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Short abstract:

This paper explores the do-ability multimorbidity in Zimbabwe. We discuss how multimorbidity is made (un)knowable within single disease data infrastructures, struggles to make multimorbidity known, and the potential of a ‘learning health systems’ approach for collective sense-making and action.

Long abstract:

Multimorbidity, commonly defined of as the experience of two-or-more long-terms conditions by one person, has been framed as among the most pressing challenges facing health systems globally. How to make multimorbidity knowable (and thus ‘doable’) within particular health system contexts, however, presents a profound challenge given the evolution of national and transnational data infrastructures around single disease categories, particularly communicable diseases. With multimorbidity currently emerging on the radars of many low-resource health systems, how do differently positioned actors within such systems make multimorbidity knowable and doable through (or despite) existing knowledge infrastructures? This paper presents findings from a participatory ethnographic study in Zimbabwe that sought to characterise the challenge of multimorbidity from multiple perspectives across Zimbabwe’s health system, including policymakers, programme managers, researchers, medical educators, health informaticians, clinicians and patients. We advance three points: (1) within existing data infrastructures, multimorbidity becomes increasingly unknowable through the process of abstraction from clinical reality, to the detriment of patient care; (2) what is known and knowable about multimorbidity stems primarily from ‘vertical’ programme datasets, favouring expansion (rather than disruption) of vertical programming and research; (3) nonetheless, actors across the health system find ways of making multimorbidity knowable beyond current parameters, often through informal workarounds. We explore the concept of a ‘learning health system’, drawn from health policy and systems research (HPSR), as a possible conceptual lens for making multimorbidity actionable, collectively, in ways that continue to elude the knowledge hierarchies, flows, and binaries of verticalized, evidence-based global health.

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Short abstract:

Using bibliometric analysis, we demonstrate how the subfield of genetic epidemiology (GE) emerged through the incorporation of epidemiological expertise into a network formerly dominated by statistical geneticists. We suggest that the formalization of GE contained complex causality within genetics.

Long abstract:

This paper takes the contemporary problem of complexity in human genomic research backwards in time, to describe how earlier frustrations in genetic disease research were contained into a new subfield, genetic epidemiology. We draw on bibliometric and citation network analysis (Leng and Leng 2021) to recreate a publication history of genetic epidemiology, from the early 20th century up to the field’s formalization around its flagship journal in the mid-1980s, just prior to the Human Genome Project’s launch. Using Web of Science indexing of 2,625 papers, we generate a network map of epidemiological studies of genetic factors and genetic studies of familial and population patterns of disease. Through this analysis, we trace how formerly distant networks of epidemiological and genetic expertise were drawn together in the 1960s through shared interest in complex or chronic disease causality. We identify how epidemiological genetics emerged alongside medical genetics but also deviated from it, as researchers struggled to isolate distinct phenotypes for complex disease and argued about the utility of classical Mendelian models of transmission in potentially non-Mendelian disease research. We suggest that the gradual incorporation of epidemiological expertise enabled researchers to maintain a viable research program on genetic contributions to multifactorial disease, providing a launching pad for the new method of genome-wide association studies. Our analysis demonstrates how an expanded network of expertise (Eyal 2013) allowed researchers to adapt to and manage etiological complexity, containing debates about the validity of particular methods and questions and redirecting intellectual interest to new ideas.

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Short abstract:

Drawing from the example of the East African Medical Survey, this paper considers mathematical statistics in the context of late-colonial modernism. Unlike the descriptive statistics then common in epidemiology, the affordances of mathematical statistics transformed what was doable with what data.

Long abstract:

The East African Medical Survey (EAMS) began in the mid 1940s to survey the health of the villages providing labour for the ill-fated Tanganyika Groundnut Scheme. This attempt to mechanise groundnut cultivation over several million acres—then one of the more costly failures of late-colonial modernism—soon fell apart but the EAMS remained, instead shifting toward the mapping and selective elimination of disease across the wider region. Six locations in Kenya and Tanganyika were selected, and thousands of people fell under detailed and invasive medical surveillance. Over the course of six years, researchers collected anthropometric and dietary data; stool, urine, blood, and skin samples; and demographic data from interviews with the many women surveyed. The correlation of social and biological data, and the construction of proxy indices from disparate data sources, was all part of the project’s stated purpose: to detail ‘a complete picture of what actually is medically wrong with the African.’ This paper considers the application of mathematical statistics as an important element of these surveys, and as a novel aspect of late-colonial modernism. In contrast to the descriptive statistics which had, until this point, dominated epidemiological and demographic surveillance in the British Empire, the application of more advanced statistical methods transformed what was doable with what data. As this paper will show, the onset of highly targeted population health interventions throughout the later twentieth century only became doable due to the statistical advances developed, in part, by the EAMS.

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Short abstract:

What does it take to solve the problem of understanding the biosocial dynamics of disease development? The paper examines how scientists conducting epigenetic research on air pollution mobilise the data infrastructures of cohort studies to turn this question into a doable problem.

Long abstract:

The emergence of environmental epigenetics in environmental health sciences came with the promise to better understand the biosocial dynamics of how environmental pollution shapes disease development across the life course and potentially generations. However, in practice, making these dynamics perceptible with the established epigenetic methods, research objects, and sequencing technologies remains challenging. Building on ethnographic fieldwork conducted at an institute of epidemiology and public health, I trace how the infrastructures of birth cohort studies turn the question of understanding the biosocial dynamics of health and disease into a doable problem in epigenetic research on air pollution exposure. First, I show how birth cohort studies have become crucial resources for epigenetic research that offer additional information to contextualise the exposure experiences cohort participants make and embody. Second, I demonstrate how environmental health scientists use these infrastructures to assemble heterogeneous types of data (derived from placental biopsies, blood samples, and brain images). In doing so, they attempt to recontextualise the epigenetic data produced with sequencing technologies to better understand the temporally dynamic and environmentally embedded process of disease development. However, such epistemic practices demand considerations of how these existing knowledge infrastructures shape and potentially constrain how researchers attempt to know the health effects of toxic exposures. I reflect upon how contemporary modes of creating scientific facts on toxicity make specific solutions to engage with the problem of environmental pollution more plausible than others.

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Short abstract:

Fields-in-the-making, genopolitics and social genomics, differ in their data modes of emergence, reflecting methods, communication and data practices. Alignment of modes of emergence with regimes of knowledge production allows insight into the epistemic and social organizational dynamics in science.

Long abstract:

Genopolitics and social genomics are emerging fields where methods from behavioral genetics, genetics and genomics are employed for researching phenomena relevant for political science and social sciences, respectively. Based on an analysis of pre-histories, methods used, infrastructures and data practices, the two fields-in-the-making are identified to have different dynamics of emergence. In terms of methodological paths, genopolitics is characterized by the divergence of methods over time while exhibiting the practices of dealing with controversy similar to behavioral genetics from where the major data/methods are borrowed. Social genomics can be characterized as a phenomenon that aligns itself with postgenomic, big data knowledge production practices, where work around the production of a data object (polygenic score) orders the emerging community in terms of data practices, communication and control. Both fields-in-the-making were restricted by the available data; however, they differed in their data modes of emergence which could be broadly characterized as ‘accumulating’ for genopolitics and ‘prototyping’ for social genomics. Such characterizations are tied to how the modes of emergences align with the prevailing regimes of knowledge production and contributes to STS in making better sense of the contemporary dynamics in epistemic and social organization in science.

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Short abstract:

The paper seeks to demonstrate the influence of epidemiological reasoning in the setup of the early information sciences and asks how infrastructures such as Eugene Garfield's citation index made an epidemiology of science doable.

Long abstract:

In 1972, the physician Kenneth Warren and the library scientist William Goffman published a paper to propose an epidemiological approach to the exponential proliferation of scientific and medical literature. Adapting a mathematical model developed to characterise the 4-factorial distribution cycle of schistosomiasis, a disease involving a parasitic worm, the authors proposed to think of scientific literature in strict analogy: the author as host, the manuscript as infective embryo, the journal as snail and the published paper as a larva infective to hosts. At the heart of their endeavour stood the hope to improve the recognition of papers of good quality (highly infectious) so that overall exposure of authors to lower quality could be reduced.

Warren and Goffman were not the only ones seeking to transfer epidemiological approaches into the understanding of knowledge distribution. Most prominently, Eugene Garfield of the Institute for Scientific Information (founded in 1956) referred retrospectively to his own work on citation-based ranking as the ‘epidemiology of science’ (1987).

With this paper I seek to illuminate the significant impact of epidemiological reasoning in the emerging information science. Rather than merely a transfer of disease models to the ecology of literature, I explore how infrastructures like Garfield’s citation index made such epidemiological approaches doable – and profitable.

The paper aims to move historiography beyond accounts of the metaphorical usage of “social contagion” across disciplines. Instead, I show that this is a history of adopting and expanding an epistemology of epidemiological reasoning to reshape the principles of scientific knowledge production.

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Short abstract:

In my presentation, I will discuss the results of a brief explorative ethnographic study focusing on the differences present within epidemiology and the more-than-human infrastructures that afford them. This study forms a ground for a future larger project on infectious disease modelling.

Long abstract:

The COVID-19 pandemic has shown the urgency of a critical engagement with epidemiology as it has come to have a wide-ranging influence on social life and policy. Although previous scholarship in social sciences has shown how epidemiology has accumulated its political capital and demonstrated the repercussions of the policies driven by different infectious disease models, still little is known about how it is done in practice. In STS epidemiology has long attracted attention mainly as a cornerstone of enactment of health as contained in populations rather than individual bodies (Law and Mol 2008). But the pandemic has highlighted the contingency and diversity of approaches within epidemiology itself with various infectious disease models competing for attention. These differences within epidemiology are yet to be thoroughly addressed by STS. In my presentation, I would like to discuss the results of a brief explorative ethnographic study with which I am trying to make a first step towards filling in this gap. I do so by examining how epidemiology is taught and done in practice through participant observation at an undergraduate course in epidemiology, within an epidemiological research group, and at an open lecture series. With this study, I distinguish between various modes of doing epidemiology and contrasting enactments of body and disease they lead to, as well as reflect on the role of more-than-human infrastructures in this process. This study is an initial step in a larger ethnographic project on infectious disease modelling the plans for which I will discuss during the presentation.

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Short abstract:

Drawing on historical sources and methods of analysis, this paper argues that authorizing strategies used to maintain the promise of modern epidemiology as an alternative to autocratic health governance have had long-term costs for the authority of public health measures.

Long abstract:

In spring 1931, Detroit health official John Gordon proposed that modern science could solve the problem of cooperation in public health. The existing public health system expected trust in the judgement of experts, certified by the state and other accrediting institutions. Gordon, to the contrary, anticipated a modern public health system in which trust resided in a new ecological science of health and disease, capable of producing highly granular facts about dynamic disease occurrence. Such a system promised to align public health codes with the liberal ideal of personal freedom, constituting a more democratic approach bound to improve cooperation.

Despite a century of technical development beyond Gordon’s wildest imagination, epidemiology has yet to solve the problem of cooperation. Indeed, by the third year of the Covid-19 pandemic, the US government had determined that epidemiologically-informed social policy was no longer doable, resorting instead to the anti-social policy that critics have described as “you do you.” And yet, the discourse of “trust in science” has been maintained even through such moments of breakdown.

This paper examines Gordon’s system at an early moment of breakdown, demonstrating that, in contrast to the democratizing discourse, power in Gordon’s system remains concentrated in “epidemiological specialists” who manage the system through ad hoc negotiations, and who citizens are expected to trust implicitly. I identify the authorizing strategies used to maintain the promise of modern epidemiology as an alternative to autocracy and argue that these strategies have had long term costs for the authority of public health measures.