Diabetes and the hospitalized patient : A cluster analytic framework for characterizing the role of sex, race and comorbidity from 2006 to 2011.

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.

Systems engineering methods for enhancing the value stream in public health preparedness: the role of Markov models, simulation, and optimization.

OBJECTIVES: Large-scale incidents such as the 2009 H1N1 outbreak, the 2011 European Escherichia coli outbreak, and Hurricane Sandy demonstrate the need for continuous improvement in emergency preparation, alert, and response systems globally. As questions relating to emergency preparedness and response continue to rise to the forefront, the field of industrial and systems engineering (ISE) emerges, as it provides sophisticated techniques that have the ability to model the system, simulate, and optimize complex systems, even under uncertainty. METHODS: We applied three ISE techniques--Markov modeling, operations research (OR) or optimization, and computer simulation--to public health emergency preparedness. RESULTS: We present three models developed through a four-year partnership with stakeholders from state and local public health for effectively, efficiently, and appropriately responding to potential public health threats: (1) an OR model for optimal alerting in response to a public health event, (2) simulation models developed to respond to communicable disease events from the perspective of public health, and (3) simulation models for implementing pandemic influenza vaccination clinics representative of clinics in operation for the 2009-2010 H1N1 vaccinations in North Carolina. CONCLUSIONS: The methods employed by the ISE discipline offer powerful new insights to understand and improve public health emergency preparedness and response systems. The models can be used by public health practitioners not only to inform their planning decisions but also to provide a quantitative argument to support public health decision making and investment.

Modeling the impact of comorbidity on breast cancer patient outcomes.

The objective of this paper is to model the impact of comorbidity on breast cancer patient outcomes (e.g., length of stay and disposition). Previous studies suggest that comorbidities may significantly affect mortality risks for breast cancer patients. The 2006 AHRQ Nationwide Inpatient Sample (NIS) is used to analyze the relationships among comorbidities (e.g., hypertension, diabetes, obesity, and mental disorder), total charges, length of stay, and patient disposition as a function of age and race. A multifaceted approach is used to quantify these relationships. A causal study is performed to explore the effect of various comorbidities on patient outcomes. Least squares regression models are developed to evaluate and compare significant factors that influence total charges and length of stay. Logistic regression is used to study the factors that may cause patient mortality or transferring. In addition, different survival models are developed to study the impact of comorbidity on length of stay with censoring information. This study shows the interactions and relationship among various comorbidities and breast cancer. It shows that certain hypertension may not increase length of stay and total charges; diabetes behaves differently among general population and breast cancer patients; mental disorder has an impact on patient disposition that affects true length of stay and charges, and obesity may have limited effect on patient outcomes. Moreover, this study will help to better understand the expenditure patterns for population subgroups with several chronic conditions and to quantify the impact of comorbidities on patient outcomes. Lastly, it also provides insight for breast cancer patients with comorbidities as a function of age and race.