System dynamics model of cervical cancer vaccination and screening interventions in Kenya
Bibliographic record
Abstract
Objectives \n \nThis paper presents a simulation model for evaluating the possible effects of a screening and vaccination campaign against Human Papillomavirus [HPV] in Kenya. \nMethod \n \nA System Dynamics model was developed using the iThink™ computer simulation package. The model was based on data extracted from epidemiological, demographic and published research and where data was not available, expert opinion was sought. The deterministic model stratified the population by vaccination status, screening status and HPV infection status. The model was simulated to estimate outputs for the next 50 years from 2011. Cost Utility indicators of Disability Adjusted Life Years (DALYs) and cost per averted DALY were used for economic evaluation. \nResults \n \nThe model predicted that catch up vaccination had the greatest impact in reducing the prevalence of cervical cancer. This was followed by Primary vaccination, with early detection through Screening having the lowest impact of the three choices of interventions in respect of averted cases of cervical cancer and DALY estimates. \nConclusion \n \nKenya as a country should consider adoption of secondary /catch up vaccination as an immediate measure to curb cervical cancer followed by primary vaccination of pre-adolescent girls. Screening should be a complementary measure(s). This model provides a policy decision support vehicle that can allow for choice between different interventions based on their expected outcomes. It also allows modification to accommodate new research results and information to assess the clinical impact of different policies and interventions in cervical cancer management in Kenya. \nKeywords: \nDynamic; Simulation; Cervical; Cancer; Kenya \nclose
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".