Abstract

Neighborhood/built environments (the areas in which people live, work, and play) and community context as social and environmental determinants of health have gained prominence with the changing care needs of people living with cystic fibrosis (CF) lung disease. Select measures of these social and environmental determinants of health (referred to as “geomarkers”) are also predictors of rapid decline, which is clinically defined as a prolonged drop in lung function relative to patient and/or center-level norms. The extent to which hyper-localization (defined as increasing the spatiotemporal precision of social and environmental exposures) aids in prediction of rapid decline remains unclear. Linear mixed effects (LME) models have been historically used for predicting rapid decline in CF, but there are few options to properly incorporate spatial correlation and induce simultaneous variable selection. The objective of this work is to develop a Bayesian spatial linear mixed effects model to predict rapid decline using geomarkers.

We describe an application of the proposed model for predicting rapid lung function decline (measured as FEV1 % predicted/year) in a Midwest U.S. cohort of pediatric CF patients aged 6-20 years. We consider a breadth of demographic and clinical characteristics alongside geomarkers, which focus on neighborhood/built environments and social/community context. Our innovative Bayesian model uses a “spike and slab” prior, accounting for spatial correlation based on ZIP code distances. We evaluate model fits and prediction accuracies. Our proposed model results in improved model fit and predictive accuracy, compared to other Bayesian and frequentist LME models with different spatial correlation assumptions. We describe how a combination of demographic, clinical, and geomarker variables can be selected as optimal predictors based on the posterior inclusion probabilities and Bayesian false discovery rate controlling rule. Our findings suggest that incorporating spatiotemporal effects and geomarkers results in an improved prediction tool. We discuss how predicting the timing and extent of rapid lung function decline can help clinicians to proactively adjust treatment plans and improve patient outcomes.