Abstract
ObjectiveTo 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.Data SourcesCohort of 267,014 people aged over 45 in NSW, Australia.Study DesignPatterns of patient flow were used to create weighted hospital service area networks (weighted‐HSANs) to 79 large public hospitals of admission. Multiple‐membership multilevel models on rates of preventable hospitalization, modeling participants structured within weighted‐HSANs, were contrasted with models clustering on 72 hospital service areas (HSAs) that assigned participants to a discrete geographic region.Data Collection/Extraction MethodsLinked survey and hospital admission data.Principal FindingsBetween‐hospital variation in rates of preventable hospitalization was more than two times greater when modeled using weighted‐HSANs rather than HSAs. Use of weighted‐HSANs 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.ConclusionMultiple‐membership multilevel models can analytically capture information lost on patient attribution when creating discrete health care catchments. Weighted‐HSANs have broad potential application in health services research and can be used across methods for creating patient catchments.
Highlights
Central to this process is the ability to analyze data at a level at which variation is meaningful
“preventable” hospitalizations are internationally used as an indicator of access to and quality of primary care (Kruzikas et al 2004; NHPA 2015), and population variation in preventable hospitalization is often partitioned into geographic “primary care service areas” reflecting natural markets of primary care supply (Mobley et al 2006; Chang et al 2011)
Participants were admitted to a mean of 15 different hospitals, which formed the basis of the weighting for the HSAN (Table 1)
Summary
Central to this process is the ability to analyze data at a level at which variation is meaningful. Preventable hospitalizations can be influenced by other health system factors, such as hospitals—which may have a different propensity to admit patients based on factors including the availability of beds (Fisher et al 2000; Shwartz et al 2011) This hypothesis remains poorly explored, as such analyses require attributing population variation in admission to the hospital level, and the few studies which have explored these associations (Krakauer et al 1996; Basu, Friedman, and Burstin 2002; Zhan et al 2004; Fiorentini et al 2011; O’Cathain et al 2013; Berlin et al 2014) mostly used ecological measures of hospital services at the geographic level. Defining hospitals’ patient catchments to capture hospital-level variation poses particular difficulties, as patients may not have a designated hospital for admission, administrative data cannot determine a patient’s likely hospital where they have not had an admission, and in most health systems choice of hospital is driven by geographic proximity and by provider and patient choice, as well as financial factors such as private health insurance arrangements
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