ArticleSpatial and spatio-temporal epidemiology2020
Automated delineation of cancer service areas in northeast region of the United States: A network optimization approach.
Article in Spatial and spatio-temporal epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Physics-Informed Graph Learning for Spatially Contiguous and Capacity-Constrained Hospital Service Area Delineation.Computers, environment and urban systems · 2026Article
- Study on edge villages based on cross-region healthcare-seeking behavior.International journal of health geographics · 2025Article
- Travel burden and bypassing closest site for surgical cancer treatment for urban and rural oncology patients.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2025Article
- A multi-constraint Monte Carlo Simulation approach to downscaling cancer data.Health & place · 2025Article
- Application of Community Detection Methods to Identify Emergency General Surgery-Specific Regional Networks.JAMA network open · 2024Article
- Cross-provincial inpatient mobility patterns and their determinants in China.BMC health services research · 2024Article
- The interaction of rurality and rare cancers for travel time to cancer care.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2023Article
- Delineation of Cancer Service Areas Anchored by Major Cancer Centers in the United States.Cancer research communications · 2022Article
- Research on Maternal Service Area and Referral System in Hubei Province, China.International journal of environmental research and public health · 2022Article
- GIS-Automated Delineation of Hospital Service Areas in Florida: From Dartmouth Method to Network Community Detection Methods.Annals of GIS · 2022Article
- Incorporating broadband durability in measuring geographic access to health care in the era of telehealth: A case example of the 2-step virtual catchment area (2SVCA) Method.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Spatial Behavior of Cancer Care Utilization in Distance Decay in the Northeast Region of the U.S.Travel behaviour & society · 2021Article
- Network Optimization Approach to Delineating Health Care Service Areas: Spatially Constrained Louvain and Leiden Algorithms.Transactions in GIS : TG · 2021Article
- Estimating a Large Travel Time Matrix Between Zip Codes in the United States: A Differential Sampling Approach.Journal of transport geography · 2020Article
- Analysis of Patients' Mobility Patterns: Insights From a Process Mining-Based Longitudinal Study.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveDerivation 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
settingMedicare 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
methodsUsing 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
findingsDelineating 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.
conclusionsDeriving 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.
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