Organisation for Economic Co-operation and Development

Note: CPA data for 2018 and 2019 are projections from the 2016 Survey on Forward Spending Plans. Country Programmable Aid (CPA), outlined in our Development Brief  and also known as “core” aid, is the portion of aid donors programme for individual countries, and over which partner countries could have a significant say. CPA is much closer than ODA to capturing the flows of aid that goes to the partner country, and has been proven in several studies to be a good proxy of aid recorded at country level. CPA was developed in 2007 in close collaboration with DAC members. It is derived on the basis of DAC statistics and was retroactively calculated from 2000 onwards

The country statistical profiles provide a broad selection of indicators, illustrating the demographic, economic, environmental and social developments, for all OECD members. The dataset also covers the five key partner economies with which the OECD has developed an enhanced engagement program with (Brazil, China, India, Indonesia and South Africa) ,accession countries (Colombia, Costa Rica and Lithuania) , Peru and the Russian Federation. The user can easily compare indicators across all countries. Total fertility rates - Unit of measure used: Number of children born to women aged 15 to 49

OECD Health Data 2016 offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems.

OECD Health Data 2017 offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems.B1:B4

This dataset presents core population health indicators, covering life expectancy; mortality indicators such as causes of mortality (by ICD codes), maternal and infant mortality, avoidable (preventable+treatable) mortality; potential years of life lost; perceived health status (also disaggregated by socio-economic status and education level); low birthweight; incidence of key communicable diseases; cancer incidence; injuries in road traffic accidents; and absence from work due to illness.

This dataset reports data on key aspects of the utilisation of healthcare, such as consultations with doctors and dentists, immunisation of children and the population aged 65+, cancer screening, hospital indicators such as discharges or average length of stay, diagnostic exams, surgical procedures and waiting times.

Non-medical determinants of health: Unhealthy lifestyles and poor environments cause millions of people to die prematurely. Smoking, harmful alcohol use, physical inactivity and obesity are the root cause of many chronic conditions. This dataset presents the latest data for tobacco consumption (including daily smokers by age and sex), vaping (by age and sex), alcohol consumption, fruits and vegetables consumption, as well as measured and self-reported data on overweight and obesity.

A key set of information for policy analysis is i) how much revenue is collected; ii) in what ways is it collected; iii) from which institutional units of the economy are revenues raised for each particular financing scheme; and iv) which financing schemes receive those revenues. This dataset provides information about the contribution mechanisms the particular financing schemes use to raise their revenues. Understanding the nature of the flows is of importance from the perspective of both health and public finance policy. For example, the classification of revenues make it possible to distinguish between public and private funding of health care finance. Understanding how resources are raised by financing schemes is important for many countries, as many health systems are struggling with the issue of funding. The classification of revenues of financing schemes is suitable for tracking the collection mechanisms of a financing framework. Furthermore, the new classification makes it possible to analyse the contribution of the institutional units to health financing.