Modeling the Impact of Retention Interventions on Mother-to-Child Transmission of HIV: Results From INSPIRE Studies in Malawi, Nigeria, and Zimbabwe
Bibliographic record
Abstract
BACKGROUND: Six implementation research studies in Malawi, Nigeria, and Zimbabwe tested approaches for improving retention in care among women living with HIV. We simulated the impact of their interventions on the probability of HIV transmission during pregnancy and breastfeeding. METHODS: A computer-based state-transition model was developed to estimate the impact of the retention interventions. Patient-level data from the 6 studies were aggregated and analyzed, and weighted averages of mother-to-child transmission (MTCT) of HIV probabilities were presented. The average MTCT probability of the more successful interventions was applied to national estimates to calculate potential infections averted if these interventions were taken to scale. RESULTS: Among the total cohort of 5742 HIV-positive women, almost 80% of all infant infections are attributed to the roughly 20% of HIV-positive pregnant and breastfeeding women not retained on antiretroviral therapy. Higher retention in the arms receiving interventions resulted in an overall lower estimated MTCT probability of 9.9% compared with 12.3% in the control arms. In the 2 studies that showed a statistically significant effect, Prevention of MTCT Uptake and Retention (PURE) and Mother Mentor (MoMent), the difference in transmission rates between intervention and control arms was 4.1% and 7.3%, respectively. Scaling up retention interventions nationally in the 3 countries could avert an average of almost 3000 infant infections annually. CONCLUSIONS: Linking HIV-positive pregnant women to antiretroviral therapy and retaining them is essential for addressing the remaining gaps and challenges in HIV/AIDS care and the elimination of MTCT. At national level, even modest improvements in retention translates into large numbers of infant infections averted.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".