Breaking Barriers with Data Equity: The Essential Role of Data Disaggregation in Achieving Health Equity.

IF 21.4 1区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Annual Review of Public Health Pub Date : 2025-01-30 DOI:10.1146/annurev-publhealth-072523-093838

Ninez A Ponce, Tara Becker, Riti Shimkhada

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Abstract

Achieving health equity necessitates high-quality data to address disparities that have remained stagnant or even worsened over time despite public health interventions. Data disaggregation, the breakdown of data into detailed subcategories, is crucial in health disparities research. It reveals and contextualizes hidden trends and patterns about marginalized populations and guides resource allocation and program development for specific needs in these populations. Data disaggregation underpins data equity, which uses community engagement to democratize data and develop better solutions for communities. Years of research on disaggregation show that researchers must collaborate closely with communities for adequate representation. However, despite generally positive support for this approach in health disparities research, data disaggregation faces methodological and political challenges. This review offers a framework for understanding data disaggregation in the context of data equity and highlights critical aspects of implementation, including challenges, opportunities, and recent policy and community-based efforts to address hurdles.

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实现健康公平需要高质量的数据，以解决尽管采取了公共卫生干预措施，但长期停滞不前甚至恶化的差异问题。数据分类，即把数据细分为详细的子类别，在健康差异研究中至关重要。它揭示了边缘化人群的隐性趋势和模式，并将其具体化，指导资源分配和项目开发，以满足这些人群的特定需求。数据分类是数据公平的基础，它利用社区参与来实现数据民主化，并为社区制定更好的解决方案。多年的分类研究表明，研究人员必须与社区密切合作，以获得充分的代表性。然而，尽管这种方法在健康差异研究中得到了普遍积极的支持，数据分类仍面临着方法论和政治上的挑战。本综述为在数据公平的背景下理解数据分类提供了一个框架，并强调了实施的关键方面，包括挑战、机遇以及近期政策和社区为解决障碍所做的努力。

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来源期刊

Annual Review of Public Health 医学-公共卫生、环境卫生与职业卫生

CiteScore

26.60

自引率

1.40%

发文量

审稿时长

>12 weeks

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