title: Identifying Specific Combinations of Multimorbidity that Contribute to Health Care Resource Utilization: An Analytic Approach.
abstract: Multimorbidity affects the majority of elderly adults and is associated with higher health costs and utilization, but how specific patterns of morbidity influence resource use is less understood.
  
  
  
  The objective was to identify specific combinations of chronic conditions, functional limitations, and geriatric syndromes associated with direct medical costs and inpatient utilization.
  
  
  
  Retrospective cohort study using the Health and Retirement Study (2008-2010) linked to Medicare claims. Analysis used machine-learning techniques: classification and regression trees and random forest.
  
  
  
  A population-based sample of 5771 Medicare-enrolled adults aged 65 and older in the United States.
  
  
  
  Main covariates: self-reported chronic conditions (measured as none, mild, or severe), geriatric syndromes, and functional limitations. Secondary covariates: demographic, social, economic, behavioral, and health status measures.
  
  
  
  Medicare expenditures in the top quartile and inpatient utilization.
  
  
  
  Median annual expenditures were $4354, and 41% were hospitalized within 2 years. The tree model shows some notable combinations: 64% of those with self-rated poor health plus activities of daily living and instrumental activities of daily living disabilities had expenditures in the top quartile. Inpatient utilization was highest (70%) in those aged 77-83 with mild to severe heart disease plus mild to severe diabetes. Functional limitations were more important than many chronic diseases in explaining resource use.
  
  
  
  The multimorbid population is heterogeneous and there is considerable variation in how specific combinations of morbidity influence resource use. Modeling the conjoint effects of chronic conditions, functional limitations, and geriatric syndromes can advance understanding of groups at greatest risk and inform targeted tailored interventions aimed at cost containment.
authors.affiliation: *Department of Epidemiology & Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH †Department of Sociology, University of Nebraska-Lincoln, Lincoln, NE ‡Department of Health Policy and Management, George Washington University Milken Institute School of Public Health, Washington, DC §Department of Family Medicine, Michigan State University, East Lansing, MI ∥Department of Family Medicine and Community Health, Case Western Reserve University School of Medicine, Cleveland, OH.
authors.forename: Nicholas K
authors.initials: NK
authors.lastname: Schiltz
authors.forename: David F
authors.initials: DF
authors.lastname: Warner
authors.forename: Jiayang
authors.initials: J
authors.lastname: Sun
authors.forename: Paul M
authors.initials: PM
authors.lastname: Bakaki
authors.forename: Avi
authors.initials: A
authors.lastname: Dor
authors.forename: Charles W
authors.initials: CW
authors.lastname: Given
authors.forename: Kurt C
authors.initials: KC
authors.lastname: Stange
authors.forename: Siran M
authors.initials: SM
authors.lastname: Koroukian
mesh_terms: D000203:Activities of Daily Living; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D005260:Female; D015438:Health Behavior; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006304:Health Status; D006801:Humans; D000069550:Machine Learning; D008297:Male; D006278:Medicare / Q000191:economics* / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D057566:Self Report; D012959:Socioeconomic Factors; D014481:United States