Evidence map›Paper›PMID 25092832›Full record

ArticleHealth affairs (Project Hope)2014

California emergency department closures are associated with increased inpatient mortality at nearby hospitals.

Charles Liu, Tanja Srebotnjak, Renee Y Hsia

Open access · greenAbstract read
In one paragraph

Article in Health affairs (Project Hope), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
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

12 citing papers in PubMed, 33 citations in OpenAlex.

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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

3 authors at 3 institutions in 2 countries.

Charles LiuCharles Liu is a medical student at Harvard Medical School, in Boston, Massachusetts.
Tanja SrebotnjakTanja Srebotnjak is a senior fellow at the Ecologic Institute, in San Mateo, California.
Renee Y HsiaRenee Y. Hsia (renee.hsia@emergency.ucsf.edu) is an associate professor in the Department of Emergency Medicine and the Institute of Health Policy Studies, University of California, San Francisco.
Harvard University · USPublic Policy Institute of California · USUniversity of Alkafeel · IQ

Funding

Clinical and Transtional Science InstituteKL2TR000143 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRANDIS, JENNIFER RUBIN · 2012 to 2015
$11.9M
NCATS NIH HHS KL2 TR000143NCATS NIH HHS KL2TR000143
6 · The paper itself

Abstract

Between 1996 and 2009 the annual number of emergency department (ED) visits in the United States increased by 51 percent while the number of EDs nationwide decreased by 6 percent, which placed unprecedented strain on the nation's EDs. To investigate the effects of an ED's closing on surrounding communities, we identified all ED closures in California during the period 1999-2010 and examined their association with inpatient mortality rates at nearby hospitals. We found that one-quarter of hospital admissions in this period occurred near an ED closure and that these admissions had 5 percent higher odds of inpatient mortality than admissions not occurring near a closure. This association persisted whether we considered ED closures as affecting all future nearby admissions or only those occurring in the subsequent two years. These results suggest that ED closures have ripple effects on patient outcomes that should be considered when health systems and policy makers decide how to regulate ED closures.

Indexed as

Health Facility ClosureAdultAgedCaliforniaEmergency Service, HospitalHospital MortalityHumansMiddle AgedPatient AdmissionRetrospective StudiesYoung AdultOrganization and Delivery of Care

Identifiers

PMID25092832
PMCPMC4214135
OpenAlexW2167017002

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.