Ethical issues in public health surveillance: a systematic qualitative review
Open accessAbstract
BACKGROUND: Public health surveillance is not ethically neutral and yet, ethics guidance and training for surveillance programmes is sparse. Development of ethics guidance should be based on comprehensive and transparently derived overviews of ethical issues and arguments. However, existing overviews on surveillance ethics are limited in scope and in how transparently they derived their results. Our objective was accordingly to provide an overview of ethical issues in public health surveillance; in addition, to list the arguments put forward with regards to arguably the most contested issue in surveillance, that is whether to obtain informed consent. METHODS: Ethical issues were defined based on principlism. We assumed an ethical issue to arise in surveillance when a relevant normative principle is not adequately considered or two principles come into conflict. We searched Pubmed and Google Books for relevant publications. We analysed and synthesized the data using qualitative content analysis. RESULTS: Our search strategy retrieved 525 references of which 83 were included in the analysis. We identified 86 distinct ethical issues arising in the different phases of the surveillance life-cycle. We further identified 20 distinct conditions that make it more or less justifiable to forego informed consent procedures. CONCLUSIONS: This is the first systematic qualitative review of ethical issues in public health surveillance resulting in a comprehensive ethics matrix that can inform guidelines, reports, strategy papers, and educational material and raise awareness among practitioners.
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Inherent bias in medical input data, such as class imbalance, can be reflected in model outputs and can influence model explanations.
“In addition, the inherent bias in the input (medical) data (e.g., class imbalance) can be reflected in the model's outputs i.e., model might prefer a specific class as compared to other classes and this bias might influence the explanations of the model [170].”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [170]
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- Authors
- Klingler, C., Silva, D. S., Schuermann, C., Reis, A. A., Saxena, A., & Strech, D.
- Affiliations
- Institut für Ethik, Geschichte und Theorie der Medizin (DE)
- Simon Fraser University (CA)
- Medizinische Hochschule Hannover (DE)
- World Health Organization (CH)
- Corresponding author
- Klingler, Corinna
- Venue
- BMC Public Health
- Published
- 2017-04-04
- Volume
- 17
- Issue
- 1
- Pages
- 295-295
- ISSN
- 1471-2458 (ISSN-L)
- PMC
- PMC5381137
- OpenAlex
- W2605151798
- Cited by
- 84
- References
- 48
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 5 · 2023: 13 · 2022: 10 · 2021: 14 · 2020: 11 · 2019: 5 · 2018: 6
- FWCI
- 5.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Public Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
- Funders
- Carlo Schmid Programme for Internships in International Organisations and EU-Institutions
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Health Sciences › Health Professions › General Health Professions › Public Health Policies and Education
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- disease surveillance, ethics of surveillance, informed consent, principlism, ethical conflicts
- MeSH terms
- Humans, Ethics, Research, Public Health Surveillance
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