Estimating long-term pollution exposure effects through inverse probability weighting methods with Cox proportional hazards models.

ABSTRACT:

Background

Fine particulate matter (PM_2.5) is associated with negative health outcomes in both the short and long term. However, the cohort studies that have produced many of the estimates of long-term exposure associations may fail to account for selection bias in pollution exposure as well as covariate imbalance in the study population; therefore, causal modeling techniques may be beneficial.

Methods

Twenty-nine years of data from the National Health Interview Survey (NHIS) was compiled and linked to modeled annual average outdoor PM_2.5 concentration and restricted-use mortality data. A series of Cox proportional hazards models, adjusted using inverse probability weights, yielded causal risk estimates of long-term exposure to ambient PM_2.5

SUBMITTER: Higbee JD 

PROVIDER: S-EPMC7319228 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature