ArticlePopulation health management2019
Balancing Adherence and Expense: The Cost-Effectiveness of Two-Sample vs One-Sample Fecal Immunochemical Test.
Article in Population health management, 2019. 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.
- One- versus two-sample fecal immunochemical testing for colorectal cancer screening: a systematic review and meta-analysis of participation, diagnostic performance, and cost-effectiveness.BMC gastroenterology · 2026Pooled it
- Detection of advanced colorectal neoplasia and relative colonoscopy workloads using quantitative faecal immunochemical tests: an observational study exploring the effects of simultaneous adjustment of both sample number and test positivity threshold.BMJ open gastroenterology · 2020Observational
- Patient randomized trial of a targeted navigation program to improve rates of follow-up colonoscopy in community health centers.Contemporary clinical trials · 2020Article
- Mailed FIT (fecal immunochemical test), navigation or patient reminders? Using microsimulation to inform selection of interventions to increase colorectal cancer screening in Medicaid enrollees.Preventive medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Colorectal cancer (CRC) causes more than 50,000 deaths each year in the United States but early detection through screening yields survival gains; those diagnosed with early stage disease have a 5-year survival greater than 90%, compared to 12% for those diagnosed with late stage disease. Using data from a large integrated health system, this study evaluates the cost-effectiveness of fecal immunochemical testing (FIT), a common CRC screening tool. A probabilistic decision-analytic model was used to examine the costs and outcomes of positive test results from a 1-FIT regimen compared with a 2-FIT regimen. The authors compared 5 diagnostic cutoffs of hemoglobin concentration for each test (for a total of 10 screening options). The principal outcome from the analysis was the cost per additional advanced neoplasia (AN) detected. The authors also estimated the number of cancers detected and life-years gained from detecting AN. The following costs were included: program management of the screening program, patient identification, FIT kits and their processing, and diagnostic colonoscopy following a positive FIT. Per-person costs ranged from $33 (1-FIT at 150ng/ml) to $92 (2-FIT at 50ng/ml) across screening options. Depending on willingness to pay, the 1-FIT 50 ng/ml and the 2-FIT 50 ng/ml are the dominant strategies with cost-effectiveness of $11,198 and $28,389, respectively, for an additional AN detected. The estimates of cancers avoided per 1000 screens ranged from 1.46 to 4.86, depending on the strategy and the assumptions of AN to cancer progression.
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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.