The sensitivity of health effect estimates from time-series studies to fine particulate matter component sampling schedule

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Abstract

The US Environmental Protection Agency air pollution monitoring data have been a valuable resource commonly used for investigating the associations between short-term exposures to PM2.5 chemical components and human health. However, the temporally sparse sampling on every third or sixth day may affect health effect estimation. We examined the impact of non-daily monitoring data on health effect estimates using daily data from the Denver Aerosol Sources and Health (DASH) study. Daily concentrations of four PM2.5 chemical components (elemental and organic carbon, sulfate, and nitrate) and hospital admission counts from 2003 through 2007 were used. Three every-third-day time series were created from the daily DASH monitoring data, imitating the US Speciation Trend Network (STN) monitoring schedule. A fourth, partly irregular, every-third-day time series was created by matching existing sampling days at a nearby STN monitor. Relative risks (RRs) of hospital admissions for PM2.5 components at lags 0-3 were estimated for each data set, adjusting for temperature, relative humidity, longer term temporal trends, and day of week using generalized additive models, and compared across different sampling schedules. The estimated RRs varied somewhat between the non-daily and daily sampling schedules and between the four non-daily schedules, and in some instances could lead to different conclusions. It was not evident which features of the data or analysis were responsible for the variation in effect estimates, although seeing similar variability in resampled data sets with relaxation of the every-third-day constraint suggests that limited power may have had a role. The use of non-daily monitoring data can influence interpretation of estimated effects of PM2.5 components on hospital admissions in time-series studies.

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