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Share of Household Income, 2010-2019
– Bottom 40%: 21%
– Top 20%: 38%
– Bottom 20%: 8%
Gini Coefficient, 2010-2019: 28
Palma Index of Income Inequality, 2010-2019: 1.0

Note: Gini coefficient – Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.
Palma index of income inequality – Palma index is defined as the ratio of the richest 10% of the population’s share of gross national income divided by the poorest 40%’s share.

Source: United Nations Children’s Fund, The State of the World’s Children 2023: For every child, vaccination, UNICEF Innocenti – Global Office of Research and Foresight, Florence, April 2023.

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Price levels in the healthcare sector, 2021 (OECD average = 100): 125
Population reporting unmet needs for medical care, by income level, 2021
– Lowest quintile: 1.7%
– Highest quintile: 0.6%
– Total: 1.2%
Population reporting unmet needs for dental care, by income level, 2021
– Lowest quintile: 4.9%
– Highest quintile: 0.4%
– Total: 1.8%

Source: OECD (2023), Health at a Glance 2023: OECD Indicators, OECD Publishing, Paris, doi.org/10.1787/7a7afb35-en.

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“Caps on user charges contribute to equity in access to care (see Section 3.3.1 Collection), with the exceptions of dental care and medical devices. In 2022, 1% of Swedish respondents in an EU-wide survey reported making informal payments, compared with the EU27 average of 4% (European Commission, 2022).

“The level of unmet health care needs in Sweden is slightly below the EU average (Fig. 7.1). In 2021, 1.3% of Swedish respondents in an EU-wide survey reported unmet needs for medical examination or treatment due to costs, distance or waiting lists [Denmark 1.3%, Norway 0.9% (2020), Finland 4.4%, the United Kingdom 4.5% (2018), the Kingdom of the Netherlands 0.2%, EU27 average 2%]. Unmet needs for health care due to costs or distance are at a very low level in Sweden (0.1%). Instead, 1.2% in Sweden reported unmet need because of waiting times, which is above the EU27 average of 0.9% (Eurostat, 2023a). Among those who abstain from seeking care, people stating their health status as bad are overrepresented. Surveys also show that individuals with university or college education, those that are born outside Europe or have poor self-rated health state less often that they have access to the care they need (AHCSA, 2022a).”

Source: Janlöv N, Blume S, Glenngård AH, Hanspers K, Anell A, Merkur S. Sweden: Health system review. Health Systems in Transition, 2023; 25(3): i–198.

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“Contributing to an intersectional and thus more precise epidemiological perspective based on AIHDA [Analysis of Individual Heterogeneity and Discriminatory Accuracy] [15, 26], our study provides an improved understanding of the socioeconomic and demographic distribution of bad self-rated general health in the Swedish population.

“Investigating differences in average risk between socioeconomic and demographic groups, our study replicates previous findings using conventional methodological approaches and showing the existence of inequalities in bad SRH [Self Rated Health] between categories of income, immigration status and gender. However, by adopting a stratified intersectional approach we provide a more nuanced map of inequalities that is normally hidden in conventional studies of socioeconomic differences in health. Thus, compared with the reference stratum comprising high-income native men, the risk of bad SRH successively increased across intersectional strata to a level 7.0 times higher in the strata encompassing low-income immigrant women. This maximum level was preceded by a risk 5.6 times higher among low-income immigrant men, a risk 4.7 higher among middle-income immigrant women, and a risk 3.8 times higher among low-income native women.

“The results from the intersectional AIHDA show the salience of gender, immigration status and income as stratification forces in Sweden [13]. At the same time, the analysis reveals that the impacts of these dimensions on the outcome are affected by each other [13]. While all strata comprising women had a higher average risk than those including males with the same income and immigration status, strata comprising women showed both relatively low and very high risk of SRH. This is in correspondence with the foundational insight of intersectionality scholarship that the positions of gendered subjects are fundamentally mediated by factors including racialization and class [19]. Along similar lines, all strata encompassing immigrants had a higher risk than those including natives with the same income and gender. Meanwhile, whereas most strata comprising persons with immigrant status were among those experiencing very high risk, two strata – those including males and females with high income – carried a lower risk. This shows that the relevance of immigration status for SRH in Sweden is clearly affected by factors including gender and income. The association between higher risk and lower income was, however, quite consistent.

“In addition, the intersectional AIHDA goes beyond conventional analysis based on probabilistic measurements of average risk (e.g., odds ratios, prevalence ratios or relative risks) [11]. AIHDA stresses the relevance of incorporating complementary information on DA when evaluating differences between groups’ average risks, thereby taking into account the individual heterogeneities within, and overlap between, intersectional strata. In our study, the accuracy of the socioeconomic and demographic information for discriminating individuals with bad SRH from those with good SRH was low. This small DA expresses the existence of false negatives (i.e., individuals with bad SRH in strata with a low average risk for bad SRH) and false positives (i.e., individuals with good SRH in strata with a high average risk for bad SRH). Our study thereby provides a detailed mapping of health disparities alongside complementary information on its DA, and yields improved information for potential public health interventions. From an ethical point of view, focusing on specific population groups because of their higher risk of ill-health may convey a risk of stigmatization, but, on the other hand, the benefits of focused public health interventions may outweigh that harm. Using an AIHDA approach, we can better evaluate to what degree a universal intervention needs be proportionately targeted to specific groups with high average risk. A low DA supports universal intervention while a high DA merits targeted efforts. In this way, intersectional AIHDA can contribute to precision public health within Marmot’s framework of proportionate universalism [4, 51].”

Source: Wemrell, M., Karlsson, N., Perez Vicente, R. et al. An intersectional analysis providing more precise information on inequities in self-rated health. Int J Equity Health 20, 54 (2021). https://doi.org/10.1186/s12939-020-01368-0.

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World Health Systems Facts is a project of the Real Reporting Foundation. We provide reliable statistics and other data from authoritative sources regarding health systems and policies in the US and sixteen other nations.

Page last updated March 31, 2025 by Doug McVay, Editor.