Global variations in mortality in adults after initiating antiretroviral treatment
Bibliographic record
Abstract
BACKGROUND: UNAIDS models use data from the International epidemiology Databases to Evaluate AIDS (IeDEA) collaboration in setting assumptions about mortality rates after antiretroviral treatment (ART) initiation. This study aims to update these assumptions with new data, to quantify the extent of regional variation in ART mortality and to assess trends in ART mortality. METHODS: Adult ART patients from Africa, Asia and the Americas were included if they had a known date of ART initiation during 2001-2017 and a baseline CD4 cell count. In cohorts that relied only on passive follow-up (no patient tracing or linkage to vital registration systems), mortality outcomes were imputed in patients lost to follow-up based on a meta-analysis of tracing study data. Poisson regression models were fitted to the mortality data. RESULTS: 464 048 ART patients were included. In multivariable analysis, mortality rates were lowest in Asia and highest in Africa, with no significant differences between African regions. Adjusted mortality rates varied significantly between programmes within regions. Mortality rates in the first 12 months after ART initiation were significantly higher during 2001-2006 than during 2010-2014, although the difference was more substantial in Asia and the Americas [adjusted incidence rate ratio (aIRR) 1.43, 95% CI: 1.22-1.66] than in Africa (aIRR 1.07, 95% CI: 1.04-1.11). CONCLUSION: There is substantial variation in ART mortality between and within regions, even after controlling for differences in mortality by age, sex, baseline CD4 category and calendar period. ART mortality rates have declined substantially over time, although declines have been slower in Africa.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| 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".