Evidence map›Paper›PMID 22422056›Full record

ArticleMedical care2012

Evaluating the effect of hospital and insurance type on the risk of 1-year mortality of very low birth weight infants: controlling for selection bias.

Songthip Ounpraseuth, C Heath Gauss, Janet Bronstein, Curtis Lowery, Richard Nugent, Richard Hall

Abstract read
In one paragraph

Article in Medical care, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 24% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

6 authors at 2 institutions in 1 country.

Songthip OunpraseuthDepartment of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA. stounpraseuth@uams.edu
C Heath Gauss
Janet Bronstein
Curtis Lowery
Richard Nugent
Richard Hall
University of Arkansas for Medical Sciences · USUniversity of Alabama at Birmingham · US

Funding

CTSA INFRASTRUCTURE FOR PEDIATRIC RESEARCHUL1RR029884 · NCRR · UNIV OF ARKANSAS FOR MED SCIS · PI LOWERY, CURTIS L · 2009 to 2011
$11.4M
NCRR NIH HHS 1UL1RR029884NCRR NIH HHS RR02146NCRR NIH HHS UL1 RR029884
6 · The paper itself

Abstract

objectivesWe examined the effect of hospital type and medical coverage on the risk of 1-year mortality of very low birth weight (VLBW) infants while adjusting for possible selection bias.

methodsThe study population was limited to singleton live birth infants having birth weight between 500 and 1500 g with no congenital anomalies who were born in Arkansas hospitals between 2001 and 2007. Propensity score (PS) matching and PS covariate adjustment were used to mitigate selection bias. In addition, a conventional multivariable logistic regression model was used for comparison purposes.

resultsGenerally, all 3 analytical approaches provided consistent results in terms of the estimated relative risk, absolute risk reduction, and the number needed to treat. Using the PS matching method, VLBW infants delivered at a hospital with a neonatal intensive care unit (NICU) were associated with a 35% relative decrease (95% bootstrap confidence interval, 18.5%-48.9%) in the risk of 1-year mortality as compared with those infants delivered at non-NICU hospitals. Furthermore, our results showed that on average, 16 VLBW infants (95% bootstrap confidence interval, 11-32), would need to be delivered at a hospital with an NICU to prevent 1 additional death at 1 year. However, there was not a difference in the risk of 1-year mortality between VLBW infants born to Medicaid-insured versus non-Medicaid-insured women.

conclusionsEstimated relative risk of infant mortality was significantly lower for births that occurred in hospitals with an NICU; therefore, greater efforts should be made to deliver VLBW neonates in an NICU hospital.

Indexed as

Infant MortalityInfant, Very Low Birth WeightArkansasBirth WeightFemaleHospitalsHumansInfantInfant, NewbornInsurance, HealthIntensive Care Units, NeonatalLogistic ModelsMaleModels, StatisticalPropensity ScoreRisk

Identifiers

PMID22422056
PMCPMC3306601
OpenAlexW2056605831

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.