Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Nuffield Department of Population Health, University of Oxford, Oxford, UK.
BMJ Glob Health. 2023 Mar;8(3). doi: 10.1136/bmjgh-2022-010157.
Despite growing consensus on the need for equitable data sharing, there has been very limited discussion about what this should entail in practice. As a matter of procedural fairness and epistemic justice, the perspectives of low-income and middle-income country (LMIC) stakeholders must inform concepts of equitable health research data sharing. This paper investigates published perspectives in relation to how equitable data sharing in global health research should be understood.
We undertook a scoping review (2015 onwards) of the literature on LMIC stakeholders' experiences and perspectives of data sharing in global health research and thematically analysed the 26 articles included in the review.
We report LMIC stakeholders' published views on how current data sharing mandates may exacerbate inequities, what structural changes are required in order to create an environment conducive to equitable data sharing and what should comprise equitable data sharing in global health research.
In light of our findings, we conclude that data sharing under existing mandates to share data (with minimal restrictions) risks perpetuating a neocolonial dynamic. To achieve equitable data sharing, adopting best practices in data sharing is necessary but insufficient. Structural inequalities in global health research must also be addressed. It is thus imperative that the structural changes needed to ensure equitable data sharing are incorporated into the broader dialogue on global health research.
尽管人们越来越一致认为需要公平的数据共享,但实际上对于这应该包括哪些内容,讨论非常有限。作为程序公平和认识论正义的问题,低收入和中等收入国家(LMIC)利益相关者的观点必须为公平的健康研究数据共享的概念提供信息。本文研究了已发表的观点,以了解全球健康研究中的公平数据共享应该如何理解。
我们对有关 LMIC 利益相关者在全球健康研究中数据共享经验和观点的文献进行了范围界定审查(2015 年以后),并对综述中纳入的 26 篇文章进行了主题分析。
我们报告了 LMIC 利益相关者对当前数据共享授权可能加剧不平等的看法,为了创造有利于公平数据共享的环境需要进行哪些结构性变革,以及全球健康研究中的公平数据共享应包括哪些内容。
根据我们的发现,我们得出的结论是,在现有的数据共享授权下(有最小的限制)共享数据存在使新殖民主义动态永久化的风险。为了实现公平的数据共享,采用数据共享的最佳实践是必要的,但还不够。全球健康研究中的结构性不平等也必须得到解决。因此,必须将确保公平数据共享所需的结构性变革纳入全球健康研究的更广泛对话中。