Invited commentary: improving spatial exposure data for everyone-life-course social context and ascertaining residential history.

Measuring age-specific, contextual exposures is crucial for life-course epidemiology research. Longitudinal residential data offer a "golden ticket" to cumulative exposure metrics and can enhance our understanding of health disparities. Residential history can be linked to myriad spatiotemporal databases to characterize environmental, socioeconomic, and policy contexts that a person has experienced throughout life. However, obtaining accurate residential history is challenging in the United States due to the limitations of administrative registries and self-reports. In a recent article, Xu et al (Am J Epidemiol. 2024;193(2):348-359) detailed an approach to linking residential history sourced from LexisNexis Accurint to a Wisconsin-based research cohort, offering insights into challenges with collection of residential history data. Researchers must analyze the magnitude of selection and misclassification biases inherent to ascertaining residential history from cohort data. A life-course framework can provide insights into why the frequency and distance of moves is patterned by age, birth cohort, racial/ethnic identity, socioeconomic status, and urbanicity. Historical and contemporary migration patterns of marginalized people seeking economic and political opportunities must guide interpretations of residential history data. In this commentary, we outline methodological priorities for use of residential history in health disparities research, including contextualizing residential history data with determinants of residential moves, triangulating spatial exposure assessment methods, and transparently quantifying measurement error.