Air Pollution Effects on Daily Clinic Visits for Lower Respiratory Illness

Abstract

We use complete clinic visit records and environmental monitoring data at 50 city districts and townships in Taiwan during the year 1998, to estimate the association between air pollution and daily clinic visits for lower respiratory illness by using a small area design and hierarchical modeling. A classical method was applied to estimate population at risk so that the daily clinic visit counts can be converted to daily rates of lower respiratory illness for each area. The temporal and spatial patterns of these multiple time series of rates are modeled in two phases. The results show that daily clinic visit rates were associated with current-day concentrations of nitrogen dioxides, carbon monoxide, sulfur dioxides and particulate matter with a diameter less than 10. People aged over 65 were most susceptible and estimated pollution effects decreased as exposure time lag increased. The analysis also suggested that several community-specific variables modified the effects of air pollution, such as community's population density and yearly air pollution levels. This paper demonstrates the use of a small area design and Bayesian hierarchical modeling technique to assess acute health effects of air pollution.