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Using Weighted Hospital Service Area Networks to Explore Variation in Preventable Hospitalization

Objective: To demonstrate the use of multiplemembership multilevel models, which analytically structure patients in a weighted network of hospitals, for exploring betweenhospital variation in preventable hospitalizations.

Data Sources: Cohort of 267,014 people aged over 45 in NSW, Australia.

Study Design: Patterns of patient flow were used to create weighted hospital service area networks (weightedHSANs) to 79 large public hospitals of admission. Multiplemembership multilevel models on rates of preventable hospitalization, modeling participants structured within weightedHSANs, were contrasted with models clustering on 72 hospital service areas (HSAs) that assigned participants to a discrete geographic region.

Data Collection/Extraction Methods: Linked survey and hospital admission data.

Principal Findings: Betweenhospital variation in rates of preventable hospitalization was more than two times greater when modeled using weightedHSANs rather than HSAs. Use of weightedHSANs permitted identification of small hospitals with particularly high rates of admission and influenced performance ranking of hospitals, particularly those with a broadly distributed patient base. There was no significant association with hospital bed occupancy.

Conclusion: Multiplemembership multilevel models can analytically capture information lost on patient attribution when creating discrete health care catchments. WeightedHSANs have broad potential application in health services research and can be used across methods for creating patient catchments.

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