Author : Jennifer Badger
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)
Book Synopsis The Geography of Long Term Exposure to Particulate Matter 2.5 and COVID-19 Mortality; An Assessment of the Fragility and Spatial Sensitivity of a Significant Finding by : Jennifer Badger
Download or read book The Geography of Long Term Exposure to Particulate Matter 2.5 and COVID-19 Mortality; An Assessment of the Fragility and Spatial Sensitivity of a Significant Finding written by Jennifer Badger and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Air pollution is directly linked to death. In December 2020, a UK coroner ruled that air pollution was the cause of a fatal asthma attack that led to the 2013 death of nine-year-old Ella Adoo-Kissi Debrah who lived adjacent to a busy motorway (BBC News, 2022). The assignment of air pollution as the official cause of death on a death certificate was the first of its kind in the world (Reynolds, 2020). Though this was the first official assignment of air pollution as a cause of death, there are numerous studies linking air pollution exposure with mortality all over the world. Before the COVID-19 pandemic, the air pollutant PM 2.5 was identified as the "largest environmental risk factor in the United States" (Goodkind et al. 2019, p. 8780) and the cause of more annual premature deaths than traffic accidents and homicides combined (Goodkind et al. 2019). With the onset of the COVID-19 pandemic, researchers began assessing the impact of air pollution exposure on COVID-19 incidence and death. In a widely received, nationwide study linking air pollution exposure to COVID-19 mortality, Harvard T.H. Chan School of Public Health researchers, Wu et al., produced significant findings linking the impact of long term exposure to PM 2.5 to COVID-19 mortality across the contiguous United States. This 2020 study, published in ScienceAdvances, has been cited over 600 times, covered by 131 news outlets and downloaded over 15,000 times. Georeferenced data is routinely used in public health research such as this, however, the substantive influence of geography in the relationship between the treatment and outcome variable is often not considered in the model specifications, research design, nor the sampling strategy (Goldhagen et al., 2005; Matisziw, Grubesic, and Wei 2008). Additionally, the mechanism of data aggregation to an administrative unit may spatially misrepresent the data (Delmelle et al., 2022). As air pollution is a local, regional, and transboundary phenomenon (Nordenstam et. al, 1998; Goodkind, 2019), spatial autocorrelation, or spatially similar values, in the long term exposure to PM 2.5 among U.S. counties is likely. Despite the inclusion of maps indicating strong spatial trends in the long term exposure to PM 2.5 and COVID-19 mortality, the possible presence of spatial autocorrelation at the local level or spatial heterogeneity at the regional level was not investigated by the authors. Epidemiological studies invoking large, areal units may misrepresent the underlying, spatial processes of environmental health-hazards and produce unreliable treatment effect estimates when relating air pollution exposure to disease (Fotheringham and Wong, 1991; Kolak and Anselin, 2019). In this thesis, the fragility of the Wu et al. treatment effect estimate to unobserved confounding is assessed utilizing an alternative sensitivity analysis framework. This framework revealed that the estimate derived by Wu et al. (2020) is much more fragile to confounding than reported by the authors. Spatial analysis was then applied to investigate the possibility of spatial regimes (e.g. hotspots) in the treatment and outcome variables which may contribute to biased or inefficient treatment effect estimates. Strong levels of spatial autocorrelation and regional spatial heterogeneity in the long term exposure to PM 2.5, and to a lesser extent in the COVID-19 mortality rate, were confirmed by both computational and exploratory spatial data analysis. The highly variable associations between long term exposure to PM 2.5 and COVID-19 Mortality per U.S. Census Region or EPA Climatically Consistent Region delivered the expected result that the relationship between the treatment and outcome variable changes with changes in the sub-National definition of place. An understanding of the geography of the ubiquitous, locally variable and far-reaching PM 2.5, and its related health-hazard risks can contribute to an uncovering of the politics, power relations, and socioenvironments that coproduce differential access to clean air and the resulting uneven health burdens experienced by Black, LatinX, Asian-American, and immigrant communities. This is an essential step towards disentangling the relationships rendering clean air no longer an "open-access good" (V ron, 2006).
