Priority mental, neurological and substance use disorders in rural Kenya: Traditional health practitioners’ and primary health care workers’ perspectives

Background: Over 75% of people with mental neurological and substance use disorders (MNSD) live in low and middle-income countries with limited access to specialized care. The World Health Organization’s Mental Health Gap Action Program (mhGAP) aims to address the human resource gap but it requires contextualization. Aim: We conducted a qualitative study in rural coastal Kenya to explore the local terms, perceived causes and management modalities of priority MNSD listed in the mhGAP, to inform implementation in this setting. Methods: We conducted 8 focus group discussions with primary health care providers and traditional health practitioners and used the framework method to conduct thematic analysis. We identified local terms, perceived causes and treatment options for MNSD. We also explored possibilities for collaboration between the traditional health practitioners and primary health care providers. Results: We found local terms for depression, psychoses, epilepsy, disorders due to substance use and self-harm/ suicide but none for dementia. Child and adolescent mental and behavioral problems were not regarded as MNSD but consequences of poor parenting. Self-harm/suicide was recognized in the context of other MNSD. Causes of MNSD were broadly either biological or supernatural. Treatment options were dependent on perceived cause of illness. Most traditional health practitioners were willing to collaborate with primary health care providers mainly through referring cases. Primary health care providers were unwilling to collaborate with traditional health practitioners because they perceived them to contribute to worsening of patients’ prognoses. Conclusions: Local terms and management modalities are available for some priority MNSD in this setting. Community level case detection and referral may be hindered by lack of collaboration between traditional health practitioners and primary health care providers. There is need for training on the recognition and management of all priority MNSD

Burden, causes, and outcomes of people with epilepsy admitted to a rural hospital in Kenya

Objective: People with epilepsy (PWE) develop complications and comorbidities often requiring admission to hospital, which adds to the burden on the health system, particularly in low-income countries. We determined the incidence, disability-adjusted life years (DALYs), risk factors, and causes of admissions in PWE. We also examined the predictors of prolonged hospital stay and death using data from linked clinical and demographic surveillance system. Methods: We studied children and adults admitted to a Kenyan rural hospital, between January 2003 and December 2011, with a diagnosis of epilepsy. Poisson regression was used to compute incidence and rate ratios, logistic regression to determine associated factors, and the DALY package of the R-statistical software to calculate years lived with disability (YLD) and years of life lost (YLL). Results: The overall incidence of admissions was 45.6/100,000 person-years of observation (PYO) (95% confidence interval [95% CI] 43.0–48.7) and decreased with age (p \u3c 0.001). The overall DALYs were 3.1/1,000 (95% CI, 1.8–4.7) PYO and comprised 55% of YLD. Factors associated with hospitalization were use of antiepileptic drugs (AEDs) (odds ratio [OR] 5.36, 95% CI 2.64–10.90), previous admission (OR 11.65, 95% CI 2.65–51.17), acute encephalopathy (OR 2.12, 95% CI 1.07–4.22), and adverse perinatal events (OR 2.87, 95% CI 1.06–7.74). Important causes of admission were epilepsy-related complications: convulsive status epilepticus (CSE) (38%), and postictal coma (12%). Age was independently associated with prolonged hospital stay (OR 1.02, 95% CI 1.00–1.04) and mortality (OR, 1.07, 95% CI 1.04–1.10). Significance: Epilepsy is associated with significant number of admissions to hospital, considerable duration of admission, and mortality. Improved supply of AEDs in the community, early initiation of treatment, and adherence would reduce hospitalization of PWE and thus the burden of epilepsy on the health system

Differential Plasmodium falciparum surface antigen expression among children with Malarial Retinopathy

Retinopathy provides a window into the underlying pathology of life-threatening malarial coma (“cerebral malaria”), allowing differentiation between 1) coma caused by sequestration of Plasmodium falciparum-infected erythrocytes in the brain and 2) coma with other underlying causes. Parasite sequestration in the brain is mediated by PfEMP1; a diverse parasite antigen that is inserted into the surface of infected erythrocytes and adheres to various host receptors. PfEMP1 sub-groups called “DC8” and “DC13” have been proposed to cause brain pathology through interactions with endothelial protein C receptor. To test this we profiled PfEMP1 gene expression in parasites from children with clinically defined cerebral malaria, who either had or did not have accompanying retinopathy. We found no evidence for an elevation of DC8 or DC13 PfEMP1 expression in children with retinopathy. However, the proportional expression of a broad subgroup of PfEMP1 called “group A” was elevated in retinopathy patients suggesting that these variants may play a role in the pathology of cerebral malaria. Interventions targeting group A PfEMP1 may be effective at reducing brain pathology

Prevalence and mortality of epilepsies with convulsive and non-convulsive seizures in Kilifi, Kenya

Objectives: The prevalence of all epilepsies (both convulsive and non-convulsive seizures) in Low- and Middle- Income Countries (LMIC), particularly sub-Saharan Africa is unknown. Under estimation of non-convulsive ep- ilepsies in data from these countries may lead to inadequate and sub-optimal allocation of resources to control and prevent epilepsy. We determined the prevalence of all types of epilepsies and compared the mortality be- tween convulsive seizures and non-convulsive seizures in a resource limited rural area in Kenya. Methods: Trained clinicians identified cases of epilepsy in a randomly selected sample of 4,441 residents in the Kilifi Health and Demographic Surveillance System site using a cross-sectional survey design. Seizure types were classified by epileptologists using the current guidelines of the International League Against Epilepsy (ILAE). We estimated prevalence for epilepsy with convulsive seizures and non-convulsive seizures and for epilepsy with non-convulsive seizures only and compared premature mortality between these groups of seizures. Results: Of the 4441 people visited, 141 had lifetime epilepsy and 96 active epilepsy, which is a crude prevalence of 31.7/1,000 persons (95% CI: 26.6-36.9) and 21.6/1,000 (95% CI: 17.3-25.9), respectively. Both convulsive and non-convulsive seizures occurred in 7% people with epilepsy (PWE), only convulsive seizures in 52% and only non-convulsive seizures in 35% PWE; there was insufficient information to classify epilepsy in the remainder 6%. The age- and sex-adjusted prevalence of lifetime people was 23.5/1,000 (95% CI: 11.0-36.0), with the adjusted prevalence of epilepsy with non-convulsive seizures only estimated at 8.2/1,000 (95%CI:3.9-12.6). The mortality rate in PWE was 6.3/1,000 (95%CI: 3.4-11.8), compared to 2.8/1,000 (2.3-3.3) in those without epilepsy; hazard ratio (HR) =2.31 (1.22-4.39; p=0.011). The annual mortality rate was 11.2/1,000 (95%CI: 5.3- 23.4) in PWE with convulsive and non-convulsive seizures and none died in PWE with non-convulsive seizures alone. Conclusions: Our study shows that epilepsy with non-convulsive seizures is common and adds to the prevalence of previously reported estimates of active convulsive epilepsy. Both epilepsy with convulsive seizures and that with non-convulsive seizures should be identified for optimising treatment and for planning resource allocation

Magnetic resonance imaging findings in Kenyans and South Africans with active convulsive epilepsy: an observational study

Objective: Focal epilepsy is common in low- and middle-income countries. The frequency and nature of possible underlying structural brain abnormalities have, however, not been fully assessed. Methods: We evaluated the possible structural causes of epilepsy in 331 people with epilepsy (240 from Kenya and 91 from South Africa) identified from community surveys of active convulsive epilepsy. Magnetic resonance imaging (MRI) scans were acquired on 1.5-Tesla scanners to determine the frequency and nature of any underlying lesions. We estimated the prevalence of these abnormalities using Bayesian priors (from an earlier pilot study) and observed data (from this study). We used a mixed-effect modified Poisson regression approach with the site as a random effect to determine the clinical features associated with neuropathology. Results: MRI abnormalities were found in 140 of 240 (modeled prevalence = 59%, 95% confidence interval [CI]: 53%–64%) of people with epilepsy in Kenya, and in 62 of 91 (modeled prevalence = 65%, 95% CI: 57%–73%) in South Africa, with a pooled modeled prevalence of 61% (95% CI: 56%–66%). Abnormalities were common in those with a history of adverse perinatal events (15/23 [65%, 95% CI: 43%–84%]), exposure to parasitic infections (83/120 [69%, 95% CI: 60%–77%]) and focal electroencephalographic features (97/142 [68%, 95% CI: 60%–76%]), but less frequent in individuals with generalized electroencephalographic features (44/99 [44%, 95% CI: 34%–55%]). Most abnormalities were potentially epileptogenic (167/202, 82%), of which mesial temporal sclerosis (43%) and gliosis (34%) were the most frequent. Abnormalities were associated with co-occurrence of generalized non-convulsive seizures (relative risk [RR] = 1.12, 95% CI: 1.04–1.25), lack of family history of seizures (RR = 0.91, 0.86–0.96), convulsive status epilepticus (RR = 1.14, 1.08–1.21), frequent seizures (RR = 1.12, 1.04–1.20), and reported use of anti-seizure medication (RR = 1.22, 1.18–1.26). Significance: MRI identified pathologies are common in people with epilepsy in Kenya and South Africa. Mesial temporal sclerosis, the most common abnormality, may be amenable to surgical correction. MRI may have a diagnostic value in rural Africa, but future longitudinal studies should examine the prognostic role

Evaluation of Kilifi epilepsy education programme: a randomized controlled trial

Objectives: The epilepsy treatment gap is largest in resource-poor countries.Weevaluated the efficacy of a 1-day health education program in a rural area of Kenya. The primary outcome was adherence to antiepileptic drugs (AEDs) as measured by drug levels in the blood, and the secondary outcomes were seizure frequency and Kilifi Epilepsy Beliefs and Attitudes Scores (KEBAS). Methods: Seven hundred thirty-eight people with epilepsy (PWE) and their designated supporter were randomized to either the intervention (education) or nonintervention group. Data were collected at baseline and 1 year after the education intervention was administered to the intervention group. There were 581 PWE assessed at both time points. At the end of the study, 105 PWE from the intervention group and 86 from the nonintervention group gave blood samples, which were assayed for the most commonly used AEDs (phenobarbital, phenytoin, and carbamazepine). The proportions of PWE with detectable AED levels were determined using a standard blood assay method. The laboratory technicians conducting the assays were blinded to the randomization. Secondary outcomes were evaluated using questionnaires administered by trained field staff. Modified Poisson regression was used to investigate the factors associated with improved adherence (transition from nonoptimal AED level in blood at baseline to optimal levels at follow-up), reduced seizures, and improved KEBAS, which was done as a post hoc analysis. This trial is registered in ISRCTN register under ISRCTN35680481. Results: There was no significant difference in adherence to AEDs based on detectable drug levels (odds ratio [OR] 1.46, 95% confidence interval [95% CI] 0.74–2.90, p = 0.28) or by self-reports (OR 1.00, 95% CI 0.71–1.40, p = 1.00) between the intervention and nonintervention group. The intervention group had significantly fewer beliefs about traditional causes of epilepsy, cultural treatment, and negative stereotypes than the nonintervention group. There was no difference in seizure frequency. A comparison of the baseline and follow-up data showed a significant increase in adherence—intervention group (36–81% [p \u3c 0.001]) and nonintervention group (38–74% [p \u3c 0.001])—using detectable blood levels. The number of patients with less frequent seizures (≤3 seizures in the last 3 months) increased in the intervention group (62–80% [p = 0.002]) and in the nonintervention group (67–75% [p = 0.04]). Improved therapeutic adherence (observed in both groups combined) was positively associated with positive change in beliefs about risks of epilepsy (relative risk [RR] 2.00, 95% CI 1.03–3.95) and having nontraditional religious beliefs (RR 2.01, 95% CI 1.01–3.99). Reduced seizure frequency was associated with improved adherence (RR 1.72, 95% CI 1.19–2.47). Positive changes in KEBAS were associated with having tertiary education as compared to none (RR 1.09, 95% CI 1.05–1.14). Significance: Health education improves knowledge about epilepsy, but once only contact does not improve adherence. However, sustained education may improve adherence in future studies

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

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

Incidence and risk factors for neonatal tetanus in admissions to Kilifi County Hospital, Kenya.

BACKGROUND: Neonatal Tetanus (NT) is a preventable cause of mortality and neurological sequelae that occurs at higher incidence in resource-poor countries, presumably because of low maternal immunisation rates and unhygienic cord care practices. We aimed to determine changes in the incidence of NT, characterize and investigate the associated risk factors and mortality in a prospective cohort study including all admissions over a 15-year period at a County hospital on the Kenyan coast, a region with relatively high historical NT rates within Kenya. METHODS: We assessed all neonatal admissions to Kilifi County Hospital in Kenya (1999-2013) and identified cases of NT (standard clinical case definition) admitted during this time. Poisson regression was used to examine change in incidence of NT using accurate denominator data from an area of active demographic surveillance. Logistic regression was used to investigate the risk factors for NT and factors associated with mortality in NT amongst neonatal admissions. A subset of sera from mothers (n = 61) and neonates (n = 47) were tested for anti-tetanus antibodies. RESULTS: There were 191 NT admissions, of whom 187 (98%) were home deliveries. Incidence of NT declined significantly (Incidence Rate Ratio: 0.85 (95% Confidence interval 0.81-0.89), P<0.001) but the case fatality (62%) did not change over the study period (P = 0.536). Younger infant age at admission (P = 0.001) was the only independent predictor of mortality. Compared to neonatal hospital admittee controls, the proportion of home births was higher among the cases. Sera tested for antitetanus antibodies showed most mothers (50/61, 82%) had undetectable levels of antitetanus antibodies, and most (8/9, 89%) mothers with detectable antibodies had a neonate without protective levels. CONCLUSIONS: Incidence of NT in Kilifi County has significantly reduced, with reductions following immunisation campaigns. Our results suggest immunisation efforts are effective if sustained and efforts should continue to expand coverage