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Residential Mobility and Geospatial Disparities in Colon Cancer Survival

  • Temple University
  • State of New Jersey
  • Rutgers - The State University of New Jersey, New Brunswick

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Background: Identifying geospatial cancer survival disparities is critical to focus interventions and prioritize efforts with limited resources. Incorporating residential mobility into spatial models may result in different geographic patterns of survival compared with the standard approach using a single location based on the patient's residence at the time of diagnosis. Methods: Data on 3,949 regional-stage colon cancer cases diagnosed from 2006 to 2011 and followed until December 31, 2016, were obtained from the New Jersey State Cancer Registry. Geographic disparity based on the spatial variance and effect sizes from a Bayesian spatial model using residence at diagnosis was compared with a time-varying spatial model using residential histories [adjusted for sex, gender, substage, race/ethnicity, and census tract (CT) poverty]. Geographic estimates of risk of colon cancer death were mapped. Results: Most patients (65%) remained at the same residence, 22% changed CT, and 12% moved out of state. The time-varying model produced a wider range of adjusted risk of colon cancer death (0.85–1.20 vs. 0.94–1.11) and resulted in greater geographic disparity statewide after adjustment (25.5% vs. 14.2%) compared with the model with only the residence at diagnosis. Conclusions: Including residential mobility may allow for more precise estimates of spatial risk of death. Results based on the traditional approach using only residence at diagnosis were not substantially different for regional stage colon cancer in New Jersey. Impact: Including residential histories opens up new avenues of inquiry to better understand the complex relationships between people and places, and the effect of residential mobility on cancer outcomes.

Original languageEnglish
Pages (from-to)2119-2125
Number of pages7
JournalCancer Epidemiology Biomarkers and Prevention
Volume29
Issue number11
DOIs
StatePublished - Nov 1 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Bayes Theorem
  • Colonic Neoplasms/epidemiology
  • Humans
  • New Jersey/epidemiology
  • Population Dynamics
  • Residence Characteristics

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