A set of training materials for professionals working in intervention epidemiology, public health microbiology and infection control and hospital hygiene.
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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. It is therefore important to be able to predict the impact of removing a particular exposure (or risk factor) on the incidence of disease in the population. This information can help policy makers 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. In order 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.
In order 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 as a measure of the effect of an exposure 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.
Measures of impact should help to answer questions like these:
For the public health policy maker it is helpful to answer these questions from two perspectives:
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
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. These are a range of measures that have been developed to express these public health concepts.
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.
After reading this chapter, you will be better able to:
understand the concept of impact numbers.
Measures of Impact
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