An Alternative Approach to Measuring Health Inequality in Norway and Implications for Municipal Priority Setting

Abstract

Background: The health state of a population can be measured in different ways. Common measures are life expectancy at birth or quality-adjusted life expectancy. However, the distribution of health may also be of interest to inform priority setting in health. Whereas there is a growing literature on the social distribution of health, knowledge of pure health inequalities may be useful to inform fair and equitable priority setting in both specialist and municipal health services. Method: We aimed to investigate the distribution of life years and quality-adjusted life years in Norway using recent mortality tables and population norms for health-related quality of life at different ages. We propose and contrast two alternative methods for estimating what we term expected quality-adjusted lifespans – the amount of quality-adjusted life years an individual who dies at a certain age will have achieved through their life. The first method assumes a relationship between increasing chronological age and decreasing health-related quality of life, and the second assumes that decreasing health-related quality of life is correlated with shorter remaining lifespan (time-to-death). The first method is similar to established methods to estimate quality-adjusted life expectancy, whereas the second is a novel approach. We estimate average, expected quality-adjusted lifespans in quintiles across a cohort of newborns in Norway in 2022. We further provide quality-adjusted life expectancies by age and sex. Results: Our results show that the gap in unweighted lifespans between individuals in the highest and lowest quintile is more than 30 years, whereas the gap in expected quality-adjusted lifespans is between 25 and 27 quality-adjusted life years, depending on the method used. We find that the time-to-death model yields lower estimates of expected quality-adjusted lifespans in the lower end of the distribution and higher estimates in the higher end, and thus higher inequality, compared to the chronological age model. Conclusion: Our estimates show that the expected distribution of both unweighted and quality-adjusted lifespans in Norway is uneven. While our results may be of relevance across all levels of priority setting in health, we argue that our study may be of particular interest to policy makers who wish to take distributive effects of priority setting in the municipal health sector into account. We argue that priority setting in the municipal health services should give higher priority to individuals and groups who are at risk of falling into the lower end of the expected quality-adjusted lifespan distribution. Examples include addiction and mental illness, severe disability, and chronic illness with early onset. We invite future research on this topic.

Keywords

• Inequalities in health
• quality-adjusted life years
• priority setting in health