Dr Sloan Mahone: List of publications

BackgroundA support group care model including self-financing is a promising strategy to improve care for patients with diabetes or hypertension in resource-constrained settings.ObjectivesWe investigated the uptake, feasibility, and impact of a self-financing patient support group care model on cardiometabolic parameters among adult patients with uncontrolled diabetes or hypertension in informal settlements in Nairobi, Kenya.MethodsA two-group prospective quasi-experimental study was conducted. The outcomes were changes in mean glycated haemoglobin (HbA1c), systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index, and waist-hip ratio in control versus intervention communities, assessed 6 months after intervention implementation.ResultsAt baseline, 118 patients with diabetes (intervention, 60; control, 58) and 176 with hypertension (intervention, 87; control, 89) were enrolled. At endline, 81 patients with diabetes and 137 with hypertension were surveyed. In the intervention arm, HbA1c decreased from 10.8% to 9.0% (mean difference [95% CI]: -1.7 [-2.4, -0.9] p p = 0.005). Difference-in-difference analysis showed a notably greater reduction in HbA1c in the intervention arm (-0.942 [0.463] p p = 0.022) and in the control arm, from 160.1 mmHg to 152.5 mmHg (-7.6 [-12.9, -2.3] p = 0.005). DBP in the intervention arm changed from 99.1 mmHg to 97.9 mmHg (-1.1 [4.2, 1.9] p = 0.462) and in the control arm from 99.7 mmHg to 94.8 mmHg (-4.9 [7.8, -2.0] p = 0.001).ConclusionsA self-financing patient support group care model is feasible, improves cardiometabolic parameters and can be a strategy to manage diabetes, hypertension, and other chronic diseases in low-resource settings.

<p><strong>Introduction:</strong> Epilepsy is a prevalent neurological condition globally, especially in Sub-Saharan Africa. In Ghana, it is one of the top five causes of premature death and disability, impacting children’s neurological development and learning outcomes. Although 25% of epilepsy cases are preventable, many go undiagnosed and untreated. People with epilepsy face social stigma and rejection, hindering their ability to seek healthcare, employment, and social interactions, which worsens their self-esteem and social integration.</p> <br> <p><strong>Purposes:</strong> The study aims to address the critical gaps in understanding and supporting people living with epilepsy (PLWE), through recording and analysing their experiences, and seeks to inform policy and practice, finally contributing to improve healthcare delivery, reduce stigma, and enhance support systems for PLWE.</p> <br> <p><strong>Methods:</strong> A qualitative research design was employed for the study. Oral histories (OHs) were conducted with ten PLWE. These interviews encourage interviewees to focus on the life stories and experiences that mean the most to them and can be shared in their own words The interview questions were constructed in English and conducted in English, Twi, Ga and Dangme.</p> <br> <p><strong>Results:</strong> The residents of Shai Osudoku and Ningo Prampram hold robust religious convictions and engage in profound spiritual practices as is the case throughout Ghana. When faced with health challenges, the PLWE and their caregivers often seek assistance from faith-based spiritual healers, including pastors or religious leaders, as their initial source of support and healing. These healers provide care by offering prayers, spiritual counselling, and incorporating various religious rituals into their healing practices. It was revealed that many PLWE, caregivers and community members perceived epilepsy differently. Some perceived it as a spiritual or demonic disease. Whilst others attributed to curse and disease of witches, idols, or gods. However, the healthcare practitioners perceived epilepsy as a medical condition. Also, it was found that individuals faced challenges, such as stigma and discrimination.</p> <br> <p><strong>Conclusion:</strong> It is recommended that public education on epilepsy be intensified. Furthermore, there is the need to ensure that PLWE have access to improved healthcare to increase their quality of life.</p>

The availability of quality and timely data for routine monitoring of mental, neurological and substance use (MNS) disorders is a challenge, particularly in Africa. We assessed the feasibility of using an open-source data science technology (R Shiny) to improve health data reporting in Nairobi City County, Kenya. Based on a previously used manual tool, in June 2022, we developed a digital online data capture and reporting tool using the open-source Kobo toolbox. Primary mental health care providers (nurses and physicians) working in primary healthcare facilities in Nairobi were trained to use the tool to report cases of MNS disorders diagnosed in their facilities in real-time. The digital tool covered MNS disorders listed in the World Health Organization’s (WHO) Mental Health Gap Action Program Intervention Guide (mhGAP-IG). In the digital system, data were disaggregated as new or repeat visits. We linked the data to a live dynamic reproducible dashboard created using R Shiny, summarising the data in tables and figures. Between January and August 2023, 9064 cases of MNS disorders (4454 newly diagnosed, 4591 revisits and 19 referrals) were reported using the digital system compared to 5321 using the manual system in a similar period in 2022. Reporting in the digital system was real-time compared to the manual system, where reports were aggregated and submitted monthly. The system improved data quality by providing timely and complete reports. Open-source applications to report health data is feasible and acceptable to primary health care providers. The technology improved real-time data capture, reporting, and monitoring, providing invaluable information on the burden of MNS disorders and which services can be planned and used for advocacy. The fast and efficient system can be scaled up and integrated with national and sub-national health information systems to reduce manual data reporting and decrease the likelihood of errors and inconsistencies.

&lt;h4&gt;Background&lt;/h4&gt;WHO estimates that more than 50 million people worldwide have epilepsy and 80% of cases are in low-income and middle-income countries. Most studies in Africa have focused on active convulsive epilepsy in rural areas, but there are few data in urban settings. We aimed to estimate the prevalence and spatial distribution of all epilepsies in two urban informal settlements in Nairobi, Kenya.&lt;h4&gt;Methods&lt;/h4&gt;We did a two-stage population-based cross-sectional study of residents in a demographic surveillance system covering two informal settlements in Nairobi, Kenya (Korogocho and Viwandani). Stage 1 screened all household members using a validated epilepsy screening questionnaire to detect possible cases. In stage 2, those identified with possible seizures and a proportion of those screening negative were invited to local clinics for clinical and neurological assessments by a neurologist. Seizures were classified following the International League Against Epilepsy recommendations. We adjusted for attrition between the two stages using multiple imputations and for sensitivity by dividing estimates by the sensitivity value of the screening tool. Complementary log-log regression was used to assess prevalence differences by participant socio-demographics.&lt;h4&gt;Findings&lt;/h4&gt;A total of 56 425 individuals were screened during stage 1 (between Sept 17 and Dec 23, 2021) during which 1126 were classified as potential epilepsy cases. A total of 873 were assessed by a neurologist in stage 2 (between April 12 and Aug 6, 2022) during which 528 were confirmed as epilepsy cases. 253 potential cases were not assessed by a neurologist due to attrition. 30 179 (53·5%) of the 56 425 individuals were male and 26 246 (46·5%) were female. The median age was 24 years (IQR 11-35). Attrition-adjusted and sensitivity-adjusted prevalence for all types of epilepsy was 11·9 cases per 1000 people (95% CI 11·0-12·8), convulsive epilepsy was 8·7 cases per 1000 people (8·0-9·6), and non-convulsive epilepsy was 3·2 cases per 1000 people (2·7-3·7). Overall prevalence was highest among separated or divorced individuals at 20·3 cases per 1000 people (95% CI 15·9-24·7), unemployed people at 18·8 cases per 1000 people (16·2-21·4), those with no formal education at 18·5 cases per 1000 people (16·3-20·7), and adolescents aged 13-18 years at 15·2 cases per 1000 people (12·0-18·5). The epilepsy diagnostic gap was 80%.&lt;h4&gt;Interpretation&lt;/h4&gt;Epilepsy is common in urban informal settlements of Nairobi, with large diagnostic gaps. Targeted interventions are needed to increase early epilepsy detection, particularly among vulnerable groups, to enable prompt treatment and prevention of adverse social consequences.&lt;h4&gt;Funding&lt;/h4&gt;National Institute for Health Research using Official Development Assistance.

<p><strong>Background</strong> 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.</p> <p><strong>Methods</strong> 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.</p> <p><strong>Findings</strong> 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%).</p> <p><strong>Interpretation</strong> 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.</p> <p><strong>Funding</strong> The Wellcome Trust, the UK National Institute of Health Research, and the Oxford NIHR Biomedical Research Centre.</p>

Transcultural psychiatry as a discipline has a well-documented history and is now the subject of numerous retrospectives that chart the development and the shifting conceptual agendas of the field. In contrast, what we have come to think of as “colonial psychiatry” exists primarily as a historiographical category within which historians of medicine, psychiatry, or imperialism may contextualize the imposition of Western categories of “normal” psychology and deviance, race, and difference, as well as greater attention paid to the lived experience of colonialism and the politics of resistance. Ultimately, these disparate but entangled bodies of literature engage with larger questions of the universality of experience and expressions of suffering and distress amidst unequal power relations.

In 1957, Kenya's government psychiatrist and director of the colony's Mathari Mental Hospital travelled to western Kenya to investigate the practice of trepanation among the Gusii people in Kisii District. Applied to relieve pressure on the brain by scraping away a portion of the skull with a hooked knife, trepanation was exceptionally rare by the 20th century, but remained common in Kisii where the operations are conducted by a group of skilled practitioners. This article uses materials from psychiatrist Edward Margetts' personal papers, including photographs, diaries and clinical notes, to describe and examine the practice of trepanation in Kisii in the 1950s, concluding with a discussion of the social meaning of trepanation and trauma in modern Kenya. © 2014 © 2014 Taylor & Francis.

Psychiatry and Empire brings together scholars in the History of Medicine to explore questions of race, gender and power relations in former colonial states across Africa, Asia, the Caribbean and the Pacific. Focusing on the intellectual histories of concepts of mental illness, mental healing and strategies of coping and resistance, this volume advances our understanding of the rise of modern psychiatry as it collided with, and sometimes underpinned, the psychology of colonial rule.

This article is concerned with the discipline of psychiatry in colonial East Africa as it emerged out of the crime and disorder problem to become an intellectually significant ‘East African School’ of psychiatry. The process of lunacy certification, in particular, provides a snapshot of the medical and political tensions that existed among the medical establishment, the prison system and the colonial courts, all of whom sought to define collective African behaviour. This historical article utilises archaic terminology, such as ‘lunatic’ or ‘lunacy’, as these categories were in use at the time.

This article opens with a retelling of colonial accounts of the 'mania of 1911', which took place in the Kamba region of Kenya Colony. The story of this 'psychic epidemic' and others like it would be recounted over the years as evidence depicting the predisposition of Africans to episodic mass hysteria. This use of medical and psychological language in primarily non-medical contexts serves to highlight the intellectual and political roles psychiatric ideas played in colonial governance. The salience of such ideas was often apparent in the face of increasing social tension, charismatic leadership and a proliferation of East African prophetic movements. This article addresses the attempts by the colonial authorities to understand or characterize, in psychological terms, a progression of African 'rebellious types' in society that often took the form of prophets and visionaries, but were diagnosed as epileptic, neurotic or suffering from 'religious mania'. © 2006 Cambridge University Press.