All-cause mortality in HIV-positive adults starting combination antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVE: To estimate mortality in HIV-positive patients starting combination antiretroviral therapy (ART) and to discuss different approaches to calculating correction factors to account for loss to follow-up. METHODS: A total of 222 096 adult HIV-positive patients who started ART 2009-2014 in clinics participating in the International epidemiology Databases to Evaluate AIDS collaboration in 43 countries in sub-Saharan Africa, Asia Pacific, Latin America, and North America were included. To allow for underascertainment of deaths due to loss to follow-up, two correction factors (one for the period 0-6 months on ART and one for later periods) or 168 correction factors (combinations of two sexes, three time periods after ART initiation, four age groups, and seven CD4 groups) based on tracing patients lost in Kenya and data linkages in South Africa were applied. Corrected mortality rates were compared with a worst case scenario assuming all patients lost to follow-up had died. RESULTS: Loss to follow-up differed between regions; rates were lowest in central Africa and highest in east Africa. Compared with using two correction factors (1.64 for the initial ART period and 2.19 for later), applying 168 correction factors (range 1.03-4.75) more often resulted in implausible mortality rates that exceeded the worst case scenario. Corrected mortality rates varied widely, ranging from 0.2 per 100 person-years to 54 per 100 person-years depending on region and covariates. CONCLUSION: Implausible rates were less common with the simpler approach based on two correction factors. The corrected mortality rates will be useful to international agencies, national programmes, and modellers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".