Despite increased prominence and funding of global health initiatives, attempts to scale up health services in developing countries are failing, with serious implications for achieving the Millennium Development Goals. A new paper argues that a key first step is to get a more realistic understanding of health systems, using the lens of complex adaptive systems.
Much ongoing work in development and humanitarian aid is based on the idea of ‘scaling up’ effective solutions. Healthcare is one of the areas where this idea has played a central role – from WHO’s Health for All in the 1960s to UNICEF’s child healthcare programmes, from rolling out HIV-AIDS, malaria and TB treatments to the package of interventions delivered to achieve the Millennium Development Goal on health.
However, despite the fact there are many cost-effective solutions to health problems faced in developing countries, many agencies are still frustrated in their attempts to deliver them at scale. This may be because of a widepread failure to understand the nature of health systems.
Melissa Leach, director of the superb STEPS Centre, has described health systems as:
complex systems made up of networks of many heterogeneous components that interact non-linearly. While pathways of change can be shaped by governance and are influenced by path dependencies, they are not entirely controllable or predictable; there will always be uncertainties and unintended consequences and new ‘emergent’ interactions and behaviours.’
If we accept this eminently sensible description, then it is little wonder that scaling up efforts continue to be frustrated. The paper by Ligia Paina and David H Peters, published in Health Policy and Planning in August, argues that there is a drastic need for a shift in thinking:
…from the current models around scaling up health services, which revolve around linear, predictable processes, to models that embrace uncertainty, non-linear processes, the uniqueness of local context and emergent characteristics.”
Their argument is supported by the fact that existing assumptions about the nature and hoped-for successes of scaling up have led to a lot of disappointments. Moreover, these efforts ‘offer little insight on how to scale up effective interventions in the future.’
The paper explores 5 concepts of complexity science, illustrated below.
The implications include paying more attention to local context, incentives and institutions, as well as anticipating certain types of unintended consequences that can undermine scaling up efforts, and developing and implementing programmes that engage key actors through transparent use of data for ongoing problem-solving and adaptation.”
The authors close with a proposal that future efforts to scale up should adapt and apply complex systems models and methodologies which have been used in other fields but which remain underused in public health. These include network scinece approaches, modelling techniques, and tools to better understand systems dynamics.
The potential benefits are clearly stated:
This can help policy makers, planners, implementers and researchers to explore different and innovative approaches for reaching populations in need with effective, equitable and efficient health services.”
These ideas are already being applied in practice, The authors are involved in a capacity strengthening programme on complexity and health systems in China. Separately, the WHO has published a guide to using systems analysis in health systems strengthening, which builds on a number of the concept described above.
These are all fascinating developments, and suggest that health may be a key area where the ideas from complexity science can prove of tangible value for development and humanitarian work.
Interested readers can hear an podcast about the article here, with David Peters talking about his ideas and experiences (and me saying a few words about the history of complexity science and the relevance for health efforts.) David has also blogged about it here.