Time From HIV Diagnosis to Viral Suppression: Survival Analysis of Statewide Surveillance Data in Alabama, 2012 to 2014
Bibliographic record
Abstract
BACKGROUND: Evaluation of the time from HIV diagnosis to viral suppression (VS) captures the collective effectiveness of HIV prevention and treatment activities in a given locale and provides a more global estimate of how effectively the larger HIV care system is working in a given geographic area or jurisdiction. OBJECTIVE: This study aimed to evaluate temporal and geographic variability in VS among persons with newly diagnosed HIV infection in Alabama between 2012 and 2014. METHODS: With data from the National HIV Surveillance System, we evaluated median time from HIV diagnosis to VS (<200 c/mL) overall and stratified by Alabama public health area (PHA) among persons with HIV diagnosed during 2012 to 2014 using the Kaplan-Meier approach. RESULTS: Among 1979 newly diagnosed persons, 1181 (59.67%) achieved VS within 12 months of diagnosis; 52.6% (353/671) in 2012, 59.5% (377/634) in 2013, and 66.9% (451/674) in 2014. Median time from HIV diagnosis to VS was 8 months: 10 months in 2012, 8 months in 2013, and 6 months in 2014. Across 11 PHAs in Alabama, 12-month VS ranged from 45.8% (130/284) to 84% (26/31), and median time from diagnosis to VS ranged from 5 to 13 months. CONCLUSIONS: Temporal improvement in persons achieving VS following HIV diagnosis statewide in Alabama is encouraging. However, considerable geographic variability warrants further evaluation to inform public health action. Time from HIV diagnosis to VS represents a meaningful indicator that can be incorporated into public health surveillance and programming.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".