Inferring population HIV incidence trends from surveillance data of recent HIV infection among HIV testing clients
Bibliographic record
Abstract
BACKGROUND: Measuring recent HIV infections from routine surveillance systems could allow timely and granular monitoring of HIV incidence patterns. We evaluated the relationship of two recent infection indicators with alternative denominators to true incidence patterns. METHODS: We used a mathematical model of HIV testing behaviours, calibrated to population-based surveys and HIV testing services programme data, to estimate the number of recent infections diagnosed annually from 2010 to 2019 in Côte d'Ivoire, Malawi, and Mozambique. We compared two different denominators to interpret recency data: those at risk of HIV acquisition (HIV-negative tests and recent infections) and all people testing HIV positive. Sex and age-specific longitudinal trends in both interpretations were then compared with modelled trends in HIV incidence, testing efforts and HIV positivity among HIV testing services clients. RESULTS: Over 2010-2019, the annual proportion of the eligible population tested increased in all countries, while positivity decreased. The proportion of recent infections among those at risk of HIV acquisition decreased, similar to declines in HIV incidence among adults (≥15 years old). Conversely, the proportion of recent infections among HIV-positive tests increased. The female-to-male ratio of the proportion testing recent among those at risk was closer to 1 than the true incidence sex ratio. CONCLUSION: The proportion of recent infections among those at risk of HIV acquisition is more indicative of HIV incidence than the proportion among HIV-positive tests. However, interpreting the observed patterns as surrogate measures for incidence patterns may still be confounded by different HIV testing rates between population groups or over time.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".