Heterogeneity and confinement of HIV prevalence among pregnant women calls for decentralized HIV interventions: Analysis of data from three rounds of HIV sentinel surveillance in Karnataka: 2013–2017
Bibliographic record
Abstract
BACKGROUND: The HIV sentinel surveillance (HSS) serves to estimate the levels and trend of HIV prevalence among high-risk, bridge, and general population and monitors HIV management at national, state, and district levels. Data from HSS are valuable in understanding the risk factors associated with HIV transmission with particular demographic characteristics. OBJECTIVES: The objective was to analyze the sociodemographic profile of the pregnant mothers attending the antenatal care (ANC) clinics in Karnataka, in order to understand the dynamics of HIV within the general population in Karnataka. MATERIALS AND METHODS: Study design: this was a cross-sectional study conducted using consecutive sampling method. Study setting: the surveillance was conducted at select antenatal clinics, in Karnataka, India, between January and March, in the years 2013, 2015, and 2017. Methodology: in total, 74,278 eligible pregnant women aged between 15 and 49 years, attending the sentinel sites for the first time during the surveillance period, were included in the study. Information on their sociodemographic characteristics and blood samples was collected. RESULTS: HIV prevalence among the ANC clinic attendees has significantly declined, reaching a recent stabilization. The risk factors significantly associated with HIV among pregnant women were age, education, occupation, and marital status. HIV is highly concentrated in the northern and southern districts of Karnataka. CONCLUSION: Despite the declining trends of HIV prevalence in Karnataka, the epidemic is heterogeneous and concentrated within the state, calling for decentralized region-specific interventions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".