SOCIAL AND ECONOMIC PREDICTORS FOR THE COURSE AND PROGNOSIS OF THE SEVERE MENTAL DISORDERS- THE SEP-MD DATA LINKAGE STUDY

严重精神障碍病程和预后的社会和经济预测因素 - SEP-MD 数据关联研究

• 批准号: ES/S002715/1
• 负责人: Jayati Das-Munshi
• 金额: $ 20.61万
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FS002715%2F1
• 关键词: SOCIAL; ECONOMIC; PREDICTORS; COURSE; PROGNOSIS

Despite more than thirty years of studies into the severe mental illnesses (SMI), including schizophrenia; central challenging questions remain around which factors most strongly predict course and outcomes of these conditions. People with SMI experience an 11 to 17 year reduction in life expectancy, an increased possibility of in-patient psychiatric admissions, and lower levels of employment, compared to the general population. Ethnic minority groups with SMI may experience even greater inequalities- with higher rates of compulsory admissions and social exclusion, compared to White British service users with SMI. Previous international work has suggested that the course of SMI may show strong variation with the environment, yet there has only been limited research conducted on whether social/ environmental factors potentially modify the course of SMI, with conclusions from studies affected by people being lost to follow up, sample sizes being too small, and with too few people of an ethnic minority background taking part, in order to enable robust conclusions. Traditional studies which follow people with SMI over time are time-intensive, expensive to run and logistically challenging to conduct, as tracing people over time is difficult. This project will address these important issues by linking UK Census data with the electronic clinical records from one of the largest secondary mental healthcare providers in Europe. This is an innovative data linkage as UK census is conducted every ten years and collects detailed information around important social indicators (for example, relating to household poverty, social isolation, migration status, employment and long-term worklessness) which are not usually available in good detail in health records. The clinical records will come from a major UK Mental Health Trust- South London & Maudsley Trust, which provides near-monopoly secondary mental healthcare to an ethnically-diverse, geographically defined urban area in London, with a catchment of approximately 1.2 million people.Through the linkage we will create a cohort of 19,800 people with severe mental illnesses followed over a minimum of eight years and we will be able to assess the association of social factors such as household poverty, social isolation, ethnicity and migration status, alongside clinical factors such as diagnosis, prescriptions and severity of illness with our main outcomes- mortality, in-patient admissions (compulsory and voluntary) and long-term worklessness. We will aim to assess the characteristics of where people live (for example the effect of living in socially fragmented neighbourhoods or more deprived areas) alongside person-level level characteristics (e.g. being socially isolated, living in poverty, tobacco use, prescriptions, diagnosis and illness severity) and how these are associated with mortality risks in people with SMI. The linkage will also enable us to assess the association of social and clinical factors with in-patient psychiatric admissions (including compulsory detentions under the UK Mental Health Act). Finally the linkage will allow us to assess clinical and social factors predicting long-term worklessness, defined as being unemployed for more than a year and/ or never holding employment, in a sample of people with SMI. Our analyses will assess the interplay of all of these associations with ethnicity and migration status. Through our study we will also consider whether people with severe mental illnesses who are multiply disadvantaged- i.e.disadvantaged across a range of indicators, experience extreme health inequalities. We have developed the research questions with our non-Academic partners, Black Thrive and the Synergi Collaborative Centre, and will intend for the findings to directly inform public policy, service development and current research and discourses regarding the inequalities experienced by people living with severe mental illnesses.

尽管对包括精神分裂症在内的严重精神疾病（SMI）进行了30多年的研究；最具挑战性的问题仍然是哪些因素最能预测这些疾病的过程和结果。与一般人群相比，重度精神障碍患者的预期寿命缩短11至17年，精神科住院的可能性增加，就业水平较低。与患有重度精神障碍的英国白人服务使用者相比，患有重度精神障碍的少数民族群体可能会经历更大的不平等——有更高的强制入学率和社会排斥率。先前的国际研究表明，重度精神分裂症的过程可能会随着环境的变化而表现出强烈的变化，然而，关于社会/环境因素是否可能改变重度精神分裂症的过程的研究却很有限，这些研究的结论受到随访人员丢失的影响，样本量太小，少数民族背景的人太少，为了能够得出强有力的结论。长期跟踪重度精神障碍患者的传统研究耗时长，运行成本高，并且在后勤上具有挑战性，因为长期追踪人们是困难的。该项目将通过将英国人口普查数据与欧洲最大的二级精神保健提供者之一的电子临床记录联系起来，解决这些重要问题。这是一种创新的数据联系，因为联合王国人口普查每十年进行一次，收集有关重要社会指标的详细信息（例如，与家庭贫困、社会孤立、移徙状况、就业和长期失业有关的指标），这些指标通常在健康记录中没有很详细的资料。临床记录将来自英国主要的精神健康信托基金——南伦敦和莫兹利信托基金，该信托基金为伦敦一个种族多样化、地理上明确的城市地区提供近乎垄断的二级精神保健服务，该地区约有120万人。通过这种联系，我们将建立一个有19 800名严重精神疾病患者的队列，随访至少8年，我们将能够评估家庭贫困、社会孤立、种族和移民身份等社会因素，以及诊断、处方和疾病严重程度等临床因素与我们的主要结果——死亡率、住院（强制和自愿）和长期失业——之间的关系。​这种联系还将使我们能够评估社会和临床因素与住院精神病患者的关系（包括英国精神卫生法规定的强制拘留）。最后，这种联系将使我们能够评估预测长期失业的临床和社会因素，定义为失业超过一年和/或从未有过工作，在重度精神障碍患者的样本中。我们的分析将评估所有这些关联与种族和移民身份的相互作用。通过我们的研究，我们还将考虑那些多重弱势的严重精神疾病患者，即在一系列指标上处于弱势的人，是否经历了极端的健康不平等。我们与非学术合作伙伴，Black Thrive和Synergi协作中心一起开发了研究问题，并打算将研究结果直接告知公共政策，服务开发以及当前关于严重精神疾病患者所经历的不平等的研究和话语。

项目成果

The association between migration and ethnicity on use of the Improving Access to Psychological Treatment (IAPT) programme : a general population cohort study

移民与种族之间的关系与使用改善心理治疗（IAPT）计划的关系：一项一般人群队列研究

• 发表时间: 2021
• 期刊: Social Psychiatry Psychiatric Epidemiology
• 作者: Bhavsar V.

Explaining ethnic variations in adolescent mental health: a secondary analysis of the Millennium Cohort Study.

• DOI: 10.1007/s00127-021-02167-w
• 发表时间: 2022-04
• 期刊: Social psychiatry and psychiatric epidemiology
• 影响因子: 4.4
• 作者: Ahmad G;McManus S;Bécares L;Hatch SL;Das-Munshi J
• 通讯作者: Das-Munshi J

Access to Health Services in Older Minority Ethnic Groups with Dementia: A Systematic Review.

痴呆症老年族裔群体获得卫生服务：系统评价。

• DOI: 10.1111/jgs.16929
• 发表时间: 2021-03
• 期刊: Journal of the American Geriatrics Society
• 影响因子: 6.3
• 作者: Co M;Couch E;Gao Q;Mac-Ginty S;Das-Munshi J;Prina M
• 通讯作者: Prina M

Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register.

• DOI: 10.1136/bmjopen-2021-054414
• 发表时间: 2022-01-24
• 期刊: BMJ open
• 影响因子: 2.9
• 作者: Bendayan R;Kraljevic Z;Shaari S;Das-Munshi J;Leipold L;Chaturvedi J;Mirza L;Aldelemi S;Searle T;Chance N;Mascio A;Skiada N;Wang T;Roberts A;Stewart R;Bean D;Dobson R
• 通讯作者: Dobson R

Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK.

• DOI: 10.1136/bmjopen-2020-042274
• 发表时间: 2021-03-25
• 期刊: BMJ open
• 影响因子: 2.9
• 作者: Chilman N;Song X;Roberts A;Tolani E;Stewart R;Chui Z;Birnie K;Harber-Aschan L;Gazard B;Chandran D;Sanyal J;Hatch S;Kolliakou A;Das-Munshi J
• 通讯作者: Das-Munshi J