HFACT research comprises the analysis of existing data, modelling, primary data collection and policy evaluation, shared across four cross-cutting methodological areas. These methods areas are:
- Requires counterfactural analysis, e.g. outcomes in a world with the policy v. outcomes in the world without the policy
- Innovative quantitative methods to describe trends and associations
- Data visualization (heatmaps)
- Concentration indices, decomposition analyses
- Characterizing importance of predictors for health outcomes
- (Causal) policy impact evaluations using aggregate data
- Dynamic panel data approaches
- Differences-in-differences
- Synthetic controls
- (Causal) impact evaluation methods with micro data
- Matching methods to create comparison group
- Instrumental variables and regression discontinuity designs
- Methods to estimate heterogenous policy effects
- Quantile regressions
- Causal machine learning
Measuring the distribution of health financing - Estimating the marginal change in health inequalities from a marginal change in expenditure on health
- Estimating the impact of policy on health inequalities, incorporating these equity impacts in economic evaluation to inform policy choice
- Spending to improve population health
- Increased interest and research in estimating the marginal productivity of health expenditure
- Policy objectives to improve population health often involves both overall increase in population health and reduction in health inequalities
- Economic evaluation of policies can inform the prioritization of investments related to health financing
- Equity informative economic evaluation extends the analysis to estimate the distribution of policy costs and benefits
- Potential applications
- Health benefit package design to reflect equity objective
- Distributional impact of raising health funds through taxation on unhealthy commodities and removal of subsidies
- Value of investing to level up access and utilisation in left behind groups
Key features- Outcomes of relevance to the decision-makers involved, based on their objectives and responsibilities
- Compare intervention against relevant alternative courses of action
- Capture outcomes and opportunity costs
- Consideration of decision uncertainty – is more evidence needed?
- What can economic evaluation and decision analysis contribute?
- When evaluating health care policies, these approaches can help extend analyses to establish whether a policy represents value for money by considering impacts on:
- the policy relevant outcomes of interest,
- the costs and associated opportunity costs,
- the magnitude and consequences of uncertainty
- Further method development is necessary but a multi-disciplinary approach bringing together health economics and adjacent fields will improve the evaluation of policies
- When evaluating health care policies, these approaches can help extend analyses to establish whether a policy represents value for money by considering impacts on:
Political agents and processes are central to public policy but tend to remain as an ‘add-on’ for research.- Political Economy Analysis (PEA) can significantly contribute to explain the ‘whys’ and ‘hows’ of policy impacts and their determinants
- Tool kit for PEA includes:
- Role/impacts of power,
- Interests, capacities and incentives across key groups
- Account for different political and institutional systems:
- Political parties & leadership
- Bureaucracy & governance
- Social, ethnic and ideological affiliations
- Useful for retrospective analysis of past policies and likely roadmap for current and future policies
- Tool kit for PEA includes:
