Spatio-Temporal Analysis and Prediction by Logistic Regression of Respiratory Diseases in India

Abstract

Respiratory illnesses rank among the top causes of death and disability in India, influenced by factors such as limited healthcare access, air pollution, smoking, allergens, and a lack of awareness. Despite government efforts to improve respiratory health policies, increase awareness, enhance healthcare facilities, and promote preventive measures, the incidence of respiratory diseases has been on the rise in recent years. This study uses Geographic Information System (GIS) technology to analyze the spatial and temporal distribution patterns of respiratory diseases, aiming to improve our understanding of the contributing factors. Principal component extraction and spatial statistical analyses were utilized to identify the main respiratory illnesses and their geographical distribution. The study concentrated on three major respiratory diseases Tuberculosis, Pneumonia, and Acute Respiratory Distress Syndrome (ARDS) which are related to each other diseases. The findings shows significant variations in the geographical distribution of these diseases across the time period 2019-2021. This spatio-temporal data is essential for enhancing current prevention, control, and treatment strategies for respiratory illnesses in the study area. The methodology applied in this study can be adapted to other regions with similar geographical characteristics and patient data. The study investigated the association of 14 variables with respiratory illnesses. The results indicate that certain variables are associated with an increased risk of frequent flare-ups and hospital admissions due to respiratory diseases. Furthermore, the severity of flare-ups leading to hospital admissions is significantly linked to the presence of comorbidities. These critical and easily measurable variables provide valuable insights for the optimal management of ambulatory patients with respiratory diseases.