Munung Nchangwi Syntia, Royal Charmaine D, de Kock Carmen, Awandare Gordon, Nembaware Victoria, Nguefack Seraphin, Treadwell Marsha, Wonkam Ambroise
Hastings Cent Rep. 2024 Dec;54 Suppl 2:S84-S92. doi: 10.1002/hast.4933.
Effectively addressing ethical issues in precision medicine research in Africa requires a holistic social contract that integrates biomedical knowledge with local cultural values and Indigenous knowledge systems. Drawing on African epistemologies such as ubuntu and ujamaa and on our collective experiences in genomics and big data research for sickle cell disease, hearing impairment, and fragile X syndrome and the project Public Understanding of Big Data in Genomics Medicine in Africa, we envision a transformative shift in health research data governance in Africa that could help create a sense of shared responsibility between all stakeholders in genomics and data-driven health research in Africa. This shift includes proposing a social contract for genomics and data science in health research that is grounded in African communitarianism such as solidarity, shared decision-making, and reciprocity. We make several recommendations for a social contract for genomics and data science in health, including the coproduction of genomics knowledge with study communities, power sharing between stakeholders, public education on the ethical and social implications of genetics and data science, benefit sharing, giving voice to data subjects through dynamic consent, and democratizing data access to allow wide access by all research stakeholders. Achieving this would require adopting participatory approaches to genomics and data governance.
要有效解决非洲精准医学研究中的伦理问题，需要一份全面的社会契约，将生物医学知识与当地文化价值观和本土知识体系结合起来。借鉴诸如乌班图和乌贾马等非洲认识论，以及我们在镰状细胞病、听力障碍和脆性X综合征的基因组学和大数据研究以及非洲基因组医学大数据公众理解项目中的集体经验，我们设想非洲卫生研究数据治理发生变革性转变，这有助于在非洲基因组学和数据驱动的卫生研究的所有利益相关者之间营造一种共同责任感。这种转变包括为卫生研究中的基因组学和数据科学提出一份基于非洲社群主义（如团结、共同决策和互惠）的社会契约。我们就卫生领域基因组学和数据科学的社会契约提出了若干建议，包括与研究社区共同生成基因组学知识、利益相关者之间分享权力、对遗传学和数据科学的伦理和社会影响进行公众教育、利益分享、通过动态同意让数据主体发声，以及使数据获取民主化，以便所有研究利益相关者都能广泛获取。实现这一目标需要在基因组学和数据治理中采用参与式方法。
