{"doi":"10.1111/jrh.12792","title":"Urban–rural differences in cancer mortality: Operationalizing rurality","abstract":"OBJECTIVE: To assess urban-rural differences in cancer mortality across definitions of rurality as (1) established binary cut-points, (2) data-driven binary cut-points, and (3) continuous. METHODS: We used Surveillance, Epidemiology, and End Results (SEER) data between 2000 and 2016 to identify incident adult screening-related cancers. Analyses were based on one testing and four validation cohorts (all n = 26,587). Urban-rural status was defined by Rural-Urban Continuum Codes, National Center for Health Statistics codes, and the Index of Relative Rurality. Each was modeled using established binary cut-points, data-driven cut-points, and as continuous. The primary outcome was 5-year cancer-specific mortality. RESULTS: Compared to established cut-points, data-driven cut-points classified more patients as rural, resulted in larger White populations in rural areas, and yielded 7%-14% lower estimates of urban-rural differences in cancer mortality. Further, hazard of cancer mortality increased 4%-67% with continuous rurality measures, revealing important between-unit differences. CONCLUSIONS: Different cut-points introduce variation in urban-rural differences in mortality across definitions, whereas using urban-rural measures as continuous allows rurality to be conceptualized as a continuum, rather than a simple aggregation. POLICY IMPLICATIONS: Findings provide alternative cut-points for multiple measures of rurality and support the consideration of utilizing continuous measures of rurality in order to guide future research and policymakers.","journal":"The Journal of Rural Health","year":2023,"id":351620,"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":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8144,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":938401,"name":"Jeffrey Franks","orcid":"0000-0001-9076-9072","position":1,"is_corresponding":false},{"id":262392,"name":"Smita Bhatia","orcid":"0000-0002-7755-5683","position":2,"is_corresponding":false},{"id":557785,"name":"Kelly Kenzik","orcid":"0000-0002-6402-8446","position":3,"is_corresponding":false},{"id":725545,"name":"Elizabeth Davis","orcid":"0000-0002-6480-2083","position":0,"is_corresponding":true}],"reference_count":12,"raw_metadata":null,"created_at":"2026-07-19T01:12:45.897709Z","pmid":"37644650","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":[]}