P1-S5.20 Multi-level analysis of the predictors of HIV prevalence among pregnant women enrolled in sentinel surveillance in four Southern India states
Bibliographic record
Abstract
Background The heterogeneity of HIV epidemic in the districts of four Southern states of India is reflected in HIV prevalence in pregnant women (ANC HIV prevalence) as well. Earlier studies have attempted to identify district level high risk population parameters that influenced ANC HIV prevalence. It is important to identify other district and individual level factors that influence ANC HIV prevalence to plan effective interventions. Methods The data from cross-sectional studies, known as integrated biological and behavioural assessments (IBBA), carried out between 2004 and 2007 among female sex workers (FSWs), their clients and men who have sex with men (MSM) in 24 districts were used to generate district-level variables concerning high-risk sub-population. The data on HIV sentinel surveillance in the ANC population (dependent variable) were obtained from the National AIDS Control Organization (NACO) for the same years. Other district level data were obtained from various governmental agencies and other reliable sources. Multilevel logistic regression analysis was used to identify individual and district-level factors associated with ANC HIV prevalence. Results The mean annual ANC HIV prevalence between 2004 to 2007 in the 24 districts considered ranged from 0.25 to 3.25%. In the multilevel model, individual level factors such as age ≥25 years [Adjusted OR (AOR): 1.49; 95% CI 1.27 to 1.76], being illiterate (AOR: 1.64; 95% CI 1.07 to 2.53) and being employed (AOR: 1.38; 95% CI 1.17 to 1.64) were significantly associated with high risk of being infected by HIV. HIV prevalence among FSWs at the district level, which was a significant high risk population parameter in the earlier studies, remained significant in the current study (AOR: 1.03; 95% CI 1.01 to 1.05). The only other district level factor which was considered in the final model was percentage of women who married below age of 18 years (AOR: 1.02; 95% CI 1.00 to 1.04). Conclusion HIV prevalence among FSWs is a key determinant of HIV prevalence among pregnant women in Southern India. Illiteracy of women and high prevalence of women marrying under 18 years at district level are both indicators of low socio economic status. Therefore in addition to targeted interventions for FSWs, awareness programs among individuals from lower socio economic status might help in reducing incidence of HIV in pregnant women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.028 | 0.001 |
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".