Evidence map›Paper›PMID 38192593›Full record

ArticleEClinicalMedicine2023

Combined population genomic screening for three high-risk conditions in Australia: a modelling study.

Paul Lacaze, Clara Marquina, Jane Tiller, Adam Brotchie, Yoon-Jung Kang, Melissa A Merritt, Robert C Green, Gerald F Watts, Kristen J Nowak, Ranjit Manchanda and 5 more

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
4.7field-weighted citation impact, top 5% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. The value of large-scale programmes in human genomics.European journal of human genetics : EJHG · 2025
    Article
  11. Article
  12. Measuring perceived utility of genomic sequencing: Development and validation of the GENEtic Utility (GENE-U) scale for adult screening.Genetics in medicine : official journal of the American College of Medical Genetics · 2024
    Article
  13. Article
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

15 authors at 6 institutions in 3 countries.

Paul LacazeSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.
Clara MarquinaHealth Economics and Policy Evaluation Research (HEPER) Group, Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, VIC 3052, Australia.
Jane TillerSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.
Adam BrotchieSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.
Yoon-Jung KangThe Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, NSW 2011, Australia.
Melissa A MerrittThe Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, NSW 2011, Australia.
Robert C GreenMass General Brigham, Broad Institute, Ariadne Labs and Harvard Medical School, Boston, MA, 02114, USA.
Gerald F WattsSchool of Medicine, University of Western Australia, Perth, WA 6009, Australia.
Kristen J NowakPublic and Aboriginal Health Division, Western Australia Department of Health, East Perth, WA, 6004, Australia.
Ranjit ManchandaWolfson Institute of Population Health, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
Karen CanfellThe Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, NSW 2011, Australia.
Paul JamesParkville Familial Cancer Centre, Peter McCallum Cancer Centre, Melbourne, VIC, 3000, Australia.
Ingrid WinshipDepartment of Genomic Medicine, Royal Melbourne Hospital City Campus, Parkville, VIC, 3050, Australia.
John J McNeilSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.
Zanfina AdemiSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.
Monash University · AUThe University of Sydney · AUThe Royal Melbourne Hospital · AUThe University of Western Australia · AUBroad Institute · USQueen Mary University of London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: No previous health-economic evaluation has assessed the impact and cost-effectiveness of offering combined adult population genomic screening for mutliple high-risk conditions in a national public healthcare system. Methods: This modeling study assessed the impact of offering combined genomic screening for hereditary breast and ovarian cancer, Lynch syndrome and familial hypercholesterolaemia to all young adults in Australia, compared with the current practice of clinical criteria-based testing for each condition separately. The intervention of genomic screening, assumed as an up-front single cost in the first annual model cycle, would detect pathogenic variants in seven high-risk genes. The simulated population was 18-40 year-olds (8,324,242 individuals), modelling per-sample test costs ranging AU$100-$1200 (base-case AU$200) from the year 2023 onwards with testing uptake of 50%. Interventions for identified high-risk variant carriers follow current Australian guidelines, modelling imperfect uptake and adherence. Outcome measures were morbidity and mortality due to cancer (breast, ovarian, colorectal and endometrial) and coronary heart disease (CHD) over a lifetime horizon, from healthcare-system and societal perspectives. Outcomes included quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER), discounted 5% annually (with 3% discounting in scenario analysis). Findings: Over the population lifetime (to age 80 years), the model estimated that genomic screening per-100,000 individuals would lead to 747 QALYs gained by preventing 63 cancers, 31 CHD cases and 97 deaths. In the total model population, this would translate to 31,094 QALYs gained by preventing 2612 cancers, 542 non-fatal CHD events and 4047 total deaths. At AU$200 per-test, genomic screening would require an investment of AU$832 million for screening of 50% of the population. Our findings suggest that this intervention would be cost-effective from a healthcare-system perspective, yielding an ICER of AU$23,926 (∼£12,050/€14,110/US$15,345) per QALY gained over the status quo. In scenario analysis with 3% discounting, an ICER of AU$4758/QALY was obtained. Sensitivity analysis for the base case indicated that combined genomic screening would be cost-effective under 70% of simulations, cost-saving under 25% and not cost-effective under 5%. Threshold analysis showed that genomic screening would be cost-effective under the AU$50,000/QALY willingness-to-pay threshold at per-test costs up to AU$325 (∼£164/€192/US$208). Interpretation: Our findings suggest that offering combined genomic screening for high-risk conditions to young adults would be cost-effective in the Australian public healthcare system, at currently realistic testing costs. Other matters, including psychosocial impacts, ethical and societal issues, and implementation challenges, also need consideration. Funding: Australian Government, Department of Health, Medical Research Future Fund, Genomics Health Futures Mission (APP2009024). National Heart Foundation Future Leader Fellowship (102604).

Indexed as

Cost-effectiveness analysisGenomicsGenomic testingHealth-economic evaluationPopulation screeningPreventionPublic health

Identifiers

PMID38192593
PMCPMC10772163
OpenAlexW4388490603

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.