Social epidemiology and systems thinking
Social epidemiology is concerned with the social variation in, and the social determinants of the distribution of health and disease [1
]. This branch of epidemiology is fundamentally interested in the influences of social factors--such as individual attributes (i.e., social class and ethnicity) [2
]; behaviors (i.e., diet and physical activity) [4
]; constructs of social interaction (i.e., social support and social cohesion) [6
]; contextual influences (i.e., neighborhoods and regions) [5
]; and the influences of the allocation of individuals in space (i.e. race/income segregation) on the distribution of health and disease in populations [8
]. By patterning exposure to disease risk factors, social factors themselves become fundamental determinants of health [10
]. Social epidemiology has made great strides during the past two decades. However, as the field grows, it is becoming readily apparent that some of the its tools may be limiting. In particular, the reductionist linear models that are the lingua franca
of epidemiologic analyses are limiting in important ways.
First, the dynamics of populations, in terms of health and disease, emerge from the behaviors and interactions of the heterogeneous individuals that comprise them. In this way, interaction undergirds many of the mechanisms that mediate the social production of health and disease. These interactions may operate both on the macro-scale between social exposures acting at multiple levels, and on the micro-scale between individuals within populations. Interactions challenge the current epidemiologic toolkit in several ways. As social factors may interact in complex ways to determine health and disease risk, the current "risk factor" approach to epidemiology, which emphasizes decontextualized, independent effect measures for exposures may not be appropriate [11
]. For example, studies have demonstrated that the relation between ethnicity and health indicators may be modified by ethnic density in the area of residence; this observation has been documented for health indicators including adverse birth outcomes [13
], asthma [15
], psychopathology [16
], suicide [17
], and mortality [20
]. This observation challenges our current approaches because it suggests that the relation between ethnicity and health may be heterogeneous, and that conceptualizing this relationship independently of social context may be flawed. Furthermore, social variability in health may be mediated by the degree and nature of social interaction within and between social groups. In this regard, several studies have shown that social interaction may transmit non-infectious disease outcomes [21
]. Furthermore, research about the health influences of social interactions suggests that population-level modes of social interaction, such as social cohesion, social capital, and social support, may shape population health and disease distribution [6
]. Ultimately, however, social interaction does not lend itself to the reductionist analytic paradigm that we employ, as potentially important social interactions between individuals in a population violate the central assumption of independence of observations in regression approaches.
Second, population dynamics feature nonlinearity, whereby change in disease risk is not always proportional to the change in exposure, and feedback, where disease can modulate exposure just as exposure can modulate disease. These dynamics are not often explored in social epidemiology, although they may have profound implications for population health. For instance, a central observation in social epidemiology is that low social status predicts poor health [27
]. However, poor health can also predict low social status [28
]. Therefore, mutually reinforcing in a positive feedback loop, low social status and poor health may ultimately converge, with reinforcing implications for a third social ill--inequality (which itself plausibly feeds back on low social status and poor health) [29
]. As is characteristic of positive feedback loops, the relationships between social status, health, and inequality are likely to feature nonlinear, accelerating behavior because of amplification at each turn of the loop. As an illustration of the inability of the current epidemiologic paradigm and toolset to negotiate these dynamics, consider the use of directed acyclic graphs (DAGs) in traditional epidemiologic analyses. DAGs are mental models used to specify and formalize the causal relationships between exposures and outcomes. However, like the regression models they educate, these mental models, by definition, forbid cyclical relationships between exposure and outcome, and therefore the feedback and reciprocity that likely characterize the true relationships between them.
Third, the counterfactual conceptual framework that underpins epidemiologic inquiry falls short when considering both fundamental social causes and macrosocial causes of disease. Our etiologic understanding of the social determinants of disease rests on the counterfactual exercise of contrasting outcome occurrence probabilities corresponding to two or more mutually-exclusive exposures [11
]. However, social factors, of fundamental importance in social epidemiology, such as race, ethnicity and gender, are attributes
of individuals, rather than exposures. Because these attributes are fundamental to identity, authors have argued that the counterfactual approach is theoretically implausible [30
]. Similarly, understanding macrosocial causes requires the assumption that a counterfactual universe could be unchanged barring a large-scale social cause. However, causes across levels are inevitably interlinked, suggesting that an alternate universe comparable to the present universe, save changes in a macrosocial influence, may also be implausible.
These three challenges may be limiting the progress of social epidemiology at this stage in its evolution [34
], and have resulted in calls to adopt newer methods that can overcome them [32
]. Several authors have suggested the adoption of systems approaches in social epidemiology as a way past these challenges [37
]. "Systems thinking" suggests that complex dynamic systems, such as populations, which feature multiple interdependent components whose interactions may include feedback, non-linearity, and lack of centralized control [40
], are best understood holistically [41
]. This epistemological approach is best contrasted to "reductionism", which suggests that systems are best understood by aggregating information gathered via the independent study of their components. By contrast, a systems approach implies that the dynamics and behavior of a system are different, qualitatively, from those of the sum of its parts. A systems approach, therefore, emphasizes the dynamics of relationships
between components of a system, rather than the characteristics
of those components themselves [41
Two systems approaches that may be particularly useful in social epidemiology include social network analysis and agent-based modeling. With respect to social epidemiology, the first involves the characterization of the structures of social networks or subsets of these networks to understand their influence on health behaviors and outcomes. The second involves the use of stochastic computer simulations of simulated individuals, in simulated space, over simulated time to understand how macro-level health and disease distribution patterns may emerge from explicitly programmed, micro-level health behaviors, social interactions, and movement of these individuals in their environments.
A developing body of work has begun to apply these approaches to address social epidemiologic research questions. For example investigators have used these approaches to better understand the social etiology of complex conditions, [21
] such as obesity [24
]. Particularly compelling is a recent high-profile study by Christakis and Fowler [24
], which used social network analysis to demonstrate the spread of obesity via social relationships in a social network. Another study used stochastic networks nested within agent-based models (ABMs) to assess strategies for population-level obesity prevention [43
]. Two more recent studies used agent-based models to understand the mechanisms underlying socioeconomic disparities in diet quality [47
], and to assess how resource allocation may influence socioeconomic disparities in walking behavior [48
While these papers are good early examples of the adoption of complex systems approaches to social epidemiologic inquiry, the field remains young. Here, we aim to synthesize the extant literature that has called for applications of these approaches in population health. We will begin with an examination of each method and its approach, and then examine each method's strengths and applicability with regard to social epidemiologic research, suggesting particular avenues where each may be appropriate. Finally, we will discuss limitations to the application of each method in the field.