Revisiting the estimations of PM2.5-attributable mortality with advancements in PM2.5 mapping and mortality statistics

Abstract

With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM_2.5) and mortality statistics are increasingly used to examine the health effects of PM_2.5. Choices of these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM_2.5-attributable mortality by taking advantage of recent advancements in high resolution mapping of PM_2.5concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM_2.5-mortality for the years of 2000, 2005, 2010 and 2015 using three PM_2.5 concentration datasets [Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR)] at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM_2.5-mortality from the 10 km CTM-derived PM_2.5 dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM_2.5 datasets. The estimated PM_2.5-mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM_2.5 concentrations reduction, the uses of the two newly developed 1 km resolution PM_2.5 datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM_2.5 concentration is the primary factor that leads to the PM_2.5-attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM_2.5-attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. CAPSULE: We revisited the estimations of PM_2.5-attributable mortality with advancements in PM_2.5 mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations.

Keywords: GIS; Geospatial uncertainty; Mortality; PM(2.5); Public health.