Cancer screening after the age of 75: Nationwide population-based trends
Bibliographic record
Abstract
ObjectivesCancer screening among older adults above 75 years of age is frequent despite generally not being recommended in evidence-based guidelines. We aimed to describe trends in prostate, cervical, breast, and colorectal cancer screening after the age of 75.SettingThis descriptive cross-sectional study analysed the 2007, 2012, 2017, and 2022 waves of the nationwide population-based Swiss Health Survey. Residents of Switzerland were invited through state-stratified multistage probability sampling.MethodsFor each wave, we calculated weighted overall and sex- and age-stratified proportions of any, prostate, cervical, breast, and colorectal cancer screening in the past 12 months explicitly for preventive non-symptomatic purposes among older adults above 75 years of age.ResultsAnalytical sample sizes ranged from 1450 (2007) to 2276 (2022). Across waves, populations aged and had increasing education levels. Over time, any cancer screening in the past 12 months was undertaken by one in four older adults aged above 75 (25.4% in 2007; 24.3% in 2022), where proportions were persistently higher among men (31.8% in 2007; 28.3% in 2022) than women (21.3% in 2007; 20.8% in 2022). In all waves, screening decreased with increasing age (2022: 29.7% among people aged 76-80, 14.8% among people aged 86 years and above). Prostate cancer screening decreased from 26.0% (2007) to 21.0% (2022), with no substantial changes for other screening types.ConclusionsCancer screening after the age of 75 has been frequent and stable across time despite not being recommended, emphasising the need for further evidence on screening effectiveness and harms among older adults.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".