Decolonising Health Data: Beyond The Numbers
Sam Jarada – The Public Trust
The Diagnostic and Statistical Manual of Mental Disorders (DSM) outlines how millions of people are studied, diagnosed and treated. However, distress, trauma and grief, for example, which fall outside Western descriptions of depression or PTSD, are ignored, misunderstood or misclassified.
Therefore, many of these guidelines don’t “fit” in Global South countries, which refers to those considered “lower” income, like in Africa or Asia, compared to European or North American countries.
The way mental health can be misdiagnosed in the Global South isn’t due to false data. It’s due to the Eurocentric/Western frameworks imposed on these communities, termed data colonialism, so the underlying local perspectives of the Global South are often sidelined to preserve foreign priorities because of how knowledge is dictated and defined [1,2].
So, who does data really serve? And how do we ensure data helps the people it describes?
How Colonialism Shapes Data Systems
Colonialism didn’t end with political independence. Its legacy morphed into priorities, tools and systems governing modern global health data. These systems were created to classify, control and take knowledge, serving colonial states and shaping data collection, ownership and usage in the Global South [3,4].
- Erasing local knowledge with artificial categories
Colonial states, namely the UK and France, imposed broad frameworks to classify health conditions, infectious diseases, and social realities, usually without modifying them to specific Global South communities. Unfortunately, these frameworks continue to exist in global health metrics such as the DSM [1,2] and the International Classification of Diseases (ICD) of the World Health Organisation [5].
These categories from the DSM and the ICD influence funding, policy and intervention within the Global South, and that poses complications. They aren’t necessarily wrong, but they’re incomplete. For instance, foreign healthcare workers in the Global South may misclassify symptoms for diseases because local frameworks were erased during the colonial period for understanding health [6]
- Data ownership and collection
Colonialism was about resource extraction. Now it’s about data/information/who defines information. In the Global South, health data is usually collected by foreign non-governmental organisations (NGOs) and international organisations and researchers with scarce benefits for the local communities they took data from [7].
Funders like the World Bank tend to prioritise data matching their agenda, such as vaccination rates, over local communities' needs, like access to mental health. Prior research, like the scoping review by Huffstetler et al., highlights how donor shifts and transitions in funding priorities from long-term community-focused health systems to short-term, high-impact interventions can exacerbate gaps in community needs [8]. This creates a distorted view of what’s measured, neglecting people’s needs and misallocating resources.
- Data as a tool for power and “neutrality”
Global health data systems are broadly seen as apolitical, neutral and objective, but that’s not true. Data is always shaped by power. Colonialism defined what’s viewed as “valid” knowledge. The ICD and Global Burden of Disease frameworks are the global standards, yet they’re rooted in Western perspectives [9,10].
In Global South countries, for example, maternal health data focuses on mortality and birth rates but overlooks broader factors like poverty and access to clinics. When compared to how census data in colonial eras was used for tracking and controlling populations, health data is utilised for justifying interventions, whether it’s upgrading hospitals via foreign funds or programmes reliant on donors. However, it rarely addresses structural problems causing health crises.
Sudan: A case study on Foreign “Experts” Erasing Local Wisdom
Sudan’s health data currently represents global inequities where colonial legacy, donor-driven agendas, and the influence of foreign "experts" erase local community knowledge and distort their needs. Administrators utilised health records, disease surveillance and census data to control and monitor populations rather than to improve public health [11].
As a result, their health information system still focuses on demographic tracking, such as disease prevalence, over community-centred health outcomes. After Sudan declared independence in 1956, its health data systems became more linked to donor priorities and foreign aid. For example, a 2020 report driven by donor input had broader health metrics, coinciding more with global health goals than the needs of the Sudanese population [12].
Consultancies, foreign researchers and NGOs usually collect and analyse health data with scarce input from local institutions or communities in Sudan [12,13]. For example, a 2020 report from the World Health Organisation highlighted gaps in governance and evidence use, with research managed by actors with poor coordination, limited dissemination and lack of evidence use in national decision‑making [12].
NGOs like Médecins Sans Frontières (MSF) collect substantial data in Sudan, but the majority of it isn’t always shared with local organisations [14]. This gatekeeping prevents local action and analysis, as well as potentially corroborates that Sudanese institutions are “unreliable.”
Sudan has a vibrant tradition of local health knowledge, where traditional healers are vital in many communities. They offer diagnostic and therapeutic services deeply rooted in cultural practices [14,15]. Unfortunately, these traditional healing systems are overlooked by medical practitioners and formal health services in favour of foreign models and experts [15,16].
They’re seen with suspicion, excluded from policy and planning, and forced to work on the fringes of care. Similar trends are seen among community midwives and other lay health workers, notably in conflict-affected areas in Sudan, like the Nuba Mountains. They offer most of the available maternal and reproductive care while facing low pay and a lack of recognition compared to employed professionals [15,16].
Solution: Community-Driven Data Sovereignty
The dominance of foreign frameworks, exploitative practices and sidelining local knowledge in global health data isn’t something we must accept. It’s our choice. To truly decolonise health data, we need to shift the power dynamics from institutions to communities, moving from control to collaboration, and from extraction to reciprocity. We need practical steps to foster community-driven data sovereignty, a model where the data is owned, governed, and utilised by the very people it represents.
We need to rethink who truly owns this information. Instead of viewing data as another commodity, we should start seeing data as a collective resource, managed by the communities themselves for their own advantage. This requires structural changes in how data is funded, designed, and used.
For community-driven data sovereignty to truly thrive, we need alternative systems that put local needs, knowledge, and accountability front and centre. They must be not only scalable and sustainable but flexible enough to fit various contexts. After all, data sovereignty goes beyond collecting and owning data. It’s ensuring ethical and effective data usage. Communities must have the tools they need to hold institutions accountable and to champion their own priorities.
Conclusion
Health data isn’t just a collection of numbers; it tells a story about who’s in charge. For years, colonial systems have influenced how we gather, own, and utilise health data, often sidelining local insights and prioritising external agendas. This is what we call data colonialism: a framework where those in power define the narrative, and the act of extraction is disguised as progress.
However, alternatives exist. Community-driven data sovereignty is one approach, where the data belongs to, is managed by, and is utilised by the very people represented. These models demonstrate that a different path is not only possible but necessary. The movement to decolonise health data is fundamentally about shifting power dynamics.
Sources
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