Mental disorders are a leading cause of global disability, driven primarily by depression and anxiety. Most of the disease burden is in Low and Middle Income Countries (LMICs), where 75% of adults with mental disorders have no service access. Despite nearly 15 years of efficacy studies showing that local non- specialists can provide evidence-based care for depression and anxiety in LMICs, few studies have advanced to the critical next step: identifying how non-specialists might best apply treatments with proven efficacy in the ?real world? using existing delivery platforms and responding to common clinical dilemmas, such as what treatment to start with, and how and when to modify treatment. Our research team has worked in western Kenya for 5 years with a UCSF-Kenya collaboration (Family AIDS Care and Education Services [FACES]) that supports integrated HIV services at over 70 primary healthcare facilities in Kisumu County. Primary care populations in Kenya have high prevalence of Major Depressive Disorder (MDD) (26%) and Posttraumatic Stress Disorder (PTSD) (35%) ? 2 and 4 times higher than in the U.S., respectively. Given the need for personalized treatment to achieve remission (?cure? or absence of disease) and the scarcity of mental health specialists in LMICs, successful reduction of population- level disability caused by depression and anxiety requires (1) evidence-based strategies for first-line and second-line (non-remitter) treatment delivered by non-specialists, and (2) identification of patient-level moderators of treatment outcome to inform personalized, resource-efficient non-specialist treatment algorithms. To address these needs, we propose to partner with local and national mental health stakeholders in Kenya to identify (1) evidence-based strategies for first-line and second-line treatment delivered by non- specialists integrated with primary care (Aim 1), and investigate (2) presumed mediators of treatment outcome (Aim 2) and determine (3) patient-level moderators of treatment effect to inform personalized, resource-efficient non-specialist treatment algorithms (Aim 3). We will use a Sequential, Multiple Assignment Randomized Trial (SMART) in which 2,710 participants with MDD, PTSD, or both will be randomized to non-specialist-delivered Interpersonal Psychotherapy (IPT) or to fluoxetine; non-remitters will be re-randomized to switch treatment or to combination therapy. The results of this research will be significant in three ways: (1) they will determine the effectiveness of non-specialist delivered first- and second-line treatment for MDD and/or PTSD in LMICs, (2) they will investigate presumed mechanisms of action for IPT and fluoxetine in a large population, (3) they will produce predictive algorithms essential for optimal sequencing of treatment for MDD and/or PTSD in low resource settings ? a critical barrier for addressing a leading global cause of disability.

Public Health Relevance

The proposed research is relevant to public health because it addresses depression and anxiety, leading causes of global disability. Despite carrying the vast majority of the global mental disorder burden, 75% of adults with mental disorders in Low and Middle Income Countries have no access to services. This study will use a Sequential Multiple Assignment Randomized Trial to test strategies for using local non-specialists to deliver evidence-based mental health care and will inform treatment algorithms essential for personalizing care to achieve rapid remission of mental disorders in low resource settings.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
1R01MH113722-01A1
Application #
9649042
Study Section
Special Emphasis Panel (ZMH1)
Program Officer
Williams, Makeda J
Project Start
2019-06-01
Project End
2024-03-31
Budget Start
2019-06-01
Budget End
2020-03-31
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of California San Francisco
Department
Type
University-Wide
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94118