Department of International Health, Johns Hopkins School of Public Health, Baltimore, MD, USA.
MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada.
Curr HIV/AIDS Rep. 2024 Aug;21(4):208-219. doi: 10.1007/s11904-024-00702-3. Epub 2024 Jun 25.
Big Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response.
Social, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.
大数据科学可用于在国家艾滋病规划的背景下切实指导资源分配,并为干预措施确定优先事项。在本次综述中,我们讨论了将大数据科学建立在公平和社会正义原则基础上的重要性,以优化全球艾滋病应对的效率和效果。
在艾滋病研究背景下,已经确定了大数据科学的社会、伦理和法律方面的考虑因素。然而,减轻这些挑战的努力有限。其后果包括艾滋病领域内的学科隔阂、社区缺乏有意义的参与和所有权,以及对分析的潜在误解或滥用,这可能会进一步加剧健康不平等。大数据科学可以通过帮助识别以前未发现或研究不足的艾滋病感染和传播途径中的差距,包括对健康结果和相关合并症的影响,从而支持艾滋病应对工作。然而,如果没有一个公平的指导框架,以及通过平衡的伙伴关系与社区进行有意义的合作,仅仅依赖大数据可能会继续加剧边缘化人群内部和之间的不平等。