Evidence map›Paper›PMID 33963508›Full record

ArticleJournal of general internal medicine2021

Evaluating the Contribution of Patient-Provider Communication and Cancer Diagnosis to Racial Disparities in End-of-Life Care Among Medicare Beneficiaries.

Cleo A Samuel-Ryals, Olive M Mbah, Sharon Peacock Hinton, Sarah H Cross, Bryce B Reeve, Stacie B Dusetzina

Open access · bronzeAbstract read
In one paragraph

Article in Journal of general internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.4field-weighted citation impact, top 12% 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

7 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
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  4. Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias.medRxiv : the preprint server for health sciences · 2024
    Article
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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

6 authors at 3 institutions in 2 countries.

Cleo A Samuel-RyalsDepartment of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA. casamuel@email.unc.edu.ORCID 0000-0001-7536-3998
Olive M MbahDepartment of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
Sharon Peacock HintonDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
Sarah H CrossSanford School of Public Policy, Duke University, Durham, NC, USA.
Bryce B ReeveDepartment of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA.
Stacie B DusetzinaDepartment of Health Policy, Vanderbilt University School of Medicine, Nashville, TN, USA.
University of North Carolina at Chapel Hill · USDuke University · USVanderbilt University · US

Funding

Cancer Care Quality Training ProgramT32CA116339 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ETHAN M. BASCH, Stephanie Brooke Wheeler · 2018 to 2026
$3.0M
Symptom and Healthcare Access Reporting and Evaluation (SHARE) for Supportive Care EquityK01CA218473 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SAMUEL-RYALS, CLEO A · 2018 to 2021
$641k
NCI NIH HHS 1 K01 CA218473-01A1NCI NIH HHS K01 CA218473NCI NIH HHS T32 CA116339
6 · The paper itself

Abstract

backgroundThe quality of end-of-life (EOL) care in the USA remains suboptimal, with significant variations in care by race and across disease subgroups. Patient-provider communication may contribute to racial and disease-specific variations in EOL care outcomes.

objectiveWe examined racial disparities in EOL care, by disease group (cancer vs. non-cancer), and assessed whether racial differences in patient-provider communication accounted for observed disparities.

designRetrospective cohort study using the 2001-2015 Surveillance, Epidemiology, and End Results - Consumer Assessment of Healthcare Providers and Systems data linked with Medicare claims (SEER-CAHPS). We employed stratified propensity score matching and modified Poisson regression analyses, adjusting for clinical and demographic characteristics

participantsBlack and White Medicare beneficiaries 65 years or older with cancer (N=2000) or without cancer (N=11,524). MAIN MEASURES: End-of-life care measures included hospice use, inpatient hospitalizations, intensive care unit (ICU) stays, and emergency department (ED) visits, during the 90 days prior to death. KEY

resultsWhen considering all conditions together (cancer + non-cancer), Black beneficiaries were 26% less likely than their Whites counterparts to enroll in hospice (adjusted risk ratio [ARR]: 0.74, 95%CI: 0.66-0.83). Among beneficiaries without cancer, Black beneficiaries had a 32% lower likelihood of enrolling in hospice (ARR: 0.68, 95%CI: 0.59-0.79). There was no racial difference in hospice enrollment among cancer patients. Black beneficiaries were also at increased risk for ED use (ARR: 1.12, 95%CI: 1.01-1.26). Patient-provider communication did not explain racial disparities in hospice or ED use. There were no racial differences in hospitalizations or ICU admissions.

conclusionWe observed racial disparities in hospice use and ED visits in the 90 days prior to death among Medicare beneficiaries; however, hospice disparities were largely driven by patients without cancer. Condition-specific differences in palliative care integration at the end-of-life may partly account for variations in EOL care disparities across disease groups.

Indexed as

Hospice CareNeoplasmsTerminal CareAgedCommunicationHealthcare DisparitiesHumansMedicareRetrospective StudiesUnited Statesend-of-life carepatient-provider communicationracial disparities

Identifiers

PMID33963508
PMCPMC8606371
OpenAlexW3159440133

What OpenQuestion holds

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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.