Consultation

The Second WHO European Health Equity Status Report – 2025

Healthy prosperous lives for all

The purpose of this document is to provide information to Member States to support their review and any comments on the data indicators and graphs that will be included in the 2nd Health Equity Status Report that will be launched in November 2025.

Introduction

The WHO European Health Equity Status Report initiative (HESRi), launched in 2019, provides data-driven insights to support policy actions that address health inequities (1).

Since its launch the work has inspired local and national authorities to strengthen their health equity policies, has informed the work of international bodies (EU institutions and agencies, OECD), and has been a catalyst for new alliances across countries and communities of practice. The work has spearheaded political commitments and the creation of practical resources to drive action on health equity and well-being – enabling governments and health authorities to progress efforts towards creating a healthy life for all.

Continuing this work, WHO Europe is currently preparing a Second Pan European Health Equity Status Report. This will bring together the latest available status and trends in health equity and well-being within and across countries of the WHO European Region.

The second HESRi Report will delve into Health equity across the life course with a spotlight on children and young people and on healthy ageing. This is in the light of major demographic shifts in the age structure of populations within countries in the European Region. The 2nd HESRi report will use innovative techniques to capture health equity status and trends of men and women at different stages across the life course. It will show how these are associated with key features of i) health systems; ii) social relations and participation, education and learning iii) living conditions; iv) employment and work and v) income security, and set out options and solutions for reducing inequities and accelerating progress for healthy and prosperous lives for all, with co-benefits for societies with higher trust and life satisfaction, more resilient public systems and fiscal stability.

Figure 0.1 Essential conditions for a healthy life

The 2nd Health Equity Status Report will be launched in Europe in November 2025

Background

• The 2nd Health Equity Status Report (HESRi2) is a comprehensive review of the status and trends in health inequities and of the essential conditions needed for all to be able to live a healthy life in the WHO European Region.

• Improving health and well-being for all, reducing health inequities and ensuring no one is left behind will bring wider economic, social and environmental benefits to Member States.

• This report seeks to change the common perceptions that health inequity is too complex to address and that it is unclear what actions to take and which policies and approaches will be effective.

• The HESRi2 captures the progress made in implementing a range of policies with a strong effect on reducing inequities and demonstrates the link between levels of investment, coverage and uptake of these policies, as well as the gaps in the essential conditions needed to live a healthy, prosperous life.

• The report will support WHO and Member States to implement the health equity priorities set out in the draft 2nd European Program of Work (EPW2) (2)

Indicators that measure poverty risk, income security and the adequacy and coverage of social protection and welfare policies that

protect health over the life-course

Indicators that measure the health impact of employment and working conditions, including availability, accessibility,

security, wages, physical and mental demands, and exposure to unsafe work

Indicators that measure differential opportunities, access and exposure to living conditions and environmental factors that

impact health and well-being

Indicators of human capital that measure the access availability and affordability of education, learning and literacy;

Indicators of social capital that measure social inclusion exclusion, levels of trust, safety and participation of individuals and communities

in society.

Indicators that measure the availability, accessibility, affordability and quality of prevention, treatment and health-care services

andprogrammes

Health Health Services

Health Social and Human Capital

Health iving Conditions

Health Employment and Working Conditions

Health Income Security and Social

Protection

HESRi2 data

• The HESRi2 analysis and findings are generated from a new dataset. It brings together three types of data (Fig. 0.1) and uses innovations in analytical methodologies to provide a better understanding of health equity, the pathways that generate equity and inequities, and how policy interventions are associated with the rate of progress to reduce gaps in health and well-being, across the countries of the WHO European Region (Annex 1).

• The HESRi2 data and analysis provide the following benefits.

1. Country-specific data allow governments to strengthen decision–making, tailoring their action and investment for health equity accordingly.

2. Analysis supports ministries of health to demonstrate how events and decisions made in other sectors contribute to and interact with inequities in health and well-being.

3. Evidence that enables national and subnational governments and health authorities to improve policy coherence, leading to improved equity in health and in life chances.

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HESRi2 uses a range of data analysis and visualizations to support a robust understanding of the current status of health inequities within countries. It also captures whether there

have been significant reductions or increases in these inequities over a period of 10–15 years (trend analysis data) (Annex 1).

• Gradient charts are used to show the socioeconomic gradient for an indicator, such as self-reported health, by examining how levels of the indicator vary between subgroups of people. Either three or five subgroups are defined according to markers of socioeconomic status - for example, level of education (three groups) (Fig. 0.2), or income or wealth (five groups). For people belonging to each subgroup, the average level of the indicator is calculated and represented in the chart by a different coloured dot.

• Gap and trend charts are used to show the difference, or gap, in average levels of the indicator in the most advantaged subgroup compared to the most disadvantaged subgroup. For example, the charts show the difference between those in the highest and lowest income quintiles or between those with highest level of education (degree-level) and those with least (lower-secondary or below). The traffic light symbols in these figures also show whether the size of the gap for each country has narrowed, widened, or stayed the same over a specified time frame (e.g. Fig 0.3).

• Decomposition charts are used to show how shortcomings in each of the five essential conditions, when combined, contribute to the gap for a given health indicator, such as mental health or limiting illness. The decomposition charts enable policymakers to see more clearly the relative weight of each condition in contributing to (in)equity in a specific health indicator (Annex 2).

• Equity tracker charts show, for each country, a summary outcome for either individual indicators or groups of indicators. The outcomes include the level of inequality or the direction of the trend in inequality. The groups of indicators are the five essential conditions, while the individual indicators are either health outcomes or variables contributing to one of the essential conditions (Annex 2).

Health equity – recent status and trends

1) Health and wellbeing status

Enshrined in the WHO constitution (3) is the principle that “health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.” Underlying this are, among other principles “the enjoyment of the highest attainable standard of health is one of the fundamental rights of every human being without distinction of race, religion, political belief, economic or social condition” and “Governments have a responsibility for the health of their peoples which can be fulfilled only by the provision of adequate health and social measures. In this section of the report, the current status and trends in inequalities in health and wellbeing outcomes within each member state in the European Region are summarized. The graphs likely to be used to illustrate this are shown below. A full list of indicators from which the summary will be drawn is given in Annex 4. The social conditions which give rise to and can be a consequence of the inequities in health and wellbeing are summarized in the section that follows this one.

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2) Essential conditions for health and wellbeing

The analysis presented in the first HESRi report identified the five essential conditions needed to live a healthy life in Europe in the 21st Century as listed in Figure 0.1. This section provides a brief rationale for each of these five conditions and graphs that will be used illustrate status and trends in the indicators related to these conditions. A full list of the HESRi indicators relating to each condition is in Annex 3. Commentary on each condition will draw on a selection of these indicators.

2.1 Health systems

Health systems have a key role in addressing equity in health. Inequities in health will result if the provision of prevention and treatment are unequally distributed in the population – the inverse care law (4). But the health care system can do more than this – it can contribute to the reduction of inequities resulting from social conditions that lead to ill health and a lack of wellbeing. To do both of these, the health system must be universal – providing care for everyone, offering high quality care to all, affordable – ensuring that any out of pocket payments do not place a disproportionate burden on anyone and are responsive to need – do not leave anyone with unmet healthcare needs.

Universal health coverage has an equity impact through ensuring that everyone can use appropriate and effective health services without experiencing financial hardship, irrespective of ability to pay, age, ethnicity, disability, geographical location, race, religion, sex, or sexual orientation. People experience financial hardship when out-of-pocket (OOP) payments are large in relation to their ability to pay for health services. ack of financial protection can lead to or deepen poverty, undermine health, and exacerbate health and socioeconomic inequities. Where health systems fail to provide adequate financial protection, people may be forced to choose between using health services and meeting other basic needs such as food, housing and heating. This has a negative impact on health and well-being, which in turn further increases the risk of socioeconomic vulnerability and exclusion (5). Policies that distribute more resources to areas with greater health, social and economic needs have a positive impact on reducing health gaps between social groups and geographical areas (6).

The quality of health care has an equity impact because health outcomes improve with better quality care (7). Therefore, equitable provision of good quality care reduces gaps in outcomes. Differences in the quality of care arise from differences in the geographical distribution of health services in low- and high-income neighbourhoods or other forms of discrimination that result in lower-income households receiving lower-quality care and poorer health outcomes.

Unequal quality of health care is frequently found between providers catering to low- or high-income neighbourhoods and between private and public providers, mirroring inequities in socioeconomic status and health outcomes. Disaggregation by education level, income and sex allows this indicator to monitor equity in the quality of health care received based on socioeconomic status.

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2.2 Social and human capital

Early childhood development lays the foundation for physical and mental health and well- being outcomes throughout the life course (8). Investment that reduces health risks and vulnerability in the prenatal period and early childhood help parents with lower resources give their children a healthy start in life. Equal access to good quality education from an early age has a strong impact on reducing differences in opportunities and risks, which has both direct and indirect impacts on health. Continued access to education and lifelong learning opportunities has a direct effect on promoting social and economic inclusion and mental well-being). It also has indirect effects linked to increased health and social literacy, such as awareness of health risks and behaviours and mediating life chances and the effects of social and economic shocks (8,9).

Mattering - experiences of feeling valued and adding value (10) -is related to positive outcomes when present, and negative results when absent. When people are treated with dignity and respect, the overall sense of mattering and wellbeing increases. When people perceive that they do not matter, their overall wellbeing suffers greatly. This is the case because people have a fundamental need to experience social worth. Meaningful participation in society, trust in others and being able to influence decisions affecting health and life chances have important direct and indirect health effects. They contribute to stronger individual and social resilience and lower levels of morbidity and poor mental health. Exposure to low-trust environments characterized by higher crime rates, social isolation and lack of ability to influence politics and decision-making in society are strongly associated with poor mental health and a higher risk of morbidity (11).

Participatory public services empower people to take control over their lives and the determinants of their own health. Investment in civic participation, reducing crime and generating social connections have positive impacts on the health and well-being of individuals through their engagement with, and trust in, the local community and wider society (6,12).

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2.3 Living conditions

Shelter is a fundamental human need, providing safety, a sense of belonging, peace and security. Poor housing and poor health are inextricably linked. People living in unaffordable, poor-quality or insecure housing are more likely to report poor health and to suffer from a variety of health problems (13,14).

Differences in the quality of the local environment in which people live have an impact on health equity. ow-income households and neighbourhoods are less likely than those who are wealthier to be in, or have access to, safe, health-promoting environments (14). ocal environments that are detrimental to physical health may also generate stress and anxiety, providing an additional link between physical environment and mental health (14,15). Policies that shape geographical planning, construction and management of public spaces can reduce health inequities due to the local environment (6).

Commercial organizations influence all aspects of life and can therefore have either a beneficial or harmful influence on health equity. Marketing and product exposure, to influence the consumption of alcohol, tobacco and food items, affect the risks of noncommunicable diseases and mortality. This has an impact on health equity because commercial influence varies with how well people are educated about the health risks of detrimental consumption behaviours, whether their economic situation and social environment encourage or discourage them from such consumption behaviours and whether they have easy access to healthy alternatives.

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2.4 Employment and working conditions

Productive participation in the labor market can have a positive impact on health equity at an individual level - through its effects on material conditions and on life chances – and at a societal level – through creating the macro-economic resources that underpin policies to enable people to prosper. People who have experienced long-term unemployment are at a higher risk of premature mortality. Young people who have experienced long-term unemployment are more likely than those who have not experienced unemployment to report risky health behaviours, at all levels of socioeconomic status.

Good work is an essential requirement for health. Job insecurity, temporary employment and poor or stressful working conditions are associated with poor mental health, self- reported ill health and an increased risk of fatal and non-fatal cardiovascular events. Excessive hours and few contractual rights directly and indirectly contribute to inequities in physical and mental health. Work-related stress follows a social gradient (16).

Systematic differences in exposure to poor working conditions are closely associated with gender norms and the workers’ age and education level. Discrimination in the labour market exists between sexes. socioeconomic and migrant groups, and persons with disabilities. Ensuring equitable participation in secure and decent employment has the potential to address health and social inequities, including gender inequities, by providing equal opportunities to obtain a secure income (16).

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2.5 Income security and social protection

The risk of poverty directly correlates with early-onset morbidity and premature mortality. Repeated or long-term exposure to financial insecurity predicts the likelihood of poor self- reported health, depression and the inability to meet the basic needs to live a healthy life, such as food, fuel and shelter (15). Non-stigmatizing social protection policies have positive effects on reducing health inequities related to income insecurity and poverty.

Investment in social protection for families and early childhood reduces health inequities by weakening the link between socioeconomic disadvantage and poor child health outcomes, including infant mortality and adverse child development (8,17). Such investment contributes to meeting children’s basic needs and helps them reach their full potential in life and health, irrespective of their or their parents’ sex, disability, race, ethnicity, origin, religion, and economic or other status (18).

Social protection programmes that support older people have an impact on health equity by maintaining their capacity to avoid health risks, access health services and perform daily tasks, especially for those with financial and health vulnerabilities. Older people, especially those from low-income backgrounds, are likely to have greater health-care needs or caring responsibilities for partners, and financial support through pension systems helps reduce their risk of ill health, disability and social exclusion (19). Women live longer than men, spending more of their lives in ill-health and are more likely to have caring responsibilities. Social protection and pension provision among women are key to addressing the resulting gender gaps in health, wellbeing and social conditions of older women.

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3) Deep dive into equity across the life course

3.1 Demographic change

The age structure of the region is changing, but not in exactly the same way in every Member State. Two simple summaries of the age structure of a country are the ratio of children (ages 0 to15) to working age adults (ages 15 to 64) and the ratio of old people (ages 65 and over) to working age adults. These ratios affect both the number and types of health events that occur in a country – often forming the focus of the health system – and also the capacity of the working age population to fund public services – principally health, social protection, education and pensions – required by children and the elderly, particularly the most vulnerable.

In every member State in the region, the ratio of older people (aged 65 and over) to those of working age (ages 15 to 64) increased in the ten years to 2024. At the same time, the ratio for those below working age (aged under 15) increased in around half of Member States and decreased in the other half of States. While a decrease in the ratio of young people reduces the short-term burden, in the longer term it suggests a reduction in the size of the working population. Although the two scenarios ultimately require similar policy responses, these will differ in terms of timescales and implementation plans.

These changes form the backdrop to the life course approach taken in this section, emphasizing what is happening to inequalities in children and young people, how ageing healthily varies across social groups and how gender differences persist across the region.

Health advantage and disadvantage accumulates across the life course – from the point of conception (and before), the conditions in which a child’s parents find themselves influences the child’s development, health and life chances. These influences are amplified by the education, training and employment opportunities experienced at the end of schooling. These, in turn, affect the income that a person can obtain, the control they have over subsequent life chances and the extent to which they matter in society. These can then influence the pension they receive in retirement. At each stage in this journey, the risk of developing a health problem increases and this can affect life chances and is affected by the conditions experienced. It is this combination of the essential conditions and health that therefore affect whether or not an individual is likely to age healthily.

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3.2 Children and young people

As indicated above, child development is important to both health in childhood and adolescence and to thriving as an adult – the ability to obtain and maintain the skills needed in adulthood, good work, decent housing and live healthily in a healthy environment.

Psychological factors are important in achieving this trajectory to good health. This includes a sense of wellbeing and mattering, life satisfaction, freedom from bullying and depression and a positive digital life. These are all interconnected. Failing physical or mental health at an early age can easily derail this trajectory to good health.

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3.3 Healthy ageing

As described above, healthy ageing is a process that evolves through life and, consequently, the key equity aspects of this process are summarized in the preceding sections. In this section, the focus is therefore on how people prepare for old age – in terms of pension and long-term care provision- and the consequences this has for equity in old age – differences in pension coverage and health status.

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3.4 Gender

Gender differences permeate all aspects of life. Wherever the data permits, the preceding sections provide disaggregation by sex and highlight the existence of differences in the essential conditions and outcomes. In this section, the emphasis is on drawing tother the story painted by the data and identifying factors that are specially about gender roles – such as parenting – and gender-related violence. The absence of routine data on many gender-related issues is of concern.

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4) Modelling

In the report, a variety of modelling tools are used to indicate (a) quantify the role of the five essential conditions (or their component indicators) in creating the equity gap in specific outcomes (such as limiting illness) in different contexts (such as life course stage, gender, sub-region) (b) the association between a change in one or more component indicators (such as poverty) and the size of the equity gap, indicating the potential, quantifiable reduction in the equity gap that could be achieved by taking action on specific social conditions (c) conversely, quantifying the extent to which improved health outcomes are associated with improved social conditions (e.g. improved mental health is associated with reduced unemployment). The technical details of the methodologies used in each type of modelling are summarized in Annex 2in the health equity gap.

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5) Summary – equity trackers

The equity trackers provide an array of traffic lights that summarise, for each Member State, either (a) the level of inequality in each essential condition (or in the component indicators of an essential condition) or (b) whether the trend in inequality has shown a significant widening over time. These dashboards enable Member States to see, at a glance, where they are making progress and, conversely, where they need to focus greater effort.

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References

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2. Developing the Second European Programme of Work 2026-2030 (EPW2):2025 https: www.who.int europe activities developing-the-second-european-programme-of- work

3. WHO constitution (with amendments): 2025 https: www.who.int about governance constitution

4. Tudor Hart, J The inverse care law. The ancet, 1971, Volume 297, Issue 7696, 405 - 412 https: doi.org 10.1016 S0140-6736(71)92410-X

5. Cylus J, Thomson S, Evetovits T. Catastrophic health spending in Europe: equity and policy implications of different calculation methods. Bull World Health Organ. 2018;96(9):599–609. doi:http: dx.doi.org 10.2471 B T.18.209031.

6. Poverty, social exclusion and health systems in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2010 http: www.euro.who.int __data assets pdf_file 0004 127525 e94499.pdf?ua=1.

7. egido-Quigley H, McKee M, Nolte E, Glinos I. Assuring the quality of health care in the European Union. Geneva: World Health Organization; 2008 (Observatory Studies Series No. 12; http: www.euro.who.int __data assets pdf_file 0007 98233 E91397.pdf?ua=1.

8. Jensen BB, Currie C, Dyson A, Eisenstadt N and Melhuish E. Early years, family and education task group: report. Review of social determinants of health and the health divide in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2013 https: iris.who.int bitstream handle 10665 344815 9789289000567-eng.pdf?sequence=1

9. Closing the gap in a generation: health equity through action on the social determinants of health. Final report of the commission on social determinants of health. Geneva: World Health Organization; 2008 (https: apps.who.int iris bitstream handle 10665 43943 9789241563703_eng.pdf?sequen ce=1.

10. Kruglanski, A.W., Prilleltensky, I., Raviv, A. (Eds.). (2025). The Routledge International Handbook of Human Significance and Mattering (1st ed.). Routledge. https: doi.org 10.4324 9781003424437

11. Wilkinson RG, Pickett KE. Income inequality and social dysfunction. Annu Rev Sociol.2009;35:493–511. doi: 10.1146 annurev-soc-070308-115926.

12. Rocco , Suhrcke M. Is social capital good for health? A European perspective. Copenhagen: WHO Regional Office for Europe; 2012 http: www.euro.who.int __data assets pdf_file 0005 170078 Is-Social-Capital-good-for- your-health.pdf?ua=1.

13. Braubach M, Jacobs D, Ormandy D. Environmental burden of disease associated with inadequate housing: a method guide to the quantification of health effects of selected housing risks in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2011 http: www.euro.who.int __data assets pdf_file 0017 145511 e95004sum.pdf?ua=1.

14. Friedli . Mental health, resilience and inequalities. Copenhagen: WHO Regional Office for Europe; 2009 http: www.euro.who.int __data assets pdf_file 0012 100821 E92227.pdf?ua=1.

15. Bell R. Psychosocial pathways and health outcomes: informing action on health inequalities. ondon: UC Institute of Health Equity; 2017 http: www.instituteofhealthequity.org resourcesreports psychosocial-pathways-and- health-outcomes-informing-action-on-health-inequalities psychosocial-pathways-and- health-outcomes.pdf.

16. Siegrist J, Rosskam E, eka S. Work and worklessness: final report of the task group on employment and working conditions, including occupation, unemployment and migrant workers. Review of social determinants of health and the health divide in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2016 (http: www.euro.who.int __data assets pdf_file 0004 334354 EWC-task-report.pdf?ua=1.

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Annex 1. Methods to derive indicators

A1.1 Selection of indicators

Indicators were selected for inclusion in the 2nd Health Equity Status Report (HESRi2) Dataset to provide a pragmatic set of measures that were based on accessible data and cover health equity status and policy progress across the five underlying conditions of health equity: Health Services, Social and Human Capital iving Conditions, Employment and Working Conditions and Income Security and Social Protection. Certain key criteria were considered in selecting indicators, in consultation with the Scientific Expert Advisory Group and the HESRi2 Country Partners Group.

The HESRi2 aimed to include indicators that:

• clearly related to priority areas for action on health equity for which there is a clear evidence base and reflected in the WHO Euro Health Equity Policy Tool;

• related to areas of action in which Member States have already made commitments;

• were relevant to the full range of country contexts across the WHO European Region;

• provided a balance of measures across the five underlying conditions for health equity;

• provided as much coverage of countries across the Region, consistent with ensuring a good representation of inequalities in countries that do not provide data to the Statistical Office of the European Union (i.e. to Eurostat);

• used data recoded on international or intranational databases that had been collected though consistent processes across countries in order to support more reliable comparisons;

• used data that could be disaggregated by socioeconomic status, or that were related to policies and expenditure known to have an impact on health inequities;

• used openly accessible data from international datasets, which could be feasibly collected and analysed given the available timescale and resources;

• used data that were reasonably current and that could be updated to track trends.

Initial, the list of 108 HESRi1indicators were reviewed, to assess the feasibility of updating them. New indicators were added, either to replace those that could not be updated or to address topics that were in scope, but not covered in HESRi1. This process was reviewed by the Scientific Expert Advisory Group and the HESRi2 Country Partners Group and through

wide consultation against the criteria listed above; as a result,122 indicators were selected for inclusion in the Health Equity Dataset.

A1.2 Data sources

Data from several sources were used to derive the indicators featured in the Health Equity Dataset. Data were obtained from sources such as household and other population-based surveys, administrative and financial data systems and surveillance systems. Where obtainable, publicly available data were used. For example, a number of indicators were derived from datasets containing national-level data published by the Organisation for Economic Cooperation and Development (OECD), World Bank, International abour Organization I O), United Nations Children’s Fund (UNICEF), Food and Agriculture Organization (FAO), International Monetary Fund (IMF), World Value Survey (WVS), Global Data ab (GD ), Global Burden of Disease (GBD), European Institute for Gender Equality (EIGE), World Justice Project, and the Eurostat database from the Statistical Office of the EU. Eurostat data were obtained through a data-sharing agreement (RPP 85 2018- FS-EU-SI C- EHIS). The responsibility for all conclusions drawn from the data lies entirely with the authors.

Where data were not publicly available, survey microdata, which contained information available at the individual level, were used to derive indicators. Official requests were made to access survey microdata, using (for example) the European Union Statistics on Income and iving Conditions instrument (EU-SI C), the WHO STEPwise approach to Surveillance (STEPS) tool, and the European Health Interview Survey (EHIS; included in the list below). Empirical data collected from these nationally representative surveys, among others, were compiled and processed to create indicators.

Details of each of the databases, surveys and other sources used and the acronyms used for them in this document are listed below.

Acronym Database/survey details EHIS European Health Interview Survey [online database]. Luxembourg:

Statistical Office of the European Union (Eurostat) (https://ec.europa.eu/eurostat/web/microdata/european-health- interview-survey).

EQLS European Quality of Life Surveys (https://www.eurofound.europa.eu/surveys/european-quality-of- lifesurveys). European Foundation for the Improvement of Living and Working Conditions (Eurofound). European Quality of Life Survey integrated data file, 2003–2016 (data collection). 3rd Edition. Colchester: UK Data Service; 2018 (SN: 7348) http://doi.org/10.5255/UKDA-SN-7348- 3).

ESS European Social Survey (http://www.europeansocialsurvey.org/). ESS Cumulative Data Wizard, ESS 1-8. Data file edition 1.0. Bergen: Norwegian Centre for Research Data; 2018 (doi:10.21338/NSD-ESS- CUMULATIVE).

EU-LFS European Union Labour Force Survey [website]. Data and publication. Luxembourg: Statistical Office of the European Union (Eurostat) (https://ec.europa.eu/eurostat/statistics- explained/index.php/EU_labour_force_survey_%E2%80%93_data_and_p ublication).

Eurostat Eurostat [online database]. Luxembourg: Statistical Office of the European Union (http://ec.europa.eu/eurostat/data/database).

EU-SILC European Union Statistics on Income and Living Conditions [online database]. Luxembourg: Statistical Office of the European Union (Eurostat) (http://ec.europa.eu/eurostat/web/microdata/europeanunion- statistics-on-income-and-living-conditions).

EWCS European Working Conditions Survey (https://www.eurofound.europa.eu/surveys/european- workingconditions-surveys). Eurofound. European Working Conditions Survey integrated data file, 1991–2015 (data collection). 7th Edition. Colchester: UK Data Service; 2018 (SN: 7363) (http://doi.org/10.5255/UKDASN-7363-7).

GBD Global Burden of Disease Collaborative Network [online database]. Seattle (WA): Institute for Health Metrics and Evaluation (IHME) (http://ghdx.healthdata.org/organizations/global-burden- diseasecollaborative-network).

GDL Global Data Lab [online database]. Nijmegen: Raboud University Institute for Management Research (https://globaldatalab.org/).

HBSC Health Behaviour in School-aged Children [online database]. Bergen: HSBC Data Management Centre (https://www.uib.no/en/hbscdata).

ILO ILOSTAT [online database]. Statistics of the International Labour Organization. Geneva: International Labour Organization (http://www.ilo.org/ilostat/).

MICS Multiple Indicator Cluster Surveys [online database]. New York (NY): United Nations Children’s Fund (http: mics.unicef.org surveys).

OECD OECD. Stat data warehouse [online database]. Paris: Organisation for Economic Co-operation and Development (https://stats.oecd.org/).

PISA OECD PISA [online database]. Programme for International Student Assessment. Paris: Organisation for Economic Co-operation and Development (http://www.oecd.org/pisa/data/).

STEPS WHO STEPwise approach to Surveillance [website]. Geneva: World Health Organization (https://www.who.int/ncds/surveillance/steps/en/).

UNICEF UNICEF data [online database]. New York (NY): United Nations Children’s Fund (https://data.unicef.org).

WHO EHIG

European Health Information Gateway [online database]. Copenhagen: WHO Regional Office for Europe (https://gateway.euro.who.int/en/).

WHO GHED

WHO Global Health Expenditure Database [online database]. Geneva: World Health Organization (https://apps.who.int/nha/database).

WHO GHO

WHO Global Health Observatory data repository [online database]. Geneva: World Health Organization (http://www.who.int/gho/en/).

WHO UNICEF

WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene [online database]. Geneva: World Health Organization and United Nations Children’s Fund (https://washdata.org/data).

UN IGME

UN Inter-agency Group for Child Mortality Estimation (https://data.unicef.org/resources/un-inter-agency-group-for-child- mortality-estimation-unigme/)

WJP World Justice Project Rule of Law Index [online database]. Washington (DC): World Justice Project (https://worldjusticeproject.org/our- work/wjp-rule-law-index).

World Bank

World Bank Data Catalogue [online database]. Washington (DC): The World Bank Group (https://data.worldbank.org/data-catalog).

WVS Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez- Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen (eds.). 2022. World Values Survey: All Rounds - Country-Pooled Datafile. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. Dataset Version 3.0.0. doi:10.14281/18241.17

A1.3 Data processing

Following data collection, individual datasets containing primary data were stored in a secure data warehouse for processing. In order to maximize country coverage, indicators were created by combining datasets from different sources, if comparable data were available. Data processing involved, where necessary, data cleaning and suppression of implausible values and cells based on small samples (counts of <50 respondents). Data were aggregated by country and year, as well as characteristics such as sex, age group, education level and income, using survey weights where available to adjust for sampling errors and biases. To facilitate comparison between countries with different age profiles, health outcome indicators were directly age standardized with the WHO World Standard Population. For some analyses, countries were aggregated by geographical region, as follows: Caucasus, central Asia, central Europe, Nordic countries, Russian Federation, South-eastern Europe western Balkans, southern Europe and western Europe (see Annex 3).

For the majority of indicators, income quintiles were calculated based on equivalized disposable household income. Where possible, levels of education were based on the International Standard Classification of Education (ISCED) 2011, or the earlier classification, ISCED 1997. The ISCED educational levels are often aggregated to form a three-category education variable, with: (1) low-level education, which is pre-primary to lower secondary education only; (2) mid-level education, which represents upper-secondary to post- secondary nontertiary education; and (3) high-level education, which is tertiary education. Indicators within HESRi2 were disaggregated by this three-category variable for educational level, where available. Where an indicator is not disaggregated by either income or education, but is available for regions within a country, these regions are grouped by income per capita. Although poorer regions tend to have worse outcomes than richer ones this is not always the case because other factors may affect regional differences in some countries (e.g. the urban rural composition of regions).

Data processing was conducted using R statistical software (version 3.4.3). A panel of regional experts, scientific advisors and members of international organizations approved the methods used and the interpretation of the indicators, prior to publication.

The primary equity measure derived was the absolute difference between the most and least disadvantaged groups (e.g. based on education or income). The trend indicators are based on the (linear) slope index of inequality to calculate an annual rate of change, as well as statistical significance. They use all available data for the indicator since 2010 to calculate the trend. Trends were assessed as increasing if the linear trend across all the data points available was significantly greater than 0 (p<0.1) or decreasing if the linear trend across all the data points available was significantly lower than 0 (p<0.1); otherwise, the trend was labelled as having no noticeable change.

A1.4 Limitations

Certain limitations need to be taken into account when interpreting the indicators and users should refer to the original source documentation to assess the quality of the data collection and measurement methods.

Differences in methodologies – such as the extent to which samples are representative of populations – and differences in survey instruments and definitions may limit the comparisons that can be made between countries and within countries over time. Individuals from lower socioeconomic groups are often underrepresented in population- based surveys, which may limit generalizability. Additionally, self-reported outcomes may be influenced by response biases and cultural differences, and, when comparing countries with diverse health services, differences in self-reported health outcomes may reflect access to care, rather than real differences in morbidity.

Whilst every effort has been made to achieve as much coverage of countries across the Region as possible, consistent with ensuring a good representation of inequalities in countries not providing data to Eurostat , only 17of the selected indicators cover 50 or more of the countries in the WHO European Region. This highlights the need for a coordinated approach to monitoring health equity within the Region going forward.

Annex 2. Modelling

A2.1 Decomposition analysis

Demanding microdata requirements for the decomposition analysis means that this analysis is only possible for inequities in a select number of health indicators.

Decompositions of contributors to self-reported health, limiting illness, mental health and life satisfaction are possible using microdata from ESS and EUSI C for 32 countries in the WHO European Region, consisting of the 27 EU countries, Iceland, Norway, Serbia, Switzerland, and the United Kingdom. There is sufficient data for underlying conditions in all five areas to be analysed, showing that differences in conditions in all five areas are statistically significant in explaining inequities in these health indicators (Fig. 2.2).

A2.2 Technical details of the decomposition method

The idea behind the decomposition analysis is to explain the differences in health indicators that were observed between socioeconomic groups in Section 1 by a set of contributing factors that differ systematically between these groups.

This helps to understand the multisectoral conditions behind why health inequities exist between groups of people within countries, even when effective health services are in place that aim to narrow or eliminate inequities in health and health care. For example, differences in health may be explained by differences in housing conditions and working conditions, as well as by differences in quality of health care. Even if countries are able to narrow inequities in one factor, inequities may still remain in others, emphasizing the importance of taking a multisectoral approach to tackling health inequity.

The decomposition analysis reveals the extent to which each factor contributes to health inequities compared to each of the other factors. The method used in the Health Equity Status Report (HESR) is a simulation-based decomposition that relies on coefficients from a single, pooled regression model, a principle related to the variant of the Oaxaca decomposition method proposed by Neumark (1) and Oaxaca Ransom (2). The decomposition is based on regression analysis of the relationships between self-reported health and the indicators of underlying conditions. It should be noted that while this analysis provides an explanation of health inequity in terms of statistical associations, the regression model used does not constitute a causal analysis and therefore the results should not be interpreted as a stand-alone guide to policy – the decomposition results should be interpreted with care and only in context with other evidence.

The analysis first quantifies the overall health inequality by calculating a health gradient, in this case the Slope Index of Inequality. The decomposition then proceeds through a counterfactual simulation for each underlying condition. To determine the contribution of a single factor (e.g. employment), we simulate a scenario where that factor is set to its optimal value for all individuals in the population. By predicting health outcomes in this simulated scenario and recalculating the health gradient, we can measure how much the overall inequality is reduced. This reduction is considered the contribution of that specific factor to the total health gap.

A2.3 Equity Tracker

he ineq ali y index in he eq i y racker wa calc la ed in he followin way: i. for ho e indica or ha are di a re a ed by ocioeconomic a :

a. he lope index i calc la ed acro ocioeconomic ro p b. where here are m ltiple mea re of ocioeconomic a ch a ed cation and income, a

in le compo i e ocioeconomic rank i fir crea ed by avera in he e variable , and hen ed o calc la e he lope index

ii. he mea re calc la ed in ep (i) i andardized by cen erin i on he mean for all co n rie and dividin by i andard deviation. In hi way, he me ric ed i mea red in andard deviation .

Where he indica or i no di a re a ed by ocioeconomic a b ha an obvio relation hip wi h ineq ali y direc ly (e. . pover y ra e , Gini), he e were j andardized a (ii) above. nn al mea re were e tima ed for each indica or. da a may be mi in for ome year for differen co n rie and differen indica or , we moo h o da a be ween year by:

i. akin he 3-year movin avera e for each indica or ii. E timatin mi in year by akin he linear rend be ween non-mi in year of da a wi hin

co n rie or ro p of indica or ( ch a each of he five e ential condition ), he andardized mea re for each indica or were hen combined acro indica or ca e orie , by akin he avera e for all non-mi in andardized indica or in each ro p for each co n ry. imilar approach wa ed o avera e acro ro p of year . inally, o crea e he - o- index een in he racker , he e contin o domain core are conver ed in o q intile . Wi hin each domain, all co n rie are ranked by heir core and divided in o five eq al ro p , wi h ' ' repre entin he lowe ineq ali y and ' ' repre entin he hi he ineq ali y.

A .4 m g w g m q w g q g . This is produced by taking the annual measures of the standardized indicators over the years 2010-2023, as derived in A2.3 and fitting a linear regression model with country fixed effects. This estimates the extent to which change in equity in each of the conditions over time within a country is associated with change in equity in self-reported health (based on the standardized slope index as in A2.3). The model also includes sex and year as control

variables to account for average differences between sexes and for underlying linear time trends. For all indicators in the chart a reduction in equity in conditions is independently associated with a reduction in health equity (and vice versa). A .5 M g ff m m .

This is based on a logistic regression model with the outcome measure, such as poor health, social isolation measured as a binary outcome (e.g. poor health vs. not poor health), controlling for sex, age, age squared and age-sex interactions. The intervention is measured as a three-way interaction between the country’s pension coverage, the standardized expenditure per pension recipients and an indicator of whether a respondent is over the country’s effective pension age for that year. This model is then used to simulate how the outcome changes, within each sex and income quintile. The simulation specifically models the effect of a best-practice scenario (i.e. pension coverage at the maximum observed level and expenditure at least at the European average) to estimate the potential impact on population health and well-being. A .6 H w b m g k b

m k x .

) L m g

First, the percent of 16–24-year-olds who report a limiting illness for each educational group for each country and year is combined with I O data on the percent (in each education group for each country and year) who are not in education, employment or training (NEET). This is used in a linear regression model with percent NEET as the outcome and the one to three- year lagged values of limiting illness as the exposure with country fixed effects. The coefficients for each lagged year are combined to estimate the effect of the cumulative trending limiting illness, over three years, on NEET. A comparable analysis is then performed using youth unemployment as the outcome variable in place of NEET. ) D

As there are only two time points for this indicator, a simpler approach is used. The difference between the two time points in depression prevalence for each country and the difference in the two labour market outcomes, NEET prevalence and youth unemployment, for each country are calculated. Then, to estimate the association, two separate regression models are fitted. In the first model, the change in the NEET rate is regressed on the change in depression. In the second, the change in youth unemployment is regressed on the change in depression.

Annex 3. Country clusters

Member States have been grouped according to policy and political commonalities. The clusters also aim to reflect the countries that Member States compare themselves to. This grouping does not coincide with preexisting WHO country groupings.

Country Cluster

Annex 4 List of HESRi Database indicators

Indicator title Indicator definition Data sources

Latest year

Health status Morbidity Alcohol consumption Percentage of adults aged 18-64 years who drink

alcohol daily (age adjusted) STEPS EHIS 2023

Alcohol consumption (risky single occasion drinking)

Percentage of adults aged 18-64 years engaging in risky single occasion drinking at least once a month (age adjusted)

STEPS EHIS 2023

Children reporting multiple health complaints

Percentage of children aged 11-15 experiencing two or more health complaints weekly

HBSC 2022

DALYs due to occupational exposure

DALYs attributable to occupational risks and exposures (age standardized rate per 100,000)

GBD 2021

Limitations in activities of daily living

Percentage of adults reporting limitations in daily activities due to health problems

EU-SILC ESS 2024

Obesity/Overweight Percentage of adults aged 18-64 years who are obese (age adjusted)

STEPS EHIS 2023

Prevalence of self- reported CVD

Percentage of adults aged 18-64 years reporting cardiovascular disease (age adjusted)

STEPS EHIS 2023

Prevalence of self- reported diabetes

Percentage of adults aged 18-64 years reporting diabetes (age adjusted)

STEPS EHIS 2023

Serious injuries in children Percentage of children aged 11-15 having required medical treatment for 3 or more injuries in past year

HBSC 2018

Smoking Percentage of adults aged 18-64 years who are current smokers

STEPS EHIS 2023

Mortality

Infant mortality rate Infant mortality rate for children aged less than 1 year (per 1000 live births)

World Bank Eurostat GDL

2019

Life expectancy at birth Life expectancy at birth in years GDL 2022

Tuberculosis mortality Age-standardized tuberculosis death rate per 100,000 population

WHO 2022

Under-five mortality rate

Under-five mortality rate (per 1000 live births) UN IGCME 2022

Wellbeing

Adult life satisfaction Percentage of adults reporting poor life satisfaction

ESS WVS 2023

Adult self-reported health

Percentage of adults reporting poor or fair health

EU-SILC ESS WVS

2024

Center for Epidemiological Studies Depression Score (CES- D8)

Proportion of people at high risk of clinical depression

ESS 2014

Children pressured by schoolwork

Percentage of children aged 11-15 reporting being pressured by schoolwork

HBSC 2022

Children's life satisfaction

Percentage of children aged 11-15 years reporting poor life satisfaction

HBSC PISA 2022

Children's self-reported health

Percentage of children aged 11-15 years reporting poor or fair health

HBSC 2022

Physical inactivity in children

Percentage of children aged 15 years who are physically active

PISA 2022

Problematic social media use in children

Percentage of children aged 11-15 showing symptoms of social media disorder

HBSC 2022

Self-reported work- related health condition

Percentage of adults aged 15-64 years reporting a work-related health problem (age adjusted)

EU-LFS via Eurostat

2020

Health services

Access to health care

Avoidable hospital admissions

Number of avoidable hospital admissions per 100,000 people

Eurostat GDL

2021

Cancer screening (cervical) Percentage of women reporting ever having a cervical cancer screening

STEPS EHIS

2023

Hospital beds Available hospital beds per 100,000 inhabitants

Eurostat OECD GDL

2022

Informal care Percentage of those aged 18-64 years who provide informal care for people with disabilities at least once a week (age adjusted)

EHIS 2019

People with diabetes treated Percentage of people aged 18-64 with diabetes who are receiving treatment

STEPS 2023

People with hypertension treated

Percentage of people aged 18-64 with hypertension who are receiving treatment

STEPS 2023

Physicians or doctors Number of physicians or doctors per 100,000 inhabitants

Eurostat OECD GDL

2022

Public expenditure on health Current health expenditure as a percentage of GDP

WHO 2023

Public expenditure on long- term care

Total expenditure on long-term care as a percentage of GDP

Eurostat OECD

2023

Public spending on public health services

General government expenditure on public health services as a percentage of GDP

IMF Eurostat OECD

2023

Self-reported unmet needs for health care

Percentage of people reporting unmet needs for health care

EU-SILC WVS

2024

Financial protection for health

Household out-of-pocket payments on long-term care

Household out-of-pocket payments on long- term care as a percentage of GDP

OECD 2023

Households with catastrophic health spending

Percentage of households with out-of-pocket payments that are greater than 40% of their capacity to pay for health care

WHO Barcelona Office

2022

Households with impoverishing health spending

Percentage of households that are impoverished or further impoverished after out-of-pocket payments

WHO Barcelona Office

2022

Out-of-pocket expenses Out-of-pocket expenses as a percentage of current health expenditure

WHO 2023

Social and human capital

Human capital

Adult literacy rate Percentage of adults aged 15+ years who are literate

UNESCO via World Bank

2023

Children developmentally on track

Percentage of children aged 36-59 months who are developmentally on track

MICS 2019

Children/youth minimally proficient in reading and maths

Percentage of children aged 15 years achieving minimum proficiency in mathematics and reading

PISA 2022

Government expenditure on early childhood education

Government expenditure on early childhood education in purchasing power standards (PPS) per child under 5 years of age

Eurostat 2021

Participation in early childhood education

Percentage of children aged 36 to 59 months participating in early childhood education

UNICEF Eurostat

2022

Participation in education and training

Percentage of adults aged 25-64 years participating in formal and non-formal education and training

Eurostat 2023

Young people not in employment/education/training

Percentage of young people who are not in employment, education or training

ILO Eurostat

2023

Social Capital

Children's exposure to Bullying Percentage of children aged 11-15 reporting being bullied in school 2-3 times a month

HBSC 2022

Children's exposure to Cyberbullying

Percentage of children aged 11-15 reporting being cyberbullied 1-2 times a month

HBSC 2022

Equal treatment under the law and absence of discrimination

Score from 0-1 on the World Justice Project Rule of Law Index measuring equal treatment under the law and absence of discrimination

World Justice Project

2024

Freedom of choice and control over life

Percentage of people aged 18+ years reporting low levels of freedom of choice and control over their own life

WVS 2022

Frequency of meeting socially Percentage of people aged 16+ years who meet with family/friends less than once a month

EU-SILC ESS

2023

Gender Equality Index Score from 1-100 on the Gender Equality Index

EIGE 2024

Having someone to ask for help Percentage of people aged 16+ years who don't have someone to ask for help

EU-SILC 2022

Participation in voluntary activities

Percentage of people aged 16+ years participating in formal voluntary activities

EU-SILC 2022

Perceived ability to influence politics

Percentage of people aged 18+ years reporting having no ability to influence politics

ESS 2023

Self-reported social support in young people

Percentage of children aged 11-15 years reporting low peer support

HBSC 2022

Social Institutions and Gender Index

Score from 0-100 on the Social Institutions and Gender Index

OECD 2023

Trust in others Percentage of adults aged 18+ years reporting low trust in other people

ESS WVS 2023

Trust in politicians Percentage of people aged 16+ years reporting low levels of trust in politicians

ESS 2023

Living environment

Commercial determinants

Daily breakfast consumption in schoolchildren

Percentage of children aged 11-15 missing breakfast on any schoolday

HBSC 2022

Food insecurity Percentage of people who cannot afford to eat an adequate meal

EU-SILC WVS

2023

Food Price Index Food Price Index (2017=100) Eurostat World Bank

2025

Moderate or severe food insecurity

Percentage of the population experiencing moderate or severe food insecurity (3-year average)

FAO 2022

Tobacco tax Tobacco taxes as a percentage of price of the most sold brand

WHO 2022

Value-added tax (VAT) on alcohol

Value-added tax (VAT) on alcohol WHO 2019

Digital environment

Internet use Percentage of people aged 16+ years who never use the internet

ESS WVS 2023

Environment

Annual mean PM10 concentrations

Annual mean concentration of PM10 in cities WHO 2022

Annual PM2.5 emissions Total annual PM2.5 emissions (ton) GDL 2022

DALYs due to air pollution DALYs attributable to air pollution (age standardized rate per 100,000)

GBD 2021

Diesel cars Diesel powered passenger cars as a percentage of all registered passenger cars

Eurostat 2023

Feeling unsafe from crime in own home

Percentage of adults aged 18+ years feeling unsafe from crime in their own home

WVS 2022

Feeling unsafe walking alone after dark

Percentage of adults feeling unsafe when walking alone in their area after dark

ESS 2023

Intentional homicide Intentional homicide by intimate partner or family member, victims per 100,000 population

Eurostat 2023

Neighbourhood security Percentage of people aged 16+ years who don't feel secure living in their neighbourhood

WVS 2023

Physical violence in children Percentage of children aged 11-15 reporting having been in 3 or more physical fights in past year

HBSC 2018

Pollution/grime/other environmental problems

Percentage of people reporting pollution, grime or other environmental problems in their area

EU-SILC 2023

Public spending on housing and community amenities

General government expenditure on housing and community amenities as a percentage of GDP

IMF Eurostat OECD

2023

Reported crime, violence and vandalism

Percentage of people aged 16+ years reporting local problems with crime, violence or vandalism

EU-SILC 2023

Road deaths Estimated road traffic deaths per 100,000 population

WHO 2019

Housing

Basic drinking water services Percentage of people without at least basic drinking water services (an improved source within a 30 minute round trip to collect water)

WHO UNICEF

2022

Basic sanitation services Percentage of people without at least basic sanitation services (improved sanitation facilities that are not shared with other households)

WHO UNICEF

2022

DALYs due to unsafe sanitation

DALYs attributable to unsafe sanitation (age standardized rate per 100,000)

GBD 2021

Households receiving housing allowance

Percentage of households receiving housing allowance

OECD 2022

Housing cost overburden rate Percentage of people living in a household where housing costs are more than 40% of disposable household income (net of housing allowances)

EU-SILC 2023

Housing overcrowding Percentage of people living in an overcrowded dwelling

EU-SILC 2023

Inability to adequately heat home

Percentage of people who cannot afford to keep their home adequately warm

EU-SILC 2023

Severe housing deprivation rate

Percentage of people living in an overcrowded dwelling that also lacks a bath and indoor toilet, or is damp or too dark

EU-SILC 2023

Employment and working conditions

Job security

Employment rate Percentage of those in employment among those aged 16-64 years

EU-SILC ESS WVS

Labour force participation rate

Percentage of the population either working or looking for work

ILO 2024

Proportion of the poorest quintile population covered by labour market programs

Percentage of the poorest quintile in the population covered by unemployment benefits and active labor market programs

World Bank

2020

Public expenditure on labour market policies

Public expenditure on labour market programmes (active and passive) as a percentage of GDP

OECD 2022

Redundancy pay at 2 years of tenure, in months

Number of months of redundancy pay at two years of job tenure

ILO 2020

Temporary employees Temporary employees as percentage of the total number of employees aged 20-64 years

Eurostat 2023

Unemployment benefit coverage

Percentage of unemployed persons receiving regular periodic social security unemployment benefits

ILO 2023

Unemployment rate Percentage of unemployed persons in the labour force

ILO 2024

Working conditions

Accidents at work Percentage of workers aged 15-64 years reporting at least one non-fatal accident at work in the past 12 months

EULFS via Eurostat

2020

Average wages/earnings Average nominal monthly earnings of employees in 2021 USD PPP

ILO 2023

Collective bargaining coverage

Percentage of employees whose pay and/or conditions of employment are determined by one or more collective agreements

ILO 2020

Disability employment ratio Ratio of employment rates between people without disabilities and people with disabilities, aged 16-64 years

EU-SILC 2023

Feeling about household's income

Percentage of people aged 16+ years reporting difficulties living on their household's income

ESS 2023

Gone without cash income Percentage of people aged 16+ years reporting they sometimes or often have to get by without an income

WVS 2022

In-work poverty Percentage of employed persons aged 18+ years with income below 60% of median equivalised disposable income (after social transfers)

EU-SILC 2023

Labour inspectors Average number of labour inspectors per 10,000 employed persons

ILO 2023

Labour share of GDP (wages and social protection transfers)

Percentage of GDP allocated to labour compensation, comprising wages and social protection transfers

ILO 2024

Low financial satisfaction Percentage of people aged 16+ years reporting low levels of satisfaction with the financial situation of their household

WVS

Low job control Percentage of the population aged 16-64 years, reporting low levels of control over how their daily work is organised

ESS

Minimum wage Statutory nominal gross monthly minimum wage in constant 2021 USD PPP

ILO 2024

Working excessive hours Percentage of workers working 40+ hours per week

ILO 2024

Working excessive hours (more than 45 hours per week)

Percentage of the population aged 16-64 years, working more than 45 hours per week

ESS

Income security and social protection Poverty and inequality

Adequacy of social assistance programmes

Social assistance transfers received by beneficiaries as a percentage of their total income or consumption

World Bank

2021

Coverage of social assistance programmes

Percentage of the population participating in social assistance programs

World Bank

2021

Disability poverty ratio Ratio of poverty rates (percentage below 60% of median income) between people with disabilities and people without disabilities, aged 16-64 years

EU-SILC 2023

Gini index Gini index of income inequality from 0-100 World Bank

2022

Incidence of social assistance programmes

Social assistance program beneficiaries in each income quintile as a percentage of total number of social assistance program beneficiaries

World Bank

2021

Poverty Percentage of the population with income below 60% of median equivalised disposable income (EUSILC) or national poverty lines (World Bank)

EU-SILC World Bank

2024

Public spending on social protection

General government expenditure on social protection as a percentage of GDP

IMF Eurostat OECD

2023

Public spending on social protection for children and families

General government expenditure on social protection for children and families, as a percentage of GDP

Eurostat 2023

Public spending on social protection for sickness and disability

General government expenditure on social protection for sickness and disability, as a percentage of GDP

Eurostat 2023

Public spending on social protection for unemployment

General government expenditure on social protection for unemployment, as a percentage of GDP

Eurostat 2023

Risk of child poverty Percentage of children aged 0-18 at risk of poverty or social exclusion

Eurostat 2024

Vulnerable people covered by social assistance programmes

Percentage of poor persons covered by social protection systems

ILO 2023

Supporting older people Pension coverage Percentage of the population above statutory

pensionable age receiving an old age pension ILO 2023

Pension expenditure per beneficiary

Annual pension expenditure per beneficiary for old-age pensions in purchasing power standards (PPS)

Eurostat 2022

Pension net replacement rate

Individual net pension entitlement as a percentage of net pre-retirement earnings

OECD 2022

Supporting parenting

Length of paid maternity and parental leave

Length of paid maternity and parental leave available to mothers in weeks

OECD 2021

Length of paid paternity and parental leave

Length of paid paternity and parental leave available for fathers in weeks

OECD 2021

Maternity benefits Percentage of women giving birth receiving maternity cash benefits

ILO 2023

Annex references

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3. Hendramoorthy M, Kupek E, Petrou S. Self-reported health and socio-economic inequalities in England, 1996–2009: Repeated national cross-sectional study. Soc Sci Med. 2015;136–137: 135–146 (https: doi.org 10.1016 j.socscimed.2015.05.026, accessed 1 April 2019).

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5. Subramanian V, Huijts T, Avendano M. Self-reported health assessments in the 2002 World Health Survey: how do they correlate with education? Bull World Health Organ. 2009;88(2):131–138.

6. Functional and activity limitations statistics [website]. uxembourg: Statistical Office of the European Union; 2017 (https: ec.europa.eu eurostat statistics- xplained index.php Functional_and_activity_limitations_statistics, accessed 1 April 2019).

7. Brown C, Harrison D, Burns H, Ziglio E. Governance for health equity: taking forward the equity values and goals of Health 2020 in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2014 (http: www.euro.who.int en publications abstracts governance-for-health-equity, accessed 1 April 2019).

8. OECD guidelines on measuring subjective well-being. Paris: Organisation for Economic Co-operation and Development; 2013 (https: www.ncbi.nlm.nih.gov books NBK189567 , accessed 1 April 2019).

The Second WHO European Health Equity Status Report – 2025

Healthy prosperous lives for all

The purpose of this document is to provide information to Member States to support their review and any comments on the data indicators and graphs that will be included in the 2nd Health Equity Status Report that will be launched in November 2025.

Introduction

The WHO European Health Equity Status Report initiative (HESRi), launched in 2019, provides data-driven insights to support policy actions that address health inequities (1).

Since its launch the work has inspired local and national authorities to strengthen their health equity policies, has informed the work of international bodies (EU institutions and agencies, OECD), and has been a catalyst for new alliances across countries and communities of practice. The work has spearheaded political commitments and the creation of practical resources to drive action on health equity and well-being – enabling governments and health authorities to progress efforts towards creating a healthy life for all.

Continuing this work, WHO Europe is currently preparing a Second Pan European Health Equity Status Report. This will bring together the latest available status and trends in health equity and well-being within and across countries of the WHO European Region.

The second HESRi Report will delve into Health equity across the life course with a spotlight on children and young people and on healthy ageing. This is in the light of major demographic shifts in the age structure of populations within countries in the European Region. The 2nd HESRi report will use innovative techniques to capture health equity status and trends of men and women at different stages across the life course. It will show how these are associated with key features of i) health systems; ii) social relations and participation, education and learning iii) living conditions; iv) employment and work and v) income security, and set out options and solutions for reducing inequities and accelerating progress for healthy and prosperous lives for all, with co-benefits for societies with higher trust and life satisfaction, more resilient public systems and fiscal stability.

Figure 0.1 Essential conditions for a healthy life

The 2nd Health Equity Status Report will be launched in Europe in November 2025

Background

• The 2nd Health Equity Status Report (HESRi2) is a comprehensive review of the status and trends in health inequities and of the essential conditions needed for all to be able to live a healthy life in the WHO European Region.

• Improving health and well-being for all, reducing health inequities and ensuring no one is left behind will bring wider economic, social and environmental benefits to Member States.

• This report seeks to change the common perceptions that health inequity is too complex to address and that it is unclear what actions to take and which policies and approaches will be effective.

• The HESRi2 captures the progress made in implementing a range of policies with a strong effect on reducing inequities and demonstrates the link between levels of investment, coverage and uptake of these policies, as well as the gaps in the essential conditions needed to live a healthy, prosperous life.

• The report will support WHO and Member States to implement the health equity priorities set out in the draft 2nd European Program of Work (EPW2) (2)

Indicators that measure poverty risk, income security and the adequacy and coverage of social protection and welfare policies that

protect health over the life-course

Indicators that measure the health impact of employment and working conditions, including availability, accessibility,

security, wages, physical and mental demands, and exposure to unsafe work

Indicators that measure differential opportunities, access and exposure to living conditions and environmental factors that

impact health and well-being

Indicators of human capital that measure the access availability and affordability of education, learning and literacy;

Indicators of social capital that measure social inclusion exclusion, levels of trust, safety and participation of individuals and communities

in society.

Indicators that measure the availability, accessibility, affordability and quality of prevention, treatment and health-care services

andprogrammes

Health Health Services

Health Social and Human Capital

Health iving Conditions

Health Employment and Working Conditions

Health Income Security and Social

Protection

HESRi2 data

• The HESRi2 analysis and findings are generated from a new dataset. It brings together three types of data (Fig. 0.1) and uses innovations in analytical methodologies to provide a better understanding of health equity, the pathways that generate equity and inequities, and how policy interventions are associated with the rate of progress to reduce gaps in health and well-being, across the countries of the WHO European Region (Annex 1).

• The HESRi2 data and analysis provide the following benefits.

1. Country-specific data allow governments to strengthen decision–making, tailoring their action and investment for health equity accordingly.

2. Analysis supports ministries of health to demonstrate how events and decisions made in other sectors contribute to and interact with inequities in health and well-being.

3. Evidence that enables national and subnational governments and health authorities to improve policy coherence, leading to improved equity in health and in life chances.

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HESRi2 uses a range of data analysis and visualizations to support a robust understanding of the current status of health inequities within countries. It also captures whether there

have been significant reductions or increases in these inequities over a period of 10–15 years (trend analysis data) (Annex 1).

• Gradient charts are used to show the socioeconomic gradient for an indicator, such as self-reported health, by examining how levels of the indicator vary between subgroups of people. Either three or five subgroups are defined according to markers of socioeconomic status - for example, level of education (three groups) (Fig. 0.2), or income or wealth (five groups). For people belonging to each subgroup, the average level of the indicator is calculated and represented in the chart by a different coloured dot.

• Gap and trend charts are used to show the difference, or gap, in average levels of the indicator in the most advantaged subgroup compared to the most disadvantaged subgroup. For example, the charts show the difference between those in the highest and lowest income quintiles or between those with highest level of education (degree-level) and those with least (lower-secondary or below). The traffic light symbols in these figures also show whether the size of the gap for each country has narrowed, widened, or stayed the same over a specified time frame (e.g. Fig 0.3).

• Decomposition charts are used to show how shortcomings in each of the five essential conditions, when combined, contribute to the gap for a given health indicator, such as mental health or limiting illness. The decomposition charts enable policymakers to see more clearly the relative weight of each condition in contributing to (in)equity in a specific health indicator (Annex 2).

• Equity tracker charts show, for each country, a summary outcome for either individual indicators or groups of indicators. The outcomes include the level of inequality or the direction of the trend in inequality. The groups of indicators are the five essential conditions, while the individual indicators are either health outcomes or variables contributing to one of the essential conditions (Annex 2).

Health equity – recent status and trends

1) Health and wellbeing status

Enshrined in the WHO constitution (3) is the principle that “health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.” Underlying this are, among other principles “the enjoyment of the highest attainable standard of health is one of the fundamental rights of every human being without distinction of race, religion, political belief, economic or social condition” and “Governments have a responsibility for the health of their peoples which can be fulfilled only by the provision of adequate health and social measures. In this section of the report, the current status and trends in inequalities in health and wellbeing outcomes within each member state in the European Region are summarized. The graphs likely to be used to illustrate this are shown below. A full list of indicators from which the summary will be drawn is given in Annex 4. The social conditions which give rise to and can be a consequence of the inequities in health and wellbeing are summarized in the section that follows this one.

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2) Essential conditions for health and wellbeing

The analysis presented in the first HESRi report identified the five essential conditions needed to live a healthy life in Europe in the 21st Century as listed in Figure 0.1. This section provides a brief rationale for each of these five conditions and graphs that will be used illustrate status and trends in the indicators related to these conditions. A full list of the HESRi indicators relating to each condition is in Annex 3. Commentary on each condition will draw on a selection of these indicators.

2.1 Health systems

Health systems have a key role in addressing equity in health. Inequities in health will result if the provision of prevention and treatment are unequally distributed in the population – the inverse care law (4). But the health care system can do more than this – it can contribute to the reduction of inequities resulting from social conditions that lead to ill health and a lack of wellbeing. To do both of these, the health system must be universal – providing care for everyone, offering high quality care to all, affordable – ensuring that any out of pocket payments do not place a disproportionate burden on anyone and are responsive to need – do not leave anyone with unmet healthcare needs.

Universal health coverage has an equity impact through ensuring that everyone can use appropriate and effective health services without experiencing financial hardship, irrespective of ability to pay, age, ethnicity, disability, geographical location, race, religion, sex, or sexual orientation. People experience financial hardship when out-of-pocket (OOP) payments are large in relation to their ability to pay for health services. ack of financial protection can lead to or deepen poverty, undermine health, and exacerbate health and socioeconomic inequities. Where health systems fail to provide adequate financial protection, people may be forced to choose between using health services and meeting other basic needs such as food, housing and heating. This has a negative impact on health and well-being, which in turn further increases the risk of socioeconomic vulnerability and exclusion (5). Policies that distribute more resources to areas with greater health, social and economic needs have a positive impact on reducing health gaps between social groups and geographical areas (6).

The quality of health care has an equity impact because health outcomes improve with better quality care (7). Therefore, equitable provision of good quality care reduces gaps in outcomes. Differences in the quality of care arise from differences in the geographical distribution of health services in low- and high-income neighbourhoods or other forms of discrimination that result in lower-income households receiving lower-quality care and poorer health outcomes.

Unequal quality of health care is frequently found between providers catering to low- or high-income neighbourhoods and between private and public providers, mirroring inequities in socioeconomic status and health outcomes. Disaggregation by education level, income and sex allows this indicator to monitor equity in the quality of health care received based on socioeconomic status.

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2.2 Social and human capital

Early childhood development lays the foundation for physical and mental health and well- being outcomes throughout the life course (8). Investment that reduces health risks and vulnerability in the prenatal period and early childhood help parents with lower resources give their children a healthy start in life. Equal access to good quality education from an early age has a strong impact on reducing differences in opportunities and risks, which has both direct and indirect impacts on health. Continued access to education and lifelong learning opportunities has a direct effect on promoting social and economic inclusion and mental well-being). It also has indirect effects linked to increased health and social literacy, such as awareness of health risks and behaviours and mediating life chances and the effects of social and economic shocks (8,9).

Mattering - experiences of feeling valued and adding value (10) -is related to positive outcomes when present, and negative results when absent. When people are treated with dignity and respect, the overall sense of mattering and wellbeing increases. When people perceive that they do not matter, their overall wellbeing suffers greatly. This is the case because people have a fundamental need to experience social worth. Meaningful participation in society, trust in others and being able to influence decisions affecting health and life chances have important direct and indirect health effects. They contribute to stronger individual and social resilience and lower levels of morbidity and poor mental health. Exposure to low-trust environments characterized by higher crime rates, social isolation and lack of ability to influence politics and decision-making in society are strongly associated with poor mental health and a higher risk of morbidity (11).

Participatory public services empower people to take control over their lives and the determinants of their own health. Investment in civic participation, reducing crime and generating social connections have positive impacts on the health and well-being of individuals through their engagement with, and trust in, the local community and wider society (6,12).

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2.3 Living conditions

Shelter is a fundamental human need, providing safety, a sense of belonging, peace and security. Poor housing and poor health are inextricably linked. People living in unaffordable, poor-quality or insecure housing are more likely to report poor health and to suffer from a variety of health problems (13,14).

Differences in the quality of the local environment in which people live have an impact on health equity. ow-income households and neighbourhoods are less likely than those who are wealthier to be in, or have access to, safe, health-promoting environments (14). ocal environments that are detrimental to physical health may also generate stress and anxiety, providing an additional link between physical environment and mental health (14,15). Policies that shape geographical planning, construction and management of public spaces can reduce health inequities due to the local environment (6).

Commercial organizations influence all aspects of life and can therefore have either a beneficial or harmful influence on health equity. Marketing and product exposure, to influence the consumption of alcohol, tobacco and food items, affect the risks of noncommunicable diseases and mortality. This has an impact on health equity because commercial influence varies with how well people are educated about the health risks of detrimental consumption behaviours, whether their economic situation and social environment encourage or discourage them from such consumption behaviours and whether they have easy access to healthy alternatives.

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2.4 Employment and working conditions

Productive participation in the labor market can have a positive impact on health equity at an individual level - through its effects on material conditions and on life chances – and at a societal level – through creating the macro-economic resources that underpin policies to enable people to prosper. People who have experienced long-term unemployment are at a higher risk of premature mortality. Young people who have experienced long-term unemployment are more likely than those who have not experienced unemployment to report risky health behaviours, at all levels of socioeconomic status.

Good work is an essential requirement for health. Job insecurity, temporary employment and poor or stressful working conditions are associated with poor mental health, self- reported ill health and an increased risk of fatal and non-fatal cardiovascular events. Excessive hours and few contractual rights directly and indirectly contribute to inequities in physical and mental health. Work-related stress follows a social gradient (16).

Systematic differences in exposure to poor working conditions are closely associated with gender norms and the workers’ age and education level. Discrimination in the labour market exists between sexes. socioeconomic and migrant groups, and persons with disabilities. Ensuring equitable participation in secure and decent employment has the potential to address health and social inequities, including gender inequities, by providing equal opportunities to obtain a secure income (16).

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2.5 Income security and social protection

The risk of poverty directly correlates with early-onset morbidity and premature mortality. Repeated or long-term exposure to financial insecurity predicts the likelihood of poor self- reported health, depression and the inability to meet the basic needs to live a healthy life, such as food, fuel and shelter (15). Non-stigmatizing social protection policies have positive effects on reducing health inequities related to income insecurity and poverty.

Investment in social protection for families and early childhood reduces health inequities by weakening the link between socioeconomic disadvantage and poor child health outcomes, including infant mortality and adverse child development (8,17). Such investment contributes to meeting children’s basic needs and helps them reach their full potential in life and health, irrespective of their or their parents’ sex, disability, race, ethnicity, origin, religion, and economic or other status (18).

Social protection programmes that support older people have an impact on health equity by maintaining their capacity to avoid health risks, access health services and perform daily tasks, especially for those with financial and health vulnerabilities. Older people, especially those from low-income backgrounds, are likely to have greater health-care needs or caring responsibilities for partners, and financial support through pension systems helps reduce their risk of ill health, disability and social exclusion (19). Women live longer than men, spending more of their lives in ill-health and are more likely to have caring responsibilities. Social protection and pension provision among women are key to addressing the resulting gender gaps in health, wellbeing and social conditions of older women.

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3) Deep dive into equity across the life course

3.1 Demographic change

The age structure of the region is changing, but not in exactly the same way in every Member State. Two simple summaries of the age structure of a country are the ratio of children (ages 0 to15) to working age adults (ages 15 to 64) and the ratio of old people (ages 65 and over) to working age adults. These ratios affect both the number and types of health events that occur in a country – often forming the focus of the health system – and also the capacity of the working age population to fund public services – principally health, social protection, education and pensions – required by children and the elderly, particularly the most vulnerable.

In every member State in the region, the ratio of older people (aged 65 and over) to those of working age (ages 15 to 64) increased in the ten years to 2024. At the same time, the ratio for those below working age (aged under 15) increased in around half of Member States and decreased in the other half of States. While a decrease in the ratio of young people reduces the short-term burden, in the longer term it suggests a reduction in the size of the working population. Although the two scenarios ultimately require similar policy responses, these will differ in terms of timescales and implementation plans.

These changes form the backdrop to the life course approach taken in this section, emphasizing what is happening to inequalities in children and young people, how ageing healthily varies across social groups and how gender differences persist across the region.

Health advantage and disadvantage accumulates across the life course – from the point of conception (and before), the conditions in which a child’s parents find themselves influences the child’s development, health and life chances. These influences are amplified by the education, training and employment opportunities experienced at the end of schooling. These, in turn, affect the income that a person can obtain, the control they have over subsequent life chances and the extent to which they matter in society. These can then influence the pension they receive in retirement. At each stage in this journey, the risk of developing a health problem increases and this can affect life chances and is affected by the conditions experienced. It is this combination of the essential conditions and health that therefore affect whether or not an individual is likely to age healthily.

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3.2 Children and young people

As indicated above, child development is important to both health in childhood and adolescence and to thriving as an adult – the ability to obtain and maintain the skills needed in adulthood, good work, decent housing and live healthily in a healthy environment.

Psychological factors are important in achieving this trajectory to good health. This includes a sense of wellbeing and mattering, life satisfaction, freedom from bullying and depression and a positive digital life. These are all interconnected. Failing physical or mental health at an early age can easily derail this trajectory to good health.

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3.3 Healthy ageing

As described above, healthy ageing is a process that evolves through life and, consequently, the key equity aspects of this process are summarized in the preceding sections. In this section, the focus is therefore on how people prepare for old age – in terms of pension and long-term care provision- and the consequences this has for equity in old age – differences in pension coverage and health status.

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3.4 Gender

Gender differences permeate all aspects of life. Wherever the data permits, the preceding sections provide disaggregation by sex and highlight the existence of differences in the essential conditions and outcomes. In this section, the emphasis is on drawing tother the story painted by the data and identifying factors that are specially about gender roles – such as parenting – and gender-related violence. The absence of routine data on many gender-related issues is of concern.

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4) Modelling

In the report, a variety of modelling tools are used to indicate (a) quantify the role of the five essential conditions (or their component indicators) in creating the equity gap in specific outcomes (such as limiting illness) in different contexts (such as life course stage, gender, sub-region) (b) the association between a change in one or more component indicators (such as poverty) and the size of the equity gap, indicating the potential, quantifiable reduction in the equity gap that could be achieved by taking action on specific social conditions (c) conversely, quantifying the extent to which improved health outcomes are associated with improved social conditions (e.g. improved mental health is associated with reduced unemployment). The technical details of the methodologies used in each type of modelling are summarized in Annex 2in the health equity gap.

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5) Summary – equity trackers

The equity trackers provide an array of traffic lights that summarise, for each Member State, either (a) the level of inequality in each essential condition (or in the component indicators of an essential condition) or (b) whether the trend in inequality has shown a significant widening over time. These dashboards enable Member States to see, at a glance, where they are making progress and, conversely, where they need to focus greater effort.

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References

1. Health Equity Status Report initiative (HESRi) products: 2019 https: www.who.int europe initiatives health-equity-status-report-initiative hesri-products

2. Developing the Second European Programme of Work 2026-2030 (EPW2):2025 https: www.who.int europe activities developing-the-second-european-programme-of- work

3. WHO constitution (with amendments): 2025 https: www.who.int about governance constitution

4. Tudor Hart, J The inverse care law. The ancet, 1971, Volume 297, Issue 7696, 405 - 412 https: doi.org 10.1016 S0140-6736(71)92410-X

5. Cylus J, Thomson S, Evetovits T. Catastrophic health spending in Europe: equity and policy implications of different calculation methods. Bull World Health Organ. 2018;96(9):599–609. doi:http: dx.doi.org 10.2471 B T.18.209031.

6. Poverty, social exclusion and health systems in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2010 http: www.euro.who.int __data assets pdf_file 0004 127525 e94499.pdf?ua=1.

7. egido-Quigley H, McKee M, Nolte E, Glinos I. Assuring the quality of health care in the European Union. Geneva: World Health Organization; 2008 (Observatory Studies Series No. 12; http: www.euro.who.int __data assets pdf_file 0007 98233 E91397.pdf?ua=1.

8. Jensen BB, Currie C, Dyson A, Eisenstadt N and Melhuish E. Early years, family and education task group: report. Review of social determinants of health and the health divide in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2013 https: iris.who.int bitstream handle 10665 344815 9789289000567-eng.pdf?sequence=1

9. Closing the gap in a generation: health equity through action on the social determinants of health. Final report of the commission on social determinants of health. Geneva: World Health Organization; 2008 (https: apps.who.int iris bitstream handle 10665 43943 9789241563703_eng.pdf?sequen ce=1.

10. Kruglanski, A.W., Prilleltensky, I., Raviv, A. (Eds.). (2025). The Routledge International Handbook of Human Significance and Mattering (1st ed.). Routledge. https: doi.org 10.4324 9781003424437

11. Wilkinson RG, Pickett KE. Income inequality and social dysfunction. Annu Rev Sociol.2009;35:493–511. doi: 10.1146 annurev-soc-070308-115926.

12. Rocco , Suhrcke M. Is social capital good for health? A European perspective. Copenhagen: WHO Regional Office for Europe; 2012 http: www.euro.who.int __data assets pdf_file 0005 170078 Is-Social-Capital-good-for- your-health.pdf?ua=1.

13. Braubach M, Jacobs D, Ormandy D. Environmental burden of disease associated with inadequate housing: a method guide to the quantification of health effects of selected housing risks in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2011 http: www.euro.who.int __data assets pdf_file 0017 145511 e95004sum.pdf?ua=1.

14. Friedli . Mental health, resilience and inequalities. Copenhagen: WHO Regional Office for Europe; 2009 http: www.euro.who.int __data assets pdf_file 0012 100821 E92227.pdf?ua=1.

15. Bell R. Psychosocial pathways and health outcomes: informing action on health inequalities. ondon: UC Institute of Health Equity; 2017 http: www.instituteofhealthequity.org resourcesreports psychosocial-pathways-and- health-outcomes-informing-action-on-health-inequalities psychosocial-pathways-and- health-outcomes.pdf.

16. Siegrist J, Rosskam E, eka S. Work and worklessness: final report of the task group on employment and working conditions, including occupation, unemployment and migrant workers. Review of social determinants of health and the health divide in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2016 (http: www.euro.who.int __data assets pdf_file 0004 334354 EWC-task-report.pdf?ua=1.

17. Baker P, Hone T, Reeves A, Avendano M, Millet C. Does government expenditure reduce inequalities in infant mortality rates in low- and middle-income countries? A time- series, ecological analysis of 48 countries from 199 to 2013. Health Econ Policy aw. 2019;14(2):249–73.doi: 10.1017 S1744133118000269

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19. World report on ageing and health. Geneva: World Health Organization; 2015 (https: apps.who.int iris bitstream handle 10665 186463 9789240694811_eng.pdf?seque nce=1.

Annex 1. Methods to derive indicators

A1.1 Selection of indicators

Indicators were selected for inclusion in the 2nd Health Equity Status Report (HESRi2) Dataset to provide a pragmatic set of measures that were based on accessible data and cover health equity status and policy progress across the five underlying conditions of health equity: Health Services, Social and Human Capital iving Conditions, Employment and Working Conditions and Income Security and Social Protection. Certain key criteria were considered in selecting indicators, in consultation with the Scientific Expert Advisory Group and the HESRi2 Country Partners Group.

The HESRi2 aimed to include indicators that:

• clearly related to priority areas for action on health equity for which there is a clear evidence base and reflected in the WHO Euro Health Equity Policy Tool;

• related to areas of action in which Member States have already made commitments;

• were relevant to the full range of country contexts across the WHO European Region;

• provided a balance of measures across the five underlying conditions for health equity;

• provided as much coverage of countries across the Region, consistent with ensuring a good representation of inequalities in countries that do not provide data to the Statistical Office of the European Union (i.e. to Eurostat);

• used data recoded on international or intranational databases that had been collected though consistent processes across countries in order to support more reliable comparisons;

• used data that could be disaggregated by socioeconomic status, or that were related to policies and expenditure known to have an impact on health inequities;

• used openly accessible data from international datasets, which could be feasibly collected and analysed given the available timescale and resources;

• used data that were reasonably current and that could be updated to track trends.

Initial, the list of 108 HESRi1indicators were reviewed, to assess the feasibility of updating them. New indicators were added, either to replace those that could not be updated or to address topics that were in scope, but not covered in HESRi1. This process was reviewed by the Scientific Expert Advisory Group and the HESRi2 Country Partners Group and through

wide consultation against the criteria listed above; as a result,122 indicators were selected for inclusion in the Health Equity Dataset.

A1.2 Data sources

Data from several sources were used to derive the indicators featured in the Health Equity Dataset. Data were obtained from sources such as household and other population-based surveys, administrative and financial data systems and surveillance systems. Where obtainable, publicly available data were used. For example, a number of indicators were derived from datasets containing national-level data published by the Organisation for Economic Cooperation and Development (OECD), World Bank, International abour Organization I O), United Nations Children’s Fund (UNICEF), Food and Agriculture Organization (FAO), International Monetary Fund (IMF), World Value Survey (WVS), Global Data ab (GD ), Global Burden of Disease (GBD), European Institute for Gender Equality (EIGE), World Justice Project, and the Eurostat database from the Statistical Office of the EU. Eurostat data were obtained through a data-sharing agreement (RPP 85 2018- FS-EU-SI C- EHIS). The responsibility for all conclusions drawn from the data lies entirely with the authors.

Where data were not publicly available, survey microdata, which contained information available at the individual level, were used to derive indicators. Official requests were made to access survey microdata, using (for example) the European Union Statistics on Income and iving Conditions instrument (EU-SI C), the WHO STEPwise approach to Surveillance (STEPS) tool, and the European Health Interview Survey (EHIS; included in the list below). Empirical data collected from these nationally representative surveys, among others, were compiled and processed to create indicators.

Details of each of the databases, surveys and other sources used and the acronyms used for them in this document are listed below.

Acronym Database/survey details EHIS European Health Interview Survey [online database]. Luxembourg:

Statistical Office of the European Union (Eurostat) (https://ec.europa.eu/eurostat/web/microdata/european-health- interview-survey).

EQLS European Quality of Life Surveys (https://www.eurofound.europa.eu/surveys/european-quality-of- lifesurveys). European Foundation for the Improvement of Living and Working Conditions (Eurofound). European Quality of Life Survey integrated data file, 2003–2016 (data collection). 3rd Edition. Colchester: UK Data Service; 2018 (SN: 7348) http://doi.org/10.5255/UKDA-SN-7348- 3).

ESS European Social Survey (http://www.europeansocialsurvey.org/). ESS Cumulative Data Wizard, ESS 1-8. Data file edition 1.0. Bergen: Norwegian Centre for Research Data; 2018 (doi:10.21338/NSD-ESS- CUMULATIVE).

EU-LFS European Union Labour Force Survey [website]. Data and publication. Luxembourg: Statistical Office of the European Union (Eurostat) (https://ec.europa.eu/eurostat/statistics- explained/index.php/EU_labour_force_survey_%E2%80%93_data_and_p ublication).

Eurostat Eurostat [online database]. Luxembourg: Statistical Office of the European Union (http://ec.europa.eu/eurostat/data/database).

EU-SILC European Union Statistics on Income and Living Conditions [online database]. Luxembourg: Statistical Office of the European Union (Eurostat) (http://ec.europa.eu/eurostat/web/microdata/europeanunion- statistics-on-income-and-living-conditions).

EWCS European Working Conditions Survey (https://www.eurofound.europa.eu/surveys/european- workingconditions-surveys). Eurofound. European Working Conditions Survey integrated data file, 1991–2015 (data collection). 7th Edition. Colchester: UK Data Service; 2018 (SN: 7363) (http://doi.org/10.5255/UKDASN-7363-7).

GBD Global Burden of Disease Collaborative Network [online database]. Seattle (WA): Institute for Health Metrics and Evaluation (IHME) (http://ghdx.healthdata.org/organizations/global-burden- diseasecollaborative-network).

GDL Global Data Lab [online database]. Nijmegen: Raboud University Institute for Management Research (https://globaldatalab.org/).

HBSC Health Behaviour in School-aged Children [online database]. Bergen: HSBC Data Management Centre (https://www.uib.no/en/hbscdata).

ILO ILOSTAT [online database]. Statistics of the International Labour Organization. Geneva: International Labour Organization (http://www.ilo.org/ilostat/).

MICS Multiple Indicator Cluster Surveys [online database]. New York (NY): United Nations Children’s Fund (http: mics.unicef.org surveys).

OECD OECD. Stat data warehouse [online database]. Paris: Organisation for Economic Co-operation and Development (https://stats.oecd.org/).

PISA OECD PISA [online database]. Programme for International Student Assessment. Paris: Organisation for Economic Co-operation and Development (http://www.oecd.org/pisa/data/).

STEPS WHO STEPwise approach to Surveillance [website]. Geneva: World Health Organization (https://www.who.int/ncds/surveillance/steps/en/).

UNICEF UNICEF data [online database]. New York (NY): United Nations Children’s Fund (https://data.unicef.org).

WHO EHIG

European Health Information Gateway [online database]. Copenhagen: WHO Regional Office for Europe (https://gateway.euro.who.int/en/).

WHO GHED

WHO Global Health Expenditure Database [online database]. Geneva: World Health Organization (https://apps.who.int/nha/database).

WHO GHO

WHO Global Health Observatory data repository [online database]. Geneva: World Health Organization (http://www.who.int/gho/en/).

WHO UNICEF

WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene [online database]. Geneva: World Health Organization and United Nations Children’s Fund (https://washdata.org/data).

UN IGME

UN Inter-agency Group for Child Mortality Estimation (https://data.unicef.org/resources/un-inter-agency-group-for-child- mortality-estimation-unigme/)

WJP World Justice Project Rule of Law Index [online database]. Washington (DC): World Justice Project (https://worldjusticeproject.org/our- work/wjp-rule-law-index).

World Bank

World Bank Data Catalogue [online database]. Washington (DC): The World Bank Group (https://data.worldbank.org/data-catalog).

WVS Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez- Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen (eds.). 2022. World Values Survey: All Rounds - Country-Pooled Datafile. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. Dataset Version 3.0.0. doi:10.14281/18241.17

A1.3 Data processing

Following data collection, individual datasets containing primary data were stored in a secure data warehouse for processing. In order to maximize country coverage, indicators were created by combining datasets from different sources, if comparable data were available. Data processing involved, where necessary, data cleaning and suppression of implausible values and cells based on small samples (counts of <50 respondents). Data were aggregated by country and year, as well as characteristics such as sex, age group, education level and income, using survey weights where available to adjust for sampling errors and biases. To facilitate comparison between countries with different age profiles, health outcome indicators were directly age standardized with the WHO World Standard Population. For some analyses, countries were aggregated by geographical region, as follows: Caucasus, central Asia, central Europe, Nordic countries, Russian Federation, South-eastern Europe western Balkans, southern Europe and western Europe (see Annex 3).

For the majority of indicators, income quintiles were calculated based on equivalized disposable household income. Where possible, levels of education were based on the International Standard Classification of Education (ISCED) 2011, or the earlier classification, ISCED 1997. The ISCED educational levels are often aggregated to form a three-category education variable, with: (1) low-level education, which is pre-primary to lower secondary education only; (2) mid-level education, which represents upper-secondary to post- secondary nontertiary education; and (3) high-level education, which is tertiary education. Indicators within HESRi2 were disaggregated by this three-category variable for educational level, where available. Where an indicator is not disaggregated by either income or education, but is available for regions within a country, these regions are grouped by income per capita. Although poorer regions tend to have worse outcomes than richer ones this is not always the case because other factors may affect regional differences in some countries (e.g. the urban rural composition of regions).

Data processing was conducted using R statistical software (version 3.4.3). A panel of regional experts, scientific advisors and members of international organizations approved the methods used and the interpretation of the indicators, prior to publication.

The primary equity measure derived was the absolute difference between the most and least disadvantaged groups (e.g. based on education or income). The trend indicators are based on the (linear) slope index of inequality to calculate an annual rate of change, as well as statistical significance. They use all available data for the indicator since 2010 to calculate the trend. Trends were assessed as increasing if the linear trend across all the data points available was significantly greater than 0 (p<0.1) or decreasing if the linear trend across all the data points available was significantly lower than 0 (p<0.1); otherwise, the trend was labelled as having no noticeable change.

A1.4 Limitations

Certain limitations need to be taken into account when interpreting the indicators and users should refer to the original source documentation to assess the quality of the data collection and measurement methods.

Differences in methodologies – such as the extent to which samples are representative of populations – and differences in survey instruments and definitions may limit the comparisons that can be made between countries and within countries over time. Individuals from lower socioeconomic groups are often underrepresented in population- based surveys, which may limit generalizability. Additionally, self-reported outcomes may be influenced by response biases and cultural differences, and, when comparing countries with diverse health services, differences in self-reported health outcomes may reflect access to care, rather than real differences in morbidity.

Whilst every effort has been made to achieve as much coverage of countries across the Region as possible, consistent with ensuring a good representation of inequalities in countries not providing data to Eurostat , only 17of the selected indicators cover 50 or more of the countries in the WHO European Region. This highlights the need for a coordinated approach to monitoring health equity within the Region going forward.

Annex 2. Modelling

A2.1 Decomposition analysis

Demanding microdata requirements for the decomposition analysis means that this analysis is only possible for inequities in a select number of health indicators.

Decompositions of contributors to self-reported health, limiting illness, mental health and life satisfaction are possible using microdata from ESS and EUSI C for 32 countries in the WHO European Region, consisting of the 27 EU countries, Iceland, Norway, Serbia, Switzerland, and the United Kingdom. There is sufficient data for underlying conditions in all five areas to be analysed, showing that differences in conditions in all five areas are statistically significant in explaining inequities in these health indicators (Fig. 2.2).

A2.2 Technical details of the decomposition method

The idea behind the decomposition analysis is to explain the differences in health indicators that were observed between socioeconomic groups in Section 1 by a set of contributing factors that differ systematically between these groups.

This helps to understand the multisectoral conditions behind why health inequities exist between groups of people within countries, even when effective health services are in place that aim to narrow or eliminate inequities in health and health care. For example, differences in health may be explained by differences in housing conditions and working conditions, as well as by differences in quality of health care. Even if countries are able to narrow inequities in one factor, inequities may still remain in others, emphasizing the importance of taking a multisectoral approach to tackling health inequity.

The decomposition analysis reveals the extent to which each factor contributes to health inequities compared to each of the other factors. The method used in the Health Equity Status Report (HESR) is a simulation-based decomposition that relies on coefficients from a single, pooled regression model, a principle related to the variant of the Oaxaca decomposition method proposed by Neumark (1) and Oaxaca Ransom (2). The decomposition is based on regression analysis of the relationships between self-reported health and the indicators of underlying conditions. It should be noted that while this analysis provides an explanation of health inequity in terms of statistical associations, the regression model used does not constitute a causal analysis and therefore the results should not be interpreted as a stand-alone guide to policy – the decomposition results should be interpreted with care and only in context with other evidence.

The analysis first quantifies the overall health inequality by calculating a health gradient, in this case the Slope Index of Inequality. The decomposition then proceeds through a counterfactual simulation for each underlying condition. To determine the contribution of a single factor (e.g. employment), we simulate a scenario where that factor is set to its optimal value for all individuals in the population. By predicting health outcomes in this simulated scenario and recalculating the health gradient, we can measure how much the overall inequality is reduced. This reduction is considered the contribution of that specific factor to the total health gap.

A2.3 Equity Tracker

he ineq ali y index in he eq i y racker wa calc la ed in he followin way: i. for ho e indica or ha are di a re a ed by ocioeconomic a :

a. he lope index i calc la ed acro ocioeconomic ro p b. where here are m ltiple mea re of ocioeconomic a ch a ed cation and income, a

in le compo i e ocioeconomic rank i fir crea ed by avera in he e variable , and hen ed o calc la e he lope index

ii. he mea re calc la ed in ep (i) i andardized by cen erin i on he mean for all co n rie and dividin by i andard deviation. In hi way, he me ric ed i mea red in andard deviation .

Where he indica or i no di a re a ed by ocioeconomic a b ha an obvio relation hip wi h ineq ali y direc ly (e. . pover y ra e , Gini), he e were j andardized a (ii) above. nn al mea re were e tima ed for each indica or. da a may be mi in for ome year for differen co n rie and differen indica or , we moo h o da a be ween year by:

i. akin he 3-year movin avera e for each indica or ii. E timatin mi in year by akin he linear rend be ween non-mi in year of da a wi hin

co n rie or ro p of indica or ( ch a each of he five e ential condition ), he andardized mea re for each indica or were hen combined acro indica or ca e orie , by akin he avera e for all non-mi in andardized indica or in each ro p for each co n ry. imilar approach wa ed o avera e acro ro p of year . inally, o crea e he - o- index een in he racker , he e contin o domain core are conver ed in o q intile . Wi hin each domain, all co n rie are ranked by heir core and divided in o five eq al ro p , wi h ' ' repre entin he lowe ineq ali y and ' ' repre entin he hi he ineq ali y.

A .4 m g w g m q w g q g . This is produced by taking the annual measures of the standardized indicators over the years 2010-2023, as derived in A2.3 and fitting a linear regression model with country fixed effects. This estimates the extent to which change in equity in each of the conditions over time within a country is associated with change in equity in self-reported health (based on the standardized slope index as in A2.3). The model also includes sex and year as control

variables to account for average differences between sexes and for underlying linear time trends. For all indicators in the chart a reduction in equity in conditions is independently associated with a reduction in health equity (and vice versa). A .5 M g ff m m .

This is based on a logistic regression model with the outcome measure, such as poor health, social isolation measured as a binary outcome (e.g. poor health vs. not poor health), controlling for sex, age, age squared and age-sex interactions. The intervention is measured as a three-way interaction between the country’s pension coverage, the standardized expenditure per pension recipients and an indicator of whether a respondent is over the country’s effective pension age for that year. This model is then used to simulate how the outcome changes, within each sex and income quintile. The simulation specifically models the effect of a best-practice scenario (i.e. pension coverage at the maximum observed level and expenditure at least at the European average) to estimate the potential impact on population health and well-being. A .6 H w b m g k b

m k x .

) L m g

First, the percent of 16–24-year-olds who report a limiting illness for each educational group for each country and year is combined with I O data on the percent (in each education group for each country and year) who are not in education, employment or training (NEET). This is used in a linear regression model with percent NEET as the outcome and the one to three- year lagged values of limiting illness as the exposure with country fixed effects. The coefficients for each lagged year are combined to estimate the effect of the cumulative trending limiting illness, over three years, on NEET. A comparable analysis is then performed using youth unemployment as the outcome variable in place of NEET. ) D

As there are only two time points for this indicator, a simpler approach is used. The difference between the two time points in depression prevalence for each country and the difference in the two labour market outcomes, NEET prevalence and youth unemployment, for each country are calculated. Then, to estimate the association, two separate regression models are fitted. In the first model, the change in the NEET rate is regressed on the change in depression. In the second, the change in youth unemployment is regressed on the change in depression.

Annex 3. Country clusters

Member States have been grouped according to policy and political commonalities. The clusters also aim to reflect the countries that Member States compare themselves to. This grouping does not coincide with preexisting WHO country groupings.

Country Cluster

Annex 4 List of HESRi Database indicators

Indicator title Indicator definition Data sources

Latest year

Health status Morbidity Alcohol consumption Percentage of adults aged 18-64 years who drink

alcohol daily (age adjusted) STEPS EHIS 2023

Alcohol consumption (risky single occasion drinking)

Percentage of adults aged 18-64 years engaging in risky single occasion drinking at least once a month (age adjusted)

STEPS EHIS 2023

Children reporting multiple health complaints

Percentage of children aged 11-15 experiencing two or more health complaints weekly

HBSC 2022

DALYs due to occupational exposure

DALYs attributable to occupational risks and exposures (age standardized rate per 100,000)

GBD 2021

Limitations in activities of daily living

Percentage of adults reporting limitations in daily activities due to health problems

EU-SILC ESS 2024

Obesity/Overweight Percentage of adults aged 18-64 years who are obese (age adjusted)

STEPS EHIS 2023

Prevalence of self- reported CVD

Percentage of adults aged 18-64 years reporting cardiovascular disease (age adjusted)

STEPS EHIS 2023

Prevalence of self- reported diabetes

Percentage of adults aged 18-64 years reporting diabetes (age adjusted)

STEPS EHIS 2023

Serious injuries in children Percentage of children aged 11-15 having required medical treatment for 3 or more injuries in past year

HBSC 2018

Smoking Percentage of adults aged 18-64 years who are current smokers

STEPS EHIS 2023

Mortality

Infant mortality rate Infant mortality rate for children aged less than 1 year (per 1000 live births)

World Bank Eurostat GDL

2019

Life expectancy at birth Life expectancy at birth in years GDL 2022

Tuberculosis mortality Age-standardized tuberculosis death rate per 100,000 population

WHO 2022

Under-five mortality rate

Under-five mortality rate (per 1000 live births) UN IGCME 2022

Wellbeing

Adult life satisfaction Percentage of adults reporting poor life satisfaction

ESS WVS 2023

Adult self-reported health

Percentage of adults reporting poor or fair health

EU-SILC ESS WVS

2024

Center for Epidemiological Studies Depression Score (CES- D8)

Proportion of people at high risk of clinical depression

ESS 2014

Children pressured by schoolwork

Percentage of children aged 11-15 reporting being pressured by schoolwork

HBSC 2022

Children's life satisfaction

Percentage of children aged 11-15 years reporting poor life satisfaction

HBSC PISA 2022

Children's self-reported health

Percentage of children aged 11-15 years reporting poor or fair health

HBSC 2022

Physical inactivity in children

Percentage of children aged 15 years who are physically active

PISA 2022

Problematic social media use in children

Percentage of children aged 11-15 showing symptoms of social media disorder

HBSC 2022

Self-reported work- related health condition

Percentage of adults aged 15-64 years reporting a work-related health problem (age adjusted)

EU-LFS via Eurostat

2020

Health services

Access to health care

Avoidable hospital admissions

Number of avoidable hospital admissions per 100,000 people

Eurostat GDL

2021

Cancer screening (cervical) Percentage of women reporting ever having a cervical cancer screening

STEPS EHIS

2023

Hospital beds Available hospital beds per 100,000 inhabitants

Eurostat OECD GDL

2022

Informal care Percentage of those aged 18-64 years who provide informal care for people with disabilities at least once a week (age adjusted)

EHIS 2019

People with diabetes treated Percentage of people aged 18-64 with diabetes who are receiving treatment

STEPS 2023

People with hypertension treated

Percentage of people aged 18-64 with hypertension who are receiving treatment

STEPS 2023

Physicians or doctors Number of physicians or doctors per 100,000 inhabitants

Eurostat OECD GDL

2022

Public expenditure on health Current health expenditure as a percentage of GDP

WHO 2023

Public expenditure on long- term care

Total expenditure on long-term care as a percentage of GDP

Eurostat OECD

2023

Public spending on public health services

General government expenditure on public health services as a percentage of GDP

IMF Eurostat OECD

2023

Self-reported unmet needs for health care

Percentage of people reporting unmet needs for health care

EU-SILC WVS

2024

Financial protection for health

Household out-of-pocket payments on long-term care

Household out-of-pocket payments on long- term care as a percentage of GDP

OECD 2023

Households with catastrophic health spending

Percentage of households with out-of-pocket payments that are greater than 40% of their capacity to pay for health care

WHO Barcelona Office

2022

Households with impoverishing health spending

Percentage of households that are impoverished or further impoverished after out-of-pocket payments

WHO Barcelona Office

2022

Out-of-pocket expenses Out-of-pocket expenses as a percentage of current health expenditure

WHO 2023

Social and human capital

Human capital

Adult literacy rate Percentage of adults aged 15+ years who are literate

UNESCO via World Bank

2023

Children developmentally on track

Percentage of children aged 36-59 months who are developmentally on track

MICS 2019

Children/youth minimally proficient in reading and maths

Percentage of children aged 15 years achieving minimum proficiency in mathematics and reading

PISA 2022

Government expenditure on early childhood education

Government expenditure on early childhood education in purchasing power standards (PPS) per child under 5 years of age

Eurostat 2021

Participation in early childhood education

Percentage of children aged 36 to 59 months participating in early childhood education

UNICEF Eurostat

2022

Participation in education and training

Percentage of adults aged 25-64 years participating in formal and non-formal education and training

Eurostat 2023

Young people not in employment/education/training

Percentage of young people who are not in employment, education or training

ILO Eurostat

2023

Social Capital

Children's exposure to Bullying Percentage of children aged 11-15 reporting being bullied in school 2-3 times a month

HBSC 2022

Children's exposure to Cyberbullying

Percentage of children aged 11-15 reporting being cyberbullied 1-2 times a month

HBSC 2022

Equal treatment under the law and absence of discrimination

Score from 0-1 on the World Justice Project Rule of Law Index measuring equal treatment under the law and absence of discrimination

World Justice Project

2024

Freedom of choice and control over life

Percentage of people aged 18+ years reporting low levels of freedom of choice and control over their own life

WVS 2022

Frequency of meeting socially Percentage of people aged 16+ years who meet with family/friends less than once a month

EU-SILC ESS

2023

Gender Equality Index Score from 1-100 on the Gender Equality Index

EIGE 2024

Having someone to ask for help Percentage of people aged 16+ years who don't have someone to ask for help

EU-SILC 2022

Participation in voluntary activities

Percentage of people aged 16+ years participating in formal voluntary activities

EU-SILC 2022

Perceived ability to influence politics

Percentage of people aged 18+ years reporting having no ability to influence politics

ESS 2023

Self-reported social support in young people

Percentage of children aged 11-15 years reporting low peer support

HBSC 2022

Social Institutions and Gender Index

Score from 0-100 on the Social Institutions and Gender Index

OECD 2023

Trust in others Percentage of adults aged 18+ years reporting low trust in other people

ESS WVS 2023

Trust in politicians Percentage of people aged 16+ years reporting low levels of trust in politicians

ESS 2023

Living environment

Commercial determinants

Daily breakfast consumption in schoolchildren

Percentage of children aged 11-15 missing breakfast on any schoolday

HBSC 2022

Food insecurity Percentage of people who cannot afford to eat an adequate meal

EU-SILC WVS

2023

Food Price Index Food Price Index (2017=100) Eurostat World Bank

2025

Moderate or severe food insecurity

Percentage of the population experiencing moderate or severe food insecurity (3-year average)

FAO 2022

Tobacco tax Tobacco taxes as a percentage of price of the most sold brand

WHO 2022

Value-added tax (VAT) on alcohol

Value-added tax (VAT) on alcohol WHO 2019

Digital environment

Internet use Percentage of people aged 16+ years who never use the internet

ESS WVS 2023

Environment

Annual mean PM10 concentrations

Annual mean concentration of PM10 in cities WHO 2022

Annual PM2.5 emissions Total annual PM2.5 emissions (ton) GDL 2022

DALYs due to air pollution DALYs attributable to air pollution (age standardized rate per 100,000)

GBD 2021

Diesel cars Diesel powered passenger cars as a percentage of all registered passenger cars

Eurostat 2023

Feeling unsafe from crime in own home

Percentage of adults aged 18+ years feeling unsafe from crime in their own home

WVS 2022

Feeling unsafe walking alone after dark

Percentage of adults feeling unsafe when walking alone in their area after dark

ESS 2023

Intentional homicide Intentional homicide by intimate partner or family member, victims per 100,000 population

Eurostat 2023

Neighbourhood security Percentage of people aged 16+ years who don't feel secure living in their neighbourhood

WVS 2023

Physical violence in children Percentage of children aged 11-15 reporting having been in 3 or more physical fights in past year

HBSC 2018

Pollution/grime/other environmental problems

Percentage of people reporting pollution, grime or other environmental problems in their area

EU-SILC 2023

Public spending on housing and community amenities

General government expenditure on housing and community amenities as a percentage of GDP

IMF Eurostat OECD

2023

Reported crime, violence and vandalism

Percentage of people aged 16+ years reporting local problems with crime, violence or vandalism

EU-SILC 2023

Road deaths Estimated road traffic deaths per 100,000 population

WHO 2019

Housing

Basic drinking water services Percentage of people without at least basic drinking water services (an improved source within a 30 minute round trip to collect water)

WHO UNICEF

2022

Basic sanitation services Percentage of people without at least basic sanitation services (improved sanitation facilities that are not shared with other households)

WHO UNICEF

2022

DALYs due to unsafe sanitation

DALYs attributable to unsafe sanitation (age standardized rate per 100,000)

GBD 2021

Households receiving housing allowance

Percentage of households receiving housing allowance

OECD 2022

Housing cost overburden rate Percentage of people living in a household where housing costs are more than 40% of disposable household income (net of housing allowances)

EU-SILC 2023

Housing overcrowding Percentage of people living in an overcrowded dwelling

EU-SILC 2023

Inability to adequately heat home

Percentage of people who cannot afford to keep their home adequately warm

EU-SILC 2023

Severe housing deprivation rate

Percentage of people living in an overcrowded dwelling that also lacks a bath and indoor toilet, or is damp or too dark

EU-SILC 2023

Employment and working conditions

Job security

Employment rate Percentage of those in employment among those aged 16-64 years

EU-SILC ESS WVS

Labour force participation rate

Percentage of the population either working or looking for work

ILO 2024

Proportion of the poorest quintile population covered by labour market programs

Percentage of the poorest quintile in the population covered by unemployment benefits and active labor market programs

World Bank

2020

Public expenditure on labour market policies

Public expenditure on labour market programmes (active and passive) as a percentage of GDP

OECD 2022

Redundancy pay at 2 years of tenure, in months

Number of months of redundancy pay at two years of job tenure

ILO 2020

Temporary employees Temporary employees as percentage of the total number of employees aged 20-64 years

Eurostat 2023

Unemployment benefit coverage

Percentage of unemployed persons receiving regular periodic social security unemployment benefits

ILO 2023

Unemployment rate Percentage of unemployed persons in the labour force

ILO 2024

Working conditions

Accidents at work Percentage of workers aged 15-64 years reporting at least one non-fatal accident at work in the past 12 months

EULFS via Eurostat

2020

Average wages/earnings Average nominal monthly earnings of employees in 2021 USD PPP

ILO 2023

Collective bargaining coverage

Percentage of employees whose pay and/or conditions of employment are determined by one or more collective agreements

ILO 2020

Disability employment ratio Ratio of employment rates between people without disabilities and people with disabilities, aged 16-64 years

EU-SILC 2023

Feeling about household's income

Percentage of people aged 16+ years reporting difficulties living on their household's income

ESS 2023

Gone without cash income Percentage of people aged 16+ years reporting they sometimes or often have to get by without an income

WVS 2022

In-work poverty Percentage of employed persons aged 18+ years with income below 60% of median equivalised disposable income (after social transfers)

EU-SILC 2023

Labour inspectors Average number of labour inspectors per 10,000 employed persons

ILO 2023

Labour share of GDP (wages and social protection transfers)

Percentage of GDP allocated to labour compensation, comprising wages and social protection transfers

ILO 2024

Low financial satisfaction Percentage of people aged 16+ years reporting low levels of satisfaction with the financial situation of their household

WVS

Low job control Percentage of the population aged 16-64 years, reporting low levels of control over how their daily work is organised

ESS

Minimum wage Statutory nominal gross monthly minimum wage in constant 2021 USD PPP

ILO 2024

Working excessive hours Percentage of workers working 40+ hours per week

ILO 2024

Working excessive hours (more than 45 hours per week)

Percentage of the population aged 16-64 years, working more than 45 hours per week

ESS

Income security and social protection Poverty and inequality

Adequacy of social assistance programmes

Social assistance transfers received by beneficiaries as a percentage of their total income or consumption

World Bank

2021

Coverage of social assistance programmes

Percentage of the population participating in social assistance programs

World Bank

2021

Disability poverty ratio Ratio of poverty rates (percentage below 60% of median income) between people with disabilities and people without disabilities, aged 16-64 years

EU-SILC 2023

Gini index Gini index of income inequality from 0-100 World Bank

2022

Incidence of social assistance programmes

Social assistance program beneficiaries in each income quintile as a percentage of total number of social assistance program beneficiaries

World Bank

2021

Poverty Percentage of the population with income below 60% of median equivalised disposable income (EUSILC) or national poverty lines (World Bank)

EU-SILC World Bank

2024

Public spending on social protection

General government expenditure on social protection as a percentage of GDP

IMF Eurostat OECD

2023

Public spending on social protection for children and families

General government expenditure on social protection for children and families, as a percentage of GDP

Eurostat 2023

Public spending on social protection for sickness and disability

General government expenditure on social protection for sickness and disability, as a percentage of GDP

Eurostat 2023

Public spending on social protection for unemployment

General government expenditure on social protection for unemployment, as a percentage of GDP

Eurostat 2023

Risk of child poverty Percentage of children aged 0-18 at risk of poverty or social exclusion

Eurostat 2024

Vulnerable people covered by social assistance programmes

Percentage of poor persons covered by social protection systems

ILO 2023

Supporting older people Pension coverage Percentage of the population above statutory

pensionable age receiving an old age pension ILO 2023

Pension expenditure per beneficiary

Annual pension expenditure per beneficiary for old-age pensions in purchasing power standards (PPS)

Eurostat 2022

Pension net replacement rate

Individual net pension entitlement as a percentage of net pre-retirement earnings

OECD 2022

Supporting parenting

Length of paid maternity and parental leave

Length of paid maternity and parental leave available to mothers in weeks

OECD 2021

Length of paid paternity and parental leave

Length of paid paternity and parental leave available for fathers in weeks

OECD 2021

Maternity benefits Percentage of women giving birth receiving maternity cash benefits

ILO 2023

Annex references

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