HIV Testing Uptake and Prevalence Among Adolescents and Adults in a Large Home-Based HIV Testing Program in Western Kenya
Bibliographic record
Abstract
OBJECTIVE: To describe HIV testing uptake and prevalence among adolescents and adults in a home-based HIV counseling and testing program in western Kenya. METHODS: Since 2007, the Academic Model Providing Access to Healthcare program has implemented home-based HIV counseling and testing on a large scale. All individuals aged ≥13 years were eligible for testing. Data from 5 of 8 catchments were included in this analysis. We used descriptive statistics and multivariate logistic regression to examine testing uptake and HIV prevalence among adolescents (13-18 years), younger adults (19-24 years), and older adults (≥25 years). RESULTS: There were 154,463 individuals eligible for analyses as follows: 22% adolescents, 19% younger adults, and 59% older adults. Overall mean age was 32.8 years and 56% were female. HIV testing was high (96%) across the following 3 groups: 99% in adolescents, 98% in younger adults, and 94% in older adults (P < 0.001). HIV prevalence was higher (11.0%) among older adults compared with younger adults (4.8%) and adolescents (0.8%) (P < 0.001). Those who had ever previously tested for HIV were less likely to accept HIV testing (adjusted odds ratio: 0.06, 95% confidence interval: 0.05 to 0.07) but more likely to newly test HIV positive (adjusted odds ratio: 1.30, 95% confidence interval: 1.21 to 1.40). Age group differences were evident in the sociodemographic and socioeconomic factors associated with testing uptake and HIV prevalence, particularly, gender, relationship status, and HIV testing history. CONCLUSIONS: Sociodemographic and socioeconomic factors were independently associated with HIV testing and prevalence among the age groups. Community-based treatment and prevention strategies will need to consider these factors.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".