The impact of “Option B” on HIV transmission from mother to child in Rwanda: An interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: Nearly a quarter of a million children have acquired HIV, prompting the implementation of new protocols-Option B and B+-for treating HIV+ pregnant women. While efficacy has been demonstrated in randomized trials, there is limited real-world evidence on the impact of these changes. Using longitudinal, routinely collected data we assessed the impact of the adoption of WHO Option B in Rwanda on mother to infant transmission. METHODS: We used interrupted time series analysis to evaluate the impact of Option B on mother-to-child HIV transmission in Rwanda. Our primary outcome was the proportion of HIV tests in infants with positive results at six weeks of age. We included data for 20 months before and 22 months after the 2010 policy change. RESULTS: Of the 15,830 HIV tests conducted during our study period, 392 tested positive. We found a significant decrease in both the level (-2.08 positive tests per 100 tests conducted, 95% CI: -2.71 to -1.45, p < 0.001) and trend (-0.11 positive tests per 100 tests conducted per month, 95% CI: -0.16 to -0.07, p < 0.001) of test positivity. This represents an estimated 297 fewer children born without HIV in the post-policy period or a 46% reduction in HIV transmission from mother to child. CONCLUSIONS: The adoption of Option B in Rwanda contributed to an immediate decrease in the rate of HIV transmission from mother to child. This suggests other countries may benefit from adopting these WHO guidelines.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 |
| 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".