Mechanisms and consequences of depression-related multimorbidity over the life course: coordinated analysis of population and primary care data

Lead Research Organisation: King's College London
Department Name: Psychological Medicine

Abstract

Multimorbidity (MM), defined as the co-existence of two or more mental and/or physical chronic conditions, represents a complex challenge for clinicians, participants, and their families. Current preventive efforts have partly failed because they have focused on one disease at a time and too late in life. For this reason, there is a critical need to identify groups of individuals at risk of MM earlier in life, and to develop interventions to prevent MM and its adverse health outcomes.
A number of surveys have shown that not only clinical depression the most common illness in any MM cluster, it also is the most important for harming a patient's quality of life. Compared with most diseases that occur (and begin to cluster) in later years, depression tends to begin much earlier in adult life. As most existing research on MM has focused on older adults (over 60 years), this means an important component has been relatively ignored. What exactly is the relationship between the onset of depression in early adult years and later MM clusters? A life course approach that follows groups of people over many years is the best way of addressing this question.
In the proposed study we will analyse three of the world largest birth cohorts (all based in the UK) that contain around 40,000 participants who have been regularly studied in the decades since their birth. We will identify those participants with a record of depression in young and mid-adult years (20 to 64 years of age) and examine how their early onset psychological difficulties interacted with later illnesses, both physical and mental. In particular, we will explore these relationships across time and within different subgroups, defined by gender and socioeconomic background.
In addition, we plan to evaluate the clinical value of the cohort findings with data collected from routine general practice. The CPRD database now contains millions of patients' records and we anticipate examining in excess of 200,000 patients with depression. These large numbers should be sufficient to identify important mechanisms and consequences of depression related MM with reasonable precision. Dr Dregan (PI) has developed a protocol for accessing electronic health records from the primary care database, which will inform this part of the proposed study. Thus, we are confident that we will access to a large number of individuals in order to identify the important risk factors (including physiological, behavioural habits, treatment, and social support) for depression-related multimorbidity clusters across young and mid-adult years. We should be able to determine which depression-related MM clusters have the most harmful impacts during young or mid-adult years and test how these relationships are affected by gender, social class, and specific attributes of depression (such as sleep disturbance).
We will use advanced statistical methods to produce the most reliable results possible. With the large cohort and primary care databases, these important questions that will inform future clinical trials and improve public health, can be addressed at a considerably lower cost than a randomised trial. We will also use the requested resources to enable other researchers to use cohort data to address important research questions.

Technical Summary

Background: Depression is the commonest individual disease in multimorbidity clusters and is the most significant in terms of affecting patients' quality of life. Unlike most diseases in multimorbidity clusters, however, depression tends to start earlier in adult life. Its subsequent course and interactions with later diseases, both physical and mental, is relatively under-investigated.

Methods: We will employ cohort data from 1946 National Study of Human Development, 1958 National Child Development Study, and the 1970 British Cohort Study to develop a platform for life course research on depression related multimorbidity. Primary care data from the Clinical Practice Research Datalink (CPRD) will be used to validate findings from the cohort studies. The analytical methods will include latent class analysis, phenotype network analysis, and multiple mediator mediation analyses. Analyses will be stratified be gender and social class to assess the heterogeneity among different population subgroups.

Expected outcomes: The proposed study will generate the necessary evidence to inform the development of future clinical trials aimed at preventing or delaying the onset of depression-related multimorbidity among highly at-risk subgroups. It will also generate novel understanding about modifiable factors with the greatest adverse impact on functioning and healthcare outcomes, supporting the NHS strategy to improving the outcomes of people with multimorbidity. We anticipate that links with King's Health Partners will facilitate the delivery of impact from this research. The research will have an international impact by developing a platform for comparative research methodology, for evaluating general population and clinical multimorbidity data, that can be applied across a wide range of topics of clinical and public health importance.

Planned Impact

The most important potential of the proposed project is on informing future clinical trials aimed at improving the health and wellbeing of millions of people living with depression and coexisting disorders worldwide. Specifically, findings from combined analyses will inform future CPRD trials about those patient-reported consequences of depression-related multimorbidity that should be included as primary outcomes. The comprehensive data, covering both general and clinical populations, will provide robust evidence about subgroups of people at greater risk of adverse functional and health outcomes that should be a priority for targeted interventions.

Identification of multiple processes that underline the development of depression-related multimorbidity has the potential to support clinicians to make better informed decisions when discussing and recommending preventative therapies to patients with complex mental and physical health needs. For example, currently the focus on clinical practice is on treating depression with anti-depressant therapy. The rich and individual-level data proposed in this application would allow for an understanding of the main modifiable processes leading from depression to multi-morbid disorders across subgroups of people. If the findings of the study demonstrate that the anti-depressant drugs are associated with a specific morbidity, but not others it would inform the development of future interventions comparing the effectiveness of antidepressant treatment in patients with different clusters of depression-related multimorbidity.

The study findings may also influence on public health initiatives. Currently there is very limited evidence about what clusters of depression-related multimorbidity carry the greatest burden for subgroups and the healthcare system. In the context of depression-related multimorbidity the challenge is identifying the most effective way to prevent the onset of a co-existing disorders across subgroups of people at greater risk of multimorbidity. Our study exploring the combined effects of multiple processes would help identify those modifiable factors that should be the target of future preventative interventions in specific subgroups. This view may need to be revised if our study shows that is the combined effect of multiple processes that are more important for the development of multimorbidity rather than individual factors. Providing answers to such pertinent questions to practice and policy would lead to an exceptionally high and lasting impact on science and public health at a fraction of cost of a single randomised trial.

Despite increased acknowledgment by clinicians and policy makers, there is limited knowledge to guide the development of interventions with the greatest potential to improve the day to day functioning of people with depression-related multimorbidity, particularly among younger adults living with lifelong multiple disorders. The rich nature of our study data would also enable an understanding of those combination of depression-related multimorbidity clusters with the greatest negative effect on patients' wellbeing, such as physical capability and social inclusion. Such evidence would have important implications for patients and their carers understanding of those factors with the greatest negative impact on day to day wellbeing and functioning.

Another potential application of our study findings is with regards to the education and training of future and current healthcare professionals about the main determinants and consequences of depression-related multimorbidity across different subgroups, including women and socially deprived groups. For instance, it could inform the development of technologies (e.g. mobile apps) aimed at enhancing junior practitioners understanding about the complexities of caring for patients with co-existing depression and physical disorders.

Publications

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Arias De La Torre J (2021) Diagnostic promiscuity: the use of real-world data to study multimorbidity in mental health. in The British journal of psychiatry : the journal of mental science

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Arias De La Torre J (2021) Prevalence and age patterns of depression in the United Kingdom. A population-based study. in Journal of affective disorders

 
Description Advancing Mental Health equalities strategies NHS England - Associate member of the Mental Health Equalities Data Quality and Research Subgroup
Geographic Reach National 
Policy Influence Type Membership of a guideline committee
Impact I was invited to become an associate member of the Mental Health Equalities Data Quality and Research Subgroup and my research has informed, and continues to do so, the development and roll out of the Patient and Carer Race Equality Framework (PCREF) with the ultimate aim to improve access, experience and outcomes of BAME communities nationally. Specifically, I was asked to present data related to my MRC award highlighting existing data challenges (e.g. poor data recording on ethnic minorities within EHRs ( e.g. Minimum Mental Health Dataset) and cohort studies and the need for using a uniform terminology. This work has led to gradual improvements in the collection and recording of patients socio-demographic information in NHS Digital-supported databases (e.g. MMHDS), facilitating auditing and research initiatives aimed at reducing health inequalities at national level.
URL https://www.england.nhs.uk/wp-content/uploads/2020/10/00159-advancing-mental-health-equalities-strat...
 
Description Determinants of MLTCs among young adults with mental disorders: a data-linkage study
Amount £146,000 (GBP)
Organisation Guy’s & St Thomas’ Charity 
Sector Charity/Non Profit
Country United Kingdom
Start 01/2020 
End 12/2022
 
Description Ethnic inequalities in mortality and service use in people with mental disorders and multimorbidities during the COVID-19 pandemic: Mixed methods study
Amount £170,260 (GBP)
Funding ID COVID-19 research programme (Health Foundation) 
Organisation The Health Foundation 
Sector Charity/Non Profit
Country United Kingdom
Start 12/2020 
End 12/2021
 
Description Investigating MUltimorbidity ThroUgh cApacity building (MUTUAL)
Amount £199,728 (GBP)
Funding ID MC_PC_MR/T037423/1 
Organisation Medical Research Council (MRC) 
Sector Public
Country United Kingdom
Start 05/2020 
End 10/2021
 
Description Mental and physical multimorbidity patterns among working-age ethnic minority adults in Lambeth
Amount £124,000 (GBP)
Funding ID EIC180702 (MLTC Challenge Fund) 
Organisation Guy’s & St Thomas’ Charity 
Sector Charity/Non Profit
Country United Kingdom
Start 11/2019 
End 10/2021
 
Description The COVID-19 Clinical Neuroscience Study (COVID-CNS)
Amount £2,341,638 (GBP)
Funding ID MR/V03605X/1 
Organisation Medical Research Council (MRC) 
Sector Public
Country United Kingdom
Start 10/2020 
End 04/2022
 
Title Pooled cohort data harmonisation 
Description We have developed guidelines for harmonising depression and multimorbidity-related measures across multiple cohort studies 
Type Of Material Improvements to research infrastructure 
Year Produced 2021 
Provided To Others? No  
Impact This algorithm/tool will be available for open access by researchers, policy makers, and clinicians and will support the analysis of pooled cohort data 
 
Title Cross-cohort data harmonisation 
Description As part of our project we have combined and analysed data across three birth cohorts (1946, 1958, and 1970) leading to important recommendations and guidelines for data harmonisation related to multimorbidity research. We are in the process of writing up the protocol for pooling cohort data for analytical and research purposes that would be made available to all researchers, government organisations, and members of the public. 
Type Of Material Data handling & control 
Year Produced 2021 
Provided To Others? Yes  
Impact This is work in progress, as we intend to publish the protocol for pooled cohort data analysis later this year. We have already been approached by academics at other UK institutions for advice on cohort data harmonisation. 
 
Description Associate member of an international research group 
Organisation University of Barcelona
Country Spain 
Sector Academic/University 
PI Contribution Jorge Arias-de la Torre has been invited to become an associate member of the CIBER Epidemiology and Public Health Madrid, Spain and the Institute of Biomedicine, University of León, León, Spain.
Collaborator Contribution The association has led to several publications and development of grants submitted for EU funding.
Impact 1. Prevalence and variability of current depressive disorder in Europe. A population-based study of 27 countries. The Lancet Public Health (2021), In Press. 2. Arias de la Torre J, Vilagut G, Ronaldson A, Dregan A, Ricci-Cabello I, Hatch SL, Serrano-Blanco A, Valderas JM, Hotopf M, Alonso J. Prevalence and age patterns of depression in the United Kingdom. A population-based study. J Affect Disord. 2021 Jan 15;279:164-172.
Start Year 2020
 
Description The Mental Health Equalities Data Quality and Research Subgroup 
Organisation Care Quality Commission (CQC)
Country United Kingdom 
Sector Public 
PI Contribution I am an Associated Member by invitation of the The Mental Health Equalities Data Quality and Research Subgroup part of the NHS Race and Health Observatory (2020) with the aim to improve access, experience and outcomes for BAME communities. I was invited to present and offer recommendations for improving quality of ethnicity-related data within the British birth cohort studies and linked electronic health records.
Collaborator Contribution My role also involves setting priorities for enhancing the accuracy and completeness of inequalities-related demographic data across all NHS Digital supported databases (e.g. Mental Health Minimum Dataset, Clinical Practice Research Datalink) with the aim to facilitate research into health inequality programme evaluation.
Impact Developed a set of priorities for Data Quality improvement (e.g. sexual orientation, disability, ethnicity, accommodation status) that will be prioritise for implementation within the next 6 months. This is a multi-disciplinary collaboration across different academic institutions and governmental organisations lead by the NHS England and NHS Improvement.
Start Year 2020
 
Description The Mental Health Equalities Data Quality and Research Subgroup 
Organisation Improvement Service
Country United Kingdom 
Sector Charity/Non Profit 
PI Contribution I am an Associated Member by invitation of the The Mental Health Equalities Data Quality and Research Subgroup part of the NHS Race and Health Observatory (2020) with the aim to improve access, experience and outcomes for BAME communities. I was invited to present and offer recommendations for improving quality of ethnicity-related data within the British birth cohort studies and linked electronic health records.
Collaborator Contribution My role also involves setting priorities for enhancing the accuracy and completeness of inequalities-related demographic data across all NHS Digital supported databases (e.g. Mental Health Minimum Dataset, Clinical Practice Research Datalink) with the aim to facilitate research into health inequality programme evaluation.
Impact Developed a set of priorities for Data Quality improvement (e.g. sexual orientation, disability, ethnicity, accommodation status) that will be prioritise for implementation within the next 6 months. This is a multi-disciplinary collaboration across different academic institutions and governmental organisations lead by the NHS England and NHS Improvement.
Start Year 2020
 
Description The Mental Health Equalities Data Quality and Research Subgroup 
Organisation King's College Hospital NHS Foundation Trust (NCH)
Country United Kingdom 
Sector Public 
PI Contribution I am an Associated Member by invitation of the The Mental Health Equalities Data Quality and Research Subgroup part of the NHS Race and Health Observatory (2020) with the aim to improve access, experience and outcomes for BAME communities. I was invited to present and offer recommendations for improving quality of ethnicity-related data within the British birth cohort studies and linked electronic health records.
Collaborator Contribution My role also involves setting priorities for enhancing the accuracy and completeness of inequalities-related demographic data across all NHS Digital supported databases (e.g. Mental Health Minimum Dataset, Clinical Practice Research Datalink) with the aim to facilitate research into health inequality programme evaluation.
Impact Developed a set of priorities for Data Quality improvement (e.g. sexual orientation, disability, ethnicity, accommodation status) that will be prioritise for implementation within the next 6 months. This is a multi-disciplinary collaboration across different academic institutions and governmental organisations lead by the NHS England and NHS Improvement.
Start Year 2020
 
Description The Mental Health Equalities Data Quality and Research Subgroup 
Organisation MQ Transforming Mental Health
Country United Kingdom 
Sector Charity/Non Profit 
PI Contribution I am an Associated Member by invitation of the The Mental Health Equalities Data Quality and Research Subgroup part of the NHS Race and Health Observatory (2020) with the aim to improve access, experience and outcomes for BAME communities. I was invited to present and offer recommendations for improving quality of ethnicity-related data within the British birth cohort studies and linked electronic health records.
Collaborator Contribution My role also involves setting priorities for enhancing the accuracy and completeness of inequalities-related demographic data across all NHS Digital supported databases (e.g. Mental Health Minimum Dataset, Clinical Practice Research Datalink) with the aim to facilitate research into health inequality programme evaluation.
Impact Developed a set of priorities for Data Quality improvement (e.g. sexual orientation, disability, ethnicity, accommodation status) that will be prioritise for implementation within the next 6 months. This is a multi-disciplinary collaboration across different academic institutions and governmental organisations lead by the NHS England and NHS Improvement.
Start Year 2020
 
Description The Mental Health Equalities Data Quality and Research Subgroup 
Organisation NHS England
Country United Kingdom 
Sector Public 
PI Contribution I am an Associated Member by invitation of the The Mental Health Equalities Data Quality and Research Subgroup part of the NHS Race and Health Observatory (2020) with the aim to improve access, experience and outcomes for BAME communities. I was invited to present and offer recommendations for improving quality of ethnicity-related data within the British birth cohort studies and linked electronic health records.
Collaborator Contribution My role also involves setting priorities for enhancing the accuracy and completeness of inequalities-related demographic data across all NHS Digital supported databases (e.g. Mental Health Minimum Dataset, Clinical Practice Research Datalink) with the aim to facilitate research into health inequality programme evaluation.
Impact Developed a set of priorities for Data Quality improvement (e.g. sexual orientation, disability, ethnicity, accommodation status) that will be prioritise for implementation within the next 6 months. This is a multi-disciplinary collaboration across different academic institutions and governmental organisations lead by the NHS England and NHS Improvement.
Start Year 2020
 
Description The Mental Health Equalities Data Quality and Research Subgroup 
Organisation Public Health England
Country United Kingdom 
Sector Public 
PI Contribution I am an Associated Member by invitation of the The Mental Health Equalities Data Quality and Research Subgroup part of the NHS Race and Health Observatory (2020) with the aim to improve access, experience and outcomes for BAME communities. I was invited to present and offer recommendations for improving quality of ethnicity-related data within the British birth cohort studies and linked electronic health records.
Collaborator Contribution My role also involves setting priorities for enhancing the accuracy and completeness of inequalities-related demographic data across all NHS Digital supported databases (e.g. Mental Health Minimum Dataset, Clinical Practice Research Datalink) with the aim to facilitate research into health inequality programme evaluation.
Impact Developed a set of priorities for Data Quality improvement (e.g. sexual orientation, disability, ethnicity, accommodation status) that will be prioritise for implementation within the next 6 months. This is a multi-disciplinary collaboration across different academic institutions and governmental organisations lead by the NHS England and NHS Improvement.
Start Year 2020
 
Description The Mental Health Equalities Data Quality and Research Subgroup 
Organisation University College London
Country United Kingdom 
Sector Academic/University 
PI Contribution I am an Associated Member by invitation of the The Mental Health Equalities Data Quality and Research Subgroup part of the NHS Race and Health Observatory (2020) with the aim to improve access, experience and outcomes for BAME communities. I was invited to present and offer recommendations for improving quality of ethnicity-related data within the British birth cohort studies and linked electronic health records.
Collaborator Contribution My role also involves setting priorities for enhancing the accuracy and completeness of inequalities-related demographic data across all NHS Digital supported databases (e.g. Mental Health Minimum Dataset, Clinical Practice Research Datalink) with the aim to facilitate research into health inequality programme evaluation.
Impact Developed a set of priorities for Data Quality improvement (e.g. sexual orientation, disability, ethnicity, accommodation status) that will be prioritise for implementation within the next 6 months. This is a multi-disciplinary collaboration across different academic institutions and governmental organisations lead by the NHS England and NHS Improvement.
Start Year 2020
 
Description The Mental Health Equalities Data Quality and Research Subgroup 
Organisation University of Manchester
Country United Kingdom 
Sector Academic/University 
PI Contribution I am an Associated Member by invitation of the The Mental Health Equalities Data Quality and Research Subgroup part of the NHS Race and Health Observatory (2020) with the aim to improve access, experience and outcomes for BAME communities. I was invited to present and offer recommendations for improving quality of ethnicity-related data within the British birth cohort studies and linked electronic health records.
Collaborator Contribution My role also involves setting priorities for enhancing the accuracy and completeness of inequalities-related demographic data across all NHS Digital supported databases (e.g. Mental Health Minimum Dataset, Clinical Practice Research Datalink) with the aim to facilitate research into health inequality programme evaluation.
Impact Developed a set of priorities for Data Quality improvement (e.g. sexual orientation, disability, ethnicity, accommodation status) that will be prioritise for implementation within the next 6 months. This is a multi-disciplinary collaboration across different academic institutions and governmental organisations lead by the NHS England and NHS Improvement.
Start Year 2020
 
Description Lambeth Public Health Invited speaker 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Policymakers/politicians
Results and Impact I was invited to present my MRC research grant to the local public health group at the Lambeth Council, South London with the aim of promoting multimorbidity-research work to local policy makers for policy development. The meeting was attended by 20 participants, including local councillors which raised questions and discussion on the scalability/applicability of research findings to local populations at increased vulnerability of multimorbidity (e.g. ethnic minorities). The work has triggered increased interest from local policy makers in funding similar research projects focused on ethnic inequalities, which led to a successful grant application to the The Health Foundation that involved collaboration with local public health specialists. In addition, the presentation helped identify some of the limitations of existing real-world data regarding mental-health related multimorbidity, such as poor capture of demographic data (e.g. ethnicity, marital status, religion) in electronic health records - this discussion informed the agenda of the NHS Mental Health Equalities Data Quality and Research Subgroup.
Year(s) Of Engagement Activity 2020