The slogan that ‘the virus doesn’t discriminate’ has been belied by the emergence of stark and persistent disparities in rates of infection, hospitalisation, and death from Covid-19 between various social groups. I focus on two groups that have been disproportionately affected, and that have been constructed or designated as particularly ‘at-risk’ during the Covid-19 pandemic: racial or ethnic minorities and fat people. I trace the range of narratives that have arisen in the context of explaining these disparities, in both the (...) scientific literature and wider expert and public discourse. I show that the scientific and public narratives around these groups have differed significantly, revealing contested and competing conceptions of the basis of these categories themselves. These different conceptions have important impacts on the kinds of interventions that become possible or desirable. I show that in the case of racial or ethnic disparities, genetic narratives have been combatted by a strong focus on structural racism as a driver of pandemic inequalities. However, in the case of fatness, individualising and stigmatising narratives have dominated discussions. I suggest that, given racial or ethnic differences in prevalence of fatness, and scholarship casting anti-fatness as historically racialised, the stigmatisation of fatness disproportionately affects racial or ethnic minorities in terms of placing individual blame or responsibility for the increased burden of Covid-19 on these groups. Despite widespread acknowledgement of the role of structural racism in driving racial inequalities in the burden of Covid-19, anti-obesity rhetoric and research provides a ‘backdoor’ to placing blame on individuals from racial minorities. (shrink)

Pandemics first and foremost hit those who are most vulnerable, and the COVID-19 pandemic is not different. Although the infection rate in the nation’s poorest neighborhoods is twice as it is in th...

The rise of neuroplasticity has led to new fields of study about the relation between social inequalities and neurobiology, including investigations into the “neuroscience of poverty.” The neural phenotype of poverty proposed in recent neuroscientific research emerges out of classed, gendered, and racialized inequalities that not only affect bodies in material ways but also shape scientific understandings of difference. An intersectional, sociomaterial approach is needed to grasp the implications of neuroscientific research that aims to both produce and repair neurobiological difference. (...) Following Benjamin’s critique of the “carceral imagination” of technoscience, this article considers how such research may fix in terms of helping, or in contrast, fix by classifying and reifying, vulnerable subjects. I address the potential for biosocial determinism in linking neural phenotypes and social problems. I use an intersectional approach to consider the presence and absence of race in this body of research and explore how some methodological and conceptual framings of the “brain on poverty” mark poor and minority children for intervention in concert with neoliberal approaches to poverty. (shrink)

Large-scale data systems are increasingly envisioned as tools for justice, with big data analytics offering a key opportunity to advance health equity. Health systems face growing public pressure to collect data on patient “social factors,” and advocates and public officials seek to leverage such data sources as a means of system transformation. Despite the promise of this “data-driven” strategy, there is little empirical work that examines big data in action directly within the sites of care expected to transform. In this (...) article, I present a case study on one such initiative, focusing on a large public safety-net health system’s initiation of sexual orientation and gender identity data collection within the clinical setting. Drawing from ethnographic fieldwork and in-depth interviews with providers, staff, and administrators, I highlight three main challenges that elude big data’s grasp on inequality: provider and staff’s limited understanding of the social significance of data collection; patient perception of the cultural insensitivity of data items; and clinic need to balance data requests with competing priorities within a constrained time window. These issues reflect structural challenges within safety-net care that big data alone are unable to address in advancing social justice. I discuss these findings by considering the present data-driven strategy alongside two complementary courses of action: diversifying the health professions workforce and clinical education reform. To truly advance justice, we need more than “just data”: we need to confront the fundamental conditions of social inequality. (shrink)