The economic evaluation of human papillomavirus vaccination strategies against cervical cancer in women in Lao PDR: a mathematical modelling approach
Bibliographic record
Abstract
BACKGROUND: Cervical cancer, a preventable disease, is the third leading cause of cancer morbidity and mortality in the Lao People's Democratic Republic (Lao PDR). Since many cervical cancers are linked to human papilloma virus (HPV) infection, vaccination against this virus may lead to a reduction in these types of cancer. The study described here is the first to compare the cost-effectiveness of different HPV vaccination options in Lao PDR. METHODS: A dynamic compartment model was created. The model included routine screening activities already in place, as well as theoretical interventions that included a 10-year old girl-only vaccination programme combined with/without a 10-year old boy vaccination programme and/or a catch-up component. The simulation was run over 100 years. In base case analyses, we assumed 70 % vaccination coverage with lifelong protection and 100 % efficacy against HPV types 16/18. The outcomes of interest were the incremental cost per Disability-Adjusted Life Year (DALY) averted. RESULTS: In base case analyses, according to the WHO definition of cost-effectiveness thresholds, vaccinating 10-year-old girls was very cost-effective. Adding a catch-up vaccination element for females aged 11-25 years was also very cost-effective, costing 1559 international dollars (I$) per DALY averted. Increasing the age limit of the catch-up vaccination component to 75 years old showed that this remained a cost-effective option (I$ 5840 per DALY averted). Adding a vaccination programme for 10-year-old boys was not found to be cost-effective unless a short time simulation (30 years or less) was considered, along with a catch-up vaccination component for both males and females. CONCLUSIONS: Adding a catch-up female vaccination component is more attractive than adding a 10-year-old boy vaccination component.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".