Epidemiology is not just about identifying risk factors for disease but also about evaluating control measures or public health interventions to reduce or eliminate the effect of these risk factors. Therefore, it is important to predict the impact of removing a particular exposure (or risk factor) on the incidence of disease in the population. This information can help policymakers decide on how best to allocate resources to ensure the most beneficial impact on public health.

Many diseases are caused by more than one exposure. For example, primary hepatic cancer may be caused by exposure to excess alcohol consumption, hepatitis B infection or hepatitis C infection. To assess the potential public health impact of a hepatitis B vaccination strategy on the incidence of primary hepatic cancer, we need a way of quantifying disease burden associated specifically with hepatitis B infection.

To do this, we need a way of measuring the proportion of the disease that can be attributed to the exposure. The relative risk (or risk ratio) is used to measure exposure's effect on an individual's risk of disease. However, to assess the impact more generally, we also need to know the number of individuals that are exposed (the prevalence of exposure). This chapter, therefore, begins by exploring the concepts of relative risk versus attributable risk.

Impact among the exposed and in the population

Measures of impact should help to answer questions like these:

• How much of the disease can be attributed to a particular exposure?
• How much of the disease can be prevented by eliminating a particular exposure?

For the public health policy maker it is helpful to answer these questions from two perspectives:

• What is the impact on people who are exposed to the risk factor?
• What is the impact on the population as a whole?

This chapter explains how impact may be measured in both the exposed group and in the entire population. It gives examples of how these measures are calculated and explains what they mean.

Details are given of how to calculate each of the following measures:

• Attributable risk among the exposed
• Attributable fraction among the exposed
• Attributable fraction in cohort studies
• Preventable fraction in cohort studies
• Attributable fraction in case-control studies
• Attributable risk in the population
• Attributable fraction in the population

Impact numbers

In clinical medicine, the number needed to treat (NNT) is used as a measure of treatment effect. It is the number of persons that need to be treated to achieve one beneficial outcome (e.g. cure) or to prevent one adverse outcome (e.g. relapse).

However, this measure has limited usefulness in a public health context when the impact of an exposure on the risk of disease is being assessed. In this situation we are more interested in calculating, for example, the number of people in a population among whom one case may be attributed to the exposure. This chapter therefore concludes with a brief discussion of impact numbers. A range of measures have been developed to express these public health concepts.

Further reading

Suggestions are given for further reading about the general principles of measuring impact, and some examples of the use of measures of impact in field epidemiology.

Credits

FEM Editor 2007

• Meirion Evans

Original Authors

• Alain Moren
• Marta Valenciano
• Thomas Grein

FEM Contributors

• Vladimir Prikazsky
• Arnold Bosman
• Lisa Lazareck
• Meirion Evans