Factors associated with engagement in HIV care for young people living with perinatally acquired HIV in England: An exploratory observational cohort study
Bibliographic record
Abstract
Identifying which young people living with perinatally acquired HIV (PHIV) are less likely to engage in care is crucial to allow targeted interventions to support them to attend clinic. We adapted an existing Engagement in Care (EIC) algorithm for adults with HIV in England, for use in young people. We applied it to data from young people with PHIV in the Adolescents and Adults Living with Perinatal HIV (AALPHI) cohort. The algorithm predicts the timing of the next scheduled clinic visit, within 1-6 months of current visit, based on routine clinical data. Follow-up was 12-months from AALPHI baseline interview. Each person-month was classified as engaged in care or not. Logistic regression models (allowing for clustered data) were used to explore baseline characteristics associated with being engaged in care, adjusting for a priori variables (time from interview, sex, age, ethnicity, country of birth). Potential characteristics were across 7 domains: sociodemographic; risk behaviour practices; mental health; cognition; clinic setting; HIV management and experience; and HIV clinical markers. Of 316 young people, 187(59%) were female, 271(86%) of black ethnicity and 184(58%) born abroad. At baseline, median [IQR] age was 17[15-18] years, and 202(69%) had viral load ≤50 copies/ml(c/mL). 87% of 3,585 person-months were classified as engaged in care. Characteristics independently associated with poorer odds of being engaged in care were: Asian/mixed/other ethnicity, vs. black ethnicity (OR 0.44, 95% CI 0.25, 0.78, p = 0.02); ever self-harmed, vs. not (OR 0.55, 95% CI 0.32, 0.95, p = 0.03); on antiretroviral therapy (ART) and self-assessed bad/not so good adherence (OR 0.46, 95% CI 0.25, 0.84) or not on ART (OR 0.64, 95% CI 0.64, 1.21) vs. on ART and good/excellent adherence (p = 0.04)); baseline VL>50c/mL, vs VL≤50c/mL (OR 0.47, 95% CI 0.30, 0.75, p = 0.002). These characteristics can help identify individuals requiring enhanced support to maintain service engagement.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".