Prevalence of cryptococcal meningitis in Asian countries: a systematic review and meta-analysis
Bibliographic record
Abstract
Cryptococcal meningitis (CM) is a disease caused by pathogenic Cryptococcus spp., leading to neurologic manifestations such as headache, confusion or behavioral changes, and photophobia. The disease typically affects immunocompromised hosts such as persons living with HIV/AIDS and other comorbidities such as hypertension, diabetes, and tuberculosis infection. CM remains a major public health concern, affecting a number of countries globally with an estimated 223,000 cases annually. The exact prevalence of CM in specific regions across the world remains poorly documented, with underlying factors related to the geographic distribution of the disease still not fully understood. To address this concern, the current study aims to estimate the proportion of cases of CM across Asian countries and describe the geographic distribution of the disease through a systematic review and meta-analyses of related studies. All studies reporting the prevalence of CM across Asia were extracted from OVID Medline, Scopus, and EBSCO CINAHL. A review of title and abstracts was done independently by eight reviewers. The quality of the studies was assessed using the Newcastle–Ottawa scale [ 42 ]. Meta-analysis was performed using R v.4.1.1, using the ‘meta’, ‘tidyverse’, and ‘metafor’ packages (version 4.19–0). Based on the results of the current study, the pooled estimated prevalence of CM among studied patient populations presenting to care across Asia was 0.08% (95% CI 0.06–0.1). Across countries, the highest estimated prevalence of CM cases was from China, followed by Cambodia, and Pakistan. A number of factors such as the prevalence of HIV infections, male predominance, and the presence of other comorbid infections such as tuberculosis and candidiasis were identified as possible underlying factors affecting the prevalence of CM. Overall, further investigations are necessary to accurately describe the geographic distribution of cases of CM across Asian countries. The findings of the study reinforce the importance of local surveillance systems and routine diagnostic workup to better describe the current burden of disease attributable to CM, which should be implemented within local contexts based on regional disease patterns. Regular monitoring and widespread awareness of the impact of CM can improve the overall outcomes of CM patients and to mitigate the prevalence of the disease in the general population.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.009 | 0.013 |
| 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) | 0.003 | 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".