P-627. Modeling the Clinical and Economic Impact of Increasing Varicella Vaccination Coverage in Panama
Bibliographic record
Abstract
Abstract Background Panama implemented two-dose universal varicella vaccination (UVV) in 2018. First-dose vaccination coverage rates (VCRs) declined from 91% (2018) to 70% (2021) during the COVID-19 pandemic. We quantified the clinical and economic impact of increasing UVV VCRs over a 10-year time horizon. Methods A previously published age-structured dynamic transmission model was adapted to Panama using country-specific demographic, healthcare resource use, cost, and epidemiological data or comparable proxy. Four UVV strategies (1st dose: 15 months; 2nd dose: 4 years) were evaluated, with 1st dose VCR assumed to be (A) 70% (status quo), (B) 80%, (C) 90%, and (D) 95% for the strategies A-D (Table 1). Second dose VCR was assumed to be 50% of first dose VCR. Outcomes were evaluated over a 10-year time horizon (2022-2031) and included cumulative varicella cases, outpatient cases, hospitalizations, deaths, payer (direct) costs, and societal (direct and indirect) costs. Costs were reported in 2023 USD with 3% annual discounting. Results Under the status quo (strategy A), we estimated 232,381 cumulative varicella cases, 159,123 outpatient cases, and 7,320 hospitalizations over 10 years, resulting in total payer and societal costs of $29,335,329 and $36,893,235, respectively. Increasing 1st dose/2nd dose VCR from 70%/35% (Strategy A) to 80%/40% (Strategy B) will avert approximately 3.6% of varicella cases, 4% of hospitalizations, and 1.8% of deaths. Increasing VCR to 90%/45% (Strategy C) will avert 6.8% of all cases, 7.7% of hospitalizations, and 3.5% of deaths compared to reference Strategy A. The impact of increasing VCRs to 95%/47.5% (Strategy D) resulted in a further reduction in varicella cases by about 20% more than Strategy C. Strategies B-D increased payer costs by 2%-6% ($0.02-$0.04 per person per year [PPPY]) and societal costs by 1%-4% ($0.01-$0.03 PPPY) over the 10-year time horizon versus reference strategy A (Figure 1). Conclusion Increasing UVV VCRs compared to the status quo will result in improved clinical outcomes with marginal increases in payer and societal costs. Disclosures Colleen Burgess, MS, Merck & Co., Inc., Rahway, NJ, USA: Contractor Salome Samant, MBBS, MPH, Merck & Co., Inc., Rahway, NJ, USA: Employee- earned salary and own stock|Merck & Co., Inc., Rahway, NJ, USA: Stocks/Bonds (Public Company) Luciana Hirata, PhD, Merck & Co., Inc., Rahway, NJ, USA.: I am an employee of MSD subsidiary of Merck & Co., Inc., Rahway, NJ, USA. and may hold stock or stock options in Merck & Co., Inc. Cintia I. Parellada, MD, PhD, Merck & Co., Inc: Employee|Merck & Co., Inc: Stocks/Bonds (Private Company) Manjiri D. Pawaskar, PhD, merck: employee|merck: Stocks/Bonds (Public Company) John C. Lang, PhD, MSc, MSc, BSc, Merck & Co., Inc.: Stocks/Bonds (Private Company)|Merck Canada Inc.: Employee
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".