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

精神疾病是英国残疾的最大原因，影响深远，跨越教育，工作和健康，贯穿整个生命历程。迫切需要监测公共心理健康和评估政策和干预措施影响的分析方法。在这项工作中，我将使用数据科学方法来研究健康行为及其在社会群体中的传播，特别关注学校中的青少年自我伤害行为（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。在英格兰，平均每个中学教室有三名儿童会自残，其中至少有一名儿童因10-19岁的自残或暴力伤害而住院。青春期的自我伤害是过早死亡（特别是自杀和药物/酒精使用原因）和未来A&E就诊和住院的预测。自我伤害的原因多种多样，非常复杂，反映了生物、社会和个性因素以及环境触发因素，这些因素可能是适当的干预目标（例如，暴露于他人的自我伤害行为）。我们目前缺乏对学校自我伤害行为规模和集群的客观衡量，因为这一领域的研究经常使用横截面或面板（重复调查）设计，无法捕捉事件的精确时间，或者没有映射到学校。确定同伴群体和个人自残行为的预测因素将为学校和医院的有针对性的预防战略提供信息。拟议的工作将数据科学方法应用于一系列去识别的电子健康记录和大规模教育数据集，以识别不同的自我伤害演示，并调查学校同龄人群体中这些健康行为的时间和顺序。健康轨迹（例如未来的死亡风险）将被描述为不同的自我伤害表现形式。这一过程的第一步是开发系统的方法，用于在一系列医疗保健环境中识别自我伤害表现（“临床表型”），包括全科医学，A&E和医院。接下来，将研究环境影响（描述“麻烦”）的影响，作为社会群体中自我伤害临床表型的风险因素。这一阶段的工作借鉴了英格兰和苏格兰的相关健康和教育数据，以创建有关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。数据科学方法（例如自然语言处理）将用于从医疗记录中的自由文本中提取信息，并将机器学习，统计和流行病学方法相结合，用于开发算法，以检测学校同龄人群体中最具临床重要性的自我伤害表现。

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.