Using moving total mortality counts to obtain improved estimates for the effect of air pollution on mortality

Abstract

In many cities of the United States, measurements of ambient particulate matter air pollution (PM) are available only once every 6 days. Time-series studies conducted in these cities that investigate the relationship between mortality and PM are restricted to using a single day's PM as the measure of PM exposure. This is undesirable because current evidence suggests that the effects of PM on mortality are spread over multiple days. And studies have shown that using a single day's PM as the measure of PM exposure can result in estimates that have a large negative bias. In this article, I introduce a new model for estimating the mortality effects of PM when only every-sixth-day PM data are available. This new model uses information available in the daily mortality time series to infer otherwise lost information about the effect of PM on mortality over a period of more than a single day. This new model typically offers an increase in both statistical estimation precision and accuracy compared with existing models.