ArticleHealth services research2019
Comparing methods of grouping hospitals.
Article in Health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Star Ratings and Willingness to Travel for Hospital Care.Health economics · 2026Article
- Development of an Atlas for US Pediatric Acute Care.JAMA pediatrics · 2026Article
- Adoption of Health Information Technologies by Area Socioeconomic Deprivation Among US Hospitals.JAMA health forum · 2025Article
- Performance of health care service area definitions for capturing variation in inpatient care and social determinants of health.Health services research · 2024Article
- Geographic and Physician-Level Variation in the Use of Hypofractionated Radiation Therapy for Breast Cancer in the U.S.: A Cross-Classified Multilevel Analysis.Advances in radiation oncology · 2024Article
- Risk stratification models for predicting preventable hospitalization in commercially insured late middle-aged adults with depression.BMC health services research · 2023Article
- Comparing methods of grouping hospitals.Health services research · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveTo compare the performance of widely used approaches for defining groups of hospitals and a new approach based on network analysis of shared patient volume. STUDY
settingNon-federal acute care hospitals in the United States. STUDY
designWe assessed the measurement properties of four methods of grouping hospitals: hospital referral regions (HRRs), metropolitan statistical areas (MSAs), core-based statistical areas (CBSAs), and community detection algorithms (CDAs). DATA EXTRACTION
methodsWe combined data from the 2014 American Hospital Association Annual Survey, the Census Bureau, the Dartmouth Atlas, and Medicare data on interhospital patient travel patterns. We then evaluated the distinctiveness of each grouping, reliability over time, and generalizability across populations. PRINCIPLE
findingsHospital groups defined by CDAs were the most distinctive (modularity = 0.86 compared to 0.75 for HRRs and 0.83 for MSAs; 0.72 for CBSA), were reliable to alternative specifications, and had greater generalizability than HRRs, MSAs, or CBSAs. CDAs had lower reliability over time than MSAs or CBSAs (normalized mutual information between 2012 and 2014 CDAs = 0.93).
conclusionsCommunity detection algorithm-defined hospital groups offer high validity, reliability to different specifications, and generalizability to many uses when compared to approaches in widespread use today. They may, therefore, offer a better choice for efforts seeking to analyze the behaviors and dynamics of groups of hospitals. Measures of modularity, shared information, inclusivity, and shared behavior can be used to evaluate different approaches to grouping providers.
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