Regional Estimates of Cardiovascular and Pulmonary Mortality Associations with Atmospheric Particulate Matter in Thailand

Abstract

P-060 Introduction: Several epidemiological studies have confirmed the associations and estimated the risk of cardiovascular and pulmonary mortality due to exposure to PM10. However the effect due to the error introduced by techniques previously used to interpolate PM10 and the effect of other gaseous pollutants in characterizing the strength of the associations remain in question. Therefore we incorporate the Bayesian Maximum Entropy (BME) method of modern spatiotemporal Geostatistics in the exposure assessment analysis to investigate the short term effect that daily PM10 exposure has on daily cardiovascular and pulmonary mortality in different regions of Thailand. Methods: We obtained from Thai agencies measurements of hourly PM10 and related co-pollutant concentrations collected from 1998 to 2003 at 51 monitoring stations across Thailand, corresponding to a total of 1,899,331 air pollutant measurements. Similarly mortality counts classified by cause of death (ICD10) were obtained and aggregated daily for 925 district areas. The BME method was used to construct accurate spatiotemporal maps of PM10 and co-pollutants that rigorously take into account the uncertainty associated with monitoring measurements as well as the natural space/time variability of the air pollutants. These maps provided the most accurate estimate to date of the PM10 exposure for each district location and day for which cardiopulmonary mortality was reported. The strength of the associations were then investigated through a case-crossover design and using multivariate conditional logistic regression to control for the potential confounding effect of co-pollutants. Results: Spatiotemporal BME exposure maps showed considerable variability of Thailand air pollutant fields. The observed associations were stronger for pulmonary mortality than for cardio mortality. The positive pulmonary mortality association to PM10 was detected in most regions of Thailand. High odds ratios were observed in central part of Thailand and in Bangkok neighbourhood. The pattern of delayed effect was observed when PM10 of previous day lags was regressed in the model. Discussion and Conclusions: The more pronounced positive associations found in the central Thailand were possibly due to industrialization, urbanization, and traffic emission sources while the positive associations found in other areas could be related to sources from agricultural biomass burning, forest fire, wind-blown dust, and sea spray. A previous study has shown that risk reported in a single city study may be biased. Our work overcomes this problem by doing a rigorous analysis of variations across both space and time in Thailand, and this analysis can be applied to other countries.