Poisson-type Multivariate Transfer Function Model Reveals Short-term Effects of Ambient Air Pollutants on Hospital Emergency room Visits for Cerebro-cardiovascular Diseases

TL;DR

This study uses a Poisson-type multivariate transfer function model to quantify the short-term effects of ambient air pollutants on hospital emergency visits for cerebro-cardiovascular diseases in Beijing, revealing specific lag times and risk ratios.

Contribution

It introduces the application of a Poisson-type multivariate transfer function model to analyze the impact of air pollutants on health outcomes, offering advantages over traditional regression methods.

Findings

SO2, NO2, and PM10 increase hospital visits with specific risk ratios.

Lags for pollutants are 2 days for SO2, 1 day for NO2 and PM10.

Temperature and humidity do not significantly affect hospital visit counts.

Abstract

Laboratory experiments have shown that cardiovascular diseases are positively correlated to the concentration of ambient air pollutants, such as SO2, NO2, PM10, etc. It has also been repeatedly reported in many countries that increased concentration of ambient air pollutants leads to rise in hospital emergency room visitss for these diseases. These studies mainly adopt either regression analysis or preliminary models in time series analysis, while the multivariable transfer function model, a relatively newly developed model, has multiple advantages over the conventional linear regression on analyzing time series. This study attempts to quantify the association between concentrations ambient air pollutants and hospital emergency room visitss for cerebro-cardiovascular diseases in Beijing using a Poisson-type multivariate transfer function model. The results show that the RR values of SO2, NO2 and PM10 for a 50 g/m3 increase are 1.129, 1.092 and 1.069 respectively. The lags for the three pollutants are estimated to be 2 days, 1 day and 1 day respectively. Compared with the ambient pollutants, daily average temperature and relative humidity do not influence the daily count of hospital emergency room visits significantly.