Background: Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm studies. Further, an understanding of patterns in and between exposure metrics that are based on specific hazards of the storm can help in designing tropical cyclone epidemiological research. Objectives: a ) Provide an open-source data set for tropical cyclone exposure assessment for epidemiological research; and b ) investigate patterns and agreement between county-level assessments of tropical cyclone exposure based on different storm hazards. Methods: We created an open-source data set with data at the county level on exposure to four tropical cyclone hazards: peak sustained wind, rainfall, flooding, and tornadoes. The data cover all eastern U.S. counties for all land-falling or near-land Atlantic basin storms, covering 1996–2011 for all metrics and up to 1988–2018 for specific metrics. We validated measurements against other data sources and investigated patterns and agreement among binary exposure classifications based on these metrics, as well as compared them to use of distance from the storm’s track, which has been used as a proxy for exposure in some epidemiological studies. Results: Our open-source data set was typically consistent with data from other sources, and we present and discuss areas of disagreement and other caveats. Over the study period and area, tropical cyclones typically brought different hazards to different counties. Therefore, when comparing exposure assessment between different hazard-specific metrics, agreement was usually low, as it also was when comparing exposure assessment based on a distance-based proxy measurement and any of the hazard-specific metrics. Discussion: Our results provide a multihazard data set that can be leveraged for epidemiological research on tropical cyclones, as well as insights that can inform the design and analysis for tropical cyclone epidemiological research. https://doi.org/10.1289/EHP6976
Severe air pollution episodes in Europe and the USA in the early- to mid-twentieth century caused large health impacts, spurring national legislation. Similarly severe episodes currently affect developing regions, as exemplified by a particularly extreme episode in January 2013 in Beijing, China. We investigated associations between this episode and medical visits at a Beijing hospital. We obtained fine particulate matter (PM2.5) measurements from the US State Department’s Embassy monitor and daily counts of all-cause, cardiovascular, and respiratory emergency visits, and outpatient visits from a nearby hospital in the Liufang Nanli community. We analyzed whether risks increased during this episode (with daily PM2.5 ≥ 350 μg/m3) using generalized linear modeling, controlling for potential confounders. The episode brought exceptionally high PM2.5 (peak daily average, 569 μg/m3). Risk increased during the episode for all-cause (relative risk 1.29 [95% CI 1.13, 1.46]), cardiovascular (1.55 [0.90, 2.68]) and respiratory (1.33 [1.10, 1.62]) emergency medical visits, and respiratory outpatient visits (1.16 [1.00, 1.33]). Relative risks of all-cause (0.95 [0.82, 1.10]) and cardiovascular (0.83 [0.67, 1.02]) outpatient visits were not statistically significant. Results were robust to modeling choices and episode definitions. This episode was extraordinarily severe, with maximum daily PM2.5 concentration nearly 22-fold above the World Health Organization guideline. During the episode, risk increased for all-cause, cardiovascular, and respiratory emergency medical visits, and respiratory outpatient visits, consistent with previous US-based research. However, no association was found for all-cause or cardiovascular outpatient visits. China-based studies like this one provide critical evidence in developing efforts regarding air pollution remediation in China.
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