Redlining & Health Outcomes Research

OVERVIEW

This project asks a direct question: when neighborhoods were graded as higher-risk under historic redlining maps, did that correlate with worse health outcomes decades later? I focused on tracing evidence quality across studies, separating descriptive correlation from causal claims, and identifying where methods were strong versus where confounders were under-controlled.

WHAT I DID

• Reviewed public-health and epidemiology studies linking HOLC grade patterns to chronic disease outcomes.
• Documented dataset scope, modeling choices, and control-variable strategies across papers.
• Compared how different studies handled validation and confounders before drawing conclusions.
• Wrote a synthesis that highlights agreement zones and high-uncertainty claims.

RESULTS / IMPACT

• Most reviewed studies show directionally worse health outcomes in historically redlined areas.
• The strongest claims came from papers that explicitly modeled socioeconomic confounders.
• Result quality depends heavily on study design; not every observed disparity supports a causal statement.
• TODO: add paper-specific effect sizes where available from the final write-up.

LESSONS + NEXT STEPS

• Inference discipline matters: policy-relevant writing should distinguish correlation from causation line-by-line.
• Next step is a tighter evidence table with methods, controls, and effect-size comparability across studies.