scholarly journals Characteristics of Super-Utilizers of Care at the University Hospitals of Geneva using Latent Class Analysis

2021 ◽  
Author(s):  
Gilles Cohen ◽  
Pascal Briot ◽  
Pierre Chopard
Keyword(s):  
Health Care ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Clinical Information ◽  
Patient Characteristics ◽  
University Hospital ◽  
Significant Heterogeneity ◽  
Class Analysis ◽  
University Hospitals ◽  
The University

In hospitalized populations, there is significant heterogeneity in patient characteristics, disease severity, and treatment responses, which generally translates into very different related outcomes and costs. A better understanding of this heterogeneity can lead to better management, more effective and efficient treatments by personalizing care to better meet patients' profiles. Thus, identifying distinct clinical profiles among patients can lead to more homogenous subgroups of patients. Super-utilizers (SUs) are such a group, who contribute a substantial proportion of health care costs and utilize a disproportionately high share of health care resources. This study uses cost, utilization metrics and clinical information to segment the population of patients (N=32,759) admitted to the University Hospital of Geneva in 2019 and thus identifies the characteristics of its SUs group using Latent Class Analysis. This study shows how cluster analysis might be valuable to hospitals for identifying super-utilizers within their patient population and understanding their characteristics.

scholarly journals Thinking About the End of Life When It Is Near: A Comparison of German and Portuguese Centenarians

2018 ◽  
Vol 41 (3) ◽  
pp. 265-285
Author(s):  
Kathrin Boerner ◽  
Daniela S. Jopp ◽  
Kyungmin Kim ◽  
Abigail Butt ◽  
Óscar Ribeiro ◽  
...  
Keyword(s):  
Health Care ◽  
Latent Class Analysis ◽  
End Of Life ◽  
Health Care Professionals ◽  
Latent Class ◽  
Individual Characteristics ◽  
Class Analysis ◽  
Class 1 ◽  
Afterlife Beliefs

This study examined how common thinking of and planning for the end of life (EOL) is among German and Portuguese centenarians, and whether patterns of EOL views are shaped by cultural and individual characteristics. A significant portion of centenarians in both countries reported not thinking about the EOL, not believing in the afterlife, and not having made EOL arrangements. Latent class analysis identified three EOL patterns: Class 1 ( EOL thoughts with EOL arrangements and afterlife beliefs), Class 2 ( EOL arrangements and afterlife beliefs without EOL thoughts), and Class 3 ( Overall low endorsement of EOL items). The proportion of Portuguese centenarians was higher in Class 1 and of German centenarians higher in Classes 2 and 3. Centenarians’ demographic, social, and health characteristics were significantly different across EOL patterns. As lack of EOL planning can result in poor EOL quality, enhancing communication among centenarians, family, and health-care professionals seems imperative.

scholarly journals Differential Health Care Use, Diabetes-Related Complications, and Mortality Among Five Unique Classes of Patients With Type 2 Diabetes in Singapore: A Latent Class Analysis of 71,125 Patients

Diabetes Care ◽  
2020 ◽  
Vol 43 (5) ◽  
pp. 1048-1056 ◽  
Author(s):  
Jun Jie Benjamin Seng ◽  
Yu Heng Kwan ◽  
Vivian Shu Yi Lee ◽  
Chuen Seng Tan ◽  
Sueziani Binte Zainudin ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Health Care ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Health Care Use ◽  
Class Analysis ◽  
Complications And Mortality

scholarly journals Sociodemographic aspects and health care-related outcomes: a latent class analysis of informal dementia care dyads

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Henrik Wiegelmann ◽  
Karin Wolf-Ostermann ◽  
Werner Brannath ◽  
Farhad Arzideh ◽  
Jan Dreyer ◽  
...  
Keyword(s):  
Health Care ◽  
Latent Class Analysis ◽  
Support Services ◽  
Latent Class ◽  
Informal Caregiver ◽  
Adult Child ◽  
Dementia Care ◽  
Class Analysis ◽  
Person With Dementia

Abstract Background Studies revealed the importance to assess dementia care dyads, composed of persons with dementia and their primary informal caregivers, in a differentiated way and to tailor support services to particular living and care circumstances. Therefore, this study aims first to identify classes of dementia care dyads that differ according to sociodemographic, care-related and dementia-specific characteristics and second, to compare these classes with regard to healthcare-related outcomes. Methods We used data from the cross-sectional German DemNet-D study (n = 551) and conducted a latent class analysis to investigate different classes of dementia care dyads. In addition, we compared these classes with regard to the use of health care services, caregiver burden (BIZA-D), general health of the informal caregiver (EQ-VAS) as well as quality of life (QoL-AD) and social participation (SACA) of the person with dementia. Furthermore, we compared the stability of the home-based care arrangements. Results Six different classes of dementia care dyads were identified, based on best Bayesian Information Criterion (BIC), significant likelihood ratio test (p <  0.001), high entropy (0.87) and substantive interpretability. Classes were labelled as “adult child parent relationship & younger informal caregiver”, “adult child parent relationship & middle aged informal caregiver”, “non family relationship & younger informal caregiver”, “couple & male informal caregiver of older age”, “couple & female informal caregiver of older age”, “couple & younger informal caregiver”. The classes showed significant differences regarding health care service use. Caregiver burden, quality of life of the person with dementia and stability of the care arrangement differed also significantly between the classes. Conclusion Based on a latent class analysis this study indicates differences between classes of informal dementia care dyads. The findings may give direction for better tailoring of support services to particular circumstances to improve healthcare-related outcomes of persons with dementia and informal caregivers.

scholarly journals Differences in Clinical Outcomes of Adults Referred to a Homeless Transitional Care Program Based on Multimorbid Health Profiles: A Latent Class Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Colin M. Smith ◽  
Jacob Feigal ◽  
Richard Sloane ◽  
Donna J. Biederman
Keyword(s):  
Health Care ◽  
Substance Use ◽  
Latent Class Analysis ◽  
Clinical Outcomes ◽  
Substance Use Disorder ◽  
Latent Class ◽  
Transitional Care ◽  
Medical Illness ◽  
Class Analysis ◽  
High Comorbidity

Background: People experiencing homelessness face significant medical and psychiatric illness, yet few studies have characterized the effects of multimorbidity within this population. This study aimed to (a) delineate unique groups of individuals based on medical, psychiatric, and substance use disorder profiles, and (b) compare clinical outcomes across groups.Methods: We extracted administrative data from a health system electronic health record for adults referred to the Durham Homeless Care Transitions program from July 2016 to June 2020. We used latent class analysis to estimate classes in this cohort based on clinically important medical, psychiatric and substance use disorder diagnoses and compared health care utilization, overdose, and mortality at 12 months after referral.Results: We included 497 patients in the study and found 5 distinct groups: “low morbidity” (referent), “high comorbidity,” “high tri-morbidity,” “high alcohol use,” and “high medical illness.” All groups had greater number of admissions, longer mean duration of admissions, and more ED visits in the 12 months after referral compared to the “low morbidity” group. The “high medical illness” group had greater mortality 12 months after referral compared to the “low morbidity” group (OR, 2.53, 1.03–6.16; 95% CI, 1.03–6.16; p = 0.04). The “high comorbidity” group (OR, 5.23; 95% CI, 1.57–17.39; p &lt; 0.007) and “high tri-morbidity” group (OR, 4.20; 95% CI, 1.26–14.01; p &lt; 0.02) had greater 12-month drug overdose risk after referral compared to the referent group.Conclusions: These data suggest that distinct groups of people experiencing homelessness are affected differently by comorbidities, thus health care programs for this population should address their risk factors accordingly.

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