Automated delineation of cancer service areas in northeast region of the United States: A network optimization approach. (June 2020)

• Record Type: Journal Article
• Authors: Wang, Fahui; Wang, Changzhen; Hu, Yujie; Weiss, Julie; Alford-Teaster, Jennifer; Onega, Tracy
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 33(2020)
• Publication Date: 2020-06
• Subjects: Cancer services areas (CSAs) -- Hospital service areas (HSAs) -- Hospital referral regions (HRRs) -- GIS -- Regionalization -- Network community detection -- Localization index (LI) -- Northeast region; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2020.100338
• Languages: English
• ISSNs: 1877-5845

Abstract

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.