Abstract
Background: Poisson and negative binomial (NB) regression models are commonly used to assess the association between air pollution and hospital admissions for cardiovascular and respiratory diseases. This study aims to utilize the New Poisson-Generalized Lindley (NPGL) regression model to evaluate the relationship between air pollutants and daily hospital admissions for these diseases in elderly individuals.
Study design: An ecological cross-sectional study.
Methods: This study gathered data on daily air pollutant concentrations, meteorological parameters, and the number of hospitalizations for cardiovascular and respiratory patients from the Environmental Protection Organization, the General Directorate of Meteorology, Farshchian Heart Hospital, and Shahid Beheshti Hospital in Hamadan. 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 in the NB and Poisson models for most pollutants. Specifically, the relative risk (RR) for CO was calculated at (RR=1.307, 95% CI 1.270, 1.345). For each unit increase in CO concentration, cardiovascular hospitalizations increased by 30.7%. A significant and direct association was found between exposure to all pollutants, except PM2.5, and hospitalizations for respiratory diseases (P < 0.05).
Conclusion: The results demonstrate a significant association between air pollution and hospital admissions for cardiovascular and respiratory diseases among the elderly, particularly for CO. This study highlights the need for policymakers to implement health programs to mitigate the effects of air pollution on vulnerable elderly populations.