by Sabrina Li, University of Oxford
This blog is reposted with permission from the RGS-IBG Geographies of Health and Wellbeing Research Group. The original post can be viewed here.
It is both a strange and exciting time for a health geographer working at the intersection of human health and the environment. More than ever before, the concepts of geography have shaped our understanding of the Covid-19 pandemic. It has redefined our relationships with space and place. It has shown us that at every spatial level, we are all interconnected in some way, shape, or form, and that our actions can have a ripple effect on society.
Maps have always been an important tool for understanding the distribution of diseases and ill health. Today, GIS and map-based dashboards are essential for learning the locations of Covid-19 clusters at different spatial scales, and are not only adopted by government agencies for disease surveillance but also by citizens to learn about the progression of the pandemic. In late January, when the Covid-19 pandemic was still in its infancy, I started working with a group of researchers based in the Department of Zoology at Oxford, the University of Washington, Northeastern University, and the Boston Children’s Hospital to collate cases with individual-level epidemiological information (e.g. gender, age, symptoms, travel history, etc.) into a line list (table with detailed data on each case), which was then mapped onto the Healthmap dashboard in real time. Our project satisfied a growing need for a centralised repository of detailed case data within the epidemiology community; many used our repository to model the early phases of the epidemic in various countries. Surprisingly, prior to this pandemic, a line list of cases during an outbreak was rarely made available for open access in real time. However there are a plethora of benefits to doing so, including accelerating our understanding of the routes of geographic spread and its associated risks. These advantages are highlighted in our correspondence to the Lancet Infectious Diseases, where we discuss the strengths of open data sharing for improving public health planning and surveillance.
Despite the versatility of modern GIS technologies in improving our preparedness and response, we were still unprepared to address the detrimental impacts of this pandemic on certain population groups in our society, especially at the local level. While it may come naturally for every health geographer to concur that the social determinants of health shape our life course and underpins health inequities, this notion was not obvious to others until the pandemic unmasked the brutal reality of deep racial and socioeconomic divides in our society. This has been amplified in Brazil, which at the time of writing has the second highest number of Covid-19 cases in the world behind the United States.
Working with a multidisciplinary group of researchers from the UK-Brazil Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE), we found that access to Covid-19 testing in the Greater Metropolitan area of São Paulo varied by socioeconomic status such as income per capita. In our most recent paper (currently under review in Nature Medicine), we highlight that importation of Covid-19 cases into Brazil came from people traveling abroad in the United States and Europe. We mapped these cases and found that in the early phases of the epidemic, cases came from residents of high-income neighbourhoods. Over two-thirds of the confirmed cases in the early phases of the epidemic in Brazil came from private labs, where the cost per test was approximately between 300-690 Brazilian real (~$56-$130 USD). This hindered access to testing for many, as the cost of a single test was equivalent to two-thirds of the minimum monthly salary of a person living in Brazil.
Driven by this finding, we are currently exploring the impacts of the Covid-19 pandemic on population groups stratified by socioeconomic status, race, and access to healthcare facilities in São Paulo state. In light of recent events, the role and impacts of structural racism on public health has become a common narrative. In particular, this sparked discussion on how race should be incorporated in social science research. As race is indicative of heredity, is race truly a risk factor for Covid-19 infections, or is it underlying systemic racism that disproportionally exposes structurally disadvantaged populations to the pandemic? Moreover, as health geographers, how do we reckon with the interactions between race, history, and the social determinants of health in our research? This is a much-needed discourse moving forward, but it is evident that health geographers will play an important role in understanding the health and social impacts of the current pandemic and its repercussions in a post-pandemic society.
About the author: Sabrina Li is a second-year DPhil student at the University of Oxford. Her research explores how human-environment interactions drive the spread of yellow fever virus in Brazil. She is also a member of the UK-Brazil CADDE initiative and Oxford Martin School Programme on Pandemic Genomics.
This blog is reposted with permission from the RGS-IBG Geographies of Health and Wellbeing Research Group. The original post can be viewed here. If you would like to know more about, or join the GHWRG, please visit their website here: https://ghwrg.wordpress.com/
Suggested further reading
Sparke, M, Anguelov, D. (2020). Contextualising coronavirus geographically. Transactions of the Institute of British Geographers. https://doi.org/10.1111/tran.12389
Hirsch, LA. In the wake: Interpreting care and global health through Black geographies. Area. 2019; 52: 314– 321. https://doi.org/10.1111/area.12573