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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".