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Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis

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  • Nilakshi T. Waidyatillake

    (Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
    Department of Medical Education, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia)

  • Patricia T. Campbell

    (Department of Infectious Diseases, Melbourne Medical School, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia)

  • Don Vicendese

    (Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
    Department of Mathematics and Statistics, La Trobe University, Bundoora, VIC 3086, Australia)

  • Shyamali C. Dharmage

    (Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia)

  • Ariadna Curto

    (Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia)

  • Mark Stevenson

    (Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
    Transport Health and Urban Design Research Lab, Melbourne School of Design, The University of Melbourne, Melbourne, VIC 3010, Australia)

Abstract

Background: We present a systematic review of studies assessing the association between ambient particulate matter (PM) and premature mortality and the results of a Bayesian hierarchical meta-analysis while accounting for population differences of the included studies. Methods: The review protocol was registered in the PROSPERO systematic review registry. Medline, CINAHL and Global Health databases were systematically searched. Bayesian hierarchical meta-analysis was conducted using a non-informative prior to assess whether the regression coefficients differed across observations due to the heterogeneity among studies. Results: We identified 3248 records for title and abstract review, of which 309 underwent full text screening. Thirty-six studies were included, based on the inclusion criteria. Most of the studies were from China ( n = 14), India ( n = 6) and the USA ( n = 3). PM 2.5 was the most frequently reported pollutant. PM was estimated using modelling techniques (22 studies), satellite-based measures (four studies) and direct measurements (ten studies). Mortality data were sourced from country-specific mortality statistics for 17 studies, Global Burden of Disease data for 16 studies, WHO data for two studies and life tables for one study. Sixteen studies were included in the Bayesian hierarchical meta-analysis. The meta-analysis revealed that the annual estimate of premature mortality attributed to PM 2.5 was 253 per 1,000,000 population (95% CI: 90, 643) and 587 per 1,000,000 population (95% CI: 1, 39,746) for PM 10 . Conclusion: 253 premature deaths per million population are associated with exposure to ambient PM 2.5 . We observed an unstable estimate for PM 10 , most likely due to heterogeneity among the studies. Future research efforts should focus on the effects of ambient PM 10 and premature mortality, as well as include populations outside Asia. Key messages: Ambient PM 2.5 is associated with premature mortality. Given that rapid urbanization may increase this burden in the coming decades, our study highlights the urgency of implementing air pollution mitigation strategies to reduce the risk to population and planetary health.

Suggested Citation

  • Nilakshi T. Waidyatillake & Patricia T. Campbell & Don Vicendese & Shyamali C. Dharmage & Ariadna Curto & Mark Stevenson, 2021. "Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis," IJERPH, MDPI, vol. 18(14), pages 1-21, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7655-:d:596816
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    References listed on IDEAS

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