Predictors of Psychotherapy Impact Analysis of 6 RCTs in Non Western Countries

 描述(由申请人提供)：低收入和中等收入国家(LMIC)的精神健康问题负担沉重。尽管与这些问题相关的负担很高，但患有这些问题的人与接受任何形式的循证治疗的人之间的差距很大。与LMIC治疗差距相关的因素之一是缺乏训练有素的精神卫生专业人员，这导致了越来越多的运动，倡导在任务分担模式中使用非专家来提供循证治疗。尽管越来越多的严格研究显示，通过非专科精神卫生提供者的任务分担策略提供的心理治疗干预具有良好的效果，但我们对影响这些干预影响的程度的修改因素和人口统计特征的了解存在差距，因为干预试验没有被提供关于哪些治疗对哪些参与者最有效的信息很少的小组来检查效果。研究方法：本研究将评估个体、症状特定和背景因素对使用和使用的调节效果 使用来自四大洲LMIC的六个随机对照试验的数据，研究非专家提供者提供的心理治疗的影响。这项综合将使用综合数据分析 汇集数据(即，使用协调的编码和分析方法，最大限度地提高比较的精确度，在众多研究中组合和分析个人水平的数据)。具体目标包括目标1：建立一个合并基线样本为n=1611的试验答复者的综合数据库。目标2：确定个体因素，如性别、年龄、教育程度、婚姻状况和创伤暴露；目标3：确定与症状、障碍和损害相关的因素，如症状严重程度和增加的损害；目标4：确定背景因素和治疗因素，如不安全程度、提供者培训、提供者性别和使用的特定治疗；次要目标：探索症状共病的作用。进一步了解治疗成功的预测因素，包括完成治疗、减少症状和改善功能，将确定进一步调整培训和治疗方案，以满足LMIC中比目前能够治疗的更广泛人群的需求，这是传播和扩大有希望的治疗、改善LMIC人口的心理健康和减轻全球疾病负担的重要一步。

 DESCRIPTION (provided by applicant): The burden of mental health problems in low- and middle-income countries (LMIC) is substantial. Although the burden associated with these problems is high, the gap between those who suffer from them and those who receive any sort of evidence-based treatment is large. Among the factors associated with this treatment gap in LMIC is the scarcity of trained mental health professionals, leading to the growing movement advocating use of non-specialists in task-sharing models to provide evidence-based treatments. Despite the growing number of rigorous studies showing promising effects of psychotherapeutic interventions provided through task-sharing strategies by non-specialist mental health providers, a gap exists in our knowledge as to modifying factors and demographic characteristics that influence the magnitude of these intervention impacts because the intervention trials have not been powered to examine effects by sub-groups providing little information about which treatments work best for which participants. Study Methods: The proposed study will evaluate the modifying effects of individual, symptom specific and contextual factors on the utilization and impacts of psychotherapy provided by non-specialist providers using data from six randomized controlled trials in LMIC across 4 continents. This synthesis will use integrative data analysis of pooled data (i.e., combining and analyzing individual-level data across numerous studies using coordinated coding and analysis methods that maximize precision of comparisons). The Specific Aims include Aim 1: building a consolidated database of respondents from the trials which have a combined baseline sample of n=1611. And the following aims which look at different factors that may predict differential utilization and impacts of psychotherapy provided by non-specialist providers: Aim 2: Identify individual factors such as sex, age, education, marital status, and trauma exposure; Aim 3: Identify factors related to symptoms, disorders, and impairment such as symptom severity, and increased impairment; Aim 4: Identify contextual and treatment factors such as degree of insecurity, provider training, provider sex and specific therapy used; and a Secondary Aim: Explore the role of symptom comorbidity. Furthering our understanding of predictors of treatment success, which includes treatment completion, reduced symptomatology, and improved functioning, will identify further adaptations of training and treatment protocols to meet the needs of a wider range of populations in LMIC than is currently able to be treated, an important step for disseminating and scaling up promising treatments, improving the mental health of the population in the LMICs, and reducing the Global Burden of Disease.