Sleep is vital to our existence; it underpins a myriad of brain and bodily functions, and supports optimal functioning across a variety of different domains including cognitive functioning, emotional regulation, tissue repair and growth, and our immune system, among others. It is not surprising, therefore, that when sleep disturbance is experienced it can lead to impairments in performance and functioning. In addition, when sleep and circadian disruption are experienced regularly, such as in the context of insomnia, individuals can be at increased risk of developing a range of physical and mental health disorders including cardiovascular disease, depression, and anxiety. Such findings reinforce the need to address sleep disturbance and also highlight that it is not only sleep duration that is important but also timing and regularity of sleep–wake patterns.

The concluding chapter reflects on the everyday lives of sex workers, police officers and public health officials in China under Xi Jinping, and considers policy implications of the book’s findings.

This chapter is about the influence of transnational actors on China’s sex worker health policies. While the policing of prostitution in China is a story of domestic law and politics, the public health approach to regulating sex work in China starts in the international global health community. It then makes its way into central government health institutions in Beijing, and trickles down into the lives of local state health workers and the sex workers in their community. These transnational roots matter: they have shaped both the content of sex work health policies and the public health officials who manage their administration. Indeed, the approach that China’s health policies and officials endorse for gauging the prevalence of HIV/AIDS and reducing its occurrence among sex workers, and the language these authorities use, reflect best practices in the global public health community. Yet the obstacles that Chinese health agents encounter result in practices that fall short of these ideals and harm sex workers. That often grim reality is the subject of the next chapter. What I highlight in this chapter is how the global public health community working in China to support the creation of HIV/AIDS policies seems disengaged from what actually happens on the ground.

This chapter introduces the regulation of prostitution in China as a case study of law in everyday life. It presents China’s three tiers of sex workers, the state’s interests in the sex industry, and patterns of prostitution policy implementation. It shows how the study of prostitution and its regulation in China expands our understanding of state–society relations, and of sex work and its regulation across space and time.

This chapter is about the local health officials who implement China’s surveillance and behavioral outreach health policies for estimating the prevalence of HIV/AIDS and reducing its occurrence among sex workers. These policies set out clear guidelines for targeting certain types and numbers of sex workers for HIV/AIDS testing and outreach, with the goal of obtaining accurate knowledge of the overall sex worker population and reaching out to the individuals who present the greatest concerns to public health. These policies are also designed to protect the individual rights of sex workers, a prerequisite for obtaining higher quality data and increasing the likelihood that public health interventions will yield safer sexual behaviors. Yet frontline health workers often deviate from these rules, as obstacles within China’s health bureaucracy complicate proper policy implementation. Local health officials must also contend with two powerful entities that are predisposed to oppose their work: the sex industry and the police. Taken together, these challenges lead health agents to focus their testing and outreach efforts on hostesses instead of low-tier sex workers – even though women in the low tier are most in need of health interventions – and result in other irregularities in policy implementation with grave public health consequences.

The conclusion of the Second World War marked a significant turning point in global dynamics, particularly evidencing the decline of British global supremacy. Economic crises engendered by the war, coupled with the political repercussions of Indian independence, accelerated the dissolution of the British Empire. One salient indicator of this decline was Iran’s decisive move toward the nationalisation of its oil industry, a pivotal moment extensively analysed in this chapter. The Labour government in Britain, assuming power at the war’s end, aimed to revise its policies to maintain its monopoly in the Iranian oil sector by improving workers’ conditions. However, these efforts proved too limited and belated to effectively counter the rapid political developments in Iran, ultimately leaving Britain without a favourable strategic position in the Iranian context. The narrative then shifts to explore the working and living conditions within the Iranian oil industry in the late 1940s, highlighting the increasing poverty, entrenched housing, and health problems. It also examines the oil company’s response to the emerging labour movement and delves into the workers’ role in the nationalisation process. Additionally, the discussion encompasses the broader impacts of the withdrawal of British experts from Iran, focusing on the long-term effects on the lives and work of industry employees. These events significantly shaped the socio-economic landscape of the region and influenced the global power structures in the post-war era.

This chapter proposes a framework for estimating the investment in human capital from health improvement or activities that improve life expectancy and reduce morbidity rates. The measurement framework builds on and extends the Jorgenson-Fraumeni income-based approach for estimating human capital to account for the effect of health on human capital. This economic approach to measuring health human capital differs from the welfare-based approach that estimates the economic effect of health improvements on the quality of life and well-being of individuals. The framework is then implemented for Canada, and the investment in health human capital for the period from 1970 to 2020 is estimated. The estimated investment in health human capital based on the income approach was found to be lower than health expenditures in Canada. This suggests that much of the health expenditures should be classified as consumption rather than as an investment that increases earnings.

This chapter examines Gaza’s socio-spatial organization and the demographic features of its population. It presents Gaza’s main urban features during the late Ottoman period, including divisions into neighborhoods, main landmarks and thoroughfares. It then offers an in-depth portrayal of Gazan society, including data on economy and lifestyles, social hierarchies, marriage patterns, migration and health, based on a detailed analysis of the Ottoman census of 1905 and surviving court records (1857–1861), in light of evidence from the literature, maps and images.

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.