Evidence map›Paper›PMID 38358739›Full record

ArticleJAMA network open2024

Trends in Patient Transfers From Overall and Caseload-Strained US Hospitals During the COVID-19 Pandemic.

Sadia H Sarzynski, Alex G Mancera, Christina Yek, Ning An Rosenthal, Alex Kartashov, John L Hick, Steven H Mitchell, Maniraj Neupane, Sarah Warner, Junfeng Sun and 3 more

Abstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Hospital Capacity Pooling in Pandemics: Simulation-Based Mortality Impacts and Diversification Mechanisms.Risk analysis : an official publication of the Society for Risk Analysis · 2026
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  6. Declining mortality after open pelvis fracture in North America.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2025
    Article
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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

13 authors.

Sadia H SarzynskiCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.
Alex G ManceraCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.
Christina YekCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.
Ning An RosenthalPINC-AI Applied Sciences, Premier, Inc, Charlotte, North Carolina.
Alex KartashovPINC-AI Applied Sciences, Premier, Inc, Charlotte, North Carolina.
John L HickHennepin Healthcare, Minneapolis, Minnesota.
Steven H MitchellDepartment of Emergency Medicine, University of Washington, Seattle.
Maniraj NeupaneCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.
Sarah WarnerCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.
Junfeng SunCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.
Cumhur Y DemirkaleCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.
Bruce SwihartCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.
Sameer S KadriCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.

Funding

Big Data - Epidemiology of Critical Illness and SepsisZIACL090045 · CLC · CLINICAL CENTER · PI KADRI, SAMEER · 2017 to 2025
$0k
6 · The paper itself

Abstract

Importance: Transferring patients to other hospitals because of inpatient saturation or need for higher levels of care was often challenging during the early waves of the COVID-19 pandemic. Understanding how transfer patterns evolved over time and amid hospital overcrowding could inform future care delivery and load balancing efforts. Objective: To evaluate trends in outgoing transfers at overall and caseload-strained hospitals during the COVID-19 pandemic vs prepandemic times. Design, Setting, and Participants: This retrospective cohort study used data for adult patients at continuously reporting US hospitals in the PINC-AI Healthcare Database. Data analysis was performed from February to July 2023. Exposures: Pandemic wave, defined as wave 1 (March 1, 2020, to May 31, 2020), wave 2 (June 1, 2020, to September 30, 2020), wave 3 (October 1, 2020, to June 19, 2021), Delta (June 20, 2021, to December 18, 2021), and Omicron (December 19, 2021, to February 28, 2022). Main Outcomes and Measures: Weekly trends in cumulative mean daily acute care transfers from all hospitals were assessed by COVID-19 status, hospital urbanicity, and census index (calculated as daily inpatient census divided by nominal bed capacity). At each hospital, the mean difference in transfer counts was calculated using pairwise comparisons of pandemic (vs prepandemic) weeks in the same census index decile and averaged across decile hospitals in each wave. For top decile (ie, high-surge) hospitals, fold changes (and 95% CI) in transfers were adjusted for hospital-level factors and seasonality. Results: At 681 hospitals (205 rural [30.1%] and 476 urban [69.9%]; 360 [52.9%] small with <200 beds and 321 [47.1%] large with ≥200 beds), the mean (SD) weekly outgoing transfers per hospital remained lower than the prepandemic mean of 12.1 (10.4) transfers per week for most of the pandemic, ranging from 8.5 (8.3) transfers per week during wave 1 to 11.9 (10.7) transfers per week during the Delta wave. Despite more COVID-19 transfers, overall transfers at study hospitals cumulatively decreased during each high national surge period. At 99 high-surge hospitals, compared with a prepandemic baseline, outgoing acute care transfers decreased in wave 1 (fold change -15.0%; 95% CI, -22.3% to -7.0%; P < .001), returned to baseline during wave 2 (2.2%; 95% CI, -4.3% to 9.2%; P = .52), and displayed a sustained increase in subsequent waves: 19.8% (95% CI, 14.3% to 25.4%; P < .001) in wave 3, 19.2% (95% CI, 13.4% to 25.4%; P < .001) in the Delta wave, and 15.4% (95% CI, 7.8% to 23.5%; P < .001) in the Omicron wave. Observed increases were predominantly limited to small urban hospitals, where transfers peaked (48.0%; 95% CI, 36.3% to 60.8%; P < .001) in wave 3, whereas large urban and small rural hospitals displayed little to no increases in transfers from baseline throughout the pandemic. Conclusions and Relevance: Throughout the COVID-19 pandemic, study hospitals reported paradoxical decreases in overall patient transfers during each high-surge period. Caseload-strained rural (vs urban) hospitals with fewer than 200 beds were unable to proportionally increase transfers. Prevailing vulnerabilities in flexing transfer capabilities for care or capacity reasons warrant urgent attention.

Indexed as

COVID-19Sprains and StrainsAdultHospitals, UrbanHumansPandemicsPatient TransferRetrospective Studies

Identifiers

PMID38358739
PMCPMC10870187

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Registered trials

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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.