This book grew out of our collective experience of teaching introductory epidemiology both in the classroom and to distance students enrolled in public health and health studies programmes in the School of Public Health (formerly the Department of Social and Preventive Medicine and then School of Population Health), University of Queensland. It began life as a detailed set of course notes that we wrote because we could not find a single epidemiology text that covered all of the areas we felt were important in sufficient detail. As the notes were to be used primarily by distance students, we tried hard to make them accessible with lots of examples, minimal jargon and equations, and by engaging readers in ‘doing’ epidemiology along the way. Feedback from students and colleagues convinced us that the notes were both approachable and practical and the result is this text, which we offer as a practical introduction to epidemiology for those who need an understanding of health data they meet in their everyday working lives, as well as for those who wish to pursue a career in epidemiology.

The first revision of the text reflected evolution, not revolution. We listened to the feedback we received from instructors and students and tried to simplify and clarify some of the trickier bits of the original text while maintaining a very ‘hands-on’ approach. We added new material to reflect contemporary epidemiological practice in public health and re-ordered some of the existing elements to improve the flow and enhance the continuity between chapters. New and expanded topics included a look at how we measure the burden of disease, greater discussion of issues relevant to ethics and privacy, appendices covering life tables and calculation of confidence intervals for common epidemiological measures, and a glossary.

This, the third edition, reflects further evolution. With our new co-author Professor Andrew Page, and inspired by colleagues at a workshop on methods of teaching modern epidemiology convened by Professors Diana Safarti and John Lynch at the University of Otago, New Zealand in 2014, we have injected some more modern approaches to causal thinking, bias and confounding. These changes are most obvious in Chapter 4 (Study Design), which we have restructured to show more clearly how each design contrasts with the ‘ideal’ (counterfactual) experiment, Chapters 7 (Bias) and 8 (Confounding), and Chapter 10 (Causation).

As you saw in Chapter 1, one of the main uses of epidemiology is to identify the causes of disease, and this is of fundamental importance in all areas of public health – if we can work out what is causing ill health then we can work to prevent it. In Chapter 2 we looked at the ways in which we can measure the occurrence of disease and touched on some ways in which we can compare different populations. However, while measuring the occurrence of a disease in a population can tell us about the health of that population, it does not directly shed much light on the underlying causes of the disease. To identify the aspects of people or their environment (exposures) that might lead to the onset of disease, we need to compare disease occurrence in groups with and without the exposures of interest. In Chapter 4 we looked at some of the study designs that we can use to do this; now we will look more closely at the measures we use to quantify the associations between ‘exposures’, or potential causes of disease, and the disease itself. By quantifying the association between an exposure and disease we can start to make judgements as to whether the exposure might actually cause the disease (we will discuss causality in more detail in Chapter 10). If we believe that it is causing disease, we can then identify the importance of that exposure in terms of its overall effect on the health of a community.

In this chapter we will look at the ways in which we calculate, use and interpret these ‘measures of association’, so-called because they describe the association between an exposure and a health outcome. An understanding of these measures will help you to interpret reports regarding the causes of ill health and the effects of particular exposures or interventions on the burden of illness in a community.

In the preceding chapters we have covered the core principles and methods of epidemiology and have shown you some of the main areas where epidemiological evidence is crucial for policy and planning. You will also have gained a sense of the breadth and depth of the subject from the examples throughout the book. To finish off we will take a broader look at the role of epidemiological practice and logic in improving health. This process where research evidence is used to change practice or policy is known as translation (see Box 16.1).

Translating epidemiological research into practice

When epidemiological evidence is both sufficient and sound it has the potential for direct translation into public health (and clinical) practice and policy. In addition to providing primary research evidence to identify and test potential interventions to improve health, you saw in Chapter 11 the fundamental role of epidemiology in knowledge synthesis through systematic reviews and meta-analyses to better inform all stages of the research and translation continuum. You have also encountered many examples of the application of simple descriptive tools to evaluate disease control programmes once they have been implemented. However, policy makers must have confidence in the quality of our data and the soundness and impartiality of our interpretations of those data. As you have seen, the evidence will often not include any data from experiments. How then can we give assurances as to the soundness of our data and their meaning?

So what is epidemiology anyway? As shown in Box 1.1, the Concise Oxford Dictionary (1964) defined it accurately, but not very helpfully, as ‘the science of epidemics’. In 1970, MacMahon and Pugh came up with something a bit more concrete: ‘the study of the distribution and determinants of disease’. Their definition succinctly identifies the two core strands of traditional epidemiology: who is developing disease (and where and when), and why are they developing it? The next definition, from the 2008 edition of the Dictionary of Epidemiology (Porta, 2008), takes things two steps further by broadening the scope to include health in general, not just disease, as well as highlighting the essential role of epidemiology in translating research findings into health policy and medical practice to control disease. The most recent definition (Porta, 2014) elaborates further still but, in doing so, loses some of the elegance of the earlier versions.

Epidemiology, therefore, is about measuring disease or other aspects of health, identifying the causes of ill-health and intervening to improve health; but what do we mean by ‘health’? Back in 1948, the World Health Organization (WHO, 1948) defined it as ‘… a state of physical, mental and social well-being’. 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.

The rates and measures that we explored in Chapter 2 provide a variety of ways to describe the health of a population and thus also enable us to compare patterns of health and disease between populations and over time. This allows us to answer the core questions relating to disease burden that are the essential first step in setting health planning and service priorities. 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).

Although descriptive data may be collected specifically to answer a defined question, they often come from governments, health care providers and statistical agencies that routinely collect vast amounts of information. Summary data – often the various forms of rate which you met in Chapter 2 – can be accessed from published reports and, increasingly, from online databanks. In some cases it is also possible to obtain information from which the rates are calculated at the individual level. These descriptive data are essential to identify health problems and for health planning and, although they cannot usually answer the question ‘Why?’, they may provide the first ideas about causality and thus generate hypotheses that can then be tested in more formal ‘analytic’ studies that we will discuss in Chapter 4. As you will come to see in later chapters, descriptive studies also play a critical and often under-appreciated role in monitoring the effects of large-scale interventions.

In this chapter we will 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.

While it is important to be able to read and interpret individual papers, as we have noted previously the results of a single study are never going to provide the complete answer to a question. To move towards this we need to review the literature more widely. There can be a number of reasons for doing this, some of which require a more comprehensive approach than others. If the aim is simply to increase our personal understanding of a new area then a few papers might provide adequate background material. Traditional narrative reviews, which give less emphasis to complete coverage of the literature and tend to be more qualitative, have value for exploring areas of uncertainty or novelty, but it is harder to scrutinise them for flaws. In contrast, a major decision regarding policy or practice should be based on a systematic review and perhaps a meta-analysis of all the relevant literature and it is this systematic approach that we will focus on here.

What is a systematic review?

A systematic review should be a helpful synthesis of all of the relevant data – highlighting patterns but not hiding differences. Although its primary data units are whole studies rather than individuals, it should still have a clearly formulated research question and be conducted with the same rigour as its component studies. So how should we go about conducting a systematic review? This is a major undertaking and excellent guidelines are widely available for would-be reviewers (see e.g. the Cochrane Collaboration website www.cochrane.org) so we will not attempt to cover all of the issues here. But, in brief, it involves:

The goal of public health is to improve the overall health of a population by reducing the burden of disease and premature death. To do this we need to be able to quantify the levels of ill-health or disease in a population in order to monitor our progress towards eliminating existing problems and to identify the emergence of new problems. Many different measures are used by researchers and policy makers to describe the health of populations. You have already met some of these for example the attack rate, which was used to investigate the source of the food poisoning outbreak in the previous chapter. In this chapter we will introduce some more of the most commonly used measures so that you can use and interpret them correctly.