Identifying Patterns of Depression Comorbidities Using Association Rule Learning: Insights from Maryland Medicaid Data

Abstract

Objectives: This study aimed to identify association rules in patients with multiple chronic conditions, with a focus on patternsinvolving depression, a highly prevalent psychiatric disorder and a significant risk factor for suicide. Understandingcomorbidity patterns in patients with depression is critical for targeting screening efforts, enabling early diagnosis, and improvingchronic disease management. Methods: Maryland Medicaid claims data from 2021 to 2022 were analyzed to examinethe co-occurrence of depression with 62 other chronic conditions using association rule learning. Analyses were stratifiedby sex and age group to identify patterns specific to demographic subgroups. Thresholds for case numbers and confidencelevels were applied to ensure that identified rules were both clinically meaningful and statistically robust. Results: The studyshowed a marked increase in the number of association rules with advancing age, particularly among women compared tomen. In total, 582 association rules were identified, providing important insights into comorbidity structures. Conclusions:This study demonstrates the utility of association rule learning for detecting clinically relevant patterns of depression comorbidities,including variations by age and sex. The identified rules could inform clinical practice by improving targeted screening,facilitating early diagnosis, and guiding management strategies for patients with multiple chronic conditions.