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After the Maps: Assessing Longitudinal Neighborhood Change After Historical Redlining with Present-Day Breast Cancer Survival

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Abstract

Historical redlining is a 1930s residential segregation policy that graded neighborhoods according to race, class, and land-use and has been linked to worse breast cancer outcomes today. Since redlining grades were determined by socioeconomic and environmental attributes, evaluating neighborhood change presents a unique opportunity to study the relative contributions of socioeconomic and physical environment to breast cancer. We evaluated whether longitudinal neighborhood change, specifically gentrification and disinvestment, is associated with survival among breast cancer cases today. We used historical redlining grade and labor class census data from 1940 to 2010 to create neighborhood change typologies: stable high neighborhood socioeconomic status (NSES), disinvested, gentrified, and stable low NSES; additional criteria were used for a second, stricter definition of stable types (“truly stable”). Typologies were linked to 43,023 breast cancer cases diagnosed 2008–2018 from New York State. Cox models estimated hazard ratios (HR) associated with neighborhood change types, using both stable high NSES and stable low NSES as referents in separate models. We found significant survival differences according to neighborhood change type. All neighborhood change typologies had higher mortality risks compared to stable high NSES, regardless of how stable NSES was defined. This was consistent in models using stable low NSES as the referent when stable NSES was defined broadly, where only stable high NSES was associated with higher survival (HR = 0.73, 95% CI 0.67–0.80) and gentrification and disinvestment did not show differential mortality risks. However, when stable NSES is defined conservatively (truly stable NSES), we found evidence of survival benefits for disinvestment (HR = 0.76, 95% CI 0.65, 0.89) and gentrification (HR = 0.76, 95% CI 0.67, 0.87) compared to truly stable low NSES neighborhoods, and the survival benefit for truly stable high NSES was strengthened (HR = 0.43, 95% CI 0.35, 0.52), though sample size is reduced in the truly stable NSES models. Our results suggest survival is always highest among consistently high NSES neighborhoods but that gentrification and disinvestment may offer buffers compared to redlined neighborhoods with consistently low NSES. This study demonstrates the complexity of neighborhood change and suggests contemporary socioeconomic conditions and underlying physical environment may both be significant domains for breast cancer outcomes.

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Data Availability

Historical redlining score data are publicly available for download at: https://www.openicpsr.org/openicpsr/project/141121/version/V2/view. Census data are publicly available at data.cenus.gov. Breast cancer data were sourced from the NYSCR. These data cannot be shared because of privacy. Access to NYSCR data requires approval from the NYSCR. The NYSCR can be contacted at: [email protected].

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Acknowledgements

We thank and acknowledge the NYSCR for access to data. NYSCR data used in this study was supported in part by cooperative agreement 6NU58DP006309 awarded to the New York State Department of Health by the Centers for Disease Control and Prevention (CDC) and by Contract 75N91018D00005 (Task Order 75N91018F00001) from the National Cancer Institute (NCI), National Institutes of Health, Department of Health and Human Services. Its contents are solely the responsibility of the New York State Department of Health and do not necessarily represent the official views of the CDC or the NCI. SM Lima is supported by a Postdoctoral Training Fellowship (T32 CA261787).

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Authors and Affiliations

  1. Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA

    Sarah M. Lima, Tia M. Palermo & Heather M. Ochs-Balcom

  2. Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA

    Sarah M. Lima

  3. Policy Research Solutions LLC (PRESTO), Buffalo, NY, USA

    Tia M. Palermo

  4. Bureau of Cancer Epidemiology, Division of Chronic Disease Prevention, New York State Department of Health, Albany, NY, USA

    Furrina F. Lee & Tabassum Z. Insaf

  5. Department of Epidemiology and Biostatistics, University at Albany, Albany, NY, USA

    Tabassum Z. Insaf

  6. Department of Geography, University at Buffalo, Buffalo, NY, USA

    Jared Aldstadt

  7. Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA

    Lili Tian

  8. Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA

    Helen C. S. Meier

  9. Department of Urban and Regional Planning, School of Architecture and Planning, University at Buffalo, Buffalo, NY, USA

    Henry Louis Taylor Jr.

  10. Community Health Equity Research Institute, University at Buffalo, Buffalo, NY, USA

    Henry Louis Taylor Jr.

  11. Roswell Park Comprehensive Cancer Center, Cancer Prevention and Control, Buffalo, NY, USA

    Deborah O. Erwin

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Contributions

Sarah M. Lima: conceptualization, design, formal analysis, methodology, visualization, writing—original draft, and writing—review and editing. Tia M. Palermo: conceptualization, design, methodology, supervision, and writing—review and editing. Furrina F. Lee: data curation, methodology, and writing—review and editing. Tabassum Z. Insaf: data curation, methodology, and writing—review and editing. Jared Aldstadt: methodology and writing—review and editing. Lili Tian: methodology and writing—review and editing. Helen C. S. Meier: data curation, methodology, and writing—review and editing. Henry Louis Taylor Jr.: conceptualization and writing—review and editing. Deborah O. Erwin: writing—review and editing. Heather M. Ochs-Balcom: conceptualization, design, methodology, supervision, and writing—review and editing.

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Lima, S.M., Palermo, T.M., Lee, F.F. et al. After the Maps: Assessing Longitudinal Neighborhood Change After Historical Redlining with Present-Day Breast Cancer Survival. J Urban Health (2026). https://doi.org/10.1007/s11524-026-01166-z

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