Why isn’t it possible to sum up the death toll from different risk factors?
Deaths caused by each risk factor can’t be added up. By understanding why, we will have a better understanding of how many lives can be saved with each intervention.
People may be exposed to different risk factors over their lifetime – like smoking, air pollution and obesity – which can increase their risk of disease and death.
However, the number of deaths caused by each risk factor can’t simply be added up. In this article, I explain why not – and why this is important.
How to understand this concept
There are several reasons why the number of deaths caused by each risk factor can’t simply be added up.
One reason is that multiple risk factors can contribute to a person’s death. Another reason is that these risk factors can interact with each other.
A simple analogy to understand this idea is with the idiom “the straw that broke the camel’s back.”
A camel carrying many items can eventually break its back, even though individual items are light enough to be carried. We could prevent the camel’s back from breaking by removing the hay bale, or removing other luggage, or freeing it from its rider.
In the same way, people can be exposed to different risk factors simultaneously, and they can overlap and interact with each other.
A real-life example is that DNA mutations can be caused by many carcinogens and some pathogens. Our cells have ‘checkpoints’ that prevent DNA mutations from leading to cancer. But, if a sufficient number of mutations occur, they will surpass the checkpoint, and a tumor can develop. This can eventually lead to death.
If people have multiple risk factors, then it’s more likely that the threshold will be surpassed. Having one risk factor can also make someone more vulnerable to another risk factor later on.
This means their death can be prevented in multiple ways. Liver cancer, for example, can be caused by chronic alcohol consumption and some hepatitis viruses, so reducing alcohol consumption or preventing hepatitis virus infections would both reduce the risk of death.
A worked example
In the figure, I have illustrated this with a worked example.
It shows that when multiple risk factors are present, the number (or fraction) of deaths from risk factors can’t be simply summed up.
Above you can see five hypothetical people who died and their previous exposure to risk factors. In pink, you can see their risk from hepatitis C virus, in blue is their risk from smoking, and in yellow is their risk from alcohol consumption.
Each risk factor increased their chance of death, according to the amount of exposure they’ve had. Some of the containers are full – this shows that some people have had sufficient risk exposure to cause death within a given timeframe.
Let’s look at what happens if some of the risk factors were removed.
If one risk factor – for example, hepatitis C – was removed, it reduced the number of deaths by 60%. This is known as the population-attributable fraction of hepatitis C, which means that 60% of deaths were caused by hepatitis C, in the hypothetical example.
If a different risk factor – smoking – was removed, it also reduced the number of deaths by 60%. So 60% of deaths were also caused by smoking.
How many deaths were caused by both of them, taken together? If both risk factors were removed, the number of deaths was reduced by 80%, not 120%.
This illustrates how the deaths from risk factors are not additive – they cannot be added up, and they do not sum up to 100%.
Similarly, if two risk factors have a population-attributable fraction of 50% and 30%, it does not mean that 20% of deaths are caused by unexplained risk factors.1
Instead, researchers can use other methods to understand the combined contribution of multiple risk factors. These estimates are based on the order of risk factors: for example, they may look at the share of deaths caused by obesity after smoking has been accounted for.2
By interpreting the fractions correctly, we can have a better understanding of the impact of different risk factors combined.
Below, you can see the full illustration.
Conclusion
Although it may sound simple to attribute a person’s death to a single risk factor, this is usually not straightforward. Over a lifetime, people are exposed to various risk factors, which can overlap and interact with each other. This also means the same deaths can be prevented in multiple ways.
By understanding the effects of different risk factors, we can identify better ways to save lives.
Acknowledgements
I would like to thank Edouard Mathieu, Hannah Ritchie, and Max Roser for their valuable feedback on this article.
I’m also very grateful to Julia Rohrer and Ruben Arslan for inspiring the analogy & figure to explain why multiple risk factors can’t be summed up.
Endnotes
Rowe, A. K., Powell, K. E., & Flanders, W. D. (2004). Why population-attributable fractions can sum to more than one. American Journal of Preventive Medicine, 26(3), 243–249. https://doi.org/10.1016/j.amepre.2003.12.007
Pearce, N. (2011). Epidemiology in a changing world: Variation, causation and ubiquitous risk factors. International Journal of Epidemiology, 40(2), 503–512. https://doi.org/10.1093/ije/dyq257
Poole, C. (2015). A history of the population attributable fraction and related measures. Annals of Epidemiology, 25(3), 147–154. https://doi.org/10.1016/j.annepidem.2014.11.015
Rückinger, S., Von Kries, R., & Toschke, A. M. (2009). An illustration of and programs estimating attributable fractions in large scale surveys considering multiple risk factors. BMC Medical Research Methodology, 9(1), 7. https://doi.org/10.1186/1471-2288-9-7
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Saloni Dattani (2023) - “Why isn’t it possible to sum up the death toll from different risk factors?” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/why-isnt-it-possible-to-sum-up-the-deaths-from-different-risk-factors' [Online Resource]BibTeX citation
@article{owid-why-isnt-it-possible-to-sum-up-the-deaths-from-different-risk-factors,
author = {Saloni Dattani},
title = {Why isn’t it possible to sum up the death toll from different risk factors?},
journal = {Our World in Data},
year = {2023},
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}
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