Conceptualizing Community Engagement for Mental and Brain Health Research in Low- and Middle-Income Countries: A Case of Kilifi County, Kenya.

Community engagement (CE) has increasingly been recognized as a critical element for successful health promotion and intervention programs. However, the term CE has been used to mean different things in different settings. In this article, we explore how CE has been conceptualized in the field of mental and brain health in Kilifi County, Kenya. We used ethnographic methods encompassing focused group discussions, key informant interviews, and observations with 65 participants, purposively recruited from Kilifi County. Data were transcribed verbatim and thematically analyzed. Our findings show that community members and stakeholders had diverse perceptions of and experiences with CE. Factors such as trust between researchers and community members, sensitization, and awareness creation were key for acceptance of research projects. Partial involvement in research, lack of access to information, poverty and socio-economic challenges, and financial expectations from researchers hindered CE and led to resistance to participation in research projects. For effective CE, there is a need to work closely with community gatekeepers, create awareness of the research projects, use local languages, and ensure continuous engagement that promotes equitable research participation. Our findings suggest that tacit knowledge, context, and mechanisms for research are all critical features of CE and should be considered to enhance acceptance and sustainability of mental and brain health interventions in Kenya.

The Shortfalls of Mental Health Compartment Models: A Call to Improve Mental Health Investment Cases in Developing Countries.

OBJECTIVES: There are irregularities in investment cases generated by the Mental Health Compartment Model. We discuss these irregularities and highlight the costing techniques that may be introduced to improve mental health investment cases. METHODS: This analysis uses data from the World Bank, the World Health Organization Mental Health Compartment Model, the United Nations Development Program, the Kenya Ministry of Health, and Statistics from the Kenyan National Commission of Human Rights. RESULTS: We demonstrate that the Mental Health Compartment Model produces irrelevant outcomes that are not helpful for clinical settings. The model inflated the productivity gains generated from mental health investment. In some cases, the model underestimated the economic costs of mental health. Such limitation renders the investment cases poor in providing valuable intervention points from the perspectives of both the users and the providers. CONCLUSIONS: There is a need for further calibration and validation of the investment case outcomes. The current estimated results cannot be used to guide service provision, research, and mental health programming comprehensively.

Use of Mobile Technology to Identify Behavioral Mechanisms Linked to Mental Health Outcomes in Kenya: Protocol for Development and Validation of a Predictive Model.

Objective: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya. Approach: The study will deploy a mobile app platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya. Expectation: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches. Conclusion : A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.

Use of mobile technology to identify behavioral mechanisms linked to mental health outcomes in Kenya: protocol for development and validation of a predictive model.

OBJECTIVE: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya. APPROACH: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya. EXPECTATION: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches. CONCLUSION: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.