Evidence map›Paper›PMID 25996638›Full record

ArticleGenetics in medicine : official journal of the American College of Medical Genetics2016

Economic evidence on identifying clinically actionable findings with whole-genome sequencing: a scoping review.

Michael P Douglas, Uri Ladabaum, Mark J Pletcher, Deborah A Marshall, Kathryn A Phillips

Abstract readScoping Review
In one paragraph

Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 3 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

14 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Article
  6. Article
  7. Whole exome sequencing in molecular diagnostics of cancer decreases over time: evidence from a cost analysis in the French setting.The European journal of health economics : HEPAC : health economics in prevention and care · 2021
    Article
  8. Evaluating the resource implications of different service delivery models for offering additional genomic findings.Genetics in medicine : official journal of the American College of Medical Genetics · 2021
    Article
  9. Article
  10. Cost Analyses of Genomic Sequencing: Lessons Learned from the MedSeq Project.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2018
    Article
  11. Article
  12. Review
  13. What are people willing to pay for whole-genome sequencing information, and who decides what they receive?Genetics in medicine : official journal of the American College of Medical Genetics · 2016
    Article
  14. 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

5 authors.

Michael P DouglasDepartment of Clinical Pharmacy, Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, California, USA.ORCID http://orcid.org/0000-0001-8517-6678
Uri LadabaumDivision of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, San Francisco, California, USA.
Mark J PletcherDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA.
Deborah A MarshallDepartment of Community Health Sciences, Cumming School of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada.
Kathryn A PhillipsDepartment of Clinical Pharmacy, Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, California, USA.

Funding

Risk-Benefit Trade-Offs for Whole Genome SequencingR01HG007063 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PHILLIPS, KATHRYN A · 2013 to 2016
$2.5M
NHGRI NIH HHS R01 HG007063
6 · The paper itself

Abstract

The American College of Medical Genetics and Genomics (ACMG) recommends that mutations in 56 genes for 24 conditions are clinically actionable and should be reported as secondary findings after whole-genome sequencing (WGS). Our aim was to identify published economic evaluations of detecting mutations in these genes among the general population or among targeted/high-risk populations and conditions and identify gaps in knowledge. A targeted PubMed search from 1994 through November 2014 was performed, and we included original, English-language articles reporting cost-effectiveness or a cost-to-utility ratio or net benefits/benefit-cost focused on screening (not treatment) for conditions and genes listed by the ACMG. Articles were screened, classified as targeting a high-risk or general population, and abstracted by two reviewers. General population studies were evaluated for actual cost-effectiveness measures (e.g., incremental cost-effectiveness ratios (ICER)), whereas studies of targeted populations were evaluated for whether at least one scenario proposed was cost-effective (e.g., ICER of ≤$100,000 per life-year or quality-adjusted life-year gained). A total of 607 studies were identified, and 32 relevant studies were included. Identified studies addressed fewer than one-third (7 of 24; 29%) of the ACMG conditions. The cost-effectiveness of screening in the general population was examined for only 2 of 24 conditions (8%). The cost-effectiveness of most genetic findings that the ACMG recommends for return has not been evaluated in economic studies or in the context of screening in the general population. The individual studies do not directly address the cost-effectiveness of WGS.

Indexed as

Genome, HumanSequence Analysis, DNACost-Benefit AnalysisGenetic Diseases, InbornGenetics, MedicalGenetic TestingHumansMutation

Identifiers

PMID25996638
PMCPMC4654986

What OpenQuestion holds

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Registered trials

None linked

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.