MATERIALS AND METHODS: This scoping review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework developed by the Joanna Briggs Institute (JBI). A total of 10 studies were identified as eligible from the title and abstract review. The mixed method quality appraisal tool (MMAT) version 2018 was used to assess the quality of the included quantitative studies.
RESULTS: The results showed that poverty, unemployment, low education levels, migrant status, community support, male gender, substance abuse, and regional disparities significantly impact the occurrence of TB LTFU in Southeast Asia.
CONCLUSION: The findings have significant implications for public health in Southeast Asia. Addressing these socioeconomic barriers through community-based strategies, educational initiatives, and policy reforms is vital for improving treatment outcomes and overall public health.
OBJECTIVES: This study reviewed the scope of diabetes datasets, health information ecosystems, and human resource capacity in four countries to assess whether a diabetes phenotyping algorithm (developed under a companion study) could be successfully applied.
METHODS: The capacity assessment was undertaken with four countries: Trinidad, Malaysia, Kenya, and Rwanda. Diabetes programme staff completed a checklist of available diabetes data variables and then participated in semi-structured interviews about Health Information System (HIS) ecosystem conditions, diabetes programme context, and human resource needs. Descriptive analysis was undertaken.
RESULTS: Only Malaysia collected the full set of the required diabetes data for the diabetes algorithm, although all countries did collect the required diabetes complication data. An HIS ecosystem existed in all settings, with variations in data hosting and sharing. All countries had access to HIS or ICT support, and epidemiologists or biostatisticians to support dataset preparation and algorithm application.
CONCLUSIONS: Malaysia was found to be most ready to apply the phenotyping algorithm. A fundamental impediment in the other settings was the absence of several core diabetes data variables. Additionally, if countries digitise diabetes data collection and centralise diabetes data hosting, this will simplify dataset preparation for algorithm application. These issues reflect common LMIC health systems' weaknesses in relation to diabetes care, and specifically highlight the importance of investment in improving diabetes data, which can guide population-tailored prevention and management approaches.