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Ethical, Legal, and Regulatory Issues for the Implementation of Omics-Based Risk Prediction of Women's Cancer: Points to Consider.
Public Health Genomics 2018 September 18
BACKGROUND AND OBJECTIVE: Advances in omics open new opportunities for cancer risk prediction and risk-based screening interventions. However, implementation of risk prediction in clinical practice may impact the ethical, legal, and regulatory aspects of current cancer screening programs. In order to support decision-making, we analyzed the ethical, legal, and regulatory issues and developed a set of Points to Consider to support management of these issues.
METHODS: We analyzed the legal and policy frameworks applicable to breast and cervical cancer screening programs in 7 European countries. We identified the most relevant issues to be considered, and we developed considerations for their management, based on the literature, the legal and policy frameworks, and our experience with similar issues.
RESULTS: The considerations focus on five topics: (A) health services planning, (B) information and invitation, (C) consent and data/sample collection, (D) risk calculation and communication of results, and (E) storage of data and residual samples.
CONCLUSION: Current frameworks might not be adequate to implement a risk prediction approach using omics factors due to the different characteristics of such approaches.
METHODS: We analyzed the legal and policy frameworks applicable to breast and cervical cancer screening programs in 7 European countries. We identified the most relevant issues to be considered, and we developed considerations for their management, based on the literature, the legal and policy frameworks, and our experience with similar issues.
RESULTS: The considerations focus on five topics: (A) health services planning, (B) information and invitation, (C) consent and data/sample collection, (D) risk calculation and communication of results, and (E) storage of data and residual samples.
CONCLUSION: Current frameworks might not be adequate to implement a risk prediction approach using omics factors due to the different characteristics of such approaches.
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