Gdoc/Admin

Causes of Death

What are people dying from?

This question is essential to guide decisions in public health, and find ways to save lives.

Many leading causes of death receive little mainstream attention. If news reports reflected what children died from, they would say that around 1,400 young children die from diarrheal diseases, 1,000 die from malaria, and 1,900 from respiratory infections – every day.

This can change. Over time, death rates from these causes have declined across the world.

A better understanding of the causes of death has led to the development of technologies, preventative measures, and better healthcare, reducing the chances of dying from a wide range of different causes, across all age groups.

In the past, infectious diseases dominated. But death rates from infectious diseases have fallen quickly – faster than other causes. This has led to a shift in the leading causes of death. Now, non-communicable diseases – such as heart diseases and cancers – are the most common causes of death globally.

More progress is possible, and the impact of causes of death can fall further.

On this page, you will find global data and research on leading causes of death and how they can be prevented. This includes the number of people dying from each cause, their death rates, how they differ between age groups, and their trends over time.

This data can also help understand the burden of disease more broadly, and offer a lens to see the impacts of healthcare and medicine, habits and behaviours, environmental factors, health infrastructure, and more.

Key Insights on Causes of Death

Globally, non-communicable diseases are the most common causes of death

The chart shows what people died from globally, in 2019. Each box represents one cause, and its size is proportional to the number of deaths it caused.

The most common causes of death globally — shown in blue — were from ‘non-communicable diseases’.

This includes heart disease, cancer, and chronic respiratory diseases. They tend to develop gradually over time and aren’t infectious themselves.1

Heart diseases were the most common cause, responsible for a third of all deaths globally. Cancers were in second, causing almost one-in-five deaths. Taken together, heart diseases and cancers are the cause of every second death.

In red are infectious diseases, which are responsible for around 1-in-7 deaths. These include pneumonia, diarrheal diseases, tuberculosis, HIV/AIDS, and malaria.

A smaller share – around 4% – was from neonatal and maternal deaths. A similar share was from accidents.

Violent deaths were less common, with 1.3% dying from suicide and less than 1% dying from interpersonal violence such as homicide or battle deaths.

In this article, we cover this in more detail:

Causes of death globally: what do people die from?

To make progress towards a healthier world we need to have a good understanding of what health problems we face today.

What you should know about this data
  • This data comes from the most recent publication of the Global Burden of Disease study by the Institute for Health Metrics and Evaluation (IHME) in 2019 and the Global Terrorism Database.
  • These estimates assign each death a single cause, based on data on the ‘underlying cause of death’ listed on death certificates, verbal autopsies, and statistical modeling. This is a simplification, as people often have multiple diseases or injuries that contribute to their death, which may also be listed on death certificates.
  • This chart shows data on causes of death globally for 2019, the year before the Covid-19 pandemic started.
Tree map of causes of death globally in 2019, with non-communicable diseases in blue, communicable or infectious diseases in red, and injuries in green. The most common causes of deaths are non-communicable diseases such as heart diseases and cancers, while injuries and especially deaths from violence are rare.

Millions of young children die from preventable causes each year

Every child’s death is a tragedy. Globally, the scale of child mortality is immense: five million children under five die yearly. That’s around 14,000 each day.

In the chart, you can see what they die from. The size of each box corresponds to the number of children under five years old who die from each cause.

The most common causes for young children are different from the leading causes across the entire population – which was shown in the previous key insight.

Infectious diseases were most common – they kill an estimated 2.2 million children annually. They include pneumonia, diarrheal diseases, malaria, and meningitis.

Next were birth disorders, such as preterm birth, neonatal asphyxia (suffocation), and trauma, which caused an estimated 1.9 million child deaths.

Several other causes, such as heart abnormalities and malnutrition, were responsible for around 100,000 deaths each.

These figures are astonishing because many of these causes are preventable. With vaccination, basic medication, rehydration treatment, nutrition supplementation, and neonatal healthcare, a large share of child deaths could be prevented.

In this article, we cover this in more detail:

What are children dying from and what can we do about it?

Here we look at the number of children dying by each cause – from pneumonia to diarrheal diseases, malaria and malnutrition. We also present the range of interventions that are available to prevent children from dying.

What you should know about this data
  • This data comes from the most recent publication by the Institute for Health Metrics and Evaluation (IHME) in 2019 (at the time of writing in August 2023).
  • These estimates assign each death a single cause, based on data on the ‘underlying cause of death’ listed on death certificates, verbal autopsies, and statistical modeling. This is a simplification, as people often have multiple diseases or injuries that contribute to their death, which may also be listed on death certificates.
  • This chart shows data on causes of death globally for 2019, the year before the Covid-19 pandemic started.
Treemap of the number of deaths from each cause in children under five years old in 2019. IHME via Our World in Data.

Causes of death have changed over time and vary by age

What people die from has changed dramatically over time.

In the chart, you can see long-run historical trends in death rates in France.2 This is shown across age groups (on the y-axis) between 1925 and 1999 (on the x-axis). The colors represent the death rates.

As you can see, the rates of some causes of death rise exponentially with age; the shades are much darker along the vertical axis.

Take respiratory diseases as an example. In 1999, people aged 40 to 44 had a death rate 24 times higher than those aged 20 to 24. For those aged 60 to 64, it was almost 440 times higher.

In contrast, external causes, which include accidents, violence, falls, and suicides, tend to rise more slowly with age. The shades get darker slowly along the vertical axis.3

Causes of death have also changed over time.

The effects of major events – such as World War Two and the AIDS epidemic – are visible on the chart. They led to large surges in death rates from external causes and infectious diseases.

Another example is that death rates from infectious diseases and respiratory diseases have declined over time – especially from the mid-20th century onwards, with the rise of antibiotics, vaccines, and public healthcare.4

What you should know about this data
  • The underlying data for this chart comes from the Institut National d'Études Démographiques, which covers causes of death nationally in France between 1925 and 1999.
  • Causes of death were categorized into categories according to the 9th edition of the International Classification of Diseases (ICD-9) manual. Data from recent years comes from the 10th edition of the International Classification of Diseases (ICD-10) manual and has not been harmonized with older data.
  • Annual mortality rates are shown for five-year age bands.
  • This data has been processed by Our World in Data. To recreate this chart, you can find scripts here.
A Lexis plot showing the change in causes of death over time (between 1925 to 1999), by age group. The plot uses national data from France, with mortality rate shown as purple-blue-green shades, getting darker with increasing mortality rates.

Death rates from communicable and non-communicable disease vary widely around the world

Some causes of death are far more common in some parts of the world than others.

In poorer countries in Africa and Asia – where clean water, sanitation, and access to healthcare are lacking – people are much more likely to die from infectious diseases, maternal, neonatal, and nutritional causes.

However, in wealthier countries, people are much more likely to die from 'non-communicable diseases' instead, which include heart diseases and cancers.

This is because of two related points.

First, infectious diseases are much more common in poorer countries, and treatment is often lacking.

Second, deaths from infectious diseases were much more common in wealthier countries in the past. As these causes of death are reduced or eliminated, people tend to live longer and die from other causes instead.5

Therefore, the data needs to be ‘age-standardized’ to see how causes of death vary between countries, among people of the same age. In the chart, you can see an age-standardized comparison of these causes of death.

As you can see, countries with higher death rates from communicable diseases also tend to have higher death rates from non-communicable diseases.

This results from poorer healthcare, income, and living standards, which affect the chances of surviving many kinds of diseases.

You can also move the slider to see how they have shifted over time. Annual death rates have been reduced over time for both categories – but they have dropped faster for communicable diseases.

What you should know about this data
  • This data comes from the publication by the Institute for Health Metrics and Evaluation (IHME) in 2019.
  • These estimates assign each death a single cause, based on data on the ‘underlying cause of death’ listed on death certificates, verbal autopsies, and statistical modeling. This is a simplification, as people often have multiple diseases or injuries that contribute to their death, which may also be listed on death certificates.
  • This chart shows data on causes of death globally for 2019, the year before the Covid-19 pandemic started.

Underlying data on causes of death is limited in many countries

The most common way to track and understand causes of death is to rely on data from death certificates – where doctors describe the chain of events that led to each person’s death and the disease or injury that caused it.

These are registered in Vital Registration systems and shared with the World Health Organization annually.

However, in many countries, this data is lacking. You can see this in the chart.

This is because of several factors – a lack of doctors, nurses, medical records, and hospitals, and a poorly functioning Vital Registration system.

Instead, our understanding of causes of death in poor countries often comes from other studies, such as ‘verbal autopsies’, which are not conducted regularly.

To improve our understanding of causes of death worldwide, we need better-functioning Vital Registration systems, medical records, and training for doctors and nurses to collect data where it’s lacking.

In this article, we cover this in more detail:

How are causes of death registered around the world?

When people die, the cause of their death is usually officially registered in their country’s national system. How is the cause determined?

What you should know about this data
  • The number of deaths in each country is estimated based on data from censuses, household surveys, and historical trends.

A range of risk factors affect the chances of death

In the data we present on causes of death, we show each death as caused by a single disease, event, or injury.

However, people have often been exposed to various risk factors earlier in life, affecting their chances of premature death.

These risk factors can include behaviors – such as smoking and low exercise – and environmental factors, such as air pollution, unsafe water, and pathogens.

Researchers try to estimate the number of deaths caused by each risk factor, using available data.

For example, researchers estimate the number of deaths that could be prevented if no one smoked, by using estimates of the risk caused by smoking, and the levels of smoking in the population.6

In the chart, you can see the estimated number of deaths caused by a range of risk factors.7

The chart shows that risk factors such as high blood pressure, smoking, and air pollution are each estimated to cause millions of deaths yearly.

These risk factors are not exclusive: people can be exposed to multiple risk factors, and some are related to each other. Therefore, the numbers do not sum up to the total number of deaths.

In this article, we cover this in more detail:

How do researchers estimate the death toll caused by each risk factor, whether it’s smoking, obesity or air pollution?

Risk factors are important to understand because they can help us identify how to save lives. How do researchers estimate their impact?

What you should know about this data
  • These estimates come from the Global Burden of Disease study by the Institute for Health Metrics and Evaluation (IHME) in 2019.
  • The researchers estimate the number of deaths caused by each risk factor in several steps. First, they estimate the increased mortality risk caused by each risk factor. This is measured relative to a theoretical minimum (for example, the absence of the risk factor or its reduction to an optimum level). Next, they estimate the number of people exposed to the risk factor. Finally, they combine these to estimate the number of deaths caused by the risk factor.
  • The number of deaths attributed to each risk factor does not sum up to the total number of deaths. This is because risk factors are not mutually exclusive: people may be exposed to multiple risk factors, and the number of deaths caused by each risk factor is calculated separately.
  • In the linked article above, this is explained in more detail.

Research & Writing

Interactive Charts on Causes of Death

Endnotes

  1. Although communicable and non-communicable diseases are shown separately, it is now understood that infectious diseases contribute to several non-communicable diseases.

    This includes Helicobacter pylori and stomach cancer, human papillomavirus and cervical cancer, hepatitis C virus and liver cancer, Chlamydia pneumoniae and atherosclerosis, Streptococcus pneumoniae and chronic respiratory diseases, and others.

    In addition, infectious diseases can increase the risk of dying from non-communicable diseases. For example, several respiratory pathogens, such as the influenza virus, increase the risk of heart attacks and strokes.

    Mercer, A. J. (2018). Updating the epidemiological transition model. Epidemiology and Infection, 146(6), 680–687. https://doi.org/10.1017/S0950268818000572

    Behrouzi, B., Bhatt, D. L., Cannon, C. P., Vardeny, O., Lee, D. S., Solomon, S. D., & Udell, J. A. (2022). Association of Influenza Vaccination With Cardiovascular Risk: A Meta-analysis. JAMA Network Open, 5(4), e228873. https://doi.org/10.1001/jamanetworkopen.2022.8873

  2. This chart was inspired by related figures in the paper:

    Schöley, J., & Willekens, F. (2017). Visualizing compositional data on the Lexis surface. Demographic Research, 36, 627–658. https://doi.org/10.4054/DemRes.2017.36.21

  3. Out of these, death rates from falls have an especially strong age gradient.

    Rockett, I. R. H., Regier, M. D., Kapusta, N. D., Coben, J. H., Miller, T. R., Hanzlick, R. L., Todd, K. H., Sattin, R. W., Kennedy, L. W., Kleinig, J., & Smith, G. S. (2012). Leading Causes of Unintentional and Intentional Injury Mortality: United States, 2000–2009. American Journal of Public Health, 102(11), e84–e92. https://doi.org/10.2105/AJPH.2012.300960

    Remund, A., Camarda, C. G., & Riffe, T. (2018). A Cause-of-Death Decomposition of Young Adult Excess Mortality. Demography, 55(3), 957–978. https://doi.org/10.1007/s13524-018-0680-9

    Rockett, I. R., Regier, M. D., Kapusta, N. D., Coben, J. H., Miller, T. R., Hanzlick, R. L., Todd, K. H., Sattin, R. W., Kennedy, L. W., Kleinig, J., & others. (2012). Leading causes of unintentional and intentional injury mortality: United States, 2000–2009. American Journal of Public Health, 102(11), e84–e92.

    Percentage of Deaths from External Causes,* by Age Group† - United States, 2017. (2019). MMWR. Morbidity and Mortality Weekly Report, 68(32), 710. https://doi.org/10.15585/mmwr.mm6832a7

  4. Armstrong, G. L. (1999). Trends in Infectious Disease Mortality in the United States During the 20th Century. JAMA, 281(1), 61. https://doi.org/10.1001/jama.281.1.61

    Wise, D. A. (Ed.). (2004). Perspectives on the economics of aging. University of Chicago Press. Chapter 9. Cutler, D., & Meara, E. (2001). Changes in the Age Distribution of Mortality Over the 20th Century (No. w8556; p. w8556). National Bureau of Economic Research. https://doi.org/10.3386/w8556

    Drevenstedt, G. L., Crimmins, E. M., Vasunilashorn, S., & Finch, C. E. (2008). The rise and fall of excess male infant mortality. Proceedings of the National Academy of Sciences, 105(13), 5016–5021. https://doi.org/10.1073/pnas.0800221105

    Jayachandran, S., Lleras-Muney, A., & Smith, K. V. (2010). Modern Medicine and the Twentieth Century Decline in Mortality: Evidence on the Impact of Sulfa Drugs. American Economic Journal: Applied Economics, 2(2), 118–146. https://doi.org/10.1257/app.2.2.118

  5. Mercer, A. J. (2018). Updating the epidemiological transition model. Epidemiology and Infection, 146(6), 680–687. https://doi.org/10.1017/S0950268818000572

    Vaupel, J. W. (2010). Biodemography of human ageing. Nature, 464(7288), 536–542. https://doi.org/10.1038/nature08984

    Vaupel, J. W., & Yashin, A. I. (1985). Heterogeneity’s Ruses: Some Surprising Effects of Selection on Population Dynamics. The American Statistician, 39(3), 176–185. https://doi.org/10.1080/00031305.1985.10479424

    Schöley, J., & Willekens, F. (2017). Visualizing compositional data on the Lexis surface. Demographic Research, 36, 627–658. https://doi.org/10.4054/DemRes.2017.36.21

  6. For other risk factors, like obesity, they estimate the number of deaths that could be prevented if the risk factor was reduced to an ‘optimal level’, such as a BMI range.

  7. Murray, C. J. L., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., Abd-Allah, F., Abdelalim, A., Abdollahi, M., Abdollahpour, I., Abegaz, K. H., Abolhassani, H., Aboyans, V., Abreu, L. G., Abrigo, M. R. M., Abualhasan, A., Abu-Raddad, L. J., Abushouk, A. I., Adabi, M., … Lim, S. S. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1223–1249. https://doi.org/10.1016/S0140-6736(20)30752-2

Cite this work

Our articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:

Saloni Dattani, Fiona Spooner, Hannah Ritchie and Max Roser (2023) - “Causes of Death” Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/causes-of-death' [Online Resource]

BibTeX citation

@article{owid-causes-of-death,
    author = {Saloni Dattani and Fiona Spooner and Hannah Ritchie and Max Roser},
    title = {Causes of Death},
    journal = {Our World in Data},
    year = {2023},
    note = {https://ourworldindata.org/causes-of-death}
}
Our World in Data logo

Reuse this work freely

All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.

All of our charts can be embedded in any site.