2018 journal article
Diabetes and the hospitalized patient: A cluster analytic framework for characterizing the role of sex, race and comorbidity from 2006 to 2011
HEALTH CARE MANAGEMENT SCIENCE, 21(4), 534–553.
author keywords: Diabetes; Comorbidities; Patient outcomes; Cluster analysis; Generalized linear models
MeSH headings : Adult; Aged; Aged, 80 and over; Cluster Analysis; Comorbidity; Diabetes Mellitus / epidemiology; Diabetes Mellitus / ethnology; Female; Hospital Charges / statistics & numerical data; Hospitalization / economics; Hospitalization / statistics & numerical data; Humans; Length of Stay; Logistic Models; Male; Middle Aged; Outcome Assessment, Health Care; Patient Discharge / statistics & numerical data; Racial Groups / statistics & numerical data; Sex Factors; United States
TL;DR:
Results showed that, although hospitalized women had better outcomes than men, the impact of diabetes was worse for women, and non-White patients had longer lengths of stay and higher total charges.
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UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: November 5, 2018
In the US, one in four adults has two or more chronic conditions; this population accounts for two thirds of healthcare spending. Comorbidity, the presence of multiple simultaneous health conditions in an individual, is increasing in prevalence and has been shown to impact patient outcomes negatively. Comorbidities associated with diabetes are correlated with increased incidence of preventable hospitalizations, longer lengths of stay (LOS), and higher costs. This study focuses on sex and race disparities in outcomes for hospitalized adult patients with and without diabetes. The objective is to characterize the impact of comorbidity burden, measured as the Charlson Weighted Index of Comorbidities (WIC), on outcomes including LOS, total charges, and disposition (specifically, probability of routine discharge home). Data from the National Inpatient Sample (2006-2011) were used to build a cluster-analytic framework which integrates cluster analysis with multivariate and logistic regression methods, for several goals: (i) to evaluate impact of these covariates on outcomes; (ii) to identify the most important comorbidities in the hospitalized population; and (iii) to create a simplified WIC score. Results showed that, although hospitalized women had better outcomes than men, the impact of diabetes was worse for women. Also, non-White patients had longer lengths of stay and higher total charges. Furthermore, the simplified WIC performed equivalently in the generalized linear models predicting standardized total charges and LOS, suggesting that this new score can sufficiently capture the important variability in the data. Our findings underscore the need to evaluate the differential impact of diabetes on physiology and treatment in women and in minorities.