Multinomial logistic model for prediction of epidemiological indicators among urban dwellers exposed to outdoor air pollution

Abstract

Human exposure to environmental contaminants is unprecedented. World Health Organization has highlighted environmental disease burden a major health risk for developing countries. In the successive years (2012-14), two major hospitals in Rawalpindi city have witnessed rapid increase in air borne health anomalies among population such as upper respiratory tract infections, chest congestion, allergic response etc. Human exposure to air pollutants in urban environment cause considerable disease burden however, a scientific assessment is necessary to attribute health risks. Based on this premise, this study was conducted in urban area of Rawalpindi city with an aim to analyze population exposure to dust and particulate matter (PM) in relation to their vulnerability for disease susceptibility. Data from hospital records as well as through questionnaire-based survey was collected about exposure estimates, work environment, disease history, socio-demographic aspects and health risk type. The observed population (males=452 and females=128) of adult ages had numerous exposure durations ranging from 4 to 12 h day-1. Chi square test revealed ‘age’ and ‘occupation’ significant but ‘gender’ inconsistent with respiratory symptoms. A Cronbach’s alpha value of 0.74 was maintained for reliability of health variables. Multinomial logistic-regression model showed ‘shortness of breath’ (β= 2.62; odds ratio=13.8; 95% CI = 3.63-52.41) has highest risk factor followed by ‘eye redness’ (β= 1.14; odds ratio=3.1; 95% CI =1.55-6.30). Overall a direct relationship between exposure to dust and PM with population illness was observed especially during construction of Rawalpindi Metro Bus Project in 2014. We conclude that degradation of environmental health has strong negative impact on general urban population that has not only lowered their functional capacity but also placed them at higher risk category. To overcome this challenge, prevention of human exposure to air pollution can be an effective intervention measure especially for people who are more vulnerable.