Health System Performance Assessment Framework for the Slovak Republic

4. Using the Slovak HSPA framework: Case study on equity

Abstract

Equity is integrated across domains of the Slovak HSPA framework, as various indicators included allow for breakdowns relevant to assess equity by geographic, demographic or socio-economic characteristics. The chapter provides an illustrative use case of the Slovak HSPA indicators through dedicated subsections linked to specific policy-relevant focus areas: geographic equity in preventive and outpatient healthcare, demographic equity in cardiovascular disease and socio-economic equity in health status.

The subsections construct simple narratives connecting input, process and output indicators across domains, illustrating how indicators can be conceptually linked throughout the Slovak HSPA framework. The Slovak HSPA framework allows moving beyond isolated indicators towards a structured assessment and coherent understanding of health and care performance, in order to uncover undesired performance guide policy recommendations.

Chapter 4 presents a case study on the use of the Slovak HSPA framework to analyse a particular theme. The topic of equity was highlighted as a priority by Slovak stakeholders throughout the framework’s development, particularly in relation to regional disparities and marginalised populations, socio-economic status, and demographic characteristics. While equity was initially included as a distinct domain in the first draft of the framework following stakeholder workshops (see Chapter 2), subsequent revisions embedded the topic throughout the framework, rather than as a standalone pillar.

Figure 4.1 highlights distribution of indicators across domains that allow for an assessment of geographic, demographic or socio-economic differences in HSPA indicators. These are present across all thematic areas, allowing for equity to be traced through the framework. This would facilitate assessment of different types of equity that may emerge at multiple points within the health system and cannot be comprehensively captured through isolated indicators.

Figure 4.1. Indicators that allow for equity assessment in the Slovak HSPA framework

Note: The numbers shown capture the number of indicators with breakdowns that would allow to assess some aspect of equity in the health system. Not all available breakdowns were counted as contributing to equity analyses. As many indicators can used to assess more than one equity dimension, the numbers are not summative.

Source: Developing HSPA Framework for the Slovak Republic project.

The case study is presented in three parts that showcase how equity can be assessed within the Slovak HSPA framework by identifying relevant indicators throughout framework domains, reviewing available data, and highlighting additional data sources where gaps remain. Section 4.1 looks at equity by geography (36 indicators), Section 4.2 by demographic distribution (54 indicators) and Section 4.3 by socio-economic variables (25 indicators). Given the large number of indicators with disaggregations, each section is illustrated through a particular health system topic. The subsections demonstrate cohesion of the framework through logical connections between the domains, illustrate that data for many indicators is readily available for analyses, and provide examples of possible data visualisations. The subsections do not provide an in-depth analysis but should be taken as examples of conceptual linkages.

4.1. Equity in geographic distribution of health variables: preventive and outpatient healthcare

Workshops held with Slovak health system stakeholders highlighted regional within-country variation as a key priority for a health system assessment. Administratively, the Slovak Republic can be divided into four regions according to the NUTS2 classification, eight regions (NUTS3 level) or 79 districts. Granularity of analysis needed and available differs by indicator and issue examined.

The Slovak HSPA framework incorporates a total of 36 indicators throughout that can be used to assess geographic differences in health system performance through a comprehensive understanding of the connections between health system inputs, process and outcome indicators (Figure 4.1). This case study focusses in particular on prevention and access to primary and outpatient care.

4.1.1. Variation in regional distribution of healthcare workforce implies differences in access to first‑contact and specialist care

Geographic differences in health system resources can lead to geographical healthcare service access issues. Table 4.1 details selected indicators with regional breakdowns on the availability of primary and outpatient care workforce.

Table 4.1. Selection of resource indicators that capture geographic disparities in supply of primary and outpatient care workforce

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Indicator source
Resources	Workforce	General practitioners per population	International, regional1	NCZI	OECD (JQNMHS2: Health employment and education)
	Workforce	Outpatient care: Physicians' job posts (full-time equivalents, FTE) per population	Regional By region, district and medical specialty	ÚDZS	ÚDZS (Data on evaluation of networks)

Notes:

1. Breakdown is available through national sources for the same indicator (see Annex D for further information).

2. Joint Questionnaire on Non Monetary Health Statistics.

Source: Developing HSPA Framework for the Slovak Republic project.

Understanding the workforce distribution across the country is vital to understand whether some areas might fall short on physician availability. The indicator on General practitioners per population can be examined by geographical area, indicating where there could be issues with essential access. General practitioners act as important gatekeepers in access to secondary care in the Slovak Republic (OECD/The Health Foundation, 2025[1]), and their adequacy can affect access or lead to delays in reaching specialist providers. The indicator Outpatient care: Physicians’ job posts (in full-time equivalents (FTE)) per population is available by both region and district, and as it is provided in full time equivalent workers. The measurement of FTEs is closely aligned with appointment availability while its breakdown by medical speciality allows for an understanding of the distribution of specific physician services.

4.1.2. Provider network adequacy and unmet needs indicators can pinpoint geographic access gaps

Indicators in the Health services area, specifically the Access domain can complement the Workforce indicators by providing an assessment of the adequacy of the providers’ network, with a geographically sensitive indication of whether the number of providers (not individual workers) is sufficient for the population (Table 4.2).

Table 4.2. Selection of Slovak HSPA indicators that capture geographic disparities in supply and access to primary and outpatient care services

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Indicator source
Healthcare services	Access	GP providers as % of optimal network	Regional by regions, districts, HICs	ÚDZS	ÚDZS (Data on evaluation of networks)
	Access	Outpatient specialist providers as % of optimal network	Regional By regions, districts, HICs and medical specialty	ÚDZS	ÚDZS (Data on evaluation of networks)
	Access	Self-reported unmet needs for medical examination by main reason declared	International, regional By NUTS2, age, education, income, main reason declared, sex	ŠÚ SR	Eurostat (EU-SILC1)

Notes: 1. EU Survey on Income and Living Conditions.

Source: Developing HSPA Framework for the Slovak Republic project.

These indicators provide district‑level data on: General practitioner providers as a percentage of the optimal number, and Outpatient providers as a percentage of the optimal number, allowing identification of districts where basic ambulatory service coverage may be falling short. Figure 4.2 categorises districts by percentage of current outpatient provider supply compared to the optimal supply assessed based on needs, showing the proportion of districts with particularly low supply or lacking data. As the data can be further assessed by region, an assessment should consider where district adequacy might be low despite apparently reasonable regional averages to identify pockets of undersupply, issues in workforce distribution, or organisational issues in the provider network.

Figure 4.2. Distribution of districts by percentage of optimal capacity fulfilment of the network of general outpatient care

Note: Data refer to 2025.

Source: ÚDZS (2026[2]) Údaje o vyhodnotení sietí podľa zákona č. 578/2004 Z. z. [Data on evaluation of networks under Act No 578/2004], https://www.ÚDZS-sk.sk/urad/udaje-o-vyhodnoteni-sieti-poskytovatelov-ambulantnej-starostlivosti-podla-zakona-c-578-2004-z-z/

In the Access domain, the indicator on Self-reported unmet needs for medical examination by main reason declared from the EU-SILC survey can be used to understand at a NUTS2 level of disaggregation whether people in any part of the country are more likely to report unmet needs, and in particular whether travel or waiting times are more frequently reported as constraints. Although disaggregated at the level of larger regions, this helps to understand the service users’ perspective on provider network adequacy.

4.1.3. Indicators on preventive care utilisation can reveal where access constraints may be suppressing uptake

A coherent geographic narrative benefits from connecting supply and access to further measures of service utilisation to distinguish where low supply plausibly suppresses use, and places where uptake remains low despite supply being adequate. Several indicators in the Slovak framework can support these links (Table 4.3).

Table 4.3. Selection of Slovak HSPA indicators that capture geographic disparities in utilisation of preventive care

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Indicator source
Prevention and risk factors	Prevention	Childhood vaccination rates among eligible children	International1, regional By region, district, vaccine	ÚVZ	ÚVZ (Evaluation of administrative vaccination control)
	Prevention	Preventive check-ups attendance, based on patients’ entitlement under Act No. 577/2004	Regional By region, HICs, type of preventive check-up	ÚDZS	ÚDZS (Report on the status of public health insurance implementation)
	Prevention	Colorectal cancer screening	International, regional2 By region2, district2, age, gender, education1, income1	NSC [NCZI]	OECD (JQNMHS3: Healthcare activities)

Notes:

1. Available when using contextualising indicators.

2. Regional comparison is available through national sources for the same indicator (see Annex D for further information).

3. Joint Questionnaire on Non Monetary Health Statistics.

Source: Developing HSPA Framework for the Slovak Republic project.

Childhood vaccination rates among eligible children connects to primary care as national immunisation schedules are facilitated through comprehensive follow-up and advice offered through primary care services. Figure 4.3 shows a specific breakdown by vaccine type of this indicator, highlighting coverage with the first dose of MMR vaccine by region in comparison to international recommendations from the WHO to reach 95% coverage for herd immunity. Among adults, Preventive check-ups attendance and Colorectal cancer screening can be used to identify where uptake of preventive services is lower and to test whether preventive care use aligns with network adequacy and physician capacity. Figure 4.3 B shows a possible view on cancer screening within regions, highlighting the time trend in screening coverage in Prešov region – the lowest-ranking region in terms of colorectal cancer screening coverage in the Slovak Republic in 2024. The data within the region can be further disaggregated to understand district-level uptake, and the time trend can be compared to other regions to understand specific drivers of low uptake. For colorectal cancer screening, the Slovak Republic has reported activities to expand the role of primary care in cancer screening while screening tests are also distributed by mail or in communities (OECD/European Commission, 2026[3]).

Figure 4.3. Mandatory vaccination across regions and colorectal cancer screening time trend in a specific region

Note: A. Line shows level needed for the WHO-recommended 95% coverage for herd immunity. B. Grey bars show number invited, while diamond markers show coverage with screening in a two‑year period.

Source: ÚVZ (2024[4]), Evaluation of administrative vaccination control, https://www.uvzsr.sk/documents/d/uvz/ko-2024-sr_komentar-pdf; NCZI (2025[5]), Oncology screening programmes in the Slovak Republic 2021-2024, https://www.nczisk.sk/Statisticke_vystupy/Tematicke_statisticke_vystupy/Onkologicke_skriningove_programy/Pages/default.aspx.

4.1.4. Consultation rates can signal differences in early access and service efficiency

Capturing early access efficiency over inpatient care, the indicator Number of patient visits to outpatient clinics in the Care provision efficiency domain is available by region, with additional breakdowns by district, age and medical speciality (Table 4.4).

Table 4.4. Selection of Slovak HSPA indicators that capture geographic disparities in utilisation outpatient care

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Indicator source
Efficiency	Care provision efficiency	Number of consultations with a physician per person	International, regional1 By type of consultation, age1, region1, district1, medical specialty1	NCZI	OECD (JQNMHS2: Healthcare activities)

Notes:

1. Comparison is available national sources for the same indicator (see Annex D for further information).

2. Joint Questionnaire on Non Monetary Health Statistics.

Source: Developing HSPA Framework for the Slovak Republic project.

It can be viewed as a utilisation intensity measure and interpreted alongside provider availability and network adequacy. Although only available at the whole‑country level, the indicator on Stage of cancer at diagnosis (placeholder, data available) can inform discussion on whether screening and early access to healthcare is functioning to reach people with diagnosis when the disease is in earlier stages, where treatments are less intensive, more effective and less costly.

If areas with weaker provider availability also show systematically lower preventive service utilisation, this would support an interpretation of structural access constraints. If utilisation remains low even where adequacy indicators are near‑optimal, this suggests other determinants may be limiting effective access, such as transport barriers, appointment systems, health literacy, or local service organisation.

4.1.5. Outcome indicators can support a geographically sensitive lens on whether gaps persist in health status and mortality

Outcome indicators allow the HSPA to assess whether geographic differences persist in health status and impact specific outcomes (Table 4.5).

Table 4.5. Slovak HSPA indicators that capture geographic disparities in outcomes sensitive to primary and outpatient healthcare

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Indicator source
Out-comes	General health outcomes	Life expectancy at birth	International, regional By gender, NUTS2	OVZ	Eurostat (Population and social conditions: Mortality)
	General health outcomes	Causes of mortality	International, regional1 By sex, cause, NUTS2, region1, district1	ŠÚ SR [MoH (SZ)]	Eurostat (Population and social conditions: Mortality)
	General health outcomes	Mortality rates for children under 5 years of age	International, regional1 By sex, age, region1	MoH (SZ)	Eurostat (Population and social conditions: Mortality)
	Disease‑specific health outcomes	Trends in the incidence of selected vaccine‑preventable diseases	Regional By region, type of a disease	ÚVZ	ÚVZ ([NCZI Yearbook]
	Disease‑specific health outcomes	Cancer mortality	International, regional1 By region1, district1, sex, age group	NSC [NCZI]	NCZI (Thematic outputs: Oncology)

Note: 1. Comparison is available national sources for the same indicator (see Annex D for further information).

Source: Developing HSPA Framework for the Slovak Republic project.

An overall picture can be achieved with the indicator Life expectancy is available by NUTS2 region (Figure 4.4 A). This indicator allows to compare Slovak regions to other European countries and regions via the Eurostat database. With the same indicator, gender differences in life expectancy can be analysed by region and over time, incorporating contextual information to infer whether some regions are performing well in reducing gaps. While providing an overall measure of longevity, the indicator analyses can be complemented by specific mortality causes (Causes of mortality) to understand what is bringing down life expectancy in different geographical areas.

Figure 4.4. Selected indicators with geographic distribution under the Outcomes thematic area

Note: A. EU13 refers to the 13 countries that joined the EU in 2004 or after. B. Based on age‑standardisation to the world population. Data refer to 2015‑2022.

Source: A. Eurostat (2025[6]), Eurostat database. B. OECD/European Commission (2025[7]), EU Country Cancer Profile: Slovak Republic 2025, https://doi.org/10.1787/e40472f4-en.

While the indicators above are affected by health service availability, several geographically-disaggregated outcome measures have particularly strong links to services. Mortality under age 5 is highly linked to access to care (particularly neonatal healthcare) and coverage of basic services. In the Slovak Republic, the indicator is available by region, allowing for a geographically sensitive interpretation. Communicable disease control is represented through Trends in the incidence of selected vaccine‑preventable diseases, measured per 100 000 population and disaggregated by region and type of disease. This indicator is closely linked to primary care services and vaccine uptake, and reflects an important outcome measure sensitive to factors relevant to achieving vaccine coverage sufficient for herd immunity.

The indicator of Cancer mortality (Figure 4.4 B) is available by both region and district, showcasing an examination of outcomes in specific population groups and diseases. Understanding these outcomes is supported by the framework, which in the above sections demonstrates capture aspects of risks, supply, access and utilisation patterns for a coherent narrative, alongside policy context. If regions with low screening participation also show higher cancer mortality (or less improvement over time), the combined indicator set supports an initial analysis about preventable outcome gaps, which can be explored deeper in a thematic analysis. If patterns diverge (e.g. low screening but not worse mortality), the analyses can explicitly explore alternative explanations than causality (case‑mix, stage distribution, treatment pathways, or data artefacts).

Taken together, the geographical lens supports a structured approach to equity by mapping supply and network adequacy (inputs), assessing access and utilisation differences (processes), and examining whether outcomes differ systematically by geography (outcomes), particularly for causes sensitive to health service support. Where feasible, HSPA reporting can present results visually (maps by region/district) and connect them to policy levers such as workforce planning, network regulation, and service reconfiguration. Not presented here, Slovak HSPA indicators with geographical disaggregations can capture variations in care quality and emergency service response speed to look at territorial differences in care responsiveness, particularly relevant where rurality and travel times may create uneven access to timely care.

4.2. Equity by demographic characteristics: Cardiovascular disease (CVD)

Demographic equity focusses on whether health system performance differs by variables such as sex, age, migrant status, ethnicity, country of birth or other variables that can result in differences in needs, risk exposure, care‑seeking patterns, and the responsiveness of services. In the Slovak HSPA, demographic breakdowns are available for a total of 54 indicators across domains, most often by age and sex/gender. Sex is usually captured in the case of administrative data, while survey data usually uses self-reported gender as the main variable.

Section 4.2 on demographic equity focusses on a path that can be used to analyse the cardiovascular diseases care system, as cardiovascular diseases consistently account for the largest share of mortality in the Slovak Republic, at 46% of all deaths in 2023 (OECD/European Observatory on Health Systems and Policies, 2025[8]).

4.2.1. Differences in behavioural determinants are key to understand cardiovascular disease inequalities

Behavioural determinants are key drivers of cardiovascular disease, while differences between groups result in uneven patterns. The Slovak HSPA includes several indicators with various demographic breakdowns under the Prevention and Risk factors area (Table 4.6).

Table 4.6. Selection of Slovak HSPA indicators that capture demographic differences in behavioural determinants

	Thematic area/ Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Custodian of the dataset
Prevention and risk factors	Behavioural determinants	Daily smokers of cigarettes	International By age, sex, education, income, economic activity, number of cigarettes smoked	ÚVZ [ŠÚ SR]	Eurostat (EHIS1)
	Behavioural determinants	Body mass index	International By age, sex, education, income	ÚVZ [ŠÚ SR]	Eurostat (EHIS1)

Note: 1. European Health Interview Survey.

Source: Developing HSPA Framework for the Slovak Republic project.

For example, Daily smokers of cigarettes and Body Mass Index (BMI) capture major modifiable CVD risk factors and are stratified by age, sex and socio-economic variables (Figure 4.5). These indicators additionally allow for an intersectional analysis to identify high‑risk groups (e.g. low‑income middle‑aged men or women) for targeted obesity prevention and health promotion interventions. Risk factors affect cardiovascular disease prevalence and can be associated with long-term outcomes in treatment and disease management.

Figure 4.5. Gender differences in rates of obesity and overweight

Note: Overweight (including obesity) rates among adults, by gender. Data refer to European Health Interview Survey (EHIS) 2019.

Source: OECD/European Commission (2022[9]), Health at a Glance: Europe 2022. State of Health in the EU Cycle.

4.2.2. For people at risk of or with cardiovascular disease, healthcare availability is valuable to support self-management

Possible demographic differences in service availability and resulting use can play an important role in management of health risks or disease. Table 4.7 highlights indicators that can be understood by demographic variables.

Table 4.7. Selection of Slovak HSPA indicators that capture demographic differences in service availability and use

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Custodian of the dataset
Resources	Digitalisation and data	Teleconsultations per capita, and as a share of all consultations – placeholder	National By age, gender, activity, HIC	IZA	NCZI
Efficiency	Care provision efficiency	Visits to general practitioners, specialists and dentists, by time interval since last visit.	International By economic activity, sex, age	ŠÚ SR [IZA]	Eurostat (EHIS1 survey data)

Note: 1. European Health Interview Survey.

Source: Developing HSPA Framework for the Slovak Republic project.

Under Data and digitalisation domain, the indicator Teleconsultations per capita (and share of all consultations) can be examined by age and gender, thus providing a lens on digital equity. If older adults or lower‑income groups have lower uptake of teleconsultations, this may indicate barriers in digital literacy, technology access, or suitability of digital care models for chronic CVD management – relevant because telehealth can support continuity of cardiology care, medication adjustments, and follow‑up after acute events.

In the Care profession efficiency domain, Visits to general practitioners, specialists and dentists stratified by economic activity, sex, and age indicators allow assessment of whether particular demographic groups (e.g. unemployed middle‑aged men) under‑utilise primary care, presenting an effective preventive measure. The indicator is available by time interval since last visit. Lower contact with GPs can result in delayed diagnosis of conditions central to preventing heart attacks and strokes (hypertension, atrial fibrillation, or hyperlipidaemia).

4.2.3. Healthcare services can be delivered or received differently by population groups

Healthcare services, including primary, acute and integrated care are crucial to understand differences in outcomes for patients. Table 4.8 reflects selected Slovak HSPA indicators relevant to service quality and integration, all possible to disaggregate by gender.

Table 4.8. Selection of Slovak HSPA indicators that capture demographic differences

	Thematic area/ Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Custodian of the dataset
Healthcare services	Quality and Safety	30‑day mortality rate following hospital admission for ischaemic stroke (patient/linked data)	International By gender	NCZI	OECD (HCQO1)
	Care co‑ordination	Primary care quality: Avoidable hospital admissions	International By disease2, gender	NCZI	OECD (HCQO)
	Care co‑ordination	Congestive heart failure: mortality or readmission within 365 after discharge	International By gender	NCZI	OECD (HCQO)
	Care co‑ordination	Ischaemic stroke mortality or readmission within 365 after discharge	International By gender	NCZI	OECD (HCQO)

Note: 1. Healthcare Quality and Outcomes

1. Congestive heart failure.

Source: Developing HSPA Framework for the Slovak Republic project.

In the Quality and Safety domain, the indicator on 30‑day mortality after hospital admission for ischaemic stroke reflects quality of acute care, with systems and processes now in place in many OECD countries to identify suspected ischaemic stroke patients and to deliver acute reperfusion therapy quickly. Breakdowns by sex enable analysis of inequities within these processes. Sex differences in symptom recognition, timely transportation of patients, timely diagnosis, evidence‑based medical interventions and access to high-quality specialised facilities such as stroke units (OECD, 2025[10]) have a profound impact on mortality. International data allows for comparisons of sex differences between countries, allowing to separate plausible biological differences from care quality, as well as analyses of time trends.

Among people with chronic cardiovascular diseases, or after experiencing acute cardiovascular events, care co‑ordination indicators allow for an assessment of effective follow-up and support for long-term management of risks. Avoidable hospital admissions for Congestive Heart Failure can be examined by gender, highlighting differences in primary care effectiveness in managing hypertension, diabetes, or heart failure. It is additionally published in two age groups (over 45 and over 65) and compared to other countries by gender to understand performance (Figure 4.6). Higher avoidable admissions in certain demographic groups may signal issues such as unmet needs, lower adherence, or poorer continuity of care.

Figure 4.6. Avoidable hospital admissions for congestive heart failure (2023 or nearest year)

Note: Data refer to people over the age of 45 years.

Source: OECD Health Statistics 2025.

Two additional placeholder indicators would provide insights to differences in chronic disease management. Mortality or readmission within 365 after discharge due to Ischaemic stroke or Congestive heart failure by gender allow assessment of whether long‑term outcomes after major CVD events differ by demographic group. Differences could point to inequities in post‑acute rehabilitation, cardiac rehabilitation uptake, specialist follow‑up, or medication adherence (Barrenho et al., 2022[11]).

4.2.4. Cardiovascular disease outcomes often differ by gender

Analyses of cardiovascular disease outcomes benefit from consideration of biological differences, as some outcomes may be more likely in a specific gender or age group due to differences biological factors, however the magnitude of the differences is usually impacted by differences in risk factors and services described above. Table 4.9 outlines selected CVD outcomes.

Table 4.9. Disease prevalence and mortality can be captured by demographic factors

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Custodian of the dataset
Outcomes	Disease‑specific health outcomes	Prevalence of cardiovascular diseases1	International By disease type, sex, economic activity status	ŠÚ SR	Eurostat (EHIS2)
	Disease‑specific health outcomes	Cardiovascular mortality	International, regional3 By gender, region	ŠÚ SR	Eurostat (Causes of death: General mortality)
	General health outcomes	Causes of treatable and preventable mortality of residents	International By sex, cause	OVZ	Eurostat (Causes of death: public health themes)

Notes:

1. Myocardial infarction, vascular heart disease or angina pectoris, high blood pressure, stroke/stroke, high blood cholesterol or lipid levels. 2. European Health Interview Survey.

2. Regional comparison is available through national sources for the same indicator (see Annex D for further information).

Source: Developing HSPA Framework for the Slovak Republic project.

In the Disease‑specific outcomes domain, the indicator Prevalence of cardiovascular diseases allows investigation of to what extent the prevalence of conditions such as myocardial infarction, stroke, or risks such as high blood pressure, cholesterol and lipid levels differ between men and women. Such disparities may reflect differences in occupational exposures, chronic stress, lifestyle risk factors, or access to preventive care. Cardiovascular mortality can be analysed across gender and regional lines and compared to other countries (Figure 4.7). Importantly, demographic breakdowns also support interpretation of indicators where system functioning interacts with population structure. Ageing regions may show different patterns of service use and outcomes that need to be contextualised against demographic composition.

Figure 4.7. Cardiovascular disease mortality rate, people under the age of 65 (2022)

Note: Data refer to the age‑sex standardised mortality rate among people under 65. The EU average is weighted.

Source: OECD (2025[10]), The State of Cardiovascular Health in the European Union, https://doi.org/10.1787/ea7a15f4-en.

In the General disease outcomes domain, the indicator Causes of treatable and preventable mortality looks at specific causes of mortality deemed mostly preventable or treatable in people under 75 years old, allowing for analyses focussed on mortality statistically considered as “premature”. Sex disaggregation by specific causes offers insights to particular CVD conditions that cause mortality in younger people.

Overall, the demographic equity lens helps ensure that HSPA reporting does not rely on population averages alone. Instead, it supports a systematic approach to answering whether the system performs comparably for women and men or across age groups, and where targeted improvements, such as prevention, access expansion, or service design adjustments, may be needed. International comparisons help to understand the demographic disparities beyond biological differences. It should be noted demographic equity is not limited to sex and age differences, and availability of data and relevance of problems affecting minorities (Box 4.1) or migrant populations should be considered.

Box 4.1. Marginalised Roma Communities in the Slovak Health System Performance Assessment framework

Inequities affecting marginalised Roma communities (MRK) were highlighted throughout the HSPA development process. Although no feasible indicators were identified to directly compare MRK population health with that of the majority population, elements of equity related to MRK can be captured through district level disaggregation and consideration to their distribution across the country. Data from 2010 indicates an uneven distribution of Roma across regions, and at a district level, up to one‑quarter of the population in some districts identified as Roma compared to 6.5% of the national population (Matlovičová et al., 2012[12]). On a municipality level, nine municipalities in 2010 were more than 75% Roma. Certain indicators that can be disaggregated at least on a district level, such as Under‑5 mortality or Childhood vaccination rates among eligible children. Additionally, indicators disaggregated by socio-economic groups can partially reflect specific populations.

4.3. Equity by socio-economic status: Health status differences

Socio-economic equity concerns whether health and health system performance differ by education, income, and other socio-economic characteristics such as employment, housing, financial hardship and others. These gradients often reflect both differences in exposure to risk factors and differences in ability to navigate and afford care, even in systems with broad coverage. In the Slovak HSPA, 25 indicators explicitly support socio-economic disaggregation, enabling HSPA reporting to monitor whether the system is delivering equitable outcomes and access across social groups, assess trends and differences in outcomes linking them to various factors throughout the system. This case study focusses morbidity and health status differences, outlining selected contributing factors across the Slovak HSPA.

4.3.1. Health status is impacted by health literacy, in turn affecting risk factors and engagement in prevention programmes

Health status is affected by a variety of factors, with health literacy, risks and preventive service utilisation playing a key role across the life course. Table 4.10 outlines selected indicators in the Slovak HSPA that can reflect socio-economic differences in these variables.

Table 4.10. Selected indicators with socio-economic disaggregations in the Prevention and risk factors domain

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Source
Prevention and risk factors	Health literacy	Health literacy – Patients reporting that most health issues are too complex for them to understand	International By age, gender, education, income	IZA	OECD (PaRIS) Placeholder
	Behavioural determinants	Daily smokers of cigarettes	International By age, sex, education, income, economic activity, number of cigarettes smoked	ÚVZ [ŠÚ SR]	Eurostat (EHIS1)
	Behavioural determinants	Performing health-enhancing physical activity	International By age, sex, education, income	ÚVZ [ŠÚ SR]	Eurostat (EHIS1)
	Behavioural determinants	Body mass index (underweight, normal weight, moderate obesity and obesity)	International By age, sex, education, income	ÚVZ [ŠÚ SR]	Eurostat (EHIS1)
	Prevention	Mammography screening within the past two years (women 50‑69)	International, regional2 By age, education3, income3, region2, district2	NSC [NCZI]	OECD (JQNMHS4: Healthcare activities)

Notes: 1. European Health Interview Survey.

1. Regional comparison is available through national sources for the same indicator (see Annex D for further information).

2. Available when using contextualising indicators.

3. Joint Questionnaire on Non-Monetary Health Statistics.

Source: Developing HSPA Framework for the Slovak Republic project.

The WHO defines health literacy as the ability to access, understand, appraise and use information and services in ways that promote and maintain good health and well-being. The Slovak HSPA will capture Health literacy through a placeholder indicator on Patients reporting that most health issues are too complex for them to understand from the PaRIS survey, with breakdowns by age, gender, education and income. This indicator enables analysis of whether certain socio-economic groups face aggravated barriers in understanding health information, which may in turn affect preventive behaviours, care‑seeking and adherence to treatment. For example, Morris et al. (2013[13]) identified that adults with low health literacy were more likely to report avoiding doctor’s visits while Baccolini (2022[14]) found adequate health literacy increases participation in breast, cervical, and colorectal cancer screening and constitutes an important factor in recognition of symptom seriousness.

In the Slovak HSPA, indicators on cancer screening, such as Mammography screening within the past two years (women 50‑69) can be viewed with breakdowns by education and income, when contextualising indicators that capture self-reported screening from the European Health Interview Survey (EHIS) are used (Annex D).

Risk factors are vital to understand development of morbidity patterns. As they commonly differ between population groups, an analysis by socio-economic breakdowns helps to understand trends and effectiveness of prevention measures. Within the Behavioural determinants domain, indicators Daily smokers of cigarettes, Body mass index (underweight, normal weight, moderate obesity and obesity) and Performing health‑enhancing physical activity are designed to be disaggregated by age, sex, education and income (Figure 4.8). It should be noted the Slovak HSPA relies on the EHIS survey for reporting on risk factors, while EU-SILC is used to illustrate in Figure 4.8.

Figure 4.8. Socio-economic distribution within the Outcomes and the Prevention and Risk Factors thematic areas

Source: OECD/European Observatory on Health Systems and Policies (2025[8]), Country Health Profile 2025: Slovak Republic, https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/12/country-health-profile-2025-country-notes_7e72146d/slovak-republic_a9b397e5/02c29a7f-en.pdf.

4.3.2. Disadvantaged socio-economic groups often experience barriers to healthcare access or may receive care that is of lower quality or less people centred

While risks and prevention are key to understand socio-economic differences in health status, these can be reinforced and exacerbated by differences in access to care and characteristics of the care received. The Slovak HSPA includes indicators relevant to capture differences in healthcare access, use and experience across socio-economic groups (Table 4.11).

Table 4.11. Selected socio-economic disaggregation in the healthcare services domain

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Source
Healthcare services	Access	Self-reported unmet needs for medical examination by main reason declared	International, regional By main reason declared; education, income, sex, age; by NUTS 2 regions	ŠÚ SR	Eurostat (EU-SILC1)
	Access	Share of households with catastrophic health spending by consumption quintile	International By consumption quintile	IZA	WHO (Financial protection in the European Region)
	People centredness	Experienced quality – patients who report they are satisfied with medical care provided by their primary care centre in the past 12 months	International By age, gender, education, income	IZA	OECD (PaRIS) Placeholder

Note: 1. EU Survey on Income and Living Conditions.

Source: Developing HSPA Framework for the Slovak Republic project.

In the Access domain, Self‑reported unmet needs for medical examination by main reason declared, disaggregated by education, income, age, sex and NUTS 2 regions, allows monitoring of whether disadvantaged groups are more likely to delay or forgo care they need, and for which reasons (cost, waiting times, distance, etc.). This enables the HSPA to distinguish whether unmet needs are primarily related to financial barriers, waiting times, or distance, and whether these reasons differ across socio-economic groups – an important input for targeted policy responses.

Complementing this, Share of households with catastrophic health spending, disaggregated by consumption quintile, directly measures the distribution of financial hardship associated with healthcare across the income/consumption spectrum and reflects adequacy of financial protection mechanisms in international comparisons (Figure 4.9). The indicator most widely used to measure financial hardship associated with OOP payments for households (Cylus, Thomson and Evetovits, 2018[15]). Its breakdown by consumption quintiles allows to assess whether burdens are concentrated among disadvantaged groups. Understanding of the causes of this needs to be accompanied by policy information, such as the structure of OOP payments for various services, and other indicators, such as those capturing breakdowns of OOPs from expenditure data on a national level (Access domain). For example, the Slovak Republic does not have co-payments at point of care for primary care (OECD/The Health Foundation, 2025[1]).

Figure 4.9. Share of households with catastrophic health spending by consumption quintile

Note: Data in countries on the left of the chart are for 2019 or the latest available year before the COVID‑19 pandemic; data in countries on the right of the chart are for 2020 or later. Data for 2020 and 2021 should be interpreted with caution due to shifts in healthcare use and spending during the peak years of the pandemic. Catastrophic health spending is defined as OOP payments that exceed a predefined percentage (here 40%) of the resources available to a household to pay for healthcare. Results are disaggregated into quintiles by consumption per person using the OECD equivalence scale. Microdata from national household budget surveys are used to calculate this indicator.

Source: OECD (2024[16]), Health at a Glance: Europe 2024: State of Health in the EU Cycle, https://doi.org/10.1787/b3704e14-en.

Equity considerations also extend to how care is experienced. In the People centredness domain, the placeholder indicator Experienced quality – patients who report they are satisfied with medical care provided by their primary care centre in the past 12 months from the PaRIS survey could be disaggregated by age, gender, education and income, as it enables assessment of whether patient‑reported experiences of care vary systematically by socio-economic status. This in turn can reflect how patients experience primary care support in management of their underlying risks or chronic conditions, which impact health. Data from the OECD PaRIS survey indicates people who rate their care as more person-centred also have better physical and mental health, and better well-being (OECD, 2025[17]).

4.3.3. Outcomes area includes indicators that reflect socio-economic differences in health status

Overall health status can currently be captured by socio-economic variables through indicators based on survey microdata. Understanding of differences between socio-economic groups is valuable to interpret for example any country-level time trend improvements. Table 4.12 shows health status indicators available by socio-economic groups.

Table 4.12. Socio-economic indicators of health status

Area	Domain	Name of the indicator	Comparability and indicator breakdown	Responsibility in HSPA	Source
Outcomes	General health outcomes	Self-perceived health	International By age, sex, education, income	ŠÚ SR	Eurostat (EU-SILC1)
	General health outcomes	People having a long-standing illness or health problem (self-perceived)	International By age, sex education, income	MoH (SZ)	Eurostat (EU-SILC1)

Note: 1. EU Survey on Income and Living Conditions.

Source: Developing HSPA Framework for the Slovak Republic project.

Self‑perceived health (Figure 4.10) and People having a long‑standing illness or health problem (self‑perceived) have available breakdowns by education and income, derived from the EU-SILC survey. These provide a high‑level, comparable view of socio-economic health gradients both disaggregated by age, sex, education and income, provide direct measures of differences in experienced health. These indicators support analysis of whether any improvements in overall health outcomes over time are shared equitably across social groups, or the extent to which gaps persist between more and less advantaged populations.

Figure 4.10. Distribution in the percentage of people reporting good or very good self-perceived health by income quintile (2024)

Note: The EU average is weighted.

Source: OECD (2024[16]), Health at a Glance: Europe 2024: State of Health in the EU Cycle, https://doi.org/10.1787/b3704e14-en.

While the current Slovak HSPA indicator set already enables extensive socio-economic disaggregation, further improvements are possible. Implementation of the Patient-Reported Indicators Survey (PaRIS) will allow for expanded use of survey‑based indicators (such as Health literacy and Experienced quality), while administrative data could in the future further identify disparities across pathways. Where feasible via sufficient data infrastructure capability and data protection requirements, linking administrative data with socio-economic variables would support more granular and longitudinal equity analyses, strengthening the role of HSPA as a tool for identifying and addressing avoidable health inequalities in the Slovak Republic. While linkage to social data is unavailable, location-based deprivation indices can be explored to disaggregate some of the indicators.

4.4. Understanding equity in health system performance through the Slovak HSPA framework

Overall, the equity case study shows how the Slovak HSPA can operationalise equity as a cross‑cutting analytical theme. By linking indicators across inputs (availability and distribution of resources), processes (access, timeliness, affordability and utilisation), and outcomes (health status and avoidable harm), the HSPA framework supports a coherent assessment that aligns with the Slovak HSPA purposes to monitor regional differences, compare internationally, evaluate time trends and understand healthcare quality. This approach can be used to understand equity across different population group lenses (geographic, demographic and socio-economic equity) within specific policy-relevant topics (e.g. cardiovascular care, cancer pathways, primary care strengthening) while maintaining an explicit equity perspective through consistent subgroup breakdowns. While no dedicated indicators reflect specific population groups such as the Roma population (Box 4.1), disaggregation can be relevant to inform on inequities. Additionally, use of area-based multiple deprivation indices could in the future provide further insights to interpret geographic breakdowns for some of the indicators.

By embedding equity across domains, the framework enables coherent narratives that connect population health differences to variations in risk exposure, service availability, access, utilisation, quality and financial protection, complemented by policy and contextual information to conduct analyses. Relevant data are available for many indicators and can be meaningfully combined to support policy‑relevant interpretation, even when analyses remain illustrative. Importantly, the framework can support dialogue and in-depth thematic examinations where inequities arise and which policy levers may be most effective. As such, the Slovak HSPA provides a practical foundation for integrating equity into routine performance assessment and for monitoring whether reforms contribute to reducing avoidable health inequalities over time.

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