Short-Term Effects of Air Pollution on Hospital Admissions of the Elderly with Respiratory and Cardiovascular Diseases Using a New Poisson-Generalized Lindley Model

Background: Poisson and negative binomial (NB) regression models are commonly used to investigate the association between air pollution and hospital admissions for cardiovascular and respiratory diseases. This study utilized the new Poisson-generalized Lindley (NPGL) regression model to evaluate the relationship between air pollutants and daily hospital admissions for these diseases among elderly individuals. Study Design: An ecological cross-sectional study. Methods: The data related to daily air pollutant concentrations, meteorological parameters, and the number of hospitalizations for cardiovascular and respiratory patients were gathered from the Environmental Protection Organization, the General Directorate of Meteorology, Farshchian Heart Hospital, and Shahid Beheshti Hospital in Hamadan. Then, the relationship between air pollution and daily hospital admissions was assessed using Poisson, NB, and NPGL models. Results: The findings indicated that the accuracy of the regression coefficients estimated in the NPGL model was higher than that in the NB and Poisson models for most pollutants. Specifically, the relative risk for carbon monoxide (CO) was calculated at 1.307 (95% confidence interval: 1.270–1.345). Cardiovascular hospitalization increased by 30.7% for each unit increase in CO concentration. A significant and direct association was found between exposure to all pollutants, except for PM2.5, and hospitalization for respiratory diseases (P<0.05). Conclusion: Overall, there was a significant relationship between air pollution and hospital admissions for cardiovascular diseases (CVDs) and respiratory diseases among the elderly, particularly regarding CO. This study indicates the need for policymakers to implement health programs to mitigate the effects of air pollution on vulnerable elderly populations.