Particulate matter and premature mortality : A Bayesian meta-analysis
Journal article
Waidyatillake, Nilakshi T., Campbell, Patricia T., Vicendese, Don, Dharmage, Shyamali C., Curto, Ariadna and Stevenson, Mark. (2021). Particulate matter and premature mortality : A Bayesian meta-analysis. International Journal of Environmental Research and Public Health. 18(14), p. Article 7655. https://doi.org/10.3390/ijerph18147655
Authors | Waidyatillake, Nilakshi T., Campbell, Patricia T., Vicendese, Don, Dharmage, Shyamali C., Curto, Ariadna and Stevenson, Mark |
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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). PM2.5 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 PM2.5 was 253 per 1,000,000 population (95% CI: 90, 643) and 587 per 1,000,000 population (95% CI: 1, 39,746) for PM10. Conclusion: 253 premature deaths per million population are associated with exposure to ambient PM2.5. We observed an unstable estimate for PM10, most likely due to heterogeneity among the studies. Future research efforts should focus on the effects of ambient PM10 and premature mortality, as well as include populations outside Asia. Key messages: Ambient PM2.5 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. |
Keywords | Bayesian hierarchical meta-analysis; particulate matter; PM2.5; PM10; premature mortality |
Year | 2021 |
Journal | International Journal of Environmental Research and Public Health |
Journal citation | 18 (14), p. Article 7655 |
Publisher | Multidisciplinary Digital Publishing Institute (MDPI AG) |
ISSN | 1661-7827 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/ijerph18147655 |
PubMed ID | 34300107 |
Scopus EID | 2-s2.0-85110300870 |
PubMed Central ID | PMC8303514 |
Open access | Published as ‘gold’ (paid) open access |
Page range | 1-21 |
Funder | Melbourne Energy Institute, University of Melbourne |
National Health and Medical Research Council (NHMRC) | |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 19 Jul 2021 |
Publication process dates | |
Accepted | 14 Jul 2021 |
Deposited | 18 Oct 2023 |
Grant ID | APP1136250 |
https://acuresearchbank.acu.edu.au/item/8zw89/particulate-matter-and-premature-mortality-a-bayesian-meta-analysis
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Publisher's version
OA_Waidyatillake_2021_Particulate_matter_and_premature_mortality_A.pdf | |
License: CC BY 4.0 | |
File access level: Open |
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