Population-Level Impact of Avahan in Karnataka State, South India Using Multilevel Statistical Modelling Techniques
Bibliographic record
Abstract
OBJECTIVE: To assess the population-level impact of "Avahan," the India AIDS Initiative of the Bill & Melinda Gates Foundation, between 2003 and 2008 in Karnataka state, India. DESIGN: Secondary data analysis using all consistent data collection sites from antenatal clinic (ANC) sentinel surveillance data from 2003 to 2008 in Karnataka. METHODS: A multilevel logistic regression model considering individual- and district-level variables was developed to compare time trends in HIV prevalence among young ANC women (younger than 25 years of age) between Avahan (18) and non-Avahan (9) districts. District-level random effects were considered for the intercept and time. The impact was assessed using interaction terms between district type (Avahan vs. non-Avahan) and time. The number of cases averted was estimated, comparing predicted ANC HIV prevalence in the presence versus the absence of Avahan. Data from the National Family Health Survey Round 3 (2006) were used to extrapolate these numbers to the general population. RESULTS: HIV prevalence among young ANC women declined from 1.46% (2003) to 0.83% (2008). The HIV prevalence trend was significantly different between Avahan and non-Avahan districts (P = 0.046). Overall, 87,035 cases of HIV infection were estimated to have been averted in the Karnataka general population because of Avahan during the 2003-2008 period (range under varying assumptions: 55,160-150,784). CONCLUSIONS: Our results suggest that Avahan has had a significant impact on the HIV epidemic in the general population of Karnataka. These results suggest that targeted interventions similar to Avahan should be implemented and scaled up in all concentrated and mixed HIV epidemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".