The topic of data sovereignty has seen a resurgence in the past decade. Among other complex underlying reasons, the expanding development and availability of large electronic health record and healthcare claims data; enhanced computational tools to analyze data; proliferation of high-speed and high-capacity data servers; and the ethical social and commercial concerns associated with the procurement, analysis, and storage of data have played a part in accelerating these debates. The fields of health services research and epidemiology in particular have been able to leverage electronic health record data and healthcare claims data to undertake varied and previously elusive observational studies. These studies can examine complex healthcare questions such as those related to the impact of policies on population health outcomes and the safety of medications. Colloquially, this resource has been referred to as ‘big data.’ The data here is any data that is ‘generated, created, collected and retained in any form or medium related to an individual patient, in, during, or part of a clinical or clinical research encounter .’ The big refers to the increased volume and breadth and depth of the data amassed, across ever-growing populations and over longer periods of time .
Another parallel phenomenon has been the uptake of the notion of decoloniality in global health discourse. The term ‘decolonizing’ has been ascribed to endeavors across the spectrum from educational syllabi re-construction to upending whole fields of inquiry. ‘Decolonizing’ global health discourse has been especially energized as the COVID-19 pandemic illuminated the widening chasm of healthcare inequities (related to mortality and access to the COVID-19 vaccine for example) between wealthier colonizing nation states and low-income states that were previously colonized or remain colonized [3, 4]. That the tenor and locus of these conversations has largely continued to focus on colonizing or formerly colonizing nation state actors has been criticized for reifying the paternalistic, colonial, hierarchical structure, and power dynamics woven into the very fabric of the field of global health . Epidemiologist Madhu Pai elaborates on the contours of this dynamic conversation and in doing so cites scholar Themrise Khan, who opines: ‘The focus should instead be on the South, by the South itself. Not the South being a focus of the North. Southern countries need to trust each other more and come together as a coalition to support each other. There is no reason for us to continue looking to the West as the harbingers of prosperity’ . In this vein, several scholars have previously posed the million dollar question on the prospects of decolonizing the deeply colonial enterprise of ‘global health,’ asking whether the very process of decolonization of global health would usher in its demise [7, 8]. These perspectives, along with the belief that ‘decolonization is not a metaphor’  were foremost on my mind as I considered writing this piece.
The promise and perils of ‘big data’ analytics in shaping how we approach strategies to achieving health equity have been written about elsewhere . There has been less exploration of the imperative of data sovereignty on informing the ethics of health services research and global health more broadly, especially in the context of decoloniality in an era of ‘big data’. This viewpoint will offer a brief and preliminary exploration of some questions that may drive this effort: is ‘decolonizing’ health data necessary? If so, what are the stakes, and who sets the terms? What would a decolonized data infrastructure necessary for health systems equity globally look like?
For the purposes of this brief, I rely on the following definition of data sovereignty that conceives of it as a ‘right’: Data sovereignty is the right of a nation to collect and manage its own data .While this definition emphasizes the particular rights of nations, especially indigenous peoples and nations , I deploy a rights-based definition that also sees patients, as individuals and as groups, as having a distinct and exceptional right to determine the parameters of the collection, utilization, accessibility, and use of their data. I am less interested here in the calls for patient ownership of data for purposes of monetization per se, but rather in their wresting power over and control of their data from private, profit-driven healthcare data corporations or non-native governmental and non-governmental organizations, who, save for a few exceptions, have been the key material beneficiaries and stewards of the big data enterprise on the global scale.
Many powerful international organizations, including the World Health Organization (WHO)  and the United States Agency for International Development (USAID) , have been invested in supporting the burgeoning electronic health records systems that ultimately funnel data to growing big data analytic firms (also referred to as electronic medical record or health information systems) of low and middle-income countries. On the face of it, if consistently and appropriately used, electronic health records or EMRs provide a longitudinal picture of the continuum of care for patients . The World Health Organization (WHO) report on the Global Strategy for Digital Health outlines their vision for accelerated adoption of data analytics as such:
The vision of the global strategy is to improve health for everyone, everywhere by accelerating the development and adoption of appropriate, accessible, affordable, scalable and sustainable person centric digital health solutions to prevent, detect and respond to epidemics and pandemics, developing infrastructure and applications that enable countries to use health data to promote health and well-being, and to achieve the health-related Sustainable Development Goals and the triple billion targets of WHO’s Thirteenth General Programme of Work, 2019–2023.
While the healthcare benefits—namely related to improving continuity and quality of care and disease surveillance—of building an architecture and supporting infrastructure for data procurement, storage, and sharing within and across healthcare institutions are outlined in the WHO report, what is not addressed is the potential for intentional and unintentional misuse of data that may exacerbate and widen within country and between country health inequities and put particularly vulnerable patient populations in harm’s way. We also know from a vast array of research that racism and bias are woven into machine learning and data algorithms that are broadly deployed for healthcare decision-making and policy-shaping in the United States [16, 17]. Often unbeknownst to them, these patient populations may be subject to ‘potentially stigmatizing, discriminating or exclusionary consequences .’
It is no stretch then to ask how emergent health data systems, in many cases funded by US and EU development and donor agencies , will be constructed and linked, and in whose hands they will fall? On the one hand, these questions gesture to the concept of data ownership, on the other, and for the purposes of this piece, they are gesturing to first and foremost to a notion of data sovereignty.
For example, while the MEASURE Evaluation program funded for the past two decades by USAID sought, ‘to help improve data collection, data quality, and the global capacity for research ,’ it does not assert a claim of sovereignty (of any kind and in any formulation) or of power to the nations and populations in which they are based. Ultimately, those countries are beholden to the whims of the grant maker or donor agency (in this case USAID) who will determine, perhaps in consultation with the community or perhaps not, what methodologies will be used to construct and analyze data, what vendors will be contracted to house the data, and what data points and populations will be incorporated (or ignored) by design. Granted, there are community-based information systems as well as data demand and use programs that ostensibly center the populations being supported. Yet these programs do not explicitly address the myriad of questions and challenges that are plaguing better-financed health systems that are not reliant on international aid for the development of their data infrastructure, including the US. Perhaps this is by design. While intimations of accountability with respect to ensuring patient privacy and data quality are invoked, there is very little by way of tools for accountability and redress if ever the privacy of individual patients or patient groups is compromised. Additional salient questions especially centered on data governance remain unanswered: who ultimately controls stewardship of the data? Where will the data be stored and who and what entities will have access? For what purposes? Have safeguards been instituted to ensure appropriate use of the data to improve health outcomes and mitigate disparities? Who sets the terms for these safeguards and what quality control mechanisms have been built to ensure safeguards are actually working to prevent harm? Many of these conversations and debates remain compartmentalized. This fragmentation is in part due to the nature of developing context specific and responsive programs but also as a result of the multitude of gatekeepers at the helm, each with undoubtedly shared yet competing interests.
In order to envisage a decolonized health data infrastructure that supports health equity within and between formerly colonized and colonizing countries and populations, one must first envisage what a decolonized global health architecture would look like. Medical scholar Ijeoma Nnodim Opara, in the powerful piece entitled ‘It’s Time to Decolonize the Decolonization Movement’ , states that a key to a decolonizing mission is centering the knowledge of ‘indigenous and (neo) colonized decolonial and anticolonial liberation movement scholars, thinkers, strategists, and activists,’ upending of the power dynamic that drives and underlies most global health endeavors, as well as an emphasis on intersectionality and interdependence. In addition, Opara states that decolonizing the decolonization movement requires the work ‘be defined, led by, and the benefits reaped by the Majority people of the world.’ This anticolonial and revolutionary ethos should animate all aspects of building a decolonized data infrastructure as well. There are particularities to data infrastructure construction however, and some of these conditions are outlined as below:
It is past time advocates for a global health system rooted in equity and repair speak to each other rather than past each other. Such collective conversations enable the development of strategies that are not only more effective but more just, representative, and accountable. They also commit to a non-totalizing agenda that does not treat people as monoliths, but rather as individuals within communities each with their own sets of needs and perspectives.
In this brief viewpoint, I sought to preliminarily explore the primary questions and conditions that may help us map a decolonized global health data architecture whose construction is led by the very people who ‘populate the dataset.’ The benefits of any such approaches taken singularly, without a parallel effort to dismantle the power asymmetry and the colonial and capitalist hierarchies that foreclose the full actualization of health for the ‘wretched of the earth ,’ will indeed be limited. What is clear is that we must move beyond platitudes and work towards solutions that force imperial powers and their corporate machinery out of the trade of selling in the sickness and health of populations and demand that they, and international aid agencies writ large, cede control and redistribute the wealth they have accumulated through colonialism (and other practices of exploitation) back to the patients, people, and populations whose stories are being told (and sold) through the accumulation of data.
The author has no competing interests to declare.
Liddell K, Simon DA, Lucassen A. Patient data ownership: who owns your health? Journal of Law and the Biosciences. 2021; 8(2). lsab023. DOI: https://doi.org/10.1093/jlb/lsab023
Zwitter A. Big Data ethics. Big Data & Society; July 2014. DOI: https://doi.org/10.1177/2053951714559253
Bambra C, Riordan R, Ford J, et al. The COVID-19 pandemic and health inequalities. J Epidemiol Community Health. 2020; 74: 964–968. DOI: https://doi.org/10.1136/jech-2020-214401
Sparke M, Williams OD. Neoliberal disease: COVID-19, co-pathogenesis and global health insecurities. Environment and Planning A: Economy and Space. 2022; 54(1): 15–32. DOI: https://doi.org/10.1177/0308518X211048905
Opara IN. It’s time to decolonize the decolonization movement. PLOS Blogs, Speaking of Medicine and Health; July 29, 2021. Accessed https://speakingofmedicine.plos.org/2021/07/29/its-time-to-decolonize-the-decolonization-movement/.
Pai M. Decolonizing global health: a moment to reflect on a movement; July 22, 2021. Accessed January 10, 2022. https://www.forbes.com/sites/madhukarpai/2021/07/22/decolonizing-global-health-a-moment-to-reflect-on-a-movement/?sh=67e287a25386.
Abimbola S, Pai M. Will global health survive its decolonisation? The Lancet. 396(10263): 1627–1628. DOI: https://doi.org/10.1016/S0140-6736(20)32417-X
Benton A. Twitter, ‘Still pretty sure you can’t decolonize global health and still have global health at the end.’ Accessed at: https://twitter.com/Ethnography911/status/1444796667513868290.
Ibrahim SA, Charlson ME, Neill DB. Big Data analytics and the struggle for equity in health care: The promise and perils. Health Equity. 2020; 4(1): 99–101. Published 2020 Apr 1. DOI: https://doi.org/10.1089/heq.2019.0112
Rainie SC, Schultz JL, Briggs E, Riggs P, Palmanteer-Holder NL. Data as a strategic resource: Self-determination, governance, and the data challenge for indigenous nations in the United States. The International Indigenous Policy Journal. 2017; 8(2). DOI: https://doi.org/10.18584/iipj.2017.8.2.1
Kukutai T, Taylor J. (eds.) Indigenous data sovereignty: Toward an agenda. Centre for Aboriginal Economic Policy Research. Canberra, Australia: Australian National University Press; 2016. DOI: https://doi.org/10.22459/CAEPR38.11.2016
World Health Organization. Global strategy on digital health 2020–2025. 2021. Licence: CC BY-NC-SA 3.0 IGO. Accessed at: https://www.who.int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf.
Silvestre E. How electronic health records strengthen the health systems of low- and middle-income countries: Learning from Eswatini and Mexico. Chapel Hill, NC: MEASURE Evaluation, University of North Carolina; 2018.
Healthcare Information and Management Systems Society (HIMSS). EHR definition. Published 2018. Accessed July 12, 2018. http://www.himss.org/ASP/topics_ehr.asp.
Benjamin R. Race after technology: Abolitionist tools for the new Jim Code. Medford, MA: Polity Press; 2019. DOI: https://doi.org/10.1093/sf/soz162
Noble SU. Algorithms of oppression: How search engines reinforce racism. New York University Press; 2018. DOI: https://doi.org/10.2307/j.ctt1pwt9w5
Jawhari B, Ludwick D, Keenan L, et al. Benefits and challenges of EMR implementations in low resource settings: a state-of-the-art review. BMC Med Inform Decis Mak. 2016; 16(116). DOI: https://doi.org/10.1186/s12911-016-0354-8
USAID. MEASURE evaluation website. Accessed: January 15, 2022. https://www.measureevaluation.org/
Arora P. Bottom of the data pyramid: Big Data and the global south. International Journal of Communication. [S.l.]. 2016; 10: 19. ISSN 1932-8036. Accessed Jan 16, 2022. https://ijoc.org/index.php/ijoc/article/view/4297/1616.
Hummel P, Braun M, Tretter M, Dabrock P. Data sovereignty: A review. Big Data & Society; January 2021. DOI: https://doi.org/10.1177/2053951720982012