During the 2011 World Conference on Social Determinants of Health, the Rio Political Declaration on Social Determinants of Health was adopted. The declaration expressed a global political commitment for the implementation of a social determinants of health (SDH) approach to reduce health inequities. Social determinants of health are defined by the World Health Organisation (WHO) as the conditions in which people are born, grow up, live, work and age. These conditions influence a person’s opportunity to be healthy, risk of illness and life expectancy. Social inequities in health – the unfair and avoidable differences in health status across groups in society – are those that result from the uneven distribution of social determinants. All of these drive health inequity – systematic disparities in health between social groups who have different levels of underlying social advantage or disadvantage such as food, shelter, clean water, sanitation, proper clothing and have limited access to medical care, education and finance.
Video: Dr Hans Rosling’s 200 Countries, 200 Years, 4 Minutes – use of data to visualize social determinants of health across the globe.
Health inequity poses a major obstacle to improving population health and wellbeing in African countries, accordingly, approaches to improve population health must include identifying and addressing these inequities. In striving to improve health through addressing SDH, there is often a lack of population health data, against which one can measure the health impact of interventions and simultaneously there are insufficient disaggregated data beyond region and country level to facilitate policy interventions that take into account social determinants. The dual challenge of lack of basic population data about disease and an absence of information about what impact, if any, interventions involving social determinants of health is having, is further exasperated by situation where even when population data is available through surveys for example, methodologies are often criticised for exhibiting different degrees of accuracy, simply put, lack of completeness, validity, and reliability of data means that researchers often don’t know who is sick or what people are being exposed to that, if addressed, could prevent disease and improve health.
Finding solutions to social problems are often inseparable from the statistics. A good example of this comes from Kenya’s Ministry of Health, which in 2013 were faced with a worrying rate of women dying in childbirth. Data showed that more than a third of women were giving birth at home and that many of them were not accessing health facilities because of distance and financial costs. The answer to the question of whether women were choosing home birth because of cultural values or access became clear when data was collected and analyzed, the problem was with access, the solution was to remove the financial barrier through policy by making maternity services free in all public health facilities in Kenya.
Evaluating data can demonstrate and visualise the link between variations in health outcomes, level of poverty and income inequality, within and in-between different countries. However, as noted by Eshetu and Woldesenbet (2011), only by identifying causal factors and root causes at continental, country level and between population groups, can policy options be prioritised to effectively improve the delivery of basic health services and the efficient deployment of often scarce resources.
Where are the data gaps?
Lack of high-quality, policy-relevant and actionable data on inequalities within populations often means that development solutions seldom focus on the people who need them most, making it extra challenging to meet development framework goals, including the Sustainable Development Goals, where nations are tasked with achieving universal health coverage and providing access to safe and affordable medicines and vaccines for all by 2030.
The Centre for Global Development joint working group, the Data for African Development Working Group, posed the question of how to address and improve data in Africa so Governments, international institutions, and donors can better target development efforts. In their 2014 report, they identified broad recommendation that ranged from building quality control mechanisms into data collection to improve accuracy to encouraging open data based on international metadata standards to tackle the underlying problems surrounding the production, analysis, and use of basic data that have inhibited progress in Africa’s data revolution.
While data generated by state institutions can at times be influenced by political and interest groups, INGO’s, donors and private sector also generate large volumes of data on various projects. Indeed, a cursory search on the internet identifies a number of platforms that are already compiling and sharing data for international development research (African Development Bank Group, Our World in Data, Development Initiatives). It is perhaps an important objective going forward that these key stakeholders work together where there are data gaps to ensure better information sharing and coordination.
While there have been gains in the frequency and quantity of censuses and household surveys over the past 30 years or so, the focus has become too engrossed on collecting more and not necessarily better or collaborative data (Taylor et al., 2014). While the need for more and better quality data is essential, we should also be equally focused on unlocking capacity to build standards that enable collaboration, scale solutions and find innovative ways to build the required data skills that will enable the spread of data benefits around the African continent.
The good news is that there is now more and more initiative that are bringing together various sectors and countries to work together to create shared networks and libraries of indicators (for example Resource Watch, Kwantu, Health Data Collaborative, Open Data for Development to name a few). These digital information communities are allowing more and more people and organisations to come together to share, compare, analyse and discuss the production and use of data to drive better decision making on sustainable development and proactive approach to healthcare.
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- ECOSOC (2009, June). ehealth: Use of Information And Communication Technology In Health. Background paper presented at the Africa Regional Ministerial Meeting on ehealth, Accra, Ghana. Retrieved from http://www.un.org/en/ecosoc/newfunct/pdf/ghanabackgroundnote.pdf
- Eshetu, W. and Woldesenbet, S. (2011). Are There Particular Social Determinants of Health for The World’s Poorest Countries? African Health Sciences, 11(1): 108–115.
- Roser, M. (2016). Health Inequality. OurWorldInData.org [Online Resource]. Retrieved from: https://ourworldindata.org/health-inequality/
- Taylor, L. and Schroeder, R. (2014). Is Bigger Better? The Emergence of Big Data as A Tool for International Development Policy, GeoJournal, 80: 503–518.
- Worku, E.B. and Woldesenbet, S.A. (2015). Poverty and Inequality – But of What – As Social Determinants of Health in Africa? African Health Sciences, 15(4): 1330–1338. Retrieved from http://doi.org/10.4314/ahs.v15i4.36