Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

Background: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy

Prevalence and lifestyle-related risk factors of obesity and unrecognized hypertension among bus drivers in Ghana

Obesity and hypertension are public health problems associated with cardiovascular events worldwide. Bus drivers, whose lifestyle is primarily sedentary and characterized by poor eating habits are at increased risk. This study determined the prevalence and lifestyle-related risk factors of obesity and hypertension among Inter-Regional Metromass Bus Drivers (IRMBDs) in Ghana. This cross-sectional study recruited 527 professional drivers from Metromass Bus stations in Accra and Kumasi Metropolis, Ghana. Structured questionnaires were administered to obtain socio-demographic and lifestyle characteristics from all participants. Anthropometric measurements including body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR) and blood pressure (BP) were determined. The prevalence of unrecognized hypertension was 38.7%. The prevalence of obesity using BMI, WC, and WHR as obesity indices were 19.0%, 19.9%, and 19.4%, respectively. Use of sleep inhibitors, long-duration sitting and eating late at night were independent risk factors for obesity, regardless of the obesity index used (p \u3c 0.05). Physical inactivity, high caloric intake and eating at stressful periods were independent risk factors for obesity based on WC and WHR measurements (p \u3c 0.05). Ageing, smoking history, alcoholic beverage intake, sleep inhibitor drug use, high calorie intake, long-duration sitting, eating late and under stressful conditions were independent risk factors for hypertension (p \u3c 0.05). There is a high prevalence of unrecognized hypertension and obesity among IRMBDs which were associated with individual lifestyle and behaviours. Increased awareness through educational and screening programs will trigger lifestyle modifications that will reduce cardio-metabolic disease onset and offer clues for better disease predictive, preventive and personalized medicine