Dataset Information

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

ABSTRACT:

Objective

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.

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 (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 methods

Linked survey and hospital admission data.

Principal findings

Between-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.

Conclusion

Multiple-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.

SUBMITTER: Falster MO

PROVIDER: S-EPMC6056604 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Falster Michael O MO Jorm Louisa R LR Leyland Alastair H AH

Health services research 20170922

<h4>Objective</h4>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.<h4>Data sources</h4>Cohort of 267,014 people aged over 45 in NSW, Australia.<h4>Study design</h4>Patterns of patient flow were used to create weighted hospital service area networks (weighted-HSANs) to 79 large public hospitals of admission. Multiple-membership multile ...[more]

PMID: 28940236

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Project description:Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014-2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.

Project description:OBJECTIVES:Reducing potentially preventable hospitalization (PPH) among older adults with dementia is a goal of Healthy People 2020, yet no tools specifically identify patients with dementia at highest risk. The objective was to develop a risk prediction model to identify older adults with dementia at high imminent risk of PPH. DESIGN:A 30-day risk prediction model was developed using multivariable logistic regression. Patients from fiscal years (FY) 2009 to 2011 were split into development and validation cohorts; FY2012 was used for prediction. SETTING:Community-dwelling older adults (?65 years of age) with dementia who received care through the Veterans Health Administration. PARTICIPANTS:There were 1 793 783 participants. MEASUREMENTS:Characteristics associated with hospitalization risk were (1) age and other demographic factors; (2) outpatient, emergency department, and inpatient utilization; (3) medical and psychiatric diagnoses; and (4) prescribed medication use including changes to psychotropic medications (eg, initiation or dosage increase). Model discrimination was determined by the C statistic for each of the three cohorts. Finally, to determine whether predicted 30-day risk strata were stable over time, the observed PPH rate was calculated out to 1 year. RESULTS:In the development cohort, .6% of patients experienced PPH within 30?days. The C statistic for the development cohort was .83 (95% confidence interval [CI] = .83-.84) and .83 in the prediction cohort (95% CI = .82-.84). Patients in the top 10% of predicted 30-day PPH risk accounted for more than 50% of 30-day PPH admissions in all three cohorts. In addition, those predicted to be at elevated 30-day risk remained at higher risk throughout a year of follow-up. CONCLUSION:It is possible to identify older adults with dementia at high risk of imminent PPH, and their risk remains elevated for an entire year. Given the negative outcomes associated with acute hospitalization for those with dementia, healthcare systems and providers may be able to engage these high-risk patients proactively to avoid unnecessary hospitalization. J Am Geriatr Soc 67:2077-2084, 2019.

Dataset Information

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

Objective

Data sources

Study design

Data collection/extraction methods

Principal findings

Conclusion

Publications

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

Similar Datasets

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