Abstract
Background
Banning smoking in work and public settings leads to immediate reductions in disease burden. However, no previous studies have looked specifically at the impact smoking bans may have on depression.
Methods
The 2006 Behavioral Risk Factor Surveillance System (BRFSS) uses a cross-sectional design representative of the non-institutionalized civilian US population. Never smoker survey participants ≥18 years of age were selected from the BRFSS (n=41,904) with their self-report of depressive symptoms in the last 2 weeks, as assessed by the Patient Health Questionnaire. Models with adjustment for survey design, sociodemographics, alcohol consumption, and work and home smoking policies were considered.
Results
Following covariate adjustment, the risk of major depression was significantly higher for those living where smoking was allowed anywhere in the home versus those living in homes with complete smoking bans and in those who indicated that smoking was permitted in their work areas versus those reporting complete workplace smoking bans.
Conclusions
Findings from the present analysis support policies that ban smoking in all workplace settings. Interventions designed to eliminate smoking in the home are also needed.
Keywords: Secondhand smoke, Depression, Tobacco policy, Mental health policy
Introduction
Secondhand smoke (SHS) exposure causes premature death and disease in persons who do not smoke [1]. Pooled data from 1988 to 2002 indicate that approximately 43% of the United States (US) population was exposed to SHS [2]. Depression is a common and often chronic disorder, affecting over 32 million US adults in their lifetime and over 13 million US adults in the last 12 months [3]. Depression has been associated with an increased risk for premature mortality [4, 5], morbidity [6], and decreased worker productivity [7]. In fact, depression is the third leading cause of disability-adjusted life years in developed countries [8].
It is well established that smokers are at a greater risk for depression [9–13], although the exact direction of this relationship is not clear. Smokers may smoke to “self-medicate” in response to their depression [12], and/or smoking may precede the onset of their depression [13]. Alternatively, a shared genetic predisposition may explain this relationship [9]. A paucity of research has examined potential relationships between SHS exposure and depression. Some cross-sectional evidence suggests that SHS exposure may be associated with depression among never smokers [14, 15]; however, no research has evaluated the relationship between SHS policies at work and SHS rules at home with the risk for depression.
Methods
Description of Surveys
The Behavioral Risk Factor Surveillance System (BRFSS) (http://www.cdc.gov/BRFSS/), a telephone-based survey, is conducted every year by the Centers for Disease Control and Prevention. The survey is designed to be representative of all demographic groups in the non-institutionalized US population. Complete data from never smokers 18 years or older were obtained from the 2006 BRFSS. This study was approved by the University of Miami Institutional Review Board. These optional modules were asked by Alabama, Arkansas, Georgia, Indiana, Iowa, Louisiana, Missouri, Nevada, New Hampshire, North Dakota, Oklahoma, Virgin Islands, Virginia, West Virginia, Wisconsin, and Wyoming (http://apps.nccd.cdc.gov/BRFSSModules/ModByCat.asp?Yr=2006).
Measures
SHS Policy
In the BRFSS, participants were asked: “Which statement best describes the rules about smoking inside your home?” For smoking policies at work, participants were asked: “Which of the following best describes your place of work’s official smoking policy for indoor public or common areas, such as lobbies, rest rooms, and lunch rooms?” and “Which of the following best describes your place of work’s official smoking policy for work areas?” Persons who were not employed were not asked the work questions (for item responses, see Table 1).
Table 1.
Demographic characteristics, health characteristics, and reported home and work-place SHS policies of eligible sample: 2006 BRFSS
| Numbera | Percentage | SE of percentage | Mean | SE of mean | |
|---|---|---|---|---|---|
| Age | 41,490 | 43.99 | 0.15 | ||
| Gender | |||||
| Male | 13,391 | 43.07 | 0.44 | ||
| Female | 28,513 | 56.92 | 0.44 | ||
| Race/ethnicity | |||||
| Non-Hispanic White | 32,648 | 75.13 | 0.41 | ||
| Non-Hispanic Black | 5,351 | 13.82 | 0.32 | ||
| Non-Hispanic other | 1,123 | 3.98 | 0.22 | ||
| Non-Hispanic multiracial | 668 | 1.56 | 0.12 | ||
| Hispanic | 1,686 | 5.49 | 0.24 | ||
| Education | |||||
| <High school | 3,536 | 7.74 | 0.22 | ||
| =High school | 12,468 | 28.76 | 0.38 | ||
| Attended college | 10,708 | 25.91 | 0.37 | ||
| Graduated college | 15,100 | 37.57 | 0.39 | ||
| Drinks per day | 41,259 | 0.22 | 0.00 | ||
| General health | 41,782 | 2.29 | 0.00 | ||
| Home rules | |||||
| No official policy | 3,976 | 8.33 | 0.20 | ||
| Allowed in some places | 1,757 | 4.71 | 0.19 | ||
| Allowed anywhere | 422 | 1.08 | 0.08 | ||
| Not allowed anywhere | 35,749 | 85.86 | 0.28 | ||
| Work policy in public areasb | |||||
| No official policy | 831 | 4.33 | 0.27 | ||
| Allowed in some places | 2,157 | 12.94 | 0.44 | ||
| Allowed anywhere | 256 | 1.40 | 0.13 | ||
| Not allowed anywhere | 16,897 | 81.31 | 0.51 | ||
| Work policy in work areasb | |||||
| No official policy | 733 | 4.17 | 0.28 | ||
| Allowed in some places | 1,332 | 7.75 | 0.32 | ||
| Allowed anywhere | 172 | 0.81 | 0.08 | ||
| Not allowed anywhere | 17,940 | 87.25 | 0.42 | ||
| Depressive symptoms | 41,904 | 2.96 | 0.03 | ||
Sample size varies due to item nonresponse
Asked only if employed
Current Depression
The Patient Health Questionnaire (PHQ-8) [16] was used to measure depression. A summed score of 10 or greater from a range of 0–24 was used to classify “major depression.” This cut-off score has 100% sensitivity and 95% specificity for diagnosing major depression [16]. The continuous score was also used, representing the cumulative number of “depressive symptoms.”
Covariates
Age, race/ethnicity, gender, education, general health, and alcohol consumption were measured by participant self-report. Age was measured in years; participants were classified as: non-Hispanic White, non-Hispanic Black, Hispanic, or non-Hispanic other; male or female; and did not graduate high school, graduated high school, attended college or technical school, or graduated from college or technical school. Alcohol consumption was classified using a continuous measure of average number of drinks per day in the past year. General health was coded as excellent (1), very good (2), good (3), fair (4), or poor (5) and was treated as an ordinal variable.
Sample Selection
Participants were asked if they had smoked 100 or more cigarettes in their lifetime. Those responding “yes” were asked if they currently smoked cigarettes. Self-identified current smokers and former smokers were excluded from the analysis. Analyses were performed with “never smokers” (defined as those who reported not smoking 100 or more cigarettes in their lifetime [n=41,904]).
Statistical Analysis
Multivariable logistic regressions were performed with adjustments for survey design and potential confounders using SAS version 9.2. The model estimated the effects of SHS home and work policies on the dichotomous measure of depression using the SAS command PROC SURVEY LOGISTIC, controlling for sociodemographic and clinical characteristics as described above. STATA version 10.0 was also used to analyze a continuous measure of depression with the same predictor and covariates as in the logistic regression, also with adjustment for survey design. Zero-inflated Poisson regression was utilized because the depressive symptoms measure was positively skewed with an excess of zeros.
Results
Demographic and Health Characteristics and Participant-Reported Home and Workplace Secondhand Smoke Policies
The average age of the sample was 44 years, nearly 57% were female, 75% were non-Hispanic White, and nearly 62% reported at least some college education (Table 1). Approximately eight in 10 participants reported that smoking was not permitted in their homes and at their place of work. The mean depression score was 2.96.
Covariates and Depression
In our multivariable models, age was inversely related with both major depression and depressive symptoms (Table 2). In addition, the average number of drinks per day was positively associated with both major depression and depressive symptoms. Poor general health was positively associated with major depression and depressive symptoms. Males were significantly less likely to have major depression and depressive symptoms than females. Overall, we found few significant differences in major depression and depressive symptoms among the different race/ethnic subpopulations. Education was significantly and inversely related with both major depression and depressive symptoms.
Table 2.
Association between SHS rules at home, policy in public areas, and depression and depressive symptoms in never smokers: 2006 BRFSS
| Variable | Depression/depressive symptoms |
||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Rules at home (n=40,327) |
Policy in public workplace areas (n =19,571)a |
Policy in work areas (n=19, 597)b |
|||||||||||||
| Logistic regression |
Zero-inflated Poisson regression |
Logistic regression |
Zero-inflated Poisson regression |
Logistic regression |
Zero-inflated Poisson regression |
||||||||||
| OR | 95% CI | β | SE | p value | OR | 95% CI | β | SE | p value | OR | 95% CI | β | SE | p value | |
| Age (years) | 0.98 | 0.97–0.98 | −0.01 | 0.00 | <0.001 | 0.97 | 0.96–0.98 | −0.01 | 0.00 | <0.001 | 0.97 | 0.96–0.98 | −0.01 | 0.00 | <0.001 |
| Alcohol consumption | 1.00 | 1.00–1.00 | 0.00 | 0.00 | <0.01 | 1.00 | 1.00–1.00 | 0.00 | 0.00 | <0.01 | 1.00 | 1.00–1.00 | 0.00 | 0.00 | <0.01 |
| General health | 2.37 | 2.12–2.65 | 0.28 | 0.01 | <0.001 | 1.86 | 1.48–2.34 | 0.23 | 0.01 | <0.001 | 1.86 | 1.47–2.34 | 0.23 | 0.02 | <0.001 |
| Gender | |||||||||||||||
| Male | 0.72 | 0.58–0.90 | −0.11 | 0.02 | <0.001 | 0.72 | 0.48–1.10 | −0.08 | 0.04 | 0.04 | 0.72 | 0.47–1.11 | −0.08 | 0.04 | 0.04 |
| Female | 1.00 | Reference | 1.00 | Reference | 1.00 | Reference | |||||||||
| Race/ethnicity | |||||||||||||||
| Non-Hispanic White | 1.18 | 0.65–2.15 | 0.06 | 0.07 | 0.37 | 0.74 | 0.28–1.95 | −0.01 | 0.13 | 0.90 | 0.73 | 0.27–2.01 | −0.02 | 0.13 | 0.88 |
| Non-Hispanic Black | 1.56 | 0.82–2.97 | 0.16 | 0.08 | 0.04 | 1.23 | 0.41–3.64 | 0.11 | 0.14 | 0.42 | 1.20 | 0.39–3.64 | 0.10 | 0.14 | 0.46 |
| Non-Hispanic other | 1.62 | 0.82–3.21 | 0.17 | 0.09 | 0.07 | 1.17 | 0.39–3.46 | 0.14 | 0.16 | 0.36 | 1.19 | 0.39–3.64 | 0.14 | 0.16 | 0.38 |
| Non-Hispanic multiracial | 1.21 | 0.61–2.38 | 0.21 | 0.09 | 0.02 | 0.65 | 0.20–2.04 | −0.01 | 0.16 | 0.97 | 0.66 | 0.20–2.14 | 0.00 | 0.16 | 0.98 |
| Hispanic | 1.00 | Reference | 1.00 | Reference | 1.00 | Reference | |||||||||
| Education | |||||||||||||||
| Did not graduate high school | 2.49 | 1.80–3.44 | 0.29 | 0.04 | <0.001 | 2.30 | 1.10–4.78 | 0.27 | 0.08 | 0.001 | 2.20 | 1.11–4.35 | 0.26 | 0.07 | 0.001 |
| Graduated high school | 1.76 | 1.37–2.24 | 0.16 | 0.02 | <0.001 | 2.26 | 1.42–3.60 | 0.20 | 0.04 | <0.001 | 2.20 | 1.38–3.48 | 0.20 | 0.04 | <0.001 |
| Attended college | 1.77 | 1.42–2.20 | 0.16 | 0.02 | <0.001 | 2.28 | 1.65–3.16 | 0.23 | 0.04 | <0.001 | 2.64 | 1.61–3.17 | 0.23 | 0.04 | <0.001 |
| Graduated from college | 1.00 | Reference | 1.00 | Reference | 1.00 | Reference | |||||||||
| Rules/policy | |||||||||||||||
| No official policy | 1.25 | 1.02–1.53 | 0.08 | 0.02 | <0.01 | 1.38 | 0.77–2.44 | 0.07 | 0.08 | 0.37 | 1.82 | 0.98–3.38 | 0.13 | 0.10 | 0.22 |
| Allowed in some places | 1.40 | 1.08–1.83 | 0.11 | 0.03 | <0.01 | 1.03 | 0.63–1.85 | 0.03 | 0.05 | 0.54 | 1.20 | 0.73–1.99 | 0.07 | 0.06 | 0.23 |
| Allowed anywhere | 2.15 | 1.24–3.71 | 0.38 | 0.09 | <0.001 | 2.12 | 0.87–5.17 | 0.23 | 0.15 | 0.14 | 2.40 | 1.17–4.95 | 0.25 | 0.11 | 0.02 |
| Not allowed anywhere | 1.00 | Reference | 1.00 | Reference | 1.00 | Reference | |||||||||
Based on the question: “Which of the following best describes your place of work’s official smoking policy for indoor public or common areas, such as lobbies, rest rooms, and lunch rooms?”
Based on the question: “Which of the following best describes your place of work’s official smoking policy for work areas?”
Secondhand Smoke Rules/Policy at Home/Work and Major Depression
The strongest associations between SHS home rules, workplace policies, and risk for depression were observed when comparing never smokers living and working in environments with no smoking restrictions versus never smokers in environments with complete smoking restrictions (Table 2). In the models adjusted for age, race/ethnicity, gender, education, general health, and alcohol consumption, the risk of major depression for those living where smoking was allowed anywhere in the home or allowed in work areas versus those reporting complete home and workplace smoking restrictions was increased (odds ratio [OR]=2.15 [95% confidence interval=1.24–3.71] and OR=2.40 [95% confidence interval=1.17–4.95], respectively); although elevated, there was no statistically significant association between smoking policies in public work areas and major depression (OR=2.12 [95% confidence interval=0.87–5.17]).
Similarly, those living where smoking was allowed anywhere in the home or in work areas had more depressive symptoms compared with those reporting complete smoking bans (β=0.38, p<0.001 and β=0.25, p=0.02, respectively); there was no significant association between public work area smoking policies and depressive symptoms (β= 0.23, p=0.14). There were also no significant interactions between gender and policies at work/home (all ps>0.20).
Discussion
Forty-three percent of the US population is potentially exposed to SHS [2], and over 35 million US adults may suffer at least one lifetime episode of major depression [3]. To the authors’ knowledge, this is the first study to assess the relationship between smoking policies at work and at home with an increased risk for depression among a representative national sample of never smokers in the US. Our findings can be interpreted in the context of the Behavioral Ecological Model [17]. According to this model, efforts to prevent exposure to SHS should be done at different levels, i.e., from macrosystemic (culture) to microsystemic (individual). Individual interventions can be briefly but effectively performed by clinicians to increase cessation rates and reduce SHS exposure in the home [18, 19]. At the macrosystemic or cultural level, banning smoking in all public places would dramatically reduce population-level exposure to SHS. By intervening at different levels, an antismoking culture is created, and thus, there would be less cigarette smokers and less exposure to SHS [20, 21]. Although not specifically focused on depression, other studies have shown that banning smoking in workplaces and public settings leads to immediate reductions in overall hospital admissions and disease-specific symptoms in workers and the public at large [22]. Therefore, banning smoking in all places may lead to a decrease in the rates of depression. On the other hand, if the temporal association is that depression leads to increased smoking and increased SHS exposure, banning smoking would still be beneficial because depressed persons would be less likely to smoke and not be exposed to SHS.
Strengths and Limitations
An advantage of the present analysis is that it uses a nationally representative sample of the US population. Furthermore, depression was measured using the PHQ, a well-validated measure [16, 23]. However, the individual history of depression was not assessed. Because of the absence of biochemical confirmation, we must also assume that actual SHS exposure varies in a dose–response fashion with the presence and extent of reported home and workplace smoking policies.
Multiple and systematic replications of previous studies [14, 15] provide additional argument for a causal association. Furthermore, experiments or controlled studies that examine large-scale policies may not be practically or ethically undertaken. Thus, results from this study, as well from previous studies [14, 15], may be the best basis for inference, as well as providing further support for comprehensive public policies which seek to further lower SHS exposure in the population.
In conclusion, this study suggests that the lack of both smoking policies at work and at home that limit or ban indoor smoking is associated with depression. Continued progress toward protecting all workers from SHS exposure is consistent with the present study findings. Interventions designed to eliminate smoking in the home are also needed. Modest financial incentives to prevent smoking at home already exist, most directly through owner and renter insurance rates which are typically higher for smokers [24]. However, public health campaigns and similar interventions that promote greater awareness of the negative consequences associated with smoking and SHS are also warranted to encourage individuals to stop smoking at home.
Acknowledgments
This study was completed while Frank C. Bandiera was a doctoral student in epidemiology at the University of Miami. Mr. Bandiera is a recipient of an individual predoctoral grant from the National Institute of Mental Health to study the association between asthma and mental health outcomes and to receive advanced training in statistical methods, genetic epidemiology, and behavioral medicine (1F31MH084567). This work was funded in part by grants from the Flight Attendant Medical Research Institute (FAMRI; Dr. Lee, principal investigator) and from the National Institute of Occupational Safety and Health (NIOSH; R01 OH03915; Dr. Fleming, principal investigator).
Abbreviations
- BRFSS
Behavioral Risk Factor Surveillance System
- SHS
Secondhand smoke
- DALYS
Disability-adjusted life years
- CDC
Centers for Disease Control and Prevention
- OR
Odds ratio
Contributor Information
Frank C. Bandiera, Department of Epidemiology and Public Health, Miller School of Medicine, University of Miami, P.O. Box 016069 (D-4-11), Miami, FL 33101, USA
Alberto J. Caban-Martinez, Department of Epidemiology and Public Health, Miller School of Medicine, University of Miami, P.O. Box 016069 (D-4-11), Miami, FL 33101, USA
Kristopher L. Arheart, Department of Epidemiology and Public Health, Miller School of Medicine, University of Miami, P.O. Box 016069 (D-4-11), Miami, FL 33101, USA
Evelyn P. Davila, Department of Epidemiology and Public Health, Miller School of Medicine, University of Miami, P.O. Box 016069 (D-4-11), Miami, FL 33101, USA
Lora E. Fleming, Department of Epidemiology and Public Health, Miller School of Medicine, University of Miami, P.O. Box 016069 (D-4-11), Miami, FL 33101, USA
Noella A. Dietz, Department of Epidemiology and Public Health, Miller School of Medicine, University of Miami, P.O. Box 016069 (D-4-11), Miami, FL 33101, USA
John E. Lewis, Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA
David Fabry, Department of Otolaryngology, Miller School of Medicine, University of Miami, Miami, FL, USA
David J. Lee, Email: dlee@med.miami.edu, Department of Epidemiology and Public Health, Miller School of Medicine, University of Miami, P.O. Box 016069 (D-4-11), Miami, FL 33101, USA
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