Use of novel cancer staging model to analyze healthcare costs of commercially insured patients with colorectal cancer.

Abstract

35 Background: Earlier colorectal cancer diagnosis can improve patient outcomes, but the payer cost impact of earlier stage at cancer diagnosis is not well understood because stage information is not directly available in administrative claims data. This data gap impairs researchers’ ability to fully evaluate the financial impact of earlier neoplasia detection resulting from colorectal cancer screening. In prior work, we used SEER-Medicare data to develop machine learning algorithms to assign AJCC stages to colon (CC) and rectal (RC) cancer patients using treatment flags derived from claims data. These algorithms were 83% (CC) and 69% (RC) accurate at assigning incident cancer patients to their SEER-recorded AJCC stage. This new work applied those validated algorithms to commercial claims data to study the healthcare costs by stage of patients identified with incident CC or RC. Methods: Newly diagnosed CC and RC patients were identified in Milliman’s 2019-2020 Consolidated Health Cost Guidelines Sources Data for commercially insured patients. Patients were assigned a cancer stage using a claims-based predictive multinomial logistic regression model (R Statistical Software - v4.1.2 R Core Team 2021; nnet package - Venables and Ripley 2002). Allowed medical and pharmacy costs were summarized by month, starting from each patient’s initial claim with a cancer diagnosis through the end of the patient’s enrollment or through September 2022. Patients with each cancer type were then grouped by stage, and mean cumulative costs at 12-, 24-, and 36-months were calculated. Results: CC patients with stage 4 disease incur costs over 4x greater than those with stages 2B or lower and 1.7x greater than patients with stages 2B/3 during the first year after diagnosis. Cost differentials by stage are smaller among patients with RC. RC patients with stage 4 disease incur costs 2.2x greater than those with stages 2B or lower and 1.3x greater than patients with stages 2B/3 over the first year after diagnosis. For both CC and RC, substantial differences in cost persist over a 3-year period post-diagnosis. Conclusions: We applied a novel cancer staging model to commercial claims to estimate patients’ healthcare costs by stage at diagnosis. CC and RC patients’ costs increase substantially as their stages at diagnosis increase. In addition to improving patient outcomes, diagnosis of CC and RC at earlier stages among commercial patients could reduce treatment costs. [Table: see text]