Person:

Norheim, Ole

Priority setting is inevitable on the path towards universal health coverage. All countries experience a gap between their population’s health needs and what is economically feasible for governments to provide. Can priority setting ever be fair and ethically acceptable? Fairness requires that unmet health needs be addressed, but in a fair order. Three criteria for priority setting are widely accepted among ethicists: cost-effectiveness, priority to the worse-off, and financial risk protection. Thus, a fair health system will expand coverage for cost-effective services and give extra priority to those benefiting the worse-off, whilst at the same time providing high financial risk protection. It is considered unacceptable to treat people differently according to their gender, race, ethnicity, religion, sexual orientation, social status, or place of residence. Inequalities in health outcomes associated with such personal characteristics are therefore unfair and should be minimized. This commentary also discusses a third group of contested criteria, including rare diseases, small health benefits, age, and personal responsibility for health, subsequently rejecting them. In conclusion, countries need to agree on criteria and establish transparent and fair priority setting processes.

Background: This study aims at quantifying the level and changes over time of inequality in age-specific mortality and life expectancy between the 19 Norwegian counties from 1980 to 2014. Methods: Data on population and mortality by county was obtained from Statistics Norway for 1980–2014. Life expectancy and age-specific mortality rates (0–4, 5–49 and 50–69 age groups) were estimated by year and county. Geographic inequality was described by the absolute Gini index annually. Results: Life expectancy in Norway has increased from 75.6 to 82.0 years, and the risk of death before the age of 70 has decreased from 26 to 14% from 1980 to 2014. The absolute Gini index decreased over the period 1980 to 2014 from 0.43 to 0.32 for life expectancy, from 0.012 to 0.0057 for the age group 50–69 years, from 0.0038 to 0.0022 for the age group 5–49 years, and from 0.0009 to 0.0006 for the age group 0–4 years. It will take between 2 and 32 years (national average 7 years) until the counties catch up with the life expectancy in the best performing county if their annual rates of increase remain unchanged. Conclusion: Using the absolute Gini index as a metric for monitoring changes in geographic inequality over time may be a valuable tool for informing public health policies. The absolute inequality in mortality and life expectancy between Norwegian counties has decreased from 1980 to 2014. Electronic supplementary material The online version of this article (10.1186/s12939-018-0771-7) contains supplementary material, which is available to authorized users.

Background: Setting Millennium Development Goals and Sustainable Development Goals for health has largely focused on defining specific targets of mortality and morbidity reduction over given time periods. Yet, less attention has been devoted to setting targets for the systemic determinants of health delivery, such as access and financial risk protection (FRP)—prevention of medical impoverishment. We examined candidate targets for FRP among low and middle-income countries by 2040. Methods: We used a data set on estimates of incidence of catastrophic health expenditure (CHE)—medical expenditure exceeding 40% of household capacity to pay—among 110 countries over 1995–2007, augmented by estimates of the percentage of out-of-pocket expenditure out of total health expenditure (OOPEXP), the share of health expenditure as a percentage of gross domestic product (HEXGDP) and the gross domestic product per capita (GDPC). Using a simple model and 2040 estimates for OOPEXP, HEXGDP and GDPC from the World Bank, the International Monetary Fund and the Institute for Health Metrics and Evaluation, we projected CHE incidence by 2040 for four country income groups. Results: We predicted that the 2040 incidence of CHE among households would be: 2.13% (Uncertainty interval: 0.60-6.87) among low-income countries, 1.15% (0.32–3.81) among lower-middle-income countries and 0.65% (0.18–2.21) among upper-middle-income countries. By 2040, the probability of achieving CHE <1.00% would be: 0.1 for low-income countries, 0.4 for lower-middle-income countries and 0.7 for upper-middle-income countries; for CHE <0.50%, it would be 0 for low-income countries, 0.1 for lower-middle-income countries and 0.3 for upper-middle-income countries. Conclusions: Historical trends of CHE rates can help define post-2015 targets for FRP. The projected achievements suggest that elimination of medical impoverishment will not be achieved by 2040 and that countries must urgently enact dramatic changes in policy to ensure FRP to their populations.