Published online before print September 21, 2009, doi: 10.1161/CIRCULATIONAHA.109.870691
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(Circulation. 2009;120:1373-1379.)
© 2009 American Heart Association, Inc.
Epidemiology and Prevention |
Declines in Acute Myocardial Infarction After Smoke-Free Laws and Individual Risk Attributable to Secondhand Smoke
From the School of Pharmacy (J.M.L.) and Department of Medicine (Cardiology), Cardiovascular Research Institute, and Center for Tobacco Control Research and Education, School of Medicine (S.A.G.), University of California, San Francisco.
Correspondence to James M. Lightwood, PhD, Department of Clinical Pharmacy, University of California, 3333 California St, Suite 420, San Francisco, CA 94118. E-mail lightwoodj{at}pharmacy.ucsf.edu
Received April 3, 2009; accepted July 16, 2009.
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Abstract |
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Background— The estimated effects of recent pubic and workplace smoking restriction laws suggest that they produce significant declines in community rates of heart attack. The consistency of these declines with existing estimates of the relative risk of heart attack in individuals attributable to passive smoking exposure is poorly understood. The objective is to determine the consistency of estimates of reductions in community rates of heart attacks resulting from smoking restriction laws with estimates of the relative risk of heart disease in individuals exposed to passive smoking.
Methods and Results— Meta-analyses of existing estimates of declines in community rates were compared with a mathematical model of the relationship between individual risk and community rates. The outcome measure is the ratio of community rates of acute myocardial infarction (after divided by before implementation of a smoking restriction law). There is a significant drop in the rate of acute myocardial infarction hospital admissions associated with the implementation of strong smoke-free legislation. The primary reason for heterogeneity in results of different studies is the duration of follow-up after adoption of the law. The pooled random-effects estimate of the rate of acute myocardial infarction hospitalization 12 months after implementation of the law is 0.83 (95% confidence interval, 0.80 to 0.87), and this benefit grows with time. This drop in admissions is consistent with a range of plausible individual risk and exposure scenarios.
Conclusion— Passage of strong smoke-free legislation produces rapid and substantial benefits in terms of reduced acute myocardial infarctions, and these benefits grow with time.
Key Words: epidemiology • meta-analysis • myocardial infarction • prevention • smoking • tobacco smoke pollution
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Introduction |
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Passive smoking increases the risk of coronary heart disease and acute myocardial infarction (AMI) in nonsmokers,1–3 which has led to the adoption of many laws making public areas and workplaces 100% smoke free. The risk of AMI falls rapidly after smoking cessation,4 and the effects of secondhand smoke (SHS) on many biological mediators that lead to heart disease occur rapidly and are nearly as large as those of smoking.5–10 It is reasonable to expect that implementing strong smoke-free laws would lead to a reduction in AMI. Several such laws in North America and Europe have been studied as natural experiments to estimate the reduction in community AMI risk,11–24 with reductions ranging from 11% (Italy12 and Ireland15) to 40% (Montana22). These reductions seemed large compared with early estimates of an
30% increase in individual risk of heart disease associated with chronic SHS exposure.5,25 These estimates may underestimate the actual individual risks associated with passive smoking because of a downward bias resulting from exposure misclassification; Whincup et al26 reported substantially higher risks of major coronary heart disease when stratifying risk based on levels of cotinine (a biomarker for SHS) than self-report, which should give a more accurate risk estimate because of more accurate SHS exposure assessment. Here, we update previously published meta-analyses of community risk27,28 (that considered only the first 4 and 8 studies, respectively) by adding more studies and accounting for the length of follow-up. The present analysis demonstrates that the community risk reduction associated with smoke-free laws grows with time and is consistent with a wide range of actual observed individual risk and exposure scenarios.
Editorial see p 1339
Clinical Perspective on p 1379
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Methods |
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This study uses a simple random-effects meta-analysis and a meta-regression to estimate the reduction in the community rate of heart attack as a function of time since smoke-free laws went into effect. Simulation estimates of the reduction in community rates of heart attack calculated as a function of individual AMI risk resulting from exposure to passive smoking produce estimates consistent with the observed reductions in community risk.
Study Identification
The first article reporting a drop in AMI after implementation of a comprehensive smoke-free law (in Helena, Mont) was published in 2004.22 We used PubMed, Science Citation Index, and Social Science Citation Index to locate 9 articles published since then. In addition, we identified 3 studies (Ireland, Massachusetts, and France) reported at meetings. One city (Pueblo, Colo) was used to publish 2 reports: 1 on results through the first 18 months after the law went into effect13 and 2 after 36 months.11 Data from Piedmont, Italy, were used as part of 1 study 2 months after the law went into effect24 and another of Piedmont alone after 6 months.12 Although there was some overlap in the data used in Pueblo (reuse of the same baseline data) and Italy (Piedmont represented 25% of the total cases in the 2-month study), we treated these 4 studies as independent observations. Table I in the online-only Data Supplement provides detailed descriptions of all the studies and available data on changes in SHS exposure.
Meta-Analysis
The studies were combined using a random-effects meta-analysis. The expected relative reduction in community rates of AMI as a function of follow-up period was estimated from 
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where RRAMI,j is the relative reduction in community rate of AMI in study j, Tj is the follow-up period after adoption of the smoking law in study j, 
j is the error term, and β0 and β1 are regression parameters. See Table I of the online-only Data Supplement for detailed descriptions of all the studies found. Calculations were done with Stata 9.0 procedures metan, metareg, and metabias.29
Model for Simulation Estimates of Expected Reduction in AMI
The simulation estimates used a model of the community rates of AMI as a function of the distribution of individual relative risks resulting from current active and passive smoking exposure, the prevalence of active and passive smoking, quitting smoking because of a smoking restriction law, and the effect of lower SHS exposure among people who continue to have some exposure after the law goes into effect.
The incidence rates of AMI in the community before (rb) and after (ra) the smoking law are
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and pt,p is the prevalence of exposure in nonsmokers to passive smoking in before (t=b) or after (t=a) the law, pt,c is the prevalence of current active smokers in period t, pq is the prevalence of smokers who quit as a result of the law, Rc is the relative risk of AMI resulting from current active smoking, Rp is the relative risk of AMI resulting from exposure to passive smoking before the law, Rq is the relative risk of AMI among recent quitters, D is the dose-response effect of lower SHS exposure among people who continue to be exposed after the law went into effect (D=1 if no dose response and D<1 if there is dose response), and i is the base rate of AMI resulting from other causes.
The prevalence of passive, pt,p, and current active smoking, pt,c, are population prevalences. The population prevalence of passive smoking exposure, pt,p, is lower than the corresponding prevalence in the nonsmoking population, which is pt,p/(1–ptc).
The parameter estimates are average values for 1 year before and after the law. The proportion of current active smokers quitting as a result of a smoking law, pq, and relative risk of current active smokers who quit as a result of the law are assumed to be the average values over 6 to 18 months after the law.
The model assumes that only nonsmokers are affected by passive smoking and current smokers are not affected by other smokers’ passive smoke. The AMI risk for recent quitters is determined only by their residual risk resulting from past current active smoking (and not current passive smoking), which underestimates AMI risk over the year after cessation. The base incidence rate of community AMI, i, is assumed constant before and after the law.
Substituting Equation 3 into Equation 2 and dividing Equation 1 by that result yields the ratio of community rates of AMI after compared with before the smoking law, RRAMI:
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The base rate of AMI resulting from other causes, i, is assumed to be constant and thus cancels out in Equation 4.
Model Parameters and Distributions
Risk of AMI Associated With Passive Smoking
We consider 4 distributions of individual relative risks. The low estimate of 1.31 (95% confidence interval [CI], 1.21 to 1.41), from the Barnoya and Glantz5 meta-analysis of 29 long-term studies of the risk of AMI and other cardiovascular disease outcomes, is based (with 1 exception26) on self-reported SHS, which tends to underestimate total exposure. These studies likely underestimate individual risk associated with SHS because people considered in the unexposed reference (the denominator) in the risk estimate should have been in the exposed group (the numerator).
Whincup et al26 published the only longitudinal study in which subjects were classified on the basis of cotinine levels at baseline, with the reference group consisting of subjects with serum cotinine 
0.7 ng/mL. This cutoff is high enough to include both unexposed nonsmokers and light passive smokers. (This decision may have been necessary because, in England during the 1978 to 1980 period, at baseline almost everyone had some detectable exposure to SHS.) Not surprisingly, Whincup et al26 found higher risks than the studies based on self-report.
We considered 3 possible risks from the Whincup et al.26 study. The risk of 1.49 (95% CI, 1.03 to 2.14) represents the AMI relative risk during the 20-year follow-up for the midrange of baseline serum cotinine levels (1.5 to 2.7 ng/mL). The other 2 estimates are 1.95 (95% CI, 1.09 to 3.48), representing the risk from comparing people with cotinine >0.7 and <0.7 ng/mL followed up for 5 to 9 years, and 3.73 (95% CI, 1.32 to 10.58) for people followed up for 0 to 4 years. Because cotinine was measured only at baseline, the reliability of this baseline cotinine as a measure of actual exposure is probably most representative of actual exposure during the 0- to 4-year follow-up.
Three Exposure Scenarios
The community effects of reduced SHS exposure were estimated for 3 cases to describe the range of prevalence of passive smoking exposure before and after a smoking restriction law, each based on cotinine-validated population data. We considered a person unexposed if his or her level of serum cotinine was below the limit of detection (0.0530,31 or 0.1032 ng/mL). We estimated D, the reduction in SHS exposure among people who continued to be exposed as the ratio of geometric mean cotinine after the law to geometric mean cotinine before the law.
The first case (large drop in exposure) is based on cotinine measured in 1999 to 2002 in the large US National Heath and Nutrition Examination Study (NHANES) among nonsmokers 
20 years of age,31 which represents the average experience of US communities with and without smoking laws. Forty-six percent of nonsmokers living in jurisdictions without a law had detectable cotinine (0.05 ng/mL) compared with 13% living in jurisdictions covered by strong laws, a 33% difference in prevalence. The ratio of the geometric mean cotinine with laws compared with those without the law was 0.19.
The second case (moderate drop in exposure from moderate base) is based on the New York State population with detectable cotinine (0.05 ng/mL) before and after the strong state smoking restriction law went into effect.18,30 The survey sample excluded New York City and Nassau County, which had already passed strong smoking restriction legislation. The sample, however, did include 3 counties (Dutchess, Suffolk, and Westchester) that had strong local smoke-free laws in place, representing 27% of the sample (personal communication, American Nonsmokers’ Rights Foundation, January 27, 2009). Including these counties in the sample probably reduced the baseline level of cotinine exposure and change associated with implementation of the state law. The prevalence of nonsmokers with detectable cotinine dropped from 68% to 52%, a 16% drop, comparing before and after the state law went into effect. The geometric mean cotinine after the law was 0.54 of that before the law.
The third case (moderate drop in exposure from high base) is based on results before and after the Scottish smoke-free law went into effect.32 Before the law, 89% of nonsmokers had detectable (0.10 ng/mL) levels of cotinine, which fell to 72% after the law, a 17% drop. The high level of SHS exposure before the law was comparable to that observed in the United States in 1988 to 1991, before most smoking restriction legislation was enacted.33 (No SEs are available for Scottish exposure prevalence. Assuming lack of correlation with the other parameters, the omission of the SEs reduces the SE of the estimated community risk and therefore will underestimate the variance of RRAMI and produce conservative results.) The geometric mean cotinine after the law was 0.61 of that before the law. The high level of exposed people following the law may reflect the fact that exposure to SHS at home remained high after the law.
Parameters Common to All Scenarios
The population prevalence of current smoking before a law, pb,c, was set to 23% (95% CI, 21 to 25) for all scenarios, the pooled-random effects estimate from the latest available year for the places that enacted laws.34–36 The distribution of the proportion of active current smokers quitting within a year of the law, pq, was from a meta-analysis of the effects of smoke-free workplaces.37 Distributions of the individual relative risks for current smokers, Rc, and those who quit as a result of the smoke-free law, Rq, were estimated by use of gender-specific rates for adults4 with never-smokers as the reference group. The parameters and their distributions are shown in the Table.
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Simulation
The simulation estimates of the ratio of community rates of AMI after divided by before the law, RRAMI, were calculated for 48 combinations of parameters: the passive smoking exposure scenarios (3 cases), individual risk for AMI associated with passive smoking (4 cases), presence or absence of a dose-response effect among people still exposed to SHS after the law took effect (2 cases), and presence or absence of smoking cessation resulting from the law (2 cases). The combinations of individual relative risk, dose-response, and smoking cessation (16 cases) were called scenarios and were grouped by the 3 groups of passive smoking exposure parameters.
Crystal Ball Version 5.238 was used for the estimates using a Monte Carlo simulations with 20 000 trials. Calculations and details on sources of the parameters are given in the online-only Data Supplement.
Sensitivity Analysis
Several sensitivity analyses were used to determine the robustness of the results to changes in the sample and statistical method. These analyses include dropping the initial (18-month) estimate from Pueblo, Colo, and the estimate for Piedmont, Italy, from the Vaselli et al24 study so that all remaining observations were independent; adjustment for an insignificant trend in AMI baseline incidence, i, for estimates in Scotland21 and Piedmont,12 which had not accounted for possible secular trends in AMI; and use of nonparametric tests for statistical significance of the slope parameter and alternative random-effects estimators. Sensitivity analyses for the simulation included alternative formulas for dose-response in nonsmokers who remained exposed to passive smoking before and after the smoke-free law, alternative methods of pooling current smoking prevalence, and different age adjustments for average relative risk resulting from current smoking.
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Results |
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Meta-Analysis
We used a random-effects model for the meta-analyses because of significant heterogeneity in the estimates, which yielded a pooled RRAMI of 0.81 (95% CI, 0.78 to 0.85; Figure 1). A funnel plot and Begg test did not suggest publication bias (Figure 2). Some of the heterogeneity could be due to differences in end points, the confounding variables considered, the analytical methods, changes in level of SHS exposure after the law, and duration of follow-up.
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There was a significant relationship between the duration of study follow-up and RRAMI (Figure 3), with ln RRAMI falling by 0.0113/month (SE, 0.002; P<0.0005). The intercept was not significantly different from 0 (–0.046; SE, 0.037; P=0.242). Duration of follow-up accounted for 76% of the between-study variance. The meta-regression between ln RRAMI and duration of study (Figure 3) provided a good estimate of changes in risk over time for the observed period. We used the results of the meta-regression in Figure 3 to standardize all relative risks and associated upper and lower bounds of the 95% CIs to the values that would be observed at 12 months after implementation of each law with
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where T is the duration of study follow-up in months. This adjustment substantially reduced the variability in the estimated values of RRAMI(12) (Figure 4). The random-effects meta-analysis of the resulting values adjusted to 12 months yielded a pooled risk estimate of 0.83 (95% CI, 0.80 to 0.87). Although lower than before, there was still significant heterogeneity, probably reflecting the differences noted above (other than study duration). Geographic heterogeneity may be an important factor in remaining heterogeneity; after adjustment, the reductions in AMI risk appear slightly larger in the United States than in Europe and Canada.
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Simulation Results
Eight of the 48 scenarios produced point estimates of RRAMI that were within the 95% CI for the pooled risk estimate produced by the meta-analysis of the individual studies at 12 months, RRAMI(12) (95% CI, 0.80 to 0.87), and 28 of the 48 scenarios produced interval estimates (defined as the 95% CI) of RRAMI that overlapped the 95% CI for the pooled estimate of individual risks (Figure 5). The scenarios that are most consistent with the pooled community risk ratio at 1 year, RRAMI(12), are those with individual SHS relative risks of 1.95. The presence of a dose-response and effect on quitting were not required to obtain reasonable agreement with the observed population risk. The sensitivity analyses did not produce noticeable changes in the meta-analysis or simulation results.
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Discussion |
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After adjustment for variable length of follow-up, the individual community studies yield remarkably consistent elevations of the community benefits of reduced AMIs after implementation of strong smoke-free legislation (Figure 3), with an
15% drop during the first year and continuing exponential declines, reaching 36% in 3 years (the limit of currently available data). The fact that the intercept of the risk reduction with time has an intercept of 0 is consistent with the results of the very large study of the effects of a strong smoke-free law in New York State,17 which tested for an immediate offset (change in intercept) and interaction between the presence of a law and time after the law went into effect. The New York study found no immediate shift in the AMI rate immediately after the law went into effect but discovered that the rate of AMI declined continuously with time after the law. Results from Bowling Green, Ohio,18 and Pueblo, Colo,11 also showed that AMI rates fell with time. Eight simulation scenarios have point estimates closest to the point estimates of the meta-analysis at 1 year (Figure 3) and are within their 95% CIs. All 8 scenarios use an individual relative risk of 1.94 (from Whincup et al26 for 5 to 9 years of follow-up). Six of these scenarios assumed a dose-response relationship (lower SHS exposures among people still exposed after the law) except 1, which used the large drop scenario. Four of the 8 scenarios included smokers quitting as a result of the law.
Richiardi et al39 published a mathematical model based on the assumption that the effect of tobacco smoke exposure was a large immediate and short-term increase in individual AMI risk (relative risk of 4.5 in the first hour of passive smoking) combined with a 50% reduction in SHS exposure that was consistent with a reduction in community-level risk of 5% to 15%, less than has actually been observed. In contrast, we assume the smaller individual risks that have been observed in long-term epidemiological studies. By accounting for the fact that the community risk reductions fall with time after the law (something not in the Richiardi et al analysis) and adjusting the observed risks to those corresponding to 12 months of follow-up, our model provides results consistent with the full range of observed risks (Figure 4).
Limitations
The largest shortcoming in the simulation is the lack of biomarker-based levels of general population exposure to SHS before and after these laws went into effect. Most of the available data relate to self-reported levels of SHS exposure, which understate exposure,40,41 with biomarker data (cotinine) generally limited to restaurant and bar workers. The restaurant and bar worker studies are useful for documenting high compliance with the laws, but biomarker data for population samples would be more useful for evaluating health effects of the law in the entire population. It is important to publish not only the changes in arithmetic and geometric mean cotinine levels but also results that can be used to estimate the fraction of the population with very low levels (<0.05 ng/mL in serum) to estimate changes in prevalence of exposure. Changes in current smoking as a result of smoking laws can noticeably affect the decline in community rates and should also be measured.
All studies with adequate data were included in the meta-regression to avoid potential bias resulting from selection and adjustment of individual estimates. With 1 exception23 (omitted from the meta-analysis because of small sample size), there are no reliable data on smoking status at the time of admission for subjects in the studies. It would be desirable to have cotinine measures on admitted patients to assess both smoking status and level of SHS exposure. Such data would allow estimates of any differential effects of smoke-free laws on nonsmokers and smokers and estimates of the effects of the laws on cessation among people at risk of AMI. Collection and reporting such data would be useful in further refining the linkages level of SHS and risk of AMI.
We did not account for the effect of lower SHS exposure among people who quit smoking after implementation of the law or the fact that continuing smokers reduce consumption when smoke-free policies go into effect;37 these omissions mean that our model probably underestimates the effect of the law.
The parameters in our model were treated as independently distributed, even though some may covary such as relative risks of current and all former smokers and correlation of prevalence of current smoking and exposure to passive smoking. The parameter distributions were taken from observational studies and may be subject to associated biases.
Conclusions
Analysis of reductions in community rates of AMI shows that the measured effects of smoking laws are consistent after accounting for the follow-up periods of different studies. Simulation estimates of expected reductions in community rates, based on existing estimates of individual relative risk of AMI resulting from exposure to passive smoking, are consistent with each other. This analysis shows, using data from 5 countries, that passage of strong smoke-free legislation produces rapid and substantial benefits in terms of reduced AMIs and that these benefits grow with time.
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Acknowledgments |
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Sources of Funding
This work was funded by National Cancer Institute grant CA-061021. The funding agency played no role in the design or conduct of the research or in the preparation of the manuscript.
Disclosures
None.
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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/cgi/content/full/CIRCULATIONAHA.109.870691/DC1.
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