The Burden of Neonatal Invasive Candidiasis in Low- and Middle-income Countries: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Invasive Candida infection remains a significant threat to neonates worldwide. Most evidence on neonatal invasive candidiasis (NIC) comes from high-income countries, leaving the burden and characteristics of NIC in low- and middle-income countries (LMICs) poorly described. This study aimed to investigate the incidence, case-fatality rates (CFR), epidemiology, and etiology of NIC in LMICs. Methods We conducted a systematic literature review and meta-analyses of all eligible studies in 17 databases published from inception until April 2022 focusing on microbiologically confirmed NIC in LMICs. Findings A total of 257 articles were included, with 10 994 NIC cases from 27 LMICs. The overall incidence rate was 2.6% (95% confidence interval [CI], 2.2–3.0). Regional disparities were evident, with South-East Asia reporting the highest incidence rate (6.3%; 95% CI, 3.2–10.3). The mean gestational age and birth weight were 31.4 weeks (standard deviation, 3.3) and 1530 g (standard deviation, 644.6), respectively. Among 10 087 included isolates, the predominant species was C albicans (39.0%), followed by C parapsilosis (24.8%), with marked differences in species distribution across World Health Organization regions. Fluconazole was the most commonly used agent for NIC treatment (55.4%; 1567/2826). Overall, 24.8% (1128/6613) of isolates with available data were resistant to fluconazole. The pooled estimated CFR was 18.7% (95% CI, 15.5–22.1). Conclusions A higher NIC incidence rate and CFR in LMICs is noted compared to high-income countries, although infected babies were less premature with a higher birth weight. The proportion of fluconazole-resistant isolates was high. Prevention and treatment strategies for NIC need to be targeted to LMIC settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".