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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".