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 pattern of prescribing, validate these groups by comparison with other indicators of mental illness, and characterise the population most at risk of increasing mental ill health. National Health Service (NHS) prescription data were linked to the Scottish Longitudinal Study (SLS), a 5.3% sample of the Scottish population (N = 151,418). Antidepressant prescription status over the previous 6 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. Five distinct prescription pattern groups were observed, indicating: no prescriptions (76%), occasional prescriptions (10%), continuation of prior use of prescriptions (8%), a 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 be in the group that started a new course of antidepressant prescriptions. The use of sequence analysis for classifying individual antidepressant trajectories offers a novel approach for capturing population-level changes in mental health risk. By classifying individuals into groups based on their anti-depressant medication use we can better identify how over time, mental health is associated with individual risk factors and contextual factors at the local level and the macro political and economic scale.

中文翻译：

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使用序列分析对个体抗抑郁药的轨迹进行分类以监测人群的心理健康

在过去的十年中，欧洲国家和美国的抗抑郁药处方有所增加，部分原因是新的精神疾病病例数量增加。本文展示了一种创新方法，该方法使用大量苏格兰人口的行政数据对精神健康状况中的人口水平变化进行分类。我们旨在确定处方方式有相似变化的个人群体，通过与其他精神疾病指标进行比较来验证这些人群，并确定最有可能增加精神疾病风险的人群。国家卫生局（NHS）处方数据与苏格兰纵向研究（SLS）相关联，该研究是苏格兰人口的5.3％样本（N = 151,418）。记录可获得数据的每个月（2009年1月至2014年12月）过去6个月的抗抑郁药处方状态，并通过最佳匹配计算出序列相异性。分层聚类用于创建具有相似变化模式的参与者组，并使用多级逻辑回归来了解组成员身份。观察到五个不同的处方模式组，表明：没有处方（76％），偶发处方（10％），继续使用先前的处方（8％），开始新的处方过程（4％）或停止服用处方（ 3％）。社会地位低下的年轻白人女性参与者，居住在被剥夺社会的邻居中，独自生活，被分居/离婚或从劳动力市场中剔除，更有可能开始新的抗抑郁药治疗。序列分析用于对个体抗抑郁药的轨迹进行分类提供了一种新颖的方法来捕获心理健康风险人群水平的变化。通过根据抗抑郁药的使用情况将个人分类，我们可以更好地确定随着时间的推移，心理健康与地方一级以及宏观政治和经济规模上的个人风险因素和背景因素之间的关系。