Evidence map›Paper›PMID 31567860›Full record

ArticleMedical care2019

Relative Effects of the Hospital Readmissions Reduction Program on Hospitals That Serve Poorer Patients.

Jason H Wasfy, Vijeta Bhambhani, Emma W Healy, Christine Choirat, Francesca Dominici, Rishi K Wadhera, Changyu Shen, Yun Wang, Robert W Yeh

Abstract read
In one paragraph

Article in Medical care, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Review
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

9 authors.

Jason H WasfyCardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Vijeta BhambhaniCardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Emma W HealyCardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Christine ChoiratSwiss Data Science Center, ETH Zurich and EPFL, Lausanne, Switzerland.
Francesca DominiciDepartment of Biostatistics, Harvard T.H. Chan School of Public Health.
Rishi K WadheraThe Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School.
Changyu ShenThe Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School.
Yun WangDepartment of Biostatistics, Harvard T.H. Chan School of Public Health.
Robert W YehThe Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School.

Funding

Harvard Clinical and Translational Science CenterKL2TR001100 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2013 to 2017
$8.5M
Explaining Rising Heart Failure Mortality in MedicareK23HL148525 · NHLBI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI WADHERA, RISHI KUMAR · 2019 to 2023
$863k
NCATS NIH HHS KL2 TR001100NHLBI NIH HHS K23 HL148525
6 · The paper itself

Abstract

importanceHospitals that serve poorer populations have higher readmission rates. It is unknown whether these hospitals effectively lowered readmission rates in response to the Hospital Readmissions Reduction Program (HRRP).

objectiveTo compare pre-post differences in readmission rates among hospitals with different proportion of dual-eligible patients both generally and among the most highly penalized (ie, low performing) hospitals.

designRetrospective cohort study using piecewise linear model with estimated hospital-level risk-standardized readmission rates (RSRRs) as the dependent variable and a change point at HRRP passage (2010). Economic burden was assessed by proportion of dual-eligibles served.

settingAcute care hospitals within the United States.

participantsMedicare fee-for-service beneficiaries aged 65 years or older discharged alive from January 1, 2003 to November 30, 2014 with a principal discharge diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia. MAIN OUTCOME AND MEASURE: Decrease in hospital-level RSRRs in the post-law period, after controlling for the pre-law trend.

resultsFor AMI, the pre-post difference between hospitals that service high and low proportion of dual-eligibles was not significant (-65 vs. -64 risk-standardized readmissions per 10000 discharges per year, P=0.0678). For CHF, RSRRs declined more at high than low dual-eligible hospitals (-79 vs. -75 risk-standardized readmissions per 10000 discharges per year, P=0.0006). For pneumonia, RSRRs declined less at high than low dual-eligible hospitals (-44 vs. -47 risk-standardized readmissions per 10000 discharges per year, P=0.0003). Among the 742 highest penalized hospitals and all conditions, the pre-post decline in rate of change of RSRRs was less for high dual-eligible hospitals than low dual-eligible hospitals (-68 vs. -74 risk-standardized readmissions per 10000 discharges per year for AMI, -88 vs. -97 for CHF, and -47 vs. -56 for pneumonia, P<0.0001 for all). CONCLUSIONS AND RELEVANCE: For all hospitals, differences in pre-post trends in RSRRs varied with disease conditions. However, for the highest-penalized hospitals, the pre-post decline in RSRRs was greater for low than high dual-eligible hospitals for all penalized conditions. These results suggest that high penalty, high dual-eligible hospitals may be less able to improve performance on readmission metrics.

Indexed as

AgedAged, 80 and overComorbidityFee-for-Service PlansFemaleHeart FailureHumansMaleMedicaidMedicareMyocardial InfarctionOwnershipPatient ReadmissionPneumoniaPovertyResidence Characteristics

Identifiers

PMID31567860
PMCPMC6856430

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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.