Konzo Disease: A household survey conducted in Mutondo community, Luampa district, Zambia
Bibliographic record
Abstract
Introduction:Konzo is a neglected neurological disease characterized by sudden, non-progressive paralysis of the lower limbs, commonly linked to chronic consumption of cyanogenic cassava combined with protein deficiency. Since 2007, sporadic cases have been reported in Western Zambia, with recent clustering in Mutondo zone of Nakayembe catchment in Luampa District. This study aimed to describe household-level risk factors and provide a crude prevalence estimate for Konzo in Mutondo community. Methods:We conducted a descriptive cross-sectional household survey in Mutondo Zone, a rural community under Nakayembe Rural Health Centre in Luampa District. Data were collected using a structured questionnaire administered electronically via KoboToolbox to 50 household heads or their next of kin aged 18 years and above. Information was obtained on demographic and socio-economic characteristics, cassava dependency, processing and consumption practices, protein intake, and knowledge of Konzo. A suspected Konzo case was defined as any individual presenting with symmetric, non-progressive paraparesis consistent with the WHO case definition. Data were analyzed using R software, with categorical variables summarized as frequencies and proportions, and continuous variables as medians. Results:Of the 50 households surveyed, 32 (64%) were headed by males. Thirty-six percent (36%) of household heads had no formal education, and all respondents (100%) were engaged in informal employment. Nearly all (94%) depended on cassava as their main staple, and a quarter (26%) reported inadequate processing practices. Protein intake was low, with over 90% consuming protein-rich foods less than twice per week. While most respondents (94%) had heard of Konzo, only 38% recognized its link to cassava or recognised preventive measures. Thirty-seven suspected cases of Konzo were identified, yielding a crude prevalence of about 12% in the surveyed population. Conclusion:Konzo remains a significant public health concern in Mutondo Community, driven by heavy cassava reliance, poor processing practices, and low dietary protein intake. Targeted interventions to improve cassava processing, diversify food sources, and enhance community awareness are urgently needed to prevent further cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".