Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Jan;56(1):e1-e9.
doi: 10.1097/MLR.0000000000000628.

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

Deborah J Rinehart  1 , Carlos Oronce  2 , Michael J Durfee  1 , Krista W Ranby  3 , Holly A Batal  1 , Rebecca Hanratty  1 , Jody Vogel  1 , Tracy L Johnson  1
Affiliations

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

Deborah J Rinehart et al. Med Care. 2018 Jan.

Abstract

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.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest: Authors declare no conflict of interest.

Figures

Figure 1
Figure 1. Probability of Social, Medical and Mental Health/Substance Use Indicators by 5 Latent Classes
1a. The estimated probability (Y axis) for each indicator variable (X axis) in the latent class analysis is shown for each of the 5 classes. On the horizontal axis, the overall sample prevalence for each indicator is given (%) and the indicators are grouped by social, medical, and mental health/substance use. The separate lines demonstrate how the classes differ across the indicators and provide information on which indicators are more prevalent for each of the 5 classes. 1b and 1c presents the same data but separates it by the high homelessness classes (1b) and the primarily medical classes (1c).
See this image and copyright information in PMC

References

    1. Allen S, Croke A. The faces of Medicaid: the complexities of caring for people with chronic illnesses and disabilities. Princeton, NJ: Center for Health Care Strategies; 2000.
    1. Kronick R, Bella M, Gilmer T. The faces of Medicaid III: refining the portrait of people with multiple chronic conditions. Princeton, NJ: Center for Health Care Strategies; 2009.
    1. Riley GF. Long-term trends in the concentration of Medicare spending. Health Aff (Millwood) 2007;26(3):808–816. - PubMed
    1. Szekendi MK, Williams MV, Carrier D, Hensley L, Thomas S, Cerese J. The characteristics of patients frequently admitted to academic medical centers in the United States. J Hosp Med. 2015;10(9):563–568. - PMC - PubMed
    1. Yoon J, Zulman D, Scott JY, Maciejewski ML. Costs associated with multimorbidity among VA patients. Med Care. 2014;52(Suppl 3):S31–36. - PMC - PubMed