Mitigating social and health inequities: Community participation and Chagas disease in rural Argentina
Bibliographic record
Abstract
Chagas disease (CD) causes 12,500 deaths annually in Latin America. As a neglected disease primarily associated with poverty, it is a major driver of health inequity. Argentina's efforts to control vector transmission have been unsuccessful. Using new survey data (n=400 households), we compare the social patterning of the burden of CD by examining socio-demographic predictors of self-reported CD and the presence of vinchucas in two areas of rural northern Argentina known to have experienced different interventions in surveillance and control. Our analyses suggest that Avellaneda, an area known for horizontal intervention strategies which nurture community participation is quite distinct from Silipica, an area which has experienced a vertical intervention strategy since 1990. Avellaneda has higher level of self-reported Chagas infection and lower level of vinchuca presence; Silipica has pronounced and statistically significant differences patterned by the head of household's level of educational attainment. A greater awareness of the disease and its transmission, along with community mobilisation and spraying, may bring about more self-reported CD and less vinchuca presence in Avellaneda than in Silipica. This suggests that strategies based on community participation may be effective in reducing the social patterning of the burden of disease, even in poor places.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".