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

Community knowledge, attitudes, and practices regarding epilepsy in Mahenge, Tanzania: A socio-anthropological study in an onchocerciasis-endemic area with a high prevalence of epilepsy

Background Throughout Africa, epilepsy is a highly stigmatized condition. It is often considered to be contagious. This study aimed to assess community knowledge, attitude, and practices toward epilepsy in four villages namely Mdindo, Msogezi, Mzelezi, and Sali within Mahenge division, in Morogoro region, Tanzania. These villages are located in an onchocerciasis–endemic area with a high prevalence of epilepsy. Methods A qualitative cross-sectional study was conducted between June and July 2019 within the framework of a multi-disciplinary research project investigating the association between onchocerciasis and epilepsy. Focus group discussions (FGDs) and in-depth interviews (IDIs) were held with persons with epilepsy (PWE) and their caretakers, community resource persons, and program coordinators of the neglected tropical diseases program. Results The main symptoms of epilepsy were well described by all participants in all villages. PWE and caretakers in all villages considered epilepsy to be a major health problem and some participants ranked it second in importance after malaria. The reported perceived causes of epilepsy included febrile seizures during childhood (locally known as degedege), heredity, evil spirits, and inhaling flatus or touching secretions from PWE, especially during seizures. Knowledge about the association between epilepsy and onchocerciasis was low. People with epilepsy are disregarded, stigmatized, and marginalized from various opportunities such as conjugal rights, schooling, leadership roles, and property inheritance. Traditional healers are often the first contact when seeking care after a person develops epilepsy. Conclusion Epilepsy is a major health burden and public health concern in the Mahenge area. The negative attitudes toward PWE and misconceptions about the causes of epilepsy contribute to delays in seeking care at health facilities. Findings from this study will be used to optimize the comprehensive community-based epilepsy treatment program that was recently initiated in the area