Comparing and Contrasting the Impact of the COVID-19 Pandemic in the European Union

Linda Hantrais, Marie-Thérèse Letablier

• Publisher: Routledge
• Year: 2020
• Print ISBN:
  9780367691721
• eBook ISBN:
  9781000330205
• Topic: Politics & International Relations
• Subtopic: Diseases & Allergies
• Index: Politics & International Relations

Comparing and Contrasting the Impact of the COVID-19 Pandemic in the European Union challenges the use of uncontextualised comparisons of COVID-19 cases and deaths in member states during the period when Europe was the epicentre of the pandemic. This timely study looks behind the headlines and the statistics to demonstrate the value for knowledge exchange and policy learning of comparisons that are founded on an in-depth understanding of key socio-demographic and public health indicators within their policy settings. The book adopts innovative, integrated, multi-disciplinary international perspectives to track and assess a fast-moving topical subject in an accessible format. It offers a template for analysing policy responses to the COVID-19 pandemic and for using evidence-based comparisons to inform and support policy development.

1 European national public healthcare systems compared

This chapter presents the socio-economic and public health settings within which coronavirus was to reach pandemic proportions. It begins with a review of public health data sources and their limitations before examining the reliability and comparability of the data collected in different socio-demographic, economic and political contexts. The chapter demonstrates the importance of situating comparisons in relation not only to key demographic indicators (population size, density and age distribution, and household composition), but also to the socio-economic environments in which national health systems are embedded. Both healthcare status and the capacity to prevent and react to health emergencies are examined. The review covers funding arrangements, levels of expenditure, provision of hospital and institutional care beds, various aspects of medical capacity, interventions to prevent diseases and preparedness to deal with public health emergencies. The conclusion considers how, in combination, socio-demographic characteristics and public health indicators can contribute to the comparative analysis of the impacts of COVID-19 in different policy settings.

Public health data sources and their limitations

Despite attempts throughout the EU’s history to harmonise social provisions, major differences have persisted not only in the overall level of state spending per capita on social protection and the structure of contributions, but also in methods of delivery and performance of national systems (Hantrais, 2007, 2019, 2020a). Problems of comparability and reliability associated with public health data both over time and across countries often seem intractable. Media coverage of the pandemic is prone to look for the best or worst performing countries on selected indicators. The inference is that practices adopted in other countries could serve as models. However, the league tables compiled by data journalists rarely comment on the reasons why policy responses might be effective in a specific societal context, and why they may, or may not, be transferable to different socio-economic and political settings.

A plethora of data are available to track and monitor the social situation across the EU in the early 21st century. Eurostat, the EU’s statistical office, regularly collects and collates national data based, as far as possible, on the same definitions and standards, and covering the same time periods. In the healthcare area, the EU has established a set of 88 European Core Health Indicators (ECHI) providing a comprehensive knowledge system of allegedly comparable health data for use in monitoring health and informing policymaking at EU level (European Commission, n.d.a, n.d.b). These indicators map demographic characteristics and socio-economic situations in EU member states. They cover health status, including causes of death, the incidence of selected communicable diseases and healthy life expectancy; determinants of health; and health interventions, extending across service provision, expenditures on health and health promotion policies. Since 1990, the EU’s Mutual Information System on Social Protection (MISSOC, 2020) collates annually updated, detailed information supplied by government departments in member states about national legislation, benefits and conditions. MISSOC provides separate tables for healthcare and sickness cash benefits.

In recognition of national diversity in social rights, in 2016 the European Commission (n.d.c) established a Social Scoreboard designed to track the comparative performance of member states. The Commission uses the Scoreboard as a ‘screening device’ and monitoring tool to enable wider assessment of the social situation in individual member states. The Scoreboard focusses on comparative data in three areas: equal opportunities and access to the labour market; dynamic labour markets and fair working conditions; and public support/social protection and inclusion. This last category includes a section on healthcare, presenting indicators for self-reported unmet need for medical care; healthy life years at the age of 65; and out-of-pocket expenditure on healthcare.

Together these sources provide comprehensive, though not always complete or very recent, datasets on public healthcare that can be used to situate and contextualise the responses of national governments to COVID-19. When used for comparative purposes, contextual data about health systems come with many of the same caveats as indicators of the spread of the pandemic and the policy measures introduced to deal with it (Chapters 2 and 3). Although countries are instructed to compile ECHI information using common definitions and criteria, national statistical offices are not always able or willing to observe these instructions due to the non-availability of certain data and variations in sources and methods of collection.

Obstacles such as these result in under-reporting, under-diagnosis and missing data. In a report on the state of health in the EU, OECD/European Union (2018, pp. 100, 102, 114, 126, 170) refer to numerous data limitations. Under-reporting is, for example, estimated to be as high as 40% in the case of HIV and tuberculosis. Caution is advised in presenting and interpreting cases of vaccine-preventable diseases due to diversity in surveillance systems, case definitions and reporting practices. In recording the prevalence of diseases, some countries collect data on acute but not chronic cases. Furthermore, variations between countries may reflect differences in testing, immunisation and screening programmes, as well as sample size and data collection methods. Where self-reporting is used in surveys, for instance to measure healthy life expectancy or unmet medical need, extra caution is required since subjective assessments may be influenced by socio-cultural factors. Reported causes of death vary considerably both between and within countries, as was starkly revealed during the pandemic (Chapter 2). Variations may be explained by the capacity to test for the virus, differences in medical protocols, coding practices and death certification processes, as well as the presence of underlying life-threatening conditions, and the account taken of the setting in which deaths occur. The failure to situate the number of deaths in relation to socio-demographic factors (population size, density and age distribution, gender, ethnicity, living and working conditions) further distorts the picture provided by daily trend figures.

An additional problem in contextualising daily information about cases and deaths across the EU during the pandemic is that full datasets, such as those prepared by ECHI, take time to complete and to check and are often published after several years’ delay. For example, the most recent European-wide data on public health laboratory capacity available when the crisis began dated from 2016; indicators for the number of curative hospital care beds or unmet need in relation to population size referred to 2017; complete datasets were not available at EU level with information about residential care homes. Data that can be accessed provide an indication of potential preparedness, but they do not record the actual capacity to deal with a major health crisis when it strikes. More useful comparative data must be sought by tracking the capacity of health services to scale up provision in the weeks following the outbreak of the disease. The numbers of excess deaths directly and indirectly attributable to the pandemic provide a better indication of the pandemic’s impact, but they may only be known sometime after the event when governments are being held to account for their actions, or they may never be known.

Public health in socio-demographic contexts

In selecting contextual indicators that might be helpful in situating and interpreting information about COVID-19 cases and deaths as well as policy measures to control and treat the disease, this section focusses on socio-demographic and public health data based on internationally agreed definitions and collection methods. Due reference is made to the caution that must be exercised if these data are to have any explanatory value.

Demographic statistics

Comparisons of the number of COVID-2019 cases and deaths reported on a given date, or during a specified period, are often made without reference to the corresponding population size, density or age distribution. These fundamental demographic data might be expected to influence observations, as can be illustrated using Eurostat demographic statistics for the 28 EU member states in pre-pandemic years (Figure 1.1).

Figure 1.1 Demographic statistics.

Sources: Eurostat (n.d.e): old-age-dependency ratio (% of total population aged over 65), 2019; Eurostat (n.d.g): population density (km2), 2018; Eurostat (n.d.h): population size (millions), 2019.

Six countries comprise between 38 (Poland) and 83 (Germany) million inhabitants (left axis), and 15 between 0.5 (Malta) and 9.8 (Hungary) million. Population density (right axis) ranges from 18 (Finland) to 1,548 (Malta) inhabitants per square kilometre. Old-age-dependency ratios (% of total population aged over 65, left axis) range between 20.7 (Luxembourg) and 35.7 (Italy).

All things being equal, large countries (Germany, France, the UK, Italy and Spain) would be expected to report many more cases of infections and deaths in absolute numbers than small countries (Slovenia, Lithuania, Latvia, Estonia, Cyprus, Luxembourg and Malta). Alternatively, population density might be a more influential factor, in which case the Netherlands, Belgium and the two small island states, Malta and Cyprus, would be expected to record relatively high rates of infection and COVID-19 deaths. Large urban agglomerations (London, Paris, the Ruhr, Madrid) might also be expected to register large numbers of cases and deaths as they become hotspots due to both their size and population density. Infections are known to be more likely to result in death among older people, who are, in turn, more likely to have underlying health conditions. The implication is that countries with high old-age-dependency ratios (Italy, Finland, Greece and Portugal) would be expected to record relatively high rates. In Chapter 2, these predictions are tested with reference to absolute and relative figures for COVID-19 cases and deaths.

Public health expenditure

The extent to which demographic factors may be mitigated by public health policy can be assessed by comparing selected healthcare indicators. An important difference between member states concerns the ways in which public health systems are funded and structured (Figure 1.2).

Figure 1.2 Public health expenditure.

Sources: Eurostat (n.d.c): expenditure on health (% GDP), 2010/2018; Eurostat (2020b, table 1): per capita spending (in PPS), 2017.

Eurostat data for the years prior to the outbreak of the pandemic show that expenditure on health as a proportion of GDP fell in most EU28 member states. Between 2010 and 2018, the decline was most marked in Ireland, Greece and Lithuania, whereas the increase was greatest in Croatia. All member states spent more on health than on education, where expenditure also fell over the same period. The Irish case may be explained by the significant increase in GDP in 2015 when some big economic operators relocated to Ireland, resulting in a substantial reduction in the proportion of GDP spent on social protection (OECD/European Union, 2018, p. 134). Despite these changing patterns, the same countries remained at each end of the scale: Denmark, Austria and France spent the largest proportion of their GDP on health in 2018, and Romania, Latvia and Cyprus the smallest proportion.

When adjusted to take account of differences in price levels between EU member states, per capita spending on health in purchasing power standards (PPS) was highest in Germany, Luxembourg, Sweden and the Netherlands in 2017; it was lowest in Romania, Latvia, Bul...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• List of figures
• List of tables
• List of boxes
• Notes on the authors
• Preface
• 1 European national public healthcare systems compared
• 2 Comparing the impacts of COVID-19 across EU member states
• 3 Tracking and comparing government responses to COVID-19
• 4 The impact of COVID-19 in policy contexts
• 5 Contextualising the impact of the COVID-19 pandemic within the European Union
• Postface
• References
• Index