ArticleEClinicalMedicine2023
Combined population genomic screening for three high-risk conditions in Australia: a modelling study.
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.
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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.
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- The Cost Effectiveness of Genomic Medicine in Cancer Control: A Systematic Literature Review.Applied health economics and health policy · 2025Pooled it
- Opportunistic Genomic Screening for Familial Hypercholesterolemia to Improve Low-Density Lipoprotein Cholesterol: A Randomized Clinical Trial.JAMA network open · 2026Trial
- BRCA1 and BRCA2 population screening in Latvia: feasibility, engagement, and early lessons from a pilot study.European journal of human genetics : EJHG · 2026Article
- Internal Validity of the French Version of the PARDI-AR-Q and Subtypes Analysis Among Children and Adolescents.Child psychiatry and human development · 2026Article
- Primary care stakeholder perspectives on clinical implementation of patient-reported hereditary cancer risk-assessment.BMC primary care · 2026Article
- Genomic risk model to implement precision prostate cancer screening in clinical care: the ProGRESS study.Nature cancer · 2026Article
- Feasibility and outcomes of the DNA Screen nationwide adult genomic screening pilot.Nature health · 2026Article
- Findings from comprehensive genome sequencing in the Canadian population: Results from the GENCOV Study.Genetics in medicine open · 2026Article
- Opportunistic salpingectomy for prevention of ovarian cancer among the general population.Current opinion in obstetrics & gynecology · 2025Review
- The value of large-scale programmes in human genomics.European journal of human genetics : EJHG · 2025Article
- Genomic Screening at a Single Health System.JAMA network open · 2025Article
- 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 · 2024Article
- Economic Evaluation of Population-Based BRCA1 and BRCA2 Testing in Canada.JAMA network open · 2024Article
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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).
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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.