Effective Strategies for Fecal Immunochemical Tests (FIT) Programs to Improve Colorectal Cancer Screening Uptake Among Populations with Limited Access to the Healthcare System: A Rapid Systematic Review
Bibliographic record
Abstract
Abstract Background Colorectal cancer (CRC) is one of the leading causes of cancer death globally. CRC screening can reduce the incidence and mortality of CRC. However, socially disadvantaged groups may disproportionately benefit less from screening programs due to their limited access to healthcare. This poor access to healthcare services is further aggravated by intersecting, cumulative social factors associated with their sociocultural background and living conditions. This rapid review systematically reviewed and synthesized evidence on the effectiveness of Fecal Immunochemical Test (FIT) programs in increasing CRC screening in populations who do not have a regular healthcare provider or who have limited healthcare system access.Methods We used three databases: Ovid MEDLINE, Embase, and EBSCOhost CINAHL. We searched for systematic reviews, meta-analysis, and quantitative and mixed-methods studies focusing on effectiveness of FIT programs (request or receipt of FIT kit, completion rates of FIT screening, and participation rates in follow-up colonoscopy after FIT positive results). For evidence synthesis, deductive and inductive thematic analysis was conducted. The findings were also classified using the Cochrane Methods Equity PROGRESS-PLUS framework. The quality of the included studies was assessed.Results Findings from the 25 included primary studies were organized into three intervention design-focused themes. Delivery of culturally-tailored programs (e.g., use of language and interpretive services) were effective in increasing CRC screening. Regarding the method of delivery for FIT, specific strategies combined with mail-out programs (e.g., motivational screening letter) or in-person delivery (e.g., demonstration of FIT specimen collection procedure) enhanced the success of FIT programs. The follow-up reminder theme (e.g., spaced out and live reminders) were generally effective. Additionally, we found evidence of the social determinants of health affecting FIT uptake (e.g., place of residence, race/ethnicity/culture/language, gender and/or sex).Conclusions Findings from this rapid review suggest multicomponent interventions combined with tailored strategies addressing the diverse, unique needs and priorities of the population with no regular healthcare provider or limited access to the healthcare system may be more effective in increasing FIT screening. Decision-makers and practitioners should consider equity and social factors when developing resources and coordinating efforts in the delivery and implementation of FIT screening strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".