As so many health systems move towards social health insurance, the issue of who should receive this at no or low cost is becoming increasingly important. The topic can quickly become highly technical and move well beyond most health practitioners’ core skills – comparing different ‘proxies’ and formulae for assessing wealth and income, as well as methods for collecting and verifying this data.
For this reason, there can be a temptation to disengage, and adopt whatever data are already available, regardless of their quality or appropriateness for the task. This is especially the case in newly-established coverage schemes, as there are so many other components to design and implement at the same time.
Yet at its core, so called ‘population targeting’ is not a technical task, but one that is foundational to public trust in the health system. If people do not believe that the funding for these health reforms are well spent – that they help those who are unable to help themselves, while not unduly benefiting some groups over others, or being gamed – they will not sustain a popular mandate to continue and expand. Nor will they produce the social solidarity that is at the heart of the best universal health coverage systems.
For this reason, improving the identification of poor and vulnerable people is becoming an increasing priority for many countries working towards UHC. Over the past year, colleagues from 11 JLN member countries have participated in a learning collaborative on how they can improve their approaches, with two of the countries – Ghana and Liberia – undertaking ‘live’ implementation projects with the support of these peers.
The resulting work is released this week as both a Handbook to help understand where improvements are needed, and an Implementation Toolbox of curated resources to make these changes happen.
The overriding message of this learning is that health should not ‘go it alone’ in the pursuit of more efficient and effective ways of identifying the poor – but neither should it be a passive recipient of external targeting data.
By far the most important priority for the participants was to improve how they worked with the many other agencies and social programs who also need to target the poor and vulnerable: childcare and school programs, housing and food benefits, national ID and electoral registers, and many others. Many of these sectors are well ahead of health in terms of the knowledge and systems available for population targeting, and it would be wasteful for health to set up its own separate systems that duplicated these.
At the same time, as more countries roll out integrated social registries – centralized, cross-government databases of poor and vulnerable households from which all agencies can draw down the data they need – it is important that health not does lose its voice. UHC programs typically cover a much wider proportion of the population than, say, cash transfer schemes, and there may be other groups who health wish to target. Furthermore, positioning health as a passive recipient of targeting data misses the value that the health sector can provide in terms of scale and reach as a contributor to accurate and up-to-date targeting databases, with its immense network of clinics, offices, community health workers and hospitals. This benefits other sectors, who have not always felt that their health colleagues have sufficiently engaged with these national targeting efforts.
The ultimate call to health leaders is to engage with their colleagues across government, and with social protection in particular, while having a clear understanding of the model of inter-agency working that will be best for health. The ‘nuts and bolts’ of this are the main focus of the handbook and implementation toolbox, which for the first time offer a set of resources specifically curated for health practitioners to help them better coordinate with targeting initiatives across government, as well as linking their health datasets with those of others.