title: Testing the harvesting hypothesis by time-domain regression analysis. II: covariate effects.
abstract: This article extends the previous work of Fung et al. (2004) investigating the ability of the time-scale log-linear regression model, proposed by Dominici et al. (2003), to detect mortality displacement (sometimes known as harvesting) in time-series data relating air pollution to excess mortality. We conducted a simulation study based on two different compartment models of the death process: pure frailty model and mixed frailty model. We assume that nonaccidental death only affects frail population in a pure frailty model and affects both frail people and other individuals in the mixed frailty model. With a pure frailty model and a moderate-size pollution effect, we identified a characteristic mortality displacement pattern in the different time-scale coefficients of log relative risk. However, once a covariate like temperature was introduced into the model, such a mortality displacement pattern disappeared. Furthermore, a false mortality displacement effect was present in the incorrectly specified model, when temperature was not taken into account. We believe that time-scale regression has limited value for detecting mortality displacement in time-series data.
authors: Department of Mathematics and Statistics, University of Windsor, Windsor, Ontario, Canada.
authors: Karen
authors: K
authors: Fung
authors: Daniel
authors: D
authors: Krewski
authors: Rick
authors: R
authors: Burnett
authors: Tim
authors: T
authors: Ramsay
authors: Yue
authors: Y
authors: Chen
mesh_terms: D000397:Air Pollution / Q000009:adverse effects*; D006801:Humans; D015233:Models, Statistical; D009026:Mortality / Q000639:trends*; D012044:Regression Analysis; D012107:Research Design; D012306:Risk; D013997:Time Factors