Terex Maps, Salida, CO, United States.
NIRAS International Consulting, Vantaa, Finland.
Sci Total Environ. 2021 Jul 20;779:146426. doi: 10.1016/j.scitotenv.2021.146426. Epub 2021 Mar 13.
The social determinants of individuals' health (e.g., socio-economic, demographic, and genetic conditions) play a major role in the health of an entire population. However, in comparison to environmental data, global data on the social determinants of health is spatially coarse, infrequently updated, and costly to measure. From global mapping efforts of the recent COVID-19 pandemic it is clear that social data is not meeting the fine spatial quality needed for mapping vulnerable populations and transmission pathways. Most maps produced generalized to larger administrative units (such as counties, states), and have not identified distinct areas of vulnerable populations apart from the surrounding environment where no population resides. We present a framework that uses environmental determinants of health, instead of social ones. Other studies that link the environment to human health have done so by analyzing one ecosystem service (such as clean air) to the health of the population. Instead of relating one ecosystem service to the health of the population, this framework breaks the environmental features that produce the ecosystem service into parts (forest, temperature, precipitation). Each feature is then related to human health. With the amount of data available it is feasible to include change in monitored features over time, and create predictors for the impact of the change of monitored features on the health of populations. This framework generalizes ecosystem services and disservices into one value that an environmental feature provides. This helps to manage uncertainty of how an individual ecosystem service affects health. Application of this framework will allow for fine scale monitoring of vulnerable populations and transmission pathways of various infectious diseases. This framework is particularly relevant to newly emerging infectious diseases, such as COVID19, whose socially determinant risk factors are unknown (or data scarce) and to which we have to respond in a rapid manner.
个体健康的社会决定因素(例如社会经济、人口和遗传条件)对整个人口的健康起着重要作用。然而,与环境数据相比,全球健康社会决定因素数据的空间粒度较粗,更新频率较低,且测量成本较高。从最近 COVID-19 大流行的全球绘图工作中可以清楚地看出,社会数据不符合绘制脆弱人群和传播途径所需的精细空间质量。大多数生成的地图概括到较大的行政单位(如县、州),并且没有除无人居住的周围环境之外确定脆弱人群的独特区域。我们提出了一个使用健康环境决定因素而不是社会决定因素的框架。其他将环境与人类健康联系起来的研究是通过分析一种生态系统服务(如清洁空气)对人口健康的影响来实现的。该框架没有将一种生态系统服务与人口健康联系起来,而是将产生生态系统服务的环境特征分解为各个部分(森林、温度、降水)。然后,每个特征都与人类健康相关联。有了可用的数据量,就可以包括随时间监测特征的变化,并创建监测特征变化对人口健康影响的预测因子。该框架将生态系统服务和不利因素概括为环境特征提供的一个值。这有助于管理单个生态系统服务如何影响健康的不确定性。该框架的应用将允许对各种传染病的脆弱人群和传播途径进行精细监测。该框架特别适用于新出现的传染病,如 COVID19,其社会决定因素风险因素未知(或数据稀缺),我们必须迅速应对。