So far, we have examined the history of our planet through the lens of a geologist, in which we observe the products captured in the rock record and try to interpret how they originate in the context of the complex interactions of geological processes over long geological timescales. This includes relocation of land masses through plate tectonics over hundreds of million-year Wilson cycles, as discussed in Chapters 5 and 10, to shorter-term sea-level and climate changes that occur over tens of thousands of years associated with orbital cycles reviewed in Chapter 18. We have also reviewed some of the major catastrophes in Earth history, with a focus on the mass extinctions that marked the end of the Paleozoic and Mesozoic eras (Chapters 11 and 13). In this chapter, we consider the idea that human activities over fewer than 300 years are now so profound that they might leave a permanent record in the geology of our planet.

In the study approaches we have looked at, the main purpose of investigation has been to understand and quantify relationships – relationships between exposures and outcomes, or between interventions and effects. And, just like the common plot line of a romantic tale, in this chapter we will consider how we can work out if those relationships are the ‘real deal’. How do we know we have measured what we think we have (is this really love?) and how much of the effect we have measured is entirely due to the exposure or intervention (or just a holiday thing)?

In epidemiology, we are interested in conducting studies to measure disease occurrence and look for causes of disease. Such studies can be applied to public health, allowing us to modify the causes for disease prevention. In the previous chapters, we learned about several commonly used public health measures and routine collections of health data. They form the basis of descriptive epidemiology and enable us to describe the frequency and patterns of health-related issues in relation to person, place and time characteristics. It is important to note that descriptive studies cannot be used to establish causal relationships but are useful for generating hypotheses. These hypotheses need to be tested in analytical studies to determine whether the ‘exposure’ of interest is associated with the changes in disease morbidity or mortality to search for the possible causes of the disease.

Importantly, the journey of learning epidemiology is like equipping you with knowledge and skills essential to critical thinking and problem-solving in your study or future career. The knowledge and skills will help you make scientifically informed decisions to improve population health. They include designing a study and applying quantitative research methods to collect data and identify ‘problems’. The data collected in the process will allow you to assess the measures of disease morbidity and mortality and make comparisons across populations, geographic locations, or different time points. Such comparisons allow us to determine potential ‘health problems’ in a relative way, which leads to further epidemiological investigations to search for possible ‘clues’ for ‘problem-solving’. In this chapter, we will explore this basic function of epidemiology: describing patterns of health problems, which is known as descriptive epidemiology.

For a case-control study to be a suitable design, we need a good idea about the outcome of interest (or condition) described by a strong case definition. But what if we know quite a bit about the exposures we are interested in, but we are a little hazy on the potential outcomes associated with those exposures? If we consider a scientific question like the one posed in this chapter – What happens if you eat pizza and chips every day?’ – we have specifically identified the exposures of interest, but can only guess what the outcomes might be. Okay, we could probably make fairly educated guesses about some of the potential outcomes (weight gain being foremost among these), but there remains a level of uncertainty about their timing, magnitude and variety. What is really needed to answer a question like this is a ‘cohort study’, a type of observational study in which ‘cohorts’ of people (population groups who share certain characteristics, such as being in the same work environment, or who are born in the same year) are sorted into groups on the basis of whether they have or have not been exposed to specific health-related factors.

In Chapter 6 we heard about how we can identify and quantify associations between exposures and health outcomes within populations, and even between countries. We learnt how useful cross-sectional studies were for looking at a range of risk factors and outcomes as they exist in a defined population at a particular point in time. While they have a great number of advantages, it can sometimes be difficult to sort out the direction of the relationships identified using cross-sectional approaches – that is, current risk factors or exposures may not necessarily have caused current outcomes or diseases. If we want to move towards thinking about potential causal relationships, we need an approach that allows us to determine the relative strength of relationships between exposures and outcomes and provide some hints about temporality – that is, to give us a start on determining if the exposure preceded the health event. We will need this type of study to address question posed for this chapter – what might be causing all those headaches that health science students seem to complain about.

When students are asked about the difficulties they experience in their epidemiology classes, one of the biggest barriers they report is the language their teachers use to describe the concepts being explained (note, it is the language rather than the concepts themselves). And here’s the thing: it is epidemiologists who are largely to blame, not the teachers! Being a relatively young discipline, it is not unusual to come across different words being used to describe the same concept, or the same word being used to describe different concepts – sometimes fundamentally different. Confusing, right?

A fundamental problem in descriptive epidemiology is how to make meaningful and robust comparisons between different populations, or within the same population over different periods. The problem has several dimensions. First, the data we have to work with (e.g. incident and prevalent cases, and deaths) is rarely usable in its raw form. We must therefore transform it in some way before undertaking the comparison itself. Second, our data usually tells us about fundamentally different attributes of the populations we are seeking to compare. If we are only ever interested in comparing any one of these attributes at a time (mortality, for example), then one of several simple and well-established transformations is all that is typically required. Increasingly, however, epidemiologists are being asked to bring these attributes together into more integrated and meaningful comparisons.