Trends in antenatal human immunodeficiency virus prevalence in Western Kenya and Eastern Uganda: evidence of differences in health policies?
Bibliographic record
Abstract
OBJECTIVE: To observe recent trends in human immunodeficiency virus (HIV) prevalence in antenatal clinic attendees to determine if previously noted falls in HIV prevalence are occurring on both sides of the Kenyan-Ugandan border. Design An ecologic study was conducted at the district level comparing HIV prevalence rates over time using data available through reports published by the Kenyan and Ugandan Ministries of Health and UNAIDS. METHODS: Sentinel sites were compared with respect to population, ethnicity, language group, and the prevalence of circumcision practice. The prevalence of HIV found at each sentinel site was recorded for the years 1990-2000 and analysed visually and by conducting bivariate correlations. RESULTS: Ethnographic analysis revealed a wide mix of ethnic and language groups and circumcision rates on both sides of the border. All sentinel surveillance sites in Uganda showed trends towards decreasing HIV prevalence, with three of five sites showing statistically significant declines (r = -0.87, -0.85, -0.86, P < 0.05). In contrast, all of the surveillance sites in Kenya showed trends toward increasing HIV prevalence, with two of the five sites showing statistically significant increases (r = 0.62, 0.84, P < 0.05). CONCLUSIONS: The declines in HIV prevalence occurring in Uganda are not being seen in geographically proximal districts of Kenya. No obvious differences in ethnic groupings or their associated prevalence of circumcision appeared to explain these differences. This suggests that decreasing HIV prevalence in Uganda is not due to the natural course of the epidemic but reflects real success in terms of HIV control policies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".