One health approach to toxocariasis in quilombola communities of southern Brazil
Bibliographic record
Abstract
BACKGROUND: Toxocariasis has been listed among the most neglected parasitic diseases worldwide, with approximately one fifth of the global population exposed, particularly those living under poverty. In Brazil, communities of descendants of enslaved blacks (quilombola) have historically had some of the highest rates of vulnerability and poverty, characterized by lack of health assistance, poor quality of life, and nutritional insecurity. METHODS: A cross-sectional sampling of quilombola individuals living in four communities of southern Brazil, as well as their dogs and the soil, was carried out from December 2021 to March 2022. Sociodemographic and other information such as water source, alimentary habits, and dog and cat ownership were gathered using a semi-structured questionnaire for assessing toxocariasis risk factors. Human serum samples were tested by ELISA for anti-Toxocara spp. IgG antibody detection was carried out on dog feces and hair, and soil samples were surveyed for presence of Toxocara spp. eggs. RESULTS: Overall, 172/208 individuals (82.7%, 95% CI = 77.0-87.2) were seropositive, the highest seroprevalence rate to date in Brazil. Male gender (P = 0.029), educational level (P = 0.026), and drinking water source (P = 0.043) were associated with seropositivity by univariate analysis. Final logistic regression revealed increased odds (P = 0.017, OR = 7.6, 95% CI = 1.5-42.7) to have seropositivity in individuals > 50 years old (< 10 years old). As expected, individuals with soil contact were more likely seropositive (P = 0.038, OR = 4.4, 95% CI = 1.1-18.8). Although retrieved in only 5/96 (5.2%) dog feces, Toxocara spp. eggs were found in 18/60 (30.0%) soil samples. CONCLUSIONS: The high vulnerability and seroprevalence observed in quilombola communities clearly demand a One Health approach for detection, monitoring, and prevention of infection by Toxocara spp. in both human and dog populations.
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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 | 0.000 | 0.000 |
| 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".