Decomposing Variations on Cluster Level for Binary Outcomes in Application to Cancer Care Disparity Studies.

Abstract

Objective: To develop a method to decompose the observed variance of binary outcomes (proportions) aggregated by regional clusters to determine targets for quality improvement efforts to reduce regional variations.

Data sources and study setting: Data from the 2018 linkage of the Surveillance, Epidemiology, and End Results-Medicare database.

Study design: We developed a method to decompose the observed regional-level variance into four attributions: random, patients' characteristics, regional cluster, and unexplained. To demonstrate the efficacy of the method, we conducted a series of numerical studies. We applied this method to our cohort to analyze endocrine therapy receipt 3-5 years after diagnosis, using health service area (HSA) as the regional cluster.

Data extraction methods: Our cohort included Stages I-III breast cancer patients diagnosed at ages 66-79 between 2007 and 2013 who received cancer surgery and were enrolled in Medicare Parts A and B.

Principal findings: After decomposition, 39% of the total variation was explained by HSAs, which was higher than that in some other breast cancer measures, such as the proportion of Stage I at diagnosis (4%), previously reported. This suggests geospatial efforts have a great potential to address the regional variation regarding this measure.

Conclusions: Our variance decomposition method provides direct information about attributable variance in the proportions at a cluster level. This technique can help in the identification of intervention targets to improve regional variations in the quality of care and clinical outcomes.