Economic Evaluation of Screening Strategies Combined with HPV Vaccination of Preadolescent Girls for the Prevention of Cervical Cancer in Vientiane, Lao PDR
Bibliographic record
Abstract
BACKGROUND: Several approaches to reduce the incidence of invasive cervical cancers exist. The approach adopted should take into account contextual factors that influence the cost-effectiveness of the available options. OBJECTIVE: To determine the cost-effectiveness of screening strategies combined with a vaccination program for 10-year old girls for cervical cancer prevention in Vientiane, Lao PDR. METHODS: A population-based dynamic compartment model was constructed. The interventions consisted of a 10-year old girl vaccination program only, or this program combined with screening strategies, i.e., visual inspection with acetic acid (VIA), cytology-based screening, rapid human papillomavirus (HPV) DNA testing, or combined VIA and cytology testing. Simulations were run over 100 years. In base-case scenario analyses, we assumed a 70% vaccination coverage with lifelong protection and a 50% screening coverage. The outcome of interest was the incremental cost per Disability-Adjusted Life Year (DALY) averted. RESULTS: In base-case scenarios, compared to the next best strategy, the model predicted that VIA screening of women aged 30-65 years old every three years, combined with vaccination, was the most attractive option, costing 2 544 international dollars (I$) per DALY averted. Meanwhile, rapid HPV DNA testing was predicted to be more attractive than cytology-based screening or its combination with VIA. Among cytology-based screening options, combined VIA with conventional cytology testing was predicted to be the most attractive option. Multi-way sensitivity analyses did not change the results. Compared to rapid HPV DNA testing, VIA had a probability of cost-effectiveness of 73%. Compared to the vaccination only option, the probability that a program consisting of screening women every five years would be cost-effective was around 60% and 80% if the willingness-to-pay threshold is fixed at one and three GDP per capita, respectively. CONCLUSIONS: A VIA screening program in addition to a girl vaccination program was predicted to be the most attractive option in the health care context of Lao PDR. When compared with other screening methods, VIA was the primary recommended method for combination with vaccination in Lao PDR.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".