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Diabetes and the hospitalized patient

机译:糖尿病和住院患者

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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.
机译:在美国,四分之一的成年人患有两种或两种以上的慢性病;这一人群占医疗支出的三分之二。共病是指一个人同时存在多种健康状况,其患病率正在增加,并已被证明会对患者的预后产生负面影响。与糖尿病相关的共病与可预防的住院率增加、住院时间延长(LOS)和费用增加相关。这项研究主要关注患有和不患有糖尿病的住院成年患者在结果方面的性别和种族差异。目的是描述以查尔森加权共病指数(WIC)衡量的共病负担对包括服务水平、总费用和处置(特别是常规出院回家的概率)在内的结果的影响。来自全国住院患者样本(2006-2011年)的数据被用于构建一个聚类分析框架,该框架将聚类分析与多变量和逻辑回归方法相结合,以实现以下几个目标:(i)评估这些协变量对结果的影响;(ii)确定住院人群中最重要的共病;以及(iii)创建简化的WIC分数。结果显示,尽管住院女性的预后优于男性,但糖尿病对女性的影响更大。此外,非白人患者的住院时间更长,总费用更高。此外,简化的WIC在预测标准化总费用和服务水平的广义线性模型中表现相当,这表明这一新评分可以充分捕捉数据中的重要可变性。我们的发现强调了评估糖尿病对女性和少数民族生理和治疗的不同影响的必要性。

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