Unveiling the Impact of Social and Environmental Determinants of Health on Lung Function Decline in Cystic Fibrosis through Data Integration using the US Registry

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.