Multiscale analysis of cancer service areas in the United States.

The purpose of delineating Cancer Service Areas (CSAs) is to define a reliable unit of analysis, more meaningful than geopolitical units such as states and counties, for examining geographic variations of the cancer care markets using geographic information systems (GIS). This study aims to provide a multiscale analysis of the U.S. cancer care markets based on the 2014-2015 Medicare claims of cancer-directed surgery, chemotherapy, and radiation. The CSAs are delineated by a scale-flexible network community detection algorithm automated in GIS so that the patient flows are maximized within CSAs and minimized between them. The multiscale CSAs include those comparable in size to those 4 census regions, 9 divisions, 50 states, and also 39 global optimal CSAs that generates the highest modularity value. The CSAs are more effective in capturing the U.S. cancer care markets because of its higher localization index, lower cross-border utilizations, and shorter travel time. The first two comparisons reveal that only a few regions or divisions are representative of the underlying cancer care markets. The last two comparisons find that among the 39 CSAs, 54% CSAs comprise multiple states anchored by cities near inner state borders, 28% are single-state CSAs, and 18% are sub-state CSAs. Their (in)consistencies across state borders or within each state shed new light on where the intervention of cancer care delivery or the adjustment of cancer care costs are needed to meet the challenges in the U.S. cancer care system. The findings could guide stakeholders to target public health policies for more effective coordination of cancer care in improving outcomes and reducing unnecessary costs.

Spatial Behavior of Cancer Care Utilization in Distance Decay in the Northeast Region of the U.S.

PURPOSE: Spatial behavior of patients in utilizing health care reflects their travel burden or mobility, accessibility for medical service, and subsequently outcomes from treatment. This paper derives the best-fitting distance decay function to capture the spatial behaviors of cancer patients in the Northeast region of the U.S., and examines and explains the spatial variability of such behaviors across sub-regions. PRINCIPAL RESULTS: (1) 46.8%, 85.5%, and 99.6% of cancer care received was within a driving time of 30, 60 and 180 minutes, respectively. (2) The exponential distance decay function is the best in capturing the travel behavior of cancer patients in the region and across most sub-regions. (3) The friction coefficient in the distance decay function is negatively correlated with the mean travel time. (4) The best-fitting function forms are associated with network structures. (5) The variation of the friction coefficient across sub-regions is related to factors such as urbanicity, economic development level, and market competition intensity. MAJOR CONCLUSIONS: The distance decay function offers an analytic metric to capture a full spectrum of travel behavior, and thus a more comprehensive measure than average travel time. Examining the geographic variation of travel behavior needs a reliable analysis unit such as organically defined "cancer service areas", which capture relevant health care market structure and thus are more meaningful than commonly-used geopolitical or census area units.

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

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.