Evidence map›Paper›PMID 40581741›Full record

SynthesisJournal of the National Cancer Institute2025

Benefits of colorectal cancer screening using fecal immunochemical testing with varying positivity thresholds by age and sex.

Matthias Harlass, Amy B Knudsen, Daan Nieboer, Luuk A van Duuren, Karen M Kuntz, Carolyn M Rutter, Pedro Nascimento de Lima, Nicholson Collier, Jonathan Ozik, Anne I Hahn and 5 more

Abstract readMeta-Analysis
In one paragraph

Synthesis in Journal of the National Cancer Institute, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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.

Matthias HarlassDepartment of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID 0000-0002-3670-1402
Amy B KnudsenInstitute for Technology Assessment, Massachusetts General Hospital, Boston, MA, United States.ORCID 0000-0003-1872-996X
Daan NieboerDepartment of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID 0000-0002-4403-6537
Luuk A van DuurenDepartment of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID 0000-0002-6286-8062
Karen M KuntzDivision of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN, United States.ORCID 0000-0001-6335-7590
Carolyn M RutterBiostatistics Program, Public Health Sciences Division, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Center, Seattle, WA, United States.ORCID 0000-0002-4396-8594
Pedro Nascimento de LimaEngineering and Applied Sciences Department, RAND Corporation, Arlington VA, United States.ORCID 0000-0001-9057-198X
Nicholson CollierDecision and Infrastructure Sciences, Argonne National Laboratory, Lemont, IL, United States.ORCID 0000-0002-2376-4156
Jonathan OzikDecision and Infrastructure Sciences, Argonne National Laboratory, Lemont, IL, United States.ORCID 0000-0002-3495-6735
Anne I HahnDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID 0000-0003-4061-2303
Fernando Alarid-EscuderoDepartment of Health Policy, School of Medicine, Stanford University, Stanford, CA, United States.ORCID 0000-0001-5076-1172
Ann G ZauberDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID 0000-0002-1764-5994
John M InadomiDepartment of Internal Medicine, The Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0001-6776-8661
Reinier G S MeesterDepartment of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID 0000-0003-4645-1221
Iris Lansdorp-VogelaarDepartment of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID 0000-0002-9438-2753

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Comparative Modeling of Effective Policies for Colorectal Cancer ControlU01CA253913 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI RUTTER, CAROLYN M, ZAUBER, ANN GRAHAM · 2020 to 2025
$6.5M
Cancer Intervention and Surveillance Modelling Network U01CA253913Department of Energy Office of Science User FacilityDepartment of Energy Office of Science User Facility DE-AC02-06CH11357Laboratory Computing Resource Center at Argonne National LaboratoryNational Energy Research Scientific Computing CenterNational Energy Research Scientific Computing Center ALCC-ERCAP0029912NCI NIH HHS P30 CA008748NCI NIH HHS U01 CA253913NIH HHS
6 · The paper itself

Abstract

backgroundFecal immunochemical test (FIT) performance for colorectal cancer screening varies by age and sex, yet most FIT-based screening programs use uniform positivity thresholds. This study assessed the potential benefits of stratifying FIT thresholds based on age and sex.

methodsWe conducted a meta-analysis of FIT sensitivity and specificity at various positivity thresholds by age and sex. We then used these estimates in 2 microsimulation models of colorectal cancer and projected lifetime clinical outcomes, incremental costs, and quality-adjusted life-years (QALYs) gained from age- and sex-stratified FIT strategies. FIT thresholds ranged from 10 to 50 µg hemoglobin per gram of feces.

resultsFor current uniform FIT screening (20 µg hemoglobin/gram of feces), models projected 85.67 to 122.15 QALYs gained at incremental costs of ‒$982 to $504 per 1000 individuals compared with no screening. At equivalent costs to current uniform screening, only 1 model found stratified FIT approaches cost-effective, yielding a marginal increase of 1.04 and 1.10 QALYs gained/1000 female and male individuals, respectively. At a willingness-to-pay threshold of $100 000/QALYs gained, both models found stratified FIT cutoffs to be the best strategy, with cutoffs being equal to or higher for males and lowest at older ages (70-75 years). Uniform strategies showed comparable effectiveness, falling within 1 quality-adjusted life-day per person of efficient strategies at up to $112 more per person. Results were sensitive to FIT test performance characteristics and 1-time setup costs.

conclusionStratifying FIT thresholds by age and sex may be cost-effective compared to current screening. The gain in expected health benefits with stratified FIT screening, however, is likely small.

Indexed as

Colorectal NeoplasmsEarly Detection of CancerFecesHemoglobinsMass ScreeningOccult BloodAdultAgedAge FactorsCost-Benefit AnalysisCost-Effectiveness AnalysisFemaleHumansMaleMiddle AgedQuality-Adjusted Life YearsHemoglobins

Identifiers

PMID40581741
PMCPMC12597507

What OpenQuestion holds

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LicenceCC BY
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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.