Sub-regional Trend of HIV Infection Among Antenatal Clinic Attendees in Andhra Pradesh (2003-2019)
Bibliographic record
Abstract
INTRODUCTION: The analyses of local risks at sub-national levels and tailored interventions are being emphasized for effective HIV management. Andhra Pradesh (AP) is a high HIV prevalence state in India, with the second-highest number of People with HIV (PWH) and a consistent decline in HIV prevalence at the state level. Probing further into the region and district-wise levels and trends of HIV prevalence will provide critical insights into sub-regional epidemic patterns. Hence, this paper analyzes the regional and district-level trends of HIV prevalence among pregnant women attending antenatal clinics (ANC) from 2003 to 2019 in AP, South India. METHODS: HIV prevalence data collected from pregnant women in AP during HIV Sentinel Surveillance (HSS) between 2003 and 2019 was used for trend analysis. The consistent sites were grouped into three regions (Coastal Andhra, Rayalaseema and Uttar Andhra), totaling 39 sites, including 21 rural and 18 urban sites. Regional and district-level HIV prevalence was analyzed using the Chisquare trend test, and spatial analysis was done using QGIS software. For the last three HSS rounds, HIV prevalence based on sociodemographic variables was calculated to understand the factors contributing to HIV positivity in each region. RESULTS: In total, 143,211 pregnant women were recruited. HIV prevalence in AP was 0.30% (95% CI: 0.22 - 0.39) in 2019. The prevalence was 0.31%, 0.35% and 0.22% in Coastal Andhra, Rayalaseema and Uttar Andhra, respectively. HIV prevalence had significantly (P < 0.05) declined in all regions. The overall trend indicated that the HIV prevalence was higher among older pregnant women and in urban regions. However, recent trends consistently report HIV positivity among young and new pregnant mothers, illiterates, and rural regions. CONCLUSION: The overall trend indicated that the HIV prevalence was higher among older pregnant women and in urban regions. However, recent trends consistently report HIV positivity among young and new pregnant mothers, illiterates, and rural regions. Identifying the contextual risk patterns associated with HIV transmission will further improve HIV preventive and management programs among the general population.
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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 | 0.001 | 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) | 0.002 | 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".