Application of Community Detection Methods to Identify Emergency General Surgery-Specific Regional Networks

Abstract

Importance: There is growing interest in developing coordinated regional systems for nontraumatic surgical emergencies; however, our understanding of existing emergency general surgery (EGS) care communities is limited.

Objective: To apply network analysis methods to delineate EGS care regions and compare the performance of this method with the Dartmouth Health Referral Regions (HRRs).

Design, setting, and participants: This cross-sectional study was conducted using the 2019 California and New York state emergency department and inpatient databases. Eligible participants included all adult patients with a nonelective admission for common EGS conditions. Interhospital transfers (IHTs) were identified by transfer indicators or temporally adjacent hospitalizations at 2 different facilities. Data analysis was conducted from January to May 2024.

Exposure: Admission for primary EGS diagnosis.

Main outcomes and measures: Regional EGS networks (RENs) were delineated by modularity optimization (MO), a community detection method, and compared with the plurality-based Dartmouth HRRs. Geographic boundaries were compared through visualization of patient flows and associated health care regions. Spatial accuracy of the 2 methods was compared using 6 common network analysis measures: localization index (LI), market share index (MSI), net patient flow, connectivity, compactness, and modularity.

Results: A total of 1 244 868 participants (median [IQR] age, 55 [37-70 years]; 776 725 male [62.40%]) were admitted with a primary EGS diagnosis. In New York, there were 405 493 EGS encounters with 3212 IHTs (0.79%), and 9 RENs were detected using MO compared with 10 Dartmouth HRRs. In California, there were 839 375 encounters with 10 037 IHTs (1.20%), and 14 RENs were detected compared with 24 HRRs. The greatest discrepancy between REN and HRR boundaries was in rural regions where one REN often encompassed multiple HRRs. The MO method was significantly better than HRRs in identifying care networks that accurately captured patients living within the geographic region as indicated by the LI and MSI for New York (mean [SD] LI, 0.86 [1.00] for REN vs 0.74 [1.00] for HRR; mean [SD] MSI, 0.16 [0.13] for REN vs 0.32 [0.21] for HRR) and California (mean [SD] LI, 0.83 [1.00] for REN vs 0.74 [1.00] for HRR; mean [SD] MSI, 0.19 [0.14] for REN vs 0.39 [0.43] for HRR). Nearly 27% of New York hospitals (37 of 139 hospitals [26.62%]) and 15% of California hospitals (48 of 336 hospitals [14.29%]) were reclassified into a different community with the MO method.

Conclusions and relevance: Development of optimal health delivery systems for EGS patients will require knowledge of care patterns specific to this population. The findings of this cross-sectional study suggest that network science methods, such as MO, offer opportunities to identify empirical EGS care regions that outperform HRRs and can be applied in the development of coordinated regional systems of care.

Figure.. Distribution of Modularity Optimization–Derived Regional Emergency General Surgery (EGS) Networks and Dartmouth Health Referral Regions (HRRs)

Regions with black boundaries indicate Dartmouth HRRs, while the various colored regions indicate modularity optimization Regional EGS Networks (RENS). Circles indicate hospitals, with red circles indicating a level 1 or 2 trauma center and orange circles indicating non–level 1 or 2 trauma centers. Larger circles denote higher volumes. The blue lines of varying thicknesses represent interhospital transfer flows between hospitals. In an optimally detected community, all interhospital transfer flows would remain within a region’s boundaries.