Roadblocks in Chagas disease care in endemic and nonendemic countries: Argentina, Colombia, Spain, and the United States. The NET-Heart project
Bibliographic record
Abstract
BACKGROUND: Chagas disease (CD) is endemic in Latin America; however, its spread to nontropical areas has raised global interest in this condition. Barriers in access to early diagnosis and treatment of both acute and chronic infection and their complications have led to an increasing disease burden outside of Latin America. Our goal was to identify those barriers and to perform an additional analysis of them based on the Inter American Society of Cardiology (SIAC) and the World Heart Federation (WHF) Chagas Roadmap, at a country level in Argentina, Colombia, Spain, and the United States, which serve as representatives of endemic and nonendemic countries. METHODOLOGY AND PRINCIPAL FINDINGS: This is a nonsystematic review of articles published in indexed journals from 1955 to 2021 and of gray literature (local health organizations guidelines, local policies, blogs, and media). We classified barriers to access care as (i) existing difficulties limiting healthcare access; (ii) lack of awareness about CD and its complications; (iii) poor transmission control (vectorial and nonvectorial); (iv) scarce availability of antitrypanosomal drugs; and (v) cultural beliefs and stigma. Region-specific barriers may limit the implementation of roadmaps and require the application of tailored strategies to improve access to appropriate care. CONCLUSIONS: Multiple barriers negatively impact the prognosis of CD. Identification of these roadblocks both nationally and globally is important to guide development of appropriate policies and public health programs to reduce the global burden of this disease.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".