Optimising diagnosis and treatment of tuberculosis infection in community and primary care settings in two urban provinces of Viet Nam: a cohort study
Bibliographic record
Abstract
OBJECTIVES: To end tuberculosis (TB), the vast reservoir of 1.7-2.3 billion TB infections (TBIs) must be addressed, but achieving global TB preventive therapy (TPT) targets seems unlikely. This study assessed the feasibility of using interferon-γ release assays (IGRAs) at lower healthcare levels and the comparative performance of 3-month and 9-month daily TPT regimens (3HR/9H). DESIGN, SETTING, PARTICIPANTS AND INTERVENTION: This cohort study was implemented in two provinces of Viet Nam from May 2019 to September 2020. Participants included household contacts (HHCs), vulnerable community members and healthcare workers (HCWs) recruited at community-based TB screening events or HHC investigations at primary care centres, who were followed up throughout TPT. PRIMARY AND SECONDARY OUTCOMES: We constructed TBI care cascades describing indeterminate and positivity rates to assess feasibility, and initiation and completion rates to assess performance. We fitted mixed-effects logistic and stratified Cox models to identify factors associated with IGRA positivity and loss to follow-up (LTFU). RESULTS: Among 5837 participants, the indeterminate rate was 0.8%, and 30.7% were IGRA positive. TPT initiation and completion rates were 63.3% (3HR=61.2% vs 9H=63.6%; p=0.147) and 80.6% (3HR=85.7% vs 9H=80.0%; p=0.522), respectively. Being male (adjusted OR=1.51; 95% CI: 1.28 to 1.78; p<0.001), aged 45-59 years (1.30; 1.05 to 1.60; p=0.018) and exhibiting TB-related abnormalities on X-ray (2.23; 1.38 to 3.61; p=0.001) were associated with positive IGRA results. Risk of IGRA positivity was lower in periurban districts (0.55; 0.36 to 0.85; p=0.007), aged <15 years (0.18; 0.13 to 0.26; p<0.001), aged 15-29 years (0.56; 0.42 to 0.75; p<0.001) and HCWs (0.34; 0.24 to 0.48; p<0.001). The 3HR regimen (adjusted HR=3.83; 1.49 to 9.84; p=0.005) and HCWs (1.38; 1.25 to 1.53; p<0.001) showed higher hazards of LTFU. CONCLUSION: Providing IGRAs at lower healthcare levels is feasible and along with shorter regimens may expand access and uptake towards meeting TPT targets, but scale-up may require complementary advocacy and education for beneficiaries and providers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".