Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF). SERDIF was evaluated with researchers studying climate-related health hazards of vasculitis disease activity across European countries (N_P1 = 10, N_P2 = 17, N_P3 = 23). Usability metrics consistently improved, indicating SERDIF’s effectiveness in linking complex environmental and health datasets. Furthermore, SERDIF-enabled epidemiologists to study environmental factors in a pregnancy cohort in Lombardy, showcasing its versatility beyond rare diseases. This framework offers for the first time a user-friendly, FAIR-compliant design for environment-health data linkage with export capabilities enabling data analysis to mitigate health risks posed by climate change.

气候变化加剧的环境因素极大地加重了全球疾病负担，对弱势群体（例如患有罕见疾病的人）产生了不成比例的影响。研究人员需要创新的动态数据链接方法来开发风险预测模型，特别是对于病因不明但潜在环境触发因素的血管炎等疾病。作为回应，我们提出了语义环境和罕见疾病数据集成框架（SERDIF）。 SERDIF 与研究欧洲各国血管炎疾病活动与气候相关的健康危害的研究人员一起进行了评估（ N _P1 = 10、 N _P2 = 17、 N _P3 = 23）。可用性指标持续改进，表明 SERDIF 在链接复杂的环境和健康数据集方面的有效性。此外，SERDIF 使流行病学家能够研究伦巴第妊娠队列中的环境因素，展示其在罕见疾病之外的多功能性。该框架首次为环境健康数据链接提供了用户友好、符合公平标准的设计，并具有导出功能，使数据分析能够减轻气候变化带来的健康风险。