Distribution of Candida species isolated from people living with human immunodeficiency virus with oropharyngeal and oral candidiasis in Africa in the era of universal test and treat policy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The introduction of antiretroviral therapy (ART) and the implementation of the human immunodeficiency virus (HIV) universal test and treat (UTT) policy have led to a decline in the incidence of opportunistic infections. However, oropharyngeal and oral candidiasis remain prevalent and continue to pose challenges among people living with human immunodeficiency virus (PLHIV) in Africa, indicating the need for a better understanding of the distribution of Candida species responsible for these infections. This systematic review and meta-analysis aimed to determine the distribution of Candida species isolated from PLHIV with oropharyngeal and oral candidiasis in Africa in the era of UTT policy. METHODS: The review followed the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines. A comprehensive search was conducted to identify eligible studies to be included in the meta-analysis and analysed using a random effects model in STATA version 17. The risk of bias was assessed using the Joanna Briggs Institute quality assessment tool. RESULTS: Fourteen studies with 4281 participants were included in the review. Overall, 2095 Candida isolates were reported, 78.7% (1650/2095) of which were C. albicans, 19.6% (410/2095), non-albicans Candida (NAC), and 1.7% (35/2095) could not be identified to the Candida specific species level. The most prevalent NAC species were C. glabrata (26.3%), followed by C. tropicalis (24.9%), C. krusei (15.6%), C. parapsilosis (11%), and C. dubliniensis (6.3%). The pooled prevalence of oropharyngeal and oral candidiasis was 48% (95% CI 34-62%). The prevalence of oropharyngeal candidiasis was higher in the pre-UTT era, at 56% (95% CI 40-72%, p < 0.001), than in the post-UTT era, at 34% (95% CI 10-67%, p < 0.001). The risk of bias assessment revealed that 71.4% (10/14) of the included studies had a low risk of bias and that 28.6% (4/14) had a moderate risk of bias. CONCLUSIONS: While C. albicans remain, the predominant species causing oropharyngeal and oral candidiasis among PLHIV in Africa, NAC species also contribute significantly to the infection burden. Despite ART and UTT policies, oropharyngeal candidiasis remains prevalent, emphasizing the need for targeted interventions.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".