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

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.