Syndemics: A theory in search of data or data in search of a theory?

Abstract

The concept of a syndemic was proposed more than two decades ago to explain how large-scale social forces might give rise to co-occurring epidemics that synergistically interact to undermine health in vulnerable populations. This conceptual instrument has the potential to help policymakers and program implementers in their endeavors to improve population health. Accordingly, it has become an increasingly popular heuristic for advocacy, most notably in the field of HIV treatment and prevention. However, most empirical studies purporting to validate the theory of syndemics actually do no such thing. Tomori et al. (2018) provide a novel case study from India illustrating how the dominant empirical approach fails to promote deeper understanding about how hazardous alcohol use, illicit drug use, depression, childhood sexual abuse, and intimate partner violence interact to worsen HIV risk among men who have sex with men. In this commentary, I relate the theory of syndemics to other established social science and public health theories of disease distribution, identify possible sources of conceptual and empirical confusion, and provide concrete suggestions for how to validate the theory using a mixed-methods approach. The hope is that more evidence can be mobilized -- whether informed by the theory of syndemics or not -- to improve health and psychosocial wellbeing among vulnerable populations worldwide.

Keywords: HIV; Mixed methods; Multilevel analysis; Population health; Social determinants of health; Stigma; Syndemic; Syndemics.

Fig 1

A simplified framework for understanding the relationship between findings based on the sum score approach vs. policy or programmatic recommendations for multicomponent interventions. Panel 1 depicts what is commonly observed in the literature on syndemics: investigators fit a regression model following the sum score approach (X) and then draw on the findings to advocate for complex, integrated, and/or multicomponent interventions (Y). The dashed line indicates that a conclusion of Y based on findings from X is incorrect, given the underlying math (Tsai & Venkataramani, 2016). Panel 2 recognizes that one of the most commonly used specifications of the sum score essentially encodes an assumption that there exists a sufficient cause interaction (Tsai et al., 2017). Such an assumption does not, however, militate for a multicomponent intervention. The findings of X would instead lead directly to the conclusion that a single component intervention (Y’) would be sufficient to prevent the outcome, because the mechanism of disease -- which the sufficient cause represents -- requires the presence of all health risks to operate. Panel 3 illustrates that if a multicomponent intervention is the desired policy or programmatic outcome, use of the sum score approach will not lead to this conclusion. One possibility, however, is to demonstrate that there are multiple sufficient pathways to disease (X’), as is often observed in the setting of enteric pathogens and diarrheal disease (Eisenberg, Scott, & Porco, 2007; Wagner & Lanoix, 1958). In none of these scenarios does X lead to Y.

Fig. 2

A simplified typology of three ways to operationalize how co-occurring epidemics relate to each other. A, B, and C can be thought of either as diseases at the individual level or as epidemics at the population level. Panel 1 depicts mutually causal epidemics, described by Singer (1996): A and B are mutually causal, B and C are mutually causal, and A and C are mutually causal. Panel 2 depicts synergistically interacting epidemics, highlighted in Singer and Clair (2003): A and B both cause C, and their total effect on C exceeds the sum of their individual effects alone. Panel 3 depicts serially causal epidemics: A causes B, which then causes C. This model is related to theories of accumulating health risks.