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 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.

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.

Association between human leukocyte antigen class II (HLA-DRB and -DQB) alleles and outcome of exposure to Mycobacterium tuberculosis: a cross-sectional study in Nairobi, Kenya.

Introduction: human leukocyte antigen (HLA) class II alleles play an important role in the early immune response to tuberculosis (TB) by presenting antigenic peptides to CD4+ T cells, hence polymorphisms in those genes can influence the efficiency of the immune response to infection and progression to active disease. Methods: an analytical cross-sectional study of adult pulmonary tuberculosis (PTB) patients at Mbagathi County Hospital, Nairobi and their HHCs. Sociodemographic data were captured on questionnaires and clinical data extracted from patient files. Intravenous blood samples were drawn for interferon-gamma release assay (IGRA) to determine latent tuberculosis infection (LTBI) among HHCs, and for extraction of DNA used in typing of HLA-DQB1 and HLA-DRB1 alleles by PCR sequence specific primer amplification. Chi-square and Fisher's exact test were used to compare the HLA type II allele frequencies of LTBI negative HHCs, LTBI positive HHCs and active TB patients. Logistic regression was used to adjust for HIV status. Results: the HLA-DQB1 and HLA-DRB1 alleles were analyzed in 17 PTB and 37 HHCs. Nineteen (19) HHCs were LTBI positive, while 18 were LTBI negative. The frequency of DRB3*1 was 0.17-fold lower [95% CI=0.03-0.83] among PTB patients compared to HHCs before adjustment for HIV status (p=0.048). The frequency of the DRB5*2 allele was significantly higher (p=0.013) among PTB patients (23.5%) compared to HHCS (0.00%). After adjusting for HIV status, the frequency of DRB1*14 was 12-fold higher [95% CI=1.11-138.2] among PTB patients compared to HHCs (p=0.040). Conclusion: the higher frequencies of HLA-DRB5*2 and HLA-DRB1*14 alleles in PTB patients suggest a likely association with progression to active PTB. The higher frequency of HLA-DRB3*1 allele among LTBI negative HHCs shows its likely protective role against M. tuberculosis infection in this population.

Latent tuberculosis among household contacts of pulmonary tuberculosis cases in Nairobi, Kenya.

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.