Improving the Measurement of Financial Protection in Health Systems

1. Improving the Measurement of Financial Protection in Health Systems Dr Rodrigo Moreno-Serra Centre for Health Policy, Imperial College London r.moreno-serra@imperial.ac.uk PCPH, Imperial College London, 5th October 2011

2. Background • Financial protection (FP): extent to which people are protected from the financial consequences of illness • Key objective of health care (HC) systems, multidimensional • Financial hardship and lack of access to HC due to costs still widespread (WHR 2010) • FP may suffer in a context of economic downturn • Monitoring FP is crucial for sound health policy

3. FP measurement: where are we? • Focus on households’ living standards before and after direct payments for health (OOPs) • OOPs reported in household surveys Catastrophic spending • OOPs cross set threshold in terms of share of disposable income Impoverishing spending • OOPs push household income below a chosen poverty line

4. FP metrics: criticisms • Measurement of capacity to pay, effects of lost income etc... • Effect of financial barriers to access: the elephant in the room • Ability to pay may deter access to necessary HC • Linked to equity but indicator of FP extent • Sole focus on incurred spending may provide misleading picture of FP

5. Catastrophic spending incidence and DTP3 immunization coverage among 1 year-olds, 87 countries (various years) Source: Immunization data from WHO. Catastrophic spending incidence data from Xu et al. (2007). Financial catastrophe is defined as OOPs for health reaching at least 40% of a household’s non-subsistence income.

6. Financial barriers to access in high-income countries with low incidence of financial catastrophe Source: IHPS, Commonwealth Fund (Schoen et al. 2010).

7. Financial Protection Measures: Suggested Areas for Development

8. I. Complementing conventional FP indicators • Coverage indicators • WHR 2010 • Generally feasible route • But often limited information available • Role of various other determinants of coverage levels • Access surveys • E.g., IHPS (Commonwealth Fund), World Health Surveys (WHO), LSMS (World Bank) • Need implementation on routine and comparable basis

9. II. Improving conventional FP indicators • ‘Need-adjusted’ FP metrics • Estimate expected utilization and OOPs according to ‘medical need’ characteristics • Adjust catastrophic and impoverishing spending incidence (expected incidence) • May yield very different policy conclusions from conventional analysis (e.g., Pradhan and Prescott 2002) • But methodologically challenging

10. III. An exploratory tool: Data Envelopment Analysis (DEA) • Based on economic concept of production frontier • Through linear programming, find units that achieve same (or better) outputs at lower use of inputs • Efficiency = actual/optimal performance (OQA/OQ1) • Can examine efficiency based on multiple outputs (e.g., FP indicators) and inputs

11. DEA applied to FP assessment • Question: How do developing countries compare concerning efficiency in ‘producing’ FP given available resources (constraints)? • Criteria for efficiency analysis: FP indicators relative to total health spending (THE) per capita (input orientation) • Gets at the issue of achievable FP performance

12. DEA applied to FP assessment

13. DEA applied to FP assessment

14. Concluding remarks • Financial barriers: distorting effects on conventional FP assessments • Despite recent progress, we need better FP metrics for: • Policy guidance • International performance comparisons • Huge potential gains from a health policy perspective