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.

晚年的抑郁症很常见，代价高昂，并可能对受影响的人，他们的亲属和社会产生毁灭性的后果。尽管如此，它通常不被承认和治疗，特别是在中低收入国家。巴西人口正在迅速老龄化，60岁或60岁以上的人已经有2 000万。巴西的医疗保健系统，特别是精神卫生部门，在应对这一挑战方面准备不足。迫切需要为中低收入国家的老年人制定具有成本效益的抑郁症治疗方案。巴西的初级保健系统是一个很好的环境，可以引入和评估一项干预措施，以覆盖这一被忽视的人口中的很大一部分。拟议的干预措施（PROACTIVE）旨在克服治疗老年抑郁症患者的障碍，如患者在社会上孤立和难以获得服务，缺乏提供有效干预措施的熟练和得到支持的工作人员，以及照顾老年人的工作人员之间缺乏协调和问责制。PROACTIVE将是一项双组随机对照试验，旨在比较在常规护理基础上增加社会心理、基于社区的干预措施的有效性和成本效益，这些干预措施主要由现有初级保健系统雇用的社区卫生工作者在家中提供。该干预措施将与“增强型”常规护理进行比较，以减少巴西圣保罗60岁或60岁以上贫困社会经济背景的成年人的抑郁症和改善功能。初级保健家庭健康单位将被随机分配到这两个治疗组之一。将根据单位的规模，邀请单位内所有或随机抽样的家庭保健小组参加。PROACTIVE包括8到11个家庭会议，取决于抑郁症的严重程度，在17周内交付。第一阶段是为所有参与者提供的，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复发预防策略;疗程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，以协助提供和问责干预措施，并接受进一步的培训和监督。对于那些在干预后没有改善的参与者，将在监督和定期团队会议上进行讨论，如果需要，将采取其他临床决策。对照组将接受“加强”的常规护理，以改善对按照“高风险”方案治疗的高风险病例的识别和定期评估。PROACTIVE的主要结局指标是9项患者健康问卷（PHQ-9）。我们将使用意向治疗分析比较进入试验后8个月和12个月时各组病例（PHQ-9<10）的恢复情况。还将测量几个次要结果，包括生活质量和功能水平。将衡量两个武器的直接和间接费用，以进行成本效益分析。我们预计在20个FHU（集群）中共招募了1，440名注册参与者，在8个月的恢复中产生86.5%的功效，差异为15个百分点（25%至40%）。我们已经制定并成功地测试了在圣保罗初级保健（RCUK/FAPESP）拟议的干预措施的可行性和可接受性。该项目有可能对抑郁症老年人的健康产生及时和重大的影响，进一步减少对巴西和大多数LMIC已经承受压力的专业精神卫生资源的依赖。巴西的初级保健模式正在其他几个低收入国家推广，如果能够证明其成本效益，这将有助于提高这种干预措施在其他低收入国家的可移植性。

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

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.

• 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

Acceptability and fidelity of a psychosocial intervention (PROACTIVE) for older adults with depression in a basic health unit in São Paulo, Brazil: a qualitative study.

• DOI: 10.1186/s12889-021-12402-3
• 发表时间: 2021-12-14
• 期刊: BMC public health
• 影响因子: 4.5
• 作者: Henrique MG;de Paula Couto MCP;Araya R;Mendes AV;Nakamura CA;Hollingworth W;van de Ven P;Peters TJ;Scazufca M
• 通讯作者: Scazufca 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