Type 2 diabetes in rural Uganda : prevalence, risk factors, perceptions and implications for the health system

Background: Between 2010 and 2030, a 69% increase in type-2 diabetes is expected in low-income countries compared to a 20% increase in high income countries. Yet health system responsiveness to non-communicable diseases has been slow in sub-Saharan Africa. Data on the prevalence of type 2 diabetes and its associated factors in mainly rural settings is lacking, yet such data can guide planning for diabetes control. Objective: The aim of these studies was to assess the prevalence of type 2 diabetes, its risk factors, risk perceptions, and possible screening tools among people aged 35-60 years so as to inform primary care level intervention in rural low-income settings. Methods: Four studies (I-IV) were conducted among people aged 35-60 years in a mainly rural demographic surveillance site in eastern Uganda (2011-2013). Study I, a cross-sectional survey assessed the prevalence of diabetes-related risk factors (including overweight, hypertension and seven socio-behavioural risk factors) in 1,656 people. Study II then estimated the prevalence of abnormal glucose regulation (AGR) using fasting plasma glucose (FPG), and assessed its socio-behavioural correlates in 1,497 people. To compare the utility of FPG and glycated haemoglobin (HbA1C) rapid tests in risk stratification, a comparative survey of 795 people was nested into study II (Study III). To assess perceptions about diabetes, 12 Focus Group Discussions were conducted among people afflicted or at higher risk of type 2 diabetes (Study IV). The assessments used standard measurements and cut-offs. Results: About 18% of people aged 35-60 years were overweight, while 21% had hypertension. Women (OR 3.7; 95% CI 2.7-5.1), peri-urban dwellers (OR 2.5; 95% CI 1.5-3.0), and wealthier people (OR 4.1; 95% CI 2.4-7.0) were more likely to be overweight. Only 34% had adequate knowledge about lifestyle diseases (I). Prevalence of diabetes was 7.4% while pre-diabetes was 8.6% and 20.2% with WHO and American Diabetes Association criteria respectively (II). Prevalence of AGR was twice higher in obese people compared to those with a normal BMI (APRR 1.9, 95% CI 1.3-2.8) (II). Sufficient physical activity and diverse diet were associated with lower likelihood of AGR (II). The direct medical cost of screening one person was US$0.53, translating to US$2 per person detected with AGR. Agreement between FPG (the reference) and HbA1C in classifying diabetes was moderate (Kappa=22.9; AUC =75%), while that for AGR was low (Kappa=11.0; AUC=59%) (III). However, agreement was high (over 90%) among negative tests and in participants with risk factors for type 2 diabetes. FPG was more practical than HbA1C (III). Participants‟ strong perceptions of diabetes as a very severe disease were incongruent with their perceived urgency for lifestyle change (IV).While people with diabetes perceive obesity as „sickness‟, those without diabetes say it signifies „success‟ (IV). Poverty, access to food and large families were cited as barriers to healthy diets. Domestic work was the preferred platform for physical activity increments. Conclusions: Obesity, insufficient physical activity and unhealthy diets are possible aids to identifying people at higher risk of type 2 diabetes in primary care. Mass screening for abnormal glucose regulation may not be affordable to struggling health systems. However, rapid tests have utility in further evaluation of people with risk factors. Health education about lifestyle diseases is a priority and should target to change the community‟s notion of health and healthy lifestyles. Several challenges of adding NCD services to overstretched health systems come to light

Clinical presentation of newly diagnosed diabetes patients in a rural district hospital in Eastern Uganda

Background: Our objective was to describe the clinical presentation of new diabetes patients in a rural hospital, to enhance clinical detection in low resource settings.Methods: A case series assessment of 103 new diabetes patients consecutively enrolled at Iganga Hospital in rural Eastern Uganda was conducted. All underwent a basic clinical assessment through the clinic’s routine procedures.Following diagnosis, variables pertinent to the study (symptoms, blood pressure, anthropometry, and blood glucose) were secondarily abstracted from their clinical records. Results: Fiftty two percent of new diabetes patients were female. The mean age was 49 years (SD=14.4). Two clinical symptoms were present in almost all new patients: Frequent urination (100%) and frequent thirst (79%). Moderately occurring symptoms (i.e. 25-50% of patients) included blurred vision, frequent eating and frequent sweating. The mean duration of symptoms was 1.4 years; 48% had high blood pressure while 46% were overweight. Random blood sugar was normal for 25% of patients. The majority (71%) were classified as having ‘moderate illness’ at diagnosis. Severe illness was significantly lower among patients aged 40 or older compared to younger patients (OR 0.1; 95% CI 0.03-0.35). Conclusion: Out-patients aged 40-65 years should be prioritised for early diabetes diagnosis and associated risk factors in this setting.Keywords: Diabetes, clinical presentation, newly diagnosed, unrecognized disease

Clinical presentation of newly diagnosed diabetes patients in a rural district hospital in Eastern Uganda

Background: Our objective was to describe the clinical presentation of new diabetes patients in a rural hospital, to enhance clinical detection in low resource settings. Methods: A case series assessment of 103 new diabetes patients consecutively enrolled at Iganga Hospital in rural Eastern Uganda was conducted. All underwent a basic clinical assessment through the clinic\u2019s routine procedures.Following diagnosis, variables pertinent to the study (symptoms, blood pressure, anthropometry, and blood glucose) were secondarily abstracted from their clinical records. Results: Fiftty two percent of new diabetes patients were female. The mean age was 49 years (SD=14.4). Two clinical symptoms were present in almost all new patients: Frequent urination (100%) and frequent thirst (79%). Moderately occurring symptoms (i.e. 25-50% of patients) included blurred vision, frequent eating and frequent sweating. The mean duration of symptoms was 1.4 years; 48% had high blood pressure while 46% were overweight. Random blood sugar was normal for 25% of patients. The majority (71%) were classified as having \u2018moderate illness\u2019 at diagnosis. Severe illness was significantly lower among patients aged 40 or older compared to younger patients (OR 0.1; 95% CI 0.03-0.35). Conclusion: Out-patients aged 40-65 years should be prioritised for early diabetes diagnosis and associated risk factors in this setting

The process evaluation of a comparative controlled trial to support self-management for the prevention and management of type 2 diabetes in Uganda, South Africa and Sweden in the smart2d project.

Background. Type 2 diabetes (T2D) and its complications are increasing rapidly. Support for healthy lifestyle and self-management is paramount, but not adequately implemented in health systems in most countries. Process evaluations facilitate understanding why and how interventions work through analysing the interaction between intervention theory, implementation and context. The SMART2D project implemented and evaluated community-based support interventions for persons at high risk of or having T2D in a rural community in Uganda, an urban township in South Africa, and socio-economically disadvantaged urban communities in Sweden. This study presents comprehensive analyses of the implementation process and interaction with context. Methods. This paper reports implementation process outcomes across the three sites, guided by the MRC framework for complex intervention process evaluations and focusing on the three community strategies (peer support program; care companion; and link between facility care and community support). Data were collected through observations of peer support group meetings using a structured guide, and semi-structured interviews with project managers, implementers and participants. Results. The countries focused their in-depth implementation in accordance with the feasibility and relevance in the context. In Uganda and Sweden, the implementation focused on the peer support intervention whereas in South Africa, it centred around the CC part. The community-facility link received the least attention in the implementation. Continuous capacity building received a lot of attention, but intervention reach, dose delivered and fidelity varied substantially. Intervention- and context-related barriers affected participation. The analysis revealed how context shaped the possibilities of implementation, the delivery and participation and affected the mechanism of impact. Conclusions. Identification of the key uncertainties and conditions facilitates focus and efficient use of resources in process evaluations, and context relevant findings. The use of an overarching framework allows to collect cross-contextual evidence and a flexibility in evaluation design to adapt to the complex nature of the intervention. When designing an intervention, it is crucial to consider aspects of the implementing organization or structure, absorptive capacity, and to thoroughly assess and discuss implementation feasibility, capacity and organizational context with the implementation team and recipients. These recommendations are important for implementation and scale u

A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Despite the availability and implementation of well-known efficacious interventions for tuberculosis treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific tuberculosis patient at risk of treatment non-adherence is still a challenge. Thus, this retrospective study, based on a record review of 838 tuberculosis patients enrolled in six health facilities, presents, and discusses a machine learning approach to explore the individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district, Uganda. Five classification machine learning algorithms, logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and AdaBoost were trained, and evaluated by computing their accuracy, F1 score, precision, recall, and the area under the receiver operating curve (AUC) through the aid of a confusion matrix. Of the five developed and evaluated algorithms, SVM (91.28%) had the highest accuracy (AdaBoost, 91.05% performed better than SVM when AUC is considered as evaluation parameter). Looking at all five evaluation parameters globally, AdaBoost is quite on par with SVM. Individual risk factors predictive of non-adherence included tuberculosis type, GeneXpert results, sub-country, antiretroviral status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, cotrimoxazole preventive therapy (CPT) dapsone status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 and 6 months. Therefore, machine learning techniques, specifically classification types, can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, tuberculosis program management should consider adopting the classification machine learning techniques evaluated in this study as a screening tool for identifying and targeting suited interventions to these patients

Gender and innovation for climate-smart agriculture. Assessment of gender-responsiveness of RAN's agricultural-focused innovations

Ownership of agricultural production resources has generally been shown by research to be male dominated and to have wide disparities between males and females. The disparities are more pronounced in rural areas where women have less income, smaller pieces of land, and have inadequate market for their produce. In dealing with Climate Smart Agriculture (CSA) adoption and with agricultural technology adoption, there has been increasing recognition of the importance of focusing on the gender-based needs behind the adoption choice itself. Grounded in the belief that solutions to Africa’s resilience challenges lie in understanding what makes communities thrive in adversity, ResilientAfrica Network (RAN) is a multidisciplinary innovation lab that leverages the creativity and talent of youth, students, scholars and communities to develop and scale innovative ideas. Given how dominant agriculture is among RAN's problem sets for strengthening resilience through innovation, RAN sought to understand how supported innovations had contributed to gender-responsive climate smart agriculture solutions. What lessons could be learned from Women of Uganda Network (WOUGNET) in their history of engagement with women farmers to co-create a gender-responsive innovation process to strengthen resilience through the agricultural sector? With the support of CCAFS, RAN and WOUGNET conducted a gender assessment exercise to evaluate if the current solutions/innovations are gender responsive or not. Three innovations were selected that are primarily focused towards CSA and that had affordability and ease of use as key objectives. The gender assessment embraced a qualitative research approach. This choice was guided by the need to appreciate respondents’ understanding and experiences or perceptions of the different innovations that RAN has been nurturing and developing over the years. The respondents were purposively selected based on availability and on being located in any of the four Northern Uganda districts of Apac, Kole, Lira and Oyam where WOUGNET has actively engaged with women farmers. From the study, it was clear that men and women farmers are willing to embrace new technologies, practices and innovations in their pursuit of enhanced agricultural productivity and new opportunities. That said, a clearly gendered view emerged from the assessment in that uptake of the innovations was to a large extent driven by socio-cultural norms and expectations related to issues of ownership, work, decision making capacity, and income generation and control. For instance, use of the innovations can reduce time spent on farming activities and can open up time and space to explore new opportunities. However, if gender considerations are not taken into account, such time could be used to negatively impact on the work burden for women farmers as it may be taken that the women are now free to take on new work – even that which would have been previously done by the men in their households

Modifiable socio-behavioural factors associated with overweight and hypertension among persons aged 35 to 60 years in eastern Uganda. PLoS One 2012

Abstract Background: Few studies have examined the behavioural correlates of non-communicable, chronic disease risk in lowincome countries. The objective of this study was to identify socio-behavioural characteristics associated with being overweight or being hypertensive in a low-income setting, so as to highlight possible interventions and target groups

Prevention and management of type 2 diabetes mellitus in Uganda and South Africa : Findings from the SMART2D pragmatic implementation trial

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A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Despite the availability and implementation of well-known efficacious interventions for tuberculosis treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific tuberculosis patient at risk of treatment non-adherence is still a challenge. Thus, this retrospective study, based on a record review of 838 tuberculosis patients enrolled in six health facilities, presents, and discusses a machine learning approach to explore the individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district, Uganda. Five classification machine learning algorithms, logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and AdaBoost were trained, and evaluated by computing their accuracy, F1 score, precision, recall, and the area under the receiver operating curve (AUC) through the aid of a confusion matrix. Of the five developed and evaluated algorithms, SVM (91.28%) had the highest accuracy (AdaBoost, 91.05% performed better than SVM when AUC is considered as evaluation parameter). Looking at all five evaluation parameters globally, AdaBoost is quite on par with SVM. Individual risk factors predictive of non-adherence included tuberculosis type, GeneXpert results, sub-country, antiretroviral status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, cotrimoxazole preventive therapy (CPT) dapsone status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 and 6 months. Therefore, machine learning techniques, specifically classification types, can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, tuberculosis program management should consider adopting the classification machine learning techniques evaluated in this study as a screening tool for identifying and targeting suited interventions to these patients

Leadership Training Workshop for Health Professionals

Sub-Saharan Africa is faced with a high burden of morbidity and mortality due to infectious diseases. This burden of disease is caused by diseases that are otherwise preventable, through appropriate behaviour change at individual, household and community level. However, there are significant barriers to attaining a sustainable reduction in the burden of these disease. These barriers include: Health systems that do not promote sufficient access to preventive and curative services, inadequate human resources for health, inadequate funding for health programs, poverty, and low levels of education. ** This course is targeted to mid-level and top level health managers in the sub-Saharan region and those in regions with a similar context across the world. We hope that by the end of this short course, participants shall have received the necessary knowledge and the requisite internal motivation and energy to initiate sustainable change in health service delivery in their organisations, so as to contribute to the improvement in the health status of their service populations.http://deepblue.lib.umich.edu/bitstream/2027.42/94136/1/education-med-oernetwork-health-admin-org-admin-leadership-health-2012.zi