Social Studies of Science

In recent years and across many nations, public health has become subject to forms of governance that are said to be aimed at establishing accountability. In this introduction to a special issue, From Person to Population and Back: Exploring Accountability in Public Health, we suggest opening up accountability assemblages by asking a series of ostensibly simple questions that inevitably yield complicated answers: What is counted? What counts? And to whom, how and why does it count? Addressing such questions involves staying attentive to the technologies and infrastructures through which data come into being and are made available for multiple political agendas. Through a discussion of public health, accountability and datafication we present three key themes that unite the various papers as well as illustrate their diversity.

Epidemiological risk scores are calculative devices that mediate and enact versions of accountability in public health and preventive medicine. This article focuses on practices of accountability by following a cardiovascular risk score widely used in medical counselling in Germany. We follow the risk score in the making, in action, and in circulation to explore how the score performs in doctor-patient relations, how it recombines epidemiological results, and how it shapes knowledge production and healthcare provision. In this way, we follow the risk score’s various trajectories – from its development at the intersection of epidemiology, general medicine and software engineering, to its usage in general practitioners’ offices, and its validation infrastructures. Exploring the translations from population to individual and back that are at work in the risk score and in the primary prevention of cardiovascular disease, we examine how versions and distributions of accountability are invoked and practiced as the score is developed and put to use. The case of a simple risk score used in everyday counselling brings into relief some key shifts in configurations of accountability with emerging versions of ‘health by the algorithm’. While there is an increasing authority of algorithmic tools in the fabric of clinical encounters, risk scores are interwoven with local specificities of the healthcare system and continue to be in the making.

On May 25, 2018, the European Union’s General Data Protection Regulation (GDPR) came into force. EU citizens are granted more control over personal data while companies and organizations are charged with increased responsibility enshrined in broad principles like transparency and accountability. Given the scope of the regulation, which aims to harmonize data practices across 28 member states with different concerns about data collection, the GDPR has significant consequences for individuals in the EU and globally. While the GDPR is primarily intended to regulate tech companies, it also has important implications for data use in scientific research. Drawing on ethnographic fieldwork with researchers, lawyers and legal scholars in Sweden, I argue that the GDPR’s flexible accountability principle effectively encourages researchers to reflect on their ethical responsibility but can also become a source of anxiety and produce unexpected results. Many researchers I spoke with expressed profound uncertainty about ‘impossible’ legal requirements for research data use. Despite the availability of legal texts and interpretations, I suggest we should take researchers’ concerns about ‘unknowable’ data law seriously. Many researchers’ sense of legal ambiguity led them to rethink their data practices and themselves as ethical subjects through an orientation to what they imagined as the ‘real people behind the data’, variously formulated as a Swedish population desiring data use for social benefit or a transnational public eager for research results. The intentions attributed to people, populations and publics – whom researchers only encountered in the abstract form of data – lent ethical weight to various and sometimes conflicting decisions about data security and sharing. Ultimately, researchers’ anxieties about their inability to discern the desires of the ‘real people’ lent new appeal to solutions, however flawed, that promised to alleviate the ethical burden of personal data.

‘Personalized medicine’ might sound like the very antithesis of population science and public health, with the individual taking the place of the population. However, in practice, personalized medicine generates heavy investments in the population sciences – particularly in data-sourcing initiatives. Intensified data sourcing implies new roles and responsibilities for patients and health professionals, who become responsible not only for data contributions, but also for responding to new uses of data in personalized prevention, drawing upon detailed mapping of risk distribution in the population. Although this population-based ‘personalization’ of prevention and treatment is said to be about making the health services ‘data-driven’, the policies and plans themselves use existing data and evidence in a very selective manner. It is as if data-driven decision-making is a promise for an unspecified future, not a demand on its planning in the present. I therefore suggest interrogating how ‘promissory data’ interact with ideas about accountability in public health policies, and also with the data initiatives that the promises bring about. Intensified data collection might not just be interesting for what it allows authorities to do and know, but also for how its promises of future evidence can be used to postpone action and sidestep uncomfortable knowledge in the present.

Accountable Care Organizations (ACOs) are exemplars of so-called value-based care in the US. In this model, healthcare providers bear the financial risk of their patients’ health outcomes: ACOs are rewarded for meeting specific quality and cost-efficiency benchmarks, or penalized if improvements are not demonstrated. While the aim is to make providers more accountable to payers and patients, this is a sea-change in payment and delivery systems, requiring new infrastructures and practices. To manage risk, ACOs employ data-intensive sourcing and big data analytics to identify individuals within their populations and sort them using novel categories, which are then utilized to tailor interventions. The article uses an STS lens to analyze the assemblage involved in the enactment of population health management through practices of data collection, the creation of new metrics and tools for analysis, and novel ways of sorting individuals within populations. The processes and practices of implementing accountability technologies thus produce particular kinds of knowledge and reshape concepts of accountability and care. In the process, account-giving becomes as much a procedural ritual of verification as an accounting for health outcomes.

Accountability has become a buzzword in recent years, applied in such disparate fields as education, academia, international politics and economics. For cohort studies, epidemiological literature and guidelines put forward several ideals to assure the quality of the generated data to make it accountable. Social scientific scholarship in turn has shown that in practice, such demands can lead to tensions and needs for balance ‘on the ground’. We take such insights as a starting point to explore these balancing acts. Based on an ethnographic investigation into the examination and data-gathering situations and practices within a German cohort study, we suggest that ‘courtesy work’ is intrinsic to the development of accountable data as a foundation for the establishment and stabilization of epidemiological facts. As such, courtesy work is part of accountable care work in epidemiological studies, which is in turn a key part of the quality assurance practices that ensure the enrollment and maintenance of people in a study, as well as the production of standardized measurement results.

In this article, we examine professional discourse around the development of polygenic risk-stratified screening (PRSS) for cancer. Analyzing a range of contemporary professional literatures from Europe, North America and Australia, we explore how the drive to screen for molecular markers of cancer risk makes professionals, screening recipients and publics responsible, in different ways, for acquiring, curating and analyzing molecular data. Investigating how these responsibilities are invoked in discussions of new data practices, technologies, organizational arrangements, engagement, education and protocols for participation, we argue that agendas for PRSS for cancer are both expanding and stratifying responsibilities. Data collection is to be achieved by intensified responsibilities for including, reassuring and recruiting populations, as well as by opening and enriching the datasets on which models and preventative screening arrangements are based. Enhanced responsibilities for screening recipients and publics are also invoked, not just in relation to personal health but for population health more generally, via research participation and consenting to data re-use in the public interest. Professionals, screening recipients and publics are also to become responsible for moderating expectations of screening according to genomic designations. Together these discourses go beyond individual risk management to extend and diversify the responsibilities of practitioners, screening recipients and publics as public health genomics develops.

Psychological therapy today plays a key role in UK public mental health. In large part, this has been through the development of the (specifically English) Improving Access to Psychological Therapies (IAPT) programme. Through IAPT, millions of citizens have encountered interventions such as cognitive behaviour therapy, largely for the treatment of depression and anxiety. This article interrogates how this national response to problems of mental ill-health – and the problematization itself – was developed, accounted for, and sustained. By imbricating economic expertise with accounts of mental ill-health and mechanisms of treatment, IAPT has revivified psychological framings of pathology and therapy. However, it has done so in ways that are more familiar within biomedical contexts (e.g. through recourse to randomized controlled trial studies). Today, the initiative is a principal player in relation to which other services are increasingly developed. Indeed, in many respects IAPT has transformed from content to context within UK public mental health (in a process of what I term ‘contextification’). By documenting these developments, this paper contributes to re-centring questions about the place and role of psychology in contemporary healthcare. Doing so helps to complicate assumptions about the dominance of linear forms of (de)biomedicalization in health-systems.