A Cluster Analytic Approach to Identify Insomnia Subtypes and Their Relationship with Economic Outcomes

Abstract

INTRODUCTION: Insomnia is a heterogenous condition with respect to underlying risk factors, presentation of symptoms, comorbidities, disease course, and outcomes. Consequently, individuals with insomnia may also have varying patterns of healthcare resource utilization and costs. However, the impact of insomnia heterogeneity on economic outcomes is not known.

METHODS: We used an integrated claims-electronic health records dataset to identify individuals aged 18-64 with insomnia between 2009-2018. A k-modes clustering algorithm with a Jaccard coefficient similarity measure was used to identify clinically relevant insomnia subtypes based on sociodemographic, comorbidity, behavioral, life event, family history, medication use, vital sign, and insomnia symptom-related characteristics. An optimum cluster solution was chosen based on clinical interpretability and significance. Insomnia clusters were compared on baseline characteristics using Chi-square tests. Logistic regression models were used to identify the association between cluster membership and binary outcomes (inpatient hospitalization, emergency department [ED] visits). Generalized linear models were used to assess similar associations with count physician office visits, non-physician outpatient visits, prescription drug fills) and cost outcomes associated with all points of service.

RESULTS: A total of 17,124 individuals with insomnia met the study inclusion criteria. The cluster analysis resulted in a five-cluster solution. The clusters were labelled as ‘Insomnia associated with obesity and hypertension’ (28.6%), ‘Insomnia associated with mental health conditions and chronic pain’ (25.4%), ‘Insomnia associated with older age, high comorbidity burden, and fatigue’ (24.6%), ‘Insomnia associated with substance use disorders’ (5.2%), and ‘Insomnia associated with overweight status, alcohol use, and low comorbidity burden’ (16.2%). Relative to the reference cluster ‘Insomnia associated with overweight status, alcohol use, and low comorbidity burden’, individuals in cluster labelled as ‘Insomnia associated with older age, high comorbidity burden, and fatigue’ displayed higher total healthcare costs (cost ratio [CR]: 1.46; 95% CI: 1.32, 1.62) primarily driven by higher inpatient (CR: 1.68; 95% CI: 1.48, 1.91) and prescription drug fill (CR: 1.49; 95% CI: 1.34, 1.65) costs.

CONCLUSION: Findings from the present study can help improve our understanding about developmental trajectories for insomnia diagnosis and facilitate the design of tailored interventions that target those at the highest risk for adverse economic consequences.