24.24) | 0.000 |
| Georgia | 14.19 | (13.32 - 15.06) | 22.87 | (20.69 - 25.20) | 0.000 |
| Hawaii | 18.22 | (17.29 - 19.16) | 22.21 | (19.39 - 25.31) | 0.008 |
| Idaho | 15.14 | (14.27 - 16.01) | 22.40 | (19.73 - 25.31) | 0.000 |
| Illinois | 17.70 | (16.63 - 18.77) | 27.63 | (25.50 - 29.86) | 0.000 |
| Indiana | 14.88 | (14.22 - 15.53) | 21.49 | (19.21 - 23.95) | 0.000 |
| Iowa | 21.02 | (20.20 - 21.83) | 25.68 | (22.66 - 28.96) | 0.003 |
| Kansas | 16.56 | (15.82 - 17.30) | 26.04 | (23.01 - 29.32) | 0.000 |
| Kentucky1 | 12.78 | (11.25 - 14.32) | 20.26 | (17.88 - 22.85) | 0.000 |
| Louisiana | 16.40 | (15.45 - 17.35) | 27.52 | (24.82 - 30.40) | 0.000 |
| Maine | 15.18 | (14.44 - 15.93) | 21.28 | (18.71 - 24.10) | 0.000 |
| Maryland | 13.17 | (12.53 - 13.82) | 21.77 | (19.19 - 24.58) | 0.000 |
| Massachusetts | 16.86 | (16.10 - 17.62) | 25.12 | (22.57 - 27.86) | 0.000 |
| Michigan | 16.06 | (15.31 - 16.81) | 23.56 | (21.85 - 25.36) | 0.000 |
| Minnesota | 18.37 | (17.75 - 19.00) | 25.41 | (22.32 - 28.76) | 0.000 |
| Mississippi | 13.29 | (12.28 - 14.31) | 21.34 | (18.93 - 23.95) | 0.000 |
| Missouri | 17.52 | (16.61 - 18.43) | 24.07 | (21.29 - 27.10) | 0.000 |
| Montana | 20.54 | (19.63 - 21.44) | 24.75 | (21.90 - 27.84) | 0.005 |
| Nebraska | 19.16 | (18.35 - 19.96) | 25.94 | (23.28 - 28.78) | 0.000 |
| Nevada | 16.79 | (15.22 - 18.36) | 24.55 | (21.91 - 27.40) | 0.000 |
| New Hampshire | 16.21 | (15.12 - 17.30) | 25.61 | (23.10 - 28.29) | 0.000 |
| New Jersey | 14.97 | (14.15 - 15.78) | 23.90 | (21.56 - 26.40) | 0.000 |
| New Mexico | 14.15 | (12.92 - 15.38) | 22.90 | (20.26 - 25.79) | 0.000 |
| New York | 15.72 | (15.14 - 16.31) | 24.20 | (22.42 - 26.07) | 0.000 |
| North Carolina | 15.12 | (14.07 - 16.18) | 22.57 | (20.12 - 25.23) | 0.000 |
| North Dakota | 21.90 | (20.77 - 23.02) | 27.38 | (24.12 - 30.90) | 0.001 |
| Ohio | 17.05 | (16.36 - 17.74) | 25.76 | (23.91 - 27.70) | 0.000 |
| Oklahoma | 13.62 | (12.81 - 14.43) | 20.76 | (18.32 - 23.44) | 0.000 |
| Oregon | 16.14 | (15.28 - 17.01) | 21.75 | (19.14 - 24.61) | 0.000 |
| Pennsylvania1 | 17.03 | (15.47 - 18.59) | 24.07 | (22.17 - 26.07) | 0.000 |
| Rhode Island | 16.74 | (15.63 - 17.86) | 27.47 | (24.43 - 30.74) | 0.000 |
| South Carolina | 15.42 | (14.63 - 16.21) | 22.24 | (19.50 - 25.24) | 0.000 |
| South Dakota | 19.58 | (17.67 - 21.50) | 25.51 | (22.56 - 28.69) | 0.001 |
| Tennessee | 14.30 | (13.35 - 15.25) | 21.31 | (18.74 - 24.13) | 0.000 |
| Texas | 16.71 | (15.73 - 17.68) | 23.01 | (21.49 - 24.61) | 0.000 |
| Utah | 12.33 | (11.66 - 12.99) | 14.54 | (12.63 - 16.68) | 0.032 |
| Vermont | 16.94 | (16.05 - 17.83) | 27.15 | (24.13 - 30.39) | 0.000 |
| Virginia | 15.22 | (14.37 - 16.07) | 22.43 | (20.49 - 24.50) | 0.000 |
| Washington | 15.66 | (15.23 - 16.09) | 20.51 | (18.12 - 23.12) | 0.000 |
| West Virginia | 13.13 | (12.24 - 14.03) | 20.69 | (17.87 - 23.84) | 0.000 |
| Wisconsin | 19.27 | (18.51 - 20.04) | 30.43 | (27.51 - 33.53) | 0.000 |
| Wyoming | 16.35 | (15.22 - 17.48) | 24.86 | (21.69 - 28.32) | 0.000 |
| BRFSS = Behavioral Risk Factor Surveillance System; NSDUH = National Survey on Drug Use and Health. NOTE: Binge alcohol use is defined as drinking five or more drinks (for males) or four or more drinks (for females) on the same occasion (i.e., at the same time or within a couple of hours of each other) on at least 1 day in the past 30 days. NOTE: NSDUH estimates along with 95 percent Bayesian confidence (credible) intervals are based on the survey-weighted hierarchical Bayes estimation approach and are generated by Markov Chain Monte Carlo techniques. BRFSS estimates are based on a survey-weighted direct estimation approach. NOTE: The p value is the probability of more extreme values than the observed difference between the BRFSS and NSDUH estimates under the null hypothesis of no difference. 1 In 2023, Kentucky and Pennsylvania were unable to collect enough BRFSS data to meet the minimum requirements for inclusion in the 2023 annual aggregate dataset. Thus, the BRFSS estimates for Kentucky and Pennsylvania are based on only the 2022 BRFSS data. Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Surveys on Drug Use and Health, 2022 and 2023; Centers for Disease Control and Prevention (CDC), Behavioral Risk Factor Surveillance Systems, 2022 and 2023. |
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| State | BRFSS1 (Estimate) |
BRFSS1 (95% Confidence Interval) |
NSDUH2 (Estimate) |
NSDUH2 (95% Confidence Interval) |
|---|---|---|---|---|
| Alabama | 14.93 | (13.87 - 15.99) | 22.61 | (20.05 - 25.39) |
| Alaska | 15.62 | (14.68 - 16.57) | 15.09 | (12.96 - 17.50) |
| Arizona | 11.18 | (10.48 - 11.88) | 15.79 | (13.41 - 18.49) |
| Arkansas | 16.80 | (15.81 - 17.79) | 21.52 | (18.81 - 24.51) |
| California | 9.08 | (8.50 - 9.66) | 9.96 | (8.88 - 11.16) |
| Colorado | 10.42 | (9.83 - 11.02) | 13.27 | (11.30 - 15.53) |
| Connecticut | 9.19 | (8.52 - 9.85) | 13.73 | (11.56 - 16.24) |
| Delaware | 12.08 | (11.06 - 13.10) | 16.90 | (14.70 - 19.37) |
| District of Columbia | 10.19 | (8.95 - 11.43) | 12.57 | (10.55 - 14.92) |
| Florida | 10.90 | (10.10 - 11.70) | 13.19 | (11.86 - 14.66) |
| Georgia | 12.29 | (11.51 - 13.06) | 17.88 | (15.90 - 20.04) |
| Hawaii | 9.49 | (8.80 - 10.17) | 13.07 | (10.85 - 15.65) |
| Idaho | 11.06 | (10.34 - 11.78) | 14.03 | (11.86 - 16.53) |
| Illinois | 11.49 | (10.57 - 12.42) | 15.68 | (13.93 - 17.60) |
| Indiana | 15.28 | (14.64 - 15.93) | 19.12 | (16.97 - 21.48) |
| Iowa | 14.22 | (13.53 - 14.90) | 16.45 | (14.02 - 19.20) |
| Kansas | 14.21 | (13.52 - 14.89) | 16.42 | (14.01 - 19.16) |
| Kentucky3 | 17.39 | (15.73 - 19.05) | 21.72 | (19.30 - 24.34) |
| Louisiana | 16.23 | (15.28 - 17.18) | 22.40 | (19.90 - 25.11) |
| Maine | 14.46 | (13.72 - 15.20) | 17.60 | (15.18 - 20.30) |
| Maryland | 9.34 | (8.81 - 9.88) | 12.44 | (10.47 - 14.71) |
| Massachusetts | 10.12 | (9.46 - 10.78) | 11.41 | (9.64 - 13.46) |
| Michigan | 14.40 | (13.70 - 15.11) | 17.33 | (15.75 - 19.02) |
| Minnesota | 12.60 | (12.04 - 13.15) | 13.98 | (11.75 - 16.55) |
| Mississippi | 16.50 | (15.40 - 17.61) | 22.31 | (19.70 - 25.17) |
| Missouri | 16.02 | (15.18 - 16.87) | 20.65 | (17.96 - 23.63) |
| Montana | 13.75 | (12.97 - 14.53) | 15.68 | (13.33 - 18.36) |
| Nebraska | 12.41 | (11.76 - 13.05) | 13.67 | (11.52 - 16.16) |
| Nevada | 14.51 | (12.91 - 16.11) | 17.10 | (14.89 - 19.56) |
| New Hampshire | 10.77 | (9.91 - 11.63) | 13.70 | (11.79 - 15.87) |
| New Jersey | 9.70 | (9.03 - 10.37) | 11.36 | (9.74 - 13.20) |
| New Mexico | 13.85 | (12.69 - 15.00) | 17.45 | (15.06 - 20.12) |
| New York | 10.26 | (9.77 - 10.76) | 13.62 | (12.29 - 15.08) |
| North Carolina | 13.86 | (12.72 - 15.01) | 17.01 | (14.79 - 19.48) |
| North Dakota | 14.10 | (13.15 - 15.05) | 17.94 | (15.11 - 21.17) |
| Ohio | 16.11 | (15.45 - 16.77) | 20.43 | (18.68 - 22.31) |
| Oklahoma | 15.71 | (14.89 - 16.53) | 21.08 | (18.43 - 24.01) |
| Oregon | 11.50 | (10.74 - 12.26) | 15.84 | (13.44 - 18.58) |
| Pennsylvania3 | 14.92 | (13.24 - 16.60) | 16.74 | (15.07 - 18.56) |
| Rhode Island | 10.61 | (9.78 - 11.45) | 15.36 | (12.96 - 18.12) |
| South Carolina | 13.66 | (12.93 - 14.39) | 18.22 | (15.64 - 21.12) |
| South Dakota | 14.55 | (12.87 - 16.23) | 18.19 | (15.56 - 21.15) |
| Tennessee | 17.72 | (16.69 - 18.76) | 19.48 | (16.97 - 22.26) |
| Texas | 11.61 | (10.86 - 12.37) | 15.96 | (14.63 - 17.39) |
| Utah | 6.29 | (5.81 - 6.76) | 8.94 | (7.46 - 10.67) |
| Vermont | 12.19 | (11.35 - 13.04) | 14.93 | (12.73 - 17.44) |
| Virginia | 11.60 | (10.87 - 12.33) | 14.03 | (12.44 - 15.79) |
| Washington | 9.47 | (9.12 - 9.82) | 13.32 | (11.35 - 15.58) |
| West Virginia | 20.72 | (19.64 - 21.79) | 26.46 | (23.25 - 29.93) |
| Wisconsin | 13.07 | (12.45 - 13.68) | 15.76 | (13.53 - 18.29) |
| Wyoming | 14.72 | (13.70 - 15.74) | 18.59 | (15.75 - 21.81) |
| BRFSS = Behavioral Risk Factor Surveillance System; NSDUH = National Survey on Drug Use and Health. NOTE: NSDUH estimates along with 95 percent Bayesian confidence (credible) intervals are based on the survey-weighted hierarchical Bayes estimation approach and are generated by Markov Chain Monte Carlo techniques. BRFSS estimates are based on a survey-weighted direct estimation approach. 1 BRFSS respondents were classified as current smokers if they reported having smoked at least 100 cigarettes during their lifetime and indicated that they smoked every day or some days at the time of the survey. 2 NSDUH respondents were classified as past month cigarette users if they smoked all or part of a cigarette during the past 30 days. 3 In 2023, Kentucky and Pennsylvania were unable to collect enough BRFSS data to meet the minimum requirements for inclusion in the 2023 annual aggregate dataset. Thus, the BRFSS estimates for Kentucky and Pennsylvania are based on only the 2022 BRFSS data. Source: SAMHSA, Center for Behavioral Health Statistics and Quality, National Surveys on Drug Use and Health, 2022 and 2023; Centers for Disease Control and Prevention (CDC), Behavioral Risk Factor Surveillance Systems, 2022 and 2023. |
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1 For further details about the YRBS and the Youth Risk Behavior Surveillance System, see the following webpage: https://www.cdc.gov/healthyyouth/data/yrbs/index.htm.
2 For more details about the NSDUH, see Section 2 of 2023 National Survey on Drug Use and Health: Methodological Summary and Definitions at https://www.samhsa.gov/data/report/2023-methodological-summary-and-definitions.
3For more details about BRFSS in general, see the following webpage: https://www.cdc.gov/brfss/.
4 The District of Columbia is referred to as a “state” in this document.
5For more information about NSDUH’s small area estimation, see Section B of 2022‑2023 National Surveys on Drug Use and Health: Guide to State Tables and Summary of Small Area Estimation Methodology at https://www.samhsa.gov/data/report/2022-2023-nsduh-guide-state-tables-and-summary-sae-methodology.
6 A PDF of the complete 2023 NSDUH questionnaire is available at https://www.samhsa.gov/data/report/nsduh-2023-questionnaire. Note, there were minor differences in the 2022 and 2023 AL08 questions. A PDF of the complete 2022 NSDUH questionnaire is available at https://www.samhsa.gov/data/report/nsduh-2022-questionnaire.
7 “DK” = “don’t know” and “REF” = “refused.”
8 A PDF of the complete 2023 BRFSS questionnaire is available at https://www.cdc.gov/brfss/questionnaires/pdf-ques/2023-BRFSS-Questionnaire-508.pdf.
9 See the following website: https://www.samhsa.gov/data/report/2014-2015-nsduh-guide-state-tables-and-summary-small-area-estimation-methodology.
10 The expected value of an estimate is defined as the mean of the observed values of the estimate over repeated samples.
11 For more information about NSDUH’s small area estimation (SAE) confidence intervals, see Section B of 2022‑2023 National Survey on Drug Use and Health: Guide to State Tables and Summary of Small Area Estimation Methodology at https://www.samhsa.gov/data/report/2022-2023-nsduh-guide-state-tables-and-summary-sae-methodology.
12 The first-order Taylor series approximation is defined as
, where
is the first-order derivative of
. If
, then
.
13Kentucky and Pennsylvania BRFSS estimates are based only on 2022 data, whereas the NSDUH estimates are based on 2022‑2023 data. This difference could contribute to the estimates being significantly different.
14See Table C.4 in 2022‑2023 Guide to State Tables and Summary of SAE Methodology at https://www.samhsa.gov/data/report/2022-2023-nsduh-guide-state-tables-and-summary-sae-methodology.
15See note footnote 13.
16For details, see https://www.cdc.gov/brfss/annual_data/annual_2023.html.
Long description, Equation 1. Variance v of the estimate of the log-odds ratio, lor hat, is a function of three quantities: q1, q2, and q3. It is expressed as the sum of q1 and q2 minus q3. Quantity q1 is the variance v of the natural logarithm of Theta b hat, quantity q2 is the variance v of the natural logarithm of Theta n hat, and quantity q3 is 2 times the covariance between the natural logarithm of Theta b hat and the natural logarithm of Theta n hat.
Long description end. Return to Equation 1.
Long description, Equation 2. Variance v of the natural logarithm of Theta sub n hat is equal to the square of quantity q. Quantity q is the difference between capital U sub n and capital L sub n divided by 2 times 1.96.
Long description end. Return to Equation 2.
Long description, Equation 3. Variance v of pi hat sub b is equal to the square of quantity q. Quantity q is the difference between upper sub b and lower sub b divided by 2 times 1.96.
Long description end. Return to Equation 3.
Long description, Equation 4. Variance v of the natural logarithm of Theta sub b hat is equal to the variance v of the natural logarithm of pi hat sub b divided by 1 minus pi hat sub b, which is then approximately equal to the variance v of pi hat sub b multiplied by the square of quantity q. Quantity q is the reciprocal of pi hat sub b multiplied by 1 minus pi hat sub b.
Long description end. Return to Equation 4.