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Group-Based Trajectory Modeling of Healthcare Financial Charges in Inflammatory Bowel Disease: A Comprehensive Phenotype

Abstract

Objectives: Inflammatory bowel disease (IBD) is a heterogeneous group of chronic inflammatory gastrointestinal conditions with variable disease courses often requiring significant healthcare expenditures. We aimed to identify disease trajectory patterns based on longitudinal financial expenditures and to assess the association of classic disease activity parameters with financial charges.

Methods: This was an analysis of a consented, prospective, natural history IBD registry (2009-2013) from a tertiary IBD center of 2,203 patients and their associated medical charges excluding pharmacy expenses. We applied group-based trajectory modeling to longitudinal healthcare financial charges to determine patterns of charges. We assessed the association between charge patterns and disease activity, quality of life, healthcare utilization, and medication requirement.

Results: The final model included 1,600 IBD patients with 5-year charges. We identified six distinct trajectories over the study period. Consistently High charges were associated with Crohn's disease (66.0% Consistently High patients, P<0.01), perianal involvement (22.6%, P<0.01), ulcerative colitis extent (89.7% extensive, P=0.01), prior IBD surgery (52.5%, P<0.01), and depression/anxiety (36.2%, P<0.01). Compared with other trajectories, Consistently High charges had higher 5-year disease activity indices (Harvey-Bradshaw P<0.01; ulcerative colitis activity index P<0.01), elevated C-reactive protein rates (72.3%, P<0.01), IBD surgery (64.5%, P<0.01), hospitalization (97.2%, P<0.01), corticosteroid (70.9%, P<0.01) and antitumor necrosis factor requirement (50.4%, P<0.01), and worse quality of life (P<0.01). Annual trends in parameters were reflected in temporal changes in financial charges. The majority of financial burden stemmed from inpatient care.

Conclusions: Healthcare financial charges represent a novel phenotype in IBD that reflect trends in classic disease activity parameters and allow for subgroup identification of temporal disease trajectories.

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