title: Aggregation and the measurement of health care costs.
abstract: OBJECTIVE
  This study evaluated the extent to which the causes of variation in health care costs differ by the level at which observations are made.
  
  
  METHODS
  More than 40 U.S. and international studies providing empirical estimates of the sources of variation in health care costs were reviewed and arrayed by size of observational units. A simplified graphical analysis demonstrating how estimated correlation coefficients change with the level and type of aggregation is presented.
  
  
  RESULTS
  As the unit of observation becomes larger, association between health care costs and health status/morbidity becomes weaker and smaller in magnitude, while correlation with income (per capita GDP) becomes stronger and larger. Individual expenditure variation within a particular health care system is largely due to differences in health status, but across systems, morbidity has almost no effect on costs. For nations, differences in per capita income explain over 90 percent of the variation in both time series and cross section.
  
  
  CONCLUSIONS
  Units of observation used for analysis of health care costs must be matched to the units at which decision making occurs. The observed pattern of empirical results is consistent with a multilevel allocative model incorporating aggregate capacity constraints. To the extent that macro constraints determine total budgets at the national level, policy interventions at the micro level (substitution of generic pharmaceuticals, use of CEA for allocation of treatments, controls on construction and technology, etc.) can act to improve efficiency, equity and average health status, but will not usually reduce aggregate average per capita costs of medical care.
authors.affiliation: Department of Risk, Insurance and Healthcare Management, Temple University, Philadelphia, PA, USA.
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Getzen
mesh_terms: D019049:Developed Countries / Q000706:statistics & numerical data; D003906:Developing Countries / Q000706:statistics & numerical data; D017048:Health Care Costs / Q000706:statistics & numerical data*; D005102:Health Expenditures / Q000706:statistics & numerical data; D006291:Health Policy; D006296:Health Services / Q000706:statistics & numerical data*; D006304:Health Status; D006801:Humans; D017059:Models, Econometric; D012456:Salaries and Fringe Benefits / Q000706:statistics & numerical data*; D014481:United States