Comparing those diagnosed early versus late in their HIV infection: implications for public health
Bibliographic record
Abstract
Routine HIV surveillance cannot distinguish between recent and older infections: HIV-positive individuals reported soon or long after infection are both considered new diagnoses from a surveillance perspective, notwithstanding the time since infection. This lack of specificity makes it difficult to understand the jurisdiction-specific trends in HIV epidemiology needed for prevention planning. Previous efforts have been made to discern such timing of infection, but these methodologies are not easily applied in a public health setting. We wished to develop a simple protocol, using routinely collected information, to classify newly diagnosed infections as recent or older, and to enumerate and characterize recent versus older infections. Applying our methodology to a review of HIV cases reported between January 2011 and December 2014, we classified 62% of cases; one-third of these were recent infections. Although men who have sex with men (MSM) and persons from HIV-endemic countries (HEC) disproportionally accounted for new HIV diagnoses, the dynamics of HIV transmission within these groups differed dramatically: MSM accounted for the majority of recent infections, whereas persons from HEC accounted for the majority of older infections. Among older infections, one-quarter were previously unaware of their infection. Categorizing cases in this manner yielded greater, jurisdiction-specific understanding of HIV, and guides subpopulation-specific interventions.
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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 | 0.043 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".