AIDS-defining causes of death from autopsy findings for HIV-positive individuals in sub-Saharan Africa in the pre- and post-ART era: A systematic review and meta-analyses
Bibliographic record
Abstract
Background: The lack of representative data on causes of death in sub-Saharan Africa (SSA) hampers our understanding of the regional burden of HIV and impact of interventions. In spite of the roll-out of antiretroviral therapy (ART) programs, HIV-infected individuals are still dying from complications of AIDS in SSA. We reviewed autopsy findings in SSA to observe whether the prevalence of 14 AIDS-defining illnesses changed from the pre-ART era to the post-ART era. Methods: We conducted a systematic review of autopsy findings in SSA using Medline, CINAHL, Evidence Based Medicine, EMBASE, Scopus, Web of Science, and abstracts from the Conference on Retroviruses and Opportunistic Infections, for literature published between January 1, 1990 and September 30, 2018. We focused on 14 AIDS-defining illnesses as causes of death. Results: In total, 33 studies were identified, including 9 from South Africa, 4 from the Ivory Coast, and the rest from eastern regions of sub-Saharan Africa. Of these, 18 studies were included in the meta-analyses for each of the AIDS-defining illnesses for adults. A ‘mixed group’ of studies that included adults and children was used for separate meta-analyses. Most opportunistic infections (OIs) showed a decrease in prevalence, with the notable exception of tuberculosis (TB), which showed a 13% increase in adult deaths and a 5% increase in mixed population group deaths. Kaposi’s sarcoma and non-Hodgkin’s lymphoma both showed a notable increase in prevalence, and liver disease showed a 10% increase in prevalence in the adult group. Conclusions: Even though ART has reduced the contribution of OIs to causes of death for people infected with HIV in SSA, targeted and strategic efforts are needed in order to strengthen existing prevention, diagnosis, and treatment of TB. More research is required to understand the complex role ARTs have on liver and kidney diseases.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".