Abstract
Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed.The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties.The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets.Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback.
Highlights
Methods of diagrammatic modelling have been greatly developed in the past two decades
Pearl has pointed out that association and causation have entirely separate languages, with terms such as regression, likelihood and “controlling for” belonging to the probabilistic group, as they refer to the observed joint distribution and to ways of manipulating it statistically; whereas terms such as effect, confounding and intervention refer to a causal relationship (Figure 1)[3,4]
We describe how diagrams can be employed to improve the analysis of such systems, and in the course of doing so we note that generic properties of the systems can be observed that are independent of the specific content, even though the diagrams themselves have been constructed solely from empirical evidence - no structure has been imposed on them
Summary
Causal methods of diagramming and modelling have been greatly developed in the past two decades. Apart from in the context of infectious diseases, they have been under-exploited in their potential to model the larger system in which health is generated or undermined This approach would accord with wider developments in biology. The cyclical relationship between labour productivity, household income, dietary intake, and nutritional and health status has been described as the “core nexus”, under conditions of absolute poverty This reinforcing (or “positive”) feedback can be beneficial or harmful in practice, depending on circumstances – see text. As well as its scientific function, this has practical advantages in terms of designing interventions Such methods are applicable to all branches of epidemiology, including infectious diseases epidemiology, chronic disease epidemiology, environmental and occupational epidemiology, and social epidemiology - and especially to their inter-relationship, e.g. simultaneous consideration of social and environmental influences
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