Knowledge about tuberculosis transmission and prevention and perceptions of health service utilization among index cases and contacts in Brazil: Understanding losses in the latent tuberculosis cascade of care
Bibliographic record
Abstract
INTRODUCTION: Tuberculosis contacts are candidates for active and latent tuberculosis infection screening and eventual treatment. However, many losses occur in the different steps of the contacts' cascade of care. Reasons for this are poorly understood. OBJECTIVE: To describe the different steps where losses in the contact cascade occur and to explore knowledge and attitudes regarding tuberculosis transmission/prevention and perceptions about tuberculosis services in order to understand the reasons for losses from the tuberculosis service users' perspective. DESIGN: We collected routine data from the index case and contact registry books and from patients' records to build the cascade of care of contacts in 12 health facilities in three Brazilian cities with high tuberculosis incidence rates. During a knowledge, attitudes and practices (KAP) survey, trained interviewers administered a semi-structured questionnaire to 138 index cases and 98 contacts. RESULTS: Most of the losses in the cascade occurred in the first two steps (contact identification, 43% and tuberculin skin testing placement, 91% of the identified contacts). Among KAP-interviewed contacts, 67% knew how tuberculosis is transmitted, 87% knew its key symptoms and 81% declared they would take preventive therapy if prescribed. Among KAP-interviewed index cases, 67% knew they could spread tuberculosis, 70% feared for the health of their families and 88% would like their family to be evaluated in the same services. CONCLUSION: Only a small proportion of contacts are evaluated for active and latent tuberculosis, despite their-and their index cases'-reasonable knowledge, positive attitudes towards prevention and satisfaction with tuberculosis services. In these services, education of service users would not be a sufficient solution. Healthcare workers' and managers' perspective, not explored in this study, may bring more light to this subject.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".