Are the Acute Effects of PM10 on Mortality in NMMAPS the Result of Inadequate Control for Weather and Season? A Sensitivity Analysis Using Flexible Distributed Lag Models
Time series studies have linked daily variations in non-accidental deaths with daily variations in ambient particulate matter (PM) air pollution, while controlling for qualitatively larger influences of weather and season. Although time series analyses typically include non-linear terms for weather and season, questions remain as to whether models to date have completely controlled for these important predictors. In this paper, we use two flexible versions of distributed lag models to control extensively for the confounding effects of weather and season. One version builds on the current approach to controlling for weather, while the other version offers a new approach. We conduct a comprehensive sensitivity analysis of the PM-mortality relationship by applying these methods to the recently updated National Morbidity, Mortality, and Air Pollution Study (NMMAPS) database that comprises air pollution, weather, and mortality time series from 1987 to 2000 for 100 US cities. We combine city-specific estimates of the short term effects of PM on mortality using a Bayesian hierarchical model. We conclude that within the broad classes of models considered, national average estimates of PM relative risk are consistent with previous NMMAPS estimates and are robust to model specification for weather and seasonal confounding.