BUTIMBA: Intensifying the Hunt for Child TB in Swaziland through Household Contact Tracing
Bibliographic record
Abstract
BACKGROUND: Limited data exists to inform contact tracing guidelines in children and HIV-affected populations. We evaluated the yield and additionality of household contact and source case investigations in Swaziland, a TB/HIV high-burden setting, while prioritizing identification of childhood TB. METHODS: In partnership with 7 local TB clinics, we implemented standardized contact tracing of index cases (IC) receiving TB treatment. Prioritizing child contacts and HIV-affected households, screening officers screened contacts for TB symptoms and to identify risk factors associated with TB. We ascertained factors moderating the yield of contact tracing and measured the impact of our program by additional notifications. RESULTS: From March 2013 to November 2015, 3,258 ICs (54% bacteriologically confirmed; 70% HIV-infected; 85% adults) were enrolled leading to evaluation of 12,175 contacts (median age 18 years, IQR 24-42; 45% children; 9% HIV-infected). Among contacts, 196 TB cases (56% bacteriologically confirmed) were diagnosed resulting in a program yield of 1.6% for all forms of TB. The number needed to screen (NNS) to identify a bacteriologically confirmed TB case or all forms TB case traced from a child IC <5 years was respectively 62% and 40% greater than the NNS for tracing from an adult IC. In year one, we demonstrated a 32% increase in detection of bacteriologically confirmed child TB. Contacts were more likely to have TB if <5 years (OR = 2.0), HIV-infected (OR = 4.9), reporting ≥1 TB symptoms (OR = 7.7), and sharing a bed (OR = 1.7) or home (OR = 1.4) with the IC. There was a 1.4 fold increased chance of detecting a TB case in households known to be HIV-affected. CONCLUSION: Contact tracing prioritizing children is not only feasible in a TB/HIV high-burden setting but contributes to overall case detection. Our findings support WHO guidelines prioritizing contact tracing among children and HIV-infected populations while highlighting potential to integrate TB and HIV case finding.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".