Determinants of time from HIV infection to linkage-to-care in rural KwaZulu-Natal, South Africa
Bibliographic record
Abstract
OBJECTIVE: To estimate time from HIV infection to linkage-to-care and its determinants. Linkage-to-care is usually assessed using the date of HIV diagnosis as the starting point for exposure time. However, timing of diagnosis is likely endogenous to linkage, leading to bias in linkage estimation. DESIGN: We used longitudinal HIV serosurvey data from a large population-based HIV incidence cohort in KwaZulu-Natal (2004-2013) to estimate time of HIV infection. We linked these data to patient records from a public-sector HIV treatment and care program to determine time from infection to linkage (defined using the date of the first CD4 cell count). METHODS: We used Cox proportional hazards models to estimate time from infection to linkage and the effects of the following covariates on this time: sex, age, education, food security, socioeconomic status, area of residence, distance to clinics, knowledge of HIV status, and whether other household members have initiated antiretroviral therapy. RESULTS: We estimated that it would take an average of 4.9 years for 50% of HIV seroconverters to be linked to care (95% confidence intervals: 4.2-5.7). Among all cohort members who were linked to care, the median CD4 cell count at linkage was 350 cells/μl (95% confidence interval: 330-380). Men and participants aged less than 30 years were found to have the slowest rates of linkage-to-care. Time to linkage became shorter over calendar time. CONCLUSION: Average time from HIV infection to linkage-to-care is long and needs to be reduced to ensure that HIV treatment-as-prevention policies are effective. Targeted interventions for men and young individuals have the largest potential to improve linkage rates.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".