According to the Lancet report on the global burden of disease published in October 2020, air pollution is among the five highest risk factors for global health, reducing life expectancy on average by 20 months. This paper describes a data-driven method for establishing causal relationships within and between two multivariate time series data streams derived from wearable sensors: personal exposure to airborne particulate matter of aerodynamic sizes less than 2.5um (PM2.5) gathered from the Airspeck monitor worn on the person and continuous respiratory rate (breaths per minute) measured by the Respeck monitor worn as a plaster on the chest. Results are presented for a cohort of 113 asthmatic adolescents using the PCMCI+ algorithm to learn the short-term causal relationships between lags of \pm exposure and respiratory rate. We consider causal effects up to a maximum delay of 8 hours, using data at both a 1 minute and 15 minute resolution in different experiments. For the first time a personalised exposure-response relationship between PM2.5 exposure and respiratory rate has been demonstrated to exist for short-term effects in asthmatic adolescents during their everyday lives. Our results lead to recommendations for work on specific open problems in causal discovery, to increase the feasibility of this approach for similar epidemiology studies in the future.
翻译:根据2020年10月发表的关于全球疾病负担的《柳叶刀报告》,空气污染是全球健康五大危险因素之一,平均预期寿命减少20个月,本文介绍了一种数据驱动方法,用以在穿损传感器产生的两个多变时间序列数据流内部和之间建立因果关系:个人接触空气动力小于2.5毫米(PM2.5)的空气中气动微粒物质,以及呼吸率持续上升(每分钟呼吸率),由呼吸器监测仪测量,该监测器将呼吸系统磨损为胸口的石膏;利用PCMCI+算法,为113名哮喘类青少年提供结果,以了解每小时接触和呼吸率滞后之间的短期因果关系;我们利用不同实验中1分钟和15分钟分辨率的数据,考虑最高延迟8小时的因果影响;首次证明PM2.5接触与呼吸率之间的个人接触-反应关系,对于哮喘青少年日常生活中的短期影响是存在的;我们的结果导致对未来因果发现中的具体公开问题进行研究的建议,从而增加未来因果发现的可行性。