IntroductionSchizophrenia and bipolar disorder account for a large proportion of the global burden of disease. Despite their enormous impact, little is known about their pathophysiology. Given the high heritability of schizophrenia and bipolar disorder, unbiased genetic studies offer the opportunity to gain insight into their neurobiology. However, advances in understanding the genetic architecture of schizophrenia and bipolar disorder have been based almost exclusively on subjects of Northern European ancestry. The Neuropsychiatric Genetics of African Populations-Psychosis (NeuroGAP-Psychosis) project aims to expand our understanding of the causes of schizophrenia and bipolar disorder through large-scale sample collection and analyses in understudied African populations.Methods and analysisNeuroGAP-Psychosis is a case-control study of 34 000 participants recruited across multiple sites within Ethiopia, Kenya, South Africa and Uganda. Participants will include individuals who are at least 18 years old with a clinical diagnosis of schizophrenia or bipolar disorder (‘psychosis’) or those with no history of psychosis. Research assistants will collect phenotype data and saliva for DNA extraction. Data on mental disorders, history of physical health problems, substance use and history of past traumatic events will be collected from all participants.DNA extraction will take place in-country, with genotyping performed at the Broad Institute. The primary analyses will include identifying major groups of participants with similar ancestry using the computation-efficient programme single nucleotide polymorphisms (SNP) weights. This will be followed by a GWAS within and across ancestry groups.Ethics and disseminationAll participants will be assessed for capacity to consent using the University of California, San Diego Brief Assessment of Capacity to Consent. Those demonstrating capacity to consent will be required to provide informed consent. Ethical clearances to conduct this study have been obtained from all participating sites. Findings from this study will be disseminated in publications and shared with controlled access public databases, such as the database of Genotypes and Phenotypes, dbGaP.

Background Depression during pregnancy and in the postpartum period is associated with poor outcomes for women and their children. Although effective interventions exist for common mental disorders that occur during pregnancy and the postpartum period, most cases in low- and middle-income countries go untreated because of a lack of trained professionals. Task-sharing models such as the Thinking Healthy Program have shown potential in feasibility and efficacy trials as a strategy for expanding access to treatment in low-resource settings; however, there are significant barriers to scale-up. We address this gap by adapting Thinking Healthy for automated delivery via a mobile phone. This new intervention, Healthy Moms, uses an existing artificial intelligence system called Tess (Zuri in Kenya) to drive conversations with users. Objective This prepilot study aims to gather preliminary data on the Healthy Moms perinatal depression intervention to learn how to build and test a more robust service. Methods We conducted a single-case experimental design with pregnant women and new mothers recruited from public hospitals outside of Nairobi, Kenya. We invited these women to complete a brief, automated screening delivered via text messages to determine their eligibility. Enrolled participants were randomized to a 1- or 2-week baseline period and then invited to begin using Zuri. We prompted participants to rate their mood via SMS text messaging every 3 days during the baseline and intervention periods, and we used these preliminary repeated measures data to fit a linear mixed-effects model of response to treatment. We also reviewed system logs and conducted in-depth interviews with participants to study engagement with the intervention, feasibility, and acceptability. Results We invited 647 women to learn more about Zuri: 86 completed our automated SMS screening and 41 enrolled in the study. Most of the enrolled women submitted at least 3 mood ratings (31/41, 76%) and sent at least 1 message to Zuri (27/41, 66%). A third of the sample engaged beyond registration (14/41, 34%). On average, women who engaged post registration started 3.4 (SD 3.2) Healthy Moms sessions and completed 3.1 (SD 2.9) of the sessions they started. Most interviewees who tried Zuri reported having a positive attitude toward the service and expressed trust in Zuri. They also attributed positive life changes to the intervention. We estimated that using this alpha version of Zuri may have led to a 7% improvement in mood. Conclusions Zuri is feasible to deliver via SMS and was acceptable to this sample of pregnant women and new mothers. The results of this prepilot study will serve as a baseline for future studies in terms of recruitment, data collection, and outcomes. International Registered Report Identifier (IRRID) RR2-10.2196/11800

Background: The COVID-19 pandemic has exerted considerable impact on public mental health globally. With the pandemic rapidly rising in sub-Saharan Africa including Kenya, there is need to provide evidence to guide the mental health response in the region. Objectives: The objective of this review is (1) to describe the mental health response to the COVID-19 pandemic in Kenya, guided by the Mental Health Preparedness and Action Framework (2) to offer context specific recommendations for improvement of the mental health response in Kenya. Such information could be useful in decision-making in Kenya as well as in the greater sub-Saharan Africa region. Methods: This narrative review is based on information obtained from official government documents released from 13th March 2020, the beginning of the pandemic in Kenya, up to 31st July 2020. Discussion: The COVID-19 response in Kenya has no formal mental health response plan. There is an unmet need for psychological first aid in the community. While guidelines for the management of mental health conditions during the COVID-19 pandemic have been prepared, implementation remains a major challenge due to a poorly resourced mental health system. There is no mental health surveillance system in place limiting ability to design evidence-based interventions. Conclusion: We propose four key strategies for strengthening the mental health response in order to mitigate the harmful impact of COVID-19 on public mental health in Kenya: (1) preparation of a formal mental health response plan specific to the COVID-19 pandemic with allocation of funding for the response (2) training of community health workers and community health volunteers on psychological first aid to enable access to support for those in need during the pandemic (3) scaling up of mobile health to increase access to care (4) conducting systematic and continuous text message surveys on the mental health impact of the COVID-19 pandemic in order to inform decision-making.

Background : Genetic studies of biomedical phenotypes in underrepresented populations identify disproportionate numbers of novel associations. However, current genomics infrastructure--including most genotyping arrays and sequenced reference panels--best serves populations of European descent. A critical step for facilitating genetic studies in underrepresented populations is to ensure that genetic technologies accurately capture variation in all populations. Here, we quantify the accuracy of low-coverage sequencing in diverse African populations. Results : We sequenced the whole genomes of 91 individuals to high-coverage ( > 20X) from the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study, in which participants were recruited from Ethiopia, Kenya, South Africa, and Uganda. We empirically tested two data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole genome sequencing data. We show that low-coverage sequencing at a depth of ≥4X captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1X) performed comparable to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation, with 4X sequencing detecting 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Conclusion : These results indicate that low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, including those that capture variation most common in Europeans and Africans. Low-coverage sequencing effectively identifies novel variation (particularly in underrepresented populations), and presents opportunities to enhance variant discovery at a similar cost to traditional approaches.

Background Depression during pregnancy and in the postpartum period is associated with a number of poor outcomes for women and their children. Although effective interventions exist for common mental disorders that occur during pregnancy and the postpartum period, most cases in low- and middle-income countries go untreated because of a lack of trained professionals. Task-sharing models such as the Thinking Healthy Program have shown great potential in feasibility and efficacy trials as a strategy for expanding access to treatment in low-resource settings, but there are significant barriers to scale-up. We are addressing this gap by adapting Thinking Healthy for automated delivery via a mobile phone. This new intervention, Healthy Moms , uses an existing artificial intelligence system called Tess ( Zuri in Kenya) to drive conversations with users. Objective The objective of this pilot study is to test the Healthy Moms perinatal depression intervention using a single-case experimental design with pregnant women and new mothers recruited from public hospitals outside of Nairobi, Kenya. Methods We will invite patients to complete a brief, automated screening delivered via text messages to determine their eligibility. Enrolled participants will be randomized to a 1- or 2-week baseline period and then invited to begin using Zuri. Participants will be prompted to rate their mood via short message service every 3 days during the baseline and intervention periods. We will review system logs and conduct in-depth interviews with participants to study engagement with the intervention, feasibility, and acceptability. We will use visual inspection, in-depth interviews, and Bayesian estimation to generate preliminary data about the potential response to treatment. Results Our team adapted the intervention content in April and May 2018 and completed an initial prepilot round of formative testing with 10 women from a private maternity hospital in May and June. In preparation for this pilot study, we used feedback from these users to revise the structure and content of the intervention. Recruitment for this protocol began in early 2019. Results are expected toward the end of 2019. Conclusions The main limitation of this pilot study is that we will recruit women who live in urban and periurban centers in one part of Kenya. The results of this study may not generalize to the broader population of Kenyan women, but that is not an objective of this phase of work. Our primary objective is to gather preliminary data to know how to build and test a more robust service. We are working toward a larger study with a more diverse population. International Registered Report Identifier (IRRID) DERR1-1...