The Estimated Lifetime Medical Cost of Diseases Attributable to Human Papillomavirus Infections Acquired in 2018
Bibliographic record
Abstract
INTRODUCTION: We estimated the lifetime medical costs of diagnosed cases of diseases attributable to human papillomavirus (HPV) infections acquired in 2018. METHODS: We adapted an existing mathematical model of HPV transmission and associated diseases to estimate the lifetime number of diagnosed cases of disease (genital warts; cervical intraepithelial neoplasia; and cervical, vaginal, vulvar, penile, anal, and oropharyngeal cancers) attributable to HPV infections that were acquired in 2018. For each of these outcomes, we multiplied the estimated number of cases by the estimated lifetime medical cost per case obtained from previous studies. We estimated the costs of recurrent respiratory papillomatosis in a separate calculation. Future costs were discounted at 3% annually. RESULTS: The estimated discounted lifetime medical cost of diseases attributable to HPV infections acquired in 2018 among people aged 15 to 59 years was $774 million (in 2019 US dollars), of which approximately half was accounted for by infections in those aged 15 to 24 years. Human papillomavirus infections in women accounted for approximately 90% of the lifetime number of diagnosed cases of disease and 70% of the lifetime cost attributable to HPV infections acquired in 2018 among those aged 15 to 59 years. CONCLUSIONS: We estimated the lifetime medical costs of diseases attributable to HPV infections acquired in 2018 to be $774 million. This estimate is lower than previous estimates, likely due to the impact of HPV vaccination. The lifetime cost of disease attributable to incident HPV infections is expected to decrease further over time as HPV vaccination coverage increases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".