# Effectiveness Research for Common Mental Disorders in Low and Middle Income Countries:  A sequential, multiple assignment randomized trial for non-specialist treatment strategies in Kenya

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $705,684

## Abstract

Project Abstract
 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.

## Key facts

- **NIH application ID:** 9935175
- **Project number:** 5R01MH113722-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Susan M. Meffert
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $705,684
- **Award type:** 5
- **Project period:** 2019-06-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9935175

## Citation

> US National Institutes of Health, RePORTER application 9935175, Effectiveness Research for Common Mental Disorders in Low and Middle Income Countries:  A sequential, multiple assignment randomized trial for non-specialist treatment strategies in Kenya (5R01MH113722-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9935175. Licensed CC0.

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