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

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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.