Treating the invisible: Gaps and opportunities for enhanced TB control along the Thailand-Myanmar border
Bibliographic record
Abstract
In Thailand's northwestern Tak province, contextual conditions along the border with Myanmar pose difficulties for TB control among migrant populations. Incomplete surveillance data, migrant patient mobility, and loss to follow-up make it difficult to estimate the TB burden and implement effective TB control measures. This multi-methods study examined tuberculosis, tuberculosis and human immunodeficiency virus co-infection, and multidrug-resistant tuberculosis treatment accessibility for migrants and refugees in Tak province, health system response, and public health surveillance. In this study we conducted 13 interviews with key informants working in public health or TB treatment provision to elicit information on TB treatment availability and TB surveillance practices. In addition we organized 15 focus group discussions with refugee and migrant TB, TB/HIV, and MDR-TB patients and non-patients to discuss treatment access. We analyzed the data using thematic analysis and created treatment availability maps with Google maps. The study identified surveillance, treatment, and funding gaps. Migrant TB cases are underreported in the provincial statistics due to jurisdictional interpretations and resource barriers. Our results suggest that TB/HIV and MDR-TB treatment options are limited for migrants and a heavy reliance on donor funding may lead to potential funding gaps for migrant TB services. We identified several opportunities that positively contribute to TB control in Tak province: improved diagnostics, comprehensive care, and collaboration through data sharing, planning, and patient referrals. The various organizations providing TB treatment to migrant and refugee populations along the border and the Tak Provincial Public Health Office are highly collaborative which offers a strong foundation for future TB control initiatives. Our findings suggest the need to enhance the surveillance system to include all migrant TB patients who seek treatment in Tak province and support efforts by stakeholders on both sides of the border to continue to share data and engage in collaborative planning on TB, TB/HIV, and MDR-TB treatment provision for migrant populations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".