Morbidity statistics - methodology

Latest update of text: May 2015

To be updated in December 2019

Pilot studies on diagnosis-specific morbidity statistics were conducted by 16 European Union (EU) Member States between 2005 and 2011. A Task Force of Morbidity Statistics was set up to analyse the results of these pilot studies and to formulate a set of recommendations on the feasibility of a regular collection of morbidity statistics. A report on morbidity statistics in the EU summarised the results of the task force’s work and presented a possible methodology for collecting and compiling EU morbidity statistics. The report was presented to the Working Group on Public Health Statistics in December 2013.

This background article provides an introduction to the contents of that report. It provides an overview of the demand for health indicators in the context of EU policies and an explanation as to how morbidity statistics could be used. Besides these aspects, the steps and methods that have been followed in order to establish a routine data collection of morbidity statistics are provided.

A second article on morbidity statistics provides an overview of the main findings, aspects that are problematic and proposed solutions for moving towards an EU data collection for morbidity statistics. It presents some prototypical situations the task force faced during its analysis of potential sources and estimates with regard to their accessibility, usefulness, comparability and overall quality. Case studies dealing with the quality of the identified sources and estimates are shown in the form of questions in order to make the article more readable.

Currently, Eurostat is working with the national statistical authorities of 13 EU Member States to identify the existing (or planned) sources and methods in view of producing the best possible national estimates for all morbidity indicators included in EU’s short list (see Annex 2 of the report on morbidity statistics in the EU). The exercise aims to identify major national obstacles as well as possible solutions in order to arrive at a complete set of morbidity statistics. Results from this exercise are expected to be available during 2017. This article is one of a set of background articles concerning the methodology for the production of health statistics in the EU and accompanies a number of statistical articles which make up an online publication on health statistics.

Full article

Executive summary

EU statistics for health status and health determinants currently come from surveys. However, one core subject is not covered, namely the regular collection of diagnosis-specific morbidity data with incidence and prevalence rates. A legal basis for such a data collection is provided by Regulation No 1338/2008 establishing a framework for Community statistics on public health and health and safety at work. That regulation foresees an implementation of morbidity statistics across the EU.

Eurostat has a long-standing commitment to develop the conceptual and methodological framework for establishing such a data collection on morbidity statistics. This ambitious goal has not yet been achieved as, from a methodological and operational point of view, establishing morbidity statistics is an extremely complex exercise; this is particularly true with regards to achieving geographical comparability of data.

To guide EU Member States in the pilot phase of developing this data collection, detailed guidelines were produced by the Morbidity Statistics Development Group in 2007: for each entry in the EU’s short list on morbidity the appropriate measures for data delivery on incidence and / or prevalence were indicated. Based on these, each Member State had to find appropriate sources for producing the best possible national estimates. The main criterion for the inclusion of a data source was the statistical robustness of the main data quality parameters in order to make reliable comparisons between Member States possible. Hence, like many EU statistics, the compilation of diagnosis-specific morbidity statistics was (and still is) output harmonised and did not involve harmonisation of a specific source.

Altogether 16 EU Member States participated in pilot studies on diagnosis-specific morbidity statistics between 2005 and 2011. In 2011, the Task Force on Morbidity Statistics was established to analyse the results of these studies, especially with regard to the choice of sources and the production of estimates. The task force subsequently presented a report with an in-depth analysis of the pilot studies and methodological recommendations to pave the way through the pioneering stage.

The establishment of diagnosis-based morbidity statistics will be crucial for filling an information gap on the health status of the EU’s population which has severely hampered the development of public health indicators for the EU.

The draft of the task force’s report was presented and discussed at the Technical Group on Morbidity in June 2013; the final version was presented to and endorsed by the Working Group on Public Health Statistics in December 2013.

The importance of EU statistics on diagnosis-based morbidity

EU statistics for health status and health determinants currently come from three surveys: the five-yearly European health interview survey (EHIS); the newly established disability survey (European health and social integration survey — EHSIS), and a basic set of health-related questions that are included in the annual EU statistics on income and living conditions (EU-SILC).

However, one core subject is not covered: the regular collection of diagnosis-specific morbidity data with incidence and prevalence rates. A legal basis for such a data collection is provided by Regulation 1338/2008 establishing a framework for Community statistics on public health and health and safety at work. That regulation provides for an implementation of EU-wide morbidity statistics. The Community action programme on public health covering 2008–13 and the Community statistical programme for 2008–12 planned the implementation of that Regulation as a key statistical element of a sustainable health monitoring system. In addition, the European Commission‘s Communication Solidarity in health (COM(2009) 0567 final) emphasised the importance of having regulations developed in each domain of public health statistics.

The recent European Commission staff working document ‘Investing in health’ (complementing its Communication ‘Towards Social Investment for Growth and Cohesion’ (COM(2013) 083 final), which is an accompanying document to the ‘Social Investment Package’, defines the role of health as part of the Europe 2020 strategy and points out that an improvement in health data collection is needed, in particular in using the European core health indicators (ECHI) and developing tools to assess the efficiency of health systems better. In addition, statistical information on specific chronic diseases is a key component in underpinning and addressing policies to improve labour market participation and reduce the risk of social exclusion and the risk of poverty.

Eurostat’s commitment to develop the conceptual and methodological framework for establishing a data collection on morbidity statistics dates back to the mid-1990s. Following the analysis of pilot studies in 16 Member States, the report of the Task Force on Morbidity Statistics provided a set of recommendations to pave the way through the pioneering stage.

The feasibility of compiling morbidity statistics, in particular in view of using data from different sources, is expected to be markedly enhanced by the revision of EU statistical law. That will be the legal basis to make possible and support a better use of existing sources, by improving access to and the use of administrative data, for example by merging or linking existing datasets.

The establishment of diagnosis-based morbidity statistics is crucial for filling an information gap on the health status of the EU’s population. Key elements of innovation for this approach include:

  • the best possible estimates from multiple sources that can be used (namely physicians issuing diagnoses / prescriptions or health records from registers, health institutions and insurance);
  • the possibility to compare best possible estimates on incidence and prevalence of diseases;
  • the comprehensive coverage of morbidity data;
  • the coverage of the whole population, by providing national estimates; and
  • the selection of diseases and conditions to be reported in terms of EU relevance and the public health perspective.

In 2007, Eurostat and Member States developed a methodology and a shortlist for collecting such data across the EU. It addressed diseases and conditions with major impact on health care and health-care related costs, annual death rates, or potential years of life lost. Examples range from heart / circulatory and respiratory diseases, cancer or metabolic diseases such as diabetes, to mental diseases, injuries and their consequences and external causes. An attempt to highlight the main diseases for which morbidity statistics are needed is presented in the EU’s short list on morbidity, which was followed throughout the pilot phase by the participating Member States.

The current lack of systematic and official data on morbidity has severely hampered the development of EU-wide public health indicators which are required to support health policymakers.

EU-wide diagnosis-based morbidity statistics: a difficult exercise

For selected diseases the health status of the EU’s population is known thanks to disease-registers, ad-hoc studies and as self-reported information from the European health interview survey or the EU statistics on income and living conditions. Currently, the principal and most reliable source for establishing and monitoring public health policies is information derived from causes of death statistics. While this type of source is well established and provides reliable and comparable public data for all EU Member States, data on causes of death do not provide information on incidence and prevalence of diseases and in particular lack information on comorbidities that would be necessary for a comprehensive picture of public health.

A regular and systematic data collection and dissemination of statistics [1] on diagnosis-based morbidity does not exist either for the EU or globally [2]. The reason behind this is that, from a methodological and operational point of view, the collection of morbidity statistics is an extremely complex exercise, in particular with regards to the geographical comparability of data. Specific efforts will be required in each EU Member State to produce operational definitions of variables that are based on many different available sources. So far these difficulties have hampered attempts to establish a morbidity data collection based on (mainly) administrative data similar to those already existing for causes of death or for health care data based on hospital discharges.

The latest release of the work on the global burden of diseases is a tentative step towards filling this information gap.

The demand for statistical data on diagnosis-based morbidity is increasing; however, the capability to respond to this increasing demand is constrained by limited data availability, quality, and use. A set of diagnosis-based morbidity indicators have been developed in the context of the European core health indicators [3] list, but most of these indicators are not yet collected and the list is not exhaustive. It is therefore important to collect morbidity statistics in order to have these indicators thoroughly implemented both in terms of definition and data availability [4].

The paradox for EU-wide statistics is that, while sometimes there is a wealth of information available for some specific diseases, this information can often be scattered, sparse, not representative of the total population, not collected systematically or not addressing the multidimensional characteristics of health. For many other diseases, national data are only rarely available. The result is:

  • a fragmented picture of the occurrence of diseases in the EU, often driven by the needs of single disease programme or ad-hoc data collection;
  • information only on incidence or prevalence for diseases where both indicators would be advisable;
  • an inefficient use of the available sources of collected information and allocated resources.

Lastly, it should not be forgotten that the legal framework for accessing and processing the available data from many different sources poses obstacles that need to be addressed and solved. The revision of the law on European statistics (2015/759) adopted in April 2015 should allow all members of the European statistical system to use their technical IT capabilities and this legal mandate for working towards this goal.

The pilot studies in 16 EU Member States that were analysed for the report on morbidity statistics in the EU have shown the feasibility of the proposed methodology for many of the 105 indicators (both for incidence and prevalence) included in EU’s short list on morbidity.

Demands for diagnosis-specific morbidity in EU programmes on public health

The responsibility for the organisation and delivery of health services and healthcare is largely held by the EU Member States at national and sub-national levels. However, the European Commission is asked for action whenever there is a need to complement Member States’ health policies, in particular in areas such as health promotion, prevention, research or dissemination of information by public health data collections within the European statistical system.

The provision of statistics on public health was closely linked to the Community action programme in the field of public health for 2008–13, which covered health status including morbidity and implemented the strategy ‘Together for health: a strategic approach for the EU 2008-2013’ (COM(2007) 630). The most relevant point addressing the importance of health data is the following: ‘Finally, health policy must be based on the best scientific evidence derived from sound data and information, and relevant research. The [European] Commission is in a unique position to assemble comparable data from the Member States and regions and must answer calls for better information and more transparent policy making, including through a system of indicators covering all levels (national and sub-national).’ (page 4).

In an ageing society, diagnosis-specific morbidity statistics are of particular importance in view of addressing issues such as self-management of multi-morbidity and reduction in the need for long-term care. The analysis of time series of morbidity statistics will be a major resource to enhance information and knowledge as requested by the Regulation establishing the EU’s third programme for action in the field of health (2014-2020). In fact, one of the thematic priorities is to ’Foster a health information and knowledge system to contribute to evidence-based decision-making, including the use of existing instruments and, where appropriate, further development of standardised health information and tools for monitoring health, collection and analysis of health data, and the wide dissemination of the results of the Programme.’.

The challenge of increasing health costs

A population in bad health is likely to cause higher overall expenditure due to both direct and indirect costs linked to ill-health, such as more people partially or totally inactive during their productive working years, as well as a burden from unhealthy retired people.

Spending on health is not just a cost: it is an investment in order to reduce the burden from costs for diagnosis and treatment of diseases and their resulting limitations, impairments and disabilities. Some examples (although not exhaustive) are chronic diseases (such as diabetes, mental disorders, neurodegenerative conditions, coronary heart disease and cancers) or diseases impairing the productive years of younger people, such as injuries and their long-lasting consequences.

The recent report on ageing from the Directorate-General for Economic and Financial Affairs clearly highlights the lack of comparable, quantifiable measures of health status (morbidity) that are required in order to evaluate the most likely possible scenario for estimating projections of health care costs in the EU. When data on health expenditure are available, it is assumed that age / gender specific expenditure profiles provide a proxy for health status (morbidity). In other words, higher expenditure signifies higher morbidity [5].

The ageing process in the EU is likely to raise demand for healthcare while also decreasing the relative size of the working population. It has been estimated that this could result in an increase in healthcare spending of 1–2 % of gross domestic product in EU Member States by 2050. On average, this would amount to an increase of approximately 25 % in healthcare spending as a share of gross domestic product based on the level of health expenditure (which ranged from 6 to 12 % of GDP in 2009). However, European Commission projections show that if people can remain healthy as they live longer, the rise in healthcare spending due to ageing would be halved.

On average across the EU, health spending per capita increased by 4.6 % per year in real terms between 2000 and 2009, but this was followed by a reduction of 0.6 % in 2010, a consequence of budget cuts during the financial and economic crisis (see Figure 1) [6]. Given the significant reduction in health care expenditure in some EU Member States, the importance of morbidity statistics will be even greater for monitoring the resulting impact on public health.

On the other hand, more effective prevention influences the incidence and / or the prevalence of diseases and thus has an impact on health expenditure. Following such morbidity developments gives indications for future impacts on health expenditure.

Figure 1: Annual average growth rate (in real terms) of health expenditure per capita, 2000–10 or nearest year (%)
For more updated data, see: (hlth_sha_hf) (Source: Eurostat)


This situation has consequences for the development of morbidity statistics. If the reductions in the costs per capita continue in the coming years, this may impact on the kind of sources to be identified and used for data collection — with a possible shift from the public sector to the private sector — as well as changes in the incidence and prevalence of diseases. This illustrates the concept of ’reverse causality’ meaning that morbidity estimates are not just a cause of expenditure, but also a result of it.

At present, information on diagnosis-based incidence and prevalence of diseases to assess the burden on the population or on the cost of diseases to assess the burden on health systems is not yet available in the EU in the form of a harmonised, regular data collection that is capable of delivering this information as part of published EU official statistics. This information gap is likely to have negative drawbacks on the possibility of establishing effective EU policies for health and efficient allocation of resources, both nationally and for the EU. These two components of the overall picture on health should be equally developed, and the establishment of morbidity statistics is the first step to be made towards this direction.

An example of how the problem has been addressed nationally is provided by an analysis of the cost of illness conducted by the Dutch national institute for public health and the environment in cooperation with Statistics Netherlands. The main reasons identified for the increase in costs were:

  1. ageing of the population (explained about 15 % of the cost rise over the period 1999–2010);
  2. price inflation (explained about 35 %);
  3. an interrelated set of causes such as policy changes, easier access to services, the growth of the number of patients treated, more intensive treatment and the implementation of new medical technology (explained about 50 %).

In spite of ageing, the largest increase in costs between 2005 and 2010 occurred in young people (aged 1–24). A higher use of youth care and a change in the rules for admittance to care for people with disabilities explained this rise. Although women use more health care than men, since 2005 the costs increased faster for men than for women. This was partially related to improved male health: they lived longer, and therefore used more health care, particularly at older ages.

Longer lives and health in EU social programmes

Life expectancy at birth in the EU-28 increased over the last 50 years by about 10 years due to several factors, including medical progress, improved hygiene, better living conditions, education and access to high quality health care. In 2012, life expectancy at 65 years of age in the EU-28 was 19.6 years (Figure 2).

Figure 2: Life expectancy at age 65, 2012 (years)
For more updated data, see: (demo_mlexpec) (Source: Eurostat)


As a result, a significant proportion of the population reaches advanced ages, which is associated with a change in the patterns of diseases from infectious to non-communicable diseases. With respect to the notable gain in life years, a crucial question to be addressed is how the life of the EU’s population is spent in terms of health. To answer this fundamental question the healthy life years indicator was included in 2005 as a structural indicator (as part of the Lisbon strategy), to underline that the population’s life expectancy in good health — not just length of life — was a key factor for economic growth. One of the aims of the European Commission’s pilot initiative ‘European innovation partnership on active and healthy ageing’ is to increase, by 2020, the average number of healthy life years in the EU’s population by two years.

The data on healthy life years is a general measure of population health which could be refined and complemented by using diagnosis-based data, both incidence and prevalence of the main causes of deaths. EU-wide diagnosis-based morbidity statistics will be essential for delivering such information. A statistical analysis of healthy life years is provided in a separate article.

Quality of life and well-being in Commission context

In its Communication on ‘GDP and beyond: measuring progress in a changing world’ the European Commission announced that it would work on developing indicators on quality of life and this is also a recommendation that is part of the Report by the Commission on the measurement of economic performance and social progress (JE Stiglitz, A Sen, JP Fitoussi): ‘Recommendation 6: Quality of life depends on people’s objective conditions and capabilities. Steps should be taken to improve measures of people’s health, education, personal activities and environmental conditions …’.

In the context of the EU’s quality of life indicators, health is indicated as one of the eight dimensions and within the dimension ‘health’ the emphasis is not only on physical health but — even more — on mental health.

At present, indicators associated with mental health well-being derived from official public health statistics include the death rate for suicides (administrative data), hospitalisation rates and self-reported depression (from population surveys). Undoubtedly these are insufficient indicators for providing a complete picture on such a complex and major health problem in the EU. It is therefore not surprising that European pact for mental health and well-being notes that ‘there is a need to improve the knowledge base on mental health: by collecting data on the state of mental health in the population’.

Gradually diagnosis-based statistics should replace self-reported data as sources for health indicators for specific diseases.

Current data collections completing the information on health in the EU

The results and indicators derived from some existing data collections would benefit from the extra dimension of diagnosis-specific statistics.

Morbidity as perceived and reported by people

The current sources available at Eurostat for assessing self-perceived health are EU statistics on income and living conditions and the European health interview survey. The information available from these sources will still be valuable even if and when diagnosis-based morbidity statistics are established. The two aspects of measuring health (self-perceived or diagnosed by a professional) should be seen as complementary to each other, which is in line with a modern approach of the social and economic role and integration of individuals as the main pillar of social statistics. However, some well-known limitations of surveys restrict their scope and use. In particular:

  • they miss acute serious illnesses with high fatality rates / low survival rates;
  • sample sizes often do not allow for more precise estimation of prevalence by age groups (for example, diabetes in younger age groups) as there are simply too few respondents with a given disease per age group — this is especially a problem in small EU Member States where sample sizes are relatively small;
  • overall non-response (and increases in non-response) and biased responses;
  • the financial cost and burden on respondents.

To overcome these limitations morbidity statistics will be comparatively cheap and will provide estimates based on the whole population. Morbidity information on individuals cannot therefore be represented only by diagnoses or only by perception, but hopefully by the full integration of these two components, as the complete description of the status of health should include population based diagnosis, surveys and mortality by cause. Data from health interview surveys and diagnosis-specific morbidity data are therefore complementary sources: both are essential for evidence based policymaking. This complementarity will have an invaluable impact on the data produced and their subsequent interpretation and use.

Comparisons between European health interview survey data and morbidity estimates were included in Annex 1 of the report on morbidity statistics in the EU as part of the data received from the EU Member States that undertook pilot studies.

Addressing inequalities and access to health care for the whole of the EU’s population

While the EU’s ageing population will require more and more health services, the younger part of the population, especially children, consistently show a higher risk of poverty or social exclusion. The share of people at risk of poverty or social exclusion is a Europe 2020 headline indicator: in the EU-27, for 2011, 26.3 % of people aged under 18 were at risk of poverty or social exclusion, compared with 19.9 % for people aged 65 and over [7] (see Figure 3), with remarkable differences between the EU Member States. More detailed data show that poverty is unevenly distributed, not only by educational attainment or income, but even across generations. This evidence suggests that policymakers should address the question of how health is accessed by and guaranteed to every segment of the population in the EU.

Figure 3: Proportion of people at risk of poverty or social exclusion, EU-27, 2006–12 (%)
For more updated data, see: (ilc_peps01) (Source: Eurostat)


Data for 2011 from EU statistics on income and living conditions show that unmet needs for medical examination due to barriers to access (too expensive, too far to travel or waiting lists) increase with age in the EU-27 and involve on average 3 % of the surveyed population (see Figure 4). This percentage shows that contact with the health systems for medical examination is relatively guaranteed. Note that children below the age of 18 are not included in these data.

Figure 4: Proportion of people with self-reported unmet needs for medical examination due to barriers to access (too expensive, too far to travel or waiting lists), EU-28, 2011 (%)
For more updated data, see: (hlth_silc_08) (Source: Eurostat)


In a scenario where deprivation could increase, a comparison between the demand for and access to care will be particularly relevant. Therefore, the availability of more accurate and complete information about which major diseases affect the EU’s population should contribute to:

  • reducing the burden of specific conditions on the population;
  • strengthening those parts of health systems where necessary (for example, prevention and rehabilitation);
  • responding to major public health challenges (for example, emerging diseases);
  • tackling inequalities in access to appropriate care for specific diseases (globally, for the EU, nationally or sub-nationally, or by gender, education, age or level of income);
  • establishing and monitoring adequate actions and policies.

The information currently available on health outcomes, namely causes of death statistics, only highlight the worst scenario by producing figures on those who died from a certain condition.

There is a strong demand from within the European Commission to have factual evidence, as in the case of the communication ‘Solidarity in health’ (COM(2009) 567 final) where the need for more health data is clearly identified as one of the foreseen EU actions: ‘support the further development and collection of data and health inequalities indicators by age, sex, socio-economic status and geographic dimension’. Morbidity statistics will be one of the building blocks for such evidence.

The importance of morbidity statistics in the international context

The global burden of disease 2010

An overwhelming amount of information can be derived from the global burden of disease 2010 study [8], a world-wide effort to compile health data. In a number of ways the EU is in a privileged situation with respect to access to and availability of some of the health data presented in this study. However, the study highlights the burden of disease for the population and also for national health systems. In fact, some aspects from the study are relevant for the development of EU morbidity statistics and should be taken into account :

  • the methods developed and used for measuring the burden of diseases and risk factor prevalence, estimation methods;
  • the efforts made to ensure a certain level of comparability by involving a consistent number of researchers to estimate incidence and prevalence of diseases (as well as causes of death, risk-factor exposure and attributable burden, and healthy life expectancy);
  • an innovative and comprehensive approach to cross-validation of data (mainly against evidence from mortality / causes of death), by including conditions leading to disabilities such as visual impairment, hearing loss and anaemia;
  • comorbidities that were taken into account for the estimation of the years lived with disability indicator;
  • quantification of uncertainties across all the components of the study, thus communicating to users the limitations of the estimates for different diseases, injuries and risk factors;
  • the commitment of 486 authors from 302 institutions in 50 countries who reviewed the final articles.

UN sustainable development goals

Building upon the millennium development goals one of the main outcomes of the Rio+20 Conference in 2012 was the agreement to develop a set of sustainable development goals. Health is one of the topics addressed in the context of the post 2015 process; it has been discussed as a cross-cutting issue during each two-year cycle. Selected health indicators will need to be available to monitor specific goals in the social, environmental and economic domains.

The importance of national morbidity statistics

The increasing demand and importance for having EU-wide morbidity statistics has been highlighted. However, the benefits for EU Member States to collect and report a set of data that are comparable across the EU should not to be underestimated. On the one hand, national morbidity statistics are the cornerstone for monitoring and evaluating morbidity developments in general and programmes in particular. On the other hand, national morbidity statistics that are comparable with those of other EU Member States are the basis for comprehensive planning of a different nature, for example in view of monitoring specific health and welfare programmes as compared with other Member States in terms of the need for human and physical resources, insurance costs, and expenditure for health care activities.

A specific example of the need for internationally comparable morbidity data is represented by issues on cross-border health care provisions, an increasing phenomenon and burden in several EU Member States.

The pilot studies showed that for several diseases, coverage of the total population is far from being achieved in all EU Member States. Available data miss a substantial number of cases, such as patients whose diseases are treated by general practitioners who are not part of the national data collection system. Such a lack of information may distort national estimates on the incidence and prevalence of diseases, as well as the sub-national and local allocation of resources.

Background to the report on morbidity statistics in the EU

The Morbidity Statistics Development Group (MSDG) 2006–07

Since the mid-1990s the European Commission (Eurostat and the Directorate-General for Health and Food Safety) has launched several activities aimed at establishing EU-wide diagnosis-specific morbidity statistics. Initiatives and projects are summarised in the final guidelines of the Morbidity Statistics Development Group.

Building on the activities carried out before 2006, the Morbidity Statistics Development Group was set up in order to move forward the methodological framework for diagnosis-specific morbidity statistics. The purpose of the group [9] was to develop proposals, to produce implementation plans and to monitor the progress of an EU-wide system of disease-specific morbidity statistics for a set of diseases, by identifying and using the best possible sources.

The group revised the EU’s short list on morbidity and produced guidelines (see Annex 4 of the report on morbidity statistics) and recommendations for the pilot phase of the diagnosis-based morbidity data collection; these covered variables, sources, and methods of data collection.

The pilot studies — characteristics of waves I and II

Two waves of pilot studies were carried out with common objectives (see Annex 9 of the report on morbidity statistics for the pilot studies’ technical descriptions), as follows.

  • Provide an inventory of potential national sources for diagnosis-specific morbidity data: the aim of this part of the methodological approach was to identify and to describe and evaluate the potential main national sources for diagnosis-specific morbidity statistics.
  • Develop a methodology for producing the best possible national estimates on incidence and prevalence, according to the EU’s short list on morbidity: the emphasis was to provide the best possible national estimates through a well described and valid procedure.
  • Conduct a pilot data collection: the proposed methodology was subsequently tested by a pilot project, thereby avoiding any duplication of work with on-going data collections such as for infectious diseases and cancers.

The EU Member States participating in the pilot studies were the following:

  • Wave I, 2005–06 (in the context of the pilot projects on morbidity statistics funded by the transition facility programme 2005, to assess the overall practicality and feasibility of the methodology proposed by the Morbidity Statistics Development Group) — Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Slovenia and Slovakia.
  • Wave II, 2007 and 2009 —Belgium (for a subset of the diseases included in the EU’s short list on morbidity), Germany, the Netherlands, Austria, Poland, Romania and Finland.

The Task Force on Morbidity Statistics

From 2009 to 2011, Eurostat activities on morbidity stopped for about a year and half due to a lack of resources. Activity resumed in the second half of 2011 when the Task Force on Morbidity Statistics was established at a meeting of the Working Group on Public Health Statistics.

The Task Force on Morbidity Statistics [10] assisted Eurostat in the preparation of a regular data collection on morbidity, with a focus on the revision and refinement of existing methodological tools. The goal of the earlier pilot studies was to test the Morbidity Statistics Development Group’s methodology for accurate national morbidity estimates. The aim of the Task Force on Morbidity Statistics has been to help Eurostat in assessing the quality and comparability of those estimates across the EU Member States, and to revise the methodology in the Morbidity Statistics Development Group’s guidelines and the EU’s short list on morbidity. The task force’s terms of reference are shown in Annex 5 of the report on morbidity statistics in the EU.

The Task Force on Morbidity Statistics concluded its work in the autumn of 2013.

Direct access to
Other articles
Tables
Database
Dedicated section
Publications
Methodology
Legislation
Visualisations
External links






Healthy life years and life expectancy at age 65, by sex (tsdph220)
Healthy life years and life expectancy at birth, by sex (tsdph100)


Healthy Life Years (hlth_hly)
Self-perceived health and well-being (hlth_sph)
Functional and activity limitations (hlth_fal)
Self-reported chronic morbidity (hlth_srcm)
Injuries from accidents (hlth_ifa)
Health care expenditure (hlth_sha)
Unmet needs for health care (hlth_unm)

Notes

  1. The definition of European statistics is according to Article 2 (2) of the European Commission Decision 2012/504/EU of 17 September 2012 on Eurostat.
  2. Chan M, Kazatchkine M, Lob-Levyt J, et al. Meeting the demands for results and accountability: a call for action on health data from eight health agencies. PLoS Med 2010; 7: e100023.
  3. Previously ‘European Community health indicators’.
  4. ECHI indicators relevant for diagnosis-based morbidity statistics are: 20. Cancer incidence; 21(b). Diabetes: register-based prevalence; 22. Dementia; 23(b). Depression: register-based prevalence; 24. Acute myocardial infarction (AMI); 25. Stroke; 26(b). Asthma: register-based prevalence; 27(b). Chronic obstructive pulmonary disease (COPD): register-based prevalence.
  5. The 2012 Ageing Report: Underlying Assumptions and Projection Methodologies European Economy 4|2011. (DG ECFIN) [1].
  6. OECD (2012), Health at a Glance: Europe 2012, OECD Publishing. [2].
  7. Emilio Di Meglio, Eurostat. Commission en Direct 652, page 4, 23.11–29.11.2012.
  8. Global burden of disease 2010: a multi-investigator collaboration for global comparative descriptive epidemiology. Murray CJL, Lopez AD, et al. Lancet 2012; 380:2055-2058.
  9. Participants to the Morbidity Statistics Development Group were: Jacques Bonte (Belgium), Liis Rooväli (Estonia), Roberta Crialesi (Italy), Björn Smedby (Sweden), Howard Meltzer (the United Kingdom), Marleen De Smedt (Eurostat) and Sabine Gagel (Eurostat).
  10. Authors of the report and members of the Task Force on Morbidity Statistics were: Willem Aelvoet (Belgium), Merike Rätsep (Estonia), Gráinne Cosgrove (Ireland), Ieva Strele (Latvia), Rita Gaidelyte (Lithuania), Bogdan Wojtyniak (Poland), Georgeta-Marinela Istrate (Romania), Mika Gissler (Finland), Jacques Bonte (private expert, from October 2012), Hartmut Buchow (Eurostat), Margarida Domingues de Carvalho (Eurostat, from August 2012) and Monica Pace (Eurostat). Former members of the task force were: Anne Fagot-Campagna (France,until March 2012), Howard Meltzer (the United Kingdom, until September 2012), Jean-Marc Schaefer (Eurostat, until April 2012).