Association Between Smoke-Free Legislation and Hospitalizations for Cardiac, Cerebrovascular, and Respiratory DiseasesClinical Perspective
Background—Secondhand smoke causes cardiovascular and respiratory disease. Smoke-free legislation is associated with a lower risk of hospitalization and death from these diseases.
Methods and Results—Random-effects meta-analysis was conducted by law comprehensiveness to determine the relationship between smoke-free legislation and hospital admission or death from cardiac, cerebrovascular, and respiratory diseases. Studies were identified by using a systematic search for studies published before November 30, 2011 with the use of the Science Citation Index, Google Scholar, PubMed, and Embase and references in identified articles. Change in hospital admissions (or deaths) in the presence of a smoke-free law, duration of follow-up, and law comprehensiveness (workplaces only; workplaces and restaurants; or workplaces, restaurants, and bars) were recorded. Forty-five studies of 33 smoke-free laws with median follow-up of 24 months (range, 2–57 months) were included. Comprehensive smoke-free legislation was associated with significantly lower rates of hospital admissions (or deaths) for all 4 diagnostic groups: coronary events (relative risk, 0.848; 95% confidence interval 0.816–0.881), other heart disease (relative risk, 0.610; 95% confidence interval, 0.440–0.847), cerebrovascular accidents (relative risk, 0.840; 95% confidence interval, 0.753–0.936), and respiratory disease (relative risk, 0.760; 95% confidence interval, 0.682–0.846). The difference in risk following comprehensive smoke-free laws does not change with longer follow-up. More comprehensive laws were associated with larger changes in risk.
Conclusions—Smoke-free legislation was associated with a lower risk of smoking-related cardiac, cerebrovascular, and respiratory diseases, with more comprehensive laws associated with greater changes in risk.
Secondhand smoke causes cardiovascular, respiratory, and neoplastic disease in adults, adverse reproductive outcomes in women, and delayed growth and respiratory and infectious disease in children.1–3 Smoke-free legislation, which prohibits smoking in certain settings, reduces exposure of nonsmokers to secondhand smoke and creates an environment that helps smokers cut down or quit smoking.4,5 Because of the large and rapid effects of secondhand smoke on the cardiovascular system,3,6 these laws would be expected to lead to reductions in acute myocardial infarctions (AMIs) and other cardiac events. Because it is impossible to conduct a randomized controlled trial of large-scale public policy interventions such as a smoke-free law, these laws are studied with the use of interrupted time series analysis, in which one estimates changes following the law, typically after accounting for preexisting time trends (often including seasonal variation) and other factors.7 Three previous meta-analyses of the literature concluded that smoke-free laws were followed by immediate reductions in AMI8,9 and other cardiac10 hospitalizations and that effects grew over time. The number of studies on the effect of smoke-free laws has rapidly grown since these earlier meta-analyses to include not only AMI, but also non-AMI cardiac disease, cerebrovascular accidents, and respiratory disease. These new reports add extended follow-up periods, new study populations and locations, and smoke-free laws with varying degrees of comprehensiveness (ie, workplaces only; workplaces and restaurants only; or workplaces, restaurants, and bars). This article presents a meta-analysis of these new outcomes, including assessment of a dose–response effect of the comprehensiveness of the laws.
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Study identification occurred from October 1, 2011 through November 30, 2011. Because there was already an identified literature in this area, we began our search for new studies by using Science Citation Index, Google Scholar, and PubMed to identify publications that cited the article that first reported a drop in AMI after implementation of a smoke-free law in Helena, Montana,11 3 recent meta-analyses of AMI or other cardiac outcomes,8–10 and the first article identifying a reduction in respiratory (asthma) emergency admissions after a smoke-free law.12 We also searched PubMed and Embase by the use of search terms “smoking ban,” or “smoke-free” or “smokefree” with “legislation” or “law” or “ordinance” with “acute myocardial infarction,” “heart attack,” “asthma,” “respiratory,” “pulmonary,” and “stroke.” Reference lists were reviewed for all articles located, and for the Institute of Medicine report Secondhand Smoke Exposure and Cardiovascular Effects,3 and the Cochrane review, “Legislative smoking bans for reducing secondhand smoke exposure, smoking prevalence and tobacco consumption,”4 as well. Finally, we identified relevant reports written by state public health departments and independent researchers through contacts in the tobacco control network. One non-English study13 was translated from French by using Google Translate.
We identified 47 studies: 36 peer-reviewed publications,11,12,14–47 7 abstracts,48–54 1 presentation,13 and 3 reports by state health departments.55–57 These studies cover 37 different smoke-free laws (10 national, 12 state, and 15 local).
We included studies examining the association between smoke-free laws and hospitalizations or deaths due to cardiovascular or respiratory disease with sufficient data to calculate the relative risk and confidence interval before and after or, in 2 studies,27,34 localities with and without a law. Two of the 47 studies were excluded because they did not meet these inclusion criteria. One tobacco industry–supported article41 comparing trends in AMI death rates in 6 US states that passed state laws was conducted by using nonstandard methodology that did not report or present data that permitted estimating relative risk and confidence intervals. In addition, the analysis was based on a very small number of data points, had very low power to detect changes, and did not account for the presence of a large number of comprehensive local laws in 2 states (California and New York), all of which bias the results to the null. An abstract53 based on a Malta study was excluded because of discrepancies between the results reported in the text and the figure that could not be resolved; we contacted the authors who reported they had not completed a manuscript based on the abstract.
Three studies performed separate analyses of reductions in hospitalizations following state laws on localities with no previous law versus localities with existing laws.18,32,35 In this situation, we used the estimates from localities without previous laws only to capture the full effect of the state law. One result for stroke from the New York State study18 was excluded because no information was available from localities without previous laws; other results from this study were included in our analysis.
Because the risk of coronary heart disease due to smoking decreases with age,58 in the 7 studies that stratified results on age,14,20,21,26,32,36,50 we used the results for ≤65 years of age (or the nearest alternative) for the primary meta-analysis.
For studies that presented estimates for diseases nested within diagnostic categories (eg, AMI and unstable angina classified under acute coronary syndrome),14,44,47 we used the most disaggregated level of data.
For studies that provide multiple estimates of the change in hospitalization rates for different time periods after law implementation,15,17,23,28,38,42 we used the estimate from the longest follow-up period to prevent double-counting in the meta-analysis. Separately, we performed a metaregression to test whether hospitalization rates changed over time after implementation of the law; in this case, we included all available estimates from various time points. For this regression, when a law was phased in13,29,54 (with restaurant or bar provisions typically taking effect after workplace restrictions), we used only the first implementation phase so that the postimplementation period and risk change associated with the law was measured consistently from the “no law” condition.
After screening all studies and excluding those with missing or incomplete data and those that did not meet inclusion criteria, 43 articles11–40,42–52,54–57 were selected for meta-analysis (online-only Data Supplement Tables I through V and online-only Data Supplement Figure I). The outcomes are AMI, acute coronary syndrome (ACS), acute coronary events (ACE), ischemic heart disease (IHD), angina, coronary heart disease (CHD), sudden cardiac death (SCD), stroke, transient ischemic attack (TIA), chronic obstructive pulmonary disease (COPD), asthma, lung infections, and spontaneous pneumothorax.
Median prelegislation time was 29.5 months (range, 3–99 months); median follow-up time was 24 months (range, 2–57 months) (online-only Data Supplement Table VI). Laws were categorized based on comprehensiveness: (1) laws applying only to workplaces, (2) workplaces and restaurants, and (3) workplaces, restaurants, and bars. Because many studies looked at >1 law or >1 disease outcome or stratified results by age or sex, our review collectively yielded 86 risk estimates for the meta-analysis.
Estimates of Risk Reductions Following Laws
Relative risks are estimated taking “no law” as the reference condition. Thirteen studies11,13,16,29,35,37,38,44,49,51,52,55,56 reported changes in absolute number or rates of disease events rather than the relative risk following implementation of a smoke-free law. For these, we used the frequency data published in the article or obtained by contacting the authors to estimate incidence rate reduction (as an estimate of relative risk) with the use of negative binomial regressions. Models included the effect of the law and, when applicable, seasonality, or they were structured to mirror the analysis in the published study (as detailed in online-only Data Supplement Tables I through IV). Thirty-one of the 43 articles accounted for long-term secular trends, 26 by including time as a variable in the analysis and 5 by doing time-matched comparisons with control communities. Nineteen of the articles included seasonality in their models.
All analyses were conducted using 2-sided tests with a significance level of α=0.05.
Q tests revealed statistically significant heterogeneity (P<0.001) between studies for all outcomes, with the exception of acute coronary events (2 studies20,50 with borderline heterogeneity, P=0.067). To account for this heterogeneity and to use a more conservative approach, we performed a random-effects meta-analysis for each outcome, stratified by comprehensiveness of laws with the use of Stata 10.1 or 9.2 metan.
We performed a random-effects metaregression (Stata metareg) with dummy variables for the 13 disease outcomes to determine whether they were similar enough to be grouped into diagnostic categories for further analysis. The regressions (online-only Data Supplement Table VII) showed no significant differences between hospital admissions or deaths for:
Coronary events: AMI, ACS, ACE, and IHD
Other heart disease: angina, CHD, and out-of-hospital SCD
Cerebrovascular accident: stroke and TIA
Respiratory disease: COPD, asthma, lung infection, and spontaneous pneumothorax
We performed analyses for these 4 diagnostic groups and the 13 individual outcomes.
We conducted a random-effects metaregression to test whether the risk reduction following smoke-free laws increased over time, as previously reported,8–10 for each outcome and each diagnostic group. For each study, the duration of follow-up postlegislation was used as the time measure.
To test whether the comprehensiveness of a law was associated with greater reductions in hospital admissions (or deaths in 6 cases14,20,24,32,50,54), we performed a random-effects metaregression with comprehensiveness of law as an ordinal variable (0 for workplaces only; 1 for workplaces and restaurants; 2 for workplaces, restaurants, and bars) including dummy variables for different outcomes.
We conducted a separate random-effects meta-analysis for older people that were excluded from the primary meta-analysis by using the results from 6 studies14,20,26,32,36,50 that reported the risk of coronary events in older populations (median cutoff age 70, range 60–75; online-only Data Supplement Tables I through IV).
Finally, to test for the possibility of publication bias in the meta-analysis, we performed the Egger test, examined a funnel plot (using Stata metafunnel), and conducted a Duval and Tweedie59 nonparametric trim-and-fill analysis to estimate the effects of any publication bias (with the use of Stata metatrim for a random-effects meta-analysis).
Comprehensive smoke-free laws were followed by significant reductions in hospital admissions for AMI, ACS, ACE, IHD, angina, CHD, SCD, stroke, asthma, and lung infection but not TIA, COPD, or spontaneous pneumothorax (Figure 1). Because there were only a few studies for some of these specific outcomes, we also pooled specific outcomes into 4 diagnostic groups as described in Methods to increase the number of studies in each group; comprehensive smoke-free laws were followed by significant reductions in hospital admissions for all 4 diagnostic groups (Figure 2 and online-only Data Supplement Figures II through V).
There was an overall pattern of more comprehensive laws being associated with greater reductions in hospital admissions (P=0.001 for individual outcomes, Figure 1, and P=0.002 for disease groups, Figure 2).
Contrary to previous findings,8–10 we did not find that the AMI risk reduction associated with smoke-free laws increased with time (P=0.537; online-only Data Supplement Figure VI) or other disease outcomes and diagnostic groups for which there were sufficient data to conduct this analysis (P>0.318 for all of them).
Consistent with the fact that the relative risk of coronary heart disease due to smoking declines with age,58 there was no significant change in risk of AMI or coronary events among older patients14,20,26,32,36,50 following a comprehensive smoke-free law (relative risk [RR], 0.973; 95% confidence interval [CI], 0.918–1.032 and RR, 0.980; 95% CI, 0.953–1.008, respectively).
Reductions in AMI hospitalizations were similar for females (RR, 0.897; 95% CI, 0.847–0.950) and males (RR, 0.912; 95% CI, 0.872–0.955) after smoke-free laws of all degrees of comprehensiveness.
Although the Egger test was statistically significant for publication bias (P=0.007) and the funnel plot suggested possible publication bias among the articles selected for the meta-analysis (online-only Data Supplement Figure VII), the nonparametric trim-and-fill estimate of the effects of publication bias59 produced essentially the same results as the meta-analysis of the published studies: RR, 0.839 (95% CI, 0.818–0.861) for actual studies versus RR, 0.829 (95% CI, 0.808–0.851) from the fill-and-trim analysis for all outcomes and RR, 0.846 (95% CI, 0.803–0.890) versus RR, 0.803 (95% CI, 0.764–0.84.) for studies of AMI following comprehensive laws, suggesting that publication bias is not likely to explain our findings.
Given that secondhand smoke has been established to cause cardiovascular and respiratory disease,1–3 one would expect that hospitalization for these diseases would drop when exposure to secondhand smoke is substantially reduced or eliminated. Consistent with 3 prior meta-analyses8–10 that concluded that smoke-free laws are associated with significant decreases in AMI and other cardiac hospital admissions, we found that comprehensive smoke-free laws (covering workplaces, restaurants, and bars) were associated with a 15% decrease in AMI hospitalizations. In addition, we found that the laws were followed by decreases in hospitalizations for ACS, ACE, IHD, angina, CHD, SCD, stroke, asthma, and lung infection (Figure 1), and decreased risk of hospitalizations for coronary events, other heart disease, cerebrovascular accident, and respiratory disease, as well (Figure 2). For TIA, COPD, and spontaneous pneumothorax, which demonstrated no statistically significant association, negative findings should be interpreted cautiously because of the small numbers of studies that examined these outcomes.
Our results are consistent with an earlier meta-analysis of stroke associated with secondhand smoke exposure quantified in individuals, which showed an overall risk of 1.25 (95% CI, 1.12–1.38) and a nonlinear dose–response.60 This overall risk is consistent with the reductions in hospital admissions for stroke that we observed following smoke-free laws (RR, 0.795; 95% CI, 0.680–0.930 [Figure 1], corresponding to risk increases associated with secondhand smoke of RR, 1.26; 95% CI, 1.08–1.47).
Several studies included in the meta-analysis documented reductions in healthcare costs associated with fewer hospitalizations for cardiovascular or respiratory diseases. Healthcare savings were reported at the city, state, and national levels, ranging from $302 000 in AMI expenses after 35 months in Starkville, Mississippi55 to €2.6 million ($3.3 million, 9.6% decrease from baseline) in angina-related hospitalization costs and €5.3 million ($6.9 million, 20.1% decrease from baseline) AMI-related hospitalization costs during the first year after smoke-free law implementation in Germany.46 (See online-only Data Supplement Tables I through IV for more details.)
Evidence on the association between smoke-free legislation and other health effects is emerging. A study in Ireland61 found a drop in preterm births (odds ratio, 0.75; 95% CI, 0.59–0.96) but an increase in low birthweight (odds ratio, 1.43; 95% CI, 1.10–1.85) 1 year after the smoke-free law. Another study from Scotland62 found significant decreases in babies small for gestational age (by 4.5%), preterm delivery (11.7%), and spontaneous preterm labor (11.4%).
Smoke-free legislation per se does not produce the effects that we observed, which are due to the associated reductions in secondhand smoke exposure and increases in smoking cessation that accompany these laws. As more places adopt smoke-free policies (whether by law in subordinate jurisdictions or voluntarily), the marginal effects of subsequent laws will be smaller, as was observed in New York and Massachusetts when those states passed comprehensive laws after many localities had.18,32 The passage of these laws reflects changes in social norms that also affect smoking behavior; the laws both formalize and accelerate this social change and the associated health benefits.
The interrupted time series observational studies that form the foundation for this meta-analysis alone do not establish causation. At the same time, a randomized, controlled trial of the effects of enacting legislation is impractical or impossible. The studies included in our meta-analysis consistently meet standards for high-quality interrupted time series studies7; in particular, all studies used objective measures of outcomes, and most considered secular trends and seasonality. The observed reductions in hospitalizations are, however, consistent with the known biological pathways by which tobacco smoke exposure causes disease and triggers acute events. The observation that AMI admissions in Helena, Montana11 rebounded after enforcement of its smoke-free law was suspended because of a lawsuit also supports a causal link.
Although compliance with smoke-free laws is high in general and many studies have documented drops in secondhand smoke exposure after law implementation (online-only Data Supplement Tables I through IV), we could not assume any 1 individual's level of exposure had decreased and subsequently reduced their risk of hospitalization. Few studies included in the meta-analysis measured tobacco smoke exposure or smoking status in individual cases.16,22,38,39 Because a randomized controlled trial is impossible, an analysis measuring individual smoking and secondhand smoke exposure would offer the most valid evidence regarding the effectiveness of smoke-free laws.
We entered the ordinal variable for comprehensiveness of a law (0 for workplaces only; 1 for workplaces and restaurants; 2 for workplaces, restaurants, and bars) in the metaregression to test whether more comprehensive laws were followed by greater reductions in hospital admissions (or deaths). We treated comprehensiveness of law as an ordinal, not an interval (continuous) variable, which is why we only reported the probability value for law comprehensiveness and not an effect size. Although this is a standard approach for integrating ordinal variables into regression analyses, we investigated the use of this procedure to ensure that our conclusions were not sensitive to this technique by treating law comprehensiveness as a categorical variable (together with dummy variables for the different outcome groups, as we do in the analysis in the article that treats law comprehensiveness as an interval variable) and tried recoding the law comprehensiveness by using alternative codings (0, 1, 3) and (0, 1, 4). As described in detail in the online-only Data Supplement Text, these analyses gave essentially the same results as the main analysis, indicating that the approach we use in the article produces robust evidence for a dose–response effect of the law, treating law comprehensiveness as an ordinal variable.
Although it is not usual in epidemiological studies, we did not consider multiple testing. Readers should take into account potential inflation from multiple testing when interpreting significance levels (α) and confidence intervals.
In one study,47 the authors expressed concern about misclassification between different outcomes.
Publication bias is always a concern in meta-analysis (online-only Data Supplement Figure VII). The nonparametric trim-and-fill analysis, however, indicated that adjusting for publication bias had little effect on the results.
This study provides evidence that smoke-free laws are followed by fewer hospitalizations and lower healthcare expenditures for a wide range of diseases and that comprehensive laws ending smoking in workplaces, restaurants, and bars are associated with greater effects. The general public, public health professionals, and policy makers should consider these positive associations as they develop smoke-free legislation and decide whether or not to include exceptions to these laws.
Sources of Funding
This work was supported by National Cancer Institute grants CA-61021 and CA-87472. The funding agency played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
Continuing medical education (CME) credit is available for this article. Go to http://cme.ahajournals.org to take the quiz.
The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA.112.121301/-/DC1.
- Received May 30, 2012.
- Accepted September 14, 2012.
- © 2012 American Heart Association, Inc.
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Secondhand smoke causes cardiovascular and respiratory disease, and implementation of smoke-free legislation is followed by drops in hospitalizations and deaths from these diseases. This meta-analysis of 45 studies of 33 smoke-free laws found that smoke-free legislation was associated with significantly lower rates of hospital admissions (or deaths) for coronary events, other heart disease, cerebrovascular accidents, and respiratory disease. There was a dose-response relationship between the strength of the law; more comprehensive laws (including workplaces, restaurants, and bars) had the largest health benefits. This study provides strong evidence not only of the health benefits of smoke-free laws, but also of the need to enact comprehensive laws without exceptions.