WHO Environmental Burden of Disease Series, No. 18

Second-hand smoke

Assessing the burden of disease at national and local levels

Mattias Öberg Maritta S. Jaakkola Annette Prüss-Üstün Christian Schweizer Alistair Woodward

A Microsoft Excel spreadsheet for calculating the estimates described in this document can be obtained from WHO/PHE. E-mail contact: [email protected]

Public Health and the Environment, Geneva European Centre for Environment and Health, Rome Free Initiative, Geneva

2010 Second-hand smoke: Assessing the burden of disease at national and local levels

WHO Library Cataloguing-in-Publication Data

Second-hand smoke: assessing the burden of disease at national and local levels / by Mattias Öberg … [et al].

(Environmental burden of disease series ; no. 18)

1.Tobacco smoke pollution - adverse effects. 2.Tobacco smoke pollution - analysis. 3.. 4.Risk factors. 5.Cost of illness. 6.Air pollution, Indoor. I.Öberg, M. II.Jaakkola, M.S. III.Prüss-Üstün, Annette. IV.Schweizer, C. V.Woodward, Alistair. VI.World Health Organization. VII.Series.

ISBN 978 92 4 159914 6 (NLM classification: HD 9130.6) ISSN 1728-1652

Suggested citation

Öberg M, Jaakkola MS, Prüss-Üstün A, Schweizer C, Woodward A. Second-hand smoke: Assessing the environmental burden of disease at national and local levels . Geneva, World Health Organization, 2010 (WHO Environmental Burden of Disease Series, No. 18).

© World Health Organization 2010

All rights reserved. Publications of the World Health Organization can be obtained from WHO Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland (tel.: +41 22 791 3264; fax: +41 22 791 4857; e-mail: [email protected] ). Requests for permission to reproduce or translate WHO publications – whether for sale or for noncommercial distribution – should be addressed to WHO Press, at the above address (fax: +41 22 791 4806; e-mail: [email protected] ).

The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement.

The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.

All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use.

The named authors alone are responsible for the views expressed in this publication.

Printed in Geneva.

Table of contents Preface...... vi Affiliations and acknowledgements...... vii List of acronyms and abbreviations ...... viii Summary...... ix 1. Introduction...... 1 1.1 Purpose and organization of the guide ...... 1 1.2 Definition of SHS...... 1 1.3 Properties of SHS ...... 2 1.4 Various population subgroups...... 3 1.4.1 Adults...... 3 1.4.2 Children...... 4 1.4.3 Sensitive groups ...... 5 1.5 Choosing the study population...... 5 1.6 Attributable and avoidable burden of disease...... 6 2. Summary of method ...... 7 2.1 Step 0: Define parameters of interest ...... 7 2.1.1 Step 0a: Set the framework ...... 7 2.1.2 Step 0b: Select diseases of interest...... 7 2.2 Step 1: Obtain key data...... 8 2.2.1 Step 1a: Collect health statistics...... 8 2.2.2 Step 1b: Assess of exposure within the study population ...... 8 2.2.3 Step 1c: Estimate the disease burden among adult non-smokers...... 8 2.3 Step 2: Calculate the population attributable fractions...... 8 2.4 Step 3: Calculate attributable burdens...... 8 2.5 Step 4: Describe uncertainty...... 9 2.6 Step 5: Summary and conclusions...... 9 3. Estimates of ...... 10 3.1 Recent major reviews...... 10 3.2 Selecting health effects associated with exposure to SHS ...... 11 3.3 Developmental effects...... 14 3.3.1 Low ...... 14 3.3.2 Preterm delivery...... 15 3.3.3 SIDS...... 16 3.3.4 Other developmental effects...... 17 3.4 Respiratory effects in children ...... 17 3.4.1 Lower respiratory tract infections...... 17 3.4.2 Pulmonary function ...... 21 3.4.3 Chronic respiratory symptoms ...... 22 3.4.4 ...... 23 3.4.5 in children ...... 26 3.5 Respiratory effects in adults...... 29 3.5.1 Asthma...... 29 3.5.2 Chronic obstructive pulmonary disease...... 32 3.5.3 Acute irritant effects ...... 33 3.5.4 Chronic respiratory symptoms ...... 33 3.5.5 Lung function impairment...... 34

iii Second-hand smoke: Assessing the burden of disease

3.6 ...... 34 3.6.1 ...... 34 3.6.2 ...... 36 3.6.3 Nasal sinus and nasopharyngeal carcinomas ...... 37 3.6.4 Childhood cancer...... 38 3.7 Cardiovascular diseases...... 38 3.7.1 Ischaemic heart disease ...... 39 3.7.2 Stroke ...... 39 3.8 Solid fuel use as a potential confounder ...... 40 4. Estimates of exposure...... 42 4.1 Considerations in exposure assessment in view of disease burden estimation ...... 42 4.1.1 Equivalence of exposure definition in surveys and in epidemiological studies...... 42 4.1.2 Time period of exposure ...... 43 4.2 Types of exposure assessment ...... 43 4.2.1 Use of questionnaires ...... 44 4.2.2 Use of ...... 44 4.2.3 Other measures of exposure ...... 45 4.3 Conducting an exposure ...... 45 4.4 Available surveys of SHS exposure...... 46 4.4.1 International databases on adults...... 46 4.4.2 International databases on children...... 47 4.4.3 National databases ...... 47 5. Availability of disease statistics ...... 48 5.1 National statistics ...... 48 5.2 Poor availability and alternative sources of statistics...... 48 6. Estimating the total disease burden related to SHS...... 49 6.1 Diseases to be included in the disease burden estimate...... 49 6.2 Calculation of the disease burden for children...... 49 6.3 Calculation of the disease burden for adults...... 50 7. Uncertainty ...... 51 7.1 Uncertainty with estimates of exposure ...... 51 7.2 Changes in exposure and disease prevalence over time ...... 51 7.3 Uncertainty of effect size...... 52 7.4 Study population and disease burden...... 52 8. Case-study ...... 54 8.1 Step 0: Define parameters of interest ...... 54 8.2 Step 1: Obtain key data...... 54 8.2.1 Step 1b: Exposure ...... 54 8.2.2 Step 1c: Burden of disease from selected health outcomes...... 55 8.3 Step 2: Calculate population attributable fractions...... 56 8.4 Step 3: Calculate attributable burdens...... 56 8.5 Step 4: Describe uncertainty...... 58 8.6 Step 5: Summary and conclusions...... 59 9. WHO policy...... 60 10. References ...... 65 Annex ...... 80

iv

List of tables Table 1: Selected recent major reports addressing adverse health effects from SHS exposure ...... 10 Table 2 : Recommended risk estimates and summary of conclusions of recent reviews of health effects of exposure to SHS in children ...... 12 Table 3: Recommended risk estimates and summary of conclusions of recent reviews of health effects of exposure to SHS in adult non-smokers ...... 13 Table 4: Odds ratios for postnatal exposure to SHS and SIDS...... 16 Table 5: Studies on lower respiratory infections and SHS exposure in developing countries...... 19 Table 6: Summary of pooled odds ratios (random effects model) for respiratory symptoms related to parental smoking among children...... 23 Table 7: Pooled odds ratios for the effect of smoking by either parent on middle ear disease a ...... 24 Table 8: Summary of pooled odds ratios (random effects model) for prevalence of asthma among children associated with parental smoking...... 27 Table 9: Available data on SHS exposure and smoking prevalence among adults in Country X...... 54 Table 10: Population in Country X in 2004 ...... 55 Table 11: Disease burden (deaths and DALYs) from selected outcomes in Country X in 2004 ...... 55 Table 12: Population attributable fraction for active smoking in Country X for adult asthma, lung cancer and IHD...... 56 Table 13: Population attributable fractions from SHS for Country X...... 56 Table 14: Burden of disease from SHS for Country X...... 58 Table 15: Sensitivity analysis of SHS mortality in Country X...... 59 Table A1: Population attributable fractions of lung cancer among men for active smoking, by country, 2004 ....80 Table A2: Population attributable fractions of lung cancer among women for active smoking, by country, 2004...... 84 Table A3: Population attributable fractions of ischaemic heart disease among men for active smoking, by country, 2004 ...... 88 Table A4: Population attributable fractions of ischaemic heart disease among women for active smoking, by country, 2004 ...... 92 Table A5: Population attributable fractions of asthma among men for active smoking, by country, 2004...... 96 Table A6: Population attributable fractions of asthma among women for active smoking, by country, 2004 ....100

v Second-hand smoke: Assessing the burden of disease

Preface The total burden of disease in a population and how that burden is distributed are important pieces of information for defining and prioritizing strategies to protect population health. Estimates of the avoidable burden of disease indicate the gains in health that could be achieved by targeted interventions. To assist policy-makers, the World Health Organization (WHO) has developed methods to carry out burden of disease analyses and has estimated the impacts worldwide of 26 risk factors, including climate change (Ezzati et al., 2002; WHO, 2002).

The Environmental Burden of Disease series aims to help countries generate reliable information for policy-making by presenting methods for estimating the environmental burden of disease at national, regional and local levels. The introductory volume in the series outlines the general method (Prüss-Üstün et al., 2003), whereas subsequent volumes focus on specific environmental risk factors. The guides on specific risk factors are organized similarly, first outlining the evidence linking the risk factor to health, and then describing a method for estimating the health impact of that risk factor on the population. All the guides take a practical, step-by- step approach and use numerical examples. The methods described can be adapted to local, regional and national levels and can be tailored to suit data availability.

Second-hand smoke (SHS) is one of the most important and most widespread exposures in the indoor environment. It affects a large proportion of the population, as smoking is prevalent (up to three quarters of adult men in some countries) and is seldom confined to outdoor settings. Children are commonly exposed to SHS when their parents are smokers. Some countries have passed legislation that prohibits smoking in the workplace, but elsewhere workers in the entertainment and food industries are frequently exposed to SHS on a daily basis. It has been known for many years that tobacco smoke is hazardous to health, and there is now a substantial literature that documents the risks associated specifically with exposure to SHS.

Other volumes of this series are available from the WHO web site. 1 For calculation sheets and support, please contact WHO ( [email protected] ).

1 http://www.who.int/quantifying_ehimpacts/national/en/index.html .

vi

Affiliations and acknowledgements Mattias Öberg is at the Institute of Environmental Medicine of the Karolinska Institute, Stockholm, Sweden. Maritta S. Jaakkola is at the Institute of Occupational and Environmental Medicine, University of Birmingham, Birmingham, , and at the Respiratory Medicine Unit, Institute of Clinical Medicine, University of Oulu, Oulu, Finland. Annette Prüss-Üstün is in the Department of and Environment at WHO, Geneva, Switzerland. Christian Schweizer is at the WHO European Centre for Environment and Health, Rome, Italy. Alistair Woodward is at the University of Auckland, Auckland, New Zealand.

We thank Leda Nemer from the WHO Regional Office for Europe, WHO European Centre for Environment and Health, Rome, for initiating the project and contributing to defining the scope as well as coordination. Their initiative was crucial for the development of the project. The Swedish Board of Health and Welfare is acknowledged for providing financial support.

Johnathan Samet, chair of the Department of Preventive Medicine, University of Southern California, Los Angeles, of America (USA), , professor of medicine at the University of California at San Francisco, USA, and Dr Daniel Ferrante, Dr Armando Peruga and Dr Roberto Bertollini from WHO provided valuable scientific comments on this report. We greatly appreciate the support received from Luminita Sanda, WHO, Geneva. Marla Sheffer of Ottawa, Canada, edited the report.

vii Second-hand smoke: Assessing the burden of disease

List of acronyms and abbreviations

ALRI acute lower respiratory tract infection ARI acute respiratory infection Cal-EPA California Environmental Protection Agency (USA) CI confidence interval COPD chronic obstructive pulmonary disease DALY disability-adjusted life year EBD environmental burden of disease

FEV 1 forced expiratory volume in 1 s GYTS Global Youth Tobacco Survey IARC International Agency for Research on Cancer IHD ischaemic heart disease ISAAC International Study of Asthma and Allergies in Childhood LBW LRI lower respiratory tract infection OR odds ratio PAF population attributable fraction RR relative risk SHS second-hand smoke SIDS sudden infant syndrome USA United States of America WHO World Health Organization

viii

Summary This guide proposes a method for estimating the disease burden from exposure to second-hand smoke (SHS) at the national, regional or local level. It summarizes the evidence on the exposure–risk relationships between SHS exposure and various health outcomes, reviews commonly available exposure data and proposes a method for combining these into an estimate of the burden of disease.

Exposure–risk relationships are summarized on the basis of the most recent reviews and meta-analyses. Relevant health outcomes are classified according to levels of evidence; outcomes with the most secure evidence of cause and effect include acute lower respiratory tract infection and asthma in children and asthma, lung cancer and ischaemic heart disease in adults.

Exposure estimates are reviewed in terms of their suitability for the quantification of disease burden and their general availability. Suitable points in time for exposure data are discussed, as the various outcomes have different time lags between exposure to SHS and development of signs and symptoms of disease.

To combine exposure and exposure–risk relationships and produce an estimate of disease burden due to SHS, the population attributable fraction is multiplied by the relevant disease burden of each health outcome in non-smokers. Uncertainty in the resulting estimates and the evaluation of this uncertainty are also discussed.

The burden of disease due to SHS worldwide has been estimated, based on the method outlined here and exposure data that are available at the national level. This estimate is documented in a separate publication (Öberg et al., 2010).

ix

Introduction

1. Introduction 1.1 Purpose and organization of the guide The relationship between exposure to second-hand smoke (SHS) and many health outcomes in children and adults has been examined closely in the epidemiological and experimental literature. Many reviews of the literature have been published by national and international organizations as well as by researchers within the field. This guide is based predominantly on recent reviews, rather than on original studies.

Although efforts have been made to reduce SHS exposure, many individuals are still exposed at home, at work and in public places. This guide is designed to help public health professionals in the assessment of the environmental burden of disease (EBD) from SHS exposure. It is structured into the following sections:

− Section 1 : The definition and properties of SHS, specific vulnerabilities to SHS and other methodological issues around the burden of disease estimation. − Section 2 : A comprehensive summary of the general method for determining EBD from SHS exposure. − Section 3 : The most recent evidence linking SHS exposure with a variety of health effects, together with the evidence-based exposure–risk relationships (e.g. odds ratios) for the selected outcomes. − Section 4 : Description of the most commonly available types of exposure information in national and international databases. − Section 5 : Overview of possible sources of health statistics. − Section 6 : Outline of the estimation of disease burden from SHS based on the previously described input parameters. − Section 7 : Sources of uncertainty and approaches for addressing this uncertainty. − Section 8 : A case-study with numerical examples to illustrate the methodology. − Section 9 : World Health Organization (WHO) policy on SHS.

The Annex provides proposed country estimates for population attributable fractions of active smoking, which are necessary parameters for estimating the burden due to SHS.

1.2 Definition of SHS SHS, which is also called environmental tobacco smoke, is formed from the burning of and other tobacco products and from smoke exhaled by the smoker. There is a recent trend to avoid using the term environmental tobacco smoke, because it implies that tobacco smoke may be treated as background or ambient (the term was originally introduced by the ). The descriptor “second-hand” captures the involuntary nature of the exposure, whereas “environmental” does not. Exposure to SHS can take place in the home, the workplace or other environments that are accessible to the public (e.g. bars, public transport). The exposure is frequently named “” or “involuntary smoking”. However, these terms suggest that

1 Second-hand smoke: Assessing the burden of disease

involuntary or passive exposure is not acceptable, but that voluntary or active exposure to tobacco smoke is acceptable. In this guidance document, we follow the official position from the Tobacco Free Initiative, which is to use the term SHS and avoid terms such as involuntary or passive smoking.

The smoke that passes into the environment from the between puffs () is the principal contributor to SHS. Approximately one half or more of the smoke generated (by weight) is sidestream smoke emitted from the smouldering end of the cigarette (Cal-EPA, 2005). Other components of SHS are exhaled mainstream smoke and mainstream smoke emitted at the mouthpiece during puff drawing. Once released into the environment of the smoker, the components of SHS are diluted and transported by the ambient air.

1.3 Properties of SHS Cigarette smoking is the main source of SHS exposure, because it is the most prevalent form of , although specific patterns differ between countries. Tobacco smoke contains thousands of chemicals that are released during burning as gases, vapours and particles. Mainstream smoke emitted as SHS is composed primarily of (3–11%), particles (15–43%) and (1–9%) (Baker & Proctor, 1990). However, over 4000 additional constituents have been identified in mainstream smoke, and approximately 400 compounds have been measured quantitatively in both mainstream and sidestream smoke (Jenkins et al., 2000). Minor constituents include, for example, compounds diffused through the wrapper and some vapour-phase components that diffuse into the environment. The total number of chemical species in mainstream smoke is unknown but may be more than an order of magnitude higher than the number identified to date (possibly more than 100 000).

There are significant differences in biological activity between mainstream smoke and sidestream smoke, arising largely from the lower temperature and less complete combustion in the smouldering cigarette. Many constituents have a higher rate of release into sidestream smoke compared with mainstream smoke. For example, twice as much nicotine and carbon monoxide and 15 times more formaldehyde were found in sidestream smoke compared with mainstream smoke (Borgerding et al., 2000). Overall, the sidestream element of SHS has been judged to be approximately 3 times more toxic than the mainstream element (Schick & Glantz, 2005). Once emitted into the air, SHS undergoes physical and chemical changes. , deposition and chemical modifications of reactive constituents tend to decrease their concentrations in SHS. However, there is evidence that evaporation of biologically less active components may cause the remaining sidestream smoke to be more toxic on a weight for weight basis (Schick & Glantz, 2006).

Many gaseous compounds in SHS are found to be toxic to humans. Several compounds found in sidestream smoke that have carcinogenic or other non-cancer health effects are listed in a recent report by the California Environmental Protection Agency (Cal-EPA) and include, for example, , benzene, carbon monoxide, formaldehyde and N- (Cal-EPA, 2005). With few exceptions, sidestream smoke contains greater mass emissions of these compounds compared with mainstream smoke. Among the substances with known health effects released as particles are, for example, arsenic, benzo( a)pyrene, , chromium(VI) and

2 Introduction

nicotine. The ultrafine particles (<0.1 µm), which travel deep into the lungs, cause direct immunological damage, but they also carry chemicals along with them. More than 50 constituents of tobacco smoke, such as arsenic, 4-aminobiphenyl and benzene, are known or suspected to cause cancer in humans. Several compounds found in tobacco smoke have also been listed as developmental or reproductive toxicants under California’s Proposition 65 (e.g. carbon monoxide, lead and nicotine). The United States National Toxicology Program estimates that at least 250 chemicals in SHS are toxic or carcinogenic (United States Surgeon General, 2006). However, the health effects of exposure to SHS cannot be estimated from any individual constituents, and the exposure–response relationships are generally assessed by epidemiological studies using exposure to SHS as a whole mixture.

1.4 Various population subgroups Behavioural and physiological factors may influence both exposure to SHS and sensitivity to its effects. This report focuses on the general population but acknowledges that exposure and sensitivity may vary by age, predisposing conditions, standard of local medical care and previous exposures.

It is also important to distinguish between the effects from maternal smoking during and the effects from maternal exposure to SHS. This report excludes exposure of the fetus due to active maternal smoking during pregnancy.

1.4.1 Adults Much of the mortality and morbidity attributable to exposure of adults to SHS is related to cardiovascular diseases and lung cancer. The exposure of adults may occur in various places: at work, in public places and at home. Measuring total exposure may therefore be complex. However, there are considerable opportunities to reduce exposure among adults by interventions in the workplace and public environments.

Numerous studies have concluded that exposure to SHS is harmful to those who have never smoked (“never-smokers”), but there is uncertainty regarding effects on ex- smokers and current smokers. Current smokers and ex-smokers tend to be excluded from studies of the health effects of SHS, not because they are considered immune to SHS, but because it is suspected that the large impact of active smoking may mask the more subtle health effects due to SHS.

There is no known or suspected mechanism by which active smoking would protect an individual against the effect of passive smoking, apart from the saturation of pathways such as aggregation and endothelial reactivity, which mediate the acute effects of smoking on cardiovascular risk. In this instance, SHS may have a relatively small effect in addition to the effects of active smoking. Otherwise, it is perhaps more likely that smokers have an increased susceptibility to the adverse effects of SHS, as their respiratory and cardiovascular systems are already damaged by tobacco smoke. It must also be noted that active smokers tend to have higher exposures to SHS than do non- smokers. In addition to the exposure to SHS from their own cigarettes, current smokers are more likely to live with a smoking spouse and spend more time in smoky environments (Skorge et al., 2006).

3 Second-hand smoke: Assessing the burden of disease

Only a few studies have explored the effects of SHS exposure on current smokers. In China, Hong Kong Special Administrative Region, SHS exposure is found to be associated with increased acute respiratory symptoms and increased recent outpatient service utilization in current smokers (Lam et al., 2005). From the National Health Interview Survey completed in the USA in 1991, Mannino and colleagues (1997) reported that current smokers with SHS exposure at home or at work were more likely to have reported an exacerbation of chronic in the 2 weeks preceding the survey than were those not exposed to SHS, but the differences were not statistically significant. In another study, no association was found between SHS exposure and more obstructive respiratory conditions among smokers (Dayal et al., 1994).

A variety of approaches may be taken to calculate the burden of disease associated with exposure to SHS. Mortality and morbidity may be calculated for never-smokers, ex-smokers and current smokers separately, assuming similar magnitudes of effects for the three groups. Another approach, based on the paucity of relevant data on effects in the other two groups, would be to include only never-smokers. The exclusion of current smokers and ex-smokers from estimates of health impacts due to SHS exposure would have a large influence on the results, as together these two groups may amount to two thirds of some adult populations, and disease rates (especially of lung cancer) are higher in these two groups than in never-smokers. For instance, in the 1980s, about 8% of all lung cancer deaths in New Zealand occurred among lifetime non-smokers (Kawachi et al., 1989).

The choice of analytical approach may also be limited by the available data on smoking prevalence and disease burden related to active smoking. For example, it may be necessary to conduct the analysis including all non-smokers (never-smokers and ex- smokers combined). Whichever approach is taken, it should ideally be accompanied by a sensitivity analysis considering alternative study populations. Further details are provided in section 6, in the case-study (section 8) and in the assessment of disease burden from exposure to SHS at the global level (Öberg et al., 2009).

1.4.2 Children Children differ from adults in several aspects that are relevant for the health impact assessment of SHS. For instance, their developing respiratory tract is generally more sensitive to environmental pollutants, and several health effects related to SHS exposure are specific to younger age groups, such as sudden infant death syndrome (SIDS) and middle ear infections. Children have a higher breathing frequency and inhale more air per body volume compared with adults. This results in a higher exposure compared with adults in the same environment. There are also age-related differences in liver metabolism and clearance rates.

The exposure pattern for children may differ considerably from that experienced by adults. Whereas adults are exposed at home, occupationally and in public places, most of the exposure among children, especially small children, occurs at home. Day-care facilities may also be an important source of exposure for children in many countries. As the child grows older, exposure to SHS may increasingly occur in environments where children learn, play or perform sports and other relevant places. In terms of health impacts, both the dose and duration of exposure are relevant.

4 Introduction

The specific pattern of children’s exposure has implications for opportunities to reduce exposure. Another aspect is that children have limited means of avoiding SHS, as they have no free choice with respect to their environment.

1.4.3 Sensitive groups There are several groups that may be especially sensitive to health effects from SHS exposure. These groups may be defined by factors such as age, predisposing conditions or illnesses and standard of local medical care.

Neonates and children are often considered to be sensitive populations. Susceptibility to SHS exposure is enhanced by prior exposure to tobacco products early in development. Children exposed to tobacco smoke in utero through either active or passive maternal smoking during pregnancy are more affected by subsequent SHS exposure, with more pronounced respiratory symptoms (Hajnal et al., 1999), higher respiratory infection rates (Jedrychowski & Flak, 1997; Strachan & Cook, 1997; Gilliland et al., 2001) and decreased pulmonary function (Li et al., 2000; Mannino et al., 2001; Rizzi et al., 2004; Svanes et al., 2004). Thus, maternal exposure to tobacco smoke during pregnancy may create a population at greater risk for the subsequent development of SHS-associated diseases. Elderly people are also a population group with special vulnerability to the adverse effects of SHS, as many diseases are more prevalent in this age group and as older people may be more restricted to indoor environments (Jaakkola, 2002).

Individuals with pre-existing asthma or cardiovascular diseases are known to be more severely affected by SHS exposure than others. Cystic fibrosis is a recessive genetic disorder affecting the mucous lining of the lungs, leading to breathing problems and other difficulties. The Cal-EPA (1997, 2005) documents summarize the extent and magnitude of the effects of SHS on individuals with cystic fibrosis as uncertain. The evidence for an effect of SHS on cystis fibrosis–related hospitalizations is seemingly convincing. However, the evidence regarding effects on pulmonary function or disease severity is less conclusive. Another predisposing condition that may influence vulnerability to the effects of SHS exposure is chronic obstructive pulmonary disease (COPD), which is characterized by mainly irreversible airflow obstruction, difficulty breathing, wheezing, chronic cough and phlegm production.

Standards of local medical care will affect the severity of the health burden related to SHS exposure. Respiratory infections such as otitis and lower respiratory tract infection (LRI) among young children are generally self-limiting in developed countries, but they may be lethal or result in permanent disability in low-income settings.

1.5 Choosing the study population The study population in this guide refers to the population for which the disease burden is calculated. The study population may include the population of an entire country, of selected cities or of specific age groups of interest (e.g. children). The study population should be selected according to the following criteria: − Data availability and reliability : Information about relative risks, exposures and outcomes should be available for the study population. The required exposure data

5 Second-hand smoke: Assessing the burden of disease

(e.g. exposure to SHS at home, in the workplace and in public places) and health data (e.g. lung cancer mortality, incidence of acute respiratory infections) are generally available at the country level and sometimes also at the district level or city level. − Policy relevance : Disease burden is often assessed in view of formulating health- protective policies or designing specific interventions. Population groups of interest may therefore be selected in terms of interest to decision-makers, provided segregated health data are available (for selected cities, for children, etc.).

1.6 Attributable and avoidable burden of disease An assessment of the burden of disease due to the exposure of interest is a quantification of disease at a population level. Such quantification provides information on the attributable burden (“How much of the current health problem is attributable to past exposures?”) and the avoidable fraction (“How much ill-health in the future can be avoided if a particular risk factor/exposure is eliminated?”).

Attributable risk is a fundamental concept in the assessment of burden of disease, and it involves the ideas of attribution and causal inference. In most cases, there are many possible causes of a specific health outcome. Therefore, attributable risk cannot be applied at the individual level. On the whole, it is not possible to distinguish between individuals who become ill because they were exposed to SHS and individuals with the same outcome that was due to another set of causes. At the level of populations, however, the data can be used to calculate what fraction of the total burden of disease would have been avoided if the exposure to SHS had not occurred. The proportion of the total burden of disease that is due to exposure to an environmental risk factor is called the population attributable fraction for that risk factor.

6 Summary of method

2. Summary of method The method proposed in this guide is summarized by the following main steps. More detail is provided in the relevant sections.

2.1 Step 0: Define parameters of interest 2.1.1 Step 0a: Set the framework Select the study population of interest; define which geographical boundaries and age groups to include. We recommend that the study population be restricted to non- smokers (because of a lack of evidence on effects of SHS on current smokers) and, if possible, that never-smokers be distinguished from ex-smokers.

2.1.2 Step 0b: Select diseases of interest For children, outcomes with the strongest evidence of effect include:

− low birth weight (LBW), − SIDS, − LRI, − otitis media (acute and/or recurrent), − asthma (onset).

Suggestive evidence exists for:

− preterm delivery.

For adults, outcomes with the strongest evidence of effect include:

− lung cancer, − ischaemic heart disease (IHD), − asthma (onset).

Suggestive evidence exists for:

− asthma (exacerbation/severity), − COPD, − breast cancer, − stroke.

In addition, some condition-specific impacts of SHS can be addressed separately. These may include:

7 Second-hand smoke: Assessing the burden of disease

− respiratory symptoms such as wheeze, cough, phlegm and dyspnoea among children and adults, − serous otitis media among children, − decreased pulmonary function in children.

2.2 Step 1: Obtain key data 2.2.1 Step 1a: Collect health statistics For the selected population groups, collect health data for the outcomes of interest (e.g. mortality, disease incidence or a combined measure of mortality and morbidity, such as disability-adjusted life years [DALYs]).

2.2.2 Step 1b: Assess prevalence of exposure within the study population Assess exposure prevalence (i.e. percentage of population exposed to SHS) using either available information or purpose-made surveys. Exposure needs to be specified by age group.

2.2.3 Step 1c: Estimate the disease burden among adult non-smokers This may be done in a variety of ways. If there are no data to permit a direct calculation, another approach is to subtract the disease burden due to active smoking from the total number of deaths, cases or DALYs. If no country-specific estimates are available, WHO can provide prior estimates for disease burden from active smoking. Note that this is an approximation of the burden of disease among individuals who are susceptible to the effects of SHS and may be an overestimate. Not all smoking-related disease that occurs among active smokers is due to their smoking.

2.3 Step 2: Calculate the population attributable fractions Use the formula for estimating the population attributable fraction (PAF) for each health outcome:

PAF = [p(RR − 1)]/[p(RR − 1) + 1] where: p = proportion exposed to SHS in the specified age group RR = relative risk for outcome in a specified population group

2.4 Step 3: Calculate attributable burdens Calculate the attributable burden by multiplying the population attributable fraction by the disease statistics (deaths, incidence or DALYs) for the total burden in children and for the burden in adult non-smokers. If there is substantial active smoking, the burden related to this factor may need to be subtracted.

8 Summary of method

2.5 Step 4: Describe uncertainty Describe the causes of uncertainty, and analyse the sensitivity of the calculated health burden to the assumptions made. A detailed and transparent description of the procedure followed, including assumptions and modifications, should be included in the final presentation of the results.

2.6 Step 5: Summary and conclusions The sequence is repeated for each outcome considered, and the attributable burdens are added together to obtain the total EBD for SHS exposure. This quantity may also be reported on a per capita basis or separately by specific health outcomes and age/sex groupings.

9 Second-hand smoke: Assessing the burden of disease

3. Estimates of relative risk 3.1 Recent major reviews The topic of health effects of SHS exposure has been addressed in several national and international reports over the years. The scientific literature on SHS is very large and increasing. The present guide does not aim to produce a new review of the extensive literature within the field of SHS, but instead uses the most recent existing reviews and reports.

The major reports that provide recent reviews of the data are shown in Table 1. These reports have systematically searched the literature and evaluated the evidence for causality, judging the extent of the knowledge available and then making an inference as to the nature of the association. In addition, several meta-analyses have recently updated the risk estimates for selected health outcomes, and these have also been included here (see health outcome–specific sections below). A brief overview of the major reviews is provided below.

Table 1: Selected recent major reports addressing adverse health effects from SHS exposure

Agency Publication title Place and date of publication World Health Organization (WHO) International consultation on environmental tobacco Geneva, Switzerland smoke (ETS) and child health 1999 International Agency for Research Tobacco smoke and involuntary smoking Lyon, on Cancer (IARC) 2004 Cal-EPA Health effects of exposure to environmental tobacco Sacramento, California, USA smoke 1997 Cal-EPA Proposed identification of environmental tobacco Sacramento, California, USA smoke as a toxic air contaminant 2005 Surgeon General, United States The health consequences of involuntary exposure to Atlanta, Georgia, USA Department of Health and Human tobacco smoke: a report of the Surgeon General 2006 Services

In 1999, WHO published a report from an international consultation on SHS and children’s health as a response to the 1997 Declaration of the Environment Leaders of the Eight (Group of Eight) on Children’s (WHO, 1999). The consultation brought together experts from developed and developing countries to examine the effects of SHS on children’s health and to recommend interventions to reduce these harmful effects. The consultation report specifically addresses respiratory and middle ear disease, reduced fetal growth, SIDS, neurodevelopmental/behavioural outcomes, cardiovascular effects in adults and childhood cancer.

In 2004, IARC published its 83rd monograph with a focus on carcinogenic risks to humans in relation to tobacco smoke and involuntary smoking (IARC, 2004). Besides classifying tobacco smoke as carcinogenic to humans (Group 1), the monograph evaluated the data related to involuntary smoking and cancer among adults and children exposed to SHS. Based on clear evidence of a causal relationship with lung cancer, SHS is classified as carcinogenic to humans . The monograph includes a thorough review of the published literature on cancer, but it focuses on the classification of the factor SHS rather than on quantitative risk estimates.

10 Estimates of relative risk

Two of the most complete reports have been those from Cal-EPA (1997, 2005). The latter report contains the most up-to-date references of all the major reviews. As part of the environmental health hazard assessment, Cal-EPA identified specific health effects causally associated with exposure to SHS. The agency also estimated the annual excess in specific health outcomes in the USA that are attributable to SHS exposure.

The most recently published report is the 29th report of the United States Surgeon General from the Department of Health and Human Services (United States Surgeon General, 2006), although most references date from 2001–2002 or earlier. The Surgeon General’s report examines the toxicology of SHS and includes an assessment of reproductive and developmental health effects and respiratory effects of exposure to SHS in children. In addition, the report systematically evaluates the evidence for causality for health effects (i.e. cancer, and respiratory effects) in adults. It uses meta-analysis to quantitatively summarize evidence as appropriate.

Similar reviews have been published elsewhere, such as the report by the Australian National Health and Medical Research Council, including an estimate of the amount of illness caused by SHS (NHMRC, 1997).

A limitation of all these reviews is that they are restricted to the English-language scientific literature. For a few outcomes that have been studied mostly in recent years (such as adult-onset asthma and COPD), we have extended the search and include reviews carried out by individual researchers.

3.2 Selecting health effects associated with exposure to SHS In calculating the attributable burden, it is necessary to select health outcomes for which (a) there is satisfactory evidence of a causal relationship with SHS and (b) there is sufficient quantitative information (e.g. in terms of relative risk). On this basis, outcomes may be grouped in the following way:

− Level 1 : There is sufficient evidence of causality. Includes outcomes for which there is a broad consensus on a causal relationship, that are extensively studied in many populations using a variety of valid study methods, for which positive findings have been reported quite consistently and for which there are plausible mechanisms.

− Level 2 : The evidence of causality is less convincing but is regarded as strongly suggestive. Includes outcomes that have been studied less extensively.

− Level 3 : The evidence of causality is limited or inconclusive.

Evidence on various health outcomes related to SHS exposure is constantly accumulating. We therefore suggest that Level 1 form a core set of outcomes for quantification of the burden of disease caused by SHS exposure. In some instances, investigators may choose to expand the burden of disease assessment to include Level 2 effects, acknowledging that the evidence for these outcomes is weaker. Level 3 outcomes are currently not supported by strong evidence, and we recommend that they not be included at this stage. Not all Level 1 or Level 2 effects are included in the set

11 Second-hand smoke: Assessing the burden of disease

suggested for quantification. One such example is the reduction in spirometric measures of lung function, which has a clear causal relationship to SHS exposure in children, but for which no estimate of relative risk is available at this time.

A summary of outcomes recommended for quantification and outcomes with more limited evidence, with their respective evidence levels, is provided in Table 2 for children and Table 3 for adults. For the outcomes supported by stronger evidence, additional details on the selection of outcomes, the evidence underlying each outcome and the relevant relative risk are provided in the following sections. Potential confounding has generally been dealt with in these large reviews, and summaries of the conclusions are reported in selected sections below, particularly in sections on health outcomes recommended for quantification.

Table 2 : Recommended risk estimates and summary of conclusions of recent reviews of health effects of exposure to SHS in children a Recommended Agency b Age RR/OR Outcomes in children Description (years) (95% CI) Exposure Reference Level A B C Developmental effects LBW Prevalence of LBW 0 1.38 Non-smoking Windham et al., 1 *** *** *** (<2500 g) at term (1.13–1.69) mother; any 1999 exposure at work or at home Preterm delivery Incidence of births 0 1.57 Non-smoking Cal-EPA, 2005 2 n *** ** before gestation week (1.35–1.84) mother; any 37 exposure at work or at home SIDS Incidence of SIDS <1 1.94 Smoking Anderson & 1 ** *** *** (1.55–2.43) mother c Cook, 1997 Spontaneous abortion – – – – – 3 n ** * / perinatal death Congenital – – – – – 3 n * * malformation Neuropsychological – – – – – 3 ** ** n development Physical development – – – – – 3 n * * Respiratory effects LRI Incidence of ALRI and <2 1.55 Either parent United States 1 *** *** *** hospitalizations (1.42–1.69) Surgeon General, 2006 Decreased pulmonary – <14 – – – 1 n ** *** function Chronic respiratory symptoms - Wheeze Prevalence of chronic <14 1.26 Either parent United States 1 *** *** *** wheeze (1.20–1.33) Surgeon General, 2006 - Cough Prevalence of chronic <14 1.35 Either parent United States 1 *** *** *** cough (1.27–1.43) Surgeon General, 2006 Otitis media - Acute Incidence of acute <8 No reliable Either parent United States 1 – – – otitis media (excluding summary Surgeon studies on specifically estimate General, 2006 recurrent otitis) available d

12 Estimates of relative risk

Recommended Agency b Age RR/OR Outcomes in children Description (years) (95% CI) Exposure Reference Level A B C - Acute and recurrent Incidence of any acute <4 1.66 Either parent Uhari et al., 1 *** *** *** otitis media (1.33–2.06) 1996 - Recurrent Incidence of recurrent <4 1.32 Either parent United States 1 – – *** otitis media (1.14–1.52) Surgeon General, 2006 - Serous Incidence of middle <4 1.33 Either parent United States 1 – *** – ear effusions (1.12–1.58) Surgeon General, 2006 Asthma - Onset Incidence of new <14 1.32 Either parent Cal-EPA, 2005 1 n *** ** cases of asthma (1.24–1.41) - Exacerbation Asthma symptoms, – – – – 1 *** *** *** hospitalizations, etc. Cancer Childhood e – – – – – 3 ** ** ** –, not available or not relevant in view of quantification; ALRI, acute lower respiratory tract infection; CI, confidence interval; OR, odds ratio; RR, relative risk a Shaded fields represent disease outcomes proposed for quantification. b A = WHO (1999); B = Cal-EPA (2005); C = United States Surgeon General (2006). The asterisks represent the conclusion in the report regarding level of evidence, where * = the evidence of causality is concluded to be “inconclusive”, “little”, “unclear” or “inadequate”; ** = the evidence of causality is concluded to be “suggestive”, “some” or “may contribute”; *** = the evidence of causality is concluded to be “sufficient” or “supportive”; and n = not evaluated in the report. c Value is based on maternal smoking. d Based on the Cal-EPA (1997, 2005) reviews and diagnostic difficulties of acute otitis; the risk estimate of Etzel et al. (1992) of 1.38 (1.21–1.56) can be used for an interim estimate, which corresponds to the mid-point of other reviews (see discussion in relevant section below). e IARC (2004) makes a distinction between all sites (**) and individual sites (*).

Table 3: Recommended risk estimates and summary of conclusions of recent reviews of health effects of exposure to SHS in adult non-smokers a

Recommended Agency b Age RR/OR Outcomes in adults Description (years) (95% CI) Exposure Reference Level B C D Reproductive effects Female fertility – – – – – 3 ** * n Other female – – – – – 3 ** * n reproductive toxicity (e.g. menopausal age) Male reproductive – – – – – 3 * * n toxicity Respiratory effects Asthma - Induction Adult-onset incident >20 1.97 At home and/or at Jaakkola et al., 1 *** ** n asthma (1.19–3.25) work 2003 - Exacerbation Asthma symptoms, – – – – 2 *** ** n hospitalizations, etc. COPD Prevalence of – 1.55 At home Eisner et al., 2 * ** n COPD (1.09–2.21) 2005 Acute irritant – – – – – 1 *** *** n symptoms and effects Chronic respiratory – – – – – – ** ** n symptoms - Wheeze Prevalence >18 1.99 At home and/or at Leuenberger et 2 - ** n (1.41–2.82) work al., 1994 - Phlegm Prevalence >18 1.69 At home and/or at Leuenberger et 2 - - n (1.23–2.31) work al., 1994

13 Second-hand smoke: Assessing the burden of disease

Recommended Agency b Age RR/OR Outcomes in adults Description (years) (95% CI) Exposure Reference Level B C D - Dyspnoea Prevalence >18 1.44 At home and/or at Leuenberger et 2 - - n (1.18–1.75) work al., 1994 Pulmonary function – – – – – 3 ** ** n Cancer Cancer (all cancer) – – – – – 3 ** n * - Lung Incidence >15 1.21 At home United States 1 *** *** *** (1.13–1.30) Surgeon 1.22 At work General, 2006 (1.13–1.33) - Breast Incidence of 15–50 1.68 At home and/or at Johnson, 2005 2 *** ** * premenopausal (1.31–2.15) work cases Incidence of any >15 1.25 At home and/or at Cal-EPA, 2005 3 * ** * breast cancer (1.08–1.44) work - Nasal sinus cavity – – – – – 2 *** ** * - Nasopharyngeal – – – – – 3 ** * * - Cervical – – – – – 3 ** * * - Urinary tract/bladder – – – – – 3 * n * - Stomach – – – – – 3 * n * - Brain – – – – – 3 * n * - Leukaemia – – – – – 3 * n n - Lymphoma – – – – – 3 * n n Cardiovascular diseases IHD Incidence of any >15 1.27 Non-smokers at United States 1 *** *** n IHD (1.19–1.36) home or at work Surgeon General, 2006 Stroke – – 1.82 Non-smokers at Bonita et al., 2 ** ** n (1.34–2.49) home or at work 1999 –, not available or not relevant in view of quantification; CI, confidence interval; OR, odds ratio; RR, relative risk a Shaded fields represent disease outcomes proposed for quantification. b B = Cal-EPA (2005); C = United States Surgeon General (2006); D = IARC (2004). The asterisks represent the conclusion in the report regarding level of evidence, where * = the evidence of causality is concluded to be “inconclusive”, “little”, “unclear” or “inadequate”; ** = the evidence of causality is concluded to be “suggestive”, “some” or “may contribute”; *** = the evidence of causality is concluded to be “sufficient” or “supportive”; and n = not evaluated in the report.

3.3 Developmental effects 3.3.1 Low birth weight LBW is generally defined as a birth weight less than 2500 g and can result from preterm delivery or intrauterine growth retardation. To avoid double-counting in the EBD assessment, it is important to separate these two categories of LBW (see section 3.3.2). For decades, it has been known that decreased fetal growth is causally related to the mother’s active smoking, with a typical mean birth weight 150–200 g less than in newborns of non-smokers and a doubled risk for LBW (Stillman et al., 1986).

Recent reviews of the literature on SHS and LBW found a small increase in risk for LBW at term or small for gestational age associated with SHS exposure (Misra & Nguyen, 1999; Windham et al., 1999; Lindbohm et al., 2002). Windham and colleagues estimated a decrement of 28.5 g with SHS exposure of non-smoking

14 Estimates of relative risk

women. The same authors also provided a pooled risk estimate of 1.38 (1.13–1.69) 1 for LBW at term or small for gestational age (Windham et al., 1999).

Since the meta-analysis by Windham et al. (1999), further evidence has accumulated of an effect of SHS exposure on birth weight. According to the Cal-EPA (2005) report, six of seven identified recent studies found an increased risk of LBW associated with SHS exposure, two of which were statistically significant.

In summary, all review reports conclude that there is sufficient evidence to infer a causal relationship between maternal exposure to SHS during pregnancy and a small reduction in birth weight. The outcome is classified as Level 1, and the odds ratio (OR) of 1.38 (1.13–1.69) suggested in the meta-analysis by Windham et al. (1999) is recommended as a base for calculation of the attributable number of infants with LBW at term among non-smoking mothers exposed at home or at work.

3.3.2 Preterm delivery Pregnancy complications, such as placenta previa, abruptio placentae and premature membrane rupture, may lead to preterm delivery (<37 completed weeks of gestation). Although the underlying mechanisms of causation by SHS are not yet fully understood, maternal active smoking is also associated with these pregnancy complications. The body of evidence has increased over the last few years. Epidemiological studies published during the 1980s and early 1990s did not find any relationship with SHS (Martin & Bracken, 1986; Ahlborg & Bodin, 1991; Mathai et al., 1992; Fortier et al., 1994). However, since 1997, at least seven studies have been published that have found a positive association between SHS and preterm delivery (six with statistical significance). Two of these studies used biomarkers to assess exposure (Jaakkola et al., 2001; Kharrazi et al., 2004). Kharazzi and colleagues (2004) presented a relative risk (RR) of 1.78 (1.01–3.13) for preterm delivery in the top 20% of SHS-exposed mothers compared with those whose levels were below the level of detection. Cal-EPA (2005) reviewed 11 studies that were published before 2004 and that reported adjusted estimates of preterm delivery risk associated with SHS exposure during pregnancy. From these studies, a summary odds ratio of 1.57 (1.35– 1.84) was calculated.

Although this evidence comes mainly from developed countries, it is likely that the impact of SHS on prematurity is at least as large in developing countries as it is in developed countries. In developing countries, additional risk factors (such as malnutrition) are more frequent, and it is plausible that SHS will have a larger impact on children who are already at higher risk.

In conclusion, there is growing evidence of a causal relationship between SHS exposure and preterm delivery. As the evidence is relatively recent, without broad consensus in major recent reports, preterm delivery is classified as a Level 2 outcome. We suggest that the odds ratio of 1.57 (1.35–1.84) presented by Cal-EPA (2005) be used to calculate the population attributable fraction of preterm delivery for non- smoking mothers exposed to SHS either at home or at work.

1 Numbers provided in parentheses are the 95% confidence interval (CI).

15 Second-hand smoke: Assessing the burden of disease

3.3.3 SIDS SIDS is characterized as a sudden and unexpected death in the first 12 months of life without any pre-existing health problems and without any explanations in autopsy. Most commonly, the child is found dead in his or her bed between the age of 1 and 4 months. Boys are affected somewhat more frequently than girls. SIDS has been causally linked to maternal smoking, although it has been difficult to separate the prenatal effects from the effects induced by postnatal SHS exposure.

Anderson & Cook (1997), in their systematic review of 39 relevant studies, concluded that the epidemiological evidence pointed to a causal relationship between SIDS and postnatal exposure to tobacco smoke. Nine studies included data on postnatal maternal smoking, of which four controlled for prenatal smoking (Table 4). Four studies had a complete adjustment for confounders such as prenatal maternal smoking (Table 4), with the adjusted odds ratios for postnatal maternal smoking varying between 1.65 and 2.39. The pooled adjusted odds ratio for SIDS associated with postnatal SHS from a smoking mother from these studies was 1.94 (1.55–2.43).

Table 4: Odds ratios for postnatal exposure to SHS and SIDSa

Odds ratio (95% CI) Study Type Maternal Paternal Other Original studies Mitchell et al., 1993 Case–control 1.65 (1.20–2.28) 1.37 (1.02–1.84) 1.17 (0.84–1.63) Schoendorf & Kiely, 1992 Case–control (black) 2.33 (1.48–3.67) 1.03 (0.43–2.47) Case–control (white) 1.75 (1.04–2.95) 1.63 (0.58–4.74) Klonoff-Cohen et al., 1995 Case–control 2.28 (1.04–4.98) 3.46 (1.91–6.28) 2.18 (1.09–4.38) Ponsonby et al., 1995 Case–control 2.39 (1.01–6.00) Meta-analysis Anderson & Cook, 1997 1.94 (1.55–2.43) a From studies with complete adjustment for confounders such as prenatal smoking used in the meta-analysis by Anderson & Cook (1997).

The Cal-EPA (2005) report identified a further 10 studies that independently examined the effect of postpartum maternal smoking, and they all found a significant association with SIDS, with five providing adjusted odds ratios ranging from 1.43 to 5.05. Three studies (Rajs et al., 1997; Milerad et al., 1998; McMartin et al., 2002) used biomarkers of exposure and found a strong association between SIDS and elevated cotinine or nicotine levels in pericardial fluid of the victims. Among the 10 studies, 3 (Elliot et al., 1998; Dwyer et al., 1999; Carpenter et al., 2004) could not separate prenatal and postnatal maternal smoking.

Whereas the majority of the studies have investigated the impact of maternal smoking on SIDS, only a few studies have addressed paternal smoking. Cal-EPA (2005) identified three studies (Brooke et al., 1997; Mitchell et al., 1997; Carpenter et al., 2004) that found an effect of paternal smoking, whereas a fourth (Dwyer et al., 1999) did not. Cal-EPA (2005) uses the odds ratio of 1.94 from the meta-analysis by Anderson & Cook (1997) for SHS exposure from postnatal maternal smoking. All three studies reporting a relative risk for paternal smoking after adjusting for maternal smoking show a significant relationship (Cal-EPA, 2005), which may point to a similar effect.

16 Estimates of relative risk

It should be noted that the incidence of SIDS is falling rapidly in many countries, probably due to public health interventions, such as advice on sleeping position. This affects the projected attributable burden of disease, but the relative risk due to SHS may also change as other causes are removed.

In conclusion, the strength of the evidence is sufficient to conclude that SHS is an independent risk factor for SIDS, and the effect is classified as Level 1. The meta- analysis of Anderson & Cook (1997) seems to be the only comprehensive review that is available. The authors report a pooled estimated odds ratio of 1.94 (1.55–2.43) for postnatal maternal smoking. In case a separate analysis for maternal and paternal smoking is to be made, further pooled analyses may provide a more specific estimate. In the meantime, the risk estimate for exposure to maternal smoking may be considered for quantification of disease burden in relation to any parental smoking.

3.3.4 Other developmental effects Other developmental effects that have been reviewed include spontaneous abortion and perinatal death, congenital malformations and neuropsychological and physical development. These outcomes are classified as Level 3 because of limited evidence, and they are not yet recommended for inclusion in estimates of EBD.

3.4 Respiratory effects in children 3.4.1 Lower respiratory tract infections LRI 1 cause approximately 3.9 million deaths annually (WHO, 2004), mainly in children of the developing world. Given the high incidence, LRI are probably the major health outcomes associated with SHS exposure among children, especially in the first 2 years of life.

More than 100 original publications with data on LRI in infants and young children are available. The majority of these were reviewed by Strachan & Cook (1997). Thirty- eight studies that looked at SHS and LRI, including early chest illness and admissions to hospital for lower respiratory illness in the first 3 years of life, were included in the meta-analysis by Strachan & Cook (1997). They reported a pooled odds ratio of 1.57 (1.42–1.74) for LRI in relation to smoking by either parent and 1.72 (1.55–1.91) and 1.29 (1.16–1.44) for maternal and paternal smoking, respectively.

In a meta-analysis of 13 studies by Li and colleagues (1999), the association between SHS exposure and LRI was estimated to be 1.93 (1.66–2.25) for overall risk of hospitalization. The odds ratios for LRI from SHS exposure were 1.71 (1.33–2.20) for children 0–2 years of age, 1.57 (1.28–1.91) for children 0–6 years of age and 1.25 (0.88–1.78) for children 3–6 years of age, respectively. Based on three Chinese studies that allowed a clear separation of the effects of prenatal and postnatal smoke exposure (the mothers did not smoke), an odds ratio of 2.13 (1.52–3.00) was estimated.

The United States Surgeon General’s (2006) report provides the most recent and updated meta-analysis that combines the studies from the 1997 review (Strachan &

1 J10–J18, J20–J22 in the International Statistical Classification of Diseases and Related Health Problems (WHO, 2007a).

17 Second-hand smoke: Assessing the burden of disease

Cook, 1997) with relevant recent publications, 34 studies in total. All except one study (Nuesslein et al., 1999) found an elevated risk of LRI associated with parental smoking. The meta-analysis merged several LRI end-points, such as lower respiratory infections/illnesses, wheezing illness and hospitalizations for LRI, and reported a pooled odds ratio of lower respiratory illnesses associated with either parent smoking of 1.55 (1.42–1.69). Fixed effects/unadjusted values have been used where available; 1.60 (1.38–1.84) is the corresponding value with random effects. Maternal smoking seemed to be associated with a higher risk (1.61; 1.47–1.75) than paternal smoking (1.31; 1.16–1.48) (fixed effects). With random effects, the pooled odds ratio is 1.66 (1.42–1.94) for maternal smoking; for paternal smoking, the number of studies is too small to allow an estimate to be calculated.

Less than half of the studies considered in the United States Surgeon General’s (2006) report reported an analysis of the impact of confounding. However, studies that controlled for potential confounding variables (e.g. family history of chest symptoms, birth weight, day care, use of cooking fuel) found only little or modest change in the effects of parental smoking after their adjustment. The relationship was therefore considered as largely independent of potential confounding variables.

After the first 2 years of life, the effect of SHS on the risk of LRI is much weaker, according to the United States Surgeon General’s (2006) report, which did not perform a formal analysis of the size of the risk in older children. The meta-analysis of Li et al. (1999), however, did provide estimates of the risk of LRI from SHS exposure by age; the excess risk (odds ratio) for children aged 3–6 years (1.25; 0.88–1.78) was about one third of that for children aged 0–2 years (1.71; 1.33–2.20). An odds ratio of one third of the excess risk from the United States Surgeon General’s (2006) report (i.e. an odds ratio of 1.18) could therefore be used as a conservative estimate for the age group between 2 and 6 years.

In conclusion, there is consensus on the causal relationship between SHS exposure and LRI, based on abundant evidence. The evidence is strongest for the first 2 years of life, and the effect is smaller in older age groups. The LRI among children aged 0–2 years is classified as a Level 1 outcome. An odds ratio of 1.55 (1.42–1.69) presented by the United States Surgeon General (2006) (and possibly an odds ratio of 1.18 for the age group 2–6 years) is recommended for the calculation of the population attributable fraction due to smoking by either parent, as this review provides a quantitative pooled risk estimate including the most recent evidence. In addition, this estimate is very close to the numbers estimated by Strachan & Cook (1997).

Risk estimate for developing countries Most studies have been conducted in developed countries, and exposure situations may be different in developing countries (e.g. presence of indoor smoke from solid fuel use, different pathogens involved, different number of cigarettes smoked at home by smoking parent, different ventilation in warmer climates). Therefore, studies from developing countries are analysed separately here. The United States Surgeon General’s (2006) report states that studies in developing countries have tended not to find an increased risk associated with exposure of children to parental smoking. However, this is not evident in Table 5, which summarizes the studies in the United States Surgeon General’s (2006) report and others identified by an independent search of the literature.

18 Estimates of relative risk

Table 5: Studies on lower respiratory infections and SHS exposure in developing countries

Sample Included in Exposure/ Study Design/ population size Outcome SG report OR (95% CI) smoker Tropical countries Armstrong & Cohort 587 ALRI No 1.9 (1.1–3.4) Father; either Campbell, 1991 Aged <5 years for 11–20 compared parent a Gambia with 0–10 c/day Azizi et al., 1995 Case–control 593 Severe ARI, No 0.99 (0.72–1.18) Father; either Aged <5 years mainly PN/BR parent a Malaysia (inpatients) Broor et al., 2001 Case–control 512 ALRI No 1.24 (0.83–1.86) Father; either Aged <5 years parent a India Campbell et al., Cohort 280 LRI No 2.35 (0.90–6.14) Father; either 1989 b Youngest sibling for 1–5 c/day parent a Gambia 3.81 (1.14–14.1) for 6–10 c/day 5.18 (1.57–9.25) for ≥11 c/day De Francisco et al., Case–control 543 ALRI mortality No 1.57 (0.96–2.56) Either parent 1993 Aged <5 years Gambia Dharmage et al., Case–control 200 ALRI (inpatients) No 1.76 (0.97–3.2) Father; either 1996 Aged <5 years parent a Sri Lanka 5.0 (1.7–14.6) Other smoker c Fonseca et al., Case–control 1300 PN No 1.44 (0.78–2.66) Any smoker 1996 c Aged <5 years for >40 c/day Brazil Gutierrez-Ramirez Case–control 285 PN (inpatients) No 3.44 (2.11–5.6) Any smoker et al., 2007 Aged <10 years Mexico Johnson & Case–control 206 Severe PN No 1.50 (0.69–3.29) Any smoker Aderele, 1992 Aged <5 years Nigeria Kumar et al., 2004 Case–control 100 Severe PN No 0.85 (0.46–1.37) Father Aged <5 years (inpatients) India Mahalanabis et al., Case–control 162 Severe PN No 0.85 (0.51–1.44) Father 2002 Aged 2–35 months (inpatients) India O’Dempsey et al., Case–control 239 Pneumococcal No 2.99 (1.10–8.15) Parents/ 1996 Aged <5 years infection others smoke Gambia in room Prietsch et al., Cross-sectional 771 ALRI No 1.22 (0.80–1.86) Mother 2008 survey for 1–9 c/day Aged <5 years 1.41 (1.07–1.86) Brazil for ≥10 c/day Savitha et al., 2007 Case–control 208 ALRI (inpatients) No 4.71 (2.62–8.48) Family history Aged <5 years of smoking India Victora et al., 1994 Case–control 1020 PN (inpatients) Yes 0.94 (0.72–1.22) Either parent Aged <2 years 1.02 (0.72–1.22) Mother Brazil 0.89 (0.64–1.24) Father Non-tropical countries Ajeel et al., 1991 Case–control 442 BR/PN No 0.94 (0.64–1.39) Father; either Aged <5 years (inpatients) parent a Iraq Chen et al., 1988 Cohort 2227 BR/PN Yes 1.25 (1.02–1.52) Father; either Aged <18 months parent a China

19 Second-hand smoke: Assessing the burden of disease

Sample Included in Exposure/ Study Design/ population size Outcome SG report OR (95% CI) smoker Gürkan et al., 2000 Case–control 58 ALRI symptoms Yes 2.0 (0.6–6.8) Either parent Aged 2–18 months and RSV antigen 3.6 (0.7–18.3) Mother Turkey (outpatients) 1.1 (0.2–4.8) Father Jin & Rossignol, Cohort 1007 BR/PN Yes 1.78 (1.18–2.68) Father; either 1993 Aged <18 months (inpatients) parent a China Kossove, 1982 Case–control 150 BR/PN No 2.18 (0.34–13.53) Any smoker Aged <1 year South Africa Richards et al., Survey 726 LRI Yes 1.75 (1.07–2.87) Either parent 1996 Aged <2 years 2.18 (1.25–3.78) Mother South Africa Véjar et al., 2000 Case–control 141 ALRI death No 4.1 (1.6–11.0) Mother Aged <5 years for >1 c/day Chile ARI, acute respiratory infection; BR, acute ; c/day, cigarettes per day; PN, pneumonia; RSV, respiratory syncytial virus; SG, United States Surgeon General (2006) a Very little smoking observed in mothers; therefore, the value for either parent is assumed to correspond to the value observed for fathers. b Not included in the analysis, as data set partly overlaps with Armstrong & Campbell (1991) study (J.R. Armstrong Schellenberg, personal communication). c Not included in the analysis, as not representative for studied relationship.

An inverse variance-weighted analysis of the studies in the United States Surgeon General’s (2006) report from developing countries (Chen et al., 1988; Jin & Rossignol, 1993; Victora et al., 1994; Richards et al., 1996; Gürkan et al., 2000) results in an odds ratio of 1.24 (1.08–1.43) (fixed effects) for parental smoking. The studies cited in the United States Surgeon General’s (2006) report include only one study from non- temperate countries, in which the greatest differences in exposure patterns might be expected. This study shows no effect (Victora et al., 1994). In a later publication (Victora, 1999), the authors comment on the discrepancy with other studies and observe that most positive studies included bronchospastic conditions as well as pneumonia, whereas the study published in 1994 was restricted to children with alveolar infiltrates.

The pooled analysis of all identified studies from developing countries (Table 5) results in an odds ratio (fixed effects, adjusted values used where available) of 1.34 (1.22–1.47). With a random effects analysis, reflecting to a certain extent that different effects are being measured, the odds ratio would be higher (1.52; 1.25–1.84) and practically identical to the one observed for all countries (including developed countries). Including only non-temperate countries (17 studies) does not provide a difference in results (fixed effects: 1.34, 1.20–1.49; random effects: 1.54, 1.22–1.95). Excluding studies analysing the effects of only mothers smoking also provides no difference in effects (fixed effects: 1.32, 1.20–1.46; random effects: 1.50, 1.21–1.85).

It appears from the Global Youth Tobacco Survey (GYTS) (CDC & WHO, 2008) that in some developing regions, the percentage of children exposed to any SHS in the home tends to be higher than the percentage having a smoking parent. Homes tend to be smaller and more crowded, and parents are often not the only smokers in the home. In Africa, for example, 3 times more children live in a home where others smoke in their presence than those having a parent who smokes. In other regions, the difference

20 Estimates of relative risk

is smaller. An analysis of studies with exposure to any smoker in the home results in a higher odds ratio of 2.74 (1.88–3.99) (fixed effects). This is, however, based on only four studies.

Only two studies have explicitly adjusted for exposure to solid fuel combustion and other factors, and both reported that the odds ratio for the risk of severe LRI from SHS remained essentially unchanged (Chen et al., 1988: unadjusted OR = 1.33, adjusted OR = 1.31; Jin & Rossignol, 1993: unadjusted OR = 2.0, adjusted OR = 2.4). On this basis, one might conclude that neither exposure to biomass combustion nor any of the other analysed parameters explain the observed relationship between exposure to SHS and LRI.

Studies on SHS exposure and LRI in developing countries are heterogeneous in terms of (a) exposure measurement, (b) relationship between assessed exposure variable and actual exposure, (c) measured outcome and (d) housing conditions—both physical parameters such as ventilation and crowding issues. In addition, potential confounders have rarely been adequately controlled for. Also, the most commonly assessed exposure parameter—namely, the parents being smokers—may be a less accurate indicator in developing countries, where, as mentioned above, often more than two adults share the home of the children. Despite these shortcomings, the research shows consistent relationships between exposure to SHS in low-income settings and LRI. The quality of studies may be weaker than in developed countries, and results probably vary greatly between countries due to different exposure patterns. Furthermore, the majority of studies in developing countries included children up to 5 years of age (14 out of 20 studies), and the somewhat lower odds ratio could partly be explained by this age range. We therefore suggest that burden of disease estimates use the overall odds ratio as proposed by the United States Surgeon General’s (2006) report.

Given the heterogeneity in factors affecting exposure across settings and continents, the lower odds ratio estimates from the pooled analysis of all developing country studies (fixed effects: 1.34; 1.22–1.47) may be used in the sensitivity analysis. It is also recommended that the health impacts be estimated separately from maternal and paternal smoking in the sensitivity analysis, using the odds ratios of 1.61 and 1.31, respectively, instead of using only parental smoking as an indicator of exposure. This is particularly important when the paternal to maternal smoking ratio varies substantially from what applies in developed countries, where most of the epidemiological studies to date have been performed.

To further increase the confidence in the effects estimate or increase precision, more high-quality studies based on adequate exposure measurement would be needed. For analysis of health impacts limited to a specific country, use of local evidence may provide more accurate results.

3.4.2 Pulmonary function There is a broad consensus that childhood exposure to SHS is associated with small decrements in several spirometric measures of lung function in preadolescents and adolescents in the range of 0.5–7%. The evidence shows that parental smoking reduces the maximum lung function level achieved, although the clinical significance of these decrements remains somewhat unclear. Nonetheless, a reduced maximum lung function level increases the risk for future chronic lung disease. The outcome is

21 Second-hand smoke: Assessing the burden of disease

therefore classified as Level 1. However, as reduced pulmonary function is not a disease per se but is related to several respiratory diseases, such as asthma and COPD, and generally no health statistics on pulmonary function per se are available at the country level, we do not recommend addressing this effect in the disease burden estimates.

3.4.3 Chronic respiratory symptoms Chronic respiratory symptoms include cough, phlegm, wheeze and dyspnoea (difficulty breathing or breathlessness). Again, these symptoms are not a disease per se, but are related to several respiratory diseases; therefore, we do not recommend including them in disease burden estimates, as this might result in double-counting. For example, wheeze is a common but nonspecific manifestation of asthma, and it is also related to other underlying causes, such as respiratory infections. Nevertheless, we will provide estimates that could be used to calculate the population attributable fraction, in case there would be specific interest in estimating the population attributable fraction of chronic respiratory symptoms due to SHS.

Children of parents who smoke show increased prevalence of respiratory symptoms, usually cough and wheezing, in most studies. The evidence is particularly strong for infants and preschool children, but there is also clear evidence of causality for school- aged children.

A meta-analysis of seven studies of wheezing illness in early childhood (Strachan & Cook, 1997) showed that maternal smoking increased the risk of wheezing (OR = 1.98; 1.71–2.30). The same authors also reviewed evidence of an effect of SHS on respiratory symptoms in school-aged children (Cook & Strachan, 1997). Since 1997, many studies have been based on data from the questionnaire developed for the International Study of Asthma and Allergies in Childhood (ISAAC) (Asher et al., 1995). The ISAAC protocol focuses more on asthma and related symptoms such as wheeze than on cough, phlegm or breathlessness.

The findings of the United States Surgeon General (2006) report are consistent with the previous reviews, with pooled odds ratios for children 11 years of age or younger of 1.26 and 1.35 for wheeze and cough, respectively (Table 6). There is clear evidence of an increased risk of wheeze and cough in relation to parental smoking, regardless of whether the smoker is the mother or the father. However, the number of studies on outcomes such as phlegm and breathlessness is insufficient to give reliable estimates for calculating attributable fractions.

22 Estimates of relative risk

Table 6: Summary of pooled odds ratios (random effects model) for respiratory symptoms related to parental smoking among children a

No. of Odds ratio for smoking (95% CI) Symptom studies Either parent One parent Both parents Mother only Father only Wheeze 31 1.26 (1.20–1.33) 7 1.18 (1.10–1.26) 10 1.41 (1.23–1.63) 21 1.28 (1.21–1.35) 12 1.13 (1.08–1.20) Cough 13 1.35 (1.27–1.43) 4 1.27 (1.14–1.41) 5 1.64 (1.48–1.81) 4 1.34 (1.17–1.54) 3 1.22 (1.12–1.3) a Adapted from United States Surgeon General (2006) report.

In conclusion, there is sufficient evidence to conclude that there is a causal relationship between parental smoking and chronic cough and wheeze among children of school age. These chronic respiratory symptoms are classified as Level 1.

The attributable burden of chronic wheeze and cough could be estimated, and we suggest the use of odds ratios of 1.26 (1.20–1.33) and 1.35 (1.27–1.43), respectively, for exposure by either parent, based on the review by the United States Surgeon General (2006). As mentioned, although not recommended for routine inclusion in a burden of disease estimate due to the possibility of double-counting, the suggested odds ratios may be considered by countries that have a special interest in the impact of chronic respiratory symptoms and have statistics on the occurrence of wheeze and cough in children.

3.4.4 Otitis media Otitis media, 1 or middle ear infections, is one of the most commonly diagnosed problems in outpatient paediatrics. Eustachian tube dysfunction of whatever etiology results in a sustained pressure differential between the middle ear and the surrounding atmosphere, with subsequent effusion of serous fluid into the middle ear. This produces a sensation of fullness and temporarily decreased hearing. The acute otitis media occurs when this fluid becomes infected with bacteria, resulting in pain, fever and a potential for tympanic membrane perfusion. Serious secondary complications may lead to sustained hearing loss and consequently to communication difficulties and educational impairment in children.

Uhari and colleagues (1996) conducted a meta-analysis of the effects of exposure to SHS on acute otitis (including both acute and recurrent otitis) media using studies from 1966 to 1994. The summary relative risk for acute otitis media incidence was 1.66 (1.33–2.06).

1 H65–H66 in the International Statistical Classification of Diseases and Related Health Problems (WHO, 2007a).

23 Second-hand smoke: Assessing the burden of disease

In its 1997 report, Cal-EPA reviewed 22 studies. However, only three studies were considered to be convincing (Iversen et al., 1985; Strachan et al., 1989; Etzel et al., 1992). These studies used precise measures of exposure to SHS (obtained from biomarkers) and/or accurate counts of outcomes based on periodic prospective screening. All three found statistically significant associations. The study by Etzel et al. (1992) was the strongest; it reported that children below the age of 3 years have a 38% excess prevalence of middle ear effusion (1.38; 1.21–1.56). Seven more studies have been identified in the Cal-EPA (2005) report. Of these, four found a significant positive association between SHS and otitis media. None of the newly reviewed studies used both prospective otitis media screening and biomarkers, as was the case in the study by Etzel et al. (1992). The negative studies were all cross-sectional or of “unknown” design.

In 1998, Strachan & Cook (1998a) published a quantitative meta-analysis related to parental smoking and middle ear disease. It was possible to calculate estimates for all outcomes except acute otitis media. Evidence for middle ear disease was consistent, with pooled odds ratios if either parent smoked of 1.48 (1.08–2.04) for recurrent otitis media and 1.38 (1.23–1.55) for middle ear effusion.

The recent United States Surgeon General (2006) report identified a total of 59 studies on the association between SHS and otitis. Meta-analyses included studies that separate acute and recurrent otitis (Table 7). Only three studies investigated acute otitis media, and this result should therefore be interpreted cautiously.

Table 7: Pooled odds ratios for the effect of smoking by either parent on middle ear disease a

No. of Odds ratio (95% CI) Outcome studies Either parent Maternal Paternal Acute otitis media b 3 0.99 (0.7–1.4) Recurrent otitis media 9 1.32 (1.14–1.52) 1.37 (1.19–1.59) 0.90 (0.70–1.15) Middle ear effusions 7 1.33 (1.12–1.58) Outpatients referred for middle ear effusions 7 1.20 (0.90–1.60) 1.84 (1.54–2.20) 1.49 (1.13–1.96) a Adapted from the United States Surgeon General (2006) report. b Specifically excluding recurrent (i.e. mostly acute recurrent) otitis media.

Although the epidemiological data strongly support a relationship between SHS exposure and middle ear disease and propose odds ratios for recurrent otitis media (1.32; 1.14–1.52) and for middle ear effusions (1.33; 1.12–1.58) (United States Surgeon General, 2006), statements on estimates for acute otitis media (without specific reference to recurrent otitis) have been more vague.

The United States Surgeon General (2006) analysis reviewed the impact of potential confounders and concluded that confounding was unlikely to have a significant impact on risk estimates of recurrent and acute otitis media.

In summary, the United States Surgeon General (2006) concluded that “the evidence is sufficient to infer a causal relationship between parental smoking and middle ear disease in children, including acute and recurrent otitis media and chronic middle ear effusion”. The report by Cal-EPA (2005) agrees with this conclusion. The analysis by the United States Surgeon General (2006) of the three studies addressing acute otitis

24 Estimates of relative risk

media (and excluding recurrent acute otitis media) did not, however, provide a meaningful conclusion. The Cal-EPA (2005) report, however, also states that the “limitations of available data on the chronicity of physical findings, as well as the differing patterns of recruitment in various studies make it impossible to distinguish separate relationships between ETS [environmental tobacco smoke] exposure and acute serous otitis media, chronic serous otitis media, and acute infectious otitis media”. The analyses of Strachan & Cook (1998a) and the United States Surgeon General (2006) also support the conclusion that many of the studies on acute otitis media provide insufficient or too variable data for a meta-analysis.

The available pooled, or high-quality, risk estimates for acute otitis media available today are the following:

− The meta-analysis by Uhari et al. (1996) for acute and recurrent otitis media results in an odds ratio of 1.66 (1.33–2.06). − Odds ratios of 1.32 (1.14–1.52) for recurrent otitis media, 1.33 (1.12–1.58) for middle ear effusions and 0.99 (0.70–1.40) for acute otitis media (based on three studies only) have been suggested by the United States Surgeon General (2006). − The Cal-EPA (2005) report uses the incidence density ratio of 1.38 (1.21–1.56) of a high-quality study by Etzel et al. (1992), the only one based on prospective screening for otitis media and biomarkers. − The review of Strachan & Cook (1998a) concluded that the “Evidence for middle ear disease is remarkably consistent, with pooled odds ratios if either parent smoked of 1.48 (95% CI 1.08 to 2.04) for recurrent otitis media, 1.38 (1.23 to 1.55) for middle ear effusion, and 1.21 (0.95 to 1.53) for outpatient or inpatient referral for glue ear. Odds ratios for acute otitis media are in the range 1.0 to 1.6.”

For the estimation of burden of disease from SHS, the ideal risk estimate is a pooled relative risk for an outcome that matches the definition of cases in the available disease statistics. Available disease statistics generally include all acute episodes of otitis media, including single and recurrent ones. The closest matching relative risk is probably the pooled value of 1.66 by Uhari et al. (1996). This estimate, however, includes studies with both single and recurrent episodes. Pooled estimates of acute otitis media excluding recurrent otitis (e.g. OR = 0.99; United States Surgeon General, 2006) are not particularly suitable for our purposes, because they exclude recurrent episodes, which may constitute an important part of the relevant disease statistics. As a second choice, the high-quality study by Etzel et al. (1992), with an incidence density ratio of 1.38 (1.21–1.56), can be used. This study is expected to have an increased precision, as it uses biomarkers for exposure to SHS. It is also close to the converging value of about 1.3 for relevant pooled risk estimates (for not exactly matching but largely overlapping outcome definitions) and corresponds to the midpoint of the range indicated by Strachan & Cook (1998a). The study by Etzel et al. (1992) was also used for disease burden estimation in the Cal-EPA (2005) study. It is therefore recommended for use as an interim value for estimating the disease burden of otitis from SHS exposure.

For the transferability of the recommended relative risk or odds ratio, developed from studies carried out in developed countries, to developing countries, the same issues as

25 Second-hand smoke: Assessing the burden of disease

those described in the section on LRI arise (i.e. linked to differing numbers of cigarettes smoked at home by smoking parent, different ventilation in warmer climates, different ratio of smoking mothers to smoking fathers, other adults smoking at home, smaller housing, etc.). In the context of these uncertainties, it is recommended that a sensitivity analysis be carried out with alternative assumptions, in particular a separate analysis of maternal and paternal smoking. As no suitable values are available for risk of otitis from exposure to SHS from maternal/paternal smoking, this may be summarized by applying the selected risk estimate (OR = 1.38) to children exposed to maternal smoking only (and not to paternal smoking, simply because no specific value for paternal smoking is currently available). The value by Uhari et al. (1996) (OR = 1.66) can also be used in the sensitivity analysis.

These estimates would be applicable for the first 3 years of life. An estimate carried out on the basis of this risk estimate may be viewed as preliminary, given the important uncertainties. Consensus supports a causal relationship in principle, but the quantification of health impacts of acute otitis from SHS exposure is supported by more limited evidence compared with the other outcomes addressed in this document. This is partly due to diagnostic difficulties and a mismatch between studied outcomes and available health statistics (although “acute and recurrent otitis” combined may in practice be considered as a reasonable approximation of “acute otitis”).

3.4.5 Asthma in children Asthma 1 is a chronic inflammatory disease of the airways that is characterized by reversible airflow obstruction and accompanied by periodic attacks of wheezing, shortness of breath and a feeling of tightness in the chest. Asthma is the most common chronic respiratory condition of childhood. It has been suggested that the onset of new cases as well as the exacerbation (additional episodes and increased severity) of childhood asthma have increased substantially in recent years in many parts of the world. SHS has been suggested to be related to both the onset and the exacerbation of childhood asthma.

The definition of asthma in studies is often based on “asthma ever”, which is typically a positive response to the question “Has this child ever had asthma?” Some studies have focused on current asthma, defined, for example, as an asthma attack in the last 12 months or the child being currently on asthma treatment, whereas other studies have specifically asked whether the diagnosis had been made by a physician. Many recent studies on childhood asthma have based their case definition on positive airway hyper- responsiveness in lung function tests in addition to symptoms in the last 12 months (Mathers et al., 2001).

Onset of asthma in children This section provides an overview of the evidence on the relationship between SHS exposure and induction of asthma in childhood. An early meta-analysis on asthma (using asthma prevalence as the outcome) found an odds ratio of 1.21 (1.10–1.34) related to parental smoking (Cook & Strachan, 1997). The effect estimate was 1.50 (1.29–1.73) when both parents smoked and 1.36 (1.20–1.55) and 1.07 (0.92–1.24) for maternal and paternal smoking, respectively. Strachan & Cook (1998b) identified

1 J45–J46 in the International Statistical Classification of Diseases and Related Health Problems (WHO, 2007a).

26 Estimates of relative risk

several longitudinal studies (Fergusson & Horwood, 1985; Neuspiel et al., 1989; Sherman et al., 1990; Martinez et al., 1992; Lewis et al., 1995; Strachan et al., 1996) that assessed the incidence of asthma or wheezing as the outcome. The incidence of asthma or wheezing was related to maternal smoking, but the effect was stronger up to age 6 (four studies, summary OR = 1.31, 1.22–1.41) and less strong thereafter (four studies, summary OR = 1.13, 1.04–1.22) (Strachan & Cook, 1998b).

A more recent review (J.A. Jaakkola & Jaakkola, 2002) identified two additional longitudinal studies of asthma or asthma-like symptoms published after this meta- analysis (Nafstad et al., 1997; Gold et al., 1999) that provided additional evidence of the effect of early-life exposure to SHS on asthma. In 2006, the United States Surgeon General’s report presented a meta-analysis with odds ratios for the effect of smoking by either parent on prevalence of asthma (Table 8). If either parent smoked, the odds ratio for asthma prevalence was 1.23 (1.14–1.33). The United States Surgeon General’s (2006) report concluded that the evidence is suggestive of but not sufficient to infer a causal relationship between SHS exposure and onset of childhood asthma. However, it concluded that the evidence is sufficient to infer a causal relationship between SHS exposure and the onset of wheeze illness in early childhood as well as between SHS exposure and ever having asthma among children of school age.

Table 8: Summary of pooled odds ratios (random effects model) for prevalence of asthma among children associated with parental smoking a

Odds ratio (95% CI) Number of studies Either parent Both parents Mother only Father only 31 1.23 (1.14–1.33) 10 1.42 (1.30–1.56) 21 1.33 (1.24–1.43) 12 1.07 (0.97–1.18) a Adapted from the United States Surgeon General (2006) report.

The meta-analysis based on the most updated epidemiological evidence was compiled by Cal-EPA (2005). They examined the association between exposure to SHS in the home and the development of childhood asthma based on studies with strong design (i.e. studies with diagnosed asthma as their outcome rather than just the symptom wheezing and studies that had adjusted for important confounders). They identified 85 studies covering over 460 000 children and representing 29 countries. Analyses based on 29 studies that controlled for the child’s history of atopy and personal smoking, and in which all age groups were combined, gave a pooled odds ratio for new-onset asthma of 1.32 (1.24–1.41). Preschool children appeared to be more at risk (1.44; 1.04–1.99) than older children (1.26; 1.19–1.32), but it is notable that the risk was not limited to young children or those exposed during pregnancy. The risk of asthma increased with increasing duration of passive smoking, suggesting an exposure–response relationship: the relative risk was 1.22 (1.16–1.34) for 5 years of postnatal SHS exposure and 1.42 (1.28–1.70) for 10 years of such exposure. Cal-EPA (2005) concluded that there is sufficient evidence to conclude that SHS exposure is causally related to new-onset asthma among children.

27 Second-hand smoke: Assessing the burden of disease

Potential mechanisms underlying the link between SHS exposure and asthma in general are discussed in more detail in section 3.5 (Respiratory effects in adults), but other mechanisms may also play a role in infants. These include impaired development of airways during pregnancy and in infancy in those exposed to SHS and the influence of SHS on development of immunological responses—for example, the balance between Th1 and Th2 T-cells (United States Surgeon General, 2006).

In conclusion, the evidence for a relationship between SHS exposure and onset of asthma is considered sufficient (Cal-EPA, 2005). Accordingly, we classify onset of asthma as a Level 1 effect and suggest that the odds ratio of 1.32 (1.24–1.41) be used for calculation of attributable cases among children less than 15 years old (Cal-EPA, 2005).

For estimating the SHS-related burden of asthma in developing countries, maternal and paternal smoking could also be estimated separately (given the issues raised in the section on LRI above). As no suitable values are available for the risk of asthma from exposure to SHS from maternal/paternal smoking separately, the selected odds ratio could be applied, in a sensitivity analysis, to children exposed to maternal smoking only (and not to paternal smoking, simply because no specific value for paternal smoking is currently available).

Asthma exacerbation/severity Numerous studies have shown that parental smoking is a causal factor for exacerbation of asthma in children with a pre-existing disease (Cal-EPA, 2005), in addition to increasing the risk of new asthma in previously healthy children. Different outcomes of exacerbation and/or severity of asthma have been studied, including the frequency and severity of asthma symptoms, use of asthma medications, school absenteeism, use of health-care services and hospitalizations, and changes in lung function parameters, such as peak expiratory flow, forced expiratory volume in 1 s (FEV 1) and forced vital capacity. Cross-sectional studies of asthma are known to have some bias related to the fact that parents may avoid smoking if their children show symptoms of asthma. The presence of asthma could also influence the reporting of exposure. Both these sources of bias are likely to lead to an underestimation of the exposure–effect relationship. In longitudinal studies on asthma severity, effects of passive smoking on increased asthmatic symptoms, more and prolonged use of medication and increased school absenteeism have been detected most consistently (Gilliland et al., 2003; Cal-EPA, 2005).

Currently, there is a consensus among scientists about SHS causing increased severity and exacerbations of asthma in children who already have the disease (Cal-EPA, 2005). Thus, exacerbation/severity of asthma is classified as a Level 1 outcome. During the last decade, several epidemiological studies have affirmed the causal connection to outcomes such as moderate or severe asthma and secondary effects such as school absenteeism.

To date, no summary estimate has been reported for the effect of SHS exposure on severity or exacerbations of asthma. We recommend that the odds ratio for induction of asthma be used in the disease burden calculation (see above) and do not recommend that asthma severity be included separately, to avoid potential double-counting. The calculation of DALYs from asthma is based on both new cases and episodes of

28 Estimates of relative risk

exacerbation, so it takes into account the nature of asthma—i.e. disability from chronic airway inflammation that sometimes flares up in acute exacerbations.

3.5 Respiratory effects in adults Several biological mechanisms are plausible for the ability of SHS exposure to affect the airways and cause non-cancer respiratory effects, such as asthma, chronic respiratory symptoms and COPD (Cal-EPA, 1997, 2005; M.S. Jaakkola & Jaakkola, 2002). SHS contains many irritant substances that can induce hypersecretion of mucus and inflammation in the airways and perhaps even in lung parenchyma.

Long-term exposure could lead to chronic inflammatory diseases of the airways. Inflammatory cells release proteolytic enzymes, and tobacco smoke inhibits antiproteases. Tobacco smoke also seems to increase epithelial permeability to environmental allergens and has been shown to enhance allergic reactions to some inhalable allergens. In addition, tobacco smoke impairs host defence mechanisms by weakening immunological responses and mucociliary clearance, making the host more vulnerable to respiratory infections.

Repeated respiratory infections may predispose the person to COPD. There is also some evidence that SHS exposure may cause bronchoconstriction and increased microvascular leakage, which are typical for asthma (United States Surgeon General, 2006).

3.5.1 Asthma The amount of research that has addressed the effects of SHS on asthma is lower in adults than in children, but it has increased during recent years. The previous major reviews concluded that the evidence on SHS exposure and asthma in adults was limited and that more studies on this topic were needed, but reviews including more updated references showed that the evidence base has strengthened considerably in recent years (M.S. Jaakkola & Jaakkola, 2002, 2006; Cal-EPA, 2005). The studies on SHS and adult asthma are divided into those investigating induction of asthma as the outcome and those studying exacerbation of a pre-existing disease.

Onset of adult asthma The review by M.S. Jaakkola & Jaakkola (2002) identified five studies on SHS exposure and adult asthma and a sixth study that was reported as an abstract at the time, whereas the 2005 updated review by Cal-EPA (2005) identified another nine studies investigating either childhood or adulthood SHS exposure and adult asthma. The studies on adult asthma included three cohort studies (Greer et al., 1993; Hu et al., 1997; Skorge et al., 2005), one incident case–control study (Jaakkola et al., 2003), two prevalent case–control studies (Flodin et al., 1995; Thorn et al., 2001) and eight cross- sectional studies.

The studies were conducted in different parts of the world and showed rather consistently an increased risk of asthma in relation to adult SHS exposure both at home and at work, although not all studies detected statistically significant effects. The effect estimate, usually odds ratio, ranged from 1.14 to 4.7. Many studies found a stronger effect in relation to workplace SHS exposure than in relation to home exposure. Several studies showed evidence of a dose–response relationship with increasing SHS

29 Second-hand smoke: Assessing the burden of disease

exposure, measured as increasing hours per day of SHS exposure (Leuenberger et al., 1994), increasing duration of exposure (Greer et al., 1993; Leuenberger et al., 1994; Iribarren et al., 2001; Janson et al., 2001), increasing number of cigarettes to which the person was exposed daily (Leuenberger et al., 1994; Hu et al., 1997; Jaakkola et al., 2003) and increasing cumulative exposure index over a lifetime (Jaakkola et al., 2003). A large multicentre cross-sectional European Community Respiratory Health Survey also linked SHS exposure to greater bronchial hyper-responsiveness (Janson et al., 2001). Three studies confirmed the relationship between SHS exposure and new adult- onset asthma (Greer et al., 1993; Hu et al., 1997; Jaakkola et al., 2003). Whereas the United States Surgeon General (2006) concluded, based on a limited number of studies, that the evidence is suggestive but not sufficient to infer a causal relationship between SHS exposure and adult-onset asthma, Cal-EPA (2005) included updated references, including studies published since 2000, and concluded that the evidence supports a causal relationship between SHS and adult-onset asthma.

Asthma was defined in the studies in variable ways, being most commonly based on reporting of doctor-diagnosed asthma, but some studies also carried out clinical and lung function investigations confirming the diagnosis with objective measures (Thorn et al., 2001; Jaakkola et al., 2003). Although the studies reviewed in the Cal-EPA (2005) report had variable control for confounding factors, the majority of studies adjusted for a variable set of confounders, and the best-quality studies adjusted for a large set of potential confounding factors. Overall, the Cal-EPA (2005) report concluded that the observed relationship between SHS exposure and asthma is probably not explained by confounding.

The evidence on the relationship between childhood SHS exposure and adult asthma has been less consistent, although an 11-year Norwegian follow-up study of approximately 2800 adults suggested that maternal smoking in pregnancy (OR = 2.9; 1.6–5.5) and during childhood (OR = 1.9; 1.1–3.2) significantly increases the risk of asthma in adulthood (Skorge et al., 2005). However, the number of studies addressing this exposure in relation to adult asthma is limited, so no definite conclusions can be reached yet.

In summary, there is consistent evidence of a causal relationship between SHS exposure and induction of asthma in adults (Cal-EPA, 2005; Gilmour et al., 2006; Jaakkola & Jaakkola, 2006). Most of the studies have controlled for bias and confounding and have shown evidence of a meaningful temporal relationship and of an exposure–response relationship. There is also evidence that SHS exposure is related to other non-cancer respiratory effects, such as chronic respiratory symptoms, including wheezing, COPD and exacerbation of asthma, providing evidence of coherence, and there are plausible biological mechanisms that could underlie the non-cancer respiratory effects of SHS exposure (M.S. Jaakkola & Jaakkola, 2002; Cal-EPA, 2005). The strongest evidence on the relationship between SHS exposure and induction of adult asthma comes from the Finnish Environment and Asthma Study, which was a population-based incident case–control study that corresponded to a follow-up of approximately 100 000 adults for 5.8 years in a defined geographic area in southern Finland. It based the diagnosis of asthma on extensive clinical and lung function investigations, ascertained detailed SHS exposure history both at home and at work in the previous 12 months and over a lifetime, and adjusted for a large set of potential confounders (Jaakkola et al., 2003). Evidence on onset of adult asthma is

30 Estimates of relative risk

classified as Level 1. The odds ratio suggested for the disease burden calculation is 1.97 (1.19–3.25) (adjusted value) for SHS exposure at work and/or at home based on the Finnish study (Jaakkola et al., 2003). Although this study covers a study population above the age of 20 years, application of the relative risk to the population of 15 years and above may be considered.

Exacerbation and severity of asthma Subjects with asthma have chronic inflammation in their airways and consequently may be particularly susceptible to the adverse effects of SHS exposure. In community- and hospital-based surveys, 69–78% of adult asthma patients report that SHS aggravates their symptoms (Dales et al., 1992; Abramson et al., 1995; Tarlo et al., 2000).

The review by M.S. Jaakkola & Jaakkola (2002) identified five studies addressing the effects of SHS exposure on exacerbation or severity of pre-existing asthma in adults. The updated review by Cal-EPA (2005) identified another four studies on this topic. The studies on asthma exacerbation include three longitudinal, one nested case–control and five cross-sectional studies. They were conducted in different parts of the world, mainly in Canada, India and the USA.

The studies have shown that in adult subjects with asthma, SHS exposure at home and/or at work is related to increased occurrence of respiratory symptoms, including cough, breathlessness and wheezing at work, increased use of bronchodilator and steroid medication, reduced general health, increased health-care utilization, including emergency department visits and hospitalizations, increased bronchial hyper- responsiveness and reduced spirometric lung function. In a 7-day follow-up study of 50 asthmatics, SHS exposure, measured by a personal nicotine badge, was associated with increased symptoms and medication use, both effects showing an exposure– response relationship (Eisner et al., 2001). The Third National Health and Nutrition Examination Survey (NHANES III) in the USA found that adult asthmatics, especially women, with the highest serum cotinine levels had significantly lower lung function levels compared with those with low SHS exposure levels (Eisner, 2002). Similar lung function decrements in adults with asthma related to SHS exposure were detected in the large cross-sectional Swiss study, showing evidence of an exposure–response relationship with daily exposure and years of exposure (Kunzli et al., 2000).

Some controlled chamber SHS exposure studies have been published showing somewhat inconsistent results due to small sample sizes and variable exposure times (M.S. Jaakkola & Jaakkola, 2002; Cal-EPA, 2005). Overall, they suggest that there is a subpopulation of asthmatics who experience increased respiratory symptoms, decreased lung function and increased bronchial hyper-responsiveness in response to short-term SHS exposure. This sensitivity seems to be reproducible over 2 years.

In summary, the studies show rather consistently that SHS exposure affects adverse pre-existing asthma in adults. There is evidence of an exposure–response relationship and of a meaningful temporal relationship. The previous section on induction of asthma discussed the coherence of evidence and biological plausibility of non-cancer respiratory effects of SHS exposure. Some experimental SHS exposure studies suggest that at least a subpopulation of asthmatics experience symptoms and lung function changes compatible with exacerbation of asthma when exposed to SHS for a few

31 Second-hand smoke: Assessing the burden of disease

hours. The review by M.S. Jaakkola & Jaakkola (2002) concluded that there is consistent, but limited, evidence of the effects of SHS exposure on exacerbation of asthma, whereas the updated Cal-EPA (2005) report concluded that the evidence is consistent with a causal effect of SHS on adult asthma exacerbation. Adult asthma exacerbation is classified as Level 2. Because the studies investigated very variable outcomes describing different aspects of asthma control, it is difficult to give one summary effect estimate.

3.5.2 Chronic obstructive pulmonary disease COPD is a chronic disease that develops slowly over many years. It is characterized by chronic cough and phlegm production and mainly irreversible airflow limitation. Studies on the effects of SHS exposure on chronic respiratory symptoms and lung function (reviewed in sections 3.5.4 and 3.5.5) can be seen as investigating the early phases of COPD, whereas studies focusing on diagnosed COPD investigate the advanced stages.

The review of non-cancer respiratory effects of SHS exposure by M.S. Jaakkola & Jaakkola (2002) identified six studies on COPD, including three longitudinal and three case–control studies. An updated review by Jaakkola & Jaakkola (2006) identified additionally one cross-sectional study and two longitudinal studies on this topic. Most of the studies reported an effect of SHS exposure at home on COPD, the odds ratio ranging between 1.2 and 5.6. Two longitudinal studies (Robbins et al., 1993; Vineis et al., 2005) and one cross-sectional study (Eisner et al., 2005) assessed SHS exposure at work also, the risk estimates ranging between 1.3 and 1.8. Several studies showed evidence of an exposure–response relationship between SHS exposure, measured as packs per day or years of exposure, and the risk of COPD.

Outcome was defined in variable ways, including reported diagnosis made by a physician, lung function measurements and diagnostic codes in mortality statistics. The older studies included asthma in their definition of COPD, and a more recent longitudinal study combined COPD and lung cancer to form the outcome.

The most recent cross-sectional study was population based by design and included approximately 2000 adults 55–75 years of age from 48 states in the USA (Eisner et al., 2005). It assessed the relationship of lifetime SHS exposure at home and at work to COPD, defined as self-reported physician diagnosis of chronic bronchitis, emphysema or COPD. An increased risk of COPD was observed in relation to the highest quartile of home exposure (OR = 1.55; 1.09–2.21) and work exposure (OR = 1.36; 1.02–1.84), after adjustment for confounders. When applying a more specific diagnosis based on emphysema and COPD only, the odds ratios were 2.38 (1.42–3.90) and 1.79 (1.21– 2.65) for home and work exposures, respectively.

Recent reviews have concluded that there is limited, but increasing, evidence that SHS exposure is related to the risk of COPD, so COPD is assessed as Level 2 (M.S. Jaakkola & Jaakkola, 2002, 2006; United States Surgeon General, 2006). If any country would like to include this in their burden of disease assessment, 1.55 (1.09– 2.21) is suggested as the best estimate of relative risk. It is based on the recent large population-based study in the USA that quantified SHS exposure carefully (Eisner et al., 2005).

32 Estimates of relative risk

3.5.3 Acute irritant effects SHS contains many agents that may cause mucosal irritation (Lee et al., 1993). Irritant effects have been found in both cross-sectional (Jones et al., 2001; Mizoue et al., 2001) and longitudinal studies (Eisner et al., 1998; Wieslander et al., 2000) of occupationally exposed individuals. In addition, experimental studies have assessed irritant symptoms (e.g. nasal congestion, excessive secretions, subjective complaints as well as increased eye blink frequency) as measures of upper respiratory tract irritation.

Subjective complaints of odour, annoyance and eye irritation seem to appear at lower sidestream smoke concentrations than for nose and throat irritation, rhinorrhoea and cough. Although the symptoms of nasal irritation may appear to be minor adverse health consequences, they have the potential to negatively affect daily functioning and quality of life.

In summary, SHS exposure has been linked to a variety of objectively documented and quantified symptoms involving the upper respiratory tract (Cal-EPA, 2005). Acute irritant symptoms are classified as Level 1. However, as irritant effects are generally not included in the health statistics at the country level and acute symptoms are considered reversible after exposure cessation, we do not recommend including this effect in the disease burden estimates.

3.5.4 Chronic respiratory symptoms Numerous studies have been published on the relationship between SHS exposure and chronic respiratory symptoms in adults (M.S. Jaakkola & Jaakkola, 2002, 2006; Cal- EPA, 2005). The earlier studies provided somewhat inconsistent results, but the more recent, often better-quality studies suggest that there is an increased risk of chronic respiratory symptoms in relation to SHS exposure at home and/or at work. Both cross- sectional and longitudinal (Schwartz & Zeger, 1990; Jaakkola et al., 1996; Eisner et al., 1998; Allwright et al., 2005; Jayet et al., 2005; Menzies et al., 2006) studies have been carried out in different parts of the world. Several studies provided evidence of an exposure–response relationship and adjusted for confounders. Both asthma-type symptoms (i.e. wheezing, dyspnoea and chest tightness) and bronchitis symptoms (i.e. cough and phlegm production) have been related to SHS exposure. Three studies, one from California, USA (Eisner et al., 1998), one from (Allwright et al., 2005) and one from Scotland (Menzies et al., 2006), found significant decreases in upper and lower respiratory symptoms after state-wide or national smoke-free bans were introduced (Jaakkola & Jaakkola, 2006). The recent reviews concluded that SHS exposure is likely to play a role in the genesis of chronic respiratory symptoms in adults, and the evidence is assessed as Level 2.

However, as generally no health statistics on chronic respiratory symptoms are available at the country level, we do not recommend including this effect in the disease burden estimates. Inclusion might lead to double-counting, as chronic respiratory symptoms are often related to respiratory diseases, such as asthma and COPD. For countries that do have statistics to estimate the attributable fractions for chronic respiratory symptoms, we recommend the use of risk estimates from an individual high-quality study (Leuenberger et al., 1994). This study included a large number of individuals (4197 non-smokers) and reported dose–response relationships. Odds ratios were calculated for wheezing (1.99; 1.41–2.82), phlegm (1.69; 1.23–2.31) and dyspnoea (1.44; 1.18–1.75).

33 Second-hand smoke: Assessing the burden of disease

3.5.5 Lung function impairment More than 20 studies from different parts of the world have addressed the relationship between SHS exposure and lung function in adulthood (reviewed by M.S. Jaakkola & Jaakkola, 2002; Cal-EPA, 2005). The reviews have concluded that SHS exposure may produce small, but measurable, decrements in spirometric lung function in adults (Cal- EPA, 2005) and that this effect may be dose dependent and, consequently, observable only in countries and occupations where SHS exposure levels are high (M.S. Jaakkola & Jaakkola, 2002). A meta-analysis was published in 1999 based on nine cross- sectional studies (Carey et al., 1999). It found a significant, although relatively small, reduction in FEV 1 related to SHS exposure, the effect estimate being −2.7% (−4.1% to −1.2%). Recent studies have suggested that patients with asthma experience larger adverse effects on lung function related to SHS exposure than do subjects free from a pre-existing airway disease (Kunzli et al., 2000; Eisner, 2002; Cal-EPA, 2005). The evidence on lung function is assessed as Level 3, as more longitudinal studies on this topic are needed before making more definite conclusions. As the studies usually assess the outcome in terms of reduction in lung function rather than increase in the risk of lung function impairment, it is not possible to suggest a risk estimate for assessing disease burden.

3.6 Cancer The list of the types of cancers caused by active smoking is long and increasing. According to the recent IARC (2004) monograph, smoking has been linked to cancers of the lung, oral cavity, pharynx, larynx, oesophagus (squamous cell carcinoma and adenocarcinoma), pancreas, urinary bladder and renal pelvis, nasal cavities and nasal sinuses, stomach, liver, kidney (renal cell carcinoma) and uterine cervix, as well as myeloid leukaemia (IARC, 2004). SHS contains similar to those that are inhaled by smokers; consequently, there has been a concern for a long time that involuntary smoking also causes cancer. However, with the exception of lung cancer, relatively few studies have examined associations between SHS and malignant disease. In this document, we include data on sites with sufficient or strongly suggestive evidence of causality. These include lung, breast, nasal sinus and nasopharynx.

3.6.1 Lung cancer Lung cancer 1 is the most common cause of cancer death in the world, and its major cause is active tobacco smoking. Since the publications by several international authorities in 1986 (IARC, 1986; NRC, 1986; United States Surgeon General, 1986), lung cancer has been firmly established as a health effect causally related to SHS exposure. Several meta-analyses on SHS exposure and the risk of lung cancer have been conducted (Mengersen et al., 1995; Law & Hackshaw, 1996; Dockery & Trichopoulos, 1997; Hackshaw et al., 1997; Boffetta et al., 1998; Hackshaw, 1998; Wells, 1998b; Zhong et al., 2000; Taylor et al., 2001; United States Surgeon General, 2006). The following sections provide a summary of the evidence according to the type of exposure.

1 Trachea, bronchus and lung cancer: C33–C34 in the International Statistical Classification of Diseases and Related Health Problems (WHO, 2007a).

34 Estimates of relative risk

Hackshaw and colleagues (1997) pooled 37 published studies on lung cancer and obtained an estimated relative risk of 1.24 (1.13–1.36) for non-smokers who lived with a smoker. A similar result (1.20; 1.12–1.29) was found by Zhong and colleagues (2000) for lung cancer risk among non-smoking women living with a smoking husband. The most recent analysis of the United States Surgeon General (2006) uses data from 52 studies on spousal SHS exposure. The risk estimate for lung cancer among male and female non-smokers who were ever exposed to SHS from their spouses was 1.21 (1.13–1.30). Men showed a slightly higher odds ratio (1.37; 1.05– 1.79) than women (1.22; 1.13–1.31). A study from New Zealand suggests that the relative risk due to exposure to SHS in the home may be higher in recent years than in earlier years when exposure to SHS outside the home was more common (Hill et al., 2007).

Exposure to SHS at the workplace is common in many countries. The United States Surgeon General (2006) report reviewed a total of 25 epidemiological studies, which have provided information on occupational exposure to SHS and the risk of lung cancer among lifetime non-smokers. Despite geographic differences in the prevalence and intensity of exposure, the effect of occupational SHS exposure on the risk of lung cancer is remarkably consistent. The pooled relative risk for lifetime non-smokers in the meta-analysis was found to be 1.22 (1.13–1.33).

The report of the United States Surgeon General (2006) has reviewed in detail the role of potential confounding in the reported associations between SHS and lung cancer risk, as it has been continuously questioned because of methodological concerns. Main concerns included, for example, publication bias or potential confounding by factors such as lifestyle variables or occupational exposures.

Although many of the earlier studies of SHS and lung cancer did not consider lifestyle variables such as diet in the statistical analysis, most of the larger studies published since the 1990s have accounted for these factors and have found that the effect of SHS remained after adjusting for them (United States Surgeon General, 2006). In analyses of workplace SHS exposures, Zhong and colleagues (1999) documented that the strong association between workplace SHS exposure and lung cancer risk remained even after making additional adjustments for other occupational exposures. The body of epidemiological research now includes a number of large studies that were designed specifically to limit misclassification and confounding. The estimated risk for lung cancer associated with involuntary smoking has changed little as new evidence has become available.

Exposure to SHS during childhood may be a risk factor for having lung cancer in adulthood. However, the results of the studies summarized by IARC (2004) are contradictory and inconclusive.

In conclusion, lung cancer among non-smokers is classified as a Level 1 outcome causally related to SHS both from spousal smoking and from exposure at work, and the suggested odds ratios are 1.21 (1.13–1.30) and 1.22 (1.13–1.33) (random effects/adjusted values where available), respectively. The odds ratios are very similar, regardless of the source of exposure. As the meta-analysis for spousal smoking is based on a larger number of studies, we recommend that its odds ratio be used for estimating disease burden from any exposure.

35 Second-hand smoke: Assessing the burden of disease

3.6.2 Breast cancer The results of studies on the causal association between SHS exposure and breast cancer are not consistent, and conclusions vary among the major reviewing reports. The IARC (2004) monograph on involuntary smoking concluded that the evidence did not support a causal association between breast cancer and SHS. The report of the United States Surgeon General (2006) concluded that the evidence is suggestive, and the Cal-EPA (2005) report found the evidence to be conclusive for SHS exposure as a cause of premenopausal breast cancer. Three meta-analyses have appeared in the recent literature, one as a published paper (Khuder & Simon, 2000), one in a book chapter (Morabia et al., 2001) and another in a published letter (Wells, 1998c). In addition, the reports by Cal-EPA (2005) and the United States Surgeon General (2006) present one meta-analysis each.

Khuder & Simon (2000) conducted a meta-analysis of 11 studies published between 1984 and 2000 that examined the association between SHS and breast cancer. Their analysis showed a pooled odds ratio of 1.41 (1.14–1.75). A positive dose–response relationship was reported in all seven studies that measured the level of SHS exposure, with a significant test for trend in two studies. All studies in the analysis found elevated risks, thus supporting an association of SHS exposure with breast cancer.

Morabia et al. (2001) conducted a meta-analysis of five case–control studies and one prospective study and provided a pooled risk estimate indicating significant associations between SHS exposure and breast cancer (OR = 1.7; 1.3–2.3). Four of these studies were also evaluated by Wells (1998c), who derived a pooled risk estimate of 1.71 (1.30–2.25).

Although published before the United States Surgeon General’s (2006) report, the Cal- EPA (2005) report presents the meta-analysis on breast cancer and SHS that includes the most recent studies. The analysis covers published studies between 1984 and 2005, with a focus on studies among women who reported being never-smokers. Cal-EPA (2005) considered 23 studies published between 2000 and 2005, whereas the United States Surgeon General (2006) report considered 5 of those studies. Similarly, IARC (2004) evaluated mostly studies published prior to 1999, with four studies published between 2000 and 2002.

In the meta-analysis by the United States Surgeon General (2006), the breast cancer risk in lifetime non-smokers was significantly associated with any adult SHS exposure overall (1.20; 1.08–1.35); however, with stratification by menopausal status, the association was limited to premenopausal women (1.64; 1.25–2.14). No effect was observed among postmenopausal women (1.00; 0.88–1.12). Passive smoking would be expected to expose breast tissue to the carcinogens in SHS, as would active smoking. However, the current evidence that active smoking causes no overall increase in breast cancer risks weighs against a causal role for involuntary smoking. The United States Surgeon General (2006) therefore concludes that the evidence for a causal relationship between SHS and breast cancer is “suggestive”.

Nineteen studies were included in the meta-analysis by Cal-EPA (2005), which yielded a summary risk estimate of 1.25 (1.08–1.44) for breast cancer overall in all exposed women. When the analysis was limited to the five studies that satisfied the exposure

36 Estimates of relative risk

assessment criteria (all major sources of lifetime SHS exposure), the summary risk estimate was 1.91 (1.53–2.39). Cal-EPA, in collaboration with Dr Kenneth Johnson (Public Health Agency of Canada), also conducted a meta-analysis of data for women who were premenopausal or younger than age 50 at diagnosis. A version of this analysis has also been published (Johnson, 2005). Fourteen studies where premenopausal breast cancer risk estimates in relation to SHS could be established yielded a summary risk estimate of 1.68 (1.31–2.15), whereas the risk estimate was 2.19 (1.68–2.84) based on the 5 of the 14 studies with more complete exposure assessment.

In conclusion, the most recent meta-analysis presents data that imply a causal relationship between SHS exposure and breast cancer among premenopausal women. The relationship with all cases of breast cancer seems to be somewhat less evident. Because of different conclusions by recent reviews on whether the evidence linking SHS exposure to breast cancer in non-smoking women is suggestive or significant, we classify breast cancer as a Level 2 outcome. We suggest that the summary risk estimate 1.68 (1.31–2.15) for breast cancer among never-smoking premenopausal women (or <50 years), presented in the most updated meta-analysis by Cal-EPA (2005), be used for calculation of the population attributable fraction of breast cancer in case this outcome is included. If health statistics do not stratify on menopausal status, a risk estimate of 1.25 (1.08–1.44) for breast cancer overall may be applied (Cal-EPA, 2005).

3.6.3 Nasal sinus and nasopharyngeal carcinomas Cancers of the nasal cavity and paranasal sinuses are extremely rare. Researchers have observed a 1.5- to 5-fold greater risk of these cancers in association with heavy smoking. Few studies have investigated the relationship between SHS exposure and the risk of cancers in the nasal sinus cavity and nasopharyngeal carcinoma among lifetime non-smokers. Nasal sinus cancer was significantly associated with SHS exposure in one prospective cohort study (Hirayama, 1984) and two case–control studies, although without a statistically significant increase in risk (Fukuda & Shibata, 1990; Zheng et al., 1993) after adjusting for potential confounders. According to the prospective study (Hirayama, 1984), which included 28 cases of death due to nasal sinus cancer, the relative risk increased with the intensity of husbands’ smoking in the following way: compared with women married to non-smokers, the relative risk was 1.7 (0.7–4.2), 2.0 (0.6–6.3) and 2.6 (1.0–6.3) for women whose husbands smoked 1– 14, 15–19 and >20 cigarettes per day, respectively. The dose-dependent increase in risk was statistically significant ( P < 0.03). However, the number of cases makes it difficult to study dose–response relationships and to draw a firm conclusion about causality.

The association of SHS exposure with nasopharyngeal carcinoma was investigated in three case–control studies among lifetime non-smokers (Yu et al., 1990; Cheng et al., 1999; Yuan et al., 2000) and one population-based case–control study (Vaughan et al., 1996). All four found a relationship with active smoking, but only one (Yuan et al., 2000) found a relationship with SHS exposure. Yuan and colleagues (2000) observed among non-smoking women a significantly increased risk associated with spousal smoking (adjusted OR = 3.09; 1.48–6.46), any household smoking (2.88; 1.39–5.96) and co-workers smoking less than 3 h per day (2.47; 1.12–5.44). However, the

37 Second-hand smoke: Assessing the burden of disease

association between SHS exposure and risk of nasopharyngeal carcinoma among men was considerably weaker, suggesting potential sex differences.

In conclusion, nasal sinus cancer is considered a Level 2 outcome, whereas nasopharyngeal carcinoma is considered a Level 3 outcome owing to limited evidence available. Because of the absence of recent studies and low number of reported cases, we do not recommend that these conditions be included in the calculation of the disease burden due to SHS.

3.6.4 Childhood cancer Cancer among children younger than 15 years of age is rare. However, in many developed countries, childhood cancer is, together with accidents, the most common cause of death in children. The most common cancers among children are leukaemia, brain tumours and lymphoma. The IARC (2004) monograph evaluated the association between parental smoking and childhood cancers. The evaluation was done for all cancers combined and separately for specific sites.

Several studies reported statistically significant increases in overall cancer risk with SHS exposure, often with supporting dose–response data (Sorahan et al., 1995, 1997a, 1997b, 2001). Few studies distinguish between postnatal and in utero exposure. Relevant exposure may also have occurred before conception (e.g. heritable mutations of male germ cells). Exposures during these three periods are likely to be correlated, in particular in relation to smoking by the father. Studies on paternal tobacco smoking suggest a small increased risk. However, bias and confounding cannot be ruled out.

IARC (2004) identified 14 studies that examined SHS as a risk for childhood cancer in general. Based on 12 of these studies, Boffetta et al. (2000) conducted a meta-analysis. The results suggest a small increase in risk for all cancers in relation to active maternal smoking during pregnancy (RR = 1.1, 1.0–1.2), but no increase for specific cancer sites. Results on postnatal exposure to SHS were too sparse to allow any conclusions to be drawn.

In summary, there is no clear evidence of an association between SHS and childhood cancer, and the effect is therefore classified as Level 3.

3.7 Cardiovascular diseases Cardiovascular diseases are the leading cause of death in most countries, and active smoking is one of the most important modifiable risk factors for both IHD and stroke. Relevant physiological variables and end-points of cardiovascular disease have been measured in relation to SHS exposure, including , ischaemic stroke, coronary flow velocity reserve, flow-mediated dilatation, aortic responsiveness and elasticity, arterial intima-media thickness, and high- and low-density lipoprotein cholesterol. SHS has been associated with a number of measurable physiological and biochemical changes that may underlie at least in part the increased risk of IHD and stroke.

38 Estimates of relative risk

3.7.1 Ischaemic heart disease IHD 1 is the most frequent cause of death in the world. Any increase in risk due to SHS may therefore have a substantial impact on EBD. Few of the risk estimates in individual studies on IHD and SHS are statistically significant. However, pooled estimates from meta-analyses consistently show a significant increase in risk of approximately 30% (Wells, 1994, 1998a; Law et al., 1997; He et al., 1999; Thun et al., 1999).

The most recent meta-analysis presented by the United States Surgeon General (2006) report includes nine cohort studies and seven case–control studies. All but two of the cohort studies were conducted in the USA; in contrast, all but one of the case–control studies were conducted outside the USA. Six studies included only women, nine studies included both sexes and one study included only men. All study participants were non-smokers, and in most studies they were lifetime non-smokers. Based on these studies, SHS exposure was associated with an increased risk for IHD mortality (fatal events), morbidity (non-fatal events) and symptoms. The overall pooled estimate of the risk of IHD related to any SHS exposure among non-smokers was 1.27 (1.19– 1.36).

The effects of home and workplace exposures are expected to be additive. No stratified meta-analyses were performed by the United States Surgeon General (2006) as a result of limitations in the precision of the estimates. Nonetheless, point estimates are similar for men and women and by exposure venue (i.e. work or home). Few studies have examined the relationship between occupational SHS exposure and risk of IHD. Steenland (1999) combined five studies for workplace exposure and estimated the pooled risk as 1.21 (1.04–1.41). The estimated summary odds ratio from another recent stratified meta-analysis related to exposure at home was 1.25 (1.17–1.33) for all coronary events (Thun et al., 1999).

Numerous studies have considered potential confounding factors in the analysis, and an association between SHS and IHD has been consistently observed in multiple populations. In its review, the United States Surgeon General’s (2006) report has concluded that the consistency of the association of SHS exposure with risk of coronary heart disease and the persistence of an association with controls for con- founding weigh heavily against residual confounding as the sole explanation.

In conclusion, IHD is classified as Level 1, meaning that it has a causal relationship with SHS. We suggest that the relative risk of 1.27 (1.19–1.36) (adjusted values used where available), estimated by the most recent review by the United States Surgeon General (2006), be used to estimate the burden of disease caused by SHS exposure at home or at work.

3.7.2 Stroke Stroke, also known as cerebrovascular disease, 2 is also one of the major causes of death. Few studies have addressed the possible association of passive smoking with stroke, although active smoking has been shown to be a significant risk factor

1 I20–I25 in the International Statistical Classification of Diseases and Related Health Problems (WHO, 2007a). 2 I60–I69 in the International Statistical Classification of Diseases and Related Health Problems (WHO, 2007a) .

39 Second-hand smoke: Assessing the burden of disease

(Jousilahti et al., 2002). Of the six studies identified in the United States Surgeon General (2006) report, two reported a statistically significant increase in the risk of stroke among those with SHS exposure—Sandler et al. (1989) (only for women) and Bonita et al. (1999). Bonita and colleagues (1999) estimated the adjusted odds ratio for non-fatal and fatal stroke related to SHS at home or at work as 1.82 (1.34–2.49), and this was significant for both males and females. Two other studies reported elevated, but statistically non-significant, risks of stroke from exposure at home (Donnan et al., 1989; You et al., 1999). It is important to note that the positive findings were obtained from case–control studies; none of the cohort studies that have examined the relationship between SHS and stroke have reported an increased risk. The six published studies also vary in their definition of stroke.

In conclusion, stroke in relation to SHS exposure among non-smokers is biologically plausible, but there is currently inconsistent epidemiological evidence. It is classified as a Level 2 outcome, and the risk estimate of 1.82 (1.34–2.49) in relation to SHS exposure at home or at work (Bonita et al., 1999) is recommended if a country wants to include stroke in its EBD calculations.

3.8 Solid fuel use as a potential confounder Several parameters are potential confounders of the relationship between SHS and the health outcomes described in this section. These include, for example, indoor smoke from solid fuel use and socioeconomic status. Although socioeconomic status has often already been addressed in the original studies, solid fuel use is not practised in the countries where the health effects have mainly been studied, namely in North America and Europe.

Solid fuel use is the household combustion of coal or biomass. Worldwide, approximately 50% of all households and 90% of rural households utilize solid fuels for cooking or heating. In situations of inefficient stoves and in poorly ventilated conditions, solid fuel use generates substantial emissions of many health-damaging pollutants. Women and their youngest children are most exposed because of their household roles. The disease burden from solid fuel use is most significant in populations living in rural areas of developing countries.

Many of the chemicals released from SHS are also found in environments with solid fuel use. In addition, the health outcomes are similar. Solid fuel use has, for example, been firmly associated with LRI in young children, as well as lung cancer in adult women. There is also moderate evidence for an association with lung cancer in adult men as well as asthma in both children and adults. Few epidemiological studies separating the effects of SHS exposure from those of solid fuel use or investigating the potential interaction between these two exposures have been reported. For LRI, for example, the two studies that have explicitly controlled for biomass combustion found no significant effect of exposure to biomass combustion on the odds ratio for the risk of severe LRI from SHS exposure: Chen et al. (1988), unadjusted OR = 1.33, adjusted OR = 1.31; Jin & Rossignol (1993), unadjusted OR = 2.0, adjusted OR = 2.4.

If the use of solid fuel influenced the effect of SHS exposure, there would be heterogeneity among studies in different regions. A recent meta-analysis compiled the studies of SHS and lung cancer from Europe, the USA and Asia and found good

40 Estimates of relative risk

consistency in effect estimates across continents, in study design and with respect to dose–response relationships (Taylor et al., 2007). Similar results have been reported in an earlier meta-analysis (Hackshaw et al., 1997). In the latter study, there was a statistically significant difference between China (excluding Hong Kong Special Administrative Region) and China, Hong Kong Special Administrative Region, compared with other locations, but only from one study that reported an implausibly low odds ratio (0.79).

We can conclude that there is no evidence that simultanous exposure to solid fuel use would reduce the effect of SHS, and the recommendation is to use the common procedure for estimating burden of disease due to SHS, regardless of solid fuel use.

41 Second-hand smoke: Assessing the burden of disease

4. Estimates of exposure 4.1 Considerations in exposure assessment in view of disease burden estimation Exposure assessment is crucial when assessing the health impacts of SHS in a population. When planning to assess exposure in view of estimating the disease burden, two aspects require particular attention:

1) the matching of exposure with the definition used in epidemiological studies, 2) the timing of exposure.

4.1.1 Equivalence of exposure definition in surveys and in epidemiological studies To estimate the burden of disease attributable to SHS, it is important to obtain and apply exposure information that matches as closely as possible the data used to derive the exposure–response relationship. Most epidemiological studies are questionnaire based and use a binary classification of exposure to separate the study population into those exposed to SHS and those not exposed.

The definition of exposure used in many epidemiological studies and surveys is often similar to “having one or more parent who smokes” for children (e.g. GYTS; CDC & WHO, 2008) and “being exposed during most days and/or most nights” in adults (e.g. European Community Respiratory Health Survey; Janson et al., 2001). In developed regions, where most epidemiological studies have been performed, relatively few differences are expected that would differentiate substantially the proxy of having a parent who smokes from the biologically effective dose. However, with housing, climatic and behavioural variations, important differences might appear, such as the following:

− The number of cigarettes smoked by smoker : The more cigarettes smoked by the smoker, the higher is the expected exposure for someone exposed to the smoker, all other circumstances being equal. Whereas in developed regions the mean cigarette consumption is about 16 per smoker per day, it can be much less in developing regions, sometimes as low as 2 per smoker per day. The consumption can also reach 25 per smoker per day, in particular in certain countries in the western Pacific or eastern European regions (Mackay & Eriksen, 2002). − Housing conditions : Housing conditions affecting the proximity to smokers, the volume of indoor spaces, ventilation systems or the number of people per household also have an impact on the effective dose of SHS. In developing countries, the occupancy of housing tends to be higher, resulting in higher exposure to SHS per smoker. − Climatic conditions and urbanization : Warmer climates and larger rural populations may result in more cigarettes being smoked outdoors and therefore lower the exposure to SHS. − Behaviour : Increased awareness of the health impacts from exposure to SHS may result in smoking parents avoiding exposure of their young children to SHS. Also, people in poorer communities take more puffs per cigarette and leave shorter stubs than do smokers in affluent communities. Studies conducted in the USA (Knight et

42 Estimates of exposure

al., 1996) illustrate that cotinine levels tend to be higher in black Americans than in white Americans, although the average number of cigarettes smoked per day tends to be higher among whites. However, regarding lung cancer, for example, studies conducted across Europe, Asia and North America showed no sign of important heterogeneity (Taylor et al., 2007).

It is therefore recommended that various hypotheses be studied through a sensitivity analysis, such as including only daily smokers of certain developing countries with low mean numbers of cigarettes consumed daily. The global assessment of the health impacts from SHS exposure (Öberg et al. 2009) provides an example.

4.1.2 Time period of exposure For outcomes that undergo several steps of development, there may be a considerable lag between exposure and effect (Jaakkola & Samet, 1999). Based on what is known for active smoking, it has been estimated that the latency period for lung cancer is between 10 and 20 years. The interval between exposure to SHS and cardiovascular effects is likely to be shorter, with some changes occurring more or less immediately (e.g. platelet activity), whereas other long-term effects may take at least 1–5 years to develop. If available, historical data on smoking prevalence should be used for lung cancer (10–20 years) and IHD (1–5 years).

Owing to uncertainty about the length of the latency period for many diseases and the lack of historical exposure data, it may be necessary to assume constant exposure to SHS over time in burden of disease estimates. Continuous data on exposure levels are rarely available for describing exposure–response relationships or for assessing exposure in large groups.

This guide applies a binary classification for exposure to divide the study population into those exposed to SHS and those not exposed, recognizing that this usually means in practice a division between “heavily exposed” and “less heavily exposed”. studies have shown that exposure to SHS is almost ubiquitous in most populations. Where smoking occurs in public places, there are few individuals who are truly “not exposed” to SHS.

4.2 Types of exposure assessment Several methods to assess SHS exposure have been used, including questionnaires, measurement of indoor air concentrations of SHS constituents, personal monitoring and biomarkers in saliva, urine, blood and hair.

The most precise measurements of exposure to individual constituents of SHS are obtained from personal monitoring and biomarkers of dose, but it is seldom feasible to use these measures in large population groups (Jaakkola & Jaakkola, 1997; Jaakkola & Samet, 1999). Therefore, epidemiological studies generally rely on questionnaire measures. Air monitors and biomarkers measure SHS exposure over a short period of time (hours to weeks), whereas many health effects are associated with long-term exposure (months to years), which supports questionnaires as the method to measure relevant exposure.

43 Second-hand smoke: Assessing the burden of disease

To estimate the burden of disease related to SHS, it is important to obtain and apply exposure information that matches as closely as possible the data used to derive the exposure–response relationship. In most instances, the epidemiological studies are questionnaire based, and such exposure assessments are therefore preferred as the basis for estimation of disease burden. Other measures of exposure may be more relevant to the development and evaluation of interventions to reduce smoking prevalence and exposure to SHS.

4.2.1 Use of questionnaires The most frequently used methods to determine SHS exposure are questionnaires and interviews. The information obtained is then often used to classify subjects into categorical groups of SHS exposure (e.g. exposed at work, at home or in other places). In these surveys, the subject reports his or her own exposure history. Questionnaires have the advantage of being able to ask questions about not only the current exposure but also past exposures, which may be more relevant for many health effects. For children, the information is given by parents or other guardians, and the most frequent proxy for SHS exposure of children is parental smoking. Parental smoking is often divided into maternal and paternal smoking, which provides the possibility to separate in utero exposure from postnatal SHS exposure at home. Even if the home is the major exposure site for children, locations outside the home may also contribute to SHS exposure.

Misclassification of exposure status can be of concern in studies using questionnaires alone to assess SHS exposure. Misclassification can be differential (i.e. biasing the estimates in either direction) or non-differential (i.e. always leading to underestimation). Misclassification can result from a number of factors, including limited questions (e.g. spousal smoking asked only), deception in reporting and inadequate recall of exposure. It has been shown that qualitative information on childhood exposure to SHS provided by subjects who are now adults and information on spousal smoking are of good reliability. However, quantitative information on the amounts smoked (i.e. number of cigarettes or hours of smoking per day) is less reliable (McLaughlin et al., 1987; Coultas et al., 1989). Yet it is still likely that people are able to recall relatively reliably whether they were heavily or lightly exposed. One way to control for deception in reporting is to use biomarkers. Studies have shown that a proportion of subjects reporting no exposure to SHS have measurable biomarker concentrations, indicating that the subject forgot, denied or was not aware of the SHS exposure (Coultas et al., 1987). In the interpretation of results, it has to be kept in mind that the overall impact of misclassification is likely to cause underestimation of the health impacts of SHS exposure.

4.2.2 Use of biomarkers Biomarkers are used to measure dose from recent exposure of an individual. The last few months are the longest period that can be measured with biomarkers at the present time, and this period is often not as relevant as past exposures in terms of health effects. In addition, biomarkers are not currently a relevant measure of exposure for quantifying health impacts for population groups, as it is expensive to use these on a large scale and because risk estimates are generally based on other measures of exposure. We summarize the use of biomarkers here because these measures may be valuable when causality is evaluated.

44 Estimates of exposure

Biomarkers of SHS exposure are tobacco smoke constituents, their metabolites or adducts that can be assessed directly by the analysis of body fluids (i.e. urine, saliva and serum) or hair. By using biomarkers, it is possible to characterize exposure more accurately, to identify individuals misreporting their smoking status and to estimate the relative degree of exposure. The most frequently used biomarker of SHS exposure is cotinine, a major metabolite of nicotine. Cotinine levels are the most frequently used method to distinguish active smokers from SHS-exposed and unexposed non-smokers. SHS-exposed non-smokers have approximately 1% of the urine cotinine levels found in active smokers. Nevertheless, cotinine concentrations in the occasional smoker are similar to those of the heavily SHS-exposed non-smoker. Another common biomarker is carboxyhaemoglobin, a sign of exposure to carbon monoxide. Other biomarkers that have been used as indicators of exposure to tobacco smoke include nicotine, thiocyanate, hydroxyproline, N-nitrosoproline, aromatic amines and certain protein or deoxyribonucleic acid adducts.

With few exceptions, the presence of nicotine or cotinine in physiological fluids can be attributed to exposure to tobacco smoke. The different constituents mirror different timing of exposure. Nicotine has a rather short half-life (2 h) and is therefore a good indicator of exposure during the previous hours. Cotinine has a longer half-life, making it a good indicator of exposure during the previous 2–3 days. Nicotine in hair has been recently used as a biomarker that relates to SHS exposure over the last 1–2 months.

The use of other nicotine products, such as (snuff), or patches, will affect the nicotine/cotinine levels significantly. Analysis of thiocyanate (formed in the liver from hydrogen cyanide that is present in the vapour phase of tobacco smoke) has been suggested to distinguish smokers from individuals using smokeless tobacco or other nicotine-containing products (Haley et al., 1983; Hauth et al., 1984; Jarvis et al., 1987).

4.2.3 Other measures of exposure The measurement of individual SHS constituents in the indoor air (e.g. airborne nicotine, respirable suspended ) is also a common way to measure SHS exposure. However, the complexity of SHS makes it difficult to find the optimal tracer or proxy compound, and indoor air concentrations seldom match the data needed for EBD assessment (i.e. the measure of exposure used in major epidemiological studies).

Personal monitoring is another method for measuring exposure (to individual constituents of SHS) that can give a high specificity and accuracy for individuals, but it has limited value in calculations of EBD. However, it may illustrate the importance of different sites of exposure during a single day.

4.3 Conducting an exposure survey As mentioned previously, exposure to SHS for the purpose of estimating health impacts in population groups is best assessed by using questionnaires. A purpose-made survey can be conducted locally, or existing survey information available at the national or international level can be used (see section 4.4 for international sources of information on exposure).

45 Second-hand smoke: Assessing the burden of disease

A well-designed survey that is statistically representative of the larger population of interest can be conducted, using relevant and clearly worded questionnaires. Such questionnaires generally rely on a binary classification of exposure (i.e. exposed or not exposed), although biomarker studies have shown that most members of a non- smoking population are likely to be exposed to SHS to some degree. Assessing exposure in order to estimate the burden of disease generally has the following purposes:

− to identify whether the individual is exposed, − to identify the main source of exposure (at work, at home, at other locations), − to identify the current and past active smoking status of the individual. Some questions for a survey on children’s exposure could include the following:

1. Is the mother/father a habitual smoker? 2. Is the mother/father/other regularly smoking at home? 3. Is the child regularly exposed to SHS in any other environments?

For some outcomes, the prenatal exposure may be important; therefore, it is also important to ask:

4. Was the child’s mother smoking during pregnancy? 5. Was the child’s mother exposed to SHS during pregnancy?

For adults, the questions could include:

1. Does the spouse or do other adults smoke regularly at home? 2. Is the person regularly exposed to SHS at work? 3. Is the person regularly exposed to SHS in other environments?

The smoking history is of importance for many outcomes; therefore, it is also important to ask:

4. Has the person ever been a regular smoker?

4.4 Available surveys of SHS exposure Information on exposure to SHS may already be available for the population of interest when starting an estimation of health impacts. It is advisable to search for such sources before conducting an additional survey. Potential sources include international assessments of SHS exposure (e.g. GYTS), scientific projects, such as long-running cohort studies where SHS has been included, and local assessments, such as household surveys.

4.4.1 International databases on adults In certain regions, multicountry studies have assessed SHS exposure among adults. One such example is the European Community Respiratory Health Survey, which has estimated SHS exposure at home and at work in 14 countries using a questionnaire (Janson et al., 2001).

46 Estimates of exposure

4.4.2 International databases on children Several international initiatives have collected data on children’s exposure to SHS. WHO and the United States Centers for Disease Control and Prevention developed the GYTS to measure tobacco use among young people (13–15 years) across countries using a common methodology and core questionnaire. Between 1999 and 2003, the GYTS was completed in almost 130 countries. The survey also enquired about children’s exposure to SHS in their home or in other places during the previous 7 days and about current smoking habits of the parents. 1

Several international scientific networks have collected data on SHS exposure from collaborating centres. One such network is ISAAC, which investigates asthma, allergic rhinitis and eczema by promoting a standardized methodology. By August 2002, 230 centres from 90 countries had registered for Phase Three. 2 Questionnaires on risk factors include two questions related to parental smoking and SHS exposure:

1. Does/did the child’s mother smoke at present/during the child’s first year of life/during pregnancy? 2. Does anybody at present smoke inside the child’s home?

In addition to international databases on children’s exposure to SHS, exposure estimates can be modelled using data available on adult smoking prevalence. For this purpose, it can be assumed that families consist of one father and one mother living together and that smoking prevalence and number of children are not related; using such assumptions, the percentage of children with at least one smoking parent can be modelled using the following formula:

Any parental smoking = Male smoking prevalence + female smoking prevalence − (male smoking prevalence × female smoking prevalence)

4.4.3 National databases On a national level, surveys or cohort studies may have been performed among the general population to assess risk factors in general or specific to certain diseases, such as asthma or IHD. Exposure to SHS may have been included. Certain countries may also have included SHS exposure in their regular surveys of environment and health. This may be an important tool to measure the efficiency of legislation and other interventions to control SHS exposure.

1 More information is available at the following web address: http://www.cdc.gov/tobacco/global/gyts/index.htm . 2 More information is available at the following web address: http://isaac.auckland.ac.nz .

47 Second-hand smoke: Assessing the burden of disease

5. Availability of disease statistics 5.1 National statistics National statistics, or data detailed by subnational units, on the incidence, prevalence, mortality or DALYs (if a national burden of disease study has been conducted) of diseases linked to SHS exposure are the data of choice for quantifying the disease burden using the proposed method. Where vulnerable groups, such as children, are of interest, the disease statistics should also present segregated illness rates specific to these groups. The diseases of interest are all those that the users of this guide would like to quantify in terms of attributable fraction due to exposure to SHS.

Where multiple studies or data sets on disease frequency exist, these should be checked for consistency, and explanations for possible variations (e.g. difference in definition of disease) should be searched for.

5.2 Poor availability and alternative sources of statistics Where the required disease data are not available or are not detailed enough for the study population of interest, it may be possible to develop an approximate or preliminary estimate based on the disease statistics extrapolated from a larger region, applying the assumption that disease rates vary only a little. However, such approximation should be carried out with caution. For example, if active smoking patterns show significant differences, it is likely that the rates of the diseases relevant for SHS exposure may also vary substantially; therefore, such an extrapolation would be inappropriate.

Disease statistics for some of the relevant diseases may also be available from alternative sources, such as the Demographic and Health Surveys 1, which provide survey results for numerous developing countries online or upon request.

For comparison, disease statistics and rates are also available from WHO on a regional basis and for all WHO Member States 2. This comprehensive database contains estimates for deaths and DALYs for about 100 major diseases and injury categories for 190 countries.

It is also possible to improve national health information and statistics through, for example, purpose-designed surveys. The Health Metrics Network 3, an initiative coordinated by WHO, supports the development of better health information systems at the country level.

1 http://www.measuredhs.com/ 2 http://www.who.int/healthinfo/global_burden_disease/en/index.html 3 http://www.who.int/healthmetrics/en/

48 Estimating the total disease burden related to SHS

6. Estimating the total disease burden related to SHS 6.1 Diseases to be included in the disease burden estimate As many of the health outcomes suspected to be associated with SHS are not currently supported by sufficient evidence for quantification, it is likely that only part of the disease burden associated with SHS can be quantified. The total disease burden related to SHS that can currently be estimated is the sum of the individual disease burdens supported by evidence rated “Level 1”, as described in this guide. The user may wish to select the diseases to be included in this estimate according to the relevance for the study population or consider only outcomes affecting specific age groups, such as children. The diseases that can generally be added up (in terms of deaths and DALYs) include the following Level 1 disease outcomes for which disease statistics are often available:

1. For children: − LBW − SIDS − LRI (<5 years) − Otitis media (acute and/or recurrent) (<3 years) − Onset of asthma (<14 years)

2. For adults − Lung cancer − IHD − Onset of asthma

Symptoms or Level 2 or 3 diseases are not currently recommended for inclusion in the disease burden estimate. However, as Level 2 means that there is some evidence, although the evidence concerning causality is not definite at the moment, whether or not such outcomes should be included depends on the purpose for which the burden of disease assessment is being performed. Additional evidence may also accumulate with time, and the evidence levels should be revisited.

6.2 Calculation of the disease burden for children The disease burden (expressed as mortality, incidence or DALYs) should be estimated, for each disease considered, by multiplying the population attributable fraction by the measure of disease:

AB = PAF SHS × B where: AB = burden attributable to SHS PAF SHS = population attributable fraction for SHS B = total burden, in deaths, cases or DALYs

49 Second-hand smoke: Assessing the burden of disease

The formula should be applied to each disease and age group separately. Deaths and DALYs attributable to SHS can then be added up; in contrast, incident cases of different diseases are not comparable and cannot be added.

6.3 Calculation of the disease burden for adults The method for estimating SHS disease burden for adults is similar to that for children. The one major difference is that smokers should normally be deducted from the study population. The reason for this is not that smokers are not considered to be susceptible to the health effects of SHS exposure, but rather that almost all studies of the health effects of SHS exposure have been based on non-smokers (and mostly never-smokers only).

There are several ways in which this restriction may be carried out, depending on the availability of data. Separate reporting on number of cases or the rate of disease among non-smokers and smokers may allow the direct estimate of burden in these two groups (Woodward & Laugesen, 2001).

An alternative approach to approximate the burden in non-smokers, in the absence of the relevant disease rates among non-smokers, is to subtract from the total burden of disease the burden attributed to active smoking before applying the population attributable fraction due to SHS:

Bns = [B − (B × PAFsm )] × (1 − p sm )

AB = PAF SHS × B ns where: B = total burden, in deaths, cases or DALYs Bns = burden in non-smokers, in deaths, cases or DALYs psm = smoking rate AB = burden attributable to SHS PAF sm = population attributable fraction for active smoking PAF SHS = population attributable fraction for SHS

The multiplication of the burden not due to active smoking by the prevalence of non- smoking aims to correct the overcount, from including the burden in smokers caused by causes other than smoking, that would otherwise be caused by this approach. This is an approximation only and may cause an overcorrection.

The population attributable fractions for active smoking are provided in the Annex, Tables A1.1 to A1.6, for each WHO Member State (Ezzati & Lopez, 2004; C. Mathers, personal communication, 2009), based on comparative risk assessment.

50 Uncertainty

7. Uncertainty The approach described in this guide carries important uncertainties that come from both the methodology and the data sources. At present, however, there is no straightforward mechanism for capturing different sources of uncertainty and for calculating lower and upper bounds for estimates generated by local assessments. If reliable information was available on the variability of both relative risks and exposure, it would be possible to perform a probabilistic analysis using Monte Carlo simulation. 1 However, this information is rarely available, and the effort required to execute such an approach seems unsuitable for most local assessments.

Given these limitations, we recommend that “best estimates” be accompanied by a sensitivity analysis based on several alternative hypotheses, which show the range of alternative results that can be obtained. In general, attributable burden estimates should be looked upon as approximations intended to guide policy decisions, rather than a precise prediction of the number of lives that will be saved by interventions.

Specific sources that may contribute to the uncertainty around estimates of health impacts from exposure to SHS are discussed in the following sections.

7.1 Uncertainty with estimates of exposure “Exposure to SHS” in fact relates to a wide range of exposures. A variety of factors affect the degree and quality of SHS exposure. Examples include the type of smoke item (e.g. cigarette, cigar or pipe), ventilation, demographics, climate and season, location relative to other sources of air pollution and time–activity patterns. Unfortunately, given the current information available on SHS as well as the methodological aspects of the studies based on which the relative risks were identified, the influence of modifying factors cannot be reliably quantified.

One uncertainty related to exposure that can be quantified is the confidence interval for the exposure distribution in a survey of SHS exposure based on a random population- based sample. Application of the upper and lower limits of this confidence interval could be presented in a sensitivity analysis.

7.2 Changes in exposure and disease prevalence over time Exposure is generally assessed only at one or two specific points in time. Smoking patterns have changed in many countries over time owing to changes in smoking behaviour, legislation and other interventions.

The latency periods for different outcomes cover a full spectrum—from hours for exacerbation of asthma and angina, to days for exacerbation of asthma, to months for induction of asthma and LBW, and to years for induction of asthma, IHD and lung cancer. If there have been substantial changes in smoking rates, this would warrant a

1 A Monte Carlo simulation calculates multiple scenarios of a model by repeatedly sampling values from the probability distributions for the uncertain input variables. It provides a probability distribution around the resulting values.

51 Second-hand smoke: Assessing the burden of disease

discussion, and in extreme circumstances sensitivity analyses may be performed assuming a range of exposures. In this guide, we recommend that historical exposure data for lung cancer (10–20 years) and IHD (1–5 years) be referred to.

Also, the disease rates may have changed. For example, the sleeping position affects the total number of deaths from SIDS. If parents change the sleeping position of their children so that more children sleep on their backs, the total number of deaths from SIDS will decrease, as will the number of attributable cases caused by SHS, although these latter deaths will make up a greater fraction of the total. This is also true for populations that are exposed to indoor smoke from solid fuel (i.e. if the use of solid fuel decreases, the total number of related diseases will fall, as will the number of attributable cases caused by SHS).

7.3 Uncertainty of effect size Although some of the relative risks used in this guide rely on meta-analyses that include data from several studies, they may still be subject to random and systematic error. Furthermore, the effect sizes are associated with the specific conditions of the populations and risks covered in those studies. As, for example, local housing conditions, ventilation and smoking habits may differ, those relative risks may not entirely cover the situation in the population under study. It is possible to perform local assessments of relative risks, in particular in areas where conditions differ significantly from the conditions of the populations covered by most of the available studies (i.e. North America and western Europe).

Finally, the effect of important co-exposures that have not been studied extensively, such as exposure to indoor pollution from solid fuel use, may introduce additional uncertainty. Recent meta-analyses of lung cancer and SHS have, however, not found important differences in risk estimates for health effects of SHS between different continents.

In order to partly explore possible ranges of a burden of disease estimate, the confidence intervals for the relative risks (odds ratios) associated with health outcomes, as reported in this guide, may be used to test the sensitivity of results. It should be noted that only the health outcomes with the strongest evidence are recommended for quantification in this guide, and quantification is further limited by the availability of suitable disease statistics. However, evidence is building up for additional health outcomes that may be considered in the future. It is also possible to quantify additional health outcomes attributable to SHS and describe them as supported by weaker evidence.

7.4 Study population and disease burden In this guide, we have excluded current smokers. This implies that current smokers are not susceptible to SHS, although certain studies indicate that the effect in smokers is similar. Smokers are certainly exposed to SHS (often to a higher extent than non- smokers) and may even be more susceptible, as they have more smoking-caused pre- existing or subclinical conditions that make them more vulnerable. Their exclusion from these calculations may underestimate the impact of SHS.

52 Uncertainty

The inclusion or exclusion of ex-smokers poses similar uncertainties, and ex-smokers can often not be addressed separately, because the number of ex-smokers or the disease rates in ex-smokers may not be known. This can be addressed in a sensitivity analysis.

53 Second-hand smoke: Assessing the burden of disease

8. Case-study This case-study is included to give the reader a comprehensive picture of how to use the present methods guide at the national, regional or local level.

8.1 Step 0: Define parameters of interest Country X, a fictitious country with 1.3 million inhabitants, was chosen as an example, with a smoking prevalence that is similar to that in many other countries around the world. The case-study uses the most recent estimations of exposure, disease burden and population from international databases available at WHO. The case-study follows the steps outlined in section 2. With respect to health outcomes, the case-study focuses on effects with a strong level of evidence, where health statistics are available. In addition, it utilizes the relative risks presented in Table 3.2.

8.2 Step 1: Obtain key data For the selected population group (i.e. all 1.3 million habitants in Country X), exposure and health data for the outcomes of interest (mortality and DALYs) are, for the purpose of this example, extracted from the WHO global database for burden of disease. 1 This database relates to the year 2004 and includes six of the selected outcomes—namely, LRI, otitis media and onset of asthma in children; and asthma, lung cancer and IHD in adults. If available, these as well as other end-points (such as LBW and SIDS) may be extracted from national, regional or local sources.

8.2.1 Step 1b: Exposure For children, the prevalence of any parental smoking is taken from the GYTS. In this survey, 59% of schoolchildren (13–15 years) report that they have at least one smoking parent in the household (Table 9). We assume that this figure applies also to parents of children aged 0–13 years.

Table 9: Available data on SHS exposure and smoking prevalence among adults in Country X

% Year Male smoking prevalence 49 2004 Female smoking prevalence 25 2004 Any parental smoking 59 2002 Any SHS exposure among non-smoking men 28 2004 Any SHS exposure among non-smoking women 28 2004 Any SHS exposure among non-smoking men 48 1994 Any SHS exposure among non-smoking women 33 1994

Exposure of adults was estimated from data on exposure to SHS obtained from a repeated regional health survey. In this survey, the participants were asked whether they had regularly (most days and/or nights) been exposed to tobacco smoke in the previous 12 months. Of the non-smoking participants, 28% of men and 28% of women reported exposure to SHS during the year 2004. In the previous survey (1994), the exposure to SHS was higher for men (48%) and slightly higher for women (33%). The

1 http://www.who.int/healthinfo/global_burden_disease/estimates_country/en/index.html .

54 Case study

1994 exposure data are later used to estimate the number of lung cancers attributable to SHS.

8.2.2 Step 1c: Burden of disease from selected health outcomes Data on health, including deaths and DALYs, for the selected outcomes (i.e. LRI, otitis media, asthma in both children and adults, lung cancer and IHD), as well as demographic data, were assumed to be taken from the WHO health database for burden of disease (Tables 10 and 11).

Table 10: Population in Country X in 2004

Population group a Population size Number of children <5 years 64 000 Number of children <15 years 211 000 Number of men ≥15 years 513 000 Number of women ≥15 years 625 000 a The disease data available from various sources, such as the WHO database, do not necessarily match the age ranges of observed health impacts from SHS. Certain assumptions need to be made to obtain figures for matching age groups, or additional details need to be sought from the data source.

Table 11: Disease burden (deaths and DALYs) from selected outcomes in Country X in 2004

Number of Outcome Number of deaths DALYs deaths/1000 DALYs/1000 LRI, children <2 years 4 141 0.1 2.3 a Otitis media, children <3 years 0 46 0.0 0.7 a Asthma, children <15 years 0 718 0.0 3.4 Asthma, men ≥15 years b 22 917 0.0 1.8 Asthma, women ≥15 years 25 632 0.0 1.0 Lung cancer, men ≥15 years 530 4 520 1.0 8.8 Lung cancer, women ≥15 years 154 1 340 0.2 2.1 IHD, men ≥15 years 2 570 19 660 5.0 38 IHD, women ≥15 years 2 960 13 130 4.7 21 Total 6 944 41 139 5.1 30 a Rate in age group 0–4 years. b The health effect has been studied in adults above 20 years of age, but is assumed here to be applicable to all adults ≥18 years of age.

In order to estimate the burden of disease among non-smokers, the burden attributable to active smoking can be taken from WHO if no local data are available (Table 12; see tables in the Annex). Owing to a higher smoking prevalence among men, a greater fraction of the disease burden is related to smoking in men compared with women. For example, only 6% of the lung cancer among the male population is estimated to be related to causes other than active smoking (e.g. SHS).

55 Second-hand smoke: Assessing the burden of disease

Table 12: Population attributable fraction for active smoking in Country X for adult asthma, lung cancer and IHD a

Outcome 15–29 years 30–44 years 45–59 years 60–69 years 70–79 years >80 years Asthma, men 0.47 0.47 0.36 0.38 0.31 0.26 Asthma, women 0.28 0.28 0.14 0.15 0.16 0.08 Lung cancer, men NA 0.83 0.91 0.95 0.94 0.91 Lung cancer, women NA 0.61 0.59 0.67 0.65 0.40 IHD, men NA 0.82 0.56 0.37 0.18 0.02 IHD, women NA 0.29 0.27 0.18 0.10 0.03 NA, not applicable a Population attributable fractions are available for several age groups above 30 years (see tables in Annex). Population attributable fractions for asthma 15–29 years are assumed to be the same as for 30–44 years.

8.3 Step 2: Calculate population attributable fractions

To estimate the population attributable fraction from SHS (PAF SHS ), the relative risk and the exposure level are inserted into the following equation:

PAF SHS = [p(RR − 1)]/[p(RR − 1) + 1] where: p = the proportion in the age group exposed to SHS RR = the relative risk for outcome and population group

For example, the PAF SHS for IHD among men 30–44 years of age in Country X is [0.28(1.27 – 1)] / [0.28(1.27 – 1) + 1] = 0.069.

The results of this procedure for all selected outcomes are given in Table 13. Low and high estimates are based on confidence intervals for the values of relative risks used for this estimation.

Table 13: Population attributable fractions from SHS for Country X

PAF SHS Outcome Relative risk (95% CI) Low Central High LRI, children <2 years 1.55 (1.42–1.69) 0.20 0.25 0.29 Otitis media, children <3 years 1.38 (1.21–1.56) 0.11 0.18 0.25 Asthma, children <15 years 1.32 (1.24–1.41) 0.12 0.16 0.20 Asthma, men ≥15 years 1.97 (1.19–3.25) 0.050 0.21 0.38 Asthma, women ≥15 years 1.97 (1.19–3.25) 0.050 0.21 0.38 Lung cancer, men ≥15 years 1.21 (1.13–1.30) 0.058 0.091 0.13 Lung cancer, women ≥15 years 1.21 (1.13–1.30) 0.041 0.065 0.091 IHD, men ≥15 years 1.27 (1.19–1.36) 0.050 0.069 0.090 IHD, women ≥15 years 1.27 (1.19–1.36) 0.050 0.069 0.090

8.4 Step 3: Calculate attributable burdens To calculate the attributable burden among children, the resulting population attributable fraction is multiplied by the chosen measure of disease burden to estimate the attributable burden, according to the following equation:

56 Case study

AB = PAF SHS × B where: AB = burden attributable to SHS PAF SHS = population attributable fraction for SHS B = total burden, in deaths, cases or DALYs

For example, the central estimate of the number of DALYs due to LRI is calculated as:

0.25 × 141 = 35 where 0.25 is the PAF SHS taken from Table 13, row 1, and 141 is the B taken from Table 11, row 1.

To calculate the attributable burden among adults, the population attributable fraction is multiplied by the chosen measure of disease burden among the non-smoking population, according to the following equations:

Bns = [B − (B × PAF sm )] × (1 − p sm )

AB = PAFSHS × B ns where: B = total burden, in deaths, cases or DALYs Bns = burden in non-smokers, in deaths, cases or DALYs psm = smoking prevalence AB = burden attributable to SHS PAF sm = population attributable fraction for active smoking PAF SHS = population attributable fraction for SHS

The calculation of attributable burden among adults is made by age group, and then the results for the different age groups are added up. For example, the number of deaths due to IHD among non-smoking men between the ages of 45 and 59 years is calculated (based on the fact that this subgroup consists of 116 300 persons and has an annual number of deaths related to IHD of 372) as:

[372 – (372 × 0.56)] × (1 − 0.49) = 83 where 0.56 is the PAF sm taken from Table 12, row 5, and 0.49 is the p sm taken from Table 9, row 1.

This calculation is then repeated for all age groups, and the total burden among non- smoking men (all ages) is 973.

Thereafter, the central estimate of the number of deaths due to IHD among men is calculated as:

0.069 × 973 = 67 where 0.069 is the PAF SHS taken from Table 13, row 8.

Repeating these calculations for all selected outcomes provides the results listed in Table 14.

57 Second-hand smoke: Assessing the burden of disease

Table 14: Burden of disease from SHS for Country X

Number of Outcome deaths DALYs Number of deaths/1000 DALYs/1000 LRI, children <2 years 1 35 0.02 0.55 Otitis media, children <3 years 0 8 0.00 0.13 Asthma, children <14 years 0 114 0.00 0.54 Asthma, men ≥15 years 2 55 0.00 0.11 Asthma, women ≥15 years 3 77 0.00 0.12 Lung cancer, men ≥15 years 2 15 0.00 0.03 Lung cancer, women ≥15 years 3 25 0.00 0.04 IHD, men ≥15 years 67 419 0.13 0.82 IHD, women ≥15 years 143 597 0.23 0.96 Total 221 1345 0.16 1.00

The final results can be expressed as total disease burden attributable to SHS or examined on a per capita basis or separately by specific disease outcomes and age/sex groupings (Table 14). The estimated burden of disease from SHS for the selected outcomes in Country X in the year 2004 is 221 deaths and 1345 DALYs. The majority of deaths are related to IHD. SHS causes about 7% of the disease burden in deaths and 8% in DALYs related to these six outcomes among non-smokers. Sixty-eight per cent of the lethal burden related to SHS among non-smoking adults is carried by women, and 12% of the disease burden in DALYs is carried by children less than 14 years of age.

8.5 Step 4: Describe uncertainty The uncertainties in the applied approach derive from the data compiled for the analysis, as described in section 7. All included estimates (i.e. exposure–risk relationship, exposure assessment and total burden of disease) have a factor of uncertainty and error. The impact of this type of error is difficult to estimate. Exposure assessment from Country X may also contain errors/uncertainties that are difficult to control. On the other hand, the exposure–response relationships are based on the most recent meta-analyses or, if meta-analyses are not available, on individual high-quality studies.

In an effort to provide some presentation of uncertainty, calculations of population attributable fractions were repeated with various alternative hypotheses. For example, the high and low estimates of the total number of deaths in Country X are calculated to be 156 and 339 deaths, respectively (Table 15). These low and high scenarios cannot be interpreted as the statistical bounds around the central estimates. They simply illustrate a sensitivity analysis based on uncertainties regarding the exposure–response relationship and various alternative assumptions made in the estimation of attributable burden. Additional sensitivity analyses could be performed utilizing various assumptions regarding exposure, exposure–response or method for burden of disease estimation.

58 Case study

Table 15: Sensitivity analysis of SHS mortality in Country X

Effect on number of deaths Assumption in best estimate Alternative condition per year 1. Best estimate RR/OR from meta- Upper 95% confidence limit of all RRs/ORs Increased by 31% (290 deaths) analyses or original papers Lower 95% confidence limit of all RRs/ORs Decreased by 29% (156 deaths) 2. Current smokers are not Regard all non-smoking burden in smokers as Increased by 53% (339 deaths) susceptible to SHS influenced by SHS 3. The non-smoking burden in ex- Ex-smokers are not susceptible to the effect of Decreased by 24% (167 deaths) smokers is influenced by SHS SHS, and 25% of non-smokers are ex- smokers 4. Parental smoking (coupled to Ratio of paternal to maternal smoking is lower Decreased by 0.5% (220 deaths) available risk estimates) is an in studied population, and exposure intensity adequate indicator of exposure for is not compensated by other factors, such as the studied population additional adult smokers in the household, smaller housing, etc.

8.6 Step 5: Summary and conclusions The presentation of results should be tailored to the target audience of the report.

59 Second-hand smoke: Assessing the burden of disease

9. WHO policy This section presents two excerpts from WHO’s recent publication, Protection from exposure to second-hand tobacco smoke. Policy recommendations (WHO, 2007b).

*****

… several countries and hundreds of subnational and local jurisdictions have successfully implemented laws requiring indoor workplaces and public places to be 100% smoke-free without encountering significant challenges in enforcement. The evidence from these jurisdictions consistently demonstrates not only that smoke-free environments are enforceable, but that they are popular and become more so following implementation. These laws have no negative impact—and often have a positive one— on businesses in the hospitality sector and elsewhere. Their outcomes—an immediate reduction in heart attacks and respiratory problems—also have a positive impact on health.

These experiences offer numerous, consistent lessons learnt, which policy-makers should consider to ensure the successful implementation of public policies that effectively protect the population from SHS exposure. These lessons include the following:

1. Legislation that mandates smoke-free environments—not voluntary policies—is necessary to protect public health; 2. Legislation should be simple, clear and enforceable, and comprehensive; 3. Anticipating and responding to the tobacco industry’s opposition, often mobilized through third parties, is crucial; 4. Involving civil society is central to achieving effective legislation; 5. Education and consultation are necessary to ensure smooth implementation; 6. An implementation and enforcement plan as well as an infrastructure for enforcement are essential; and 7. Implementation of smoke-free environments must be monitored and, ideally, their impact measured and experiences documented.

In light of the accumulated evidence, local, subnational 1 and national governments worldwide are increasingly implementing smoke-free policies in workplaces and public places to protect people from the dangers of SHS. Jurisdictions that have implemented smoke-free workplaces and public places have observed an immediate drop in levels of SHS, a decline in levels of SHS components in the population as well as significant and immediate health improvements in workers previously exposed to SHS.

At the same time, smoke-free environments have been found to be very effective as a policy by making it easier for smokers to cut down or quit and by

1 Subnational level refers to all jurisdictions other than the local, municipal level and the national or federal level of a country. It may include states, provinces, cantons, departments or similar jurisdictions.

60 WHO policy

reducing smoking initiation. Furthermore, smoke-free laws enjoy popular support and high levels of compliance when properly implemented; they forcefully deliver the message that smoking is not socially acceptable.

Recent progress has highlighted the feasibility of achieving smoke-free environments and heightened worldwide interest in promoting them. Developed and developing countries like Ireland, New Zealand, Scotland and Uruguay, as well as territories 1 such as , have built on the implementation of smoke-free laws at the local and subnational level that began in North America in the late 1970s. With almost universal success, they have since enacted and implemented laws to protect workers and the public from SHS in almost all indoor workplaces and public places (including bars and casinos), achieving strong popular support. Other countries are interested in learning from their experiences.

Since the 1970s, tobacco companies have considered smoke-free laws to be the “most dangerous development to the viability of the tobacco industry that has yet occurred” (Roper Organization, 1978). The tobacco industry—usually working through front groups operating with its support—vigorously opposes the passage and implementation of smoke-free laws, whether at local, subnational or national level. Tobacco companies continue to misrepresent the evidence on the health effects of SHS exposure and even claim that WHO has concluded that SHS is not dangerous. In fact, WHO has consistently concluded the opposite: SHS kills.

Finally, the obligations under WHO’s Framework Convention on Tobacco Control (WHO FCTC; WHO, 2003), to which more than 140 WHO Member States and the European Community are Parties, 2 are further driving the need for clearer guidance from WHO on protection from SHS. Article 8 of the WHO FCTC, Protection from exposure to tobacco smoke , requires Parties to:

Adopt and implement in areas of existing national jurisdiction as determined by national law and actively promote at other jurisdictional levels the adoption and implementation of effective legislative, executive, administrative and/or other measures, providing for protection from exposure to tobacco smoke in indoor workplaces, public transport, indoor public places and, as appropriate, other public places.

*****

Recommendations on protection from exposure to second-hand tobacco smoke

In light of the deleterious health effects and the frequency of exposure to SHS (an exposure that carries significant social and economic costs); the cost-effectiveness, feasibility and popularity of smoke-free policies; and the successful experience of a rapidly growing number of jurisdictions worldwide, WHO makes the following recommendations to protect workers and the public from exposure to SHS.

1 A territory is a geographical area distinct from a WHO Member State for which the United Nations makes no assumption regarding its political or administrative affiliation. 2 147 parties as of 1 June 2007.

61 Second-hand smoke: Assessing the burden of disease

Recommendation 1: 100% smoke-free environments, not ventilation Remove the pollutant—tobacco smoke—through implementation of 100% smoke-free environments. This is the only effective strategy to reduce exposure to tobacco smoke in indoor environments to safe levels and to provide an acceptable level of protection from the dangers of SHS exposure. Ventilation and smoking areas, whether separately ventilated from non-smoking areas or not, do not reduce exposure to a safe level of risk and are not recommended.

Second-hand tobacco smoke causes serious and fatal diseases in adults and children. There is no safe level of exposure to SHS. Ventilation and health experts agree that ventilation is not a solution to this significant health issue. In 2006, the United States Surgeon General’s report concluded (Conclusions 3 and 10 on page 649), “Establishing smoke-free workplaces is the only effective way to ensure that second- hand smoke exposure does not occur in the workplace. Exposure of non-smokers to second-hand smoke cannot be controlled by air cleaning or mechanical air exchange.”

Recommendation 2: Universal protection by law Enact legislation requiring all indoor workplaces and public places to be 100% smoke-free environments. Laws should ensure equal protection for all. Voluntary policies are not an acceptable response to protection. Under some circumstances, the principle of universal, effective protection may require specific quasi-outdoor and outdoor workplaces to be smoke-free.

There is no scientific basis for exempting particular types of spaces or categories of the population from protection; all individuals are vulnerable to the harm caused by SHS exposure. The critical principle bearing on universal application of smoke-free legislation is the protection of human rights. The right to the highest attainable standard of health, the right to life and the right to a healthy environment are found within international human rights laws and many national constitutions. Exposure to SHS clearly hinders the exercise of these and other fundamental rights and freedoms found within human rights law (Selin & Vasquez, 2006).

Legislation protecting all workers is necessary to safeguard these rights. Voluntary policies are incompatible with the responsibility of governments to protect public health and are not effective. Just three months after Ireland implemented its smoke- free legislation, 97% of pubs were smoke-free. Five years into a voluntary agreement in the United Kingdom, less than 1% of pubs were smoke-free.

Recommendation 3: Proper implementation and adequate enforcement of the law Passing smoke-free legislation is not enough. Its proper implementation and adequate enforcement require relatively small but critical efforts and means.

All governments—whether in high- or low-income jurisdictions—must be prepared to invest reasonable resources in achieving and enforcing smoke-free laws. Investment in tobacco control is an explicit obligation under Article 26 of the WHO FCTC. 1 Costs

1 Article 26 provides that “each Party shall provide financial support in respect of its national activities intended to achieve the objective of the Convention” and that Parties shall promote “the utilization of bilateral, regional, subregional and other multilateral channels to provide funding for the development

62 WHO policy

for implementing smoke-free laws may include promotional campaigns to build support for the law, commissioning public opinion polls, educational materials on implementation, compliance monitoring systems, staffing a phone number to respond to public complaints and a temporary increase in the number of inspectors assigned to monitor initial implementation.

Governments should also be prepared to face challenges to the law even after successful implementation. These may include lobbying campaigns by tobacco industry front groups to roll back the law or a legal challenge in the courts. While legal challenges to smoke-free laws have been upheld only in rare circumstances (usually based on inadequate consultation prior to implementation of a law or pre-emption of a law by a superseding jurisdiction), governments should take actions before and after implementation of the law to ensure the sustainability of the law (Nixon et al., 2004). These actions include a comprehensive public education campaign, consultation with stakeholders, assurance that the law is consistent in protecting public health, and providing data showing that the law is being enforced fairly.

Recommendation 4: Public education to reduce SHS exposure in the home Implement educational strategies to reduce SHS exposure in the home, recognizing that smoke-free workplace legislation increases the likelihood that people (both smokers and non-smokers) will voluntarily make their homes smoke-free.

All individuals have the right to be informed about the risks of SHS exposure, how to exercise their right to a healthy environment and how to protect their families from SHS harm (WHO, 1999). Since the home is often the highest source of SHS exposure for children and for adults who do not work outside the home, policies need to be developed to address this setting if public health is to be adequately protected. Education can be an effective strategy in promoting protection from SHS in the home (WHO, 1999; Thompson et al., 2006).

Smoke-free workplaces result in lower levels of tobacco consumption among smokers and are associated with a greater likelihood of workers implementing smoke-free policies in their homes (Borland et al., 1999, 2006; Merom & Rissel, 2001). Therefore, smoke-free workplace legislation should be included as one of the primary strategies in protecting individuals from SHS in the home.

Education to promote smoke-free homes can be part of campaigns implemented to build public support for smoke-free legislation, which have included messages from the health professionals informing smokers, particularly as parents, of the impact of SHS exposure in the home and have urged them to make their homes smoke-free (Program Training and Consultation Centre, 2002; New South Wales Cancer Council, 2003; Health Canada, 2006; United States Environmental Protection Agency, 2006).

To complement mass media campaigns, health warnings on tobacco packages are a very cost-effective public education medium that are guaranteed to reach all smokers. Most countries with picture-based warnings include warnings related to SHS. In

and strengthening of multisectoral comprehensive tobacco control programmes of developing country Parties and Parties with economies in transition.”

63 Second-hand smoke: Assessing the burden of disease

Canada, more than one fourth of smokers reported that picture warnings implemented in 2000 motivated them to smoke less inside the home (Environics Research Group, 2002).

64 References

10. References

Abramson MJ, Kutin JJ, Rosier MJ, Bowes G (1995) Morbidity, medication and trigger factors in a community sample of adults with asthma. Medical Journal of Australia , 162:78–81. Ahlborg G Jr, Bodin L (1991) Tobacco smoke exposure and pregnancy outcome among working women. A prospective study at prenatal care centers in Orebro County, Sweden. American Journal of , 133:338–347. Ajeel N, Al-Sadoon I, Yacoub A (1991) Risk factors for acute respiratory infections among hospitalised children in Basrah: a case control study. Bahrain Medical Bulletin , 13(1):14–18. Allwright S, Paul G, Greiner B, Mullally BJ, Pursell L, Kelly A, Bonner B, D’Eath M, McConnell B, McLaughlin JP, O’Donovan D, O’Kane E, Perry IJ (2005) Legislation for smoke-free workplaces and health of workers in Ireland: before and after study. BMJ (Clinical Research Ed.) , 331:1117. Anderson HR, Cook DG (1997) Passive smoking and sudden infant death syndrome: review of the epidemiological evidence. Thorax , 52:1003–1009. Armstrong JR, Campbell H (1991) Indoor air pollution exposure and lower respiratory infections in young Gambian children. International Journal of Epidemiology , 20:424–429. Asher MI, Keil U, Anderson HR, Beasley R, Crane J, Martinez F, Mitchell EA, Pearce N, Sibbald B, Stewart AW, Strachan D, Weiland SK, Williams HC (1995) International study of asthma and allergies in childhood (ISAAC): rationale and methods. European Respiratory Journal , 8:483–491 (http://www.erj.ersjournals.com/cgi/reprint/8/3/483 , accessed 12 September 2009). Azizi BH, Zulkifli HI, Kasim MS (1995) Protective and risk factors for acute respiratory infections in hospitalized urban Malaysian children: a case control study. The Southeast Asian Journal of Tropical Medicine and Public Health , 26:280–285. Baker RR, Proctor CF (1990). The origins and properties of environmental tobacco smoke. Environment International , 16:231–245. Boffetta P, Agudo A, Ahrens W, Benhamou E, Benhamou S, Darby SC, Ferro G, Fortes C, Gonzalez CA, Jockel KH, Krauss M, Kreienbrock L, Kreuzer M, Mendes A, Merletti F, Nyberg F, Pershagen G, Pohlabeln H, Riboli E, Schmid G, Simonato L, Tredaniel J, Whitley E, Wichmann HE, Winck C, Zambon P, Saracci R (1998) Multicenter case–control study of exposure to environmental tobacco smoke and lung cancer in Europe. Journal of the National Cancer Institute , 90:1440–1450. Boffetta P, Tredaniel J, Greco A (2000). Risk of childhood cancer and adult lung cancer after childhood exposure to passive smoke: a meta-analysis. Environmental Health Perspectives , 108:73–82. Bonita R, Duncan J, Truelsen T, Jackson RT, Beaglehole R (1999) Passive smoking as well as active smoking increases the risk of acute stroke. Tobacco Control , 8:156–160.

65 Second-hand smoke: Assessing the burden of disease

Borgerding MF, Bodnar JA, Wingate DE (2000) The 1999 Benchmark Study—final report. A research study conducted after consultation with the Massachusetts Department of Public Health . Louisville, KY, Brown and Williamson Tobacco (cited in IARC, 2004). Borland R, Mullins R, Trotter T, White V (1999) Trends in environmental tobacco smoke restrictions in the home in Victoria, Australia. Tobacco Control , 8:226– 271. Borland R, Yong H-H, Cummings KM, Hyland A, Anderson S, Fong GT (2006) Determinants and consequences of smoke-free homes: findings from the International Tobacco Control (ITC) Four Country Survey. Tobacco Control , 15(Suppl. 3):iii42–iii50. Brooke H, Gibson A, Tappin D, Brown H (1997) Case–control study of sudden infant death syndrome in Scotland, 1992–5. BMJ (Clinical Research Ed.) , 314:1516– 1520. Broor S, Pandey RM, Ghosh M, Maitreyi RS, Lodha R, Singhal T, Kabra SK (2001) Risk factors for severe acute lower respiratory tract infection in under-five children. Indian Pediatrics , 38:1361–1369. Cal-EPA (1997) Health effects of exposure to environmental tobacco smoke. Final report, September . Sacramento, CA, California Environmental Protection Agency, Office of Environmental Health Hazard Assessment (http://www.oehha.org/air/environmental_tobacco/finalets.html , accessed 12 September 2009). Cal-EPA (2005) Proposed identification of environmental tobacco smoke as a toxic air contaminant . Sacramento, CA, California Environmental Protection Agency, Air Resources Board ( http://www.oehha.org/air/environmental_tobacco/2005etsfinal.html , accessed 12 September 2009). Campbell H, Armstrong JR, Byass P (1989) Indoor air pollution in developing countries and acute respiratory infection in children. Lancet , 333:1012. Carey IM, Cook DG, Strachan DP (1999) The effects of environmental tobacco smoke exposure on lung function in a longitudinal study of British adults. Epidemiology , 10:319–326. Carpenter RG, Irgens LM, Blair PS, England PD, Fleming P, Huber J, Jorch G, Schreuder P (2004) Sudden unexplained infant death in 20 regions in Europe: case control study. Lancet , 363:185–191. CDC, WHO (2008) Global Youth Tobacco Survey (GYTS). Developed by the Centres for Disease Control and Prevention and the World Health Organization. Atlanta, GA, Centers for Disease Control and Prevention, Office on Smoking and Health ( http://www.cdc.gov/tobacco/global/gyts/index.htm ). Chen Y, Li WX, Yu SZ, Qian WH (1988) Chang-Ning epidemiological study of children’s health. I: Passive smoking and children’s respiratory diseases. International Journal of Epidemiology , 17:348–355. Cheng YJ, Hildesheim A, Hsu MM, Chen IH, Brinton LA, Levine PH, Chen CJ, Yang CS (1999) Cigarette smoking, consumption and risk of nasopharyngeal carcinoma in Taiwan. Cancer Causes & Control: CCC , 10:201–207.

66 References

Cook DG, Strachan DP (1997) Health effects of passive smoking. 3. Parental smoking and prevalence of respiratory symptoms and asthma in school age children. Thorax , 52:1081–1094. Coultas DB, Howard CA, Peake GT, Skipper BJ, Samet JM (1987) Salivary cotinine levels and involuntary tobacco smoke exposure in children and adults in New Mexico. The American Review of Respiratory Disease , 136:305–309. Coultas DB, Peake GT, Samet JM (1989) Questionnaire assessment of lifetime and recent exposure to environmental tobacco smoke. American Journal of Epidemiology , 130:338–347. Dales RE, Kerr PE, Schweitzer I, Reesor K, Gougeon L, Dickinson G (1992) Asthma management preceding an emergency department visit. Archives of Internal Medicine , 152:2041–2044. Dayal HH, Khuder S, Sharrar R, Trieff N (1994) Passive smoking in obstructive respiratory disease in an industrialized urban population. Environmental Research , 65:161–171. de Francisco A, Morris J, Hall AJ, Armstrong Schellenberg JR, Greenwood BM (1993) Risk factors for mortality from acute lower respiratory tract infections in young Gambian children. International Journal of Epidemiology , 22:1174– 1182. Dharmage SC, Rajapaksa LC, Fernando DN (1996) Risk factors of acute lower respiratory tract infections in children under five years of age. The Southeast Asian Journal of Tropical Medicine and Public Health , 27:107–110. Dockery DW, Trichopoulos D (1997) Risk of lung cancer from environmental exposures to tobacco smoke. Cancer Causes & Control: CCC , 8:333–345. Donnan GA, McNeil JJ, Adena MA, Doyle AE, O’Malley HM, Neill GC (1989) Smoking as a risk factor for cerebral ischaemia. Lancet , 2:643–647. Dwyer T, Ponsonby AL, Couper D (1999) Tobacco smoke exposure at one month of age and subsequent risk of SIDS—a prospective study. American Journal of Epidemiology , 149:593–602. Eisner MD (2002) Environmental tobacco smoke exposure and pulmonary function among adults in NHANES III: impact on the general population and adults with current asthma. Environmental Health Perspectives , 110:765–770. Eisner MD, Smith AK, Blanc PD (1998) Bartenders’ respiratory health after establishment of smoke-free bars and taverns. JAMA: the Journal of the American Medical Association , 280:1909–1914. Eisner MD, Katz PP, Yelin EH, Hammond SK, Blanc PD (2001) Measurement of environmental tobacco smoke exposure among adults with asthma. Environmental Health Perspectives , 109:809–814. Eisner MD, Balmes J, Katz PP, Trupin L, Yelin EH, Blanc PD (2005) Lifetime environmental tobacco smoke exposure and the risk of chronic obstructive pulmonary disease. Environmental Health , 4:7. Elliot J, Vullermin P, Robinson P (1998) Maternal cigarette smoking is associated with increased inner airway wall thickness in children who die from sudden infant

67 Second-hand smoke: Assessing the burden of disease

death syndrome. American Journal of Respiratory and Critical Care Medicine , 158:802–806. Environics Research Group (2002) Evaluation of the new warnings on cigarette packs . Toronto, Ontario. Etzel RA, Pattishall EN, Haley NJ, Fletcher RH, Henderson FW (1992) Passive smoking and middle ear effusion among children in day care. Pediatrics , 90:228–232. Ezzati M, Lopez AD (2004) Smoking and oral tobacco use. In: Ezzati M, Lopez AD, Rodgers A, Murray CJL, eds. Comparative quantification of health risks. Geneva, World Health Organization. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ (2002) Selected major risk factors and global and regional burden of disease. Lancet , 360:1347–1360. Fergusson DM, Horwood LJ (1985) Parental smoking and respiratory illness during early childhood: a six-year longitudinal study. Pediatric Pulmonology , 1:99– 106. Flodin U, Jonsson P, Ziegler J, Axelson O (1995) An epidemiologic study of bronchial asthma and smoking. Epidemiology , 6:503–505. Fonseca W, Kirkwood BR, Victora CG, Fuchs SR, Flores JA, Misago C (1996) Risk factors for childhood pneumonia among the urban poor in Fortaleza, Brazil: a case–control study. Bulletin of the World Health Organization , 74:199–208. Fortier I, Marcoux S, Brisson J (1994) Passive smoking during pregnancy and the risk of delivering a small-for-gestational-age infant. American Journal of Epidemiology , 139:294–301. Fukuda K, Shibata A (1990) Exposure–response relationships between woodworking, smoking or passive smoking, and squamous cell neoplasms of the maxillary sinus. Cancer Causes & Control: CCC , 1:165–168. Gilliland FD, Li YF, Peters JM (2001) Effects of maternal smoking during pregnancy and environmental tobacco smoke on asthma and wheezing in children. American Journal of Respiratory and Critical Care Medicine , 163:429–436. Gilliland FD, Berhane K, Islam T, Wenten M, Rappaport E, Avol E, Gauderman WJ, McConnell R, Peters JM (2003) Environmental tobacco smoke and absenteeism related to respiratory illness in schoolchildren. American Journal of Epidemiology , 157:861–869. Gilmour MI, Jaakkola MS, London SJ, Nel AE, Rogers CA (2006) How exposure to environmental tobacco smoke, outdoor air pollutants, and increased pollen burdens influences the incidence of asthma. Environmental Health Perspectives , 114:627–633. Gold DR, Burge HA, Carey V, Milton DK, Platts-Mills T, Weiss ST (1999) Predictors of repeated wheeze in the first year of life: the relative roles of cockroach, birth weight, acute lower respiratory illness, and maternal smoking. American Journal of Respiratory and Critical Care Medicine , 160:227–236. Greer JR, Abbey DE, Burchette RJ (1993) Asthma related to occupational and ambient air pollutants in nonsmokers. Journal of Occupational Medicine , 35:909–915.

68 References

Gürkan F, Kiral A, Dagli E, Karakoc F (2000) The effect of passive smoking on the development of respiratory syncytial virus . European Journal of Epidemiology , 16:465–468. Gutierrez-Ramirez SF, Molina-Salinas GM, Garcia-Guerra JF, Vargas-Villarreal J, Mata-Cardenas BD, Gonzalez-Salazar F (2007) [Environmental tobacco smoke and pneumonia in children living in Monterrey, Mexico.] Revista de Salud Pública (Bogotá, Colombia) , 9:76–85 (in Spanish). Hackshaw AK (1998) Lung cancer and passive smoking. Statistical Methods in Medical Research , 7:119–136. Hackshaw AK, Law MR, Wald NJ (1997) The accumulated evidence on lung cancer and environmental tobacco smoke. BMJ (Clinical Research Ed.) , 315:980–988. Hajnal BL, Braun-Fahrlander C, Grize L, Gassner M, Varonier HS, Vuille JC, Wuthrich B, Sennhauser FH (1999) Effect of environmental tobacco smoke exposure on respiratory symptoms in children. SCARPOL Team. Swiss Study on Childhood Allergy and Respiratory Symptoms with Respect to Air Pollution, Climate and Pollen. Schweizerische Medizinische Wochenschrift , 129:723–730. Haley NJ, Axelrad CM, Tilton KA (1983) Validation of self-reported smoking behavior: biochemical analyses of cotinine and thiocyanate. American Journal of Public Health , 73:1204–1207. Hauth JC, Hauth J, Drawbaugh RB, Gilstrap LC 3rd, Pierson WP (1984) Passive smoking and thiocyanate concentrations in pregnant women and newborns. Obstetrics and Gynecology , 63:519–522. He J, Vupputuri S, Allen K, Prerost MR, Hughes J, Whelton PK (1999) Passive smoking and the risk of coronary heart disease—a meta-analysis of epidemiologic studies. New England Journal of Medicine , 340:920–926. Health Canada (2006) S.S.D. Second-hand smoke diseases . Ottawa, Ontario (http://www.hc-sc.gc.ca/hc-ps/alt_formats/hecs-sesc/pdf/tobac-tabac/res/media/fullfillment- eng.pdf , accessed 12 September 2009). Hill SE, Blakely T, Kawachi I, Woodward A (2007) Mortality among lifelong nonsmokers exposed to secondhand smoke at home: cohort data and sensitivity analyses. American Journal of Epidemiology , 165:530–540. Hirayama T (1984) Cancer mortality in nonsmoking women with smoking husbands based on a large-scale cohort study in Japan. Preventive Medicine , 13:680–690. Hu FB, Persky V, Flay BR, Richardson J (1997) An epidemiological study of asthma prevalence and related factors among young adults. Journal of Asthma , 34:67– 76. IARC (1986) Tobacco smoking. Lyon, International Agency for Research on Cancer (IARC Monographs on the Evaluation of the Carcinogenic Risk of Chemicals to Humans, Vol. 38). IARC (2004) Tobacco smoke and involuntary smoking. Lyon, International Agency for Research on Cancer (IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Vol. 83).

69 Second-hand smoke: Assessing the burden of disease

Iribarren C, Friedman GD, Klatsky AL, Eisner MD (2001) Exposure to environmental tobacco smoke: association with personal characteristics and self reported health conditions. Journal of Epidemiology and Community Health , 55:721– 728. Iversen M, Birch L, Lundqvist GR, Elbrond O (1985) Middle ear effusion in children and the indoor environment: an epidemiological study. Archives of Environmental Health , 40:74–79. Jaakkola JJ, Jaakkola MS (2002) Effects of environmental tobacco smoke on the respiratory health of children. Scandinavian Journal of Work, Environment & Health , 28(Suppl. 2):71–83. Jaakkola JJ, Jaakkola N, Zahlsen K (2001) Fetal growth and length of gestation in relation to prenatal exposure to environmental tobacco smoke assessed by hair nicotine concentration. Environmental Health Perspectives , 109:557–561. Jaakkola MS (2002) Environmental tobacco smoke and health in the elderly. The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology , 19:172–181. Jaakkola MS, Jaakkola JJ (1997) Assessment of exposure to environmental tobacco smoke. The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology , 10:2384–2397. Jaakkola MS, Jaakkola JJ (2002) Effects of environmental tobacco smoke on the respiratory health of adults. Scandinavian Journal of Work, Environment & Health , 28(Suppl. 2):52–70. Jaakkola MS, Jaakkola JJ (2006) Impact of smoke-free workplace legislation on exposures and health: possibilities for prevention. The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology , 28:397–408. Jaakkola MS, Samet JM (1999) Occupational exposure to environmental tobacco smoke and health risk assessment. Environmental Health Perspectives , 107(Suppl. 6):829–835. Jaakkola MS, Jaakkola JJ, Becklake MR, Ernst P (1996) Effect of passive smoking on the development of respiratory symptoms in young adults: an 8-year longitudinal study. Journal of Clinical Epidemiology , 49:581–586. Jaakkola MS, Piipari R, Jaakkola N, Jaakkola JJ (2003) Environmental tobacco smoke and adult-onset asthma: a population-based incident case–control study. American Journal of Public Health , 93:2055–2060. Janson C, Chinn S, Jarvis D, Zock JP, Toren K, Burney P (2001) Effect of passive smoking on respiratory symptoms, bronchial responsiveness, lung function, and total serum IgE in the European Community Respiratory Health Survey: a cross-sectional study. Lancet , 358:2103–2109. Jarvis MJ, Tunstall-Pedoe H, Feyerabend C, Vesey C, Saloojee Y (1987) Comparison of tests used to distinguish smokers from nonsmokers. American Journal of Public Health , 77:1435–1438. Jayet PY, Schindler C, Schwartz J, Kunzli N, Zellweger JP, Ackermann-Liebrich U, Leuenberger P (2005) Passive smoking exposure among adults and the

70 References

dynamics of respiratory symptoms in a prospective multicenter cohort study. Scandinavian Journal of Work, Environment & Health , 31:465–473. Jedrychowski W, Flak E (1997) Maternal smoking during pregnancy and postnatal exposure to environmental tobacco smoke as predisposition factors to acute respiratory infections. Environmental Health Perspectives , 105:302–306. Jenkins RA, Guerin MR, Tomkins BA (2000) Properties and measures of environmental tobacco smoke. In: The chemistry of environmental tobacco smoke: composition and measurement . Boca Raton, FL, Lewis Publishers/CRC Press, pp. 77–106 (cited in IARC, 2004). Jin C, Rossignol AM (1993) Effects of passive smoking on respiratory illness from birth to age eighteen months, in Shanghai, People’s Republic of China. Journal of Pediatrics , 123:553–558. Johnson AW, Aderele WI (1992) The association of household pollutants and socio- economic risk factors with the short-term outcome of acute lower respiratory infections in hospitalized pre-school Nigerian children. Annals of Tropical Paediatrics , 12:421–432. Johnson KC (2005) Accumulating evidence on passive and active smoking and breast cancer risk. International Journal of Cancer , 117:619–628. Jones S, Love C, Thomson G, Green R, Howden-Chapman P (2001) Second-hand smoke at work: the exposure, perceptions and attitudes of bar and workers to environmental tobacco smoke. Australian and New Zealand Journal of Public Health , 25:90–93. Jousilahti P, Patja K, Salomaa V (2002) Environmental tobacco smoke and the risk of cardiovascular disease. Scandinavian Journal of Work, Environment & Health , 28(Suppl. 2):41–51. Kawachi I, Pearce NE, Jackson RT (1989) Deaths from lung cancer and ischaemic heart disease due to passive . New Zealand Medical Journal , 102:337–340. Kharrazi M, DeLorenze GN, Kaufman FL, Eskenazi B, Bernert JT Jr, Graham S, Pearl M, Pirkle J (2004) Environmental tobacco smoke and pregnancy outcome. Epidemiology , 15:660–670. Khuder SA, Simon VJ Jr (2000) Is there an association between passive smoking and breast cancer? European Journal of Epidemiology , 16:1117–1121. Klonoff-Cohen HS, Edelstein SL, Lefkowitz ES, Srinivasan IP, Kaegi D, Chang JC, Wiley KJ (1995) The effect of passive smoking and tobacco exposure through breast milk on sudden infant death syndrome. JAMA: the Journal of the American Medical Association , 273(1):795–798. Knight JM, Eliopoulos C, Klein J, Greenwald M, Koren G (1996) Passive smoking in children. Racial differences in systemic exposure to cotinine by hair and urine analysis. Chest , 109(2):446–450. Kossove D (1982) Smoke-filled rooms and lower respiratory disease in infants. South African Medical Journal , 61:622–624.

71 Second-hand smoke: Assessing the burden of disease

Kumar S, Awasthi S, Jain A, Srivastava RC (2004) Blood zinc levels in children hospitalized with severe pneumonia: a case control study. Indian Pediatrics , 41:486–491. Kunzli N, Schwartz J, Stutz EZ, Ackermann-Liebrich U, Leuenberger P (2000) Association of environmental tobacco smoke at work and forced expiratory lung function among never smoking asthmatics and non-asthmatics. The SAPALDIA-Team. Swiss Study on Air Pollution and Lung Disease in Adults. Sozial- und Präventivmedizin , 45:208–217. Lam TH, Ho LM, Hedley AJ, Adab P, Fielding R, McGhee SM, Leung GM, Aharonson-Daniel L (2005) Secondhand smoke and respiratory ill health in current smokers. Tobacco Control , 14:307–314. Law MR, Hackshaw AK (1996) Environmental tobacco smoke. British Medical Bulletin , 52:22–34. Law MR, Morris JK, Wald NJ (1997) Environmental tobacco smoke exposure and ischaemic heart disease: an evaluation of the evidence. BMJ (Clinical Research Ed.) , 315:973–980. Lee LY, Gerhardstein DC, Wang AL, Burki NK (1993) Nicotine is responsible for airway irritation evoked by cigarette smoke inhalation in men. Journal of Applied Physiology , 75:1955–1961. Leuenberger P, Schwartz J, Ackermann-Liebrich U, Blaser K, Bolognini G, Bongard JP, Brandli O, Braun P, Bron C, Brutsche M (1994) Passive smoking exposure in adults and chronic respiratory symptoms (SAPALDIA Study). Swiss Study on Air Pollution and Lung Diseases in Adults, SAPALDIA Team. American Journal of Respiratory and Critical Care Medicine , 150:1222–1228. Lewis S, Richards D, Bynner J, Butler N, Britton J (1995) Prospective study of risk factors for early and persistent wheezing in childhood. European Respiratory Journal , 8:349–356. Li JS, Peat JK, Xuan W, Berry G (1999) Meta-analysis on the association between environmental tobacco smoke (ETS) exposure and the prevalence of lower respiratory tract infection in early childhood. Pediatric Pulmonology , 27:5–13. Li YF, Gilliland FD, Berhane K, McConnell R, Gauderman WJ, Rappaport EB, Peters JM (2000) Effects of in utero and environmental tobacco smoke exposure on lung function in boys and girls with and without asthma. American Journal of Respiratory and Critical Care Medicine , 162:2097–2104. Lindbohm ML, Sallmen M, Taskinen H (2002) Effects of exposure to environmental tobacco smoke on reproductive health. Scandinavian Journal of Work, Environment & Health , 28(Suppl. 2):84–96. Mackay J, Eriksen M (2002) Tobacco atlas . Geneva, World Health Organization. Mahalanabis D, Gupta S, Paul D, Gupta A, Lahiri M, Khaled MA (2002) Risk factors for pneumonia in infants and young children and the role of solid fuel for cooking: a case–control study. Epidemiology and Infection , 129:65–71. Mannino DM, Siegel M, Rose D, Nkuchia J, Etzel R (1997) Environmental tobacco smoke exposure in the home and worksite and health effects in adults: results from the 1991 National Health Interview Survey. Tobacco Control , 6:296–305.

72 References

Mannino DM, Moorman JE, Kingsley B, Rose D, Repace J (2001) Health effects related to environmental tobacco smoke exposure in children in the United States: data from the Third National Health and Nutrition Examination Survey. Archives of Pediatrics & Adolescent Medicine , 155:36–41. Martin TR, Bracken MB (1986) Association of low birth weight with passive smoke exposure in pregnancy. American Journal of Epidemiology , 124:633–642. Martinez FD, Cline M, Burrows B (1992) Increased incidence of asthma in children of smoking mothers. Pediatrics , 89:21–26. Mathai M, Vijayasri R, Babu S, Jeyaseelan L (1992) Passive maternal smoking and birthweight in a south Indian population. British Journal of Obstetrics and Gynaecology , 99:342–343. Mathers CD, Lopez AD, Murray CJ (2001) The burden of disease and mortality by condition: data, methods, and results for 2001. In: Global burden of disease . Geneva, World Health Organization ( http://www.dcp2.org/pubs/GBD/3/FullText , accessed 12 September 2009). McLaughlin JK, Dietz MS, Mehl ES, Blot WJ (1987) Reliability of surrogate information on cigarette smoking by type of informant. American Journal of Epidemiology , 126:144–146. McMartin KI, Platt MS, Hackman R, Klein J, Smialek JE, Vigorito R, Koren G (2002) Lung tissue concentrations of nicotine in sudden infant death syndrome (SIDS). Journal of Pediatrics , 140:205–209. Mengersen KL, Tweedie RL, Biggerstaff B (1995) The impact of method choice on meta-analysis. The Australian Journal of Statistics , 37:19–44. Menzies D, Nair A, Williamson PA, Schembri S, Al-Khairalla MZ, Barnes M, Fardon TC, McFarlane L, Magee GJ, Lipworth BJ (2006) Respiratory symptoms, pulmonary function, and markers of inflammation among bar workers before and after a legislative ban on smoking in public places. JAMA: the Journal of the American Medical Association , 296:1742–1748. Merom D, Rissel C (2001) Factors associated with smoke-free homes in NSW: results from the 1998 NSW health survey. Australia and New Zealand Journal of Public Health , 25(4):339–345. Milerad J, Vege A, Opdal SH, Rognum TO (1998) Objective measurements of nicotine exposure in victims of sudden infant death syndrome and in other unexpected child deaths. Journal of Pediatrics , 133:232–236. Misra DP, Nguyen RH (1999) Environmental tobacco smoke and low birth weight: a hazard in the workplace? Environmental Health Perspectives , 107(Suppl. 6):897–904. Mitchell EA, Ford RP, Stewart AW, Taylor BJ, Becroft DM, Thompson JM, Scragg R, Hassall IB, Barry DM, Allen EM (1993) Smoking and the sudden infant death syndrome. Pediatrics , 91(5):893–896. Mitchell EA, Tuohy PG, Brunt JM, Thompson JM, Clements MS, Stewart AW, Ford RP, Taylor BJ (1997) Risk factors for sudden infant death syndrome following the prevention campaign in New Zealand: a prospective study. Pediatrics , 100:835–840.

73 Second-hand smoke: Assessing the burden of disease

Mizoue T, Reijula K, Andersson K (2001) Environmental tobacco smoke exposure and overtime work as risk factors for in Japan. American Journal of Epidemiology , 154:803–808. Morabia A, Lash T, Aschengrau A (2001) Passive cigarette smoking and breast cancer. In: Watson RR, Witten R, eds. Environmental tobacco smoke . Boca Raton, FL, CRC Press, pp. 177–191. Nafstad P, Kongerud J, Botten G, Hagen JA, Jaakkola JJ (1997) The role of passive smoking in the development of bronchial obstruction during the first 2 years of life. Epidemiology , 8:293–297. Neuspiel DR, Rush D, Butler NR, Golding J, Bijur PE, Kurzon M (1989) Parental smoking and post-infancy wheezing in children: a prospective cohort study. American Journal of Public Health , 79:168–171. New South Wales Cancer Council (2003) Car and home smoke free zone—give your child a healthy future. New South Wales (http://www.smokefreezone.org/index.cfm/page_id/1118, accessed 27 March 2007). NHMRC (1997) The health effects of passive smoking . Canberra, National Health and Medical Research Council. Nixon M, Mahmoud L, Glantz SA (2004) Tobacco industry litigation to deter local public health ordinances: the industry usually loses in court. Tobacco Control , 13:65–73. NRC (1986) Environmental tobacco smoke: measuring exposures and assessing health effects. Washington, DC, National Research Council, Board on Environmental Studies and Toxicology, Committee on Passive Smoking. Nuesslein TG, Beckers D, Rieger CH (1999) Cotinine in meconium indicates risk for early respiratory tract infections. Human & Experimental Toxicology , 18:283– 290. Öberg M, Woodward A, Jaakkola M, Prüss-Ustün A (2010) Global estimate of the burden of disease from second-hand smoke . Geneva, World Health Organization. O’Dempsey TJ, McArdle TF, Morris J, Lloyd-Evans N, Baldeh I, Laurence BE, Secka O, Greenwood BM (1996) A study of risk factors for pneumococcal disease among children in a rural area of west Africa. International Journal of Epidemiology , 25:885–893. Ponsonby AL, Dwyer T, Kasl SV, Cochrane JA (1995) The Tasmanian SIDS case– control study: univariable and multivariable risk factor analysis. Paediatric and Perinatal Epidemiology , 9(3):256–272. Prietsch SO, Fischer GB, Cesar JA, Lempek BS, Barbosa LV Jr, Zogbi L, Cardoso OC, Santos AM (2008) Acute lower respiratory illness in under-five children in Rio Grande, Rio Grande do Sul State, Brazil: prevalence and risk factors. Cadernos de Saúde Pública / Ministério da Saúde, Fundação Oswaldo Cruz, Escola Nacional de Saúde Públicai , 24:1429–1438. Program Training and Consultation Centre (2002) Smoke-free homes and asthma pilot sites: media campaigns. Toronto, Ontario ( http://www.ptcc-cfc.on.ca/english/ Resources/Resource-Search/Resource/?rid=12333 , accessed 12 November 2007).

74 References

Prüss-Üstün A, Mathers C, Corvalán C, Woodward A (2003) Introduction and methods: assessing the environmental burden of disease at national and local levels. Geneva, World Health Organization (Environmental Burden of Disease Series, No. 1; http://www.who.int/quantifying_ehimpacts/publications/9241546204/en/index.html , accessed 12 September 2009). Rajs J, Rasten-Almqvist P, Falck G, Eksborg S, Andersson BS (1997) Sudden infant death syndrome: postmortem findings of nicotine and cotinine in pericardial fluid of infants in relation to morphological changes and position at death. Pediatric Pathology & Laboratory Medicine: Journal of the Society for Pediatric Pathology, affiliated with the International Paediatric Pathology Association , 17:83–97. Richards GA, Terblanche AP, Theron AJ, Opperman L, Crowther G, Myer MS, Steenkamp KJ, Smith FC, Dowdeswell R, van der Merwe CA, Stevens K, Anderson R (1996) Health effects of passive smoking in adolescent children. South African Medical Journal , 86:143–147. Rizzi M, Sergi M, Andreoli A, Pecis M, Bruschi C, Fanfulla F (2004) Environmental tobacco smoke may induce early lung damage in healthy male adolescents. Chest , 125:1387–1393. Robbins AS, Abbey DE, Lebowitz MD (1993) Passive smoking and chronic respiratory disease symptoms in non-smoking adults. International Journal of Epidemiology , 22:809–817. Roper Organization (1978) A study of public attitudes toward cigarette smoking and the tobacco industry in 1978 . Vol. 1. Prepared for the . The Roper Organization, Inc. ( http://legacy.library.ucsf.edu/tid/yuf92f00 , accessed 4 January 2007). Sandler DP, Comstock GW, Helsing KJ, Shore DL (1989) Deaths from all causes in non-smokers who lived with smokers. American Journal of Public Health , 79:163–167. Savitha MR, Nandeeshwara SB, Pradeep Kumar MJ, ul-Haque F, Raju CK (2007) Modifiable risk factors for acute lower respiratory tract infections. Indian Journal of Pediatrics , 74:477–482. Schick S, Glantz S (2005) Philip Morris toxicological experiments with fresh sidestream smoke: more toxic than mainstream smoke. Tobacco Control , 14:396–404. Schick S, Glantz SA (2006) Sidestream cigarette smoke toxicity increases with aging and exposure duration. Tobacco Control , 15:424–429. Schoendorf KC, Kiely JL (1992) Relationship of sudden infant death syndrome to maternal smoking during and after pregnancy. Pediatrics , 90(6):905–908. Schwartz J, Zeger S (1990) Passive smoking, air pollution, and acute respiratory symptoms in a diary study of student nurses. The American Review of Respiratory Disease , 141:62–67. Selin H, Vasquez J (2006) Exposure to second-hand smoke in the Americas: a human rights perspective. Washington, DC, Pan American Health Organization.

75 Second-hand smoke: Assessing the burden of disease

Sherman CB, Tosteson TD, Tager IB, Speizer FE, Weiss ST (1990) Early childhood predictors of asthma. American Journal of Epidemiology , 132:83–95. Skorge TD, Eagan TM, Eide GE, Gulsvik A, Bakke PS (2005) The adult incidence of asthma and respiratory symptoms by passive smoking in uterus or in childhood. American Journal of Respiratory and Critical Care Medicine , 172:61–66. Skorge TD, Eagan TM, Eide GE, Gulsvik A, Bakke PS (2006) Exposure to environmental tobacco smoke in a general population. Respiratory Medicine , 101(2):277–285. Sorahan T, Lancashire R, Prior P, Peck I, Stewart A (1995) Childhood cancer and parental use of alcohol and tobacco. Annals of Epidemiology , 5:354–359. Sorahan T, Lancashire RJ, Hulten MA, Peck I, Stewart AM (1997a) Childhood cancer and parental use of tobacco: deaths from 1953 to 1955. British Journal of Cancer , 75:134–138. Sorahan T, Prior P, Lancashire RJ, Faux SP, Hulten MA, Peck IM, Stewart AM (1997b) Childhood cancer and parental use of tobacco: deaths from 1971 to 1976. British Journal of Cancer , 76:1525–1531. Sorahan T, McKinney PA, Mann JR, Lancashire RJ, Stiller CA, Birch JM, Dodd HE, Cartwright RA (2001) Childhood cancer and parental use of tobacco: findings from the inter-regional epidemiological study of childhood cancer (IRESCC). British Journal of Cancer , 84:141–146. Steenland K (1999) Risk assessment for heart disease and workplace ETS exposure among nonsmokers. Environmental Health Perspectives , 107(Suppl. 6):859– 863. Stillman RJ, Rosenberg MJ, Sachs BP (1986) Smoking and reproduction. Fertility and Sterility , 46:545–566. Strachan DP, Cook DG (1997) Health effects of passive smoking. 1. Parental smoking and lower respiratory illness in infancy and early childhood. Thorax , 52:905– 914. Strachan DP, Cook DG (1998a) Health effects of passive smoking. 4. Parental smoking, middle ear disease and adenotonsillectomy in children. Thorax , 53:50–56. Strachan DP, Cook DG (1998b) Health effects of passive smoking. 6. Parental smoking and childhood asthma: longitudinal and case–control studies. Thorax , 53:204–212. Strachan DP, Jarvis MJ, Feyerabend C (1989) Passive smoking, salivary cotinine concentrations, and middle ear effusion in 7 year old children. BMJ (Clinical Research Ed.) , 298:1549–1552. Strachan DP, Butland BK, Anderson HR (1996) Incidence and prognosis of asthma and wheezing illness from early childhood to age 33 in a national British cohort. BMJ (Clinical Research Ed.) , 312:1195–1199. Svanes C, Omenaas E, Jarvis D, Chinn S, Gulsvik A, Burney P (2004) Parental smoking in childhood and adult obstructive lung disease: results from the European Community Respiratory Health Survey. Thorax , 59:295–302.

76 References

Tarlo SM, Leung K, Broder I, Silverman F, Holness DL (2000) Asthmatic subjects symptomatically worse at work: prevalence and characterization among a general asthma clinic population. Chest , 118:1309–1314. Taylor R, Cumming R, Woodward A, Black M (2001) Passive smoking and lung cancer: a cumulative meta-analysis. Australia and New Zealand Journal of Public Health , 25:203–211. Taylor R, Najafi F, Dobson A (2007) Meta-analysis of studies of passive smoking and lung cancer: effects of study type and continent. International Journal of Epidemiology , 36(5):1048–1059. Thompson G, Wilson N, Howden-Chapman P (2006) Population level policy options for increasing the prevalence of smokefree homes. Journal of Epidemiology and Community Health , 60:298–304. Thorn J, Brisman J, Toren K (2001) Adult-onset asthma is associated with self- reported mold or environmental tobacco smoke exposures in the home. Allergy , 56:287–292. Thun M, Henley J, Apicella L (1999) Epidemiologic studies of fatal and nonfatal cardiovascular disease and ETS exposure from spousal smoking. Environmental Health Perspectives , 107(Suppl. 6):841–846. Uhari M, Mantysaari K, Niemela M (1996) A meta-analytic review of the risk factors for acute otitis media. Clinical Infectious Diseases: an Official Publication of the Infectious Diseases Society of America , 22:1079–1083. United States Environmental Protection Agency (2006) Take the smoke-free homes pledge . Washington, DC ( http://www.epa.gov/smokefree/pledge/index.html , accessed 27 March 2007). United States Surgeon General (1986) The health consequences of involuntary smoking. A report of the Surgeon General. Rockville, MD, United States Department of Health and Human Services, Public Health Service, Centers for Disease Control, Center for Health Promotion and Education, Office on Smoking and Health. United States Surgeon General (2006) The health consequences of involuntary exposure to tobacco smoke: a report of the Surgeon General. Atlanta, GA, United States Department of Health and Human Services, Centers for Disease Control and Prevention, Coordinating Center for Health Promotion, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health ( http://www.surgeongeneral.gov/library/secondhandsmoke/ , accessed 12 September 2009). Vaughan TL, Shapiro JA, Burt RD, Swanson GM, Berwick M, Lynch CF, Lyon JL (1996) Nasopharyngeal cancer in a low-risk population: defining risk factors by histological type. Cancer Epidemiology, Biomarkers & Prevention: a Publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology , 5:587–593. Véjar L, Casteran JC, Navarrete P, Sanchez S, LeCerf P, Castillo C (2000) [Risk factors for home deaths due to pneumonia among low socioeconomic level Chilean children, Santiago de Chile (1994).] Revista Médica de Chile , 128:627–632 (in Spanish).

77 Second-hand smoke: Assessing the burden of disease

Victora CG (1999) Risk factors for acute lower respiratory infections. In: Benguigui Y, Antuñano FJL, Schmunis G, Yunes J, eds. Respiratory infections in children . Washington, DC, Pan American Health Organization, Regional Office of the World Health Organization. Victora CG, Fuchs SC, Flores JA, Fonseca W, Kirkwood B (1994) Risk factors for pneumonia among children in a Brazilian metropolitan area. Pediatrics , 93:977–985. Vineis P, Airoldi L, Veglia P, Olgiati L, Pastorelli R, Autrup H, Dunning A, Garte S, Gormally E, Hainaut P, Malaveille C, Matullo G, Peluso M, Overvad K, Tjonneland A, Clavel-Chapelon F, Boeing H, Krogh V, Palli D, Panico S, Tumino R, Bueno-De-Mesquita B, Peeters P, Berglund G, Hallmans G, Saracci R, Riboli E (2005) Environmental tobacco smoke and risk of respiratory cancer and chronic obstructive pulmonary disease in former smokers and never smokers in the EPIC prospective study. BMJ (Clinical Research Ed.) , 330:277. Vork KL, Broadwin RL, Blaisdell RJ (2007) Developing asthma in childhood from exposure to second-hand tobacco smoke—insights from a meta-regression. Environmental Health Perspectives , 115(10):1394–1400. Wells AJ (1994) Passive smoking as a cause of heart disease. Journal of the American College of Cardiology , 24:546–554. Wells AJ (1998a) Heart disease from passive smoking in the workplace. Journal of the American College of Cardiology , 31:1–9. Wells AJ (1998b) Lung cancer from passive smoking at work. American Journal of Public Health , 88:1025–1029. Wells AJ (1998c) Re: “Breast cancer, cigarette smoking, and passive smoking”. American Journal of Epidemiology , 147:991–992 [erratum in American Journal of Epidemiology , 148:314]. WHO (1999) International consultation on environmental tobacco smoke (ETS) and child health, 11–14 January 1999 . Geneva, World Health Organization (WHO/NCD/TFI/99.10). WHO (2002) World health report 2002 . Geneva, World Health Organization. WHO (2003) WHO Framework Convention on Tobacco Control . Geneva, World Health Organization. WHO (2004) World health report 2004 . Geneva, World Health Organization. WHO (2007a) International statistical classification of diseases and related health problems , 10th rev . Geneva, World Health Organization (http://apps.who.int/classifications/apps/icd/icd10online/ ). WHO (2007b) Protection from exposure to second-hand tobacco smoke. Policy recommendations . Geneva, World Health Organization (http://whqlibdoc.who.int/publications/2007/9789241563413_eng.pdf ). Wieslander G, Lindgren T, Norback D, Venge P (2000) Changes in the ocular and nasal signs and symptoms of aircrews in relation to the ban on smoking on intercontinental flights. Scandinavian Journal of Work, Environment & Health , 26:514–522.

78 References

Windham GC, Eaton A, Hopkins B (1999) Evidence for an association between environmental tobacco smoke exposure and birthweight: a meta-analysis and new data. Paediatric and Perinatal Epidemiology , 13:35–57. Woodward A, Laugesen M (2001) How many deaths are caused by second hand cigarette smoke? Tobacco Control , 10:383–388. You RX, Thrift AG, McNeil JJ, Davis SM, Donnan GA (1999) Ischemic stroke risk and passive exposure to spouses’ cigarette smoking. Melbourne Stroke Risk Factor Study (MERFS) Group. American Journal of Public Health , 89:572– 575. Yu MC, Garabrant DH, Huang TB, Henderson BE (1990) Occupational and other non- dietary risk factors for nasopharyngeal carcinoma in Guangzhou, China. International Journal of Cancer , 45:1033–1039. Yuan JM, Wang XL, Xiang YB, Gao YT, Ross RK, Yu MC (2000) Non-dietary risk factors for nasopharyngeal carcinoma in Shanghai, China. International Journal of Cancer , 85:364–369. Zheng W, McLaughlin JK, Chow WH, Chien HT, Blot WJ (1993) Risk factors for cancers of the nasal cavity and paranasal sinuses among white men in the United States. American Journal of Epidemiology , 138:965–972. Zhong L, Goldberg MS, Gao Y-T, Jin F (1999) A case–control study of lung cancer and environmental tobacco smoke among nonsmoking women living in Shanghai, China. Cancer Causes & Control: CCC , 10(6):607–616. Zhong L, Goldberg MS, Parent ME, Hanley JA (2000) Exposure to environmental tobacco smoke and the risk of lung cancer: a meta-analysis. Lung Cancer , 27:3–18.

79 Second-hand smoke: Assessing the burden of disease

Annex 1

Table A1: Population attributable fractions of lung cancer among men for active smoking, by country, 2004

PAF sm for lung cancer among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Afghanistan 0.89 0.87 0.84 0.80 0.75 Albania 0.88 0.92 0.94 0.94 0.90 Algeria 0.02 0.78 0.88 0.89 0.91 Andorra 0.79 0.92 0.92 0.92 0.90 Angola 0.00 0.85 0.82 0.74 0.72 Antigua and Barbuda 0.00 0.30 0.00 0.00 0.79 Argentina 0.68 0.89 0.91 0.89 0.86 Armenia 0.87 0.94 0.97 0.95 0.90 Australia 0.15 0.77 0.88 0.91 0.90 Austria 0.50 0.88 0.90 0.90 0.88 Azerbaijan 0.83 0.86 0.92 0.86 0.74 Bahamas 0.00 0.77 0.69 0.75 0.67 Bahrain 0.00 0.48 0.90 0.93 0.94 Bangladesh 0.67 0.82 0.91 0.91 0.93 Barbados 0.00 0.27 0.66 0.77 0.75 Belarus 0.79 0.93 0.95 0.92 0.89 Belgium 0.70 0.93 0.95 0.95 0.94 Belize 0.00 0.76 0.69 0.84 0.89 Benin 0.00 0.42 0.55 0.56 0.61 Bhutan 0.21 0.64 0.84 0.80 0.84 Bolivia (Plurinational State of) 0.46 0.68 0.80 0.78 0.76 Bosnia and Herzegovina 0.86 0.92 0.94 0.93 0.87 Botswana 0.00 0.66 0.77 0.77 0.78 Brazil 0.58 0.81 0.86 0.83 0.81 Brunei Darussalam 0.00 0.67 0.85 0.86 0.83 Bulgaria 0.78 0.93 0.93 0.88 0.72 Burkina Faso 0.00 0.58 0.79 0.80 0.80 Burundi 0.00 0.49 0.65 0.58 0.64 Cambodia 0.71 0.81 0.86 0.88 0.82 Cameroon 0.00 0.45 0.77 0.67 0.72 Canada 0.50 0.84 0.92 0.93 0.93 Cape Verde 0.00 0.00 0.55 0.48 0.54 Central African Republic 0.00 0.64 0.73 0.52 0.53 Chad 0.13 0.66 0.74 0.52 0.54 Chile 0.00 0.69 0.83 0.86 0.85 China 0.52 0.49 0.54 0.60 0.62 Colombia 0.00 0.62 0.81 0.82 0.78 Comoros 0.00 0.26 0.51 0.47 0.62 Congo 0.00 0.65 0.81 0.83 0.86 Cook Islands 0.00 0.00 0.62 0.73 0.86 Costa Rica 0.00 0.23 0.63 0.75 0.76 Côte d’Ivoire 0.00 0.82 0.87 0.81 0.80 Croatia 0.75 0.93 0.95 0.94 0.92

1 It should be noted that only aggregate data are available for Serbia and Montenegro, although WHO recognizes both as Member States.

80 Annex Table A1 (continued)

PAF sm for lung cancer among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Cuba 0.71 0.90 0.91 0.91 0.91 Cyprus 0.41 0.81 0.87 0.86 0.85 Czech Republic 0.63 0.93 0.95 0.94 0.91 Democratic People’s Republic of Korea 0.18 0.71 0.84 0.83 0.81 Democratic Republic of the Congo 0.29 0.59 0.66 0.00 0.00 Denmark 0.52 0.87 0.92 0.93 0.92 Djibouti 0.00 0.37 0.49 0.18 0.18 Dominica 0.00 0.82 0.81 0.83 0.55 Dominican Republic 0.00 0.69 0.83 0.84 0.88 Ecuador 0.00 0.35 0.63 0.68 0.68 Egypt 0.74 0.70 0.74 0.66 0.55 El Salvador 0.00 0.00 0.25 0.37 0.35 Equatorial Guinea 0.12 0.69 0.76 0.53 0.58 Eritrea 0.00 0.34 0.54 0.47 0.58 Estonia 0.83 0.91 0.95 0.94 0.91 Ethiopia 0.00 0.35 0.44 0.09 0.26 Fiji 0.00 0.00 0.00 0.00 0.00 Finland 0.00 0.80 0.89 0.91 0.90 France 0.83 0.93 0.93 0.92 0.90 Gabon 0.00 0.82 0.81 0.78 0.79 Gambia 0.00 0.11 0.75 0.84 0.88 Georgia 0.76 0.82 0.87 0.81 0.64 Germany 0.64 0.88 0.91 0.92 0.90 Ghana 0.00 0.46 0.58 0.48 0.46 Greece 0.67 0.92 0.93 0.93 0.93 Grenada 0.73 0.82 0.82 0.77 0.00 Guatemala 0.45 0.60 0.77 0.81 0.84 Guinea 0.64 0.65 0.65 0.72 0.75 Guinea-Bissau 0.09 0.54 0.61 0.59 0.66 Guyana 0.21 0.43 0.55 0.49 0.16 Haiti 0.00 0.17 0.53 0.55 0.73 Honduras 0.00 0.60 0.85 0.87 0.86 Hungary 0.84 0.96 0.96 0.94 0.92 Iceland 0.67 0.79 0.84 0.90 0.88 India 0.24 0.61 0.77 0.71 0.79 Indonesia 0.56 0.81 0.91 0.90 0.89 Iran (Islamic Republic of) 0.00 0.50 0.73 0.74 0.71 Iraq 0.91 0.92 0.93 0.91 0.91 Ireland 0.08 0.82 0.91 0.93 0.91 Israel 0.32 0.81 0.89 0.87 0.84 Italy 0.57 0.88 0.92 0.94 0.92 Jamaica 0.43 0.77 0.84 0.83 0.84 Japan 0.39 0.79 0.86 0.91 0.93 Jordan 0.58 0.85 0.89 0.86 0.86 Kazakhstan 0.83 0.93 0.96 0.94 0.89 Kenya 0.00 0.30 0.66 0.62 0.65 Kiribati 0.80 0.47 0.00 0.00 0.00 Kuwait 0.00 0.00 0.65 0.80 0.81 Kyrgyzstan 0.71 0.84 0.91 0.85 0.65 Lao People’s Democratic Republic 0.72 0.79 0.89 0.87 0.87 Latvia 0.77 0.92 0.94 0.94 0.90 Lebanon 0.19 0.85 0.89 0.86 0.85

81 Second-hand smoke: Assessing the burden of disease

PAF sm for lung cancer among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Lesotho 0.00 0.61 0.77 0.78 0.82 Liberia 0.25 0.45 0.54 0.47 0.56 Libyan Arab Jamahiriya 0.52 0.67 0.77 0.77 0.76 Lithuania 0.60 0.93 0.94 0.93 0.86 Luxembourg 0.63 0.89 0.92 0.93 0.92 Madagascar 0.00 0.36 0.57 0.52 0.60 Malawi 0.00 0.19 0.38 0.69 0.74 Malaysia 0.47 0.84 0.92 0.92 0.86 Maldives 0.23 0.86 0.95 0.97 0.97 Mali 0.00 0.00 0.62 0.67 0.69 Malta 0.00 0.84 0.92 0.92 0.90 Marshall Islands 0.51 0.58 0.76 0.81 0.88 Mauritania 0.00 0.43 0.59 0.56 0.63 Mauritius 0.46 0.62 0.82 0.84 0.80 Mexico 0.00 0.41 0.74 0.80 0.82 Micronesia (Federated States of) 0.00 0.00 0.00 0.00 0.08 Monaco 0.84 0.90 0.91 0.91 0.89 Mongolia 0.60 0.86 0.94 0.94 0.94 Morocco 0.58 0.77 0.85 0.83 0.82 Mozambique 0.00 0.00 0.00 0.00 0.14 Myanmar 0.72 0.85 0.90 0.89 0.89 Namibia 0.00 0.69 0.78 0.78 0.80 Nauru 0.00 0.00 0.95 0.00 0.00 Nepal 0.57 0.76 0.84 0.80 0.79 Netherlands 0.64 0.88 0.93 0.95 0.94 New Zealand 0.00 0.75 0.87 0.90 0.90 Nicaragua 0.34 0.59 0.77 0.76 0.83 Niger 0.41 0.57 0.78 0.82 0.85 Nigeria 0.08 0.45 0.35 0.27 0.34 Niue 0.12 0.07 0.60 0.72 0.87 Norway 0.17 0.82 0.91 0.91 0.88 Oman 0.00 0.51 0.73 0.73 0.79 Pakistan 0.08 0.67 0.87 0.84 0.88 Palau 0.00 0.32 0.78 0.79 0.81 Panama 0.00 0.54 0.77 0.74 0.79 Papua New Guinea 0.00 0.08 0.21 0.00 0.00 Paraguay 0.34 0.80 0.86 0.87 0.86 Peru 0.00 0.28 0.70 0.75 0.76 Philippines 0.73 0.88 0.90 0.85 0.79 Poland 0.77 0.94 0.96 0.95 0.92 Portugal 0.70 0.89 0.90 0.88 0.84 Qatar 0.00 0.00 0.64 0.60 0.69 Republic of Korea 0.59 0.83 0.93 0.95 0.96 Republic of Moldova 0.74 0.91 0.91 0.85 0.73 Romania 0.80 0.93 0.94 0.89 0.73 Russian Federation 0.84 0.93 0.93 0.91 0.83 Rwanda 0.00 0.00 0.10 0.57 0.62 Saint Kitts and Nevis 0.00 0.76 0.56 0.42 0.00 Saint Lucia 0.25 0.44 0.15 0.30 0.74 Saint Vincent and the Grenadines 0.87 0.80 0.62 0.73 0.35 Samoa 0.00 0.54 0.53 0.12 0.23

82 Annex Table A1 (continued)

PAF sm for lung cancer among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years San Marino 0.00 0.81 0.92 0.94 0.94 Sao Tome and Principe 0.49 0.81 0.87 0.87 0.88 Saudi Arabia 0.00 0.45 0.74 0.77 0.77 Senegal 0.00 0.15 0.46 0.56 0.63 Serbia and Montenegro 0.88 0.95 0.95 0.92 0.79 Seychelles 0.00 0.43 0.83 0.87 0.00 Sierra Leone 0.44 0.67 0.66 0.61 0.64 Singapore 0.28 0.75 0.90 0.92 0.92 Slovakia 0.69 0.92 0.94 0.94 0.87 Slovenia 0.78 0.93 0.93 0.94 0.93 Solomon Islands 0.00 0.27 0.52 0.52 0.64 Somalia 0.43 0.65 0.66 0.40 0.27 South Africa 0.64 0.85 0.87 0.85 0.85 Spain 0.76 0.92 0.93 0.92 0.91 Sri Lanka 0.47 0.78 0.83 0.80 0.81 Sudan 0.00 0.00 0.33 0.35 0.24 Suriname 0.57 0.71 0.79 0.83 0.78 Swaziland 0.00 0.56 0.81 0.81 0.85 Sweden 0.00 0.72 0.83 0.85 0.80 Switzerland 0.49 0.85 0.90 0.90 0.88 Syrian Arab Republic 0.00 0.77 0.80 0.73 0.75 Tajikistan 0.00 0.48 0.62 0.61 0.32 Thailand 0.60 0.83 0.89 0.89 0.86 The former Yugoslav Republic of 0.93 0.93 0.93 0.90 0.78 Macedonia Timor-Leste 0.46 0.75 0.88 0.86 0.85 Togo 0.00 0.46 0.57 0.56 0.62 Tonga 0.00 0.00 0.59 0.73 0.86 Trinidad and Tobago 0.61 0.80 0.73 0.74 0.59 Tunisia 0.35 0.73 0.86 0.85 0.85 Turkey 0.89 0.92 0.94 0.92 0.94 Turkmenistan 0.74 0.84 0.86 0.74 0.50 Tuvalu 0.71 0.59 0.84 0.76 0.84 Uganda 0.00 0.46 0.54 0.05 0.07 Ukraine 0.75 0.92 0.93 0.90 0.78 United Arab Emirates 0.00 0.00 0.52 0.62 0.75 United Kingdom of Great Britain and 0.31 0.83 0.91 0.93 0.92 Northern Ireland United Republic of Tanzania 0.00 0.60 0.72 0.59 0.61 United States of America 0.60 0.86 0.93 0.93 0.92 Uruguay 0.78 0.92 0.93 0.92 0.90 Uzbekistan 0.57 0.66 0.79 0.67 0.32 Vanuatu 0.00 0.30 0.80 0.80 0.87 Venezuela (Bolivarian Republic of) 0.50 0.75 0.80 0.79 0.76 Viet Nam 0.43 0.81 0.91 0.90 0.89 Yemen 0.58 0.63 0.74 0.40 0.32 Zambia 0.15 0.65 0.71 0.71 0.73 Zimbabwe 0.32 0.71 0.81 0.81 0.83

83 Second-hand smoke: Assessing the burden of disease

Table A2: Population attributable fractions of lung cancer among women for active smoking, by country, 2004

PAF sm for lung cancer among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Afghanistan 0.75 0.49 0.10 0.00 0.00 Albania 0.77 0.69 0.65 0.57 0.10 Algeria 0.00 0.00 0.00 0.15 0.00 Andorra 0.37 0.37 0.00 0.03 0.03 Angola 0.72 0.02 0.18 0.00 0.00 Antigua and Barbuda 0.00 0.27 0.00 0.55 0.37 Argentina 0.36 0.65 0.56 0.44 0.45 Armenia 0.65 0.50 0.68 0.60 0.20 Australia 0.24 0.67 0.76 0.79 0.73 Austria 0.51 0.78 0.67 0.66 0.63 Azerbaijan 0.72 0.54 0.55 0.38 0.00 Bahamas 0.00 0.10 0.59 0.18 0.75 Bahrain 0.00 0.21 0.36 0.80 0.89 Bangladesh 0.49 0.51 0.49 0.54 0.65 Barbados 0.00 0.00 0.08 0.00 0.00 Belarus 0.00 0.00 0.14 0.05 0.02 Belgium 0.34 0.68 0.68 0.58 0.55 Belize 0.00 0.21 0.23 0.21 0.00 Benin 0.00 0.00 0.00 0.00 0.00 Bhutan 0.00 0.00 0.00 0.00 0.31 Bolivia (Plurinational State of) 0.00 0.25 0.42 0.35 0.02 Bosnia and Herzegovina 0.77 0.59 0.52 0.61 0.28 Botswana 0.00 0.23 0.04 0.00 0.00 Brazil 0.28 0.35 0.53 0.46 0.22 Brunei Darussalam 0.00 0.68 0.89 0.84 0.73 Bulgaria 0.51 0.56 0.46 0.37 0.20 Burkina Faso 0.00 0.00 0.29 0.44 0.06 Burundi 0.00 0.00 0.00 0.12 0.00 Cambodia 0.54 0.51 0.47 0.58 0.41 Cameroon 0.00 0.00 0.00 0.00 0.00 Canada 0.63 0.82 0.88 0.87 0.81 Cape Verde 0.00 0.00 0.00 0.00 0.00 Central African Republic 0.00 0.00 0.00 0.00 0.00 Chad 0.00 0.00 0.00 0.00 0.00 Chile 0.00 0.31 0.55 0.60 0.63 China 0.16 0.12 0.28 0.49 0.34 Colombia 0.09 0.26 0.62 0.63 0.33 Comoros 0.00 0.00 0.00 0.00 0.00 Congo 0.00 0.00 0.00 0.00 0.00 Cook Islands 0.44 0.00 0.29 0.24 0.56 Costa Rica 0.00 0.00 0.30 0.54 0.55 Côte d’Ivoire 0.04 0.00 0.00 0.00 0.00 Croatia 0.62 0.71 0.71 0.61 0.46 Cuba 0.54 0.80 0.80 0.78 0.76 Cyprus 0.07 0.18 0.36 0.38 0.00 Czech Republic 0.23 0.75 0.74 0.71 0.62 Democratic People’s Republic of Korea 0.35 0.46 0.64 0.66 0.49 Democratic Republic of the Congo 0.00 0.00 0.00 0.00 0.00

84 Annex Table A2 (continued)

PAF sm for lung cancer among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Denmark 0.45 0.86 0.88 0.88 0.81 Djibouti 0.08 0.00 0.00 0.00 0.00 Dominica 0.88 0.79 0.76 0.56 0.67 Dominican Republic 0.28 0.45 0.68 0.66 0.50 Ecuador 0.15 0.02 0.25 0.39 0.16 Egypt 0.52 0.19 0.00 0.00 0.00 El Salvador 0.00 0.00 0.14 0.10 0.00 Equatorial Guinea 0.00 0.00 0.00 0.00 0.00 Eritrea 0.00 0.00 0.00 0.00 0.00 Estonia 0.61 0.59 0.67 0.65 0.40 Ethiopia 0.00 0.00 0.19 0.34 0.15 Fiji 0.00 0.00 0.00 0.00 0.00 Finland 0.00 0.56 0.57 0.58 0.56 France 0.66 0.74 0.58 0.53 0.47 Gabon 0.00 0.13 0.00 0.00 0.00 Gambia 0.00 0.00 0.00 0.00 0.00 Georgia 0.35 0.08 0.27 0.00 0.00 Germany 0.46 0.75 0.70 0.66 0.60 Ghana 0.00 0.00 0.00 0.00 0.00 Greece 0.09 0.48 0.51 0.57 0.63 Grenada 0.00 0.53 0.67 0.46 0.00 Guatemala 0.10 0.10 0.45 0.56 0.46 Guinea 0.27 0.00 0.00 0.00 0.00 Guinea-Bissau 0.00 0.00 0.00 0.00 0.00 Guyana 0.04 0.28 0.00 0.00 0.00 Haiti 0.00 0.00 0.00 0.00 0.00 Honduras 0.29 0.16 0.60 0.64 0.56 Hungary 0.75 0.89 0.84 0.78 0.76 Iceland 0.62 0.82 0.89 0.86 0.83 India 0.00 0.00 0.00 0.00 0.03 Indonesia 0.62 0.50 0.67 0.67 0.71 Iran (Islamic Republic of) 0.00 0.00 0.00 0.00 0.00 Iraq 0.39 0.63 0.64 0.63 0.49 Ireland 0.33 0.72 0.84 0.86 0.81 Israel 0.22 0.51 0.60 0.65 0.60 Italy 0.38 0.58 0.57 0.60 0.59 Jamaica 0.00 0.28 0.51 0.48 0.29 Japan 0.00 0.43 0.48 0.60 0.78 Jordan 0.13 0.08 0.18 0.14 0.39 Kazakhstan 0.67 0.55 0.71 0.66 0.26 Kenya 0.00 0.00 0.00 0.00 0.00 Kiribati 0.00 0.00 0.00 0.77 0.00 Kuwait 0.00 0.00 0.03 0.47 0.40 Kyrgyzstan 0.46 0.28 0.44 0.26 0.00 Lao People’s Democratic Republic 0.43 0.66 0.67 0.62 0.62 Latvia 0.00 0.39 0.34 0.49 0.35 Lebanon 0.00 0.00 0.06 0.00 0.00 Lesotho 0.00 0.00 0.00 0.00 0.00 Liberia 0.00 0.00 0.00 0.00 0.00 Libyan Arab Jamahiriya 0.07 0.00 0.00 0.00 0.00 Lithuania 0.13 0.45 0.27 0.32 0.39 Luxembourg 0.32 0.71 0.77 0.66 0.57

85 Second-hand smoke: Assessing the burden of disease

PAF sm for lung cancer among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Madagascar 0.00 0.00 0.00 0.01 0.00 Malawi 0.00 0.00 0.00 0.00 0.00 Malaysia 0.33 0.47 0.74 0.77 0.72 Maldives 0.43 0.77 0.90 0.93 0.93 Mali 0.00 0.00 0.00 0.00 0.00 Malta 0.00 0.23 0.47 0.22 0.00 Marshall Islands 0.84 0.62 0.42 0.46 0.70 Mauritania 0.00 0.00 0.00 0.00 0.00 Mauritius 0.46 0.23 0.23 0.41 0.23 Mexico 0.00 0.00 0.36 0.48 0.56 Micronesia (Federated States of) 0.00 0.00 0.00 0.00 0.00 Monaco 0.00 0.53 0.42 0.42 0.41 Mongolia 0.26 0.41 0.79 0.85 0.75 Morocco 0.00 0.00 0.00 0.00 0.00 Mozambique 0.00 0.00 0.00 0.04 0.00 Myanmar 0.60 0.73 0.78 0.75 0.73 Namibia 0.00 0.00 0.00 0.00 0.00 Nauru 0.61 0.74 0.71 0.72 0.00 Nepal 0.20 0.11 0.16 0.09 0.00 Netherlands 0.66 0.86 0.84 0.80 0.63 New Zealand 0.51 0.77 0.85 0.81 0.71 Nicaragua 0.00 0.23 0.40 0.39 0.19 Niger 0.00 0.00 0.00 0.00 0.00 Nigeria 0.00 0.00 0.00 0.00 0.00 Niue 0.60 0.19 0.00 0.18 0.59 Norway 0.21 0.78 0.86 0.83 0.59 Oman 0.00 0.00 0.00 0.00 0.00 Pakistan 0.00 0.00 0.00 0.00 0.22 Palau 0.71 0.50 0.50 0.21 0.53 Panama 0.00 0.00 0.34 0.40 0.25 Papua New Guinea 0.00 0.00 0.00 0.00 0.00 Paraguay 0.00 0.00 0.37 0.28 0.00 Peru 0.34 0.07 0.39 0.52 0.31 Philippines 0.47 0.50 0.60 0.52 0.24 Poland 0.41 0.80 0.76 0.69 0.61 Portugal 0.28 0.37 0.28 0.28 0.30 Qatar 0.00 0.00 0.00 0.00 0.00 Republic of Korea 0.30 0.42 0.64 0.77 0.88 Republic of Moldova 0.43 0.52 0.56 0.27 0.00 Romania 0.41 0.58 0.54 0.51 0.20 Russian Federation 0.17 0.41 0.44 0.42 0.31 Rwanda 0.14 0.00 0.00 0.00 0.00 Saint Kitts and Nevis 0.00 0.00 0.00 0.10 0.00 Saint Lucia 0.00 0.00 0.61 0.27 0.10 Saint Vincent and the Grenadines 0.83 0.71 0.54 0.00 0.64 Samoa 0.25 0.00 0.00 0.00 0.00 San Marino 0.00 0.00 0.32 0.58 0.60 Sao Tome and Principe 0.02 0.44 0.59 0.70 0.53 Saudi Arabia 0.00 0.00 0.00 0.00 0.00 Senegal 0.00 0.00 0.00 0.00 0.00 Serbia and Montenegro 0.63 0.80 0.75 0.64 0.11

86 Annex Table A2 (continued)

PAF sm for lung cancer among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Seychelles 0.00 0.00 0.00 0.61 0.00 Sierra Leone 0.00 0.00 0.00 0.00 0.00 Singapore 0.27 0.48 0.58 0.78 0.85 Slovakia 0.53 0.60 0.54 0.52 0.49 Slovenia 0.00 0.74 0.68 0.70 0.67 Solomon Islands 0.00 0.00 0.00 0.00 0.00 Somalia 0.38 0.00 0.00 0.00 0.00 South Africa 0.46 0.49 0.54 0.57 0.42 Spain 0.53 0.55 0.23 0.13 0.20 Sri Lanka 0.02 0.00 0.00 0.00 0.00 Sudan 0.00 0.11 0.00 0.00 0.00 Suriname 0.00 0.37 0.53 0.33 0.26 Swaziland 0.00 0.00 0.00 0.00 0.00 Sweden 0.25 0.75 0.79 0.72 0.57 Switzerland 0.37 0.77 0.74 0.68 0.52 Syrian Arab Republic 0.00 0.00 0.00 0.00 0.00 Tajikistan 0.45 0.36 0.39 0.00 0.00 Thailand 0.53 0.67 0.72 0.69 0.62 The former Yugoslav Republic of Macedonia 0.27 0.45 0.40 0.42 0.26 Timor-Leste 0.16 0.33 0.58 0.59 0.57 Togo 0.00 0.00 0.00 0.00 0.00 Tonga 0.71 0.49 0.27 0.35 0.66 Trinidad and Tobago 0.06 0.00 0.00 0.00 0.53 Tunisia 0.00 0.00 0.00 0.00 0.00 Turkey 0.43 0.00 0.39 0.50 0.23 Turkmenistan 0.53 0.20 0.30 0.21 0.00 Tuvalu 0.88 0.57 0.41 0.46 0.68 Uganda 0.00 0.00 0.00 0.25 0.05 Ukraine 0.18 0.33 0.35 0.27 0.00 United Arab Emirates 0.00 0.00 0.19 0.33 0.45 United Kingdom of Great Britain and Northern 0.30 0.78 0.84 0.87 0.81 Ireland United Republic of Tanzania 0.00 0.00 0.00 0.00 0.00 United States of America 0.58 0.81 0.89 0.89 0.85 Uruguay 0.63 0.65 0.45 0.34 0.33 Uzbekistan 0.61 0.24 0.36 0.11 0.00 Vanuatu 0.66 0.56 0.00 0.29 0.48 Venezuela (Bolivarian Republic of) 0.46 0.62 0.64 0.64 0.62 Viet Nam 0.35 0.40 0.58 0.57 0.63 Yemen 0.25 0.06 0.26 0.00 0.00 Zambia 0.00 0.00 0.00 0.00 0.00 Zimbabwe 0.14 0.00 0.36 0.43 0.24

87 Second-hand smoke: Assessing the burden of disease

Table A3: Population attributable fractions of ischaemic heart disease among men for active smoking, by country, 2004

PAF sm for IHD among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Afghanistan 0.82 0.44 0.14 0.05 0.01 Albania 0.82 0.59 0.35 0.18 0.02 Algeria 0.02 0.30 0.19 0.10 0.02 Andorra 0.80 0.58 0.27 0.14 0.02 Angola 0.00 0.41 0.12 0.04 0.00 Antigua and Barbuda 0.00 0.05 0.00 0.00 0.01 Argentina 0.69 0.51 0.25 0.10 0.01 Armenia 0.82 0.66 0.47 0.20 0.02 Australia 0.16 0.29 0.19 0.12 0.02 Austria 0.52 0.48 0.23 0.12 0.01 Azerbaijan 0.82 0.43 0.27 0.08 0.01 Bahamas 0.00 0.30 0.07 0.04 0.00 Bahrain 0.00 0.10 0.22 0.15 0.03 Bangladesh 0.68 0.36 0.25 0.12 0.02 Barbados 0.00 0.04 0.06 0.04 0.01 Belarus 0.80 0.63 0.37 0.15 0.02 Belgium 0.71 0.61 0.36 0.21 0.03 Belize 0.00 0.28 0.07 0.07 0.02 Benin 0.00 0.08 0.04 0.02 0.00 Bhutan 0.22 0.18 0.14 0.05 0.01 Bolivia (Plurinational State of) 0.48 0.21 0.11 0.05 0.01 Bosnia and Herzegovina 0.82 0.60 0.33 0.15 0.01 Botswana 0.00 0.19 0.10 0.05 0.01 Brazil 0.60 0.35 0.17 0.07 0.01 Brunei Darussalam 0.00 0.20 0.15 0.08 0.01 Bulgaria 0.79 0.64 0.29 0.09 0.01 Burkina Faso 0.00 0.15 0.11 0.05 0.01 Burundi 0.00 0.11 0.05 0.02 0.00 Cambodia 0.72 0.35 0.17 0.09 0.01 Cameroon 0.00 0.09 0.10 0.03 0.01 Canada 0.52 0.40 0.26 0.16 0.02 Cape Verde 0.00 0.00 0.04 0.01 0.00 Central African Republic 0.00 0.18 0.08 0.02 0.00 Chad 0.14 0.19 0.08 0.02 0.00 Chile 0.00 0.22 0.13 0.08 0.01 China 0.53 0.11 0.04 0.02 0.00 Colombia 0.00 0.17 0.12 0.06 0.01 Comoros 0.00 0.04 0.03 0.01 0.00 Congo 0.00 0.19 0.12 0.07 0.01 Cook Islands 0.00 0.00 0.05 0.04 0.01 Costa Rica 0.00 0.04 0.05 0.04 0.01 Côte d’Ivoire 0.00 0.37 0.18 0.06 0.01 Croatia 0.76 0.63 0.36 0.19 0.02 Cuba 0.72 0.52 0.24 0.12 0.02 Cyprus 0.42 0.34 0.18 0.08 0.01 Czech Republic 0.65 0.61 0.36 0.17 0.02 Democratic People’s Republic of Korea 0.19 0.23 0.14 0.07 0.01 Democratic Republic of the Congo 0.31 0.15 0.06 0.00 0.00

88 Annex Table A3 (continued)

PAF sm for IHD among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Denmark 0.54 0.45 0.27 0.16 0.02 Djibouti 0.00 0.07 0.03 0.00 0.00 Dominica 0.00 0.36 0.12 0.07 0.00 Dominican Republic 0.00 0.21 0.14 0.07 0.01 Ecuador 0.00 0.06 0.05 0.03 0.00 Egypt 0.75 0.22 0.08 0.03 0.00 El Salvador 0.00 0.00 0.01 0.01 0.00 Equatorial Guinea 0.12 0.22 0.09 0.02 0.00 Eritrea 0.00 0.06 0.04 0.01 0.00 Estonia 0.82 0.56 0.37 0.18 0.02 Ethiopia 0.00 0.06 0.02 0.00 0.00 Fiji 0.00 0.00 0.00 0.00 0.00 Finland 0.00 0.33 0.20 0.13 0.02 France 0.82 0.63 0.29 0.14 0.02 Gabon 0.00 0.36 0.12 0.05 0.01 Gambia 0.00 0.02 0.09 0.07 0.01 Georgia 0.77 0.37 0.18 0.06 0.00 Germany 0.66 0.47 0.25 0.14 0.02 Ghana 0.00 0.10 0.04 0.01 0.00 Greece 0.68 0.58 0.31 0.17 0.02 Grenada 0.74 0.36 0.13 0.05 0.00 Guatemala 0.46 0.15 0.09 0.06 0.01 Guinea 0.66 0.19 0.06 0.03 0.01 Guinea-Bissau 0.10 0.13 0.05 0.02 0.00 Guyana 0.22 0.09 0.04 0.01 0.00 Haiti 0.00 0.02 0.04 0.02 0.01 Honduras 0.00 0.16 0.15 0.09 0.01 Hungary 0.82 0.67 0.43 0.20 0.02 Iceland 0.69 0.32 0.14 0.12 0.01 India 0.26 0.16 0.10 0.03 0.01 Indonesia 0.57 0.35 0.24 0.11 0.02 Iran (Islamic Republic of) 0.00 0.11 0.08 0.04 0.00 Iraq 0.82 0.58 0.30 0.13 0.02 Ireland 0.09 0.36 0.25 0.16 0.02 Israel 0.33 0.34 0.20 0.09 0.01 Italy 0.59 0.48 0.28 0.17 0.02 Jamaica 0.44 0.29 0.14 0.07 0.01 Japan 0.41 0.32 0.16 0.13 0.03 Jordan 0.60 0.42 0.20 0.08 0.01 Kazakhstan 0.82 0.61 0.44 0.19 0.02 Kenya 0.00 0.05 0.06 0.02 0.00 Kiribati 0.81 0.10 0.00 0.00 0.00 Kuwait 0.00 0.00 0.06 0.05 0.01 Kyrgyzstan 0.73 0.39 0.25 0.07 0.00 Lao People’s Democratic Republic 0.73 0.32 0.21 0.09 0.01 Latvia 0.78 0.60 0.35 0.18 0.02 Lebanon 0.20 0.42 0.21 0.08 0.01 Lesotho 0.00 0.16 0.09 0.05 0.01 Liberia 0.27 0.09 0.04 0.01 0.00 Libyan Arab Jamahiriya 0.53 0.20 0.10 0.05 0.01 Lithuania 0.62 0.61 0.35 0.16 0.01 Luxembourg 0.64 0.51 0.28 0.16 0.02

89 Second-hand smoke: Assessing the burden of disease

PAF sm for IHD among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Madagascar 0.00 0.07 0.04 0.02 0.00 Malawi 0.00 0.03 0.02 0.03 0.01 Malaysia 0.48 0.39 0.26 0.14 0.01 Maldives 0.24 0.43 0.40 0.31 0.05 Mali 0.00 0.00 0.05 0.03 0.00 Malta 0.00 0.40 0.27 0.15 0.02 Marshall Islands 0.53 0.14 0.09 0.06 0.01 Mauritania 0.00 0.08 0.04 0.02 0.00 Mauritius 0.47 0.17 0.13 0.07 0.01 Mexico 0.00 0.08 0.08 0.05 0.01 Micronesia (Federated States of) 0.00 0.00 0.00 0.00 0.00 Monaco 0.82 0.52 0.24 0.12 0.02 Mongolia 0.61 0.44 0.32 0.19 0.03 Morocco 0.59 0.29 0.15 0.07 0.01 Mozambique 0.00 0.00 0.00 0.00 0.00 Myanmar 0.74 0.41 0.22 0.10 0.02 Namibia 0.00 0.22 0.10 0.05 0.01 Nauru 0.00 0.00 0.35 0.00 0.00 Nepal 0.58 0.28 0.15 0.05 0.01 Netherlands 0.66 0.49 0.29 0.20 0.03 New Zealand 0.00 0.27 0.18 0.12 0.02 Nicaragua 0.36 0.15 0.10 0.04 0.01 Niger 0.43 0.14 0.10 0.06 0.01 Nigeria 0.09 0.09 0.02 0.01 0.00 Niue 0.12 0.01 0.05 0.04 0.01 Norway 0.18 0.36 0.23 0.13 0.01 Oman 0.00 0.11 0.08 0.04 0.01 Pakistan 0.08 0.20 0.17 0.07 0.01 Palau 0.00 0.05 0.10 0.05 0.01 Panama 0.00 0.13 0.10 0.04 0.01 Papua New Guinea 0.00 0.01 0.01 0.00 0.00 Paraguay 0.35 0.33 0.16 0.09 0.01 Peru 0.00 0.05 0.07 0.04 0.01 Philippines 0.74 0.47 0.21 0.07 0.01 Poland 0.79 0.65 0.42 0.22 0.02 Portugal 0.71 0.50 0.21 0.10 0.01 Qatar 0.00 0.00 0.05 0.02 0.00 Republic of Korea 0.61 0.38 0.30 0.20 0.04 Republic of Moldova 0.75 0.55 0.25 0.07 0.01 Romania 0.81 0.64 0.32 0.10 0.01 Russian Federation 0.82 0.62 0.31 0.12 0.01 Rwanda 0.00 0.00 0.00 0.02 0.00 Saint Kitts and Nevis 0.00 0.28 0.04 0.01 0.00 Saint Lucia 0.26 0.09 0.01 0.01 0.01 Saint Vincent and the Grenadines 0.82 0.33 0.05 0.04 0.00 Samoa 0.00 0.13 0.03 0.00 0.00 San Marino 0.00 0.35 0.28 0.19 0.03 Sao Tome and Principe 0.51 0.35 0.18 0.09 0.01 Saudi Arabia 0.00 0.09 0.08 0.05 0.01 Senegal 0.00 0.02 0.03 0.02 0.00 Serbia and Montenegro 0.82 0.67 0.35 0.14 0.01

90 Annex Table A3 (continued)

PAF sm for IHD among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Seychelles 0.00 0.08 0.14 0.09 0.00 Sierra Leone 0.46 0.20 0.06 0.02 0.00 Singapore 0.29 0.27 0.22 0.14 0.02 Slovakia 0.71 0.57 0.34 0.17 0.01 Slovenia 0.79 0.62 0.31 0.19 0.02 Solomon Islands 0.00 0.04 0.03 0.02 0.00 Somalia 0.44 0.19 0.06 0.01 0.00 South Africa 0.65 0.41 0.18 0.07 0.01 Spain 0.77 0.58 0.29 0.15 0.02 Sri Lanka 0.49 0.31 0.14 0.06 0.01 Sudan 0.00 0.00 0.02 0.01 0.00 Suriname 0.59 0.23 0.11 0.07 0.01 Swaziland 0.00 0.13 0.12 0.06 0.01 Sweden 0.00 0.24 0.14 0.08 0.01 Switzerland 0.50 0.41 0.23 0.11 0.01 Syrian Arab Republic 0.00 0.30 0.11 0.04 0.01 Tajikistan 0.00 0.10 0.05 0.02 0.00 Thailand 0.61 0.37 0.20 0.10 0.01 The former Yugoslav Republic of Macedonia 0.82 0.63 0.31 0.12 0.01 Timor-Leste 0.47 0.27 0.19 0.08 0.01 Togo 0.00 0.10 0.04 0.02 0.00 Tonga 0.00 0.00 0.04 0.04 0.01 Trinidad and Tobago 0.63 0.33 0.08 0.04 0.00 Tunisia 0.37 0.25 0.16 0.07 0.01 Turkey 0.82 0.60 0.34 0.15 0.03 Turkmenistan 0.75 0.40 0.16 0.04 0.00 Tuvalu 0.72 0.15 0.14 0.04 0.01 Uganda 0.00 0.09 0.04 0.00 0.00 Ukraine 0.76 0.59 0.31 0.12 0.01 United Arab Emirates 0.00 0.00 0.03 0.02 0.01 United Kingdom of Great Britain and Northern 0.32 0.37 0.24 0.15 0.02 Ireland United Republic of Tanzania 0.00 0.16 0.08 0.02 0.00 United States of America 0.62 0.44 0.28 0.17 0.02 Uruguay 0.79 0.60 0.30 0.14 0.02 Uzbekistan 0.59 0.19 0.11 0.03 0.00 Vanuatu 0.00 0.05 0.12 0.05 0.01 Venezuela (Bolivarian Republic of) 0.52 0.27 0.11 0.05 0.01 Viet Nam 0.45 0.34 0.24 0.11 0.01 Yemen 0.60 0.18 0.08 0.01 0.00 Zambia 0.16 0.19 0.07 0.03 0.01 Zimbabwe 0.33 0.23 0.12 0.06 0.01

91 Second-hand smoke: Assessing the burden of disease

Table A4: Population attributable fractions of ischaemic heart disease among women for active smoking, by country, 2004

PAF sm for IHD among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Afghanistan 0.44 0.19 0.01 0.00 0.00 Albania 0.47 0.36 0.17 0.07 0.00 Algeria 0.00 0.00 0.00 0.01 0.00 Andorra 0.13 0.13 0.00 0.00 0.00 Angola 0.40 0.01 0.02 0.00 0.00 Antigua and Barbuda 0.00 0.08 0.00 0.07 0.02 Argentina 0.13 0.32 0.12 0.04 0.03 Armenia 0.33 0.20 0.19 0.08 0.01 Australia 0.07 0.34 0.26 0.18 0.10 Austria 0.21 0.47 0.18 0.10 0.07 Azerbaijan 0.40 0.23 0.12 0.03 0.00 Bahamas 0.00 0.03 0.13 0.01 0.11 Bahrain 0.00 0.06 0.06 0.18 0.25 Bangladesh 0.20 0.21 0.09 0.06 0.07 Barbados 0.00 0.00 0.01 0.00 0.00 Belarus 0.00 0.00 0.02 0.00 0.00 Belgium 0.12 0.35 0.19 0.07 0.05 Belize 0.00 0.06 0.03 0.02 0.00 Benin 0.00 0.00 0.00 0.00 0.00 Bhutan 0.00 0.00 0.00 0.00 0.02 Bolivia (Plurinational State of) 0.00 0.08 0.07 0.03 0.00 Bosnia and Herzegovina 0.46 0.27 0.11 0.08 0.02 Botswana 0.00 0.07 0.00 0.00 0.00 Brazil 0.09 0.12 0.11 0.05 0.01 Brunei Darussalam 0.00 0.35 0.47 0.24 0.10 Bulgaria 0.21 0.24 0.09 0.03 0.01 Burkina Faso 0.00 0.00 0.04 0.04 0.00 Burundi 0.00 0.00 0.00 0.01 0.00 Cambodia 0.23 0.21 0.09 0.07 0.03 Cameroon 0.00 0.00 0.00 0.00 0.00 Canada 0.30 0.54 0.44 0.28 0.15 Cape Verde 0.00 0.00 0.00 0.00 0.00 Central African Republic 0.00 0.00 0.00 0.00 0.00 Chad 0.00 0.00 0.00 0.00 0.00 Chile 0.00 0.10 0.12 0.08 0.07 China 0.05 0.03 0.04 0.05 0.02 Colombia 0.03 0.08 0.15 0.09 0.02 Comoros 0.00 0.00 0.00 0.00 0.00 Congo 0.00 0.00 0.00 0.00 0.00 Cook Islands 0.17 0.00 0.04 0.02 0.05 Costa Rica 0.00 0.00 0.04 0.06 0.05 Côte d’Ivoire 0.01 0.00 0.00 0.00 0.00 Croatia 0.29 0.38 0.21 0.08 0.03 Cuba 0.24 0.50 0.31 0.17 0.12 Cyprus 0.02 0.05 0.06 0.03 0.00 Czech Republic 0.07 0.43 0.24 0.13 0.06 Democratic People’s Republic of Korea 0.12 0.18 0.16 0.10 0.04 Democratic Republic of the Congo 0.00 0.00 0.00 0.00 0.00

92 Annex Table A4 (continued)

PAF sm for IHD among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Denmark 0.18 0.61 0.46 0.30 0.15 Djibouti 0.02 0.00 0.00 0.00 0.00 Dominica 0.56 0.49 0.26 0.07 0.08 Dominican Republic 0.09 0.17 0.19 0.10 0.04 Ecuador 0.04 0.00 0.04 0.04 0.01 Egypt 0.22 0.06 0.00 0.00 0.00 El Salvador 0.00 0.00 0.02 0.01 0.00 Equatorial Guinea 0.00 0.00 0.00 0.00 0.00 Eritrea 0.00 0.00 0.00 0.00 0.00 Estonia 0.29 0.27 0.18 0.10 0.03 Ethiopia 0.00 0.00 0.02 0.03 0.01 Fiji 0.00 0.00 0.00 0.00 0.00 Finland 0.00 0.25 0.13 0.08 0.05 France 0.34 0.42 0.13 0.06 0.04 Gabon 0.00 0.04 0.00 0.00 0.00 Gambia 0.00 0.00 0.00 0.00 0.00 Georgia 0.12 0.02 0.04 0.00 0.00 Germany 0.18 0.44 0.20 0.10 0.06 Ghana 0.00 0.00 0.00 0.00 0.00 Greece 0.03 0.19 0.10 0.07 0.07 Grenada 0.00 0.22 0.18 0.05 0.00 Guatemala 0.03 0.03 0.08 0.07 0.03 Guinea 0.09 0.00 0.00 0.00 0.00 Guinea-Bissau 0.00 0.00 0.00 0.00 0.00 Guyana 0.01 0.09 0.00 0.00 0.00 Haiti 0.00 0.00 0.00 0.00 0.00 Honduras 0.10 0.05 0.14 0.09 0.05 Hungary 0.43 0.67 0.36 0.17 0.12 Iceland 0.29 0.54 0.46 0.27 0.17 India 0.00 0.00 0.00 0.00 0.00 Indonesia 0.30 0.20 0.18 0.10 0.09 Iran (Islamic Republic of) 0.00 0.00 0.00 0.00 0.00 Iraq 0.14 0.30 0.16 0.09 0.04 Ireland 0.11 0.39 0.37 0.27 0.15 Israel 0.07 0.21 0.14 0.10 0.06 Italy 0.13 0.26 0.13 0.08 0.06 Jamaica 0.00 0.09 0.10 0.05 0.02 Japan 0.00 0.16 0.09 0.08 0.13 Jordan 0.04 0.02 0.02 0.01 0.03 Kazakhstan 0.34 0.24 0.21 0.10 0.01 Kenya 0.00 0.00 0.00 0.00 0.00 Kiribati 0.00 0.00 0.00 0.17 0.00 Kuwait 0.00 0.00 0.00 0.05 0.03 Kyrgyzstan 0.18 0.09 0.08 0.02 0.00 Lao People’s Democratic Republic 0.16 0.33 0.18 0.09 0.06 Latvia 0.00 0.14 0.05 0.05 0.02 Lebanon 0.00 0.00 0.01 0.00 0.00 Lesotho 0.00 0.00 0.00 0.00 0.00 Liberia 0.00 0.00 0.00 0.00 0.00 Libyan Arab Jamahiriya 0.02 0.00 0.00 0.00 0.00 Lithuania 0.04 0.17 0.04 0.03 0.03 Luxembourg 0.11 0.38 0.27 0.10 0.05

93 Second-hand smoke: Assessing the burden of disease

PAF sm for IHD among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Madagascar 0.00 0.00 0.00 0.00 0.00 Malawi 0.00 0.00 0.00 0.00 0.00 Malaysia 0.11 0.18 0.24 0.16 0.10 Maldives 0.16 0.46 0.50 0.40 0.28 Mali 0.00 0.00 0.00 0.00 0.00 Malta 0.00 0.07 0.09 0.02 0.00 Marshall Islands 0.56 0.29 0.08 0.05 0.09 Mauritania 0.00 0.00 0.00 0.00 0.00 Mauritius 0.18 0.07 0.03 0.04 0.01 Mexico 0.00 0.00 0.06 0.05 0.05 Micronesia (Federated States of) 0.00 0.00 0.00 0.00 0.00 Monaco 0.00 0.22 0.07 0.04 0.03 Mongolia 0.08 0.15 0.30 0.25 0.11 Morocco 0.00 0.00 0.00 0.00 0.00 Mozambique 0.00 0.00 0.00 0.00 0.00 Myanmar 0.28 0.40 0.28 0.15 0.10 Namibia 0.00 0.00 0.00 0.00 0.00 Nauru 0.29 0.41 0.21 0.13 0.00 Nepal 0.06 0.03 0.02 0.01 0.00 Netherlands 0.34 0.62 0.36 0.19 0.07 New Zealand 0.21 0.46 0.38 0.20 0.09 Nicaragua 0.00 0.07 0.07 0.04 0.01 Niger 0.00 0.00 0.00 0.00 0.00 Nigeria 0.00 0.00 0.00 0.00 0.00 Niue 0.28 0.06 0.00 0.01 0.06 Norway 0.06 0.47 0.39 0.22 0.06 Oman 0.00 0.00 0.00 0.00 0.00 Pakistan 0.00 0.00 0.00 0.00 0.01 Palau 0.39 0.20 0.10 0.02 0.05 Panama 0.00 0.00 0.05 0.04 0.01 Papua New Guinea 0.00 0.00 0.00 0.00 0.00 Paraguay 0.00 0.00 0.06 0.02 0.00 Peru 0.12 0.02 0.07 0.06 0.02 Philippines 0.18 0.20 0.14 0.06 0.01 Poland 0.15 0.51 0.26 0.11 0.06 Portugal 0.09 0.13 0.04 0.02 0.02 Qatar 0.00 0.00 0.00 0.00 0.00 Republic of Korea 0.10 0.16 0.16 0.16 0.24 Republic of Moldova 0.16 0.22 0.12 0.02 0.00 Romania 0.15 0.26 0.11 0.06 0.01 Russian Federation 0.05 0.15 0.08 0.04 0.02 Rwanda 0.04 0.00 0.00 0.00 0.00 Saint Kitts and Nevis 0.00 0.00 0.00 0.01 0.00 Saint Lucia 0.00 0.00 0.14 0.02 0.00 Saint Vincent and the Grenadines 0.55 0.38 0.12 0.00 0.07 Samoa 0.08 0.00 0.00 0.00 0.00 San Marino 0.00 0.00 0.05 0.07 0.06 Sao Tome and Principe 0.00 0.16 0.14 0.12 0.04 Saudi Arabia 0.00 0.00 0.00 0.00 0.00 Senegal 0.00 0.00 0.00 0.00 0.00 Serbia and Montenegro 0.30 0.50 0.25 0.09 0.01

94 Annex Table A4 (continued)

PAF sm for IHD among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Seychelles 0.00 0.00 0.00 0.08 0.00 Sierra Leone 0.00 0.00 0.00 0.00 0.00 Singapore 0.09 0.19 0.13 0.18 0.19 Slovakia 0.23 0.27 0.11 0.06 0.04 Slovenia 0.00 0.42 0.19 0.12 0.08 Solomon Islands 0.00 0.00 0.00 0.00 0.00 Somalia 0.14 0.00 0.00 0.00 0.00 South Africa 0.18 0.19 0.11 0.07 0.03 Spain 0.22 0.24 0.03 0.01 0.01 Sri Lanka 0.00 0.00 0.00 0.00 0.00 Sudan 0.00 0.03 0.00 0.00 0.00 Suriname 0.00 0.13 0.11 0.03 0.01 Swaziland 0.00 0.00 0.00 0.00 0.00 Sweden 0.08 0.43 0.29 0.13 0.05 Switzerland 0.13 0.45 0.24 0.11 0.04 Syrian Arab Republic 0.00 0.00 0.00 0.00 0.00 Tajikistan 0.17 0.12 0.06 0.00 0.00 Thailand 0.23 0.34 0.22 0.11 0.06 The former Yugoslav Republic of Macedonia 0.09 0.17 0.07 0.04 0.01 Timor-Leste 0.05 0.11 0.13 0.08 0.05 Togo 0.00 0.00 0.00 0.00 0.00 Tonga 0.39 0.20 0.04 0.03 0.08 Trinidad and Tobago 0.02 0.00 0.00 0.00 0.05 Tunisia 0.00 0.00 0.00 0.00 0.00 Turkey 0.16 0.00 0.07 0.05 0.01 Turkmenistan 0.22 0.06 0.04 0.02 0.00 Tuvalu 0.56 0.25 0.07 0.05 0.08 Uganda 0.00 0.00 0.00 0.02 0.00 Ukraine 0.05 0.11 0.06 0.02 0.00 United Arab Emirates 0.00 0.00 0.02 0.03 0.03 United Kingdom of Great Britain and Northern 0.10 0.47 0.37 0.27 0.15 Ireland United Republic of Tanzania 0.00 0.00 0.00 0.00 0.00 United States of America 0.26 0.52 0.46 0.32 0.19 Uruguay 0.31 0.32 0.08 0.03 0.02 Uzbekistan 0.29 0.08 0.06 0.01 0.00 Vanuatu 0.34 0.24 0.00 0.02 0.04 Venezuela (Bolivarian Republic of) 0.18 0.30 0.16 0.09 0.06 Viet Nam 0.12 0.14 0.13 0.07 0.07 Yemen 0.08 0.01 0.04 0.00 0.00 Zambia 0.00 0.00 0.00 0.00 0.00 Zimbabwe 0.04 0.00 0.06 0.04 0.01

95 Second-hand smoke: Assessing the burden of disease

Table A5: Population attributable fractions of asthma among men for active smoking, by country, 2004

PAF sm for asthma among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Afghanistan 0.47 0.26 0.15 0.10 0.09 Albania 0.47 0.39 0.35 0.31 0.23 Algeria 0.00 0.16 0.20 0.19 0.24 Andorra 0.44 0.38 0.28 0.24 0.24 Angola 0.00 0.24 0.13 0.08 0.08 Antigua and Barbuda 0.00 0.02 0.00 0.00 0.11 Argentina 0.31 0.31 0.25 0.19 0.17 Armenia 0.47 0.46 0.47 0.34 0.23 Australia 0.04 0.16 0.19 0.22 0.23 Austria 0.18 0.29 0.23 0.21 0.19 Azerbaijan 0.47 0.25 0.27 0.16 0.08 Bahamas 0.00 0.16 0.07 0.08 0.06 Bahrain 0.00 0.05 0.23 0.27 0.36 Bangladesh 0.30 0.20 0.25 0.22 0.30 Barbados 0.00 0.02 0.06 0.09 0.09 Belarus 0.45 0.43 0.37 0.26 0.21 Belgium 0.33 0.40 0.36 0.35 0.36 Belize 0.00 0.15 0.07 0.13 0.21 Benin 0.00 0.04 0.04 0.04 0.05 Bhutan 0.05 0.09 0.14 0.10 0.15 Bolivia (Plurinational State of) 0.16 0.10 0.11 0.10 0.09 Bosnia and Herzegovina 0.47 0.40 0.34 0.27 0.18 Botswana 0.00 0.10 0.10 0.09 0.11 Brazil 0.23 0.19 0.17 0.13 0.12 Brunei Darussalam 0.00 0.10 0.15 0.15 0.14 Bulgaria 0.43 0.44 0.29 0.17 0.08 Burkina Faso 0.00 0.07 0.11 0.10 0.12 Burundi 0.00 0.05 0.06 0.04 0.06 Cambodia 0.34 0.19 0.17 0.17 0.13 Cameroon 0.00 0.04 0.10 0.06 0.08 Canada 0.18 0.23 0.27 0.28 0.29 Cape Verde 0.00 0.00 0.04 0.03 0.04 Central African Republic 0.00 0.09 0.08 0.03 0.04 Chad 0.03 0.10 0.08 0.03 0.04 Chile 0.00 0.11 0.14 0.15 0.15 China 0.19 0.05 0.04 0.04 0.05 Colombia 0.00 0.08 0.13 0.12 0.11 Comoros 0.00 0.02 0.03 0.02 0.05 Congo 0.00 0.09 0.12 0.13 0.17 Cook Islands 0.00 0.00 0.05 0.07 0.17 Costa Rica 0.00 0.02 0.05 0.08 0.10 Côte d’Ivoire 0.00 0.20 0.18 0.11 0.12 Croatia 0.39 0.43 0.36 0.33 0.27 Cuba 0.34 0.33 0.24 0.22 0.26 Cyprus 0.13 0.19 0.18 0.15 0.16 Czech Republic 0.27 0.41 0.37 0.30 0.25 Democratic People’s Republic of Korea 0.05 0.12 0.14 0.13 0.13 Democratic Republic of the Congo 0.08 0.07 0.06 0.00 0.00

96 Annex Table A5 (continued)

PAF sm for asthma among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Denmark 0.19 0.27 0.27 0.29 0.27 Djibouti 0.00 0.03 0.03 0.01 0.01 Dominica 0.00 0.20 0.12 0.13 0.04 Dominican Republic 0.00 0.11 0.14 0.14 0.19 Ecuador 0.00 0.03 0.05 0.06 0.07 Egypt 0.38 0.11 0.08 0.05 0.04 El Salvador 0.00 0.00 0.01 0.02 0.02 Equatorial Guinea 0.03 0.11 0.09 0.03 0.04 Eritrea 0.00 0.03 0.04 0.03 0.04 Estonia 0.47 0.36 0.38 0.31 0.26 Ethiopia 0.00 0.03 0.02 0.00 0.01 Fiji 0.00 0.00 0.00 0.00 0.00 Finland 0.00 0.18 0.20 0.23 0.23 France 0.47 0.43 0.29 0.24 0.22 Gabon 0.00 0.20 0.12 0.10 0.11 Gambia 0.00 0.01 0.09 0.13 0.20 Georgia 0.41 0.20 0.18 0.11 0.06 Germany 0.28 0.28 0.26 0.25 0.22 Ghana 0.00 0.04 0.04 0.03 0.03 Greece 0.30 0.38 0.31 0.29 0.29 Grenada 0.37 0.20 0.13 0.09 0.00 Guatemala 0.15 0.07 0.10 0.11 0.15 Guinea 0.28 0.09 0.06 0.07 0.09 Guinea-Bissau 0.02 0.06 0.05 0.04 0.06 Guyana 0.05 0.04 0.04 0.03 0.01 Haiti 0.00 0.01 0.04 0.03 0.08 Honduras 0.00 0.08 0.16 0.16 0.18 Hungary 0.47 0.47 0.44 0.33 0.27 Iceland 0.30 0.17 0.15 0.21 0.19 India 0.06 0.08 0.10 0.07 0.11 Indonesia 0.21 0.19 0.24 0.20 0.22 Iran (Islamic Republic of) 0.00 0.05 0.08 0.08 0.07 Iraq 0.47 0.38 0.30 0.24 0.24 Ireland 0.02 0.20 0.25 0.28 0.26 Israel 0.09 0.19 0.20 0.17 0.15 Italy 0.22 0.29 0.28 0.30 0.27 Jamaica 0.14 0.15 0.15 0.13 0.15 Japan 0.12 0.17 0.16 0.24 0.32 Jordan 0.23 0.24 0.20 0.15 0.17 Kazakhstan 0.47 0.41 0.45 0.33 0.22 Kenya 0.00 0.02 0.06 0.05 0.06 Kiribati 0.46 0.05 0.00 0.00 0.00 Kuwait 0.00 0.00 0.06 0.10 0.12 Kyrgyzstan 0.34 0.22 0.25 0.14 0.06 Lao People’s Democratic Republic 0.35 0.17 0.21 0.17 0.18 Latvia 0.42 0.40 0.35 0.32 0.24 Lebanon 0.05 0.24 0.21 0.15 0.16 Lesotho 0.00 0.08 0.10 0.10 0.13 Liberia 0.07 0.04 0.04 0.03 0.04 Libyan Arab Jamahiriya 0.18 0.10 0.10 0.09 0.09 Lithuania 0.24 0.40 0.35 0.28 0.18 Luxembourg 0.27 0.31 0.28 0.28 0.27

97 Second-hand smoke: Assessing the burden of disease

PAF sm for asthma among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Madagascar 0.00 0.03 0.04 0.03 0.05 Malawi 0.00 0.01 0.02 0.06 0.09 Malaysia 0.16 0.22 0.27 0.25 0.17 Maldives 0.06 0.25 0.40 0.47 0.47 Mali 0.00 0.00 0.05 0.06 0.07 Malta 0.00 0.23 0.27 0.26 0.24 Marshall Islands 0.18 0.07 0.09 0.11 0.19 Mauritania 0.00 0.04 0.04 0.04 0.05 Mauritius 0.15 0.08 0.13 0.14 0.12 Mexico 0.00 0.04 0.08 0.10 0.13 Micronesia (Federated States of) 0.00 0.00 0.00 0.00 0.00 Monaco 0.47 0.33 0.24 0.22 0.21 Mongolia 0.24 0.25 0.33 0.32 0.33 Morocco 0.23 0.15 0.16 0.13 0.14 Mozambique 0.00 0.00 0.00 0.00 0.01 Myanmar 0.36 0.24 0.22 0.19 0.21 Namibia 0.00 0.11 0.10 0.09 0.12 Nauru 0.00 0.00 0.36 0.00 0.00 Nepal 0.22 0.15 0.15 0.11 0.11 Netherlands 0.28 0.30 0.29 0.34 0.35 New Zealand 0.00 0.14 0.18 0.21 0.24 Nicaragua 0.10 0.07 0.10 0.08 0.14 Niger 0.13 0.07 0.10 0.12 0.15 Nigeria 0.02 0.04 0.02 0.01 0.02 Niue 0.03 0.00 0.05 0.07 0.18 Norway 0.04 0.20 0.24 0.24 0.20 Oman 0.00 0.05 0.08 0.07 0.11 Pakistan 0.02 0.10 0.18 0.14 0.20 Palau 0.00 0.02 0.10 0.10 0.12 Panama 0.00 0.06 0.10 0.08 0.11 Papua New Guinea 0.00 0.00 0.01 0.00 0.00 Paraguay 0.10 0.18 0.16 0.17 0.17 Peru 0.00 0.02 0.07 0.08 0.09 Philippines 0.36 0.28 0.22 0.14 0.11 Poland 0.42 0.45 0.43 0.36 0.27 Portugal 0.33 0.31 0.22 0.18 0.15 Qatar 0.00 0.00 0.05 0.04 0.07 Republic of Korea 0.24 0.21 0.30 0.34 0.43 Republic of Moldova 0.37 0.35 0.25 0.14 0.08 Romania 0.47 0.43 0.33 0.19 0.08 Russian Federation 0.47 0.42 0.31 0.22 0.14 Rwanda 0.00 0.00 0.00 0.04 0.05 Saint Kitts and Nevis 0.00 0.15 0.04 0.02 0.00 Saint Lucia 0.07 0.04 0.01 0.01 0.09 Saint Vincent and the Grenadines 0.47 0.18 0.05 0.07 0.02 Samoa 0.00 0.06 0.04 0.00 0.01 San Marino 0.00 0.19 0.28 0.33 0.35 Sao Tome and Principe 0.17 0.19 0.18 0.16 0.20 Saudi Arabia 0.00 0.04 0.08 0.09 0.10 Senegal 0.00 0.01 0.03 0.04 0.05 Serbia and Montenegro 0.47 0.47 0.36 0.25 0.11

98 Annex Table A5 (continued)

PAF sm for asthma among men Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Seychelles 0.00 0.04 0.14 0.17 0.00 Sierra Leone 0.15 0.10 0.06 0.04 0.06 Singapore 0.08 0.14 0.22 0.25 0.29 Slovakia 0.33 0.37 0.34 0.30 0.18 Slovenia 0.43 0.42 0.32 0.33 0.30 Solomon Islands 0.00 0.02 0.03 0.03 0.06 Somalia 0.14 0.09 0.06 0.02 0.01 South Africa 0.27 0.23 0.18 0.14 0.16 Spain 0.41 0.38 0.29 0.26 0.25 Sri Lanka 0.16 0.16 0.14 0.11 0.12 Sudan 0.00 0.00 0.02 0.02 0.01 Suriname 0.22 0.12 0.11 0.13 0.10 Swaziland 0.00 0.06 0.12 0.11 0.15 Sweden 0.00 0.12 0.14 0.14 0.12 Switzerland 0.17 0.24 0.23 0.21 0.20 Syrian Arab Republic 0.00 0.16 0.12 0.07 0.09 Tajikistan 0.00 0.05 0.05 0.04 0.02 Thailand 0.24 0.21 0.21 0.18 0.17 The former Yugoslav Republic of Macedonia 0.47 0.43 0.31 0.21 0.11 Timor-Leste 0.15 0.14 0.20 0.16 0.16 Togo 0.00 0.05 0.04 0.04 0.05 Tonga 0.00 0.00 0.05 0.07 0.17 Trinidad and Tobago 0.25 0.18 0.08 0.08 0.05 Tunisia 0.10 0.13 0.16 0.14 0.16 Turkey 0.47 0.40 0.35 0.26 0.34 Turkmenistan 0.37 0.22 0.17 0.08 0.03 Tuvalu 0.34 0.07 0.14 0.08 0.15 Uganda 0.00 0.04 0.04 0.00 0.00 Ukraine 0.39 0.39 0.31 0.21 0.11 United Arab Emirates 0.00 0.00 0.03 0.05 0.09 United Kingdom of Great Britain and Northern 0.09 0.21 0.24 0.27 0.28 Ireland United Republic of Tanzania 0.00 0.07 0.08 0.04 0.05 United States of America 0.24 0.26 0.29 0.29 0.28 Uruguay 0.43 0.40 0.30 0.24 0.23 Uzbekistan 0.22 0.09 0.11 0.05 0.02 Vanuatu 0.00 0.02 0.12 0.10 0.18 Venezuela (Bolivarian Republic of) 0.18 0.14 0.11 0.10 0.09 Viet Nam 0.14 0.19 0.24 0.20 0.20 Yemen 0.23 0.09 0.08 0.02 0.02 Zambia 0.04 0.09 0.07 0.07 0.08 Zimbabwe 0.09 0.12 0.12 0.11 0.14

99 Second-hand smoke: Assessing the burden of disease

Table A6: Population attributable fractions of asthma among women for active smoking, by country, 2004

PAFsm for asthma among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Algeria 0.42 0.09 0.01 0.00 0.00 Afghanistan 0.46 0.19 0.14 0.12 0.01 Albania 0.00 0.00 0.00 0.02 0.00 Andorra 0.13 0.06 0.00 0.00 0.00 Angola 0.39 0.00 0.02 0.00 0.00 Antigua and Barbuda 0.00 0.04 0.00 0.11 0.07 Argentina 0.12 0.17 0.10 0.08 0.10 Armenia 0.32 0.10 0.15 0.13 0.03 Australia 0.07 0.18 0.22 0.28 0.26 Austria 0.20 0.28 0.15 0.17 0.18 Azerbaijan 0.39 0.12 0.09 0.06 0.00 Bahamas 0.00 0.01 0.11 0.02 0.28 Bahrain 0.00 0.03 0.05 0.29 0.51 Bangladesh 0.19 0.10 0.08 0.11 0.19 Barbados 0.00 0.00 0.01 0.00 0.00 Belarus 0.00 0.00 0.01 0.01 0.00 Belgium 0.11 0.19 0.15 0.12 0.14 Belize 0.00 0.03 0.03 0.03 0.00 Benin 0.00 0.00 0.00 0.00 0.00 Bhutan 0.00 0.00 0.00 0.00 0.05 Bolivia (Plurinational State of) 0.00 0.03 0.06 0.05 0.00 Bosnia and Herzegovina 0.45 0.14 0.09 0.14 0.05 Botswana 0.00 0.03 0.00 0.00 0.00 Brazil 0.09 0.05 0.09 0.08 0.04 Brunei Darussalam 0.00 0.19 0.41 0.36 0.26 Bulgaria 0.20 0.12 0.07 0.06 0.03 Burkina Faso 0.00 0.00 0.03 0.07 0.01 Burundi 0.00 0.00 0.00 0.01 0.00 Cambodia 0.22 0.10 0.07 0.12 0.08 Cameroon 0.00 0.00 0.00 0.00 0.00 Canada 0.29 0.34 0.38 0.41 0.37 Cape Verde 0.00 0.00 0.00 0.00 0.00 Central African Republic 0.00 0.00 0.00 0.00 0.00 Chad 0.00 0.00 0.00 0.00 0.00 Chile 0.00 0.05 0.10 0.13 0.18 China 0.04 0.01 0.03 0.09 0.06 Colombia 0.03 0.04 0.12 0.15 0.06 Comoros 0.00 0.00 0.00 0.00 0.00 Congo 0.00 0.00 0.00 0.00 0.00 Cook Islands 0.16 0.00 0.03 0.03 0.14 Costa Rica 0.00 0.00 0.04 0.11 0.14 Côte d’Ivoire 0.01 0.00 0.00 0.00 0.00 Croatia 0.28 0.21 0.17 0.14 0.10 Cuba 0.23 0.30 0.26 0.27 0.29 Cyprus 0.02 0.02 0.05 0.06 0.00 Czech Republic 0.07 0.25 0.20 0.21 0.18 Democratic People’s Republic of Korea 0.12 0.09 0.13 0.16 0.11 Democratic Republic of the Congo 0.00 0.00 0.00 0.00 0.00

100 Annex Table A6 (continued)

PAFsm for asthma among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Denmark 0.17 0.40 0.40 0.44 0.35 Djibouti 0.02 0.00 0.00 0.00 0.00 Dominica 0.55 0.29 0.22 0.12 0.21 Dominican Republic 0.09 0.08 0.16 0.17 0.12 Ecuador 0.04 0.00 0.03 0.06 0.02 Egypt 0.21 0.02 0.00 0.00 0.00 El Salvador 0.00 0.00 0.01 0.01 0.00 Equatorial Guinea 0.00 0.00 0.00 0.00 0.00 Eritrea 0.00 0.00 0.00 0.00 0.00 Estonia 0.28 0.14 0.15 0.16 0.08 Ethiopia 0.00 0.00 0.02 0.05 0.02 Fiji 0.00 0.00 0.00 0.00 0.00 Finland 0.00 0.12 0.10 0.13 0.14 France 0.33 0.24 0.11 0.10 0.11 Gabon 0.00 0.02 0.00 0.00 0.00 Gambia 0.00 0.00 0.00 0.00 0.00 Georgia 0.12 0.01 0.03 0.00 0.00 Germany 0.17 0.25 0.17 0.17 0.16 Ghana 0.00 0.00 0.00 0.00 0.00 Greece 0.02 0.09 0.08 0.12 0.18 Grenada 0.00 0.11 0.15 0.08 0.00 Guatemala 0.03 0.01 0.07 0.12 0.10 Guinea 0.08 0.00 0.00 0.00 0.00 Guinea-Bissau 0.00 0.00 0.00 0.00 0.00 Guyana 0.01 0.04 0.00 0.00 0.00 Haiti 0.00 0.00 0.00 0.00 0.00 Honduras 0.09 0.02 0.11 0.16 0.14 Hungary 0.42 0.47 0.31 0.27 0.30 Iceland 0.28 0.33 0.40 0.40 0.39 India 0.00 0.00 0.00 0.00 0.00 Indonesia 0.29 0.10 0.15 0.17 0.24 Iran (Islamic Republic of) 0.00 0.00 0.00 0.00 0.00 Iraq 0.14 0.16 0.13 0.15 0.11 Ireland 0.11 0.22 0.32 0.40 0.36 Israel 0.07 0.10 0.11 0.16 0.16 Italy 0.13 0.13 0.10 0.13 0.16 Jamaica 0.00 0.04 0.08 0.09 0.05 Japan 0.00 0.08 0.07 0.13 0.31 Jordan 0.03 0.01 0.02 0.02 0.08 Kazakhstan 0.33 0.12 0.18 0.17 0.04 Kenya 0.00 0.00 0.00 0.00 0.00 Kiribati 0.00 0.00 0.00 0.26 0.00 Kuwait 0.00 0.00 0.00 0.08 0.08 Kyrgyzstan 0.17 0.04 0.06 0.04 0.00 Lao People’s Democratic Republic 0.16 0.17 0.15 0.14 0.18 Latvia 0.00 0.06 0.04 0.09 0.06 Lebanon 0.00 0.00 0.01 0.00 0.00 Lesotho 0.00 0.00 0.00 0.00 0.00 Liberia 0.00 0.00 0.00 0.00 0.00 Libyan Arab Jamahiriya 0.02 0.00 0.00 0.00 0.00 Lithuania 0.03 0.08 0.03 0.05 0.08 Luxembourg 0.10 0.21 0.22 0.17 0.15

101 Second-hand smoke: Assessing the burden of disease

PAFsm for asthma among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Madagascar 0.00 0.00 0.00 0.00 0.00 Malawi 0.00 0.00 0.00 0.00 0.00 Malaysia 0.11 0.09 0.20 0.26 0.26 Maldives 0.16 0.27 0.44 0.55 0.55 Mali 0.00 0.00 0.00 0.00 0.00 Malta 0.00 0.03 0.07 0.03 0.00 Marshall Islands 0.55 0.15 0.06 0.08 0.23 Mauritania 0.00 0.00 0.00 0.00 0.00 Mauritius 0.17 0.03 0.03 0.07 0.04 Mexico 0.00 0.00 0.05 0.09 0.14 Micronesia (Federated States of) 0.00 0.00 0.00 0.00 0.00 Monaco 0.00 0.11 0.06 0.07 0.08 Mongolia 0.08 0.07 0.25 0.38 0.28 Morocco 0.00 0.00 0.00 0.00 0.00 Mozambique 0.00 0.00 0.00 0.00 0.00 Myanmar 0.27 0.23 0.23 0.23 0.26 Namibia 0.00 0.00 0.00 0.00 0.00 Nauru 0.28 0.23 0.18 0.21 0.00 Nepal 0.06 0.01 0.02 0.01 0.00 Netherlands 0.33 0.41 0.31 0.29 0.18 New Zealand 0.21 0.27 0.33 0.30 0.25 Nicaragua 0.00 0.03 0.05 0.06 0.03 Niger 0.00 0.00 0.00 0.00 0.00 Nigeria 0.00 0.00 0.00 0.00 0.00 Niue 0.27 0.03 0.00 0.02 0.16 Norway 0.06 0.28 0.34 0.33 0.16 Oman 0.00 0.00 0.00 0.00 0.00 Pakistan 0.00 0.00 0.00 0.00 0.04 Palau 0.38 0.10 0.08 0.03 0.13 Panama 0.00 0.00 0.04 0.06 0.04 Papua New Guinea 0.00 0.00 0.00 0.00 0.00 Paraguay 0.00 0.00 0.05 0.04 0.00 Peru 0.11 0.01 0.05 0.10 0.06 Philippines 0.18 0.10 0.12 0.10 0.04 Poland 0.14 0.31 0.22 0.19 0.17 Portugal 0.09 0.06 0.03 0.04 0.05 Qatar 0.00 0.00 0.00 0.00 0.00 Republic of Korea 0.10 0.07 0.13 0.25 0.49 Republic of Moldova 0.15 0.11 0.10 0.04 0.00 Romania 0.15 0.13 0.09 0.10 0.03 Russian Federation 0.05 0.07 0.06 0.07 0.06 Rwanda 0.04 0.00 0.00 0.00 0.00 Saint Kitts and Nevis 0.00 0.00 0.00 0.01 0.00 Saint Lucia 0.00 0.00 0.12 0.04 0.01 Saint Vincent and the Grenadines 0.54 0.21 0.09 0.00 0.19 Samoa 0.08 0.00 0.00 0.00 0.00 San Marino 0.00 0.00 0.04 0.13 0.16 Sao Tome and Principe 0.00 0.08 0.11 0.19 0.13 Saudi Arabia 0.00 0.00 0.00 0.00 0.00 Senegal 0.00 0.00 0.00 0.00 0.00 Serbia and Montenegro 0.29 0.30 0.21 0.15 0.02

102 Annex Table A6 (continued)

PAFsm for asthma among women Country 30–44 years 45–59 years 60–69 years 70–79 years >80 years Seychelles 0.00 0.00 0.00 0.14 0.00 Sierra Leone 0.00 0.00 0.00 0.00 0.00 Singapore 0.08 0.09 0.11 0.27 0.43 Slovakia 0.22 0.14 0.09 0.10 0.11 Slovenia 0.00 0.24 0.15 0.20 0.21 Solomon Islands 0.00 0.00 0.00 0.00 0.00 Somalia 0.13 0.00 0.00 0.00 0.00 South Africa 0.17 0.09 0.09 0.12 0.09 Spain 0.21 0.12 0.03 0.02 0.03 Sri Lanka 0.00 0.00 0.00 0.00 0.00 Sudan 0.00 0.01 0.00 0.00 0.00 Suriname 0.00 0.06 0.09 0.05 0.04 Swaziland 0.00 0.00 0.00 0.00 0.00 Sweden 0.07 0.24 0.24 0.21 0.15 Switzerland 0.13 0.26 0.19 0.18 0.12 Syrian Arab Republic 0.00 0.00 0.00 0.00 0.00 Tajikistan 0.17 0.06 0.05 0.00 0.00 Thailand 0.22 0.18 0.18 0.18 0.17 The former Yugoslav Republic of Macedonia 0.08 0.08 0.05 0.07 0.04 Timor-Leste 0.04 0.05 0.11 0.13 0.15 Togo 0.00 0.00 0.00 0.00 0.00 Tonga 0.38 0.10 0.03 0.05 0.21 Trinidad and Tobago 0.02 0.00 0.00 0.00 0.13 Tunisia 0.00 0.00 0.00 0.00 0.00 Turkey 0.16 0.00 0.05 0.09 0.04 Turkmenistan 0.22 0.03 0.04 0.03 0.00 Tuvalu 0.55 0.13 0.06 0.08 0.22 Uganda 0.00 0.00 0.00 0.03 0.01 Ukraine 0.05 0.05 0.04 0.04 0.00 United Arab Emirates 0.00 0.00 0.02 0.05 0.10 United Kingdom of Great Britain and Northern 0.09 0.28 0.32 0.40 0.36 Ireland United Republic of Tanzania 0.00 0.00 0.00 0.00 0.00 United States of America 0.26 0.31 0.40 0.45 0.42 Uruguay 0.30 0.17 0.07 0.05 0.06 Uzbekistan 0.28 0.03 0.05 0.01 0.00 Vanuatu 0.33 0.12 0.00 0.04 0.11 Venezuela (Bolivarian Republic of) 0.17 0.15 0.13 0.16 0.18 Viet Nam 0.12 0.07 0.11 0.12 0.18 Yemen 0.08 0.01 0.03 0.00 0.00 Zambia 0.00 0.00 0.00 0.00 0.00 Zimbabwe 0.04 0.00 0.05 0.07 0.04

103 Second-hand smoke: Assessing the burden of disease

104