(2016) Ambient Air Pollution: a Global Assessment of Exposure And

Ambient air pollution: A global assessment of exposure and burden of disease

Content

List of tables 7 List of figures7 List of annexes 9 Preface 11 Abbreviations 13 Summary 15 1. Introduction 19 2. Exposure to ambient air pollution 23

2.1. Exposure : ground measurements of PM10 and PM2.5 23 2.1.1. Methods 23 2.1.2. Results 25 2.1.3. Discussion 31

2.2 Exposure : modelled estimates of PM2.5 32 2.2.1. Methods 32 2.2.2. Results 32 2.2.3. Discussion 37 3. Burden of disease attributable to ambient air pollution 39 3.1. Methods 39 3.1.1. Source of datas 39 3.1.2. Estimation of the disease burden 39 3.1.3. Uncertainty analysis 40

3.2. Results 40

3.3. Discussion 47 4. Conclusion and way forward 49 References 51 Acknowledgment 55

Annex 1. Modelled population exposure to particulate

matter (PM2.5), by country 57

Annex 2. Deaths, YLL’s and DALY’S Attributable to Ambient Air Pollution, by country 63

List of tables

Table 1 : Ambient air pollution database : Proportion of settlements by population size

Table 2 : Total number of towns and cities in AAP database, 2016 version, by region

Table 3 : Number of cities included for the PM2.5 and PM10 comparison over a five-year period (mostly 2008-2013), by region

Table 4 : Trend for the five-year period (mostly 2008-2013) in PM2.5 or PM10 based on cities available in several versions of the database, by region

Table 5 : Deaths attributable to AAP in 2012, by disease, age and sex

Table 6 : Disability-adjusted life years (DALYs) attributable to AAP in 2012, by disease, age and sex

Table 7 : Population attributable fraction for disability-adjusted life years attributable to AAP in 2012, by disease, age, sex and region

Table A1 : Annual median concentration of particulate matter of an aerodynamic diameter

of 2.5 μm or less (PM2.5) with lower and upper bound, population-weighted and modelled, by area and country

Table A2.1 : Deaths attributable to AAP in 2012 in both sex, by disease and country

Table A2.2 : Deaths attributable to AAP in 2012 in women, by disease and country

Table A2.3 : Deaths attributable to AAP in 2012 in men, by disease and country

Table A2.4 : Years of life lost (YLLs) attributable to AAP in 2012 in both sex, by disease and country

Table A2.5 : Years of life lost (YLLs) attributable to AAP in 2012 in women, by disease and country

Table A2.6 : Years of life lost (YLLs) attributable to AAP in 2012 in men, by disease and country

Table A2.7 : Disability-adjusted life years (DALY) attributable to AAP in 2012 in both sex, by disease and country

Table A2.8 : Disability-adjusted life years (DALY) attributable to AAP in 2012 in women, by disease and country

Table A2.9 : Disability-adjusted life years (DALY) attributable to AAP in 2012 in men, by disease and country

List of figures

Figure 1 : Number of towns and cities with accessible PM10 and PM2.5 data in 2016 per urban population

Figure 2 : Location of the monitoring stations and PM2.5 concentration in nearly 3 000 human settlements, 2008-2015

7 Figure 3 : PM10 levels by region and city size, for available cities and towns latest year in the period 2008-2015

Figure 4 : PM10 levels for selected cities by region, for the last available year in the period 2011-2015

Figure 5 : PM10 levels for available mega-cities of more than 14 million habitants for the last available year in the period 2011-2015

Figure 6 : Annual mean particulate matter concentration of the assessed towns and cities compared to the WHO Air Quality Guidelines a

Figure 7 : Percentage of cities with increasing and decreasing PM2.5 or PM10 annual means over a five-year period (mostly 2008-2013), by region

Figure 8 : Percentage of city population experiencing increasing and decreasing PM2.5 or PM10 annual means over a five-year period (mostly 2008-2013), by region

3 Figure 9 : Global map of modelled annual median concentration of PM2.5, in μg / m

3 Figure 10 : Annual median exposure to ambient (outdoor) PM2.5 in μg / m , by region - urban and rural population, 2014

3 Figure 11 : Annual median exposure to ambient (outdoor) PM2.5 in μg / m , by region - urban population only, 2014

Figure 12 : Median PM2.5 concentration, by geographic region – urban and rural areas combined, 2014

Figure 13 : Modelled annual median particulate matter concentration compared to the WHO Air Quality Guidelines (AQG)

Figure 14 : Deaths attributable to AAP in 2012, by disease and region

Figure 15 : Age-standardized deaths per capita attributable to AAP in 2012, by disease and region

Figure 16 : Deaths attributable to AAP in 2012, by country

Figure 17 : DALYs attributable to AAP in 2012, by country

Figure 18 : Age-standardized deaths per 100 000 capita attributable to AAP in 2012, by country

Figure 19 : Age-standardized DALYs per 100 000 capita attributable to AAP in 2012, by country

Figure 20 : Deaths attributable to AAP in 2012, by disease

List of annexes

Annex 1 : Modelled population exposure to particulate matter of PM2.5, by country

Annex 2 : Deaths, YLLs and DALYs attributable to ambient air pollution, by country

Preface

This report presents a summary of methods Air pollution affects practically all countries in the and results of the latest World Health Organi- world and all parts of society. zation (WHO) global assessment of ambient air pollution exposure and the resulting burden The role of the health sector is crucial, and there of disease. is a need to engage with other sectors to maxi- mize the co-benefits of health, climate, environ- Air pollution has become a growing concern in ment, social and development. Saving people’s the past few years, with an increasing number lives is the overarching aim to implement policies of acute air pollution episodes in many cities aiming at tackling air pollution in the health, trans- worldwide. As a result, data on air quality is port, energy, and urban development sectors. becoming increasingly available and the science underlying the related health impacts is Dr Maria Neira also evolving rapidly. Director of Public Health, Environmental and Social Determinants of Health To date, air pollution – both ambient (outdoor) and household (indoor) – is the biggest envi- World Health Organization ronmental risk to health, carrying responsibility for about one in every nine deaths annually. Ambient (outdoor) air pollution alone kills around 3 million people each year, mainly from noncommunicable diseases. Only one person in ten lives in a city that complies with the WHO Air quality guidelines. Air pollution continues to rise at an alarming rate, and affects economies and people’s quality of life ; it is a public health emergency.

Interventions and policies for tackling air pollution issues exist and have been proven to be effective. The implementation of WHO resolution WHA68.8, which maps out a road map for enhanced global responses to the adverse effects of air pollution, provides an essential framework for decision-makers to choose and implement the most efficient policies.

Air pollution has also been identified as a global health priority in the sustainable development agenda. WHO has responsibility for stewarding three air pollution-related indicators for monitoring progress against the Sustainable Develop- ment Goals (SDGs) : in health (Goal 3), in cities (Goal 11) and in energy (Goal 7).

Abbreviations

AAP ambient air pollution AFR Africa Region ALRI acute lower respiratory disease AMR Americas Region AQG Air quality guidelines BoD burden of disease COPD chronic obstructive pulmonary disease DALY disability-adjusted life year DIMAQ data integration model for air quality EMR Eastern Mediterranean Region EUR European Region GBD global burden of disease HAP household air pollution HIC high-income countries IER integrated exposure risk IHD ischeamic heart disease LMIC low-and middle-income countries PAF population attributable fraction

3 PM2.5 particulate matter of diameter of less than 2.5 μm in μg / m

3 PM10 particulate matter of diameter of less than 10 μm in μg / m RR relative risk SDGs Sustainable Development Goals SEAR South East Asia Region UN United Nations YLD year lived with disability YLL year of life lost WHO World Health Organization WPR Western Pacific Region

Summary

Air pollution represents the biggest environ- In 2015, to respond to this major global public mental risk to health. In 2012, one out of every health threat, the 194 WHO Member States adop- nine deaths was the result of air pollution-related the first World Health Assembly resolution to ted conditions. Of those deaths, around 3 mil- “ address the adverse health effects of air pollu- lion are attributable solely to ambient (outdoor) tion ”. The following year, Member States agreed air pollution. Air pollution affects all regions, on a road map for “ an enhanced global response settings, socioeconomic groups, and age to the adverse health effects of air pollution ”. groups. While all people living in a given area breathe from the same air, there are never- Among the main elements of this road map are theless important geographical differences in the monitoring and reporting of air pollution, and exposure to air pollution. Citizens in Africa, Asia enhanced systems, structures and processes or the Middle East breathe much higher levels for monitoring and reporting health trends asso- of air pollutants that those in living other parts of ciated with air pollution and its sources. the world. Some places have air pollution levels that are several times higher than those consi- The current report presents methods and data dered safe by the World Health Organization for two of the SDG indicators reported by WHO : (WHO) Air quality guidelines.

·· SDG Indicator 11.6.2 : Annual mean Air pollution is used as a marker of sustai- levels of fine particulate matter (PM ) in nable development, as sources of air pollu- 2.5 cities (population-weighted) ; and tion also produce climate-modifying pollutants

(e.g. CO2 or black carbon). Policies to address air pollution also generate a range of bene- ·· SDG Indicator 3.9.1 : Mortality rate attribu- fits to human health, not only through air quality ted to household and ambient air pollution. improvements but also other health benefits, such as injury prevention or enabling physical activity. This report presents the updated results of mortality and morbidity attributed to ambient air pollu- Consequently, concerns about air pollution are tion (also known as ‘ burden of disease due to air reflected in the Sustainable Development Goals pollution ’). It includes information on the sources (SDGs) : Air pollution levels in cities is cited as of data on air pollution available to the WHO, an indicator for urban sustainable development on the methodology used to estimate human (SDG 11) ; access to clean energy – particular- exposure to air pollution and related burden of ly clean household fuels and technologies – is disease, as well as the actual estimates of human highlighted as an indicator for sustainable en- exposure to particulate matter of a diameter less ergy (SDG 7) ; and mortality due to air pollution than 2.5 micrometres (PM2.5) for countries and (ambient and household) is used an indicator for for the globe ; and the related national burden the health SDG goal (SDG 3). Reliable estimates of disease attributable to long-term exposure to of exposure to and impacts from air pollutants ambient (outdoor) air pollution for the year 2012. are key to better inform policy-makers, as well as development partners. These figures are neces- The global exposure assessment to air pollu- sary to track progress in air quality improvements tion consists of modelled data of population- and to help evaluate the effectiveness of policies weighted annual mean PM2.5 concentrations. aiming at reducing air pollution, as well as as- Data are modelled through combining remote sessment of how much they are contributing to satellite sensing data with ground measure- protect health. ments from the 2016 WHO ambient (outdoor) air quality database, which serves for calibration of the satellite data.

15 This database compiles information on PM2.5 1 and PM10 from measurements for about 3000 cities and towns worldwide. The modelled estimates indicate that in 2014 only about one in ten people breathe clean air, as defined by the WHO Air quality guidelines.

The burden of disease attributed to ambient air pollution was estimated using methodology developed for the Comparative Risk Assessment study. This methodology is based on combining estimates of exposure to air pollution and its distribution in the population, with results from epidemiological studies that indicate the additional disease burden from different levels of exposure to air pollution, after adjusting for other risk factors (e.g. tobacco smoke).

The results are refered to by epidemiologists as exposure-risk estimates at each level of exposure. Health outcomes, for which there is enough epidemiological evidence to be included in the analysis, comprise acute lower respiratory, chronic obstructive pulmonary disease, stroke, ischeamic heart disease and lung cancer. Many other diseases have been associated with air pollution, but are not included in this assessment because the evidence was not considered sufficiently robust.

The actual impact of air pollution on health presented here is a conservative figure, as it does not include the separate impacts of health from other air pollutants such as nitrogen oxides (NOx) or ozone (O3), and excludes health impacts where evidence is still limited (e.g. pre-term birth or low birth weight).

Through this combined approach, the current report summarizes three sets of data and the interrelationship between them : 1) The compi- lation of the PM2.5 and PM10 measurements data in the 2016 WHO ambient (outdoor) air quality database ; 2) the modelled estimates of PM2.5 ; and 3) the associated burden of disease.

1 Particulate matter of a diameter less than 25 and 10 micrometres respectively.

1. Introduction

This document summarizes the methods and the years (5). A framework of indicators for the 17 results of the latest WHO global assessment of air SDGs and 169 SDG-related targets has been pollution exposure and related burden of disease. developed, and includes three air pollution-related indicators, which fall under WHO’s reporting Exposure to air pollutants can affect human responsibility : health in various ways, leading to increased mortality and morbidity (1). Epidemiological evidence ·· SDG Indicator 3.9.1 : Mortality rate attri- on the health effects of air pollution is growing buted to household and ambient air and evolving quickly. Today, air pollution is the pollution for the health goal (SDG 3). largest environmental risk factor (2). ·· SDG Indicator 11.6.2 : Annual mean levels Reliable estimates of exposure to air pollu- of fine particulate matter (PM ) in cities tants and related impacts on health are key to 2.5 (population-weighted) for the urban sus- better inform policy-makers, as well as other tainable development goal (SDG 11). health and development partners. Such information is essential to implementing, monitoring and evaluating policies that help to tackle air ·· SDG Indicator 7.1.2 : Proportion of popu- pollution while also protecting health. lation with primary reliance on clean fuels and technologies for the sustainable The 194 WHO Member States recently adop- energy goal (SDG 3). ted a resolution to respond to the adverse health effects of air pollution (3), as well as a Reporting for Indicators 3.9.1 and 11.6.2 are road map for an enhanced global response (4). based on the methods and results presented The road map outlines goals and approaches in the current document, while the methods for for monitoring and reporting of air pollution, estimating SDG Indicator 7.1.2. are presented as well as enhancing systems, structures and elsewhere (6). processes necessary to support monitoring and reporting on health trends associated with air While a number of air pollutants are associa- pollution and its sources. ted with significant excess mortality or morbidity, including NOx, ozone, carbon monoxide Air pollution is a clear marker for sustainable deve- and sulfur dioxides in particular, PM2.5 is the air lopment, as sources of air pollution also produce pollutant that has been most closely studied climate pollutants (like CO2 or black carbon). and is most commonly used as proxy indicator of exposure to air pollution more generally (1). Policies to address air pollution generate a number of benefits to human health, not only through Particulate matter consists of a complex mixture air quality improvements but also other health of solid and liquid particles of organic and inor- benefits, such as injury prevention or enabling ganic substances suspended in the air. The ma- physical activity. Adressing air pollution there- jor components of PM are sulphates, nitrates, fore features highly in the global agenda. ammonia, sodium chloride, black carbon, mine- ral dust and water. The most health-damaging In September 2015, the United Nations (UN) particles are those with a diameter of 10 μm or General Assembly adopted Transforming our less, which can penetrate and lodge deep in- world : The 2030 Agenda for sustainable deve- side the lungs. Both short- and long-term expo- lopment, which paves the way for a renewed sure to air pollutants have been associated with global commitment to combat the world’s most health impacts. persistent development issues over the next 15

19 Small particle pollution has health impacts even Coverage of the entire exposure range for long- at very low concentrations – indeed no threshold term exposure to PM2.5 has been achieved has been identified below which no damage through the development of the integrated ex- to health is observed. Therefore, the WHO Air posure risk (IER) functions (16) that have been quality guidelines (AQGs) recommend aiming developed for the Global Burden of Disease for, and achieving, the lowest concentrations of Study (17). PM possible (1). The guideline values are : This allows for the integration of available rela-

PM2.5 PM10 tive risks information from studies of ambient air 10 µg / m3 annual mean 20 µ / m3 annual mean pollution, household air pollution, second-hand 25 µg / m3 24-hour mean 50 µg / m3 24-hour mean smoke and active smoking. The IERs have been used to derive estimates for both household- Reflecting the rising number of air pollution and ambient air pollution-attributable burden of episodes in cities worldwide – and the resulting disease for the GBD Project (12, 14, 18) and by increased awareness about its impacts on health the WHO (2, 6).

- PM2.5 is increasingly measured and monitored by national air quality monitoring networks. WHO They currently cover cause-specific mortality has been compiling annual mean concentrations such as acute lower respiratory in children under for PM2.5 and PM10 in cities since 2011 (7,8). five years of age, and chronic obstructive pulmonary disease (COPD), ischeaemic heart disease A summary of a recent update of the WHO (IHD), stroke and lung cancers in adults. ambient (outdoor) air quality database (9) containing average annual estimates for PM2.5 Other health outcomes have been associated exposure globally, by region and for 3 000 spe- with long-term exposure to particulate matter, cific human settlements is presented as part of such as adverse birth outcomes, childhood this report. respiratory disease, diabetes, atherosclerosis, and neurodevelopment and cognitive function, The present work on global exposure modelling but are not considered here (19). further develops recent innovations in global exposure estimates for PM2.5 (10,11), as used to The current report presents the latest national model global burden of disease attributable to mortality and morbidity estimates attributable to ambient air pollution (2, 12–14). ambient air pollution.

It was also presented and reviewed at the Global Platform on Air Quality and Health, ini- tiatied by WHO and other partners in 2014 (15).

Academic researchers, scientists and interna- tional organizations have combined efforts to harmonize in developing comprehensive and reliable models of population exposure to PM2.5, which can be used for health impact estimation.

2. Exposure to ambient air pollution

2.1. Exposure : ground measurements of PM10 and PM2.5 2.1.1. Methods Data sources Description of methods Primary sources of data include official reporting from countries to WHO, official national / The database compiles ground measurements sub-national reports and national/sub-national of annual mean concentrations of particulate web sites containing measurements of PM10 matter of a diameter of less than 10 μm (PM ) 10 or PM2.5. Furthermore, measurements reported or 2.5 μm (PM2.5) and aims to compile an ave- by the following regional networks were used : rage for each city / town. Years of measurements Clean Air Asia for Asia (20), and the Air quality range from 2010 to 2015, unless the latest avai- e-reporting database (21) from the European lable data precede these times, in which case Environment Agency for Europe. In the absence the most recent was used. of the above-mentioned data, data from UN agencies, development agencies, articles from peer Scope of data reviewed journals and ground measurements compiled in the framework of the Global Burden of The database covers 3 000 human settlements Disease Project (17) were used. 2 ranging in size from a few hundred to more than 10 million inhabitants. Most of these are urban The data was compiled between Septem- areas of 20 000 inhabitants or more – hence ber–November 2015 from the above-mentio- reference to an “ urban air quality data base ”. ned sources / partners, or from web searches However, about 25 % of settlements in the data- when not availbable. The web search strategy base are smaller areas of up to 20 000 residents, included the following approaches : and a limited proportion (mostly in Europe) are settlements of only a few hundred to a few thou- 1. Screening of the web sites of respective minis- sand inhabitants - although these may also be tries of environment, health, and statistics offices. located in proximity to larger urban agglomera- tions (see Table 1). 2. Web searches with the terms « air quality », « air pollution », « suspended particles », « moni- Table 1. Ambient air pollution database : Proportion toring », « PM », « PM ». of settlements by population size 10 2.5

Language variations used included English, Percentages Settlement size French, Spanish, Portuguese, Italian, and German. of observations 1 % 0 Type of data used 5 % 1 240 10 % 4 173 The database incorporated annual mean

25 % 20 000 concentrations of particulate matter (PM10 or PM ) based on daily measurements, or other 41 % 50 000 2.5 data that could be aggregated into annual mean 50 % 80 756 values. In the absence of annual means, mea- 75 % 314 719 surements covering a more limited period of the 90 % 1 150 153 year were exceptionally used and extrapolated. 95 % 2 313 328 99 % 8 741 365 Mean settlement size 543 664 2 For the full list of references, please refer to the online version Number of settlements 2977 of the WHO database (9).

23 In order to derive air quality that is largely repre- Population data - used for weighting and for sentative of human exposure in urban contexts, estimating the share of urban population co- mainly data from urban background readings, vered - were either based on : UN population residential areas, and commercial (or mixed) statistics when available for all human settle- area measurements were used. Air quality sta- ments covered (22), or census data from national tions characterized as covering particular ‘ hot statistical offices(23) . spots ’ or exclusively industrial areas were not included, unless they were contained in repor- For completeness, cities with only PM10 (or resp. ted city means and could not be disaggregated. PM2.5) reported, PM2.5 (or PM10) concentration

was calculated from PM10 (resp. PM2.5) using

This selection is in accordance with the aim national conversion factors (PM2.5 / PM10 ratio) of capturing representative values for average either provided by the country or estimated as human exposure. In contrast, measurements population-weighted averages of urban-speci- from hot spots and industrial areas are often fic conversion factors for the country. Urban- captured for the purpose of identifying the specific conversion factors were estimated as highest exposure areas, and were deemed to the mean ratio of PM2.5 to PM10 of stations for the be less representative of mean exposures for same year. If national conversion factors were most of the urban population. Hot spots were not available, regional ones were used, which either designated as such by the original were obtained by averaging country-specific reports, or were qualified as such due to their conversion factors. exceptional nature (e.g. exceptionally busy roads etc.). Omitting these, may, however, have As exposure to PM2.5 serves as indicator for led to an underestimation of the mean air pollu- burden of disease calculation, PM2.5 data were tion levels of a given city. converted from PM10 in the absence of their measurement because they were used to cali- Where the data from various sources were brate the model described below. available, only the latest data and most reliable sources were used. Only data measured since As the conversion factor PM2.5 / PM 10 may vary 2008 were included in the database. according to location (generally between 0.4 and

0.8), the converted PM10 value for individual sett- It was not always possible to retrieve or use lements may deviate from the actual value, and all publicly available data of interest. Reasons should only be considered as approximations. included language barriers, or incomplete information on data or data quality (e.g. missing year The temporal coverage represents the number of reference). Data were used as presented in of days per year covered by measurements, original sources. The indicated numbers of or any alternative qualification (as provided in monitors do not necessarily correspond to the the original sources). If data from several moni- total number of existing or operational stations toring stations in one city or town were available, in the cities, but to the numbers of stations used their average temporal coverage was used for for deriving the indicated city data. the overall average. Information on temporal coverage was not always available ; however Data processing and reporting reporting agencies often respect a specific reporting threshold for the number of days cove- Where available, urban means reported by red before reporting on a given measurement va- the original sources are included in the data- lue, or using it for estimating the city annual mean. base. Where no urban means were available, the eligible city data were averaged using appropriate denominators.

24 2.1.2. Results Data availability

The 2016 version of the WHO ambient (outdoor) air quality database consists mainly of urban air quality data - annual means for PM10 and / or PM2.5 - covering about 3 000 human settlements in 103 countries, for the years 2008-2015 (9). The regional distribution and the number of settlements, and corresponding accessible data are described in Table 2 and Figure 1, respectively.

Table 2 : Total number of towns and cities in AAP database, 2016 version, by region

Number Number of countries Total number of Region of towns / cities with data countries in region Africa (Sub-Saharan) (LMIC) 39 10 47 Americas (LMIC) 102 13 24 Americas (HIC) 524 6 11 Eastern Mediterranean (LMIC) 53 8 15 Eastern Mediterranean (HIC) 31 6 6 Europe (LMIC) 165 9 19 Europe (HIC) 1 549 33 34 South-East Asia (LMIC) 175 9 11 Western Pacific (LMIC) 225 4 21 Western Pacific (HIC) 109 5 6 Global 2 972 103 194

AAP : Ambient air pollution database ; LMIC : Low-and middle-income countries ; HIC : high-income countries.

Figure 1 : Number of towns and cities with accessible PM10 and PM2.5 data in 2016 per urban population.

Nr of towns and cities per 1 million urban inhabitants PM10 3.0

PM2.5

2.5

2.0

1.5

1.0

0.5

0.0 Afr Amr Amr Emr Emr Eur Eur Sear Wpr Wpr (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC)

PM10 / 2.5 : Fine particulate matter of 10 / 2.5 microns or less ; Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia ; Wpr: Western Pacific; LMIC : low- and middle-income countries ; HIC : high-income countries.

25 PM2.5 measurements can be directly linked to In high-income countries (HIC), 1 241 cities and estimates of health risks, and are therefore of towns with PM2.5 measures could be accessed, particular interest. In high-income countries, compared to 1639 cities and towns with PM10

PM2.5 measurements are conducted widely. measurements. In low- and middle-income countries (LMICs),

PM2.5 measures are not readily available in many The 3 000 cities and towns currently covered by countries but there have been large improve- the WHO database represent about 1.6 billion ments since in the past three years. people, or 43 % of the global urban population.

Annual mean PM2.5 measurements were avai- Ground measurements of annual mean concen- lable for 339 cities, or almost five times more tration of PM2.5 for the 3000 towns and cities is than in the 2014 version of the database. shown in Figure 2.

Annual mean PM10 measurements could be accessed in as many as 586 cities in LMICs.

Figure 2 : Location of the monitoring stations and PM2.5 concentration in nearly 3 000 human settlements, 2008-2015

PM2.5 : Fine particulate matter of 2.5 microns or less.

26 Data summary

An overview of PM10 levels for the WHO regions and selected cities is presented in Figures 3, 4 and 5.

Figure 3 : PM10 levels by region and city size, for available cities and towns latest year in the period 2008–2015

PM (μg/m3) 10 <100 000 habitants 300 100 000 - < 500 000 habitants

1 million - < 3 million habitants

1 million - < 3 million habitants 250 above 3 million habitants

all cities

200

150

100

Afr Amr Amr Emr Emr Eur Eur Sear Wpr WPr World World* (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC)

* regional urban pop weighted

PM10 : Particulate matter of 10 microns or less ; Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia ; Wpr : Western Pacific ; LMIC : low- and middle-income countries ; HIC : high-income countries. PM10 values for the world are regional urban population-weighted.

3 Figure 4 : PM10 levels for selected cities by region, for the last available year in the period 2011-2015

3 PM10 (μg/m )

400

350

300

250

200

150

100

0 Afr Amr Amr Emr Emr Eur Eur Sear Wpr Wpr (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC) Vereenigin g Pretoria Daka r Toront o Buenos Aire s Caracas Santiago Sao Paul o Mexico city Bogotá La Pa z Abu Dhab i Doha Ma'ameer Riyadh Beirut Casablanc a Tehran Greater Cairo Madrid London Berlin Roma Buchares t Belgrade Sofia Istanbu l Bangko k Colombo Dhak a Delh i Auckland Sydney Singapore Seoul Kuching Manila Beijin g Ulaanbaatar

PM10 : Particulate matter of 10 microns or less; Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia ; Wpr : Western Pacific ; LMIC : low- and middle-income countries ; HIC : high-income countries.

3 Selection criteria : For year of measurement (2011 or more recent), the largest city or capital for each country within a region (or two cities for one country if only two countries available in the region. City size ranges from 140 000 to 26 million habitants.

27 Figure 5 : PM10 levels for available mega-cities of more than 14 million habitants for the last available year in the period 2011-2015

3 PM10 (μg/m )

250

200

150

100

0 Delhi Sao Paulo Mexico city Cairo Buenos Aires Istanbul Shanghai Mumbai Beijing Dhaka Kolkata

PM10 : Particulate matter of 10 microns or less. Compliance with Air Quality Guidelines

Figure 6 shows the regional percentages of the assessed towns and cities with PM measurements experiencing PM10 or PM2.5 air pollution levels that meet or exceed the WHO Air Quality Guidelines (AQG) 3 3 4 (i.e. annual mean values of 20 µg / m (for PM10) and 10 µg / m (for PM2.5)). Globally, according to the currently available data, only 16 % of the assessed population is exposed to PM10or PM2.5 annual mean 3 levels complying with AQG levels. This increases to 27 % for the interim target 3 (i.e. IT-3 : 30 µg / m for PM10 3 3 3 and 15 µg / m for PM2.5) of the AQG, 46 % for interim target 2 (i.e. IT-2 : 50 µg / m for PM10 and 25 µg / m 3 3 for PM2.5), and 56 % for interim target 1 (IT-1 : 70 µg / m for PM10 and 35 µg / m for PM2.5). 1.2 Figure 6 : Annual mean particulate matter concentration of the assessed towns and cities, compared to the WHO Air Quality Guidelinesa exceed AQG

below AQG % of cities assessed

100%

80%

60%

40%

20%

0% Amr Wpr Eur Amr Eur Afr Emr Sear WPr Emr (HIC) (HIC) (HIC) (LMIC) (LMIC) (LMIC) (LMIC) (HIC) Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean; Eur : Europe ; Sear : South-East Asia ; Wpr : Western Pacific; LMIC: low- and middle-income countries; HIC : high-income countries ; AQG : WHO Air Quality Guidelines. a 3 3 Annual mean PM10 : 20 µg / m ; Annual mean PM2.5 : 10 µg / m .

4 For town and cities with both PM10 and PM2.5 values, PM2.5 were used. 28 Comparison of urban air pollution levels in recent years

A total of 792 towns and cities in 67 countries were selected for a more refined comparison of2.5 PM

(where available), or PM10 values over a period of five or more years (Table 3). The selection was made according to the following criteria: These cities/towns have either measured PM.2.5 or PM10 values in the three databases, and cover a period of three years or more; or these towns/cities are represented in the data that was collected in 2011, 2014 or 2016 and cover a period of four years or more. The 2011 version of the database contains data for 2010 or earlier and the 2014 version for 2012 or earlier. To compare levels of air pollution for the equivalent of a five-year period (mostly 2008–2013) for cities included in such a selection, a linear regression was made. Table 4 presents a regional summary by WHO region and income groups. Globally, annual PM levels are estimated to have increased by 8 % during the recent five-year period in the assessed cities. (The cited global increase is weighted by the regional urban population)

Table 3 : Number of cities included for the PM2.5 and PM10 comparison over a five-year period (mostly 2008-2013), by region Region Number of town and cities Number of countries Africa (Sub-Saharan) 1 2 2 America (LMIC) 13 7 America (HIC) 343 6 Eastern Mediterranean (LMIC) 16 6 Eastern Mediterranean (HIC) 6 2 Europe (LMIC) 32 5 Europe (HIC) 277 30 South-East Asia 53 5 Western Pacific (LMIC) 33 2 Western Pacific (HIC) 21 3 Global 796 68

LMIC : Low- and middle-income countries ; HIC : High-income countries. Regions with less than five cities were not included in the analysis, due to poor representatation.

Figure 7 shows the percentage of towns and cities with decreasing levels of annual mean PM2.5 or PM10 (in green), increasing levels (in light orange), and levels with changes of ≤10 % over a five-year period (in blue), by region. The variation in population living in cities with increasing or decreasing population levels is represented in Figure 8.

Table 4 : Trend for the five-year period (mostly 2008-2013) in PM2.5 or PM10 based on cities available in several versions of the database, by region 1 Region Trend over the mean period 2008-2013 2 Africa (Sub-Saharan) NA America (LMIC) America (HIC) Eastern Mediterranean (LMIC) Eastern Mediterranean (HIC) Europe (LMIC) Europe (HIC) South-East Asia Western Pacific (LMIC) Western Pacific (HIC) Global 3

1 Criteria for inclusion : cities with measured PM2.5 or PM10 values in the three database versions covering a period of three years or more, or in two versions and covering a period of four years or more. 2 : No more than 5 % change over the five-year period ; : More than 5 % decrease over the five-year period ; : More than 5 % increase over the five-year period. 3 Based on weighting by regional urban population. LMIC : Low- and middle-income countries ; HIC : high-income countries ; NA : not available. Results are based on 795 cities and are to be interpreted with caution, as : a) cities included might not ensure representativeness ; a) yearly variations due for example to climatic changes can be important ; and c) a five-year comparison does not necessarily represent trends, in particular when changes are limited. 1 Figure 7 : Percentage of cities with increasing and decreasing PM2.5 or PM10 annual means over a five-year period (mostly 2008–2013), by region

% cities increasing

Limited change (≤10% over 5 years) 1.2 % cities decreasing

100%

80%

60%

40%

20%

0% Eur Amr Eur Wpr Amr Emr Sear Emr WPr World* (LMIC) (LMIC) (HIC) (HIC) (HIC) (LMIC) (53) (HIC) (LMIC) (792) (30) (13) (277) (21) (343) (15) (6) (32)

Amr : Americas ; Emr : Eastern Mediterranean; Eur : Europe ; Sear : South-East Asia, Wpr : Western Pacific ; LMIC : Low- and middle-income countries ; HIC : high-income countries. 1 The number of cities is indicated in bracket. * The world figure is regional population-weighted.

Figure 8 : Percentage of city population experiencing increasing and decreasing PM2.5 or PM10 annual means over a five-year period (mostly 2008-2013), by region

with increasing air pollution

1.2 Limited change (≤10% over 5 years) in air pollution

with decreasing air pollution

100%

80%

60%

40%

20%

0% Eur Amr Wpr Amr Eur Emr Sear WPr Emr World (LMIC) (HIC) (HIC) (LMIC) (HIC) (LMIC) (LMIC) (HIC)

Amr : Americas ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia, Wpr : Western Pacific ; LMIC : Low- and middle- income countries ; HIC : high-income countries.

30 2.1.3. Discussion

The WHO urban ambient air quality database In addition, some existing data may have been presents data on annual mean concentration of omitted, either because it could not yet be particulate matter (PM) for almost 3 000 human accessed due to language issues or limited ac- settlements, mostly cities, in 103 countries and cessibility. Finally, the city means presented in is the most comprehensive database of its kind the database may differ from the ones calculated to date. The number of cities and towns repor- by the cities or municipalities themselves because ting on PM measurements has nearly doubled they are averaged from multiple station data. since the previous version of the database was published in 2014 (1 600 cities) (8) and nearly The comparison of air pollution levels for a given tripled since the first 2011 version (1 100 cities) set of cities over the five-year period 2008-2013 (7), which indicates an increased awareness shows a global increase of 8% in annual mean of the risks posed by air pollution. In the past concentrations of PM2.5. High-income regions two years, the number of ground measurements of the Americas, Europe and Western Pacific available in high-income countries has doubled, demonstrate decreasing air pollution, while the while there has been a 50 % increase in low-and other regions have increasing levels. middle-income countries. However, such a comparison of air pollution levels Monitoring the air quality is the first, but key, has a number of limitations. First of all, when PM2.5 step to be taken by public authorities, both at measurements were not available, PM10 values the national and city levels, to tackle the mul- (converted into PM2.5) were used for the compari- tisectoral challenge of addressing air pollution. son, which may have influenced the results. However, there are still huge monitoring and reporting gaps between high-income countries Second, the period of comparison is relatively and low- and middle-income counterparts. short. Yearly variations may for example be in- Most air quality data still comes from Europe fluenced by weather patterns and data within a and the Americas, with regions such as Africa, five-year period may not be sufficient to reflect a South East Asia, and Eastern Mediterranean longer-term trend. A longer time period of com- lagging behind. parison would be required to confirm any trends.

The data presented in the latest database Third, although sampling locations are reaso- reflects a number of important limitations. The nably stable over time, they may have changed ground measurements are of limited compara- within the period of comparison, and variations bility because of various reasons : a) Differing in annual mean PM levels for a given city may locations of measurement stations ; b) varying reflect different sampling locations rather than a measurement methods ; c) different temporal genuine trend. As the data collected is used for coverage of certain measurements (i.e. if only calibrating the model that derives global PM2.5 part of the year was covered, the measurement estimates, data quality is crucial, as well as loca- may significantly deviate from the annual mean tion details and the type of monitoring stations : due to seasonal variability) ; d) possible inclu- information that is also used in the modeling exer- sion of data that should not have been eligible cise. Efforts and investments should be made to for this database due to insufficient information encourage cities and countries to monitor air to ensure compliance ; e) differences in sizes of quality closely, using standard, good quality urban areas covered (i.e. for certain countries, and comparable methods and instruments, as only measurements for larger cities were found, well as making this information widely available. whereas for others also cities with just a few thou- sand inhabitants were available) ; and f) varying To date, only particulate matter concentrations quality of measurements. have been compiled in the database. Additional

pollutants – such as nitreous oxide (NO2), ozone

(O3) or others – might be added to the database in the future.

31 2.2. Exposure : modelled estimates of PM2.5 2.2.1. Methods 2.2.2. Results

Assessment of the global effects of air pollution Global exposure to PM2.5 has been modelled requires a comprehensive set of estimates of for the year 2014 in order to provide a com- exposure for all populations. Ground monito- prehensive global coverage of estimates of ring networks have provided the primary source air quality. The key parameter is the annual of this information but, although coverage has median concentration of PM2.5, which is highly increased, there remain regions in which there is relevant for estimating health impacts. Modelled no or very little monitoring. exposure to ambient PM2.5 levels provides more comprehensive information for countries than Ground measurement data therefore need to measured data, which only provide data for a be supplemented with information from other selection of towns and cities. A comprehensive sources, such as estimates from satellite retrie- set of estimated exposures such as this will be vals of aerosol optical depth and chemical required, when estimating the health impacts of transport models. The recently developed Data ambient air pollution for a given country. Integration Model for Air Quality (DIMAQ) incor- porates data from multiple sources in order to Estimates of exposure are required for all areas, provide estimates of exposures to PM2.5 at high including those that may not be covered by spatial resolution (0.1o × 0.1o) globally. ground monitoring networks. Estimates of air quality, expressed in terms of median concen-

Sources of data include : Ground measurements trations of PM2.5 are now available for all regions from 6 003 monitoring locations around the world of the world, including areas in which PM2.5 (9,10), satellite remote sensing ; population monitoring is not available (Figure 9) . It can estimates ; topography ; and information on local be viewed on an interactive map at www.who. monitoring networks and measures of specific int/phe/health_topics/outdoorair/databases/en. contributors of air pollution from chemical trans- Estimates of exposure, by country, are pre- port models. The DIMAQ model calibrates data sented in Table A1 of Annex 1. from these sources with ground measurements. The relationships between the various sources of data may be complex and will vary between regions due to differences in the composition of

PM2.5, and other factors.

DIMAQ has a hierarchical structure within which calibration equations are produced for individual countries using, as a priority, data from that country where available. Where data within a country is insufficient to produce accurate estimates, it is supplemented with regional information. When calibration equations have been established and tested, the model is used to estimate exposures, together with associated measures of uncertainty across the globe. These high-resolution estimates can be used to produce air quality profiles for individual countries, regions and globally.

A full description of the model development and evaluation is available separately (24).

32 In the majority of regions of the world, annual Based on the modelled data, 92 % of the world median concentrations of air pollution are population are exposed to PM2.5 air pollution higher than the WHO guideline levels of 10 concentrations tha are above the annual mean μg / m3 (Figures 10 and 11). Exposures are WHO AQG levels of 10 μg / m3 (Figure 13). With particularly high in the Eastern Mediterranean, the exception of the region of the Americas, all South-East Asian and Western Pacific Regions. regions – both HIC and LMIC – have less than Air pollution does not exclusively originate from 20 % of the population living in places in com- human activity, and can be greatly influenced pliance with the WHO AQG. by dust storms, for example, particularly in areas close to deserts. This is partially illustra- ted in Figure 12, where modelled annual median

PM2.5 concentration by area (rural / urban) is presented and where rural areas in the African and Eastern Mediterranean Regions show greater concentrations than urban ones.

3 Figure 9 : Global map of modelled annual median concentration of PM2.5, in µg / m

PM2.5 : Fine particulate matter of 2.5 microns or less.

33 Figure 10 : Annual median exposure to ambient (outdoor) mean annual concentration of PM2.5 in µg / m3, by region - urban and rural population population, 2014

3 PM2.5 (μg/m )

100 91

60 55 55 49

40 32

17 20 14 15 9

0 Afr Amr Amr Emr Emr Eur Eur Sear Wpr WPr World (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC)

3 WHO AQG for PM2.5: 10 μg/m

Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean ; Eur: Europe ; Sear : South-East Asia, Wpr : Western Pacific ; LMIC: low- and middle-income countries ; HIC : high-income countries ; PM2.5 : particulate matter with an aerodynamic diameter of 2.5 µm or less. WHO AQG : WHO Air Quality Guidelines.

3 Figure 11 : Annual median exposure to ambient (outdoor) PM2.5 in µg / m , by region - urban population only, 2014

3 PM2.5 (μg/m )

120

104

100

59 59 54 60

43 37 40 28

18 15 16 20 9

0 Afr Amr Amr Emr Emr Eur EUR Sear WPr WPr World (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC)

3 WHO AQG for PM2.5: 10 μg/m Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia, Wpr : Western Pacific ; LMIC : low- and middle-income countries ; HIC : high-income countries. PM2.5 : particulate matter with an aerodynamic diameter of 2.5 µPM2.5 : pa. WHO AQG : WHO Air Quality Guidelines.

34 Figure 12 : Median PM2.5 concentration, by geographic region - urban and rural areas combined, 2014

3 PM2.5 (μg/m )

100 92 89

urban

rural 80

60 52 49 49 46 37 29 35 40 30 25

19 15 20 14 13 12 8 7 8 6

0 Afr Amr Amr Emr Eur Eur Eur Sear Wpr WPr (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC) (HIC) (LMIC) .

Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia, Wpr : Western Pacific ; LMIC : low- and middle-income countries ; HIC : high-income countries. Note : This graph represents the median PM2.5 of regional urban and rural areas. These estimates are not in relation to the number of people exposed to those levels.

Figure 13 : Modelled annual median particulate matter concentration compared to the 1.2 WHO Air Quality Guidelines (AQG) a

exceed AQG

below AQG

100%

80%

60%

40%

76 20% 20 18 16

2 1.0 0.7 0.50.0 0.0 8% 0% Amr Amr Eur Wpr Sear Eur Afr WPr Emr Emr World (HIC) (LMIC) (HIC) (HIC) (LMIC) (LMIC) (LMIC) (HIC) (LMIC) Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia ; Wpr : Western Pacific ;

LMIC : Low- and middle-income countries ; HIC : high-income countries ; AQG : WHO Air Quality Guidelines.a Annual mean PM10 : 3 3 20 µg / m ; Annual mean PM2.5 : 10 µg / m .

35 2.2.3. Discussion

Global population exposure to PM2.5 matter has Finally, where measurements of PM2.5 are not avai- been modelled for the year 2014 to provide a lable, PM10 measurements are used after conver- comprehensive global coverage of estimates sion to PM2.5 using either city, country or regional of air quality. Annual mean concentration of conversion factors. Conversion factors typically

PM2.5 is highly relevant for estimating health range between 0.4 - 0.8 depending on location. impacts and is used as an exposure indicator for Localised conversion factors are likely to be more calculating the burden of disease attributable to accurate, but the ability to calculate them relies ambient air pollution. on availability of localised data. The potential for inaccuracies in conversion factors means that The model used for this study currently has model outputs for areas using large numbers of several limitations, however. The first limitation converted values may be less accurate than those relates to population data. While the quality of based on measurements of PM2.5 and extra cau- estimates of population data and population tion should be taken in their interpretation. density, used to calculate the average estimates of PM2.5 for urban and rural areas is gene- Wherever possible, estimates of PM2.5 have rally good for high-income countries, it can be been computed using standardized categories relatively poor for some low- and middle-income and methods in order to enhance cross-natio- areas. Furthermore, the definition of urban / rural nal comparability. This approach may result in may greatly vary by country. some cases in differences between the estimates presented here and the official national The second limitation relates to resolution : statistics prepared and endorsed by indivi- The grid size is 0.1° x 0.1° (11 x 11 km close dual WHO Member States. These differences to the equator, but smaller towards the poles). between WHO and national statistics may be This resolution may cause limitations when larger in countries with small cities and settle- considering local situations. However, finer ments which may not be fully represented by resolutions are planned for future studies. the resolution of the WHO model. This may be Regarding lack of monitoring data in countries, compounded for isolated regions where air the model produces a calibration equation for pollution is primarily from local sources and is each country using country level data as a prio- experienced at very local levels. rity, with regional data being used to supple- ment local information for countries with sparse, or no, ground monitoring data. It is acknowle- dged that the estimates for data-poor countries may be relatively imprecise and that this may result in apparent changes in levels of air pollution at borders with data-poor countries. In order to achiever reduced uncertainty in modelled data it is important that countries continue and / or improve ground measurement programmes.

3. Burden of disease attributable to ambient air pollution 3.1. Methods

The burden of disease attributable to ambient air pollution was estimated for the year 2012 based on Comparative Risk Assessment methods (25) and in consultation with expert groups for the Global Burden of Disease (GBD) study (12, 14). This methodology is based on combining exposure to air pollution and its distribution in the where z is the annual mean concentration of population with exposure-risk estimates at each PM2.5, zcf is the counterfactual PM2.5 concentra- level of exposure. This results in a population tion, α, γ and δ are the parameter estimates. attributable fraction of disease burden, which is The IERs are age-specific for both IHD and stroke. then applied to the health outcome of interest. Demographic data 3.1.1. Source of the data Population data from the UN Population Division Health data (21) were used.

The total number of deaths, years of life lost (YLLs), years lived with disability (YLDs) and 3.1.2. Estimation of disease disability-adjusted life years (DALYs) by disease, burden country, sex and age group have been developed by the World Health Organization (26). The percentage of the population exposed to PM2.5 was provided by country, in increments of 1 µg / m3 ; relative risks were calcu- Exposure data lated for each PM2.5 increment, based on the integrated exposure-response functions (IER). Annual mean concentration of PM were 2.5 The counterfactual concentration was selec- modelled according to the description in the ted to be a uniform distribution with lower and section Exposure : modelled estimates of PM . 2.5 upper limits of 5.9 and 8.7 µg / m3 respectively, The model output provided the percentage of given by the average of the minimum and and the population exposed to PM by country, in 2.5 5th percentiles of outdoor air pollution cohort increments of 1 µg / m3. exposure distribution, as described elsewhere (14, 16). The country population attributable Exposure-risk relationship fractions (PAF) for ALRI, COPD, lung cancer, stroke and IHD were calculated using the The integrated exposure-response (IER) func- following formula, for each sex and age group : tions, developed for the GBD 2010 study (16) and further updated for the GBD 2013 study (14 ; 27), were used for acute lower respiratory infections (ALRI), lung cancer, chronic obstructive pulmonary disease (COPD), stroke and 3 ischaemic heart disease (IHD) and can be des- where i is the level of PM2.5 in µg / m , and Pi is cribed with the following formula (16) : the percentage of the population exposed to that level of air pollution, and RR is the relative risk.

39 The attributable burden is calculated by multi- plying the population-attributable fraction (PAF) 3.2. Results by the health outcome, for each health outcome, Globally, 3 million deaths were attributable to sex and age group : ambient air pollution (AAP) in 2012. About 87 % of these deaths occur in LMICs , which repre- AB = PAF x health outcome sent 82 % of the world population. where AB is the attributable burden, PAF is the The WHO Western Pacific and South East Asian population-attributable fraction and the health out- regions bear most of the burden with 1.1 mil- come of interest, e.g. deaths, DALYs, YLLs, etc. lion and 799 000 deaths, respectively. In other DALYs are calculated by adding the years of regions, about 211 000 deaths occur in Sub- life lived with disability (YLDs) and the YLLs. Saharan Africa, 194 000 in the Eastern Mediter- The relative risks (RRs) for YLDs were adjus- ranean region, 190 000 in Europe, and 93 000 ted by a scalar of 1 for ALRI, lung cancer, and in the Americas. The remaining deaths occur in COPD ; of 0.141 for IHD and of 0.553 for stroke high-income countries of Europe (289 000), the (27). The age groups included in the analysis Americas (44 000), Western Pacific (44 000), were children less than five years of age for and Eastern Mediterranean (10 000) (Figure 14). ALRI, and adults above 25 years for the other diseases as previously reported (2, 6). Age-standardized deaths and DALYs are shown in Figures 15 to 19. Age-standardized 3.1.3. Uncertainty analysis measures of deaths and disease are often used to compare countries, as they adjust for age The uncertainty intervals are based on the fol- distribution differences by applying age- lowing sources of uncertainty : (a) Uncertainty specific mortality rates for each population. around the exposure to air pollution; and (b) Country estimates of deaths, YLLs and DALYs uncertainty around relative risks of the integra- are provided by disease and sex in Annex 2. ted exposure-function. To account for uncer- The methods for exposure assessment and tainty in exposure, the upper and lower expo- burden of disease estimation have been slight- sure was obtained from the model (24) for each ly updated compared with the previous esti- country, i.e. upper and lower credible limits for mate of 3.7 million deaths from AAP (2012), as the estimate of the percentage of the population released in 2014 (2). exposed to PM2.5. The variation is thought to be due to : 1) Addi- Alternative burden of disease estimates were tional evidence that has become available on calculated for each of these exposures. For the the relationship between exposure and health integrated exposure-response function, 1 000 outcomes, and the use of an updated version draws of each parameter of the function were of the integrated exposure-response functions obtained (27) and population attributable frac- leading to modified estimates depending on the tions were calculated for each draw. disease ; and 2) revised population exposure to air pollution leading to modified estimates The final uncertainty intervals were obtained as according to the region. the 2.5 and 97.5 percentiles of the draws for the alternative burden of disease calculations. As uncertainty for total mortality by cause was not taken into account, this is a partial accounting of uncertainty, and is likely to be underestimated.

40 Figure 14 : Deaths attributable to AAP in 2012, by disease and region

Number of deaths (000s)

1500 Stroke

IHD

1250 COPD 1 102 Lung cancer

ALRI 1000

799

750

500

289 211 250 194 190 93 44 44 10 0 Wpr Sear Eur Afr Emr Eur Amr Amr Wpr Emr (LMIC) (HIC) (LMIC) (LMIC) (LMIC) (HIC) (HIC) (HIC)

AAP : ambient air pollution ; Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia ; Wpr : Western Pacific ; LMIC : Low- and middle-income countries ; HIC : High-income countries

Figure 15 : Age-standardized deaths per capita attributable to AAP in 2012, by disease and region

Age-standardized deaths per 100 000 capita

80 Stroke

IHD 70 65 COPD 59 59 60 Lung cancer 55 54 ALRI

50 47

39 40

30 25

20 18

10 10 7

0 Wpr Sear Eur Emr Emr World Afr Eur Amr Wpr Amr (LMIC) (LMIC) (LMIC) (HIC) (HIC) (LMIC) (HIC) (HIC)

AAP : ambient air pollution ; Afr : Africa ; Amr : Americas ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia ; Wpr : Wes- tern Pacific ; LMIC : Low- and middle-income countries ; HIC : High-income countries.

41 Figure 16 : Deaths attributable to AAP in 2012, by country

AAP : Ambient air pollution

Figure 17 : DALYs attributable to AAP in 2012, by country

AAP : Ambient air pollution

42 Figure 18 : Age-standardized deaths per 100 000 capita attributable to AAP in 2012, by country

AAP : Ambient air pollution

Figure 19 : Age-standardized DALYs per 100 000 capita attributable to AAP in 2012, by country

AAP : Ambient air pollution

43 Almost 94 % of deaths worldwide are due to noncommunicable diseases in adults, such as cardiovas- cular diseases (stroke and ischaemic heart disease), chronic obstructive pulmonary disease and lung cancers. The remaining deaths occur in children under five years of age due to acute lower respiratory infections (Figure 20 and Tables 5 and 6).

Figure 20 : Deaths attributable to AAP in 2012, by disease

STROKE 242 250 IHD 169 250 COPD LUNG CANCER ALRI 8% 6%

36% 402 350 14% 1 078 800

36%

1 082 750

Percentage represents percentage of total AAP burden. AAP : ambient air pollution ; ALRI : acute lower respiratory disease ; COPD : chronic obstructive pulmonary disease ; IHD : ischaemic heart disease.

Table 5 : Deaths attributable to AAP in 2012, by disease, age and sex

Deaths Disease Children < 5 years Men Women Total ALRI 169 250 - - 169 250 COPD - 135 900 106 350 242 250 Lung cancer - 285 900 116 450 402 350 IHD - 606 350 472 450 1 078 800 Stroke - 540 600 542 150 1 082 750 Total 169 250 1 568 750 1 237 400 2 975 400

AAP : ambient air pollution ; ALRI : acute lower respiratory disease ; COPD : chronic obstructive pulmonary disease ; IHD : ischaemic heart disease. Men and women are adults of 25 years and above.

Table 6 : Disability-adjusted life years (DALYs) attributable to AAP in 2012, by disease, age and sex

DALYs (‘000s) Disease Children < 5 years Men Women Total ALRI 15 478 - - 15 478 COPD - 3 758 2 862 6 620 Lung cancer - 7 076 2 736 9 812 IHD - 16 782 10 105 26 887 Stroke - 13 927 12 210 26 137 Total 15 478 41 544 27 913 84 934

DALYs : disability-adjusted life years; AAP : ambient air pollution ; ALRI : acute lower respiratory disease ; COPD : chronic obstructive pulmonary disease ; IHD : ischaemic heart disease. Men and women are adults of 25 years and above.

The fraction of each individual disease attributable to ambient air pollution is presented in Table 7 for DALYs, and ranges from 8 % for COPD to 25 % for lung cancers. ALRI, stroke and IHD lie in the middle with population attributable fraction of around 16 %. 44 % % % % % % % % % 9 % 5 9 18 % 16 % 18 11 24 20 10 13 19 19 % Both ; IHD: Ischaemic IHD: ; % % % % % % % % % 9 % 5 9 8 17 % 16 % 17 10 22 19 12 18 18 % : High-income countries. Women ; HIC % % % % % % % % % 5 10% 19 % 17 % 18 11 25 20 11 14 20 10 19 % 25 years Stroke ≥ Men % % % % % % % % % 6 9 17 % 15 % 10 % 17 12 22 18 11 13 19 17 % Both : Chronic obstructive pulmonary disease pulmonary obstructive Chronic : % % % % % % % % % ; COPD ; 9 % 5 8 16 % 14 % 16 11 20 18 10 12 18 16 % : Low- and middle-income countries Women ; LMIC % % % % % % % % % 6 16 % 18 % 10 % 18 12 23 19 12 14 20 10 17 % 25 years IHD Men ≥ : Western Pacific % % % % % % % % % 5 ; Wpr 25 % 34 % 12 % 27 15 47 36 15 23 33 15 38 % : Acute lower respiratory disease respiratory lower Acute : Both ; ALRI ; % % % % % % % % % 5 24 % 34 % 11 % 27 16 47 36 15 23 32 14 38 % Women ; Sear: South-East Asia % % % % % % % % % 5 26 % 34 % 13 % 26 15 47 35 15 24 33 15 38 % : Ambient air pollution air Ambient : 25 years : Europe Lung cancer Men ≥ ; AAP ; ; Eur % % % % % % % % % 8 % 9 % 1 % 5 1 2 9 2 4 2 9 % 14 10 Both % % % % % % % % % 8 % 9 % 1 % 5 0 2 9 2 4 2 9 % 14 10 Women : Eastern Mediterranean % % % % % % % % % ; Emr 8 % 9 % 5 1 2 9 2 4 2 9 % 2 % 14 10 25 years ; PAF: population attributable fraction attributable population PAF: ; Men COPD ≥ : America % % % % % % % ; Amr % % 6 8 17% 17 % 16 12 23 18 10 15 19 17 % 10 % Children ALRI < 5 years : Africa : Population attributable fraction (PAF) for the Disability-ad justed life years (DALY) AAP in 2012, by disease, age, sex and region. : Disability-adjusted life years life Disability-adjusted : World LMIC Afr Amr (HIC) Amr (LMIC) Emr (HIC) Emr (LMIC) Eur (HIC) Eur (LMIC) Sear Wpr (HIC) Wpr (LMIC) HIC heart disease. Afr DALY Table 7

45 3.3. Discussion

In 2012, ambient air pollution from particu- The actual impact of air pollution on health late matter was responsible for about 3 million presented here is a conservative figure. Many deaths and 85 million DALYs. Burden of disease other diseases have been associated with air calculation is based on : 1) Population exposure pollution but are not included in this assessment, assessment ; 2) exposure-response functions ; because the evidence was not considered to be and 3) underlying health data. As data on air sufficiently robust, such as pre-term birth or low quality and epidemiological evidence is accu- birth weight. Also, it does not include the separate mulating rapidly, and methodolodies for both impacts of health from other air pollutants such as points 1) and 2) are being revised on a regular nitrogen oxides (NOx) or ozone (O3). basis, and these figures are evolving. As data on air pollutants, methods to assess Decision-makers and researchers need accurate population exposure and epidemiological and reliable estimates of air pollution exposure evidence accumulates, health effects of additio- and the related health impacts. Differing needs nal pollutants should be taken into account in (cause-specific vs all-cause mortality, national or the future as it has already been done for ozone regional assessment), publication schedules and (14). In addition, assessments of the health im- audience may lead to different approaches and pacts from interventions to reduce air pollution data use, and therefore different results (28–31). using rigorous epidemiological methodology The methods used in this report were developed are much needed. These would be able to take for global reporting and using the latest available account of context, of intervention packages evidence and health data that complied with and provide a stronger basis for assessing real- WHO standards ; they may therefore differ from life impacts of air quality improvement efforts. official national statistics prepared and endorsed by individual WHO Member States.

Some regions, such as the Eastern Mediterra- nean and Africa are highly affected by natural desert dust particles. This results in high health burden according to the current methods, which assume natural dust affects health the same way as PM2.5 from other sources.

The health impacts associated with exposure to dust are not yet fully understood, and are currently treated the same way as those from air pollution in industrialized countries where most of the epidemiological studies have been performed.

There is a need to for a better assessment of the health impacts from natural dust, as it could result in much lower burden than those from antropogenic particulate matter (32, 33).

4. Conclusion and way forward

Ambient air pollution kills about 3 million people In terms of monitoring and reporting, there is annually and is affecting all regions of the world, a huge gap in monitoring and reporting air although Western Pacific and South East Asia pollutants in low and middle-income regions, are the most affected. About 90 % of people especially in Africa and Asia but also other breathe air that does not comply with the WHO regions. Strengthening capacities of cities Air Quality Guidelines. to monitor their air quality with standardized methods, reliable and good quality instrumenta- The data presented here carry significant tion, and sustainable structures is key. uncertainties but constitute the best evidence available to date. Air quality data and epidemiological evidence on the effects of air pollutants on health are growing quickly. They will continue to improve exposure assessment to particulate matter and other pollutants, as well as for burden of disease estimation.

More epidemiological studies of the long-term effects of exposure to air pollution in low-income settings, where air pollution reaches unaccep- table levels, are urgently needed to better inform the exposure-response relationships. Additional evidence on health outcomes currently not assessed in the analysis, because of lack of knowledge, is also crucial.

References

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Acknowledgment

This document was prepared by Sophie Gumy, (Emory University), Aaron Cohen (Health Effects and Annette Prüss-Üstün (WHO Geneva), with Institute), Rita van Dingenen (Joint Research inputs and comments from Heather Adair-Ro- Centre, European Commission), Yang Liu (Emo- hani, Carlos Dora and Elaine Fletcher (WHO ry University), Randall Martin (Dalhousie), Lance Geneva), Gavin Shaddick and Matthew Tho- Waller (Emory University), Jason West (North mas (University of Bath, United Kingdom). Carolina) and Jim Zidek (University of British The interactive map and the static map of expo- Columbia), in addition to Annette Prüss-Us- sure to air pollution were prepared by Ravi San- tün, Sophie Gumy and Carlos Dora of WHO. thana Gopala Krishnan (WHO Geneva) ; all other The model was presented and reviewed at the maps were prepared by Florence Rusciano WHO Global Platform on Air Quality, Geneva, in (WHO Geneva). August 2015.

Exposure : ground measurements Burden of disease attributable to of PM10 and PM2.5 ambient air pollution The database was compiled during 2015 with The burden of disease was calculated by some updates in the first quarter of 2016 by Sophie Gumy and Annette Prüss-Ustün (WHO Sophie Gumy, Tara Neville, Kristina Dushaj Geneva). and Annette Prüss-Ustün (WHO Geneva) with contributions from Mazen Malkawi (WHO CEHA, Amman), Christian Gapp (WHO ECEH, Bonn), Alberto González Ortiz (European Environment Agency), Kaye Patdu and Candy Tong (Clean Air Asia), Michael Brauer (School of Population and Public Health, University of British Colum- bia, Canada), and several national institutions.

Exposure : modelled estimates of PM2.5 The model to produce the estimates was developed under the leadership of WHO and the University of Bath, United Kingdom, with the input and review of an expert group of 12 leading scientists in the area. The scientist leading the work was Gavin Shaddick (University of Bath) who was assisted by Matthew Thomas and Amelia Jobling (University of Bath). The expert group consisted of Gavin Shaddick, Michael Brauer (University of British Columbia), Aaron van Donkelaar (Dalhousie University), Rick Burnett (Health Canada), Howard Chang

Annex 1 : Modelled population exposure to particulate matter (PM2.5 ), by country Country income grouping is based on the World Bank analytical income classification of economies 1 corresponding to the year of the data.

Table A1 : Annual median concentration of particulate matter of an aerodynamic diameter of 2.5 mm or less (PM2.5) with lower and upper bound, population-weighted and modelled, by area and country

3 3 PM2.5 [µg/m ], urban and rural areas PM2.5 [µg/m ], urban areas

Region Country Median Lower Upper Median Lower Upper

Emr (LMIC) Afghanistan 46 26 80 63 41 98 Eur (LMIC) Albania 16 9 29 17 10 30 Afr Algeria 27 9 74 25 8 73 Eur (HIC) Andorra 10 6 18 11 6 17 Afr Angola 27 8 95 42 9 182

Amr (HIC) Antigua and Barbuda 13 6 28 13 6 28

Amr (LMIC) Argentina 13 8 22 14 9 24 Eur (LMIC) Armenia 21 7 66 25 7 87 Wpr (HIC) Australia 6 4 8 6 4 9 Eur (HIC) Austria 16 11 23 17 12 24 Eur (LMIC) Azerbaijan 24 8 73 26 8 86 Amr (HIC) Bahamas 13 6 28 13 6 28 Emr (HIC) Bahrain 60 43 82 60 44 82 Sear Bangladesh 84 53 131 89 58 134 Amr (HIC) Barbados 14 6 31 14 6 31 Eur (LMIC) Belarus 17 6 48 18 6 54 Eur (HIC) Belgium 15 11 22 16 11 23 Amr (LMIC) Belize 22 7 64 21 7 62 Afr Benin 31 9 107 28 7 106 Sear Bhutan 48 30 79 39 30 50 Bolivia, Plurinational 27 17 45 32 20 49 Amr (LMIC) States of

Eur (LMIC) Bosnia and Herzegovina 42 23 76 55 30 100

Afr Botswana 16 6 42 19 6 57 Amr (LMIC) Brazil 10 7 16 11 8 17 Wpr HIC Brunei Darussalam 5 3 9 5 3 9 Eur (LMIC) Bulgaria 27 18 40 30 21 42 Afr Burkina Faso 37 12 119 37 10 132

1 For more information, see : Country classification. Washington (DC): World Bank (https://datahelpdesk.worldbank. org/knowledgebase/topics/19280-country-classification, accessed February 2016).

57 3 3 PM2.5 [µg/m ], urban and rural areas PM2.5 [µg/m ], urban areas

Region Country Median Lower Upper Median Lower Upper

Afr Burundi 38 11 136 49 12 198 Afr Cabo Verde 36 14 96 NA NA NA Wpr (LMIC) Cambodia 23 7 75 25 6 99 Afr Cameroon 65 38 111 64 39 104 Amr (HIC) Canada 7 5 10 7 5 10

Afr Central African Republic 39 12 129 56 15 217

Afr Chad 39 14 115 61 18 207 Amr (HIC) Chile 20 13 30 25 18 36 Wpr (LMIC) China 54 37 80 59 42 84 Amr (LMIC) Colombia 17 11 25 18 13 26 Afr Comoros 16 8 32 16 8 32 Afr Congo 40 11 143 57 13 236 Wpr (LMIC) Cook Islands NA NA NA NA NA NA

Amr (LMIC) Costa Rica 18 12 27 19 13 27 Eur (HIC) Croatia 19 12 31 20 13 32 Amr (LMIC) Cuba 17 7 38 16 7 38 Eur (HIC) Cyprus 17 11 26 17 11 26 Eur (HIC) Czech Republic 20 14 29 21 15 29 Afr Côte d’Ivoire 22 7 71 19 5 71

Democratic People’s 27 8 92 31 8 118 Sear Republic of Korea

Democratic Republic 38 12 121 61 16 225 Afr of the Congo

Eur (HIC) Denmark 10 7 16 11 7 17 Emr (LMIC) Djibouti 39 9 159 46 10 211

Amr (LMIC) Dominica 13 6 28 13 6 28

Amr (LMIC) Dominican Republic 16 7 37 17 7 40 Amr (LMIC) Ecuador 13 9 20 13 9 20 Emr (LMIC) Egypt 93 50 171 101 54 188 Amr (LMIC) El Salvador 35 20 62 37 22 64 Afr Equatorial Guinea 33 7 153 32 8 128 Afr Eritrea 35 10 126 36 9 138 Eur (HIC) Estonia 8 5 13 8 5 13 Afr Ethiopia 30 9 94 36 10 132 Wpr (LMIC) Fiji 6 2 22 6 2 22 Eur (HIC) Finland 7 5 11 7 5 11 Eur (HIC) France 12 8 17 13 9 18 Afr Gabon 30 8 115 36 7 169 Afr Gambia 39 7 234 43 6 306

Eur (LMIC) Georgia 19 11 33 23 13 40

58 3 3 PM2.5 [µg/m ], urban and rural areas PM2.5 [µg/m ], urban areas

Region Country Median Lower Upper Median Lower Upper

Eur (HIC) Germany 14 9 20 14 10 21 Afr Ghana 24 12 46 22 10 46 Eur (HIC) Greece 12 8 18 13 9 18 Amr (LMIC) Grenada 14 6 31 14 6 32 Amr (LMIC) Guatemala 32 20 51 33 23 49 Afr Guinea 21 7 66 19 5 68 Afr Guinea-Bissau 27 7 103 29 6 138 Amr (LMIC) Guyana 15 5 45 16 5 54 Amr (LMIC) Haiti 22 7 63 25 8 79 Amr (LMIC) Honduras 35 20 60 40 23 68 Eur (LMIC) Hungary 21 14 32 23 16 33 Eur (HIC) Iceland 8 5 12 8 5 12 Sear India 62 41 95 66 45 97 Sear Indonesia 14 9 23 18 11 28 Emr (LMIC) Iran (Islamic Republic of) 42 27 63 40 28 58 Emr (LMIC) Iraq 50 18 141 51 17 154 Eur (HIC) Ireland 9 6 14 10 7 14 Eur (HIC) Israel 19 13 28 19 14 27 Eur (HIC) Italy 17 12 25 18 13 26 Amr (LMIC) Jamaica 16 11 25 17 12 25 Wpr (HIC) Japan 13 8 19 13 9 19 Emr (LMIC) Jordan 36 24 53 38 27 52 Eur (LMIC) Kazakhstan 15 6 43 21 7 67 Afr Kenya 16 9 28 17 10 29 Wpr (LMIC) Kiribati 5 1 20 NA NA NA Emr (HIC) Kuwait 75 50 112 78 53 116 Eur (LMIC) Kyrgyzstan 15 8 27 15 8 28 Lao People’s Democratic 27 9 76 34 10 106 Wpr (LMIC) Republic

Eur (HIC) Latvia 19 12 29 20 13 31 Emr (LMIC) Lebanon 30 19 45 31 21 45 Afr Lesotho 18 4 76 22 4 113 Afr Liberia 8 4 17 6 3 15 Emr (LMIC) Libya 61 22 171 58 19 175 Eur (HIC) Lithuania 18 12 28 19 13 28 Eur (HIC) Luxembourg 16 11 24 17 12 23 Afr Madagascar 17 9 33 32 19 54 Afr Malawi 22 7 72 26 7 95 Wpr (LMIC) Malaysia 15 9 24 17 10 26

Sear Maldives 16 8 29 NA NA NA

59 3 3 PM2.5 [µg/m ], urban and rural areas PM2.5 [µg/m ], urban areas

Region Country Median Lower Upper Median Lower Upper

Afr Mali 39 13 124 35 10 120 Eur (HIC) Malta 14 9 21 14 10 20 Wpr (LMIC) Marshall Islands NA NA NA NA NA NA Afr Mauritania 65 19 230 86 21 368 Afr Mauritius 14 9 23 14 8 24 Amr (LMIC) Mexico 20 13 30 20 13 31 Micronesia (Federated 6 2 22 6 2 23 Wpr (LMIC) States of) Eur (HIC) Monaco 9 6 18 9 6 18 Wpr (LMIC) Mongolia 20 13 32 32 20 51 Eur (LMIC) Montenegro 22 14 35 24 15 38 Emr (LMIC) Morocco 20 13 32 19 12 29 Afr Mozambique 17 6 47 22 7 69 Sear Myanmar 51 32 80 57 35 90

Afr Namibia 18 6 51 18 6 57 Wpr (LMIC) Nauru NA NA NA NA NA NA Sear Nepal 64 33 123 74 39 140 Eur (HIC) Netherlands 15 10 22 15 10 22 Wpr (HIC) New Zealand 5 4 8 5 4 8 Amr (LMIC) Nicaragua 24 8 74 26 7 95 Afr Niger 59 19 191 51 15 177 Afr Nigeria 39 25 61 38 24 58 Wpr (LMIC) Niue NA NA NA NA NA NA Eur (HIC) Norway 9 6 13 9 6 14 Emr (HIC) Oman 48 18 132 47 15 145 Emr (LMIC) Pakistan 60 37 97 68 43 107 Wpr (LMIC) Palau NA NA NA NA NA NA Amr (LMIC) Panama 12 7 20 13 7 23 Wpr (LMIC) Papua New Guinea 10 2 45 12 2 71 Amr (LMIC) Paraguay 15 9 25 17 10 28 Amr (LMIC) Peru 26 16 42 36 23 54 Wpr (LMIC) Philippines 22 14 35 27 17 43 Eur (HIC) Poland 24 16 35 25 18 36 Eur (HIC) Portugal 9 6 14 10 6 14 Emr (HIC) Qatar 103 67 160 105 69 159 Wpr (HIC) Republic of Korea 27 18 39 28 19 40 Eur (LMIC) Republic of Moldova 17 5 59 17 4 68 Eur (LMIC) Romania 19 13 28 20 14 29 Eur (HIC) Russian Federation 15 8 27 17 9 31 Afr Rwanda 43 13 143 51 14 185

60 3 3 PM2.5 [µg/m ], urban and rural areas PM2.5 [µg/m ], urban areas

Region Country Median Lower Upper Median Lower Upper

Amr (HIC) Saint Kitts and Nevis NA NA NA NA NA NA Amr (LMIC) Saint Lucia 15 7 31 15 7 32 Saint Vincent and the 13 6 28 NA NA NA Amr (LMIC) Grenadines

Wpr (LMIC) Samoa NA NA NA NA NA NA Eur (HIC) San Marino NA NA NA NA NA NA Afr Sao Tome and Principe 13 5 29 NA NA NA Emr (HIC) Saudi Arabia 108 67 174 127 79 205 Afr Senegal 34 21 55 43 29 64 Eur (LMIC) Serbia 19 12 31 21 14 33 Afr Seychelles 13 7 27 13 7 27 Afr Sierra Leone 17 4 69 17 3 86 Wpr (HIC) Singapore 17 9 31 17 9 31 Eur (HIC) Slovakia 19 13 29 20 14 29

Eur (HIC) Slovenia 18 12 27 19 14 28 Wpr (LMIC) Solomon Islands 5 1 18 5 1 19 Emr (LMIC) Somalia 16 5 49 17 4 66 Afr South Africa 27 18 42 31 21 48 Emr (LMIC) South Sudan 29 11 81 32 12 91 Eur (HIC) Spain 9 7 14 10 7 14 Sear Sri Lanka 27 14 51 28 15 55 Emr (LMIC) Sudan 44 14 137 53 16 178 Amr (LMIC) Suriname 16 5 51 16 4 63 Afr Swaziland 18 5 59 20 5 72 Eur (HIC) Sweden 6 4 9 6 4 9 Eur (HIC) Switzerland 12 8 18 13 8 18 Emr (LMIC) Syrian Arab Republic 34 12 98 34 11 105 Eur (LMIC) Tajikistan 41 12 138 51 14 187 Sear Thailand 25 16 37 27 19 38 The former Yugoslav 37 23 59 43 28 65 Eur (LMIC) Republic of Macedonia

Sear Timor-Leste 15 3 65 15 3 69 Afr Togo 28 8 107 26 6 113 Wpr (LMIC) Tonga NA NA NA NA NA NA Amr (HIC) Trinidad and Tobago 13 6 28 13 6 28 Emr (LMIC) Tunisia 36 22 61 35 21 59 Eur (LMIC) Turkey 34 22 50 35 24 51 Eur (LMIC) Turkmenistan 25 8 76 26 7 95 Wpr (LMIC) Tuvalu NA NA NA NA NA NA Afr Uganda 57 30 108 80 46 138 Eur (LMIC) Ukraine 16 6 44 17 6 48

61 3 3 PM2.5 [µg/m ], urban and rural areas PM2.5 [µg/m ], urban areas

Region Country Median Lower Upper Median Lower Upper

Emr (HIC) United Arab Emirates 64 39 104 64 39 106 Eur (HIC) United Kingdom 12 8 18 12 8 18

United Republic 22 11 42 24 12 47 Afr of Tanzania

Amr (HIC) United States of America 8 5 12 8 6 12

Amr (HIC) Uruguay 11 8 17 11 8 17 Eur (LMIC) Uzbekistan 32 10 102 38 12 128 Wpr (LMIC) Vanuatu 6 2 23 7 2 26 Venezuela, Bolivarian 22 13 39 24 14 41 Amr (LMIC) Republic of Wpr (LMIC) Viet Nam 26 15 43 28 17 45 Emr (LMIC) Yemen 43 11 173 42 9 208 Afr Zambia 23 9 64 29 10 90 Afr Zimbabwe 20 7 55 24 8 75

PM2.5 : Particulate matter of a diameter of 2.5 mm or less ; ALRI : Acute lower respiratory disease ; COPD : Chronic obstructive pulmonary disease ; IHD : Ischaemic heart disease. Afr : Africa ; Amr : America ; Emr : Eastern Mediterranean ; Eur : Europe ; Sear : South-East Asia ; Wpr : Western Pacific ; LMIC : Low- and middle-income countries ; HIC : High-income countries ; NA : Not available.

Wherever possible, estimates of PM2.5 have been computed using standardized categories and methods in order to enhance cross-national comparability. This approach may result in some cases in differences between the estimates presented here and the official national statistics prepared and endorsed by individual WHO Member States.

These differences between WHO and national statistics may be larger for occur in countries with small cities and settlements which may not be fully represented by the resolution of the WHO model. This may be compounded for isolated regions where air pollution is primarily from local sources and is experienced at very local levels.

62 Annex 2 : Deaths, YLLs and DALYs attributable to ambient air pollution, by country

63 81 47 41 12 51 20 18 67 15 57 17 33 38 10 58 14 21 52 40 31 53 26 14 55 58 0.2 0.3 Age-standardized rate 37 64 31 27 31 19 23 92 34 46 15 11 24 15 30 11 26 26 25 92 13 13 28 0.4 0.2 100 118 Crude rate Deaths per 100 000 capita (0, 33) (0, 25) (0, 86) (0, 64) (5, 54) (0, 40) (2, 423) (1, 2829) (132, 165) (154, 237) (744, 2452) (107, 4229) (954, 6220) (909, 3972) (2010, 3587) (159, 13717) (2135, 4294) (1983, 3045) (2753, 4373) (2381, 6922) (8961, 13570) (4577, 16150) (1377, 11428) (2660, 13385) (7033, 10210) (30245, 46036) (11292, 39983) Uncertainty interval 1 22 17 93 57 44 36 148 192 273 Total Total 1 842 7 058 9 756 2 750 2 890 4 297 9 450 3 343 2 606 2 521 3 538 8 634 4 623 11 145 11 424 37 449 26 241 b 6 6 0 20 19 39 17 13 53 780 676 496 718 973 136 3 071 5 231 1 710 2 723 1 222 2 146 1 065 1 454 9 616 3 428 1 735 10 617 Stroke b 0 13 10 58 31 76 22 18 76 89 850 767 IHD 3 813 5 087 1 491 5 256 1 643 1 642 2 656 6 680 1 276 1 126 1 398 4 031 1 120 10 291 12 987 b 3 1 5 4 3 0 39 14 25 19 14 88 10 45 301 184 633 370 687 238 563 583 960 1 500 4 375 1 232 2 901 Lung cancer b 0 0 1 0 7 0 1 3 0 20 53 63 45 57 41 29 68 72 188 131 131 192 115 100 539 195 8 316 COPD a 8 0 0 8 0 1 3 2 1 4 1 1 2 0 85 20 35 20 341 135 714 265 198 3 772 3 687 3 850 1 651 ALRI Number of deaths Afghanistan Country Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia, Plurinational States of Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Emr (LMIC) Region Eur (LMIC) Afr Eur (HIC) Afr Amr (HIC) Amr (LMIC) Eur (LMIC) Wpr (HIC) Eur (HIC) Eur (LMIC) Amr (HIC) Emr (HIC) Sear Amr (HIC) Eur (LMIC) Eur (HIC) Amr (LMIC) Afr Sear Amr (LMIC) Eur (LMIC) Afr Amr (LMIC) Wpr HIC Eur (LMIC) Afr

65 3 48 37 54 37 40 52 13 70 17 34 50 14 31 23 14 29 43 62 52 10 42 16 24 14 77 27 NA Age-standardized rate 5 30 20 32 25 26 34 16 76 14 17 29 14 66 36 20 58 24 63 33 20 25 18 20 11 51 25 NA Crude rate Deaths per 100 000 capita NA (1, 19) (79, 181) (29, 174) (82, 336) (29, 6607) (469, 786) (150, 281) (52, 1845) (222, 4510) (610, 1889) (605, 1910) (338, 6115) (688, 7767) (173, 2864) (860, 2308) (1348, 4689) (5755, 8463) (2410, 6438) (2064, 3465) (4499, 8063) (2028, 3556) (4788, 7314) (1189, 1828) (2047, 26352) (11528, 35080) (36017, 51817) (869033, 1212034) Uncertainty interval 13 NA 128 125 644 223 212 Total Total 3 001 2 934 7 000 1 886 1 215 4 340 2 822 6 502 1 244 2 842 4 127 6 110 4 970 1 138 2 008 1 771 1 498 15 596 23 034 43 531 1 032 833 b 5 78 59 45 92 NA 300 381 484 172 806 263 641 615 326 1 025 1 078 2 413 1 101 1 042 1 638 1 086 1 194 2 038 6 911 7 124 16 206 Stroke 449 373 b 6 37 31 57 NA 532 306 731 386 380 120 434 875 953 IHD 1 249 1 736 1 060 1 087 3 688 1 404 1 989 3 620 1 394 3 910 4 828 1 098 22 327 257 638 b 1 1 8 5 1 21 62 12 20 65 56 42 87 97 NA 230 503 587 748 583 975 402 164 152 1 210 3 334 2 079 228 479 Lung cancer b 7 2 3 5 0 45 62 23 64 61 89 43 26 49 71 83 64 39 20 43 76 NA 169 270 473 1 210 1 968 90 626 COPD a 1 5 2 0 7 0 3 0 0 17 32 54 85 86 46 NA 315 452 158 324 230 951 1 378 2 621 2 426 6 716 1 431 ALRI 10 520 Number of deaths Burundi Country Cabo Verde Cambodia Cameroon Canada Central African Republic Chad Chile China Colombia Comoros Congo Cook Islands Costa Rica Croatia Cuba Cyprus Czech Republic Côte d’Ivoire Democratic People’s Republic of Korea Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Afr Region Afr Wpr (LMIC) Afr Amr (HIC) Afr Afr Amr (HIC) Wpr (LMIC) Amr (LMIC) Afr Afr Wpr (LMIC) Amr (LMIC) Eur (HIC) Amr (LMIC) Eur (HIC) Eur (HIC) Afr Sear Afr Eur (HIC) Emr (LMIC) Amr (LMIC) Amr (LMIC) Amr (LMIC) Emr (LMIC) Amr (LMIC)

66 3 8 4 50 56 11 23 34 46 51 13 41 19 23 19 38 47 43 46 32 42 68 38 50 68 10 11 13 0.3 Age-standardized rate 6 6 29 25 24 14 17 24 21 90 33 22 45 21 13 21 27 30 29 20 82 49 25 34 31 15 16 35 0.2 Crude rate Deaths per 100 000 capita (0, 32) (0, 64) (4, 624) (0, 302) (0, 352) (18, 384) (1, 1609) (76, 622) (24, 675) (38, 755) (43, 1071) (543, 1904) (273, 3832) (381, 4532) (912, 1501) (2341, 4741) (3816, 7407) (1717, 6683) (1598, 2378) (1242, 1927) (6279, 9881) (4890, 20742) (3825, 15204) (6690, 13684) (12729, 34229) (32783, 81876) (22583, 30064) (14291, 26531) (515242, 744416) Uncertainty interval 2 22 21 228 319 327 385 386 464 230 681 Total Total 1 243 3 741 5 728 5 008 1 980 2 466 3 022 1 575 8 147 1 219 13 257 10 954 26 160 61 792 26 267 10 085 21 057 621 138 b 0 9 4 77 48 62 99 534 155 160 473 174 493 124 262 4 595 2 774 1 358 5 372 2 804 1 705 1 006 1 535 1 659 7 290 3 200 5 814 36 527 Stroke 195 001 b 2 75 11 12 307 233 233 138 100 880 723 120 123 801 809 403 509 IHD 2 320 3 793 2 127 1 810 2 388 4 324 4 974 8 292 13 392 16 781 16 484 249 388 b 5 0 5 1 4 4 4 17 36 31 16 75 16 98 93 376 192 844 162 728 142 411 4 256 6 821 2 000 4 951 1 460 6 391 26 334 Lung cancer b 8 1 0 1 7 0 7 1 0 22 18 52 72 70 69 32 14 81 12 35 400 126 569 160 434 173 558 1 000 COPD 110 500 a 0 0 0 4 5 1 0 3 4 0 0 1 2 62 58 12 99 362 114 968 396 690 159 574 600 5 565 2 534 1 010 ALRI 39 914 Number of deaths Equatorial Guinea Country Eritrea Estonia Ethiopia Fiji Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran (Islamic Republic of) Iraq Ireland Israel Italy Afr Region Afr Eur (HIC) Afr Wpr (LMIC) Eur (HIC) Eur (HIC) Afr Afr Eur (LMIC) Eur (HIC) Afr Eur (HIC) Amr (LMIC) Amr (LMIC) Afr Afr Amr (LMIC) Amr (LMIC) Amr (LMIC) Eur (LMIC) Eur (HIC) Sear Sear Emr (LMIC) Emr (LMIC) Eur (HIC) Eur (HIC) Eur (HIC)

67 9 9 22 43 69 21 45 59 52 42 30 31 11 58 35 11 39 35 32 23 60 16 46 18 17 0.0 NA 0.2 Age-standardized rate 6 25 24 21 61 12 14 38 29 91 29 19 33 73 20 19 17 22 14 32 31 26 20 14 20 0.0 NA 0.1 Crude rate Deaths per 100 000 capita NA (0, 22) (0, 19) (0, 14) (0, 719) (0, 621) (12, 65) (69, 135) (66, 164) (484, 869) (433, 544) (87, 4273) (109, 326) (850, 2784) (758, 2730) (597, 1453) (1269, 1704) (1993, 6971) (1363, 2268) (1183, 1679) (1408, 2758) (1671, 2678) (1953, 5715) (3548, 8036) (2794, 7902) (1322, 15858) (11488, 41945) (12475, 20528) Uncertainty interval 0 0 8 48 NA 693 487 387 236 106 129 978 252 Total Total 1 483 5 102 2 129 1 857 1 859 1 434 2 054 2 216 4 215 2 706 6 251 5 218 30 790 10 293 16 798 b 0 0 2 91 30 20 25 82 NA 349 484 117 617 620 496 274 195 674 553 369 3 111 1 780 1 984 1 207 1 773 1 601 4 269 10 463 Stroke b 0 0 4 89 75 35 23 76 NA 246 743 319 668 921 697 237 144 IHD 6 307 1 192 1 357 1 156 1 038 1 424 1 135 3 630 1 122 9 983 10 286 b 0 6 2 3 0 1 82 79 35 73 37 13 49 27 10 23 NA 159 669 122 199 209 251 223 136 670 9 743 1 336 Lung cancer b 8 0 3 7 4 4 1 1 2 0 0 34 31 33 48 25 16 60 16 47 42 23 NA 281 117 148 154 742 COPD a 8 0 0 6 1 0 1 0 2 0 0 18 63 89 12 48 82 63 30 29 NA 399 914 747 340 467 2 021 2 291 ALRI Number of deaths Jamaica Country Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Lao People’s Democratic Republic Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia (Federated States of) Monaco Amr (LMIC) Region Wpr (HIC) Emr (LMIC) Eur (LMIC) Afr Wpr (LMIC) Emr (HIC) Eur (LMIC) Wpr (LMIC) Eur (HIC) Emr (LMIC) Afr Afr Emr (LMIC) Eur (HIC) Eur (HIC) Afr Afr Wpr (LMIC) Sear Afr Eur (HIC) Wpr (LMIC) Afr Afr Amr (LMIC) Wpr (LMIC) Eur (HIC)

68 6 7 70 39 31 21 61 27 56 12 31 57 44 36 52 12 12 25 17 47 39 31 16 57 39 NA 0.3 NA NA Age-standardized rate 7 9 40 61 25 13 43 13 36 24 21 35 28 13 13 33 11 18 14 30 69 17 23 74 73 NA 0.5 NA NA Crude rate Deaths per 100 000 capita NA NA NA (0, 463) (40, 481) (54, 615) (0, 1208) (0, 4776) (284, 472) (24, 1226) (326, 634) (66, 2894) (158, 200) (779, 1389) (132, 5360) (196, 1874) (534, 1523) (6109, 9907) (2176, 5394) (3802, 9146) (3277, 5148) (7426, 13057) (9054, 13963) (18744, 26942) (38031, 56243) (48757, 71340) (22037, 34736) (21327, 31670) (11001, 17576) Uncertainty interval 20 NA NA NA 382 306 636 477 425 519 179 Total Total 1 123 8 134 3 343 9 943 4 017 1 247 6 183 1 159 4 239 1 769 3 008 22 664 46 750 59 241 28 696 26 589 11 523 14 497 b 4 36 NA NA NA 434 145 162 836 399 156 180 142 132 465 779 811 3 535 1 278 3 183 1 835 1 348 8 376 3 899 5 336 11 316 13 983 16 705 10 124 Stroke b 92 13 NA NA NA 572 160 598 671 330 257 227 186 529 647 104 IHD 3 379 5 476 3 328 1 203 1 073 9 618 1 949 2 638 1 967 6 794 20 772 14 067 11 987 b 9 3 7 60 77 58 64 25 36 26 34 NA NA NA 767 923 264 137 108 651 323 188 2 780 1 839 2 394 2 240 5 731 4 695 2 115 Lung cancer b 1 0 6 5 2 18 84 58 10 40 99 12 10 16 20 26 NA NA NA 137 652 128 599 483 284 164 1 561 1 770 5 688 COPD a 0 1 0 0 1 1 7 39 34 72 10 15 42 12 16 88 NA NA NA 369 740 165 163 1 350 1 530 3 168 1 666 ALRI 22 233 13 683 Number of deaths Mongolia Country Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Republic of Korea Republic of Moldova Romania Wpr (LMIC) Region Eur (LMIC) Emr (LMIC) Afr Sear Afr Wpr (LMIC) Sear Eur (HIC) Wpr (HIC) Amr (LMIC) Afr Afr Wpr (LMIC) Eur (HIC) Emr (HIC) Emr (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Eur (HIC) Emr (HIC) Wpr (HIC) Eur (LMIC) Eur (LMIC)

69 7 8 61 40 19 21 26 67 28 34 19 50 16 40 17 27 39 36 38 36 16 28 60 89 NA NA NA 0.0 0.2 Age-standardized rate 98 21 21 19 14 28 15 61 19 30 21 64 35 21 27 24 15 38 21 14 15 18 30 46 NA NA NA 0.0 0.4 Crude rate Deaths per 100 000 capita NA NA NA (6, 55) (0, 31) (0, 40) (1, 26) (0, 81) (0, 122) (0, 318) (0, 3196) (0, 1886) (522, 915) (543, 1479) (122, 3734) (396, 2031) (1220, 3320) (7027, 9293) (1592, 2440) (3821, 6858) (2681, 4156) (1271, 3895) (5675, 9843) (3542, 8251) (2044, 5246) (1210, 11062) (4644, 11955) (11397, 17206) (59079, 192348) Uncertainty interval 0 38 21 25 18 74 40 NA NA NA 732 188 Total Total 2 227 8 119 2 005 5 435 1 783 1 094 3 465 2 144 2 632 6 860 7 792 8 093 1 482 5 994 3 615 14 356 140 851 b 8 5 0 9 18 12 33 78 NA NA NA 940 764 617 248 813 240 471 864 295 3 335 1 952 6 486 1 765 2 273 3 161 1 496 1 189 46 216 Stroke b 6 0 15 12 10 31 47 26 NA NA NA 496 529 398 544 265 273 493 750 IHD 4 128 2 049 2 210 4 608 3 134 4 846 1 738 3 779 1 743 83 938 b 3 1 1 2 8 0 8 4 4 19 32 22 32 95 NA NA NA 362 285 416 217 365 118 408 538 9 840 1 341 1 638 1 834 Lung cancer b 0 0 1 0 0 0 7 0 34 67 88 15 16 21 10 16 43 28 66 86 NA NA NA 723 236 464 125 275 199 COPD a 1 0 5 4 0 1 5 0 0 3 2 0 0 59 33 52 NA NA NA 133 738 613 745 116 501 1 362 1 160 1 200 2 878 ALRI Number of deaths Russian Federation Country Rwanda Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Eur (HIC) Region Afr Amr (HIC) Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Afr Emr (HIC) Afr Eur (LMIC) Afr Afr Wpr (HIC) Eur (HIC) Eur (HIC) Wpr (LMIC) Emr (LMIC) Afr Emr (LMIC) Eur (HIC) Sear Emr (LMIC) Amr (LMIC) Afr Eur (HIC) Eur (HIC) Emr (LMIC) Eur (LMIC)

70 7 2 28 47 31 42 24 44 48 45 65 28 13 21 13 85 27 33 58 30 23 NA NA 108 Age-standardized rate 7 1 33 66 19 22 26 43 44 71 23 26 12 12 21 57 20 30 27 16 13 NA NA 120 Crude rate Deaths per 100 000 capita NA NA (0, 41) (0, 403) (0, 515) (564, 749) (194, 996) (287, 2322) (679, 3600) (137, 2949) (1107, 1635) (3739, 5540) (1134, 5197) (673, 79137) (3513, 7645) (4424, 7612) (6239, 10044) (5387, 22534) (1539, 80804) (7937, 23064) (3028, 10148) (17790, 26921) (27197, 38289) (19758, 34835) Uncertainty interval 2 NA NA 210 351 655 713 Total Total 1 366 1 454 4 631 3 667 7 989 5 765 6 113 6 667 2 411 1 856 22 375 32 668 54 507 16 355 38 043 16 282 27 340 b 1 58 96 NA NA 660 566 944 172 217 854 616 7 302 1 527 2 905 3 749 2 108 5 893 4 936 1 582 2 144 10 053 12 824 15 644 Stroke b 1 73 NA NA 390 372 223 399 308 519 366 IHD 9 669 2 465 2 425 1 624 7 366 1 599 9 984 3 466 5 081 2 849 14 813 38 707 22 560 b 0 26 12 28 62 36 24 NA NA 292 492 114 126 169 444 814 122 123 4 323 6 498 2 644 4 803 8 836 4 897 Lung cancer b 2 2 0 23 19 55 13 14 18 51 NA NA 976 102 192 268 424 110 721 195 126 135 1 065 1 127 COPD a 1 3 9 4 0 50 45 63 12 33 NA NA 104 484 240 130 722 125 591 997 700 3 141 1 912 1 415 ALRI Number of deaths Thailand Country The former Yugoslav Republic of Macedonia Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United Republic of Tanzania United States of America Uruguay Uzbekistan Vanuatu Venezuela, Bolivarian Republic of Viet Nam Yemen Zambia Zimbabwe Sear Region Eur (LMIC) Sear Afr Wpr (LMIC) Amr (HIC) Emr (LMIC) Eur (LMIC) Eur (LMIC) Wpr (LMIC) Afr Eur (LMIC) Emr (HIC) Eur (HIC) Afr Amr (HIC) Amr (HIC) Eur (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Emr (LMIC) Afr Afr Age group includes 25 years and above. Age group includes less than 5 years old.

AAP: Ambient air pollution; ALRI: Acute lower respiratory disease; COPD: Chronic obstructive pulmonary disease; IHD: Ischaemic heart disease. Afr: Africa; Amr: America; Emr: Eastern Mediterranean; Eur: Mediterranean; Eastern Emr: America; Amr: Africa; Afr: disease. heart Ischaemic IHD: disease; pulmonary obstructive Chronic COPD: disease; respiratory lower Acute ALRI: pollution; air Ambient AAP: Europe; Sear: South-East Asia; Wpr: Western Pacific; LMIC: Low- and middle-income countries; HIC: High-income NA: Not available. a b

71 9 7 8 52 11 40 28 27 41 38 40 21 49 27 31 46 12 11 45 81 42 34 47 17 13 0.2 0.2 Age-standardized rate 27 11 15 23 84 90 22 25 13 10 25 10 20 42 13 32 77 38 64 26 24 27 17 20 0.1 0.4 107 Crude rate Deaths per 100 000 capita (0, 17) (2, 25) (0, 28) (0, 37) (0, 14) (0, 12) (1, 246) (46, 58) (69, 105) (0, 1312) (44, 6579) (34, 1756) (913, 1410) (832, 1594) (450, 1944) (407, 2839) (960, 1694) (352, 1217) (625, 4980) (3271, 4734) (1179, 3387) (1268, 2005) (4463, 6739) (1930, 6862) (1094, 5831) (4747, 17119) (12074, 18726) Uncertainty interval 0 8 86 17 20 52 25 43 10 160 915 Total Total 4 006 2 269 1 164 1 623 1 258 4 578 1 284 1 962 1 372 1 161 5 544 4 851 3 096 4 294 11 201 15 084 b 0 7 9 9 3 3 90 29 17 12 873 830 454 398 553 656 296 351 440 924 1 850 4 634 1 206 5 422 2 537 1 359 1 752 Stroke b 0 9 6 5 49 31 10 13 25 24 579 623 509 499 387 797 721 400 653 IHD 1 896 5 110 3 289 3 345 1 176 2 000 2 375 1 828 b 0 2 1 7 7 1 1 7 6 1 0 21 45 70 52 65 63 77 14 169 124 319 859 250 118 443 1 144 Lung cancer b 0 1 0 0 0 3 0 9 0 0 83 33 44 42 35 14 11 12 27 19 47 81 21 88 54 223 3 729 COPD a 8 0 1 1 1 9 0 1 1 1 1 0 3 0 0 3 90 17 60 36 763 120 323 149 1 728 1 798 1 452 ALRI Number of deaths Country Bulgaria Brunei Darussalam Burkina Faso Brazil Bosnia and Herzegovina Botswana Belgium Bolivia, Plurinational States of Belize Benin Bhutan Bangladesh Barbados Belarus Bahamas Bahrain Austria Azerbaijan Australia Albania Algeria Andorra Antigua and Barbuda Argentina Armenia Afghanistan Angola Region Eur (LMIC) Wpr HIC Afr Amr (LMIC) Eur (LMIC) Afr Eur (HIC) Amr (LMIC) Amr (LMIC) Afr Sear Sear Amr (HIC) Eur (LMIC) Amr (HIC) Emr (HIC) Eur (HIC) Eur (LMIC) Wpr (HIC) Eur (LMIC) Afr Eur (HIC) Amr (HIC) Amr (LMIC) Eur (LMIC) Emr (LMIC) Afr

73 8 8 9 2 24 23 11 64 14 36 50 11 21 18 40 46 45 20 29 14 36 47 60 28 51 32 42 NA Age-standardized rate 5 24 20 10 46 17 19 22 31 12 58 32 14 21 58 26 52 15 12 24 31 13 70 23 31 19 26 NA Crude rate Deaths per 100 000 capita NA (0, 9) (53, 98) (12, 78) (35, 81) (19, 896) (36, 150) (9, 2996) (607, 941) (82, 1470) (194, 331) (279, 863) (289, 882) (378, 1041) (941, 1605) (132, 2638) (293, 3351) (845, 1444) (102, 2163) (589, 2082) (890, 12333) (2194, 3323) (2017, 3643) (1086, 2877) (2737, 4005) (5520, 16614) (16245, 23495) (389791, 542399) Uncertainty interval 6 94 78 56 57 NA 797 767 546 270 568 570 838 Total Total 1 035 1 297 1 791 2 147 7 288 2 786 2 932 1 945 1 168 3 322 1 416 1 323 19 682 10 952 462 436 b 3 41 25 87 27 38 NA 299 180 350 145 440 540 881 685 255 886 536 500 205 171 557 455 7 743 3 211 3 836 1 249 Stroke 219 575 b 3 25 40 14 14 NA 380 478 575 180 693 863 147 580 173 344 385 146 421 824 615 210 IHD 1 830 2 226 1 702 1 555 10 018 129 846 b 0 2 2 0 8 4 1 61 45 64 12 25 22 16 22 83 11 NA 564 199 146 353 364 299 237 234 1 561 67 281 Lung cancer b 0 3 1 1 2 18 44 10 21 18 31 13 33 19 16 29 39 30 11 62 28 22 NA 868 571 199 126 42 903 COPD a 0 0 0 3 0 1 1 7 2 1 38 20 35 23 14 66 NA 490 114 652 123 186 133 625 4 665 1 028 2 831 1 165 ALRI Number of deaths Country Ecuador Egypt El Salvador Dominican Republic Dominica Denmark Djibouti Croatia Cuba Cyprus Democratic People’s Republic of Korea Democratic Republic of the Congo Cook Islands Costa Rica Czech Republic Côte d’Ivoire Congo Comoros Chad Chile Colombia Central African Republic China Canada Cameroon Cambodia Cabo Verde Burundi Region Amr (LMIC) Emr (LMIC) Amr (LMIC) Amr (LMIC) Amr (LMIC) Eur (HIC) Emr (LMIC) Eur (HIC) Amr (LMIC) Eur (HIC) Sear Afr Wpr (LMIC) Amr (LMIC) Eur (HIC) Afr Afr Afr Afr Amr (HIC) Amr (LMIC) Afr Wpr (LMIC) Amr (HIC) Afr Wpr (LMIC) Afr Afr

74 7 7 8 3 9 2 7 5 46 51 31 57 26 29 40 44 44 41 13 16 16 38 38 42 46 30 42 19 0.2 Age-standardized rate 6 5 30 25 13 13 30 22 43 18 73 27 30 26 28 23 38 17 12 21 86 19 22 22 13 23 23 11 0.1 Crude rate Deaths per 100 000 capita (0, 32) (0, 13) (0, 95) (0, 160) (1, 292) (0, 722) (7, 148) (15, 480) (18, 355) (11, 296) (36, 295) (378, 615) (533, 832) (239, 840) (196, 2313) (707, 2867) (736, 1096) (137, 1882) (2657, 5481) (2973, 4618) (2013, 3923) (1156, 2365) (1514, 5912) (1973, 8087) (9706, 12963) (6297, 11395) (5497, 15113) (14114, 35705) (214832, 312709) Uncertainty interval 9 1 10 88 305 501 678 104 220 912 171 152 146 185 547 Total Total 4 016 9 128 3 824 1 544 3 028 2 178 1 219 1 868 4 349 5 263 11 306 26 941 11 652 259 977 b 2 4 0 71 47 86 72 27 35 87 35 139 878 227 815 236 957 515 764 245 1 394 3 513 3 434 1 676 3 133 1 602 2 055 18 347 96 383 Stroke b 5 5 0 53 61 45 64 26 169 209 334 408 398 373 853 116 100 130 911 IHD 1 884 6 858 3 808 5 875 2 164 1 039 6 033 1 044 1 547 89 727 b 2 1 2 0 5 1 9 5 9 0 1 59 37 52 68 16 33 10 204 469 139 714 149 196 1 661 1 341 6 202 2 238 1 153 Lung cancer b 6 0 6 0 2 0 2 0 0 8 0 3 69 14 66 35 33 32 12 33 23 46 11 51 177 224 297 246 47 210 COPD a 0 0 2 2 0 2 0 0 2 4 0 0 1 0 45 69 50 21 23 466 289 264 178 313 450 152 1 081 2 049 ALRI 20 455 Number of deaths Country Iraq Ireland Iran (Islamic Republic of) Israel Italy Indonesia Hungary Iceland India Haiti Honduras Guyana Greece Grenada Guatemala Guinea Guinea-Bissau Germany Ghana Georgia Estonia Finland France Gambia Eritrea Ethiopia Fiji Gabon Equatorial Guinea Region Emr (LMIC) Eur (HIC) Emr (LMIC) Eur (HIC) Eur (HIC) Sear Eur (LMIC) Eur (HIC) Sear Amr (LMIC) Amr (LMIC) Amr (LMIC) Eur (HIC) Amr (LMIC) Amr (LMIC) Afr Afr Eur (HIC) Afr Eur (LMIC) Eur (HIC) Eur (HIC) Eur (HIC) Afr Afr Afr Wpr (LMIC) Afr Afr

75 6 8 5 12 14 44 11 62 35 27 17 11 45 24 36 29 21 47 45 27 34 18 33 53 18 NA 0.1 0.0 Age-standardized rate 9 6 9 18 15 12 24 25 34 18 17 27 70 17 18 20 21 34 26 83 10 18 17 56 23 NA 0.1 0.0 Crude rate Deaths per 100 000 capita NA (0, 6) (0, 7) (0, 7) (4, 22) (26, 64) (0, 307) (30, 57) (0, 387) (39, 122) (284, 674) (44, 2267) (417, 599) (116, 147) (492, 656) (225, 402) (578, 1113) (865, 1379) (910, 2659) (380, 1283) (353, 1253) (677, 1121) (865, 3052) (599, 7483) (5620, 9341) (1469, 3998) (1379, 3124) (4383, 15975) Uncertainty interval 4 0 0 95 51 16 45 NA 459 831 117 213 856 923 508 131 983 573 322 Total Total 7 620 2 685 2 436 1 146 1 961 1 441 2 229 4 895 11 883 b 1 0 5 0 35 12 47 17 42 NA 194 924 820 331 342 664 120 305 160 296 291 815 239 184 2 255 1 015 1 744 5 052 Stroke b 2 0 9 0 53 32 37 14 48 75 NA 123 647 433 765 371 489 325 274 591 639 477 276 114 IHD 4 307 1 342 2 948 4 089 b 0 0 6 4 6 0 5 1 1 9 0 20 31 34 12 24 35 62 34 18 31 21 18 NA 505 220 121 2 670 Lung cancer b 0 0 0 0 1 4 2 2 6 2 1 0 9 2 10 42 47 23 17 23 24 10 14 43 14 63 NA 348 COPD a 0 0 1 0 0 0 0 0 3 5 0 9 3 13 13 31 38 21 38 28 NA 205 129 383 410 167 892 1 046 ALRI Number of deaths Country Monaco Mexico Micronesia (Federated States of) Mauritius Marshall Islands Mauritania Malta Malaysia Maldives Mali Libya Lithuania Luxembourg Malawi Liberia Madagascar Lesotho Lao People’s Democratic Republic Latvia Lebanon Kuwait Kyrgyzstan Kiribati Kazakhstan Kenya Japan Jordan Jamaica Region Eur (HIC) Amr (LMIC) Wpr (LMIC) Afr Wpr (LMIC) Afr Eur (HIC) Wpr (LMIC) Sear Afr Emr (LMIC) Eur (HIC) Eur (HIC) Afr Afr Afr Afr Wpr (LMIC) Eur (HIC) Emr (LMIC) Emr (HIC) Eur (LMIC) Wpr (LMIC) Eur (LMIC) Afr Wpr (HIC) Emr (LMIC) Amr (LMIC)

76 5 9 9 5 41 25 28 10 45 26 34 19 14 10 28 51 25 58 42 29 49 56 19 27 53 22 NA NA NA 0.2 Age-standardized rate 7 6 9 14 88 58 18 71 62 23 14 13 15 33 20 19 33 26 12 13 31 43 12 23 32 41 NA NA NA 0.4 Crude rate Deaths per 100 000 capita NA NA NA (30, 38) (0, 488) (0, 230) (0, 2366) (22, 275) (81, 837) (10, 583) (21, 253) (97, 161) (25, 1211) (206, 588) (114, 223) (64, 2509) (316, 567) (953, 2307) (3523, 5338) (4830, 7618) (1498, 2377) (3505, 6257) (1773, 4193) (2851, 4570) (9373, 13862) (8520, 13378) (9511, 13676) (27433, 92271) (17347, 25554) (22064, 31251) Uncertainty interval 34 10 NA NA NA 762 450 214 302 174 177 554 167 130 458 Total Total 4 431 1 512 6 327 1 948 4 728 1 726 2 859 1 569 3 769 67 714 11 651 11 074 21 283 26 390 11 493 b 7 3 42 91 69 66 63 NA NA NA 415 433 651 204 494 942 190 103 636 198 1 914 2 767 4 514 4 297 1 588 7 008 9 068 5 491 1 991 26 465 Stroke b 5 21 85 84 88 50 47 NA NA NA 273 847 200 475 559 146 290 313 224 IHD 1 100 1 024 3 037 5 284 5 226 1 446 4 347 9 764 3 257 1 506 39 313 b 5 9 1 2 6 3 67 38 22 59 11 25 21 85 19 13 NA NA NA 432 312 636 438 692 132 361 1 326 1 682 1 678 1 208 Lung cancer b 6 1 9 5 3 6 3 0 2 5 0 7 89 51 66 64 43 19 33 28 NA NA NA 192 171 160 902 276 893 1 313 COPD a 0 1 3 7 5 1 0 8 0 5 0 40 62 72 18 75 30 16 15 NA NA NA 756 354 644 567 159 1 313 9 519 5 883 ALRI Number of deaths Country Portugal Qatar Republic of Korea Republic of Moldova Romania Russian Federation Poland Peru Philippines Paraguay Nauru Nepal Netherlands Niger Nigeria Niue Norway Pakistan Palau Panama Papua New Guinea New Zealand Nicaragua Oman Namibia Myanmar Mozambique Montenegro Morocco Mongolia Region Eur (HIC) Emr (HIC) Wpr (HIC) Eur (LMIC) Eur (LMIC) Eur (HIC) Eur (HIC) Amr (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Sear Eur (HIC) Afr Afr Wpr (LMIC) Eur (HIC) Emr (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Wpr (HIC) Amr (LMIC) Emr (HIC) Afr Sear Afr Eur (LMIC) Emr (LMIC) Wpr (LMIC)

77 6 4 92 41 30 13 32 32 24 31 26 11 11 28 51 26 24 12 56 15 19 26 33 NA NA NA 0.1 0.0 Age-standardized rate 48 16 21 18 12 22 11 19 19 26 28 30 16 58 29 14 50 15 25 18 20 15 18 NA NA NA 0.4 0.0 Crude rate Deaths per 100 000 capita NA NA NA (0, 52) (0, 27) (0, 10) (0, 15) (0, 20) (2, 24) (0, 183) (0, 926) (0, 1582) (160, 899) (55, 1714) (227, 389) (208, 566) (577, 1757) (426, 3981) (768, 1165) (562, 1531) (1070, 2741) (1261, 2862) (2095, 5350) (5511, 8281) (2117, 3689) (1250, 1928) (1649, 2892) (2746, 3695) Uncertainty interval 0 7 19 32 11 13 16 NA NA NA 660 110 979 313 420 887 962 Total Total 1 889 2 097 1 190 2 581 3 635 6 928 2 915 1 612 2 306 3 199 1 026 b 6 0 2 4 7 9 55 16 NA NA NA 700 513 172 980 420 961 219 421 134 122 323 398 452 1 537 3 690 1 080 1 559 Stroke b 0 4 6 4 6 12 27 13 NA NA NA 893 328 215 759 113 112 198 829 203 263 222 IHD 1 418 1 249 1 987 1 745 1 072 1 429 b 2 1 2 0 0 3 0 0 7 1 36 96 14 40 10 63 95 12 92 NA NA NA 148 324 579 100 111 363 Lung cancer b 0 4 0 0 6 4 4 0 6 0 0 0 47 12 22 26 16 96 75 10 33 24 90 16 NA NA NA 162 COPD a 0 0 1 0 0 2 0 1 1 0 0 2 0 48 23 13 29 NA NA NA 214 509 524 627 353 265 329 1 224 ALRI Number of deaths Country Tajikistan Sweden Switzerland Syrian Arab Republic Swaziland Spain Suriname South Africa South Sudan Sri Lanka Sudan Somalia Solomon Islands Slovakia Slovenia Singapore Serbia Seychelles Sierra Leone Senegal Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Rwanda Saint Kitts and Nevis Saint Lucia Region Eur (LMIC) Eur (HIC) Eur (HIC) Emr (LMIC) Afr Eur (HIC) Amr (LMIC) Afr Emr (LMIC) Sear Emr (LMIC) Emr (LMIC) Wpr (LMIC) Eur (HIC) Eur (HIC) Wpr (HIC) Eur (LMIC) Afr Afr Afr Amr (LMIC) Wpr (LMIC) Eur (HIC) Afr Emr (HIC) Afr Amr (HIC) Amr (LMIC)

78 ; 1 5 8 9 22 24 49 21 71 20 19 39 48 26 89 32 33 17 29 40 33 21 NA NA Age-standardized rate : Eastern Mediterranean Eastern : ; Emr ; 1 5 13 15 23 25 11 16 53 16 23 11 20 62 33 33 20 18 21 52 28 NA NA 118 : America : Crude rate Deaths per 100 000 capita ; Amr ; : Africa : NA NA (0, 14) (0, 200) (0, 187) (74, 380) (70, 1524) (100, 137) (442, 650) (295, 1633) (483, 2310) (145, 1154) (1242, 4274) (608, 36782) (1763, 3091) (245, 41329) (1568, 3416) (2767, 4473) (1489, 2188) (8312, 14709) (3717, 10905) (2222, 10324) (7563, 11200) (10666, 14818) : Not available. Uncertainty interval ; NA 1 99 NA NA 968 278 118 727 137 543 Total Total 1 084 2 781 2 461 2 575 7 685 3 552 7 429 1 624 1 833 9 412 11 502 17 113 28 673 12 701 : Ischaemic heart disease. Afr disease. heart Ischaemic : ; IHD ; b 0 39 44 29 NA NA 365 379 997 748 120 980 482 732 296 342 7 292 3 362 2 560 7 479 2 207 1 393 5 054 3 406 : High-income countries Stroke ; HIC b 0 59 83 38 NA NA 194 210 117 692 660 189 141 IHD 1 060 1 253 2 177 9 355 4 622 2 843 1 037 6 273 1 010 4 320 20 654 b 0 4 8 9 41 10 29 36 15 13 66 26 29 53 NA NA 344 111 439 862 1 245 4 004 2 166 1 424 : Chronic obstructive pulmonary disease pulmonary obstructive Chronic : Lung cancer b ; COPD ; : Low- and middle-income countries 0 4 4 7 0 1 8 23 13 64 62 90 76 47 97 26 43 NA NA 557 378 207 411 219 COPD ; LMIC a 0 2 3 6 1 1 54 15 26 53 19 23 42 NA NA 345 473 631 230 303 841 101 231 1 335 ALRI Number of deaths : Western Pacific ; Wpr : Acute lower respiratory disease respiratory lower Acute : ; ALRI ; Country Zimbabwe Zambia Yemen Vanuatu Venezuela, Bolivarian Republic of Viet Nam United States of America Uruguay Uzbekistan Ukraine United Arab Emirates United Kingdom United Republic of Tanzania Tuvalu Uganda Turkey Turkmenistan Tunisia Togo Tonga Trinidad and Tobago Timor-Leste The former Yugoslav Republic of Macedonia Thailand ; Sear: South-East Asia : Ambient air pollution air Ambient : : Europe Region Afr Afr Emr (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Amr (HIC) Amr (HIC) Eur (LMIC) Eur (LMIC) Emr (HIC) Eur (HIC) Afr Wpr (LMIC) Afr Eur (LMIC) Eur (LMIC) Emr (LMIC) Afr Wpr (LMIC) Amr (HIC) Sear Eur (LMIC) Sear Age group includes 25 years and above. Age group includes less than 5 years old.

AAP a b Eur

79 80 51 48 17 56 24 26 95 20 69 23 37 45 13 88 21 22 56 39 35 67 24 18 73 65 55 0.3 0.3 Age-standardized rate 37 64 35 31 35 22 27 37 50 18 12 29 18 38 11 26 27 26 11 15 29 34 0.4 0.2 107 110 100 130 Crude rate Deaths per 100 000 capita (0, 18) (0, 13) (0, 48) (0, 35) (3, 29) (0, 24) (1, 177) (1, 1520) (86, 107) (84, 132) (67, 2474) (379, 1237) (750, 6449) (540, 3382) (100, 7150) (455, 2028) (759, 2607) (4497, 6829) (2638, 9293) (1548, 7547) (1047, 1890) (1301, 2703) (1070, 1635) (1480, 2370) (3756, 5483) (1202, 3536) (6486, 22871) (18175, 27311) Uncertainty interval 9 0 12 51 32 96 24 19 928 106 113 Total Total 5 601 6 573 3 962 5 462 1 589 1 518 2 334 4 873 2 085 1 322 1 357 1 915 4 628 2 354 1 677 22 365 15 041 b 3 3 8 9 9 6 0 22 24 46 340 786 324 200 566 940 320 512 518 624 862 570 1 318 2 694 1 364 5 195 4 982 1 578 Stroke b 8 5 9 0 34 18 51 12 45 39 451 838 922 845 777 380 617 775 541 322 IHD 1 985 3 088 2 880 1 481 6 946 3 392 7 877 2 135 b 2 1 9 3 3 2 7 8 0 26 18 12 43 24 10 224 121 515 306 437 186 493 913 458 791 1 057 3 516 1 757 Lung cancer b 0 0 0 0 4 0 1 2 0 11 84 77 32 35 26 46 74 26 18 33 56 39 23 100 111 317 112 4 587 COPD a 5 0 0 5 0 0 2 1 0 3 1 0 1 0 49 75 12 18 12 192 392 145 108 888 753 1 974 2 235 2 121 ALRI Number of deaths Country Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia, Plurinational States of Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Region Emr (LMIC) Eur (LMIC) Afr Eur (HIC) Afr Amr (HIC) Amr (LMIC) Eur (LMIC) Wpr (HIC) Eur (HIC) Eur (LMIC) Amr (HIC) Emr (HIC) Sear Amr (HIC) Eur (LMIC) Eur (HIC) Amr (LMIC) Afr Sear Amr (LMIC) Eur (LMIC) Afr Amr (LMIC) Wpr HIC Eur (LMIC) Afr Afr

81 4 44 58 49 44 56 18 81 20 39 55 17 43 28 20 40 45 87 55 12 48 18 24 16 93 30 57 NA Age-standardized rate 6 21 34 28 28 38 19 81 15 19 32 16 75 41 25 64 26 69 34 21 28 18 19 13 55 26 35 NA Crude rate Deaths per 100 000 capita NA (0, 10) (16, 96) (44, 100) (96, 184) (32, 949) (45, 186) (11, 236) (17, 3612) (273, 455) (88, 1395) (582, 888) (116, 2348) (320, 1007) (325, 1046) (191, 3480) (392, 4417) (480, 1269) (3017, 4457) (1324, 3561) (1216, 2024) (2479, 4422) (1078, 1953) (2582, 3990) (1081, 14021) (6010, 18471) (19760, 28330) (478904, 669949) Uncertainty interval 6 70 69 NA 644 676 374 145 593 118 974 975 731 140 Total Total 1 519 3 677 1 049 2 395 1 654 3 571 1 546 2 337 3 324 2 823 8 308 12 082 23 849 570 396 b 2 39 33 84 20 50 42 NA 521 129 176 564 542 752 229 366 545 509 119 291 316 146 1 163 1 157 3 700 3 289 8 463 Stroke 229 798 b 3 23 18 80 32 49 NA 634 911 639 160 388 702 213 233 711 814 253 523 495 475 IHD 2 133 1 126 1 917 2 080 2 602 12 310 127 791 b 1 8 1 6 2 1 4 40 12 43 44 26 62 91 53 NA 147 269 350 449 437 622 846 203 101 1 772 1 515 161 198 Lung cancer b 5 1 2 2 0 5 35 12 34 33 49 27 13 31 40 50 45 18 10 24 32 NA 107 144 639 274 1 100 47 724 COPD a 0 3 1 0 4 0 2 0 0 11 92 17 31 50 49 26 39 NA 182 266 201 779 117 461 1 456 1 398 3 885 5 855 ALRI Number of deaths Country Cabo Verde Cambodia Cameroon Canada Central African Republic Chad Chile China Colombia Comoros Congo Cook Islands Costa Rica Croatia Cuba Cyprus Czech Republic Côte d’Ivoire Democratic People’s Republic of Korea Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Region Afr Wpr (LMIC) Afr Amr (HIC) Afr Afr Amr (HIC) Wpr (LMIC) Amr (LMIC) Afr Afr Wpr (LMIC) Amr (LMIC) Eur (HIC) Amr (LMIC) Eur (HIC) Eur (HIC) Afr Sear Afr Eur (HIC) Emr (LMIC) Amr (LMIC) Amr (LMIC) Amr (LMIC) Emr (LMIC) Amr (LMIC) Afr

82 4 4 68 18 28 12 38 50 68 18 42 26 32 23 38 49 47 48 39 59 80 45 54 89 13 16 18 26 0.4 Age-standardized rate 7 7 28 27 17 21 25 24 95 37 21 52 24 14 21 29 33 29 23 91 55 28 39 36 16 19 41 27 0.3 Crude rate Deaths per 100 000 capita (0, 19) (0, 32) (2, 334) (0, 208) (1, 892) (0, 193) (40, 328) (13, 379) (19, 399) (26, 591) (533, 887) (257, 468) (304, 1065) (988, 3824) (862, 1283) (136, 1950) (184, 2220) (709, 1095) (2282, 9311) (1176, 2379) (1801, 3489) (3300, 5271) (4032, 8209) (2902, 12647) (6932, 19142) (7934, 15165) (18554, 46214) (12870, 17101) (300418, 431873) Uncertainty interval 1 13 11 697 167 181 200 215 243 126 896 377 718 371 Total Total 7 994 6 605 1 873 2 700 2 829 1 068 1 247 1 478 4 324 6 069 14 508 34 851 14 961 11 929 361 161 b 0 5 2 21 27 68 87 88 52 53 289 594 748 237 491 720 266 782 123 164 2 540 1 172 2 240 1 128 3 776 1 807 2 380 98 618 18 179 Stroke b 1 7 7 73 55 59 70 177 118 133 957 482 350 393 475 234 300 132 IHD 1 409 2 246 1 083 7 359 1 349 2 161 9 626 3 090 4 484 10 906 159 661 b 8 0 4 1 2 2 2 27 21 11 59 94 11 46 56 84 64 180 159 694 991 524 273 3 103 4 583 1 286 3 609 4 730 20 132 Lung cancer b 1 0 1 5 0 4 1 8 0 6 6 11 80 11 30 39 38 37 19 46 93 21 349 323 703 257 104 335 63 291 COPD a 0 0 0 3 8 3 1 0 2 2 0 0 1 1 5 38 64 90 54 211 517 218 376 310 311 544 3 516 1 453 ALRI 19 459 Number of deaths Country Eritrea Estonia Ethiopia Fiji Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran (Islamic Republic of) Iraq Ireland Israel Italy Jamaica Region Afr Eur (HIC) Afr Wpr (LMIC) Eur (HIC) Eur (HIC) Afr Afr Eur (LMIC) Eur (HIC) Afr Eur (HIC) Amr (LMIC) Amr (LMIC) Afr Afr Amr (LMIC) Amr (LMIC) Amr (LMIC) Eur (LMIC) Eur (HIC) Sear Sear Emr (LMIC) Emr (LMIC) Eur (HIC) Eur (HIC) Eur (HIC) Amr (LMIC)

83 13 54 91 25 52 75 60 65 39 33 11 73 51 15 42 35 37 29 57 22 49 26 20 12 89 59 0.0 NA 0.2 Age-standardized rate 6 31 25 67 14 19 41 31 37 17 38 77 23 20 16 27 19 31 38 27 25 15 23 48 82 0.0 NA 0.1 101 Crude rate Deaths per 100 000 capita NA (0, 8) (0, 15) (8, 43) (0, 12) (0, 332) (0, 314) (39, 78) (39, 100) (68, 204) (317, 397) (42, 2006) (314, 779) (462, 822) (187, 311) (776, 1049) (697, 8392) (468, 1502) (404, 1477) (678, 1150) (766, 1080) (829, 1646) (796, 1302) (1130, 3920) (1047, 3058) (2166, 4914) (1326, 3904) (7055, 26040) (6837, 11188) Uncertainty interval 0 0 4 61 32 78 NA 910 356 937 926 175 119 519 158 665 252 Total Total 5 398 2 873 1 145 1 001 1 222 1 070 2 253 1 264 3 814 2 533 9 178 18 907 b 0 0 1 75 75 45 12 15 13 47 81 NA 244 965 326 315 200 114 343 210 968 542 954 677 175 236 5 410 1 367 2 014 Stroke b 0 0 2 41 38 22 14 44 92 NA 468 715 245 718 344 565 647 605 659 646 326 475 114 347 112 IHD 6 197 3 358 2 288 5 677 b 0 5 1 8 3 6 0 1 47 26 55 87 25 29 21 17 47 58 NA 138 548 165 147 220 189 111 450 831 7 072 Lung cancer b 0 2 5 9 2 2 1 1 1 0 0 1 25 74 17 19 24 15 37 12 23 24 13 11 NA 218 106 107 393 COPD a 9 0 8 0 3 0 0 0 0 1 0 0 0 35 50 28 44 33 18 16 24 NA 232 504 364 211 262 1 129 1 245 ALRI Number of deaths Country Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Lao People’s Democratic Republic Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia (Federated States of) Monaco Mongolia Montenegro Region Wpr (HIC) Emr (LMIC) Eur (LMIC) Afr Wpr (LMIC) Emr (HIC) Eur (LMIC) Wpr (LMIC) Eur (HIC) Emr (LMIC) Afr Afr Emr (LMIC) Eur (HIC) Eur (HIC) Afr Afr Wpr (LMIC) Sear Afr Eur (HIC) Wpr (LMIC) Afr Afr Amr (LMIC) Wpr (LMIC) Eur (HIC) Wpr (LMIC) Eur (LMIC)

84 8 34 24 68 26 61 16 39 56 46 42 56 15 15 32 20 61 56 10 32 25 72 54 91 48 NA 0.3 NA NA Age-standardized rate 8 27 14 44 12 39 28 24 37 30 13 14 36 13 22 15 36 80 20 10 29 76 84 23 NA 0.5 NA NA 110 Crude rate Deaths per 100 000 capita NA NA NA (0, 235) (0, 721) (16, 206) (14, 644) (33, 362) (0, 2412) (67, 2850) (212, 411) (327, 936) (42, 1685) (128, 162) (111, 1037) (657, 1790) (3247, 5344) (3922, 6802) (1220, 3089) (2028, 4950) (1779, 2772) (5520, 8637) (6137, 9965) (9234, 13274) (20692, 30689) (26659, 40154) (13500, 21370) (11904, 17834) (31229, 100241) Uncertainty interval 11 NA NA NA 130 692 334 310 250 305 709 145 Total Total 4 366 1 773 5 216 2 291 3 325 2 291 1 007 7 092 1 496 8 170 1 201 11 171 25 467 32 852 17 622 14 938 73 137 b 2 58 66 73 90 29 NA NA NA 642 342 209 894 114 260 697 364 378 488 1 544 5 825 1 595 6 975 7 637 5 827 3 863 1 985 2 569 19 752 Stroke b 7 42 84 NA NA NA 285 727 381 514 184 168 143 101 329 374 943 274 IHD 1 873 2 219 1 882 5 271 1 103 8 841 6 704 1 538 3 757 11 008 44 624 b 6 2 7 37 39 79 19 25 17 87 30 12 NA NA NA 683 484 132 339 256 150 1 572 1 147 2 032 1 605 4 053 3 369 1 683 8 158 Lung cancer b 6 0 6 4 3 6 1 56 25 74 21 55 10 62 13 17 18 NA NA NA 669 869 376 439 312 195 113 531 4 375 COPD a 1 0 0 5 7 7 0 1 5 8 18 42 91 24 91 48 72 NA NA NA 210 784 887 385 910 409 1 855 7 800 ALRI 12 714 Number of deaths Country Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Republic of Korea Republic of Moldova Romania Russian Federation Rwanda Region Emr (LMIC) Afr Sear Afr Wpr (LMIC) Sear Eur (HIC) Wpr (HIC) Amr (LMIC) Afr Afr Wpr (LMIC) Eur (HIC) Emr (HIC) Emr (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Eur (HIC) Emr (HIC) Wpr (HIC) Eur (LMIC) Eur (LMIC) Eur (HIC) Afr

85 24 23 27 78 31 45 27 49 22 55 25 30 53 40 10 54 40 20 25 11 80 86 35 NA NA NA 0.0 0.2 Age-standardized rate 24 19 13 30 15 71 22 30 26 71 41 23 29 26 19 49 24 16 13 21 39 43 39 NA NA NA 0.0 0.4 Crude rate Deaths per 100 000 capita NA NA NA (3, 31) (0, 15) (0, 19) (1, 16) (0, 54) (0, 70) (0, 135) (0, 961) (0, 1614) (336, 914) (294, 527) (67, 2020) (825, 1274) (693, 2138) (759, 7106) (227, 1132) (975, 2507) (4282, 5604) (2164, 3974) (1422, 2228) (5876, 8928) (3555, 6154) (2549, 6608) (2278, 5388) (10209, 15731) Uncertainty interval 0 21 10 12 11 42 78 21 NA NA NA 895 674 419 821 Total Total 4 921 1 043 3 128 1 852 1 165 7 429 1 442 4 279 4 877 4 458 3 898 1 726 12 962 b 9 4 5 3 0 4 16 24 NA NA NA 366 872 294 126 392 106 252 444 785 123 983 489 1 776 2 796 1 312 1 624 3 896 Stroke b 9 6 3 6 0 18 19 15 NA NA NA 266 195 346 153 160 278 979 422 850 IHD 2 699 1 221 1 139 2 622 1 885 3 101 2 361 5 349 b 2 1 1 1 5 0 6 3 2 20 12 18 78 59 NA NA NA 270 978 190 304 153 266 260 442 1 059 1 510 2 899 Lung cancer b 0 0 0 0 9 7 0 6 0 3 0 43 55 12 15 27 98 16 44 39 NA NA NA 145 302 178 124 757 COPD a 1 0 3 3 0 1 2 0 0 2 2 0 0 30 19 29 67 61 NA NA NA 348 392 736 651 676 287 1 654 ALRI Number of deaths Country Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Thailand Region Amr (HIC) Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Afr Emr (HIC) Afr Eur (LMIC) Afr Afr Wpr (HIC) Eur (HIC) Eur (HIC) Wpr (LMIC) Emr (LMIC) Afr Emr (LMIC) Eur (HIC) Sear Emr (LMIC) Amr (LMIC) Afr Eur (HIC) Eur (HIC) Emr (LMIC) Eur (LMIC) Sear

; 9 2 64 33 43 32 57 68 53 89 28 16 24 20 35 48 69 36 23 NA NA 131 103 : Eastern Mediterranean ; Emr Age-standardized rate : America 8 1 80 20 22 32 52 54 80 25 29 13 13 27 61 25 35 31 18 12 NA NA ; Amr 123 Crude rate Deaths per 100 000 capita : Africa NA NA (0, 28) (0, 216) (0, 316) (663, 986) (464, 612) (118, 617) (64, 1425) (142, 1168) (632, 2888) (382, 1968) (2246, 3354) (3471, 5568) (366, 37875) (1942, 4227) (882, 44069) (2662, 4521) (1779, 5876) (3006, 12230) (4221, 12158) (16480, 23516) (11414, 20146) : Not available. Uncertainty interval ; NA 2 : Ischaemic heart disease. Afr NA NA 823 110 727 214 537 434 888 Total Total 2 798 2 043 4 436 8 926 3 190 8 597 3 652 3 886 1 327 19 967 25 834 20 930 15 838 ; IHD b 1 29 52 97 NA NA 319 270 795 462 134 834 475 251 4 999 1 512 5 346 1 542 1 128 2 531 2 376 8 352 1 147 : High-income countries Stroke ; HIC b 1 35 NA NA 249 184 140 963 340 907 192 309 172 IHD 1 454 8 540 1 388 4 523 5 362 2 213 2 903 1 789 18 054 13 205 b 9 0 18 19 88 60 49 21 94 14 82 NA NA 239 463 134 333 470 5 636 2 205 2 637 4 833 3 651 : Chronic obstructive pulmonary disease Lung cancer ; COPD b : Low- and middle-income countries 1 2 9 0 5 15 12 59 29 95 63 10 64 71 28 NA NA 653 192 217 343 106 570 COPD ; LMIC a 1 1 6 7 2 0 27 25 77 37 18 71 NA NA 252 139 419 361 785 523 355 1 806 1 072 ALRI Number of deaths : Western Pacific ; Wpr : Acute lower respiratory disease ; ALRI Country The former Yugoslav Republic of Macedonia Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United Republic of Tanzania United States of America Uruguay Uzbekistan Vanuatu Venezuela, Bolivarian Republic of Viet Nam Yemen Zambia Zimbabwe : South-East Asia : Ambient air pollution : Europe; Sear Region Eur (LMIC) Sear Afr Wpr (LMIC) Amr (HIC) Emr (LMIC) Eur (LMIC) Eur (LMIC) Wpr (LMIC) Afr Eur (LMIC) Emr (HIC) Eur (HIC) Afr Amr (HIC) Amr (HIC) Eur (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Emr (LMIC) Afr Afr Age group includes 25 years and above. Age group includes less than 5 years old.

Eur AAP a b

87 7 5 363 424 657 508 451 254 333 979 261 334 390 659 1 302 1 547 1 195 1 010 1 183 1 764 1 007 1 072 1 794 1 467 1 348 1 399 2 456 Age-standardized rate 5 7 344 239 898 864 439 494 511 736 345 587 865 475 614 374 315 2 392 1 204 1 941 1 446 1 938 1 849 1 170 2 155 1 904 1 270 Crude rate YLLs 100 000 per capita (0, 637) (0, 1160) (0, 1428) (11, 564) (5, 2127) (133, 1216) (51, 14794) (25, 48436) (5402, 8140) (3769, 4651) (2607, 85184) (57791, 91575) (4528, 296175) (40925, 83926) (15359, 48721) (35531, 64880) (37122, 190585) (68806, 107340) (63334, 295345) (67634, 731631) (25610, 160025) (142448, 206211) (114860, 370840) (445478, 704450) (130976, 457620) (301813, 1053085) (938727, 1382416) Uncertainty interval 21 806 972 377 440 Total Total 6 682 9 353 1 393 4 207 1 631 88 439 74 303 55 079 65 026 51 919 36 575 174 680 696 511 239 884 121 024 214 908 204 523 324 026 439 587 109 506 566 080 1 142 024 b 5 85 268 361 159 367 288 1 412 3 551 1 080 7 774 63 521 52 792 31 729 28 907 28 263 57 557 12 773 42 857 53 614 28 109 11 355 88 567 14 364 Stroke 239 367 134 408 239 167 b 14 357 457 230 252 627 IHD 2 307 2 245 2 221 1 026 79 453 34 000 22 852 31 861 29 104 30 965 46 867 26 617 60 815 23 954 16 575 354 716 109 178 144 985 136 594 264 736 120 813 b 1 78 92 57 22 564 463 271 125 614 300 1 412 2 223 9 666 9 762 1 147 7 393 4 553 26 434 74 360 14 849 37 223 15 144 19 267 16 426 27 755 120 661 Lung cancer b 0 4 4 1 3 63 19 11 645 925 936 145 346 3 462 1 869 1 045 1 356 1 875 3 240 1 195 4 660 2 788 3 243 1 023 1 839 10 115 COPD 167 797 a 0 0 7 6 58 84 79 212 750 341 271 146 123 736 1 810 1 854 3 222 7 710 ALRI 64 834 24 091 17 953 30 968 12 253 149 811 342 278 334 716 349 663 Number of YLLs Bulgaria Brunei Darussalam Burkina Faso Benin Bhutan Bolivia, Plurinational States of Bosnia and Herzegovina Botswana Brazil Argentina Armenia Barbados Belarus Belize Afghanistan Algeria Andorra Angola Antigua and Barbuda Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Belgium Albania Country Eur (LMIC) Wpr HIC Afr Afr Sear Amr (LMIC) Eur (LMIC) Afr Amr (LMIC) Amr (LMIC) Eur (LMIC) Amr (HIC) Eur (LMIC) Amr (LMIC) Emr (LMIC) Afr Eur (HIC) Afr Amr (HIC) Wpr (HIC) Eur (HIC) Eur (LMIC) Amr (HIC) Emr (HIC) Sear Eur (HIC) Eur (LMIC) Region

89 64 NA 938 868 318 408 332 694 618 217 423 603 555 329 706 319 1 684 1 356 1 797 1 906 1 507 1 008 1 490 1 876 1 396 1 528 1 296 1 809 Age-standardized rate NA 685 606 373 105 360 761 290 622 387 439 511 793 433 320 1 733 1 328 1 681 2 179 1 691 1 278 1 371 1 158 1 611 1 069 1 104 1 887 1 312 Crude rate YLLs 100 000 per capita NA (17, 460) 26807904) (19543716, (934, 7945) (1883, 4261) (3291, 6130) (667, 128437) (3063, 14957) (1208, 35599) (4994, 74410) (28745, 97338) (7214, 157893) (47970, 79146) (24516, 86406) (22313, 58308) (30163, 45749) (8044, 133450) (39613, 70634) (10973, 17978) (72493, 282653) (26116, 393384) (54884, 662681) (90511, 139522) (294477, 449621) (146456, 422581) (117756, 207859) (977014, 1389703) (616636, 2080128) Uncertainty interval NA 314 Total Total 3 037 5 583 9 120 4 892 61 334 36 782 64 913 54 779 37 787 51 936 44 756 21 650 89 923 56 253 14 889 175 494 277 111 101 596 363 999 168 604 244 295 398 968 116 373 1 174 059 1 326 396 23 052 132 b NA 131 729 1 622 4 828 1 691 7 739 2 398 4 463 3 710 30 947 32 829 29 697 69 515 10 233 22 243 13 452 41 983 66 022 14 794 13 981 20 142 22 408 14 539 Stroke 405 956 169 397 212 525 10 174 964 b NA 792 953 146 IHD 8 325 1 576 7 771 2 784 9 162 17 513 21 865 35 272 50 212 20 685 26 362 10 805 89 424 22 393 96 799 45 243 25 151 18 890 65 292 42 354 26 245 579 459 144 887 5 586 044 b 41 29 27 NA 692 366 688 205 135 6 850 2 369 2 280 1 292 2 589 8 768 3 958 3 438 1 327 1 489 10 836 13 416 18 375 61 176 88 190 29 092 23 280 14 614 5 192 605 Lung cancer b 3 40 39 NA 120 342 925 116 349 634 634 821 361 1 264 1 172 4 061 1 386 1 545 1 341 4 440 1 221 1 756 1 589 1 288 41 035 23 675 11 364 COPD 1 487 398 a 7 91 13 13 34 NA 461 257 593 167 1 550 2 870 4 155 4 895 7 684 7 813 ALRI 28 605 41 024 14 382 29 392 86 432 20 907 125 078 237 841 220 184 611 121 129 982 955 030 Number of YLLs Burundi Cabo Verde Cambodia Canada Central African Republic Chad China Cameroon Chile Colombia Comoros Congo Cook Islands Country Egypt El Salvador Côte d’Ivoire Democratic People’s Republic of Korea Democratic Republic of the Congo Djibouti Dominica Dominican Republic Ecuador Cyprus Czech Republic Denmark Cuba Costa Rica Croatia Afr Afr Wpr (LMIC) Amr (HIC) Afr Afr Wpr (LMIC) Afr Amr (HIC) Amr (LMIC) Afr Afr Wpr (LMIC) Region Emr (LMIC) Amr (LMIC) Afr Sear Afr Emr (LMIC) Amr (LMIC) Amr (LMIC) Amr (LMIC) Eur (HIC) Eur (HIC) Eur (HIC) Amr (LMIC) Amr (LMIC) Eur (HIC)

90 9 72 52 570 833 554 203 921 292 426 251 781 245 271 205 905 1 899 1 211 1 493 1 184 1 386 1 008 1 400 1 159 1 188 1 545 1 685 1 203 1 690 Age-standardized rate 7 482 582 470 968 107 341 788 594 879 810 103 452 769 297 583 277 698 923 994 1 550 1 009 1 355 1 136 1 719 1 048 1 771 1 365 1 217 Crude rate YLLs 100 000 per capita (0, 720) (2, 1095) (0, 9613) 23179478) (2, 11267) (16469071, (32, 27740) (94, 11818) (790, 34369) (553, 18343) (997, 20463) (1203, 39255) (2213, 20886) (58770, 90213) (35558, 54901) (46465, 92862) (23614, 94105) (16838, 28299) (10043, 187988) (11065, 180089) (76667, 306566) (33231, 120529) (130357, 208824) (230550, 632146) (143698, 295100) (234110, 439693) (606437, 802052) (213090, 452214) (231594, 1134357) (918449, 2295002) Uncertainty interval 63 346 496 Total Total 7 341 5 601 5 979 23 239 74 147 45 045 12 714 18 938 73 280 90 033 10 561 59 524 22 850 12 941 117 287 171 188 116 861 216 907 478 060 224 647 709 084 348 333 703 207 327 596 1 731 346 19 590 004 b 9 64 204 983 926 5 041 3 057 3 452 5 070 2 281 4 408 2 016 30 979 31 806 11 548 40 672 12 286 43 169 25 925 84 821 80 135 26 883 15 798 79 372 80 659 Stroke 117 458 180 270 905 691 5 230 651 b 52 182 258 IHD 3 472 3 845 3 146 3 180 3 903 2 279 9 644 4 190 8 473 7 594 22 249 82 644 21 192 21 300 20 015 67 410 40 220 52 610 43 084 64 045 226 878 131 513 422 105 131 867 439 333 7 412 934 b 0 95 29 600 115 120 497 161 685 182 588 832 4 319 2 571 2 437 5 102 2 304 9 349 3 110 53 191 18 944 12 822 37 894 19 790 805 530 104 132 156 418 130 442 137 053 Lung cancer b 5 2 0 20 20 16 829 167 293 340 181 925 183 619 528 179 3 224 1 131 1 326 1 843 9 482 1 718 1 030 9 519 6 796 8 513 3 600 19 399 COPD 2 516 699 a 0 3 2 92 11 15 92 41 323 300 354 462 209 8 980 5 279 1 109 5 636 ALRI 62 630 14 444 35 958 52 024 10 347 87 880 32 875 54 425 91 681 505 240 229 870 3 624 190 Number of YLLs Guinea Guinea-Bissau Hungary India Guatemala Guyana Haiti Honduras Iceland France Gabon Gambia Georgia Germany Ghana Greece Grenada Finland Ethiopia Fiji Equatorial Guinea Eritrea Estonia Italy Israel Iran (Islamic Republic of) Iraq Ireland Indonesia Country Afr Afr Eur (LMIC) Sear Amr (LMIC) Amr (LMIC) Amr (LMIC) Amr (LMIC) Eur (HIC) Eur (HIC) Afr Afr Eur (LMIC) Eur (HIC) Afr Eur (HIC) Amr (LMIC) Eur (HIC) Afr Wpr (LMIC) Afr Afr Eur (HIC) Eur (HIC) Eur (HIC) Emr (LMIC) Emr (LMIC) Eur (HIC) Sear Region

91 0 4 NA 514 855 962 730 259 361 949 326 955 631 696 970 524 206 419 193 477 1 856 1 116 1 285 1 398 1 458 1 084 1 699 1 414 Age-standardized rate 0 3 NA 330 743 554 396 792 627 817 746 256 609 984 638 639 449 528 437 346 346 533 1 799 1 471 1 131 1 772 1 585 1 269 Crude rate YLLs 100 000 per capita NA (0, 820) (0, 247) (0, 642) (2, 29331) (290, 1520) (10, 28899) (1361, 2703) (1342, 3308) (2957, 8644) (33458, 53573) (2564, 190738) (35243, 68435) (26497, 44009) (24739, 35054) (22858, 73035) (38246, 51207) (13699, 17117) (10198, 18271) (28099, 73826) (91743, 206243) (76607, 243779) (26356, 110316) (35223, 411307) (89698, 380125) (146075, 450530) (219824, 753378) (315925, 512238) Uncertainty interval 0 3 NA 130 Total Total 1 138 2 109 2 605 6 707 44 367 10 708 51 310 15 355 36 100 29 997 73 203 55 569 15 363 44 646 14 599 47 930 289 795 160 693 116 732 176 677 266 644 271 641 555 095 422 185 b 0 1 26 NA 437 485 441 2 614 5 165 9 136 5 021 3 157 6 809 9 893 2 071 45 034 43 660 29 538 10 720 54 751 15 471 15 521 77 917 49 073 17 023 13 027 Stroke 178 114 102 089 b 0 2 72 NA 515 712 IHD 1 509 2 180 2 248 4 817 6 353 3 846 31 393 92 655 18 014 27 513 33 857 25 039 21 964 18 765 16 997 36 055 10 165 31 325 20 810 158 918 193 571 234 186 b 0 0 96 52 31 NA 854 635 413 163 856 332 577 1 701 5 763 3 975 6 886 4 812 5 274 3 593 2 293 2 148 2 131 4 326 18 812 19 050 31 617 178 246 Lung cancer b 0 0 1 26 55 19 74 31 NA 949 303 107 418 320 150 930 784 673 125 757 525 3 651 2 897 1 179 1 153 2 713 3 516 11 848 COPD a 2 0 0 1 0 63 67 38 NA 519 718 181 2 670 5 755 2 761 7 458 8 047 1 112 4 399 1 648 5 726 ALRI 82 914 67 818 36 161 30 826 42 444 208 016 183 436 Number of YLLs Mali Luxembourg Malaysia Maldives Malta Madagascar Malawi Marshall Islands Lithuania Liberia Libya Lebanon Lesotho Lao People’s Democratic Republic Latvia Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Jamaica Japan Jordan Mauritania Mauritius Micronesia (Federated States of) Monaco Mexico Country Afr Eur (HIC) Wpr (LMIC) Sear Eur (HIC) Afr Afr Wpr (LMIC) Eur (HIC) Afr Emr (LMIC) Emr (LMIC) Afr Wpr (LMIC) Eur (HIC) Eur (LMIC) Afr Wpr (LMIC) Emr (HIC) Eur (LMIC) Amr (LMIC) Wpr (HIC) Emr (LMIC) Afr Afr Wpr (LMIC) Eur (HIC) Amr (LMIC) Region

92 6 NA NA NA 937 830 683 700 871 279 132 750 672 439 166 303 427 910 699 345 989 1 764 1 627 1 438 1 879 1 585 1 518 1 356 1 298 Age-standardized rate 9 NA NA NA 726 683 448 646 485 220 383 518 382 312 279 369 330 486 1 239 1 368 1 315 1 077 2 118 1 657 1 322 1 029 1 624 1 450 1 579 Crude rate YLLs 100 000 per capita NA NA NA (2, 8155) 3415964) 2848577) (2226669, (1923609, (0, 63261) (0, 105853) (473, 21655) (5902, 7403) (6237, 10602) (1177, 16247) (6041, 56939) (9288, 17927) (1548, 54610) (1426, 15150) (24192, 42989) (3830, 289097) (15460, 43378) (42901, 109773) (89177, 139093) (178845, 291920) (574461, 817291) (224082, 383002) (221491, 569811) (447950, 666269) (190134, 290586) (237754, 382435) (755255, 1195918) Uncertainty interval NA NA NA 379 Total Total 8 537 6 642 34 787 10 276 37 986 81 300 13 594 11 065 32 822 26 428 33 044 66 160 10 442 239 598 175 861 690 714 373 601 296 130 115 073 988 443 559 991 241 090 314 939 2 788 027 2 345 359 b 69 NA NA NA 2 758 4 358 4 887 2 263 1 366 4 354 2 967 89 658 33 533 12 529 52 736 10 835 75 614 13 830 34 847 12 486 11 902 80 020 18 514 Stroke 299 332 439 419 382 748 312 146 157 769 103 680 b NA NA NA 233 IHD 3 343 2 372 6 968 5 653 3 875 6 055 5 199 90 870 16 718 16 537 30 705 18 186 87 564 23 421 47 787 12 011 14 263 57 125 40 336 144 488 303 549 534 583 445 708 247 551 142 088 b 70 NA NA NA 247 299 722 783 824 1 609 2 392 1 690 1 679 8 853 2 947 7 990 1 211 2 759 5 388 23 822 76 488 27 843 41 779 70 507 15 470 65 445 99 218 57 942 144 742 Lung cancer b 4 14 64 87 NA NA NA 463 219 717 128 185 280 244 301 498 1 781 1 402 2 457 2 151 2 172 8 840 4 051 3 267 31 521 37 949 17 656 13 981 COPD 115 082 a 3 30 16 56 NA NA NA 119 889 126 676 3 567 3 080 6 569 1 364 3 819 1 089 1 424 7 962 ALRI 33 467 67 160 14 797 14 990 122 599 138 885 287 404 151 164 2 018 551 1 242 440 Number of YLLs Morocco Mozambique Myanmar Mongolia Montenegro Namibia Nauru Nicaragua Niger Nepal Netherlands New Zealand Nigeria Country Niue Oman Pakistan Norway Palau Peru Philippines Qatar Panama Papua New Guinea Paraguay Poland Portugal Republic of Korea Republic of Moldova Romania Emr (LMIC) Afr Sear Wpr (LMIC) Eur (LMIC) Afr Wpr (LMIC) Amr (LMIC) Afr Sear Eur (HIC) Wpr (HIC) Afr Region Wpr (LMIC) Emr (HIC) Emr (LMIC) Eur (HIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Emr (HIC) Amr (LMIC) Wpr (LMIC) Amr (LMIC) Eur (HIC) Eur (HIC) Wpr (HIC) Eur (LMIC) Eur (LMIC)

93 0 3 NA NA NA 477 495 382 156 893 177 876 871 870 501 374 448 764 831 1 511 1 195 1 089 1 116 1 287 1 190 1 782 1 373 Age-standardized rate 0 7 NA NA NA 441 500 695 861 275 921 324 692 484 489 412 546 701 679 2 229 1 004 1 459 1 363 1 089 1 690 1 310 1 410 Crude rate YLLs 100 000 per capita NA NA NA (0, 699) (0, 3022) (37, 668) (1, 3573) (0, 1577) (0, 14612) (0, 29479) (147, 1308) (0, 190034) (7270, 36208) (6893, 261160) (10098, 17970) (54710, 85063) (13225, 34817) (56163, 167326) (65942, 227535) (23835, 210056) (72749, 115746) (87989, 160382) (359039, 545313) (137899, 235835) (224919, 623811) (180637, 234079) (1387484, 349265) Uncertainty interval 0 NA NA NA 482 905 629 458 975 Total Total 2 177 8 520 14 338 26 028 70 925 25 931 93 611 108 622 146 376 149 684 454 770 128 043 188 138 410 512 102 136 206 697 126 637 3 193 932 b 0 NA NA NA 177 401 125 851 126 318 3 886 4 266 2 263 5 657 25 587 13 242 23 942 26 621 52 962 88 336 20 502 16 506 80 188 20 763 38 856 Stroke 962 217 173 456 b 0 NA NA NA 273 334 418 948 266 169 IHD 8 353 5 133 1 277 14 621 14 751 56 725 52 275 12 040 13 264 13 343 42 799 14 426 47 899 117 189 120 850 105 918 1 939 666 b 0 21 78 82 53 24 NA NA NA 622 729 983 210 284 103 5 147 3 569 9 276 6 520 1 145 42 769 10 034 44 936 10 779 10 690 37 806 264 830 Lung cancer b 1 8 0 3 5 10 13 NA NA NA 843 400 162 413 425 280 430 158 1 113 1 691 4 972 5 122 4 578 1 566 1 711 15 103 10 186 COPD a 9 0 1 7 84 11 33 NA NA NA 237 159 131 411 451 366 2 981 4 718 5 322 ALRI 12 116 66 948 67 662 55 711 123 652 105 342 108 895 261 209 Number of YLLs Russian Federation Rwanda Saint Kitts and Nevis Samoa Saint Vincent and the Grenadines Saint Lucia Country San Marino Somalia Solomon Islands South Africa South Sudan Spain Sri Lanka Sudan Slovenia Suriname Sweden Switzerland Seychelles Sierra Leone Singapore Slovakia Swaziland Sao Tome and Principe Saudi Arabia Senegal Serbia Eur (HIC) Afr Amr (HIC) Wpr (LMIC) Amr (LMIC) Amr (LMIC) Region Eur (HIC) Emr (LMIC) Wpr (LMIC) Afr Emr (LMIC) Eur (HIC) Sear Emr (LMIC) Eur (HIC) Amr (LMIC) Eur (HIC) Eur (HIC) Afr Afr Wpr (HIC) Eur (HIC) Afr Afr Emr (HIC) Afr Eur (LMIC)

; : America ; Amr 43 NA NA 643 926 594 595 277 651 168 326 689 811 935 692 1 392 2 346 1 148 1 309 1 106 1 208 3 120 1 402 1 512 2 196 1 720 : Africa Age-standardized rate 27 NA NA 796 772 800 661 310 473 573 249 444 571 730 872 630 1 562 1 561 1 095 1 080 1 130 2 323 1 196 2 489 1 609 1 172 : Not available. Crude rate YLLs 100 000 per capita ; NA : Ischaemic heart disease. Afr NA NA (0, 1225) ; IHD (0, 17545) (0, 13046) (4262, 21261) (26226, 38493) (23971, 31545) (3800, 148164) (93603, 218308) (66432, 185137) (11608, 122182) (95177, 139946) (36834, 171202) (28321, 198935) (411912, 622205) (704766, 988251) (324787, 543839) (17925, 1638357) (103839, 415531) (158205, 377239) (35395, 1660176) (221078, 657777) (123682, 211212) (476947, 835297) (121702, 458316) Uncertainty interval : High-income countries ; HIC 66 NA NA Total Total 8 814 8 862 32 306 73 867 27 723 15 099 91 774 158 950 123 871 518 516 117 542 846 068 120 193 423 492 300 985 278 521 784 015 460 001 170 373 659 901 291 678 128 949 1 128 219 b 26 NA NA 1 487 2 284 7 432 3 844 36 705 30 309 13 709 17 523 36 037 28 621 80 384 56 059 55 715 40 153 66 717 22 490 15 793 Stroke 160 335 239 376 247 172 125 646 328 906 110 472 : Chronic obstructive pulmonary disease b 30 NA NA IHD : Low- and middle-income countries 9 960 1 911 5 548 6 473 8 822 95 816 43 109 11 519 61 326 74 229 48 986 16 984 45 516 94 773 89 708 14 882 216 933 368 778 796 790 139 234 249 788 111 775 465 743 ; COPD ; LMIC b 3 NA NA 399 795 733 683 3 004 8 103 4 046 4 194 2 085 1 009 4 196 3 389 2 549 14 568 14 135 73 112 97 796 14 596 21 645 114 602 196 101 147 882 192 562 Lung cancer : Western Pacific b ; Wpr 0 47 38 NA NA 434 501 393 261 417 986 1 328 1 955 1 987 1 531 4 878 5 411 6 760 2 667 4 491 2 395 3 378 17 204 20 007 17 575 12 278 COPD : Acute lower respiratory disease a ; ALRI 8 NA NA 101 258 829 324 9 442 4 573 4 057 : South-East Asia 5 734 1 135 2 959 ALRI 10 533 45 494 43 924 21 805 11 766 65 481 11 407 53 762 90 477 63 624 285 051 173 615 128 485 Number of YLLs ; Sear : Europe ; Eur : Ambient air pollution ; AAP Syrian Arab Republic Tajikistan Thailand The former Yugoslav Republic of Macedonia Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Country Uganda Ukraine United Arab Emirates United Kingdom United Republic of Tanzania United States of America Uruguay Uzbekistan Venezuela, Bolivarian Republic of Viet Nam Yemen Vanuatu Zambia Zimbabwe : Eastern Mediterranean : year of life lost Emr (LMIC) Eur (LMIC) Sear Eur (LMIC) Sear Afr Wpr (LMIC) Amr (HIC) Emr (LMIC) Eur (LMIC) Eur (LMIC) Wpr (LMIC) Region Afr Eur (LMIC) Emr (HIC) Eur (HIC) Afr Amr (HIC) Amr (HIC) Eur (LMIC) Amr (LMIC) Wpr (LMIC) Emr (LMIC) Wpr (LMIC) Afr Afr Age group includes 25 years and above. Age group includes less than 5 years old.

YLL a b Emr

95 3 5 817 835 145 392 290 814 216 270 531 814 173 765 193 369 875 817 679 271 822 2 411 1 530 1 002 1 188 1 398 Age-standardized rate 5 4 688 328 401 373 468 920 276 249 601 255 376 206 860 768 466 274 1 905 1 129 1 573 1 239 1 529 1 111 1 531 1 805 Crude rate YLLs 100 000 per capita (3, 196) (0, 270) (2, 804) (0, 549) (0, 429) (54, 516) (9, 18958) (28, 7816) (769, 28210) (1113, 1388) (2394, 3597) (6722, 21586) (9822, 63392) (13832, 25322) (1303, 111090) (13638, 27183) (30416, 47782) (17331, 87911) (23046, 36220) (55552, 79643) (51571, 180805) (28357, 298561) (23507, 109730) (216653, 341781) (376188, 563712) (122028, 428178) Uncertainty interval 7 132 187 610 525 373 348 Total Total 1 249 2 959 4 975 16 213 80 252 18 571 20 241 43 345 77 671 21 218 39 254 56 034 29 437 67 713 274 983 127 925 179 980 461 536 281 677 b 2 42 75 154 161 389 154 128 782 7 540 5 650 4 003 5 538 2 200 50 570 60 940 27 801 25 327 13 611 20 372 13 218 15 582 14 770 30 522 Stroke 118 505 112 013 b 4 75 102 326 209 580 164 146 891 IHD 6 703 9 858 7 434 1 133 56 435 48 877 18 643 10 759 39 628 22 479 78 697 55 352 13 261 10 627 10 886 30 608 127 197 b 7 1 13 38 30 22 46 412 122 163 232 199 1 506 2 648 3 641 1 573 5 910 1 479 1 637 7 552 1 162 2 923 4 492 10 842 30 572 30 138 Lung cancer b 1 0 5 1 1 1 0 49 26 152 945 305 410 352 201 642 684 254 348 769 2 231 1 326 1 179 4 195 1 326 76 788 COPD a 0 2 3 0 59 67 24 52 50 89 311 285 117 109 801 765 3 274 5 424 1 571 8 134 ALRI 13 523 10 928 29 279 163 099 131 797 156 975 Number of YLLs Country Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Bolivia, Plurinational States of Benin Bhutan Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Region Emr (LMIC) Eur (LMIC) Afr Eur (HIC) Afr Amr (HIC) Amr (LMIC) Eur (LMIC) Wpr (HIC) Eur (HIC) Eur (LMIC) Amr (HIC) Emr (HIC) Sear Amr (HIC) Eur (LMIC) Eur (HIC) Amr (LMIC) Amr (LMIC) Afr Sear Eur (LMIC) Afr Amr (LMIC) Wpr HIC Eur (LMIC)

96 45 NA 320 841 772 604 206 224 411 405 162 374 172 332 558 266 1 265 1 264 1 539 1 402 1 617 1 159 1 655 1 275 1 086 1 089 1 626 1 452 Age-standardized rate 80 NA 297 664 589 487 266 236 941 620 241 803 332 905 366 482 243 1 483 1 053 1 334 1 486 1 511 1 122 1 868 1 030 1 297 1 690 1 129 Crude rate YLLs 100 000 per capita NA (7, 192) (388, 3450) (764, 1734) (905, 1664) (240, 51828) (4012, 6661) (413, 15866) (1281, 6329) (3168, 69417) (3080, 52022) (2382, 35051) (9185, 24474) (49112, 87387) (10207, 35361) (12380, 41630) (17099, 28858) (15057, 25987) (33732, 51588) (53548, 172413) (30987, 123588) (62793, 180873) (11078, 171839) (22547, 273082) (132518, 201931) (277324, 933537) (397446, 568777) Uncertainty interval (8349919, 11390232) (8349919, NA 132 Total Total 2 417 1 236 5 482 1 330 9 357 3 840 70 635 22 568 76 204 44 769 14 113 26 282 23 461 20 896 34 997 43 102 24 553 18 711 111 633 163 697 118 666 106 521 164 242 595 889 478 890 9 809 480 b 65 NA 718 705 350 6 730 2 483 5 138 9 591 1 757 6 988 9 925 2 195 1 020 7 639 6 414 21 975 24 820 12 534 14 319 33 935 14 996 10 136 28 254 69 820 Stroke 107 418 173 092 4 804 159 b 57 NA 375 262 679 633 IHD 4 314 6 009 6 348 3 521 9 384 7 426 2 991 2 467 7 167 33 582 16 091 15 395 21 765 10 087 15 560 23 933 18 112 38 170 12 026 59 455 227 785 2 620 152 b 0 7 57 18 68 NA 679 346 853 127 289 494 280 441 837 7 179 2 475 5 073 5 301 3 536 8 457 8 563 4 361 1 546 1 455 36 368 17 040 1 453 252 Lung cancer b 9 1 20 29 60 NA 338 809 579 523 163 627 684 548 171 271 572 567 513 331 179 268 1 904 1 423 9 559 4 619 16 469 COPD 674 323 a 3 3 46 70 14 12 NA 221 596 272 114 5 994 1 303 2 060 3 163 3 408 ALRI 11 128 69 235 56 736 12 056 16 868 93 313 59 202 10 325 44 505 257 595 105 721 423 559 Number of YLLs Colombia Comoros Congo Cook Islands Country Burkina Faso Burundi Cabo Verde Cambodia Cameroon Canada Central African Republic Chad Chile China Costa Rica Croatia Cuba Cyprus Czech Republic Côte d’Ivoire Democratic People’s Republic of Korea Dominica Democratic Republic of the Congo Denmark Djibouti Dominican Republic Ecuador Egypt Amr (LMIC) Afr Afr Wpr (LMIC) Region Afr Afr Afr Wpr (LMIC) Afr Amr (HIC) Afr Afr Amr (HIC) Wpr (LMIC) Amr (LMIC) Eur (HIC) Amr (LMIC) Eur (HIC) Eur (HIC) Afr Sear Amr (LMIC) Afr Eur (HIC) Emr (LMIC) Amr (LMIC) Amr (LMIC) Emr (LMIC)

97 5 28 50 706 140 595 119 590 759 111 769 184 244 364 479 647 639 1 520 1 033 1 156 1 208 1 349 1 241 1 131 1 003 1 308 1 148 1 346 Age-standardized rate 4 70 87 560 740 754 210 563 304 573 213 665 906 431 854 567 349 422 806 476 946 1 282 1 018 1 017 1 421 1 093 1 204 1 293 Crude rate YLLs 100 000 per capita (0, 267) (0, 472) (0, 2690) (9, 9739) (1, 4706) (26, 4170) (339, 7915) (202, 6677) (960, 8644) (350, 14948) (557, 17488) (9802, 39451) (5454, 87458) (4782, 87626) (14364, 21967) (24625, 97125) (19451, 38983) (11623, 42388) (26208, 40349) (14479, 22554) (51699, 82224) (79044, 171590) (86734, 420277) (84431, 233666) (70401, 144587) (362860, 912704) (240070, 319522) (6517074, 9293312) Uncertainty interval 17 184 139 Total Total 4 917 3 837 2 148 1 928 5 310 8 267 3 049 18 067 24 872 69 620 30 836 32 175 56 873 18 435 33 119 54 875 10 399 67 570 122 792 688 528 279 224 264 415 176 808 110 054 7 800 910 b 3 86 32 987 419 470 1 044 4 181 6 807 1 821 2 278 5 486 5 713 2 357 1 347 31 022 79 310 48 508 21 127 12 865 42 801 45 407 13 587 21 027 15 169 14 276 Stroke 416 064 2 414 822 b 12 89 55 692 IHD 2 504 3 712 1 535 1 224 1 305 1 380 7 345 8 842 1 633 1 497 42 680 10 301 22 058 19 899 16 390 77 785 22 515 14 852 10 371 10 740 33 186 130 612 157 167 2 306 544 b 0 7 45 40 51 57 50 287 190 221 837 179 499 985 184 5 539 1 269 1 054 6 718 3 235 1 906 1 425 38 394 13 159 27 891 52 206 18 701 198 706 Lung cancer b 4 0 7 1 7 2 80 62 57 58 709 293 587 346 136 747 464 532 511 119 308 1 287 5 405 3 380 1 147 3 828 1 265 COPD 1 023 759 a 0 1 6 1 0 21 38 118 398 188 141 142 1 823 2 052 1 870 4 513 4 088 6 300 ALRI 42 264 98 053 26 208 13 773 40 887 16 147 23 931 28 475 185 983 1 857 079 Number of YLLs Iraq Ireland Indonesia Iran (Islamic Republic of) Country El Salvador Eritrea Equatorial Guinea Estonia Ethiopia Fiji Finland France Georgia Germany Gabon Gambia Ghana Greece Grenada Guatemala Haiti Honduras Guinea Guinea-Bissau Guyana Hungary Iceland India Emr (LMIC) Eur (HIC) Emr (LMIC) Sear Region Amr (LMIC) Afr Afr Eur (HIC) Afr Wpr (LMIC) Eur (HIC) Eur (HIC) Eur (LMIC) Eur (HIC) Afr Afr Afr Eur (HIC) Amr (LMIC) Amr (LMIC) Amr (LMIC) Amr (LMIC) Afr Afr Amr (LMIC) Eur (LMIC) Eur (HIC) Sear

98 0 3 NA 141 157 387 106 801 569 677 510 399 887 308 936 466 157 979 925 553 346 221 289 324 1 156 1 000 1 217 1 844 1 224 Age-standardized rate 0 2 NA 202 405 422 266 493 542 254 763 967 389 743 247 629 276 699 755 398 210 422 340 280 1 218 1 282 1 079 1 744 1 059 Crude rate YLLs 100 000 per capita NA (0, 266) (0, 229) (89, 483) (5, 14403) (1, 14082) (475, 946) (462, 1110) (944, 2783) (5787, 9707) (4160, 7270) (3377, 4283) (8829, 28663) (7740, 11146) (1237, 97031) (14405, 19191) (11532, 47352) (10520, 17164) (13555, 25798) (13377, 21057) (33213, 74942) (11770, 30374) (85411, 155902) (68199, 233248) (13855, 162555) (37221, 161936) (33671, 107996) (71065, 214508) (127788, 209804) Uncertainty interval 0 1 NA 735 362 883 Total Total 7 837 5 858 3 815 9 463 7 751 5 141 2 160 16 767 21 820 31 499 14 176 19 452 17 558 78 214 59 415 58 341 19 883 124 455 173 361 106 000 115 291 139 273 172 027 b 0 0 NA 241 111 187 766 1 999 3 232 6 189 1 106 7 257 7 234 4 591 2 721 3 037 1 274 7 263 5 576 4 847 41 127 69 710 38 564 20 579 26 546 15 345 19 113 25 223 50 304 Stroke b 0 0 NA 198 193 526 IHD 2 644 1 893 7 237 2 040 7 543 8 807 4 737 1 133 1 009 9 671 8 793 3 022 1 168 46 326 57 353 59 786 12 521 11 953 11 035 13 221 30 873 17 242 85 593 b 0 0 38 18 16 NA 416 650 917 220 488 731 930 270 844 695 153 710 165 107 157 2 982 3 294 1 109 1 627 6 370 34 371 44 831 12 182 Lung cancer b 0 5 6 0 31 24 33 44 24 62 14 NA 191 675 191 884 345 260 461 149 128 423 579 383 810 212 2 489 1 110 5 323 COPD a 0 2 0 0 22 15 41 27 63 NA 142 285 792 424 230 2 500 3 472 1 862 3 415 2 797 1 165 1 175 ALRI 80 928 15 152 37 173 34 742 94 987 11 696 18 624 Number of YLLs Country Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Lao People’s Democratic Republic Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia (Federated States of) Region Eur (HIC) Eur (HIC) Amr (LMIC) Wpr (HIC) Emr (LMIC) Eur (LMIC) Afr Wpr (LMIC) Emr (HIC) Eur (LMIC) Wpr (LMIC) Eur (HIC) Emr (LMIC) Afr Afr Emr (LMIC) Eur (HIC) Eur (HIC) Afr Afr Wpr (LMIC) Sear Afr Eur (HIC) Wpr (LMIC) Afr Afr Amr (LMIC) Wpr (LMIC)

99 4 90 91 NA NA NA 101 456 675 579 699 212 671 620 222 335 478 366 940 525 566 192 921 1 226 1 461 1 315 1 774 1 441 1 363 Age-standardized rate 7 NA NA NA 235 876 755 601 578 470 985 388 523 314 175 214 308 381 333 746 999 208 189 311 1 235 1 838 1 471 1 174 1 329 Crude rate YLLs 100 000 per capita NA NA NA (0, 85) (1, 3452) (0, 25810) (0, 44276) (657, 8699) (164, 8766) (525, 5875) (851, 1088) (1744, 2968) (2781, 5459) (495, 18432) (8606, 15357) (2485, 23519) (5561, 15661) (1796, 125238) (17038, 44484) (38661, 61080) (61770, 93155) (75973, 121784) (95851, 244409) (276447, 393004) (104679, 181830) (269559, 429507) (159581, 236994) (971393, 1487889) (836044, 1220982) Uncertainty interval 45 NA NA NA 151 966 Total Total 2 384 5 543 4 096 4 373 3 998 12 408 76 279 32 750 15 591 10 785 11 961 50 254 11 463 77 586 28 114 100 528 332 184 139 481 160 870 354 247 199 140 1 215 453 1 012 379 b 13 39 NA NA NA 183 5 183 1 039 2 716 7 307 4 917 1 451 1 135 1 352 1 248 4 929 5 752 8 838 46 775 15 537 37 045 25 683 16 256 71 253 33 284 Stroke 140 657 211 378 203 293 120 160 b 24 71 NA NA NA 763 525 IHD 5 370 7 904 1 225 7 483 6 966 1 986 1 887 1 607 2 433 4 731 4 018 36 185 81 429 36 272 14 942 18 681 81 408 17 101 17 486 128 099 235 717 143 385 b 8 88 37 46 NA NA NA 298 567 602 664 152 270 265 526 188 988 2 611 4 557 1 254 7 660 1 588 33 762 16 884 14 486 11 666 18 623 43 455 25 880 Lung cancer b 0 3 2 97 39 92 39 76 96 79 18 NA NA NA 164 539 773 323 141 1 024 1 041 7 184 1 116 3 457 2 612 1 067 17 927 19 509 27 645 COPD a 0 2 5 12 52 26 52 NA NA NA 469 730 413 254 661 1 393 1 417 2 719 6 763 1 679 6 541 ALRI 14 418 51 463 58 409 32 168 68 621 119 158 864 235 534 059 Number of YLLs Country Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Niger Nigeria Niue Oman Pakistan Palau Norway Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Republic of Korea Republic of Moldova Region Eur (HIC) Wpr (LMIC) Eur (LMIC) Emr (LMIC) Afr Sear Afr Wpr (LMIC) Sear Eur (HIC) Wpr (HIC) Amr (LMIC) Afr Afr Wpr (LMIC) Emr (HIC) Emr (LMIC) Wpr (LMIC) Eur (HIC) Amr (LMIC) Wpr (LMIC) Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Eur (HIC) Emr (HIC) Wpr (HIC) Eur (LMIC)

100 0 2 75 NA NA NA 599 864 963 362 415 733 721 541 268 238 532 217 949 863 534 318 923 115 906 1 069 1 745 1 112 1 024 Age-standardized rate 0 5 NA NA NA 844 383 403 534 552 594 995 289 324 953 476 756 160 576 934 307 717 242 539 1 114 1 585 1 610 1 310 1 187 Crude rate YLLs 100 000 per capita NA NA NA (0, 316) (0, 765) (57, 508) (10, 193) (0, 1028) (0, 1312) (0, 7592) (0, 91290) (0, 12682) (3517, 6189) (4386, 11710) (6952, 59988) (2578, 13745) (24511, 73623) (61575, 80767) (32542, 51479) (32284, 57582) (20620, 31809) (3022, 118382) (28646, 99084) (44272, 76426) (31883, 72663) (87652, 137810) (96165, 266403) (160882, 243388) (520667, 1652256) Uncertainty interval 0 NA NA NA 353 218 479 134 810 258 Total Total 8 695 4 948 4 481 9 821 47 640 70 884 41 749 45 734 49 203 26 586 66 086 65 159 37 943 60 692 53 250 114 372 203 369 175 475 1 217 745 b 0 90 42 65 NA NA NA 175 173 380 2 525 7 209 1 852 5 622 1 558 2 243 47 638 11 399 32 992 10 119 19 350 10 455 92 604 10 960 12 742 19 846 40 639 12 004 Stroke 469 392 b 0 92 78 NA NA NA 121 118 367 733 150 IHD 5 777 6 568 6 434 3 911 1 565 3 002 5 798 3 950 51 272 30 851 15 556 16 185 46 663 16 655 35 253 20 719 33 724 699 149 b 6 6 9 0 21 46 36 40 NA NA NA 227 434 101 347 426 2 877 2 149 2 838 1 467 8 154 2 741 1 174 3 442 2 703 10 999 40 171 10 113 14 627 Lung cancer b 2 0 4 2 0 6 2 62 53 83 NA NA NA 861 378 536 584 164 139 238 403 314 171 425 3 421 1 552 3 244 1 644 1 867 COPD a 4 2 0 1 33 49 11 78 11 15 NA NA NA 203 130 215 3 602 5 612 2 612 1 208 2 071 4 394 ALRI 29 859 24 092 32 049 56 878 46 232 47 572 111 077 Number of YLLs Country Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Region Eur (LMIC) Eur (HIC) Afr Amr (HIC) Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Afr Emr (HIC) Afr Eur (LMIC) Afr Afr Wpr (HIC) Eur (HIC) Eur (HIC) Wpr (LMIC) Emr (LMIC) Afr Emr (LMIC) Eur (HIC) Sear Emr (LMIC) Amr (LMIC) Afr Eur (HIC) Eur (HIC) Emr (LMIC)

101 : Eastern ; Emr 28 NA NA 467 726 834 375 722 718 968 535 192 547 118 178 486 465 754 659 2 306 1 232 2 302 1 153 1 712 1 380 : America ; Amr Age-standardized rate : Africa 17 NA NA 589 740 438 730 723 173 369 486 193 282 421 476 980 779 614 1 505 1 080 1 042 1 741 1 012 2 015 1 337 Crude rate YLLs 100 000 per capita : Not available. ; NA NA NA (0, 394) : Ischaemic heart disease. Afr (0, 7918) (0, 4369) (3368, 4580) (1432, 6853) (9174, 13331) (5638, 58792) (1868, 73001) (31910, 88088) (32733, 47528) (13926, 65576) (6286, 705171) (45119, 79002) (12002, 89782) (38097, 167686) (67053, 161462) (13418, 668580) (92850, 278471) (49171, 191644) (160611, 238599) (232719, 318894) (137192, 230643) (157770, 276307) ; IHD Uncertainty interval 21 NA NA Total Total 4 015 2 971 3 959 4 959 59 134 11 216 35 630 40 087 45 746 63 075 57 711 45 323 : High-income countries 200 428 275 375 179 301 490 993 119 027 118 910 307 117 194 276 217 697 120 610 ; HIC b 8 NA NA 699 936 6 440 8 826 1 207 1 934 9 023 8 510 17 297 69 191 15 248 13 180 36 036 29 299 23 795 55 912 60 608 17 683 29 396 Stroke 103 457 126 850 118 649 b 9 : Chronic obstructive pulmonary disease NA NA 916 IHD 3 106 5 493 1 714 1 867 1 897 5 156 4 112 20 221 85 872 21 482 26 246 17 673 42 355 17 305 30 067 34 617 31 496 130 958 349 292 158 082 100 577 ; COPD b : Low- and middle-income countries 1 NA NA 259 113 187 853 877 477 420 893 893 285 940 1 175 1 481 2 071 3 746 9 256 37 822 24 932 11 243 43 579 85 539 35 930 ; LMIC Lung cancer b 5 0 20 96 69 NA NA 140 177 754 642 285 405 1 024 3 690 6 887 2 388 1 267 3 256 1 083 6 262 1 897 1 126 7 587 1 578 COPD : Western Pacific : Acute lower respiratory disease a ; Wpr 3 48 NA NA 129 311 537 166 ; ALRI 3 853 2 121 1 750 9 142 4 801 2 342 1 323 4 943 ALRI 19 417 21 021 76 307 27 448 20 914 57 248 42 962 31 355 121 133 Number of YLLs : South-East Asia ; Sear : Ambient air pollution ; AAP : Europe Country Tajikistan Thailand The former Yugoslav Republic of Macedonia Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United Republic of Tanzania United States of America Uruguay Uzbekistan Vanuatu Venezuela, Bolivarian Republic of Viet Nam Yemen Zambia Zimbabwe ; Eur : year of life lost Region Eur (LMIC) Sear Eur (LMIC) Sear Afr Wpr (LMIC) Amr (HIC) Emr (LMIC) Eur (LMIC) Eur (LMIC) Wpr (LMIC) Afr Eur (LMIC) Emr (HIC) Eur (HIC) Afr Amr (HIC) Amr (HIC) Eur (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Emr (LMIC) Afr Afr Age group includes 25 years and above. Age group includes less than 5 years old.

Mediterranean YLL a b

102 6 9 388 640 644 466 539 752 349 491 474 471 620 2 498 1 142 1 311 2 067 2 255 1 751 1 192 2 270 1 706 1 206 1 150 1 586 1 848 Age-standardized rate 9 6 627 597 654 767 476 356 868 443 806 932 273 959 416 411 1 904 1 410 1 040 2 308 2 467 1 423 2 876 1 298 2 354 3 011 Crude rate YLLs 100 000 per capita (8, 369) (0, 367) (0, 879) (0, 734) (3, 1322) (73, 702) (23, 6978) (16, 29638) (2655, 3263) (3008, 4544) (8611, 27165) (1714, 57014) (21683, 39595) (27226, 56771) (15454, 96650) (2914, 185453) (38356, 59565) (34654, 55437) (79130, 277004) (39170, 433070) (39258, 185786) (19753, 102678) (86678, 126655) (562668, 818621) (228829, 362678) (179748, 625151) Uncertainty interval 13 868 598 245 254 458 Total Total 1 022 2 958 3 723 4 378 36 508 31 679 43 808 20 362 66 161 64 990 49 185 44 866 259 607 680 488 291 097 196 101 134 657 126 852 106 967 414 834 b 3 43 83 135 206 206 631 692 140 7 124 3 771 5 817 6 824 1 352 25 812 37 997 73 469 32 230 14 498 22 486 15 689 16 147 13 493 32 999 Stroke 120 662 127 353 b 10 155 150 700 419 294 211 IHD 1 416 9 872 1 641 1 112 28 224 64 378 87 717 69 550 20 206 16 759 38 337 89 633 16 520 18 600 12 225 18 218 48 845 186 039 227 519 b 1 44 15 87 62 56 735 179 231 451 365 225 7 115 3 047 8 093 5 914 1 061 90 088 15 626 26 380 10 516 13 507 20 203 11 926 21 942 44 222 Lung cancer b 3 1 6 2 3 0 96 18 38 194 621 613 583 994 391 672 697 2 429 1 843 1 917 2 061 1 198 1 106 2 136 5 920 91 010 COPD a 0 4 3 0 20 79 34 71 34 425 465 154 232 123 4 436 6 829 1 053 1 651 1 045 9 819 ALRI 17 446 13 163 35 556 179 202 918 192 688 Number of YLLs Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Bhutan Bolivia, Plurinational States of Benin Bosnia and Herzegovina Botswana Brunei Darussalam Bulgaria Brazil Country Emr (LMIC) Eur (LMIC) Afr Eur (HIC) Afr Amr (HIC) Amr (LMIC) Eur (LMIC) Wpr (HIC) Eur (HIC) Eur (LMIC) Amr (HIC) Emr (HIC) Sear Amr (HIC) Eur (LMIC) Eur (HIC) Amr (LMIC) Sear Amr (LMIC) Afr Eur (LMIC) Afr Wpr HIC Eur (LMIC) Amr (LMIC) Region

103 86 NA 448 418 710 504 517 511 901 266 648 404 1 755 1 563 2 163 1 971 1 223 2 042 1 144 1 981 1 722 1 052 1 183 1 514 1 503 1 993 2 335 2 148 Age-standardized rate NA 729 483 786 131 856 404 963 618 512 424 442 541 338 1 888 1 540 2 490 1 987 1 560 1 850 1 503 1 711 1 232 1 414 1 281 2 086 1 607 1 940 Crude rate YLLs 100 000 per capita NA (10, 268) 15425025) (11194228, (545, 4495) (1118, 2532) (449, 76799) (2383, 4474) (1783, 8628) (743, 19728) (3900, 88470) (6962, 11315) (5012, 81450) (2582, 39379) (16358, 55711) (30841, 50306) (14300, 51058) (24523, 44731) (56758, 88046) (13093, 33841) (83654, 241698) (41494, 159067) (61363, 198427) (68369, 120451) (15084, 221544) (31916, 389541) (161935, 247712) (579142, 820949) (339547, 1146426) Uncertainty interval NA 182 Total Total 1 800 9 408 3 166 3 562 5 280 41 452 35 052 99 290 56 827 22 669 54 927 32 211 35 357 27 383 73 271 26 045 97 970 12 293 158 445 128 251 200 302 137 774 234 726 730 507 695 169 13 242 651 b 66 NA 917 379 973 5 096 2 345 1 953 6 722 7 551 7 155 1 378 7 567 2 268 17 833 12 653 15 378 27 972 18 414 35 580 12 273 10 216 37 769 20 008 99 577 Stroke 232 864 105 107 5 370 805 b 89 NA 531 578 943 IHD 4 804 6 171 6 491 2 105 5 304 12 481 18 937 19 876 17 910 11 504 28 446 14 337 26 794 16 158 41 359 13 125 27 131 55 842 11 724 58 629 85 432 351 675 2 965 892 b 23 20 29 67 NA 398 238 734 346 995 148 851 8 115 4 375 1 516 5 764 1 047 2 412 4 408 1 983 1 752 14 824 11 078 20 530 11 197 51 822 44 136 3 739 353 Lung cancer b 2 91 30 20 56 NA 861 793 649 759 685 178 191 716 586 550 170 303 366 1 060 2 639 1 022 2 536 1 244 6 745 14 116 24 566 COPD 813 075 a 0 5 45 97 20 10 NA 955 239 320 143 4 521 8 387 1 567 2 836 4 405 ALRI 16 548 24 156 80 575 68 342 18 264 70 780 10 582 41 927 353 527 126 871 132 121 531 472 Number of YLLs Chad Chile Cabo Verde Central African Republic China Burkina Faso Burundi Cambodia Cameroon Canada Cook Islands Costa Rica Cuba Congo Croatia Cyprus Czech Republic Dominica Dominican Republic Colombia Comoros Côte d’Ivoire Democratic People’s Republic of Korea Denmark Djibouti Ecuador Egypt Democratic Republic of the Congo Country Afr Amr (HIC) Afr Afr Wpr (LMIC) Afr Afr Wpr (LMIC) Afr Amr (HIC) Wpr (LMIC) Amr (LMIC) Amr (LMIC) Afr Eur (HIC) Eur (HIC) Eur (HIC) Amr (LMIC) Amr (LMIC) Afr Afr Sear Eur (HIC) Emr (LMIC) Amr (LMIC) Emr (LMIC) Amr (LMIC) Afr Region

104 13 79 93 819 432 982 305 411 627 778 674 273 363 402 2 272 1 852 2 082 1 375 1 469 1 028 1 479 1 076 1 557 1 667 1 242 1 272 1 641 1 372 2 289 1 116 Age-standardized rate 10 689 620 966 138 476 688 127 908 763 905 592 546 345 834 395 772 1 799 1 694 1 416 1 130 1 179 2 189 1 193 2 155 1 064 1 071 1 509 1 104 1 229 Crude rate YLLs 100 000 per capita (1, 623) (0, 454) (0, 6924) (1, 6562) (67, 7669) (22, 18053) (353, 11663) (442, 19418) (636, 21766) (625, 12550) (1235, 12241) (5457, 92622) (15796, 23782) (13804, 54657) (21079, 32352) (32568, 49874) (5224, 100360) (26861, 53910) (21206, 78220) (11058, 18604) (51558, 209821) (78527, 126682) (73314, 150588) (144873, 714179) (146398, 398718) (366337, 482565) (134138, 280733) (148539, 284331) (553692, 1381328) Uncertainty interval (9946795, 13893270) (9946795, 47 207 312 Total Total 6 724 3 831 3 673 7 404 4 292 8 024 19 720 34 653 26 610 62 411 59 987 10 671 41 028 12 840 42 444 57 858 15 014 444 669 147 287 103 618 301 252 114 593 204 804 423 984 223 878 1 042 818 11 789 093 b 6 32 507 513 118 972 3 558 1 294 8 991 1 631 6 801 1 710 2 792 5 835 2 684 2 409 68 949 22 042 19 645 17 530 15 810 13 060 42 019 34 727 13 296 49 637 38 245 Stroke 489 627 100 960 2 815 829 b 40 127 170 IHD 1 587 5 931 2 655 2 679 1 841 2 347 1 800 1 839 5 090 5 829 12 092 41 987 47 511 10 929 12 671 49 458 11 509 23 830 30 095 28 232 12 350 89 187 85 187 149 092 308 721 264 939 5 106 390 b 0 45 63 21 64 138 301 642 464 318 121 417 1 226 6 104 1 147 1 452 4 265 1 805 2 412 1 842 6 368 76 241 34 490 15 709 14 251 98 659 24 735 96 071 606 825 104 213 Lung cancer b 0 3 2 12 13 13 513 121 326 204 174 794 124 579 972 567 620 521 109 100 337 8 371 1 256 1 959 5 653 2 313 5 134 4 307 13 994 COPD 1 492 940 a 1 5 0 2 15 54 20 71 67 237 181 710 274 158 2 332 3 584 3 410 4 892 5 833 8 144 ALRI 19 103 28 092 46 994 19 811 34 155 49 417 28 217 319 258 131 817 1 767 111 Number of YLLs El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland France Gabon Haiti Honduras Hungary Iceland India Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Iraq Ireland Israel Indonesia Iran (Islamic Republic of) Italy Country Amr (LMIC) Afr Afr Eur (HIC) Afr Wpr (LMIC) Eur (HIC) Eur (HIC) Afr Amr (LMIC) Amr (LMIC) Eur (LMIC) Eur (HIC) Sear Afr Eur (LMIC) Eur (HIC) Afr Eur (HIC) Amr (LMIC) Amr (LMIC) Afr Afr Amr (LMIC) Emr (LMIC) Eur (HIC) Eur (HIC) Emr (LMIC) Eur (HIC) Sear Region

105 1 6 NA 318 830 845 367 999 914 687 521 664 344 680 514 292 1 370 2 410 1 172 1 881 1 727 1 591 1 367 1 259 1 604 2 382 1 032 1 652 1 859 Age-standardized rate 0 4 NA 617 776 735 602 823 517 886 732 712 730 412 636 750 264 998 449 833 463 1 979 1 210 1 297 2 353 1 930 1 477 1 608 1 852 Crude rate YLLs 100 000 per capita NA (0, 554) (0, 413) (0, 163) (5, 14504) (1, 15258) (884, 1760) (200, 1038) (879, 2202) (1984, 5863) (1268, 93704) (6015, 11014) (23823, 32044) (10319, 12834) (13887, 44370) (15868, 26907) (16998, 23914) (14831, 62964) (21655, 42641) (19974, 32584) (16326, 43464) (15576, 27640) (21034, 248898) (52573, 218190) (42789, 135764) (58183, 131272) (74960, 236062) (150665, 521111) (188088, 302420) Uncertainty interval 0 2 84 NA 776 Total Total 8 742 7 603 5 567 1 373 4 547 1 722 57 317 27 879 11 548 33 748 21 924 20 534 41 704 98 463 31 858 26 809 28 047 22 379 160 644 156 350 102 352 250 159 381 734 150 522 b 0 1 13 NA 244 326 255 3 576 6 838 2 050 9 767 4 545 2 300 2 128 1 305 8 287 1 340 8 209 5 145 5 046 7 346 14 193 39 353 28 494 28 204 24 547 51 785 19 811 Stroke 108 404 b 0 1 48 NA 514 322 984 IHD 2 924 8 125 1 115 9 221 2 679 9 455 1 171 3 332 13 573 99 132 23 534 19 372 13 157 14 028 20 636 61 782 14 151 15 368 16 478 11 167 148 593 136 217 b 0 0 22 35 79 NA 636 125 584 260 420 990 470 225 1 714 3 677 1 375 1 659 2 484 4 081 3 647 3 280 5 956 4 919 1 392 15 755 12 441 19 435 133 415 Lung cancer b 0 0 1 94 50 31 26 12 63 15 NA 566 439 412 469 117 269 192 601 566 729 241 314 300 1 829 2 087 6 526 2 841 2 541 COPD a 0 0 0 0 0 24 36 26 NA 433 687 290 118 856 3 226 4 575 2 537 4 043 1 495 2 958 1 596 2 174 ALRI 21 009 45 741 33 076 23 819 19 130 102 509 113 028 Number of YLLs Jamaica Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Lao People’s Democratic Republic Latvia Lebanon Lesotho Luxembourg Madagascar Malawi Malaysia Maldives Mauritius Mexico Micronesia (Federated States of) Monaco Japan Liberia Libya Lithuania Mali Malta Marshall Islands Mauritania Mongolia Country Amr (LMIC) Emr (LMIC) Eur (LMIC) Afr Wpr (LMIC) Emr (HIC) Eur (LMIC) Wpr (LMIC) Eur (HIC) Emr (LMIC) Afr Eur (HIC) Afr Afr Wpr (LMIC) Sear Afr Amr (LMIC) Wpr (LMIC) Eur (HIC) Wpr (HIC) Afr Emr (LMIC) Eur (HIC) Afr Eur (HIC) Wpr (LMIC) Afr Wpr (LMIC) Region

106 8 NA NA NA 991 804 699 355 176 388 525 851 866 521 253 736 535 1 484 1 820 1 566 1 969 1 724 1 665 1 105 1 806 1 376 1 773 1 437 2 403 Age-standardized rate 11 NA NA NA 856 795 425 585 266 343 429 773 424 651 430 427 377 663 1 997 1 398 1 174 2 395 1 837 1 462 1 306 1 932 1 941 2 073 2 973 Crude rate YLLs 100 000 per capita NA NA NA (2, 4745) 1928134) 1627624) (1255275, (1087099, (0, 37428) (0, 61639) (510, 7554) (893, 9278) (4488, 7635) (298, 12905) (973, 36242) (5050, 6317) (3551, 33421) (6500, 12470) (9812, 27736) (2079, 163848) (25883, 65343) (50539, 78013) (102817, 170336) (297718, 424251) (119368, 201233) (125664, 325508) (485332, 766829) (287762, 429651) (128161, 197684) (149781, 244879) (855234, 2699613) Uncertainty interval NA NA NA 228 Total Total 6 153 4 733 6 691 9 498 6 444 5 676 99 582 48 550 22 396 15 643 21 084 64 819 21 359 38 046 139 069 358 529 156 649 212 731 360 851 163 504 634 196 200 568 1 572 574 1 332 980 1 976 187 b 30 NA NA NA 1 719 1 642 6 523 5 918 3 437 1 615 3 107 1 127 6 973 6 734 1 183 9 676 42 883 17 996 38 569 27 053 18 591 86 516 46 736 56 042 Stroke 228 041 158 676 179 455 191 986 492 824 b NA NA NA 161 516 IHD 2 579 8 814 1 147 4 982 3 592 3 623 3 767 9 532 7 993 3 350 1 240 54 684 15 938 11 220 63 058 51 291 15 762 29 107 40 024 22 849 90 816 175 451 298 866 166 143 302 322 b 34 NA NA NA 159 570 554 518 254 1 826 1 007 4 295 1 014 1 694 2 233 7 810 6 402 1 024 4 400 21 211 24 895 42 726 13 357 58 841 73 338 46 822 46 943 101 287 224 659 Lung cancer b 2 11 89 48 93 45 NA NA NA 628 122 394 169 206 201 357 1 242 1 128 1 416 1 056 6 229 2 984 2 406 10 471 13 594 18 440 87 437 10 524 11 682 COPD a 2 18 66 11 29 73 NA NA NA 420 635 677 422 764 1 662 3 850 8 227 2 140 8 255 4 360 6 505 ALRI 19 049 71 136 80 476 34 992 82 543 168 246 708 381 1 154 316 Number of YLLs Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Nigeria Niue Norway Oman Palau Panama Papua New Guinea Paraguay Niger Pakistan Peru Philippines Poland Portugal Qatar Republic of Korea Republic of Moldova Romania Russian Federation Country Eur (LMIC) Emr (LMIC) Afr Sear Afr Wpr (LMIC) Sear Eur (HIC) Wpr (HIC) Amr (LMIC) Afr Wpr (LMIC) Eur (HIC) Emr (HIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Amr (LMIC) Afr Emr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Eur (HIC) Emr (HIC) Wpr (HIC) Eur (LMIC) Eur (LMIC) Eur (HIC) Region

107 4 0 NA NA NA 799 245 585 246 639 540 802 960 742 521 841 569 1 819 1 467 1 312 1 359 1 895 1 503 1 649 1 235 1 302 1 235 1 520 2 378 Age-standardized rate 8 0 NA NA NA 665 392 516 409 621 478 558 815 768 672 659 969 960 918 1 772 1 539 1 288 1 242 1 046 1 179 1 844 1 689 1 609 1 618 Crude rate YLLs 100 000 per capita NA NA NA (0, 383) (0, 812) (0, 7019) (0, 2264) (90, 800) (24, 475) (0, 1994) (0, 16816) (0, 98733) (4634, 22482) (8807, 23110) (6573, 11794) (31633, 93698) (40207, 64267) (34039, 53329) (34560, 97055) (3850, 142755) (37287, 128451) (16480, 150243) (93595, 159445) (61740, 145681) (55695, 102887) (128740, 357480) (250916, 383741) (119012, 153380) (198075, 302170) Uncertainty interval 0 NA NA NA 371 552 496 264 324 Total Total 4 039 1 367 9 391 16 207 60 982 51 861 52 933 17 236 84 525 90 100 64 737 44 339 80 903 80 289 127 446 235 036 105 700 318 088 135 813 251 401 b 0 60 87 84 NA NA NA 705 471 225 144 2 023 3 132 9 297 2 034 7 621 13 879 33 116 47 697 24 701 13 012 91 144 14 188 10 644 10 046 12 982 47 197 19 506 80 853 Stroke b 0 77 NA NA NA 268 544 582 213 156 188 IHD 8 090 9 433 8 844 7 857 6 829 8 953 3 568 5 351 40 070 81 936 31 556 62 092 22 888 75 067 32 344 26 613 74 187 131 061 b 0 41 68 57 15 18 43 NA NA NA 164 395 711 183 557 382 5 834 4 371 7 293 2 395 1 828 7 813 7 941 3 681 34 615 11 866 76 780 27 692 30 310 Lung cancer b 1 3 6 1 9 4 0 74 NA NA NA 242 218 903 931 465 261 292 109 710 163 1 377 3 328 3 256 3 025 1 030 1 126 6 942 13 514 COPD a 0 5 5 0 0 18 82 51 NA NA NA 159 148 248 235 197 1 773 2 647 6 139 2 711 5 589 ALRI 26 077 37 090 31 619 35 613 59 110 61 323 66 774 150 132 Number of YLLs Sweden Switzerland Singapore Spain Sri Lanka Sudan Suriname Swaziland Syrian Arab Republic Tajikistan Rwanda Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Slovakia Slovenia South Africa South Sudan Thailand Solomon Islands Somalia Country Eur (HIC) Eur (HIC) Wpr (HIC) Eur (HIC) Sear Emr (LMIC) Amr (LMIC) Afr Emr (LMIC) Eur (LMIC) Afr Amr (HIC) Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Afr Emr (HIC) Afr Eur (LMIC) Afr Afr Eur (HIC) Eur (HIC) Afr Emr (LMIC) Sear Wpr (LMIC) Emr (LMIC) Region

108

; : America 59 NA NA 836 620 371 761 222 509 914 725 1 611 1 016 1 394 1 517 1 780 4 029 1 677 2 255 2 764 1 141 1 239 2 079 ; Amr Age-standardized rate : Africa : Not available. 36 NA NA 857 888 356 582 661 306 619 965 721 991 646 2 046 1 149 1 438 1 552 2 925 1 381 3 041 1 889 1 361 ; NA Crude rate YLLs 100 000 per capita : Ischaemic heart disease. Afr NA NA (0, 831) (0, 9630) (0, 8678) ; IHD (5972, 63385) (2771, 14446) (1910, 75197) (20591, 26974) (17019, 25188) (62330, 92490) (22665, 105624) (11122, 933659) (64682, 247933) (91061, 215775) (21813, 992433) (16339, 109153) (78529, 132226) (72537, 266686) (471676, 669656) (187570, 313203) (127912, 379504) (318315, 559363) : High-income countries Uncertainty interval ; HIC 45 NA NA Total Total 4 799 5 891 38 237 23 765 21 090 77 455 74 447 10 140 71 237 46 452 637 226 159 610 244 191 181 959 476 898 570 692 265 725 442 204 107 298 171 068 b 18 NA NA 788 8 697 1 348 6 225 7 268 1 910 7 283 31 919 20 789 15 442 44 348 26 760 54 560 65 038 13 467 22 470 37 321 Stroke 120 322 135 919 210 257 : Chronic obstructive pulmonary disease b 21 : Low- and middle-income countries NA NA 995 IHD 6 025 3 834 6 854 4 577 4 709 9 726 28 211 39 845 47 983 31 313 15 118 96 879 77 158 64 706 58 212 447 498 307 662 237 820 149 210 ; COPD ; LMIC b 2 NA NA 537 287 546 588 398 6 622 3 168 2 123 1 608 3 303 1 609 2 496 13 282 61 870 54 217 10 850 12 389 107 023 171 170 111 953 Lung cancer : Western Pacific b ; Wpr 0 27 34 NA NA 294 325 890 296 193 581 133 1 233 2 490 4 144 3 504 1 584 6 016 2 594 1 269 9 988 1 800 13 120 COPD : Acute lower respiratory disease a 4 ; ALRI 53 NA NA 129 518 599 158 : South-East Asia 2 452 2 307 6 964 3 392 1 636 6 465 ALRI 22 903 12 664 97 308 38 033 32 269 32 848 71 237 47 514 163 918 Number of YLLs ; Sear : Europe ; Eur : Ambient air pollution ; AAP The former Yugoslav Republic of Macedonia Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United Republic of Tanzania United States of America Uruguay Uzbekistan Vanuatu Zimbabwe Venezuela, Bolivarian Republic of Viet Nam Yemen Zambia Country : Eastern Mediterranean : year of life lost Eur (LMIC) Sear Afr Wpr (LMIC) Amr (HIC) Emr (LMIC) Eur (LMIC) Eur (LMIC) Wpr (LMIC) Afr Eur (LMIC) Emr (HIC) Eur (HIC) Afr Amr (HIC) Amr (HIC) Eur (LMIC) Wpr (LMIC) Afr Amr (LMIC) Wpr (LMIC) Emr (LMIC) Afr Region Age group includes 25 years and above. Age group includes less than 5 years old.

Emr YLL a b

109 7 5 267 353 445 680 372 996 271 521 463 354 404 708 1 114 1 420 1 587 1 274 1 033 1 219 1 329 1 810 2 501 1 097 1 833 1 493 1 375 Age-standardized rate 5 7 816 362 619 254 959 882 454 353 887 492 506 524 647 388 355 2 185 1 227 1 993 2 436 1 470 1 928 1 290 1 959 1 880 1 196 Crude rate DALYs 100 000 per capita DALYs (0, 657) (0, 1518) (0, 1184) (11, 588) (5, 2223) (139, 1322) (52, 15664) (27, 50732) (5679, 8840) (4152, 5354) (2634, 87447) (4586, 302118) (43193, 88932) (59030, 94725) (15554, 49730) (37186, 68807) (37519, 196565) (69920, 110141) (68199, 744254) (64676, 304961) (25959, 165568) (144728, 210701) (116116, 380407) (450258, 714931) (133038, 474968) (1019971, 1565478) (308804, 1087305) Uncertainty interval 21 856 390 451 Total Total 1 019 7 132 9 678 1 702 1 444 4 731 68 602 90 278 76 293 37 169 56 001 54 681 207 374 123 348 715 482 177 952 243 908 573 208 332 068 444 375 220 501 111 935 1 267 255 b 5 91 381 281 163 318 393 1 452 3 578 8 730 1 161 43 834 12 539 31 842 29 100 28 593 64 096 52 956 88 934 14 504 53 900 59 090 12 978 28 579 Stroke 244 532 135 436 249 310 b 15 467 364 234 254 636 IHD 2 333 2 282 1 050 2 277 24 910 23 087 32 124 29 394 80 138 34 433 16 723 47 195 31 157 27 161 61 409 145 781 360 298 121 757 138 062 110 617 270 583 b 1 92 79 58 22 574 465 277 304 126 620 2 253 1 426 9 915 4 593 1 186 9 713 7 450 15 300 28 045 14 995 74 921 26 629 19 433 37 546 16 581 121 865 Lung cancer b 0 7 4 19 39 22 15 296 604 511 2 086 2 955 2 765 1 005 2 468 3 084 5 245 4 872 9 245 7 537 6 552 5 339 1 391 2 110 2 214 16 749 COPD 271 662 a 0 0 8 8 60 93 99 372 155 227 745 762 274 161 1 876 3 245 1 844 7 910 ALRI 65 079 24 333 18 982 31 601 12 283 150 221 343 358 335 543 353 835 Number of DALYs Belarus Barbados Belgium Belize Benin Bhutan Bolivia, Plurinational States of Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Country Eur (LMIC) Amr (HIC) Eur (HIC) Amr (LMIC) Afr Sear Amr (LMIC) Eur (LMIC) Afr Amr (LMIC) Wpr HIC Eur (LMIC) Afr Emr (LMIC) Eur (LMIC) Afr Eur (HIC) Afr Amr (HIC) Amr (LMIC) Eur (LMIC) Wpr (HIC) Eur (HIC) Eur (LMIC) Amr (HIC) Emr (HIC) Sear Region

111 68 NA 919 976 334 422 341 639 334 726 573 228 437 619 344 718 1 546 1 734 1 869 1 412 1 953 1 029 1 547 1 424 1 559 1 859 1 348 1 947 Age-standardized rate NA 643 710 112 392 775 372 448 335 817 405 453 525 300 644 1 737 1 762 1 721 1 363 2 203 1 313 1 136 1 175 1 345 1 645 1 915 1 105 1 425 Crude rate DALYs 100 000 per capita DALYs NA (18, 479) (944, 8146) (1947, 4671) (3391, 6374) (742, 136028) (1269, 37551) (3114, 15828) (5078, 77106) (7373, 166998) (50086, 83865) (24857, 90402) (11394, 18978) (40452, 72760) (8281, 138762) (22877, 60802) (30999, 47707) (73231, 290149) (29201, 101381) (92827, 144282) (26334, 402957) (55592, 684015) (300100, 462590) (147516, 429122) (121249, 216618) (622224, 2127257) Uncertainty interval (1005641, 1460546) (20007452, 27642228) (20007452, NA 325 Total Total 3 220 5 684 5 059 9 427 62 979 38 908 68 215 56 287 57 647 92 658 15 582 22 679 53 300 46 264 39 087 178 415 105 261 372 709 280 064 174 580 119 780 247 874 407 373 1 345 967 1 220 357 23 675 602 b NA 792 135 1 631 6 178 1 695 3 908 4 818 2 408 8 025 31 014 10 307 30 327 69 837 32 959 23 227 13 521 43 318 21 071 66 204 14 899 23 308 15 179 14 295 Stroke 171 148 213 509 409 090 10 327 235 b NA 808 965 148 IHD 8 431 2 819 9 306 8 000 1 604 17 749 35 678 50 839 21 070 22 111 26 963 10 900 90 746 65 908 45 623 26 516 42 846 98 002 25 417 19 161 22 603 146 091 586 048 5 662 869 b 43 29 28 NA 379 704 698 218 136 6 929 2 402 1 336 1 314 1 502 2 700 8 864 3 995 3 463 2 299 10 968 13 530 18 561 29 383 14 733 23 481 88 784 61 584 5 243 327 Lung cancer b 6 99 NA 600 269 106 641 970 357 897 2 676 3 404 3 427 3 830 2 848 2 154 6 759 3 128 4 355 1 457 2 323 1 366 1 854 11 197 28 316 26 209 75 577 COPD 1 814 679 a 8 92 43 13 26 NA 470 291 225 699 1 647 2 889 4 922 7 811 7 979 4 305 ALRI 41 185 28 900 29 494 15 196 21 123 88 057 627 493 125 544 238 434 220 466 130 378 957 459 Number of DALYs Burundi Central African Republic Cabo Verde Cambodia Cameroon Canada Chad Chile China Congo Cook Islands Colombia Comoros Country Czech Republic Côte d’Ivoire Costa Rica Croatia Cuba Cyprus Democratic People’s Republic of Korea Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Afr Afr Afr Wpr (LMIC) Afr Amr (HIC) Afr Amr (HIC) Wpr (LMIC) Afr Wpr (LMIC) Afr Amr (LMIC) Region Eur (HIC) Afr Amr (LMIC) Eur (HIC) Amr (LMIC) Eur (HIC) Sear Afr Eur (HIC) Emr (LMIC) Amr (LMIC) Amr (LMIC) Amr (LMIC) Emr (LMIC) Amr (LMIC)

112 9 54 76 215 966 258 823 307 438 567 590 858 923 213 266 288 1 986 1 589 1 449 1 745 1 184 1 221 1 239 1 526 1 197 1 403 1 033 1 239 1 732 Age-standardized rate 7 361 819 462 106 793 621 900 830 482 496 979 599 112 953 713 289 321 615 1 623 1 394 1 074 1 250 1 805 1 025 1 376 1 149 1 758 1 019 Crude rate DALYs 100 000 per capita DALYs (0, 744) (0, 9832) (2, 1153) (2, 11455) (99, 12139) (33, 28707) (563, 19051) (803, 36006) (2248, 22304) (1221, 40273) (1051, 21435) (23967, 98360) (47163, 95143) (60240, 93177) (36378, 56936) (18152, 30809) (82127, 325137) (34043, 123906) (10157, 193099) (11182, 182819) (240916, 663000) (146097, 304536) (132904, 214253) (622548, 834161) (216810, 468258) (246725, 465377) (236069, 1189241) (933959, 2358064) Uncertainty interval (17046204, 24531628) (17046204, 64 508 363 Total Total 6 122 5 774 7 423 10 788 13 222 19 416 61 162 74 692 76 302 92 262 23 599 46 363 13 475 24 690 229 756 730 670 499 543 229 888 119 237 118 179 175 035 726 027 335 900 367 253 1 769 100 20 506 014 b 9 73 208 2 296 3 484 5 088 1 003 1 064 5 057 3 072 2 229 5 075 49 578 15 840 26 204 93 848 80 391 11 989 31 085 27 629 40 891 12 462 32 665 81 437 86 663 Stroke 118 253 913 075 183 554 5 290 851 b 52 261 187 IHD 2 297 3 187 9 785 3 219 4 235 3 969 3 511 3 864 7 745 8 876 69 974 40 618 65 895 53 339 21 493 22 490 43 631 21 539 20 255 83 414 232 055 444 628 425 114 133 068 136 139 7 455 375 b 0 29 96 184 503 595 163 839 692 610 117 120 5 139 2 324 4 350 2 590 2 455 3 145 9 436 12 909 19 160 53 568 38 258 19 894 813 674 104 981 158 013 138 292 131 947 Lung cancer b 0 6 7 29 35 50 359 735 522 426 862 306 1 858 4 716 1 611 5 674 2 118 2 172 1 725 2 082 5 035 8 828 1 178 26 564 14 904 41 378 23 441 12 191 COPD 3 282 841 a 2 4 0 16 14 50 507 723 116 318 354 126 314 5 653 5 313 1 119 9 110 ALRI 33 084 10 424 88 159 36 351 62 880 14 488 52 298 55 660 92 673 507 048 231 727 3 663 274 Number of DALYs Equatorial Guinea Gabon Eritrea France Gambia Ethiopia Georgia Estonia Fiji Finland Germany Ghana Grenada Guatemala Guinea Greece Guinea-Bissau Guyana Haiti India Honduras Hungary Iceland Country Indonesia Iran (Islamic Republic of) Iraq Ireland Israel Italy Afr Afr Afr Eur (HIC) Afr Afr Eur (LMIC) Eur (HIC) Wpr (LMIC) Eur (HIC) Eur (HIC) Afr Amr (LMIC) Amr (LMIC) Afr Eur (HIC) Afr Amr (LMIC) Amr (LMIC) Sear Amr (LMIC) Eur (LMIC) Eur (HIC) Region Sear Emr (LMIC) Emr (LMIC) Eur (HIC) Eur (HIC) Eur (HIC)

113 0 5 NA 539 219 715 977 660 978 331 879 276 537 993 751 375 432 200 496 1 117 1 718 1 020 1 417 1 494 1 333 1 141 1 904 1 484 1 791 Age-standardized rate 0 3 NA 543 462 659 650 484 637 765 259 854 421 807 346 998 761 571 650 358 360 554 1 604 1 153 1 806 1 505 1 823 1 312 1 260 Crude rate DALYs 100 000 per capita DALYs NA (0, 839) (0, 664) (0, 259) (2, 29905) (10, 30274) (300, 1623) (1445, 2894) (1388, 3448) (3042, 9097) (10442, 18856) (39283, 53271) (14565, 18708) (25707, 37039) (36273, 73079) (23119, 74453) (26937, 44974) (34144, 55014) (2604, 197879) (28663, 77709) (24539, 43902) (35469, 419254) (91167, 388740) (26680, 113554) (77621, 249698) (93772, 214821) (233989, 798287) (147304, 460299) (325031, 532267) Uncertainty interval 0 3 NA 135 Total Total 2 242 1 194 2 700 6 974 46 102 15 007 10 842 53 638 16 543 31 392 15 731 56 379 74 641 45 404 36 795 35 384 49 540 587 030 269 757 179 853 276 428 293 800 119 442 165 821 436 407 b 0 1 28 NA 533 457 477 6 927 2 626 3 307 5 295 5 189 9 388 9 953 2 152 13 218 78 448 15 711 54 855 49 352 45 192 17 153 15 752 11 126 29 624 45 039 12 598 Stroke 197 984 106 252 b 0 2 73 NA 740 521 IHD 4 888 2 210 2 287 1 532 6 466 3 892 21 028 10 325 19 015 25 355 34 331 36 802 31 584 31 545 17 135 22 155 27 763 18 331 93 314 16 670 199 245 159 618 237 465 b 0 0 53 96 31 NA 870 170 865 426 641 339 583 2 143 4 353 5 322 6 916 3 987 2 308 1 716 2 163 3 622 4 852 5 811 1 714 19 205 18 970 32 017 180 791 Lung cancer b 0 0 2 54 49 NA 283 903 598 172 102 355 615 797 159 7 141 1 722 4 387 1 197 2 782 3 440 3 767 6 954 1 095 1 925 2 764 5 688 1 876 16 438 COPD a 0 2 1 1 0 45 89 66 NA 766 563 188 1 868 5 781 8 099 1 138 7 488 5 781 2 873 4 423 2 810 3 605 ALRI 83 240 36 207 68 297 30 906 44 235 184 200 208 354 Number of DALYs Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Lebanon Lesotho Liberia Libya Luxembourg Madagascar Mali Kyrgyzstan Lao People’s Democratic Republic Latvia Lithuania Malawi Malaysia Maldives Malta Marshall Islands Country Mauritania Micronesia (Federated States of) Monaco Mongolia Mauritius Mexico Amr (LMIC) Wpr (HIC) Emr (LMIC) Eur (LMIC) Afr Wpr (LMIC) Emr (HIC) Emr (LMIC) Afr Afr Emr (LMIC) Eur (HIC) Afr Afr Eur (LMIC) Wpr (LMIC) Eur (HIC) Eur (HIC) Afr Wpr (LMIC) Sear Eur (HIC) Wpr (LMIC) Region Afr Wpr (LMIC) Eur (HIC) Wpr (LMIC) Afr Amr (LMIC)

114 6 NA NA NA 849 959 704 731 295 889 138 780 312 439 688 461 934 172 759 364 1 620 1 605 1 529 1 689 1 533 1 945 1 394 1 316 1 009 Age-standardized rate 9 NA NA NA 743 695 468 512 659 230 404 287 377 530 401 323 378 511 1 677 1 383 2 253 1 367 1 399 1 146 2 153 1 057 1 484 1 645 1 609 Crude rate DALYs 100 000 per capita DALYs NA NA NA (2, 8487) (0, 65734) (0, 108018) (497, 22733) (6605, 8757) (6362, 10866) (1212, 17457) (6124, 58700) (9601, 19412) (1478, 15738) (1609, 56590) (3973, 296744) (15764, 44631) (45388, 116450) (92834, 147778) (182357, 300280) (592047, 858889) (234391, 415553) (223946, 583674) (199622, 306641) (456917, 683994) (241726, 390976) (2250524, 3466303) (1992965, 3003450) (1400443, 4408243) (771359, 1237902) Uncertainty interval NA NA NA 394 Total Total 8 726 7 627 10 716 38 748 85 829 11 558 14 325 10 759 26 992 33 825 33 929 67 046 178 918 245 171 718 432 379 751 315 230 120 804 253 512 572 883 320 959 2 822 073 2 452 808 3 228 897 1 015 174 b 74 NA NA NA 4 387 2 797 2 489 4 967 3 050 4 455 1 556 33 718 90 543 10 966 52 962 77 065 15 535 12 070 35 789 85 509 12 978 18 817 Stroke 441 650 390 112 973 217 302 550 315 928 104 935 160 893 b NA NA NA 238 IHD 2 414 3 402 5 787 7 071 5 281 6 172 3 975 17 078 92 069 18 339 31 120 88 479 24 287 14 433 48 595 59 488 12 354 40 569 307 259 540 886 145 603 449 535 143 543 249 921 1 950 243 b 71 NA NA NA 255 317 727 833 791 8 988 1 645 2 409 1 690 2 980 2 782 8 052 1 217 5 422 71 703 23 994 76 965 27 934 42 168 15 578 66 053 58 336 266 721 100 350 145 980 Lung cancer b 7 84 NA NA NA 551 278 631 188 477 601 480 749 802 3 429 4 689 1 080 7 262 3 703 5 586 7 319 6 093 42 132 26 139 54 071 53 739 31 229 14 825 COPD 203 138 a 4 34 24 65 NA NA NA 136 929 131 847 3 110 6 673 1 406 3 939 1 435 1 264 8 053 ALRI 12 576 33 877 68 013 15 096 15 256 123 049 139 242 288 091 152 429 2 022 045 1 246 970 Number of DALYs Mozambique Morocco Myanmar Namibia Montenegro Nauru Russian Federation Niue Palau Country New Zealand Nicaragua Niger Nepal Netherlands Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Portugal Qatar Republic of Korea Republic of Moldova Poland Romania Afr Emr (LMIC) Sear Afr Eur (LMIC) Wpr (LMIC) Wpr (LMIC) Wpr (LMIC) Region Wpr (HIC) Amr (LMIC) Afr Sear Eur (HIC) Afr Eur (HIC) Emr (HIC) Emr (LMIC) Amr (LMIC) Wpr (LMIC) Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Emr (HIC) Wpr (HIC) Eur (LMIC) Eur (HIC) Eur (LMIC) Eur (HIC)

115 0 3 NA NA NA 510 489 781 874 891 511 896 402 383 162 931 462 901 193 672 1 251 1 438 1 804 1 112 1 165 1 323 1 240 1 429 2 390 Age-standardized rate 0 7 NA NA NA 515 452 556 748 704 494 727 899 502 286 961 425 709 351 820 807 1 037 1 440 1 703 1 337 1 472 1 385 1 119 1 590 Crude rate DALYs 100 000 per capita DALYs NA NA NA (0, 723) (0, 1623) (37, 689) (0, 3114) (1, 3745) (0, 15241) (0, 30558) (150, 1358) (0, 192638) (7841, 39714) (10517, 18898) (13553, 35797) (55687, 87238) (6961, 264867) (57267, 175976) (74892, 120816) (89674, 164494) (66740, 232766) (24974, 219257) (95388, 228829) (67187, 190103) (190290, 253571) (371260, 576900) (141976, 249981) (228768, 650658) (427557, 656102) Uncertainty interval 0 NA NA NA 931 495 993 467 650 Total Total 2 246 8 735 14 990 96 965 72 417 26 600 28 158 112 186 129 313 102 909 220 583 147 735 474 754 152 094 133 596 196 219 422 022 163 854 126 069 542 214 b 0 NA NA NA 409 181 319 128 871 137 4 105 5 980 2 277 5 055 25 683 39 455 20 545 81 537 20 953 16 907 13 293 24 060 29 352 54 977 88 820 37 340 30 568 Stroke 174 714 167 934 b 0 NA NA NA 339 276 171 268 962 424 IHD 5 232 8 521 1 301 14 892 48 463 13 372 14 689 13 533 43 118 14 970 58 237 53 148 12 379 96 682 43 484 107 302 122 366 118 065 219 454 b 0 79 21 24 53 83 NA NA NA 633 288 735 991 211 108 5 196 1 163 6 575 3 600 9 387 3 029 38 000 10 749 10 886 45 262 43 157 10 083 14 588 115 769 Lung cancer b 5 0 6 18 25 10 35 NA NA NA 948 441 373 313 3 582 2 987 1 234 4 088 1 077 2 819 2 578 9 962 1 295 4 291 3 397 15 152 26 689 13 844 29 447 COPD a 8 0 1 86 11 17 41 NA NA NA 453 409 139 429 272 167 5 842 3 132 4 737 9 610 ALRI 67 395 67 756 56 072 10 953 45 592 123 951 105 724 109 253 262 610 Number of DALYs Rwanda Saint Kitts and Nevis Country Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Serbia Seychelles Sierra Leone Slovenia Solomon Islands Somalia Senegal Singapore Slovakia South Africa South Sudan Spain Sri Lanka Sudan Suriname Swaziland Switzerland Sweden Syrian Arab Republic Tajikistan Thailand Afr Amr (HIC) Region Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Afr Emr (HIC) Eur (LMIC) Afr Afr Eur (HIC) Wpr (LMIC) Emr (LMIC) Afr Wpr (HIC) Eur (HIC) Afr Emr (LMIC) Eur (HIC) Sear Emr (LMIC) Amr (LMIC) Afr Eur (HIC) Eur (HIC) Emr (LMIC) Eur (LMIC) Sear

116 ; : America : ; Amr ; 44 NA NA 942 607 624 291 679 176 707 845 336 964 725 : Africa : 1 180 1 345 1 141 1 239 3 151 1 472 1 532 2 230 1 759 Age-standardized rate 27 NA NA 810 675 340 495 588 261 586 763 457 888 647 1 602 1 116 1 115 1 159 2 347 1 232 2 519 1 635 1 194 ; NA: Not available. Crude rate DALYs 100 000 per capita DALYs : Ischaemic heart disease. Afr disease. heart Ischaemic : ; IHD ; NA NA (0, 1282) (0, 17939) (0, 13424) (4369, 21975) (26810, 39716) (25728, 35715) (3907, 154648) (11746, 126011) (97577, 145576) (37051, 173952) (28730, 204403) (332422, 563596) (18112, 1665776) (108260, 437232) (37219, 1752538) (126302, 218023) (493511, 882658) (161705, 391100) (223133, 674971) (123087, 471950) : High-income countries (720077, 1018543) Uncertainty interval ; HIC 68 NA NA Total Total 8 924 9 049 33 150 75 267 30 479 15 523 94 267 121 282 867 624 121 404 435 999 314 497 821 795 174 845 688 818 286 193 297 057 467 389 131 267 1 141 785 b : Chronic obstructive pulmonary disease pulmonary obstructive Chronic : 26 NA NA 1 512 2 346 7 722 3 964 13 840 17 590 36 526 28 808 80 711 61 260 41 108 56 141 67 112 22 568 15 960 Stroke 242 166 252 438 333 882 129 932 126 711 ; COPD ; b 30 : Low- and middle-income countries NA NA IHD 1 927 5 597 6 586 9 051 10 109 11 664 62 046 74 505 49 772 17 250 95 764 46 340 90 415 15 113 371 760 800 250 141 450 113 987 473 323 251 377 ; LMIC b 3 NA NA 797 405 739 694 8 149 4 068 4 228 2 102 4 233 1 037 3 472 2 578 14 246 73 745 98 923 21 843 14 709 197 655 149 065 195 036 Lung cancer : Western Pacific : Acute lower respiratory disease respiratory lower Acute : b ; Wpr 1 99 ; ALRI ; NA NA 938 105 406 1 506 4 333 2 224 9 571 2 539 4 212 8 070 6 715 9 039 2 071 2 815 33 004 14 192 11 617 37 308 20 221 COPD a 8 NA NA 115 269 866 333 4 583 4 130 5 780 1 247 3 283 : South-East Asia ALRI 44 102 23 038 11 799 11 918 54 576 65 552 90 821 63 863 287 096 174 605 129 343 Number of DALYs ; Sear : Ambient air pollution air Ambient : ; AAP ; : Europe ; Eur The former Yugoslav Republic of Macedonia Timor-Leste Togo Tonga Country Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United Republic of Tanzania United States of America Uruguay Venezuela, Bolivarian Republic of Viet Nam Yemen Uzbekistan Vanuatu Zambia Zimbabwe : Disability-adjusted life years life Disability-adjusted : Eur (LMIC) Sear Afr Wpr (LMIC) Region Amr (HIC) Emr (LMIC) Eur (LMIC) Eur (LMIC) Wpr (LMIC) Afr Eur (LMIC) Emr (HIC) Eur (HIC) Afr Amr (HIC) Amr (HIC) Amr (LMIC) Wpr (LMIC) Emr (LMIC) Eur (LMIC) Wpr (LMIC) Afr Afr Age group includes 25 years and above. Age group includes less than 5 years old.

DALY a b Emr: Eastern Mediterranean

117 5 3 280 388 898 849 699 846 210 184 782 575 910 234 282 300 836 154 403 858 832 1 434 1 260 1 582 1 027 1 566 2 456 Age-standardized rate 4 6 911 283 220 787 480 405 270 282 673 500 945 290 386 343 412 708 1 132 1 580 1 847 1 357 1 556 1 268 1 593 1 929 1 147 Crude rate DALYs 100 000 per capita DALYs (0, 440) (0, 592) (2, 852) (3, 207) (0, 279) (57, 562) (28, 8200) (9, 19920) (2504, 3873) (1236, 1615) (793, 29275) (9979, 66106) (6807, 22037) (23611, 37699) (56679, 81887) (17530, 90675) (30990, 49214) (14658, 29478) (1328, 113898) (14672, 27269) (54219, 177204) (28588, 304451) (24129, 114297) (52525, 188627) (125632, 444600) (411938, 645789) (218979, 346868) Uncertainty interval 8 370 396 551 641 192 138 Total Total 3 137 5 121 1 418 30 375 69 294 57 113 40 189 22 859 79 062 44 525 21 603 19 005 82 959 16 477 290 803 113 627 517 418 182 222 131 566 278 465 b 2 77 45 795 134 164 413 177 167 2 216 6 098 4 482 5 753 7 599 30 801 14 913 15 641 13 306 24 911 20 908 13 838 27 952 26 124 61 381 50 706 Stroke 114 395 122 603 b 5 77 900 149 168 598 212 337 103 IHD 1 149 7 869 6 768 30 936 11 024 10 737 13 393 16 307 55 743 81 010 22 749 10 112 10 845 18 777 40 289 49 531 56 907 129 836 b 1 7 47 23 30 38 13 201 232 682 164 123 420 4 527 2 949 1 175 7 617 1 657 1 489 5 960 1 579 1 514 3 666 2 696 30 319 30 763 10 919 Lung cancer b 0 8 6 2 3 10 11 397 128 628 169 981 538 279 2 248 1 393 1 137 1 272 7 641 2 293 1 206 1 011 2 860 2 259 3 171 4 545 COPD 124 061 a 0 4 3 0 96 55 68 25 74 69 781 812 125 118 290 315 1 582 8 612 5 438 3 368 ALRI 29 398 11 044 69 435 13 818 158 982 132 214 163 611 Number of DALYs Brunei Darussalam Bulgaria Benin Bhutan Bolivia, Plurinational States of Bosnia and Herzegovina Botswana Brazil Burkina Faso Belize Belarus Belgium Bangladesh Barbados Azerbaijan Bahrain Austria Bahamas Armenia Australia Angola Antigua and Barbuda Argentina Andorra Albania Algeria Afghanistan Country Wpr HIC Eur (LMIC) Afr Sear Amr (LMIC) Eur (LMIC) Afr Amr (LMIC) Afr Amr (LMIC) Eur (LMIC) Eur (HIC) Sear Amr (HIC) Eur (LMIC) Emr (HIC) Eur (HIC) Amr (HIC) Eur (LMIC) Wpr (HIC) Afr Amr (HIC) Amr (LMIC) Eur (HIC) Eur (LMIC) Afr Emr (LMIC) Region

118 48 NA 649 221 807 618 344 573 277 182 172 392 428 421 238 863 333 1 300 1 210 1 683 1 698 1 452 1 519 1 137 1 300 1 118 1 673 1 316 Age-standardized rate 86 NA 521 284 614 585 941 379 495 252 349 254 833 971 642 250 678 309 1 525 1 155 1 549 1 890 1 515 1 183 1 044 1 331 1 716 1 086 Crude rate DALYs 100 000 per capita DALYs NA (7, 201) (794, 1923) (949, 1774) (394, 3551) (275, 55444) (1306, 6762) (451, 16835) (4206, 7135) (3288, 74301) (2416, 36284) (9466, 25696) (3171, 54429) (12597, 43574) (18119, 31154) (14805, 23050) (34810, 53813) (15445, 26983) (10363, 37219) (50791, 91728) (63304, 183941) (31354, 127378) (11168, 175802) (22900, 284130) (135168, 208012) (411728, 603736) (280122, 956296) Uncertainty interval (8550689, 11763242) (8550689, NA 137 Total Total 1 323 3 995 9 847 1 406 5 802 2 468 27 070 15 136 25 044 77 680 46 687 18 773 25 179 19 444 44 692 36 229 21 558 23 271 73 547 120 064 167 768 501 693 108 013 168 544 605 299 10 085 465 b 67 NA 710 379 720 5 176 3 148 4 319 7 817 6 559 1 024 2 355 7 163 1 841 6 764 15 059 34 090 10 058 14 644 12 565 10 366 28 339 70 728 10 543 22 568 Stroke 174 599 107 876 4 871 317 b 58 NA 268 646 695 381 IHD 3 569 9 499 6 519 7 689 6 119 7 291 2 568 3 056 4 355 22 058 15 599 10 413 12 147 24 216 18 265 38 787 60 000 15 784 10 216 34 217 230 911 2 653 784 b 7 0 19 69 61 NA 131 292 860 352 444 871 281 498 5 128 5 340 2 503 1 063 1 558 1 464 8 638 4 403 8 518 3 561 7 247 17 162 36 613 1 467 555 Lung cancer b 2 99 39 55 NA 295 432 642 184 511 599 310 914 1 248 1 765 1 317 4 751 1 679 1 742 1 081 1 342 1 568 1 061 3 132 33 741 11 985 11 800 COPD 827 725 a 3 9 46 13 18 97 NA 226 640 130 323 1 896 3 225 3 487 2 073 6 383 1 312 ALRI 16 947 93 449 56 966 12 198 45 281 59 397 10 431 11 178 265 085 106 010 424 752 Number of DALYs Central African Republic Chad Cameroon Canada Chile China Burundi Cabo Verde Cambodia Country El Salvador Dominican Republic Ecuador Egypt Djibouti Dominica Czech Republic Côte d’Ivoire Democratic People’s Republic of Korea Democratic Republic of the Congo Denmark Cuba Cyprus Croatia Costa Rica Congo Cook Islands Colombia Comoros Afr Afr Afr Amr (HIC) Amr (HIC) Wpr (LMIC) Afr Afr Wpr (LMIC) Region Amr (LMIC) Amr (LMIC) Amr (LMIC) Emr (LMIC) Emr (LMIC) Amr (LMIC) Eur (HIC) Afr Sear Afr Eur (HIC) Amr (LMIC) Eur (HIC) Eur (HIC) Amr (LMIC) Afr Wpr (LMIC) Afr Amr (LMIC)

119 5 54 30 376 498 253 670 660 196 810 782 121 625 125 171 722 148 160 1 603 1 174 1 377 1 016 1 324 1 161 1 285 1 249 1 409 1 067 1 196 Age-standardized rate 4 91 73 360 435 962 816 874 585 493 930 456 694 233 593 314 434 574 768 779 220 224 1 352 1 224 1 106 1 330 1 456 1 044 1 052 Crude rate DALYs 100 000 per capita DALYs (0, 278) (1, 499) (1, 4795) (0, 2792) (28, 4329) (977, 9312) (10, 10191) (206, 6985) (360, 8373) (563, 17970) (355, 15697) (4839, 90054) (5526, 88794) (9977, 41692) (6403, 10916) (26949, 41843) (11941, 43983) (14879, 23560) (52992, 84973) (19837, 40182) (71527, 149172) (89154, 248086) (27007, 106138) (88717, 444275) (91124, 167870) (80850, 179471) (247234, 334417) (369868, 942950) (6791629, 9916369) Uncertainty interval 17 190 147 Total Total 3 089 8 488 5 545 2 008 3 935 2 221 8 714 5 162 34 205 55 808 33 195 10 570 57 512 19 083 69 483 31 582 75 830 25 729 112 589 186 789 273 914 289 758 133 231 126 874 705 745 8 227 991 b 3 88 36 512 462 994 5 912 2 365 1 355 5 559 2 287 1 837 6 828 2 320 1 140 15 218 21 126 14 720 13 926 45 531 13 022 47 119 24 400 48 890 80 783 44 680 31 377 Stroke 419 319 2 441 642 b 90 57 13 698 IHD 8 990 1 652 1 506 7 456 1 398 1 322 1 252 1 557 3 782 2 825 2 570 10 860 10 491 33 555 15 109 22 854 16 583 80 057 21 080 22 904 48 418 43 279 158 464 132 955 2 324 770 b 8 0 51 52 57 40 45 186 993 503 843 181 223 192 290 1 919 1 433 3 273 6 760 3 008 1 282 5 576 18 816 52 661 28 077 13 257 34 730 38 802 200 791 Lung cancer b 2 3 0 21 14 10 947 181 398 924 836 212 730 319 138 956 523 144 1 048 2 235 2 679 6 639 2 082 8 504 5 208 3 907 10 461 15 725 COPD 1 385 386 a 2 0 1 8 0 50 38 25 150 156 403 314 191 194 6 321 4 151 4 551 1 886 2 060 ALRI 16 336 28 597 24 064 41 021 13 873 26 794 42 735 98 944 186 856 1 875 402 Number of DALYs Guatemala Guinea India Grenada Guinea-Bissau Guyana Haiti Honduras Hungary Iceland Ghana Greece Gambia Georgia Germany Gabon France Fiji Finland Equatorial Guinea Eritrea Estonia Ethiopia Country Iran (Islamic Republic of) Israel Italy Iraq Ireland Indonesia Amr (LMIC) Afr Sear Amr (LMIC) Afr Amr (LMIC) Amr (LMIC) Amr (LMIC) Eur (LMIC) Eur (HIC) Afr Eur (HIC) Afr Eur (LMIC) Eur (HIC) Afr Eur (HIC) Wpr (LMIC) Eur (HIC) Afr Afr Eur (HIC) Afr Region Emr (LMIC) Eur (HIC) Eur (HIC) Emr (LMIC) Eur (HIC) Sear

120 3 0 NA 306 337 108 233 718 369 588 172 955 573 117 830 528 424 913 313 982 485 401 1 289 1 890 1 017 1 003 1 172 1 251 Age-standardized rate 2 0 NA 360 292 248 443 280 777 713 227 553 989 299 772 414 289 512 414 761 250 665 436 1 099 1 768 1 235 1 313 1 110 Crude rate DALYs 100 000 per capita DALYs NA (0, 90) (0, 239) (0, 275) (94, 535) (5, 15094) (1, 14350) (982, 3001) (482, 1171) (513, 1034) (3654, 4806) (4273, 7536) (8961, 29373) (8138, 11994) (12030, 32197) (11699, 49033) (10740, 17627) (13716, 21749) (1252, 100625) (34118, 78886) (14869, 20090) (14039, 28032) (71709, 219164) (34195, 111029) (13978, 166466) (37893, 166207) (74186, 254465) (132184, 219783) Uncertainty interval 1 0 48 NA 924 390 797 Total Total 2 287 4 197 7 940 5 205 6 047 20 645 22 213 32 226 79 800 60 768 60 667 14 519 18 070 17 416 10 050 20 560 179 012 141 189 107 508 117 639 188 692 b 0 0 14 NA 805 204 121 263 4 875 7 322 1 150 7 343 4 727 5 795 6 262 2 812 3 051 1 279 7 367 3 289 52 245 25 294 26 596 19 671 15 385 38 851 20 724 79 696 Stroke b 0 0 24 NA 536 196 211 IHD 1 189 3 076 2 087 7 611 8 952 8 902 7 338 4 839 1 153 1 024 9 816 1 927 87 176 12 051 17 343 13 437 31 173 11 156 60 090 12 858 59 983 b 0 8 0 16 40 18 NA 159 108 166 491 716 222 700 158 738 853 273 655 924 937 419 1 119 6 419 3 322 1 640 12 314 45 592 Lung cancer b 0 1 0 68 18 28 48 75 NA 851 475 301 978 133 214 635 510 266 103 7 804 2 686 1 736 1 297 2 160 1 748 1 833 1 221 2 522 COPD a 0 0 1 0 2 67 28 18 52 NA 437 250 900 309 1 874 1 244 2 527 3 497 3 430 2 809 1 219 ALRI 19 473 11 735 95 151 15 174 37 332 34 976 81 300 Number of DALYs Mexico Micronesia (Federated States of) Monaco Mauritania Mauritius Marshall Islands Kyrgyzstan Mali Malta Kiribati Kuwait Lao People’s Democratic Republic Madagascar Malawi Malaysia Maldives Jordan Kazakhstan Kenya Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Japan Jamaica Country Amr (LMIC) Wpr (LMIC) Eur (HIC) Afr Afr Wpr (LMIC) Eur (LMIC) Afr Eur (HIC) Wpr (LMIC) Emr (HIC) Wpr (LMIC) Afr Afr Wpr (LMIC) Sear Emr (LMIC) Eur (LMIC) Afr Eur (HIC) Emr (LMIC) Afr Afr Emr (LMIC) Eur (HIC) Eur (HIC) Wpr (HIC) Amr (LMIC) Region

121 4 96 95 NA NA NA 208 937 617 623 974 544 231 346 492 387 650 226 687 727 692 599 479 1 438 1 834 1 474 1 403 1 524 1 251 Age-standardized rate 7 NA NA NA 334 218 226 773 222 315 392 352 184 333 412 535 489 617 590 896 787 1 350 1 141 1 030 1 226 1 870 1 490 1 052 1 290 Crude rate DALYs 100 000 per capita DALYs NA NA NA (1, 3598) (0, 45340) (0, 26994) (982, 1345) (543, 6148) (177, 9276) (671, 9291) (520, 19390) (2890, 5964) (1811, 3107) (5690, 16231) (2529, 24390) (8773, 15800) (40460, 65542) (18165, 47613) (1852, 129366) (89467, 141783) (66174, 100564) (96959, 250625) (77555, 125559) (276926, 449216) (163918, 245221) (109737, 197727) (285845, 415517) (865895, 1286013) (982557, 1511846) Uncertainty interval NA NA NA 157 Total Total 1 152 4 143 4 603 4 347 5 761 2 485 28 546 83 383 11 999 11 048 12 316 53 142 15 961 34 826 77 904 12 692 117 212 205 208 366 909 163 656 148 843 347 000 103 143 1 057 323 1 231 458 b 41 NA NA NA 216 8 993 6 001 1 293 4 999 1 391 1 243 1 479 4 977 8 069 2 732 5 217 1 058 48 246 35 854 72 796 16 694 25 781 37 666 15 641 47 169 Stroke 121 997 206 188 212 374 142 251 b 74 NA NA NA 542 800 IHD 4 182 2 484 4 807 1 642 1 944 2 019 7 037 7 862 8 083 1 245 5 429 17 602 51 974 18 267 82 504 19 058 15 138 36 702 82 007 36 762 145 146 238 540 129 845 b 37 49 90 NA NA NA 994 188 268 530 272 153 668 613 304 570 1 602 7 709 1 266 4 587 2 641 11 074 26 216 43 761 18 781 11 813 17 011 14 513 33 928 Lung cancer b 4 89 42 NA NA NA 291 183 152 191 245 141 207 509 262 332 2 272 2 706 5 651 2 919 3 201 1 823 1 885 1 960 11 757 64 594 18 743 27 385 30 229 COPD a 9 2 31 55 61 14 NA NA NA 666 340 496 749 488 3 646 6 811 1 734 6 762 2 769 1 432 1 411 ALRI 69 227 32 578 51 682 58 586 14 612 536 188 119 487 865 909 Number of DALYs Republic of Moldova Romania Republic of Korea Poland Portugal Qatar Papua New Guinea Paraguay Peru Philippines Panama Pakistan Palau Norway Oman New Zealand Nicaragua Niger Nigeria Niue Nepal Netherlands Nauru Mozambique Myanmar Namibia Mongolia Morocco Montenegro Country Eur (LMIC) Eur (LMIC) Wpr (HIC) Eur (HIC) Eur (HIC) Emr (HIC) Wpr (LMIC) Amr (LMIC) Amr (LMIC) Wpr (LMIC) Amr (LMIC) Emr (LMIC) Wpr (LMIC) Eur (HIC) Emr (HIC) Wpr (HIC) Amr (LMIC) Afr Afr Wpr (LMIC) Sear Eur (HIC) Wpr (LMIC) Afr Sear Afr Wpr (LMIC) Emr (LMIC) Eur (LMIC) Region

122 2 0 80 NA NA NA 951 130 941 331 908 569 234 972 749 276 246 549 761 559 375 425 878 2 349 1 147 1 073 1 765 1 129 1 014 Age-standardized rate 6 0 NA NA NA 734 267 563 171 965 319 794 614 505 544 592 298 335 978 618 396 413 876 1 532 1 209 1 324 1 622 1 023 1 608 Crude rate DALYs 100 000 per capita DALYs NA NA NA (0, 788) (0, 327) (0, 7901) (0, 1391) (0, 1070) (10, 202) (58, 530) (0, 13190) (0, 92534) (3707, 6608) (2860, 15447) (7494, 64193) (4528, 12130) (32706, 77627) (32252, 90532) (46274, 83474) (3053, 120293) (65190, 87950) (21087, 32809) (33590, 53983) (33048, 59479) (25080, 78080) (29030, 101715) (98058, 279848) (167188, 259127) (527150, 1680821) Uncertainty interval 0 NA NA NA 267 842 488 138 365 224 Total Total 4 586 8 984 5 246 10 844 55 568 60 211 40 452 66 368 64 706 66 783 76 067 49 564 27 293 43 414 46 980 49 465 181 229 213 564 1 234 984 b 0 71 43 92 NA NA NA 390 174 179 2 621 1 565 5 647 2 662 1 955 7 413 12 320 17 412 14 080 40 873 20 868 11 018 93 281 33 455 10 477 19 624 10 217 11 449 Stroke 475 189 b 0 93 79 NA NA NA 152 745 373 119 123 IHD 4 102 5 899 3 082 3 997 1 609 6 485 6 700 5 904 34 104 20 390 17 329 21 118 35 646 47 429 31 332 16 335 15 818 704 440 b 0 7 9 6 41 37 47 21 NA NA NA 429 350 102 438 231 3 478 2 707 1 182 8 211 1 187 2 756 2 167 1 479 2 890 2 860 14 737 10 159 40 510 Lung cancer b 3 0 4 2 7 18 10 NA NA NA 624 159 737 680 105 189 406 461 1 834 1 764 6 272 1 271 4 155 5 538 1 792 1 229 9 006 1 802 11 699 COPD a 1 0 3 5 19 95 15 53 13 34 NA NA NA 204 223 151 4 604 2 080 1 281 2 853 5 839 ALRI 19 463 47 751 57 026 46 418 32 095 24 267 30 079 111 778 Number of DALYs Sweden Switzerland Syrian Arab Republic Tajikistan Swaziland South Sudan Spain Sri Lanka Sudan Suriname Solomon Islands Somalia South Africa Sao Tome and Principe Saudi Arabia Sierra Leone Singapore Slovakia Slovenia San Marino Senegal Serbia Seychelles Saint Vincent and the Grenadines Samoa Russian Federation Rwanda Saint Lucia Saint Kitts and Nevis Country Eur (HIC) Eur (HIC) Emr (LMIC) Eur (LMIC) Afr Emr (LMIC) Eur (HIC) Sear Emr (LMIC) Amr (LMIC) Wpr (LMIC) Emr (LMIC) Afr Afr Emr (HIC) Afr Wpr (HIC) Eur (HIC) Eur (HIC) Eur (HIC) Afr Eur (LMIC) Afr Amr (LMIC) Wpr (LMIC) Eur (HIC) Afr Amr (LMIC) Amr (HIC) Region

123 : ; Amr 29 : Africa NA NA 186 497 782 691 126 501 574 204 570 984 745 386 753 849 492 754 1 417 1 742 1 220 2 330 1 265 Age-standardized rate : Not available. ; NA 18 NA NA 293 509 795 631 205 435 502 389 202 750 451 761 750 622 1 001 1 362 1 047 2 043 1 764 1 062 1 117 Crude rate DALYs 100 000 per capita DALYs : Ischaemic heart disease. Afr ; IHD NA NA (0, 421) (0, 4540) (0, 8103) ; HIC: High-income countries (1478, 7170) (3773, 5558) (1935, 76231) (5711, 60632) (9434, 13886) (12202, 92538) (46343, 82205) (6375, 718941) (14031, 66968) (33829, 50181) (49769, 198270) (93815, 286989) (14581, 715016) (68771, 168585) (40268, 178286) (166229, 300933) (140980, 240534) (239712, 333430) (168140, 254341) Uncertainty interval 22 NA NA Total Total 5 155 4 601 3 057 4 068 58 870 46 566 65 179 46 343 41 833 36 305 11 606 123 202 197 906 232 643 325 901 122 794 125 480 497 797 185 457 285 561 211 486 b : Chronic obstructive pulmonary disease 8 NA NA 971 710 9 062 8 595 1 996 1 267 8 860 6 495 29 562 61 115 18 123 65 810 24 012 31 666 36 198 13 273 15 483 72 694 Stroke 121 354 129 729 104 840 : Low- and middle-income countries ; COPD b ; LMIC 9 NA NA 924 IHD 5 262 4 229 1 949 1 926 1 735 5 565 3 173 31 832 30 503 35 674 17 686 43 328 18 017 26 370 21 809 87 104 101 309 161 524 351 024 132 386 b : Western Pacific 1 NA NA 913 290 950 899 433 480 882 861 188 114 259 3 773 9 319 2 085 1 489 36 278 86 655 44 053 11 354 25 178 38 210 ; Wpr Lung cancer : Acute lower respiratory disease b 0 30 49 ; ALRI NA NA 140 599 657 395 4 228 3 237 1 127 1 320 2 044 3 874 5 841 3 325 7 024 1 001 1 897 9 545 18 044 10 443 13 423 COPD : South-East Asia a ; Sear 4 55 NA NA 171 591 329 134 5 190 1 467 2 365 4 817 1 785 9 734 2 126 3 934 ALRI 27 482 57 659 43 131 31 471 21 293 76 790 21 109 122 133 Number of DALYs : Ambient air pollution ; Eur: Europe ; AAP Uzbekistan Yemen Zambia Zimbabwe Uruguay Vanuatu Venezuela, Bolivarian Republic of Viet Nam United States of America United Republic of Tanzania United Kingdom Ukraine United Arab Emirates Turkmenistan Uganda Tunisia Turkey Tuvalu Trinidad and Tobago Togo Tonga The former Yugoslav Republic of Macedonia Timor-Leste Thailand Country ; Emr: Eastern Mediterranean : Disability-adjusted life years Eur (LMIC) Emr (LMIC) Afr Afr Amr (HIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Amr (HIC) Afr Eur (HIC) Eur (LMIC) Emr (HIC) Eur (LMIC) Afr Emr (LMIC) Eur (LMIC) Wpr (LMIC) Amr (HIC) Afr Wpr (LMIC) Eur (LMIC) Sear Sear Region Age group includes 25 years and above. Age group includes less than 5 years old.

America DALY a b

124 7 9 399 655 658 488 554 803 364 482 514 496 645 2 543 1 161 1 339 2 110 2 286 1 781 1 309 2 297 1 750 1 292 1 173 1 626 1 880 Age-standardized rate 9 6 644 610 668 800 490 398 956 462 976 426 842 289 428 1 928 1 433 1 064 2 331 2 500 1 450 2 909 1 323 1 000 2 409 3 058 Crude rate DALYs 100 000 per capita DALYs (8, 381) (0, 378) (0, 927) (0, 749) (3, 1371) (76, 762) (23, 7471) (17, 30851) (2915, 3739) (3175, 4967) (8739, 27712) (1732, 58207) (22548, 41572) (15613, 99461) (38942, 60930) (2936, 188471) (28453, 59454) (35306, 57027) (80265, 286385) (39517, 439803) (39993, 190704) (19971, 105879) (87855, 128993) (231285, 368064) (607961, 919646) (183508, 642890) Uncertainty interval 13 252 259 893 623 485 Total Total 1 060 3 313 3 995 4 557 20 692 36 996 33 078 67 410 50 089 45 744 66 234 45 918 262 154 200 502 137 543 294 743 749 837 128 312 108 658 424 679 b 3 46 86 151 216 749 217 657 147 6 905 7 225 4 248 1 363 6 440 25 948 74 055 32 966 38 228 14 741 15 794 22 926 13 680 33 295 16 202 Stroke 126 707 130 137 b 10 157 152 713 424 299 215 IHD 9 955 1 679 1 433 1 133 28 419 20 313 88 531 70 328 17 049 64 849 38 661 18 732 90 038 17 040 18 370 49 202 12 350 189 574 230 462 b 1 45 15 88 62 57 765 181 456 233 230 373 3 078 8 134 7 219 5 961 1 078 15 767 26 627 10 621 91 102 13 643 20 428 12 046 22 101 44 602 Lung cancer b 4 2 9 0 12 11 29 325 852 342 608 168 4 367 3 692 3 080 1 130 4 700 1 202 1 458 1 749 1 197 1 691 2 997 1 628 9 108 COPD 147 601 a 0 5 4 0 30 87 35 87 38 430 472 156 247 131 4 542 6 845 1 064 1 663 1 063 ALRI 17 783 13 289 35 681 10 370 203 330 179 747 194 853 Number of DALYs Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Afghanistan Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Bhutan Bolivia, Plurinational States of Bosnia and Herzegovina Botswana Brunei Darussalam Bulgaria Belize Benin Brazil Country Eur (LMIC) Afr Eur (HIC) Afr Amr (HIC) Amr (LMIC) Eur (LMIC) Wpr (HIC) Eur (HIC) Emr (LMIC) Eur (LMIC) Amr (HIC) Emr (HIC) Sear Amr (HIC) Eur (LMIC) Eur (HIC) Sear Amr (LMIC) Eur (LMIC) Afr Wpr HIC Eur (LMIC) Amr (LMIC) Afr Amr (LMIC) Region

125 90 NA 466 526 435 731 517 926 279 533 666 416 1 800 1 624 2 213 2 059 1 282 2 093 2 021 1 184 1 205 1 784 1 076 1 535 2 046 1 565 2 182 Age-standardized rate NA 503 137 769 810 869 437 420 990 633 462 528 555 348 1 937 1 578 2 515 1 893 1 585 2 016 1 540 1 746 1 450 1 300 2 115 1 268 1 973 Crude rate DALYs 100 000 per capita DALYs NA (10, 278) 15895066) (11453348, (553, 4594) (484, 80716) (1151, 2749) (2438, 4604) (781, 20723) (1806, 9060) (3987, 92741) (7184, 11848) (5158, 84332) (2633, 40840) (31945, 52726) (16613, 57810) (14482, 53167) (24965, 45828) (57985, 90584) (13393, 35088) (84219, 245180) (61961, 203255) (41865, 162771) (70274, 124947) (15190, 227195) (32356, 399937) (164959, 254600) (342501, 1171149) Uncertainty interval NA 188 Total Total 1 897 3 217 9 780 3 653 5 432 43 171 23 772 35 909 58 574 33 016 36 090 56 428 75 089 12 832 28 121 26 820 160 001 130 281 100 735 204 941 101 033 139 861 238 829 740 668 13 590 137 b 68 NA 920 975 413 3 030 5 132 6 757 2 068 7 736 2 463 1 383 7 362 7 736 17 900 13 170 28 045 18 449 15 682 35 747 20 750 12 765 10 706 37 864 Stroke 100 420 105 633 5 455 918 b 90 NA 539 585 958 IHD 4 862 6 545 6 250 2 125 5 432 12 612 19 274 14 552 18 126 11 631 20 078 28 781 56 529 16 300 27 062 41 691 27 358 13 270 59 216 86 091 11 870 3 009 085 b 24 29 68 21 NA 407 248 744 352 157 870 8 190 5 840 4 426 1 542 1 004 1 055 4 460 2 437 1 828 1 999 11 314 11 172 14 963 20 745 52 171 3 775 771 Lung cancer b 4 51 60 NA 305 169 331 857 459 173 465 724 2 065 1 531 1 428 2 579 1 725 1 685 6 446 1 241 3 626 1 262 1 786 2 787 16 331 14 409 COPD 986 954 a 0 5 45 25 17 NA 244 128 377 161 1 007 8 813 1 577 2 850 4 587 4 492 ALRI 24 238 80 786 68 578 16 702 18 315 70 981 10 692 362 408 127 017 132 424 532 707 Number of DALYs Chad Chile China Cameroon Canada Central African Republic Burkina Faso Burundi Cabo Verde Cambodia Country Congo Cook Islands Colombia Comoros Costa Rica Croatia Cuba Cyprus Czech Republic Côte d’Ivoire Democratic People’s Republic of Korea Denmark Djibouti Dominica Dominican Republic Democratic Republic of the Congo Ecuador Afr Amr (HIC) Wpr (LMIC) Afr Amr (HIC) Afr Afr Afr Afr Wpr (LMIC) Region Afr Wpr (LMIC) Afr Amr (LMIC) Amr (LMIC) Eur (HIC) Amr (LMIC) Eur (HIC) Eur (HIC) Afr Sear Eur (HIC) Emr (LMIC) Amr (LMIC) Amr (LMIC) Afr Amr (LMIC)

126 98 13 81 843 440 319 429 641 793 696 284 2 363 2 141 1 029 1 488 1 054 1 508 2 409 1 900 1 389 1 126 1 610 1 695 1 277 1 301 1 677 1 409 2 333 1 136 Age-standardized rate 11 993 705 133 710 632 141 497 942 792 927 604 560 357 851 1 874 1 448 1 192 2 230 1 661 1 727 1 141 1 221 2 189 1 086 1 089 1 531 1 136 1 254 Crude rate DALYs 100 000 per capita DALYs (2, 655) (0, 466) (0, 7043) (1, 6661) 14606630) (70, 7830) (10262166, (23, 18586) (356, 12068) (448, 20309) (648, 22302) (648, 13074) (5505, 94026) (1255, 13005) (21499, 33372) (16184, 24659) (13988, 56667) (27173, 54989) (33300, 51335) (5271, 103042) (21633, 80081) (79789, 129379) (54470, 219288) (74456, 155437) (147255, 745078) (593804, 856619) (152163, 415287) (375126, 499638) (135965, 288780) (561844, 1415551) Uncertainty interval 47 216 319 Total Total 3 901 6 854 3 766 7 678 4 335 8 314 27 280 60 667 20 314 35 433 43 110 63 429 10 928 42 097 13 029 59 067 105 552 456 756 153 926 718 664 117 299 312 754 209 026 436 269 1 063 355 12 278 024 b 6 37 541 553 120 6 903 3 706 1 302 9 012 1 647 1 717 2 802 6 077 2 692 1 090 17 945 69 363 19 766 25 178 13 182 34 860 15 867 46 729 13 703 50 060 Stroke 234 492 493 756 102 771 2 849 209 b 40 130 171 IHD 2 678 1 599 6 003 2 717 1 865 2 358 1 821 1 859 5 176 12 798 49 859 12 190 42 991 11 048 48 894 24 035 30 485 12 503 11 630 28 522 89 788 355 137 151 998 311 673 266 650 5 130 605 b 0 45 63 22 65 647 139 305 469 322 123 424 1 462 1 236 6 149 1 157 4 296 1 821 2 431 1 863 34 752 76 905 44 422 15 887 14 319 99 490 25 001 612 883 105 353 Lung cancer b 0 4 4 19 21 29 773 221 902 417 463 309 881 889 245 161 1 158 2 800 2 633 8 265 2 995 1 070 1 224 4 921 18 060 41 837 25 653 12 981 COPD 1 897 456 a 1 0 6 3 15 66 25 197 316 716 409 168 4 960 2 409 3 593 3 427 5 873 8 167 ALRI 19 211 28 234 42 776 47 138 20 016 34 282 49 938 28 866 320 192 132 783 1 787 871 Number of DALYs Estonia Fiji Honduras Hungary Iceland India El Salvador Equatorial Guinea Eritrea Ethiopia Finland France Gabon Haiti Egypt Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Country Iraq Ireland Indonesia Iran (Islamic Republic of) Eur (HIC) Wpr (LMIC) Amr (LMIC) Eur (LMIC) Eur (HIC) Sear Amr (LMIC) Afr Afr Afr Eur (HIC) Eur (HIC) Afr Amr (LMIC) Emr (LMIC) Afr Eur (LMIC) Eur (HIC) Afr Eur (HIC) Amr (LMIC) Amr (LMIC) Afr Afr Amr (LMIC) Region Emr (LMIC) Eur (HIC) Emr (LMIC) Sear

127 1 6 NA 387 422 332 850 878 386 681 349 937 703 531 708 535 1 405 2 433 1 227 1 902 1 764 1 618 1 703 1 396 1 285 1 065 1 031 1 912 1 680 Age-standardized rate 0 4 NA 420 807 644 798 747 643 856 544 901 652 769 267 750 731 465 859 752 424 1 999 1 225 1 319 2 391 1 036 1 968 1 878 1 521 Crude rate DALYs 100 000 per capita DALYs NA (0, 565) (0, 425) (5, 15185) (1, 15553) (929, 1859) (205, 1088) (903, 2280) (2032, 6098) (6146, 11337) (1292, 97277) (11742, 19891) (24404, 33165) (10909, 13904) (14021, 45095) (15025, 64545) (16099, 27405) (17553, 25044) (20321, 33313) (22209, 45072) (16624, 45511) (21184, 252935) (53320, 222532) (43326, 138723) (59344, 135959) (75546, 241047) (155315, 298202) (157715, 544352) (192803, 312522) Uncertainty interval 0 2 NA 804 Total Total 8 961 7 791 5 637 1 445 1 776 4 686 15 976 28 686 12 346 34 166 42 415 22 276 21 342 27 335 58 674 33 079 28 895 158 790 100 053 234 023 162 249 398 337 152 611 105 154 257 395 b 0 1 NA 270 336 273 2 755 3 638 6 956 2 157 9 831 8 409 4 661 2 483 2 138 5 331 1 347 8 345 1 347 5 078 28 628 28 260 41 983 39 597 14 239 19 899 25 368 54 007 Stroke 118 288 b 0 1 NA 530 325 997 IHD 6 050 2 961 8 238 9 523 1 134 9 379 1 186 2 703 3 390 13 691 23 944 19 494 13 253 14 177 16 607 20 894 87 721 99 528 15 540 14 241 62 141 139 262 150 289 b 0 0 35 80 NA 648 130 592 268 475 424 230 6 429 1 724 3 697 1 384 1 672 2 503 4 113 3 682 4 958 3 287 5 979 1 000 97 217 15 883 12 551 19 703 135 199 Lung cancer b 0 0 53 97 31 25 91 NA 655 180 602 620 947 222 687 332 401 6 983 1 087 2 639 1 934 1 704 1 467 4 620 1 561 3 529 4 269 8 634 1 026 COPD a 0 0 1 0 88 27 37 38 NA 119 457 701 313 968 121 3 255 4 603 2 550 4 058 2 972 1 654 1 566 ALRI 21 032 45 908 33 321 24 762 19 171 102 900 113 203 Number of DALYs Israel Italy Jamaica Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Lao People’s Democratic Republic Latvia Lebanon Lesotho Lithuania Luxembourg Madagascar Malawi Japan Liberia Libya Malaysia Maldives Mali Malta Marshall Islands Country Mauritius Mexico Micronesia (Federated States of) Mauritania Eur (HIC) Eur (HIC) Amr (LMIC) Emr (LMIC) Eur (LMIC) Afr Wpr (LMIC) Emr (HIC) Eur (LMIC) Wpr (LMIC) Eur (HIC) Emr (LMIC) Afr Eur (HIC) Eur (HIC) Afr Afr Wpr (HIC) Afr Emr (LMIC) Wpr (LMIC) Sear Afr Eur (HIC) Wpr (LMIC) Region Afr Amr (LMIC) Wpr (LMIC) Afr

128 8 NA NA NA 301 824 733 375 183 881 398 538 883 543 260 796 557 2 413 1 506 1 012 1 882 1 666 1 761 2 040 1 124 1 765 1 847 1 404 1 794 Age-standardized rate 11 NA NA NA 874 477 806 445 614 787 276 445 352 437 664 449 438 430 690 1 630 2 026 1 448 1 247 1 858 2 433 1 531 1 335 1 968 1 964 Crude rate DALYs 100 000 per capita DALYs NA NA NA (0, 169) (2, 4915) 1716701) (1127121, (0, 38748) (0, 62769) (527, 8166) (911, 9593) (4548, 7760) (310, 13468) (5621, 7412) (3599, 34318) (6705, 13452) (9961, 28424) (1004, 37264) (15755, 28108) (2118, 167374) (27229, 68919) (52367, 82270) (104639, 174822) (306289, 443553) (124723, 217702) (126988, 333049) (493810, 788684) (292598, 439065) (133261, 206292) Uncertainty interval (1268077, 1954374) 87 NA NA NA 236 Total Total 6 241 4 955 6 955 9 978 6 615 6 475 22 692 51 003 22 787 15 944 21 509 21 930 67 663 38 500 142 028 101 014 371 432 166 387 216 095 367 674 648 265 170 129 1 590 615 1 395 486 b 14 33 NA NA NA 1 739 7 382 1 655 7 466 5 989 1 246 3 488 1 659 3 162 7 071 6 977 1 340 9 824 43 374 18 077 39 398 27 181 88 097 19 096 49 655 Stroke 160 299 229 276 183 925 193 931 b 49 NA NA NA 164 IHD 8 995 2 602 1 168 3 843 5 051 3 639 3 688 9 625 8 171 3 433 55 307 11 241 63 596 51 778 16 425 11 302 15 982 29 537 22 968 41 221 177 414 302 346 167 417 304 389 b 23 34 NA NA NA 164 267 574 562 524 1 032 1 838 1 410 1 022 4 400 1 714 2 252 6 451 7 869 1 028 4 428 21 354 43 037 13 421 25 158 59 890 47 272 74 134 102 219 Lung cancer b 1 3 42 99 NA NA NA 465 289 571 137 424 286 356 297 597 511 2 729 1 545 1 879 4 061 9 174 2 667 4 613 23 843 26 354 23 389 19 471 COPD 138 543 a 0 2 20 75 15 34 76 NA NA NA 441 657 768 506 769 2 194 1 678 3 903 8 285 2 205 8 494 ALRI 19 265 71 366 80 657 35 435 83 202 168 604 710 781 1 156 136 Number of DALYs Monaco Montenegro Morocco Mozambique Mongolia Myanmar Namibia Nauru Country Nepal Netherlands Niue New Zealand Nicaragua Nigeria Niger Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Poland Portugal Peru Philippines Qatar Republic of Korea Republic of Moldova Eur (HIC) Eur (LMIC) Emr (LMIC) Afr Wpr (LMIC) Sear Afr Wpr (LMIC) Region Sear Eur (HIC) Wpr (LMIC) Wpr (HIC) Amr (LMIC) Afr Afr Eur (HIC) Emr (HIC) Emr (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Amr (LMIC) Eur (HIC) Eur (HIC) Amr (LMIC) Wpr (LMIC) Emr (HIC) Wpr (HIC) Eur (LMIC)

129 5 0 NA NA NA 655 554 821 753 601 832 254 264 532 592 1 461 2 425 1 566 1 718 1 007 1 258 1 410 1 842 1 326 1 258 1 575 1 503 1 354 1 935 Age-standardized rate 8 0 NA NA NA 637 491 568 868 793 682 530 684 405 437 674 952 2 106 2 999 1 213 1 876 1 272 1 786 1 719 1 622 1 007 1 561 1 330 1 072 Crude rate DALYs 100 000 per capita DALYs NA NA NA (0, 397) (0, 836) (92, 828) (24, 487) (0, 2356) (0, 7345) (0, 2046) (0, 17361) (0, 100104) (4982, 24268) (8985, 23666) (6798, 12304) (32179, 97908) (41279, 66817) (34565, 54431) (3884, 144582) (56535, 105013) (37723, 131050) (17124, 155444) (95676, 166551) (62671, 151214) (151783, 249488) (125044, 165703) (130714, 370769) (203996, 317620) (861636, 2729202) Uncertainty interval 0 NA NA NA 566 271 505 329 382 Total Total 1 405 4 149 9 744 62 721 53 551 17 314 82 333 53 345 17 616 85 726 93 144 45 125 80 952 203 747 144 516 131 513 240 793 261 190 108 286 1 993 913 b 0 89 85 66 NA NA NA 229 145 482 711 2 435 3 317 2 150 9 494 7 646 56 690 14 234 48 082 10 735 19 832 10 069 34 109 47 946 81 433 13 043 15 272 25 020 Stroke 498 028 b 0 78 NA NA NA 803 216 157 189 589 556 271 IHD 1 245 8 988 7 988 6 887 8 277 9 536 3 623 9 072 5 440 91 568 75 971 32 645 82 420 32 030 74 937 40 909 62 579 26 783 b 0 58 15 18 44 71 42 NA NA NA 402 726 186 164 561 385 7 860 5 909 4 408 3 717 7 326 2 413 8 026 47 262 27 841 30 525 34 946 11 881 226 211 Lung cancer b 3 6 3 0 11 15 17 NA NA NA 542 154 671 268 252 616 555 3 820 1 780 9 614 2 296 1 758 1 549 1 840 5 807 7 572 2 457 17 133 14 990 COPD a 6 5 1 3 0 52 22 86 NA NA NA 249 258 152 177 206 4 406 6 738 2 989 2 657 1 851 6 349 ALRI 37 317 31 806 35 661 59 306 61 502 66 926 150 832 Number of DALYs Romania Russian Federation Rwanda Saint Kitts and Nevis Country Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Suriname Swaziland Sweden Switzerland Singapore Slovenia South Africa South Sudan Spain Sri Lanka Sudan Syrian Arab Republic Slovakia Solomon Islands Somalia Eur (LMIC) Eur (HIC) Afr Amr (HIC) Region Amr (LMIC) Amr (LMIC) Wpr (LMIC) Eur (HIC) Afr Emr (HIC) Afr Eur (LMIC) Afr Afr Amr (LMIC) Afr Eur (HIC) Eur (HIC) Wpr (HIC) Eur (HIC) Afr Emr (LMIC) Eur (HIC) Sear Emr (LMIC) Emr (LMIC) Eur (HIC) Wpr (LMIC) Emr (LMIC)

130 : ; Amr : Africa 60 NA NA 874 850 648 386 788 232 521 934 760 2 424 1 647 1 033 1 432 4 063 1 555 1 815 1 751 2 279 2 801 1 275 2 120 1 171 Age-standardized rate ; NA: Not available. 37 NA NA 998 868 903 388 604 676 318 633 737 981 664 1 646 2 091 1 171 2 949 1 475 1 583 1 417 3 074 1 916 1 022 1 383 Crude rate DALYs 100 000 per capita DALYs : Ischaemic heart disease. Afr ; IHD : High-income countries NA NA (0, 862) (0, 9831) (0, 8886) ; HIC (6048, 65378) (2830, 14824) (1966, 78393) (34933, 99535) (17335, 25832) (63618, 95445) (21954, 30159) (22782, 107012) (11207, 947809) (67307, 259080) (92671, 222554) (79967, 135829) (73256, 273680) (16519, 111871) (259399, 401893) (479717, 685447) (191462, 323201) (22891, 1038384) (129023, 387995) (326446, 582343) Uncertainty interval 46 NA NA Total Total 4 857 5 992 65 858 21 544 38 962 75 061 79 448 25 878 10 368 47 701 72 396 330 728 582 063 643 988 163 399 189 017 495 894 250 542 173 855 269 482 109 666 456 175 b : Chronic obstructive pulmonary disease 18 NA NA 802 7 345 8 730 1 375 6 454 1 968 7 364 13 155 95 240 15 535 21 044 32 129 29 594 64 122 44 513 37 550 65 596 22 985 13 506 Stroke 137 326 122 709 212 528 ; COPD : Low- and middle-income countries b ; LMIC 21 NA NA IHD 6 936 1 003 6 099 3 863 4 637 9 851 4 822 23 094 48 135 40 237 15 324 28 654 98 122 31 755 58 582 65 261 78 313 132 350 239 374 449 226 311 799 150 068 b : Western Pacific 2 NA NA 538 291 551 604 404 1 847 6 661 3 186 1 623 3 334 2 143 2 560 1 628 77 559 13 386 62 391 54 870 10 936 12 524 172 478 108 381 112 787 ; Wpr Lung cancer : Acute lower respiratory disease b ; ALRI 0 56 69 NA NA 543 849 266 944 1 634 2 437 1 223 6 246 1 940 5 776 4 196 9 778 7 169 4 812 2 168 3 478 1 496 19 903 19 581 19 264 COPD : South-East Asia a ; Sear 5 60 NA NA 134 537 656 163 5 676 2 457 2 345 6 982 3 416 1 815 6 727 ALRI 26 128 22 993 13 304 97 815 38 070 33 283 71 684 47 691 32 392 164 963 Number of DALYs : Ambient air pollution ; Eur: Europe ; AAP Tajikistan Thailand The former Yugoslav Republic of Macedonia Timor-Leste Togo Tonga Country Tuvalu Trinidad and Tobago Tunisia Turkey Turkmenistan Ukraine United Arab Emirates United Kingdom United Republic of Tanzania United States of America Uruguay Uganda Uzbekistan Vanuatu Venezuela, Bolivarian Republic of Viet Nam Yemen Zambia Zimbabwe ; Emr: Eastern Mediterranean : Disability-adjusted life years Eur (LMIC) Sear Eur (LMIC) Sear Afr Wpr (LMIC) Region Wpr (LMIC) Amr (HIC) Emr (LMIC) Eur (LMIC) Eur (LMIC) Eur (LMIC) Emr (HIC) Eur (HIC) Afr Amr (HIC) Amr (HIC) Afr Eur (LMIC) Wpr (LMIC) Amr (LMIC) Wpr (LMIC) Emr (LMIC) Afr Afr Age group includes 25 years and above. Age group includes less than 5 years old.

DALY a b America

131