Testing the harvesting hypothesis by time-domain regression analysis. II: covariate effects.
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.
Fung
Karen
K
Department of Mathematics and Statistics, University of Windsor, Windsor, Ontario, Canada.
Krewski
Daniel
D
Burnett
Rick
R
Ramsay
Tim
T
Chen
Yue
Y
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