title: Testing the harvesting hypothesis by time-domain regression analysis. I: baseline analysis.
abstract: Although the association between air pollution and daily mortality is well established, the mechanisms by which air pollution results in excess mortality are not yet well understood. In particular, there exists debate over whether air pollution has a direct effect on mortality in the general population or simply shortens the life span of frail individuals, a hypothesis referred to as "harvesting." The goal of this investigation is to test the harvesting hypothesis using the time-domain regression method of Dominici et al. (2003a). We conducted simulations based on a two-compartment model that divides the population into a larger group of healthy individuals and a frail subpopulation. Death from air pollution is assumed to take place in two steps, by first moving from healthy population to the frail pool, then death with probability related to the level of air pollution. Using time-domain analysis, we seek to identify data patterns that would be characteristic of harvesting under different scenarios. For a pure harvesting model, time-domain analysis indicates that mortality is associated with a short-term air pollution episode of less than 2 d if the mean residency time in the frail pool is short. If both entrants and deaths depend on the level of air pollution and the rates of entry to and exit from the frail pool are about the same, the log relative risk estimates are essentially unchanged at all time scales. If pollution affects mortality in the frail pool more than entrants, larger effects will occur at shorter time scales.
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: Francesca
authors: F
authors: Dominici
mesh_terms: D000397:Air Pollution / Q000009:adverse effects*; D004198:Disease Susceptibility / Q000139:chemically induced; D006801:Humans; D015233:Models, Statistical; D009026:Mortality / Q000639:trends*; D012044:Regression Analysis; D012107:Research Design; D012306:Risk; D013997:Time Factors