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 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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".