Integrating neurological expertise into One Health strategies for pediatric neurocysticercosis-associated epilepsy control in Sub-Saharan Africa: a narrative review
Bibliographic record
Abstract
BACKGROUND: Neurocysticercosis (NCC), caused by the larval stage of Taenia solium, is a leading preventable cause of epilepsy in children, particularly in Sub-Saharan Africa (SSA), where inadequate sanitation practices and limited veterinary control strategies persist. A missing link in the One Health response to NCC has been the absence of communication between neurospecialists and the other partners in control strategies. This narrative review explores how integrating neurological expertise into One Health strategies can enhance the prevention and control of NCC-associated epilepsy in children across the region. METHODS: We reviewed literature from PubMed/MEDLINE, Scopus, Google Scholar, Embase, Web of Science, Global Health, CINAHL, PsycINFO, and the African Index Medicus, including peer-reviewed articles and organizational reports published between 2013 and March 2025. FINDINGS AND CONCLUSION: Out of 1,509 records screened, 28 studies met the inclusion criteria, focusing on neuroparasitosis-associated epilepsy in children across SSA. These included research on disease burden and care challenges (n = 10), pathogenesis (n = 8), One Health control strategies (n = 4), and implementation barriers and solutions (n = 6). NCC remains a major contributor to pediatric epilepsy and associated disability in SSA. A One Health approach informed by direct input from neuro-specialists and better recording of infectious causes of epilepsy can assist teams to implement key strategies, including community education, improved sanitation, food safety measures, pig vaccination, and mass drug administration. Strengthening intersectoral collaboration and healthcare access is critical to reducing NCC burden and improving neurological outcomes for affected children.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".