Implementation and Challenges to Latent Tuberculosis Infection Care in Malaysia
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,057 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».