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.

Race/ethnicity and socio-economic differences in breast cancer surgery outcomes

“…Furthermore, previous studies have shown that hospitalized breast cancer patients present with slightly different characteristics compared with the general inpatient population, and may have different patterns of admission such as shorter lengths of stay, fewer diagnoses, more procedures on average, and significantly lower total hospital charges [30]. This raises the possibility that breast cancer patients are being admitted for specific, targeted purposes, such as surgery, and therefore minor complications may be underreported, potentially resulting in an underestimation of the associations observed in this study.…”

“…However the HCUP-NIS does not include information on patient outcomes after discharge, therefore complications and mortality occurring after hospital discharge were not included in our analysis. In addition, since the dataset includes only de-identified patient records, it was not possible to exclude duplicate records if the same patient was admitted multiple times in the same year [30].…”

Characterizing the impact of mental disorders on HIV patient length of stay and total charges

“…Studying the impact of comorbidities provides information regarding disease management, cost structure and resource utilization. Chronic diseases (CDC, 2009b) in particular, though controllable, account for billions in healthcare costs annually (CDC, 2009c;Zhang et al, 2010). In this study, hypertension, diabetes, mental disorders and obesity are selected as comorbid conditions of interest with respect to HIV, with a particular focus on mental disorders as they have been recognized to have a special relationship with HIV in the literature.…”

“…Previous studies using large hospitalization data (Zhang et al, 2010;Elixhauser et al, 1998) have resulted in small R 2 values when using least squares regression with the CoxBox transformation (Box and Cox, 1964). Since these data have a large number of variables, the proportion of variance explained by the fit is usually low when only a subset of the covariates are added to the regression models.…”

When is Age More Than Just a Number?

“…Many older patients will not only tolerate aggressive therapy, but they will thrive as a productive subset of society, making valuable contributions as elder statesmen, senior professionals and academicians, mentors, and grandparents or great-grandparents. Can we replace or supplement age as ''just a number'' with a descriptor of medical status as a function of comorbidities, as suggested by Zhang et al 1 Two manuscripts from this issue of Annals of Surgical Oncology support the importance of looking beyond age as number that might dogmatically preclude curative-intent surgery for either a common malignancy such as breast cancer or a less common tumor such as esophageal cancer. Laki et al compared outcomes following breast cancer surgery for patients C80 years, 75-80 years, and 70-75 years, finding results that were similar between these age categories and comparable to those reported in the medical literature for younger patients.…”