Funding equitable data science for better health in Africa
This article was co-authored by Grace Annan-Callcott & Ekin Bolukbasi
We’ll start by being honest with you… the title to our blog is a little misleading. This is not a ‘How to’ guide and we don’t have all the answers about how to fund health data science on the African continent.
What we do know, working in the Data for Science and Health team at Wellcome, is that as a global health funder we will need to consider both what and how we fund, to support data science for better health. We also know that there won’t be just one approach — but many, and that they will need to be codesigned with researchers, data scientists and broader community stakeholders.
So, we went to the DS-I Africa community’s annual meeting and ran a workshop to discuss what funders need to consider when supporting health data science on the continent. DS-I Africa is a grant program funded by the National Institutes of Health, to create and support a pan-continental network of data scientists and technologies. We’re sharing what we heard from the DS-I Africa community for other global health funders also working in this space.
Understand what exists already: datasets, data scientists and data platforms
The first step is to understand what exists and where the gaps are. What datasets and data platforms are in use already? What data science training is already provided, and for what career stages? Getting an accurate picture will require funders to go beyond their existing networks, and listen to researchers, policy makers and public health professionals who work in and understand the context. And it will vary hugely across different countries, regions, cities, universities, research institutes, academic disciplines and so on. With a reasonable understanding of the context, funders can then tailor their approach. We heard some specific ideas from members of the DS-I Africa community about how to fund data science in a way that is more responsive to the needs of research communities. These include:
Support the art of ‘ideation’: data scientists on the continent face a number of barriers to getting the funding they need for new or early ideas. Co-producing grants, funding smaller scale projects and testing ‘experimental’ funding processes for data science could help support early ideas. Impact isn’t always proportional to the amount of funding — small catalytic grants, or funding a few skilled and driven teams, can have a big impact further down the line.
Be intentional in expanding beyond existing ‘hubs’ of funding for data science: specific institutions and cities tend to develop as hubs for data science skills and infrastructure. These places then become ‘centres of gravity’ for funding, which means that data scientists, datasets and data infrastructure tend to be concentrated in a few fields or locations. Funders need to intentionally reach out beyond those hubs, for example by funding work that pairs recognised data science institutions with lesser-known institutions or individuals with potential.
Encourage flexibility and collaboration across existing initiatives: mandating that there’s an African lead institution during a funding call can help improve representation, but can also provide barriers. Pairing an African institution with a high-income based institution can help with administrative burden but must ensure that benefits are not solely felt by the HIC institution. Events like data hackathons or data prizes can facilitate partnerships and “matchmaking” between different sectors, geographies and experience levels. This DS-I Africa project is a nice example of pairing institutions to build research capacity.
Recognise how fundamental foundational infrastructure is
Funding models often focus on answering specific research questions, related to the organisation’s strategy and priorities. But funders also need to consider contributing to the creation and maintenance of the foundational data infrastructure that research depends on. The impact of this kind of work may be longer term, and less easy to track back to a specific grant or individual funder. But it can be crucial to future of research. Specifically, the DS-I Africa community mentioned funders should look to:
Improve infrastructure for data collection and processing: this must consider the needs of the community the data is coming from, especially for data involving marginalised or historically excluded groups. Funding for digital tools, computing infrastructure, diverse data collection and the dissemination of outcomes can all make significant impact. eLwazi is a brilliant example of this from the DS-I Africa consortium itself: it’s an open data science platform for teams to find and access data, select tools and workflows and run analyses on a variety of computing environments.
Support data harmonisation and standardisation: non-standardized data within and across countries, and the lack of standards or common frameworks for interoperability and reuse of data, are a barrier to robust data science. Initiatives that support longitudinal, population-based, harmonized and standardized data, which can be linked to other relevant sources, can have important long-term impacts. INSPIRE network was mentioned a few times as a great example of this.
Facilitate robust governance and ethical oversight: The regulatory environment around data sharing and data protection across different countries varies. This can be a significant blocker when supporting the development of robust governance structures for ethical sharing and oversight across countries. Getting this right is critical in order to deliver trustworthy systems for impactful results. To give an example from DS-I Africa, this project will develop evidence-based, context specific guidance for the governance of data science initiatives in sub-Saharan Africa.
Put communities at the centre of research design
The data scientists and researchers we heard from at DS-I Africa cared deeply about making sure their work made a difference to the communities they work in. They want funders to support them to do so. The first step is being clear on who ‘the community’ is, which means asking questions like: is it population level or specific groups? Who is most affected by the collection and use of data? Who’s most at risk? Who will actually benefit? Once these kinds of questions are clear then funders and researchers can start to make sure their needs are prioritised through the research process. Within this context, the community at DS-I Africa suggested that funders should:
Support engagement to understand concerns and hesitations that researchers may be facing: health data can feel sensitive and personal, and people may understandably be hesitant about data being used for research, particularly if there are concerns about commercial and governmental agendas. Funding to support comprehensive public engagement work in the data and health space, and in a region-specific manner, is crucial to understanding anxieties and concerns and to know how to address them. Researchers need time and resources to plan and deliver public engagement work, which funders can help with. A number of the DS-I Africa projects have a strong focus on engagement — this project, for example, will explore public attitudes to big data and genomic medicine and develop frameworks to support future engagement.
Reach out beyond existing contacts and networks: working in collaboration with local leaders and civil society representatives to co-develop study protocols or interventions can help reach new audiences — including those reached less frequently. Connecting with new audiences may also require using a range of communication channels and innovative methods — which requires funding to design and implement. For example, in another session at DS-I Africa we heard of a team who had run a fashion show that facilitated engagement with data on gender-based violence.
Ensure there is mutual benefit: there’s a role for funders to incentivise dissemination of the impact of research back to the community. This must go well beyond just publishing papers — and needs to be built into the research process from the outset. An great example from DS-I Africa, is the UZIMA Data Science Hub — at the centre of the Hub’s strategy engaging with key stakeholders to ‘identify pathways for dissemination and sustainability’ of the AI models the team develops.
Final thoughts
It was brilliant to join the DS-I Africa community and talk about these complex and knotty issues — huge thanks to everyone who attended our workshop. Take a look at the DS-I Africa website to learn more about the great work the teams are doing.
We’re already using what we heard to inform the design of a new initiative for data science in mental health on the African continent — more on that in the summer. More broadly, Wellcome is also trying different approaches to support more researchers from low- and middle-income countries apply for funding.
If you’re a funder working through these ideas too or a researcher with ideas related to what we’ve covered in this blog — continue the conversation with the Data for Science and Health team at Wellcome by writing to us at ContactDataForScienceAndHealth@wellcome.org.
