Mapping community viral load and social boundaries

Abstract

Recently, Castel et al.[1] sought to measure and map community viral load (CVL) in Washington, DC. The authors included the most recent viral load data in the city and two indicators of socioeconomic status, namely poverty rates and the percentage of high school diplomas, as part of their analysis [1]. Findings were distributed by geopolitical regions and were presented on various maps of the city. They show an overrepresentation of racialized and poor neighbourhoods as where individuals are most likely to have a detectable viral load and suffer from HIV-related disparities. In many ways, the findings presented by Castel et al.[1] are not new or surprising. We have known for decades that the HIV epidemic concentrates in geographical areas where individuals are most vulnerable to HIV infection, areas marked by high levels of marginalization, poverty, oppression, and social exclusion. This is clearly evident in the District of Columbia where the HIV epidemic is generalized in geographical areas where target populations are most likely to present with high viral loads and live in socioeconomic conditions that make them disproportionally affected by HIV. What is innovative is the ways that CVL allows for a new form of surveillance that can then be used to draw boundaries geographically and identify specific areas for the deployment of high impact interventions, areas that are then identified as ‘risky’ and in need of attention. Despite its sophistication, CVL draws a rather partial and incomplete portrait of the HIV epidemic. We would go as far as to say that it decontextualizes HIV transmission [2]. CVL fails to consider the personal circumstances of people who are rendered visible by mapping exercises and the implications for people living with HIV who reside in areas that are being targeted by public health authorities. Not surprisingly, CVL has led to the intensification of prevention efforts and the deployment of more aggressive interventions to drive down CVL, with little further consideration of the realities of people living with HIV on the ground. The techniques of CVL allow us to measure and map concentrations of the virus, as if they might occur at random. This belies the reality that target populations we are most interested in are often forced into particular geographic areas, economic and ethnic ‘ghettos’. We are concerned that CVL and its ‘geographic’ distribution have become a proxy for naming target populations, but with new levels of sophistication. In particular, we are concerned with the potential to increase stigma directed at populations who occupy what we term ‘viral spaces’. This phenomenon has important implications for the health and safety of people living with HIV [3], and may have broader implications for everyone living in these spaces. This is especially true of geographical areas where higher CVL overlap with concentrations of racialized people as reported by Castel et al.[1]. This could result in the combining of HIV-related stigma with racism, classism and other discriminatory practices. What will it mean to know that a particular neighbourhood is where people have HIV and where people get HIV? What are the implications for those living there and for those living outside the well defined boundaries of this particular neighbourhood? We noticed Castel et al.[1] reporting measurements of CVL alongside ‘ward of residence’ and insurance status. The study is able to represent pockets of higher CVL in neighbourhoods that we imagine might be recognizable to those living in the District of Columbia, and surely recognizable to those for whom mapping risk is their trade. We appreciate the ways CVL is useful for epidemiological surveillance, but are concerned over the implications for actuarial surveillance and the possibility of decreased insurance options for individuals because of where they live. Additionally, we call for more discussions on the use of surveillance data and the ethics of CVL as a mapping exercise. Do the individuals whose viral load is calculated know that these data will be used to draw boundaries between ‘sick’ and ‘healthy’? Are they given an option over how their medical data will be used, including when used in aggregate, for such purposes? These issues have yet to be addressed in the literature on CVL. In our view, it is imperative that researchers engage in discussions and debates around the use of this new biomarker because it may very well contribute to the production of new spaces of exclusion for people living with HIV and others living in areas where the highest viral loads are found. For this reason, we argue that CVL merit closer examination. Acknowledgements Conflicts of interest There are no conflicts of interest.