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: 10681458
• 负责人: Susan M. Meffert
• 金额: $ 59.88万
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10681458
• 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; Mental Depression; Mental Health; Mental Health Services; Mental disorders; Participant; Patients; Physicians; Population; Post-Traumatic Stress Disorders; Primary Care; Primary Health Care; Psychotherapy; Public Health; Randomized; Randomized, Controlled Trials; 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; 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; primary care patient; recruit; relapse prevention; residence; response; scale up; treatment effect; treatment strategy; 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; Mental Depression; Mental Health; Mental Health Services; Mental disorders; Participant; Patients; Physicians; Population; Post-Traumatic Stress Disorders; Primary Care; Primary Health Care; Psychotherapy; Public Health; Randomized; Randomized, Controlled Trials; 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; 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; primary care patient; recruit; relapse prevention; 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，由抑郁和动画造成的主要原因。 伯恩大多数疾病都处于低收入和中等收入国家（LMIC），其中75％的成年人有精神 疾病无法访问服务。尽管有近15年的效率研究表明，当地的非 - 专家可以为LMIC中的抑郁症和动画提供基于证据的护理，很少有研究提前 下一步的关键：确定非专家如何最好地应用具有证明有效性的治疗方法 “现实世界”使用现有的交付平台并应对常见的临床困境，例如什么 从一开始，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，并与UCSF-Kenya合作（家庭 艾滋病护理和教育服务[面部]）支持70多名主要的艾滋病毒服务 Kisumu县的医疗机构。肯尼亚的初级保健人口主要患病率很高 抑郁症（MDD）（26％）和创伤后应激障碍（PTSD）（35％） - 2和4倍 分别比美国。考虑到需要个性化治疗以实现缓解（“治愈”或 缺乏疾病）和LMIC中心理健康专家的稀缺性，成功减少了人口 - 抑郁和焦虑引起的水平残疾需要（1）基于证据的一线策略 非专家提供的二线（非驱动器）治疗，（2）患者级别的识别 治疗结果的主持人，以告知个性化，资源有效的非专业治疗 算法。 为了满足这些需求，我们建议与地方和国家心理健康利益相关者合作 肯尼亚以确定（1）非 - 与初级保健（AIM 1）集成的专家，并调查（2）提出的治疗结果介体 （AIM 2）并确定（3）患者级治疗效果的主持人，以告知个性化的资源效率 非专科治疗算法（AIM 3）。我们将使用顺序的多个分配随机试验 （智能），其中有2,710名具有MDD，PTSD或两者的参与者将被随机分配给非专业人士。 人际心理治疗（IPT）或氟西汀；非造物会将重新转换以切换治疗或 结合疗法。 这项研究的结果将以三种方式重要：（1）他们将确定 非专家在LMIC中提供了MDD和/或PTSD的一线和二线治疗，（2）他们将 研究大量人群中IPT和氟西汀的作用机制，（3）它们将产生 低资源中MDD和/或PTSD治疗的最佳测序必不可少的预测算法 设置 - 解决主要全球残疾原因的关键障碍。