72 research outputs found

    Frequent hospital readmissions for Clostridium difficile infection and the impact on estimates of hospital-associated C. difficile burden

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    ObjectiveClostridium difficile infection (CDI) is associated with hospitalization and may cause readmission following admission for any reason. We aimed to measure the incidence of readmissions due to CDI.DesignRetrospective cohort study.PatientsAdult inpatients in Orange County, California, who presented with new-onset CDI within 12 weeks of discharge.MethodsWe assessed mandatory 2000-2007 hospital discharge data for trends in hospital-associated CDI (HA-CDI) incidence, with and without inclusion of postdischarge CDI (PD-CDI) events resulting in rehospitalization within 12 weeks of discharge. We measured the effect of including PD-CDI events on hospital-specific CDI incidence, a mandatory reporting measure in California, and on relative hospital ranks by CDI incidence.ResultsFrom 2000 to 2007, countywide hospital-onset CDI (HO-CDI) incidence increased from 15 per 10,000 to 22 per 10,000 admissions. When including PD-CDI events, HA-CDI incidence doubled (29 per 10,000 in 2000 and 52 per 10,000 in 2007). Overall, including PD-CDI events resulted in significantly higher hospital-specific CDI incidence, although hospitals had disproportionate amounts of HA-CDI occurring postdischarge. This resulted in substantial shifts in some hospitals' rankings by CDI incidence. In multivariate models, both HO and PD-CDI were associated with increasing age, higher length of stay, and select comorbidities. Race and Hispanic ethnicity were predictive of PD-CDI but not HO-CDI.ConclusionsPD-CDI events associated with rehospitalization are increasingly common. The majority of HA-CDI cases may be occurring postdischarge, raising important questions about both accurate reporting and effective prevention strategies. Some risk factors for PD-CDI may be different than those for HO-CDI, allowing additional identification of high-risk groups before discharge

    Cost Savings of Universal Decolonization to Prevent Intensive Care Unit Infection: Implications of the REDUCE MRSA Trial

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    ObjectiveTo estimate and compare the impact on healthcare costs of 3 alternative strategies for reducing bloodstream infections in the intensive care unit (ICU): methicillin-resistant Staphylococcus aureus (MRSA) nares screening and isolation, targeted decolonization (ie, screening, isolation, and decolonization of MRSA carriers or infections), and universal decolonization (ie, no screening and decolonization of all ICU patients).DesignCost analysis using decision modeling.MethodsWe developed a decision-analysis model to estimate the health care costs of targeted decolonization and universal decolonization strategies compared with a strategy of MRSA nares screening and isolation. Effectiveness estimates were derived from a recent randomized trial of the 3 strategies, and cost estimates were derived from the literature.ResultsIn the base case, universal decolonization was the dominant strategy and was estimated to have both lower intervention costs and lower total ICU costs than either screening and isolation or targeted decolonization. Compared with screening and isolation, universal decolonization was estimated to save $171,000 and prevent 9 additional bloodstream infections for every 1,000 ICU admissions. The dominance of universal decolonization persisted under a wide range of cost and effectiveness assumptions.ConclusionsA strategy of universal decolonization for patients admitted to the ICU would both reduce bloodstream infections and likely reduce healthcare costs compared with strategies of MRSA nares screening and isolation or screening and isolation coupled with targeted decolonization

    Quantifying the Exposure to Antibiotic-Resistant Pathogens Among Patients Discharged From a Single Hospital Across All California Healthcare Facilities

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    ObjectiveTo assess the time-dependent exposure of California healthcare facilities to patients harboring methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), extended-spectrum β-lactamase (ESBL)-producing Escherichia coli and Klebsiella pneumoniae, and Clostridium difficile infection (CDI) upon discharge from 1 hospital.MethodsRetrospective multiple-cohort study of adults discharged from 1 hospital in 2005-2009, counting hospitals, nursing homes, cities, and counties in which carriers were readmitted, and comparing the number and length of stay of readmissions and the number of distinct readmission facilities among carriers versus noncarriers.ResultsWe evaluated 45,772 inpatients including those with MRSA (N=1,198), VRE (N=547), ESBL (N=121), and CDI (N=300). Within 1 year of discharge, MRSA, VRE, and ESBL carriers exposed 137, 117, and 45 hospitals and 103, 83, and 37 nursing homes, generating 58,804, 33,486, and 15,508 total exposure-days, respectively. Within 90 days of discharge, CDI patients exposed 36 hospitals and 35 nursing homes, generating 7,318 total exposure-days. Compared with noncarriers, carriers had more readmissions to hospitals (MRSA:1.8 vs 0.9/patient; VRE: 2.6 vs 0.9; ESBL: 2.3 vs 0.9; CDI: 0.8 vs 0.4; all P<.001) and nursing homes (MRSA: 0.4 vs 0.1/patient; VRE: 0.7 vs 0.1; ESBL: 0.7 vs 0.1; CDI: 0.3 vs 0.1; all P<.001) and longer hospital readmissions (MRSA: 8.9 vs 7.3 days; VRE: 8.9 vs 7.4; ESBL: 9.6 vs 7.5; CDI: 12.3 vs 8.2; all P<.01).ConclusionsPatients harboring antibiotic-resistant pathogens rapidly expose numerous facilities during readmissions; regional containment strategies are needed

    Long-Term Care Facilities: Important Participants of the Acute Care Facility Social Network?

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    Background: Acute care facilities are connected via patient sharing, forming a network. However, patient sharing extends beyond this immediate network to include sharing with long-term care facilities. The extent of long-term care facility patient sharing on the acute care facility network is unknown. The objective of this study was to characterize and determine the extent and pattern of patient transfers to, from, and between long-term care facilities on the network of acute care facilities in a large metropolitan county. Methods/Principal Findings: We applied social network constructs principles, measures, and frameworks to all 2007 annual adult and pediatric patient transfers among the healthcare facilities in Orange County, California, using data from surveys and several datasets. We evaluated general network and centrality measures as well as individual ego measures and further constructed sociograms. Our results show that over the course of a year, 66 of 72 long-term care facilities directly sent and 67 directly received patients from other long-term care facilities. Long-term care facilities added 1,524 ties between the acute care facilities when ties represented at least one patient transfer. Geodesic distance did not closely correlate with the geographic distance among facilities. Conclusions/Significance: This study demonstrates the extent to which long-term care facilities are connected to the acute care facility patient sharing network. Many long-term care facilities were connected by patient transfers and further added many connections to the acute care facility network. This suggests that policy-makers and health officials should account for patient sharing with and among long-term care facilities as well as those among acute care facilities when evaluating policies and interventions. © 2011 Lee et al

    Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic

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    Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method—the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds
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