The positive effects of population-based preferential sampling in environmental epidemiology.
In environmental epidemiology, exposures are not always available at subject locations and must be predicted using monitoring data. The monitor locations are often outside the control of researchers, and previous studies have shown that "preferential sampling" of monitoring locations can adversely affect exposure prediction and subsequent health effect estimation. We adopt a slightly different definition of preferential sampling than is typically seen in the literature, which we call population-based preferential sampling. Population-based preferential sampling occurs when the location of the monitors is dependent on the subject locations. We show the impact that population-based preferential sampling has on exposure prediction and health effect estimation using analytic results and a simulation study. A simple, one-parameter model is proposed to measure the degree to which monitors are preferentially sampled with respect to population density. We then discuss these concepts in the context of PM2.5 and the EPA Air Quality System monitoring sites, which are generally placed in areas of higher population density to capture the population's exposure.
Department of Biostatistics, Harvard University, 655 Huntington Avenue, Boston, MA 02115, USA jantonel@hsph.harvard.edu.
Joseph
J
Antonelli
RAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA.
Matthew
M
Cefalu
Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada.
Luke
L
Bornn
D004781:Environmental Exposure; D004784:Environmental Monitoring / Q000706:statistics & numerical data; D004812:Epidemiologic Methods; D006801:Humans; D008962:Models, Theoretical; D012107:Research Design
Air pollution epidemiology; Preferential sampling; exposure estimation