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The 2022 version of the index compares 26 European OECD members. It follows 23 indicators, starting with general ones (like life expectancy) and going to more specific aspects (hospital admissions with diabetes, 5-year leukemia survival rates, etc.). See the rankings for a more specific description of the indicators. Most of the data comes from OECD and Eurostat, but other sources are also used.
The data was indexed on a 0-1 scale with the best country result valued at 1 and the worst at 0. The results from individual indicators (Perinatal mortality + Neonatal mortality + Infant mortality and Ischemic stroke mortality + Hemorrhagic stroke mortality are grouped and averaged into two groups) are summed.
There are three sets of results:
The Health for Money (HFM) Index 2022 consists of 25 main indicators:
These are the indicators on the basis of which the basic index is calculated. Two of them (Top universities, Covid deaths per million) are calculated on the basis of auxiliary indicators. The Health expenditure GDP and Health expenditure per capita indicators are used to weigh the base index.
In addition, the index includes two indicators created by calculating the average of multiple related variables. Firstly, the “Average – mortalities” indicator comprises Infant mortality, Neonatal mortality, and Perinatal mortality. Secondly, the “Average stroke mortality” comprises Ischemic stroke mortality and Hemorrhagic stroke mortality.
The following 3 auxiliary indicators were also used in the preparation of the main variables (or their indexation):
Their use is described in the sections "Calculating indicators" and "Indexing indicators."
Previous versions of the index also included 3 indicators which were subsequently removed due to their obsolescence[1]:
These indicators have been replaced by the indicator Death due to cancer per 100 000.
Data for most indicators are exported from external datasets and directly incorporated into the HFM index calculations. However, three indicators are prepared in a different way:
The Top Universities Index indicates the number of medical schools in a country that are among the top 500 in the world. No such dataset currently exists, so the Top universities indicator is based on the QS Ranking by subject (medicine). The medical faculties among the top 500 in the world are counted and then weighted by the population of the country. For this purpose, the Population World Bank data dataset is used.
The Our World in Data portal publishes (and regularly updates) an Excel file on its website that contains day-by-day covid data from the start of the pandemic, for each country in the world. Data processed in this way allows the number of deaths for a particular year to be calculated, and then (based on the Population World Bank dataset) the number of deaths from covid per million inhabitants for a particular year to be calculated.
The measles incidence indicator is prone to significant variation for countries that experience (local) measles outbreaks. Given the way the indicators are indexed, and the final index calculated, such a deviation could significantly skew a country's results.
For this reason, this indicator is calculated as the average measles incidence over the last 5 years. This means that, for example, the 2019 figure is the average of the years 2015-2019. The result of such a calculation will be affected by a possible measles outbreak, but without unduly distorting the final index for the country in question.
Most indicators are indexed in the same way - the highest and lowest value in a given year is identified, and then the difference between the two values (the range) is calculated. An index is then calculated for each country, reflecting where the country is on the scale of values.
For indicators where higher values indicate better performance (e.g. life expectancy), a country's position on the scale (index) is calculated using the following formula:
Index = (Country value - lowest value) / Range
For indicators where higher values imply worse performance (e.g. maternal mortality in childbirth), a country's position on the scale is calculated using the following formula:
Index = 1 - (Country value - lowest value) / Range
In some cases, the nature of the indicator requires (for various reasons) a different approach to indexation. These cases are described in this section:
The auxiliary indicator Average Wage Index measures the average wage in a given country in relation to the European Union (EU) average wage. It is calculated using the following formula:
Average wage index = Country average / EU average wage
This calculation measures the "distance" between a country's average wage and the EU average wage. Unlike other indices, it can therefore exceed 1 (in cases where the country's wage is higher than the EU average). The Average Wage Index is used in the preparation of the Health expenditure per capita Index.
The Health expenditure per capita (HEpC) indicator is calculated on the basis of the average per capita government expenditure on health and the Average wage Index. The calculation is based on the assumption that 60% of health expenditure is used for wages. The final equation is as follows:
HEpC index = (HEpC x 0,4) + (HEpC x 0,6) / Average wage index
For most indicators, the data used come from the OECD database (subcategory Health). In some cases, however, OECD data are missing, in which case one of two scenarios occurs:
The HFM Index also includes some specific indicators that are compiled by institutions focusing on specific areas (e.g. Quacquarelli Symonds (QS), a UK-based company ranking the world's best universities, or the European Centre for Disease Prevention and Control (ECDC), which monitors the incidence of measles).
For each version of the HFM index, t-1 data are used (e.g. the 2022 index is based on 2021 data). Where data for a given year is missing, data from the next available year backward are used. Data from the following year are used only if the following year is the first year for which data are collected.
In some cases, it is not possible to find a source that provides the necessary data. In such cases, the values are estimated on the basis of values for other countries. In order to make the estimates as accurate as possible, the countries have been divided into two groups:
We assume that the states in each group have similar economic characteristics and, as a result, have less dispersion in health system performance (compared to the dispersion of all states). Thus, an estimate for a country is based on the values of the other countries in its group.
[1] The most recent data for these indicators are from the 2010-2014 period (the indicators are measured at 5-year intervals)