Evidence map›Paper›PMID 36675679›Full record

ArticleJournal of personalized medicine2022

Clinical Cancer and Direct-to-Consumer Genetic Test Result-Sharing Behavior: Findings from HINTS 2020.

Sukh Makhnoon, Robert Yu, Susan K Peterson, Sanjay Shete

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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

4 citing papers in PubMed.

  1. Use of Family Relationships in Commercial Claims Data to Characterize Clinical Events of Patients withAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    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

4 authors.

Sukh MakhnoonDepartment of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Robert YuDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Susan K PetersonDepartment of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Sanjay SheteDepartment of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0001-7622-376X

Funding

Multilevel investigation of uncertain and reclassified genomic variants in clinical oncologyK99CA256216 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MAKHNOON, SUKH · 2021 to 2022
$216k
NCI NIH HHS 5P30CA016672NCI NIH HHS K99CA256216
6 · The paper itself

Abstract

introductionSharing genetic test results with different stakeholders such as family members, healthcare providers and genetic counselors (HCP/GCs), spouses/partners, and friends is a health behavior of clinical importance in genomic medicine.

methodsUsing nationally representative population-based data collected from the Health Information National Trends Survey (HINTS 5, cycle 4), we identified the prevalence and factors associated with genetic test result-sharing behavior for high-risk cancer tests, genetic health risk tests, and ancestry tests within four groups: HCP/GCs, first-degree relatives (FDRs), spouse/partner, and friend/other.

resultsOverall, 68.4% of those who underwent high-risk cancer genetic testing shared their results with FDRs, whereas 89.9% shared with HCP/GCs. In adjusted multivariable analyses, women were nine times more likely than men to share ( DISCUSSION: These data add empirical evidence about the population prevalence of genetic information sharing and serve as a metric for public engagement with genetic testing.

Indexed as

cancercommunicationdirect-to-consumergenetic testingresult sharing

Identifiers

PMID36675679
PMCPMC9866851

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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.