Search

Abstract Background Epilepsy remains a significant public health concern in Tanzania, with affected individuals enduring stigma, whether through actions or perceptions. Myths, misunderstandings, and misconceptions about epilepsy have persisted due to a multitude of factors. Here, we assessed attitudes and perceptions toward epilepsy in Mahenge. Methods A cross-sectional study utilising a mixed-methods approach was undertaken in eight villages in the Ulanga district of Mahenge, integrating a semi-structured questionnaire and focus group discussions (FGDs). The questionnaire involved 778 community members, and 15 FGDs were conducted (seven groups with people with epilepsy and eight without). Descriptive statistics, chi-square, and logistic regression were used for quantitative analysis, while we used NVivo version 14 for thematic analysis of qualitative data. Results Of 778 participants, over half were women (425, 54.6%) with a median age of 41 years (IQR: 30–55) and most had completed primary education (79.9%). The majority of participants were aware of epilepsy (96.8%), yet they displayed low knowledge (51%), negative attitudes (45.5%), and perceptions (42.1%) towards the disorder. A low level of understanding was significantly associated with negative attitudes (Adjusted Odds Ratio [AOR] = 1.89, 95%CI: 1.41–2.53) and perceptions (AOR = 3.22, 95%CI: 2.05–5.04) towards epilepsy. In the qualitative analysis, often hereditary factors and infections were named as causes of epilepsy, along with misconceptions involving witchcraft and divine punishment. There was also a misconception about the contagiousness of epilepsy. Traditional healers were often the initial point of treatment. Epilepsy-related stigma was evident, with individuals with epilepsy facing derogatory labels, social isolation, and barriers to education. Lastly, there was a lack of understanding regarding a possible association between epilepsy and onchocerciasis. Conclusions Despite high awareness of epilepsy, there is insufficient understanding, negative attitudes, and perceptions, including misconceptions and stigma about this neurologic condition. Community-based education programmes are essential for promoting proper healthcare-seeking behaviour and dispelling myths.

Summary: 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. Funding: The Wellcome Trust, the UK National Institute of Health Research, and the Oxford NIHR Biomedical Research Centre.