Use of sequence analysis for classifying individual antidepressant trajectories to monitor population mental health
Abstract Background Over the past decade, antidepressant prescriptions have increased in European countries and the United States, partly due to an increase in the number of new cases of mental illness. This paper demonstrates an innovative approach to the classification of population level change in mental health status, using administrative data for a large sample of the Scottish population. We aimed to identify groups of individuals with similar patterns of change in usage, validate these groups with other indicators of mental illness, and characterise the population most at risk of increasing mental ill health. Methods NHS prescription data were linked to the Scottish Longitudinal Study, a 5.3% sample of the Scottish population (N=151,418). Antidepressant prescription status over the previous six months was recorded for every month for which data were available (January 2009-December 2014), and sequence dissimilarity was computed by optimal matching. Hierarchical clustering was used to create groups of participants who had similar patterns of change, with multi-level logistic regression used to understand group membership. Results Five distinct prescription pattern groups were observed, indicating: no prescriptions (76%), occasional prescriptions (10%), continuation of prior use of prescriptions (8%), new course of prescriptions started (4%) or ceased taking prescriptions (3%). Young, white, female participants, of low social grade, residing in socially deprived neighbourhoods, living alone, being separated/divorced or out of the labour force, were more likely to have become prescribed a new course of antidepressants. Conclusions The use of sequence analysis for classifying individual antidepressant trajectories offers a novel approach for capturing population-level changes in mental health. By classifying individuals into groups based on their anti-depressant medication use we can better identify the determinants of changes in mental health, from compositional, contextual, to macro political and economic factors.