{"doi":"10.1002/alz.12705","title":"Neighborhood segregation and cognitive change: Multi‐Ethnic Study of Atherosclerosis","abstract":"INTRODUCTION: We investigated associations between neighborhood racial/ethnic segregation and cognitive change. METHODS: We used data (n = 1712) from the Multi-Ethnic Study of Atherosclerosis. Racial/ethnic segregation was assessed using Getis-Ord (Gi*) z-scores based on American Community Survey Census tract data (higher Gi* = greater spatial clustering of participant's race/ethnicity). Global cognition and processing speed were assessed twice, 6 years apart. Adjusted multilevel linear regression tested associations between Gi* z-scores and cognition. Effect modification by race/ethnicity, income, education, neighborhood socioeconomic status, and neighborhood social support was tested. RESULTS: Participants were on average 67 years old; 43% were White, 11% Chinese, 29% African American/Black, 17% Hispanic; 40% had high neighborhood segregation (Gi* > 1.96). African American/Black participants with greater neighborhood segregation had greater processing speed decline in stratified analyses, but no interactions were significant. DISCUSSION: Segregation was associated with greater processing speed declines among African American/Black participants. Additional follow-ups and comprehensive cognitive batteries may further elucidate these findings. HIGHLIGHTS: A study of neighborhood racial/ethnic segregation and change in cognition. Study was based on a racially and geographically diverse, population-based cohort of older adults. Racial/ethnic segregation (clustering) was measured by the Getis-ord (Gi*) statistic. We saw faster processing speed decline among Black individuals in segregated neighborhoods.","journal":"Alzheimer s & Dementia","year":2022,"id":249009,"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":29,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7671,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":589680,"name":"Oanh L. Meyer","orcid":"0000-0002-1093-0477","position":1,"is_corresponding":false},{"id":515302,"name":"Miranda R. Jones","orcid":"0000-0003-4863-104X","position":2,"is_corresponding":false},{"id":653915,"name":"Duyen Tran","orcid":"0000-0002-9110-5670","position":3,"is_corresponding":false},{"id":691224,"name":"Michaela Booker","orcid":"0000-0002-4361-7647","position":4,"is_corresponding":false},{"id":691223,"name":"Diana Mitsova","orcid":"0000-0002-4239-2378","position":5,"is_corresponding":false},{"id":366567,"name":"Rachel Peterson","orcid":"0000-0001-6818-7009","position":6,"is_corresponding":false},{"id":108598,"name":"James E. Galvin","orcid":"0000-0001-5678-245X","position":7,"is_corresponding":false},{"id":676476,"name":"James R. Bateman","orcid":"0000-0002-3646-9148","position":8,"is_corresponding":false},{"id":258460,"name":"Kathleen M. Hayden","orcid":"0000-0002-7745-3513","position":9,"is_corresponding":false},{"id":325846,"name":"Timothy M. Hughes","orcid":"0000-0002-2919-7199","position":10,"is_corresponding":false},{"id":439560,"name":"Lilah M. Besser","orcid":"0000-0001-9945-0877","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T00:24:19.356023Z","pmid":"35869977","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":[]}