Mortality burden attributable to long-term ambient PM 2.5 exposure in China: using novel exposure-response functions with multiple exposure windows
Atmospheric Environment, ISSN: 1352-2310, Vol: 246, Page: 118098
2021
- 27Citations
- 43Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Exposure to ambient fine particulate matter (PM 2.5 ) increases the mortality burden. Exposure windows and exposure-response functions (ERFs) are two critical components of accurate mortality burden estimation. We explored the potential heterogeneity of exposure windows and reassessed the PM 2.5 -attributable mortality burden in China with novel ERFs. Based on 1 km × 1 km satellite-retrieved PM 2.5 and population data, provincial-level age structure, and mortality data, we applied the recent Global Exposure Mortality Model (GEMM) with multiple exposure windows (1-year to 6-year during 2010–2015) to estimate age-specific PM 2.5 -attributable mortality burden in China in 2015. Then, the Global Burden of Disease (GBD) 2017 Integrated Exposure-Response (IER) and Log-Linear (LL) models were exercised for comparative analysis. The PM 2.5 -attributable mortality was the highest with a 3-year average exposure window (2013–2015). The GEMM-based total premature deaths were 133.2% [95% confidence interval (95% CI): 93.6%–226.4%] and 14.2% (95% CI: 13.9%–16.8%) higher than the values obtained from the GBD2017 IER model and LL model, respectively. The national mortality burden attributable to PM 2.5 was 1.94 (95% CI: 1.63–2.23) million, of which IHD and stroke were the leading causes, accounting for 27.3% and 23.0% of the total burden respectively. The mortality burden for the people over 80 years old was 0.62 (95%CI: 0.52–0.71) million, accounting for 31.9% (95%CI: 31.8%–32.0%) of the total burden. This study demonstrates the potential heterogeneity of PM 2.5 -attributable mortality burden associated with different exposure windows, especially when there are spatial-temporal variations in PM 2.5 concentrations. The model comparison results suggest that the health impacts attributed to long-term PM 2.5 exposure in China may be much higher than previously estimated. The population over 80 years old has the highest PM 2.5 -attributable mortality burden. These findings have important policy implications for addressing air pollution at the provincial and national level in China.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S135223102030830X; http://dx.doi.org/10.1016/j.atmosenv.2020.118098; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85096984931&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S135223102030830X; https://api.elsevier.com/content/article/PII:S135223102030830X?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S135223102030830X?httpAccept=text/plain; https://dul.usage.elsevier.com/doi/; https://dx.doi.org/10.1016/j.atmosenv.2020.118098
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know