Particulate Air Pollution and Daily Mortality in Kathmandu Valley,Nepal: Associations and Distributed Lag

摘要

The distributed lag effect of ambient particulate air pollution that can be attributed to all cause mortality inKathmandu valley, Nepal is estimated through generalized linear model (GLM) and generalized additive model (GAM)with autoregressive count dependent variable. Models are based upon daily time series data on mortality collected fromthe leading hospitals and exposure collected from the 6 six strategically dispersed fixed stations within the valley. Thedistributed lag effect is estimated by assigning appropriate weights governed by a mathematical model in which weightsincreased initially and decreased later forming a long tail. A comparative assessment revealed that autoregressive semiparametricGAM is a better fit compared to autoregressive GLM. Model fitting with autoregressive semi-parametric GAMshowed that a 10 μg m-3 rise in PM10 is associated with 2.57 % increase in all cause mortality accounted for 20 days lageffect which is about 2.3 times higher than observed for one day lag and demonstrates the existence of extended lag effectof ambient PM10 on all cause deaths. The confounding variables included in the model were parametric effects of seasonaldifferences measured by Fourier series terms, lag effect of mortality, and nonparametric effect of temperature representedby loess smoothing. The lag effects of ambient PM10 remained constant beyond 20 days.