Our hunt for causes is complicated by two emerging observations. First, factors at multiple levels, including biological, behavioural, group and macro-social levels, all have implications for the production and distribution of health.7
Secondly, these factors frequently influence one another and, in addition, are sometimes influenced by the health indicators of interest. We illustrate each of these observations by focusing on obesity, synthesizing a well-established body of work in the area.
The rapid increase in obesity in the USA and other countries during the past 15 years has been the subject of much academic discussion. Recent publications have even suggested that the increase in childhood obesity in the USA might result in the first reversal of life expectancy in this country within the past century (excepting a very brief reversal during the 1918 influenza pandemic).8
The academic focus on the etiology of the ‘obesity epidemic’ has been intense, and the evidence now suggests that a diverse range of factors influence obesity. These include but are not limited to: (i) endogenous factors such as genes and factors influencing their expression; (ii) individual-level factors such as behaviours (size of food portions, dietary habits, exercise, television-viewing patterns), education, income; (iii) neighbourhood-level factors such as availability of grocery stores, suitability of the walking environment, advertising of high caloric foods; (iv) school-level factors such as availability of high-caloric foods and beverages and health education; (v) district or state-level policies that regulate marketing of high caloric foods; (vi) national-level surplus food programmes, other food distribution programmes and support for various agricultural products; and (vii) from a lifecourse perspective history of breastfeeding, maternal health and parental obesity.9,10
So, what is the cause of obesity?
This example highlights the problem for the dominant epidemiological causal paradigm, namely, how do we think about, and analytically grapple with, the potential contribution of factors at all these levels of influence when we are centrally concerned with isolating independent and actionable ‘causes’?
We may think of three different possible solutions to this problem.
The first is conceptual. One might argue that factors that are at higher levels of influence and exert their influence indirectly by way of other factors are not truly ‘causes’. However, this solution is unsatisfying. For example, if legislation eliminated the production of cigarettes then (eventually) most lung cancer would be eliminated. Would it not make sense then to argue that such legislation was causally related to the occurrence of lung cancer in the population? If we accept, as the evidence amply suggests, that these higher level influences do indeed matter and do, in some way, influence the likelihood of obesity, a pragmatic scientific approach1
would suggest that we should indeed consider these factors as part of a set of causes, or at the very least as causes of causes11
and hence worthy of our epidemiological interest.12
A counterfactual approach suggests that a better understanding of causation is an understanding of how interventions to change a variable would ultimately lead to changes in an outcome variable of interest, compared with a counterfactual scenario in which no intervention occurred. This would involve not only an estimate of the strength, but also the timing of the effects, as some causal variables might produce more immediate effects, whereas others might have a slower but more lasting impact. This approach is agnostic as to the level at which such interventions occur.
The second solution could be to extend the tools that we currently use within epidemiology to deal with these other factors. Largely in response to this question there has been in the past decade a dramatic increase in the use of multilevel, or hierarchical, regression models within epidemiology. These models allow epidemiologists to consider the contribution of factors at multiple levels while taking into account factors at other levels that may confound the relation between the central factor (cause) of interest and the key disease outcome. Unfortunately, although useful, multilevel methods do little to help deal with a fundamental limitation of all regression-based models, namely that these models are concerned with assessing the relation between ‘independent’ variables and ‘outcomes’ of interest. Therefore, this approach, as commonly used, does little to take into account the dynamic and reciprocal relations between some ‘exposures’ and ‘outcomes’, discontinuous relations or changes in the relations between ‘exposures’ and ‘outcomes’ over time.
Going back to our obesity example, even though individual exercise patterns are linked to the risk of obesity,13
obesity is also a determinant of individual exercise patterns.14
Similarly, although dietary habits are clearly linked to risk of obesity,14
individual dietary habits are in turn shaped both by social networks15
and by the availability of food in an individual's neighbourhood.16
Also, it is likely that the relation among all the key factors, or causes, of obesity is not easily parameterized. It would be a substantial assumptive stretch to argue that there is a linear relation, for example, between suitability of the walking environment and risk of obesity, and even more of a stretch to argue that any hypothesized parametric relation is consistent across all the relations of interest in shaping obesity. Therefore, in this one example we can see a reciprocal relation between putative ‘exposures’ and ‘outcomes’, clear interrelations between key independent variables of interest, and absence of clear predictable parametric relations. Regression models, although clearly helpful at identifying isolated relations between covariates while taking into account potential confounders, are poorly suited to deal with these complications. Interrelations among individuals can also lead to violations of the stable unit treatment value assumption (SUTVA), since a treatment that affects the obesity of one individual could also affect the obesity of his/her friends.
A third possible solution—and the focus of this article—is the adoption of complex systems dynamic computational models. Complex systems approaches in general allow us to take into account both the influence of causal influence at multiple levels and the interrelations among causal covariates that strain most widely used analytic methods. There have been previous calls for the adoption of complex systems dynamic methods to epidemiology,17
and the conceptual basis for these arguments can be traced back as far as Morris (1957).18,19
These calls have grown in recent years, and recent writing in the field has gone further in showing how these methods can substantially move us forward in our thinking.20–22