The Estimated Direct Lifetime Medical Costs of Sexually Transmitted Infections Acquired in the United States in 2018
Bibliographic record
Abstract
BACKGROUND: We estimated the lifetime medical costs attributable to sexually transmitted infections (STIs) acquired in 2018, including sexually acquired human immunodeficiency virus (HIV). METHODS: We estimated the lifetime medical costs of infections acquired in 2018 in the United States for 8 STIs: chlamydia, gonorrhea, trichomoniasis, syphilis, genital herpes, human papillomavirus (HPV), hepatitis B, and HIV. We limited our analysis to lifetime medical costs incurred for treatment of STIs and for treatment of related sequelae; we did not include other costs, such as STI prevention. For each STI, except HPV, we calculated the lifetime medical cost by multiplying the estimated number of incident infections in 2018 by the estimated lifetime cost per infection. For HPV, we calculated the lifetime cost based on the projected lifetime incidence of health outcomes attributed to HPV infections acquired in 2018. Future costs were discounted at 3% annually. RESULTS: Incident STIs in 2018 imposed an estimated $15.9 billion (25th-75th percentile: $14.9-16.9 billion) in discounted, lifetime direct medical costs (2019 US dollars). Most of this cost was due to sexually acquired HIV ($13.7 billion) and HPV ($0.8 billion). STIs in women accounted for about one fourth of the cost of incident STIs when including HIV, but about three fourths when excluding HIV. STIs among 15- to 24-year-olds accounted for $4.2 billion (26%) of the cost of incident STIs. CONCLUSIONS: Incident STIs continue to impose a considerable lifetime medical cost burden in the United States. These results can inform health economic analyses to promote the use of cost-effective STI prevention interventions to reduce this burden.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".