Combining Weather and Pollution Indicators with Insurance Claims for Identifying and Predicting Asthma Prevalence and Hospitalizations

Abstract

This study examines how particular risk factors, including weather, age, gender, and environmental exposures, affect asthma prevalence and hospitalization rates in southwestern Pennsylvania, the 5^th most populous state in the U.S.A, located in the northeastern part of the country. This research uses novel machine learning approaches, advanced programming and data analysis techniques with medical trends and patient outcomes, highlighting the applications of statistical computing in human healthcare. This study examines how outcomes such as asthma prevalence, hospitalizations, acute length of stay, emergency department visits, and readmission rates correlate with clinical history and seasonal daily environmental data for particular age groups and gender. This study found that asthma prevalence, asthma-related hospitalizations, emergency department visits, and hospital readmissions negatively correlate with temperature and the presence of PM2.5 and positively correlate with NO2.