A flexible hierarchical framework for improving inference in area-referenced environmental health studies.

ABSTRACT: Study designs where data have been aggregated by geographical areas are popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. However, the resulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typically are not available from routinely collected data. We propose a framework to improve inference drawn from such studies exploiting information derived from individual-level survey data. The latter are summarized in an area-level scalar score by mimicking at ecological level the well-known propensity score methodology. The literature on propensity score for confounding adjustment is mainly

SUBMITTER: Pirani M 

PROVIDER: S-EPMC7613972 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature