Global matters of epidemiology and the ethical challenges of addressing the health of populations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.210 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.104 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.018 | 0.033 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".