Characteristics of patient populations that determine the effectiveness of collabortive care interventions for depression: meta-analysis of individual participant data inpeople with long term conditions

Substantial advances have occurred in primary care over recent years to improve depression care, with one promising intervention being ‘collaborative care’. Collaborative care is an enhanced way to manage depression in primary care and involves multiple components, including using a case manager (sometimes a nurse) to oversee care coordination, often with the help of a specialist mental health professional. There is now ample evidence that collaborative care is more effective than usual care for people with depression or anxiety and that these effects are cost-effective. However, while some authors suggest that research should now look at ways to implement collaborative care there is still uncertainty about which patients will benefit the most from collaborative care.

In the UK the National Institute for Health and Care Excellence (NICE) recommend that collaborative care be used only when patients have a long term condition and depression and also have significant disability. However, other types of patients with depression may benefit from collaborative care but few studies have looked at the types of patients (i.e. older, females, presence of more depression symptoms) whose depression symptoms improve the most when they receive collaborative care. We aim to contact researchers who have tested collaborative care models to get the information they collected about their patients and the types of treatments they received (i.e. medication or psychological treatments and number of sessions). Using powerful statistical methods called individual participant data meta-analysis we aim to identify patient characteristics that can predict whether a patient is likely to benefit from receiving collaborative care and inform future guidance about delivering collaborative care in primary care settings.