Efficacy of 4 % deltamethrin-impregnated collars against canine visceral leishmaniasis across different areas and the sociocultural burden of collar loss in a middle-income country
Bibliographic record
Abstract
Despite efforts to control visceral leishmaniasis (VL), the disease remains a major burden in low- and middle-income countries. In South America, insecticide-impregnated dog collars help prevent disease transmission, as dogs are the main reservoirs in urban areas. This study evaluated the efficacy of 4 % deltamethrin-impregnated collars (DMC) against canine VL (CVL) over a 24-month period in an endemic area of Brazil. We compared 941 DMC dogs with 1032 control dogs (C) across four geographic areas with similar baseline disease prevalence. The difference between the DMC and C cohorts was statistically significant ( p < 0.05), and the study achieved an overall efficacy of 63 %, 51 %, 48 %, and 58 % at the first, second, third, and fourth follow-ups, respectively. Among dogs that remained protected, efficacy was 74 %, 67 %, 100 %, and 100 % across the follow-ups, whereas in dogs that lost their collars between follow-ups, efficacy was 45 %, 10 %, 23 %, and − 11 %. Collar loss between follow-ups was associated with a 2.25-fold increase in the odds of CVL (OR 2.25, p < 0.05). No statistically significant geographical variation in collar loss was observed, and most losses were potentially preventable. However, infrequently bathed dogs had significantly higher odds of CVL (OR 9.93, p < 0.05). These results help demystify sociocultural stigmas related to collar loss and support the development of targeted public health education initiatives. Ensuring collar retention, incorporating owners' cultural behaviors to promote consistent collar use, and integrating educational actions within the One Health and Health Promotion frameworks are crucial to maximizing the success of large-scale dog interventions in public health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".