5 research outputs found

    The impact of educational camp on glycemic control of Rwandan type 1 diabetes youth

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    In Rwanda, the prevalence of known type 1 diabetes mellitus in seven  districts of the country is 16.4 per 100,000 in young adults under 25 years old. The objective of this study was to compare the glycemic control of type 1 diabetes youth before and after the diabetes camp in Rwanda. A quasi experimental design using a longitudinal approach to compare the glycemic control before and after camp was used; 97 type 1 diabetes youth of both sexes, average age of 21 years were assigned into 8 groups and every group attended 5 days of diabetes education at the camp. Medical records about glycated hemoglobin levels before and 3 months after the camp were extracted from the database of Rwanda Diabetes Association and were analyzed to identify the impact of the educational camp. The mean  difference between the glycemic control before and 3 months after the camp revealed a statistically  significant decrease of 2.1% HbA1c (P-value = 0.02). As conclusion, this study found that diabetes educational camp is an effective strategy to improve Rwandan type 1 diabetes youth’s glycemic control. ________________________________________________________________________Key words: Rwanda, type 1 diabetes, youth, camp, glycemic contro

    Identifying the necessary capacities for the adaptation of a diabetes phenotyping algorithm in countries of differing economic development status

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    Background In 2019, the World Health Organization recognised diabetes as a clinically and pathophysiologically heterogeneous set of related diseases. Little is currently known about the diabetes phenotypes in the population of low- and middle-income countries (LMICs), yet identifying their different risks and aetiology has great potential to guide the development of more effective, tailored prevention and treatment. 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