ArticlePloS one2020
Cost-effectiveness analysis of genetic diagnostic strategies for Lynch syndrome in Italy.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- The Cost Effectiveness of Genomic Medicine in Cancer Control: A Systematic Literature Review.Applied health economics and health policy · 2025Pooled it
- Universal Tumor Screening in Colorectal Cancer: Role of MMR Immunohistochemistry for Lynch Syndrome and Early-Onset CRC.Cancers · 2026Article
- Cost-Utility Analysis of Universal Lynch Syndrome Screening among Colorectal Cancer Patients in a Low-Middle-Income Country.Yonsei medical journal · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lynch syndrome (LS) is an autosomal dominant condition caused by pathogenic variants in mismatch repair (MMR) genes that predispose individuals to different malignancies, such as colorectal cancer (CRC) and endometrial cancer. Current guidelines recommended testing for LS in individuals with newly diagnosed CRC to reduce cancer morbidity and mortality in relatives. Economic evaluations in support of such approach, however, are not available in Italy. We developed a decision-analytic model to analyze the cost-effectiveness of LS screening from the perspective of the Italian National Health System. Three testing strategies: the sequencing of all MMR genes without prior tumor analysis (Strategy 1), a sequential IHC and MS-MLPA analysis (Strategy 2), and an age-targeted strategy with a revised Bethesda criteria assessment before IHC and methylation-specific MLPA for patients ≥ than 70 years old (Strategy 3) were analyzed and compared to the "no testing" strategy. Quality Adjusted Life Years (QALYs) in relatives after colonoscopy, aspirin prophylaxis and an intensive gynecological surveillance were estimated through a Markov model. Assuming a CRC incidence rate of 0.09% and a share of patients affected by LS equal to 2.81%, the number of detected pathogenic variants among CRC cases ranges, in a given year, between 910 and 1167 depending on the testing strategy employed. The testing strategies investigated, provided one-time to the entire eligible population (CRC patients), were associated with an overall cost ranging between €1,753,059.93-€10,388,000.00. The incremental cost-effectiveness ratios of the Markov model ranged from €941.24 /QALY to €1,681.93 /QALY, thus supporting that "universal testing" versus "no testing" is cost-effective, but not necessarily in comparison with age-targeted strategies. This is the first economic evaluation on different testing strategies for LS in Italy. The results might support the introduction of cost-effective recommendations for LS screening in Italy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.