Estimating the Attributable Disease Burden and Effects of Interhospital Patient Sharing on <i>Clostridium difficile</i> Infections

Sewell, Daniel K.; Simmering, Jacob E.; Justice, Samuel; Pemmaraju, Sriram V.; Segre, Alberto M.; Polgreen, Philip M.

doi:10.1017/ice.2019.73

Cited by 5 publications

(3 citation statements)

References 50 publications

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“…Following the work of Sewell et al, we fit a static NAM using as a response variable the monthly average number of CDI cases and as covariates the monthly average number of admissions, the median length of stay, the median number of diagnoses, and the proportion of patients over 65. The adjacency matrix A was constructed such that A i j was the monthly average number of transfers from j to i divided by the monthly average number of admissions in hospital j (the diagonal elements A ii were fixed at zero); we row‐normalized the adjacency matrix when fitting the NAM.…”

Section: Patient‐sharing Networkmentioning

confidence: 99%

Analysis of network interventions with an application to hospital‐acquired infections

Sewell

Program²

2019

Statistics in Medicine

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Regional interventions to prevent the spread of hospital‐acquired infections, vaccination campaigns, and information dissemination strategies are examples of treatment interventions applied to members of a network with the intent of effecting a network‐wide change. In designing clinical trials or determining policy changes, it may not be cost effective or otherwise possible to treat all actors of a network. There is a notable lack of study designs and statistical frameworks with which to plan a network‐wide intervention in this context and analyze the resulting data. This paper builds off of the network autocorrelation model in order to provide such a framework for a pre‐post study design. We derive key quantitative measures of the network‐wide treatment effect, exact formulas for power analyses of these measures, and extensions for the context in which the network is unknown. As the treatment assignation is part of the network‐wide treatment, we provide methods for determining the assignation which optimizes the overall treatment effect over all members of the network subject to any arbitrary set of implementation costs and cost constraint. We implement these methods on Clostridioides difficile data for the state of California, where the hospitals are linked through patient sharing.

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Section: Patient‐sharing Networkmentioning

confidence: 99%

Analysis of network interventions with an application to hospital‐acquired infections

Sewell

Program²

2019

Statistics in Medicine

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“…In fact, the proportion of shared patients between hospitals was correlated with genetic similarity of MRSA strains from those hospitals [4]. Clostridium difficile infections are likewise shown to be associated with a hospital's network connectivity based on transferred patients [5,6]. There is also differential quality of care effects of inter-hospital critical care transfers, with transferred patients more like to go to higher-quality centers [7,8] and huband-spoke networks for stroke and myocardial infarction with reliance on multiple sites of care [9,10].…”

Section: Introductionmentioning

confidence: 98%

Association of hospital centrality in inter-hospital patient-sharing networks with patient mortality and length of stay

Bergmark

Jin²,

Semco³

et al. 2023

PLoS ONE

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Objective The interdependence of hospitals is underappreciated in patient outcomes studies. We used a network science approach to foreground this interdependence. Specifically, within two large state-based interhospital networks, we examined the relationship of a hospital’s network position with in-hospital mortality and length of stay. Methods We constructed interhospital network graphs using data from the Healthcare Cost and Utilization Project and the American Hospital Association Annual Survey for Florida (2014) and California (2011). The exposure of interest was hospital centrality, defined as weighted degree (sum of all ties to a given hospital from other hospitals). The outcomes were in-hospital mortality and length of stay with sub-analyses for four acute medical conditions: pneumonia, heart failure, ischemic stroke, myocardial infarction. We compared outcomes for each quartile of hospital centrality relative to the most central quartile (Q4), independent of patient- and hospital-level characteristics, in this retrospective cross-sectional study. Results The inpatient cohorts had 1,246,169 patients in Florida and 1,415,728 in California. Compared to Florida’s central hospitals which had an overall mortality 1.60%, peripheral hospitals had higher in-hospital mortality (1.97%, adjusted OR (95%CI): Q1 1.61 (1.37, 1.89), p<0.001). Hospitals in the middle quartiles had lower in-hospital mortality compared to central hospitals (%, adjusted OR (95% CI): Q2 1.39%, 0.79 (0.70, 0.89), p<0.001; Q3 1.33%, 0.78 (0.70, 0.87), p<0.001). Peripheral hospitals had longer lengths of stay (adjusted incidence rate ratio (95% CI): Q1 2.47 (2.44, 2.50), p<0.001). These findings were replicated in California, and in patients with heart failure and pneumonia in Florida. These results show a u-shaped distribution of outcomes based on hospital network centrality quartile. Conclusions The position of hospitals within an inter-hospital network is associated with patient outcomes. Specifically, hospitals located in the peripheral or central positions may be most vulnerable to diminished quality outcomes due to the network. Results should be replicated with deeper clinical data.

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“…Several empirical studies have shown that inter-facility patient movement plays an important role in the dissemination of antimicrobial resistant organisms and CDI throughout healthcare systems, including acute care facilities. [6][7][8] Inter-facility patient sharing, 9,10 including both "direct" same-day patient transfers and "indirect" inter-facility patient movement with intervening non-hospital stays, may contribute to transmission between hospitals. The regional structures of most healthcare systems means that the majority of patient sharing occurs within healthcare regions 11 and genetic similarities of antibiotic resistant organisms reflect regional transfer patterns.…”

Section: Introductionmentioning

confidence: 99%

Interfacility patient sharing and Clostridioides difficile infection incidence in the Ontario hospital system: A 13-year cohort study

Brown

McGeer

Schwartz

et al. 2019

Infect. Control Hosp. Epidemiol.

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Objective: Interfacility patient movement plays an important role in the dissemination of antimicrobial-resistant organisms throughout healthcare systems. We evaluated how 3 alternative measures of interfacility patient sharing were associated with C. difficile infection incidence in Ontario acute-care facilities. Design: The cohort included adult acute-care facility stays of ≥3 days between April 2003 and March 2016. We measured 3 facility-level metrics of patient sharing: general patient importation, incidence-weighted patient importation, and C. difficile case importation. Each of the 3 patient-sharing metrics were examined against the incidence of C. difficile infection in the facility per 1,000 stays, using Poisson regression models. Results: The analyzed cohort included 6.70 million stays at risk of C. difficile infection across 120 facilities. Over the 13-year period, we included 62,189 new cases of healthcare-associated CDI (incidence, 9.3 per 1,000 stays). After adjustment for facility characteristics, general importation was not strongly associated with C. difficile infection incidence (risk ratio [RR] per doubling, 1.10; 95% confidence interval [CI], 0.97–1.24; proportional change in variance [PCV], −2.0%). Incidence-weighted (RR per doubling, 1.18; 95% CI, 1.06–1.30; PCV, −8.4%) and C. difficile case importation (RR per doubling, 1.43; 95% CI, 1.29–1.58; PCV, −30.1%) were strongly associated with C. difficile infection incidence. Conclusions: In this 13-year study of acute-care facilities in Ontario, interfacility variation in C. difficile infection incidence was associated with importation of patients from other high-incidence acute-care facilities or specifically of patients with a recent history of C. difficile infection. Regional infection control strategies should consider the potential impact of importation of patients at high risk of C. difficile shedding from outside facilities.

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Estimating the Attributable Disease Burden and Effects of Interhospital Patient Sharing on Clostridium difficile Infections

Cited by 5 publications

References 50 publications

Analysis of network interventions with an application to hospital‐acquired infections

Analysis of network interventions with an application to hospital‐acquired infections

Association of hospital centrality in inter-hospital patient-sharing networks with patient mortality and length of stay

Interfacility patient sharing and Clostridioides difficile infection incidence in the Ontario hospital system: A 13-year cohort study

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