Integrating diverse data sources offers a comprehensive view of patient health and holds potential for improving clinical decision-making. In Cystic Fibrosis (CF), which is a genetic disorder primarily affecting the lungs, biomarkers that track lung function decline such as FEV1 serve as important predictors for assessing disease progression. Prior research has shown that incorporating social and environmental determinants of health improves prognostic accuracy. To investigate the lung function decline among individuals with CF, we integrate data from the U.S. Cystic Fibrosis Foundation Patient Registry with social and environmental health information. Our analysis focuses on the relationship between lung function and the deprivation index, a composite measure of socioeconomic status. We used advanced multivariate mixed-effects models, which allow for the joint modelling of multiple longitudinal outcomes with flexible functional forms. This methodology provides an understanding of interrelationships among outcomes, addressing the complexities of dynamic health data. We examine whether this relationship varies with patients' exposure duration to high-deprivation areas, analyzing data across time and within individual US states. Results show a strong relation between lung function and the area under the deprivation index curve across all states. These results underscore the importance of integrating social and environmental determinants of health into clinical models of disease progression. By accounting for broader contextual factors, healthcare providers can gain deeper insights into disease trajectories and design more targeted intervention strategies.

翻译：整合多样化数据源能够全面呈现患者健康状况，并为改善临床决策提供潜力。囊性纤维化是一种主要影响肺部的遗传性疾病，追踪肺功能下降的生物标志物（如FEV1）是评估疾病进展的重要预测指标。先前研究表明，纳入社会与环境健康决定因素可提高预后准确性。为研究囊性纤维化患者的肺功能下降情况，我们将美国囊性纤维化基金会患者注册数据与社会环境健康信息进行整合。我们的分析聚焦于肺功能与剥夺指数（一种社会经济地位综合衡量指标）之间的关系。我们采用先进的多变量混合效应模型，该模型允许对具有灵活函数形式的多个纵向结局进行联合建模。该方法能够理解结局指标间的相互关系，应对动态健康数据的复杂性。我们通过分析跨时间和美国各州内部的数据，检验这种关系是否随患者暴露于高剥夺区域的时间长度而变化。结果显示，在所有州份中，肺功能与剥夺指数曲线下面积均存在显著关联。这些结果强调了将社会与环境健康决定因素整合到疾病进展临床模型中的重要性。通过考量更广泛的情境因素，医疗保健提供者能够更深入地理解疾病发展轨迹，并设计更具针对性的干预策略。