Legislative smoking bans for reducing harms from secondhand smoke exposure, smoking prevalence and tobacco consumption

Kate Frazer; Joanne E Callinan; Jack McHugh; Susan van Baarsel; Anna Clarke; Kirsten Doherty; Cecily Kelleher

doi:10.1002/14651858.CD005992.pub3

Legislative smoking bans for reducing harms from secondhand smoke exposure, smoking prevalence and tobacco consumption

Authors' declarations of interest

Version published: 04 February 2016 Version history

https://doi.org/10.1002/14651858.CD005992.pub3

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Abstract

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Background

Smoking bans have been implemented in a variety of settings, as well as being part of policy in many jurisdictions to protect the public and employees from the harmful effects of secondhand smoke (SHS). They also offer the potential to influence social norms and the smoking behaviour of those populations they affect. Since the first version of this review in 2010, more countries have introduced national smoking legislation banning indoor smoking.

Objectives

To assess the effects of legislative smoking bans on (1) morbidity and mortality from exposure to secondhand smoke, and (2) smoking prevalence and tobacco consumption.

Search methods

We searched the Cochrane Tobacco Addiction Group Specialised Register, MEDLINE, EMBASE, PsycINFO, CINAHL and reference lists of included studies. We also checked websites of various organisations. Date of most recent search; February 2015.

Selection criteria

We considered studies that reported legislative smoking bans affecting populations. The minimum standard was having an indoor smoking ban explicitly in the study and a minimum of six months follow‐up for measures of smoking behaviour. Our search included a broad range of research designs including: randomized controlled trials, quasi‐experimental studies (i.e. non‐randomized controlled studies), controlled before‐and‐after studies, interrupted time series as defined by the Cochrane Effective Practice and Organisation of Care Group, and uncontrolled pre‐ and post‐ban data.

Data collection and analysis

One author extracted characteristics and content of the interventions, participants, outcomes and methods of the included studies and a second author checked the details. We extracted health and smoking behaviour outcomes. We did not attempt a meta‐analysis due to the heterogeneity in design and content of the studies included. We evaluated the studies using qualitative narrative synthesis.

Main results

There are 77 studies included in this updated review. We retained 12 studies from the original review and identified 65 new studies. Evidence from 21 countries is provided in this update, an increase of eight countries from the original review. The nature of the intervention precludes randomized controlled trials. Thirty‐six studies used an interrupted time series study design, 23 studies use a controlled before‐and‐after design and 18 studies are before‐and‐after studies with no control group; six of these studies use a cohort design. Seventy‐two studies reported health outcomes, including cardiovascular (44), respiratory (21), and perinatal outcomes (7). Eleven studies reported national mortality rates for smoking‐related diseases. A number of the studies report multiple health outcomes. There is consistent evidence of a positive impact of national smoking bans on improving cardiovascular health outcomes, and reducing mortality for associated smoking‐related illnesses. Effects on respiratory and perinatal health were less consistent. We found 24 studies evaluating the impact of national smoke‐free legislation on smoking behaviour. Evidence of an impact of legislative bans on smoking prevalence and tobacco consumption is inconsistent, with some studies not detecting additional long‐term change in existing trends in prevalence.

Authors' conclusions

Since the first version of this review was published, the current evidence provides more robust support for the previous conclusions that the introduction of a legislative smoking ban does lead to improved health outcomes through reduction in SHS for countries and their populations. The clearest evidence is observed in reduced admissions for acute coronary syndrome. There is evidence of reduced mortality from smoking‐related illnesses at a national level. There is inconsistent evidence of an impact on respiratory and perinatal health outcomes, and on smoking prevalence and tobacco consumption.

PICOs

Population

Intervention

Comparison

Outcome

The PICO model is widely used and taught in evidence-based health care as a strategy for formulating questions and search strategies and for characterizing clinical studies or meta-analyses. PICO stands for four different potential components of a clinical question: Patient, Population or Problem; Intervention; Comparison; Outcome.

See more on using PICO in the Cochrane Handbook.

Plain language summary

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Does legislation to ban smoking reduce exposure to secondhand smoke and smoking behaviour?

Since the first national legislation banning indoor smoking in all public places was introduced in 2004, there has been an increase in the number of countries, states and regions adopting similar smoke‐free legislation banning smoking in public places and work places since this review was first published. The main reason is to protect nonsmokers from the harmful health effects of exposure to secondhand smoke. Another reason is to provide a supportive environment for people who want to quit smoking.

Study characteristics

We searched for studies that investigated the effect of introducing a ban on any measures of health, or on smoking behaviour (up to February 2015). Since the previous version of this review had shown clear evidence that introducing legislation to ban smoking in public places does reduce exposure to secondhand smoke (SHS) in those places, we did not include studies that only reported exposure to SHS. We included 77 studies from 21 countries in this updated review. Studies of health outcomes typically used data from hospitals to look for changes in rates of admissions, discharges or deaths. Most studies looked at illnesses related to the cardiovascular system (heart or blood vessels), such as heart attacks and strokes. Studies also looked at effects on respiratory health, including chronic obstructive pulmonary disease (e.g. bronchitis), asthma and lung function. Seven studies looked at the health of newborn children. Eleven studies reported death rates. The best‐quality studies collected data at multiple time points before and after the introduction of a ban in order to adjust for existing time trends. Some studies could compare events rates in areas with and without bans, or where bans were introduced at different times.

Key results

There is evidence that countries and their populations benefit from improved health after introducing smoking bans, importantly to do with the heart and blood vessels. We found evidence of reduced deaths. The impact of bans on respiratory health, on the health of newborn children, and on reducing the number of smokers and their cigarette use is not as clear, with some studies not detecting any reduction.

Quality of the evidence

Legislative bans have not been evaluated by randomized trials, and the quality of the evidence from the types of studies contributing to this review is lower. Changes in health outcomes could be due to other things, such as change in healthcare practices. However, many of the studies used methods of analysis that could control for underlying trends, and increase our confidence that any changes are caused by the introduction of bans.

Authors' conclusions

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Implications for practice

This updated review identified moderate‐quality evidence that countries and their populations benefit from enacting national legislative smoking bans with improved health outcomes from reduced exposures to passive smoke, specifically cardiovascular disease. There was also low‐quality evidence of reduced mortality for smoking‐related illnesses. The evidence on perinatal and respiratory health outcomes is not consistent, nor is the evidence on potential reductions in tobacco consumption.

Implications for research

We need research on the continued longer‐term impact of smoking bans on the health outcomes of specific subgroups of the population, such as young children, disadvantaged and minority groups. More robust research on the impact of smoking bans is warranted, especially in relation to respiratory and perinatal health outcomes. Documenting of active smoking in studies should be more consistent and should use validation methods. Documentation of ex‐smokers should include information on previous smoking history and duration of quit times. Robust study designs (including those with a control for comparison) reporting passive smoke exposures and health‐related outcomes need to include biological coherence criteria.

Summary of findings

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Summary of findings for the main comparison.

Patient or population: Smokers and nonsmokers Settings: 21 countries including 12 European countries, Turkey, USA, Canada, Australia, New Zealand, Hong Kong, Argentina, Panama, Uruguay. Intervention: Comprehensive or partial smoking bans in public places implemented by legislation Comparison: No bans (note: observational data only)
Outcomes¹	Effects of intervention	Quality of the evidence (GRADE)²	Comments
Cardiovascular health	44 studies included. 43 studies evaluated incidence of acute myocardial infarction (AMI) and acute coronary syndrome (ACS), 33 of which detected significant associations between introduction of bans and reductions in events. 6 studies evaluated stroke incidence; 5 detected significant associations between introduction of bans and reductions in events	⊕⊕⊕⊝ moderate³
Respiratory health	21 studies included. Data imprecise with conflicting results. 6 of 11 studies reported significant reductions in COPD admissions. 7 of 12 reported significant reductions in asthma admissions	⊕⊝⊝⊝ very low⁴
Perinatal health	7 studies included. Data imprecise with conflicting results; due to study designs unclear if many of observed associations due to confounding factors	⊕⊝⊝⊝ very low⁴
Mortality	11 studies included. 8 detected significant association between introduction of bans and reduced smoking‐related mortality	⊕⊕⊝⊝ low
GRADE Working Group grades of evidence High quality: Further research is very unlikely to change our confidence in the estimate of effect. Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate. Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate. Very low quality: We are very uncertain about the estimate.
¹Note, original review also included changes in environmental tobacco smoke (ETS) exposure as an outcome. Evidence was unequivocal that bans were associated with significant reductions in ETS (see Callinan 2010), and hence we did not evaluate this outcome in this update. ²As all studies are observational, starting point for GRADE rating is low. Meta‐analyses not conducted; data summarized narratively. ³Upgraded due to evidence of a dose‐response effect. ⁴Downgraded due to imprecision.

Background

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Description of the condition

Tobacco is the second major cause of mortality in the world, and currently responsible for the death of about one in ten adults worldwide (WHO 2009; WHO 2013). Measures to control the demand for and supply of tobacco products, as well as to protect public health, have been demanded through Article 8 of the Framework Convention on Tobacco Control (WHO 2003; WHO 2009; WHO 2014).

The epidemic of cigarette smoking is identified as one the greatest public health disasters of the 20th century, with over 20 million attributable deaths (USDHHS 2014). Over the past 50 years of reports by the Surgeon General, international evidence has emerged that smoking affects most organs and that there is no risk‐free level of exposure to secondhand smoke (SHS) (USDHHS 2014). The World Health Organization (WHO 2014; WHO 2015) estimates that six million people die annually from tobacco‐related diseases; 600,000 from the effects of secondhand smoke exposure.

Secondhand smoke, also known as environmental tobacco smoke (ETS) or passive smoke, is the combination of side‐stream smoke, i.e. smoke that is emitted between puffs of burning tobacco (cigarettes, pipes or cigars), and mainstream smoke, i.e. smoke that is exhaled by the smoker (NCI 1999). Secondhand smoke is a complex mixture of thousands of gases and particulate matter emitted by the combustion of tobacco products and from smoke exhaled by those smoking (NRC 1986). Secondhand smoke was declared to be carcinogenic by the International Agency for Research on Cancer (IARC 2004; IARC 2008; IARC 2009).

Negative health effects associated with exposure to SHS have been well documented and include major conditions such as lung cancer, as well as cardiovascular disease, respiratory disease and asthma, and other significant health outcomes such as eye and nasal irritation and low birth weight in babies of nonsmokers (Allwright 2002; Hackshaw 1997; NCI 1999; ANHMRC 1997; SCOTH 2004; IARC 2009; USDHHS 2014).

There has been an increase in the number of countries introducing comprehensive national indoor smoking policies banning smoking in indoor public places and work places since 2005 and the number of research papers has risen exponentially since this review was first published (Callinan 2010). The primary outcome is to protect nonsmokers from the harmful health effects of exposure to secondhand smoke and additionally to provide a supportive environment for people who want to quit smoking.

Description of the intervention

The efforts of the Framework Convention on Tobacco Control to reduce tobacco consumption worldwide (WHO 2003; WHO 2009; WHO 2013) include a demand for smoke‐free legislation, and the MPOWER provisions include protecting people from tobacco use (WHO 2008; WHO 2009; WHO 2015). Legislating for smoke‐free environments is a fundamental component of these actions.

Introducing national smoking legislation is a public policy issue. The underpinning decision‐making process is multifactorial, including epidemiological evidence of the toxicity of smoke and the associated link to a pathological endpoint, international policy evidence of acceptability and compliance and evidence of improved health outcomes. Legislative smoking bans vary in their comprehensiveness in different settings, i.e. the extent to which they allow smoking or restrict it to designated areas and where those smoking restrictions occur. Legislation prohibiting smoking indoors, including in bars and restaurants, we classify in this review as a comprehensive smoking ban, even though exemptions may occur in different settings, e.g. psychiatric units, prisons, and residential homes, including nursing homes. Less comprehensive smoking bans, such as those which allow smoking in designated rooms or areas, we classify in this review as partial bans. The primary outcome is to protect nonsmokers from the harmful health effects of exposure to secondhand smoke, and additionally to provide a supportive environment for people who want to quit smoking. Evidence from the previous review identified the impact of national smoking bans on improved respiratory and sensory symptoms, improved lung function, reduced tobacco consumption and reduced SHS exposure (Callinan 2010).

How the intervention might work

One potential outcome of smoking bans and restrictions is to reduce or eliminate the exposure of nonsmokers to the dangers of SHS. Another is to reduce tobacco consumption among smokers in specified areas including work places or general public places. While SHS in the work place increases the risk of lung cancer among nonsmokers, the elevation in risk is modest in comparison with the risk of active smoking. International evidence is emphatic, that smoking is responsible for increased mortality for smokers, and for nonsmokers through SHS exposure (WHO 2015). Ethical questions also arise in relation to individual civil liberty, and policy makers prefer not to interfere with such rights for those who smoke, except for minors. It is the harmful effect of passive smoking in nonsmokers that justifies the policy action, especially for workers. This means that the endpoint is often more likely to be an exposure measure to passive smoke than either active smoking rates or a health gain of reduced smoking‐related morbidity or mortality. Evidence from this review previously demonstrated that a smoking ban does lead to a reduction in exposure to passive smoking, specifically for the population employed in the hospitality sector. It also reported evidence of improved health outcomes (Callinan 2010).

Why it is important to do this review

This is a major public health issue affecting an estimated billion active smokers worldwide and the larger population of nonsmokers. The impact of introducing smoking legislation is to cut exposure to passive smoke. For every person who dies as a result of smoking, it is estimated that 30 or more people will live with smoking‐related illnesses (USDHHS 2014). Banning smoking is a public policy issue. The decision‐making process underpinning it is ultimately a political action which rests on a combination of evidence sources, including:

Mechanistic evidence of toxicity of smoke
Epidemiological evidence that either smoking or SHS is linked to a pathological endpoint
Policy evidence that imposing a restriction will be socially acceptable and achieve high compliance
Action research evidence that it can be successfully implemented.

Bans and policies can be implemented through public health policies or legislation affecting populations at a national, state or community level.

In setting the parameters for the original review, we adopted a strict methodological approach in keeping with the Cochrane process but with consideration for the nature of health promotion interventions in setting those parameters. Evaluation of health promotion interventions continues to generate debate in the scientific literature. Davey Smith 2000 argues that the randomized control trial is the standard for assessing health promotion interventions. Opponents of this view (Britton 2010; Green 2015) acknowledge that rigorous evaluation of studies is important, but that randomized controlled trials may not be the best approach given the complexities, processes and scope of health promotion programmes.

During the intervening period since this review was first published, there have been sustained developments to reduce exposure to tobacco and reduce consumption, with more countries signing up to the Framework Convention on Tobacco Control and enacting national smoke‐free legislation. There have been extensions of smoking bans to reduce exempted population groups. This has resulted in fewer partial smoking bans and more inclusive comprehensive bans in a wider range of settings. The evidence of health outcomes on reduced exposure, morbidity and mortality arising from the enactment of smoking bans can take time to emerge. In this review we include robust studies strengthening this evidence base and its impact at a population level.

Objectives

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To assess the effects of legislative smoking bans on (1) morbidity and mortality from exposure to secondhand smoke, and (2) smoking prevalence and tobacco consumption.

Methods

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Criteria for considering studies for this review

Types of studies

We include randomized controlled trials, non‐randomized controlled studies, controlled before‐and‐after studies, and interrupted time series, as defined by the Cochrane Effective Practice and Organisation of Care Group (EPOC 2013), and uncontrolled before‐and‐after studies, with a minimum follow‐up of six months for measures of smoking.

Types of participants

Smokers and nonsmokers exposed to comprehensive or partial smoking bans. The bans must be implemented by legislation, and may affect populations at a local, regional, or national level.

Types of interventions

Legislative bans which either ban smoking completely in all settings including the hospitality sector (comprehensive) or restrict it to designated areas (partial). The ban may be implemented at national, state or local level. For controlled studies, the intervention setting may be compared to settings without smoking bans or with less restrictive policies.

Types of outcome measures

Primary objective:

Measures of health outcomes including any measure of morbidity or mortality, e.g. cardiac admissions, respiratory health, and pulmonary function. In studies with longer follow‐up, measures of the incidence of lung cancer and cardiovascular disease may also be available. If health outcomes were reported for population subgroups defined by smoking status or by levels of or changes in SHS exposure, we extracted data for these subgroups.

Secondary objective:

Measures of smoking behaviour including prevalence of tobacco use, tobacco consumption, cessation rates. For these outcomes we required data from large population‐based studies. We also required baseline data (pre‐legislation) and a follow‐up period of a minimum of six months after introduction of a ban, to assess a sustained impact.

For this update, we have not included studies only reporting the impact of smoking bans on passive smoke exposure using self‐reported data or only measuring cotinine. An impact of bans on passive smoke exposure and a reduction in cotinine measures following reduced exposure was unequivocal from the first version of the review (Callinan 2010). We now require measured health outcomes data for studies reporting passive smoke exposure.

We required biochemical verification of exposure to environmental tobacco smoke over self‐reported perceptions. In order to assess sustained impact, we included studies which reported outcomes such as smoking behaviour at least six months after the start of the smoking ban. In the first version of the review, we excluded studies which reported environmental measures of air quality (e.g. particulate matter (PM₂.₅), respirable particles (RSP), vapour phase nicotine) as their sole measure of exposure to SHS, and we do not include these studies in this update.

Where possible, we stratified smoking behavioural outcomes by age, gender and socioeconomic status.

Search methods for identification of studies

For the original version, we searched all databases from inception to June 2009. One author subsequently conducted searches from 2009 to March 2013. For this update, the Trials Search Co‐ordinator of the Tobacco Addiction Group completed all searches from February 2009 to 26th February 2015.

The searches conducted were:

Cochrane Tobacco Addiction Group Specialised Register (up to end of February 2015); see Appendix 1 for search strategy.
MEDLINE & PubMed (via OVID, up to 26th February 2015 ); see Appendix 2 & Appendix 3 for search strategies.
EMBASE (via OVID, up to 26th February 2015); see for Appendix 4 for search strategy.
PsycINFO (via OVID, up to 26th February 2015); see Appendix 5 for search strategy.
Cumulative Index to Nursing and Allied Health Literature (CINAHL) (via Ebscoup to March 2013); see Appendix 6 for search strategy.

We did not update the searches of CINAHL beyond 2013 as they were not identifying additional studies. We also checked the reference lists and bibliographies of included studies for further articles, and we contacted other experts for published and unpublished trials. We did not exclude any publications on the basis of language or publication date.

We checked websites for relevant studies and contacted authors for details of unpublished research papers and for additional information

Data collection and analysis

For this update, JC prescreened titles and abstracts between 2009 and 2012. One author (KF) prescreened titles and abstracts (2009 to 2015) to identify studies that may be relevant or useful. Three authors (JC, AC, KD) independently screened the reduced number of titles and abstracts to assess relevance for inclusion. KF obtained the full text of potentially relevant studies. Two authors (KF, CK) independently assessed the papers to see if they met the inclusion criteria. No discrepancies emerged. At this time, we limited studies reporting passive exposure to include those also reporting specific health outcome measures. We noted all decisions. One author (KF) independently extracted the data for the individual studies, and a second author (SvB) checked the results.

Two authors (KF, JMcH) independently reviewed studies reporting active smoking measures. We held discussions with a third independent author (CK) and made a decision to limit active smoking studies to those reporting outcomes from a population level.

One author (KF) completed a 'Risk of bias' assessment using the assessment tool (Higgins 2011) for the included studies, and a second author (SvB) checked the results. The domains assessed were:

Adequate sequence generation.
Adequate allocation concealments.
Blinding of personnel/all outcomes.
Addressing incomplete outcome data.
Selective outcome reporting.
Other bias.

We assessed each domain as being at high, low or unclear risk of bias.

We completed data extraction on a specific pro forma, and extracted data on the following information, where it was available:

Country and study setting
Category of study (population‐ or institution‐based)
Size of eligible population
Number of participants or number of clusters and participants
Demographic characteristics (if relevant) of participants
Description and target of the intervention
Definition of smoking status used
Definition of exposure to secondhand smoke
Outcomes and how they were measured
Biochemical validation
Length of follow‐up
Handling of dropouts and losses to follow‐up
Adverse effects of intervention

Meta‐analysis was not possible due to the heterogeneity in study design, participants, outcomes and nature of the intervention, so we have presented summary and descriptive statistics. We report any threats to validity or other limitations described by the studies.

Results

Description of studies

See: Characteristics of included studies, Characteristics of excluded studies, Figure 1.

Figure 1

Study flow diagram.

We include 77 studies which met the eligibility criteria for this updated review. We retain 12 studies with unchanged data from the first version of the review (Cesaroni 2008; Gallus 2007; Goodman 2007; Hahn 2008; Juster 2007; Khuder 2007; Larsson 2008; Lemstra 2008; Pell 2008; Pell 2009; Sargent 2012; Seo 2007). Additional results have been reported for two previously included studies, and we have renamed them to reflect this, with original reports now listed as secondary references: Alsever 2009 (previously Bartecchi 2006), and Barone‐Adesi 2011 (previously Barone‐Adesi 2006). We have now excluded other studies previously included that reported passive smoke exposure with either self‐reported outcomes or cotinine measures. Other excluded studies are those without a six‐month follow‐up period following the ban and those that did not report smoking prevalence from national population data.

The included studies examine the effects of comprehensive or partial indoor smoke‐free legislation implemented in countries, states (regions) or at local level. We identified the effect of the implementation of national smoking bans in studies representing 21 countries. Studies with national smoking bans in countries included in this update are: Argentina (Ferrante 2012), Belgium (Cox 2013; Cox 2014), Denmark (Christensen 2014), Germany (Sargent 2012; Schmucker 2014), Hong Kong (McGhee 2014), Panama (Jan 2014), Switzerland (Bonetti 2011; Di Valentino 2015; Durham 2011; Dusemund 2015; Humair 2014; Rajkumar 2014), Turkey (Yildiz 2015) and Uruguay (Sebrié 2014).

Countries included in the earlier review are retained in the update: Canada (Gaudreau 2013; Lemstra 2008; Naiman 2010), England (Lee 2011; Liu 2013; Millett 2013; Sims 2013), France (Séguret 2014), Ireland (Cronin 2012; Goodman 2007; Kabir 2009; Kabir 2013; Kent 2012; Stallings‐Smith 2013), Italy (Barone‐Adesi 2011; Cesaroni 2008; Federico 2012; Gallus 2007; Gasparrini 2009; Gualano 2014), Netherlands (De Korte‐De Boer 2012), New Zealand (Barnett 2009), Norway (Bharadwaj 2012), Scotland (Jones 2015; Mackay 2010; Mackay 2011; Mackay 2012; Mackay 2013; Pell 2008; Pell 2009), Spain (Aguero 2013; Villalbi 2011), Sweden (Larsson 2008) and USA (Alsever 2009; Amaral 2009; Barr 2012; Basel 2014; Bruckman 2011; Bruintjes 2011; Croghan 2015; Dove 2010; Hahn 2008; Hahn 2011; Hahn 2014; Head 2012; Herman 2011; Hurt 2012; Juster 2007; Khuder 2007; Klein 2014; Landers 2014; Lippert 2012; Loomis 2012; North Carolina 2011; Page 2012; Roberts 2012; Rodu 2012; Sargent 2004; Seo 2007; Vander Weg 2012).

One study reports on the impact of national smoking bans from a number of countries including the USA, Canada, New Zealand, Scotland, Republic of Ireland, and Northern Ireland (Bajoga 2011). The majority of studies (27) are located in the USA. Other countries with multiple studies are: Scotland (7), Ireland (6), Switzerland (6), Italy (6) and England (4).

The definition used in this review for comprehensive smoking bans is prohibited smoking in work places, including restaurants and bars. We categorise legislation which permits smoking in bars and restaurants as a partial smoking ban, whether at local, state or national level. The implementation of smoking bans has varied across national jurisdictions, and exceptions for smoking rooms may be allowed within comprehensive bans. Using these definitions, we identified 18 studies reporting evidence for partial smoking bans (Aguero 2013; Amaral 2009; Bonetti 2011; Christensen 2014; Cox 2014; Di Valentino 2015; Dusemund 2015; Durham 2011; Humair 2014; Khuder 2007; Lippert 2012; Loomis 2012; McGhee 2014; Rajkumar 2014; Sargent 2004; Sargent 2012; Schmucker 2014; Villalbi 2011). We define the majority of smoking bans in place as comprehensive within this review.

The settings in this update vary considerably from the original review. For this update we identified studies reporting the impact of national smoking bans in the following settings:

42 studies used hospital registers for admissions or discharge data on specific population cohorts
20 studies used registries for national health outcomes, death rates, pregnancy and perinatal health
11 studies used population‐level country‐specific prevalence surveys reporting active exposure to smoking
4 studies are work place‐based, reporting primarily passive exposure and measured health outcomes.

We found 43 studies which reported smoking data either as a primary outcome, a descriptive variable reporting national prevalence without comparing rates before or after smoking legislation, or used as a covariate in analysis. Eleven studies (Cesaroni 2008; Christensen 2014; Cox 2014; Ferrante 2012; Head 2012; Hurt 2012; Jan 2014; Kabir 2013; Mackay 2010; Naiman 2010; Stallings‐Smith 2013) report smoking prevalence data from another data source, rather than data from their own studies. Twenty‐four studies report an impact of smoking bans on active or passive smoking (Analysis 1.1). Active smoking outcomes including prevalence, quit rate and tobacco consumption are specifically reported in 19 studies (Bajoga 2011; Bharadwaj 2012; Cesaroni 2008; Cox 2014; Federico 2012; Ferrante 2012; Gallus 2007; Gualano 2014; Hahn 2008; Hurt 2012; Jones 2015; Kabir 2009; Klein 2014; Lee 2011; Lemstra 2008; Lippert 2012; Mackay 2011; Mackay 2012; Page 2012). Combined active and passive smoking outcomes are reported in Larsson 2008. Passive smoke exposures are reported in a further four studies (Durham 2011; Goodman 2007; Pell 2008; Rajkumar 2014) with the evidence of health outcomes reported in 72 studies, including: cardiovascular outcomes (Analysis 1.1), respiratory outcomes (Analysis 2.1) and perinatal health outcomes (Analysis 3.1). Associations between indoor smoking legislation and mortality rates are reported in 11 studies included in this update (Analysis 4.1). A number of studies report multiple health outcomes or a combination of health‐related outcomes and mortality outcome data.

Study Design

We did not identify any randomized controlled trials, due to a lack of feasibility in using this methodology in population‐level studies measuring the effect of national legislative smoking bans. Of the 77 studies included in this update, 36 used an interrupted time series design measuring the impact of smoking bans using data from national registries, episodes of monthly hospital admissions or discharges, or reporting multiple prevalence surveys from population health surveys (Aguero 2013; Amaral 2009; Bajoga 2011; Barnett 2009; Barr 2012; Barone‐Adesi 2011; Basel 2014; Bruckman 2011; Christensen 2014; Cox 2013; Cox 2014; Croghan 2015; Cronin 2012; De Korte‐De Boer 2012; Federico 2012; Gasparrini 2009; Gualano 2014; Hahn 2011; Humair 2014; Jan 2014; Kabir 2013; Kent 2012; Klein 2014; Liu 2013; Mackay 2010; Mackay 2011; Mackay 2012; Mackay 2013; Millett 2013; Roberts 2012; Sargent 2012; Schmucker 2014; Sebrié 2014; Séguret 2014; Sims 2013; Stallings‐Smith 2013).

Twenty‐three studies use a quasi‐experimental (controlled before‐and‐after) study EPOC 2013 design (Alsever 2009; Bharadwaj 2012; Bonetti 2011; Bruintjes 2011; Di Valentino 2015; Dove 2010; Dusemund 2015; Ferrante 2012; Gaudreau 2013; Hahn 2008; Hahn 2014; Head 2012; Herman 2011; Jones 2015; Khuder 2007; Landers 2014; Loomis 2012; Naiman 2010; Page 2012; Rodu 2012; Sargent 2004; Seo 2007; Vander Weg 2012). Three of these studies reported using a matched control area for comparison (Hahn 2014; Khuder 2007; Seo 2007). The remaining 18 studies used before‐and‐after methods with no control group (Cesaroni 2008; Durham 2011; Gallus 2007; Goodman 2007; Hurt 2012; Juster 2007; Kabir 2009; Larsson 2008; Lee 2011; Lemstra 2008; Lippert 2012; McGhee 2014; North Carolina 2011; Pell 2008; Pell 2009; Rajkumar 2014; Villalbi 2011; Yildiz 2015). Six of these studies used a cohort design (Durham 2011; Goodman 2007; Larsson 2008; Pell 2008; Pell 2009; Rajkumar 2014).

Excluded studies

For this update, we exclude 36 studies included in the first version, as they did not meet the revised inclusion criteria for this update (Abrams 2006; Akhtar 2007; Alcouffe 1997; Allwright 2005; Biener 2007; Bondy 2009; Braverman 2008; Brownson 1995; CDC 2007; Eagan 2006; Eisner 1998; Ellingsen 2006; Farrelly 2005; Fernandez 2009; Fernando 2007; Fichtenberg 2000; Fong 2006; Fowkes 2008; Galán 2007; Gilpin 2002; Gotz 2008; Hahn 2006; Haw 2007; Helakorpi 2008; Heloma 2003; Hyland 2009; Jiménez‐Ruiz 2008; Menzies 2006; Mulcahy 2005; Mullally 2009; Palmersheim 2006; Pearson 2009; Semple 2007; Vasselli 2008; Verdonk‐Kleinjan 2009; Waa 2006). We now included two further studies as secondary references in this update (Barone‐Adesi 2006; Bartecchi 2006).

In this update, we exclude uncontrolled before‐and‐after studies reporting unverified health outcomes or those which only reported cotinine biomarkers and no other additional health outcome data, as the focus for this update is on including studies reporting reduced passive exposure that also measured health outcomes. The evidence from the first version clearly established that reduced passive smoke exposure results in reduced cotinine measures. We exclude from this update studies reporting the impact of smoking bans on smoking prevalence, tobacco cessation or quit rates which are not representative population‐level measures. See Characteristics of excluded studies for specific details.

Risk of bias in included studies

We made explicit judgements of bias according to the criteria in the Cochrane Handbook for Systematic Reviews of Interventions (Cochrane Handbook, Higgins 2011). We provide a summary of the assessments in Figure 2. The study designs used in this review for evaluating a policy‐level health promotion outcome do not fulfil the criteria used to confirm a low risk of bias, and as such we consider the evidence to be at high risk of bias for many of the studies included. However, we acknowledge that the majority of study designs included in this update used data from large hospital and national data registries, and for 23 studies include a control reference area.

Figure 2

Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

Sequence generation and allocation concealment

The non‐randomized studies used in this review did not facilitate random sequence generation, allocation concealment or blinding of participants, as smoking is a visible and active process. A number of studies used large representative population surveys which employed stratified or random sampling nationally (Bajoga 2011; Federico 2012; Gualano 2014; Jones 2015; Lee 2011; Lippert 2012; Liu 2013; Mackay 2011). Volunteer samples were reported in four studies (Durham 2011; Goodman 2007; Larsson 2008; Rajkumar 2014).

Blinding

It was not possible to blind participants in the studies included in this review, as the intervention was a national public policy and smoking is visible. The use of large data sets also negated blinding. However the large data sets obtained from hospitals used the Internation Classification of Diseases (ICD) coding to confirm principal diagnoses. Studies reporting mortality data similarly used data sets from large national registries.

Incomplete outcome data

A number of studies did not report total sample sizes. Durham 2011 and Larsson 2008 reported high attrition rates, with consequent reporting bias for outcomes. Two studies reported the use of imputed scores (Aguero 2013; Hurt 2012). Klein 2014 reported that records were excluded from the data set when smoking status or other key descriptive variables including gestational age or data on duration of pregnancy were missing. This led to the exclusion of 6.3% of cases, amounting to over 30,000 records.

Selective reporting

Within this review a large number of studies used existing data sets, and individual‐level data were not available. Whilst the outcomes associated were reported, the data sets were pre‐existing and may have given rise to bias associated with misclassification of data, i.e. residual confounding. Prevalence studies used different data sets for each survey and this can introduce bias when combining data (Bajoga 2011; Federico 2012; Gallus 2007; Gualano 2014; Jones 2015; Lee 2011; Lippert 2012; Mackay 2011). There is a reliance on self‐reported, unverified smoking status in studies included in this update. Verified smoking status (confirming either smoker or nonsmoker status) was reported by Goodman 2007; Larsson 2008; Pell 2008; Pell 2009. Pell 2009 primarily analysed data for nonsmoker outcomes, but provided a comparison for current smokers, with limited data reported.

Other bias

Other bias identified in the included studies is the lack of adjusting for confounders, as data were not available within the accessed data sets. Smoking status was self‐reported for the majority of studies covering active and passive smoke exposure. Cesaroni 2008; Christensen 2014; Cox 2014; Ferrante 2012; Head 2012; Hurt 2012; Jan 2014; Kabir 2013; Mackay 2010; Naiman 2010; Stallings‐Smith 2013 report smoking prevalence data from other data or from national surveys, and not from their main data sources. A number of these studies only provided a single smoking prevalence result, and we have not included this information in further statistical analyses (Christensen 2014; Head 2012; Kabir 2013; Mackay 2010; Naiman 2010; Stallings‐Smith 2013). Kabir 2013 included maternal smoking prevalence data for analyses reported from an earlier paper (Kabir 2009). Verified smoking status was measured in four studies (Goodman 2007; Larsson 2008; Pell 2008; Pell 2009).

A number of the studies using data from large hospital or population registries did not provide information on individual smoking status or other individual confounders. However, these data sets used statistical modelling (both linear and non‐linear) and adjustments to account for confounding of included variables. A number of studies adjusted for air quality, pollution, influenza rates and seasonality, using national data sets in an effort to reduce confounding and influence on health outcomes.

Other factors that could have led to bias include: changed prescribing practices for statins during the period of data collection (Cesaroni 2008; Christensen 2014); legislation banning trans‐fatty acids in foods, resulting in dietary changes which could influence cardiovascular outcomes (Christensen 2014). Legislative changes during the period of data collection, including an increase in the price of cigarettes, was reported by Federico 2012, Jan 2014 and Klein 2014. This may have influenced their study outcomes. Bharadwaj 2012 reported changed occupational status for pregnant women during the period of the study, and this was identified as a factor which reduced the power of the study. Page 2012 reported significant differences in demographic data between the control area and the intervention area at baseline, and the influence of this on their outcomes. Larsson 2008 reported that the study was predominantly in women, as only 30% of the study participants were men. Schmucker 2014 included ex‐smokers in the group of nonsmokers, due to a small sample size of less than 6%, and inconsistent documentation. Di Valentino 2015 detected a significant reduction in the control area which did not have a ban in place. Other new legislation, including laws banning advertising and sales of cigarettes to minors, may have influenced these outcomes.

Sample size

Two studies reported power calculations (Bajoga 2011; Lee 2011). Aguero 2013 did not analyse the impact of legislative changes on mortality, due to the small sample size reported. Fifteen studies did not report a sample size (Alsever 2009; Bajoga 2011; Bharadwaj 2012; Bruckman 2011; Gaudreau 2013; Gualano 2014; Head 2012; Herman 2011; Khuder 2007; Landers 2014; Loomis 2012; McGhee 2014; Mackay 2011; Naiman 2010; Seo 2007), although a number of these studies reported that large data sets were used with samples in excess of 1000 and up to 26,000 participants during annual data collections. Seo 2007 does not include an overall sample size, although the totals included in tables reported in the paper are suggestive of small numbers. Naiman 2010 reported population statistics and analyses based on rates per 10,000 population.

Follow‐up

The minimum period required for follow‐up was six months. The period for follow‐up extended from nine months post‐legislative bans (Kabir 2009) up to 81 months (Stallings‐Smith 2013; secondary reference Stallings‐Smith 2014). Gualano 2014 reported an eight‐year follow‐up period post‐legislation. A number of studies reported phased implementation of national smoking bans in a variety of settings. Cox 2013, De Korte‐De Boer 2012, Gaudreau 2013, ,Hahn 2014, Naiman 2010, Roberts 2012, Sebrié 2014 and Séguret 2014 report phased implementation of smoking bans in Belgium, Netherland, France, USA, Canada and Uruguay. Landers 2014 detected the impact of county‐level and state‐level bans on child and adult asthma discharge rates across multiple US states; Amaral 2009 compared the impact of local and statewide ordinances on perinatal health outcomes in California over a period of six years.

Biochemical verification

Smoking status was not reported in the majority of studies included in this update. Biochemical verification of smoking status was measured through analysis of cotinine in saliva or urine for four studies (Goodman 2007; Larsson 2008; Pell 2008; Pell 2009). Health outcomes data were verified by primary diagnosis using International Classification of Diseases (ICD) codes. Definitions of current, ex‐ or nonsmoker status in prevalence surveys were reported using WHO guidelines; cotinine measures (when present) for nonsmoking status were confirmed as those less than 15 ng/ml (See Characteristics of included studies).

Adverse events

Four included studies identified adverse events which may have influenced their study populations and reported outcomes. Humair 2014 and Sargent 2004 reported suspension of smoking bans in each of their studies during the periods of data collection. Gualano 2014 reported that 2007 was the peak year in the Italian recession and that this may have influenced smoking rates. Head 2012 reported the influence of hurricanes Katrina and Rita, which may have affected population levels during their study period.

Assessment of heterogeneity

As in the original version of the review, due to the heterogeneity in clinical variation and study designs reporting primary and secondary outcomes, we did not attempt a meta‐analysis. We offer a qualitative narrative analysis to report the outcomes in this updated review.

Effects of interventions

See: Summary of findings for the main comparison

Primary objective: Effect on health outcomes

We found evidence for health outcomes in 72 studies. A number of the studies included evidence for multiple health outcomes. We divided outcomes into cardiovascular (Analysis 1.1), respiratory (Analysis 2.1), perinatal (Analysis 3.1), and mortality (Analysis 4.1) and report trends and associations using Bradford‐Hill 1965 criteria. (Where results are described as significant they were statistically significant at the P=0.05 level unless otherwise stated).

Cardiovascular outcomes (Analysis 1.1)

We found 44 studies assessing associations between bans and cardiovascular health outcomes. Thirty‐eight studies collected data on specific cardiac outcomes (acute coronary syndrome (ACS), acute myocardial infarction (AMI)); 19 interrupted time series studies (Aguero 2013; Barnett 2009; Barone‐Adesi 2011; Barr 2012; Basel 2014; Bruckman 2011; Christensen 2014; Cronin 2012; Gasparrini 2009; Hahn 2011; Humair 2014; Jan 2014; Kent 2012; Liu 2013; Roberts 2012; Sargent 2012; Schmucker 2014; Sebrié 2014; Séguret 2014), 10 quasi‐experimental controlled before‐and‐after studies (Alsever 2009; Bonetti 2011; Bruintjes 2011; Di Valentino 2015; Ferrante 2012; Gaudreau 2013; Khuder 2007; Sargent 2004; Seo 2007; Vander Weg 2012), and nine uncontrolled before‐and‐after studies (Cesaroni 2008; Hurt 2012; Lemstra 2008; Lippert 2012; McGhee 2014; North Carolina 2011; Pell 2008; Rajkumar 2014; Yildiz 2015; see Analysis 1.1). Evidence from four quasi‐experimental controlled before‐and‐after studies (Head 2012; Herman 2011; Loomis 2012; Naiman 2010) and one uncontrolled before‐and‐after study (Juster 2007) provide evidence for both cardiac and stroke outcomes. Mackay 2013 provides evidence of the Scottish ban specifically for stroke outcomes.

Cardiac outcomes

We found consistent temporal trends with evidence of significant reductions in AMI/ACS admissions following the introduction of national smoking bans. Significant reductions in rates of admissions and discharges were evident in 12 studies (Alsever 2009; Bonetti 2011; Di Valentino 2015; Ferrante 2012; Gaudreau 2013; Head 2012; Herman 2011; Loomis 2012; Naiman 2010; Sargent 2004; Seo 2007; Vander Weg 2012), compared to their reference areas. Seven studies found similar associations (Cesaroni 2008; Hurt 2012; Juster 2007; Lemstra 2008; McGhee 2014; North Carolina 2011; Pell 2008). Studies using interrupted time series data also identified a consistent association with reduced admissions (Aguero 2013; Barnett 2009; Barone‐Adesi 2011; Bruckman 2011; Christensen 2014; Cronin 2012; Hahn 2011; Jan 2014; Kent 2012; Liu 2013; Roberts 2012; Sargent 2012; Schmucker 2014; Sebrié 2014).

Bruintjes 2011 and Khuder 2007 detected declining trends in AMI admissions, but the reductions were not statistically different to comparison areas in either study. Barr 2012 and Gasparrini 2009 observed declining trends in AMI admissions post‐ban, but no statistically significant association after adjusting for linear trends and non‐linear adjustment for secular trends. Whilst Basel 2014 reported a steep decline in AMI rates in the five years prior to the smoking ban, they found no significant results after statistical adjustment for previous ordinances. Two smaller communities in Colorado previously enacted smoke‐free legislation and identified a 27% reduction in AMI hospitalizations (Bruintjes 2011). The effect of the existing ordinances may have influenced the current results (Basel 2014).

Séguret 2014 detected a downward trend in ACS admissions over a seven‐year phased implementation of smoking bans in France. However, after adjusting for linear trends, age and gender, the results were not statistically significant. Lippert 2012 detected mixed results, predominantly reduced prevalence of heart disease, angina and AMI rates; however, increased rates were also detected in states with partial bans. Whilst Humair 2014 observed significant reductions in ACS hospital admissions post bans, the results were not significant after statistical adjustment for confounders including age, gender and secular trend. Yildiz 2015 did not observe any change in cardiac admissions.

We found a clear dose‐response effect in a number of studies included in this update. Alsever 2009 reported sustained reductions three years after a smoking ban was introduced, (statistically adjusting for secular trends) in comparison with the control area. Similar results were reported in Vander Weg 2012, who observed reducing admission trends during the phased implementation of smoking bans in settings, compared to states without bans.

Bonetti 2011 found evidence of sustained reductions in AMI rates in the second year of the ban for nonsmokers, with no change observed in the control area. Cronin 2012, Jan 2014 and Sebrié 2014 reported consistent reductions in AMI admissions at least two years after the introduction of national smoking bans. Naiman 2010 detected reduced admissions for angina after a work place ban was introduced, and further reductions in admissions for cardiovascular conditions following subsequent enactment of a ban in restaurants. Statistically significant reductions in AMI admissions were observed following the implementation of a ban in bars and the hospitality sector. The authors suggest that the statistically significant reductions in hospital admissions were unlikely to be attributable to decreased active smoking rates.

Biological coherence was observed in Schmucker 2014, with diverging trends in ST‐elevation myocardial infarction (STEMI) incidence between smokers and nonsmokers. Schmucker 2014 detected less coronary vessel disease in smokers compared to nonsmokers in those admitted for STEMI; however, statistically significant post‐ban reductions in admissions were only observed in nonsmokers, irrespective of gender and age. Greater reductions were observed in both younger nonsmokers (aged less than 65 years) and in older nonsmokers (65 years and over) in both the first and second years after the ban was introduced. Nonsmokers in the study also included a small number of ex‐smokers. Overall, current smokers in the study presented with STEMI at an earlier age (13 years younger) and were otherwise young and healthy people, their only risk factor being smoking. Di Valentino 2015 identified statistically significant reduced STEMI admissions in each of the three years after a ban was introduced, for older patients (up to 65 years), irrespective of gender. Reductions in those aged under 65 years were detected in the first year after the ban. While they noted a dose effect, the authors suggest a biological plausibility, as the results were not transient and the reduction in STEMI admissions in the older age group may include more nonsmokers. Smoking status was not recorded in this study. While the authors observed reductions in men aged 65 years or older in the control canton area (no ban), they did not observe reductions in older women. The observed reductions may have been influenced in the control area by other anti‐smoking activities and legislation (Di Valentino 2015).

Outcomes in subgroups

The majority of studies made statistical adjustments for either age, gender, smoking status (where available, Analysis 5.1) or socioeconomic status, and conducted specific sub group analyses.

Head 2012 observed statistically significant reductions in AMI admissions, irrespective of ethnic class. Overall, the greatest reductions in admissions for heart disease following smoking legislation were identified in nonsmokers (Aguero 2013; Barnett 2009; Bonetti 2011; Cronin 2012; Pell 2008; Schmucker 2014; Seo 2007), with Rajkumar 2014 reporting decreased heart rate variability in nonsmokers. Greater reductions in admission were observed among younger age groups (Barone‐Adesi 2011; Cesaroni 2008; Di Valentino 2015; Sargent 2012), irrespective of gender (Aguero 2013; Barone‐Adesi 2011; Gaudreau 2013; Hurt 2012). Schmucker 2014 observed reductions in nonsmokers, irrespective of age (Analysis 1.1).

Cesaroni 2008 identified a reduction in acute coronary events in 35‐ to 64‐year‐olds; the association was significant for men and greater for those living in lower socioeconomic areas compared to higher socioeconomic groups. Liu 2013 observed similar results. Barnett 2009 identified significant reductions in men, and those aged 55 to 74 years, but living in more affluent areas (quintile 2), with increases in admissions for younger women. The greatest decrease in admissions was seen in never‐smokers. Among younger never‐smokers (30 to 54 years) there was a statistically significant increase in AMI admissions (Barnett 2009). While Kent 2012, Roberts 2012 and McGhee 2014 detected statistically significant reductions in admissions after adjusting for age, Aguero 2013 detected significant reductions particularly in women and in people aged 65 to 74 years, with former and nonsmokers showing significantly reduced AMI rates. North Carolina 2011 observed reduced admissions, irrespective of gender and in both age groups. Further statistical modelling, using dummy false start dates, found one false date did improve results. Sargent 2012 reported a reduction in AMI rates amongst older age groups and those aged 30 to 68 years, with reduced hospitalization costs observed at one year following the smoking ban. The upper age limit in this study was 105 years and 43.5% of the cohort were retired. Di Valentino 2015 also observed reduced admissions in those aged 65 years and older, irrespective of gender for each year after the ban. A reduction in admissions in younger age groups (under 65 years) was observed in the first year after the ban. Barone‐Adesi 2011 also observed significant reductions in younger participants.

Hahn 2011 identified a reduction in AMI rates, significantly for women but not for men. The gender differences may be explained by the settings and work place bans in place. Jan 2014 also identified a reduction in AMI rates among women. The impact of a subsequent tax increase on cigarette pricing was associated with a significant reduction in AMI admissions. Liu 2013 identified significant reductions in MI admissions in both genders, after adjusting for deprivation. Significant absolute risk reductions were associated with men living in the most deprived areas compared to those living in either middle‐ranked or higher‐ranked areas.

Cronin 2012 observed significantly reduced ACS admission rates in men, in smokers and in nonsmokers after the introduction a smoking ban in Ireland. Pell 2008 observed a 14% reduction in admissions in smokers, a 19% reduction in ex‐smokers and a 21% reduction in admissions for nonsmokers. They note that of the total reduction in admissions, 67% was attributable to nonsmokers. Greater reductions were observed in men under 55 years and women under 65 years. Christensen 2014 observed significant reductions in AMI admissions; however, they could not explain the difference detected post‐ban after adjusting for age and gender and in the absence of diabetes. The authors suggest that a separate national ban on trans‐fatty acids may have influenced their study results. Bruintjes 2011 did not detect any significant difference in admissions in Greeley (Colorado, USA) when compared to the control area. However, they observed a significant reduction in AMI admissions amongst smokers when compared to nonsmokers after the introduction of the smoking ban in Greeley.

Stroke outcomes

Six studies detected an association with stroke admissions (Analysis 1.1.2), four studies using a control for comparison (Head 2012; Herman 2011; Loomis 2012; Naiman 2010), and one study using interrupted time series data (Mackay 2013). Juster 2007 used a before‐and‐after method, reporting significant reductions in AMI admission rates in New York, but not for stroke admissions.

Five studies did provide evidence of significant reductions in stroke admissions following smoking bans. Head 2012, Herman 2011, Loomis 2012 and Naiman 2010 detected significant declines in admissions compared to their control areas.

Mackay 2013 identified increasing admission rates for cerebral infarction in Scotland, prior to the introduction of a smoking ban. Following the ban, and after statistically adjusting for confounders, there was a significant reduction in admissions for cerebral infarction (8.9%), persisting for 20 months following the legislation. No interactions between subgroups were significant after adjustment for confounders (e.g. gender, age, residence or deprivation index).

Respiratory outcomes

We found 21 studies assessing the association between smoking bans and respiratory outcomes, including chronic obstructive pulmonary disease (COPD), asthma and lung function. Eleven studies reported COPD health outcomes: three studies used interrupted time series data (Croghan 2015; Humair 2014; Kent 2012). Six studies used quasi‐experimental controlled before‐and‐after methods (Dusemund 2015; Gaudreau 2013; Hahn 2014; Head 2012; Naiman 2010; Vander Weg 2012); the remaining two studies used an uncontrolled before‐and‐after design (McGhee 2014; Yildiz 2015). Six of these studies additionally reported asthma health outcomes (Croghan 2015; Gaudreau 2013; Head 2012; Humair 2014; Kent 2012; Yildiz 2015).

Six studies only reported asthma outcomes: Herman 2011; Landers 2014 (controlled before‐and‐after studies); Mackay 2010; Millett 2013; Roberts 2012 and Sims 2013 (interrupted time series data). Four uncontrolled before‐and‐after studies identified the impact of smoking bans on specific lung function outcomes (Durham 2011; Goodman 2007; Larsson 2008; Rajkumar 2014).

COPD (Analysis 2.1)

Six studies reported consistent reductions in COPD admissions associated with smoking bans. Dusemund 2015 identified a 22.4% reduction in admissions compared to the control area. Naiman 2010 reported reductions in admissions for COPD post‐ban compared to the control areas. Hahn 2014 reported, after adjusting for trends and confounders, that those living in counties with comprehensive smoke‐free bans were 22% less likely to be admitted for COPD than those living in counties with weak or no bans. A dose response was associated with smoking bans in place for more than 12 months, resulting in a 21% reduction in admissions. Protective factors identified in the study were being male, aged 45 years to 65 years, and educated at least to secondary level (Hahn 2014).

Head 2012 identified significant differences in non‐Hispanic black and white residents in Beaumont compared to the control areas, and identified ethnic differences between both groups of residents. They found significant reductions in admissions for COPD and asthma in non‐Hispanic white residents only. Vander Weg 2012 and Humair 2014 observed dose‐response associations with lower COPD admissions; at 36 months after smoking legislation when compared to controls, Humair 2014 observed reductions in COPD admissions over the four time periods of the study.

Five studies reported no significant reductions in COPD admissions. Gaudreau 2013 and Yildiz 2015 observed no significant association; Croghan 2015 identified a downward trend in COPD admissions, but this was not significant after adjusting for age and gender. Kent 2012 detected increased admissions for pulmonary diseases in general, with a significant different post‐ban for pneumonia rates, but not for COPD. McGhee 2014 also reported increased admissions for bronchitis and respiratory tract infections post‐ban, but no associations with COPD admissions.

Asthma (Analysis 2.2)

Seven of the 12 studies reported a significant association between smoking bans and reduced asthma hospitalizations. Sims 2013 observed that a significant reduction for nonsmokers was equivalent to 1900 fewer admissions for each of the first three years of the ban. Consistent reductions in asthma admissions amongst children post‐legislation ranged from 12.3% (Millett 2013), through 18.2% (Mackay 2010), up to 22% Herman 2011, whilst Gaudreau 2013 observed no association between the ban and reduced admissions for children or adults. Kent 2012 observed reduced asthma admissions in younger age groups, whilst Mackay 2010 identified increased asthma admissions among children prior to the introduction of smoke‐free legislation; admission rates reduced in children post ban and these were not significantly different in either the preschool age group or the 5‐ to 14‐year age group.

Croghan 2015 reported a step‐change reduction in visits to emergency departments for asthma. After statistical adjustment for potential underlying temporal trends in hospital visits, they observed significant reductions in hospitalization rates both for adults and for children. Millett 2013 also observed increasing admissions amongst children in the year before the ban. Post‐ban decreases were significant, even after adjusting for confounders. The authors suggest a reduction of 6802 admissions could be identified in the first three years of the ban.

Head 2012 observed significant reductions in discharge rates among white non‐Hispanic residents, but there was no significant difference in discharges for black non‐Hispanic residents. Landers 2014 identified significant reductions in admissions for adults of working age and for children after the introduction of county smoking laws. No significant associations were observed following implementation of state laws.

Gaudreau 2013, Humair 2014, Roberts 2012 and Yildiz 2015 did not detect any significant reductions in asthma admissions in adults following smoke‐free bans; Roberts 2012 observed an increase in hospitalization rates.

Lung function (Analysis 2.3)

There was evidence of improved lung function with significant reductions in passive smoke exposure reported in hospitality workers following smoking legislation (Durham 2011; Goodman 2007) (Analysis 2.3). These findings are consistent with the evidence in the earlier version of the review. Lung function improved for smokers and nonsmokers (Goodman 2007), with improvements observed in women and older participants (Durham 2011). Larsson 2008 did not observe improvements in lung function post‐bans; Rajkumar 2014 reported reduced episodes of coughing.

Inconsistent evidence emerged for COPD outcomes post‐ban, but there was more consistent evidence for reduced asthma admissions and reduced passive smoke exposure.

Perinatal outcomes (Analysis 3.1)

Seven studies identified specific perinatal health outcomes (Amaral 2009; Cox 2013; Kabir 2013; Mackay 2012 (using interrupted time series data); Bharadwaj 2012; Page 2012 (controlled before‐and‐after); Kabir 2009 (uncontrolled before‐and‐after)). The emerging evidence identifies an association between smoking bans and reductions in active smoking in pregnant women, and consequent reductions in foetal passive smoke exposure. Bharadwaj 2012 and Page 2012 detected significant reductions in maternal smoking compared to their controls.

Cox 2013 and Kabir 2009 identified a reduction in the risk of preterm deliveries after adjusting for confounders. Kabir 2009 observed an increase in the risk of low birth weight, which the authors suggest may reflect secular trends. Bharadwaj 2012 and Kabir 2013 observed a reduction in the risk of low and very low birth weights, while Mackay 2012 detected significant reductions in small‐for‐gestational‐age babies and in rates of preterm delivery among both current and nonsmokers, using a ban date three months prior to implementation. Analyses using the later start date identified an increase in preterm delivery rates following adjustment for pre‐eclampsia data.

Amaral 2009 noted that local ordinances were associated with a decrease in very low and low birth weights and an increased gestation period of 0.03 days. A dose‐response effect for a more restrictive statewide smoking ban resulted in an increased gestation period for white and higher‐educated mothers, and a significant decrease in very low birth weights. For Hispanic mothers in this study, there was a reduction in birth weights of 7.2 grams following the introduction of statewide bans. This is an inverse dose‐response effect; the authors suggest the implementation of more restrictive work place smoking bans may have led to increased smoking in the home or greater exposure to secondhand smoke in the home.

Cox 2013 observed a reduced risk of preterm births during a phased introduction of smoking bans. After the 2010 ban, there was a reduction in preterm delivery; however, there were no significant associations between the smoking ban and the risk of low and very low birth weights or small‐for‐gestation‐age. Although Page 2012 observed reduced maternal smoking, there was no significant impact of the ban on perinatal outcomes in comparison with the control area. Page acknowledges that differences in the intervention and control areas may have influenced the outcomes.

Mortality outcomes (Analysis 4.1)

We found 11 studies investigating associations between bans and mortality rates. Five studies used interrupted time series methods (Aguero 2013; Cox 2014; De Korte‐De Boer 2012; Jan 2014; Stallings‐Smith 2013); two used quasi‐experimental controlled before‐and‐after study designs (Dove 2010; Rodu 2012); and the remaining four studies used uncontrolled before‐and‐after methods (Hurt 2012; McGhee 2014; Pell 2009; Villalbi 2011).

Aguero 2013; Cox 2014; De Korte‐De Boer 2012; Dove 2010; Pell 2009; Rodu 2012; Stallings‐Smith 2013; Villalbi 2011 provide evidence of reduced smoking‐related mortality (including cardiovascular and respiratory) with consistent, temporal and dose‐response associations observed. Dove 2010 and Rodu 2012 observed temporal and consistent reductions in AMI mortality rates when compared to their control areas. Rodu identified significant reductions in mortality, but the changes were not immediate in comparison to the states where no smoking bans were in place. Dove observed a dose response of continued reducing AMI mortality rates in the second year of the ban. Similar trends were reported in Cox 2014. Stallings‐Smith 2013 and Stallings‐Smith 2014, with a follow‐up period of 81 months, observed a 13% reduction in all‐cause mortality and a 26% reduction in deaths from ischaemic heart disease (IHD), a 32% reduction in stroke deaths and a 38% reduction in COPD mortality. The 2014 paper identified significant reductions in inequalities in smoking‐related mortality. For IHD and COPD, the reductions were strongest in the most deprived tertile. Following the smoking ban in Ireland, a reduction in stroke mortality rates was observed across all socioeconomic groups. Pell 2008 detected a significant dose response associated with higher rates of ACS mortality in nonsmokers who had higher levels of measured cotinine.

Aguero 2013 and Villalbi 2011 identified reduced AMI mortality rates, with Aguero 2013 observing lower rates in women and Villalbi 2011 reporting significant reductions, even after adjusting for gender and age. McGhee 2014 observed a reduction in lung cancer diagnoses, although the authors suggest that this change was not attributable to the introduction of the smoking ban. Jan 2014 identified reducing AMI mortality rates in the pre‐ban years between 2001 and 2008, but found no significant association post‐legislation, in the 2008 to 2010 period. Whilst Hurt 2012 observed a 17% reduction in the incidence of sudden cardiac deaths in the 18‐month period post ban, the result was not statistically significant.

Active smoking and reduced secondhand exposure

Twenty‐four studies investigate associations between smoking bans and passive and active smoke exposure. Six studies used interrupted time series designs, four used a quasi‐experimental controlled before‐and‐after study design, and 14 are before‐and‐after studies with no control population (Analysis 5.1). Three studies did not report smoking status data from their main data sets, but accessed smoking prevalence data from national surveys (Cesaroni 2008; Cox 2014; Ferrante 2012).

We found active smoking measures including smoking prevalence, quit rates and tobacco consumption reported in 19 studies (Bajoga 2011; Bharadwaj 2012; Cesaroni 2008; Cox 2014; Federico 2012; Ferrante 2012; Gallus 2007; Gualano 2014; Hahn 2008; Hurt 2012; Jones 2015; Kabir 2009; Klein 2014; Lee 2011; Lemstra 2008; Lippert 2012; Mackay 2011; Mackay 2012; Page 2012). Reduced smoke exposure outcomes are reported in four studies (Durham 2011; Goodman 2007; Pell 2008; Rajkumar 2014). Larsson 2008 includes evidence of both active and secondhand exposures.

Active smoking (Analysis 5.1)

Five studies used ITS methods to analyse national or regional population smoking behaviour (Bajoga 2011 multinational; Federico 2012; Gualano 2014 Italy; Mackay 2011 Scotland; Jones 2015 Scotland and England). Bajoga 2011 covered 13 US states, four Canadian provinces, and four other areas (Republic of Ireland (ROI), Northern Ireland, Scotland, New Zealand). In all but three of these (Ireland, Delaware and New Mexico) there was already a significantly declining smoking prevalence prior to the introduction of smoking bans. After introduction of the bans there was an immediate decline in prevalence in two areas (Washington and ROI) and a faster rate of decline in a further six US states. In the other 13 locations there was no identifiable change in the trend. In Italy, Federico 2012 found some evidence of short‐term impact, while the longer period analysed by Gualano 2014 did not detect evidence that the ban had changed the pre‐existing rate of decline in prevalence. In Scotland, Mackay 2011 also detected only a short‐term impact on prevalence just before the introduction of legislation, before a return to the pre‐existing rate of decline. Jones 2015 did not detect an association in either Scotland or England, but in England there were only two data points after the ban.

One study used ITS methods to analyse smoking prevalence among low‐income pregnant women in Ohio (Klein 2014). Preconception smoking rates had been stable in the six years prior to the ban, whereas after the ban there was a small but statistically significant reduction in prevalence.

Two studies used a controlled design to analyse population prevalence data: Ferrante 2012 (comparing Sante Fe to Buenos Aires, Argentina) and Hahn 2008 (comparing Fayette County to other counties in the state of Kentucky). Bharadwaj 2012 (Norway) and Page 2012 (Pueblo City, Colorado) (controlled before‐and‐after studies) reported both active and passive health outcomes. Ferrante 2012 identified a nonsignificant decline in national smoking prevalence rates in Sante Fe compared to the control area, Buenos Aires. They noted more quit attempts in Santa Fe than in Buenos Aires prior to the introduction of smoke‐free legislation in Argentina. However, they reported no change in the proportion of daily smokers or the number of cigarettes consumed in either city.

Hahn 2008 identified significant reductions in smoking prevalence after the introduction of bans compared to control counties, even after controlling for seasonality, time trends and demographic characteristics. Bharadwaj 2012 identified reduced active smoking and higher quit rates during pregnancy amongst women working in bars and restaurants compared to women working in other settings with no bans. Page 2012 observed a reduction in maternal smoking in Pueblo when compared to the control area, but no reduction in maternal smoking in Pueblo post‐ban. The authors acknowledge that statistically significant differences between the areas at baseline reporting may have influenced the results in Pueblo.

Eight studies used uncontrolled before‐and‐after methods to measure changes in active smoking: Cesaroni 2008 (Italy); Cox 2014 (Belgium); Gallus 2007 (Italy); Hurt 2012 (Minnesota); Lee 2011 (England); Lippert 2012 (17 US States); Lemstra 2008 (Saskatoon, Canada); Mackay 2012 (Scotland). Active and passive smoking were reported in a further two uncontrolled before‐and‐after studies: Kabir 2009 (Ireland); Larsson 2008 (Sweden).

While Gallus 2007 did not identify a reduction in smoking in the years prior to the ban, they found evidence of reduced prevalence in the population after the smoking ban was introduced. Kabir 2009 also identified a reduction in Irish maternal smoking rates post‐ban and increased smoking cessation prior to pregnancy in 2005. Lee 2011 did not detect significant changes in smoking prevalence trends or in the number of cigarettes smoked per day, after controlling for time and other trends. The study reported significantly reduced smoking in cars and in homes, and increased smoking behaviours outside, with a reduced consumption of cigarettes. Similarly, Larsson 2008 did not detect any significant change in smoking prevalence in a small cohort of hospitality employees, including casino and bingo hall workers, one year following introduction of smoking bans.

Lippert 2012 reported significant reductions in smoking prevalence in 14 of the 17 US states after the introduction of smoking bans. The implementation of smoking bans in this study varied by state, ranging from either a ban in work places, restaurants and bars (Arizona, District of Columbia, Hawaii, Illinois, Iowa, Maryland, Minnesota, New Jersey, Ohio, Puerto Rico, Utah) or restaurants and bars (Colorado, New Hampshire, New Mexico). Pennsylvania had a ban in the work place, and Louisiana and Nevada had bans in work places and restaurants. The follow‐up periods ranged from two to four years after the introduction of smoking bans, and reductions in smoking prevalence were noted in all states irrespective of the comprehensiveness of the ban. The highest reduction in smoking prevalence was reported in New Hampshire; Utah was the only state reporting no change in prevalence.

Mackay 2012 detected reduced smoking prevalence after the introduction of a ban, with an increased number of people who reported they had "never smoked". They found a steep decline in smoking in the three months prior to the introduction of the ban; however, the association with reduced prevalence was not sustained during the post‐ban period. Prescribing of nicotine replacement therapy (NRT) was significantly higher prior to the legislation, with increased quit attempts. Similarly, the associations were not sustained in the post‐legislation period. Lemstra 2008 detected reduced smoking and increased quit attempts in Saskatoon after the smoking ban was introduced. The study compared their results with data from the wider state of Saskatchewan and from all of Canada, and reported significant reductions in smoking prevalence in Saskatoon compared to both comparisons areas.

Cesaroni 2008 (Italy), Cox 2014 (Belgium) and Hurt 2012 (Minnesota) all reported reduced smoking prevalence rates after smoking bans were introduced. The evidence was from specific national data sources and not from their respective study data sets. Cox 2014 reported national Belgian health survey data (pre‐/post‐ban) identifying decreased smoking prevalence and decreasing consumption specifically amongst heavy smokers (more than 20 cigarettes a day). While Hurt 2012 identified reducing trends in smoking prevalence in Minnesota from national data, they found no evidence of significant differences in smoking prevalence from specific study data.

Effects on smoking behaviour in subgroups

A number of studies in Analysis 5.1 included subgroup analyses for a combination of variables, including gender, age and socioeconomic group (Cesaroni 2008; Cox 2014; Federico 2012; Gallus 2007; Gualano 2014; Jones 2015; Kabir 2009; Klein 2014; Lee 2011).

Cox 2014 identified a reduction in national smoking prevalence post‐ban for both men and women, with specific evidence for reduced smoking trends in women. Federico 2012 found decreased smoking trends for men and women in the initial post‐ban period, but the reductions were not maintained and smoking prevalence rates returned to pre‐ban levels, especially amongst those with lower education. Cesaroni 2008 found the association to be statistically significant in men but not women, and observed greater reductions in smoking in residents living in lower socioeconomic areas than those living in higher socioeconomic areas. Gallus 2007 also observed reduced smoking prevalence post‐ban, confirming a significant reduction in smoking in men and in those aged 15 to 44 years. Gualano 2014 identified a reduction in smoking prevalence for men and women and a reduction in smoking intensity, and found reduced smoking in younger age groups, irrespective of gender, and lower prevalence rates in older women. Increased smoking trends (prevalence and consumption) were identified in women aged 45 to 64 years, but the evidence was not statistically significant. Overall, reductions in smoking prevalence were not associated with Italian smoke‐free legislation after statistical modelling Gualano 2014. Similar results were reported by Jones 2015 who found reduced consumption in men aged 18 to 34 years, but there was no significant reduction in consumption in older women and significantly higher consumption in women aged 35 to 54 years in England compared to Scotland. Evidence of reduced consumption in men aged 55 and older was reported from Scottish data (Jones 2015). The study reported inconclusive findings and limited evidence of an association with smoking prevalence after statistical adjustment.

Klein 2014 reported lower odds of preconceptual smoking amongst low‐income women after the introduction of a smoking ban, even after adjusting for multiple confounders including age, income, education, residence and parity. Kabir 2009 found similar reductions in Irish maternal smoking rates after statistical adjustment.

Lee 2011 did not identify evidence of reduced smoking prevalence after adjusting for confounders; however, they detected reduced smoking trends in older respondents, with evidence of higher smoking rates in women and in younger age groups. Significant reductions in active smoking in cars and inside homes were reported in this study, consistent with evidence in Pell 2008.

Reduced secondhand exposure (Analysis 5.2)

Studies identifying specific passive smoke exposure outcomes for this update had to include evidence of health outcomes, which we have presented in previous sections. Four uncontrolled before‐and‐after studies (Durham 2011 (Switzerland); Goodman 2007 (Ireland); Pell 2008 (Scotland); Rajkumar 2014 (Switzerland)) provided evidence of reduced passive smoke exposure in addition to health outcomes. Larsson 2008 (Sweden) provides evidence for both active smoking and secondhand exposures, using an uncontrolled before‐and‐after design.

Evidence of reduced passive smoke exposure was detected following the introduction of smoking bans, consistent with evidence from the previous version of the review (Durham 2011; Goodman 2007; Larsson 2008; Rajkumar 2014; Pell 2008) (Analysis 5.2). Health outcomes for these studies are presented in Analysis 2.1 and in Analysis 1.1 for Pell 2008.

Discussion

available in

Legislation restricting or prohibiting smoking in work places and public places is a public health measure at the population level. There were no randomized controlled trials where the intervention was a smoking ban. The predominant study designs evaluating the effectiveness of smoking bans were interrupted time series studies, quasi‐experimental before‐and‐after studies with a control area for comparison, and before‐and‐after studies with no control area for reference. Three studies used matched areas for comparison of controls (Hahn 2014; Khuder 2007; Seo 2007). While the before‐and‐after studies with no controls were often unable to control for possible confounders and changes in secular trends over time, the interrupted time series studies used statistical modelling in an attempt to adjust for these effects in analyses. However, because of uncertainty about the underlying trends, some study authors noted that their results were sensitive to the choice of model.

The evidence supports a temporal association between the introduction of national smoke‐free bans and subsequent reductions in smoking‐related morbidity and mortality. Evidence for smoking bans in improving cardiovascular, respiratory and perinatal health outcomes for both smokers and nonsmokers is persuasive. The evidence in this update identified a dose‐response association, with sustained and improved health outcomes over time, specifically cardiovascular. As the period since bans were enacted has lengthened, improvements in health outcomes have increased or have been maintained. Evidence in this review identified improved health outcomes for nonsmokers in relation to cardiovascular and asthma health outcomes and to reduced mortality rates. Evidence of a biologically plausible effect emerged in studies examining STEMI admissions. Schmucker 2014 detected reduced STEMI rates for nonsmokers compared to smokers, with identified divergent trends in the incidence of disease observed. Di Valentino 2015 also suggests a biological plausibility, with reduced STEMI admissions in those aged 65 or older; however, smoking status was not reported in this study.

Perinatal outcomes provide evidence of reduced maternal smoking and acknowledged impact on foetal health. Inconsistent evidence emerged for other outcomes, including birth weights. The benefits identified in some studies are consistent with those reported in Been 2014, Jones 2014 and Kelleher 2014; however, the studies in this review do not provide compelling evidence of a clear association between smoke‐free legislation and improved perinatal outcomes; we need more evidence to confirm or refute such associations.

Consistent evidence of reduced mortality is reported, with an observed temporal dose‐response effect. Statistically significant reductions and downward trends were noted for cardiovascular and respiratory illnesses. Evidence of a reduction in mortality in lower socioeconomic groups is persuasive, especially in Stallings‐Smith 2014, given the duration of the study period (81 months).

As in the previous version of this review, inconsistent evidence emerged of the impact of smoking bans on reducing smoking prevalence rates and tobacco consumption.

The studies in this review are heterogeneous in their design, populations and interventions, and we were unable to perform statistical comparisons or meta‐analyses. Despite the different study designs, this update provides more methodologically robust studies than those reported in the first version, incorporating large data sets facilitating modelling and regression analyses and adjusting for non‐linear trends and confounders. The majority of studies have evaluated comprehensive smoking bans; only 18 studies investigated partial bans. Significant improvements in health outcomes were reported in countries where comprehensive bans were in place and compared to areas with either no ban or partial bans. Since the first version of this review (2010), there has been an increase in countries worldwide implementing national smoke‐free bans. The FCTC (WHO 2014) identified an 84% increase in countries implementing smoking policies, and a 61% increase in countries implementing complete smoking bans.

The 2008 MPOWER evidence‐based measures include protection from tobacco smoke to reduce tobacco‐related morbidity and mortality (WHO 2009; WHO 2013). The results from the original review indicated that introducing legislative smoking bans leads to a reduction in exposure to passive smoke. Key population groups benefiting from the enactment of legislative smoking bans reported in this review include pregnant women and their babies, children and nonsmokers. There is also evidence of improved cardiovascular outcomes for smokers in three studies (Bruintjes 2011; Cronin 2012; Pell 2008).

Socioeconomic gradients indicate that men in lower socioeconomic groups are benefiting from the effect of smoke‐free legislation. In the original version of the review, the evidence of the impact for active smoking was unclear but indicated a downward trend. The studies included in this update provide some evidence of reductions in smoking prevalence. However, a number of studies did not detect evidence of a change in prevalence, or change in rate of decline in prevalence, associated with the introduction of bans, irrespective of the population studies. Four studies (Bharadwaj 2012; Kabir 2009; Klein 2014; Mackay 2012) identified declining smoking rates in pregnant women, but this was not borne out for all studies.

Limitations in studies included in this review are the absence of randomised trials. The inevitable reliance on observational data means that we can only identify correlations between the introduction of smoking restrictions, and the health and behavioural outcomes of interest. The studies using national population surveys employed random sampling or stratified sampling techniques. The data sets used in many studies were relatively large and allowed for statistical modelling and adjustment for possible confounders. Small sample sizes are reported in a number of the studies which used volunteer samples recruited within the hospitality sector. A number of studies did not report sample sizes, and individual‐level data were not available within large registry data sets, which limited analyses for confounders, e.g. smoking status and comorbidities. Other confounders included increased pricing of cigarettes during study periods, removal of trans‐fatty acids in foods, and suspension of bans. These and other factors may have led to changes in health outcomes over the study periods which could not be controlled for in analyses. These may have influenced the reported results. It is possible that some studies that did not detect changes in health outcomes have not been published and are unavailable for inclusion in this review. However, this update includes some studies that did not identify a positive impact of smoking bans. We excluded from this review studies reporting only cotinine biomarkers; studies reporting passive smoke exposure had to include a measured health outcome. This provided a wider body of literature, but there are few studies which verified smoker status. Smoker status was reported in 24 studies in this review, and verified in only four.

From a public health perspective the impact of smoking legislation is to reduce passive smoke exposure and to reduce active smoking. Since first publication of this review in 2010, the evidence is mounting and the concentration of studies clearly identifies reduced passive smoke exposure with associated reductions in morbidity and mortality post‐smoking bans. Smoking policies usually comprise multicomponent efforts to tackle smoking cessation as well as the public health objective of reducing exposure to environmental tobacco smoke. Populations exposed to smoking restrictions are likely to be exposed to other interventions. The implementation of comprehensive legislation on smoking will necessitate other tobacco control measures to prepare for its successful implementation, such as increased media awareness, telephone smoking cessation helplines, and smoking cessation support services to ensure awareness, comprehension and support for those affected by it (Callinan 2010). The effectiveness of legislative efforts will also depend on successful enforcement of smoking bans and compliance with the legislation. Other tobacco control measures, such as taxation on tobacco products, limits on advertising and sponsorship, and limits on the sale of tobacco products, may vary between jurisdictions. A comprehensive approach to tobacco control will utilize both individual and population‐based intervention strategies, causing difficulties in evaluating the effect of a single intervention such as the smoking ban legislation.

Figure 1

Study flow diagram.

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Figure 2

Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

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Study

Location/ Intervention

Outcomes

Smoking status

ITS studies

Aguero 2013

Spain, Girona

Partial

2006

All AMI events 1 January 2002 to 31 December 2008 for people aged 35 to 74 years: 3703 cases. 2142 events pre‐legislation. 3012 were admitted to hospital

AMI incidence rates significantly decreased (RR 0.89, 95% CI 0.81 to 0.97); similar significant decreases observed in mortality rates, RR 0.82 (95% CI 0.71 to 0.94). Decrease observed in both genders, particularly women (RR 0.72) and in people 65 to 74 years (RR 0.74)

Nonsmokers showed diminished incidence rates; passive smokers significant reductions in AMI RR 0.88, (95% CI 0.80 to 0.97) (AHA definition); RR 0.82, (95% CI 0.72 to 0.92) (WHO MONICA definition). Non‐significant in smokers; RR 0.93 (95% CI 0.82 to 1.05) (AHA definition); RR 0.91, (95% CI 0.80 to 1.04) (MONICA definition)

Smoking status reported

No validation

Barnett 2009

New Zealand,

Christchurch

Comprehensive

2004

Poisson regression analysis pre‐ and post‐ban. Deprivation coding for socioeconomic profile. Overall RR was 0.92 (95% CI 0.86 to 0.99) between first AMI admissions pre‐ and post‐smoke‐free legislation

Gender stratification identified a significant reduction for men RR 0.90 (95% CI 0.82 to 0.99) when compared to women RR 0.94 (95% CI 0.84 to 1.05)

Age stratification identified significant reductions for men in admissions for first AMI event in 55 to 74 year olds RR 0.86 (95% CI 0.75 to 0.99) and 75+ age group RR 0.85 (95% CI 0.73 to 0.98)

Highest RR differences in admissions were recorded for nonsmokers (aged 30 to 54 years) following smoking legislation: RR 1.71 (95% CI 1.16 to 2.52)

Significant differences noted for nonsmokers in 55 to 74 year age group, compared to regular and ex‐smokers RR 0.83 (95% CI 0.69 to 1.00). Significant reductions in admissions in those aged 55 to 74 years living in quintile 2, RR 0.76 (95% CI 0.59 to 0.97)

No significant differences observed for smokers

Smoking status reported

No validation

Barone‐Adesi 2011

Italy

20 Italian regions

Comprehensive

2005

Poisson regression analysis pre‐ and post‐ban. Mixed effects regression modelling used with fixed coefficients for national trend reporting; random coefficients reported for region‐specific deviations

Overall rate ratio (RR) 0.96 (95% CI 0.95 to 0.98) for ACE admissions among people aged 70 years and younger. This was a 4% reduction in hospital admissions post‐smoke‐free legislation

Men RR 0.97 (95% CI 0.95 to 0.98)

Women RR 0.95 (95% CI 0.93 to 0.98)

There was no effect in people aged over 70 years; RR 1.00 (95% CI 0.99 to 1.02)

No smoking status reported

Barr 2012

USA, 9 States:

Illinois, Ohio, Minnesota, New York, Washington, New Jersey, Arizona, Massachusetts, Delaware.

Comprehensive

Poisson regression modelling used. Adjustment for demographic and seasonal and secular trends in admission rates. State level modelling with county‐specific random effects used to estimate change in AMI admission rates

Approx. 64,000 admissions for AMI per year. Statistically significant results in AMI hospital admissions post‐ban were found when strict linearity of secular trends of AMI admission rates was assumed: ‐5.4% (95% CI ‐8.2 to ‐2.5)

The effect was attenuated to zero under relaxation of assumptions

No significant results identified following non‐linear adjustments for secular trends.

No smoking status reported

Basel 2014

USA, Colorado

Comprehensive

2006

Poisson regression analysis used to identify differences in monthly AMI admissions post legislation

63.9% of patients were men and 36.1% were women. Mean age 66.9 years SD ± 14.4

No significant reduction in AMI rates observed post‐legislation risk ratio (RR) 1.059 (95% CI 0.993 to 1.131)

Results identified a steep decline in AMI rates 2000 to 2005 prior to legislation. Two smaller communities in Colorado previously enacted smoke‐free legislation and identified 27% reduction in AMI hospitalizations (Bruintjes 2011)

Current study adjusted for this population of 5411 patients and adjusted population census. No significant difference post‐legislation adjusting for this group, RR 1.038 (95% CI 0.971 to 1.11)

No significant impact of smoke‐free legislation demonstrated even after accounting for pre‐existing ordinances

No smoking status reported

Bruckman 2011

USA, Ohio

Comprehensive

2007

Interrupted monthly time series study. Mixed linear modelling data adjusting for gender and age

AMI rate reduced 1.9775 per 1000 in 2005 to 1.680 per 1000 in 2009 (1680 discharges per one million Ohio residents)

For men and women the mean age adjusted discharge rate decreased over study period P < 0.0001. (men: 2.6334 vs 2.2567, P < 0.001; women: 1.432 vs 1.992, P < 0.001)

Significant decrease in discharge rates before and after statewide indoor tobacco smoke ban

No smoking status reported

Christensen 2014

Denmark

Partial ban (not fully enforced)

2007

Smoking prevalence decreased from 27% in 2003 to 21% in 2010 (National survey data)

109,094 admissions recorded during study period. Adjusted modelling for age, gender and type 2 diabetes

No significant differences in hospital admissions for AMI identified post‐ban after adjusting for age and gender

Significant differences in hospital admissions for AMI identified after adjusting for age, gender and incidence of type 2 diabetes:

1 year pre‐ban RR 0.86 (95% CI 0.79 to 0.94)

1 year post‐ban RR 0.77 (95% CI 0.71 to 0.85)

2 years post‐ban RR 0.77 (95% CI 0.70 to 0.84)

Significant reduction in number of AMI admissions may be explained by incremental enactment of smoking ban activities in Denmark and implementation of nationwide ban on trans‐fatty acids in food in 2004

Smoking status not reported from AMI data

Smoking prevalence reported from national surveys

Cronin 2012

Ireland

Comprehensive

2004

At baseline, percentage of current smokers admitted with ACS 2003/2004 was 34%. This reduced in 2005/2006 to 31% and reduced further in 2006/2007 to 29%

Pre‐legislation 205.9 ACS admissions/100,000 population. In the year following ban there was a statistically significant 12% reduction in the rate of admissions 177.9/100,000 (95% CI 164.0 to 185.1, P = 0.002)

There was no change in the rate of ACS admissions in the following year. A further 13% reduction was observed in the 3rd year post‐legislation March 2006 to March 2007; 149.2 (95% CI 139.7 to 159.2)

Reductions in admissions between 2003 to 2004 and 2004 to 2005 were due to smaller number of cases among men: 281.5 vs 233.5/100,000, P = 0.0011, and current smokers 408 vs 302 admissions, P < 0.0001; no significant change among women, former smokers, and never‐smokers

The 2nd reduction in ACS admissions 2005 compared to 2006 to 2007 was due to a reduction among men, 235.4 vs 195.2, P = 0.0021 and in current smokers 325 vs 271, P = 0.0269, and in never‐smokers 355 vs 302, P = 0.0386

There was no significant change in total deaths for all causes during the study period and the number of deaths from circulatory causes declined 6.5%

Smoking legislation was associated with early significant decrease in hospital admissions for ACS. A further reduction was noted 2 years post‐legislation

Smoking status self reported

No validation

Gasparrini 2009

Italy, Tuscany

Comprehensive

2005

2000 to 2004 pre‐ban 13,456 AMI cases registered. 2005 post‐legislation 2190 cases registered

A decrease of 5.4% in AMI rates was observed in age group 30 to 64 years post‐legislation, RR 0.95 (95% CI 0.89 to 1.00, P = 0.07 (NS)).

Adjusting for linear or non‐linear time trends (age groups in 10 year bands) or gender did not provide any statistical significant differences post‐legislation

No smoking status reported

Hahn 2011

USA, Kentucky, Lexington‐ Fayette County

Comprehensive

2004

AMI hospitalization rates in age group ≥ 35 years decreased for women after law enacted; adjusted RR 0.77 (95% CI 0.62 to 0.96, P < 0.05). A decrease in rate from 334.1/100,000 to 237.3/100,000.

The rate for men increased 424.6/100,000 to 438.4/100,000, RR 1.11 (95% CI 0.91 to 1.36, NS).

The post‐law decline for women was maintained during the study period

Gender differences observed in post‐legislation period for different workers covered by laws

Pre‐ban admission age 67.3 years, post‐ban 65.5 years, t = 3.2, P = 0.001

No smoking status reported

Humair 2014

Switzerland, Geneva

Partial ban with period of suspension

2008

10% trend in reduced admissions for ACS IRR 0.90 (95% CI 0.80 to 1.00, P = 0.24)

No smoking status included

Jan 2014

Panama

Comprehensive

2008

Adjusted RR for AMI comparing baseline with 1st post‐smoking ban period was 0.982 (95% CI 0.967 to 0.997, P = 0.023), 1.8% decrease.

The adjusted RR increased in the 2nd post‐ban period, RR 1.049 (95% CI 1.022 to 1.077, P = 0.0001)

The adjusted AMI RR for women was 1.075 (95% CI 1.033 to 1.119, P = 0.0001), NS for men

The adjusted RR reduced following the tax increase (final post‐ban period) RR 0.985 (95% CI 0.971 to 0.999, P = 0.041)

No seasonality trends or linear trends in AMI case series tests

No smoking status reported

Authors report results of reduced prevalence from other national data source

Kent 2012

Ireland

Comprehensive

2004

Significant differences in admissions for ACS observed adjusted RR 0.82 (95% CI 0.70 to 0.97, P = 0.02). Reduced admissions in aged 50 to 55 years and 60 to 69 years. No changed in admissions in other age groups

No smoking status reported

Liu 2013

England, Liverpool

Comprehensive

2007

Age‐adjusted CHD admissions increased in men by 8%, RR 1.08 (95% CI 1.06 to 1.11) and increased in women by 12%, RR 1.12 (95% CI 1.09 to 1.16)

Age‐adjusted rates for MI admissions decreased post‐legislation by 41.6% for men, RR 0.584 (95% CI 0.542 to 0.629) and 42.6% for women, RR 0.574 (95% CI 0.520 to 0.633)

Modelling identified that MI admissions reduced by 45% (95% CI 58.0 to 28.4), post‐legislation (2010 to 2011 compared to 2005/2006) in the 10 most deprived wards

In comparison, the middle‐ranked wards identified 42.3% reduction in MI admissions (95% CI 56.4 to 23.6)

For the 10 most affluent wards, MI admissions reduced 38.6% (95% CI 57.5 to 11.2).

Absolute risk difference between least‐deprived wards for first 2 years was 69.8 MI admissions/100,000 person years compared to 2010 and 2011 data, 32 MI admissions/100,000 person years; RR 0.46 (95% CI 0.044 to 4.76)

ARIMA analysis identified statistically significant effects of smoking ban for men in the most deprived wards and middle‐ranked wards

Reduction in MI admissions following smoking ban was greater than secular trends. Upstream intervention

Smoking status not reported

Roberts 2012

USA, Rhode Island

Comprehensive

2006/2007

2008/2009

AMI age‐adjusted admission rate pre‐ban (2003) was 35.2/10,000 population (95% CI 34.0 to 36.5) and post‐phase 11 of the ban in 2009, 23.1/10,000 population (95% CI 22.1 to 24.1)

Between 2003 and 2007, following the 1st implementation of the smoking ban, the number of admissions for AMI decreased 17.1%, with a reduction in reimbursed hospital costs

No smoking status reported

Sargent 2012

Germany

Federal and State bans

Partial

2007 to 2008

Cohort aged 30 to 105 years, mean 56 years. 66.5% women registered.

43.5% of cohort were retired, 39.9% of members were employed. 2.2% of cohort were hospitalized for angina pectoris, and 1.1% of cohort had been hospitalized for AMI during the study period

At 1 year follow‐up, smoking bans associated with 13.28% (95% CI 8.19 to 18.36) reduction in admissions for angina pectoris and an 8.58% (95% CI 4.99 to 12.17) reduction in AMI hospitalizations

The percent reduction in AMI did not differ with respect to gender. Reductions in admissions for AMI higher for younger participants (30 to 68 years) compared to older group, 15.77% (95% CI 10.57 to 20.97)

After the law, there was a statistically significant downward trend in admissions for angina with slope resulting in a decline of about 5 hospitalizations per month slope = −5.33 (95% CI 7.18 to 3.48). The percent reduction in angina was not significantly different for older vs younger individuals, or men vs women.

Larger reductions in hospitalizations for angina were observed in older participants,15.66% (95% CI 10.9 to 20.39)

Hospitalization costs reduced during study period. Overall the introduction of smoking ban was associated with prevention of 1880 hospitalizations and savings of EUR 7.7 million

No smoking status reported

Schmucker 2014

Germany, Breman

Partial

2008

3545 patients admitted. Mean age 63 ± 10 years. 72% were men, 20% diabetes mellitus and 44% active smokers

Smokers with STEMI were younger than nonsmokers 56 years ± 12 vs 69 ± 12, P < 0.01; men, 80% vs 66%, P < 0.01

Smokers with STEMI had significantly fewer coronary vessels diseased compared to nonsmokers, 1.76 ± 0.8 vs 1.99 ± 0.8, P < 0.01. (Nonsmokers in study included ex‐smokers in analyses)

Hospitalization rates for STEMI decreased post‐smoking ban, a reduction from 65 ± 10 per month to 55 ± 9

Number of nonsmokers admitted for STEMI significantly decreased from 39 cases/month pre‐ban to 29 cases, P < 0.01. This reduction was observed in both genders and all ages in nonsmokers. Greatest reductions in nonsmokers were in those aged ≤ 65 years, 32%, P < 0.01 and in those > 65 years, P < 0.01 (after adjusting for confounders hypertension, obesity, diabetes mellitus).

16% (P < 0.01) reduction in total STEMI admissions post‐ban.

Overall 26% reduction (P < 0.01) in admissions among nonsmokers. There was no significant difference in the number of smokers admitted for STEMI post‐smoking ban

Self‐reported smoking status

Sebrié 2014

Uruguay

Comprehensive

2006

11,135 cases identified over study period. 65% were men (n = 7287). In 2008 there was a significant drop in AMI monthly admissions ‐35.9 ± 10.1 (SE), constant 167 ± 7, a 22% drop. A similar reduction was observed for men, women and people aged 40 to 65 years and aged 56 years and older

The 2nd follow‐up analyses 2004 to 2010 identified a drop of 30.9 cases/month AMI admissions (95% CI ‐49.8 to ‐11.8, P = 0.002)

The effect of the law did not increase or decrease over time

The overall drop in AMI monthly admissions was 17%, IRR 0.829 (95% CI 0.743 to 0.925, P = 0.001) (to 2010) following smoke‐free legislation

The results from 2010 analyses confirm the sustained impact of smoke‐free legislation on AMI admissions

No smoking status reported

Séguret 2014

France

Comprehensive

1991, 2006, 2008

Adjusted for age and sex admission rates for ACS admissions observed a reduction from 269.1/100,000 2003 to 234, RR 0.87 (95% CI 0.85 to 0.89) in 2009. A reduction of 12.8%

After adjusting for linear trends, reductions linked to the ban were not significant when analysed for gender or age groups (men aged ≤ 55 years or > 55 years and women ≤ 65 years or > 65 years).

The study did not demonstrate a significant effect of a 2‐phase ban on ACS admissions. ACS rate was reducing in France during this 7‐year period

No smoking status reported

Controlled before‐and‐after studies

Alsever 2009

USA,

Pueblo City, Colorado

Control: Pueblo county outside city limits, El Paso county

Comprehensive

2003

Significant drop in admissions for AMI among residents within Pueblo city limits continued in Phase 2 of the study (follow‐up 36 months)

Decrease 152 per 100,000 person years, a decline of 19% since Phase 1 and a decline of 41% pre‐legislation RR 0.59 (95% CI 0.49 to 0.70)

Males RR 0.67 (95% CI 0.52 to 0.82); Females RR 0.48 (95% CI 0.36 to 0.60) (pre‐legislation to Phase 2)

No significant changes were observed among residents outside the city limits RR 1.03 (95% CI 0.68 to 1.39) or in El Paso County, RR 0.95 (95% CI 0.87 to 1.03)

Adjusting for secular trends in pre ban period was not significant. Sustained reduction in rates of AMI admissions observed over 3‐year period

No smoking status reported

Bonetti 2011

Switzerland, Canton Graubünden

Control Canton Lucerne

Partial

Canton Ban 2008

(National Ban up to 2010)

Adjusted for air pollution, drug prescribing and comorbidities

Statistically significant differences in admissions post‐legislation identified in Graubünden (229 and 242 admissions pre‐law; 183 and 188 admissions post‐law; P < 0.05)

Overall reduction in number of AMI admissions in Graubünden in the 2 years post‐ban; 21% lower than in the 2 pre‐ban years. The reduction most pronounced in nonsmokers, women and individuals with documented coronary artery disease, including those with prior AMI and prior coronary intervention or graft surgery

Decrease in 2nd year of ban limited to nonsmokers 151 (2006) vs 108 (2010), P < 0.05

No decrease observed in control Lucerne

No association found between magnitude of outdoor air pollution and incidence of AMI.

Use of lipid‐lowering drugs increased in Graubünden and in Lucerne

Smoking status reported

No validation

Bruintjes 2011

USA, Greeley, Colorado and surrounding area

Smoking ordinance Greeley

Control: areas outside city

Comprehensive

2003

Prevalence of smoking:

482 hospitalizations analysed in Greeley with 224 in residents of surrounding area. 23.7% active smokers in Greeley; 61.4% of patients were men. (30.0% smokers in control area).

A significant decrease in hospital incidence rates in Greeley observed post‐ordinance RR 0.73 (95% CI 0.59 to 0.90). NS result in comparison area. Difference between Greeley and comparison area was NS, P = 0.48

Regression analyses identified smokers experienced statistically significant reductions in hospitalizations in Greeley RR 0.44 (95% CI 0.29 to 0.65)

Reduction in AMI rates in smokers in surrounding area did not differ from Greeley, P = 0.38

Significant difference observed post‐ordinance, but not in comparison with surrounding area

Smoking status reported

Di Valentino 2015

Switzerland, Canton Ticino

Partial (local smoke‐free ordinance)

2007

Compared to Canton of Basel

(no ban)

Mean incidence of STEMI reduced post‐legislation in Ticino 123.7/100,000 pre‐ban, to post ban 92.9 (2007 to 2008), P = 0.002; 101.6 (2008 to 2009), P = 0.024; 89.6 (2009 to 2010), P = 0.001

Post‐ban reduction in STEMI admissions observed in age group 65 years and older irrespective of gender, each year post‐ban, P = 0.0001

In the under‐65‐year age group , the mean incidence of STEMI admissions decreased in 1st year post‐ban 109.0 vs 85.3, P = 0.01

No significant differences in annual number of STEMI admissions in Basel during the study period except in age group 65 years and older 362.3 (pre‐) vs 223.6, 234.4, 199.8. Lower STEMI admissions noted in Basel compared to Ticino during study period

No smoking status reported

Ferrante 2012

Argentina,

Santa Fe

Comprehensive

August 2006

Control: Buenos Aires City: partial October 2006

Significant reduction in in ACS admissions in Santa Fe ‐2.5 admissions/100,000, P = 0.03 and persistence change over time post‐law 0.26 fewer admissions/100,000 inhabitants per month (95% CI ‐0.39 to ‐0.13, P < 0.001). 13% reduction compared to control city, RR 0.74 (95% CI 0.63 to 0.86)

In Buenos Aires City no change post‐ban, P = 0.28 or over time P = 0.89

Slight decrease (P = 0.84, NS) in smoking prevalence during study period (2005 to 2009) from national prevalence survey. More quit attempts in Sante Fe prior to ban than in control 53.2% (95% CI 42.5% to 63.6%) vs 44.4% (95% CI 34.3% to 55.0%, P = 0.045). No change in proportion of daily smokers or cigarettes consumed

100% smoke‐free law more effective in reducing and sustaining reduction in admissions for ACS in Sante Fe

No smoking status reported from data

Prevalence reported from other data source

Gaudreau 2013

Canada, Prince Edward Island

Comprehensive 2003

Control:

New Brunswick Province

Significant reduction in mean rate of AMIs 5.92 cases/100,000 person months, P = 0.04 post‐smoking ban. The trend of admissions for angina in men reduced ‐0.44 cases/100,000 person months, P = 0.01 at 1 to 67 months post‐smoke‐free law. No significant difference when comparing age groups 35 to 64 years and 65 to 104 years

No significant difference for other cardiovascular admissions in study population

No smoking status included

Head 2012

USA, Beaumont City, Texas

Control: Tyler Texas and All Texas

Comprehensive

2006

Texas BRFSS data estimated ethnicity of current smokers 23% black, 20% white during 2005 to 2008

Discharges for all participants (non‐Hispanic black and non‐Hispanic white) declined significantly post‐legislation in Beaumont for AMI, RR 0.74 (95% CI 0.65 to 0.85) and stroke RR 0.71 (95% CI 0.62 to 0.82)

No smoking status reported from data

Reports state smoking prevalence from other data source

Herman 2011

USA, Arizona

counties with bans

Control: counties with no bans

Comprehensive

2007

Statistically significant reduction in hospital admissions comparing ban counties with no‐ban counties, AMI 159 cases, 13% reduction in cases, P = 0.01, angina 63 cases, 33% reduction, P = 0.014

No smoking status reported

Khuder 2007

USA,

Intervention city: Bowling Green, Ohio

Control city: Kent, Ohio

Partial ban

2002

Admission rates for CHD‐related diseases showed downward trend during study period

Admission rates CHD in intervention city reduced 36/10,000 population in 2002 to 22 per 10,000 in 2003; 39% decrease (95% CI 33% to 45%) and to 19/10,000 in 2005, 47% decrease (95% CI 41% to 55%).

Further ARIMA models identified a downward trend in admissions in Bowling Green, omega estimates: ω = ‐1.69, P = 0.036 compared to Kent City, ω ‐1.14, P = 0.183

No observed changes noted in Kent compared to reduced CHD admissions in Bowling Green

No smoking status reported

Loomis 2012

USA,

Florida 2003, (partial)

New York 1985, 2003 Comprehensive

Control: Oregon

(partial ban)

The effect of comprehensive smoking ban on AMI rates in aged > 35 years was significant in New York, marginally significant at 10% level in Florida

The interaction of time and law is significant for Florida and New York. This indicates rates of AMI decreasing over time post‐comprehensive legislation

Moderate smoke‐free laws in Oregon were associated with lower AMI rates β = 3.846, P < 0.05. The interaction with time was negative and significant β = ‐0.242, P < 0.01

Rates for AMI hospitalizations reduced 18.4% (95% CI 8.8 to 28.0) in Florida (annual decline of 5.3%) and 15.9% (95% CI 11.0 to 20.1), β = ‐1.483, P < 0.05 in New York

This is equivalent to 28,649 fewer age‐adjusted admissions (95% CI 20,292 to 37,006; annual decline of 4.4%) for New York

The few comprehensive smoke‐free laws in Oregon were not associated with state reduction in admissions for MI or stroke

No smoking status reported

Naiman 2010

Canada, Toronto

1999, 2001

Comprehensive

2004

13 municipalities had bans

Control cities: Durham Region, Thunder Bay (no bans)

A 39% reduction in cardiovascular conditions (95% CI 38 to 40), and a 33% reduction in admissions for respiratory conditions (95% CI 32 to 34) were observed after 2001 ban

A significant reduction in admissions for angina were observed after the first ban, –0.913 (95% CI ‐1.24 to ‐0.59, P < 0.001)

A significant reduction in admissions for all other conditions observed after the 2nd phase of the ban was enacted (restaurants)

Only a significant reduction in admissions for AMI were noted after the 3rd phase of the ban, ‐0.611 (95% CI ‐1.03 to ‐0.19, P = 0.004). Authors suggest that reduction in hospital admissions unlikely due to decreased active smoking

No significant results detected for specific age group or gender reported

Smoking status reported from national Canadian survey.

No smoking status data from main data set.

Sargent 2004

USA Helena, Montana, Ordinance

Partial ban (then suspended)

June 2002

Control: non‐residents

Reduction in monthly AMI admissions in residents Helena – 16 (95% CI ‐31.7 to ‐0. 3) post‐ordinance.

No significant decrease in admissions for those living outside of Helena

No smoking status reported

Seo 2007

USA, Monroe County

Comprehensive

2005

Control: Delaware County, Indiana

Admission rates for AMI. There was a significant decrease in Monroe County but not in matched control Delaware County from the period August 2001 to May 2003 to the period August 2003 to May 2005 during which the smoke‐free law was in effect for nonsmoking people. Monroe: 17 to 5 (95% CI ‐21.19 to ‐2.81) vs Delaware:18 to 16 (95% CI ‐13.43 to 9.43).

There were no admissions for AMI among nonsmoking people from January 1st to May 2005 when the ban was extended to include bars and clubs. Non‐significant reduction in admissions for AMI amongst smokers in Monroe from 8 pre‐law to 7 post‐law and in Delaware from 8 pre‐law to 6 post‐law during this period

There was a significant difference in AMI admissions rates from August 2003 to May 2005 between Monroe and the control area 5 vs 16, change 11 (95% CI 2.02 to 19.98)

Self‐reported smoking status

Vander Weg 2012

USA

state bans 1991 to 2008

Ban varied by state

Control: states with no bans

1991 to 2008 data analysed

Risk‐adjusted hospital admission rates for AMI reduced 20 to 21% in the 36 months post‐implementation of smoking bans in restaurants, bars and workplaces (P < 0.001 for each ban)

At baseline, counties with bans in place had higher admission rates for AMI compared to controls (and higher admissions for hip fractures)

Counties with bans in 2008 had more Medicare enrollees and larger proportion of white residents

At 36 months post‐legislation, counties with bans had significantly lower AMI admission rates compared to no bans: RR 0.79, (No CI reported) P < 0.001 (workplace ban in place). Significant downward trends over time as increase in bans in different settings

No smoking status reported

Uncontrolled before‐and‐after studies

Cesaroni 2008

Italy, Rome

Comprehensive

2005

Prevalence: men: 34.9% pre‐law period (2002 ‐ 2003) to 30.5% post‐law period (2005); women: 20.6% pre‐law to 20.4% post‐law

Significant reduction in acute coronary events in 35‐ to 64‐year‐olds from pre‐law to post‐law period, RR 0.89 (95% CI 0.85 to 0.93) and in 65‐ to 74‐year‐olds, RR 0.92 (95% CI 0.88 to 0.97)

No change in 75‐ to 84‐year‐olds, RR 1.02 (95% CI 0.98 to 1.07)

Data from the post‐law was compared with data in the previous year, the effect of the law was statistically significant on men but not on women and was greater for residents living in lower socioeconomic areas than those from higher socioeconomic areas

Fewer acute coronary events in 35‐ to 64‐year‐olds identified (11.2%)

Self‐reported smoking status from other survey

No smoking status from admissions data

Hurt 2012

USA, Minnesota, Olmsted County

2002, 2007

Comprehensive

2007

Significant differences noted pre‐ordinance 1 and post‐ordinance 2 for MI. Incidence of MI declined by 33%, P < 0.001 from 150.8 to 100.7/100,000 population adjusted (age and gender) RR 0.67 (95% CI 0.53 to 0.83, P < 0.001)

Smoking status self‐reported

Juster 2007

USA, New York

Comprehensive

2003

In 2004, hospital admissions for AMI were reduced by 8% as a result of the comprehensive ban, equivalent to 3813 fewer admissions for AMI

The smoking ban was associated with a reduction in admissions for AMI on average 0.32/100,000 persons per month in all counties in New York state (95% CI ‐0.47 to ‐0.16, P < 0.001)

No smoking status reported

Lemstra 2008

Canada, Saskatoon

Comprehensive

2004

Age‐standardized incidence rate of AMI per 100,000 population in Saskatoon 176.1 (95% CI 165.3 to 186.8) before smoke‐free ban (1st July 2000 to 30 June 2004) to 152.4 (95% CI 135.3 to 169.3) post‐ban (1 July 2004 to 30 June 2005)

Incidence rate ratio: 0.87 (95% CI 0.84 to 0.90). 13% reduction in AMI discharges in period following legislation

Smoking status reported from survey data

Lippert 2012

Country: USA,

Arizona 2007*

Colorado 2006

District of Columbia 2007

Hawaii 2006*

Illinois 2008*

Iowa 2008*

Louisiana 2007

Maryland 2008*

Minnesota 2007

Nevada 2006

New Hampshire 2007

New Jersey 2006*

New Mexico 2007

Ohio 2006*

Pennsylvania 2008

Puerto Rico 2007*

Utah 2006*

Clean Indoor Air Act

(varied implementation)

* all comprehensive bans.

Remaining states: partial bans.

7 States had significant decrease in prevalence of CHD/angina post‐ban: Arizona, District of Columbia, Hawaii, New Hampshire, New Jersey, New Mexico, Pennsylvania (state N)

Arizona: (311) 4.7% (95% CI 3.6 to 5.8) vs (346) 3.4% (95% CI 2.8 to 3.9, P ≤ 0.0001)

District of Columbia: (141) 2.9% (95% CI 2.3 to 3.5) vs (132) 2.0% (95% CI 1.6 to 2.4, P < 0.001)

Hawaii: (257) 3.4%(95% CI 2.8 to 4.0) vs (247) 2.6% (95% CI 2.2 to 3.1, P < 0.001)

New Hampshire: (377) 4.5% (95% CI 4.0 to 5.0) vs (336) 3.6% (95% CI 3.1 to 4.1, P ≤ 0.001)

New Jersey: (801) 4.6% (95% CI 4.2 to 5.0) vs (592) 3.6% (95% CI 3.2 to 4.0, P ≤ 0.0001)

New Mexico: (340) 3.8% (95% CI 3.3 to 4.3) vs (438) 3.2% (95% CI 2.8 to 3.6, P ≤ 0.01)

Pennsylvania: (891) 5.4% (95% CI 4.8 to 6.0) vs (625) 4.7% (95% CI 4.2 to 5.2, P ≤ 0.01)

2 states had increased prevalence of CHD/angina: Colorado, Louisiana

7 states/Territory had significant reductions in AMI post‐ban (state N)

District of Columbia: (149) 3.3% (95% CI 2.7 to 3.9) vs (127) 1.9% (95% CI 1.5 to 2.3, P ≤ 0.0001)

Hawaii: (260) 3.6% (95% CI 3.0 to 4.2) vs (263) 2.9% (95% CI 2.4 to 3.4, P ≤ 0.01)

Iowa: (317) 4.7% (95% CI 4.1 to 5.3) vs (344) 4.1% (95% CI 3.6 to 4.6, P < 0.05)

Minnesota: (202) 3.4% (95% CI 2.9 to 3.9) vs (271) 2.8% (95% CI 2.4 to 3.2, P < 0.05)

New Hampshire: (321) 4.0% (95% CI 3.5 to 4.5) vs (296) 3.4% (95% CI 2.9 to 3.9, P < 0.05)

New Jersey:(676) 3.9% (95% CI 3.5 to 4.3) vs (567) 3.5% (95% CI 3.1 to 4.0, P < 0.05)

Puerto Rico: (301) 4.7% (95% CI 4.1 to 5.3) vs (268) 4.0% (95% CI 3.4 to 4.7, P < 0.05)

Four states had increased prevalence of AMI post‐ban: Colorado, Louisiana, Nevada, Pennsylvania (NS)

14 States had significant decrease in prevalence of current smokers. Highest difference post‐ban observed in New Hampshire, 3% change

Self‐reported smoking status and reported health outcomes

McGhee 2014

Hong Kong

Partial

2007

Study period prior to comprehensive ban (July 2009). Partial smoking bans associated with 9% decrease in admissions for ischaemic heart disease (95% CI ‐13.59 to ‐ 4.17, P < 0.05)

No smoking status reported

North Carolina 2011

USA, North Carolina

Comprehensive

2010

Regression analyses identified a 21% decrease in emergency admissions for AMI 12 months following implementation of smoke‐free restaurant and bars legislation RR 0.79 (95% CI 0.75 to 0.83)

Reduction in admissions: men aged 18 to 59 years 2385 vs 1916; aged ≥ 60 years 3196 vs 2885

Women aged 18 to 59 years 946 vs 778; aged ≥ 60 years 2901 vs 2421

Additional modelling including interaction variables including time, gender, age category did not improve the model

Additional modelling analyses identified improved outcomes were calculated using false start dates for legislation

No smoking status reported

Pell 2008

Scotland

Comprehensive

March 2006

In people admitted for ACS in Scotland, there was no significant reduction in self‐reported number of cigarettes smoked in the pre‐ or post‐law periods or the geometric mean cotinine level, 152 to 147 ng/ml, P = 0.72

Never‐smokers reported decrease in SHS exposure and biochemically verified, serum cotinine mean 0.68 to 0.56 ng/ml; P < 0.001

No significant change for nonsmokers or ex‐smokers (all admitted for ACS) reporting "no exposure" to SHS from pre‐ to post‐law period in either "own home" or "other people's homes". Never‐smokers reporting "no exposure" in own home: 83% (565/677) pre‐law vs 86% (460/537) post‐law, P = 0.64. Never‐smokers reporting "no exposure" in "other people's homes": 91% (617/677) pre‐law vs 92% (495/537) post‐law, P = 0.34

14% reduction in ACS admissions among smokers, 19% reduction among ex‐smokers and 21% reduction in never‐smokers.

Greater reduction in admissions current smokers: women 19% (95% CI 15% to 23%) compared to men 11% (95% CI 9% to 13%)

Reductions highest in women nonsmokers 23% (95% CI 20% to 26%) compared to men nonsmokers 18% (95% CI 16% to 20%)

Greater reduction in admissions detected in male smokers aged ≤ 55 years and in women ≤ 65 years 9% (95% CI 6% to 12%) when compared to older people 8% (95% CI 15% to 21%) Similar results obtained for nonsmokers 8% (95% CI 4 to 12) vs 22% (95% CI 20 to 24).

Smoking status validated

Rajkumar 2014

Switzerland, Basel City, Basel County and Zurich

Partial

2010

Pulse wave velocity and heart rate variability parameters significantly changed (dose‐dependent) for the 55 nonsmoking hospitality employees. A 1 cpd decrease was associated with a 2.3% (95% CI 0.2 to 4.4; P < 0.031) higher root mean square of successive differences, a 5.7% (95% CI 0. to 10.2; P < 0.02) higher high‐frequency component and a 0.72% (95% CI 0.4 to 1.05; P < 0.001) lower pulse wave velocity

The measures significantly improved after introducing smoke‐free legislation and identify a decreased cardio vascular risk

SHS validated measure

Self‐reported smoking status

Yildiz 2015

Turkey,

Kocaeli City

Comprehensive

2009

Admissions for diagnoses of COPD and MI were unchanged (NS differences) post‐legislation

No smoking status reported

Analysis 1.1

Comparison 1 Cardiovascular health outcomes, Outcome 1 Cardiac outcomes.

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Study	Location/ Intervention	Outcomes	Smoking status
ITS studies
Mackay 2013	Scotland Comprehensive 2006	Pre‐legislation rates for stroke, intracerebral haemorrhage and unspecified stroke were decreasing Rates for cerebral infarction were increasing 0.97%/year Following smoke‐free legislation there was a reduction in admissions for cerebral infarction, persisting for 20 months. An 8.9% (95% CI 4.85 to 12.77, P < 0.001) stepwise reduction was observed at time of implementation No interactions between subgroups were significant after adjustment for confounders (sex, age, residence or deprivation index)	No smoking status reported
Controlled before‐and‐after studies
Head 2012	USA, Beaumont City, Texas Comprehensive 2006 Control: Tyler Texas and All Texas	Discharges for all participants (non‐Hispanic black and non‐Hispanic white) declined significantly post‐legislation in Beaumont for stroke, RR 0.71 (95% CI 0.62 to 0.82) Significant differences in stroke admissions observed for non‐Hispanic white residents in Tyler (control area) RR 0.71 (95% CI 0.58 to 0.86). Reduction in admissions for all diagnoses in all Texas (mixed policies)	No smoking status reported Reports state smoking prevalence from other data source
Herman 2011	USA, Arizona counties with bans Comprehensive 2007 Control: counties with no bans	Statistically significant reduction in hospital admissions comparing ban counties with no‐ban counties, stroke 198 cases, 14% reduction, P = 0.001	No smoking status reported
Loomis 2012	USA, Florida 2003, (partial) New York 1985, 2003 Comprehensive Control: Oregon (partial ban)	Significant reductions in hospitalizations for stroke admissions observed in Florida; 18.1% (95% CI 9.3% to 30.0%, β= ‐16.194, P < 0.01). This equates to a 5.2% reduction in hospital admissions. Moderate laws were significantly associated with a decrease in stroke hospitalizations over time, β= ‐0.122, P < 0.01. The few comprehensive smoke‐free laws in Oregon were not associated with state reduction in admissions for MI or stroke	No smoking status reported
Naiman 2010	Canada, Toronto 1999, 2001, 2004 Comprehensive 2004 13 municipalities had bans. Control cities: Durham Region, Thunder Bay (no bans)	A 39% reduction in cardiovascular conditions (95% CI 38% to 40%). No significant reductions in admissions were noted in control cities or for control conditions. No significant results for specific age group or gender reported.	Smoking status reported from national Canadian survey. No smoking status data from main data set.
Uncontrolled before‐after studies
Juster 2007	USA, New York Comprehensive 2003	No effect on stroke admissions	No smoking status reported

Analysis 1.2

Comparison 1 Cardiovascular health outcomes, Outcome 2 Stroke outcomes.

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Study	Location/ Intervention	Outcomes	Smoking status
ITS studies
Croghan 2015	USA, Minnesota, Olmstead County Comprehensive 2007	In relation to COPD, the implementation of smoke‐free legislation was not associated with a downward step change in ED visits P = 0.158 or change in trend, P = 0.313.	No smoking status reported
Humair 2014	Switzerland, Geneva Partial ban (with period of suspension) 2008	Hospitalizations for COPD significantly decreased over 4 periods of time, IRR 0.54 (95% CI 0.42 to 0.68)	No smoking status reported
Kent 2012	Ireland Comprehensive 2004	Admissions for pulmonary illness 439/100,000 population per annum to 396/100,000, 1 year post‐ban unadjusted RR 0.91 (95% CI 0.83 to 0.99, P = 0.048) and adjusted for confounders RR 0.85 (95% CI 0.72 to 0.99, P = 0.04) Significant differences observed for asthma and pneumonia, but not for COPD in any age group	No smoking status reported
Controlled before‐and‐after studies
Dusemund 2015	Switzerland, Canton of Graubünden Local ordinance: Partial 2008 Control: Rest of Switzerland (not including Graubünden or Ticino)	22.4% reduction in incidence of AECOPD admissions, IRR 0.78 (95% CI 0.68 to 0.88, P < 0.001). Rest of Switzerland, reduction 7%, IRR 0.93 (95% CI 0.91 to 0.95, P < 0.001) Greater reduction in admissions observed in Intervention Canton, P = 0.008 compared to control	No smoking status reported
Gaudreau 2013	Canada, Prince Edward Island Comprehensive 2003 Control: New Brunswick Province	No significant differences reported for respiratory admissions	No smoking status reported
Hahn 2014	USA, Kentucky Comprehensive 2004, 2008 to 2011 Control: counties with smoking policy < 12 months or no ban	Adjusting for all characteristics, population and seasonal trend factors, risk ratio of COPD hospitalizations in communities with comprehensive smoking bans was 0.781 compared to communities with a weak or no policy Chi² = 6.65, P = 0.01; 95% CI 0.647 to 0.942 The risk ratio of hospitalizations for COPD in communities with established laws was 0.789 compared to communities with new or no laws Chi² = 9.91, P = 0.02; 95% CI 0.680 to 0.914 Protective factors for reduced COPD admissions were being male, aged 45 to 64 years and living in county with higher post‐secondary education Overall the study identified those living in counties with comprehensive smoke‐free laws were 22% less likely to be hospitalized for COPD compared to those living in counties with weak or no laws. Counties that had smoking bans in place for > 12 months were 21% less likely to be hospitalized for COPD compared to communities with laws < 12 months or no laws The study found that smoke‐free policies can improve health outcomes and can negate risk factors including lower socioeconomic status and living in rural tobacco‐growing communities	No smoking status reported
Head 2012	USA, Beaumont City, Texas Comprehensive 2006 Control: Tyler Texas and All Texas	COPD discharges for non‐Hispanic black residents RR 1.04 (95% CI 0.85 to 1.27 (NS)) and non‐Hispanic white residents RR 0.64 (95% CI 0.54 to 0.75) in Beaumont. NS in control areas	No smoking status reported
Naiman 2010	Canada, Toronto Comprehensive 1999, 2001, 2004 13 municipalities had bans Control cities: Durham Region, Thunder Bay (no bans)	33% reduction in admissions for respiratory conditions, (95% CI 32 to 34) observed after 2001 ban	Smoking status reported from national Canadian survey. No smoking status reported from main data set
Vander Weg 2012	USA State bans 1991 to 2008 Control: States with no bans	36 months post‐legislation, states with bans had significantly lower COPD admission rates compared to no bans, 11% to 17%, P < 0.001 with significant decreasing trends over time as increase in bans in different settings	No smoking status reported
Uncontrolled before‐and‐after studies
McGhee 2014	Hong Kong Partial 2007	Respiratory admissions and admission for lung cancer increased	No smoking status reported
Yildiz 2015	Turkey, Kocaeli City Comprehensive 2009	Bronchitis admissions reduced 39.8%, 44,141 to 26,558 post‐ban Admissions for LRTI decreased (7048 to 6738, P < 0.01) post‐legislation. Peak admission levels noted May 2010 Admissions for diagnoses of COPD and MI were unchanged (NS differences) post‐legislation Admissions for allergic rhinitis: NS trend analysis observed. Admissions for asthma showed NS increase (6805 vs 7895)	Principal diagnostic codes used No smoking status reported

Analysis 2.1

Comparison 2 Respiratory health outcomes, Outcome 1 COPD.

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Study	Location/ Intervention	Outcomes	Smoking status
ITS studies
Croghan 2015	USA, Minnesota, Olmstead County Comprehensive 2007	Evidence supported a downward step change in ED visits for asthma, RR 0.814 (95% CI 0.722 to 0.966, P < 0.001) post‐legislation Results for adults identified similar trend, RR 0.840 (95% CI 0.729 to 0.966, P = 0.015) post‐legislation For children RR 0.751 (95% CI 0.595 to 0.947, P = 0.015) post‐legislation	No smoking status reported
Humair 2014	Switzerland, Geneva Partial ban (with period of suspension) 2008	No statistically significant changes for asthma admissions	No smoking status reported
Kent 2012	Ireland Comprehensive 2004	Significant differences post‐legislation in younger age groups for asthma admissions, RR 0.60 (95% CI 0.39 to 0.91, P = 0.016)	No smoking status included
Mackay 2010	Scotland Comprehensive 2006	Pre‐legislation, admissions for asthma in aged 0 to 14 years increased, mean rate 5.2% year, (95% CI 3.9 to 6.6). Post‐legislation, mean reduction in rate of asthma admissions 18.2% per year compared to March 26th 2006, (95% CI 14.7 to 21.8, P < 0.001) After adjusting for sex, age group, residence, or socioeconomic status, admissions for asthma increased pre‐ban 4.4%/year, (95% CI 3.3 to 5.5). Post‐legislation the rate of admissions decreased 15.1%/year, (95% CI 12.9 to 17.2) Reductions in admissions for asthma were observed in both age groups post‐legislation. 55.1% of admissions occurred in preschool children. Pre‐legislation, there was an increasing trend in admissions in this group (9.1%). Similar reductions post‐legislation; NS difference observed between the age groups (No significant differences were observed between the groups after adjusting for age, sex, area of residence and socioeconomic group) Significant reduction in emergency admissions for children with asthma observed following smoke‐free legislation	Nonsmokers as participants' children Smoking prevalence reported from other data source
Millett 2013	England Comprehensive 2007	50.1% of the 217,381 admissions were preschool‐aged during study period Pre legislation the admission rate for children with asthma was increasing 2.2%/ year, adjusted RR 1.02 (95% CI 1.02 to 1.03). Post‐legislation there was a statistically significant decrease in admission rates for childhood asthma: 8.9%, adjusted RR 0.91 (95% CI 0.89 to 0.93). Overall the legislation was associated with a 12.3% reduction in hospital admissions for childhood asthma in the 1st year Modelling analyses identified a potential reduction of 6802 admissions in the 1st 3 years following smoke‐free legislation Multivariate analyses identified post legislation reductions in asthma admissions adjusting for age, gender, socioeconomic status, area of residence and in all English regions.	Nonsmokers as participants' children
Roberts 2012	USA, Rhode Island Comprehensive 2006/2007 2008/2009	There was an increase in hospitalizations for asthma between 2003: 11.3% (95%CI 10.6 to 12.1) and 2009: 13.5% (95% CI 12.8 to 14.3)	No smoking status reported
Sims 2013	England Comprehensive 2007	502,000 admissions recorded during study period. Adjusted for seasonality, variation in population and long‐term trends Smoke‐free legislation associated with immediate 4.9% (95% CI 0.6% to 9.0%) reduction in emergency admissions for asthma in adults. This would equate to approximately 1900 admissions prevented in each of the 1st 3 years post‐legislation No regional differences were observed	All nonsmokers in study
Controlled before‐and‐after studies
Gaudreau 2013	Canada, Prince Edward Island Comprehensive ban 2003 Control: New Brunswick Province	No significant differences reported for asthma admissions in children aged 0 to 14 years or in adults	No smoking status reported
Head 2012	USA, Beaumont City, Texas Comprehensive 2006 Control: Tyler Texas and All Texas	Discharges in Beaumont reduced for white non‐Hispanic residents for asthma, RR 0.69 (95% CI 0.52 to 0.91. Black non‐Hispanic residents RR 1.00 (95% CI 0.84 to 1.21)	No smoking status reported from data Reports state smoking prevalence from other data source
Herman 2011	USA, Arizona Counties with bans Comprehensive 2007 Control: counties with no bans	Statistically significant reduction in hospital admissions comparing ban counties with no‐ban counties Asthma: 249 cases, 22% reduction, P < 0.001	No smoking status reported
Landers 2014	USA States: Comprehensive bans Arizona May 2007, Colorado July 2006, Florida July 2003, Hawaii November 2006, Iowa July 2008, Maryland February 2008, New Jersey April 2006, New York July 2003, Rhode Island May 2005, Utah May 2006, Vermont September 2005, Washington December 2005 Control States: Arkansas, Kentucky, Michigan, South Carolina, Wisconsin	Bivariate analyses identified adult asthma discharge rates associated with being non‐white 0.26, P < 0.001, living in poverty, 0.19, P < 0.001 and rate of primary care physicians in county 0.16, P < 0.001 Child asthma discharges associated with living in poverty 0.33, P < 0.001, smoking prevalence 0.24, P < 0.001 and state cigarette tax ‐0.18, P < 0.001 Multivariate adjusted models observed significant reduction in relationship between implementation of county laws and reduction in working‐age adult asthma discharges β = ‐2.44, P < 0.05 and child asthma discharges β = ‐1.32, P < 0.05 No significant effect of state laws on working‐adult or child asthma beyond effect of county laws. No effect of state laws on appendicitis discharge rates Local county laws had impact on asthma discharges	Smoking status self reported
Uncontrolled before‐after studies
Yildiz 2015	Turkey, Kocaeli City Comprehensive 2009	Admissions for asthma showed NS increase (6805 vs 7895)	No smoking status reported

Analysis 2.2

Comparison 2 Respiratory health outcomes, Outcome 2 Asthma.

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Study	Location/ Intervention	Outcomes	Smoking status
Uncontrolled before‐and‐after studies
Durham 2011	Switzerland, Canton of Vaud Local ordinance Partial 2009	ETS exposure declined significantly after introduction of new smoke free law. Smokers had lung age 5.6 years older than chronological age. 61.0% reported smoking at baseline. 54.6% at follow up. Pre law: non‐smokers inhaled equivalent of 1.4 to 7.4 cigarettes / day. Post law significantly reduced p<0.05. (Figure not given). Lung function: improved in women +3.07%, p=0.05; non‐smokers +3.91%, p=0.04; and in older participants +4.22%, p=0.004.	Lung function and smoke exposure validated Self‐report health status
Goodman 2007	Ireland Comprehensive March 2004	Total ETS exposure to SHS was 46.9 hours pre ban and 4.2 hours post ban, a decrease of 90%. Exposure to SHS outside of work: Mean 6.4 hrs pre‐law V 3.7 hrs at 1 yr post‐law (% change) ‐42%; p ≤ 0.01. FVC parameters increased significantly in never smokers, it declined in current smokers. FEV1 did not change significantly in any group; increased in non smokers. Significant reduction in carboxyhaemoglobin by 5% in the never‐smoker group, but no significant reduction in ex‐smokers and current smokers. 79% reduction in exhaled CO for never and ex smokers but no significant change in current smokers. Exhaled CO Median (interquartile range) ppm: 4.0 (IQR, 3‐5) pre law vs 2.0 (IQR, 2‐3) follow up, p <0.001. Median exhaled breath CO and salivary cotinine decreased by 79% and 81% respectively in never and ex smokers. Saliva cotinine Median (IQR) ng/ml: 5.1 (IQR 3.4‐7.6) pre law V 0.6 (IQR 0.3‐1.3) follow up, p <0.001.	Self reported exposure to SHS was validated by carboxyhaemoglobin, exhaled CO and salivary cotinine
Larsson 2008	Sweden Comprehensive June 2005	No change in median cigarettes per day: 17 cig/day to 15 cig/day at 12 month follow‐up, p for trend= 0.788, NS. No significant reduction for cigarette consumption for either gaming (casino or bingo hall) or for other hospitality employees. Small number of smokers at baseline. No change in smoking status from baseline to 12 months follow up. Small number of smokers at baseline that responded at follow‐up, n= 14. Significant reduction in the percentage of employees reporting exposure to SHS for 75% of more of their time at work. 59/91 (65%) pre ban V 1/71(1%) at follow up, p<0.001. Greater duration of SHS exposure amongst gaming employees than other hospitality employees at baseline (p value for trend= 0.029) but duration of SHS exposure was similar in both at follow up. No statistical changes in spirometry / lung function or cigarettes consumed at one year follow up.	Biochemical validation of Active and SHS exposure and urinary cotinine
Rajkumar 2014	Switzerland, Basel City, Basel County and Zurich Partial 2010	27.2% of participants (n=92) were ex smokers, the remainder being non smokers. 14.1% reported diagnosis of asthma, 62% were female respondents (n=57). SHS bio chemically measured using Monitor of Nicotine (MoNIC) passive sampling badges. Exposure to SHS decreased during the study. Of the 78 participants exposed to SHS at baseline, 55 were not exposed at follow up and their SHS exposure decreased from 2.6,95% CI 1.7 to 3.4 CE/d to 0.2, 95% CI: 0.1 to 0.2 CE/d. Lung function analyses were completed on all 62 participants. At baseline, lung function testing identified lower results compared to population range, difference marked for women forced expiratory volume (FEV). After the smoking ban, an adjusted odds ratio for cough was 0.59, 95% CI 0.36 to 0.93, and for chronic bronchitis 0.75, 95% CI 0.55 to 1.02 compared to baseline. Post ban, self reported cough decreased. Below average lung function pre legislation indicates chronic damage from long term smoke exposure. Second hand smoke exposure in 55 non smoking hospitality employed participants was 2.56, 95% CI 1.70 to 3.44 cigarette equivalents per day pre ban and was 0.16, 95% CI 0.13 to 0.20 at follow up (Rajkumar 2014).	SHS exposure bio chemically measured in air quality measurements Non smokers in study ‐ self reported

Analysis 2.3

Comparison 2 Respiratory health outcomes, Outcome 3 Lung function.

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Study	Location/ Intervention	Outcomes
ITS studies
Amaral 2009	USA, California Local smoke free ordinances 1988 to 1994. State workplace ban Partial 1995	44181 births during study period Local workplace ordinances decreased the fraction of very low birth weight births in cities with local ordinances by 0.04 percentage points The implementation of local smoking ordinances was associated with a decrease in birth weight of 1.83 grams and increased gestation by 0.03 days The statewide ordinance was associated with a reduction in birth weight of 6.58 grams, P < 0.001 reducing to non‐significant changes of ‐2.45 grams and ‐3.12 grams after adjusting for different cities and ban trajectories Subgroup analyses identified that white mothers had an increase in gestation of 0.19 days, P < 0.001 after local ordinances and a significant decrease in very low birth weights by 0.06 percentage points, P < 0.001. Education level of mothers was not associated with significant differences in birth outcomes if local ordinance was in place. The statewide ordinance was significantly associated with lower birth weight and decreased gestation for lower‐educated mothers. Mothers with high school degree education were significantly associated with increased birth weight by 10 grams and decreased fraction of very low birth weight by 0.2 percentage points The statewide smoking ordinance, after adjusting for race and ethnicity, was associated with a significant reduction in birth weight of 7.2 grams, P < 0.05 for Hispanic mothers Results suggest that state work place smoking bans had a statistically significant but small negative effect on birth weight. Local ordinances did not have a similar effect
Cox 2013	Belgium Comprehensive 2010	606,877 singleton births delivered at 24 to 44 weeks gestation 448,520 births spontaneous deliveries Reductions in risk of preterm births reduced at each phase of smoking ban legislation After 2010 comprehensive ban, there was step change in the risk of spontaneous preterm delivery; slope change ‐2.65% (95% CI ‐5.11 to ‐0.13; P = 0.04) Similar reductions noted for all births, change ‐3.5% (95% CI ‐6.35 to ‐0.57; P = 0.02) No significant effect of smoking ban on risk of low birth weight or small‐for‐gestational‐age in population or on average birth weight (adjusted modelling)
Kabir 2013	Ireland Comprehensive 2004	Maternal smoking rates from 2000 to 2008 were higher in mothers who had SGA or vSGA. Data available from 1 maternity hospital 2000 to 2008 data. Not linked to national registry data Reduced monthly rates of SGA and vSGA reductions were observed post‐legislation (adjusted modelling); 4.7% to 4.3% (vSGA) and 6.9% to 6.6% (SGA). Effects continued in the post‐ban period: vSGA ‐0.6%, P < 0.0001 and SGA ‐0.02%, P < 0.0001 Significant reduction in low birth weights observed indicates evidence of smoke‐free legislation
Mackay 2012	Scotland Comprehensive 2006	Post‐legislation there was a significant reduction in current smoking rates, 25.4% to 18.8%, P < 0.001; and an increase in never‐smokers 57.3% to 58.4%, P < 0.001 Univariate modelling identified decrease 11.07% 95% CI 6.79 to 15.15, P < 0.001) in overall preterm deliveries and a decrease 10.26% (95% CI 4.04 to 16.07, P < 0.002) in spontaneous preterm labour. Significant for current and never‐smokers (model used date 1st January 2006, not 26th March) Prior to legislation multivariate analyses observed significant decreases (after adjusting for confounders) in SGA ‐4.52% (95% CI ‐8.28 to ‐0.60, P = 0.024); vSGA ‐7.95 (95% CI ‐15.87 to ‐7.35, P = 0.048), overall preterm delivery ‐11.72% (95% CI ‐15.87 to ‐7.35, P < 0.001), and for spontaneous preterm labour ‐11.35% (95% CI ‐17.20 to ‐5.09, P = 0.001). Significant reductions for current and nonsmokers Analyses using later start date identified increase in preterm delivery rates 3.83 (95% CI 1.42 to 6.30, P = 0.002), following adjustment for pre‐eclampsia data
Controlled before‐and‐after studies
Bharadwaj 2012	Norway Intervention: Mothers who work in bars and restaurants Control: All other mothers on register Comprehensive 2004	Post‐legislation mothers in the treatment group significantly reduced their risk of < 1500 grams birth by 1.9 percentage points (P < 0.05) and < 2000 grams birth by 2.5 percentage points (P < 0.05) and a significant reduction of 2.5 percentage points in being born preterm. There was no effect on < 1000g, APGAR score or if birth defect or male birth Approximately 20% of mothers in treatment group reported smoking at start of pregnancy; 64% were not smoking at start of pregnancy. No details reported for remainder. Following the smoking ban, mothers in the treatment group were 15.4% more likely to quit smoking during pregnancy (P < 0.05). The impact of quitting smoking at start of pregnancy increased birth weights on average by 162.5 grams, P < 0.05 There was no effect on birth weight for mothers who were nonsmokers at start of pregnancy. Mothers with missing data for smoking status also had increased birth weights of 105.5 grams and may suggest underreporting of smoking status Further analyses did not identity changes in birth weight associated with self‐reported income Occupational status during pregnancy changed for the treatment group. A number of mothers changed employment from bars and restaurants. Analyses of these changes did not identify significant differences to the results The impact of fathers' smoking status on birth weight identified a decrease of 77.09 grams in the treatment group (significant at 10% level) Further analyses on the impact of birth weight on later life success predicted that at age 28 years, a 100 gram increase in birth weight could increase adult income by 1.8%. For the sample in the study, their birth weight increase of 164 grams would translate into a 2.7% increase in salary This study identified that mothers working in bars and restaurants after smoke‐free legislation was introduced were 15% more like to quit smoking and this impacted on increased birth weights and on lower incidences of preterm births
Page 2012	USA, Colorado Intervention: Pueblo Control: El Paso Comprehensive 2003	Significant differences observed at baseline between the intervention city and the comparison in relation to mother's mean age. race, ethnicity, education, alcohol consumption, marital status and anaemia Significant differences existed in relation to previous pregnancy and medical history. Mothers from Pueblo were more likely to be Hispanic, have lower education and report previous pregnancy complications Results identified significantly more mothers were smoking in the control City 8.66% pre‐ban compared to 11.89% post‐ban, P < 0.0001 The percentage of smokers in Pueblo was 16.64% at baseline and 15.07% post‐ban, P < 0.0786, NS No significant differences were noted post‐ban in intervention city in relation to LBW. In control city, there was an increase in births < 3000 grams, 29.78% to 32.02%, P < 0.0001 Unadjusted rates of preterm babies did not change over time in Pueblo but increased in the control city, 7.93% to 9.23%, P < 0.001 Multivariable logistic regression modelling, adjusted for medical conditions, and birth characteristics found no significant association among location, ban and LBW Unadjusted models for preterm births identified a 21% (23% adjusted) reduction in odds of preterm birth associated with smoking ban, P < 0.05, in Pueblo When compared to control city, the smoking ban in Pueblo was associated with a 38% reduction in odds of maternal smoking, OR 0.620 (95% CI 0.529 to 0.727, P < 0.05)
Uncontrolled before‐and‐after studies
Kabir 2009	Ireland Comprehensive 2004	1 year post‐smoking legislation, a 25% decrease in risk of preterm births was observed; OR 0.75 (95% CI 0.59 to 0.96) There was a 43% increased risk of LBW; OR 1.43 (95% CI 1.10 to 1.85) after adjusting for all potential confounders A 12% reduction in maternal smoking rates (23.4% to 20.6%) was observed post‐ban There was an increase in smoking cessation prior to pregnancy in 2005, P = 0.047 Significant decline in preterm births and maternal smoking. Increase in LBW birth risks may reflect secular trend

Analysis 3.1

Comparison 3 Perinatal health outcomes, Outcome 1 Effect on perinatal health.

Navigate to figure in Review


Study	Location and Ban	Study Design/ Outcomes
ITS studies
Aguero 2013	Spain, Girona Partial 2006	AMI admissions and mortality. AMI case fatality n = 891 Post‐ban decrease observed in AMI mortality rates, RR 0.82 (95% CI 0.71 to 0.94, P < 0.05) AMI mortality age < 65 years NS. ≥ 65 years RR 0.82 (95% CI 0.74 to 0.91, P < 0.05) (AHA/ESC definition) Subgroup analysis: women AMI mortality rates, RR 0.72 (95% CI 0.52 to 0.97, P < 0.05) men: AMI mortality rates, RR 0.85 (95% CI 0.72 to 0.99, P < 0.05) (AHA/ESC definition)
Cox 2014	Belgium, Flanders Partial 2007	Flemish Agency for Care and Health registry data on AMI deaths for people aged ≥ 30 years during 2000 to 2009. 38,992 AMI deaths recorded Decreased AMI mortality rates January 2006. Highest in women ≤ 60 years, ‐33.8% (95% CI ‐49.6 to ‐13.0) compared with effect for men ‐13.1% (95% CI ‐24.3 to ‐0.3) Estimates for aged ≥ 60 years ‐9.0% (95% CI ‐14.1 to – 3.7) for men, and ‐7.9% (95% CI ‐13.5 to ‐2.0) for women. Additional effect post‐2007 legislation for men aged ≥ 60 years with annual slope change ‐3.8% (95% CI ‐6.5 to ‐1.0) From January 2006 to December 2009, the model predicts 1715 fewer AMI deaths with smoke‐free legislation. Step change in mortality after 1st ban.
De Korte‐De Boer 2012	Netherlands, Limberg General work place ban 2004 Included hospitality sector 2008 Comprehensive 2008	Weekly incidence data on sudden cardiac arrest from ambulance registry South Limberg. 2305 sudden cardiac arrest cases recorded during study period (2002 to 2010), mean incidence 5.3 (SD 2.3) Adjusted Poisson model identified small increase in sudden cardiac death pre‐ban and reduced post‐ban 2004 ‐0.24% cases/week, P = 0.043. Equivalent to 6.8% reduction 1 year post‐ban, 22 cases. No further decrease noted after 2nd ban. This may be due to poor enforcement of 2008 legislation
Jan 2014	Panama Comprehensive 2008	Mortality regression models (January 2001 to April 2008) on changes in deaths from MI identified 0.5% annual percentage change, P < 0.05. The trend was 0.47% up to June 2010, with a trend change of ‐0.3% July 2010 to December 2012. The change was not statistically significant
Stallings‐Smith 2013	Ireland Comprehensive 2004	Impact on mortality rates. During study period 215,878 non‐trauma deaths recorded in population ≥ 35 years (2000 to 2007) Following smoke‐free legislation, there was a 13% immediate decrease in all‐cause mortality, RR 0.87 (95% CI 0.76 to 0.99) There was a 26% reduction in deaths from ischaemic heart disease, RR 0.74 (95% CI 0.63 to 0.88), a 32% reduction in deaths from stroke, RR 0.68 (95% CI 0.54 to 0.85), and a 38% reduction in COPD deaths, RR 0.62 (95% CI 0.46 to 0.83) after smoke‐free legislation Post‐ban reductions for IHD, stroke and COPD were observed in ages ≥ 65 years COPD mortality was reduced in women, RR 0.47 (95% CI 0.32 to 0.70) 15% decrease in non‐smoking‐related mortality, RR 0.85 (95% CI 0.75 to 0.97). There was a 5% increase in mortality each post‐ban year. No post‐ban annual trend reductions were detected for any smoking‐related causes of death Unadjusted estimates of 3726 smoking‐related deaths (95% CI 2305 to 4629) were probably prevented as a result of smoke‐free legislation, primarily due to reduced passive smoke exposure Follow‐up paper mortality rates and socioeconomic status (2000 to 2010) Stallings‐Smith 2014 identified smoking ban reduced inequalities in smoking‐related mortality. 2 factors emerged explaining 81% of the variance: Structural factors were characterised with high loadings on education, occupation, foreign nationality and family composition Material aspects loaded in the 2nd factor included: unemployment, housing tenure and car access No post‐ban annual trend effects were detected for any cause of death in the period 2000 to 2007 Post‐ban mortality effects of structural socioeconomic indicators identified a reduction in smoking‐related inequalities For IHD and COPD mortality rates, reductions were strongest in the most deprived tertile; decreases in stroke mortality were observed across all socioeconomic groups (Stallings‐Smith 2014)
Controlled before‐and‐after studies
Dove 2010	USA, Massachusetts Control 290 cities and towns with no bans Comprehensive 2004	AMI deaths recorded on national registry Post‐legislation statistically significant reduction in AMI mortality rates 7.4% (95% CI 3.3 to 11.4, P < 0.001); 270 fewer deaths Significant reduction in AMI mortality rates aged ≥ 75 years compared to younger, ‐9.1% (95% CI ‐13.9 to ‐4.1, P < 0.001). Higher reduction detected in women compared to men, ‐9.7% (95% CI ‐15.1 to ‐3.9, P < 0.001) No significant results in control groups State ban reduction ‐1.6% in 1st year and increased to ‐18.6%, P < 0.001, in 2nd year following legislation
Rodu 2012	USA, state bans California 1 January 1995 Utah 1 January 1995 South Dakota 1 July 2002 Delaware* 27 November 2002 Florida 1 July 2003 New York * 24 July 2003 * Comprehensive bans Remaining states no bans	Secondary analysis of AMI mortality rates aged > 45 years California: The AMI mortality rate declined pre‐ban 1992 to 1993 from 225/100,000 to 204/100,000, annual reduction of 3%. Post‐ban the AMI rate declined 2%, P = 0.16 Utah: 3 years pre‐ban, the AMI mortality rate decreased from 200 to 180/100,000; 3.3% annual reduction. In 1995, post‐ban, the rate declined 7.7%, P = 0.43 Between 1991 and 1994, no significant difference was noted in other 48 States without smoking bans at that time. South Dakota: In the 3 years pre‐ban, AMI mortality rates dropped 253 to 198/100,000, 7.2% annual reduction. In the year post‐ban, the rate increase 8.9% to 216/100,000, P = 0.007 Delaware: Pre‐ban the AMI mortality rate decreased 199 to 160/100,000, 6.6% annual decline. Post‐ban the rate decreased 8.1%, P = 0.89 Between 1999 and 2002 the AMI rate declined for the other 46 States without a ban. In 2003 the rate of AMI decline was 7.2%, significantly greater than expected, P < 0.0002. Florida: Pre‐ban the AMI mortality rate declined 169 to 132/100,000, 6.4% annual decline. Post‐ban the rate significantly reduced 8.8%, P = 0.04 New York: Pre‐ban AMI mortality rates reduced 187 to 160/100,000, 4.9% annual reduction. Post‐ban the rate significantly declined 12%, P < 0.0002 Statewide smoking bans had little or no immediate effect on AMI death rates
Uncontrolled before‐and‐after studies
Hurt 2012	USA, Minnesota, Olmsted County 2002, 2007 Comprehensive 2007	Incidence of sudden cardiac death (SCD) declined pre‐ordinance 1 and post‐ordinance 2 by 17%, P = 0.13, 109.1 to 92.0/100,000 population; RR 0.83 (95% CI 0.65 to 1.06) NS
McGhee 2014	Hong Kong Partial 2007	Hospital admission and mortality rates: Ischaemic heart disease, acute myocardial infarction, cerebrovascular disease, cardiovascular disease, respiratory disease, lung cancer, all natural causes, injury poisonings and external causes, cancer excluding lung cancer. Mortality rates for lung cancer diagnosis significantly reduced 5.65% (95% CI ‐9.73 to ‐1.39, P < 0.05) The authors suggest this is not attributable to the smoking ban, but to improved treatment and other factors as follow‐up post‐legislation is 12 months
Pell 2009	Scotland Comprehensive March 2006	Cohort study. Mortality rates in ACS admissions amongst nonsmokers All‐cause mortality increased from 10 in those with mean cotinine ≤ 0.1 ng.ml to 22 in those with cotinine > 0.9 ng/ml, P < 0.001 All‐cause mortality (after adjusting for age and gender) associated with cotinine > 0.9 ng/ml, OR 4.80 (95% CI 1.95 to 11.83, P = 0.003 Current smokers excluded from the primary analyses (n = 1831), 53 (3%) died and 78 (4%) died or were readmitted for myocardial infarction within 30 days of the index admission. The early risk of death in smokers was comparable to that among never‐smokers; however, the difference was no longer statistically significant when adjusted for differences in age
Villalbi 2011	Spain Partial 2005/2006	Secondary analysis of AMI mortality rates. 2004 to 2007 study period Reduction in AMI deaths observed 2004: Rate 119.99/100,000 population (95% CI 117.98 to 122.01) vs 2007: 102.28 (95% CI 100.49 to 104.07) Adjusted AMI mortality rates in 2004 and 2005 are similar, but in 2006 there is a 9% decline for men and 8.7% decline for women, especially aged > 64 years. In 2007 there is a statistically significant decline for men (‐4.8%), but not for women Post‐ban the annual age‐standardized AMI mortality risk was significantly reduced in the years after legislation compared to 2003/2004 rates Men: 2006: RR 0.90 (95% CI 0.88 to 0.93, P < 0.001). 2007: RR 0.86 (95% CI 0.83 to 0.88, P < 0.001) Women: 2006: RR 0.90 (95% CI 0.87 to 0.92, P < 0.001). 2007: RR 0.86 (95% CI 0.84 to 0.89, P < 0.001) The smoking ban was associated with a reduction in AMI mortality

Analysis 4.1

Comparison 4 Mortality outcomes, Outcome 1 Effect on mortality rates.

Navigate to figure in Review

Study

Location and ban

Smoking outcome

Results

ITS studies

Bajoga 2011

21 jurisdictions: 13 US states, 4 Canadian provinces, 4 countries Republic of Ireland (ROI), Scotland, New Zealand, Northern Ireland

Comprehensive

2009

Smoking prevalence surveys

Smoking status: self‐reported

In 18 jurisdictions, with exception of ROI, Delaware and New Mexico, there was a statistically significant decline in smoking prevalence prior to legislation

Immediate change noted in smoking prevalence and level of smoking in Washington ‐2.56 (95% CI ‐0.80 to ‐4.33); and in ROI ‐1.18 (95% CI ‐0.37 to ‐1.98)

Significant changes in trend post‐ban (compared to pre‐legislation) noted for 6 jurisdictions:

Delaware: ‐1.12 (95% CI ‐0.82 to ‐1.39)

Maine: ‐0.50 (95% CI ‐0.14 to ‐0.85)

New Jersey: ‐0.84 (95% CI ‐0.08 to –1.60)

New Mexico: ‐1.37 (95% CI ‐0.23 to ‐2.52)

Ohio: ‐1.43 (95% CI ‐0.33 to ‐2.54)

Rhode Island: ‐0.72 (95% CI ‐0.10 to ‐1.33)

The decline of smoking prevalence increased in these 6 jurisdictions in further post‐legislation period.

No change in smoking prevalence rates identified in 13 of 21 jurisdictions

Federico 2012

Italy

Comprehensive

2005

Smoking prevalence, quit attempts

Smoking status: self‐reported

Linear regression analyses

Smoking prevalence decreased post‐ban

Men : 37.8% (1999) to 34.4% (2010)

Women: 21.5% (1999) to 21.2% (2010)

Number of cigarettes smoked decreased over time and increased quit rates were observed

Smoking prevalence in men decreased, β = 2.6%, P = 0.002, and cessation rates increased, β = 3.3%, P = 0.006 after the ban. The rates returned to pre‐ban level subsequently

Among women the immediate change and change in smoking prevalence associated with the ban were not statistically significant. Long‐term trends in reducing smoking prevalence favoured highly educated, β = ‐0.3%

A reduction in smoking prevalence among lower‐educated women was observed, β = 1.6% decrease, P = 0.120 (NS), however significant increases in quit ratios were observed, 4.5%, P < 0.001 for low‐educated women. Trends reversed over time.

For younger‐aged 20 to 24 years, smoking ban associated with reduced prevalence for lower‐educated men, β = 1.3%, P = 0.088 (NS)

Overall the impact of ban on smoking and inequalities was short term

Gualano 2014

Italy

Comprehensive

January 2005

Smoking prevalence surveys

Smoking status: self‐reported

Annual surveys 2001 to 2013 of > 3000 adults nationally representative sample

Decrease in smoking prevalence 28.9% 2001 to 20.6% in 2013

Expected annual percentage change (EPAC) ‐2.6%, P < 0.001

Reduction in number of cigarette smoked, decrease, from 16.4/day to 12.7/day, EPAC ‐2.1%, P < 0.001

Decrease prevalence for men EPAC ‐2.9%,P < 0.001, women ‐2.5%, P < 0.001

Smoking intensity reduction greater in men:

18.8/day to 13.5 cigs/day 2013, EPAC ‐2.5, P < 0.001

Reduction in tobacco consumption in men aged 15 to 24 years, P = 0.02, and aged 25 to 44 years, P = 0.01

Women reduction in intensity 12.2 cigs/day reduced to 11.5 cigs/day. EPAC ‐1.0, P = 0.03

Significant reduction in tobacco consumption in women aged 15 to 24 years, P = 0.02; aged 25 to 44 years, P = 0.002, and aged 65 years and older, P = 0.02 Increase in consumption observed among women aged 45 to 64 years (NS)

Data show significant reduction in tobacco consumption, but no join point related to introduction of smoke‐free law

Jones 2015

England, 2007

Scotland, 2006

Comprehensive

Smoking prevalence, tobacco consumption

Smoking status: self‐reported

For waves 1 to 18 of the surveys:

12,771 pooled smoker observations in Scotland (mean 0.779), in England number of smokers pooled 50,438 (mean 0.709)

Smoking prevalence Scotland (2‐way fixed‐effect model)

Men: n = 22,210; 0.00925 (0.43)

Women: n = 24,752; 0.0197 (1.05)

Prevalence of active smoking: little effect on overall prevalence in Scotland

Smoking intensity in Scotland:

No significant differences post‐ban in number of cigarettes smoked

Scotland:(England as control)

Small variation in smoking prevalence over time. Declining trends in smoking

Intensity of smoking: estimates are not significant. Insufficient evidence to conclude smoking ban results in decrease in cigarette consumption

Linear fixed trends identified in Scotland – decreased consumption in men 55 years and older by 0.28 half‐packs/1.4 cigarettes, P < 0.01 (10% level significance)

Estimates show increase in prevalence and intensity among male 'moderate smokers' (10 to 19 cigs/day)/0.325 half‐packs/1.6 cigarettes/day, P < 0.05

England (Scotland as control):

Impact of policy at 1 year: reduction in consumption men aged 18 to 34 years 0.432 half‐packs/2.16 cigs, P < 0.05

England (Scotland as control)

Women aged 55 years and older: reduction in consumption ‐0.083 half‐packs/1.3 cigs (NS)

Increased consumption in age 35 to 54 years by 0.2625 half‐packs/1.31 cigs/day, P < 0.05

Inconclusive findings reported. Smoking bans are not effective in reducing smoking consumption

Klein 2014

USA, Ohio

Comprehensive

2007

Preconceptual smoking prevalence in low‐income women

Smoking status:

self‐reported

Mothers (pregnant and post‐partum) who gave birth March 2002 to December 2009

Spline regression analyses used. n = 483,911

Pre‐smoking ban current smokers 43.3%

Post‐smoking ban, current smokers 39.9%

Lower odds of preconceptual smoking associated with being non‐white, higher educational attainment, > 50% federal poverty level, aged less than 20 years or older than 30 years and having more than one child and living in city location (compared to ref groups). Living in rural location was associated with higher odds of preconceptual smoking among low‐income women compared with women living in suburban location: OR 1.05 95% CI 1.02 to 1.08)

April 2001 to May 2007( pre‐ban), no statistical difference in preconceptual smoking levels in low‐income women

Statistically significant differences post‐legislation OR 0.98 (95% CI 0.98 to 0.99)

For every 6 months after policy, the odds of preconception smoking decreased 11% after accounting for social demographic differences

Mackay 2011

Scotland

Comprehensive

2006

Smoking prevalence and quit attempts

Smoking status:

self‐reported

Prevalence of smoking fell 8.0% from 31.3% in period January to March 1999 to 23.7% July to September 2010. Steep decline in quarter preceding legislation.

Effect October to December 2005 (prior to legislation) smoking prevalence fell 1.7% (95% CI ‐2.38 to ‐1.02, P < 0.001). 1.7% absolute reduction in smoking prevalence. This effect was not sustained.

Quit attempts:

NRT prescribing was significantly higher prior to legislation. Following the smoking ban, prescribing costs fell by 26% per month (95% CI 17% to 35%, P < 0.001). 12 months post‐smoking ban, the prescription costs were not significantly different to 2003 to 2005 period

Quit attempts increased prior to legislation and resultant fall in smoking prevalence. The effects were not sustained

Controlled before‐and‐after studies

Bharadwaj 2012

Norway

Comprehensive

2004

Smoking prevalence and pregnancy outcomes

Smoking status:

self‐reported

Approximately 20% of mothers in treatment group (working in bars and restaurants) reported smoking at start of pregnancy, 64% were not smoking at start of pregnancy. No details reported for remainder. Following the smoking ban, mothers in the treatment group were 15.4% more likely to quit smoking during pregnancy (P < 0.05) than women working in other settings

This study identified that mothers working in bars and restaurants after smoke‐free legislation was introduced were 15% more like to quit smoking and this impacted on increased birth weights and on lower incidences of preterm births

Ferrante 2012

Argentina,

Santa Fe

Comprehensive August 2006

Control: Buenos Aires City: partial October 2006

Smoking prevalence

Smoking status reported from national prevalence data, surveys in 2005 & 2009

Non‐significant decreases in smoking prevalence in both cities over period

2005:

Santa Fe 27.3% (95% CI 24.3 to 30.5), Buenos Aires: 27.4% (95% CI 24.4 to 30.6), (difference between cities NS, P = 0.95)

2009:

Santa Fe 26.6% (95% CI 25.5 to 27.8), Buenos Aires: 26.1% (95% CI 22.8 to 29.7), (difference between cities NS, P = 0.84)

More quit attempts in Sante Fe in year prior to 2009 survey than in control, 53.2% (95% CI 42.5 to 63.6) vs 44.4% (95% CI 34.3 to 55.0, P = 0.045). No change in proportion of daily smokers or cigarettes consumed in either area between 2005 and 2009

Hahn 2008

USA,
Kentucky,Fayette County

Comprehensive
April 2004

Control: 30 counties with no smoking ban

(and remaining 112 counties)

Smoking prevalence

Smoking status:

self‐reported

Fayette County: pre‐law 25.7% (95% CI 21.2 to 30.1); post‐law 17.5% (95% CI 11.8 to 23.1) = 31.9% reduction

Control area: pre‐law 28.4% (95% CI 26.8 to 30.0); post‐law 27.6% (95% CI 25.2 to 30.0) = 2.8% reduction. Significant reduction in smoking prevalence pre‐law to post‐law periods and between intervention and control areas (Wald Chi² = 5.5, P = 0.02) after controlling for seasonality, time trends, demographic characteristics

Page 2012

USA, Pueblo City, Colorado Comprehensive

2003

Control: El Paso County, Colorado

Maternal smoking

LBW and preterm births

Smoking status:

self‐reported

Significant differences observed at baseline between the intervention city and the comparison in relation to mother's mean age. race, ethnicity, education, alcohol consumption, marital status and anaemia

Significant differences existed in relation to previous pregnancy and medical history. Mothers from Pueblo were more likely to be Hispanic, have lower education and report previous pregnancy complications

Results identified a significant increase in mother's smoking in the control city (8.66% pre‐ban compared to 11.89% post‐ban, P < 0.0001)

The percentage of mothers smoking in Pueblo was unchanged (16.64% at baseline and 15.07% post‐ban, P = 0.0786, NS)

When compared to control city, the smoking ban in Pueblo was associated with a 38% reduction in odds of maternal smoking: OR 0.620 (95% CI 0.529 to 0.727, P < 0.05)

Before‐and‐after studies (no control)

Cesaroni 2008

Italy, Rome

Comprehensive

2005

Smoking prevalence

Smoking status: self reported from national survey data

Prevalence: Men: 34.9% pre‐law period (2002 ‐ 2003) to 30.5% post‐law period (2005).
Women: 20.6% pre‐law to 20.4% post‐law

Cigarette sales decreased 2005 ‐5.5%

Data from the post‐law was compared with data in the previous year, the effect of the law was statistically significant for men but not women and was greater for residents living in lower socioeconomic areas than those from higher socioeconomic areas

Cox 2014

Belgium, Flanders

Partial

2007

Smoking prevalence reported from national data

Reports a decrease in Belgian smoking prevalence (2004 ‐ 2008) from Belgian Health Survey Active smokers stable from 1997 to 2004. but decreased significantly 2004 to 2008 for men and women. Prevalence of smoking in women reduced from 22% in 1997 to 17.9% in 2008 Prevalence of heavy smoking in population decreased (more than 20 cigs/day) from 7.7% to 4.9%

Gallus 2007

Italy

Comprehensive

January 2005

Smoking prevalence and tobacco consumption

Smoking status:

self‐reported

2001/2 vs 2003/4: No significant difference in smoking prevalence
2005/6 vs 2003/4: Significant reduction (P < 0.05) in prevalence in total population, in men and in people aged 15 to 44 years

Smoking prevalence:
2004: 26.2%; women 22.5%, men 30%
2005: 25.6%; women 22.2%, men 29.3%
2006: 24.3%; women 20.3%, men 28.6%

Reduction in mean daily cigarette consumption: 15.4 in 2004 (men: 16.7; women: 13.7), to 14.6 in 2005 (men: 16.3; women: 12.4) and 13.9 cig/day in 2006 (men: 15.1; women: 12.4)

Reduction in smokers consuming ≥ 15 cig/day from 15.2% in 2004 to 13.2% in 2005 to 11.7% in 2006

Hurt 2012

USA, Minnesota, Olmsted County

2002, 2007

Comprehensive

2007

Smoking prevalence

Smoking status:

self‐reported.

National data used for smoking prevalence.

Smoking prevalence at baseline for 25.1% (myocardial infarction; MI) and 15.7% (sudden cardiac death; SCD). No significant differences post‐ban. BRFSS data reported smoking decreased in 2000 from 19.8% to 14.9% in 2010

Significant differences noted pre‐ordinance 1 and post‐ordinance 2 for MI. Incidence of MI declined by 33%, P < 0.001 from 150.8 to 100.7/100,000 population, adjusted RR 0.6 (95% CI 0.53 to 0.83)

Incidence of SCD declined pre‐ordinance 1 and post‐ordinance 2 by 17%, P = 0.13, 109.1 to 92.0/100,000 population, RR 0.83 (95% CI 0.65 to 1.06, NS)

During period of study, prevalence of smoking declined and prevalence of hypertension, diabetes mellitus, hypercholesterolaemia and obesity remained constant or increased

Decrease in incidence of MI not explained by factors other than reduced smoking prevalence

Kabir 2009

Ireland

Comprehensive

2004

Perinatal outcomes

Maternal smoking and quit rates

Smoking status:

self‐reported

1 year post‐smoking legislation, a 25% decrease in risk of preterm births was observed; OR 0.75 (95% CI 0.59 to 0.96)

There was a 43% increased risk of LBW; OR 1.43 (95% CI 1.10 to 1.85) after adjusting for all potential confounders

A 12% reduction in maternal smoking rates (23.4% to 20.6%) was observed post‐ban

There was an increase in smoking cessation prior to pregnancy in 2005, P = 0.047. Former smokers increased from 23.9% to 24.4%

Significant decline in preterm births and maternal smoking. Increase in LBW birth risks may reflect secular trend

Larsson 2008

Sweden

Comprehensive
June 2005

ETS exposure, smoking prevalence

Active smoking and

SHS exposure measured

cotinine levels

No change in median cigarettes per day: 17 cig/day to 15 cig/day at 12 month follow‐up, P for trend = 0.788, NS. No significant reduction for cigarette consumption for either gaming (casino or bingo hall) or for other hospitality employees. Small number of smokers at baseline

No change in smoking status from baseline to 12 months follow‐up. Small number of smokers at baseline that responded at follow‐up, n = 14

Significant reduction in the percentage of employees reporting exposure to SHS for 75% of more of their time at work. 59/91 (65%) pre‐ban vs 1/71(1%) at follow‐up, P < 0.001

Greater duration of SHS exposure amongst gaming employees than other hospitality employees at baseline (P value for trend = 0.029) but duration of SHS exposure was similar in both at follow‐up

No statistical changes in spirometry/lung function or cigarettes consumed at 1 year follow‐up

Lee 2011

England

Comprehensive

July 2007

Smoking prevalence

Smoking status:

self‐reported

Response rates 61% to 73% over the period of the surveys 2003 to 2008

Current smokers decreased 25% in 2003 to 21% in 2008, Adjusted odds ratio (AOR) 0.96/year (95% CI 0.95 to 0.98, P < 0.01)

Mean number cigarettes consumed decreased 14.1 to 13.1, ‐0.28 ± 0.06, P < 0.01

The implementation of smoke‐free legislation was not associated with a statistically significant change in the trend in smoking prevalence: AOR 1.02 (95% CI 0.94 to 1.11, P = 0.596); or number of cigarettes smoked per day 0.42, SE = 0.28, P = 0.142. After controlling for time and other trends, no significant differences reported post‐ban

Older respondents less likely to smoke compared to younger aged (18 to 34 years) AOR 0.55 (95% CI 0.52 to 0.58, P < 0.001) and women more likely to smoke, AOR 1.07 (95% CI 1.03 to 1.12, P < 0.001)

Reduction in smoking at work from 15% pre‐ban to 2% post‐ban, AOR 0.12, P = 0.0005 Reduction in smoking in pubs or bars 36% to 3%, AOR 0.04, P = 0.0005

Decreased smoking in cafes/restaurants AOR 0.12, P < 0.0005 and inside homes AOR 0.67, P = 0.001

Smoking in cars decreased from 32% to 26%, AOR 0.73, P = 0.015, and smoking outside increased 45% to 63% post‐ban, AOR 2.11, P = 0.0005

No hardening of current smokers noted. As prevalence decreased so did consumption per smoker

Lemstra 2008

Canada,
Saskatoon

Comprehensive

2004

Smoking prevalence

Smoking status: self reported

Smoking prevalence decreased from 24.1% (95% CI 20.4 to 27.7) in 2003 to 18.2% (95% CI 15.7 to 20.9). Follow‐up survey in 2005 reported 19.5% current smokers (95% CI 16.9 to 21.8). 77 of the 1255 respondents reported quitting smoking in the year following the ban

Comparative data with Saskatchewan and all of Canada, identified statistically significant relative reductions in smoking prevalence in Saskatoon, P < 0.0001

Lippert 2012

Country: USA,

Arizona 2007*

Colorado 2006

District of Columbia 2007

Hawaii 2006*

Illinois 2008*

Iowa 2008*

Louisiana 2007

Maryland 2008*

Minnesota 2007

Nevada 2006

New Hampshire 2007

New Jersey 2006*

New Mexico 2007

Ohio 2006*

Pennsylvania 2008

Puerto Rico 2007*

Utah 2006*

Clean Indoor Air Act

(varied implementation)

* all Comprehensive bans.

Remaining States: Partial bans.

Smoking prevalence

Smoking status: self reported

1 year pre‐/post‐ data. Average time post‐ban 3.06 years

5 States (Colorado, Hawaii, Nevada, New Jersey, Ohio) 4‐year interval

8 states/territory (Arizona, District of Columbia, Louisiana,Minnesota,New Hampshire, New Mexico, Puerto Rico, Utah) 3‐year interval

4 States (Illinois, Iowa, Maryland, Pennsylvania) 2‐year interval

86,531,447, 28.2% population represented in 17 states

14 States had significant decrease in prevalence of current smokers. Highest difference post‐ban observed in New Hampshire, 3% change

6 states with the highest differences in current smoking status post‐ban are listed below (State N):

Colorado: (1106) 19.8% (95% CI 18.5 to 21.1) vs (1749) 17.0% (95% CI 15.9 to 18.1, P ≤ 0.0001)

Iowa: (956) 19.8% (95% CI 18.4 to 21.2) vs (882) 17.1% (95% CI 15.7 to 18.5, P ≤ 0.0001)

Maryland: (1450) 17.1% (95% CI 15.9 to 18.3) vs (1221) 15.1% (95% CI 13.9 to 16.3, P ≤ 0.0001)

New Hampshire: (1079) 18.7% (95% CI 17.4 to 20.0) vs (836) 15.7% (95% CI 14.2 to 17.3, P ≤ 0.0001)

New Jersey: (2384) 18.0% (95% CI 17.0 to 19.0) vs (1864) 15.8% (95% CI 14.7 to 16.9, P ≤ 0.0001)

New Mexico: (1263) 20.1% (95% CI 18.7 to 21.5) vs 1483) 17.9% (95% CI 16.6 to 19.2, P ≤ 0.0001)

6 states had significant increase in number of former smokers.

No state had increased prevalence of current smokers post‐legislation (Utah unchanged)

Mackay 2012

Scotland

Comprehensive

2006

ITS study of pregnancy outcomes

Smoking status

self‐reported

Post‐legislation there was a significant reduction in current smoking rates 25.4% to 18.8%, P < 0.001, and an increase in never‐smokers 57.3% to 58.4%, P < 0.001

Analysis 5.1

Comparison 5 Smoking and passive smoking outcomes, Outcome 1 Active smoking outcomes.

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Study	Country & ban	Outcome	Heading 3	Results	Heading 5
Durham 2011	Switzerland, Canton of Vaud Local ordinance Partial 2009	SHS exposure	Smoking status: self‐reported Lung function measures ETS exposure	1798 hospitality venues invited to participate. 2% response, n = 36 enrolled. 106 participants recruited from venues at baseline. 66 participants at follow‐up (31st May to 26th September 2010) ETS exposure declined significantly after introduction of new smoke‐free law Smokers had lung age 5.6 years older than chronological age Pre‐law: nonsmokers inhaled equivalent of 1.4 to 7.4 cigarettes/day. Post‐law significantly reduced P < 0.05 (figure not given) Lung function: improved in women + 3.07%, P = 0.05; nonsmokers + 3.91%, P =0.04; and in older participants + 4.22%, P = 0.004	Passive health outcomes
Goodman 2007	Ireland Comprehensive March 2004	Respiratory function, ETS exposure in hospitality workers	Self‐reported exposure to SHS was validated by carboxyhaemoglobin, exhaled CO and salivary cotinine	Total ETS exposure to SHS was 46.9 hours pre‐ban and 4.2 hours post‐ban, a decrease of 90% Exposure to SHS outside of work: Mean 6.4 hours pre‐law vs 3.7 hours at 1 year post‐law (% change) ‐42%; P ≤ 0.01 FVC parameters increased significantly in never‐smokers, it declined in current smokers. FEV1 did not change significantly in any group; increased in nonsmokers Significant reduction in carboxyhaemoglobin by 5% in the never‐smoker group, but no significant reduction in ex‐smokers and current smokers. 79% reduction in exhaled CO for never‐ and ex‐smokers but no significant change in current smokers. Exhaled CO median (interquartile range) ppm: 4.0 (IQR, 3 ‐ 5) pre‐law vs 2.0 (IQR, 2 ‐ 3) follow‐up, P < 0.001 Median exhaled breath CO and salivary cotinine decreased by 79% and 81% respectively in never‐ and ex‐smokers. Saliva cotinine median (IQR) ng/ml: 5.1 (IQR 3.4 ‐ 7.6) pre‐law vs 0.6 (IQR 0.3 ‐ 1.3) follow‐up, P < 0.001	Passive
Larsson 2008	Sweden Comprehensive June 2005	ETS exposure, smoking prevalence	Active smoking and SHS exposure measured cotinine levels	No change in median cigarettes per day: 17 cig/day to 15 cig/day at 12 month follow‐up, P for trend = 0.788, NS. No significant reduction for cigarette consumption for either gaming (casino or bingo hall) or for other hospitality employees. Small number of smokers at baseline No change in smoking status from baseline to 12 months follow up. Small number of smokers at baseline that responded at follow‐up, n= 14. Significant reduction in the percentage of employees reporting exposure to SHS for 75% or more of their time at work. 59/91 (65%) pre‐ban vs 1/71(1%) at follow‐up, P < 0.001. Greater duration of SHS exposure amongst gaming employees than other hospitality employees at baseline (P value for trend = 0.029) but duration of SHS exposure was similar in both at follow‐up. No statistical changes in spirometry/lung function or cigarettes consumed at 1‐year follow‐up	Passive Health outcomes
Pell 2008	Scotland Comprehensive 2006	SHS exposure in nonsmokers	Smoking status validated	Persons who never smoked reported decreased in SHS exposure and biochemically‐verified, serum cotinine mean 0.68 to 0.56 ng/ml; P < 0.001 post‐ban. SIgnificant reductions in both men and women, P < 0.001	Passive
Rajkumar 2014	Switzerland, Basel City, Basel County and Zurich Partial 2010	SHS exposure	SHS exposure validated	SHS biochemically measured using Monitor of Nicotine (MoNIC) passive sampling badges. Exposure to SHS decreased during the study. Of the 78 participants exposed to SHS at baseline, 55 were not exposed at follow‐up. Secondhand smoke exposure in 55 nonsmoking hospitality employees was 2.56, (95% CI 1.70 to 3.44) cigarette equivalents per day pre‐ban and was 0.16 (95% CI 0.13 to 0.20) at follow‐up	Passive

Analysis 5.2

Comparison 5 Smoking and passive smoking outcomes, Outcome 2 Passive smoking outcomes.

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Patient or population: Smokers and nonsmokers Settings: 21 countries including 12 European countries, Turkey, USA, Canada, Australia, New Zealand, Hong Kong, Argentina, Panama, Uruguay. Intervention: Comprehensive or partial smoking bans in public places implemented by legislation Comparison: No bans (note: observational data only)
Outcomes¹	Effects of intervention	Quality of the evidence (GRADE)²	Comments
Cardiovascular health	44 studies included. 43 studies evaluated incidence of acute myocardial infarction (AMI) and acute coronary syndrome (ACS), 33 of which detected significant associations between introduction of bans and reductions in events. 6 studies evaluated stroke incidence; 5 detected significant associations between introduction of bans and reductions in events	⊕⊕⊕⊝ moderate³
Respiratory health	21 studies included. Data imprecise with conflicting results. 6 of 11 studies reported significant reductions in COPD admissions. 7 of 12 reported significant reductions in asthma admissions	⊕⊝⊝⊝ very low⁴
Perinatal health	7 studies included. Data imprecise with conflicting results; due to study designs unclear if many of observed associations due to confounding factors	⊕⊝⊝⊝ very low⁴
Mortality	11 studies included. 8 detected significant association between introduction of bans and reduced smoking‐related mortality	⊕⊕⊝⊝ low
GRADE Working Group grades of evidence High quality: Further research is very unlikely to change our confidence in the estimate of effect. Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate. Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate. Very low quality: We are very uncertain about the estimate.
¹Note, original review also included changes in environmental tobacco smoke (ETS) exposure as an outcome. Evidence was unequivocal that bans were associated with significant reductions in ETS (see Callinan 2010), and hence we did not evaluate this outcome in this update. ²As all studies are observational, starting point for GRADE rating is low. Meta‐analyses not conducted; data summarized narratively. ³Upgraded due to evidence of a dose‐response effect. ⁴Downgraded due to imprecision.

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Comparison 1. Cardiovascular health outcomes

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Cardiac outcomes Show forest plot			Other data	No numeric data

1.1 ITS studies			Other data	No numeric data
1.2 Controlled before‐and‐after studies			Other data	No numeric data
1.3 Uncontrolled before‐and‐after studies			Other data	No numeric data
2 Stroke outcomes Show forest plot			Other data	No numeric data

2.1 ITS studies			Other data	No numeric data
2.2 Controlled before‐and‐after studies			Other data	No numeric data
2.3 Uncontrolled before‐after studies			Other data	No numeric data

Comparison 1. Cardiovascular health outcomes

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Comparison 2. Respiratory health outcomes

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 COPD Show forest plot			Other data	No numeric data

1.1 ITS studies			Other data	No numeric data
1.2 Controlled before‐and‐after studies			Other data	No numeric data
1.3 Uncontrolled before‐and‐after studies			Other data	No numeric data
2 Asthma Show forest plot			Other data	No numeric data

2.1 ITS studies			Other data	No numeric data
2.2 Controlled before‐and‐after studies			Other data	No numeric data
2.3 Uncontrolled before‐after studies			Other data	No numeric data
3 Lung function Show forest plot			Other data	No numeric data

3.3 Uncontrolled before‐and‐after studies			Other data	No numeric data

Comparison 2. Respiratory health outcomes

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Comparison 3. Perinatal health outcomes

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Effect on perinatal health Show forest plot			Other data	No numeric data

1.1 ITS studies			Other data	No numeric data
1.2 Controlled before‐and‐after studies			Other data	No numeric data
1.3 Uncontrolled before‐and‐after studies			Other data	No numeric data

Comparison 3. Perinatal health outcomes

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Comparison 4. Mortality outcomes

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Effect on mortality rates Show forest plot			Other data	No numeric data

1.1 ITS studies			Other data	No numeric data
1.2 Controlled before‐and‐after studies			Other data	No numeric data
1.3 Uncontrolled before‐and‐after studies			Other data	No numeric data

Comparison 4. Mortality outcomes

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Comparison 5. Smoking and passive smoking outcomes

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Active smoking outcomes Show forest plot			Other data	No numeric data

1.1 ITS studies			Other data	No numeric data
1.2 Controlled before‐and‐after studies			Other data	No numeric data
1.3 Before‐and‐after studies (no control)			Other data	No numeric data
2 Passive smoking outcomes Show forest plot			Other data	No numeric data

Comparison 5. Smoking and passive smoking outcomes

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Cochrane Review language

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Abstract

Background

Objectives

Search methods

Selection criteria

Data collection and analysis

Main results

Authors' conclusions

PICOs

PICOs

Population

Intervention

Comparison

Outcome

Plain language summary

Does legislation to ban smoking reduce exposure to secondhand smoke and smoking behaviour?

Visual summary

Authors' conclusions

Implications for practice

Implications for research

Summary of findings

Background

Description of the condition

Description of the intervention

How the intervention might work

Why it is important to do this review

Objectives

Methods

Criteria for considering studies for this review

Types of studies

Types of participants

Types of interventions

Types of outcome measures

Search methods for identification of studies

Data collection and analysis

Results

Description of studies

Study Design

Excluded studies

Risk of bias in included studies

Sequence generation and allocation concealment

Blinding

Incomplete outcome data

Selective reporting

Other bias

Sample size

Follow‐up

Biochemical verification

Adverse events

Assessment of heterogeneity

Effects of interventions

Primary objective: Effect on health outcomes

Cardiovascular outcomes (Analysis 1.1)

Cardiac outcomes

Outcomes in subgroups

Stroke outcomes

Respiratory outcomes

COPD (Analysis 2.1)

Asthma (Analysis 2.2)

Lung function (Analysis 2.3)

Perinatal outcomes (Analysis 3.1)

Mortality outcomes (Analysis 4.1)

Active smoking and reduced secondhand exposure

Active smoking (Analysis 5.1)

Effects on smoking behaviour in subgroups

Reduced secondhand exposure (Analysis 5.2)

Discussion

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