Evidence map›Paper›PMID 24307260›Full record

ArticleJournal of general internal medicine2014

Variation in the risk of readmission among hospitals: the relative contribution of patient, hospital and inpatient provider characteristics.

Siddhartha Singh, Yu-Li Lin, Yong-Fang Kuo, Ann B Nattinger, James S Goodwin

Abstract readMulticenter Study
In one paragraph

Article in Journal of general internal medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 1 pooled it
–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

47 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Observational
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  11. Trends and variation in benzodiazepine use in nursing homes in the USA.European journal of clinical pharmacology · 2022
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  12. Calibrating Readmission Risk Prediction Models for Determining Post-discharge Follow-up Timing.Journal of community hospital internal medicine perspectives · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Siddhartha SinghCenter for Patient Care and Outcomes Research, Medical College of Wisconsin, 9200 West Wisconsin Avenue, Milwaukee, WI, 53226, USA, ssingh@mcw.edu.
Yu-Li Lin
Yong-Fang Kuo
Ann B Nattinger
James S Goodwin

Funding

UTMB OAIC Research Education Component (REC)P30AG024832 · NIA · UNIVERSITY OF TEXAS MEDICAL BR GALVESTON · PI JAMES S. GOODWIN, MD, MELISSA M. MORROW · 2005 to 2026
$26.7M
Patient Centered Outcomes Research in the ElderlyR24HS022134 · AHRQ · UNIVERSITY OF TEXAS MED BR GALVESTON · PI GOODWIN, MD, JAMES S. · 2013 to 2017
$4.8M
Care of the elder hospitalized patient: the role of the hospitalistsR01AG033134 · NIA · UNIVERSITY OF TEXAS MED BR GALVESTON · PI GOODWIN, MD, JAMES S. · 2009 to 2018
$2.2M
Established Investigator Award in Cancer Prevention & ControlK05CA134923 · NCI · UNIVERSITY OF TEXAS MED BR GALVESTON · PI GOODWIN, MD, JAMES S. · 2008 to 2019
$1.0M
AHRQ HHS R24 HS022134NCI NIH HHS K05 CA134923NCI NIH HHS K05-CA134923NIA NIH HHS 1R01-AG033134NIA NIH HHS 5P30AG024832NIA NIH HHS P30 AG024832NIA NIH HHS R01 AG033134PHS HHS RP101207
6 · The paper itself

Abstract

backgroundThe risk of readmission varies among hospitals. This variation has led the Centers of Medicare and Medicaid services to reduce payments to hospitals with excess readmissions. The contribution of patient characteristics, hospital characteristics and provider type to the variation in risk of readmission among hospitals has not been determined.

objectiveTo describe the variation in risk of readmission among hospitals and partition it by patient characteristics, hospital characteristics and provider type.

designRetrospective research design of 100% Texas Medicare data using multilevel, multivariable models. SUBJECTS: A total of 514,064 admissions of Medicare beneficiaries to 272 hospitals in Texas for medical diagnoses during the years 2008 and 2009. MAIN MEASURES: Using hierarchical generalized linear models, we describe the hospital-specific variation in risk of readmission that is attributable to patients characteristics, hospital characteristics and provider type by measuring the variance and intraclass correlation coefficients. KEY

resultsOf the total variation in risk of readmission, only a small amount (0.84%) is attributed to hospitals. In further analyses modeling the components of this variation among hospitals, differences in patient characteristics in the hospitals explained 56.2% of the variation. Hospital characteristics and the type of provider explained 9.3% of the variation among hospitals and 0.08% of the total variation in risk of readmission.

conclusionsPatient characteristics are the largest contributor to variation in risk of readmission among hospitals. Measurable hospital characteristics and the type of inpatient provider contribute little to variation in risk of readmission among hospitals.

Indexed as

Clinical CompetenceInpatientsAgedAged, 80 and overCohort StudiesFemaleHospitalsHumansMaleMedicarePatient ParticipationPatient ReadmissionRetrospective StudiesRisk FactorsTexasUnited States

Identifiers

PMID24307260
PMCPMC3965757

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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.