Non-Melanoma Skin Cancer Distribution in the Russian Federation
Bibliographic record
Abstract
BACKGROUND: Non-melanoma skin cancer (NMSC) incidence has been increasing steadily around the world. The aim of the study is to describe geographic trends in incidence and mortality of NMSC in Russia between 2007 and 2017 and compare findings to other European countries. METHODS: We used geospatial analysis to map the incident cases and descriptive statistical analysis to analyze trends. Additionally, we assessed the relationship between ethnicity, geographic latitude/longitude, and NMSC incidence/mortality rates. We retrospectively analyzed the data from the Moscow Oncology Research Institute, Ministry of Health of the Russian Federation, for 2007-2017. Routine methods of descriptive epidemiology were used to study incidence and mortality rates by age groups, years, and jurisdictions (i.e., Federal Districts and Federal Subjects). RESULTS: In total, 733,723 patients were diagnosed with NMSC in Russia over the period 2007-2017, of whom 63% were women. The overall age-standardized incidence and mortality rates were 29.64/100,000 and 0.70/100,000, respectively. There was a consistent increase in age-standardized incidence rates over the study period, with a decreasing mortality rate. Geographic mapping revealed north-to-south and east-to-west gradients for NMSC. CONCLUSIONS: This study demonstrated longitudinal trends for NMSC incidence in Russia documenting that skin phototype, latitude/longitude, climate zones, and cultural practices remain dominant risk factors defining the epidemiology of NMSC. Moreover, this work identified several regions in the country (i.e., Republic of Adygea, Samara, Krasnodar Krai, etc.), where patient education/sun awareness campaigns will be useful to help reduce the risk of this malignancy.
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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".