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Reflections from four experts on the role of epidemiological and macroeconomic modelling at the Health Economics Study Group meeting

Earlier this month, the Centre for Health Economics in London (CHIL) hosted the Winter 2021 Health Economists’ Study Group meeting online. The opening session discussed the joining up of health and economic evidence to support decision-makers across the world. Discussants included Peter Piot, John Edmunds, Edwine Barasa and Gesine Meyer-Rath. We compiled a small summary of points and share the video.

Earlier this month, the Centre for Health Economics in London (CHIL) hosted the Winter 2021 Health Economists’ Study Group meeting online. A recording of the opening session featuring Peter Piot, John Edmunds, Edwine Barasa and Gesine Meyer-Rath can be found here:

 

For those who don’t have time to watch the full video (although we highly encourage you to!), we pulled some interesting take-aways from the speakers:
  • Use cases of health economics on C19 are many: optimally allocate resources, informing how to strengthen health systems, helping to plan for sustainable financing (fiscal space), looking at healthcare costs on households, planning (budget impact analysis), looking at distribution/equity
  • Collaboration between epi and macroeconomic modellers means we can help each other: for instance, economists think a lot about counterfactuals and how we should consider different courses of action which will be essential for considering different response options
  • C19 is a pandemic: it won’t be over until it is over everywhere: we need working across countries and communities
  • Economic and health advice are often set up in opposition – in the UK, the two sources of evidence come from different bodies that inform the government through parallel channels. However, they don’t need to be adversarial
  • There are many feedback loops between the two disciplines: ill-health means costs for health systems, but also unemployment, low productivity, changes to individual behaviour; on the other hand, poverty, unemployment, economic insecurity have negative impacts on health
  • In this pandemic, we have to adapt and use health frameworks that take into account societal impacts
  • We are in the ‘acute’ phase of the pandemic, but afterwards, there will be a gradual change to making C19 part of a more ‘normal’ part of health decision-making: health economics will support resource allocation then too
  • In the longer-term, there will still be many huge questions such as how to allocate healthcare budget, prevention vs treatment, distributional considerations (inequality: ethnic, intergenerational, wealth), investments
  • We need context-relevant policy selection: in SSA, socio-econ-demographic context and the C19 pandemic characteristics are different. Countries can’t follow a ‘model path’ (e.g. Kenya is very different on all counts from the UK)
  • A lot of SSA countries have implemented very stringent policies – the economic, direct and indirect health impacts will be much greater in the region so we need evidence that joins up health and economics
  • Fiscal space and budget impact analyses is another area where health economics can help LMICs: e.g. setting testing targets in Kenya at the UK level (tests/million) would absorb close to half of the total health budget
  • LMIC governments need analytics/evidence to support decisions about whether to strengthen critical care or essential care? E.g. should LMICs, where ICU capacity is low, also follow the path of high income countries by strengthening critical care? Or should they focus on essential care? Evidence shows that in Kenya, the bulk of patients needing care are those with several disease, not critical disease
  • We also need to contextualise C19 in the wider health system: a study in Republic of South Africa (RSA) shows that the cost-effectiveness of intensive care for hospitalised C19 patients is two times the cost-effectiveness threshold in the country
  • In RSA, evidence on resource quantification (e.g. number of beds, ventilators, drug forecasting) supporting the C19 response planning in the country; also supported testing planning (through developing testing algorithms) and resource projections
  • In RSA, this evidence has been ‘unusually’ used by policy-makers: for instance, by the Ministerial advisory committee on COVID (working with Minister of Health directly) and Treasury (to inform the C19 health budget)
  • We need to be clear about the known, unknown and unknowable and to learn to deal with uncertainty in the pandemic