Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework

Area of Science:

• Environmental health
• Data science
• Epidemiology

Background:

• Climate change exacerbates environmental factors, increasing the global disease burden, particularly for vulnerable populations like those with rare diseases.
• Developing risk prediction models for diseases with unknown etiology, such as vasculitis, requires advanced data linkage methods to explore potential environmental triggers.
• Existing data linkage methods are often inadequate for integrating complex, dynamic environmental and health datasets.

Purpose of the Study:

• To introduce the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF) for linking environmental and health data.
• To evaluate SERDIF's usability and effectiveness in facilitating research on climate-related health hazards and rare diseases.
• To demonstrate SERDIF's versatility in analyzing environmental factors across different health contexts, including non-rare disease cohorts.

Main Methods:

• Development of the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF) with a focus on user-friendliness and FAIR compliance.
• Evaluation of SERDIF with researchers studying climate-related health hazards of vasculitis across multiple European countries.
• Application of SERDIF by epidemiologists to investigate environmental factors within a pregnancy cohort in Lombardy.

Main Results:

• Consistent improvements in usability metrics were observed during the evaluation, confirming SERDIF's effectiveness.
• SERDIF successfully facilitated the linkage of complex environmental and health datasets for researchers.
• The framework demonstrated versatility by enabling environmental factor analysis in a pregnancy cohort, extending its utility beyond rare diseases.

Conclusions:

• SERDIF provides a novel, user-friendly, and FAIR-compliant solution for environment-health data linkage.
• The framework empowers researchers and epidemiologists to analyze data for mitigating health risks associated with climate change.
• SERDIF enhances the development of risk prediction models for rare and other diseases influenced by environmental factors.