Identifying Subgroups of Adult Superutilizers in an Urban Safety-Net System Using Latent Class Analysis: Implications for Clinical Practice

Med Care. 2018 Jan;56(1):e1-e9. doi: 10.1097/MLR.0000000000000628.


Background: Patients with repeated hospitalizations represent a group with potentially avoidable utilization. Recent publications have begun to highlight the heterogeneity of this group. Latent class analysis provides a novel methodological approach to utilizing administrative data to identify clinically meaningful subgroups of patients to inform tailored intervention efforts.

Objective: The objective of the study was to identify clinically distinct subgroups of adult superutilizers.

Research design: Retrospective cohort analysis.

Subjects: Adult patients who had an admission at an urban safety-net hospital in 2014 and 2 or more admissions within the preceding 12 months.

Measures: Patient-level medical, mental health (MH) and substance use diagnoses, social characteristics, demographics, utilization and charges were obtained from administrative data. Latent class analyses were used to determine the number and characteristics of latent subgroups that best represented these data.

Results: In this cohort (N=1515), a 5-class model was preferred based on model fit indices, clinical interpretability and class size: class 1 (16%) characterized by alcohol use disorder and homelessness; class 2 (14%) characterized by medical conditions, MH/substance use disorders and homelessness; class 3 (25%) characterized primarily by medical conditions; class 4 (13%) characterized by more serious MH disorders, drug use disorder and homelessness; and class 5 (32%) characterized by medical conditions with some MH and substance use. Patient demographics, utilization, charges and mortality also varied by class.

Conclusions: The overall cohort had high rates of multiple chronic medical conditions, MH, substance use disorders, and homelessness. However, the patterns of these conditions were different between subgroups, providing important information for tailoring interventions.

MeSH terms

  • Adult
  • Colorado
  • Female
  • Hospitalization / statistics & numerical data*
  • Hospitals, Urban / statistics & numerical data*
  • Humans
  • Male
  • Medical Overuse / statistics & numerical data*
  • Middle Aged
  • Models, Statistical*
  • Retrospective Studies
  • Safety-net Providers / statistics & numerical data*