Abstract
Breast cancer is a complex disease and may be accompanied by other multiple health conditions. The present study investigates associations between diagnosis codes in breast cancer patients using the Nationwide Inpatient Sample data. Concomitant diagnoses codes are identified by statistically significant associations between the diagnoses codes in a given breast cancer patient. These are subsequently represented in the form of a network (Breast Cancer Concomitant Diagnosis Network (BCCDN)). In contrast to more classical approaches, BCCDN provides system-level insights and convenient visualization reflected by the complex wiring patterns between the diagnoses codes. Social network analysis is used to investigate highly connected codes in the BCCDN network, and their variation across three different populations: (i) the deceased breast cancer population (ii) the elderly breast cancer population (age>65 years) and (iii) the adult breast cancer population (age <=65 years). BCCDNs were investigated across years 2005 and 2006 in order to identify associations that are robust to the stratified sampling and population heterogeneity as well as possible errors in documentation characteristic of observational healthcare data. The results presented validate known chronic comorbidities and their persistence across the deceased and elderly breast cancer population. They also provide novel associations and potential comorbidities in breast cancer patients that may warrant a more detailed investigation.
Similar content being viewed by others
References
American Cancer Society. (2013) Cancer Facts & Figures 2012. American Cancer Society, Atlanta.
Barabási AL and Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nature Reviews Genetics 5 (2), 101–113.
Benjamini Y and Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57 (1), 125–133.
Dominick KL, Dudley TK, Coffman CJ and Bosworth HB (2005) Comparison of three comorbidity measures for predicting health service use in patients with osteoarthritis. Arthritis Care & Research 53 (5), 666–672.
Eisner EJ, Zook EG, Goodman N and Macario E (2002) Knowledge, attitudes, and behavior of women ages 65 and older on mammography screening and Medicare: results of a national survey. Women Health 36 (4), 1–18.
Elixhauser A, Steiner C, Harris DR and Coffey RM (1998) Comorbidity measures for use with administrative data. Medical Care 36 (1), 8–27.
Extermann M (2007) Interaction between comorbidity and cancer. Cancer Control 14 (1), 13–22.
Farley JF, Harley CR and Devine JW (2006) A comparison of comorbidity measurements to predict healthcare expenditure. American Journal of Managed Care 12 (2), 110–119.
Garis RI and Farmer KC (2002) Examining costs of chronic conditions in a Medicaid population. Managed Care 11 (8), 43–50.
Geraci JM, Escalante CP, Freeman JL and Goodwin JS (2005) Comorbid disease and cancer: the need for more relevant conceptual models in health services research. Journal of Clinical Oncology 23 (3), 7399–7404.
Hidalgo CA, Blumm N, Barabási AL and Christakis NA (2009) A dynamic network approach for the study of human phenotypes. PLoS Computational Biology 5 (4), e1000353.
Hochberg Y and Tamhane AC (1987) Multiple Comparison Procedures. Wiley, New York.
Hurria A (2011) Embracing the complexity of comorbidity. Journal of Clinical Oncology 29 (32), 4217–4218.
Jemal A, Bray F, Center M, Ferlay J, Ward E and Forman D (2011) Global cancer statistics. CA: A Cancer Journal of Clinicians 61 (2), 69–90.
Ogle KS, Swanson GM, Woods N and Azzouz F (2000) Cancer and comorbidity: redefining chronic diseases. Cancer 88 (3), 653–663.
Piccirillo JF, Tierney RM, Costas I, Grove L and Spitznagel EL Jr. (2004) Prognostic importance of comorbidity in a hospital-based cancer registry. Journal of the American Medical Association 291 (20), 2441–2447.
Satariano WA and Raglund DR (1994) The effect of comorbidity on 3-year survival of women with primary breast cancer. Annals of Internal Medicine 120 (2), 104–110.
Stier DM et al (1999) Quantifying comorbidity in a disease-specific cohort: adaptation of the total illness burden index to prostate cancer. Urology 54 (3), 424–429.
Vogel TR, Dombrovskiy VY, Carson JL, Graham AM and Lowry SF (2010) Postoperative sepsis in the United States. Annals of Surgery 252 (6), 1065–1071.
Yancik R et al (1996) Cancer and comorbidity in older patients: a descriptive profile. Annals of Epidemiology 6 (5), 399–412.
Zhang S, Ivy JS, Payton FC and Diehl KM (2010) Modeling the impact of comorbidity on breast cancer patient outcomes. Health Care Management Science 13 (2), 137–154.
Zhang S (2011) Modeling the complexity of breast cancer under conditions of uncertainty. Ph.D. Dissertation, Edward P. Fitts Department of Industrial and Systems Engineering, Raleigh (NC), North Carolina State University.
Acknowledgements
The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through grant number UL1TR000117. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nagarajan, R., Zhang, S., Cobb Payton, F. et al. Inferring breast cancer concomitant diagnosis and comorbidities from the Nationwide Inpatient Sample using social network analysis. Health Syst 3, 136–142 (2014). https://doi.org/10.1057/hs.2014.4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1057/hs.2014.4