This chapter analyses a core set of indicators on health and health systems. Country dashboards and OECD snapshots shed light on how countries compare across six dimensions: health status, risk factors for health, access, quality and outcomes, health system capacity and resources, and on COVID‑19. Quadrant charts illustrate how much health spending is associated with access, quality and health outcomes.
Health at a Glance 2021
OECD Indicators
Health at a Glance
Chapter 1. Indicator overview: Country dashboards and major trends
Abstract
Introduction
Health indicators offer an ‘at a glance’ perspective on how healthy populations are and how well health systems perform. This introductory chapter provides a comparative overview of OECD countries across 24 core indicators, organised around six dimensions of health and health systems (Table 1.1). These indicators are selected based on how relevant and actionable they are from a policy perspective; as well as the more practical consideration of data availability across countries. The extent to which health spending is associated with health outcomes, access and quality is also explored.
Such analysis does not indicate which countries have the best performing health systems, particularly as only a small subset of the many indicators in Health at a Glance are included here. Rather, this chapter identifies some relative strengths and weaknesses. This can help policy makers determine priority action areas for their country, with subsequent chapters in Health at a Glance providing a more detailed suite of indicators, organised by topic area.
Table 1.1. Population health and health system performance: Core indicators
Note: AMI = acute myocardial infarction; BMI = body mass index; COPD = chronic obstructive pulmonary disease.
Based on these indicators, country dashboards are produced. These compare a country’s performance to others countries and to the OECD average. Comparisons are made based on the latest year available. For most indicators, this refers to 2019, or the nearest year if 2019 data are not available for a given country. For the COVID‑19 dashboard, comparisons span 2020‑21.
Country classification for each indicator is into one of three colour-coded groups:
Blue, when the country’s performance is close to the OECD average
Green, when the country’s performance is considerably better than the OECD average
Red, when the country’s performance is considerably worse than the OECD average
The exception to this grouping is for the dashboard on health system capacity and resources (Table 1.6), where indicators cannot be easily classified as showing better or worse performance. Here, lighter and darker shades of blue signal if a country has considerably less or more of a given health care resource than the OECD average.
Accompanying these country dashboards are OECD snapshots and quadrant charts. OECD snapshots provide summary statistics for each indicator. Quadrant charts illustrate simple associations (not causal relationships) between how much countries spend on health and how effectively health systems function. Figure 1.1 shows the interpretation of each quadrant, taking health outcome variables as an example. Further information on the methodology, interpretation and use of these country dashboards, OECD snapshots and quadrant charts are provided in the boxed text below.
Figure 1.1. Interpretation of quadrant charts: Health expenditure and health outcome variables
Methodology, interpretation and use
Country dashboards
The classification of countries being close to, better or worse than the OECD average is based on an indicator’s standard deviation (a common statistical measure of dispersion). Countries are classified as “close to the OECD average” (blue) whenever the value for an indicator is within one standard deviation from the OECD average for the latest year. Particularly large outliers (larger than three standard deviations) are excluded from calculations of the standard deviation to avoid statistical distortions.
For a typical indicator, about 65% of countries will be close to the OECD average, with the remaining 35% performing significantly better (green) or worse (red). When the number of countries that are close to the OECD average is higher (lower), it means that cross-country variation is relatively low (high) for that indicator.
OECD snapshots
For each indicator, the OECD average, highest and lowest values are shown; as are the three countries with the largest improvements over time in terms of changes to absolute values.
Quadrant charts
Quadrant charts plot health expenditure per capita against another indicator of interest (on health outcomes, quality of care and access). They show the percentage difference of each indicator as compared with the OECD average. The centre of each quadrant chart is the OECD average. Data from the latest available year are used. A limitation is that lagged effects are not taken into account – for example, it may take some years before higher health spending translates into longer life expectancy.
Health status
Four health status indicators reflect core aspects of both the quality and quantity of life. Life expectancy is a key indicator for the overall health of a population; avoidable mortality focuses on premature deaths that could have been prevented or treated. Diabetes prevalence shows morbidity for a major chronic disease; self-rated health offers a more holistic measure of mental and physical health. Figure 1.2 presents a snapshot on health status across the OECD and Table 1.2 provides more detailed country comparisons.
Figure 1.2. Health status across the OECD, 2019 (or nearest year)
Note: Largest improvement shows countries with largest changes in absolute value over time (% change in brackets).
Source: OECD Health Statistics 2021, IDF Diabetes Atlas 2019.
Table 1.2. Dashboard on health status, 2019 (or nearest year)
Note: ☑ Better than OECD average; ⦿ Close to OECD average; ⮽ Worse than OECD average. Hungary, Latvia, Lithuania and Mexico are excluded from the standard deviation calculation for avoidable mortality, while Mexico is excluded from diabetes prevalence.
Japan, Switzerland and Spain lead a large group of 27 OECD countries in which life expectancy at birth exceeded 80 years in 2019. A second group, including the United States and a number of central and eastern European countries, had a life expectancy between 77 and 80 years. Mexico and Latvia had the lowest life expectancy, at less than 76 years. In general, life expectancy has increased for most of the last half-century, despite some slowdown in longevity gain in recent years. However, COVID‑19 has had a dramatic effect, with life expectancy in 2020 falling for 24 of 30 OECD countries with comparable data.
Avoidable mortality rates (from preventable and treatable causes) in 2019 were lowest in Luxembourg, where less than 100 per 100 000 people died prematurely. Avoidable mortality rates were also relatively low (under 150 per 100 000 people) in Switzerland, Israel, Iceland, Japan, Italy, Korea, Australia, Sweden, Spain, the Netherlands and Norway. Latvia, Hungary, Mexico, Lithuania and the Slovak Republic had the highest avoidable mortality rates, at over 300 premature deaths per 100 000 people.
Diabetes prevalence in 2019 was highest in Mexico, Turkey, the United States and Germany, with over 10% of adults living with diabetes (age‑standardised data). Prevalence rates have stabilised in many OECD countries, especially in Western Europe, but increased markedly in Turkey. Such upward trends are due in part to rising rates of obesity and physical inactivity.
Almost 9% of adults considered themselves to be in bad health in 2019, on average across the OECD. This ranged from over 15% in Latvia, Korea, Lithuania and Portugal to under 3% in Colombia, New Zealand and Canada. However, socio-cultural differences, the share of older people and differences in survey design affect cross-country comparability. People with lower incomes are generally less positive about their health as compared with people on higher incomes, in all OECD countries.
Investing more into health systems contributes to gains in health outcomes, by offering more accessible and higher quality care. Differences in risk factors such as smoking, alcohol and obesity also explain cross-country variation in health outcomes. Social determinants of health matter too, notably income levels, better education and improved living environments.
Risk factors for health
Smoking, alcohol consumption and obesity are the three major individual risk factors for non-communicable diseases, contributing to a large share of worldwide deaths. Air pollution is also a critical environmental determinant of health. Figure 1.3 presents a snapshot on risk factors for health across the OECD and Table 1.3 provides more detailed country comparisons.
Figure 1.3. Risk factors for health across the OECD, 2019 (or nearest year)
Note: Largest improvement shows countries with largest changes in absolute value over time (% change in brackets).
Source: OECD Health Statistics 2021, OECD Environment Statistics 2020.
Table 1.3. Dashboard on risk factors for health, 2019 (or nearest year)
Note: ☑ Better than OECD average; ⦿ Close to OECD average; ⮽ Worse than OECD average. Hungary, Latvia and Lithuania excluded from standard deviation calculation for ambient air pollution. * Likely under-estimate of obesity as self-reported data.
Smoking causes multiple diseases, with the World Health Organization estimating tobacco smoking kills 8 million people in the world every year. The share of people smoking daily in 2019 ranged from around 25% or more in Turkey, Greece, Hungary, Chile and France to below 10% in Costa Rica, Mexico, Iceland and Norway. Daily smoking rates have decreased in most OECD countries over the last decade, from an average of 21.3% in 2009 to 16.5% in 2019. In the Slovak Republic and Turkey, though, smoking rates have risen slightly.
Alcohol use is a leading cause of death and disability worldwide, particularly among those of working age. Measured through sales data, Latvia reported the highest consumption in 2019 (12.9 litres of pure alcohol per person per year), followed by Austria and the Czech Republic. Turkey, Israel, Costa Rica, Colombia and Mexico have comparatively low consumption levels (under 5 litres). Average consumption fell in 29 OECD countries since 2009. Harmful drinking is of particular concern in certain countries, notably Latvia and Hungary.
Obesity is a major risk factor for many chronic diseases, including diabetes, cardiovascular diseases and cancer. Obesity rates have been increasing in recent decades in almost all OECD countries, with an average of 56% of the population being overweight or obese in 2019. Obesity rates are highest in Mexico, Chile and the United States; and lowest in Japan and Korea. Included here are data for people who are overweight (including obese) using both measured and self-reported data. Caution should be taken when comparing countries with reporting differences, since measured data are generally higher.
Air pollution is not only a major environmental threat, but also worsens health. OECD projections estimate that ambient (outdoor) air pollution may cause 6 to 9 million premature deaths a year worldwide by 2060. Premature deaths attributable to ambient particulate matter ranged from over 70 per 100 000 people in Poland and Hungary, to less than 7 deaths per 100 000 people in Iceland, New Zealand and Sweden, in 2019.
Access to care
Ensuring equitable access is critical for inclusive societies and high performing health systems. Population coverage, measured by the share of the population eligible for a core set of services and those satisfied with the availability of quality health care, offers an initial assessment of access to care. The proportion of spending covered by prepayment schemes gives further insight on financial protection. The share of populations reporting unmet need for medical care offers a measure of effective service coverage. Figure 1.4 presents a snapshot on access to care across the OECD and Table 1.4 provides more detailed country comparisons.
Figure 1.4. Access to care across the OECD, 2019 (or nearest year)
Note: Largest improvement shows countries with largest changes in absolute value over time (% change in brackets). Indicator on population coverage, satisfaction based on 2020 data.
Source: OECD Health Statistics 2021, Gallup World Poll 2020.
In terms of the share of the population eligible for coverage, most OECD countries have achieved universal (or near-universal) coverage for a core set of services. However, in Mexico and the United States, population coverage was below 90% in 2019, with coverage below 95% in a further five countries (Costa Rica, Poland, Hungary, the Slovak Republic and Colombia).
Satisfaction with the availability of quality health services offers further insights on effective coverage. On average across OECD countries, 71% of people were satisfied with the availability of quality health services where they live in 2020. Citizens in Norway, Belgium, the Netherlands and Switzerland were most likely to be satisfied (over 90%). Whereas less than 50% of citizens were satisfied in Poland (26%), Greece (38%), Chile (39%), Colombia (47%) and Mexico (48%).
The degree of cost sharing applied to those services also affects access to care. Across the OECD, around three‑quarters of all health care costs were covered by government or compulsory health insurance schemes in 2019. However, in Mexico, less than half of all health spending was covered by publicly mandated schemes; and in Latvia, Portugal, Greece and Korea only around 60% of all costs were covered. Mexico, though, has significantly expanded population coverage and financial protection over the last decade.
In terms of service coverage, on average across 27 OECD countries with comparable data, only 2.6% of the population reported that they had unmet care needs due to cost, distance or waiting times in 2019. However, in Estonia more than 15% of the population reported unmet care needs. Accessibility to health care was also limited in Greece, with around 8% of the population reporting unmet needs for health care. Socioeconomic disparities are significant in most countries, with the income gradient largest in Greece, Turkey, Latvia and Iceland.
Table 1.4. Dashboard on access to care, 2019 (or nearest year)
Note: ☑ Better than OECD average; ⦿ Close to OECD average; ⮽ Worse than OECD average. Estonia is excluded from standard deviation calculation for unmet needs.
Quality of care
Good quality care requires health services to be safe, appropriate, clinically effective and responsive to patient needs. Antibiotic prescriptions and avoidable hospital admissions for chronic obstructive pulmonary disease (COPD) are examples of indicators that measure the safety and appropriateness of primary care. Breast cancer screening is an indicator of the quality of preventive care; 30‑day mortality following acute myocardial infarction (AMI) measures the clinical effectiveness of secondary care. Figure 1.5 presents a snapshot on quality and outcome of care across the OECD and Table 1.5 provides more detailed country comparisons.
Figure 1.5. Quality of care across the OECD, 2019 (or nearest year)
Note: Largest improvement shows countries with largest changes in absolute value over time (% change in brackets).
Source: OECD Health Statistics 2021.
Table 1.5. Dashboard on quality of care, 2019 (or nearest year)
Note: ☑ Better than OECD average; ⦿ Close to OECD average; ⮽ Worse than OECD average. Latvia and Mexico are excluded from standard deviation calculation for AMI mortality. Effective cancer care reports total data for all available countries in CONCORD‑3.
The overuse, underuse or misuse of antibiotics and other prescription medicines contribute to increased antimicrobial resistance and represent wasteful spending. The total volumes of antibiotics prescribed in 2019 varied nearly four‑fold across countries, with Estonia, Sweden and Germany reporting the lowest volumes, whereas Iceland, Australia and Greece recorded the highest volumes. Across the OECD, the number of antibiotics prescribed has increased slightly over time.
COPD is a condition for which effective treatment at the primary care level is well established – and hospital admissions for this condition may signal quality issues in primary care. Admission rates varied 8‑fold across OECD countries with Italy, Mexico and Chile reporting the lowest rates and Turkey, Ireland and Australia having the highest rates in 2019. Cross-country differences are broadly similar, but with some exceptions, for avoidable hospital admissions for asthma, congestive heart failure and diabetes (see Chapter 6).
Breast cancer is the cancer with the highest incidence among women in all OECD countries, and the second most common cause of cancer death among women. Timely mammography screening is critical to identify cases, allowing treatment to start at an early stage of the disease. In 2019, mammography screening was highest in Sweden (95% of women aged 50‑69), with Denmark, Spain, Finland and Portugal also having screening rates a little over 80%. Screening rates were lowest in the Slovak Republic, Turkey, Hungary and Latvia (all under 40%). COVID‑19 had a large impact on screening programmes, with reductions in screening rates in six of the seven countries with available data for 2020.
Mortality following acute myocardial infarction (AMI) is a long-established indicator of the quality of acute care. It has been steadily declining since the 1970s in most countries, yet important cross-country differences still exist. Mexico had by far the highest 30‑day mortality following AMI (27.5 deaths per 100 admissions); rates were also relatively high in Latvia in 2019. The lowest rates were found in Iceland, the Netherlands, Norway and Australia, at around 3% or less (comparisons based on unlinked data).
Health system capacity and resources
Having sufficient health care resources is critical to a resilient health system. More resources, though, do not automatically translate into better health outcomes – the effectiveness of spending is also important. Health spending per capita summarises overall resource availability. The number of practising doctors and nurses provide further information on the supply of health workers. Hospital beds is an indicator of acute care capacity. Figure 1.6 presents a snapshot on health system capacity and resources across the OECD and Table 1.6 provides more detailed country comparisons.
Figure 1.6. Health system capacity and resources across the OECD, 2019 (or nearest year)
Note: Largest increase shows countries with largest changes in absolute value over time (% change in brackets).
Source: OECD Health Statistics 2021.
Dashboard on health system capacity and resources, 2019 (or nearest year)
Note: ⮉ Above OECD average; ⦿ Close to OECD average; ⮋ Below OECD average. Chile, Costa Rica, Greece and Portugal include all doctors licensed to practice, resulting in a large over-estimation. Japan and Korea are excluded from the standard deviation calculation for hospital beds. The United States is excluded from standard deviation calculation for HCE per capita.
Overall, countries with higher health spending and higher numbers of health workers and other resources have better health outcomes, quality and access to care. However, the absolute amount of resources invested is not a perfect predictor of better outcomes – risk factors for health and the wider social determinants of health are also critical, as is the efficient use of health care resources.
The United States spends considerably more than any other country (almost USD 11 000 per person, adjusted for purchasing power, in 2019), and also spent the most when measured as a share of GDP. Health care spending per capita is also high in Switzerland, Norway and Germany. Mexico, Turkey and Colombia spent the least, at around a quarter of the OECD average. Health spending has grown consistently across most countries over the past decades, other than a temporary slowdown following the 2008 financial crisis. With the onset of the COVID‑19 pandemic, initial data for 2020 points to a sharp increase in overall health spending, of around 5.1% on average.
A large part of health spending is translated into wages for the workforce. The number of doctors and nurses in a health system is therefore an important way of monitoring how resources are being used. The number of doctors ranged from less than 2.5 per 1 000 population in Turkey, Colombia, Poland and Mexico, to over five in Austria, Portugal and Greece, in 2019. However, numbers in Portugal and Greece are over-estimated as they include all doctors licensed to practise. On average there were just under 9 nurses per 1 000 population in OECD countries in 2019, ranging from less than 3 per 1 000 people in Colombia, Turkey, Mexico and Chile to about 18 in Switzerland and Norway.
The number of hospital beds provides an indication of resources available for delivering inpatient services. The COVID‑19 pandemic has highlighted the need to have sufficient hospital beds (particularly intensive care beds), together with sufficient numbers of doctors and nurses. Still, a surplus of beds may cause an exaggeration in their use and therefore costs, notably for patients whose outcomes may not improve from intensive care. Across OECD countries, there were on average 4.4 hospital beds per 1 000 people in 2019. Over half of OECD countries reported between 3 and 8 hospital beds per 1 000 people. Japan and Korea, though, have more hospital beds (12‑13 per 1 000 people), with relatively few beds in Mexico, Costa Rica and Colombia.
COVID‑19
The COVID‑19 pandemic has claimed millions of lives, with many more suffering ill-health as a direct or indirect consequence of the virus. As of the time of publication, about 250 million cases were reported and almost 5 million people have died from the virus. These figures are underestimates, with many more cases and deaths going undetected. Therefore, alongside COVID‑19 cases and COVID‑19 deaths, excess mortality – a measure of deaths from all causes over and above what could normally be expected for a given period of time – provides a complementary measure. Excess mortality accounts for unreported COVID‑19 deaths and deaths indirectly caused by the virus (see Chapter 2 for methodology used). Figure 1.7 presents a snapshot of COVID‑19 across the OECD and Table 1.7 provides more detailed country comparisons, including differences in vaccination rates.
Figure 1.7. Snapshot on COVID‑19 across the OECD, 2020‑21
Note: Data on excess deaths and COVID‑19 deaths up to week 26‑2021, except for Australia (week 25), Canada (week 22), and Colombia (week 18). Data on COVID‑19 cases and vaccination rates up to week 39‑2021. See Chapter 2 for methods used to calculate excess deaths.
Source: OECD Health Statistics 2021, ECDC 2021, Our World in Data 2021.
Table 1.7. Dashboard on COVID‑19, 2020‑21
Note: ☑ Better than OECD average; ⦿ Close to OECD average; ⮽ Worse than OECD average. Data on excess deaths and COVID‑19 deaths up to week 26‑2021, except for Australia (week 25), Canada (week 22), and Colombia (week 18). Data on COVID‑19 cases and vaccination rates up to week 39‑2021. See Chapter 2 for methods used to calculate excess deaths.
In all but one OECD country, more people died in the 18‑month period since January 2020 than on average in the corresponding time period between 2015‑19. The excess mortality rate was highest in Mexico (4 456 excess deaths per million people), followed by Poland (3 663), the Czech Republic (3 465), and the Slovak Republic (3 133). Excess deaths were negative in Norway, and relatively low in Korea, Iceland, Denmark, Australia and New Zealand.
Countries with the highest number of reported COVID‑19 deaths per population were, in general, countries also experiencing higher excess mortality rates, but with some notable exceptions. Reported COVID‑19 death rates up to early October 2021 were highest in Hungary and the Czech Republic. Reported COVID‑19 deaths were below 50 deaths per million people in New Zealand, Australia and Korea. Excess mortality was much higher than reported COVID‑19 deaths in Mexico and Poland – potentially indicative of underreporting of some COVID‑19 fatalities and/or additional deaths due to other factors, including the indirect consequences of the virus. Belgium, Sweden and the United Kingdom recorded substantially higher COVID‑19 fatality rates compared to excess mortality. This implies some overestimation of COVID‑19 deaths and/or reduced mortality in other areas.
Cumulative reported COVID‑19 cases up to early October 2021 exceeded or were approaching 15 000 cases per 100 000 people in the Czech Republic, Israel, the Slovak Republic and Slovenia; but were under 1 000 cases per 100 000 people in New Zealand (91), Australia (437) and Korea (624).
For vaccination rates, as of early October 2021, Portugal had the highest share of the population fully vaccinated (85.2%), followed by Iceland (80.5%) and Spain (78.6%). Vaccination rates were lowest in Colombia (33.6%) and Mexico (35.4%).
To what extent does health spending translate into better access, quality and health outcomes?
Quadrant charts plot the association between health spending and selected indicators of health system goals. They illustrate the extent to which spending more on health translates into stronger performance across three dimensions: health outcomes, quality and access to care. Note though that only a small subset of indicators for these three dimensions are compared against health spending, with quadrant charts showing simple statistical correlations rather than causal links.
Health spending and health outcomes
These quadrant charts illustrate the extent to which countries that spend more on health have better health outcomes (such associations do not guarantee a causal relationship).
Figure 1.8. Life expectancy and health expenditure
Figure 1.9. Avoidable mortality (preventable and treatable) and health expenditure
There is a clear positive association between health spending per capita and life expectancy (Figure 1.8). Amongst the 38 OECD countries, 17 countries spend more and have higher life expectancy than the OECD average (top right quadrant). A further 12 countries spend less and have lower life expectancy at birth (bottom left quadrant).
Of particular interest are countries that deviate from this basic relationship. Seven countries spend less than average but achieve higher life expectancy overall (top left quadrant). This may indicate relatively good value‑for-money of health systems, notwithstanding the fact that many other factors also have an impact on health outcomes. These seven countries are Italy, Korea, Portugal, Spain, Slovenia, Greece and Israel. The only country in the bottom right quadrant is the United States, with much higher spending than in all other OECD countries, but lower life expectancy than the OECD average.
For avoidable mortality, there is also a clear association in the expected direction (Figure 1.9). Amongst OECD countries, 18 countries spend more and have lower avoidable mortality rates (bottom right quadrant), and 11 countries spend less and have more deaths that could have been avoided (top left quadrant). Eight countries spend less than average but have lower avoidable mortality rates – the seven countries with relatively high life expectancy and low health spending, plus Chile (bottom left quadrant). The United States spends more than the OECD average and has worse avoidable mortality rates.
Health spending, access and quality of care
These quadrant charts illustrate the extent to which countries that spend more on health deliver more accessible and better quality care (such associations do not guarantee a causal relationship).
Figure 1.10. Satisfaction with availability of quality services and health expenditure
Figure 1.11. Breast cancer screening and health expenditure
In terms of access, Figure 1.10 shows a clear positive correlation between the share of the population satisfied with the availability of quality health care where they live and health spending per capita. Amongst the 37 OECD countries with available data, 17 countries spent more and had a higher share of the population satisfied with availability than the OECD average (top right quadrant). The converse was true in 14 countries (bottom left quadrant). In Ireland, health spending was 24% higher than the OECD average, but only 66% of the population were satisfied with the availability of quality health care where they live (compared to 71% being satisfied on average across the OECD). In Slovenia and the Czech Republic, health spending per capita was relatively low, but a noticeably greater share of the population were satisfied with the availability of quality health care, as compared to the OECD average.
In terms of quality of care, Figure 1.11 shows the relationship between health spending and breast cancer screening rates. Whilst there is an overall weak positive correlation between health spending and the share of women regularly screened, six countries spent less than the OECD average yet had higher cancer screening rates (top left quadrant), with eight countries spending more than the OECD average and having lower cancer screening rates (bottom right quadrant).