Risks associated with cancer screening programmes among multimorbid patients: a systematic review
Bibliographic record
Abstract
Abstract Background Cancer screening programmes have been implemented to facilitate early detection of cancer. However, there are risks associated with cancer screening, such as overdiagnosis. This means identifying problems that were never going to cause harm1. This includes identification of abnormalities that do not progress, or that progress too slowly to cause symptoms or harm during a person’s remaining lifetime in the context of multi-morbidity1,2. Aim We conducted a systematic review of the literature to explore the harms associated with overdiagnosis in multimorbid patients when they undergo three different types of cancer screening programmes. Methods The search was conducted on MEDLINE, EMBASE, PSYCHINFO and Scopus from 1960 (cancer screening programmes implementation) until November 2023. Inclusion criteria involved studies with multimorbid participants (having two or more chronic conditions), having one of three common types of cancers (i.e., breast, prostate and lung), and used a standardised method for screening (i.e., mammography, Prostate-specific antigen test, and Low Dose Computed tomography (LDCT) or Computed Tomography scans (CT)). Only peer-reviewed studies published in English were included. Four keyword sets were used, “Multimorbidity”, “Overdiagnosis “, “Patient harms” and “Cancer screening”. Two independent reviewers conducted the search and data extraction. Rayyan web application was used to help with title, and abstract screening, and to compare the independent reviews. The Newcastle-Ottawa scale was used for quality assessment. This systematic review was registered with PROSPERO database (CRD42024475175) and followed the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidance. Ethical approval was not required to undertake this review. Results A total of 200 studies resulted from all the databases search and duplicates (n=29) removed. Titles, abstracts and full-texts were screened, with seven studies meeting the inclusion criteria. Quality assessment of the included studies showed two studies of good quality and five studies of poor quality. All included studies were conducted in the United States. Breast cancer overdiagnosis increased with increasing the number of comorbidities. Rates of overdiagnosis with prostate cancer were higher compared to breast cancer, with multimorbid patients having a 15% higher risk of overdiagnosed malignancies, compared to non-multimorbid individuals. Harms of overdiagnosis were categorised into psychological (e.g. anxiety), physical (e.g. side effects of medications) and financial (e.g. costs to healthcare systems). Overdiagnosis with lung cancer was associated with increased anxiety, with more individuals dying from other causes than lung cancer in post-mortem studies, questioning the risk versus benefit of lung cancer screening result interpretation for non-progressive disease. Conclusion The emergence of sophisticated testing technologies and greater access to screening tests can contribute to overdiagnosis. We found many different types of harm related to overdiagnosis in common types of cancer. Further research is required to explore the risk of overdiagnosis with other types of cancer. Awareness of clinicians about the risks of overdiagnosis, particularly in high-risk population, such as multimorbid patients, could guide their decision-making regarding non-progressive cancer treatment. References 1. Neal CH, Helvie MA. Overdiagnosis and risks of breast cancer screening. Radiologic Clinics. 2021 Jan 1;59(1):19-27.. 2. Petrazzuoli F, Morin L, Angioni D, Pecora N, Cherubini A. Polypharmacy, Overdiagnosis and Overtreatment. The Role of Family Physicians in Older People Care. 2022:325-40.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".