Serum vitamin D status exerts effects on glucose-insulin-homeostatic states underlying Diabetes-Mellitus, Type 2 (T2DM). This has been described in white and Asian population where low Vitamin D levels predicted future impairments in beta cell function and worsening of insulin resistance. This study aimed to examine the relationship between serum vitamin D, insulin resistance and beta cell function in a sub population of black Kenyan T2DM patients. The primary objective was to determine the levels of serum 25 hydroxy (25-OH) vitamin D, and estimate the insulin resistance, and beta cell function among T2DM patients at Moi Teaching and Referral Hospital (MTRH). This was a cross sectional study. 124 T2DM patients attending the MTRH Diabetes clinic between February and May 2016 were enrolled. Patients on insulin therapy and/or thiazolidinediones were excluded. Anthropometric, clinical and demographic data was obtained. Samples were drawn for estimation of serum 25-OH vitamin D, fasting insulin levels and fasting blood glucose levels. HOMA (Homeostatic model of assessment) model was used to estimate Beta cell secretion (HOMA-B) and insulin resistance (HOMA-IR); while the Disposition index {(DI) hyperbola product of insulin sensitivity (1/HOMA-IR) and beta cell secretion} was used to estimate the beta cell function. The relationships between serum vitamin D, insulin resistance and beta cell function were explored using a linear regression model. The study participants had a mean age of 56.2 (± 9.2) years, and a mean BMI of 26.9 kg/m2 (4.3). Forty nine percent (n = 61) were males. Vitamin D deficiency was present in 71.1% (n = 88) of the respondents. Relatively low levels of insulin resistance and higher levels of beta cell dysfunction were observed {median HOMA-IR of 2.3 (0.7, 6.5) and Disposition Index (DI) of 25.5 (14.3, 47.2)}. Vitamin D levels exhibited a low positive correlation with DI [r = 0.22 (95% CI: 0.03, 0.37)], but was not significantly correlated with HOMA-IR [r = 0.07(95% CI: − 0.11, 0.25)]. These results indicate that beta cell dysfunction rather than insulin resistance as the predominant defect among black T2DM patients seeking care at the MTRH diabetes clinic. Vitamin D deficiency is also prevalent among them and exhibits a low positive correlation with beta cell dysfunction. There was no correlation observed between Vitamin D deficiency and insulin resistance.

Countries in Sub-Saharan Africa are increasingly adopting mandatory social health insurance programs. In Kenya, mandatory social health insurance is being implemented through the national health insurer, the National Hospital Insurance Fund (NHIF), but the level of coverage, affordability and financial risk protection provided by health insurance, especially for rural informal households, is unclear. This study provides as assessment of affordability of NHIF premiums, the need for financial risk protection, and the extent of financial protection provided by NHIF among rural informal workers in western Kenya.Methods We conducted a mixed methods study with a cross-sectional household survey (n = 1773), in-depth household interviews (n = 36), and 6 focus group discussions (FGDs) with community stakeholders in rural western Kenya. Health insurance status was self-reported and households were categorized into insured and uninsured. Using survey data, we calculated the affordability of health insurance (unaffordability was defined as the monthly premium being > 5% of total household expenditures), out of pocket expenditures (OOP) on healthcare and its impact on impoverishment, and incidence of catastrophic health expenditures (CHE). Logistic regression was used to assess household characteristics associated with CHE.Results Only 12% of households reported having health insurance and was unaffordable for the majority of households, both insured (60%) and uninsured (80%). Rural households spent an average of 12% of their household budget on OOP, with both insured and uninsured households reporting high OOP spending and similar levels of impoverishment due to OOP. Overall, 12% of households experienced CHE, with uninsured households more likely to experience CHE. Participants expressed concerns about value of health insurance given its cost, availability and quality of services, and financial protection relative to other social and economic household needs. Households resulted to borrowing, fundraising, taking short term loans and selling family assets to meet healthcare costs.Conclusion Health insurance coverage was low among rural informal sector households in western Kenya, with health insurance premiums being unaffordable to most households. Even among insured households, we found high levels of OOP and CHE. Our results suggest that significant reforms of NHIF and health system are required to provide adequate health services and financial risk protection for rural informal households in Kenya.

BackgroundHuman immunodeficiency virus (HIV) viral failure occurs when antiretroviral therapy fails to suppress and sustain a person’s viral load count below 1,000 copies of viral ribonucleic acid per milliliter. For those newly diagnosed with HIV and living in a setting where healthcare resources are limited, such as a low- and middle-income country, the World Health Organization recommends viral load monitoring six months after initiation of antiretroviral treatment and yearly thereafter. Deviations from this schedule are made in cases where viral failure occurs or at the discretion of the clinician. Failure to detect viral failure in a timely fashion can lead to delayed administration of essential interventions. Clinical prediction models based on information available in the patient medical record are increasingly being developed and deployed for decision support in clinical medicine and public health. This raises the possibility that prediction models can be used to detect potential for viral failure in advance of viral measurements, particularly when those measurements occur infrequently.ObjectiveOur goal is to use electronic health record data from a large HIV care program in Kenya to characterize and compare the predictive accuracy of several statistical machine learning methods for predicting viral failure at the first and second measurements following initiation of antiretroviral therapy. Predictive accuracy is measured in terms of sensitivity, specificity and area under the receiver-operator characteristic curve.MethodsWe trained and cross-validated 10 statistical machine learning models and algorithms on data from over 10,000 patients in the Academic Model Providing Access to Healthcare care program in western Kenya. These included parametric, non-parametric, ensemble, and Bayesian methods. The input variables included 50 items from the clinical record, hand picked in consultation with clinician experts. Predictive accuracy measures were calculated using 10-fold cross validation.ResultsViral load failure rate is about 20% in this patient cohort at both the first and second measurements. Ensemble techniques generally outperformed other methods. For predicting viral failure at the first follow up measure, specificity was over 90% for these methods, but sensitivity was typically in the 50–60% range. Predictive accuracy was greater for the second follow up measure, with sensitivities over 80%. Super Learner, gradient boosting and Bayesian additive regression trees consistently outperformed other methods. For a viral failure rate of 20%, the positive predictive value for the top-performing methods is between 75 and 85%, while the negative predictive value is over 95%.ConclusionEvidence from this study suggests that machine learning techniques have potential to identify patients at risk for viral failure prior to their scheduled measurements. Ultimately, prognostic virologic assessment can help guide the administration of earlier targeted intervention such as enhanced drug resistance monitoring, rigorous adherence counseling, or appropriate next-line therapy switching. External validation studies should be used to confirm the results found here.

IntroductionMany low- and middle-income countries are attempting to finance healthcare through voluntary membership of insurance schemes. This study examined willingness to prepay for health care, social solidarity as well as the acceptability of subsidies for the poor as factors that determine enrolment in western Kenya.MethodsThis study employed a sequential mixed method design. We conducted a cross-sectional household survey (n = 1,746), in-depth household interviews (n = 36), 6 FGDs with community stakeholders and key informant interviews (n = 11) with policy makers and implementers in a single county in western Kenya. Social solidarity was defined by willingness to make contributions that would benefit people who were sicker (“risk cross-subsidization”) and poorer (“income cross-subsidization”). We also explored participants' preferences related to contribution cost structure – e.g., flat, proportional, progressive, and exemptions for the poor.ResultsOur study found high willingness to prepay for healthcare among those without insurance (87.1%) with competing priorities, low incomes, poor access, and quality of health services, lack of awareness of flexible payment options cited as barriers to enrolment. More than half of respondents expressed willingness to tolerate risk and income cross-subsidization suggesting strong social solidarity, which increased with socio-economic status (SES). Higher SES was also associated with preference for a proportional payment while lower SES with a progressive payment. Few participants, even the poor themselves, felt the poor should be exempt from any payment, due to stigma (being accused of laziness) and fear of losing power in the process of receiving care (having the right to demand care).ConclusionAlthough there was a high willingness to prepay for healthcare, numerous barriers hindered voluntary health insurance enrolment in Kenya. Our findings highlight the importance of fostering and leveraging existing social solidarity to move away from flat rate contributions to allow for fairer risk and income cross-subsidization. Finally, governments should invest in robust strategies to effectively identify subsidy beneficiaries.