Rehfuess Eva A, Best Nicky, Briggs David J, Joffe Mike
MRC-HPA Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Emerg Themes Epidemiol. 2013 Dec 6;10(1):13. doi: 10.1186/1742-7622-10-13.
Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa.
Diagram-based Analysis of Causal Systems combines the use of causal diagrams with multiple routinely available data sources, using a variety of statistical techniques. In a step-by-step process, the causal diagram evolves from conceptual based on a priori knowledge and assumptions, through operational informed by data availability which then undergoes empirical testing, to integrated which synthesizes information from multiple datasets. In our application, we apply different regression techniques to Demographic and Health Survey (DHS) datasets for Benin, Ethiopia, Kenya and Namibia and a pooled World Health Survey (WHS) dataset for sixteen African countries. Explicit strategies are employed to make decisions transparent about the inclusion/omission of arrows, the sign and strength of the relationships and homogeneity/heterogeneity across settings.Findings about the current state of evidence on the complex web of socio-economic, environmental, behavioral and healthcare factors influencing childhood ALRI, based on DHS and WHS data, are summarized in an integrated causal diagram. Notably, solid fuel use is structured by socio-economic factors and increases the risk of childhood ALRI mortality.
Diagram-based Analysis of Causal Systems is a means of organizing the current state of knowledge about a specific area of research, and a framework for integrating statistical analyses across a whole system. This partly a priori approach is explicit about causal assumptions guiding the analysis and about researcher judgment, and wrong assumptions can be reversed following empirical testing. This approach is well-suited to dealing with complex systems, in particular where data are scarce.
有效的干预措施需要了解个体因果路径如何共同决定疾病。基于系统流行病学的概念，本文开发了基于图表的因果系统分析（DACS）方法来分析复杂系统，并通过研究撒哈拉以南非洲儿童急性下呼吸道感染（ALRI）的近端和远端决定因素的作用来应用该方法。
基于图表的因果系统分析将因果图的使用与多个常规可用数据源相结合，运用多种统计技术。在一个循序渐进的过程中，因果图从基于先验知识和假设的概念性图，通过由数据可用性告知的操作性图（然后进行实证检验），演变为整合了来自多个数据集信息的整合图。在我们的应用中，我们将不同的回归技术应用于贝宁、埃塞俄比亚、肯尼亚和纳米比亚的人口与健康调查（DHS）数据集以及16个非洲国家的合并世界卫生调查（WHS）数据集。采用明确的策略使关于箭头的包含/省略、关系的符号和强度以及不同环境中的同质性/异质性的决策透明化。基于DHS和WHS数据，关于影响儿童ALRI的社会经济、环境、行为和医疗保健因素复杂网络的当前证据状态的研究结果总结在一个整合因果图中。值得注意的是，固体燃料的使用由社会经济因素构成，并增加了儿童ALRI死亡率的风险。
基于图表的因果系统分析是一种组织特定研究领域当前知识状态的方法，也是一个整合整个系统统计分析的框架。这种部分先验的方法明确了指导分析的因果假设和研究者的判断，错误的假设在实证检验后可以被推翻。这种方法非常适合处理复杂系统，特别是在数据稀缺的情况下。
