Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortality
Exposure-response associations between fine particulate matter (PM2.5) and mortality have been extensively studied but potential confounding by daily minimum and maximum temperatures in the weeks preceding death has not been carefully investigated. This paper seeks to close that gap by using lagged...
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Elsevier
2024-12-01
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| Series: | Global Epidemiology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590113324000427 |
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| author | Louis Anthony Cox, Jr |
| author_facet | Louis Anthony Cox, Jr |
| author_sort | Louis Anthony Cox, Jr |
| collection | DOAJ |
| description | Exposure-response associations between fine particulate matter (PM2.5) and mortality have been extensively studied but potential confounding by daily minimum and maximum temperatures in the weeks preceding death has not been carefully investigated. This paper seeks to close that gap by using lagged partial dependence plots (PDPs), sorted by importance, to quantify how mortality risk depends on lagged values of PM2.5, daily minimum and maximum temperatures and other variables in a dataset from the Los Angeles air basin (SCAQMD). We find that daily minimum and maximum temperatures and daily mortality counts two to three weeks ago are important independent predictors of both current daily elderly mortality and current PM2.5 levels. Thus, it is important to control for these variables over a period of at least several weeks preceding death. Such detailed control for lagged confounders has not been performed in influential past papers on PM2.5-mortality associations, but appears to be essential for isolating the potential causal contributions of specific variables to mortality risk, and, therefore, a worthwhile area for future research and risk assessment modeling. |
| format | Article |
| id | doaj-art-04e3077a120a42a6ab8536a20903b94b |
| institution | Kabale University |
| issn | 2590-1133 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Global Epidemiology |
| spelling | doaj-art-04e3077a120a42a6ab8536a20903b94b2024-12-12T05:22:33ZengElsevierGlobal Epidemiology2590-11332024-12-018100176Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortalityLouis Anthony Cox, Jr0Corresponding author.; Cox Associates, Entanglement, University of Colorado at Denver, Denver, CO. USAExposure-response associations between fine particulate matter (PM2.5) and mortality have been extensively studied but potential confounding by daily minimum and maximum temperatures in the weeks preceding death has not been carefully investigated. This paper seeks to close that gap by using lagged partial dependence plots (PDPs), sorted by importance, to quantify how mortality risk depends on lagged values of PM2.5, daily minimum and maximum temperatures and other variables in a dataset from the Los Angeles air basin (SCAQMD). We find that daily minimum and maximum temperatures and daily mortality counts two to three weeks ago are important independent predictors of both current daily elderly mortality and current PM2.5 levels. Thus, it is important to control for these variables over a period of at least several weeks preceding death. Such detailed control for lagged confounders has not been performed in influential past papers on PM2.5-mortality associations, but appears to be essential for isolating the potential causal contributions of specific variables to mortality risk, and, therefore, a worthwhile area for future research and risk assessment modeling.http://www.sciencedirect.com/science/article/pii/S2590113324000427PM2.5MortalityMinimum daily temperatureConfoundingTime seriesPartial dependence plots |
| spellingShingle | Louis Anthony Cox, Jr Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortality Global Epidemiology PM2.5 Mortality Minimum daily temperature Confounding Time series Partial dependence plots |
| title | Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortality |
| title_full | Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortality |
| title_fullStr | Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortality |
| title_full_unstemmed | Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortality |
| title_short | Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortality |
| title_sort | clarifying causality and information flows between time series particulate air pollution temperature and elderly mortality |
| topic | PM2.5 Mortality Minimum daily temperature Confounding Time series Partial dependence plots |
| url | http://www.sciencedirect.com/science/article/pii/S2590113324000427 |
| work_keys_str_mv | AT louisanthonycoxjr clarifyingcausalityandinformationflowsbetweentimeseriesparticulateairpollutiontemperatureandelderlymortality |