State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, PR China.
Department of Geriatrics, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands; SPARCS Synergy Programme for Analyzing Resilience and Critical Transitions, Wageningen, the Netherlands.
Environ Int. 2018 Dec;121(Pt 1):240-249. doi: 10.1016/j.envint.2018.09.007. Epub 2018 Sep 13.
Respiratory, and cardio-cerebrovascular health-related diseases significantly threaten human health and together with air pollution form a complex pathophysiological system. Other complex biological systems show that increased variance and autocorrelations in time series may act as valid early warning signals for critical transitions. On population level, we determined the likelihood that increased variance and autocorrelation of hospital visit on cardiopulmonary disease preceded critical transitions in population health by human-pollution interactions. We investigated long-term hospital visits from a hospital in Nanjing City, China during 2006-2016 for the most important cardiopulmonary diseases likely to be influenced by air pollution: cerebrovascular accident disease (CVAD), coronary artery disease (CAD), chronic obstructive pulmonary disease (COPD), lung cancer disease (LCD), and the grouped categories of respiratory system disease (RESD) and cardio-cerebrovascular system disease (CCD). The time series of standard deviations (SDs) and autocorrelation at-lag-1 (AR-1) were studied as potential Early-Warning Indicators (EWIs) of transitions in population health. Elevated SDs provided an early warning for critical transitions in visit for LCD and overall CCD and CVAD, for the period of 2012-2013, after which a real transition of increased visit occurred for these disease categories. Statistical testing showed that these SDs were significantly increased (p < 0.1). The long-term air pollution together with intermittent pollution episodes may have triggered critical transitions in population health for cardiopulmonary disease. It is recommended to consider significant increases in variability in time series of relevant system parameters, such as visit, as early warning signs for future transitions in populations' health states.
呼吸和心脑血管相关疾病严重威胁人类健康，与空气污染一起构成了一个复杂的病理生理系统。其他复杂的生物系统表明，时间序列中增加的方差和自相关性可以作为关键转变的有效预警信号。在人群水平上，我们确定了心肺疾病就诊的方差和自相关性增加是否先于人群健康的人类-污染相互作用的关键转变。我们研究了中国南京市一家医院 2006-2016 年期间最有可能受到空气污染影响的重要心肺疾病的长期就诊情况：脑血管意外疾病（CVAD）、冠心病（CAD）、慢性阻塞性肺疾病（COPD）、肺癌疾病（LCD），以及呼吸系统疾病（RESD）和心脑血管系统疾病（CCD）的分组类别。标准差（SDs）和自相关滞后 1（AR-1）的时间序列被研究为人群健康转变的潜在预警指标（EWIs）。升高的 SDs 为 2012-2013 年期间的 LCD 和整体 CCD 和 CVAD 的就诊关键转变提供了预警，此后这些疾病类别的就诊人数出现了真正的增加。统计检验表明，这些 SDs 显著增加（p < 0.1）。长期的空气污染加上间歇性的污染事件可能已经引发了心肺疾病人群健康的关键转变。建议考虑相关系统参数（如就诊次数）时间序列中显著增加的变异性作为未来人群健康状态转变的预警信号。
