Evidence map›Paper›PMID 39207756›Full record

ArticleJAMA network open2024

Surgeon and Care Team Network Measures and Timely Breast Cancer Treatment.

Ramsey Ash, Bruno T Scodari, Andrew P Schaefer, Sarah L Cornelius, Gabriel A Brooks, A James O'Malley, Tracy Onega, Dana C Verhoeven, Erika L Moen

Abstract read
In one paragraph

Article in JAMA network open, 2024. 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
–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

7 citing papers in PubMed.

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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

9 authors.

Ramsey AshProgram in QUantitative Social Science, Dartmouth College, Hanover, New Hampshire.
Bruno T ScodariDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Andrew P SchaeferThe Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, New Hampshire.
Sarah L CorneliusDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Gabriel A BrooksThe Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, New Hampshire.
A James O'MalleyDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Tracy OnegaDepartment of Population Health Sciences, University of Utah, Salt Lake City.
Dana C VerhoevenDepartment of Health Services Research & Administration, University of Nebraska Medical Center College of Public Health, Omaha.
Erika L MoenDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Fred W Kolling IV · 1985 to 2026
$91.3M
Improving cancer health equity by targeting physician networksR37CA263936 · NCI · DARTMOUTH COLLEGE · PI MOEN, ERIKA · 2021 to 2025
$2.3M
NCI NIH HHS P30 CA023108NCI NIH HHS R37 CA263936
6 · The paper itself

Abstract

Importance: Cancer treatment delay is a recognized marker of worse outcomes. Timely treatment may be associated with physician patient-sharing network characteristics, yet this remains understudied. Objective: To examine the associations of surgeon and care team patient-sharing network measures with breast cancer treatment delay. Design, Setting, and Participants: This cross-sectional study of Medicare claims in a US population-based setting was conducted from 2017 to 2020. Eligible participants included patients with breast cancer who received surgery and the subset who went on to receive adjuvant therapy. Patient-sharing networks were constructed for treating physicians. Data were analyzed from September 2023 to February 2024. Exposures: Surgeon linchpin score (a measure of local uniqueness or scarcity) and care density (a measure of physician team familiarity) were assessed. Surgeons were considered linchpins if their linchpin score was in the top 15%. The care density of a patient's physician team was calculated on preoperative teams for surgically-treated patients and postoperative teams for adjuvant therapy-receiving patients. Main Outcomes and Measures: The primary outcomes were surgical and adjuvant delay, which were defined as greater than 60 days between biopsy and surgery and greater than 60 days between surgery and adjuvant therapy, respectively. Results: The study cohort included 56 433 patients (18 004 aged 70-74 years [31.9%]) who were mostly from urban areas (44 931 patients [79.6%]). Among these patients, 8009 (14.2%) experienced surgical delay. Linchpin surgeon status (locally unique surgeon) was not statistically associated with surgical delay; however, patients with high preoperative care density (ie, high team familiarity) had lower odds of surgical delay compared with those with low preoperative care density (odds ratio [OR], 0.58; 95% CI, 0.53-0.63). Of the 29 458 patients who received adjuvant therapy after surgery, 5700 (19.3%) experienced adjuvant delay. Patients with a linchpin surgeon had greater odds of adjuvant delay compared with those with a nonlinchpin surgeon (OR, 1.30; 95% CI, 1.13-1.49). Compared with those with low postoperative care density, there were lower odds of adjuvant delay for patients with high postoperative care density (OR, 0.77; 95% CI, 0.69-0.87) and medium postoperative care density (OR, 0.85; 95% CI, 0.77-0.94). Conclusions and Relevance: In this cross-sectional study of Medicare claims, network measures capturing physician scarcity and team familiarity were associated with timely treatment. These results may help guide system-level interventions to reduce cancer treatment delays.

Indexed as

Breast NeoplasmsMedicarePatient Care TeamSurgeonsTime-to-TreatmentAgedAged, 80 and overCross-Sectional StudiesFemaleHumansUnited States

Identifiers

PMID39207756
PMCPMC11362867

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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.