Modeling mortality fluctuations in Los Angeles as functions of pollution and weather effects

Abstract

Linear and nonlinear models are used to investigate possible associations between mortality and pollution and weather effects in Los Angeles County. State-space modeling and time and frequency domain regressions are used to modify the data base and to isolate significant weather factors and pollutants associated with increased daily mortality. Nonparametric and parametric regression methods are used to develop nonlinear dose-response profiles relating mortality to temperature and to the statistically significant pollutants. A parametric nonlinear time series model involving linear and squared terms in temperature and the logarithm of pollution provides a reasonable predictive model.