Bridging health equity and technology: Integrating social determinants of health into real-time surveillance systems

Bridging this gap between health equity and technological tools by integrating Social Determinants of Health (SDOH) into real-time population health surveillance methods is becoming essential to managing the population's health responsively and inclusively.. This conceptual overview dwells on the theoretical backgrounds, existing practices, and prospects of how SDOH can be embedded in dynamic surveillance systems in public health. Based on important frameworks, such as the WHO SDOH model, the Dahlgren and Whitehead model, syndemic theory, systems thinking, and equity-oriented surveillance, this study emphasizes how health outcomes are determined by what is captured concerning the socio-economic and environmental contexts. Although the introduction of real-time surveillance tools like electronic health records (EHRs), mobile health applications and instruments, geospatial information systems (GIS), and artificial intelligence (AI) have enhanced the speed and scope of data capture, these technologies have mostly been clinically oriented. The review also outlines key obstacles to SDOH integration. Data fragmentation, insufficient standardized indicators, privacy issues, and the inability to deal with real-time data are the key obstacles to SDOH integration. It also points at potential initiatives like cross-sector collaborations of data-sharing, implementing health equity impact indicators, and predictive analytics that can be used to detect vulnerable populations. The paper's conclusion will propose creating standardized data models, ethical data governance frameworks, more investment in public health informatics, and pilot projects to demonstrate the practicability of various models. Finally, this paper argues in favor of a paradigm shift in how surveillance is conducted in public health surveillance, which is not just about monitoring the incidence of disease but also about questioning and dealing with the social determinants of health. This form of integration can be transformative in terms of more inclusive, equitable, and proactive public health systems.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-30
DOI
https://doi.org/10.5281/zenodo.17265159
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
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article

Bridging health equity and technology: Integrating social determinants of health into real-time surveillance systems

Zenodo (CERN European Organization for Nuclear Research)
Genomics and Phylogenetic Studies
article

Bridging health equity and technology: Integrating social determinants of health into real-time surveillance systems

article en

Abstract

Bridging this gap between health equity and technological tools by integrating Social Determinants of Health (SDOH) into real-time population health surveillance methods is becoming essential to managing the population's health responsively and inclusively.. This conceptual overview dwells on the theoretical backgrounds, existing practices, and prospects of how SDOH can be embedded in dynamic surveillance systems in public health. Based on important frameworks, such as the WHO SDOH model, the Dahlgren and Whitehead model, syndemic theory, systems thinking, and equity-oriented surveillance, this study emphasizes how health outcomes are determined by what is captured concerning the socio-economic and environmental contexts. Although the introduction of real-time surveillance tools like electronic health records (EHRs), mobile health applications and instruments, geospatial information systems (GIS), and artificial intelligence (AI) have enhanced the speed and scope of data capture, these technologies have mostly been clinically oriented. The review also outlines key obstacles to SDOH integration. Data fragmentation, insufficient standardized indicators, privacy issues, and the inability to deal with real-time data are the key obstacles to SDOH integration. It also points at potential initiatives like cross-sector collaborations of data-sharing, implementing health equity impact indicators, and predictive analytics that can be used to detect vulnerable populations. The paper's conclusion will propose creating standardized data models, ethical data governance frameworks, more investment in public health informatics, and pilot projects to demonstrate the practicability of various models. Finally, this paper argues in favor of a paradigm shift in how surveillance is conducted in public health surveillance, which is not just about monitoring the incidence of disease but also about questioning and dealing with the social determinants of health. This form of integration can be transformative in terms of more inclusive, equitable, and proactive public health systems.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 47%
Genomics and Phylogenetic Studies
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