Vikela Ekhaya: A Novel, Community-based, Tuberculosis Contact Management Program in a High Burden Setting
Bibliographic record
Abstract
BACKGROUND: The prevention of tuberculosis (TB) in child contacts of TB cases and people living with human immunodeficiency virus (HIV) is a public health priority, but global access to TB preventive therapy (TPT) remains low. In 2019, we implemented Vikela Ekhaya, a novel community-based TB contact management program in Eswatini designed to reduce barriers to accessing TPT. METHODS: Vikela Ekhaya offered differentiated TB and HIV testing for household contacts of TB cases by using mobile contact management teams to screen contacts, assess their TPT eligibility, and initiate and monitor TPT adherence in participants' homes. RESULTS: In total, 945 contacts from 244 households were screened for TB symptoms; 72 (8%) contacts reported TB symptoms, and 5 contacts (0.5%) were diagnosed with prevalent TB. A total of 322 of 330 (98%) eligible asymptomatic household contacts initiated TPT. Of 322 contacts initiating TPT, 248 children initiated 3 months of isoniazid and rifampicin and 74 children and adults living with HIV initiated 6 months of isoniazid; 298 (93%) completed TPT. In clustered logistic regression analyses, unknown HIV status (adjusted odds ratio [aOR] 5.7, P = .023), positive HIV status (aOR 21.1, P = .001), urban setting (aOR 5.6, P = .006), and low income (aOR 5.9, P = .001) predicted loss from the cascade of care among TPT-eligible contacts. CONCLUSION: Vikela Ekhaya demonstrated that community-based TB household contact management is a feasible, acceptable, and successful strategy for TB screening and TPT delivery. The results of this study support the development of novel, differentiated, community-based interventions for TB prevention and control.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".