| Literature DB >> 35247797 |
Erin E Bennett1, Katie M Lynch2, Xiaohui Xu3, Eun Sug Park4, Qi Ying5, Jingkai Wei2, Richard L Smith6, James D Stewart7, Eric A Whitsel8, Melinda C Power2.
Abstract
Current efforts to characterize movers and identify predictors of moving have been limited. We used the ARIC cohort to characterize non-movers, short-distance movers, and long-distance movers, and employed best subset algorithms to identify important predictors of moving, including interactions between characteristics. Short- and long-distance movers were notably different from non-movers, and important predictors of moving differed based on the distance of the residential move. Importantly, systematic inclusion of interaction terms enhanced model fit and was substantively meaningful. This work has important implications for epidemiologic studies of contextual exposures and those treating residential mobility as an exposure.Entities:
Keywords: Epidemiology; Exposure misclassification; Residential mobility; Selection bias
Mesh:
Year: 2022 PMID: 35247797 PMCID: PMC9004423 DOI: 10.1016/j.healthplace.2022.102771
Source DB: PubMed Journal: Health Place ISSN: 1353-8292 Impact factor: 4.931