To demonstrate the use of multiple‐membership multilevel models, which analytically structure patients in a weighted network of hospitals, for exploring between‐hospital variation in preventable hospitalizations.
Cohort of 267,014 people aged over 45 in , Australia.
Patterns of patient flow were used to create weighted hospital service area networks (weighted‐s) to 79 large public hospitals of admission. Multiple‐membership multilevel models on rates of preventable hospitalization, modeling participants structured within weighted‐s, were contrasted with models clustering on 72 hospital service areas (s) that assigned participants to a discrete geographic region.
Linked survey and hospital admission data.
Between‐hospital variation in rates of preventable hospitalization was more than two times greater when modeled using weighted‐s rather than s. Use of weighted‐s 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.
Multiple‐membership multilevel models can analytically capture information lost on patient attribution when creating discrete health care catchments. Weighted‐s have broad potential application in health services research and can be used across methods for creating patient catchments.
Data Collection/Extraction Methods