Air pollution and health disparities in China : differential exposure and unequal health effect

Abstract

Severe air pollution in Chinese cities has become a global concern. Fine particulate matter (PM2.5) pollution has attracted considerable attention because of its severity and resultant detrimental effects on human health. Air pollution accelerates health disparities amongst socioeconomic groups because it may lead to increased suffering of the poor. The impoverished are more exposed and socioeconomically vulnerable to air pollution. However, research on air pollution-related health disparities (e.g. differential exposure and unequal health effect) is still in its infancy in China. This dissertation addresses three research questions. The first research question is relevant to how ignoring individual mobility and PM2.5 spatiotemporal variations produces misclassification errors in ambient PM2.5 exposure estimates. It is important to understand this question because accurate exposure estimates are significant to understand the pathway of health disparities. The impacts of ignoring individual mobility and PM2.5 spatiotemporal variations are examined using mobile phone location data and geo-informed backward propagation neural network (Geo-BPNN)-derived PM2.5 concentrations in Shenzhen. The results show that ignoring individual mobility, PM2.5 spatiotemporal variations or both leads to misclassification errors in exposure estimates. A larger misclassification error occurs in the estimate ignoring PM2.5 spatiotemporal variations than that ignoring individual mobility. The second research question addresses the first pathway of health disparities to determine whether PM2.5 exposure differs among people with different economic statuses. That is, it aims to answer who are more exposed to PM2.5 pollution. Individual total exposures during weekdays at multi-temporal scales are estimated using mobile phone location data and Geo-BPNN-derived PM2.5 concentrations in Shenzhen. Coupling the estimated PM2.5 exposure with housing price, this study examines the associations between individual- and neighbourhood-level economic statuses and individual total exposure using linear regression and two-level hierarchical linear models. The results show that positive associations exist between the two-level economic statuses and individual total exposure at a daytime, daily, weekly, monthly, seasonal or annual scale. That is, people living in areas with higher residential property prices are more exposed to PM2.5 pollution. The third research question addresses the second pathway to determine whether socioeconomic indicators modify the association between air pollution exposure and health outcome. Data collected from 295 Chinese cancer registries (county/districts) from 2006 to 2014 are used to examine socioeconomic modification effects on the association between PM2.5 exposure and annual incidence rate of lung cancer for males. The results show that there is a stronger association between PM2.5 exposure and male incidence rate in urban areas, in the lower economic or education counties. That is, male residents in urban areas, in the lower economic or education counties are faced with an enlarged effect of PM2.5 exposure on the incidence rate of lung cancer in China. The findings emphasize the need for public health intervention and urban planning initiatives targeting not only socio-economic disparities in ambient air pollution exposure, but also the urban–rural, educational or economic disparities in health effects associated with air pollution exposure, thus alleviating health disparities across socioeconomic groups. Future prediction on air pollution-associated health effects should also consider such socioeconomic disparities.