The Cost-Effectiveness of Directly Observed Highly-Active Antiretroviral Therapy in the Third Trimester in HIV-Infected Pregnant Women
Bibliographic record
Abstract
BACKGROUND: In HIV-infected pregnant women, viral suppression prevents mother-to-child HIV transmission. Directly observed highly-active antiretroviral therapy (HAART) enhances virological suppression, and could prevent transmission. Our objective was to project the effectiveness and cost-effectiveness of directly observed administration of antiretroviral drugs in pregnancy. METHODS AND FINDINGS: A mathematical model was created to simulate cohorts of one million asymptomatic HIV-infected pregnant women on HAART, with women randomly assigned self-administered or directly observed antiretroviral therapy (DOT), or no HAART, in a series of Monte Carlo simulations. Our primary outcome was the quality-adjusted life expectancy in years (QALY) of infants born to HIV-infected women, with the rates of Caesarean section and HIV-transmission after DOT use as intermediate outcomes. Both self-administered HAART and DOT were associated with decreased costs and increased life-expectancy relative to no HAART. The use of DOT was associated with a relative risk of HIV transmission of 0.39 relative to conventional HAART; was highly cost-effective in the cohort as a whole (cost-utility ratio $14,233 per QALY); and was cost-saving in women whose viral loads on self-administered HAART would have exceeded 1000 copies/ml. Results were stable in wide-ranging sensitivity analyses, with directly observed therapy cost-saving or highly cost-effective in almost all cases. CONCLUSIONS: Based on the best available data, programs that optimize adherence to HAART through direct observation in pregnancy have the potential to diminish mother-to-child HIV transmission in a highly cost-effective manner. Targeted use of DOT in pregnant women with high viral loads, who could otherwise receive self-administered HAART would be a cost-saving intervention. These projections should be tested with randomized clinical trials.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".