Chapter 1 presents the historical context and the key players who called for statistics to be collected as part of the interwar public health programs presented in later chapters. Striving to promote the health of “others” – poor rural communities, or people in foreign countries – using scientific methods, the Rockefeller Foundation and the Milbank Memorial Fund provided support to statistical initiatives with the help of bacteriologists trained in laboratory methods, who aimed to extend those principles to the social world. Missionaries’ sons born in China – with knowledge both of Chinese conditions and North American public health training – made implementation of such programs possible in China. It was these driving forces that enabled these public health programs, including related statistical practices.

Chapter 6 investigates the WHO’s malaria and tuberculosis control programs in the 1950s and 1960s, which made use of statistical collection and analysis. Numbers had become omnipresent in program design and implementation by this time, and experts at the WHO and in the Taiwanese government used their public health knowledge to justify their selection of statistics. WHO experts and Taiwanese health officers also used numbers to advocate for their programs. In particular, experts curated numbers to bolster their arguments in the context of ongoing policy debates at the WHO. Their ways of curating numbers were different as they occupied different positions within global health policy-making. Experts within the WHO mobilized their knowledge on the diseases in question to justify their selection of certain statistics over others, while their Taiwanese colleagues used numbers to present Taiwan as a viable testing ground for WHO policies, with a view to obtaining financial and technical support.

Chapter 2 details how Karl Pearson’s mathematical statistics were integrated into public health education, specifically at the Johns Hopkins School of Public Health (JHSPH) statistics department and its Chinese counterpart at the Peking Union Medical College (PUMC) in Beijing. Conceptions of the role of statistics in science differed at the two schools: the JHSPH aspired to identify the underlying statistical regularities in life and death; the PUMC was focused on testing and adapting scientific knowledge to fit Chinese public health work. The JHSPH’s transition from a biological focus in Pearson’s tradition to a focus on public health and epidemiology is also discussed. That approach was transferred – up to a point – to the PUMC. The major intermediaries in the transfer were a JHSPH alumnus and Rockefeller Foundation officer, John B. Grant, and one of his students, Yuan Yijing. Graduates of the two schools went on to use mathematical statistics in fieldwork, though they encountered resistance from locals and other experts. Statistical reporting nonetheless gained an increasingly prominent role in public health work in New York, Geneva, and Beijing. Graduates of the two schools were later employed by health organizations at different levels.

Chapter 7 explores the ways statistics were used to govern public health in the early People’s Republic of China (PRC), showcasing another way the language of statistics was spoken. The PRC was disconnected from the WHO’s statistical network during this time, and instead became part of an international health network made up of socialist countries. The chapter details the rise and fall of socialist statistics within the PRC and the methodological continuity maintained by public health researchers, despite the government’s enforced implementation of socialist statistics in the 1950s. The PRC during the period under study illustrates how statistical thinking continued to develop even as experts’ authority was called into question due to a series of anti-intellectual campaigns.

Chapter 3 focuses on the League of Nations Health Organization (LHNO) epidemiological intelligence service and the organization’s relationship with the Chinese government. Using Rockefeller funding, the LNHO strove to lead international health collaboration by creating an international health statistics reporting system. The organization’s statistical authority, however, was a patchwork, as it had to negotiate with stakeholders individually. The epidemiological intelligence service devised a tiered network for generating and sharing statistical standards and data. North Atlantic countries were included in the standard-making process, whereas other regions were relegated to the receiving end. A focus on the Chinese government’s strategies of cooperation with the service is illuminating as to the geopolitical context, which played a salient role in the epidemiological reporting network, especially given that the Republic of China saw its collaboration with the LNHO as a way to recover customs controls from the imperial powers.

Yi-Tang Lin presents the historical process by which statistics became the language of global health for local and international health organizations. Drawing on archival material from three continents, this study investigates efforts by public health schools, philanthropic foundations, and international organizations to turn numbers into an international language for public health. Lin shows how these initiatives produced an international network of public health experts who, across various socioeconomic and political contexts, opted for different strategies when it came to setting global standards and translating local realities into numbers. Focusing on China and Taiwan between 1917 and 1960, Lin examines the reception, adaptation, and appropriation of international health statistics. She presents the dynamic interplay between numbers, experts, and policy-making in international health organizations and administrations in China and Taiwan. This title is also available as Open Access.

A considerable amount of literature has been published on autonomous vehicles, robotics in healthcare, and the danger of losing jobs and human control as a result of automation. However, there has been relatively little discussion about how AI might change scientific research. AI-assisted scientific research is already offering a significant boost to the process of scientific discovery. Not surprisingly, this radical change in scientific research will have significant consequences. First, if the research process becomes automated, it may be conducted by anyone, which puts citizen science in a new context. Just as developments in hardware and software made personal computers feasible for individual use, automated research may affect science in the future. Second, unlike researchers, AI and neural networks cannot explain their thinking yet. The fully automated research expands the ‘black box’, making oversight and ethical review problematic in systems opaque to outside scrutiny. Automated research raises many further questions about regulation, safety, funding and patentability. This chapter will focus on the issues connected with privacy and data protection, from the EU GDPR point of view.

Chapter 18 covers the licensing of commercial software, data and databases. It begins with a discussion of database protection, including the history of legislative attempts to cover data and databases under copyright law, and the divergent pathways taken in the US and EU. It next addresses commercial data and database licensing practices (MD Mark v. Kerr-McGee, Eden Hannon v. Sumitomo). The chapter next discusses data privacy and protection regulations and their implementation in licensing agreements. It next moves to commercial software licensing, discussing the distinctions between source and object code. and offering some background on the legal protection of computer software through copyright, patent and trade secret law. Particular software licensing practices are discussed next, including provisions necessary to license source code, maintenance and support obligations, reverse engineering restrictions. It concludes with a discussion of licensing in the cloud and how different companies have approached this business model.

This chapter focuses on m-Health, i.e. technologies offered through mobile devices with particular regard to those having a specific health purpose. The contribution highlights that the mass use of these technologies is raising many challenges to national and European legislators, who are now facing a twofold task: assuring safety and reliance of the data generated by these products and protecting patients/consumers’ privacy and confidentiality. From the first perspective, such software may sometimes be classified as medical devices, although this classification is not always easy since there could be “border-line products”. If a software is classified as a medical device, then its safety and efficacy are guaranteed by the applicability of relevant regulations, which dictate specific prerequisites, obligations and responsibilities for manufacturers as well as distributors. From a data protection perspective, the mass use of these technologies allows the collection of huge amounts of personal data, both sensitive data (as relating to health conditions) and data that can nonetheless contribute to the creation of detailed user profiles.

This introductory chapter examines the health and wellbeing of children and young people growing up in Australia and New Zealand. Itlooks at the population characteristics of future generations, facilitators and challenges to healthy growth and development, and emerging health and social trends. It considers the health and wellbeing of children and young people as situated within their individual developmental journey. Whether born in New Zealand or Australia, or arriving as a child or young person, growth is nested within the social and cultural traditions of familyduring times of rapid social and environmental change.

The chapter begins by reviewing some of the common demographic and statistical indicator measures used to monitor the health and wellbeing of children and young people across the world. Being aware of the many different ways in which age and other measures are used to describe the health of children and young people can support the best use of the international evidence. The chapter then explores how these principles can be applied to key conditions and societal constructs relevant to the health and wellbeing of children and young people growing up in Australia and New Zealand.

Most of the existing privacy and security legal frameworks at both the federal and state level provide incomplete safeguards against many of the privacy and information security harms highlighted in earlier chapters. Many of these frameworks have long been critiqued by privacy law experts for their lack of effectiveness. The IoT amplifies these inadequacies as it compounds existing privacy and security challenges.

At the state level, the patchwork of privacy and security legislation creates varying obligations for businesses without consistently ensuring that individuals receive adequate privacy and cybersecurity protection. State legislation also suffers from several shortcomings and is often replete with gaping privacy and security holes. Even the CCPA, the first privacy statute of its kind in the United States, has several limitations. Further, varying state privacy and security legislation also enables unequal access to privacy and security between citizens of different states.

In 2015, the US Senate passed a resolution recommending the adoption of a national strategy for IoT development (IoT Resolution).1 Currently, the proposed Developing Innovation and Growing the Internet of Things Act (DIGIT) would establish a federal working group and a steering committee within the Department of Commerce.2 If the act is adopted, the working group, under the guidance of the steering committee, would be charged with evaluating and providing a report containing recommendations to Congress on multiple IoT aspects.3 These areas include identifying federal statutes and regulations that could inhibit IoT growth and impact consumer privacy and security.4

Diverse and increasingly comprehensive data about our personal lives is collected. When these personal data are linked to health records or linked to other data collected in our environment, such as that collected by state administrations or financial systems, the data have huge potential for public health research and society in general. Precision medicine, including pharmacogenomics, particularly depends on the potential of data linkage. With new capacities to analyze linked data, researchers today can retrieve and assess valuable and clinically relevant information. One way to develop such linked data sets and to make them available for research is through health data cooperatives. An example of such a health data cooperation is MIDATA – a health data cooperative recently established in Switzerland and the main focus of this chapter. In response to concerns about the present health data economy, MIDATA was founded to provide a governance structure for data storage that supports individual’s digital self-determination by allowing MIDATA members to control their own personal data flow and to store such data in a secure environment.