What does Chinese law have to say about people who are involved in sex work and the places where it occurs? Prostitution control is a universal problem for which states have adopted a variety of policies to address the public order, public health, and commercial challenges that it presents. This chapter describes that range of regulatory possibilities. It then explains the official choices that China has made, through discussions of the policing, health, and taxation rules and institutions that the People’s Republic of China (PRC) has adopted to regulate prostitution.

The Nagoya Protocol to the Convention on Biological Diversity (CBD) has threatened to impede access to genetic resources and related data for cross-border scientific research. In principle, every use of genetic resources would require a set of contracts under the CBD, in a “bilateral” regime. The related transaction costs could overwhelm many transnational research undertakings, affecting even public health responses to outbreaks and epidemics. However, the Nagoya Protocol also offers a unique opportunity to resolve this dilemma, despite struggles to define the meaning and coverage of “digital sequence information.” The coverage of genetic sequence data under the CBD remains controversial mainly because users do not know what the potential consequences of such coverage might ultimately entail. This chapter’s objective is to outline a type of coverage devised specifically for pathogens that would promote science, public health, and commercial applications while also protecting the interests of provider countries, supporting innovation, and addressing inequalities. The authors envision an agreed waiver for pathogen sequence data used for upstream scientific research purposes under the Nagoya Protocol, without compromising the duty of users to share benefits. This waiver should help alleviate the problems of definition and subject-matter coverage that have stymied multilateral action.

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.

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.

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 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.

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 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).

This chapter explores two different systems of research oversight in recent Brazilian history: the bureaucracies of the twentieth and twenty-first-century Brazilian state, and approaches developed by A’uwẽ (Xavante) aldeias over the same period in Pimentel Barbosa Indigenous Land. Focusing primarily on genetics-based research, Dent develops the concept of bureaucratic vulnerability. She argues that the way some geneticists have interpreted state regulatory systems regarding biosamples creates additional risks for Indigenous people under study. At the same time, Indigenous groups are placed in a bureaucratic double bind, where non-Indigenous experts are called on to justify and validate their claims in the eyes of the state. The protectionist state regulation contrasts with relationship-based practices that A’uwẽ interlocutors have developed over repeated interaction and years of collaboration with a group of anthropologists and public health researchers. Specifically, A’uwẽ have responded to the dual and interrelated challenges of recognition under a colonial state and the management of outside researchers through the careful modulation of researchers’ affective experience of fieldwork, working to create enduring relationships and mutual obligation.