In this chapter, we look at the analytic studies that are our main tools for identifying the causes of disease and evaluating health interventions. Unlike descriptive epidemiology, analytic studies involve planned comparisons between people with and without disease, or between people with and without exposures thought to cause (or prevent) disease. They try to answer the questions, ‘Why do some people develop disease?’ and ‘How strong is the association between exposure and outcome?’. This group of studies includes the intervention, cohort and case–control studies that you met briefly in Chapter 1. Together, descriptive and analytic epidemiology provide information for all stages of health planning, from the identification of problems and their causes to the design, funding and implementation of public health solutions and the evaluation of whether these solutions really work and are cost-effective in practice.

The goal of public health is to improve the overall health of a population by reducing the burden of disease and premature death. In order to monitor our progress towards eliminating existing problems and to identify the emergence of new problems, we need to be able to quantify the levels of ill health or disease in a population. Researchers and policy makers use many different measures to describe the health of populations. In this chapter we introduce more of the most commonly used measures so that you can use and interpret them correctly. We first discuss the three fundamental measures that underlie both the attack rate and most of the other health statistics that you will come across in health-related reports, the incidence rate, incidence proportion (also called risk or cumulative incidence) and prevalence, and then look at how they are calculated and used in practice. We finish by considering other, more elaborate measures that attempt to get closer to describing the overall health of a population. As you will see, this is not always as straightforward as it might seem.

Epidemiology is about measuring disease or other aspects of health in populations, identifying the causes of ill-health and intervening to improve health, and we come back to these three fundamental components later in the chapter. But what do we mean by ‘health’? Back in 1948, the World Health Organization defined it as ‘… a state of complete physical, mental and social well-being’ (WHO, 1948). In practice, what we usually measure is physical health, and this focus is reflected in the content of most routine reports of health data and in many of the health measures that we will consider here; however, there are now methods to capture the more elusive components of mental and social well-being as well. Importantly, the WHO recognised that it is not longevity per se that we seek, but a long and healthy life. So, instead of simply measuring ‘life expectancy’, WHO introduced the concepts of ‘health-adjusted life expectancy’ (HALE) and subsequently ‘disability-adjusted life years’ (DALYs) to enable better international comparisons of the effectiveness of health systems.

People live complicated lives and, unlike laboratory scientists who can control all aspects of their experiments, epidemiologists have to work with that complexity. As a result, no epidemiological study can ever be perfect. Even an apparently straightforward survey of, say, alcohol consumption in a community, can be fraught with problems. Who should be included in the survey? How do you measure alcohol consumption reliably? All we can do when we conduct a study is aim to minimise error as far as possible, and then assess the practical effects of any unavoidable error. A critical aspect of epidemiology is, therefore, the ability to recognise potential sources of error and, more importantly, to assess the likely effects of any error, both in your own work and in the work of others. If we publish or use flawed or biased research we spread misinformation that could hinder decision-making, harm patients and adversely affect health policy. Future research may also be misdirected, delaying discoveries that can enhance public health.

The search for the causes of disease is an obvious central step in the pursuit of better health through disease prevention. In the previous chapters we looked at how we measure health (or disease) and how we look for associations between exposure and disease. Being able to identify a relation between a potential cause of disease and the disease itself is not enough, though. If our goal is to change practice or policy in order to improve health, then we need to go one step further and decide whether the relation is causal because, if it is not, intervening will have no effect. As in previous chapters, we discuss causation mainly in the context of an exposure causing disease but, as you will see when we come to assessing causation in practice, the concepts apply equally to a consideration of whether a potential preventive measure really does improve health.

The importance of simple descriptive data was recognised by William Farr, whom we mentioned briefly in Chapter 1 for his seminal work using the newly established vital statistics register of England in the nineteenth century. As we discussed in Chapter 1, this descriptive epidemiology, concerned as it is with ‘person, place and time’, attempts to answer the questions ‘Who?’, ‘What?’, ‘Where?’ and ‘When?’. This can include anything from a description of disease in a single person (a case report) or a special survey conducted to measure the prevalence of a particular health issue in a specific population, to reports from national surveys and data collection systems showing how rates of disease or other health-related factors vary in different geographical areas or over time (time trends). In this chapter we look in more detail at some of the most common types of descriptive data and where they come from. However, before embarking on a data hunt, we first need to decide exactly what it is we want to know, and this can pose a challenge. To make good use of the most relevant descriptive data, it is critical to formulate our question as precisely as possible.