Long‐Term Exposure to Particulate Air Pollution Is Associated With 30‐Day Readmissions and Hospital Visits Among Patients With Heart Failure

Abstract

Background Long‐term air pollution exposure is a significant risk factor for inpatient hospital admissions in the general population. However, we lack information on whether long‐term air pollution exposure is a risk factor for hospital readmissions, particularly in individuals with elevated readmission rates. Methods and Results We determined the number of readmissions and total hospital visits (outpatient visits+emergency room visits+inpatient admissions) for 20 920 individuals with heart failure. We used quasi‐Poisson regression models to associate annual average fine particulate matter at the date of heart failure diagnosis with the number of hospital visits and 30‐day readmissions. We used inverse probability weights to balance the distribution of confounders and adjust for the competing risk of death. Models were adjusted for age, race, sex, smoking status, urbanicity, year of diagnosis, short‐term fine particulate matter exposure, comorbid disease, and socioeconomic status. A 1‐µg/m 3 increase in fine particulate matter was associated with a 9.31% increase (95% CI, 7.85%–10.8%) in total hospital visits, a 4.35% increase (95% CI, 1.12%–7.68%) in inpatient admissions, and a 14.2% increase (95% CI, 8.41%–20.2%) in 30‐day readmissions. Associations were robust to different modeling approaches. Conclusions These results highlight the potential for air pollution to play a role in hospital use, particularly hospital visits and readmissions. Given the elevated frequency of hospitalizations and readmissions among patients with heart failure, these results also represent an important insight into modifiable environmental risk factors that may improve outcomes and reduce hospital use among patients with heart failure.

Associations between PFAS occurrence and multimorbidity as observed in an electronic health record cohort

“…Data for this project came from the EPA CARES resource, a collection of electronic health records from the University of North Carolina Healthcare System that have been integrated with environmental exposure data for use in environmental health studies. 19 , 23 Patient addresses were considered successfully geocoded at the street or zip code level. For this study, we used a random sample of North Carolina residents who had an interaction with a University of North Carolina Healthcare System affiliated hospital or clinic at least once between January 1, 2004, and December 31, 2016, that generated an electronic health record.…”

“…16,17 One resource that has not been widely applied to PFAS research is electronic health records (EHRs). The use of EHRs in research has accelerated over the past decade, and they are now widely used within environmental epidemiology, particularly for the study of air quality 18,19 and social determinants of health. 20 One of the main advantages of EHRs is their broad assessment of health outcomes.…”

Associations between short-term exposure to PM2.5and cardiomyocyte injury in myocardial infarction survivors in North Carolina

“…We used air pollution exposure models with high correlation with ground-based monitoring and high spatiotemporal resolution 16. These models have been used in previous studies14 15 33 including ones of troponin23 and allowed for uniform, daily capture of exposures for the entire study period which would not have been possible with monitor or other sensor data. However, these models do not fully capture personal exposures, only ambient exposure at the residence.…”

“…The participants in this study came from the Environmental Protection Agency Clinical and Archived records Research for Environmental Studies (EPA CARES) resource which has been previously described 14 15. EPA CARES is a collection of electronic health records from the University of North Carolina Healthcare System (UNCHCS), collected via a series of queries to extract patient data sets based on specific characteristics.…”

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

“…Dr. Cynthia Rider is a Toxicologist at the National Institute of Environmental Health Sciences and a leading expert in the chemical safety and risk assessment of chemical mixtures ( Catlin et al, 2018 ; Ryan et al, 2019 ; Rider et al, 2021 ). Dr. Cavin Ward-Caviness is a Computational Biologist and Principal Investigator at the U.S. EPA, and he leads studies integrating geospatial exposure measures with molecular biomarkers and health outcome data to understand the impacts of chemical pollutants and social determinants of health ( Ward-Caviness et al, 2020 ; Martin et al, 2021 ; Ward-Caviness et al, 2021 ). Dr. David Reif is a Professor in the Department of Biological Sciences at North Carolina State University (NCSU) and Director of the NCSU Bioinformatics Consulting and Services Core.…”