Health Data Equity

In the field of digital public health, health data equity has become a critical focus, aiming to address fairness in access, representation, and utilization of health data across diverse populations. Despite technological advances, disparities in data collection and analysis persist, often marginalizing underserved communities. Challenges revolve around ensuring inclusivity in data sources and eliminating biases that can skew health outcomes and interventions. Understanding how different populations are represented in health data is crucial to developing equitable health interventions. While significant studies have highlighted the importance of data equity, much remains unexplored, particularly the methods for rectifying data imbalance and fostering inclusivity in digital health technologies.

This Research Topic aims to explore and advance the understanding of health data equity within digital public health. The goal is to investigate innovative strategies and tools that promote equitable data practices, ensuring that all populations, regardless of demographic or geographic background, are accurately represented and benefit from public health initiatives. We seek research addressing questions such as: How can biases in health data collection be identified and reduced? What strategies can ensure fair representation of minority groups? Moreover, research that tests hypotheses on the impact of equitable data practices on health outcomes is encouraged.

To gather further insights on health data equity, we welcome articles addressing, but not limited to, the following themes:

• Frameworks and policies promoting equitable health data access and representation
• Addressing and mitigating biases in health data analytics
• Technological innovations for inclusive data collection and utilization
• The role of AI in promoting health data equity
• Case studies showcasing successful equity-focused digital health interventions.

Submissions should provide detailed explorations and propose solutions to bridge data gaps, ultimately enhancing equitable public health interventions across populations.

This Research Topic was launched in collaboration with the 10th Digital Public Health Conference, a world-leading annual interdisciplinary event on research and innovation in digital public health, organized by University College London. We welcome submissions from speakers, attendees and the broader research community.

The Topic Editors disclose that they serve as Theme Leads at Data Science for Health, a non-profit organization. This relationship is unrelated to the research topic and does not influence the objectivity or editorial decisions associated with this Research Topic.