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      A global perspective on coal-fired power plants and burden of lung cancer

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          Abstract

          Background

          Exposure to ambient particulate matter generated from coal-fired power plants induces long-term health consequences. However, epidemiologic studies have not yet focused on attributing these health burdens specifically to energy consumption, impeding targeted intervention policies. We hypothesize that the generating capacity of coal-fired power plants may be associated with lung cancer incidence at the national level.

          Methods

          Age- and sex-adjusted lung cancer incidence from every country with electrical plants using coal as primary energy supply were followed from 2000 to 2016. We applied a Poisson regression longitudinal model, fitted using generalized estimating equations, to estimate the association between lung cancer incidence and per capita coal capacity, adjusting for various behavioral and demographic determinants and lag periods.

          Results

          The average coal capacity increased by 1.43 times from 16.01 gigawatts (GW) (2000~2004) to 22.82 GW (2010~2016). With 1 kW (KW) increase of coal capacity per person in a country, the relative risk of lung cancer increases by a factor of 59% (95% CI = 7.0%~ 135%) among males and 85% (95% CI = 22%~ 182%) among females. Based on the model, we estimate a total of 1.37 (range = 1.34 ~ 1.40) million standardized incident cases from lung cancer will be associated with coal-fired power plants in 2025.

          Conclusions

          These analyses suggest an association between lung cancer incidence and increased reliance on coal for energy generation. Such data may be helpful in addressing a key policy question about the externality costs and estimates of the global disease burden from preventable lung cancer attributable to coal-fired power plants at the national level.

          Electronic supplementary material

          The online version of this article (10.1186/s12940-019-0448-8) contains supplementary material, which is available to authorized users.

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          Most cited references36

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          Fine-particulate air pollution and life expectancy in the United States.

          Exposure to fine-particulate air pollution has been associated with increased morbidity and mortality, suggesting that sustained reductions in pollution exposure should result in improved life expectancy. This study directly evaluated the changes in life expectancy associated with differential changes in fine particulate air pollution that occurred in the United States during the 1980s and 1990s. We compiled data on life expectancy, socioeconomic status, and demographic 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 estimate the association between reductions in pollution and changes in life expectancy, with adjustment for changes in socioeconomic and demographic variables and in proxy indicators for the prevalence of cigarette smoking. A decrease of 10 microg per cubic meter in the concentration of fine particulate matter was associated with an estimated increase in mean (+/-SE) life expectancy of 0.61+/-0.20 year (P=0.004). The estimated effect of reduced exposure to pollution on life expectancy was not highly sensitive to adjustment for changes in socioeconomic, demographic, or proxy variables for the prevalence of smoking or to the restriction of observations to relatively large counties. Reductions in air pollution accounted for as much as 15% of the overall increase in life expectancy in the study areas. A reduction in exposure to ambient fine-particulate air pollution contributed to significant and measurable improvements in life expectancy in the United States. 2009 Massachusetts Medical Society
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            Akaike's information criterion in generalized estimating equations.

            W. Pan (2001)
            Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi-likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.
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              An association between air pollution and mortality in six U.S. cities.

              Recent studies have reported associations between particulate air pollution and daily mortality rates. Population-based, cross-sectional studies of metropolitan areas in the United States have also found associations between particulate air pollution and annual mortality rates, but these studies have been criticized, in part because they did not directly control for cigarette smoking and other health risks. In this prospective cohort study, we estimated the effects of air pollution on mortality, while controlling for individual risk factors. Survival analysis, including Cox proportional-hazards regression modeling, was conducted with data from a 14-to-16-year mortality follow-up of 8111 adults in six U.S. cities. Mortality rates were most strongly associated with cigarette smoking. After adjusting for smoking and other risk factors, we observed statistically significant and robust associations between air pollution and mortality. The adjusted mortality-rate ratio for the most polluted of the cities as compared with the least polluted was 1.26 (95 percent confidence interval, 1.08 to 1.47). Air pollution was positively associated with death from lung cancer and cardiopulmonary disease but not with death from other causes considered together. Mortality was most strongly associated with air pollution with fine particulates, including sulfates. Although the effects of other, unmeasured risk factors cannot be excluded with certainty, these results suggest that fine-particulate air pollution, or a more complex pollution mixture associated with fine particulate matter, contributes to excess mortality in certain U.S. cities.
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                Author and article information

                Contributors
                (+1)857-233-6146 , chl309@mail.harvard.edu
                Journal
                Environ Health
                Environ Health
                Environmental Health
                BioMed Central (London )
                1476-069X
                28 January 2019
                28 January 2019
                2019
                : 18
                : 9
                Affiliations
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Environmental Health, , Harvard T.H. Chan School of Public Health, ; 665 Huntington Avenue, Building 1, Room 1401, Boston, MA 02115 USA
                [2 ]ISNI 0000 0001 0083 6092, GRID grid.254145.3, Department of Occupational Safety and Health, , China Medical University, ; 91 Hsueh-Shih Road, Taichung, 40402 Taiwan
                [3 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Biostatistics, , Harvard T.H. Chan School of Public Health, ; 655 Huntington Avenue, Building 2, 4th Floor, Boston, MA 02115 USA
                [4 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Epidemiology, , Harvard T.H. Chan School of Public Health, ; 665 Huntington Avenue, Building 1, Room 1401, Boston, MA 02115 USA
                Author information
                http://orcid.org/0000-0002-3124-9402
                Article
                448
                10.1186/s12940-019-0448-8
                6350330
                30691464
                97ed12d5-6b68-4b23-af81-504b299873a9
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 30 July 2018
                : 11 January 2019
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                Public health
                coal capacity,coal-fired power plants,energy matrix,environmental factor,global burden disease,lung cancer incidence,pm2.5

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