title: Modeling the impact of comorbidity on breast cancer patient outcomes.
abstract: 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.
authors.affiliation: Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Campus Box 7906, Raleigh, NC 27695-7906, USA. szhang5@ncsu.edu
authors.forename: Shengfan
authors.initials: S
authors.lastname: Zhang
authors.forename: Julie Simmons
authors.initials: JS
authors.lastname: Ivy
authors.forename: Fay Cobb
authors.initials: FC
authors.lastname: Payton
authors.forename: Kathleen M
authors.initials: KM
authors.lastname: Diehl
mesh_terms: D000368:Aged; D001943:Breast Neoplasms / Q000191:economics / Q000401:mortality; D018567:Breast Neoplasms, Male / Q000191:economics / Q000401:mortality; D015897:Comorbidity; D005260:Female; D017722:Hospital Charges / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D017063:Outcome Assessment, Health Care; D016019:Survival Analysis; D014481:United States / Q000453:epidemiology