Linkage of Administrative Datasets: Enhancing Longitudinal Epidemiological Studies in the Era of “Big Data”

Reframing the environment in data-intensive health sciences

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research.

A Snapshot of Doctoral Training in Epidemiology: Positioning Us for the Future

Although epidemiology core competencies are established by the Association of Schools and Programs of Public Health for masters-level trainees, no equivalent currently exists for the doctoral level. Thus, the objective of the Doctoral Education in Epidemiology Survey (2019) was to collect information on doctoral-level competencies in general epidemiology (doctoral) degree programs and other pertinent information from accredited programs in the United States and Canada. Participants (doctoral program directors or knowledgeable representatives of the program) from 57 institutions were invited to respond to a 39-item survey (18 core competencies; 9 noncore or emerging topic-related competencies; and 12 program-related items). Participants from 55 institutions (96.5%) responded to the survey, of whom over 85% rated 11 out of 18 core competencies as "very important" or "extremely important." More than 80% of the programs currently emphasize 2 of 9 noncore competencies (i.e., competency to ( 1) develop and write grant proposals, and ( 2) assess evidence for causality on the basis of different causal inference concepts). "Big data" is the most frequently cited topic currently lacking in doctoral curricula. Information gleaned from previous efforts and this survey should prompt a dialog among relevant stakeholders to establish a cohesive set of core competencies for doctoral training in epidemiology.