Evaluating the relationship between clinical and demographic characteristics of insulin-using people with diabetes and their health outcomes: a cluster analysis application.

BACKGROUND: The aim of this study was to determine how clusters or subgroups of insulin-treated people with diabetes, based upon healthcare resource utilization, select social demographic and clinical characteristics, and diabetes management parameters, are related to health outcomes including acute care visits and hospital admissions. METHODS: This was a non-experimental, retrospective cluster analysis. We utilized Aetna administrative claims data to identify insulin-using people with diabetes with service dates from 01 January 2015 to 30 June 2018. The study included adults over the age of 18 years who had a diagnosis of type 1 (T1DM) or type 2 diabetes mellitus (T2DM) on insulin therapy and had Aetna medical and pharmacy coverage for at least 18 months (6 months prior and 12 months after their index date, defined as either their first insulin prescription fill date or their earliest date allowing for 6 months' prior coverage). We used K-means clustering methods to identify relevant subgroups of people with diabetes based on 13 primary outcome variables. RESULTS: A total of 100,650 insulin-using people with diabetes were identified in the Aetna administrative claims database and met study criteria, including 11,826 (11.7%) with T1DM and 88,824 (88.3%) with T2DM. Of these 79,053 (78.5%) people were existing insulin users. Seven distinct clusters were identified with different characteristics and potential risks of diabetes complications. Overall, clusters were significantly associated with differences in healthcare utilization (emergency room visits, inpatient admissions, and total inpatient days) after multivariable adjustment. CONCLUSIONS: This analysis of healthcare claims data using clustering methodologies identified meaningful subgroups of patients with diabetes using insulin. The subgroups differed in comorbidity burden, healthcare utilization, and demographic factors which could be used to identify higher risk patients and/or guide the management and treatment of diabetes.