Carlos Oronce
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Carlos Oronce has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Deborah Rinehart
Denver Health and Hospital Authority
1 shared publication - Holly Batal
Denver Health and Hospital Authority, Denver, CO
1 shared publication - Jody Md Vogel
Department of Emergency Medicine, Denver Health Medical Center, Denver, CO; Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO
1 shared publication - Krista Wilke Ranby
Department of Psychology, University of Colorado Denver
1 shared publication - Michael J. Durfee
Center for Health Systems Research, Office of Research, Denver Health and Hospital Authority, 777 Bannock St., M.C. 6551, Denver, CO 80204, USA
1 shared publication - Rebecca Hanratty
Ambulatory Care Services, Denver Health , Denver, CO
1 shared publication
Latest Publications
- 1 January 2018
- Published by Wolters Kluwer Health in Medical Care
- Vol. 56 (1) , e1-e9
Identifying Subgroups of Adult Superutilizers in an Urban Safety-Net System Using Latent Class Analysis
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.
- 1 January 2018
- Published by Wolters Kluwer Health in Medical Care
- Vol. 56 (1) , e1-e9
Identifying Subgroups of Adult Superutilizers in an Urban Safety-Net System Using Latent Class Analysis
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.