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. Author manuscript; available in PMC: 2019 Apr 3.
Published in final edited form as: Alcohol Clin Exp Res. 2018 Dec 10;43(1):79–90. doi: 10.1111/acer.13922

Cigarette Use Among Individuals with Alcohol Use Disorders in the United States, 2002 to 2016: Trends Overall and by Race/Ethnicity

Andrea H Weinberger 1, Lauren R Pacek 1, Daniel Giovenco 1, Sandro Galea 1, Michael J Zvolensky 1, Misato Gbedemah 1, Renee D Goodwin 1
PMCID: PMC6446564  NIHMSID: NIHMS1011454  PMID: 30408209

Abstract

Background:

Individuals with alcohol use disorders (AUDs) who smoke cigarettes experience greater health risks than those using either substance alone. Further, disparities exist in AUDs and smoking by race/ethnicity. Although smoking has declined in the general population, it is not known whether the smoking prevalence has changed over time for individuals with AUDs. The current study used representative U.S. data to estimate the prevalence of current cigarette use from 2002 to 2016 by AUD status and severity overall and by race/ethnicity.

Methods:

Data were drawn from the National Survey on Drug Use and Health, an annual cross-sectional study of U.S. individuals, from 2002 to 2016 (total analytic sample n = 837,326). Cigarette smoking prevalence was calculated annually among those with and without past-year AUD and by AUD severity level (mild, moderate, severe AUD). Time trends in smoking prevalence by AUD status and severity were tested using logistic regression for the overall sample and significant interactions were subsequently stratified by race/ethnicity (non-Hispanic [NH] White, NH Black, Hispanic, NH Other).

Results:

Cigarette use was persistently over twice as common among those with AUDs compared to without AUDs (2016: 37.84% vs. 16.29%). Cigarette use was also more common among those at each level of AUD severity criteria (2016: mild AUD 34.59%; moderate AUD 35.35%; severe AUD 52.23%). Approximately half of NH Black respondents with AUDs, and three-quarters of NH Black respondents with severe AUDs, reported smoking in 2016. The prevalence of smoking decreased significantly over time among respondents with and without AUDs; however, there were differences by race. There was no decline in smoking prevalence among NH Black respondents with AUDs over time in contrast to a significant decrease for every other racial/ethnic group with and without AUDs.

Conclusions:

Individuals with AUDs may need additional resources and interventions to quit smoking, especially NH Black individuals.

Keywords: Smoking, Cigarettes, Alcohol Use Disorders, Race, Epidemiology

ALCOHOL USE AND cigarette use are strongly associated (Adams, 2017; McKee and Weinberger, 2013) and cigarette smoking is common among a majority of adults with problematic alcohol use such as alcohol use disorders (AUDs; Weinberger et al., 2016). Approximately 14% of U.S. adults report past-year AUDs with 7.3% of adults reporting mild AUDs, 3.2% reporting moderate AUDs, and 3.4% reporting severe AUDs (Grant et al., 2015). The risks associated with alcohol misuse are well established (Tetrault and O’Connor, 2017; U.S. Department of Health and Human Services, 2016) and people with AUDs experience increased alcohol-related diseases and premature mortality (Rogers et al., 2015; Stahre et al., 2014). Further, greater disability is associated with greater severity of AUD (Grant et al., 2015).

The significant health risks of tobacco, the leading cause of preventable death and disease in the United States and a significant global cause of mortality, are also clear (USDHHS, 2014; WHO, 2012). Co-use of alcohol and tobacco increases the risk of several diseases (e.g., head and neck cancers, cirrhosis) well beyond use of either substance alone (Marrero et al., 2005; Pelucchi et al., 2006; Zheng, 2010). Use of cigarettes also has detrimental effects on aspects of cognition (e.g., executive function, memory; Durazzo et al., 2012; Wagner et al., 2013) and is associated with decreased cognitive recovery in adults with AUDs (Durazzo et al., 2006, 2014; Pennington et al., 2013). Importantly, cigarette smoking is associated with greater odds of relapse to AUDs among individuals with remitted AUDs (Weinberger et al., 2015). Consequently, reducing the prevalence of smoking among individuals with AUDs could have numerous important benefits for the health and treatment/recovery of individuals with AUDs.

The prevalence of smoking in the United States and many other industrialized countries has declined over the past 50 years (GBD 2015 Tobacco Collaborators, 2017; Jamal et al., 2016, Ng et al., 2014). Yet, there are some groups for whom tobacco control efforts may not have been as effective, such as individuals with AUDs. Understanding the extent to which cigarette use remains disproportionately prevalent among those with AUDs, and whether there have been recent changes in the prevalence of smoking, is of critical import in estimating the need for public health and clinical efforts to be directed toward people with AUDs.

Beyond examining the relationship between AUD and smoking in the general population, there are racial/ethnic disparities related both to AUDs and cigarette smoking in the United States. While AUDs are similarly or more prevalent among non-Hispanic (NH) White individuals than NH Black and Hispanic individuals, NH Black and Hispanic individuals have more persistent AUDs, greater likelihood of recurrent AUDs, and greater AUD-related negative consequences (Caetano et al., 2014; Chartier and Caetano, 2010; Vaeth et al., 2017; Witbrodt et al., 2014). Further, NH Black and Hispanic individuals with severe AUDs are less likely to use AUD treatment services and complete AUD treatment than NH White individuals (Chartier and Caetano, 2010). It should also be noted that in large samples of U.S. veterans, the prevalence of AUDs was higher among NH Black and Hispanic respondents than NH White respondents (Williams et al., 2016) while treatment utilization for AUDs did not differ by race/ethnicity (Bensley et al., 2017). Like AUDs, NH Black and Hispanic individuals report a lower prevalence of smoking, while NH Black individuals reported a similar prevalence of smoking compared to NH White individuals (Jamal et al., 2018). The leading causes of mortality among NH Black and Hispanic individuals, similar to NH White individuals, are smoking-related diseases (CDC, 2018a,b; USDHHS, 1998), yet these 2 racial/ethnic groups experience disproportionate tobacco-related medical consequences (DeLancey et al., 2008; Underwood et al., 2012, USDHHS, 1998) Identifying differences in the trends in smoking prevalence over time for those with, versus without, AUDs by race/ethnicity would further allow the targeting of public health and clinical efforts toward subgroups with the greatest AUD and smoking disparities.

The current study used annual, cross-sectional data of individuals age 12 and older in the United States to estimate the overall prevalence of current cigarette use from 2002 to 2016 among those with and without AUDs, adjusting for demographics. It was expected that persons with AUDs would report higher prevalence of smoking than persons without AUDs. As more severe AUDs are associated with increased odds of nicotine use disorder (Grant et al., 2015), the prevalence of smoking was also examined by severity of AUD to examine whether the prevalence of smoking increased with greater AUD severity. The current study also assessed racial/ethnic differences in smoking prevalence and AUD severity.

MATERIALS AND METHODS

Study Population

Study data were drawn from the National Survey on Drug Use and Health (NSDUH) public data portal (http://datafiles.samhsa.gov/) for the years 2002 to 2016. The NSDUH provides annual cross-sectional national data on the use of tobacco, other substance use, and mental health in the United States and is described in depth elsewhere (SAMHSA, 2016). A multistage area probability sample for each of the 50 states and the District of Columbia has been conducted to represent the male and female civilian noninstitutionalized U.S. population aged 12 and older. The data sets from each year were concatenated, adding a variable for the survey year. For this study, respondents in each year from 2002 to 2016 who reported current cigarette smoking status were included in the analyses of smoking prevalence (unweighted total n = 837,326).

Measures

Cigarette Smoking Prevalence.

Current (i.e., past-month) cigarette smoking prevalence was assessed using the following questions: (i) “Have you ever smoked part or all of a cigarette?” (ii) “Have you smoked at least 100 cigarettes in your entire life?” and (iii) “During the past 30 days, have you smoked part or all of a cigarette?” Current smokers were defined as respondents who reported smoking at least 100 lifetime cigarettes and at least 1 cigarette within the past 30 days. For the analyses, an “other smoking categories” group was defined as respondents who were not current smokers (e.g., never smokers, former smokers).

Alcohol Use Variables.

Past-year AUDs (alcohol dependence or alcohol abuse) were assessed using DSM-IV criteria (APA, 1994) during each annual survey. Alcohol abuse was defined as meeting 1 or more of 4 alcohol abuse criteria in the past 12 months and not meeting criteria for alcohol dependence in the past 12 months. Alcohol dependence was defined as meeting 3 or more of 7 alcohol dependence criteria. Respondents who met criteria for past-year alcohol abuse or dependence were classified as having a past-year AUD. Among respondents who were categorized as having a past-year AUD, AUD severity levels were classified as mild, moderate, or severe (1 to 3, 4 to 5, or ≥6 criteria, respectively) consistent with the approach taken by Grant and colleagues (2015). Detailed AUD criteria can be found in the NSDUH 2016 Codebook (Appendix D Recoded Substance Dependence and Abuse Variable Documentation section; SAMHSA, 2016).

Demographics.

Demographic variables were classified into the following categories: age, gender, race/ethnicity, and total annual family income.

Statistical Analysis

To estimate cigarette smoking prevalence from 2002 to 2016 by AUD status, the prevalence of current smoking and associated standard errors among the whole population and stratified by past-year AUD status (AUD, no AUD) was calculated for each year from 2002 to 2016. Time trends in the prevalence of current smoking stratified by past-year AUD status were tested using logistic regression with continuous year as the predictor for the linear time trend. These analyses were conducted twice: first with no covariates (unadjusted) and subsequently adjusting for age, gender, race/ethnicity, and total annual family income. To determine whether there were differential time trends in current smoking by AUD status, additional logistic regression analyses were run that included the 2-way interaction of year by past-year AUD versus their respective comparison group (i.e., no past-year AUD). These analyses were repeated examining AUD severity categories (mild, moderate, severe AUD vs. no AUD).

The time trend analyses described above were repeated stratified by race/ethnicity. A logistic regression of current smoking found the 3-way interactions of year by AUD status by race/ethnicity was significant so the models examining race/ethnicity were stratified by past-year AUD status. Time trends in the prevalence of current smoking by past-year AUD status group within each racial/ethnic category were tested using logistic regression with continuous year as the predictor to test the linear time trend. These analyses were conducted twice: once with no covariates (unadjusted model) and once controlling for other demographic variables (i.e., age, gender, total annual family income; adjusted model). Differential time trends in current smoking between past-year AUD status were tested by 2-way interaction of year × past-year AUD status (with past-year AUD vs. without past-year AUD status) in logistic regressions stratified by each racial/ethnic group. When analyses were repeated using the AUD severity variable, the 3-way interaction was not significant (p = 0.093) so models were not run stratifying by race/ethnicity.

Sampling weights for the NSDUH were computed to control unit-level and individual-level nonresponse and were adjusted to ensure consistency with population estimates obtained from the U.S. Census Bureau. In order to use data from the 15 years of combined data, a new weight was created upon aggregating the 15 data sets by dividing the original weight by the number of data sets combined. This approach is outlined in the NSDUH Public Use File Codebook (Center for Behavioral Health Statistics and Quality, 2017) and is consistent with our prior work (e.g., Weinberger et al., 2018). Additional details regarding the weight components and the sample weighting procedures appear in the 2016 NSDUH Methodological Resource Book (Center for Behavioral Health Statistics and Quality, 2017). Analyses were performed incorporating the NSDUH sampling weights and controlling for the complex clustered sampling using STATA SE version 13 (StataCorp, LLC, College Station, TX) and SAS-callable SUDAAN Version 11.0.1 (RTI International, Research Triangle Park, NC; http://www.rti.org/sudaan/), which used the Taylor series estimation methods to provide accurate standard errors.

RESULTS

Cigarette Smoking Prevalence from 2002 to 2016 for Those With and Without AUDs

The prevalence of cigarette smoking declined significantly from 2002 to 2016 among respondents with AUDs (51.92 to 37.84%, p < 0.001) and among respondents without AUDs (21.44 to 16.07%, p < 0.001; see Table 1 and Fig. 1). These trends remained significant after controlling for demographic covariates. The year × AUD interaction was significant in unadjusted and adjusted analyses such that the decline in smoking prevalence from 2002 to 2016 was more rapid among respondents with AUDs than among respondents without AUDs. Yet, the cigarette smoking prevalence among respondents with AUDs was more than twice that of respondents without AUDs for every year in the study period including the most recent data year (2016).

Table 1.

Cigarette Smoking Prevalence from 2002 to 2016 by AUD Status and Severity (NSDUH, Age 12 and Older)

Linear trends
Past-year AUD status Total 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 ORa
(95% CI)		 t-Test
p-value		 aORa,b
(95% CI)		 t-Test
p-value
No AUD (n) 133,188 9,702 9,966 9,591 9,459 9,216 9,131 8,829 8,731 8,832 8,922 8,315 8,014 8,384 8,285 7,811 0.98 (0.97, 0.98) −20.30 <0.001 0.98 (0.98, 0.99) −13.12 <0.001
 wt% 18.98 21.44 20.88 20.53 20.77 20.67 19.94 19.43 19.25 19.09 18.18 18.23 17.56 17.59 16.29 16.07
 SE 0.09 0.30 0.37 0.29 0.37 0.32 0.34 0.40 0.32 0.32 0.37 0.32 0.30 0.27 0.27 0.30
Any AUD (n) 35,050 2,969 2,929 2,923 2,887 1,686 2,691 2,589 2,556 2,344 2,169 2,018 1,855 1,593 1,482 1,359 0.96 (0.96, 0.97) −13.45 <0.001 0.97 (0.96, 0.98) −9.87 <0.001
 wt% 45.02 50.92 50.36 48.52 47.50 47.60 45.99 48.16 45.69 42.92 43.87 43.68 41.97 39.96 37.35 37.84
 SE 0.31 1.32 1.16 1.09 1.20 1.12 1.29 1.08 1.24 1.53 1.28 1.15 1.28 1.07 1.10 1.21
Mild AUD (n) 18,747 1,641 1,601 1,604 1,574 1,437 1,425 1,373 1,330 1,273 1,180 1,035 1,000 822 771 681 0.96 (0.95, 0.97) −10.19 <0.001 0.97 (0.96, 0.97) −7.86 <0.001
 wt% 39.83 47.51 44.75 44.75 42.42 43.20 39.34 43.64 38.98 37.46 38.40 36.30 37.44 34.76 30.93 34.59
 SE 0.45 1.51 1.46 1.58 1.63 1.37 1.49 1.75 1.63 1.70 1.33 1.44 1.82 1.34 1.48 1.79
Moderate AUD (n) 9,041 735 784 761 752 702 688 675 663 574 549 537 470 418 378 355 0.96 (0.95, 0.97) −6.39 <0.001 0.97 (0.96, 0.98) −4.87 <0.001
 wt% 47.22 50.35 52.83 47.54 51.74 50.15 51.29 52.52 51.41 44.94 43.25 46.59 42.98 42.32 42.79 35.35
 SE 0.65 2.48 2.17 2.24 2.25 2.08 2.35 2.76 2.39 3.14 2.71 2.35 2.40 2.33 2.60 2.42
Severe AUD (n) 7,262 593 544 558 561 547 578 541 563 497 440 446 385 353 333 323 0.97 (0.96, 0.99) −3.87 <0.001 0.98 (0.97, 0.99) −2.66 0.009
 wt% 60.47 62.49 66.85 64.82 60.76 61.66 60.44 56.83 61.92 60.47 66.58 62.88 58.31 56.47 52.68 52.23
 SE 0.78 2.65 3.29 2.87 2.96 2.75 2.96 3.63 2.23 2.91 3.00 2.95 3.35 2.97 2.60 2.42
Chi-square tests (p-value)c <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Chi-square tests (p-value)d <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Differential time trend: year × past-year AUD status (none vs. any) F(1,170) = 17.77 <0.001 F(1,170) = 23.87 <0.001
Differential time trend: year × past-year AUD severity status (none; mild; moderate; severe) F(3,168) = 6.98 <0.001 F(3,168) = 8.99 <0.001
Differential time trend: year × past-year no AUD versus mild AUD F(1,170) = 16.19 <0.001 F(1,170) = 18.84 <0.001
Differential time trend: year × past-year no AUD versus moderate AUD F(1,170) = 4.54 0.035 F(1,170) = 8.62 0.004
Differential time trend: year × past-year no AUD versus severe AUD F(1,170) = 0.14 0.713 F(1,170) = 0.40 0.530
Differential time trend: year × past-year mild AUD versus moderate AUD F(1,170) = 0.29 0.592 F(1,170) = 0.18 0.671
Differential time trend: year × past-year mild AUD versus severe AUD F(1,170) = 3.07 0.082 F(1,170) = 3.35 0.069
Differential time trend: year × past-year moderate AUD versus severe AUD F(1,170) = 1.03 0.311 F(1,170) = 1.44 0.233
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AUD, alcohol use disorder; NSDUH, National Survey on Drug Use and Health; OR, odds ratio; SE, standard error.

a

OR compares current smoking versus other smoker categories (i.e., former smokers, never smokers); ORs reflect the linear time trends across all years.

b

Adjusted for age (12 to 17, 18 to 25, 26 to 34, 35 to 49, and 50 years old or older); gender (male, female); total annual family income (<$20,000, $20,000 to $49,999, $50,000 or more); and race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic all other race or more than one race).

c

Chi-squared tests for the difference in percentage of current smokers by past-year AUD status (none vs. any AUD).

d

Chi-squared tests for the difference in percentage of current smokers by past-year AUD severity status (none; mild; moderate; severe).

Fig. 1.

Fig. 1.

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Cigarette smoking prevalence from 2002 to 2016 for those with and without alcohol use disorders (AUDs) and by AUD severity (mild, moderate, and severe).

When examining severity of AUDs, the prevalence of cigarette smoking declined significantly from 2002 to 2016 among respondents with mild (47.51 to 30.93%, p < 0.001), moderate (50.35 to 35.35%, p < 0.001), and severe (62.49 to 52.23%, p < 0.001) AUD, as well as among respondents without AUDs (21.44 to 16.07%, p < 0.001; see Fig. 1 and Table 1). These trends remained significant after controlling for demographic covariates. The year × AUD severity interactions were significant in unadjusted and adjusted analyses such that the decline in smoking prevalence from 2002 to 2016 was more rapid among respondents with mild and moderate AUDs than among respondents without AUDs. The rate of decline in smoking prevalence did not differ significantly between other levels of AUD severity (no AUD vs. severe AUD, mild AUD vs. moderate AUD, mild AUD vs. severe AUD, or moderate AUD vs. severe AUD). Notably, the prevalence of cigarette smoking among respondents within each severity level of AUD was higher than those without AUD in every year of the study period. In 2016, the cigarette smoking prevalence among those with mild and moderate AUDs was 2 times higher than those with no AUD and the cigarette smoking prevalence among those with severe AUD was more than 3 times higher than those with no AUD.

Cigarette Smoking Prevalence from 2002 to 2016 by AUD Status and Race/Ethnicity

The 3-way interaction of time × AUD status (AUD, no AUD) × race/ethnicity was significant in unadjusted analyses, F(3, 168) = 2.84, p = 0.040, and after adjusting for demographics, F(3, 168) = 4.49, p = 0.005; see Fig. 2. In both unadjusted and adjusted analyses, there was a significant decrease in cigarette smoking prevalence over time for respondents with any AUD and without AUDs in each racial/ethnic group except for NH Black respondents with AUDs. NH Black respondents with AUDs showed no change in smoking prevalence over time and half of Black respondents with AUDs (50.51%) reported smoking cigarettes in 2016. Among NH White respondents, the decrease in smoking prevalence over time was greater for respondents with AUDs compared to respondents without AUDs; trends over time did not differ by AUD status for the other racial/ethnic groups. The 3-way interaction of time × AUD severity (no, mild, moderate, severe AUD) × race/ethnicity was not significant.

Fig. 2.

Fig. 2.

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Cigarette smoking prevalence from 2002 to 2016 by alcohol use disorder status and race/ethnicity. Note: AUD, alcohol use disorder; NH, non-Hispanic.

The prevalence of cigarette smoking was significantly higher among respondents with mild, moderate, and severe AUDs compared to respondents without AUDs within each racial/ethnic group for every year of the study period (see Table 2). In 2016, compared to NH White respondents without AUDs, NH White respondents with mild and moderate AUDs were approximately 2 times more likely, and persons with severe AUD were approximately 2.5 times more likely to report cigarette smoking (p < 0.001) Among NH Black participants in 2016, compared to those without AUDs, persons with mild and moderate AUDs were approximately 2.5 times more likely and those with severe AUD were approximately 5 times more likely to report smoking (p < 0.001). Among Hispanic participants in 2016, persons with mild and moderate AUDs were approximately 2.5 times more likely and those with severe AUD were approximately 3 times more likely to report cigarette smoking as compared to Hispanic participants without AUDs. Among individuals of other race/ethnicity in 2016, persons with mild and moderate AUDs were approximately 2.5 times more likely and those with severe AUD were approximately 4 times more likely than other participants without AUD to report cigarette smoking (p < 0.001).

Table 2.

Cigarette Smoking Prevalence From 2002 to 2016 by AUD Status and Race/Ethnicity (NSDUH, Age 12 and Older)

Linear trends
Past-year AUD status Total 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 ORa
(95% CI)		 t-Test
p-value		 aORa,b
(95% CI)		 t-Test
p-value
No AUD NH White (n) 95,208 7,488 7,427 7,123 6,776 6,624 6,777 6,250 6,182 6,307 6,300 5,884 5,588 5,771 5,595 5,316 0.98 (0.97, 0.98) −14.14 <0.001 0.99 (0.98, 0.99) −9.12 <0.001
 wt% 20.89 22.78 22.59 22.62 22.47 22.17 21.64 21.13 20.93 20.97 20.31 20.42 19.56 19.64 18.22 18.15
 SE 0.12 0.35 0.40 0.36 0.44 0.33 0.41 0.40 0.43 0.47 0.50 0.41 0.39 0.37 0.42 0.39
NH Black (n) 13,549 855 923 871 953 896 889 935 859 927 945 878 873 930 947 868 0.98 (0.97, 0.99) −5.33 <0.001 0.98 (0.97, 0.99) −5.35 <0.001
 wt% 18.19 19.65 20.77 18.43 19.28 19.14 18.62 19.82 17.71 18.27 17.30 17.75 18.15 17.68 16.13 15.11
 SE 0.22 0.98 0.96 0.92 0.98 1.08 0.84 1.01 1.03 0.96 0.85 0.87 0.88 0.88 0.68 0.56
Hispanic (n) 13,781 820 969 918 1,055 973 928 905 972 880 887 883 865 907 940 879 0.97 (0.96, 0.97) −10.36 <0.001 0.97 (0.96, 0.98) −9.03 <0.001
 wt% 13.43 16.79 14.42 14.74 16.10 16.32 14.80 13.25 15.57 14.24 11.71 11.78 11.87 11.93 10.82 10.90
 SE 0.20 1.08 0.67 0.79 0.83 0.99 0.99 0.65 0.72 0.95 0.69 0.65 0.66 0.59 0.47 0.52
NH Other (n) 10,650 539 647 679 675 723 737 739 718 718 790 670 688 776 803 748 0.97 (0.96, 0.98) −4.86 <0.001 0.98 (0.97, 0.99) −4.08 <0.001
 wt% 13.61 18.60 13.58 13.09 14.57 16.54 15.44 14.27 12.70 12.22 14.11 13.41 11.45 12.14 12.36 11.88
 SE 0.27 1.70 1.29 0.86 1.15 1.22 1.31 0.97 1.14 0.88 1.16 0.95 0.82 0.61 0.98 0.98
Chi-square test between race/ethnicity (p-value)c <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Differential time trend: year as continuous × race/ethnicity among no AUD F(3, 168) = 5.61 0.001 F(3, 168) = 3.82 0.011
Any AUD NH White (n) 24,663 2,297 2,168 2,163 2,066 1,901 1,960 1,793 1,774 1,603 1,502 1,351 1,219 1,036 951 879 0.96 (0.96, 0.97) −11.14 <0.001 0.97 (0.96, 0.98) −8.58 <0.001
 wt% 46.34 52.38 51.12 50.40 49.06 47.71 47.69 49.85 48.66 43.53 44.32 44.85 43.11 41.27 38.03 37.98
 SE 0.38 1.64 1.24 1.34 1.23 1.37 1.47 1.29 1.55 1.71 1.42 1.54 1.37 1.24 1.34 1.47
NH Black (n) 3,029 203 206 220 245 215 202 232 225 212 180 200 184 171 155 179 0.99 (0.97,1.01) −0.81 0.418 1.00 (0.98, 1.02) −0.30 0.766
 wt% 50.71 52.74 52.51 50.40 50.87 53.16 52.53 50.23 46.49 51.05 50.49 47.10 57.32 49.16 46.91 50.51
 SE 0.88 3.79 4.94 3.70 3.97 3.22 3.63 3.58 3.60 4.36 3.87 3.24 3.24 3.59 3.70 2.98
Hispanic (n) 4,218 299 334 313 341 333 283 338 299 302 288 260 251 214 201 162 0.96 (0.94, 0.97) −5.82 <0.001 0.96 (0.94, 0.98) −4.99 <0.001
 wt% 36.01 44.55 44.26 40.29 37.88 41.84 33.21 37.71 33.30 35.79 39.51 35.89 30.33 30.71 28.58 28.16
 SE 0.72 2.65 3.21 2.83 3.01 4.11 2.68 3.55 2.54 3.34 3.21 2.17 2.96 2.43 2.88 2.96
NH Other (n) 3,140 170 221 227 235 237 246 226 258 227 199 207 201 172 175 139 0.97 (0.95, 0.99) −2.70 0.008 0.97 (0.95, 0.99) −2.66 0.009
 wt% 42.58 42.62 51.77 39.61 45.68 52.09 40.97 52.22 42.76 40.67 39.77 45.55 37.21 35.15 40.29 36.18
 SE 1.20 5.15 4.39 5.64 5.72 5.17 4.29 4.98 4.05 4.17 5.06 5.97 4.78 3.97 3.90 3.81
Chi-square test between race/ethnicity (p-value)c <0.001 0.050 0.268 0.011 0.011 0.153 <0.001 0.006 <0.001 0.018 0.124 0.035 <0.001 <0.001 <0.001 <0.001
Differential time trend: year as continuous × race/ethnicity among any AUD F(3, 1,680) = 4.28 0.006 F(3, 168) = 4.26 0.006
Mild AUD NH White (n) 13,587 1,308 1,218 1,222 1,143 1,054 1,078 975 964 896 849 724 668 543 494 461 0.96 (0.95, 0.97) −9.05 <0.001 0.96 (0.95, 0.97) −7.34 <0.001
 wt% 41.07 49.98 45.12 47.26 43.39 43.72 40.74 44.44 42.39 37.85 38.22 37.81 38.20 35.52 30.61 34.88
 SE 0.54 1.79 1.58 1.93 1.82 1.77 1.80 2.17 2.03 1.99 1.68 1.82 2.46 1.63 1.97 2.19
NH Black (n) 1,531 96 108 110 140 99 98 129 105 105 92 95 94 84 90 86 1.00 (0.98, 1.03) 0.44 0.664 1.01 (0.99, 1.03) 0.81 0.420
 wt% 44.44 43.55 44.77 42.05 46.52 47.00 41.81 46.97 34.58 47.07 46.40 36.89 55.48 47.00 45.29 42.47
 SE 1.27 5.64 5.73 4.78 5.67 5.30 4.51 5.35 5.23 4.73 4.93 4.51 4.93 5.11 4.09 3.37
Hispanic (n) 2,094 151 165 155 168 167 127 166 141 159 152 121 136 114 96 76 0.95 (0.93, 0.97) −4.27 <0.001 0.95 (0.93, 0.98) −3.90 <0.001
 wt% 30.97 40.38 39.46 35.80 31.34 27.21 35.25 27.55 31.47 34.89 27.42 25.41 26.80 26.80 21.61 26.74
 SE 0.92 3.86 5.04 4.11 3.84 4.92 3.43 3.70 3.72 4.50 4.04 3.34 3.82 3.62 2.40 4.09
NH Other (n) 1,525 86 110 117 123 117 122 103 120 113 87 95 102 81 91 58 0.96 (0.93, 0.99) −2.29 0.023 0.96 (0.93, 0.99) −2.25 0.026
 wt% 39.97 36.01 51.32 33.15 49.49 44.45 44.40 49.89 34.60 34.12 36.03 39.52 34.70 28.16 35.08 34.63
 SE 1.57 6.41 5.70 6.86 6.04 8.18 5.71 6.80 4.89 5.78 6.66 8.74 7.05 4.49 5.38 5.71
Chi-square test between race/ethnicity (p-value)c <0.001 0.050 0.490 0.031 0.037 0.546 0.008 0.147 0.007 0.122 0.305 0.163 <0.001 0.005 <0.001 0.047
Differential time trend: year as continuous × race/ethnicity among mild AUD F(3, 168) = 5.79 <0.001 F(3, 168) = 6.16 <0.001
Moderate AUD NH White (n) 6,251 558 576 560 546 478 479 464 436 387 352 343 314 268 263 227 0.96 (0.95, 0.98) −5.22 <0.001 0.97 (0.96, 0.99) −3.87 <0.001
 wt% 48.69 51.00 54.00 49.93 54.21 47.89 52.68 56.70 53.73 46.30 44.39 47.52 44.99 44.12 42.73 37.11
 SE 0.71 2.96 2.26 2.32 2.76 2.43 2.83 2.73 2.78 3.92 3.53 3.14 3.16 2.66 3.31 3.20
NH Black (n) 788 51 50 53 50 69 56 52 70 60 58 62 39 49 31 38 0.97 (0.94,1.01) −1.41 0.162 0.98 (0.94,1.01) −1.28 0.203
 wt% 51.15 56.36 53.55 39.56 53.93 60.86 57.46 44.52 62.15 45.71 52.11 58.21 47.05 40.31 46.08 40.46
 SE 2.05 8.09 9.10 7.66 8.33 7.51 6.74 5.95 7.11 8.26 6.13 7.04 7.72 6.40 8.31 8.07
Hispanic (n) 1,143 80 98 86 95 87 85 84 79 76 78 79 63 54 46 53 0.96 −2.33 0.96 −2.11
(0.93, 0.99) 0.021 (0.93, 0.99) 0.037
 wt% 39.71 41.72 47.76 40.92 46.78 47.60 45.15 39.45 33.86 41.91 39.36 37.96 34.28 35.54 37.22 28.96
 SE 1.79 6.27 6.09 6.05 5.41 6.76 6.12 7.41 5.45 7.79 6.52 5.65 7.36 4.71 7.79 5.18
NH Other (n) 859 46 60 62 61 68 68 75 78 51 61 53 54 47 38 37 0.97 (0.93,1.00) −1.80 0.073 0.96 (0.92,1.00) −1.91 0.057
 wt% 43.84 50.08 53.49 52.44 35.14 64.84 33.47 54.33 52.01 34.09 33.13 41.35 38.77 46.79 55.76 30.35
 SE 2.29 7.75 7.40 9.23 9.05 6.08 8.03 11.22 9.22 8.49 8.08 10.45 9.71 6.98 7.61 6.42
Chi-square test between race/ethnicity (p-value)c <0.001 0.414 0.741 0.246 0.195 0.157 0.129 0.053 0.006 0.741 0.355 0.193 0.482 0.390 0.501 0.436
Differential time trend: year as continuous × race/ethnicity among moderate AUD F(3, 168) = 0.08 0.970 F(3, 168) = 0.10 0.958
Severe AUD NH White (n) 4,815 431 374 381 377 369 403 354 374 320 301 284 237 225 194 191 0.97 (0.96, 0.99) −3.22 0.002 0.98 (0.96, 0.99) −2.34 0.020
 wt% 63.34 62.66 70.81 64.95 65.34 66.75 65.35 58.67 64.97 62.26 68.84 65.22 60.32 60.64 60.51 50.50
 SE 0.99 3.19 3.32 3.67 3.63 3.08 3.23 4.05 2.92 3.70 3.60 3.84 4.04 3.75 3.60 3.15
NH Black (n) 710 56 48 57 55 47 48 51 50 47 30 43 51 38 34 55 0.99 (0.95,1.03) −0.35 0.727 1.00 (0.95,1.94) −0.13 0.897
 Wt% 68.14 66.99 68.51 91.47 59.89 61.07 73.72 66.87 59.71 68.94 66.14 62.78 72.91 67.39 54.32 75.46
 SE 1.84 5.72 10.67 3.55 8.10 7.15 7.12 7.54 8.96 9.73 9.54 7.62 6.92 7.52 8.87 6.46
Hispanic (n) 981 68 71 72 78 79 71 88 79 67 58 60 52 46 59 33 0.96 (0.93, 0.99) −2.02 0.045 0.97 (0.93,1.01) −1.61 0.110
 wt% 46.36 58.46 53.29 53.18 44.58 47.00 37.00 42.19 51.50 42.21 56.92 53.67 45.76 39.75 37.29 31.65
 SE 2.00 7.61 8.76 7.52 7.60 9.49 6.79 8.90 7.02 6.60 6.40 7.63 8.80 5.57 5.93 7.41
NH Other (n) 756 38 51 48 51 52 56 48 60 63 51 59 45 44 46 44 0.98 (0.93, 1.03) −0.81 0.421 0.97 (0.92,1.01) −1.43 0.156
 wt% 52.45 58.95 51.65 45.43 54.68 59.13 41.51 60.58 57.56 70.86 67.73 66.34 42.62 39.82 39.04 49.32
 SE 2.90 10.49 11.29 12.44 8.59 9.02 7.16 7.78 11.52 8.18 10.45 8.01 12.60 9.82 9.09 9.78
Chi-square test between race/ethnicity (p-value)c <0.001 0.756 0.175 0.002 0.048 0.084 <0.001 0.090 0.330 0.056 0.399 0.410 0.076 0.009 0.008 0.002 F(3, 168) = 0.43 0.735 F(3, 168) = 0.47 0.701
Differential time trend: year as continuous × race/ethnicity among severe AUD F(3, 168) = 6.98 <0.001 F(3, 168) = 9.69 <0.001
Differential time trend: year as continuous × past-year AUD status (in 2 categories; none vs. any) among NH White participants F(1, 170) = 23.38 <0.001 F(1, 170) = 30.80 <0.001
Differential time trend: year as continuous × past-year AUD status (in 2 categories; none vs. any) among NH Black participants F(1, 170) = 1.28 0.259 F(1, 170) = 2.60 0.109
Differential time trend: year as continuous × past-year AUD status (in 2 categories; none vs. any) among Hispanic participants F(1, 170) = 1.55 0.215 F(1, 170) = 1.90 0.170
Differential time trend: year as continuous × past-year AUD status (in 2 categories; none vs. any) among NH Other race/ethnicity participants F(1, 170) = 0.13 0.722 F(1, 170) = 0.94 0.334
Differential time trend: year as continuous × past-year AUD severity status (in 4 categories) among NH White participants F(3, 168) = 9.07 <0.001 F(3, 168) = 11.27 <0.001
Differential time trend: year as continuous × past-year AUD severity status (in 4 categories) among NH Black participants F(3, 168) = 1.48 0.221 F(3, 168) = 1.69 0.171
Differential time trend: year as continuous × past-year AUD severity status (in 4 categories) among Hispanic participants F(3, 168) = 0.50 0.680 F(3, 168) = 0.76 0.520
Differential time trend: year as continuous × past-year AUD severity status (in 4 categories) among NH Other race/ethnicity participants F(3, 168) = 0.17 0.915 F(3, 168) = 0.65 0.582
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AUD, alcohol use disorder; NH, non-Hispanic; NSDUH, National Survey on Drug Use and Health; OR, odds ratio; SE, standard error; wt%, weighted percent.

a

OR compares current smoking versus other smoker categories (i.e., former smokers, never smokers); ORs reflect the linear time trends across all years.

b

Adjusted for age (12 to 17, 18 to 25, 26 to 34, 35 to 49, and 50 years old or older); gender (male, female); total annual family income (<$20,000, $20,000 to $49,999, ≥$50,000); and race/ethnicity (NH White, NH Black, Hispanic, NH all other race or more than one race).

c

Chi-squared tests for the difference in percentage of current smokers by race/ethnicity.

DISCUSSION

We examined trends in cigarette smoking prevalence over a 15-year period among individuals in the United States by AUD status and severity in the full sample and by race/ethnicity. The prevalence of smoking decreased over time among individuals with and without AUDs. Yet, the prevalence of smoking among individuals with AUDs was more than twice as high as those without AUDs in every year of the study period, including the most recent data year (2016:37.84% vs. 16.07%). When considering AUD severity, the discrepancy was even greater with more than half of persons with severe AUDs (52.23%) reporting cigarette smoking in 2016. Regarding racial/ethnic differences in AUDs and smoking, there was no change in smoking prevalence among NH Black individuals with AUDs over time while every other racial/ethnic group experienced declines in smoking. In 2016, approximately half (50.51%) of people with AUDs who identified as NH Black reported smoking cigarettes. Notably, three-quarters (75.46%) of NH Black persons with severe AUDs reported smoking in 2016. There was a decrease in cigarette smoking among both individuals with and without AUDs and a more rapid decrease in smoking among individuals with AUDs, specifically mild and moderate AUDs, compared to individuals without AUDs during the time period studied. The speed of the decline in smoking prevalence did not differ significantly between individuals with severe AUDs and those without AUDs or between those in various pairs of AUD severity levels (e.g., mild vs. moderate, severe vs. moderate). However, smoking remains significantly more common among individuals with AUDs, including those with mild, moderate, and severe AUDs, suggesting that those with AUDs of any severity level remain a highly vulnerable group disproportionately affected by cigarette use.

Alcohol and cigarettes are commonly used together and smoking quit rates are lower for individuals with AUD compared to those without AUD (Falk et al., 2006; Jiang et al., 2015; Smith et al., 2014; Weinberger et al., 2016, 2017). There are several potential pharmacological, genetic, and environmental reasons for these high levels of co-use and maintenance of smoking among people with AUDs (Adams, 2017; McKee and Weinberger, 2013; Roche et al., 2016). For example, alcohol increases craving for nicotine and nicotine self-administration (Dermody and Hendershot, 2017; Verplaetse and McKee, 2017). Treatment programs for AUDs may present an important opportunity to aid these individuals with smoking cessation efforts. Although there may be concerns about providing smoking treatment concurrent with alcohol treatment, most studies find that concurrent smoking and alcohol treatment do not yield worse outcomes than treating alcohol alone (for recent reviews, see McKelvey et al., 2017; Thurgood et al., 2016). As cigarette use increases craving to use alcohol (Cooney et al., 2007; Dermody and Hendershot, 2017; Verplaetse and McKee, 2017), is related to decreased cognitive recovery in adults with AUDs (Durazzo et al., 2006, 2014; Pennington et al., 2013), and is associated with poorer AUD outcomes (Durazzo and Meyerhoff, 2017; Weinberger et al., 2015), combined smoking and alcohol treatment may offer significant benefits for alcohol treatment outcomes.

We identified racial/ethnic differences in the relationship between AUDs and smoking. One out of every 2 NH Black individuals with AUDs reported smoking in 2016, the highest prevalence among any racial/ethnic group. Further, approximately 75% of NH Black individuals with severe AUDs reported cigarette smoking. Individuals who identified as NH Black and had AUDs also did not show any change in smoking prevalence over time in contrast to a decrease in smoking for NH Black individuals without AUDs and every other racial/ethnic group across AUD status. These findings contrast with 1 study that did not find racial/ethnic differences in the cross-sectional relationship between AUD and smoking using 2011 to 2013 NSDUH data (Higgins et al., 2016).

A better understanding is needed of the disparities in smoking among NH Black persons with AUDs compared to persons with AUDs from other racial/ethnic groups (see Zemore et al., 2018). Factors proposed to impact racial/ethnic differences in AUDs include discrimination and prejudice, racial/ethnic stigma, poverty, neighborhood factors (e.g., ethnic density), and treatment access and utilization (Mulia et al., 2009; Vaeth et al., 2017; Zemore et al., 2016). NH Black persons are less likely than NH White persons access alcohol treatment services, especially at higher level of AUD severity (Chartier and Caetano, 2010), and the percentage of NH Black persons admitted for alcohol treatment decreased from 2002 to 2012 while the percentage of NH White and Hispanic persons increased during that time period (Vaeth et al., 2017).

In addition to understanding disparities in AUDs for NH Black persons, there is also a need for more information about racial/ethnic differences in smoking behavior (e.g., Cox et al., 2011). Although NH Black individuals in the general U.S. population have a similar smoking prevalence to NH White individuals (Jamal et al., 2018) and are more likely to report a desire to quit and quit attempts (Babb et al., 2017), NH Black individuals appear to be less likely to quit smoking (Babb et al., 2017). Several of the factors suggested to play a role in racial/ethnic disparities in AUD are also related to greater prevalences of cigarette smoking (e.g., discrimination, poverty; Borrell et al., 2007; Brandolo et al., 2015; Casetta et al., 2017; Lorant et al., 2003). In addition, NH Black individuals are more likely to use menthol cigarettes, which are associated with greater nicotine dependence and greater difficulty quitting (Delnevo et al., 2011; Foulds et al., 2010; Gandhi et al., 2009; Levy et al., 2011; Okuyemi et al., 2007; Villanti et al., 2017). Little is known about menthol smoking among persons with AUDs overall or by race/ethnicity. Among patients at 24 substance use disorder (SUD) treatment programs in the United States, which included AUD treatment, the prevalence of menthol smoking of 53.3% and, within the sample of menthol users, 23.8% reported that alcohol was their primary substance (compared to 15.7% of nonmenthol smokers; Gubner et al., 2018). Menthol cigarette use, compared to nonmenthol cigarette use, was associated with NH Black and Hispanic race/ethnicity, female gender, and greater interest in quitting smoking during SUD treatment. Two U.S. studies reported that menthol use was not associated with past 30-day alcohol use (Rath et al., 2016; Villanti et al., 2018) while another U.S. study of smokers from the community found no relationship between menthol use and binge drinking, frequency of consuming alcohol, or number of drinks per drinking occasion (Cohn et al., 2017). It is not clear at this time whether menthol plays a role in the disparities of smoking among persons with and without AUD.

Together, more research is needed to understand the factors associated with AUD disparities and smoking behavior among NH Black persons as well as how these factors might interact with each other and impact smoking behavior among persons with AUDs. NH Black individuals with AUDs, especially those with severe AUDs, who smoke cigarettes may need additional targeted public health and clinical interventions, including attention to variables that may associated with smoking and AUDs among this group (e.g., stress, treatment access) and variables that may disproportionally impact quit success for NH Black persons such as menthol cigarettes. Attention to these variables may be critical to make progress in decreasing the smoking prevalence and consequences of smoking for persons with both cigarette smoking and AUDs.

There are several limitations of the current study. First, results would need to be replicated to determine generalizability to individuals who were not included in the NSDUH sample (e.g., non-U.S. adults). Second, the 15 years of data used in the current analyses were cross-sectional. It would be useful for studies with longitudinal data that can follow adults with and without AUDs over time to examine individual trajectories in AUD, cigarette smoking, and the relationship between the 2 behaviors over time as well as potential mediators and moderators of the relationship between AUD and cigarette smoking prevalence. Third, due to small sample sizes, races/ethnicities other than NH White, NH Black, and Hispanic were analyzed together as a single group. This group included races/ethnicities with both the highest (e.g., Native American) and lowest (e.g., Asian) AUD and smoking prevalences (Chartier and Caetano, 2010; Jamal et al., 2018). Similarly, subgroups within race/ethnicity (e.g., Puerto Ricans, Mexican Americans, Cuban Americans as subgroups of the Hispanic group) demonstrate differences in AUD prevalence (Vaeth et al., 2017), but could not be examined separately due to sample size restrictions. It was also outside the scope of the current investigation to examine differences in the relationship between AUD and smoking prevalence for other demographics (e.g., gender, education) or demographic differences within racial/ethnic groups (e.g., gender differences in the relationship between AUD and smoking prevalence for NH Black respondents). These are important areas for future research. Fourth, smoking and AUDs were assessed using self-report measures, which may be subject to recall biases, reporting errors, and underreporting of substance use or problems related to substance use. Fifth, while outside the scope of the current study, it will be important for future research to examine factors associated with and/or underlying the relationship between AUD and smoking especially for NH Black individuals.

CONCLUSIONS

Individuals with AUDs bear a disproportionate burden of cigarette smoking in the United States, with a prevalence that is over twice the national average, with even higher prevalences seen among those with more severe AUDs. Cigarette smoking has not declined among NH Black individuals with AUDs, whereas a decline is observed among all other racial/ethnic groups. Approximately half of NH Black individuals with AUD, and three-quarters of NH Black individuals with severe AUDs, reported smoking in 2016. Because of the increased health risks associated with the smoking among persons with AUDs (Marrero et al., 2005; Pelucchi et al., 2006; Zheng, 2010), there is a need to focus greater scientific and public health efforts on tobacco control efforts for this subgroup. Individuals with AUDs may need additional resources and interventions to help reduce the high prevalence of smoking especially for those who identify as NH Black.

FUNDING

This work was supported by NIDA (grants R01-DA20892 to RDG and K01-DA043413 to LRP).

Footnotes

CONFLICT OF INTERESTS

The authors have no conflicts of interest to report.

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