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A Simulation-Based Assessment of Alternative Explanations for Apparent Confounding in “PM Decomposition” Studies
https://orcid.org/http://orcid.org/0000-0001-6418-5188
Determining whether there is a causal link between chronic exposure to PM2.5 (atmospheric particulate matter with a diameter of 2.5 µm or less) and mortality is a fundamental challenge in determining appropriate air quality standards for PM2.5. While numerous epidemiological studies have found a statistical association between PM2.5 and mortality, unmeasured confounding in this relationship remains a concern. Several recent studies have examined the possibility of unmeasured confounding of the long-term association between PM2.5 exposure and mortality by decomposing PM2.5 into two orthogonal components — a “global” component that measures the national trend in pollution, and a “local” component that measures the local trend in pollution after controlling for the national trend. Generally, these “PM decomposition” studies find that while PM2.5 and mortality are trending downward over time at the national level, areas with steeper declines in PM2.5 do not have correspondingly steep declines in mortality. This finding is consistent with the long-term relationship between PM2.5 and mortality being confounded by some other, unmeasured, long-term trends. However, alternative explanations for these findings have been proposed under which there is still a causal link between PM2.5 and mortality. This study conducts simulation-based tests of four of these proposed alternative explanations — the omission of spatial variation in PM2.5 and mortality, confounding at the local level, measurement error, and an association between PM2.5 and mortality at a different time scale than those tested in the PM decomposition studies. We find that none of these alternative explanations can reproduce the results in the PM decomposition studies while simultaneously allowing for a causal link between PM2.5 and mortality.
A Simulation-Based Assessment of Alternative Explanations for Apparent Confounding in “PM Decomposition” Studies
https://orcid.org/http://orcid.org/0000-0001-6418-5188
Determining whether there is a causal link between chronic exposure to PM2.5 (atmospheric particulate matter with a diameter of 2.5 µm or less) and mortality is a fundamental challenge in determining appropriate air quality standards for PM2.5. While numerous epidemiological studies have found a statistical association between PM2.5 and mortality, unmeasured confounding in this relationship remains a concern. Several recent studies have examined the possibility of unmeasured confounding of the long-term association between PM2.5 exposure and mortality by decomposing PM2.5 into two orthogonal components — a “global” component that measures the national trend in pollution, and a “local” component that measures the local trend in pollution after controlling for the national trend. Generally, these “PM decomposition” studies find that while PM2.5 and mortality are trending downward over time at the national level, areas with steeper declines in PM2.5 do not have correspondingly steep declines in mortality. This finding is consistent with the long-term relationship between PM2.5 and mortality being confounded by some other, unmeasured, long-term trends. However, alternative explanations for these findings have been proposed under which there is still a causal link between PM2.5 and mortality. This study conducts simulation-based tests of four of these proposed alternative explanations — the omission of spatial variation in PM2.5 and mortality, confounding at the local level, measurement error, and an association between PM2.5 and mortality at a different time scale than those tested in the PM decomposition studies. We find that none of these alternative explanations can reproduce the results in the PM decomposition studies while simultaneously allowing for a causal link between PM2.5 and mortality.
A Simulation-Based Assessment of Alternative Explanations for Apparent Confounding in “PM Decomposition” Studies
https://orcid.org/http://orcid.org/0000-0001-6418-5188
Determining whether there is a causal link between chronic exposure to PM2.5 (atmospheric particulate matter with a diameter of 2.5 µm or less) and mortality is a fundamental challenge in determining appropriate air quality standards for PM2.5. While numerous epidemiological studies have found a statistical association between PM2.5 and mortality, unmeasured confounding in this relationship remains a concern. Several recent studies have examined the possibility of unmeasured confounding of the long-term association between PM2.5 exposure and mortality by decomposing PM2.5 into two orthogonal components — a “global” component that measures the national trend in pollution, and a “local” component that measures the local trend in pollution after controlling for the national trend. Generally, these “PM decomposition” studies find that while PM2.5 and mortality are trending downward over time at the national level, areas with steeper declines in PM2.5 do not have correspondingly steep declines in mortality. This finding is consistent with the long-term relationship between PM2.5 and mortality being confounded by some other, unmeasured, long-term trends. However, alternative explanations for these findings have been proposed under which there is still a causal link between PM2.5 and mortality. This study conducts simulation-based tests of four of these proposed alternative explanations — the omission of spatial variation in PM2.5 and mortality, confounding at the local level, measurement error, and an association between PM2.5 and mortality at a different time scale than those tested in the PM decomposition studies. We find that none of these alternative explanations can reproduce the results in the PM decomposition studies while simultaneously allowing for a causal link between PM2.5 and mortality.
A Simulation-Based Assessment of Alternative Explanations for Apparent Confounding in “PM Decomposition” Studies
Environ Model Assess
Chang, Wonjun (author) / Glasgow, Garrett (author) / Ramkrishnan, Bharat (author) / Smith, Anne E. (author)
Environmental Modeling & Assessment ; 27 ; 665-692
2022-08-01
28 pages
Article (Journal)
Electronic Resource
English
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