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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. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Background: Kenya aims to achieve universal health coverage (UHC) by 2030 and has selected the National Health Insurance Fund (NHIF) as the 'vehicle' to drive the UHC agenda. While there is some progress in moving the country towards UHC, the availability and accessibility to NHIF-contracted facilities may be a barrier to equitable access to care. We estimated the spatial access to NHIF-contracted facilities in Kenya to provide information to advance the UHC agenda in Kenya. Methods: We merged NHIF-contracted facility data to the geocoded inventory of health facilities in Kenya to assign facility geospatial locations. We combined this database with covariates data including road network, elevation, land use, and travel barriers. We estimated the proportion of the population living within 60- and 120-minute travel time to an NHIF-contracted facility at a 1-x1-kilometer spatial resolution nationally and at county levels using the WHO AccessMod tool. Results: We included a total of 3,858 NHIF-contracted facilities. Nationally, 81.4% and 89.6% of the population lived within 60- and 120-minute travel time to an NHIF-contracted facility respectively. At the county level, the proportion of the population living within 1-hour of travel time to an NHIF-contracted facility ranged from as low as 28.1% in Wajir county to 100% in Nyamira and Kisii counties. Overall, only four counties (Kiambu, Kisii, Nairobi and Nyamira) had met the target of having 100% of their population living within 1-hour (60 min) travel time to an NHIF-contracted facility. On average, it takes 209, 210 and 216 min to travel to an NHIF-contracted facility, outpatient and inpatient facilities respectively. At the county level, travel time to an NHIF-contracted facility ranged from 10 min in Vihiga County to 333 min in Garissa. Conclusion: Our study offers evidence of the spatial access estimates to NHIF-contracted facilities in Kenya that can inform contracting decisions by the social health insurer, especially focussing on marginalised counties where more facilities need to be contracted. Besides, this evidence will be crucial as the country gears towards accelerating progress towards achieving UHC using social health insurance as the strategy to drive the UHC agenda in Kenya. [ABSTRACT FROM AUTHOR]

Background: Comorbid common mental disorders (CMDs) are pervasive in refugee populations. However, limited research has explored psychosocial factors for mental disorder comorbidity in Somali refugee samples. Aims: This study aims to explore potential risk and protective factors for comorbid depression-anxiety and comorbid depression-PTSD by examining associations between trauma exposure, psychosocial factors, and mental health symptoms among a sample of Somali refugees displaced in urban Kenya. Methods: We used snowball sampling to recruit Somali youth aged 15 to 35years(N = 250, n = 143 female, n = 88 male, n = 19 unknown gender). We measured 16 common types of trauma exposure and three psychosocial factors (endorsing violence, willingness to share problems, and symptom awareness) and used the HSCL-25 and PCL-C to capture individual and comorbid CMD symptoms, using guided cutoff points and/or algorithms. We then ran a series of logistic regression analyses to examine relationships between trauma exposure, psychosocial factors, and individual and comorbid CMD symptoms. Results: Findings showed that increased trauma exposure predicted symptoms of individual and comorbid CMDs. Increased symptom awareness and endorsement of violence predicted comorbid depression-PTSD and comorbid anxiety-depression symptoms, respectively. Willingness to share problems buffered depressive symptoms but did not predict comorbidity. Conclusions: These findings revealed the high CMD comorbidity prevalence with differential effects of trauma and psychosocial factors on individual or comorbid mental disorders. This study suggests a need for transdiagnostic approaches that cut across Western diagnostic boundaries and consider culturally responsive and relevant items for mental health measures. [ABSTRACT FROM AUTHOR]