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

低收入和中等收入国家常见精神障碍的有效性研究：肯尼亚非专科治疗策略的序贯、多项分配随机试验

• 批准号: 10359165
• 负责人: Susan M. Meffert
• 金额: $ 66.85万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10359165
• 关键词: Acquired Immunodeficiency Syndrome; Address; Adult; Aftercare; Algorithms; Ambulatory Care Facilities; Antidepressive Agents; Anxiety; Caring; Clinical; Collaborations; Combined Modality Therapy; Country; County; County Hospitals; Disease; Disease remission; Education; Emotional; Family; Fluoxetine; Generations; HIV; HIV Seropositivity; Health; Health Services Accessibility; Health care facility; Healthcare; High Prevalence; Income; Intervention; Kenya; Learning; Letters; Major Depressive Disorder; Mediating; Mediator of activation protein; Mental Depression; Mental Health; Mental Health Services; Mental disorders; Participant; Patient Care; Patients; Physicians; Population; Post-Traumatic Stress Disorders; Primary Health Care; Psychotherapy; Public Health; Randomized; Randomized Controlled Trials; Relapse; Research; Resource-limited setting; Resources; Role; Sequential Multiple Assignment Randomized Trial; Services; Severities; Social support; Specialist; Symptoms; Testing; Time; Training; Treatment outcome; Visit; Work; World Health Organization; burden of illness; cost; disability; disorder later incidence prevention; effectiveness evaluation; effectiveness research; effectiveness study; efficacy research; efficacy study; evidence base; falls; follow-up; improved; interpersonal therapy; low and middle-income countries; personalized care; personalized medicine; prediction algorithm; recruit; residence; response; scale up; treatment effect; treatment strategy

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.

项目摘要 精神障碍是全球残疾的主要原因，主要是由抑郁和焦虑引起的。 大多数疾病负担发生在低收入和中等收入国家（LMIC），75%的成年精神病患者 残疾人无法获得服务。尽管近15年的疗效研究表明，当地非- 专家可以为中低收入国家的抑郁和焦虑提供循证护理，但很少有研究取得进展。 关键的下一步：确定非专家如何最好地应用已证实有效的治疗方法， “真实的世界”使用现有的交付平台，并应对常见的临床困境，例如 治疗开始，以及如何以及何时修改治疗。 我们的研究团队在肯尼亚西部工作了5年，与UCSF-肯尼亚合作（家庭） 艾滋病护理和教育服务[FACES]），支持70多个小学的艾滋病毒综合服务 基苏穆县的医疗设施。肯尼亚的初级保健人口中主要疾病的患病率很高 抑郁症（MDD）（26%）和创伤后应激障碍（PTSD）（35%）-高出2倍和4倍 在美国，分别考虑到需要个性化治疗以实现缓解（“治愈”或 低收入国家缺乏心理健康专家，人口成功减少， 由抑郁和焦虑引起的水平残疾需要（1）一线和 由非专家提供的二线（非缓解者）治疗，以及（2）确定患者水平 治疗结果的调节者，以告知个性化的、资源有效的非专家治疗 算法 为了满足这些需求，我们建议与地方和国家精神卫生利益相关者合作， 肯尼亚将确定（1）由非政府组织提供的一线和二线治疗的循证战略 专家与初级保健相结合（目标1），并调查（2）治疗结果的假定介质 (Aim 2）和确定（3）患者水平的治疗效果调节剂，以告知个性化的、资源有效的 非专家治疗算法（目标3）。我们将使用序贯、多重分配随机试验 （SMART），其中2，710名患有MDD，PTSD或两者兼而有之的参与者将被随机分配到非专家提供的 人际关系心理治疗（IPT）或氟西汀;未缓解者将重新随机分配至转换治疗或 到联合治疗 本研究的结果将在三个方面具有重要意义：（1）它们将决定 LMIC中MDD和/或PTSD的非专家一线和二线治疗，（2）他们将 调查IPT和氟西汀在大人群中的假定作用机制，（3）它们会产生 预测算法对于低资源下MDD和/或PTSD的最佳治疗顺序至关重要 环境-这是解决全球主要残疾问题的一个关键障碍。