Abstract:
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Numerous statistical studies from the field of environmental epidemiology have reported associations between short-term air pollution (e.g., PM2.5, NO2, etc.) exposure and population health effects, including mortality. Such risk models typically examine lagged risk through distributed-lag models in acknowledgement of the understanding that exposure often manifests in population health over time, rather than instantaneously. Through use of multitaper method-derived time series tools, we explore a new structural approach to the problem of lagged manifestation of air pollution exposure, which we have named _synthetic lag_. Our new method solves several problems which exist for previously developed methods, while retaining a similar philosophical approach and interpretation. We demonstrate the validity and usefulness of the new approach by comparing our results to those of classic models, using data from the National Morbidity and Mortality Air Pollution Study (NMMAPS) database.
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