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标题：Estimates of the Global Burden of Ambient [... formula ...], Ozone, and [... formula ...] on Asthma Incidence and Emergency Room Visits
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作者：Susan C. Anenberg ; Daven K. Henze ; Veronica Tinney 等
期刊名称：Environmental Health Perspectives
印刷版ISSN：0091-6765
电子版ISSN：1552-9924
出版年度：2018
卷号：126
期号：10
页码：107004
DOI：10.1289/EHP3766
出版社：OCR Subscription Services Inc
摘要：Background: Asthma is the most prevalent chronic respiratory disease worldwide, affecting 358 million people in 2015. Ambient air pollution exacerbates asthma among populations around the world and may also contribute to new-onset asthma. Objectives: We aimed to estimate the number of asthma emergency room visits and new onset asthma cases globally attributable to fine particulate matter ( PM 2.5 ), ozone, and nitrogen dioxide ( NO 2 ) concentrations. Methods: We used epidemiological health impact functions combined with data describing population, baseline asthma incidence and prevalence, and pollutant concentrations. We constructed a new dataset of national and regional emergency room visit rates among people with asthma using published survey data. Results: We estimated that 9–23 million and 5–10 million annual asthma emergency room visits globally in 2015 could be attributable to ozone and PM 2.5 , respectively, representing 8–20% and 4–9% of the annual number of global visits, respectively. The range reflects the application of central risk estimates from different epidemiological meta-analyses. Anthropogenic emissions were responsible for ∼ 37 % and 73% of ozone and PM 2.5 impacts, respectively. Remaining impacts were attributable to naturally occurring ozone precursor emissions (e.g., from vegetation, lightning) and PM 2.5 (e.g., dust, sea salt), though several of these sources are also influenced by humans. The largest impacts were estimated in China and India. Conclusions: These findings estimate the magnitude of the global asthma burden that could be avoided by reducing ambient air pollution. We also identified key uncertainties and data limitations to be addressed to enable refined estimation. https://doi.org/10.1289/EHP3766