How are high burden countries implementing policies and tools for latent tuberculosis infection? A survey of current practices and barriers
Bibliographic record
Abstract
BACKGROUND AND AIMS: Despite the World Health Organization (WHO)'s updated guidelines on tuberculosis (TB) preventive treatment, the scale-up of TB preventive therapy remains low in many high-burden countries (HBCs). We conducted a survey to better understand the current status of policy implementation and barriers for scale-up. METHODS: Survey questions pertained to HBCs' current latent TB infection (LTBI) screening and treatment strategies, and the availability of LTBI tests and newer treatments (eg, isoniazid/rifapentine [3HP]). The 19-question survey was piloted and sent out via email in June 2019 as a protected Microsoft Word document to contacts [National TB Program (NTP) staff, researchers, and health officials] in the 30 TB HBCs. Responses were accepted until February 2020. RESULTS: Thirty-seven completed surveys from 24 HBCs were received. Respondents from five countries (Brazil, Lesotho, Mozambique, Russia, Zambia) reported having LTBI guidelines that are fully implemented. Among respondents who indicated their country currently has no LTBI guideline implementation (Angola, China, DRC, India, Indonesia, Kenya, Myanmar), the most often cited barrier to implementation was the prioritization of active TB over LTBI management (n = 5, Angola, China, DRC, India, Kenya). Of the 16 countries in which respondents reported using purified protein derivative (PPD), 9 reported having experienced a PPD shortage within the past year (from time of survey). Respondents from six countries reported currently using Interferon-gamma Release Assays (IGRAs) in their NTP, and 13 cited high cost as a barrier to IGRA use. Lastly, rifapentine was stated not be available in 8 HBCs. CONCLUSION: This survey indicates limited implementation of WHO LTBI guidelines in HBCs and provides some insight into barriers to implementation, including shortage of products (eg, PPD), high costs (eg, IGRAs), and lack of regulatory approval of newer treatments (eg, rifapentine). Thus, we should work towards price reductions for LTBI tests and treatments, and the development of tests that can be more easily implemented at peripheral healthcare levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".