Chronic pain among children with cerebral palsy attending a Ugandan tertiary hospital: a cross-sectional study.

BACKGROUND: Children with cerebral palsy (CP) frequently experience chronic pain. The burden and severity of such pain is often underestimated in relation to their other impairments. Recognition and awareness of this chronic pain among children with CP constitute the cornerstone for caretakers and clinicians to improve the quality of life of those children. This study aimed to determine the prevalence of chronic pain among children with CP, and the factors associated. METHODS: A cross-sectional study of children with CP, aged 2-12 years, attending the CP rehabilitation clinic and Pediatric Neurology Clinic at Mulago Hospital, Uganda from November 2017 to May 2018. A detailed history and clinical examination were performed and the co-morbidities were determined. CP was classified using the Gross Motor Function Classification System (GMFCS), Manual Ability Classification System, Communication Function Classification System (CFCS), and the Eating and Drinking Ability Classification System (EDACS) and documented with the level of impairment in the different domains. Pain was assessed by using the revised Face, Legs, Activity, Consolability, Cry pain scale. RESULTS: A total of 224 children with CP were enrolled. The prevalence of chronic pain was 64.3%. The majority had spastic bilateral CP (77.8%), moderate pain lasting over 6 months, and none of them was on long-term pain management. Epilepsy (60.9%), behavioral problem (63.2%), hearing impairment (66,7%), learning problem (67,6%), dental caries (75%), gastro-esophageal reflux (75%), sleep disorders (79.5%), vision impairment (80%), and malnutrition (90%) were co- morbid conditions of chronic pain in children with CP in this study. The factors independently associated with chronic pain among children with CP were the GMFCS level IV & V, CFCS level IV & V, EDACS level IV & V, female children, and caretaker aged more than 30 years. CONCLUSIONS: Two-thirds of children with CP attending rehabilitation in this hospital had chronic pain. None was receiving pain management. Chronic pain was associated with the presence of multiple co-morbidities and more severe disability. Rehabilitation and care programs for children with CP should include assessment of pain in routine care and provide interventions for pain relief in children with CP even at an early age.

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

Adherence to antiepileptic drugs among children attending a tertiary health unit in a low resource setting.

INTRODUCTION: Epilepsy is one of the neglected and highly stigmatised diseases, yet it is very common affecting about 70 million people worldwide. In Uganda, the estimated prevalence of epilepsy is 13% with about 156 new cases per 100,000 people per year. Adherence to antiepileptic drugs is crucial in achieving seizure control yet in Uganda; there is lack of information on adherence to antiepileptic drugs and the factors that affect this among children. This study was therefore designed to determine the level of adherence to antiepileptic drugs and the factors that are associated with non adherence. METHODS: In a cross sectional study, 122 children who met the inclusion criteria were enrolled and interviewed using a pretested questionnaire. Assessment of adherence to antiepileptic drugs was done by self report and assay of serum drug levels of the antiepileptic drugs. Focus group discussions were held to further evaluate the factors that affect adherence. RESULTS: Age range was 6 months - 16 years, male to female ratio 1.3:1 and majority had generalised seizures 76 (62.3%). Adherence to antiepileptic drugs by self report was 79.5% and 22.1% by drug levels. Majority of the children in both adherent and non adherent groups by self report had inadequate drug doses (95/122). Children were found to be more non-adherent if the caregiver had an occupation (p-value 0.030, 95%CI 1.18-28.78). CONCLUSION: Majority of children had good adherence levels when estimated by self report. The caregiver having an occupation was found to increase the likelihood of non adherence in a child.