Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities
Language English Country Great Britain, England Media print
Document type Journal Article, Research Support, Non-U.S. Gov't, Comparative Study, Research Support, N.I.H., Extramural
Grant support
DP210102076
Australian Research Council
2015916
Australian Medical Research Future Fund
PubMed
38725299
PubMed Central
PMC11082424
DOI
10.1093/ije/dyae066
PII: 7667947
Knihovny.cz E-resources
- Keywords
- Short-term exposure, air monitoring station observation, fine particulate matter, model estimation, mortality risk comparison,
- MeSH
- Adult MeSH
- Cardiovascular Diseases * mortality MeSH
- Air Pollutants * adverse effects analysis MeSH
- Middle Aged MeSH
- Humans MeSH
- Environmental Monitoring methods MeSH
- Mortality trends MeSH
- Respiratory Tract Diseases mortality MeSH
- Particulate Matter * adverse effects analysis MeSH
- Aged MeSH
- Machine Learning MeSH
- Cities * epidemiology MeSH
- Environmental Exposure * adverse effects MeSH
- Air Pollution * adverse effects analysis MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Comparative Study MeSH
- Geographicals
- Cities * epidemiology MeSH
- Names of Substances
- Air Pollutants * MeSH
- Particulate Matter * MeSH
BACKGROUND: Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures. METHODS: We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach. RESULTS: We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-μg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively. CONCLUSIONS: Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.
Department of Environmental Health Harvard T H Chan School of Public Health Boston MA USA
Department of Environmental Health Instituto Nacional de Saúde Dr Ricardo Jorge Porto Portugal
Department of Environmental Health School of Public Health Fudan University Shanghai China
Department of Epidemiology Instituto Nacional de Saúde Dr Ricardo Jorge Lisboa Portugal
EPIUnit Instituto de Saúde Pública Universidade do Porto Porto Portugal
Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic
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