Implementation and Challenges to Latent Tuberculosis Infection Care in Malaysia
Bibliographic record
Abstract
introduction Tuberculosis (TB) caused a total death toll of 1.5 million out of 10 million infected world population in 2018.1 • The World Health Organization (WHO) and the United Nation (UN) advocate the commitment in latent tuberculosis infection (LTBI) care in countries with lower incidence of TB (TB incidence rate <100 per 100,000 population) to end the TB epidemic by the next decade. Screening and treating LTBI is one of the TB preventive strategies, targeting asymptomatic individuals infected by Mycobacterium tuberculosis that remains dormant and nontransmissible. Malaysia is a country with TB incidence rate of 92 and mortality rate of 6.6 per 100 000 population in 2018. Through LTBI identification, preventing TB reactivation among the LTBI affected specific high-risk populations with LTBI treatment could help strengthen TB control in Malaysia. However, we have limited understanding about the practice and performance of LTBI care in the local settings. Objective This systematic review aims to identify literature evidence addressing the progress and challenges to LTBI care in Malaysia. Methods Three electronic databases were searched: PubMed, EMBASE and Web of Science. Ongoing studies were searched in the National Medical Research Register (NMRR) and clinicaltrial.gov. Studies were included if they described clinical management of LTBI according to the LTBI cascade of care, including contact tracing, LTBI screening, diagnosis and/or treatment; assessed the understanding of LTBI, were conducted in Malaysia; were available in English. Local TB and LTBI management guidelines were searched in the Government portals. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the observational studies that were published. Appraisal of Guidelines for Research & Evaluation II (AGREE II) instrument was used to assess the quality of guidelines. Data from all eligible articles were extracted using a standardised data collection form and the findings were presented and described narratively. Results We identified 14 published studies, 7 ongoing studies in the NMRR registry, 1 ongoing clinical trial, 3 local guidelines describing the LTBI management. The methodological quality of the published studies and guidelines were moderately high. Discussion and Conclusion A number of published studies had focused on the initial part of the cascade of LTBI care, involving the screening and diagnosis of LTBI among specific high-risk populations in Malaysia. • Several ongoing studies start to investigate the downstream part of the cascade of LTBI care, focusing LTBI treatment. • LTBI study targeting general population has not been identified. • Improvement is needed in terms of the coordination among healthcare professionals in the multidisciplinary team, the availability of human and financial resources, the understanding and awareness of the significance to practise and accept LTBI management, and evidence-based research to contribute to health policy planning and resources allocation for LTBI care. • LTBI management is important to help identify and tackle the reservoir of TB infection, as an effort to prevent and control TB incidence, with the aim of achieving the milestones of eliminating TB epidemic in line with the WHO END TB Strategy by 2035.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".