Use of sequence analysis for classifying individual antidepressant trajectories to monitor population mental health.
Use of sequence analysis for classifying individual antidepressant trajectories to monitor population mental health.
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DOI：
10.1186/s12888-020-02952-y
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发表时间：
2020-11-23
期刊：
影响因子：
4.4
通讯作者：
Pearce J
中科院分区：
文献类型：
--
作者：
Cherrie M;Curtis S;Baranyi G;McTaggart S;Cunningham N;Licence K;Dibben C;Bambra C;Pearce J
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，部分原因是精神疾病新发病例的增加。本文展示了一种创新的方法来分类的人口水平变化的心理健康状况，使用管理数据的大样本苏格兰人口。我们的目的是确定具有相似处方模式变化模式的个体群体，通过与其他精神疾病指标的比较来验证这些群体，并确定最有可能增加精神疾病风险的人群。国民健康服务（NHS）处方数据与苏格兰纵向研究（SLS）相关，该研究占苏格兰人口的5.3%（N = 151，418）。记录了数据可用的每个月（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序列相异性。使用分层聚类来创建具有相似变化模式的参与者组，使用多水平逻辑回归来了解组成员关系。观察到五个不同的处方模式组，表明：无处方（76%），偶尔处方（10%），继续使用以前的处方（8%），开始新的处方疗程（4%）或停止服用处方（3%）。年轻、白色、女性参与者、社会地位低、居住在社会贫困的社区、独居、分居/离婚或脱离劳动力，更有可能成为开始新一轮抗抑郁药处方的一组。使用序列分析对个体抗抑郁药轨迹进行分类，为捕获心理健康风险的人群水平变化提供了一种新方法。通过根据抗抑郁药物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，心理健康如何与个人风险因素和地方层面以及宏观政治和经济规模的背景因素相关。
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.
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影响因子：
6.3
作者：
Barr B;Taylor-Robinson D;Stuckler D;Loopstra R;Reeves A;Whitehead M
通讯作者：
Whitehead M
影响因子：
4
作者：
Moncho J;Pereyra-Zamora P;Tamayo-Fonseca N;Giron M;Gómez-Beneyto M;Nolasco A
通讯作者：
Nolasco A
影响因子：
2.9
作者：
Katikireddi, Srinivasa Vittal;Niedzwiedz, Claire L.;Popham, Frank
通讯作者：
Popham, Frank
DOI：
10.1097/ede.0000000000000985
发表时间：
2019-05
期刊：
Epidemiology (Cambridge, Mass.)
影响因子：
--
作者：
Bijlsma MJ;Wilson B;Tarkiainen L;Myrskylä M;Martikainen P
通讯作者：
Martikainen P
影响因子：
4.4
作者：
Gibson K;Cartwright C;Read J
通讯作者：
Read J