Cerebral palsy in Mulago hospital, Uganda : comorbidity, diagnosis and cultural adaptation of an assessment tool

Background and aim: Cerebral palsy (CP) is the most common form of chronic motor disability that begins in early childhood and persists throughout life. The clinical features, including motor function, comorbidities and nutritional status, have not been investigated in Uganda. In addition, no assessment tool to measure functional skill development and the level of independence performance in activities of daily living has been developed for these children. The overall aim of this thesis was to describe the neurological, anthropometric and brain imaging findings of Ugandan children with CP and to develop a culturally relevant assessment tool for measuring their functional performance. Methods and participants: Five cross sectional studies (I-V) were carried out at the Mulago National Referral Hospital in Kampala and in varied rural and urban districts within Uganda. Three studies were conducted at the health facility (I-III), while two were conducted in the community (IV-V). Study I investigated the clinical types, motor function and comorbidities of children with CP. In Study II, this same cohort had their anthropometric measurements taken, as well as information about their clinical, feeding and perinatal history to determine their nutritional status and associated factors. Study III, performed on a sub sample of the original cohort, investigated the brain computed tomography (CT) scans and associated features. In Study IV, the Pediatric Evaluation Disability Inventory (PEDI) was translated and cross-culturally adapted to the Ugandan environment to create the PEDI-UG instrument. The psychometric properties of the new PEDI-UG instrument was validated in Study V. Results: Bilateral spastic CP was the main clinical subtype (45%). Severe gross and fine motor function levels were more common in the bilateral spastic and dyskinetic CP subtypes. Signs of learning disability (75%) and epilepsy (45%) were the most common comorbidities. Speech and language impairments were associated with bilateral spastic CP and severe gross and fine motor dysfunction (Study I). More than half (52%) of the children with CP were malnourished, with being underweight (42%) presented as the most common form. Malnutrition was associated more with children 5 years of age or older, and those with a history of complications during the neonatal period (Study II). The distribution of brain image patterns differed from that seen in high income countries with more primary grey matter injuries (PGMI) (44%) and normal scans (31%) and very few primary white matter injuries (4%). PGMI were more common in children with a history of hospital admission following birth (Study III). In the culturally adapted PEDI-UG, overall 178 of the original 197 PEDI items (90%) were retained, with a number of modifications in the remaining items, to create the final 185-item PEDI-UG. (Study IV). Most activities of the culturally adapted PEDI UG (95%) showed acceptable fit to the Rasch model. In addition, the caregiver assistant rating scale was changed from a six-point to four-point rating scale (Study V). Conclusions: There was a large proportion of severely affected children with CP in this cohort, with frequent malnutrition and more PGMI. These results suggest a different etiology of CP in infants born full-term between sub-Saharan Africa and high-income countries. Our findings could imply a higher occurrence of birth asphyxia, postnatally acquired infections or other varied insults around the last trimester period which may possibly benefit from improved emergency obstetric and postnatal care. The culturally adapted PEDI-UG instrument with a four categories caregiver assistant rating scale is appropriate, providing a valid measure of the functional performance of typically developing children from the age of 6 months to 7.5 years in Uganda and other similar African contexts

Pediatric cerebral palsy in Africa: A systematic review

Cerebral palsy is a common neurologic problem in children and is reported as occurring in approximately 2-2.5 of 1000 live births globally. As is the case with many pediatric neurologic conditions, very little has been reported on this condition in the African context. Resource-limited settings such as those found across the continent are likely to result in a different spectrum of etiologies, prevalence, severity as well as management approaches. This review aims to establish what has been reported on this condition from the African continent so as to better define key clinical and research questions

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.Key words: Adherence, antiepileptic drugs, children, Epilepsy, Low resource settin

Severe neurological sequelae and behaviour problems after cerebral malaria in Ugandan children

BACKGROUND: Cerebral malaria is the most severe neurological complication of falciparum malaria and a leading cause of death and neuro-disability in sub-Saharan Africa. This study aimed to describe functional deficits and behaviour problems in children who survived cerebral malaria with severe neurological sequelae and identify patterns of brain injury. FINDINGS: Records of children attending a specialist child neurology clinic in Uganda with severe neurological sequelae following cerebral malaria between January 2007 and December 2008 were examined to describe deficits in gross motor function, speech, vision and hearing, behaviour problems or epilepsy. Deficits were classified according to the time of development and whether their distribution suggested a focal or generalized injury. Any resolution during the observation period was also documented. Thirty children with probable exposure to cerebral malaria attended the clinic. Referral information was inadequate to exclude other diagnoses in 7 children and these were excluded. In the remaining 23 patients, the commonest severe deficits were spastic motor weakness (14), loss of speech (14), hearing deficit (9), behaviour problems (11), epilepsy (12), blindness (12) and severe cognitive impairment (9). Behaviour problems included hyperactivity, impulsiveness and inattentiveness as in attention deficit hyperactivity disorder (ADHD) and conduct disorders with aggressive, self injurious or destructive behaviour. Two patterns were observed; a) immediate onset deficits present on discharge and b) late onset deficits. Some deficits e.g. blindness, resolved within 6 months while others e.g. speech, showed little improvement over the 6-months follow-up. CONCLUSIONS: In addition to previously described neurological and cognitive sequelae, severe behaviour problems may follow cerebral malaria in children. The observed differences in patterns of sequelae may be due to different pathogenic mechanisms, brain regions affected or extent of injury. Cerebral malaria may be used as a new model to study the pathogenesis of ADHD

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

Prevalence and associated factors of neurodevelopmental disability among infants in eastern Uganda: a population based study.

BACKGROUND: Neurodevelopmental disability (NDD) is increasingly acknowledged as one of the important causes of disease burden in low income countries. None the less, there is a dearth of data on the burden of NDD and its determinants in these settings. We aimed to establish the prevalence and factors associated with NDD among infants in Eastern Uganda. METHODS: We assessed 487 infants aged 9-12 months within Iganga-Mayuge Health Demographic Surveillance Site in Eastern Uganda using the Malawi Developmental Assessment Tool. The tool has four domains: gross motor, fine motor, language and social domains. An infant failed a domain if she/he failed more than two parameters of the expected at his/her age. We interviewed mothers on factors that could influence the infants' neurodevelopmental outcomes. Data were analysed using STATA version 14. We used odds ratios and 95% confidence intervals to assess statistical significance of associations. RESULTS: Of the 487 infants, 62(12.7%) had an NDD in at least one of the domains. The most affected was social behaviour where 52(10.7%) infants had an NDD. Severe impairment was seen among 9(1.8%) infants with NDD in either three or four domains. Factors associated with NDD at multivariate logistic regression included: parity of more than three children (aOR = 1.8, 95% CI: 1.02-3.18); failure to cry at birth (aOR = 3.6, 95% CI: 1.46-9.17) and post-neonatal complications (aOR = 4.15, 95% CI: 1.22-14.10). Low birth weight, immediate and exclusive breast feeding were not significantly associated with NDD. CONCLUSION: We found a high NDD burden among infants particularly in the social behaviour domain. To optimise the socio-neural development of infants, programs are needed to educate and work with families on how to engage and stimulate infants. Existing immunisation clinics and community health worker strategies provide an excellent opportunity for stemming this burden

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

Prevalence and factors associated with convulsive status epilepticus in Africans with epilepsy

Objective: We conducted a community survey to estimate the prevalence and describe the features, risk factors, and consequences of convulsive status epilepticus (CSE) among people with active convulsive epilepsy (ACE) identified in a multisite survey in Africa. Methods: We obtained clinical histories of CSE and neurologic examination data among 1,196 people with ACE identified from a population of 379,166 people in 3 sites: Agincourt, South Africa; Iganga-Mayuge, Uganda; and Kilifi, Kenya. We performed serologic assessment for the presence of antibodies to parasitic infections and HIV and determined adherence to antiepileptic drugs. Consequences of CSE were assessed using a questionnaire. Logistic regression was used to identify risk factors. Results: The adjusted prevalence of CSE in ACE among the general population across the 3 sites was 2.3 per 1,000, and differed with site (p \u3c 0.0001). Over half (55%) of CSE occurred in febrile illnesses and focal seizures were present in 61%. Risk factors for CSE in ACE were neurologic impairments, acute encephalopathy, previous hospitalization, and presence of antibody titers to falciparum malaria and HIV; these differed across sites. Burns (15%), lack of education (49%), being single (77%), and unemployment (78%) were common in CSE; these differed across the 3 sites. Nine percent with and 10% without CSE died. Conclusions: CSE is common in people with ACE in Africa; most occurs with febrile illnesses, is untreated, and has focal features suggesting preventable risk factors. Effective prevention and the management of infections and neurologic impairments may reduce the burden of CSE in ACE

Electroencephalographic features of convulsive epilepsy in Africa: A multicentre study of prevalence, pattern and associated factors

Objective: We investigated the prevalence and pattern of electroencephalographic (EEG) features of epilepsy and the associated factors in Africans with active convulsive epilepsy (ACE). Methods: We characterized electroencephalographic features and determined associated factors in a sample of people with ACE in five African sites. Mixed-effects modified Poisson regression model was used to determine factors associated with abnormal EEGs. Results: Recordings were performed on 1426 people of whom 751 (53%) had abnormal EEGs, being an adjusted prevalence of 2.7 (95% confidence interval (95% CI), 2.5–2.9) per 1000. 52% of the abnormal EEG had focal features (75% with temporal lobe involvement). The frequency and pattern of changes differed with site. Abnormal EEGs were associated with adverse perinatal events (risk ratio (RR) = 1.19 (95% CI, 1.07–1.33)), cognitive impairments (RR = 1.50 (95% CI, 1.30–1.73)), use of anti-epileptic drugs (RR = 1.25 (95% CI, 1.05–1.49)), focal seizures (RR = 1.09 (95% CI, 1.00–1.19)) and seizure frequency (RR = 1.18 (95% CI, 1.10–1.26) for daily seizures; RR = 1.22 (95% CI, 1.10–1.35) for weekly seizures and RR = 1.15 (95% CI, 1.03–1.28) for monthly seizures)). Conclusions: EEG abnormalities are common in Africans with epilepsy and are associated with preventable risk factors. Significance: EEG is helpful in identifying focal epilepsy in Africa, where timing of focal aetiologies is problematic and there is a lack of neuroimaging services

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