Integrating US Registry data to reveal social and environmental determinants impacting lung function decline in cystic fibrosis patients

Integrating diverse data resources offers a comprehensive view of patient information, enhancing healthcare decision-making. In cystic fibrosis (CF), a genetic disorder affecting the lungs, biomarkers tracking lung function decline are critical predictors of disease progression. Research shows that incorporating location-specific social and environmental determinants of health improves prognostic accuracy. To investigate variability in lung function decline among CF patients, we integrate data from the US Cystic Fibrosis Foundation Patient Registry with social and environmental health information, focusing on the relationship between lung function and the community deprivation index. Our methodology employs advanced multivariate mixed-effects models, enabling simultaneous analysis of multiple longitudinal outcomes while accommodating diverse functional forms. This approach provides a nuanced 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 association between lung function and the area under the deprivation index curve across all states. These findings emphasize the value of integrating environmental and socioeconomic markers into clinical decision-making strategies. By accounting for these factors, healthcare providers can gain deeper insights into disease progression and develop more targeted intervention strategies.