Syndemic thinking in large-scale studies : case studies of disability, hypertension, and diabetes across income groups in India and China

Abstract

Syndemics consider where two or more conditions cluster, how they interact, and what macro-social processes have driven them together. Yet, syndemics emerge and interact differently across contexts and through time. This article considers how syndemics involving Type 2 diabetes (DM), disability, and income differ among men and women and between India and China. We use the WHO Study on global AGEing and adult health (SAGE) data. Using multivariable logistic regression, we assess the interaction of socio-economic factors and diseases on a multiplicative scale. We found that gender and income interact significantly in China to increase the odds of reporting hypertension and diabetes, but only for reporting diabetes in India. High income interacts with metabolic conditions to increase the odds of reporting comorbidity. Hypertension and diabetes were both independently and jointly associated with increase in the odds of being disabled in both countries, but the association varies by conditions. We argue that, first, our study reveals how these syndemics differ between countries and, second, that they differ significantly between income groups. Both findings refute the idea that a "global syndemic" exists. Instead, we emphasize the need for more ethnographic work that invests in local historical, social, and political interpretations of syndemics. Furthermore, ethnographic evidence suggests that the lowest-income communities face compounded social stress, untreated depression, and poor healthcare access alongside these clustered "metabolic" conditions. This point is most notable to demonstrate the need for chronic integrated care for not only the wealthy but also poorer people with metabolic conditions.