Assessment of common mental disorders in the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort at age 24 years using the Clinical Interview Schedule-Revised (CIS-R)

<ns3:p>Common mental disorders (CMDs), such as depression and anxiety, are highly prevalent and often manifest first between childhood and early adulthood. These disorders in young people can have wide-reaching negative impacts on health and other domains throughout the life course. Therefore, studies of CMDs in young people are essential to understand their prevalence, risk factors, and prognosis. In addition to diagnoses, studies of the individual symptom dimensions of depression and anxiety are required to understand the complex and heterogeneous presentation of CMDs. Questionnaires frequently used to assess depression and anxiety in large-scale population-based studies, though useful, are often unable to provide validated categorical diagnoses. The Clinical Interview Schedule-Revised (CIS-R) is a validated, internationally recognised, standardised semi-structured interview tool for assessing CMD symptoms in large community-based samples. It provides information on individual symptoms, symptom dimensions, and ICD-10 diagnoses for a range of CMDs. This Data Note describes CIS-R data collected from a prospective birth cohort in Southwest England, the Avon Longitudinal Study of Parents and Children (ALSPAC), at age 24 years. We summarise the available data and present them in hierarchical order from International Classification of Diseases (ICD)-10 diagnosis, symptom dimension scores, individual symptom scores, and symptom directionality. We also present the descriptive characteristics of the data. For example, we report the prevalence of ICD-10 depression (11%) and anxiety disorders (10%) and report five symptom domain scores (somatic, psychological, atypical, anxiety, and depressive cognitions) in addition to many individual symptoms. The aim of this Data Note is to enhance the visibility of available data at age 24 in ALSPAC, document data generation and coding processes, and provide information about the derived variables for use by the wider scientific community.</ns3:p>