Introduction Household Contacts (HHCs) of Pulmonary Tuberculosis (PTB) patients have a higher risk of latent tuberculosis infection (LTBI). However, its prevalence and risk factors among adults living with PTB patients are poorly documented in Kenya. Objective to determine the prevalence and risk factors for LTBI among adult HHCs of PTB patients in Kenya. Methods this was an analytical cross-sectional study of HHCs of PTB patients in Nairobi, Kenya. Socio-demographic data was captured on questionnaires and blood samples drawn for Interferon gamma (IFN-γ) quantification. Univariate and multivariate analyses using the Statistical Package for Social Scientists (SPSS) was used to determine the prevalence of LTBI and risk factors at 95% Confidence Interval (CI). Results a total of 166 PTB patients yielded 175 HHCs of whom 29.7% (52/125) were males and 70.3% (123/125) were females. A majority of HHCs [65.7% (115/175)] lived in a single-room house with the patient and [37.7% (66/175)] were in the age group 30-39-years. The overall prevalence of LTBI was 55.7%, peaking among spouses of the patients [70.0% (14/20) and the 30-39 year age group [63.5% (42/66)]. Potential risk factors for LTBI included cohabiting with a PTB patient for 8 to 12 weeks [OR = 3.6 (0.70-18.5), p = 0.107], being a spouse of the patient [OR = 2.0 (0.72-5.47), p = 0.173] and sharing a single room with the patient [OR = 1.58 (0.84 - 2.97), p = 0.158]. Conclusion the high prevalence of LTBI among adult HHCs of PTB patients in this population demonstrates the need for targeted contact-screening programs in high TB transmission settings.

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Close contacts of active pulmonary tuberculosis (PTB) patients are at higher risk of infection as the confirmed cases remain highly infectious before and while in the early stages of treatment. This work highlights the encounters and perspectives of household contacts (HHCs) of PTB patients in an urban setting in Kenya, with a focus on accessibility to health services, interactions within the community, and the risk of infection at home. A multimethod study design involving descriptive cross-sectional analysis and informal interviews was used. The study participants were recruited from tuberculosis (TB) isolation wards and outpatient clinics of Mbagathi County Hospital in Nairobi, Kenya. Data was collected using structured questionnaires and informal interviews. Results revealed improved access to treatment by PTB patients. However, the global goal of eliminating TB infections by minimizing latent tuberculosis reactivation remains a challenge in this population primarily because most of the HHCs lacked knowledge on diagnosis and treatment of latent tuberculosis infection (LTBI). Most participants were residents of informal settlements in Nairobi characterized by small and poorly planned housing structures with poor waste management systems. In most houses, the living space doubled as cooking and sleeping area. There was therefore a high exposure of spouses, children and other persons living with the patients. We recommend that further education be provided to HHCs to increase awareness on available testing and preventive treatment for LTBI, and infection prevention practices at the household level. Furthermore, additional resources should be offered to economically disadvantaged patients to support their social and treatment needs.

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.