Evidence map›Paper›PMID 38194620›Full record

ArticleJCO oncology practice2024

Importance of Patient Health Insurance Coverage and Out-of-Pocket Costs for Genomic Testing in Oncologists' Treatment Decisions.

K Robin Yabroff, Kewei Sylvia Shi, Jingxuan Zhao, Andrew N Freedman, Zhiyuan Zheng, Leticia Nogueira, Xuesong Han, Carrie N Klabunde, Janet S de Moor

Abstract read
In one paragraph

Article in JCO oncology practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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.

K Robin YabroffSurveillance and Health Equity Science, American Cancer Society, Atlanta, GA.ORCID 0000-0003-0644-5572
Kewei Sylvia ShiSurveillance and Health Equity Science, American Cancer Society, Atlanta, GA.ORCID 0000-0002-0024-4916
Jingxuan ZhaoSurveillance and Health Equity Science, American Cancer Society, Atlanta, GA.ORCID 0000-0002-2733-2000
Andrew N FreedmanDivision of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD.ORCID 0000-0003-1451-4208
Zhiyuan ZhengSurveillance and Health Equity Science, American Cancer Society, Atlanta, GA.ORCID 0000-0002-7665-3564
Leticia NogueiraSurveillance and Health Equity Science, American Cancer Society, Atlanta, GA.ORCID 0000-0001-6990-3660
Xuesong HanSurveillance and Health Equity Science, American Cancer Society, Atlanta, GA.ORCID 0000-0001-7159-7949
Carrie N KlabundeOffice of Disease Prevention, Office of the Director, National Institutes of Health, Rockville, MD.
Janet S de MoorDivision of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD.ORCID 0000-0003-2543-7412

Funding

Intramural NIH HHS Z99 CA999999Intramural NIH HHS Z99 OD999999NCI NIH HHS HHSN261201000061CNCI NIH HHS HHSN261201000081CNCI NIH HHS HHSN261201000086C
6 · The paper itself

Abstract

purposeUse of genomic testing, especially multimarker panels, is increasing in the United States. Not all tests and related treatments are covered by health insurance, which can result in substantial patient out-of-pocket (OOP) costs. Little is known about oncologists' treatment decisions with respect to patient insurance coverage and OOP costs for genomic testing.

methodsWe identified 1,049 oncologists who used multimarker tumor panels from the 2017 National Survey of Precision Medicine in Cancer Treatment. Separate multivariable ordinal logistic regressions examined associations of oncologist-, practice-, and area-level characteristics and oncologists' ratings of importance (very, somewhat, or a little/not important) of insurance coverage and OOP costs for genomic testing in treatment decisions, adjusting for oncologist years of experience, sex, race and ethnicity, specialty, use of next-generation sequencing (NGS) tests, region, tumor boards, patient insurance mix, and area-level socioeconomic characteristics.

resultsAmong oncologists, 47.3%, 32.7%, and 20.0% reported that patient insurance coverage for genomic testing was very, somewhat, or a little/not important, respectively, in treatment decisions. In addition, 56.9%, 28.0%, and 15.2% reported that OOP costs for testing were very, somewhat, or a little/not important, respectively. In adjusted analyses, oncologists who used NGS tests were more likely to report patient insurance and OOP costs as important (odds ratio [OR], 2.00 [95% CI, 1.16 to 3.45] and OR, 2.12 [95% CI, 1.22 to 3.68], respectively) in treatment decisions compared with oncologists who did not use these tests, as were oncologists who treated solid tumors, rather than only hematological cancers. More years of experience and higher percentages of Medicaid or self-paid/uninsured patients in the practice were associated with reporting insurance coverage (OR, 1.43 [95% CI, 1.09 to 1.89]) and OOP costs (OR, 1.51 [95% CI, 1.13 to 2.01]) as important. Oncologists in practices with molecular tumor boards for genomic tests were less likely to report coverage (OR, 0.63 [95% CI, 0.47 to 0.85]) and OOP costs (OR, 0.72 [95% CI, 0.53 to 0.97]) as important than their counterparts in practices without these tumor boards.

conclusionMost oncologists rate patient health insurance and OOP costs for genomic tests as important considerations in subsequent treatment recommendations. Modifiable factors associated with these ratings can inform interventions to support patient-physician decision making about care.

Indexed as

Hematologic NeoplasmsOncologistsGenetic TestingHealth ExpendituresHumansInsurance CoverageUnited States

Identifiers

PMID38194620
PMCPMC11742181

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.