Download Hi-Res ImageDownload to MS-PowerPointCite This:Environ. Sci. Technol. Lett. 2018, 5, 9, 546-551
Ambient PM2.5 Reduces Global and Regional Life Expectancy
- Joshua S. Apte*Joshua S. Apte*E-mail: [email protected]Department of Civil, Architectural and Environmental Engineering, The University of Texas, 301 East Dean Keeton Street, Stop C1700, Austin, Texas 78712, United StatesMore by Joshua S. Apte,
- Michael BrauerMichael BrauerSchool of Population and Public Health, The University of British Columbia, 2206 East Mall, Vancouver, British Columbia V6T1Z3, CanadaMore by Michael Brauer,
- Aaron J. CohenAaron J. CohenHealth Effects Institute, 75 Federal Street, Suite 1400, Boston, Massachusetts 02110, United StatesMore by Aaron J. Cohen,
- Majid EzzatiMajid EzzatiMRC-PHE Centre for Environment and Health, Imperial College London, London W2 1PG, United KingdomMore by Majid Ezzati, and
- C. Arden Pope IIIC. Arden Pope, IIIDepartment of Economics, Brigham Young University, Provo, Utah 84602, United StatesMore by C. Arden Pope, III
Copyright © 2018 American Chemical Society
Abstract
Exposure to ambient fine particulate matter (PM2.5) air pollution is a major risk for premature death. Here, we systematically quantify the global impact of PM2.5 on life expectancy. Using data from the Global Burden of Disease project and actuarial standard life table methods, we estimate global and national decrements in life expectancy that can be attributed to ambient PM2.5 for 185 countries. In 2016, PM2.5 exposure reduced average global life expectancy at birth by ∼1 year with reductions of ∼1.2–1.9 years in polluted countries of Asia and Africa. If PM2.5 in all countries met the World Health Organization Air Quality Guideline (10 μg m–3), we estimate life expectancy could increase by a population-weighted median of 0.6 year (interquartile range of 0.2–1.0 year), a benefit of a magnitude similar to that of eradicating lung and breast cancer. Because background disease rates modulate the effect of air pollution on life expectancy, high age-specific rates of cardiovascular disease in many polluted low- and middle-income countries amplify the impact of PM2.5 on survival. Our analysis adds to prior research by illustrating how mortality from air pollution substantially reduces human longevity.
1. Introduction
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Exposure to ambient fine particulate matter (PM2.5) air pollution causes important adverse health outcomes that result in premature death, including ischemic heart disease, strokes, lung cancer, chronic obstructive pulmonary disease, and respiratory infections. Despite the well-documented global burden of disease from PM2.5(1−3) (∼4.1 million deaths in 2016),(4) prior research has not systematically explored how global variations in PM2.5 exposure affect life expectancy. Here, we use an actuarial modeling approach and data from the Global Burden of Disease (GBD) 2016 study to address the question: "How much does PM2.5 air pollution shorten human life expectancy around the world?"
How air pollution affects human longevity has been a topic of continued interest for analysts at the science–policy interface of air pollution over at least the past five decades.(5−13) For the lay public and policymakers alike, health risks that substantially reduce survival time are more compelling than those that merely hasten death by a few days. In the 1980s and 1990s, much research investigated the so-called “harvesting” hypothesis that air pollution might most strongly influence the mortality of those who were already at risk of imminent death.(8−10) By the mid-2000s, the weight of evidence from several large, carefully designed long-term cohort studies suggested a substantial decrement in survival associated with air pollution mortality, because the risks of long-term PM2.5 exposure were approximately an order of magnitude greater than risks from day-to-day air quality variation.(9,14) Baccarelli and colleagues demonstrated that the U.S. communities with the most exceptional aging (e.g., populations of >85 or >100) had low ambient air pollution in addition to low rates of smoking, poverty, and obesity, providing suggestive evidence of the benefits of clean air for longevity.(15) Several groups have estimated the relationship between changes in air pollution and changes in life expectancy.(11−13,16,17) For example, Correia et al.(12) used a differences-in-differences approach to model the relationship between PM2.5 and life expectancy for 545 U.S. counties and determined that decadal-scale improvements in regional air quality resulted in ∼0.35 year of increased life expectancy at birth per 10 μg m–3 change in PM2.5. Similarly, Ebenstein et al. used a regression discontinuity approach to evaluate spatial contrasts in PM10 across China to estimate ∼0.64 year of increased life expectancy per 10 μg m–3 increment in PM10.(12,13)
The advent of globally consistent age-resolved estimates of mortality from PM2.5 from the Global Burden of Disease (GBD) collaboration(3,4) now enables systematic assessment of the global variation in the decrement from air pollution. Here, we build upon the actuarial approach of Brunekreef, Dockery, and Pope, who combined data on baseline survival curves with illustrative examples of excess mortality risk from PM2.5 to arrive at approximate estimates of life expectancy decrements for simplified exposure scenarios.(10,18)
2. Materials and Methods
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2.1. Estimation Approach
We used a standard life table method(19) to estimate the baseline life expectancy at birth e0 for each of 185 countries. For each country, we estimated abridged (i.e., multiyear interval) life tables using all-cause death rates for 23 age strata in the Global Burden of Disease 2016 data set. The Supporting Information describes procedures for computing life tables and baseline life expectancy from age-specific death rates.
We use a cause-deleted life table approach to simulate the life expectancy decrement that is attributable to PM2.5 risk factors.(10,18−20) This approach involves four steps: (i) estimating the age-specific death rate attributable to ambient PM2.5 for each location, (ii) assuming that in the absence of this risk factor, age-specific death rates would be proportionally lower, (iii) recomputing a counterfactual “cause-deleted” or “cause-eliminated” life table that would exist in the absence of this risk factor (see the Supporting Information),(19,20) and (iv) estimating the counterfactual life expectancy at birth (e0′). The life table approach requires an assumption that the baseline health status of those who die prematurely from air pollution is similar to that of the general population.(10,18−20) Finally, we attribute the difference between the baseline life expectancy and the cause-deleted counterfactual life expectancy to the life expectancy decrement caused by PM2.5: ΔLE = e0′ – e0. Figure 1 illustrates baseline and cause-deleted survival curves for two countries.
Figure 1
Figure 1. Example survival curves for observed life tables (solid lines) and simulated cause-deleted life tables (dashed lines) where ambient PM2.5 exposure is eliminated as a mortality risk factor. Life expectancy e0 can be visualized as the integral of the survival curve over the age spectrum. Life expectancy for the counterfactual case is increased after removing PM2.5 as a mortality risk. For a given country, the reduction in life expectancy attributed to PM2.5 (ΔLE) relative to a counterfactual scenario with no excess mortality risk from PM2.5 can be visualized as the area between the solid and dashed curves.
2.2. Attributable Mortality
We used data from the Global Burden of Disease 2016 study to obtain age-specific attributable death rates for each country for ambient PM2.5. Briefly, the GBD approach(3,21) involves estimating age-specific attributable mortality for each analysis region as the product of (i) age-resolved populations, (ii) age-specific background disease rates for five key causes, and (iii) regionally population-weighted age-specific population attributable fractions for PM2.5 mortality for each of the five causes, computed on the basis of nonlinear integrated-exposure-response (IER) functions(22,23) and a 0.1° × 0.1° gridded PM2.5 exposure surface.(24−26) The five GBD causes of death for which PM2.5 is a risk factor are ischemic heart disease, cerebrovascular disease (stroke), chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections. As described by Cohen et al.,(3) relative risks for the IER functions are estimated relative to a distribution of the theoretical minimum risk exposure level (TMREL) that ranges from 2.4 to 5.9 μg of PM2.5 m–3, consistent with the lowest concentrations observed in long-term epidemiological studies. We input age-specific mortality into our life table analysis as the sum of the five cause-specific death rates. Uncertainties in the GBD approach include (i) the fact that underlying cause-specific mortality data are modeled and therefore uncertain for some countries,(27) (ii) contributions to attributable mortality from diseases other than the five major causes considered here, and (iii) the assumption that the IER functions reasonably describe mortality risk from PM2.5 over the full ambient concentration spectrum.(21,28) In addition to using the published GBD attributable death rates for ambient PM2.5, we obtained(4,29) similar age-specific mortality data sets for other risk factors (e.g., tobacco smoking) and other major causes of death (e.g., cancers) to provide comparison and context.
Following Apte et al.,(21) we performed a mortality analysis for ambient PM2.5 wherein we simulate the disease burden for alternative hypothetical exposure distributions where global PM2.5 is limited to specific target concentration(s). To do so, we updated the gridded PM2.5 mortality model of Apte et al.(21) with year-2016 data to reproduce the central-tendency GBD 2016 results for ambient PM2.5 to within ±1–2% for each country. We then re-estimated attributable mortality for PM2.5 under hypothetical scenarios in which the ambient exposure concentration distribution for each grid cell was assigned to an alternative concentration, such as the World Health Organization (WHO) annual-average air quality guideline PM2.5 concentration of 10 μg m–3.
3. Results and Discussion
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In 2016, global the population-weighted median life expectancy at birth was 72.6 years [interquartile range (IQR) of 68.2–76.3 years]. For high-income countries, the average life expectancy was 80.9 years (Table 1). The lowest life expectancies are generally in sub-Saharan Africa (average of ∼62.8 years). Global population exposures to ambient PM2.5 are unequally distributed (Figure 2a). For the 2016 population,(26) 95% of the global population lived in regions where PM2.5 concentrations exceed the WHO guideline concentration of 10 μg m–3. Ambient PM2.5 concentrations for the 10th and 90th percentiles of the global concentration–population distribution span nearly an order of magnitude (11 and 97 μg m–3, respectively).
Table 1. Global and Regional Life Expectancy and Life Expectancy Decrements for Selected Risk Factors and Causes of Death
| global | East Asia | South Asia | North Africa and Middle East | sub-Saharan Africa | Latin America | high income | |
|---|---|---|---|---|---|---|---|
| baseline LE (years) | 72.5 | 76.3 | 68.7 | 73.1 | 62.8 | 75.8 | 80.9 |
| all air pollution | 1.65 | 1.90 | 2.54 | 1.54 | 1.97 | 0.73 | 0.40 |
| ambient PM2.5 | 1.03 | 1.24 | 1.56 | 1.29 | 0.94 | 0.54 | 0.37 |
| ambient ozone | 0.05 | 0.07 | 0.10 | 0.03 | 0.01 | 0.02 | 0.03 |
| household air pollution | 0.72 | 0.71 | 1.22 | 0.30 | 1.32 | 0.20 | 0.01 |
| tobacco | 1.82 | 2.39 | 1.51 | 1.60 | 0.73 | 1.23 | 1.82 |
| water sanitation | 0.57 | 0.02 | 1.02 | 0.19 | 1.53 | 0.13 | 0.01 |
| dietary risks | 2.67 | 3.10 | 2.58 | 3.13 | 1.54 | 1.82 | 1.91 |
| unsafe sex | 0.37 | 0.08 | 0.16 | 0.04 | 2.03 | 0.27 | 0.07 |
| all cancer | 2.37 | 3.03 | 1.26 | 1.70 | 1.52 | 2.31 | 3.53 |
| lung cancer | 0.41 | 0.67 | 0.12 | 0.26 | 0.09 | 0.26 | 0.72 |
| breast cancer | 0.14 | 0.09 | 0.10 | 0.14 | 0.12 | 0.16 | 0.23 |
Figure 2
Figure 2. Relationship among the global distribution of ΔLE, the life expectancy decrement from PM2.5, and global PM2.5 concentrations C. ΔLE is generally higher in countries with higher PM2.5 levels. (a) Global distribution of population with respect to annual-average PM2.5 for year 2016. Plotted data reflect local smoothing of bin-width-normalized distributions computed over 400 logarithmically spaced bins: equal-sized plotted areas reflect equal populations. Each country is colored proportionally to the ΔLE from PM2.5 exposure. (b) Cumulative distribution of ΔLE over the global population. The global population-weighted median value for ΔLE is 1.22 years, corresponding to conditions in China. Shading for each country shows the national population-weighted mean PM2.5, illustrating how ΔLE has a strong but imperfect association with PM2.5. (c) National decrements in ΔLE vs PM2.5. Owing to the supralinear concentration–response relationship of mortality with PM2.5, the slope of this distribution is higher for countries with lower average PM2.5 concentrations.
Figures 2 and 3 show the global distribution of life expectancy impacts from PM2.5. Globally, ambient PM2.5 pollution was associated with a population-weighted mean decrement in global life expectancy of 1.03 years. Among 185 countries, the population-weighted median decrement in life expectancy from PM2.5 (ΔLE) was 1.22 years [IQR of 0.67–1.51 years (Figure 2b)]. As shown in Figure 3, the life expectancy impact of ambient PM2.5 is especially large in polluted countries of Asia, Africa, and the Middle East, including Bangladesh (1.87 years), Egypt (1.85 years), Pakistan (1.56 years), India (1.53 years), Saudi Arabia (1.48 years), Nigeria (1.28 years), and China (1.25 years).
Figure 3
Figure 3. Global maps of the life expectancy decrement ΔLE from PM2.5. Panel a shows baseline ΔLE for year-2016 concentrations (global population-weighted mean and median of 1.03 and 1.22 years, respectively). Panel b shows hypothetical gains in life expectancy for an alternative exposure distribution where concentrations are limited to a maximum of 10 μg m–3, the WHO air quality guideline concentration (global-average ΔLE of ∼0.59 year). See also Table S2.
Life expectancy decrements from PM2.5 are positively correlated with national-average PM2.5 concentrations [r = 0.79 (Figure 2c)]. For countries with PM2.5 concentrations below 25 μg m–3 (including nearly all high-income countries), ΔLE and population-weighted mean PM2.5 track closely and approximately linearly, with a slope that is roughly consistent with the directly measured relationship between LE and PM2.5 of Correia et al. (average of 0.35 year LE increase per 10 μg m–3 reduction in exposure for 545 U.S. counties).(12) At higher concentrations, the nonlinear integrated exposure–response functions used to estimate mortality attributable to PM2.5 generally lead to a decreasing marginal risk change per increment in PM2.5. Further, national differences in the structure of underlying disease burden modulate the relationship between PM2.5 and life expectancy, contributing to the scatter in Figure 2c. In particular, ΔLE from PM2.5 is sensitive to age-specific death rates in each country, while death rates from PM2.5 (but not ΔLE) are also strongly influenced by the age distribution of a country’s population (see Figure S1).
To place our findings in context, we used published GBD cause- and age-specific mortality data to estimate the life expectancy decrements that are attributable to other key diseases and risks (Table 1). Relative to our core global finding of a global mean ΔLE of 1.03 years for ambient PM2.5, the full set of air pollution risk factors [including ambient O3 and household air pollution (HAP)] decreases global life expectancy by an average of 1.65 years. In regions where both ambient PM2.5 and HAP are major risk factors, the ΔLE for the combined set of household and ambient air pollutants is even larger (2.5 years in South Asia and 2.0 years in sub-Saharan Africa). For context, other major global risk factors for reduced life expectancy include dietary risks (2.7 years), tobacco smoking (1.8 years), unsafe water and sanitation (0.57 year), and unsafe sex (0.37 year). Globally, cancers result in ∼2.4 years of reduced life expectancy, while the most common cancer types (e.g., lung and breast) individually reduce life expectancy by ∼0.2–0.4 year. In the United States, the ΔLE for PM2.5 (0.38 year) is substantially larger than the impact of breast cancer (0.23 year), while in South Asia, the ΔLE for PM2.5 (1.6 years) substantially exceeds the combined impact of all cancers (1.3 years). In short, the burden of disease from air pollution results in life expectancy decrements of a magnitude similar to those of other high-priority risk factors and diseases.
Because air pollution has a disproportionate effect on the elderly, air pollution reduces life expectancy predominantly by increasing the probability of death above age 60 (Figure S2). We utilized our estimated standard life tables for each country to understand how PM2.5 affects survival from age 60 to 85, expressed as the metric 25q60 (Table S1). In high-income countries with a low PM2.5, baseline survival rates for this 25-year interval are high (∼50%) and PM2.5 exposure reduces 25q60 by ∼3%. In contrast, for high-PM2.5, high-mortality countries (e.g., South Asia), 25q60 at baseline is low (∼20–30%) and the impact of PM2.5 on elderly survival is quite large. For example, across South Asia, the probability of surviving from age 60 to 85 would have been 20% higher if PM2.5 exposure were removed as a mortality risk factor.
To illustrate how improvements in PM2.5 might result in increased life expectancy, we estimate ΔLE for alternative global exposure distributions (Figure 3 and Table S2). These simulations must be interpreted with care (see below), as they most properly reflect the LE for a hypothetical alternative reality where the distribution of PM2.5 concentrations is held constant over time at a specific value. If PM2.5 concentrations worldwide were limited to the WHO air quality guideline concentration of 10 μg m–3, global life expectancy would be on average 0.59 year longer. The benefit of reaching this stringent target would be especially large in countries with the highest current levels of pollution, with approximately 0.8–1.4 years of additional survival in countries such as Egypt, India, Pakistan, Bangladesh, China, and Nigeria. In contrast, many high-income countries already nearly meet the WHO guideline and would have much smaller LE benefits. Because limiting the maximum PM2.5 concentration in one area may also have air quality benefits for less polluted surroundings, our estimates may understate the possible LE benefits of reaching specific air quality guidelines. Halving PM2.5 globally would increase e0 globally by 0.33 year, and about 0.40–0.55 year in the most polluted countries of Asia and Africa. These benefits are large in absolute magnitude. However, because the relationship for PM2.5 and mortality has a declining slope at higher concentrations,(21−23,28,30−33) the LE benefit of halving PM2.5 for the highly polluted countries is only 25–30% of the total national ΔLE for PM2.5.
Predictions about possible improvements in life expectancy must be interpreted carefully. Mortality for any risk factor will evolve over time as a result of demographic and epidemiological transitions.(34,35) LE is strongly dependent on age-specific death rates, which tend to decrease over time in countries where general population health is improving. Thus, the life expectancy benefit of PM2.5 reductions in many polluted lower- and middle-income countries might be 20–40% lower than our core estimates if those countries also had age-specific death rates that were typical of high-income economies (Table S3). As low-income, high-mortality countries undergo epidemiological transitions, one hallmark is a trend toward “aging” populations: reducing age-specific mortality increases survival to higher ages. Because air pollution disproportionately affects the elderly, the attributable death rate for PM2.5 is expected to increase over time in many lower- and middle-income countries where populations are just beginning to “age”.(3,21) Thus, the paradoxical result is that per-capita mortality from air pollution may increase in some countries even as its life expectancy impacts fall (see Figure S1). This result can be understood in terms of diminishing returns: as populations live longer, reducing any individual risk factor will have a smaller impact on additional survival, while at the same time, competing risks for mortality will become more important. Reducing air pollution in countries at all levels of economic development could lead to substantial gains in life expectancy, gains on a par with reducing other well-recognized threats to public health.
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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.estlett.8b00360.
Detailed information about life table methods, supporting figures, and supporting tables (PDF)
Data file with results for 185 countries (XLSX)
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Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
Author Information
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- Joshua S. Apte - Department of Civil, Architectural and Environmental Engineering, The University of Texas, 301 East Dean Keeton Street, Stop C1700, Austin, Texas 78712, United States;
http://orcid.org/0000-0002-2796-3478;
- Michael Brauer - School of Population and Public Health, The University of British Columbia, 2206 East Mall, Vancouver, British Columbia V6T1Z3, Canada;http://orcid.org/0000-0002-9103-9343
The authors declare no competing financial interest.
Acknowledgments
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The authors thank K. Walker and J. Marshall for comments on this analysis. This publication was developed under Assistance Agreement R835873 awarded by the U.S. Environmental Protection Agency. It has not been formally reviewed by EPA. The views expressed in this document are solely those of authors and do not reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. A.J.C. was supported by the Health Effects Institute.
References
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This article references 35 other publications.
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No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. METHODS: We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. FINDINGS: In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0·9% (0·4-1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. INTERPRETATION: Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children. FUNDING: Bill & Melinda Gates Foundation.
- 2Cohen, A. J.; Anderson, H. R.; Ostro, B.; Pandey, K. D.; Krzyzanowski, M.; Künzli, N.; Gutschmidt, K.; Pope, A.; Romieu, I.; Samet, J. M.; Smith, K. The global burden of disease due to outdoor air pollution. J. Toxicol. Environ. Health, Part A 2005, 68, 1301– 1307, DOI: 10.1080/15287390590936166[Crossref], [PubMed], [CAS], Google Scholar2https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXmt1GktL8%253D&md5=b10517b028975a01e68829562bca9c32The Global Burden of Disease Due to Outdoor Air PollutionCohen, Aaron; Ross Anderson, H.; Ostro, Bart; Pandey, Kiran; Krzyzanowski, Michal; Kuenzli, Nino; Gutschmidt, Kersten; Pope, Arden; Romieu, Isabelle; Samet, Jonathan; Smith, KirkJournal of Toxicology and Environmental Health, Part A (2005), 68 (13-14), 1301-1307CODEN: JTEHF8; ISSN:1528-7394. (Taylor & Francis, Inc.)As part of the World Health Organization (WHO) Global Burden of Disease Comparative Risk Assessment, the burden of disease attributable to urban ambient air pollution was estd. in terms of deaths and disability-adjusted life years (DALYs). Air pollution is assocd. with a broad spectrum of acute and chronic health effects, the nature of which may vary with the pollutant constituents. Particulate air pollution is consistently and independently related to the most serious effects, including lung cancer and other cardiopulmonary mortality. The analyses on which this report is based est. that ambient air pollution, in terms of fine particulate air pollution (PM2.5), causes about 3% of mortality from cardiopulmonary disease, about 5% of mortality from cancer of the trachea, bronchus, and lung, and about 1% of mortality from acute respiratory infections in children under 5 yr, worldwide. This amts. to about 0.8 million (1.2%) premature deaths and 6.4 million (0.5%) years of life lost (YLL). This burden occurs predominantly in developing countries; 65% in Asia alone. These ests. consider only the impact of air pollution on mortality (i.e., years of life lost) and not morbidity (i.e., years lived with disability), due to limitations in the epidemiol. database. If air pollution multiplies both incidence and mortality to the same extent (i.e., the same relative risk), then the DALYs for cardiopulmonary disease increase by 20% worldwide.
- 3Cohen, A. J.; Brauer, M.; Burnett, R.; Anderson, H. R.; Frostad, J.; Estep, K.; Balakrishnan, K.; Brunekreef, B.; Dandona, L.; Dandona, R. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 2017, 389, 1907– 1918, DOI: 10.1016/S0140-6736(17)30505-6[Crossref], [PubMed], [CAS], Google Scholar3https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1cvns1yhsA%253D%253D&md5=ce8ca11f951fbb3a484e211f77e8dd25Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015Cohen Aaron J; Brauer Michael; Burnett Richard; Shin Hwashin; Anderson H Ross; Frostad Joseph; Estep Kara; Freedman Greg; Vos Theo; Murray Christopher J L; Forouzanfar Mohammad H; Balakrishnan Kalpana; Brunekreef Bert; Dandona Lalit; Dandona Rakhi; Feigin Valery; Hubbell Bryan; Jobling Amelia; Shaddick Gavin; Thomas Matthew; Kan Haidong; Knibbs Luke; Liu Yang; Martin Randall; van Donkelaar Aaron; Morawska Lidia; Pope C Arden 3rd; Straif Kurt; van Dingenen RitaLancet (London, England) (2017), 389 (10082), 1907-1918 ISSN:.BACKGROUND: Exposure to ambient air pollution increases morbidity and mortality, and is a leading contributor to global disease burden. We explored spatial and temporal trends in mortality and burden of disease attributable to ambient air pollution from 1990 to 2015 at global, regional, and country levels. METHODS: We estimated global population-weighted mean concentrations of particle mass with aerodynamic diameter less than 2·5 μm (PM2·5) and ozone at an approximate 11 km × 11 km resolution with satellite-based estimates, chemical transport models, and ground-level measurements. Using integrated exposure-response functions for each cause of death, we estimated the relative risk of mortality from ischaemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections from epidemiological studies using non-linear exposure-response functions spanning the global range of exposure. FINDINGS: Ambient PM2·5 was the fifth-ranking mortality risk factor in 2015. Exposure to PM2·5 caused 4·2 million (95% uncertainty interval [UI] 3·7 million to 4·8 million) deaths and 103·1 million (90·8 million 115·1 million) disability-adjusted life-years (DALYs) in 2015, representing 7·6% of total global deaths and 4·2% of global DALYs, 59% of these in east and south Asia. Deaths attributable to ambient PM2·5 increased from 3·5 million (95% UI 3·0 million to 4·0 million) in 1990 to 4·2 million (3·7 million to 4·8 million) in 2015. Exposure to ozone caused an additional 254 000 (95% UI 97 000-422 000) deaths and a loss of 4·1 million (1·6 million to 6·8 million) DALYs from chronic obstructive pulmonary disease in 2015. INTERPRETATION: Ambient air pollution contributed substantially to the global burden of disease in 2015, which increased over the past 25 years, due to population ageing, changes in non-communicable disease rates, and increasing air pollution in low-income and middle-income countries. Modest reductions in burden will occur in the most polluted countries unless PM2·5 values are decreased substantially, but there is potential for substantial health benefits from exposure reduction. FUNDING: Bill & Melinda Gates Foundation and Health Effects Institute.
- 4Gakidou, E.; Afshin, A.; Abajobir, A. A.; Abate, K. H.; Abbafati, C.; Abbas, K. M.; Abd-Allah, F.; Abdulle, A. M.; Abera, S. F.; Aboyans, V. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017, 390, 1345– 1422, DOI: 10.1016/S0140-6736(17)32366-8
- 5Lave, L. B.; Seskin, E. P. Does air pollution shorten lives? In Statistical and Mathematical Aspects of Pollution Problems; Pratt, J., Ed.; Marcel Dekker: New York, 1974; pp 223– 244.Google ScholarThere is no corresponding record for this reference.
- 6Lave, L. B.; Seskin, E. P. An analysis of the association between U.S. mortality and air pollution. J. Am. Stat. Assoc. 1973, 68, 284– 290, DOI: 10.1080/01621459.1973.10482421
- 7Thibodeau, L. A.; Reed, R. B.; Bishop, Y. M.; Kammerman, L. A. Air pollution and human health: a review and reanalysis. Environ. Health Persp. 1980, 34, 165– 183, DOI: 10.1289/ehp.8034165
- 8Schwartz, J. Harvesting and long term exposure effects in the relation between air pollution and mortality. Am. J. Epidemiol. 2000, 151, 440– 448, DOI: 10.1093/oxfordjournals.aje.a010228[Crossref], [PubMed], [CAS], Google Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3c7ntVWrtg%253D%253D&md5=37f4e98eb9c6ba3e2a55a373761a9d91Harvesting and long term exposure effects in the relation between air pollution and mortalitySchwartz JAmerican journal of epidemiology (2000), 151 (5), 440-8 ISSN:0002-9262.While time series analyses have demonstrated that airborne particles are associated with early death, they have not clarified how much the deaths are advanced. If all of the pollution-related deaths were advanced by only a few days, one would expect little association between weekly averages of air pollution and daily deaths. The author used the STL algorithm to classify data on air pollution, daily deaths, and weather from Boston, Massachusetts (1979-1986) into three time series: one reflecting seasonal and longer fluctuations, one reflecting short term fluctuations, and one reflecting intermediate patterns. By varying the cutoff point between short term and intermediate term, it was possible to examine harvesting on different time scales. For chronic obstructive pulmonary disease, there was evidence that most of the mortality was displaced by only a few months. For pneumonia, heart attacks, and all-cause mortality, the effect size increased with longer time scales. The percentage increase in all deaths associated with a 10-microg/m3 increase in PM2.5 rose from 2.1% (95% confidence interval: 1.5, 4.3) to 3.75% (95% confidence interval: 3.2, 4.3) as the focus moved from daily patterns to monthly patterns. This is consistent with the larger effect seen in prospective cohort studies, rather than harvesting's playing a major role.
- 9Künzli, N.; Medina, S.; Kaiser, R.; Quenel, P.; Horak, F., Jr.; Studnicka, M. Assessment of deaths attributable to air pollution: Should we use risk estimates based on time series or on cohort studies?. Am. J. Epidemiol. 2001, 153, 1050– 1055, DOI: 10.1093/aje/153.11.1050[Crossref], [PubMed], [CAS], Google Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3MzislCrtg%253D%253D&md5=e5f8a849ec24c24dbef10b04885f1610Assessment of deaths attributable to air pollution: should we use risk estimates based on time series or on cohort studies?Kunzli N; Medina S; Kaiser R; Quenel P; Horak F Jr; Studnicka MAmerican journal of epidemiology (2001), 153 (11), 1050-5 ISSN:0002-9262.Epidemiologic studies are crucial to the estimation of numbers of deaths attributable to air pollution. In this paper, the authors present a framework for distinguishing estimates of attributable cases based on time-series studies from those based on cohort studies, the latter being 5-10 times larger. The authors distinguish four categories of death associated with air pollution: A) air pollution increases both the risk of underlying diseases leading to frailty and the short term risk of death among the frail; B) air pollution increases the risk of chronic diseases leading to frailty but is unrelated to timing of death; C) air pollution is unrelated to risk of chronic diseases but short term exposure increases mortality among persons who are frail; and D) neither underlying chronic disease nor the event of death is related to air pollution exposure. Time-series approaches capture deaths from categories A and C, whereas cohort studies assess cases from categories A, B, and C. In addition, years of life lost can only be derived from cohort studies, where time to death is the outcome, while in time-series studies, death is a once-only event (no dimension in time). The authors conclude that time-series analyses underestimate cases of death attributable to air pollution and that assessment of the impact of air pollution on mortality should be based on cohort studies.
- 10Brunekreef, B. Air pollution and life expectancy: is there a relation?. Occup. Environ. Med. 1997, 54, 781– 784, DOI: 10.1136/oem.54.11.781
- 11Pope, C. A.; Ezzati, M.; Dockery, D. W. Fine-particulate air pollution and life expectancy in the United States. N. Engl. J. Med. 2009, 360, 376– 386, DOI: 10.1056/NEJMsa0805646[Crossref], [PubMed], [CAS], Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtVSltr8%253D&md5=5033a22aa3fda3604d97be40374873d8Fine-particulate air pollution and life expectancy in the United StatesPope, C. Arden, III; Ezzati, Majid; Dockery, Douglas W.New England Journal of Medicine (2009), 360 (4), 376-386CODEN: NEJMAG; ISSN:0028-4793. (Massachusetts Medical Society)Exposure to fine-particulate air pollution has been assocd. with increased morbidity and mortality, suggesting that sustained redns. in pollution exposure should result in improved life expectancy. This study directly evaluated the changes in life expectancy assocd. with differential changes in fine-particulate air pollution that occurred in the United States during the 1980s and 1990s. The authors compiled data on life expectancy, socioeconomic status, and demog. characteristics for 211 county units in the 51 U.S. metropolitan areas with matching data on fine-particulate air pollution for the late 1970s and early 1980s and the late 1990s and early 2000s. Regression models were used to est. the assocn. between redns. in pollution and changes in life expectancy, with adjustment for changes in socioeconomic and demog. variables and in proxy indicators for the prevalence of cigarette smoking. A decrease of 10 μg per cubic meter in the concn. of fine particulate matter was assocd. with an estd. increase in mean (± SE) life expectancy of 0.61 ± 0.20 yr (P = 0.004). The estd. effect of reduced exposure to pollution on life expectancy was not highly sensitive to adjustment for changes in socioeconomic, demog., or proxy variables for the prevalence of smoking or to the restriction of observations to relatively large counties. Redns. in air pollution accounted for as much as 15% of the overall increase in life expectancy in the study areas. A redn. in exposure to ambient fine-particulate air pollution contributed to significant and measurable improvements in life expectancy in the United States.
- 12Correia, A. W.; Pope, C. A., III; Dockery, D. W.; Wang, Y.; Ezzati, M.; Dominici, F. Effect of air pollution control on life expectancy in the United States. Epidemiol. 2013, 24, 23– 31, DOI: 10.1097/EDE.0b013e3182770237[Crossref], [PubMed], [CAS], Google Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3s7ptFaisA%253D%253D&md5=16d1bbfe8acb1ed660c7e4c07c59d95dEffect of air pollution control on life expectancy in the United States: an analysis of 545 U.S. counties for the period from 2000 to 2007Correia Andrew W; Pope C Arden 3rd; Dockery Douglas W; Wang Yun; Ezzati Majid; Dominici FrancescaEpidemiology (Cambridge, Mass.) (2013), 24 (1), 23-31 ISSN:.BACKGROUND: In recent years (2000-2007), ambient levels of fine particulate matter (PM2.5) have continued to decline as a result of interventions, but the decline has been at a slower rate than previous years (1980-2000). Whether these more recent and slower declines of PM2.5 levels continue to improve life expectancy and whether they benefit all populations equally is unknown. METHODS: We assembled a data set for 545 U.S. counties consisting of yearly county-specific average PM2.5, yearly county-specific life expectancy, and several potentially confounding variables measuring socioeconomic status, smoking prevalence, and demographic characteristics for the years 2000 and 2007. We used regression models to estimate the association between reductions in PM2.5 and changes in life expectancy for the period from 2000 to 2007. RESULTS: A decrease of 10 μg/m in the concentration of PM2.5 was associated with an increase in mean life expectancy of 0.35 years (SD = 0.16 years, P = 0.033). This association was stronger in more urban and densely populated counties. CONCLUSIONS: Reductions in PM2.5 were associated with improvements in life expectancy for the period from 2000 to 2007. Air pollution control in the last decade has continued to have a positive impact on public health.
- 13Ebenstein, A.; Fan, M.; Greenstone, M.; He, G.; Zhou, M. New evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River Policy. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 10384, DOI: 10.1073/pnas.1616784114[Crossref], [PubMed], [CAS], Google Scholar13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsVKltb3O&md5=bd8ed5bb6404437403c44d511ab9cb19New evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River PolicyEbenstein, Avraham; Fan, Maoyong; Greenstone, Michael; He, Guojun; Zhou, MaigengProceedings of the National Academy of Sciences of the United States of America (2017), 114 (39), 10384-10389CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)This paper finds that a 10-μg/m3 increase in airborne particulate matter [particulate matter smaller than 10 μm (PM10)] reduces life expectancy by 0.64 years (95% confidence interval = 0.21-1.07). This est. is derived from quasiexperimental variation in PM10 generated by China's Huai River Policy, which provides free or heavily subsidized coal for indoor heating during the winter to cities north of the Huai River but not to those to the south. The findings are derived from a regression discontinuity design based on distance from the Huai River, and they are robust to using parametric and nonparametric estn. methods, different kernel types and bandwidth sizes, and adjustment for a rich set of demog. and behavioral covariates. Furthermore, the shorter lifespans are almost entirely caused by elevated rates of cardiorespiratory mortality, suggesting that PM10 is the causal factor. The ests. imply that bringing all of China into compliance with its Class I stds. for PM10 would save 3.7 billion life-years.
- 14Pope, C. A.; Dockery, D. W. Health effects of fine particulate air pollution: Lines that connect. J. Air Waste Manage. Assoc. 2006, 56, 709– 742, DOI: 10.1080/10473289.2006.10464485[Crossref], [PubMed], [CAS], Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xmt1ygs7k%253D&md5=7aaf80b762054e234b73d653096e18f2Health effects of fine particulate air pollution: lines that connectPope, C. Arden, III; Dockery, Douglas W.Journal of the Air & Waste Management Association (2006), 56 (6), 709-742CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)A review. Efforts to understand and mitigate the health effects of participate matter (PM) air pollution have a rich and interesting history. This review focuses on six substantial lines of research that have been pursued since 1997 that have helped elucidate our understanding about the effects of PM on human health. There has been substantial progress in the evaluation of PM health effects at different time-scales of exposure and in the exploration of the shape of the concn.-response function. There has also been emerging evidence of PM-related cardiovascular health effects and growing knowledge regarding interconnected general pathophysiol. pathways that link PM exposure with cardiopulmonary morbidity and mortality. Despite important gaps in scientific knowledge and continued reasons for some skepticism, a comprehensive evaluation of the research findings provides persuasive evidence that exposure to fine particulate air pollution has adverse effects on cardiopulmonary health. Although much of this research has been motivated by environmental public health policy, these results have important scientific, medical, and public health implications that are broader than debates over legally mandated air quality stds.
- 15Baccarelli, A. A.; Hales, N.; Burnett, R. T.; Jerrett, M.; Mix, C.; Dockery, D. W.; Pope, C. A. Particulate air pollution, exceptional aging, and rates of centenarians: A nationwide analysis of the United States, 1980–2010. Environ. Health Perspect. 2016, 124, 1744– 1750, DOI: 10.1289/EHP197
- 16Chen, Y.; Ebenstein, A.; Greenstone, M.; Li, H. Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 12936– 12941, DOI: 10.1073/pnas.1300018110[Crossref], [PubMed], [CAS], Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsVShurfN&md5=07723e8c93b56e9a788bd269af501112Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policyChen, Yuyu; Ebenstein, Avraham; Greenstone, Michael; Li, HongbinProceedings of the National Academy of Sciences of the United States of America (2013), 110 (32), 12936-12941, S12936/1-S12936/29CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)This paper's findings suggest that an arbitrary Chinese policy that greatly increases total suspended particulates (TSPs) air pollution is causing the 500 million residents of Northern China to lose more than 2.5 billion life years of life expectancy. The quasi-exptl. empirical approach is based on China's Huai River policy, which provided free winter heating via the provision of coal for boilers in cities north of the Huai River but denied heat to the south. Using a regression discontinuity design based on distance from the Huai River, we find that ambient concns. of TSPs are about 184 μg/m3 [95% confidence interval (CI): 61, 307] or 55% higher in the north. Further, the results indicate that life expectancies are about 5.5 y (95% CI: 0.8, 10.2) lower in the north owing to an increased incidence of cardiorespiratory mortality. More generally, the anal. suggests that long-term exposure to an addnl. 100 μg/m3 of TSPs is assocd. with a redn. in life expectancy at birth of about 3.0 y (95% CI: 0.4, 5.6).
- 17Fann, N.; Kim, S.-Y.; Olives, C.; Sheppard, L. Estimated changes in life expectancy and adult mortality resulting from declining PM2.5 exposures in the contiguous United States: 1980–2010. Environ. Health Perspect. 2017, 125, 097003 DOI: 10.1289/EHP507
- 18Pope, C. A.; Dockery, D. W. Air pollution and life expectancy in China and beyond. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 12861, DOI: 10.1073/pnas.1310925110
- 19Arias, E.; Heron, M.; Tejada-Vara, B. United States life tables eliminating certain causes of death, 1999–2001. National Vital Statistics Reports 2013, 61, 9Google ScholarThere is no corresponding record for this reference.
- 20Tsai, S. P.; Lee, E. S.; Hardy, R. J. The effect of a reduction in leading causes of death: Potential gains in life expectancy. Am. J. Public Health 1978, 68, 966– 971, DOI: 10.2105/AJPH.68.10.966[Crossref], [PubMed], [CAS], Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaE1M%252FlvF2rsA%253D%253D&md5=ce7a44e2b908933facfcfa4157c5b91fThe effect of a reduction in leading causes of death: potential gains in life expectancyTsai S P; Lee E S; Hardy R JAmerican journal of public health (1978), 68 (10), 966-71 ISSN:0090-0036.The potential gains in total expectation of life and in the working life ages among the United States population are examined when the three leading causes of death are totally or partially eliminated. The impressive gains theoretically achieved by total elimination do not hold up under the more realistic assumption of partial elimination or reduction. The number of years gained by a new-born child, with a 30 per cent reduction in major cardiovascular diseases would be 1.98 years, for malignant neoplasms 0.71 years, and for motor vehicle accidents 0.21 years. Application of the same reduction to the working ages, 15 to 70 years, results in a gain of 0.43, 0.26, and 0.14 years, respectively for the three leading causes of death. Even with a scientific break-through in combating these causes of death, it appears that future gains in life expectancies for the working ages will not be spectacular. The implication of the results in relation to the current debate on the national health care policy is noted.
- 21Apte, J. S.; Marshall, J. D.; Brauer, M.; Cohen, A. J. Addressing global mortality from ambient PM2.5. Environ. Sci. Technol. 2015, 49, 8057– 8066, DOI: 10.1021/acs.est.5b01236[ACS Full Text
], [CAS], Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVShtb3P&md5=9deade47f08dc87bbe7572d6199be8e4Addressing Global Mortality from Ambient PM2.5Apte, Joshua S.; Marshall, Julian D.; Cohen, Aaron J.; Brauer, MichaelEnvironmental Science & Technology (2015), 49 (13), 8057-8066CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient fine particulate matter (PM2.5) has a large, well-documented global burden of disease. This work used high-resoln. (10 km, global-coverage) concn. data and cause-specific integrated exposure-response functions developed for the Global Burden of Disease 2010 to assess how regional and global improvements in ambient air quality could reduce attributable mortality from PM2.5. Overall, an aggressive global program of PM2.5 mitigation in accord with World Health Organization interim guidelines could avoid 750,000 (23%) of the 3.2 million deaths/yr currently (2010) attributable to ambient PM2.5. Modest improvements in PM2.5 in relatively clean regions (North America, Europe) would result in surprisingly large avoided mortality, due to demog. factors and the non-linear concn.-response relationship which describes the risk of PM in relation to several important causes of death. Major air quality improvements would be required to substantially reduce mortality from PM2.5 in more polluted regions, e.g., China and India. Forecasted demog. and epidemiol. transitions in India and China imply that to maintain PM2.5-attributable mortality rates (deaths/100,000 people-yr) const., av. PM2.5 concns. would need to decline by ∼20-30% over the next 15 years to merely offset increases in PM2.5-attributable mortality from aging populations. An effective program to deliver clean air to the most polluted regions could avoid several hundred thousand premature deaths each year. - 22Burnett, R. T.; Pope, C. A.; Ezzati, M.; Olives, C.; Lim, S. S.; Mehta, S.; Shin, H. H.; Singh, G.; Hubbell, B.; Brauer, M. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ. Health Perspect. 2014, 122, 397– 403, DOI: 10.1289/ehp.1307049[Crossref], [PubMed], [CAS], Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2cvjvFWnsA%253D%253D&md5=4ce6aad41a1f1cd5bcdd31aa405d8416An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposureBurnett Richard T; Pope C Arden 3rd; Ezzati Majid; Olives Casey; Lim Stephen S; Mehta Sumi; Shin Hwashin H; Singh Gitanjali; Hubbell Bryan; Brauer Michael; Anderson H Ross; Smith Kirk R; Balmes John R; Bruce Nigel G; Kan Haidong; Laden Francine; Pruss-Ustun Annette; Turner Michelle C; Gapstur Susan M; Diver W Ryan; Cohen AaronEnvironmental health perspectives (2014), 122 (4), 397-403 ISSN:.BACKGROUND: Estimating the burden of disease attributable to long-term exposure to fine particulate matter (PM2.5) in ambient air requires knowledge of both the shape and magnitude of the relative risk (RR) function. However, adequate direct evidence to identify the shape of the mortality RR functions at the high ambient concentrations observed in many places in the world is lacking. OBJECTIVE: We developed RR functions over the entire global exposure range for causes of mortality in adults: ischemic heart disease (IHD), cerebrovascular disease (stroke), chronic obstructive pulmonary disease (COPD), and lung cancer (LC). We also developed RR functions for the incidence of acute lower respiratory infection (ALRI) that can be used to estimate mortality and lost-years of healthy life in children < 5 years of age. METHODS: We fit an integrated exposure-response (IER) model by integrating available RR information from studies of ambient air pollution (AAP), second hand tobacco smoke, household solid cooking fuel, and active smoking (AS). AS exposures were converted to estimated annual PM2.5 exposure equivalents using inhaled doses of particle mass. We derived population attributable fractions (PAFs) for every country based on estimated worldwide ambient PM2.5 concentrations. RESULTS: The IER model was a superior predictor of RR compared with seven other forms previously used in burden assessments. The percent PAF attributable to AAP exposure varied among countries from 2 to 41 for IHD, 1 to 43 for stroke, < 1 to 21 for COPD, < 1 to 25 for LC, and < 1 to 38 for ALRI. CONCLUSIONS: We developed a fine particulate mass-based RR model that covered the global range of exposure by integrating RR information from different combustion types that generate emissions of particulate matter. The model can be updated as new RR information becomes available.
- 23Nasari, M. M.; Szyszkowicz, M.; Chen, H.; Crouse, D.; Turner, M. C.; Jerrett, M.; Pope, C. A.; Hubbell, B.; Fann, N.; Cohen, A. A class of non-linear exposure-response models suitable for health impact assessment applicable to large cohort studies of ambient air pollution. Air Qual., Atmos. Health 2016, 9, 961– 972, DOI: 10.1007/s11869-016-0398-z[Crossref], [PubMed], [CAS], Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XjsFKntLw%253D&md5=e5a2adf69b3eb1fe91035cb4b648a100A class of non-linear exposure-response models suitable for health impact assessment applicable to large cohort studies of ambient air pollutionNasari, Masoud M.; Szyszkowicz, Mieczyslaw; Chen, Hong; Crouse, Daniel; Turner, Michelle C.; Jerrett, Michael; Pope, C. Arden; Hubbell, Bryan; Fann, Neal; Cohen, Aaron; Gapstur, Susan M.; Diver, W. Ryan; Stieb, David; Forouzanfar, Mohammad H.; Kim, Sun-Young; Olives, Casey; Krewski, Daniel; Burnett, Richard T.Air Quality, Atmosphere & Health (2016), 9 (8), 961-972CODEN: AQAHAX; ISSN:1873-9326. (Springer)The effectiveness of regulatory actions designed to improve air quality is often assessed by predicting changes in public health resulting from their implementation. Risk of premature mortality from long-term exposure to ambient air pollution is the single most important contributor to such assessments and is estd. from observational studies generally assuming a log-linear, no-threshold assocn. between ambient concns. and death. There has been only limited assessment of this assumption in part because of a lack of methods to est. the shape of the exposure-response function in very large study populations. In this paper, we propose a new class of variable coeff. risk functions capable of capturing a variety of potentially non-linear assocns. which are suitable for health impact assessment. We construct the class by defining transformations of concn. as the product of either a linear or log-linear function of concn. multiplied by a logistic weighting function. These risk functions can be estd. using hazard regression survival models with currently available computer software and can accommodate large population-based cohorts which are increasingly being used for this purpose. We illustrate our modeling approach with two large cohort studies of long-term concns. of ambient air pollution and mortality: the American Cancer Society Cancer Prevention Study II (CPS II) cohort and the Canadian Census Health and Environment Cohort (CanCHEC). We then est. the no. of deaths attributable to changes in fine particulate matter concns. over the 2000 to 2010 time period in both Canada and the USA using both linear and non-linear hazard function models.
- 24Brauer, M.; Freedman, G.; Frostad, J.; van Donkelaar, A.; Martin, R. V.; Dentener, F.; Dingenen, R. v.; Estep, K.; Amini, H.; Apte, J. S. Ambient air pollution exposure estimation for the Global Burden of Disease 2013. Environ. Sci. Technol. 2016, 50, 79– 88, DOI: 10.1021/acs.est.5b03709[ACS Full Text], [CAS], Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvVyit7bM&md5=0ff17c54d051acef99c5a703b91d4c2fAmbient Air Pollution Exposure Estimation for the Global Burden of Disease 2013Brauer, Michael; Freedman, Greg; Frostad, Joseph; van Donkelaar, Aaron; Martin, Randall V.; Dentener, Frank; Dingenen, Rita van; Estep, Kara; Amini, Heresh; Apte, Joshua S.; Balakrishnan, Kalpana; Barregard, Lars; Broday, David; Feigin, Valery; Ghosh, Santu; Hopke, Philip K.; Knibbs, Luke D.; Kokubo, Yoshihiro; Liu, Yang; Ma, Stefan; Morawska, Lidia; Sangrador, Jose Luis Texcalac; Shaddick, Gavin; Anderson, H. Ross; Vos, Theo; Forouzanfar, Mohammad H.; Burnett, Richard T.; Cohen, AaronEnvironmental Science & Technology (2016), 50 (1), 79-88CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient air pollution exposure is a major risk factor for global disease. Assessing the impact of air pollution on population health and evaluating trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure ests. This work combined satellite-based ests., chem. transport model simulations, and ground measurements from 79 countries to produce global ests. of annual av. fine particle (PM2.5) and O3 concns. at 0.1° × 0.1° spatial resoln. for 5-yr intervals from 1990 to 2010 and year 2013. These ests. were used to assess population-weighted mean concns. for 1990-2013 for 188 countries. In 2013, 87% of the world population lived in areas exceeding the World Health Organization air quality guideline (10 μg/m3 PM2.5 annual av.). From 1990 to 2013, global population-weighted PM2.5 increased 20.4%, driven by trends in southern and southeastern Asia and China. Decreases in population-weighted mean PM2.5 concns. were evident in most high income countries. Population-weighted mean O3 concns. increased globally 8.9% from 1990 to 2013, with increases in most countries; modest decreases occurred in North America, parts of Europe, and several southeastern Asia countries.
- 25Shaddick, G.; Thomas, M. L.; Green, A.; Brauer, M.; van Donkelaar, A.; Burnett, R.; Chang, H. H.; Cohen, A.; van Dingenen, R.; Dora, C. Data integration model for air quality: A hierarchical approach to the global estimation of exposures to ambient air pollution. Journal of the Royal Statistical Society. Series C, Applied statistics 2018, 67, 231– 253, DOI: 10.1111/rssc.12227
- 26Shaddick, G.; Thomas, M.; Amini, H.; Broday, D. M.; Cohen, A.; Frostad, J.; Green, A.; Gumy, S.; Liu, Y.; Martin, R. V. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ. Sci. Technol. 2018, DOI: 10.1021/acs.est.8b02864
- 27Ke, C.; Gupta, R.; Xavier, D.; Prabhakaran, D.; Mathur, P.; Kalkonde, Y. V.; Kolpak, P.; Suraweera, W.; Jha, P.; Allarakha, S. Divergent trends in ischaemic heart disease and stroke mortality in India from 2000 to 2015: a nationally representative mortality study. Lancet Global Health 2018, 6, e914– e923, DOI: 10.1016/S2214-109X(18)30242-0
- 28Marshall, J. D.; Apte, J. S.; Coggins, J. S.; Goodkind, A. L. Blue skies bluer?. Environ. Sci. Technol. 2015, 49, 13929– 13936, DOI: 10.1021/acs.est.5b03154[ACS Full Text], [CAS], Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhslKltrjN&md5=bce6a7f91c3f3eacca352b57ed611efeBlue Skies Bluer?Marshall, Julian D.; Apte, Joshua S.; Coggins, Jay S.; Goodkind, Andrew L.Environmental Science & Technology (2015), 49 (24), 13929-13936CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)The largest U.S. environmental health risk is cardiopulmonary mortality from ambient PM2.5. The concn.-response (C-R) for ambient PM2.5 in the U.S. is generally assumed to be linear: from any initial baseline, a given concn. redn. would yield the same improvement in health risk. Recent evidence points to the perplexing possibility that the PM2.5 C-R for cardiopulmonary mortality and some other major endpoints might be supralinear: a given concn. redn. would yield greater improvements in health risk as the initial baseline becomes cleaner. We explore the implications of supralinearity for air policy, emphasizing U.S. conditions. If C-R is supralinear, an economically efficient PM2.5 target may be substantially more stringent than under current stds. Also, if a goal of air policy is to achieve the greatest health improvement per unit of PM2.5 redn., the optimal policy might call for greater emission redns. in already-clean locales-making "blue skies bluer"-which may be at odds with environmental equity goals. Regardless of whether the C-R is linear or supralinear, the health benefits of attaining U.S. PM2.5 levels well below the current std. would be large. For the supralinear C-R considered here, attaining the current U.S. EPA std., 12 μg m-3, would avert only ∼17% (if C-R is linear: ∼ 25%) of the total annual cardiopulmonary mortality attributable to PM2.5.
- 29Institute for Health Metrics and Evaluation. GBD 2016 Results Tool. http://ghdx.healthdata.org/gbd-results-tool, 2017 (accessed July 11, 2018).Google ScholarThere is no corresponding record for this reference.
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- 31Pope, C. A.; Burnett, R. T.; Krewski, D.; Jerrett, M.; Shi, Y.; Calle, E. E.; Thun, M. J. Cardiovascular mortality and exposure to airborne fine particulate matter and cigarette smoke: Shape of the exposure-response relationship. Circulation 2009, 120, 941– 948, DOI: 10.1161/CIRCULATIONAHA.109.857888[Crossref], [PubMed], [CAS], Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXht1WntbnE&md5=dbe35d2a7060f23487f2086272230e48Cardiovascular Mortality and Exposure to Airborne Fine Particulate Matter and Cigarette Smoke: Shape of the Exposure-Response RelationshipPope, C. Arden, III; Burnett, Richard T.; Krewski, Daniel; Jerrett, Michael; Shi, Yuanli; Calle, Eugenia E.; Thun, Michael J.Circulation (2009), 120 (11), 941-948CODEN: CIRCAZ; ISSN:0009-7322. (Lippincott Williams & Wilkins)Fine particulate matter exposure from both ambient air pollution and secondhand cigarette smoke has been assocd. with larger risks of cardiovascular mortality than would be expected on the basis of linear extrapolations of the relative risks from active smoking. This study directly assessed the shape of the exposure-response relationship between cardiovascular mortality and fine particulates from cigarette smoke and ambient air pollution. Prospective cohort data for >1 million adults were collected by the American Cancer Society as part of the Cancer Prevention Study II in 1982. Cox proportional hazards regression models that included variables for increments of cigarette smoking and variables to control for education, marital status, body mass, alc. consumption, occupational exposures, and diet were used to describe the mortality experience of the cohort. Adjusted relative risks of mortality were plotted against estd. av. daily dose of fine particulate matter from cigarette smoke along with comparison ests. for secondhand cigarette smoke and air pollution. There were substantially increased cardiovascular mortality risks at very low levels of active cigarette smoking and smaller but significant excess risks even at the much lower exposure levels assocd. with secondhand cigarette smoke and ambient air pollution. Relatively low levels of fine particulate exposure from either air pollution or secondhand cigarette smoke are sufficient to induce adverse biol. responses increasing the risk of cardiovascular disease mortality. The exposure-response relationship between cardiovascular disease mortality and fine particulate matter is relatively steep at low levels of exposure and flattens out at higher exposures.
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- 34Smith, K. R.; Ezzati, M. How environmental health risks change with development: The epidemiologic and environmental risk transitions revisited. Annu. Rev. Environ. Resources 2005, 30, 291– 333, DOI: 10.1146/annurev.energy.30.050504.144424
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Abstract
Figure 1
Figure 1. Example survival curves for observed life tables (solid lines) and simulated cause-deleted life tables (dashed lines) where ambient PM2.5 exposure is eliminated as a mortality risk factor. Life expectancy e0 can be visualized as the integral of the survival curve over the age spectrum. Life expectancy for the counterfactual case is increased after removing PM2.5 as a mortality risk. For a given country, the reduction in life expectancy attributed to PM2.5 (ΔLE) relative to a counterfactual scenario with no excess mortality risk from PM2.5 can be visualized as the area between the solid and dashed curves.
Figure 2
Figure 2. Relationship among the global distribution of ΔLE, the life expectancy decrement from PM2.5, and global PM2.5 concentrations C. ΔLE is generally higher in countries with higher PM2.5 levels. (a) Global distribution of population with respect to annual-average PM2.5 for year 2016. Plotted data reflect local smoothing of bin-width-normalized distributions computed over 400 logarithmically spaced bins: equal-sized plotted areas reflect equal populations. Each country is colored proportionally to the ΔLE from PM2.5 exposure. (b) Cumulative distribution of ΔLE over the global population. The global population-weighted median value for ΔLE is 1.22 years, corresponding to conditions in China. Shading for each country shows the national population-weighted mean PM2.5, illustrating how ΔLE has a strong but imperfect association with PM2.5. (c) National decrements in ΔLE vs PM2.5. Owing to the supralinear concentration–response relationship of mortality with PM2.5, the slope of this distribution is higher for countries with lower average PM2.5 concentrations.
Figure 3
Figure 3. Global maps of the life expectancy decrement ΔLE from PM2.5. Panel a shows baseline ΔLE for year-2016 concentrations (global population-weighted mean and median of 1.03 and 1.22 years, respectively). Panel b shows hypothetical gains in life expectancy for an alternative exposure distribution where concentrations are limited to a maximum of 10 μg m–3, the WHO air quality guideline concentration (global-average ΔLE of ∼0.59 year). See also Table S2.
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ARTICLE SECTIONSThis article references 35 other publications.
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Lancet 2012, 380, 2224– 2260, DOI: 10.1016/S0140-6736(12)61766-8[Crossref], [PubMed], [CAS], Google Scholar1https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3s3isFClug%253D%253D&md5=f35c63bad4b58d5266a7ee7c4512569bA comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010Lim Stephen S; Vos Theo; Flaxman Abraham D; Danaei Goodarz; Shibuya Kenji; Adair-Rohani Heather; Amann Markus; Anderson H Ross; Andrews Kathryn G; Aryee Martin; Atkinson Charles; Bacchus Loraine J; Bahalim Adil N; Balakrishnan Kalpana; Balmes John; Barker-Collo Suzanne; Baxter Amanda; Bell Michelle L; Blore Jed D; Blyth Fiona; Bonner Carissa; Borges Guilherme; Bourne Rupert; Boussinesq Michel; Brauer Michael; Brooks Peter; Bruce Nigel G; Brunekreef Bert; Bryan-Hancock Claire; Bucello Chiara; Buchbinder Rachelle; Bull Fiona; Burnett Richard T; Byers Tim E; Calabria Bianca; Carapetis Jonathan; Carnahan Emily; Chafe Zoe; Charlson Fiona; Chen Honglei; Chen Jian Shen; Cheng Andrew Tai-Ann; Child Jennifer Christine; Cohen Aaron; Colson K Ellicott; Cowie Benjamin C; Darby Sarah; Darling Susan; Davis Adrian; Degenhardt Louisa; Dentener Frank; Des Jarlais Don C; Devries Karen; Dherani Mukesh; Ding Eric L; Dorsey E Ray; Driscoll Tim; Edmond Karen; Ali Suad Eltahir; Engell Rebecca E; Erwin Patricia J; Fahimi Saman; Falder Gail; Farzadfar Farshad; Ferrari Alize; Finucane Mariel M; Flaxman Seth; Fowkes Francis Gerry R; Freedman Greg; Freeman Michael K; Gakidou Emmanuela; Ghosh Santu; Giovannucci Edward; Gmel Gerhard; Graham Kathryn; Grainger Rebecca; Grant Bridget; Gunnell David; Gutierrez Hialy R; Hall Wayne; Hoek Hans W; Hogan Anthony; Hosgood H Dean 3rd; Hoy Damian; Hu Howard; Hubbell Bryan J; Hutchings Sally J; Ibeanusi Sydney E; Jacklyn Gemma L; Jasrasaria Rashmi; Jonas Jost B; Kan Haidong; Kanis John A; Kassebaum Nicholas; Kawakami Norito; Khang Young-Ho; Khatibzadeh Shahab; Khoo Jon-Paul; Kok Cindy; Laden Francine; Lalloo Ratilal; Lan Qing; Lathlean Tim; Leasher Janet L; Leigh James; Li Yang; Lin John Kent; Lipshultz Steven E; London Stephanie; Lozano Rafael; Lu Yuan; Mak Joelle; Malekzadeh Reza; Mallinger Leslie; Marcenes Wagner; March Lyn; Marks Robin; Martin Randall; McGale Paul; McGrath John; Mehta Sumi; Mensah George A; Merriman Tony R; Micha Renata; Michaud Catherine; Mishra Vinod; Mohd Hanafiah Khayriyyah; Mokdad Ali A; Morawska Lidia; Mozaffarian Dariush; Murphy Tasha; Naghavi Mohsen; Neal Bruce; Nelson Paul K; Nolla Joan Miquel; Norman Rosana; Olives Casey; Omer Saad B; Orchard Jessica; Osborne Richard; Ostro Bart; Page Andrew; Pandey Kiran D; Parry Charles D H; Passmore Erin; Patra Jayadeep; Pearce Neil; Pelizzari Pamela M; Petzold Max; Phillips Michael R; Pope Dan; Pope C Arden 3rd; Powles John; Rao Mayuree; Razavi Homie; Rehfuess Eva A; Rehm Jurgen T; Ritz Beate; Rivara Frederick P; Roberts Thomas; Robinson Carolyn; Rodriguez-Portales Jose A; Romieu Isabelle; Room Robin; Rosenfeld Lisa C; Roy Ananya; Rushton Lesley; Salomon Joshua A; Sampson Uchechukwu; Sanchez-Riera Lidia; Sanman Ella; Sapkota Amir; Seedat Soraya; Shi Peilin; Shield Kevin; Shivakoti Rupak; Singh Gitanjali M; Sleet David A; Smith Emma; Smith Kirk R; Stapelberg Nicolas J C; Steenland Kyle; Stockl Heidi; Stovner Lars Jacob; Straif Kurt; Straney Lahn; Thurston George D; Tran Jimmy H; Van Dingenen Rita; van Donkelaar Aaron; Veerman J Lennert; Vijayakumar Lakshmi; Weintraub Robert; Weissman Myrna M; White Richard A; Whiteford Harvey; Wiersma Steven T; Wilkinson James D; Williams Hywel C; Williams Warwick; Wilson Nicholas; Woolf Anthony D; Yip Paul; Zielinski Jan M; Lopez Alan D; Murray Christopher J L; Ezzati Majid; AlMazroa Mohammad A; Memish Ziad ALancet (London, England) (2012), 380 (9859), 2224-60 ISSN:.BACKGROUND: Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. METHODS: We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. FINDINGS: In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0·9% (0·4-1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. INTERPRETATION: Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children. FUNDING: Bill & Melinda Gates Foundation.
- 2Cohen, A. J.; Anderson, H. R.; Ostro, B.; Pandey, K. D.; Krzyzanowski, M.; Künzli, N.; Gutschmidt, K.; Pope, A.; Romieu, I.; Samet, J. M.; Smith, K. The global burden of disease due to outdoor air pollution. J. Toxicol. Environ. Health, Part A 2005, 68, 1301– 1307, DOI: 10.1080/15287390590936166[Crossref], [PubMed], [CAS], Google Scholar2https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXmt1GktL8%253D&md5=b10517b028975a01e68829562bca9c32The Global Burden of Disease Due to Outdoor Air PollutionCohen, Aaron; Ross Anderson, H.; Ostro, Bart; Pandey, Kiran; Krzyzanowski, Michal; Kuenzli, Nino; Gutschmidt, Kersten; Pope, Arden; Romieu, Isabelle; Samet, Jonathan; Smith, KirkJournal of Toxicology and Environmental Health, Part A (2005), 68 (13-14), 1301-1307CODEN: JTEHF8; ISSN:1528-7394. (Taylor & Francis, Inc.)As part of the World Health Organization (WHO) Global Burden of Disease Comparative Risk Assessment, the burden of disease attributable to urban ambient air pollution was estd. in terms of deaths and disability-adjusted life years (DALYs). Air pollution is assocd. with a broad spectrum of acute and chronic health effects, the nature of which may vary with the pollutant constituents. Particulate air pollution is consistently and independently related to the most serious effects, including lung cancer and other cardiopulmonary mortality. The analyses on which this report is based est. that ambient air pollution, in terms of fine particulate air pollution (PM2.5), causes about 3% of mortality from cardiopulmonary disease, about 5% of mortality from cancer of the trachea, bronchus, and lung, and about 1% of mortality from acute respiratory infections in children under 5 yr, worldwide. This amts. to about 0.8 million (1.2%) premature deaths and 6.4 million (0.5%) years of life lost (YLL). This burden occurs predominantly in developing countries; 65% in Asia alone. These ests. consider only the impact of air pollution on mortality (i.e., years of life lost) and not morbidity (i.e., years lived with disability), due to limitations in the epidemiol. database. If air pollution multiplies both incidence and mortality to the same extent (i.e., the same relative risk), then the DALYs for cardiopulmonary disease increase by 20% worldwide.
- 3Cohen, A. J.; Brauer, M.; Burnett, R.; Anderson, H. R.; Frostad, J.; Estep, K.; Balakrishnan, K.; Brunekreef, B.; Dandona, L.; Dandona, R. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 2017, 389, 1907– 1918, DOI: 10.1016/S0140-6736(17)30505-6[Crossref], [PubMed], [CAS], Google Scholar3https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1cvns1yhsA%253D%253D&md5=ce8ca11f951fbb3a484e211f77e8dd25Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015Cohen Aaron J; Brauer Michael; Burnett Richard; Shin Hwashin; Anderson H Ross; Frostad Joseph; Estep Kara; Freedman Greg; Vos Theo; Murray Christopher J L; Forouzanfar Mohammad H; Balakrishnan Kalpana; Brunekreef Bert; Dandona Lalit; Dandona Rakhi; Feigin Valery; Hubbell Bryan; Jobling Amelia; Shaddick Gavin; Thomas Matthew; Kan Haidong; Knibbs Luke; Liu Yang; Martin Randall; van Donkelaar Aaron; Morawska Lidia; Pope C Arden 3rd; Straif Kurt; van Dingenen RitaLancet (London, England) (2017), 389 (10082), 1907-1918 ISSN:.BACKGROUND: Exposure to ambient air pollution increases morbidity and mortality, and is a leading contributor to global disease burden. We explored spatial and temporal trends in mortality and burden of disease attributable to ambient air pollution from 1990 to 2015 at global, regional, and country levels. METHODS: We estimated global population-weighted mean concentrations of particle mass with aerodynamic diameter less than 2·5 μm (PM2·5) and ozone at an approximate 11 km × 11 km resolution with satellite-based estimates, chemical transport models, and ground-level measurements. Using integrated exposure-response functions for each cause of death, we estimated the relative risk of mortality from ischaemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections from epidemiological studies using non-linear exposure-response functions spanning the global range of exposure. FINDINGS: Ambient PM2·5 was the fifth-ranking mortality risk factor in 2015. Exposure to PM2·5 caused 4·2 million (95% uncertainty interval [UI] 3·7 million to 4·8 million) deaths and 103·1 million (90·8 million 115·1 million) disability-adjusted life-years (DALYs) in 2015, representing 7·6% of total global deaths and 4·2% of global DALYs, 59% of these in east and south Asia. Deaths attributable to ambient PM2·5 increased from 3·5 million (95% UI 3·0 million to 4·0 million) in 1990 to 4·2 million (3·7 million to 4·8 million) in 2015. Exposure to ozone caused an additional 254 000 (95% UI 97 000-422 000) deaths and a loss of 4·1 million (1·6 million to 6·8 million) DALYs from chronic obstructive pulmonary disease in 2015. INTERPRETATION: Ambient air pollution contributed substantially to the global burden of disease in 2015, which increased over the past 25 years, due to population ageing, changes in non-communicable disease rates, and increasing air pollution in low-income and middle-income countries. Modest reductions in burden will occur in the most polluted countries unless PM2·5 values are decreased substantially, but there is potential for substantial health benefits from exposure reduction. FUNDING: Bill & Melinda Gates Foundation and Health Effects Institute.
- 4Gakidou, E.; Afshin, A.; Abajobir, A. A.; Abate, K. H.; Abbafati, C.; Abbas, K. M.; Abd-Allah, F.; Abdulle, A. M.; Abera, S. F.; Aboyans, V. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017, 390, 1345– 1422, DOI: 10.1016/S0140-6736(17)32366-8
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- 7Thibodeau, L. A.; Reed, R. B.; Bishop, Y. M.; Kammerman, L. A. Air pollution and human health: a review and reanalysis. Environ. Health Persp. 1980, 34, 165– 183, DOI: 10.1289/ehp.8034165
- 8Schwartz, J. Harvesting and long term exposure effects in the relation between air pollution and mortality. Am. J. Epidemiol. 2000, 151, 440– 448, DOI: 10.1093/oxfordjournals.aje.a010228[Crossref], [PubMed], [CAS], Google Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3c7ntVWrtg%253D%253D&md5=37f4e98eb9c6ba3e2a55a373761a9d91Harvesting and long term exposure effects in the relation between air pollution and mortalitySchwartz JAmerican journal of epidemiology (2000), 151 (5), 440-8 ISSN:0002-9262.While time series analyses have demonstrated that airborne particles are associated with early death, they have not clarified how much the deaths are advanced. If all of the pollution-related deaths were advanced by only a few days, one would expect little association between weekly averages of air pollution and daily deaths. The author used the STL algorithm to classify data on air pollution, daily deaths, and weather from Boston, Massachusetts (1979-1986) into three time series: one reflecting seasonal and longer fluctuations, one reflecting short term fluctuations, and one reflecting intermediate patterns. By varying the cutoff point between short term and intermediate term, it was possible to examine harvesting on different time scales. For chronic obstructive pulmonary disease, there was evidence that most of the mortality was displaced by only a few months. For pneumonia, heart attacks, and all-cause mortality, the effect size increased with longer time scales. The percentage increase in all deaths associated with a 10-microg/m3 increase in PM2.5 rose from 2.1% (95% confidence interval: 1.5, 4.3) to 3.75% (95% confidence interval: 3.2, 4.3) as the focus moved from daily patterns to monthly patterns. This is consistent with the larger effect seen in prospective cohort studies, rather than harvesting's playing a major role.
- 9Künzli, N.; Medina, S.; Kaiser, R.; Quenel, P.; Horak, F., Jr.; Studnicka, M. Assessment of deaths attributable to air pollution: Should we use risk estimates based on time series or on cohort studies?. Am. J. Epidemiol. 2001, 153, 1050– 1055, DOI: 10.1093/aje/153.11.1050[Crossref], [PubMed], [CAS], Google Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3MzislCrtg%253D%253D&md5=e5f8a849ec24c24dbef10b04885f1610Assessment of deaths attributable to air pollution: should we use risk estimates based on time series or on cohort studies?Kunzli N; Medina S; Kaiser R; Quenel P; Horak F Jr; Studnicka MAmerican journal of epidemiology (2001), 153 (11), 1050-5 ISSN:0002-9262.Epidemiologic studies are crucial to the estimation of numbers of deaths attributable to air pollution. In this paper, the authors present a framework for distinguishing estimates of attributable cases based on time-series studies from those based on cohort studies, the latter being 5-10 times larger. The authors distinguish four categories of death associated with air pollution: A) air pollution increases both the risk of underlying diseases leading to frailty and the short term risk of death among the frail; B) air pollution increases the risk of chronic diseases leading to frailty but is unrelated to timing of death; C) air pollution is unrelated to risk of chronic diseases but short term exposure increases mortality among persons who are frail; and D) neither underlying chronic disease nor the event of death is related to air pollution exposure. Time-series approaches capture deaths from categories A and C, whereas cohort studies assess cases from categories A, B, and C. In addition, years of life lost can only be derived from cohort studies, where time to death is the outcome, while in time-series studies, death is a once-only event (no dimension in time). The authors conclude that time-series analyses underestimate cases of death attributable to air pollution and that assessment of the impact of air pollution on mortality should be based on cohort studies.
- 10Brunekreef, B. Air pollution and life expectancy: is there a relation?. Occup. Environ. Med. 1997, 54, 781– 784, DOI: 10.1136/oem.54.11.781
- 11Pope, C. A.; Ezzati, M.; Dockery, D. W. Fine-particulate air pollution and life expectancy in the United States. N. Engl. J. Med. 2009, 360, 376– 386, DOI: 10.1056/NEJMsa0805646[Crossref], [PubMed], [CAS], Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtVSltr8%253D&md5=5033a22aa3fda3604d97be40374873d8Fine-particulate air pollution and life expectancy in the United StatesPope, C. Arden, III; Ezzati, Majid; Dockery, Douglas W.New England Journal of Medicine (2009), 360 (4), 376-386CODEN: NEJMAG; ISSN:0028-4793. (Massachusetts Medical Society)Exposure to fine-particulate air pollution has been assocd. with increased morbidity and mortality, suggesting that sustained redns. in pollution exposure should result in improved life expectancy. This study directly evaluated the changes in life expectancy assocd. with differential changes in fine-particulate air pollution that occurred in the United States during the 1980s and 1990s. The authors compiled data on life expectancy, socioeconomic status, and demog. characteristics for 211 county units in the 51 U.S. metropolitan areas with matching data on fine-particulate air pollution for the late 1970s and early 1980s and the late 1990s and early 2000s. Regression models were used to est. the assocn. between redns. in pollution and changes in life expectancy, with adjustment for changes in socioeconomic and demog. variables and in proxy indicators for the prevalence of cigarette smoking. A decrease of 10 μg per cubic meter in the concn. of fine particulate matter was assocd. with an estd. increase in mean (± SE) life expectancy of 0.61 ± 0.20 yr (P = 0.004). The estd. effect of reduced exposure to pollution on life expectancy was not highly sensitive to adjustment for changes in socioeconomic, demog., or proxy variables for the prevalence of smoking or to the restriction of observations to relatively large counties. Redns. in air pollution accounted for as much as 15% of the overall increase in life expectancy in the study areas. A redn. in exposure to ambient fine-particulate air pollution contributed to significant and measurable improvements in life expectancy in the United States.
- 12Correia, A. W.; Pope, C. A., III; Dockery, D. W.; Wang, Y.; Ezzati, M.; Dominici, F. Effect of air pollution control on life expectancy in the United States. Epidemiol. 2013, 24, 23– 31, DOI: 10.1097/EDE.0b013e3182770237[Crossref], [PubMed], [CAS], Google Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3s7ptFaisA%253D%253D&md5=16d1bbfe8acb1ed660c7e4c07c59d95dEffect of air pollution control on life expectancy in the United States: an analysis of 545 U.S. counties for the period from 2000 to 2007Correia Andrew W; Pope C Arden 3rd; Dockery Douglas W; Wang Yun; Ezzati Majid; Dominici FrancescaEpidemiology (Cambridge, Mass.) (2013), 24 (1), 23-31 ISSN:.BACKGROUND: In recent years (2000-2007), ambient levels of fine particulate matter (PM2.5) have continued to decline as a result of interventions, but the decline has been at a slower rate than previous years (1980-2000). Whether these more recent and slower declines of PM2.5 levels continue to improve life expectancy and whether they benefit all populations equally is unknown. METHODS: We assembled a data set for 545 U.S. counties consisting of yearly county-specific average PM2.5, yearly county-specific life expectancy, and several potentially confounding variables measuring socioeconomic status, smoking prevalence, and demographic characteristics for the years 2000 and 2007. We used regression models to estimate the association between reductions in PM2.5 and changes in life expectancy for the period from 2000 to 2007. RESULTS: A decrease of 10 μg/m in the concentration of PM2.5 was associated with an increase in mean life expectancy of 0.35 years (SD = 0.16 years, P = 0.033). This association was stronger in more urban and densely populated counties. CONCLUSIONS: Reductions in PM2.5 were associated with improvements in life expectancy for the period from 2000 to 2007. Air pollution control in the last decade has continued to have a positive impact on public health.
- 13Ebenstein, A.; Fan, M.; Greenstone, M.; He, G.; Zhou, M. New evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River Policy. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 10384, DOI: 10.1073/pnas.1616784114[Crossref], [PubMed], [CAS], Google Scholar13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsVKltb3O&md5=bd8ed5bb6404437403c44d511ab9cb19New evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River PolicyEbenstein, Avraham; Fan, Maoyong; Greenstone, Michael; He, Guojun; Zhou, MaigengProceedings of the National Academy of Sciences of the United States of America (2017), 114 (39), 10384-10389CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)This paper finds that a 10-μg/m3 increase in airborne particulate matter [particulate matter smaller than 10 μm (PM10)] reduces life expectancy by 0.64 years (95% confidence interval = 0.21-1.07). This est. is derived from quasiexperimental variation in PM10 generated by China's Huai River Policy, which provides free or heavily subsidized coal for indoor heating during the winter to cities north of the Huai River but not to those to the south. The findings are derived from a regression discontinuity design based on distance from the Huai River, and they are robust to using parametric and nonparametric estn. methods, different kernel types and bandwidth sizes, and adjustment for a rich set of demog. and behavioral covariates. Furthermore, the shorter lifespans are almost entirely caused by elevated rates of cardiorespiratory mortality, suggesting that PM10 is the causal factor. The ests. imply that bringing all of China into compliance with its Class I stds. for PM10 would save 3.7 billion life-years.
- 14Pope, C. A.; Dockery, D. W. Health effects of fine particulate air pollution: Lines that connect. J. Air Waste Manage. Assoc. 2006, 56, 709– 742, DOI: 10.1080/10473289.2006.10464485[Crossref], [PubMed], [CAS], Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xmt1ygs7k%253D&md5=7aaf80b762054e234b73d653096e18f2Health effects of fine particulate air pollution: lines that connectPope, C. Arden, III; Dockery, Douglas W.Journal of the Air & Waste Management Association (2006), 56 (6), 709-742CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)A review. Efforts to understand and mitigate the health effects of participate matter (PM) air pollution have a rich and interesting history. This review focuses on six substantial lines of research that have been pursued since 1997 that have helped elucidate our understanding about the effects of PM on human health. There has been substantial progress in the evaluation of PM health effects at different time-scales of exposure and in the exploration of the shape of the concn.-response function. There has also been emerging evidence of PM-related cardiovascular health effects and growing knowledge regarding interconnected general pathophysiol. pathways that link PM exposure with cardiopulmonary morbidity and mortality. Despite important gaps in scientific knowledge and continued reasons for some skepticism, a comprehensive evaluation of the research findings provides persuasive evidence that exposure to fine particulate air pollution has adverse effects on cardiopulmonary health. Although much of this research has been motivated by environmental public health policy, these results have important scientific, medical, and public health implications that are broader than debates over legally mandated air quality stds.
- 15Baccarelli, A. A.; Hales, N.; Burnett, R. T.; Jerrett, M.; Mix, C.; Dockery, D. W.; Pope, C. A. Particulate air pollution, exceptional aging, and rates of centenarians: A nationwide analysis of the United States, 1980–2010. Environ. Health Perspect. 2016, 124, 1744– 1750, DOI: 10.1289/EHP197
- 16Chen, Y.; Ebenstein, A.; Greenstone, M.; Li, H. Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 12936– 12941, DOI: 10.1073/pnas.1300018110[Crossref], [PubMed], [CAS], Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsVShurfN&md5=07723e8c93b56e9a788bd269af501112Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policyChen, Yuyu; Ebenstein, Avraham; Greenstone, Michael; Li, HongbinProceedings of the National Academy of Sciences of the United States of America (2013), 110 (32), 12936-12941, S12936/1-S12936/29CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)This paper's findings suggest that an arbitrary Chinese policy that greatly increases total suspended particulates (TSPs) air pollution is causing the 500 million residents of Northern China to lose more than 2.5 billion life years of life expectancy. The quasi-exptl. empirical approach is based on China's Huai River policy, which provided free winter heating via the provision of coal for boilers in cities north of the Huai River but denied heat to the south. Using a regression discontinuity design based on distance from the Huai River, we find that ambient concns. of TSPs are about 184 μg/m3 [95% confidence interval (CI): 61, 307] or 55% higher in the north. Further, the results indicate that life expectancies are about 5.5 y (95% CI: 0.8, 10.2) lower in the north owing to an increased incidence of cardiorespiratory mortality. More generally, the anal. suggests that long-term exposure to an addnl. 100 μg/m3 of TSPs is assocd. with a redn. in life expectancy at birth of about 3.0 y (95% CI: 0.4, 5.6).
- 17Fann, N.; Kim, S.-Y.; Olives, C.; Sheppard, L. Estimated changes in life expectancy and adult mortality resulting from declining PM2.5 exposures in the contiguous United States: 1980–2010. Environ. Health Perspect. 2017, 125, 097003 DOI: 10.1289/EHP507
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- 20Tsai, S. P.; Lee, E. S.; Hardy, R. J. The effect of a reduction in leading causes of death: Potential gains in life expectancy. Am. J. Public Health 1978, 68, 966– 971, DOI: 10.2105/AJPH.68.10.966[Crossref], [PubMed], [CAS], Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaE1M%252FlvF2rsA%253D%253D&md5=ce7a44e2b908933facfcfa4157c5b91fThe effect of a reduction in leading causes of death: potential gains in life expectancyTsai S P; Lee E S; Hardy R JAmerican journal of public health (1978), 68 (10), 966-71 ISSN:0090-0036.The potential gains in total expectation of life and in the working life ages among the United States population are examined when the three leading causes of death are totally or partially eliminated. The impressive gains theoretically achieved by total elimination do not hold up under the more realistic assumption of partial elimination or reduction. The number of years gained by a new-born child, with a 30 per cent reduction in major cardiovascular diseases would be 1.98 years, for malignant neoplasms 0.71 years, and for motor vehicle accidents 0.21 years. Application of the same reduction to the working ages, 15 to 70 years, results in a gain of 0.43, 0.26, and 0.14 years, respectively for the three leading causes of death. Even with a scientific break-through in combating these causes of death, it appears that future gains in life expectancies for the working ages will not be spectacular. The implication of the results in relation to the current debate on the national health care policy is noted.
- 21Apte, J. S.; Marshall, J. D.; Brauer, M.; Cohen, A. J. Addressing global mortality from ambient PM2.5. Environ. Sci. Technol. 2015, 49, 8057– 8066, DOI: 10.1021/acs.est.5b01236[ACS Full Text], [CAS], Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVShtb3P&md5=9deade47f08dc87bbe7572d6199be8e4Addressing Global Mortality from Ambient PM2.5Apte, Joshua S.; Marshall, Julian D.; Cohen, Aaron J.; Brauer, MichaelEnvironmental Science & Technology (2015), 49 (13), 8057-8066CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient fine particulate matter (PM2.5) has a large, well-documented global burden of disease. This work used high-resoln. (10 km, global-coverage) concn. data and cause-specific integrated exposure-response functions developed for the Global Burden of Disease 2010 to assess how regional and global improvements in ambient air quality could reduce attributable mortality from PM2.5. Overall, an aggressive global program of PM2.5 mitigation in accord with World Health Organization interim guidelines could avoid 750,000 (23%) of the 3.2 million deaths/yr currently (2010) attributable to ambient PM2.5. Modest improvements in PM2.5 in relatively clean regions (North America, Europe) would result in surprisingly large avoided mortality, due to demog. factors and the non-linear concn.-response relationship which describes the risk of PM in relation to several important causes of death. Major air quality improvements would be required to substantially reduce mortality from PM2.5 in more polluted regions, e.g., China and India. Forecasted demog. and epidemiol. transitions in India and China imply that to maintain PM2.5-attributable mortality rates (deaths/100,000 people-yr) const., av. PM2.5 concns. would need to decline by ∼20-30% over the next 15 years to merely offset increases in PM2.5-attributable mortality from aging populations. An effective program to deliver clean air to the most polluted regions could avoid several hundred thousand premature deaths each year.
- 22Burnett, R. T.; Pope, C. A.; Ezzati, M.; Olives, C.; Lim, S. S.; Mehta, S.; Shin, H. H.; Singh, G.; Hubbell, B.; Brauer, M. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ. Health Perspect. 2014, 122, 397– 403, DOI: 10.1289/ehp.1307049[Crossref], [PubMed], [CAS], Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2cvjvFWnsA%253D%253D&md5=4ce6aad41a1f1cd5bcdd31aa405d8416An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposureBurnett Richard T; Pope C Arden 3rd; Ezzati Majid; Olives Casey; Lim Stephen S; Mehta Sumi; Shin Hwashin H; Singh Gitanjali; Hubbell Bryan; Brauer Michael; Anderson H Ross; Smith Kirk R; Balmes John R; Bruce Nigel G; Kan Haidong; Laden Francine; Pruss-Ustun Annette; Turner Michelle C; Gapstur Susan M; Diver W Ryan; Cohen AaronEnvironmental health perspectives (2014), 122 (4), 397-403 ISSN:.BACKGROUND: Estimating the burden of disease attributable to long-term exposure to fine particulate matter (PM2.5) in ambient air requires knowledge of both the shape and magnitude of the relative risk (RR) function. However, adequate direct evidence to identify the shape of the mortality RR functions at the high ambient concentrations observed in many places in the world is lacking. OBJECTIVE: We developed RR functions over the entire global exposure range for causes of mortality in adults: ischemic heart disease (IHD), cerebrovascular disease (stroke), chronic obstructive pulmonary disease (COPD), and lung cancer (LC). We also developed RR functions for the incidence of acute lower respiratory infection (ALRI) that can be used to estimate mortality and lost-years of healthy life in children < 5 years of age. METHODS: We fit an integrated exposure-response (IER) model by integrating available RR information from studies of ambient air pollution (AAP), second hand tobacco smoke, household solid cooking fuel, and active smoking (AS). AS exposures were converted to estimated annual PM2.5 exposure equivalents using inhaled doses of particle mass. We derived population attributable fractions (PAFs) for every country based on estimated worldwide ambient PM2.5 concentrations. RESULTS: The IER model was a superior predictor of RR compared with seven other forms previously used in burden assessments. The percent PAF attributable to AAP exposure varied among countries from 2 to 41 for IHD, 1 to 43 for stroke, < 1 to 21 for COPD, < 1 to 25 for LC, and < 1 to 38 for ALRI. CONCLUSIONS: We developed a fine particulate mass-based RR model that covered the global range of exposure by integrating RR information from different combustion types that generate emissions of particulate matter. The model can be updated as new RR information becomes available.
- 23Nasari, M. M.; Szyszkowicz, M.; Chen, H.; Crouse, D.; Turner, M. C.; Jerrett, M.; Pope, C. A.; Hubbell, B.; Fann, N.; Cohen, A. A class of non-linear exposure-response models suitable for health impact assessment applicable to large cohort studies of ambient air pollution. Air Qual., Atmos. Health 2016, 9, 961– 972, DOI: 10.1007/s11869-016-0398-z[Crossref], [PubMed], [CAS], Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XjsFKntLw%253D&md5=e5a2adf69b3eb1fe91035cb4b648a100A class of non-linear exposure-response models suitable for health impact assessment applicable to large cohort studies of ambient air pollutionNasari, Masoud M.; Szyszkowicz, Mieczyslaw; Chen, Hong; Crouse, Daniel; Turner, Michelle C.; Jerrett, Michael; Pope, C. Arden; Hubbell, Bryan; Fann, Neal; Cohen, Aaron; Gapstur, Susan M.; Diver, W. Ryan; Stieb, David; Forouzanfar, Mohammad H.; Kim, Sun-Young; Olives, Casey; Krewski, Daniel; Burnett, Richard T.Air Quality, Atmosphere & Health (2016), 9 (8), 961-972CODEN: AQAHAX; ISSN:1873-9326. (Springer)The effectiveness of regulatory actions designed to improve air quality is often assessed by predicting changes in public health resulting from their implementation. Risk of premature mortality from long-term exposure to ambient air pollution is the single most important contributor to such assessments and is estd. from observational studies generally assuming a log-linear, no-threshold assocn. between ambient concns. and death. There has been only limited assessment of this assumption in part because of a lack of methods to est. the shape of the exposure-response function in very large study populations. In this paper, we propose a new class of variable coeff. risk functions capable of capturing a variety of potentially non-linear assocns. which are suitable for health impact assessment. We construct the class by defining transformations of concn. as the product of either a linear or log-linear function of concn. multiplied by a logistic weighting function. These risk functions can be estd. using hazard regression survival models with currently available computer software and can accommodate large population-based cohorts which are increasingly being used for this purpose. We illustrate our modeling approach with two large cohort studies of long-term concns. of ambient air pollution and mortality: the American Cancer Society Cancer Prevention Study II (CPS II) cohort and the Canadian Census Health and Environment Cohort (CanCHEC). We then est. the no. of deaths attributable to changes in fine particulate matter concns. over the 2000 to 2010 time period in both Canada and the USA using both linear and non-linear hazard function models.
- 24Brauer, M.; Freedman, G.; Frostad, J.; van Donkelaar, A.; Martin, R. V.; Dentener, F.; Dingenen, R. v.; Estep, K.; Amini, H.; Apte, J. S. Ambient air pollution exposure estimation for the Global Burden of Disease 2013. Environ. Sci. Technol. 2016, 50, 79– 88, DOI: 10.1021/acs.est.5b03709[ACS Full Text], [CAS], Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvVyit7bM&md5=0ff17c54d051acef99c5a703b91d4c2fAmbient Air Pollution Exposure Estimation for the Global Burden of Disease 2013Brauer, Michael; Freedman, Greg; Frostad, Joseph; van Donkelaar, Aaron; Martin, Randall V.; Dentener, Frank; Dingenen, Rita van; Estep, Kara; Amini, Heresh; Apte, Joshua S.; Balakrishnan, Kalpana; Barregard, Lars; Broday, David; Feigin, Valery; Ghosh, Santu; Hopke, Philip K.; Knibbs, Luke D.; Kokubo, Yoshihiro; Liu, Yang; Ma, Stefan; Morawska, Lidia; Sangrador, Jose Luis Texcalac; Shaddick, Gavin; Anderson, H. Ross; Vos, Theo; Forouzanfar, Mohammad H.; Burnett, Richard T.; Cohen, AaronEnvironmental Science & Technology (2016), 50 (1), 79-88CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient air pollution exposure is a major risk factor for global disease. Assessing the impact of air pollution on population health and evaluating trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure ests. This work combined satellite-based ests., chem. transport model simulations, and ground measurements from 79 countries to produce global ests. of annual av. fine particle (PM2.5) and O3 concns. at 0.1° × 0.1° spatial resoln. for 5-yr intervals from 1990 to 2010 and year 2013. These ests. were used to assess population-weighted mean concns. for 1990-2013 for 188 countries. In 2013, 87% of the world population lived in areas exceeding the World Health Organization air quality guideline (10 μg/m3 PM2.5 annual av.). From 1990 to 2013, global population-weighted PM2.5 increased 20.4%, driven by trends in southern and southeastern Asia and China. Decreases in population-weighted mean PM2.5 concns. were evident in most high income countries. Population-weighted mean O3 concns. increased globally 8.9% from 1990 to 2013, with increases in most countries; modest decreases occurred in North America, parts of Europe, and several southeastern Asia countries.
- 25Shaddick, G.; Thomas, M. L.; Green, A.; Brauer, M.; van Donkelaar, A.; Burnett, R.; Chang, H. H.; Cohen, A.; van Dingenen, R.; Dora, C. Data integration model for air quality: A hierarchical approach to the global estimation of exposures to ambient air pollution. Journal of the Royal Statistical Society. Series C, Applied statistics 2018, 67, 231– 253, DOI: 10.1111/rssc.12227
- 26Shaddick, G.; Thomas, M.; Amini, H.; Broday, D. M.; Cohen, A.; Frostad, J.; Green, A.; Gumy, S.; Liu, Y.; Martin, R. V. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ. Sci. Technol. 2018, DOI: 10.1021/acs.est.8b02864
- 27Ke, C.; Gupta, R.; Xavier, D.; Prabhakaran, D.; Mathur, P.; Kalkonde, Y. V.; Kolpak, P.; Suraweera, W.; Jha, P.; Allarakha, S. Divergent trends in ischaemic heart disease and stroke mortality in India from 2000 to 2015: a nationally representative mortality study. Lancet Global Health 2018, 6, e914– e923, DOI: 10.1016/S2214-109X(18)30242-0
- 28Marshall, J. D.; Apte, J. S.; Coggins, J. S.; Goodkind, A. L. Blue skies bluer?. Environ. Sci. Technol. 2015, 49, 13929– 13936, DOI: 10.1021/acs.est.5b03154[ACS Full Text], [CAS], Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhslKltrjN&md5=bce6a7f91c3f3eacca352b57ed611efeBlue Skies Bluer?Marshall, Julian D.; Apte, Joshua S.; Coggins, Jay S.; Goodkind, Andrew L.Environmental Science & Technology (2015), 49 (24), 13929-13936CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)The largest U.S. environmental health risk is cardiopulmonary mortality from ambient PM2.5. The concn.-response (C-R) for ambient PM2.5 in the U.S. is generally assumed to be linear: from any initial baseline, a given concn. redn. would yield the same improvement in health risk. Recent evidence points to the perplexing possibility that the PM2.5 C-R for cardiopulmonary mortality and some other major endpoints might be supralinear: a given concn. redn. would yield greater improvements in health risk as the initial baseline becomes cleaner. We explore the implications of supralinearity for air policy, emphasizing U.S. conditions. If C-R is supralinear, an economically efficient PM2.5 target may be substantially more stringent than under current stds. Also, if a goal of air policy is to achieve the greatest health improvement per unit of PM2.5 redn., the optimal policy might call for greater emission redns. in already-clean locales-making "blue skies bluer"-which may be at odds with environmental equity goals. Regardless of whether the C-R is linear or supralinear, the health benefits of attaining U.S. PM2.5 levels well below the current std. would be large. For the supralinear C-R considered here, attaining the current U.S. EPA std., 12 μg m-3, would avert only ∼17% (if C-R is linear: ∼ 25%) of the total annual cardiopulmonary mortality attributable to PM2.5.
- 29Institute for Health Metrics and Evaluation. GBD 2016 Results Tool. http://ghdx.healthdata.org/gbd-results-tool, 2017 (accessed July 11, 2018).Google ScholarThere is no corresponding record for this reference.
- 30Pope, C. A.; Burnett, R. T.; Turner, M. C.; Cohen, A.; Krewski, D.; Jerrett, M.; Gapstur, S. M.; Thun, M. J. Lung cancer and cardiovascular disease mortality associated with ambient air pollution and cigarette smoke: Shape of the exposure–response relationships. Environ. Health Perspect. 2011, 119, 1616– 1621, DOI: 10.1289/ehp.1103639
- 31Pope, C. A.; Burnett, R. T.; Krewski, D.; Jerrett, M.; Shi, Y.; Calle, E. E.; Thun, M. J. Cardiovascular mortality and exposure to airborne fine particulate matter and cigarette smoke: Shape of the exposure-response relationship. Circulation 2009, 120, 941– 948, DOI: 10.1161/CIRCULATIONAHA.109.857888[Crossref], [PubMed], [CAS], Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXht1WntbnE&md5=dbe35d2a7060f23487f2086272230e48Cardiovascular Mortality and Exposure to Airborne Fine Particulate Matter and Cigarette Smoke: Shape of the Exposure-Response RelationshipPope, C. Arden, III; Burnett, Richard T.; Krewski, Daniel; Jerrett, Michael; Shi, Yuanli; Calle, Eugenia E.; Thun, Michael J.Circulation (2009), 120 (11), 941-948CODEN: CIRCAZ; ISSN:0009-7322. (Lippincott Williams & Wilkins)Fine particulate matter exposure from both ambient air pollution and secondhand cigarette smoke has been assocd. with larger risks of cardiovascular mortality than would be expected on the basis of linear extrapolations of the relative risks from active smoking. This study directly assessed the shape of the exposure-response relationship between cardiovascular mortality and fine particulates from cigarette smoke and ambient air pollution. Prospective cohort data for >1 million adults were collected by the American Cancer Society as part of the Cancer Prevention Study II in 1982. Cox proportional hazards regression models that included variables for increments of cigarette smoking and variables to control for education, marital status, body mass, alc. consumption, occupational exposures, and diet were used to describe the mortality experience of the cohort. Adjusted relative risks of mortality were plotted against estd. av. daily dose of fine particulate matter from cigarette smoke along with comparison ests. for secondhand cigarette smoke and air pollution. There were substantially increased cardiovascular mortality risks at very low levels of active cigarette smoking and smaller but significant excess risks even at the much lower exposure levels assocd. with secondhand cigarette smoke and ambient air pollution. Relatively low levels of fine particulate exposure from either air pollution or secondhand cigarette smoke are sufficient to induce adverse biol. responses increasing the risk of cardiovascular disease mortality. The exposure-response relationship between cardiovascular disease mortality and fine particulate matter is relatively steep at low levels of exposure and flattens out at higher exposures.
- 32Smith, K. R.; Peel, J. L. Mind the gap. Environ. Health Perspect. 2010, 118, 1643– 1645, DOI: 10.1289/ehp.1002517
- 33Pope, C. A.; Cropper, M.; Coggins, J.; Cohen, A. Health benefits of air pollution abatement policy: Role of the shape of the concentration-response function. J. Air Waste Manage. Assoc. 2015, 65, 516– 522, DOI: 10.1080/10962247.2014.993004[Crossref], [CAS], Google Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXntFeltbY%253D&md5=cc74143a74fd12966bfd771d7ec47e34Health benefits of air pollution abatement policy: Role of the shape of the concentration-response functionPope, C. Arden, III; Cropper, Maureen; Coggins, Jay; Cohen, AaronJournal of the Air & Waste Management Association (2015), 65 (5), 516-522CODEN: JAWAFC; ISSN:1096-2247. (Taylor & Francis Ltd.)There is strong evidence that fine particulate matter (aerodynamic diam. <2.5 μm; PM2.5) air pollution contributes to increased risk of disease and death. Ests. of the burden of disease attributable to PM2.5 pollution and benefits of reducing pollution are dependent upon the shape of the concn. response (C-R) functions. Recent evidence suggests that the C-R function between PM2.5 air pollution and mortality risk may be supralinear across wide ranges of exposure. Such results imply that incremental pollution abatement efforts may yield greater benefits in relatively clean areas than in highly polluted areas. The role of the shape of the C-R function in evaluating and understanding the costs and health benefits of air pollution abatement policy is explored. There remain uncertainties regarding the shape of the C-R function, and addnl. efforts to more fully understand the C-R relationships between PM2.5 and adverse health effects are needed to allow for more informed and effective air pollution abatement policies. Current evidence, however, suggests that there are benefits both from reducing air pollution in the more polluted areas and from continuing to reduce air pollution in cleaner areas. Implications: Ests. of the benefits of reducing PM2.5 air pollution are highly dependent upon the shape of the PM2.5-mortality concn.-response (C-R) function. Recent evidence indicates that this C-R function may be supralinear across wide ranges of exposure, suggesting that incremental pollution abatement efforts may yield greater benefits in relatively clean areas than in highly polluted areas. This paper explores the role of the shape of the C-R function in evaluating and understanding the costs and health benefits of PM2.5 air pollution abatement.
- 34Smith, K. R.; Ezzati, M. How environmental health risks change with development: The epidemiologic and environmental risk transitions revisited. Annu. Rev. Environ. Resources 2005, 30, 291– 333, DOI: 10.1146/annurev.energy.30.050504.144424
- 35Salomon, J. A.; Murray, C. J. L. The epidemiologic transition revisited: Compositional models for causes of death by age and sex. Population & Development Review 2002, 28, 205– 228, DOI: 10.1111/j.1728-4457.2002.00205.x
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