title: Health-related quality of life as a predictor of pediatric healthcare costs: a two-year prospective cohort analysis.
abstract: BACKGROUND
  The objective of this study was to test the primary hypothesis that parent proxy-report of pediatric health-related quality of life (HRQL) would prospectively predict pediatric healthcare costs over a two-year period. The exploratory hypothesis tested anticipated that a relatively small group of children would account for a disproportionately large percent of healthcare costs.
  
  
  METHODS
  317 children (157 girls) ages 2 to 18 years, members of a managed care health plan with prospective payment participated in a two-year prospective longitudinal study. At Time 1, parents reported child HRQL using the Pediatric Quality of Life Inventory (PedsQL 4.0) Generic Core Scales, and chronic health condition status. Costs, based on health plan utilization claims and encounters, were derived for 6, 12, and 24 months.
  
  
  RESULTS
  In multiple linear regression equations, Time 1 parent proxy-reported HRQL prospectively accounted for significant variance in healthcare costs at 6, 12, and 24 months. Adjusted regression models that included both HRQL scores and chronic health condition status accounted for 10.1%, 14.4%, and 21.2% of the variance in healthcare costs at 6, 12, and 24 months. Parent proxy-reported HRQL and chronic health condition status together defined a 'high risk' group, constituting 8.7% of the sample and accounting for 37.4%, 59.2%, and 62% of healthcare costs at 6, 12, and 24 months. The high risk group's per member per month healthcare costs were, on average, 12 times that of other enrollees' at 24 months.
  
  
  CONCLUSIONS
  While these findings should be further tested in a larger sample, our data suggest that parent proxy-reported HRQL can be used to prospectively predict healthcare costs. When combined with chronic health condition status, parent proxy-reported HRQL can identify an at risk group of children as candidates for proactive care coordination.
authors.affiliation: RAND Health, 1700 Main Street, M-28, Santa Monica, California, 90407, USA. mseid@rand.org.
authors.forename: Michael
authors.initials: M
authors.lastname: Seid
authors.forename: James W
authors.initials: JW
authors.lastname: Varni
authors.forename: Darron
authors.initials: D
authors.lastname: Segall
authors.forename: Paul S
authors.initials: PS
authors.lastname: Kurtin
mesh_terms: D000293:Adolescent; D002140:California / Q000453:epidemiology; D002648:Child; D002662:Child Health Services / Q000191:economics* / Q000706:statistics & numerical data; D002669:Child Welfare; D002675:Child, Preschool; D002908:Chronic Disease / Q000191:economics / Q000453:epidemiology; D019988:Children with Disabilities / Q000706:statistics & numerical data; D005260:Female; D017048:Health Care Costs; D006304:Health Status; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008329:Managed Care Programs / Q000191:economics* / Q000706:statistics & numerical data; D008484:Medicaid / Q000191:economics* / Q000706:statistics & numerical data; D010290:Parents / Q000523:psychology*; D010372:Pediatrics / Q000191:economics* / Q000379:methods / Q000592:standards; D019727:Proxy; D011594:Psychometrics / Q000379:methods*; D011788:Quality of Life; D018570:Risk Assessment; D018434:Sickness Impact Profile