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Record W3096113148 · doi:10.1017/ice.2020.479

Decreased Hospitalizations and Costs From Infection in Sixteen Nursing Homes in the SHIELD OC Regional Decolonization Initiative

2020· article· en· W3096113148 on OpenAlexaboutno aff
Gabrielle Gussin, James A. McKinnell, Raveena Singh, Ken Kleinman, Amherst Loren Miller, Raheeb Saavedra, Lauren Heim, Marlene Estevez, Tabitha D. Catuna, Eunjung Lee, Thomas Tjoa, Rachel B. Slayton, Nimalie D. Stone, John A. Jernigan, Matthew Zahn, Lynn Janssen, Shruti K. Gohil, Philip A. Robinson, Steven Park, Robert A. Weinstein, Mary K. Hayden, Cassiana E. Bittencourt, Ellena M. Peterson, Susan S. Huang

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBathingMedicaidQuarter (Canadian coin)IodophorEmergency medicineHealth careSurgery

Abstract

fetched live from OpenAlex

Distinguished Oral Background: Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County, California (SHIELD OC) was a CDC-funded regional decolonization intervention from April 2017 through July 2019 involving 38 hospitals, nursing homes (NHs), and long-term acute-care hospitals (LTACHs) to reduce MDROs. Decolonization in NH and LTACHs consisted of universal antiseptic bathing with chlorhexidine (CHG) for routine bathing and showering plus nasal iodophor decolonization (Monday through Friday, twice daily every other week). Hospitals used universal CHG in ICUs and provided daily CHG and nasal iodophor to patients in contact precautions. We sought to evaluate whether decolonization reduced hospitalization and associated healthcare costs due to infections among residents of NHs participating in SHIELD compared to nonparticipating NHs. Methods: Medicaid insurer data covering NH residents in Orange County were used to calculate hospitalization rates due to a primary diagnosis of infection (counts per member quarter), hospital bed days/member-quarter, and expenditures/member quarter from the fourth quarter of 2015 to the second quarter of 2019. We used a time-series design and a segmented regression analysis to evaluate changes attributable to the SHIELD OC intervention among participating and nonparticipating NHs. Results: Across the SHIELD OC intervention period, intervention NHs experienced a 44% decrease in hospitalization rates, a 43% decrease in hospital bed days, and a 53% decrease in Medicaid expenditures when comparing the last quarter of the intervention to the baseline period (Fig. 1). These data translated to a significant downward slope, with a reduction of 4% per quarter in hospital admissions due to infection ( P < .001), a reduction of 7% per quarter in hospitalization days due to infection ( P < .001), and a reduction of 9% per quarter in Medicaid expenditures ( P = .019) per NH resident. Conclusions: The universal CHG bathing and nasal decolonization intervention adopted by NHs in the SHIELD OC collaborative resulted in large, meaningful reductions in hospitalization events, hospitalization days, and healthcare expenditures among Medicaid-insured NH residents. The findings led CalOptima, the Medicaid provider in Orange County, California, to launch an NH incentive program that provides dedicated training and covers the cost of CHG and nasal iodophor for OC NHs that enroll. Funding: None Disclosures: Gabrielle M. Gussin, University of California, Irvine, Stryker (Sage Products): Conducting studies in which contributed antiseptic product is provided to participating hospitals and nursing homes. Clorox: Conducting studies in which contributed antiseptic product is provided to participating hospitals and nursing homes. Medline: Conducting studies in which contributed antiseptic product is provided to participating hospitals and nursing homes. Xttrium: Conducting studies in which contributed antiseptic product is provided to participating hospitals and nursing homes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.377
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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