Trends in life expectancy of HIV-positive adults on antiretroviral therapy across the globe
Bibliographic record
Abstract
PURPOSE OF REVIEW: Improved virological and immunological outcomes and reduced toxicity of antiretroviral combination therapy (ART) raise the hope that life expectancy of HIV-positive persons on ART will approach that of the general population. We systematically review the literature and summarize published estimates of life expectancy of HIV-positive populations on ART. We compare their life expectancy with the life expectancy of the general or, in sub-Saharan Africa, HIV-negative populations, by time period and gender. RECENT FINDINGS: Ten relevant studies were published from 2006 to 2015. Three studies were from Canada, two from European countries, three from sub-Saharan Africa and two were multicountry studies. Life expectancy increased over time in all studies and regions. Expressed as the percentage of life expectancy in the HIV-negative or general population, estimated life expectancy at age 20 years in HIV-positive people on ART ranged from 60.3% (95% CI 58.0-62.6%) in Rwanda (2008-2011) to 89.1% (95% CI 84.7-93.6%) in Canada (2008-2012). The percentage of life expectancy in the HIV-negative or general population achieved was higher in HIV-positive women than in HIV-positive men in all countries, except for Canada wherein the opposite was the case. SUMMARY: Life expectancy in HIV-positive people on ART has improved worldwide in recent years, but important gaps remain compared with the general and HIV-negative population, and between regions and genders.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".