Social contagion? : using data science to characterise the distribution and dispersion of health behaviours in adolescence

在英国，精神疾病是导致残疾的最大原因，其影响深远，涵盖了教育、工作和健康，贯穿了整个人生历程。迫切需要监测公共心理健康和评估政策和干预措施的影响的分析方法。在这项工作中，我将使用数据科学方法来描述健康行为及其在社会群体中的传播，特别关注青少年在学校的自我伤害行为(与药物/酒精使用或暴力有关的伤害，以及故意自我伤害)。这项工作还将探索如何将相关的健康和教育数据用作学校和医院健康干预措施随机试验的平台。在英国，平均中学教室包括三名曾经自我伤害的儿童，至少有一名儿童因10-19岁的自我伤害或暴力伤害而入院。青春期的自我伤害预示着过早死亡(特别是自杀和药物/酒精使用的原因)，以及未来去急诊室就诊和住院。自残的原因是多种多样和高度复杂的，反映了生物、社会和人格因素以及环境触发因素，这些因素可能是干预的适当目标(例如，暴露在他人的自残行为中)。我们目前缺乏对学校中自我伤害行为的规模和集群的客观衡量，因为这一领域的研究经常使用横断面或小组(重复调查)设计，这些设计没有捕捉到事件的准确时间，或者没有映射到学校。确定同龄人群体和个人自残行为的预测因素将为学校和医院制定有针对性的预防战略提供依据。这项拟议的工作将数据科学方法应用于一系列未识别的电子健康记录和大规模教育数据集，以表征不同的自我伤害陈述，并调查这些健康行为在学校同龄人群体中的时间和顺序。健康轨迹(例如，未来的死亡风险)将针对不同的自我伤害表现进行描述。这一过程的第一步是开发系统的方法，在包括全科、急诊室和医院在内的一系列医疗保健环境中识别自我伤害表现(“临床表型”)。接下来，环境影响(描述“暴露组”)的影响将作为社会群体中自我伤害的临床表型的危险因素进行调查。这一阶段的工作利用英格兰和苏格兰的相关健康和教育数据，创建关于学校同龄人健康行为的时间和顺序的详细信息，以调查社会传染的证据。将应用数据科学方法(例如自然语言处理)从医疗记录的自由文本中提取信息，并将结合机器学习、统计学和流行病学方法来开发算法，以检测学校同龄人群体中临床上最重要的自我伤害表现。

Mental illness is the largest cause of disability in the UK with far-reaching consequences, spanning education, work, and health, across the life course. Analytical approaches for monitoring public mental health and evaluating the impact of policy and interventions - at scale - are urgently needed. In this work I will use data science methods to characterise health behaviours and their spread through social groups, with a particular focus on adolescent self-harm behaviours (injuries related to drug/alcohol-use or violence, and intentional self-injury) in schools. The work will also explore how linked health and education data can be used as a platform for randomised trials of health interventions in schools and hospitals.In England, the average secondary school classroom includes three children who will ever self-harm, with at least one child who is admitted to hospital with self-inflicted or violent injuries aged 10-19 years. Self-harm in adolescence is predictive of premature death (particularly with suicide and drug/alcohol-use causes) and future A&E attendances and hospital admissions. The causes of self-harm are varied and highly complex, reflecting biological, social and personality factors in tandem with environmental triggers, which could be appropriate targets for intervention (e.g. exposure to others' self-injurious behaviour). We currently lack objective measures of the scale and clustering of self-harm behaviours in schools, because studies in this field have often used cross-sectional or panel (repeated survey) designs that do not capture the precise timing of events, or are not mapped to schools. Identification of predictors of peer-group and individual self-harm behaviours will inform targeted prevention strategies in schools and hospitals. The proposed work applies data science methods to a range of de-identified electronic health records and large-scale education datasets to characterise different self-harm presentations, and investigates the timing and sequence of these health behaviours within school peer-groups. Health trajectories (e.g. future risk of death) will be characterised for different presentations of self-harm. The first step in this process is developing systematic approaches for identifying self-harm presentations ("clinical phenotypes") in a range of healthcare settings, including general practice, A&E and hospitals. Next, the influence of environmental influences (describing the "exposome") will be investigated as risk factors for clinical phenotypes of self-harm within social groups. This phase of work draws on linked health and education data for England and Scotland to create detailed information on the timing and sequence of health behaviours in schools peer-groups, to investigate evidence of social contagion. Data science methods (e.g. natural language processing) will be applied to extract information from free text in medical records, and a combination of machine learning, statistical and epidemiological methods will be used to develop algorithms for detecting the most clinically important presentations of self-harm in schools-peer groups.