Burden of Disease

OWID presents work from many different people and organizations. When citing this entry, please also cite the original data source. This entry can be cited as:

Max Roser (2017) – ‘Burden of Disease’. Published online at OurWorldInData.org. Retrieved from: https://ourworldindata.org/burden-of-disease/ [Online Resource]

The burden of disease can be measured in Disability Adjusted Life Years (DALYs) lost. DALYs are a standardized metric that allow for direct comparison of burdens of different diseases across countries and over time. Conceptually, one DALY lost is the equivalent of losing one year in good health because of either premature mortality or disability. Assessing health outcomes by both mortality and morbidity provides a more encompassing view on health outcomes than only looking at mortality or life expectancy alone.

# Empirical View

# The global burden of disease is huge

How many healthy life years are lost due to diseases, deaths, and injuries? It is a daring question, but we actually do have a good answer to it. The Global Burden of Disease estimated that in 23.5% of potential life years were lost due to premature death in the year 2013. Another 10.7% of potential healthy life years were lost due to disease and disability.

This means that in sum more than a third of potential healthy life years were lost. The global burden of disease was 34.2% in 2013.

2-dalys-lost-globally-in-2013

# The distribution of the global burden of disease

The visualisation below shows the Disability Adjusted Life Years (DALYs) lost per 100,000.

# Communicable diseases are a high burden of disease in poor countries

The disease burden due to communicable diseases alone is shown in the following map. Communicable diseases are often curable and better public health can reduce the burden considerably yet many poor countries still suffer from the burden of these diseases.

# There is much cross-country heterogeneity in terms of conditions contributing towards the burden of disease

The following visualization – produced by the Institute for Health Metrics and Evaluation (IHME) – presents a breakdown of burden-of-disease estimates for selected countries. A dedicated IHME website provides a fascinating interactive tool to explore all available data on burden of disease worldwide.

As the chart below shows, different conditions contribute differently to health outcomes depending on the specific country and gender. While in Angola there are clear challenges relating to HIV/AIDS, diarrhoea and malaria, in countries such as the US or Germany, the biggest challenges relate to cardiovascular and respiratory disease and cancer (the main cause of ‘neoplasms’).

Burden of disease by cause, country, and gender (2013 estimates) – produced by IHME Viz Hub

IHME_BurdenOfDisease_breakdown


# Correlates, Determinants, & Consequences

# Average income and the Burden of Disease

The visualisation below shows the relationship between average income – measured by GNI per capita – and the Burden of Disease. The Burden of Disease is disaggregated into the health burden due to communicable diseases and non-communicable diseases.

The chart shows that communicable diseases in particular are closely correlated to average income levels. The relationship that was estimated by Sterck et al. 20171 is shown in the legend. GNI per capita has a strong negative correlation with log DALYs lost due to communicable diseases with an elasticity of -0·88. On the other hand, the non-communicable disease burden is much less strongly associated with average income (the elasticity is estimated to be -0·13). Another conclusion we can draw from this chart is that the relationship between GNI per capita and DALYs lost due to the disease burden of communicable diseases is best captured by a log-log function.

# Average income and the Burden of Disease due to communicable diseases

The health burden due to communicable diseases vs GDP per capita is shown in the following visualisation. The correlation between both measures is apparent from the visualisation. But despite this correlation, Sterck et al. 20172 find that GNI is not a significant predictor of health outcomes once other factors are controlled for. The first of these other factors is individual poverty – relative to a health poverty line of 10.89 international-$ per day. The second factor is the epidemiological surrounding of a country which captures the health status of neighbouring countries. And the third important factor is institutional capacity.


# Measurement, Data Quality & Definitions

Disability Adjusted Life Years (DALYs) lost is a standardized metric allowing for direct comparison and summing of burdens of different diseases. Conceptually, one DALY is the equivalent of one year in good health lost because of premature mortality or disability (see Murray et al. 20153). Assessing health outcomes by both mortality and morbidity provides a more encompassing view on health outcomes than only looking at mortality or life expectancy alone.

Three categories of health conditions are distinguished:

  1. Group I DALYs lost due to communicable, maternal, perinatal and nutritional conditions;
  2. DALYs lost due to non-communicable diseases (NCD);
  3. DALYs lost due to injuries. Our main analysis focuses on Group I DALYs.

# Data Sources

# Institute for Health Metrics and Evaluation (IHME) – Burden of Disease study