Levels and trend of HIV prevalence among pregnant women in Tamil Nadu: Analysis of data from HIV sentinel surveillance (2003–2019)
Bibliographic record
Abstract
Introduction HIV Sentinel Surveillance (HSS) among pregnant women is a key indicator to estimate the HIV prevalence in the general population, as nearly 85% of HIV transmission in India is linked to the heterosexual route. Tracking the levels and trends of the HIV prevalence at state, regional, and district levels help to prioritize and facilitate a tailor-made intervention. Here, we analyze the region and district-wise levels and trends of HIV prevalence among pregnant women attending the antenatal clinics (ANC) from 2003 to 2019 in Tamil Nadu. Methods Eleven rounds of HSS data collected from pregnant women in Tamil Nadu, from 2003 to 2019 were analysed. Consistent sites were grouped into four geographical regions (North, Central, West and South), a total of 67 sites including 33 urban 31 rural, and 3 private sites. Chi-square trend test used to find linear trends and spatial analysis used to understand the geographical distribution of HIV prevalence over the years. Results In total, 2,77,444 blood samples were collected during HSS from 2003 to 2019. In 2019, the state HIV prevalence was 0.18 (95% CI: 0.13–0.23); region-wise prevalence was 0.21, 0.07, 0.32 and 0.12 in North, Central, West and South respectively. HIV prevalence in Tamil Nadu had significantly declined since 2003, which however, was not significant in all districts; with inter-district fluctuations. Conclusion HIV interventions have to be strengthened in the districts with inconsistent trends, specifically the West region. This requires a more detailed analysis of region-specific contextual factors associated with the transmission risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".