Evidence map›Paper›PMID 31390034›Full record

ArticleJAMA network open2019

Comparison of 3 Safety-Net Hospital Definitions and Association With Hospital Characteristics.

Ioana Popescu, Kathryn R Fingar, Eli Cutler, Jing Guo, H Joanna Jiang

Abstract readComparative Study
In one paragraph

Article in JAMA network open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing 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

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

  1. Pooled it
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  14. Alternative Approaches to Characterizing Disparate Care by Race, Ethnicity, and Insurance Between Hospitals.International journal of environmental research and public health · 2025
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  16. Understanding the hospital safety net Hospital resource limitations impact prostate cancer treatment beyond socioeconomic disparities.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2025
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10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Ioana PopescuDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.
Kathryn R FingarIBM Watson Health, Sacramento, California.
Eli CutlerIBM Watson Health, Sacramento, California.
Jing GuoAgency for Healthcare Research and Quality, Rockville, Maryland.
H Joanna JiangAgency for Healthcare Research and Quality, Rockville, Maryland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: No consensus exists on how to define safety-net hospitals (SNHs) for research or policy decision-making. Identifying which types of hospitals are classified as SNHs under different definitions is key to assessing policies that affect SNH funding. Objective: To examine characteristics of SNHs as classified under 3 common definitions. Design, Setting, and Participants: This cross-sectional analysis includes noncritical-access hospitals in the Healthcare Cost and Utilization Project State Inpatient Databases from 47 US states for fiscal year 2015, linked to the Centers for Medicare & Medicaid Services Hospital Cost Reports and to the American Hospital Association Annual Survey. Data were analyzed from March 1 through September 30, 2018. Exposures: Hospital characteristics including organizational characteristics, scope of services provided, and financial attributes. Main Outcomes and Measures: Definitions of SNH based on Medicaid and Medicare Supplemental Security Income inpatient days historically used to determine Medicare Disproportionate Share Hospital (DSH) payments; Medicaid and uninsured caseload; and uncompensated care costs. For each measure, SNHs were defined as those within the top quartile for each state. Results: The 2066 hospitals in this study were distributed across the Northeast (340 [16.5%]), Midwest (587 [28.4%]), South (790 [38.2%]), and West (349 [16.9%]). Concordance between definitions was low; 269 hospitals (13.0%) or fewer were identified as SNHs under any 2 definitions. Uncompensated care captured smaller (200 of 523 [38.2%]) and more rural (65 of 523 [12.4%]) SNHs, whereas DSH index and Medicaid and uncompensated caseload identified SNHs that were larger (264 of 518 [51.0%] and 158 of 487 [32.4%], respectively) and teaching facilities (337 of 518 [65.1%] and 229 of 487 [47.0%], respectively) that provided more essential services than non-SNHs. Uncompensated care also distinguished remarkable financial differences between SNHs and non-SNHs. Under the uncompensated care definition, median (interquartile range [IQR]) bad debt ($27.1 [$15.5-$44.3] vs $12.8 [$6.7-$21.6] per $1000 of operating expenses; P < .001) and charity care ($19.9 [$9.3-$34.1] vs $9.1 [$4.0-$18.7] per $1000 of operating expenses) were twice as high and median (IQR) unreimbursed costs ($32.6 [$12.4-$55.4] vs $23.6 [$9.0-$42.7] per $1000 of operating expenses; P < .001) were 38% higher for SNHs than for non-SNHs. Safety-net hospitals defined by uncompensated care burden had lower median (IQR) total (4.7% [0%-9.9%] vs 5.8% [1.2%-11.2%]; P = .003) and operating (0.3% [-8.0% to 7.2%] vs 2.3% [-3.9% to 8.9%]; P < .001) margins than their non-SNH counterparts, whereas differences between SNH and non-SNH profit margins generally were not statistically significant under the other 2 definitions. Conclusions and Relevance: Different SNH definitions identify hospitals with different characteristics and financial conditions. The new DSH formula, which accounts for uncompensated care, may lead to redistributed payments across hospitals. Our results may inform which types of hospitals will experience funding changes as DSH payment policies evolve.

Indexed as

Cross-Sectional StudiesHospitalsHumansSafety-net ProvidersUnited States

Identifiers

PMID31390034
PMCPMC6686776

What OpenQuestion holds

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LicenceCC BY
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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.