{"doi":"10.1016/j.focus.2024.100209","title":"Associations of Historical Redlining With BMI and Waist Circumference in Coronary Artery Risk Development in Young Adults","abstract":"•Historically biased mortgage lending, or redlining, used race to evaluate risk.•Black adults had high BMI and waist circumference in all neighborhoods.•White adults had low BMI and waist circumference in redlined areas.•In redlined areas, neighborhood income was higher for White than for Black adults.•Redlining may have reinforced racial obesity inequality 50 years later. IntroductionHistorical maps of racialized evaluation of mortgage lending risk (i.e., redlined neighborhoods) have been linked to adverse health outcomes. Little research has examined whether living in historically redlined neighborhoods is associated with obesity, differentially by race or gender.MethodsThis is a cross-sectional study to examine whether living in historically redlined neighborhoods is associated with BMI and waist circumference among Black and White adults in 1985–1986. Participants’ addresses were linked to the 1930s Home Owners’ Loan Corporation maps that evaluated mortgage lending risk across neighborhoods. The authors used multilevel linear regression models clustered on Census tract, adjusted for confounders to estimate main effects, and stratified, and interaction models by (1) race, (2) gender, and (3) race by gender with redlining differentially for Black versus White adults and men versus women. To better understand strata differences, they compared Census tract–level median household income across race and gender groups within Home Owners’ Loan Corporation grade.ResultsBlack adults (n=2,103) were more likely than White adults (n=1,767) to live in historically rated hazardous areas and to have higher BMI and waist circumference. Redlining and race and redlining and gender interactions for BMI and waist circumference were statistically significant (p<0.10). However, in stratified analyses, the only statistically significant associations were among White participants. White participants living in historically rated hazardous areas had lower BMI (β=−0.63 [95% CI= −1.11, −0.15]) and lower waist circumference (β=−1.50 [95% CI= −2.62, −0.38]) than those living in declining areas. Within each Home Owners’ Loan Corporation grade, residents in White participants’ neighborhoods had higher incomes than those living in Black participants’ neighborhoods (p<0.0001). The difference was largest within historically redlined areas. Covariate associations differed for men, women, Black, and White adults, explaining the difference between the interaction and the stratified models. Race by redlining interaction did not vary by gender.ConclusionsWhite adults may have benefitted from historical redlining, which may have reinforced neighborhood processes that generated racial inequality in BMI and waist circumference 50 years later.","journal":"AJPM Focus","year":2024,"id":448343,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9627,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":361193,"name":"Tamara Dubowitz","orcid":"0000-0003-4035-9782","position":1,"is_corresponding":false},{"id":581475,"name":"Kirsten Beyer","orcid":"0000-0002-7513-8528","position":2,"is_corresponding":false},{"id":581476,"name":"Yuhong Zhou","orcid":"0000-0002-3658-1308","position":3,"is_corresponding":false},{"id":305266,"name":"Kiarri N. Kershaw","orcid":"0000-0002-0063-6397","position":4,"is_corresponding":false},{"id":1267351,"name":"Waverly Duck","orcid":null,"position":5,"is_corresponding":false},{"id":413970,"name":"Feifei Ye","orcid":"0000-0001-7212-3235","position":6,"is_corresponding":false},{"id":414979,"name":"Robin Beckman","orcid":null,"position":7,"is_corresponding":false},{"id":11272,"name":"Penny Gordon-Larsen","orcid":"0000-0001-5322-4188","position":8,"is_corresponding":false},{"id":353721,"name":"James M. Shikany","orcid":"0000-0002-2424-9308","position":9,"is_corresponding":false},{"id":437999,"name":"Catarina I. Kiefe","orcid":"0000-0001-8719-6963","position":10,"is_corresponding":false},{"id":413969,"name":"Andrea S. Richardson","orcid":"0000-0001-6894-8226","position":0,"is_corresponding":true}],"reference_count":86,"raw_metadata":null,"created_at":"2026-07-19T02:02:08.018689Z","pmid":"38590394","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}