Burden of human scabies in sub‐Saharan African prisons: Evidence from the west region of Cameroon
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: There is little data on the profile and magnitude of scabies in sub-Saharan African prisons. The present study aimed to assess the prevalence and determinants of scabies in prisons of the west region of Cameroon. METHODS: We conducted a cross-sectional study from March to August 2014, and consecutively recruited volunteer detainees of three randomly selected prisons in the West Region of Cameroon. The diagnosis was based on clinical findings after assessment by two experienced and well-trained dermatologists. RESULTS: We enrolled 755 prisoners, 17 (2%) of whom were women. Their mean age was 32 ± 12 years. There were 242 cases (32%) of scabies, with significantly more cases in the most crowded prison (P < 0.0001). Men were significantly more affected than women (P = 0.004) and the prevalence of scabies significantly decreased when the level of education increased (P < 0.0001). In addition to a low level of education (adjusted odds ratio (aOR) 1.90; P < 0.0001), sharing clothes/bedding (aOR 2.72; P < 0.0001) and the number of detainees per cell > 10 (aOR 1.89; P = 0.002), but not age, duration of incarceration, number of baths/week and washing/week, were independent drivers of scabies occurrence. CONCLUSION: Almost one-third of prisoners suffered from scabies in our prisons. A low educational level, the sharing of clothes/bedding and number of detainees/cell > 10 were independent determinants of the disease. Urgent measures must be undertaken to reduce the burden of scabies in our prisons.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".