Confounding refers to a mixing or muddling of effects that can occur when the relationship we are interested in is confused by the effect of something else. It arises when the groups we are comparing are not completely exchangeable and so differ with respect to factors other than their exposure status. If one (or more) of these other factors is a cause of both the exposure and the outcome, then some or all of an observed association between the exposure and outcome may be due to that factor.

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.

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.

If the results of a study reveal an interesting association between an exposure and a health outcome, there is a natural tendency to assume that it is real. (Note: we are considering whether two things are associated. This does not imply that one causes the other to occur.) However, before we can even contemplate this possibility we have to try to rule out other possible explanations for the results. There are three main ‘alternative explanations’ that we have to consider whenever we analyse epidemiological data or read the reports of others, whatever the study design; namely, could the results be due to chance, bias or error, or confounding? We discuss the first of these, chance, in this chapter and cover bias and confounding in Chapters 7 and 8, respectively.

When we speak of prevention in the context of public health, we usually think of what is sometimes called ‘primary prevention’, which aims to prevent disease from occurring in the first place; that is, to reduce the incidence of disease. Vaccination against childhood infectious diseases is a good example of primary prevention, as is the use of sunscreen to prevent the development of skin cancer. However, somewhat confusingly, the term ‘prevention’ is also used to describe other strategies to control disease. One of these is the use of screening to advance diagnosis to a point at which intervention is more effective, often described as ‘secondary prevention’. What is sometimes called ‘tertiary prevention’ is even more remote from the everyday concept of prevention, usually implying efforts to limit disease progression or the provision of better rehabilitation to enhance quality of life among those who have been diagnosed with a disease.

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.

In this chapter we look at the ways in which we calculate, use and interpret ‘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 on the causes of ill health and the effects of particular exposures or interventions on the burden of illness in a community. Note that, while we discuss the measures in the context of an ‘exposure’ and ‘disease’, they can be used to assess the association between any measure of health status and any potential ‘cause’.

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 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.

While it is important to be able to read and interpret individual papers, 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 have value for exploring areas of uncertainty or novelty but give less emphasis to complete coverage of the literature and tend to be more qualitative, so it is harder to scrutinise them for flaws. Scoping reviews are more systematic but still exploratory. They are conducted to identify the breadth of evidence available on a particular topic, clarify key concepts and identify the knowledge gaps. 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 approach that we focus on here.

In the previous chapters we have considered the ‘nuts and bolts’ of epidemiology. In this and the next few chapters we look at how epidemiology is used in practice to improve public health. We start with ‘surveillance’ because without timely information on emerging and changing health problems, public health action can be paralysed or, at best, inefficient. In this chapter we discuss the design and use of surveillance systems that enable health officials to detect new risks and diseases such as mpox promptly, track known diseases and health problems, and generate data needed for effective health planning and resource allocation.

In the previous chapter we alluded to what is sometimes called ‘secondary’ prevention, where instead of trying to prevent disease from occurring, we try to detect it earlier, in the hope that this will enable more effective treatment and thus improved health outcomes. This is an aspect of public health that has great intuitive appeal, especially for serious conditions such as cancer, where the options for primary prevention can be very limited. However, screening programs are usually very costly exercises and they do not always deliver the expected benefits in terms of improved health outcomes. In this chapter we introduce you to the requirements for implementing a successful screening program and to some of the problems that we encounter when trying to determine whether such a program is actually beneficial in practice.

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.

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, we take a broader look at the role of epidemiological practice and logic in improving health. There is a growing desire for public health and medical research to be ‘translational’; that is, directly applicable to a population or patient. The process, whereby research evidence is used to change practice or policy, is known as ‘translation’, and the research outputs from epidemiology are critical at all stages (see Box 16.2); indeed, epidemiology has been described as ‘the epicenter of translational science’ (Hiatt, 2010).

The global pandemic of COVID-19 that began in late 2019 highlighted the importance of rapid and thorough investigations of outbreaks. The response to COVID-19 was at a scale not previously seen, involving all sectors of society, including government and private industry. To control and minimise the impact of COVID-19, huge and costly efforts were required to effectively coordinate many different organisations, many of which were not primarily concerned with public health. This type of re-focusing of resources is common in outbreak and public health emergency settings, but is rarely seen at such scale. In this chapter we look at outbreak investigation in more detail and, in doing so, focus on infectious diseases, although not exclusively, because other agents such as toxins and chemicals can also result in ‘outbreaks’ of non-communicable intoxications, injuries and cancer.

The overarching goal of public health is to maximise the health of the population, and to achieve this we need evidence about what works and what does not work. Good studies are difficult to design and implement, and interpretation of their results and conclusions is not always as straightforward as we might hope. How, then, can we make the best use of this information? In the next three chapters we look at ways to identify, appraise, integrate and interpret the literature to generate the evidence we need to inform policy and practice. In this chapter we focus on interpreting the results from a single study, because if they are not valid they will be of limited value. The central question we have to answer when we read a study report is, ‘Are the results of the study valid?’