In Sydney’s north, planning for an eruv began in the early 2000s by a group of Shabbat-observant Jews. What looked like an innocent project that did not involve much more than erecting a couple of poles in inconspicuous colours with wire attached to them, most of them on private land with the consent of the owners, became a several years-long dispute in which the imagined boundary turned into a real one for many residents, which they sought to prevent by recourse to planning law. This chapter explores how residents and councillors in St. Ives mobilised planning law to draw the acceptable boundaries of Jewishness. By analysing public documents, including a survey on the eruv commissioned by the Local Council as well as Council meeting minutes, media reports, and submissions to local newspapers, I trace the implicit religious and racial boundaries of belonging in this Australian suburb that the eruv rendered visible and I examine how the planning law regime participated in protecting these boundaries, thereby affirming White Christian settlers as rightful inhabitants of this suburban land.

The performance of ‘Chinese music’ in Australian has a long and varied history. Performances of sonic arts which display a Chinese origin or connection range across various genres including classical, folk, opera, popular and sacred music. The performers are and have been equally diverse, including immigrants, international students, visiting artists and cosmopolitans from mainland China, the Sinosphere and the population of ‘Chinese overseas’, as well as people born or permanently residing in Australia of both Chinese and non-Chinese heritage. This chapter focuses on contemporary practice of different genres and on ethnographic examples from our own experience, but as space and historical records allow, we also look back in time. Our discussion illustrates some of the main ways that music has served to enhance social connections within and beyond Australia’s Chinese community, including within an Australian sociocultural fabric that has increasingly acknowledged and valued cultural diversity and multiplicities of cultural identity.

This chapter discusses how historical exchanges with Makassan and other seafaring peoples from beyond the Arafura Sea remain a profound influence on Yolŋu music and culture that endures to this day. We explore how Yolŋu people, through their enduring ceremonial traditions, elaborately integrate song, dance and design elements to recount exchanges with Makassan seafarers, the boats in which they sailed, and the goods they carried. We also discuss how, since the mid-1980s, this autonomous history of Yolŋu exchanges with foreigners has been remembered and continues to inspire new forms of Yolŋu cultural expression that overtly reach out across cultures. Our approach is informed by our long history of researching Yolŋu song in all its forms and working together to document the Yolŋu public ceremonial song tradition known as manikay. Garawirrtja’s expertise is further grounded in his extensive training and practice as a Yolŋu elder and ceremonial singer of the manikay tradition, who maintains hereditary songs that recount Yolŋu contact histories with Makassan and other seafarers.

This article explores the importance of music to Australian Torres Strait Islanders, in their home islands and on the Australian mainland, for maintaining and sustaining connections with the historical traditions of the Torres Strait region in far northern Queensland. Beginning post-World War Two, there was a sizeable diaspora to the Australian mainland and also the gradual unravelling of race-based laws aimed at controlling the travels and personal lives of Islanders, and Aboriginal peoples. Because of the diaspora, there were some changes in Islander sociality and culture over time, place and situation, in particular regarding performance and performativity. However, aspects of Islander music practices remain similar to what had occurred traditionally, but with some modifications via adoptions, adaptations and innovations befitting new social, cultural and economic environments. This article concludes with discussion of how traditional practices have contributed to contemporary Islander music variously as culture, commerce and creativity.

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.