Fahui Wang1, Changzhen Wang1, Yujie Hu2, Julie Weiss3, Jennifer Alford-Teaster4, Tracy Onega5. 1. Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, United States. 2. Department of Geography, University of Florida, Gainesville, FL, United States; UF Informatics Institute, University of Florida, Gainesville, FL, United States. 3. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States. 4. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States; Norris Cotton Cancer Center, Lebanon, NH, United States; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States. 5. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States; Norris Cotton Cancer Center, Lebanon, NH, United States; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States; Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States. Electronic address: tracy.l.onega@dartmouth.edu.
Abstract
OBJECTIVE: Derivation of service areas is an important methodology for evaluating healthcare variation, which can be refined to more robust, condition-specific, and empirically-based automated regions, using cancer service areas as an exemplar. DATA SOURCES/STUDY SETTING: Medicare claims (2014-2015) for the nine-state Northeast region were used to develop a ZIP-code-level origin-destination matrix for cancer services (surgery, chemotherapy, and radiation). This population-based study followed a utilization-based approach to delineate cancer service areas (CSAs) to develop and test an improved methodology for small area analyses. DATA COLLECTION/EXTRACTION METHODS: Using the cancer service origin-destination matrix, we estimated travel time between all ZIP-code pairs, and applied a community detection method to delineate CSAs, which were tested for localization, modularity, and compactness, and compared to existing service areas. PRINCIPAL FINDINGS: Delineating 17 CSAs in the Northeast yielded optimal parameters, with a mean localization index (LI) of 0.88 (min: 0.60, max: 0.98), compared to the 43 Hospital Referral Regions (HRR) in the region (mean LI: 0.68; min: 0.18, max: 0.97). Modularity and compactness were similarly improved for CSAs vs. HRRs. CONCLUSIONS: Deriving cancer-specific service areas with an automated algorithm that uses empirical and network methods showed improved performance on geographic measures compared to more general, hospital-based service areas.
OBJECTIVE: Derivation of service areas is an important methodology for evaluating healthcare variation, which can be refined to more robust, condition-specific, and empirically-based automated regions, using cancer service areas as an exemplar. DATA SOURCES/STUDY SETTING: Medicare claims (2014-2015) for the nine-state Northeast region were used to develop a ZIP-code-level origin-destination matrix for cancer services (surgery, chemotherapy, and radiation). This population-based study followed a utilization-based approach to delineate cancer service areas (CSAs) to develop and test an improved methodology for small area analyses. DATA COLLECTION/EXTRACTION METHODS: Using the cancer service origin-destination matrix, we estimated travel time between all ZIP-code pairs, and applied a community detection method to delineate CSAs, which were tested for localization, modularity, and compactness, and compared to existing service areas. PRINCIPAL FINDINGS: Delineating 17 CSAs in the Northeast yielded optimal parameters, with a mean localization index (LI) of 0.88 (min: 0.60, max: 0.98), compared to the 43 Hospital Referral Regions (HRR) in the region (mean LI: 0.68; min: 0.18, max: 0.97). Modularity and compactness were similarly improved for CSAs vs. HRRs. CONCLUSIONS: Deriving cancer-specific service areas with an automated algorithm that uses empirical and network methods showed improved performance on geographic measures compared to more general, hospital-based service areas.
Keywords:
Cancer services areas (CSAs); GIS; Hospital referral regions (HRRs); Hospital service areas (HSAs); Localization index (LI); Network community detection; Northeast region; Regionalization
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