Evidence map›Paper›PMID 38815164›Full record

Trial reportNEJM evidence2024

A Trial of Automated Outbreak Detection to Reduce Hospital Pathogen Spread.

Meghan A Baker, Edward Septimus, Ken Kleinman, Julia Moody, Kenneth E Sands, Neha Varma, Amanda Isaacs, Laura E McLean, Micaela H Coady, Eunice J Blanchard and 22 more

Registry-linked trialAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in NEJM evidence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04053075 (Cluster Linkage Using Statistics to Trigger and Evaluate Response), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04053075 nacompletednot on this map

Cluster Linkage Using Statistics to Trigger and Evaluate Response (CLUSTER) Trial for Outbreak Detection and Response

TypeinterventionalSponsorHarvard Pilgrim Health CareRan2019 to 2024Enrolled2,905,455ConditionsCluster DetectionArmsRoutine cluster detection, Enhanced cluster detection
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

32 authors.

Meghan A BakerDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
Edward SeptimusDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
Ken KleinmanDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst.
Julia MoodyHCA Healthcare, Nashville.
Kenneth E SandsDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
Neha VarmaDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
Amanda IsaacsDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
Laura E McLeanHCA Healthcare, Nashville.
Micaela H CoadyDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
Eunice J BlanchardHCA Healthcare, Nashville.
Russell E PolandDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
Deborah S YokoeDepartment of Medicine, University of California, San Francisco School of Medicine, San Francisco.
John StellingDepartment of Medicine, Brigham and Women's Hospital, Boston.
Katherine HaffenrefferDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
Adam ClarkDepartment of Medicine, Brigham and Women's Hospital, Boston.
Taliser R AveryDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
Selsebil SljivoDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
Robert A WeinsteinRush University Medical Center, Chicago.
Kimberly N SmithHCA Healthcare, Nashville.
Brandon CarverHCA Healthcare, Nashville.
Brittany MeadorHCA Healthcare, Nashville.
Michael Y LinRush University Medical Center, Chicago.
Sarah S LewisDuke University Hospital, Durham, NC.
Chamaine WashingtonDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
Megha BhattaraiDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
Lauren ShimelmanDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
Martin KulldorffDepartment of Medicine, Brigham and Women's Hospital, Boston.
Sujan C ReddyCenters for Disease Control and Prevention, Atlanta.
John A JerniganCenters for Disease Control and Prevention, Atlanta.
Jonathan B PerlinHCA Healthcare, Nashville.
Richard PlattDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
Susan S HuangDivision of Infectious Diseases, University of California, Irvine School of Medicine, Irvine.

Funding

NCEZID CDC HHS U54 CK000484NCEZID CDC HHS U54 CK000607
6 · The paper itself

Abstract

backgroundDetection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown.

methodsWe conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods.

resultsReal-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92).

conclusionsAutomated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).

Indexed as

COVID-19Cross InfectionDisease OutbreaksHospitals, CommunityHumansInfection ControlSARS-CoV-2

Identifiers

PMID38815164
PMCPMC12990200

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.