A systematic review and meta-analysis of the aetiological agents of non-malarial febrile illnesses in Africa
Bibliographic record
Abstract
BACKGROUND: The awareness of non-malarial febrile illnesses (NMFIs) has been on the rise over the last decades. Therefore, we undertook a systematic literature review and meta-analysis of causative agents of non-malarial fevers on the African continent. METHODOLOGY: We searched for literature in African Journals Online, EMBASE, PubMed, Scopus, and Web of Science databases to identify aetiologic agents that had been reported and to determine summary estimates of the proportional morbidity rates (PMr) associated with these pathogens among fever patients. FINDINGS: A total of 133 studies comprising 391,835 patients from 25 of the 54 African countries were eligible. A wide array of aetiologic agents were described with considerable regional differences among the leading agents. Overall, bacterial pathogens tested from blood samples accounted for the largest proportion. The summary estimates from the meta-analysis were low for most of the agents. This may have resulted from a true low prevalence of the agents, the failure to test for many agents or the low sensitivity of the diagnostic methods applied. Our meta-regression analysis of study and population variables showed that diagnostic methods determined the PMr estimates of typhoidal Salmonella and Dengue virus. An increase in the PMr of Klebsiella spp. infections was observed over time. Furthermore, the status of patients as either inpatient or outpatient predicted the PMr of Haemophilus spp. infections. CONCLUSION: The small number of epidemiological studies and the variety of NMFI agents on the African continent emphasizes the need for harmonized studies with larger sample sizes. In particular, diagnostic procedures for NMFIs should be standardized to facilitate comparability of study results and to improve future meta-analyses. Reliable NMFI burden estimates will inform regional public health strategies.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.009 | 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) | 0.003 | 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".