Participation and health care provision of statutory skin cancer screening in Germany – a secondary data analysis
Bibliographic record
Abstract
BACKGROUND: In Germany, skin cancer screening was introduced nationwide in July 2008. From the age of 35 years, members of the statutory health insurance are eligible for screening every 2 years. OBJECTIVE: The aim of this study is to calculate the participation rates and the proportions of health care providers of statutory skin cancer screening in Germany on a population-based level. METHODS: Data were provided by a nationwide German statutory health insurance, approximately 6.1 million members, covering the years 2008/2009. Participation rates were calculated per yearly quarter and were adjusted for age, gender and federal state. RESULTS: Approximately 920,000 insurants were screened from the third quarter of 2008 until the last quarter of 2009. Mean participation rate of skin cancer screening was 30.8%. Women had higher participation rates (31.9%) than men (29.7%). After adjusting for gender and federal state, high rates for pensioners at the age of 65-74 were confirmed at 39.4% on average for all yearly quarters. One of the highest gender- and age-adjusted rates was observed in the state of Schleswig-Holstein, where a population based pilot project had been implemented before the start of the nationwide screening programme. In general, without taking into account Berlin, former East Germany had a much lower gender- and age-adjusted participation rate (23.9%) than West Germany (33.3%). At the first quarter after implementation of screening, 58.5% of the screenings were provided by dermatologists and 41.5% by general practitioners. CONCLUSION: Participation rates and health care providers of skin cancer screening can be calculated from secondary data and contribute to identify group- and region-specific participation patterns in order to improve early detection of skin cancer.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".