Current ethical and social issues in epidemiology

Consideration and Disclosure of Group Risks in Genomics and Other Data-Centric Research: Does the Common Rule Need Revision?

Harms and risks to groups and third-parties can be significant in the context of research, particularly in data-centric studies involving genomic, artificial intelligence, and/or machine learning technologies. This article explores whether and how United States federal regulations should be adapted to better align with current ethical thinking and protect group interests. Three aspects of the Common Rule deserve attention and reconsideration with respect to group interests: institutional review board (IRB) assessment of the risks/benefits of research; disclosure requirements in the informed consent process; and criteria for waivers of informed consent. In accordance with respect for persons and communities, investigators and IRBs should systematically consider potential group harm when designing and reviewing protocols, respectively. Research participants should be informed about any potential group harm in the consent process. We call for additional public discussion, empirical research, and normative analysis on these issues to determine the right regulatory and policy path forward.

Epidemiological methods in transition: Minimizing biases in classical and digital approaches

Epidemiology and Public Health have increasingly relied on structured and unstructured data, collected inside and outside of typical health systems, to study, identify, and mitigate diseases at the population level. Focusing on infectious diseases, we review the state of Digital Epidemiology at the beginning of 2020 and how it changed after the COVID-19 pandemic, in both nature and breadth. We argue that Epidemiology's progressive use of data generated outside of clinical and public health systems creates several technical challenges, particularly in carrying specific biases that are almost impossible to correct for a priori. Using a statistical perspective, we discuss how a definition of Digital Epidemiology that emphasizes "data-type" instead of "data-source," may be more operationally useful, by clarifying key methodological differences and gaps. Therefore, we briefly describe some of the possible biases arising from varied collection methods and sources, and offer some recommendations to better explore the potential of Digital Epidemiology, particularly on how to help reduce inequity.

Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic

Introduction

The COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy.

Methods

A qualitative semi-structured methodology was conducted through interviews with experts in artificial intelligence, data science, epidemiology, and/or policy making who utilized non-traditional data in Spain or Italy during the pandemic. Questions focused on barriers and facilitators to data use, as well as opportunities for improving utility and uptake within public health. Interviews were transcribed, coded, and analyzed using the framework analysis method.

Results

Non-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity.

Discussion

While the utility of non-traditional data was demonstrated during the pandemic, opportunities exist to enhance its impact. Challenges reveal a need for data governance frameworks to guide practices and policies of use. Despite the perceived benefit of collaborations and improved data infrastructure, efforts are needed to strengthen and sustain them beyond the pandemic. Lessons from these findings can guide research institutions, multilateral organizations, governments, and public health authorities in optimizing the use of non-traditional data.

Global matters of epidemiology and the ethical challenges of addressing the health of populations

PURPOSE

The American College of Epidemiology (ACE) held its 2022 Annual Meeting, September 8-11, with a conference theme of 'Pandemic of Misinformation: Building Trust in Epidemiology'. The ACE Ethics Committee hosted a symposium session in recognition of the global spotlight placed on epidemiology and public health due to the COVID-19 crisis. The ACE Ethics Committee invited previous Chairs of the Ethics Committee and current President of the International Epidemiological Association to present at the symposium session. This paper aims to highlight the ethical challenges presented during the symposium session.

METHODS

Three speakers with diverse backgrounds representing expertize from the fields of ethics, epidemiology, public health, clinical trials, pharmacoepidemiology, statistics, law, and public policy, covering perspectives from the U.S., Europe, and Southeast Asia were selected to present on the ethical challenges in epidemiology and public health applying a global theme. Dr. D. Weed presented on 'Causation, Epidemiology and Ethics'; Dr. C.M. Pandey presented on the 'Ethical Challenges in the Practice of Digital Epidemiology'; and Dr. J. Acquavella presented on 'Departures from Scientific Objectivity: A Cause of Eroding Trust in Epidemiology.'

RESULTS

The collective goal to improve the public's health was a mutually shared theme across the three distinct areas. We highlight the common ethical guidance and principle-based approaches that have served epidemiology and public health in framing and critical analysis of novel challenges, including autonomy, beneficence, justice, scientific integrity, duties to the profession and community, and developing and maintaining public trust; however, gaps remain in how best to address health inequalities and the novel emergence and pervasiveness of misinformation and disinformation that have impacted the health of the global community. We introduce an ethical framework of translational bioethics that places considerations of the social determinants of health at the forefront.

CONCLUSIONS

The COVID-19 pandemic required an expedited public health response and, at the same time, placed the profession of epidemiology and public health, its system, and structures, under the microscope like never before. This article illustrates that revisiting our foundations in research and practice and orienting contemporary challenges using an ethical lens can assist in identifying and furthering the health of populations globally.

Identifying the most important facilitators of open research data sharing and reuse in Epidemiology: A mixed-methods study

To understand how open research data sharing and reuse can be further improved in the field of Epidemiology, this study explores the facilitating role that infrastructural and institutional arrangements play in this research discipline. It addresses two research questions: 1) What influence do infrastructural and institutional arrangements have on open research data sharing and reuse practices in the field of Epidemiology? And 2) how could infrastructural and institutional instruments used in Epidemiology potentially be useful to other research disciplines? First, based on a systematic literature review, a conceptual framework of infrastructural and institutional instruments for open research data facilitation is developed. Second, the conceptual framework is applied in interviews with Epidemiology researchers. The interviews show that two infrastructural and institutional instruments have a very high influence on open research data sharing and reuse practices in the field of Epidemiology, namely (a) access to a powerful search engine that meets open data search needs and (b) support by data stewards and data managers. Third, infrastructural and institutional instruments with a medium, high, or very high influence were discussed in a research workshop involving data stewards and research data officers from different research fields. This workshop suggests that none of the influential instruments identified in the interviews are specific to Epidemiology. Some of our findings thus seem to apply to multiple other disciplines. This study contributes to Science by identifying field-specific facilitators and challenges for open research data in Epidemiology, while at the same time revealing that none of the identified influential infrastructural and institutional instruments were specific to this field. Practically, this implies that open data infrastructure developers, policymakers, and research funding organizations may apply certain infrastructural and institutional arrangements to multiple research disciplines to facilitate and enhance open research data sharing and reuse.