Risk factors and communities disproportionately affected by cervical cancer in the Russian Federation: A national population-based study
Bibliographic record
Abstract
Cervical cancer is one of the leading causes of cancer in women of childbearing age worldwide. A substantial fraction of cervical cancer is associated with Human Papilloma Virus (HPV) infection and is preventable through vaccination and screening. The aim of the study is to describe geographic and epidemiologic trends in incidence and mortality of cervical cancer in Russia during 2007–2018. Publicly accessible data from the P.A. Herzen Moscow Oncology Research Institute and the Ministry of Health of Russian Federation for 2007–2018 was used for this study. Cervical cancer incidence and mortality rates were analyzed using descriptive statistics and results were mapped to determine the geographic distribution. Potential contributing risk factors in the population were studied using univariate and multivariate Poisson regression analyses. A total of 187,013 patients were diagnosed with cervical cancer in Russia between 2007 and 2018. The average age-standardized incidence (ASIR) and mortality rates (ASMR) were 15.70/100,000 and 5.76/100,000 females, respectively, with a 27% increase in the incidence observed between 2007 and 2018. The highest ASIR was observed in the Far Eastern Federal District and the lowest in the Central Federal District. Multivariate model for cervical cancer ASIR showed that daily smoking (p = 0·0003) and syphilis (p = 0.003) were significantly associated with cervical cancer incidence. The incidence of cervical cancer in Russia is rising at a significant pace. This trend can in part be attributed to a lack of nationwide cervical cancer screening . The presented results are valuable for informing public health policy on HPV vaccinations, smoking prevention and cervical cancer screening as urgent interventions are needed to combat a troubling trend. This work was supported by the Cancer Research Society (CRS)-Canadian Institutes for Health Research (CIHR) Partnership Grant #25343 to Dr. Litvinov. Canadian Dermatology Foundation research grant to Dr. Litvinov, and by the Fonds de la recherche du Québec – Santé to Dr. Sasseville (#22648) and to Dr. Litvinov (#34753 and #36769). This research was further supported by the CIHR Catalyst Grant #428712 to Dr. Litvinov.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".