Abstract
Objectives. We examined whether support for tobacco control policies varies by demographic group, including nativity status (i.e., immigrant versus US born).
Methods. We analyzed 1995 to 2002 data from the Current Population Survey Tobacco Use Supplement (n = 543 951). The outcome was a summary attitudinal measure assessing support of smoking bans in 4 of 6 venues.
Results. US-born respondents, smokers, male respondents, Native Americans, Whites, and those who were unmarried, of lower socioeconomic status, and whose workplaces and homes were not smoke free were less likely to support smoking bans. Immigrants exhibited stronger support for banning smoking in every venue, with a generation-specific gradient in which support eroded with increasing assimilation to the United States. Levels of support were more than twice as high among immigrants as among US-born respondents (odds ratio [OR] = 2.16; 95% confidence interval [CI] = 2.08, 2.23). Naturalized citizens displayed higher support than US-born citizens, which may be relevant for mobilization of the electorate. Differences in population composition and contexts (e.g., smoke-free workplaces) only partially accounted for immigrants' stronger level of support.
Conclusions. Immigrants and their children may be valuable tobacco control allies given their supportive attitudes toward smoke-free policies.
A substantial body of evidence suggests that secondhand smoke causes numerous diseases.1–3 More than 50 compounds in secondhand smoke are known human carcinogens,4 and there is no risk-free level of exposure.5 Policies restricting smoking in public places and work sites are effective in terms of reducing population exposure to secondhand smoke,5,6 decreasing cigarette consumption, and increasing cessation and quit attempts among smokers.7 Smoke-free policies in the United States have expanded substantially over the past 2 decades; such policies, which covered only 3% of US indoor employees in 1986, covered 77% of employees in 2003.8 Since 1993, 337 localities and 30 US states have enacted 100% smoke-free policies.9,10
Although support for smoke-free policies varies across demographic groups,11–13 no previous analyses, to our knowledge, have examined smoke-free policy support by nativity. Understanding tobacco control attitudes among immigrants is important for strategic, ethical, and demographic reasons.
Opinions about where smoking should be allowed are a general indicator of public support for tobacco control policies,14 and such support is an important catalyst for enactment of these policies.15,16 Public opinion influences election outcomes and establishes the policy agenda of elected officials via voters' selection of candidates.17 Public opinion is also important in gauging the appropriateness of state ballot initiatives or referenda that offer the most direct political participation of citizens to change tobacco policies, because ballot initiatives and referenda bypass state legislative processes dominated by tobacco industry; indeed, such initiatives and referenda facilitated passage of 28 state-level tobacco measures from 1988 to 2006.18
Knowledge about public support for smoking bans derived from publicly funded data may help advocates identify voter constituencies for coalition building in election strategies and may help them target media messages, given that advocates have vastly fewer resources than industry for public opinion polling. Understanding the tobacco policy–related attitudes of different demographic groups, including immigrants, may help to protect against tobacco industry manipulation. The tobacco industry has a sophisticated understanding of the immigrant market segment19 and a long history of targeting immigrants (e.g., by investing in relationships with minority community leaders and organizations).20 The industry in turn expects recipients of its funds to advocate for its legislative agenda,20,21 which may counter the interests of a group's constituents.
Notably, many immigrants are not voters because they are not naturalized citizens, and they may also be illegal aliens. Because noncitizen immigrants lack rights and privileges and fear retribution,22 they may be less vocal than other groups in their advocacy of protective measures such as smoke-free workplaces. Finally, the growing size of the foreign-born population and their children—now representing more than 22% of the US population—provides a demographic imperative for examining this group's policy-related attitudes.23–26
In this study, we present descriptive patterns of immigrant support for smoke-free policies; we tested whether US immigrants have different levels of support for smoking bans than native-born individuals and explored explanations for these patterns. We hypothesized that immigrants would hold stronger attitudes in support of smoking bans.
Immigrants may be more likely to support smoking bans because the demographic subgroups that make up the immigrant population are typically more supportive of smoking bans than US-born individuals (a compositional explanation). Immigrants are much less likely than US-born individuals to be smokers27 and much more likely to be Hispanic or Asian.28 These demographic subgroups are also more likely to support smoking bans.14
Several different levels of context may also affect immigrants' support for smoking bans (contextual explanations), including the immediate work and home context with respect to smoke-free policies,14 their state of residence,14,28 their family socialization context, and their country of origin. For example, immigrants from different countries of origin may hold different attitudes and norms toward the acceptability of smoking that may have been cultivated in their country of origin (e.g., by smoke-free policies in that country) or may be maintained by the US-based family and community context.29
We used data from the Current Population Survey (CPS) Tobacco Use Supplement (TUS) for the years 1995–1996, 1998–1999, and 2001–2002.30 The TUS is a multistage probability sample representative of the US noninstitutionalized civilian population aged 15 years or older.
Our outcome variable, support for smoking bans in 6 venues (restaurants, hospitals, indoor work areas, bars and cocktail lounges, indoor sports venues, and indoor shopping malls), was based on TUS questions asking whether smoking should be allowed in “all areas, some areas, or not allowed at all.” We summed the number of venues in which each person supported complete smoking bans (“smoking should not be allowed at all”) and created a dichotomous variable coded 1 if a person supported smoking bans in 4 or more venues and 0 otherwise. We chose this summary measure because it is straightforward, has been applied in previous studies,14 and is relevant to policies such as those of the World Health Organization.31 The measure weighted each venue equally, which was justified in our data according to a factor analysis showing that the 6 attitudes loaded approximately equally on the first factor.
We excluded observations in which responses were missing for any attitudinal measure. Also, proxy respondents were excluded because they were not asked the questions on attitudes toward smoke-free policies.14 The final sample consisted of N = 543 951 respondents.
Our predictor of interest was nativity, categorized as first generation (foreign-born respondents), second generation (US-born respondents with at least one foreign-born parent), or third generation or higher (US-born respondents with US-born parents). We hypothesized that if family context is one vehicle by which norms and expectations about smoke-free attitudes are transmitted, we would observe gradient effects (e.g., the magnitude of second-generation support would fall between that of first- and third-generation support).
Given that voting may be an important pathway by which public opinion translates to policy enactment, we adjusted for citizenship (naturalized citizen immigrants, noncitizen immigrants, and US-born citizens). We modeled country (for countries with unweighted sample sizes above 200) or region (for countries with less representation) of birth to ensure that we had large enough categories with sufficient power to allow a meaningful examination of between-group differences. We grouped immigrant length of residence in the United States into 5-year categories.
Respondents were categorized as current smokers, former smokers, or never smokers according to whether they had smoked 100 cigarettes in their lifetime and whether they currently smoked every day, some days, or not at all, respectively.14,32 We excluded respondents whose smoking status could not be determined (0.29% of the original sample). A smoke-free home variable was based on respondents' self-report that “no one is allowed to smoke anywhere” in their home. Respondents who were employed and who worked indoors were asked whether their workplace had an official policy restricting smoking in any way. Those employed in smoke-free workplaces were compared with those employed in non-smoke-free workplaces (including workplaces that lacked tobacco policies and respondents who were not in universe for receiving the survey question).14
Study time periods, defined according to the TUS survey years, were 1995–1996, 1998–1999, and 2001–2002. Demographic covariates included gender, age (divided by 10) and age-squared (both centered at 45 years), race/ethnicity, educational attainment, marital status, annual income, occupation, employment status, and residence in California versus elsewhere in the United States (given California's strong tobacco control policy environment and its disproportionate immigrant population).
Initially we used cross tabulations and χ2 tests in SAS version 9.1 (SAS Institute Inc, Cary, NC) to test bivariate associations between the study variables and policy support. We then conducted multiple logistic regression analyses using SUDAAN version 10 (Research Triangle Institute, Research Triangle Park, NC) with the summary smoking ban measure as the outcome. Model 1 tested unadjusted bivariate data and included only nativity (generation). Models 1a through 1n added one variable at a time to model 1 to assess how immigrant generation associations with smoking ban support were affected.
Model 2 added all of the demographic variables simultaneously. Model 3 added smoking status to model 2. Model 4 added workplace and home smoking bans to model 3. Model 5 added California residence to model 4. Models 6 through 8 built on model 4 to explore whether patterns of support varied among immigrants when length of stay, country of origin, and citizenship were substituted for the first-generation variable. For all of the immigrant variables, the reference (omitted) group was third-generation respondents; the second-generation category was included as an indicator variable. We applied self-response weights and self-response replicate weights (created via a balanced repeated replication method) to adjust the variance induced by the complex multistage design of the CPS.
Figure 1 reflects tabulations from 4 variable types: support for smoking bans, venue, year, and generation. To determine the significance of the time trend slopes in Figure 1, we used logistic regression and time–generation interactions. We estimated logistic regression models of smoking ban support as the outcome for each of the 6 venues; each model included the covariates of the generation indicator variable (with US-born respondents as the reference group), the 3 time periods of the survey as an ordinal variable, and the generation–time interaction. The significance of the time trend for US-born respondents was determined by the logit coefficient P value for the time period variable, as a test of whether the time trend slope was significantly different from zero. Significant P values for the first- and second-generation respondents in the figure indicate that the time slope is significantly different for that generation group relative to third-generation respondents.
FIGURE 1 Smoking ban attitudes with percentage increases in support across time, by venue, generation, and year: Current Population Survey Tobacco Use Supplement, 1995–2002.
Note. 3rd gen, USB = third-generation US-born respondents; 2nd gen, 1 FB parent = second-generation respondents with 1 foreign-born parent; 2nd gen, 2 FB parents = second-generation respondents with 2 foreign-born parents; 1st gen, FB = first-generation foreign-born respondents. The values that appear above the bars indicate the percentage increases in support for each generation group within each venue from 1995 to 2002.
*P < .05.
Table 1 shows that in the United States public support for smoking bans increased across time. Support for banning smoking in 4 of 6 venues rose from 54.8% in 1995–1996 to 68.5% in 2001–2002. The prevalence of the US population covered by workplace smoking bans also increased over this period (data not shown). On average, 75.7% of immigrants supported banning smoking in 4 of the 6 venues, as compared with 65.7% of second-generation respondents and 59.1% of third-generation respondents. Smokers, male respondents, those who were unmarried, Native Americans, Whites, those of lower socioeconomic status, and those whose workplaces and homes were not smoke free were less likely to support smoking bans.
TABLE 1 Univariate and Bivariate Sample Distribution: Current Population Survey Tobacco Use Supplement, 1995–2002
| Variable and Category | Unweighted No. | Weighted %a | Support for Smoking Ban in 4 of 6 Venues, %ab |
| Total | 543 951 | 100.0 | 61.6 |
| Nativity | |||
| US born, US-born parents (third generation) | 446 007 | 79.6 | 59.1 |
| US born, foreign-born parents (second generation) | 49 152 | 9.0 | 65.7 |
| Foreign born (first generation) | 48 792 | 11.4 | 75.7 |
| Citizenship | |||
| US-born citizen | 495 159 | 88.64 | 59.8 |
| Foreign-born naturalized citizen | 20 074 | 4.43 | 75.3 |
| Foreign-born noncitizen | 28 718 | 6.94 | 76.0 |
| Length of stay in US, y | |||
| 0–4 | 7748 | 1.9 | 74.9 |
| 5–9 | 9011 | 2.2 | 76.0 |
| 10–14 | 7669 | 1.9 | 76.7 |
| 15–19 | 7748 | 1.9 | 75.3 |
| ≥ 20 | 23 666 | 5.1 | 73.6 |
| US born | 488 109 | 87.2 | 59.6 |
| Survey period | |||
| 1995–1996 | 185 031 | 32.3 | 54.8 |
| 1998–1999 | 174 886 | 33.4 | 61.1 |
| 2001–2002 | 184 034 | 34.3 | 68.5 |
| Smoking status | |||
| Never smoker | 302 978 | 57.5 | 72.0 |
| Former smoker | 123 893 | 21.1 | 61.5 |
| Current smoker | 117 080 | 21.5 | 33.7 |
| Smoke-free home | |||
| No | 218 117 | 39.3 | 38.9 |
| Yes | 325 834 | 60.7 | 76.2 |
| Smoke-free workplace | |||
| Noc | 373 595 | 68.7 | 58.7 |
| Yes | 170 356 | 31.3 | 67.9 |
| Race/ethnicity | |||
| Non-Hispanic White | 432 433 | 73.5 | 59.4 |
| Non-Hispanic Black | 48 958 | 11.7 | 60.5 |
| Native American | 5920 | 0.8 | 56.8 |
| Asian/Pacific Islander | 15 885 | 3.6 | 75.2 |
| Hispanic | 40 755 | 10.4 | 74.2 |
| Gender | |||
| Male | 235 667 | 48.2 | 57.4 |
| Female | 308 284 | 51.8 | 65.5 |
| Occupation | |||
| Blue collar | 80 485 | 16.2 | 51.8 |
| White collar | 222 595 | 40.6 | 65.1 |
| Service | 49 663 | 9.4 | 58.8 |
| Farmworker | 10 430 | 1.8 | 58.8 |
| Military | 60 | 0.0 | 53.2 |
| Unemployed or not in labor force | 180 718 | 32.0 | 63.1 |
| Employment status | |||
| Employed | 341 961 | 63.5 | 61.2 |
| Unemployed | 18 295 | 3.9 | 55.7 |
| Not in labor force | 183 695 | 32.6 | 63.0 |
| Income, $ | |||
| 0–9999 | 48 677 | 9.1 | 57.3 |
| 10 000–19 999 | 71 989 | 13.0 | 57.8 |
| 20 000–29 999 | 76 079 | 13.7 | 57.7 |
| 30 000–39 999 | 68 964 | 12.3 | 58.9 |
| 40 000–49 999 | 51 668 | 9.3 | 60.3 |
| 50 000–59 999 | 46 818 | 8.5 | 62.1 |
| 60 000–74 999 | 45 084 | 8.3 | 64.8 |
| ≥ 75 000 | 86 971 | 16.9 | 70.2 |
| Missing | 47 701 | 9.0 | 62.6 |
| Education | |||
| High school or less | 273 542 | 51.3 | 58.1 |
| Some college/associate degree | 141 200 | 25.5 | 61.5 |
| Bachelor's degree or more | 129 209 | 23.2 | 69.3 |
| Marital status | |||
| Married | 310 154 | 53.9 | 64.0 |
| Not married | 233 797 | 46.1 | 58.7 |
| Age, y | |||
| 15–18 | 27 684 | 7.5 | 64.1 |
| 19–34 | 99 068 | 21.4 | 59.7 |
| 35–44 | 161 386 | 29.2 | 60.2 |
| ≥ 45 | 255 813 | 42.0 | 63.0 |
| State of residence | |||
| California | 38 480 | 12.1 | 78.1 |
| Other US state (or District of Columbia) | 505 471 | 87.9 | 59.3 |
Note. All group differences were significant at P < .001. US-born and foreign-born totals are slightly different across different variables because of the structure of the Tobacco Use Supplement questionnaire.
a Self-response weights.
b The 6 venues were bars, restaurants, indoor workplaces, indoor sporting events, indoor malls, and hospitals.
c Or was not asked question.
Across all groups, smoking bans in hospitals garnered the greatest support and smoking bans in bars garnered the least (Figure 1). Within each nativity group, attitudes toward smoking bans became more supportive over time for all of the venues, and there was a gradient in support across all venues according to immigrant generation. First-generation respondents were the most supportive of bans, and third-generation respondents expressed the weakest support. However, third-generation respondents outpaced first-generation respondents on a relative basis for all but 2 of the venues and outcomes regarding increasing support, as indicated by first-generation respondents' significantly shallower slope of support across time relative to third-generation respondents (Figure 1).
Multivariate models demonstrated that foreign-born individuals were more likely than were their third-generation counterparts to support smoking bans in 4 or more of the venues both before (odds ratio [OR] = 2.16; 95% confidence interval [CI] = 2.08, 2.23; Table 2, model 1) and after (OR = 1.67; 95% CI = 1.61, 1.73; Table 3, model 2) adjustment for demographic factors. When each variable was added to model 1 in turn (Table 2, models 1a–1n), first-generation respondents' OR for support for smoking bans decreased the most with the addition of race/ethnicity (decrease of 41% versus the bivariate model 1), the home smoking ban variable (decrease of 35%), smoking status (decrease of 26%), and California residence (decrease of 25%). Similar to the bivariate analyses, all of the demographic variables had significant associations with smoking ban support in the multivariate analyses.
TABLE 2 Bivariate and Trivariate Logistic Regression Results of Smoking Ban Support in at Least 4 of 6 Venues, by Immigrant Generation: Current Population Survey Tobacco Use Supplement, 1995–2002
| Model | Variable Added to Modelb | First-Generation Respondents,a OR (95% CI) | Change in OR vs Bivariate OR, % | Second-Generation Respondents,a OR (95% CI) | Change in OR vs Bivariate OR, % |
| 1 | Generation (bivariate) | 2.16 (2.08, 2.23) | … | 1.32 (1.30, 1.35) | … |
| 1a | Survey year | 2.13 (2.06, 2.21) | −2.3 | 1.34 (1.32, 1.37) | 6.9 |
| 1b | Race/ethnicity | 1.69 (1.63, 1.74) | −40.8 | 1.21 (1.19, 1.24) | −33.6 |
| 1c | Gender | 2.19 (2.11, 2.26) | 2.3 | 1.33 (1.30, 1.35) | 2.5 |
| 1d | Occupation | 2.26 (2.18, 2.34) | 8.4 | 1.29 (1.26, 1.31) | −10.2 |
| 1e | Employment | 2.16 (2.09, 2.23) | −0.1 | 1.31 (1.28, 1.34) | −3.2 |
| 1f | Income | 2.26 (2.18, 2.34) | 8.6 | 1.34 (1.31, 1.37) | 5.7 |
| 1g | Education | 2.18 (2.11, 2.26) | 1.8 | 1.33 (1.30, 1.36) | 2.9 |
| 1h | Marital status | 2.12 (2.05, 2.20) | −3.1 | 1.33 (1.31, 1.36) | 4.1 |
| 1i | Age | 2.18 (2.10, 2.25) | 1.4 | 1.26 (1.24, 1.29) | −18.3 |
| 1j | Smoking status | 1.86 (1.79, 1.92) | −26.2 | 1.21 (1.18, 1.24) | −34.2 |
| 1k | Home smoking ban | 1.75 (1.69, 1.82) | −35.1 | 1.20 (1.18, 1.23) | −36.5 |
| 1m | Workplace smoking ban | 2.20 (2.12, 2.28) | 3.4 | 1.36 (1.34, 1.39) | 13.3 |
| 1n | California residence | 1.88 (1.81, 1.94) | −24.6 | 1.23 (1.21, 1.26) | −27.9 |
Note. CI = confidence interval; OR = odds ratio. All ORs are significant at P < .001.
a Reference group: third-generation US-born respondents.
b Only the coefficient for the association between immigrant generation and smoking ban support is shown. All models included generation and the single other variable listed.
TABLE 3 Multiple Logistic Regression Results of Smoking Ban Support in At Least 4 of 6 Venues: Current Population Survey Tobacco Use Supplement, 1995–2002
| Variable/Category | Model 1, OR (95% CI) | Model 2, OR (95% CI) | Model 3, OR (95% CI) | Model 4, OR (95% CI) | Model 5, OR (95% CI) | Model 6, OR (95% CI) | Model 7, OR (95% CI) | Model 8, OR (95% CI) |
| Generation | ||||||||
| First-generation/immigrant | 2.16† (2.08, 2.23) | 1.67† (1.61, 1.73) | 1.50† (1.45, 1.56) | 1.39† (1.34, 1.44) | 1.31† (1.26, 1.36) | |||
| Second generation | 1.32† (1.30, 1.35) | 1.11† (1.09, 1.14) | 1.09† (1.07, 1.12) | 1.06† (1.04, 1.09) | 1.03** (1.00, 1.05) | 1.06† (1.04, 1.09) | 1.05† (1.02, 1.07) | 1.05† (1.03, 1.08) |
| Third generation (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Survey period | ||||||||
| 2001–2002 | 1.71† (1.67, 1.75) | 1.76† (1.71, 1.80) | 1.55† (1.51, 1.59) | 1.57† (1.53, 1.61) | 1.55† (1.51, 1.59) | 1.55† (1.51, 1.59) | 1.55† (1.51, 1.59) | |
| 1998–1999 | 1.26† (1.24, 1.29) | 1.28† (1.25, 1.30) | 1.18† (1.16, 1.21) | 1.19† (1.17, 1.22) | 1.19† (1.16, 1.21) | 1.19† (1.16, 1.21) | 1.18*** (1.16, 1.21) | |
| 1995–1996 (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
| Race/ethnicity | ||||||||
| Hispanic | 1.97† (1.90, 2.04) | 1.70† (1.64, 1.77) | 1.53† (1.47, 1.59) | 1.39† (1.33, 1.44) | 1.52† (1.46, 1.57) | 1.51† (1.45, 1.57) | 1.43† (1.37, 1.48) | |
| Asian/Pacific Islander | 1.39† (1.32, 1.47) | 1.30† (1.23, 1.37) | 1.26† (1.19, 1.33) | 1.10† (1.04, 1.17) | 1.26† (1.19, 1.34) | 1.25† (1.18, 1.32) | 1.33† (1.23, 1.44) | |
| American Indian | 1.08 (0.98, 1.18) | 1.14** (1.03, 1.27) | 1.16*** (1.04, 1.28) | 1.11** (1.00, 1.23) | 1.16*** (1.04, 1.29) | 1.16*** (1.04, 1.28) | 1.15*** (1.04, 1.28) | |
| Non-Hispanic Black | 1.27† (1.23, 1.32) | 1.09† (1.06, 1.13) | 1.09† (1.06, 1.13) | 1.10† (1.07, 1.14) | 1.09† (1.06, 1.13) | 1.09† (1.06, 1.13) | 1.09† (1.05, 1.13) | |
| Non-Hispanic White (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
| Gender | ||||||||
| Female | 1.42† (1.40, 1.44) | 1.33† (1.31, 1.35) | 1.32† (1.30, 1.34) | 1.33† (1.31, 1.35) | 1.32† (1.30, 1.34) | 1.32† (1.30, 1.34) | 1.32† (1.30, 1.34) | |
| Male (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
| Occupation | ||||||||
| Unemployed or not in the labor force | 1.24† (1.16, 1.33) | 1.11† (1.03, 1.20) | 1.06 (0.98, 1.14) | 1.05 (0.97, 1.13) | 1.06 (0.98, 1.14) | 1.06 (0.98, 1.14) | 1.06 (0.99, 1.15) | |
| Military | 1.09 (0.52, 2.31) | 0.98 (0.45, 2.12) | 0.76 (0.36, 1.58) | 0.74 (0.34, 1.61) | 0.76 (0.36, 1.58) | 0.75 (0.36, 1.56) | 0.76 (0.37, 1.58) | |
| Farmworker | 1.25† (1.17, 1.33) | 1.12*** (1.04, 1.19) | 1.19† (1.11, 1.27) | 1.17† (1.10, 1.25) | 1.18† (1.10, 1.27) | 1.19† (1.11, 1.27) | 1.18† (1.10, 1.26) | |
| Service | 1.13† (1.10, 1.16) | 1.11† (1.08, 1.14) | 1.07† (1.04, 1.10) | 1.07† (1.04, 1.10) | 1.07† (1.04, 1.10) | 1.07† (1.04, 1.10) | 1.07† (1.04, 1.10) | |
| White collar | 1.30† (1.27, 1.33) | 1.21† (1.18, 1.24) | 1.08† (1.05, 1.11) | 1.07† (1.04, 1.10) | 1.08† (1.05, 1.11) | 1.08† (1.05, 1.11) | 1.08† (1.05, 1.11) | |
| Blue collar (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
| Employment status | ||||||||
| Not in labor force | 0.94* (0.88, 1.00) | 0.97 (0.91, 1.05) | 1.12*** (1.05, 1.20) | 1.11*** (1.04, 1.19) | 1.12*** (1.05, 1.20) | 1.12*** (1.05, 1.21) | 1.12*** (1.05, 1.20) | |
| Unemployed | 0.88† (0.84, 0.91) | 0.99 (0.95, 1.03) | 1.18† (1.13, 1.23) | 1.15† (1.10, 1.21) | 1.18† (1.13, 1.23) | 1.18† (1.13, 1.23) | 1.18† (1.13, 1.23) | |
| Employed (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
| Income, $ | ||||||||
| Missing | 1.13† (1.09, 1.16) | 1.04** (1.01, 1.08) | 1.02 (0.98, 1.06) | 1.02 (0.98, 1.06) | 1.02 (0.99, 1.06) | 1.02 (0.98, 1.06) | 1.03 (0.99, 1.06) | |
| ≥ 75 000 | 1.51† (1.46, 1.57) | 1.34† (1.29, 1.39) | 1.23† (1.18, 1.28) | 1.20† (1.15, 1.24) | 1.23† (1.19, 1.28) | 1.23† (1.19, 1.28) | 1.24† (1.19, 1.29) | |
| 60 000–74 999 | 1.31† (1.27, 1.35) | 1.19† (1.15, 1.23) | 1.12† (1.08, 1.16) | 1.11† (1.07, 1.15) | 1.12† (1.08, 1.16) | 1.12† (1.08, 1.16) | 1.13† (1.09, 1.17) | |
| 50 000–59 999 | 1.20† (1.16, 1.24) | 1.11† (1.07, 1.15) | 1.06*** (1.02, 1.10) | 1.05*** (1.02, 1.09) | 1.07*** (1.03, 1.11) | 1.06*** (1.03, 1.11) | 1.07† (1.03, 1.11) | |
| 40 000–49 999 | 1.14† (1.11, 1.18) | 1.08† (1.04, 1.12) | 1.04** (1.00, 1.08) | 1.04* (1.00, 1.07) | 1.04** (1.01, 1.08) | 1.04** (1.01, 1.08) | 1.05*** (1.01, 1.08) | |
| 30 000–39 999 | 1.07† (1.04, 1.10) | 1.04** (1.01, 1.07) | 1.02 (0.99, 1.05) | 1.02 (0.98, 1.05) | 1.02 (0.99, 1.06) | 1.02 (0.99, 1.06) | 1.02 (0.99, 1.06) | |
| 20 000–29 999 (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
| 10 000–19 999 | 0.98 (0.95, 1.01) | 1.01 (0.98, 1.04) | 1.02 (0.99, 1.06) | 1.02 (0.99, 1.06) | 1.02 (0.99, 1.06) | 1.02 (0.99, 1.06) | 1.02 (0.99, 1.06) | |
| 0–9999 | 0.97* (0.94, 1.01) | 1.05*** (1.02, 1.09) | 1.08† (1.04, 1.12) | 1.08† (1.05, 1.12) | 1.08† (1.04, 1.12) | 1.08† (1.04, 1.12) | 1.08† (1.04, 1.12) | |
| Education | ||||||||
| Bachelor's or more | 1.51† (1.48, 1.54) | 1.22† (1.20, 1.25) | 1.07† (1.05, 1.09) | 1.06† (1.04, 1.08) | 1.07† (1.05, 1.09) | 1.07† (1.05, 1.09) | 1.07† (1.05, 1.10) | |
| Some college | 1.19† (1.16, 1.21) | 1.12† (1.10, 1.14) | 1.03*** (1.01, 1.06) | 1.01 (0.99, 1.03) | 1.04† (1.02, 1.06) | 1.03† (1.01, 1.05) | 1.04† (1.02, 1.06) | |
| High school or less (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
| Marital status | ||||||||
| Not married | 0.80† (0.78, 0.81) | 0.86† (0.84, 0.88) | 0.92† (0.90, 0.94) | 0.90† (0.89, 0.92) | 0.92† (0.90, 0.94) | 0.92† (0.90, 0.94) | 0.92† (0.91, 0.94) | |
| Married (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
| Age (10-year) | 1.01† (1.01, 1.02) | 1.03† (1.03, 1.04) | 1.04† (1.04, 1.05) | 1.04† (1.03, 1.05) | 1.04† (1.04, 1.05) | 1.04† (1.04, 1.05) | 1.04† (1.04, 1.05) | |
| Age squared (10-year) | 1.05† (1.05, 1.05) | 1.02† (1.01, 1.02) | 1.01† (1.01, 1.01) | 1.01† (1.01, 1.01) | 1.01† (1.01, 1.01) | 1.01† (1.01, 1.01) | 1.01† (1.01, 1.01) | |
| Smoking status | ||||||||
| Never smoker | 4.36† (4.27, 4.45) | 2.62† (2.56, 2.68) | 2.66† (2.60, 2.71) | 2.62† (2.56, 2.68) | 2.62† (2.57, 2.68) | 2.62† (2.56, 2.68) | ||
| Former smoker | 2.86† (2.79, 2.92) | 1.88† (1.84, 1.93) | 1.89† (1.85, 1.94) | 1.88† (1.84, 1.93) | 1.89† (1.84, 1.93) | 1.88† (1.84, 1.93) | ||
| Current smoker (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
| Smoke-free home | ||||||||
| Yes | 3.41† (3.35, 3.47) | 3.34† (3.29, 3.40) | 3.41† (3.35, 3.47) | 3.41† (3.35, 3.47) | 3.40† (3.35, 3.46) | |||
| No (Ref)a | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |||
| Smoke-free workplace | ||||||||
| Yes | 1.41† (1.39, 1.44) | 1.40† (1.38, 1.43) | 1.41† (1.39, 1.44) | 1.41† (1.39, 1.44) | 1.41† (1.39, 1.44) | |||
| No (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |||
| State of residence | ||||||||
| California | 1.93† (1.85, 2.02) | |||||||
| Other US state (Ref) | 1.00 | |||||||
| Citizenship | ||||||||
| Immigrant noncitizen | 1.47† (1.41, 1.54) | |||||||
| Immigrant naturalized citizen | 1.27† (1.20, 1.35) | |||||||
| US born (Ref) | 1.00 | |||||||
| Length of stay, y | ||||||||
| ≥ 20 | 1.28† (1.22, 1.34) | |||||||
| 15–19 | 1.42† (1.32, 1.52) | |||||||
| 10–14 | 1.42† (1.32, 1.52) | |||||||
| 5–9 | 1.41† (1.32, 1.51) | |||||||
| 0–4 | 1.48† (1.37, 1.59) | |||||||
| US born (Ref) | 1.00 | |||||||
| Country/region of birth | ||||||||
| Other Central/South America | 1.45† (1.32, 1.59) | |||||||
| Colombia | 1.33** (1.07, 1.65) | |||||||
| El Salvador | 1.60† (1.31, 1.96) | |||||||
| Canada | 1.33† (1.19, 1.49) | |||||||
| Mexico | 1.70† (1.58, 1.84) | |||||||
| Other Europe | 1.24† (1.13, 1.35) | |||||||
| France | 1.02 (0.82, 1.26) | |||||||
| Russia | 0.96 (0.76, 1.20) | |||||||
| Poland | 1.09 (0.94, 1.28) | |||||||
| Italy | 1.26*** (1.07, 1.49) | |||||||
| England | 1.27*** (1.09, 1.49) | |||||||
| Germany | 1.03 (0.93, 1.13) | |||||||
| Other Caribbean | 1.28** (1.04, 1.58) | |||||||
| Jamaica | 1.40*** (1.11, 1.78) | |||||||
| Dominican Republic | 1.73† (1.42, 2.10) | |||||||
| Cuba | 1.08 (0.91, 1.29) | |||||||
| Australia | 1.52** (1.06, 2.16) | |||||||
| Middle East | 1.90† (1.65, 2.17) | |||||||
| Other Asia | 1.19** (1.00, 1.41) | |||||||
| Japan | 1.11 (0.93, 1.32) | |||||||
| Koreas | 1.59† (1.34, 1.89) | |||||||
| Vietnam | 1.43† (1.17, 1.74) | |||||||
| China | 0.96 (0.81, 1.13) | |||||||
| India | 1.22** (1.02, 1.45) | |||||||
| Philippines | 1.34*** (1.12, 1.59) | |||||||
| Africa | 1.86† (1.51, 2.28) | |||||||
| Puerto Rico | 1.27† (1.12, 1.44) | |||||||
| United States (Ref) | 1.00 | |||||||
Note. CI = confidence interval; OR = odds ratio. In models 6–8, the first-generation/immigrant variable was modeled as “citizenship,” “immigrant length of stay,” or “country of birth.”
*P < .10; **P < .05; ***P < .01; †P < .001.
a Or was not asked question.
As anticipated, differences in smoking status also attenuated the coefficient for immigrant status and smoking ban support (OR = 1.50; 95% CI = 1.45, 1.56; model 3 for first-generation respondents) relative to models 1 and 2; however, immigrants still held more supportive attitudes than did US-born respondents. Throughout the models, second-generation immigrants exhibited less support for smoking bans than did first-generation respondents but were still significantly more supportive than were third-generation respondents.
We next tested whether differences in smoking policy contexts affected associations between nativity and attitudes. Adjusting for workplace smoking policy and home smoking policy further attenuated the coefficient for support among first-generation respondents (OR = 1.39; 95% CI = 1.34, 1.44; model 4), as did California residence (model 5). Immigrants continued to be more likely than were US-born respondents to support smoking bans (OR = 1.31; 95% CI = 1.26, 1.36; model 5), but there was little difference between second-generation and third-generation respondents (OR = 1.03; 95% CI = 1.00, 1.05).
Finally, we tested patterns of support within immigrant groups. Model 6 showed that immigrants who were not US citizens (OR = 1.47; 95% CI = 1.41, 1.54) and naturalized citizens (OR = 1.27; 95% CI = 1.20, 1.35) exhibited significantly stronger support for smoking bans than did US-born respondents. Recent immigrants (those who had immigrated within the preceding 4 years) were 48% more likely to support smoking bans than were US-born respondents (OR = 1.48; 95% CI = 1.37, 1.59) and more likely to support bans than were immigrants who had been in the United States more than 20 years (OR = 1.28), with a gradient in between, even after adjustment for covariates (model 7).
Although the heterogeneity in immigrants' level of support can be discriminated according to their country or region of origin, most immigrants displayed stronger support than did US-born respondents regardless of their area of origin. For example, adjusted data (model 8) showed that immigrants from Africa (OR = 1.86), the Dominican Republic (OR = 1.73), and Mexico (OR = 1.70) exhibited the greatest support for smoking bans versus US-born respondents.
Our study produced 4 principal findings. First, American immigrants support smoking bans more strongly than do their US-born counterparts, with a clear gradient of support across immigrant generation and assimilation measures (e.g., length of stay, citizenship). Second, demographic composition and smoking policy–related contexts each explain part of this more pronounced immigrant support, although higher residual levels of support among immigrants remained. Third, there was some initial support for the family socialization theory of smoking ban support, in that second-generation immigrants exhibited a higher level of support than did third-generation immigrants, but this was largely accounted for by differences in composition and smoking policy contexts. Finally, over the 7 years spanning this analysis, all of the groups included in our study exhibited substantial secular increases in support of smoking bans.
Smoke-free policies are workplace protection measures that prevent disease and premature death.1,2 However, certain groups are less likely to be protected by such policies, including Hispanic immigrants and workers in low-wage jobs.33 Because illegal immigrants, citizen children of illegal immigrants, and refugees are more vulnerable than their counterparts who are US citizens, they may be less willing to advocate for worker-protection policies such as workplace smoking bans for fear of retribution or deportation.22 Indeed, in the past decade, states have passed hundreds of laws that restrict immigrant rights.22 Public health advocates must therefore protect immigrants' health by advocating for smoke-free policies on their behalf.
The finding that immigrants are consistently more supportive of banning smoking than their US-born, third-generation counterparts suggests that they are potential allies in the tobacco control movement as partners in coalition-building and campaign mobilization efforts. Publicizing their support may also protect them from industry manipulation in legislative battles.12
The tobacco industry has targeted and exploited vulnerable populations, such as immigrants and racial minority groups, not only to increase product sales but also to obstruct passage of smoke-free policies; indeed, the industry has a complex understanding of the immigrant market and has strategized and targeted immigrants with respect to sales and marketing since at least the 1970s.19 Profit is the industry's motivation factor, as articulated in the following Brown & Williamson (B&W) internal document: Clearly, the sole reason for B&W's interest in the Black and Hispanic communities is the actual and potential sales of B&W products within these communities and the profitability of these sales … this relatively small and often tightly knit [minority] community can work to B&W's marketing advantage, if exploited properly.34
Moreover, the tobacco industry has cultivated relationships with leaders in minority communities not only to increase their tobacco use but also for legislative ends, such as “to advance and defend industry policy positions and to … obstruct tobacco control efforts.”20(p342) For example, a Philip Morris internal memo enumerates the Hispanic and Asian constituent organizations to which the company donated a total of $145 655 in 1988. Forty-three percent of these organizations “provided support on legislative issues,” including “restrictive smoking,” “excise tax,” and “Prop 99” (a 1988 California tobacco control ballot initiative) issues.35
In other instances, the industry has compelled organizations to which it has donated to write letters to support its legislative agenda.20,21 The industry has thus forced many groups to oppose smoke-free legislative efforts that would probably have improved the health of their constituents had the efforts passed. The tobacco industry has also specifically targeted voters in Black and Hispanic communities with mass media campaigns to oppose legislation on secondhand smoke.36,37 Therefore, documenting higher levels of smoke-free policy support among immigrants (including immigrants who are US citizens and therefore voters) would provide protection against tobacco industry manipulation, including making it more difficult for minority constituency groups to lobby on behalf of the industry's positions and against the interests of constituents.
We found that population compositional variables, including race/ethnicity and smoking status, accounted for a portion of immigrants' higher level of support for smoking bans. Previous discussions of the implications of demographic differences in support for smoke-free policies have centered on compositional explanations, including insufficient control for smoking status.13 In immigrant health research, nativity and race/ethnicity are often conflated,38 even though these constructs are distinct dimensions of inequality that may influence tobacco use and support for tobacco control policy differently.
Dispelling population compositional explanations may strengthen deductions from observational studies about the contextual effects of clean indoor air policies. For example, Gilpin et al. concluded that California residents exhibited more support for smoking bans than residents of the other US states from 1993 to 1999 after controlling for demographic variables, and they attributed the association to the strong tobacco control policy environment cultivated there.14
The pattern of stronger policy support among immigrants holds for most countries of origin and erodes with longer residence in the United States. We found a gradient of support by immigrant generation, with children of immigrants (second-generation respondents) initially more supportive of smoking bans than were third-generation respondents in unadjusted models, as well as a gradient according to number of foreign-born parents. The second-generation associations may signal effects of family anti-tobacco socialization (e.g., about the unacceptability of smoking).39
Norms and expectations play an important role in conditioning behaviors and attitudes, including those related to smoking, and immigrant kin and community networks have been documented as reinforcing certain behavior-related norms and sanctioning those who defy them.40 Although we found only small residual associations among second-generation respondents after adjustment for covariates, these covariates may have overadjusted for mediators given that socioeconomic advancement is an important aspect of immigrant assimilation.41 Finally, we did not have the most appropriate data to test family socialization pathways that may be proxied by generation.
We found that, after adjustment, policy support attitudes were weaker among immigrants who had resided in the United States for longer periods than among those who had resided in the country for shorter periods. This erosion of attitudes with time aligns with other evidence indicating that, with increasing assimilation to the United States, immigrants adopt the health behaviors or health profile of US-born individuals.27,42 Given that the tobacco industry has researched, tracked, and targeted immigrants according to their level of assimilation, erosion of attitudes may reflect residual effects of tobacco advertising and marketing to immigrants.19
Americans increased their support for smoking bans from 1995 to 2002, regardless of nativity, subgroup, or venue. Our findings align with previous research12,14,43 and seem to be a result of the tobacco control movement's success in passing local and statewide laws prohibiting smoking in public places,9,10 employers' voluntary passage of smoke-free policies, and promotion of social norms that smoking is unacceptable.44 Both of these pathways (policies and norms) are important for reducing smoking rates. As with seat belt use and prevention of drunk driving,45 passage of laws limiting smoking in public areas may increase the level of support for such policies.43,46 However, even though more restrictive smoking policies might cause an increase in attitudes supporting these policies, a certain level of baseline support needs to be present before passage of such laws is politically feasible.47
Although the majority of the US population supports smoke-free policies, the country does not have national-level laws mandating smoke-free environments. Sixteen countries worldwide have passed comprehensive national smoke-free policies, including Ireland, New Zealand, and Uruguay.31 Despite current strong support for workplace smoking bans, and despite the documented, well-publicized harmful health effects of secondhand smoke,1,2 inequalities in workplace smoke-free policy coverage remain.11–13,33 These inequalities are generated by the patchwork of smoking ban policies in the United States at the local and state levels and a reliance on voluntary smoking bans that disproportionately benefit white-collar workers.48 Mandating smoke-free workplaces must be a matter of federal policy to effectively protect the health of all workers and reduce health inequalities.
We used data from the CPS, one of the few surveys representative of the US civilian noninstitutionalized population with sufficient power to allow an examination of smoking patterns across different American subgroups and with valid and reliable measures on tobacco use, tobacco control, and demographics. We adjusted for the complex CPS design using replicate weights to calculate corrected standard errors.
Because this was a repeat cross-sectional study, we cannot deduce that within-individual attitudes changed across time. However, because the CPS is rigorously designed and executed by the Census Bureau and the TUS is representative of the US and state populations, our results indicate changes in population attitudes across time. We may have underestimated support for tobacco control policies given that we assessed support in only 6 venues. Other instruments, such as the Smoking Policy Inventory, have been developed to capture a range of different tobacco control policy domains.49 The TUS should consider adopting this measure in future surveys. Finally, our workplace smoke-free policy variable may have involved measurement error because it was self-reported. Although we did not account for enforcement, US smoke-free policies are largely self-enforcing.50
We found that attitudes in support of smoke-free policies were stronger among immigrants than among their US-born, third-generation counterparts and that composition as well as context explained some but not all of the patterns observed. Increasing support for smoke-free laws among all population groups and instituting comprehensive smoke-free policies are important goals with respect to preventing tobacco-related disease and improving population health.
Acknowledgments
Theresa L. Osypuk was supported by the Robert Wood Johnson Foundation Health and Society Scholars Program at the University of Michigan and by a 2-year Association of Schools of Public Health (ASPH) and American Legacy Foundation STEP-UP to Tobacco Control dissertation grant (L2010-02). Dolores Acevedo-Garcia was supported by National Cancer Institute (grant 1 R03 CA093198-01) and by an ASPH and American Legacy Foundation grant (L4002-01/03).
The authors thank K. Viswanath for helpful comments on a prior draft of this article.
Human Participant Protection
No protocol approval was needed for this study.
