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Title: A Bayesian Multi-Outcome Analysis of Fine Particulate Matter and Cardiorespiratory Hospitalizations.

Authors: Thomas, Emma G; Braun, Danielle; Kioumourtzoglou, Marianthi-Anna; Trippa, Lorenzo; Wasfy, Jason H; Dominici, Francesca

Published In Epidemiology, (2022 Mar 01)

Abstract: BACKGROUND: Short-term fine particulate matter (PM2.5) exposure is positively associated with acute cardiovascular and respiratory events. Understanding whether this association varies across specific cardiovascular and respiratory conditions has important biologic, clinical, and public health implications. METHODS: We conducted a time-stratified case-crossover study of hospitalizations from 2000 through 2014 among United States Medicare beneficiaries aged 65+. The outcomes were hospitalizations with any of 57 cardiovascular and 32 respiratory discharge diagnoses. We estimated associations with two-day moving average PM2.5 as a piecewise linear term with a knot at PM2.5 = 25 g/m3. We used Multi-Outcome Regression with Tree-structured Shrinkage (MOReTreeS) to identify de novo groups of related diseases such that PM2.5 associations are: (1) similar within outcome groups; but (2) different between outcome groups. We adjusted for temperature, humidity, and individual-level characteristics. We introduce an R package, moretrees. RESULTS: Our dataset included 16,007,293 cardiovascular and 8,690,837 respiratory hospitalizations. Of 57 cardiovascular diseases, 51 were grouped and positively associated with PM2.5. We observed a stronger positive association for heart failure, which formed a separate group. We observed negative associations for groups containing the outcomes other aneurysm and intracranial hemorrhage. Of 32 respiratory outcomes, 31 were grouped and were positively associated with PM2.5. Influenza formed a separate group with a negative association. CONCLUSIONS: We used a new statistical approach, MOReTreeS, to uncover variation in the association between short-term PM2.5 exposure and hospitalizations for cardiovascular and respiratory causes controlling for patient characteristics, time trends, and environmental confounders.

PubMed ID: 35104259 Exiting the NIEHS site

MeSH Terms: Aged; Air Pollutants/adverse effects; Air Pollutants/analysis; Air Pollution/adverse effects; Air Pollution/analysis; Bayes Theorem; Cardiovascular Diseases*/epidemiology; Cardiovascular Diseases*/therapy; Cross-Over Studies; Environmental Exposure*/adverse effects; Environmental Exposure*/analysis; Hospitalization/statistics & numerical data; Humans; Medicare; Particulate Matter*/adverse effects; Particulate Matter*/analysis; Respiratory Tract Diseases*/epidemiology; Respiratory Tract Diseases*/therapy; United States/epidemiology

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