Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT03466346 |
| Other study ID # |
1R01MH115512 1R01MH113722-01A1 |
| Secondary ID |
1R01MH1155121R01 |
| Status |
Completed |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
August 31, 2020 |
| Est. completion date |
May 6, 2024 |
Study information
| Verified date |
May 2024 |
| Source |
University of California, San Francisco |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
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 test strategies for integrating first and second line evidence-based depression and
trauma-related disorder treatments with primary care services at a large public sector
hospital and conduct robust cost and cost-benefit analyses of each treatment to produce a
"menu" of cost-benefit options for personalized, integrated mental health care with
corresponding effectiveness and implementation values.
Description:
Mental disorders are a leading cause of global disability, driven 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 research
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 strategies for
sustainable "real world" non-specialist treatment including integration with existing
healthcare platforms and response to common clinical dilemmas, such as what treatment to
start with and how to modify it.
Given the need to personalize treatment to achieve remission (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, with (2)
confirmation of presumed mechanism of action and (3) patient-level moderators of treatment
outcome to inform personalized, non-specialist treatment algorithms.
The research team has worked in western Kenya for 6 years with a UCSF-Kenya collaboration
that supports integrated HIV services at over 70 primary healthcare facilities in Kisumu
County (Family AIDS Care and Education Services [FACES]). Primary care populations in Kenya
have high prevalence of Major Depressive Disorder (MDD) (26%) and Posttraumatic Stress
Disorder (PTSD) (35%). Kenyan leaders lack an evidence base for two essential treatments -
psychotherapy and second generation antidepressants- without which scale-up will fall short
of its potential. We conducted a randomized, controlled trial in Kisumu County of
Interpersonal Psychotherapy (IPT) delivered by non-specialists for HIV-positive patients with
MDD and PTSD. In our study, IPT achieved full remission of MDD and PTSD in the majority of
participants.
Given the high prevalence of MDD-PTSD co-morbidity, we will collaborate with the FACES team
providing services to Kisumu County Hospital (KCH) primary care outpatient clinic (~10,000
patients/month) to conduct a randomized trial of IPT versus fluoxetine for MDD and/or PTSD.
Local non-specialists will be trained in mental health care for the SMART and hired through
the Kenyan Ministry of Health to work at KCH. SMART participants will be randomized to: (1)
first line treatment with IPT or fluoxetine; (2) second line treatment for non-remitters-
treatment "switch" (e.g., IPT to fluoxetine) or treatment "combination" (e.g., addition of
IPT to fluoxetine). Research with mental health specialists in high income countries suggests
that antidepressants and psychotherapy have equivalent short-term efficacy and that
psychotherapy yields superior long-term relapse prevention. We will test the role of
previously identified mechanisms in mediating remission and key moderators of treatment
effect. Results of moderator and Q learning analyses will produce first and second-line
non-specialist treatment algorithms.
