Islam Mozharul, Valiani Arafaat A, Datta Ranjan, Chowdhury Mohammad, Turin Tanvir C
Department of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Department of Sociology, Istanbul Sabahattin Zaim University, İstanbul, Türkiye.
Camb Q Healthc Ethics. 2024 Apr 3:1-11. doi: 10.1017/S096318012400015X.
Recent studies highlight the need for ethical and equitable digital health research that protects the rights and interests of racialized communities. We argue for practices in digital health that promote data self-determination for these communities, especially in data collection and management. We suggest that researchers partner with racialized communities to curate data that reflects their wellness understandings and health priorities, and respects their consent over data use for policy and other outcomes. These data governance approach honors and builds on Indigenous Data Sovereignty (IDS) decolonial scholarship by Indigenous and non-indigenous researchers and its adaptations to health research involving racialized communities from former European colonies in the global South. We discuss strategies to practice equity, diversity, inclusion, accessibility and decolonization (EDIAD) principles in digital health. We draw upon and adapt the concept of Precision Health Equity (PHE) to emphasize models of data sharing that are co-defined by racialized communities and researchers, and stress their shared governance and stewardship of data that is generated from digital health research. This paper contributes to an emerging research on equity issues in digital health and reducing health, institutional, and technological disparities. It also promotes the self-determination of racialized peoples through ethical data management.
近期研究凸显了开展符合伦理且公平的数字健康研究的必要性，此类研究旨在保护种族化社区的权益。我们主张在数字健康领域采取相关做法，以促进这些社区的数据自决权，尤其是在数据收集和管理方面。我们建议研究人员与种族化社区合作，精心整理能够反映其健康认知和健康优先事项的数据，并尊重他们对数据用于政策及其他成果的同意权。这种数据治理方法借鉴并基于本土和非本土研究人员的去殖民化研究成果——本土数据主权（IDS），以及其在涉及全球南方前欧洲殖民地种族化社区的健康研究中的应用。我们讨论了在数字健康领域践行公平、多样性、包容性、可及性和去殖民化（EDIAD）原则的策略。我们借鉴并调整了精准健康公平（PHE）的概念，以强调由种族化社区和研究人员共同定义的数据共享模式，并强调他们对数字健康研究产生的数据的共同治理和管理。本文为数字健康领域中关于公平问题以及减少健康、机构和技术差距的新兴研究做出了贡献。它还通过符合伦理的数据管理促进了种族化群体的自决权。
