title: Assessing key cost drivers associated with caring for chronic kidney disease patients.
abstract: To examine key factors influencing chronic kidney disease (CKD) patients' total expenditure and offer recommendations on how to reduce total cost of CKD care without compromising quality.
  
  
  
  Using the 2002-2011 Medical Expenditure Panel Survey (MEPS) data, our cross-sectional study analyzed 197 patient records-79 patients with one record and 59 with two entries per patient (138 unique patients). We used three patient groups, based on international statistical classification of diseases version 9 code for condition (ICD9CODX) classification, to focus inference from the analysis: (a) non-dialysis dependent CKD, (b) dialysis and (c) transplant. Covariate information included region, demographic, co-morbid conditions and types of services. We used descriptive methods and multivariate generalized linear models to understand the impact of cost drivers. We compared actual and predicted CKD cost of care data using a hold-out sample of nine, randomly selected patients to validate the models.
  
  
  
  Total costs were significantly affected by treatment type, with dialysis being significantly higher than non-dialysis and transplant groups. Costs were highest in the West region of the U.S. Average costs for patients with public insurance were significantly higher than patients with private insurance (p < .0743), and likewise, for patients with co-morbid conditions over those without co-morbid conditions (p < .001).
  
  
  
  Managing CKD patients both before and after the onset of dialysis treatment and managing co-morbid conditions in individuals with CKD are potential sources of substantial cost savings in the care of CKD patients. Comparing total costs pre and post the United States Affordable Care Act could provide invaluable insights into managing the cost-quality tradeoff in CKD care.
authors.affiliation: McCombs School of Business, University of Texas in Austin, Austin, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Damien
authors.affiliation: McCombs School of Business, University of Texas in Austin, Austin, USA. lanham@uthscsa.com.|The University of Texas Health Science Center San Antonio, San Antonio, USA. lanham@uthscsa.com.|South Texas Veterans Health Care System, San Antonio, USA. lanham@uthscsa.com.
authors.forename: Holly J
authors.initials: HJ
authors.lastname: Lanham
authors.affiliation: Saaraa Medical Solutions, Whitehouse Station, NJ, USA.
authors.forename: Murali
authors.initials: M
authors.lastname: Parthasarathy
authors.affiliation: Piedmont Clinic, Atlanta, GA, USA.|Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Nikhil L
authors.initials: NL
authors.lastname: Shah
mesh_terms: D015897:Comorbidity; D003365:Costs and Cost Analysis; D003430:Cross-Sectional Studies; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006801:Humans; D006278:Medicare / Q000191:economics; D015999:Multivariate Analysis; D006435:Renal Dialysis / Q000191:economics; D051436:Renal Insufficiency, Chronic / Q000191:economics* / Q000628:therapy; D014481:United States
keywords: Chronic kidney disease; Co-morbid conditions; Total costs drivers