Cluster randomised controlled trial (RCT) for late life depression in socioeconomically deprived areas of São Paulo, Brazil (PROACTIVE)

巴西圣保罗社会经济贫困地区晚年抑郁症的整群随机对照试验 (RCT)（主动）

• 批准号: MR/R006229/1
• 负责人: Ricardo Araya
• 金额: $ 66.59万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FR006229%2F1
• 关键词: Cluster; randomised; controlled; trial; RCT

Depression in later life is common, costly, and can have devastating consequences for those affected, their relatives, and society. Notwithstanding this, it usually goes unrecognised and untreated, especially in low-middle income countries. The Brazilian population is ageing rapidly, with already 20 million people aged 60 or more. The Brazilian health care system, especially the mental health sector, is poorly prepared to meet this challenge. There is an urgent need to develop cost-effective depression treatment programmes for older people living in low- and middle-income countries. The Brazilian primary care system is an excellent setting to introduce and evaluate an intervention to reach a large proportion of this neglected part of the population. The intervention proposed (PROACTIVE) aims to overcome barriers for treating old people with depression, such as patients' social isolation and difficulties in accessing services, lack of skilled and supported staff to deliver effective interventions, and poor coordination and accountability among staff caring for elderly people. PROACTIVE will be a two-arm cluster randomised controlled trial aiming to compare the effectiveness and cost-effectiveness of adding to usual care a psychosocial, community-based intervention mostly delivered at home by Community Health Workers employed by the existing primary care system. The intervention will be compared with an 'enhanced' usual care in reducing depressive illness and improving functioning among adults 60 years or older from poor socioeconomic backgrounds in São Paulo, Brazil. Primary Care Family Health Units will be randomised to one of these two treatment groups. All or a random sample of the Family Health Teams within a Unit, depending on size of the Unit, will be invited to participate. PROACTIVE consists of 8 to 11 home sessions, depending on severity of depression, delivered over 17 weeks. The initial phase is given to all participants and comprises three sessions covering psycho-education and learning simple coping strategies to improve mood. The Second Phase is based on behaviour activation and relapse prevention strategies; the number of sessions depends on the severity of symptoms. Community Health Workers will be equipped with tablet computers to assist with the delivery and accountability of the intervention and to receive further training and supervision. Those participants who do not improve with the intervention, in relation to specified clinical algorithms, will be discussed in supervision and regular team meetings and, if needed, other clinical decisions will be adopted. The control group will receive 'enhanced' usual care in so far as improved identification and periodic assessments of high-risk cases treated as per a 'high-risk' protocol. The primary outcome measure of PROACTIVE is the 9-item Patient Health Questionnaire (PHQ-9). We will compare the recovery of cases (PHQ-9<10) across arms at 8 and 12 months after entering the trial, using an intention-to-treat analysis. Several secondary outcomes will be also measured including quality of life and levels of functioning. Direct and indirect costs in both arms will be measured to undertake a cost-effectiveness analysis. We anticipate recruiting a total of 1,440 participants registered in 20 FHUs (clusters), yielding 86.5% power for a 15 percentage point difference (25% to 40%) in recovery at 8 months. We have developed and successfully tested the feasibility and acceptability of the proposed intervention in primary care in São Paulo (RCUK/FAPESP). This project has the potential for a timely and major impact on the wellbeing of depressed older adults, further reducing dependency on specialised mental health resources already under strain in Brazil and most LMIC. The Brazilian primary care model is being replicated in several other LMIC, contributing to increase the portability of this intervention to other LMIC should its cost-effectiveness be demonstrated.

以后生活中的抑郁是普遍的，昂贵的，并且可能对受影响的人，亲戚和社会产生毁灭性的后果。尽管如此，它通常没有得到认可和未经处理，尤其是在中低收入的国家。巴西人口正在迅速衰老，已经有2000万人年龄在60岁以上。巴西医疗保健系统，尤其是精神卫生部门，已经准备好应对这一挑战。迫切需要为居住在低收入国家和中等收入国家的老年人制定具有成本效益的抑郁症治疗计划。巴西初级保健系统是引入和评估干预措施以达到被忽视的人群中很大一部分的绝佳场所。拟议的干预措施（积极主动）旨在克服治疗抑郁症的老年人的障碍，例如患者的社会隔离和难以获取服务，缺乏熟练和支持的员工来提供有效的干预措施，以及在携带老年人的员工中的协调和责任。积极主动将是一项两臂群集随机对照试验，旨在比较在现有初级保健系统雇用的社区卫生工作者中，大多数人在家里提供的基于社区的社区干预措施的有效性和成本效益。该干预措施将与从巴西圣保罗的社会经济背景贫穷的60岁以上的成年人中减少抑郁症和功能的“增强”通常的护理进行比较。初级保健家庭健康部门将被随机分为这两个治疗组之一。将邀请一个单位内家庭健康团队的全部或随机样本，具体取决于单位的大小，被邀请参加。积极的主动性由8至11个家庭会议组成，具体取决于抑郁症的严重程度，超过17周。初始阶段是给所有参与者的，并包括三个课程，涵盖心理教育和学习简单的应对策略以改善情绪。第二阶段是基于行为激活和中继预防策略的基础。会议的数量取决于症状的严重程度。社区卫生工作者将配备平板电脑，以协助干预的交付和问责制并接受进一步的培训和监督。那些与指定临床算法有关的参与者将在监督和常规团队会议上讨论，并在需要时讨论其他临床决定。随着对根据“高风险”方案处理的高风险案例的改进识别和定期评估，对照组将在改进的识别和定期评估中获得“增强”的常规​​护理。积极主动的主要结果度量是9项患者健康问卷（PHQ-9）。我们将使用意向性治疗分析在进入试验后的第8个月和12个月的第8个月和12个月之间对案件（PHQ-9 <10）进行比较。还将测量几个次要结果，包括生活质量和功能水平。将衡量双方的直接和间接成本以进行成本效益分析。我们预计，在20个FHU（群集）中招募了1,440名参与者，在8个月时恢复的15个百分点差异（25％至40％）的功率为86.5％。我们已经开发并成功地测试了圣保罗（RCUK/FAPESP）的初级保健干预措施的可行性和可接受性。该项目有可能及时地对沮丧的老年人的健康产生重大影响，进一步降低了对巴西和大多数LMIC已疲劳的专业心理健康资源的依赖。巴西初级保健模型正在其他几种LMIC中复制，这有助于在证明其成本效益的情况下将这种干预的可移植性增加到其他LMIC。

项目成果

Patient Health Questionnaire-9 Item Pairing Predictiveness for Prescreening Depressive Symptomatology: Machine Learning Analysis (Preprint)

患者健康问卷 9 项目配对预测抑郁症状学预筛：机器学习分析（预印本）

• DOI: 10.2196/preprints.48444
• 发表时间: 2023
• 作者: Glavin D

Patient Health Questionnaire-9 Item Pairing Predictiveness for Prescreening Depressive Symptomatology: Machine Learning Analysis.

• DOI: 10.2196/48444
• 发表时间: 2023-10-19
• 期刊: JMIR mental health
• 影响因子: 5.2
• 作者: Glavin D;Grua EM;Nakamura CA;Scazufca M;Ribeiro Dos Santos E;Wong GHY;Hollingworth W;Peters TJ;Araya R;Van de Ven P
• 通讯作者: Van de Ven P

Other PHQ-9 item pairings are better than the PHQ-2: A Machine Learning analysis

• DOI: 10.1016/j.procs.2022.09.089
• 发表时间: 2022-01-01
• 期刊: Procedia Computer Science
• 作者: Glavin, Darragh;Maekawa, Eduardo;Van de ven, Pepijn
• 通讯作者: Van de ven, Pepijn

A Machine Learning approach to optimize the assessment of depressive symptomatology

优化抑郁症状评估的机器学习方法

• DOI: 10.1016/j.procs.2022.09.090
• 发表时间: 2022
• 期刊: Procedia Computer Science
• 作者: Eduardo M

Patient health questionnaire-9 item pairing predictiveness for prescreening depressive symptomatology: machine learning analysis

患者健康问卷 - 9 项配对预测抑郁症状预筛：机器学习分析

• DOI: 10.34961/researchrepository-ul.24873888.v1
• 发表时间: 2023
• 作者: Glavin D