Population-Based Estimates and Predictors of Child and Adolescent Linkage to HIV Care or Death in Western Kenya
Bibliographic record
Abstract
BACKGROUND: Population-level estimates of linkage to HIV care among children and adolescents (CAs) can facilitate progress toward 95-95-95 goals. SETTING: This study was conducted in Bunyala, Chulaimbo, and Teso North subcounties, Western Kenya. METHODS: Linkage to care was defined among CAs diagnosed with HIV through Academic Model Providing Access to Healthcare (AMPATH)'s home-based counseling and testing initiative (HBCT) by merging HBCT and AMPATH Medical Record System data. Using follow-up data from Bunyala, we examined factors associated with linkage or death, using weighted multinomial logistic regression to account for selection bias from double-sampled visits. Based on the estimated model, we imputed the trajectory for each person in 3 subcounties until a simulated linkage or death occurred or until the end of 8 years when an individual was simulated to be censored. RESULTS: Of 720 CAs in the analytic sample, 68% were between 0 and 9 years and 59% were female. Probability of linkage among CAs in the combined 3 subcounties was 48%-49% at 2 years and 64%-78% at 8 years while probability of death was 13% at 2 years and 19% at 8 years. Single or double orphanhood predicted linkage (adjusted odds ratio [aOR]: 2.66, 95% confidence interval [CI]: 1.33 to 5.32) and death (aOR: 9.85 [95% CI: 2.21 to 44.01]). Having a mother known to be HIV-positive also predicted linkage (aOR = 1.94, 95% CI: 0.97 to 3.86) and death (aOR: 14.49, 95% CI: 3.32 to 63.19). CONCLUSION: HIV testers/counselors should continue to ensure linkage among orphans and CAs with mothers known to be HIV-positive and also to support other CAs to link to HIV care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".