Logistic Regression between Air Pollution and Mortality from Respiratory Diseases in Santa Gertrudes, SP (2014-2023)

Abstract

Air pollution is a critical environmental problem that affects human health in many regions around the world. In cities with intense industrial activity, such as Santa Gertrudes, SP, the concentration of particulate matter, such as PM10, can have significant impacts on the respiratory health of the population. This study aims, using advanced statical modeling, to investigate the relationship between PM₁₀ concentration and mortality from respiratory diseases. The main objective was to apply generalized linear models, specifically logistic regression, to analyze how the concentration of PM₁₀ influences the mortality of patients hospitalized for respiratory diseases in the city of Santa Gertrudes during the period from 2014 to 2023. The methodology involved building and comparing several logistic models to assess the effect of PM₁₀ and additional variables on mortality, such as relative humidity and wind speed. The quality of the models was assessed using the Akaike information criterion (AIC) and deviance. The results showed that PM₁₀lag₄ and “days of hospitalization” were the most statistically significant variables. The odds ratio associated with PM₁₀lag₄ indicated an increase of approximately 0.8% in the chance of death for each 1 µg/m³ increase in PM₁₀ concentration. The variable “days of hospitalization” increased the chance of death by around 6.7% per additional day of hospitalization, highlighting the need for public policies aimed at reducing pollution and properly managing health conditions in the region.