title: The role of resource allocation models in selecting clinical preventive services.
abstract: OBJECTIVE
  To demonstrate the potential value and current limitations of using resource allocation models for selecting health services.
  
  
  METHODS
  To identify the most efficient mix of preventive services that could be offered by a managed care organization (MCO) for a fixed budget, an optimization model (greatest number of life years saved) and a cost-effectiveness model (rank order of most to least cost effective) were developed. Because of the lack of cost-effectiveness analyses that met the study criteria, only 9 preventive services were selected to demonstrate each model.
  
  
  METHODS
  The 2 models were applied to a hypothetical managed care population of 100,000 enrollees with age, sex, and risk distribution similar to that of the US population. Data for the input variables were obtained from cost-effectiveness studies of 9 preventive services. Model variables included the target population, percent of enrollees who received the preventive service, the cost of the preventive service, life years saved, and cost-effectiveness ratios.
  
  
  RESULTS
  The models demonstrated that efficient allocation of finite resources can be achieved. When budgets are limited, different premises between the 2 models may yield different health consequences. However, as the budgets were increased, results from the 2 models were more closely aligned.
  
  
  CONCLUSIONS
  Resource allocation models have the potential for assisting MCOs in selecting a set of preventive services that will maximize population health. Before this potential can be fully realized, additional methodological development and cost-effectiveness studies are needed. The use of resource allocation should be examined for selecting all healthcare services.
authors.affiliation: Surveillance and Evaluation Research Branch, Division of Adolescent and School Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
authors.forename: L Y
authors.initials: LY
authors.lastname: Wang
authors.forename: A C
authors.initials: AC
authors.lastname: Haddix
authors.forename: S M
authors.initials: SM
authors.lastname: Teutsch
authors.forename: B
authors.initials: B
authors.lastname: Caldwell
mesh_terms: D002017:Budgets; D003362:Cost-Benefit Analysis; D015276:Health Care Rationing / Q000379:methods / Q000458:organization & administration*; D006302:Health Services Research / Q000191:economics; D008329:Managed Care Programs / Q000191:economics / Q000458:organization & administration*; D017146:Models, Organizational; D017410:Practice Guidelines as Topic; D011314:Preventive Health Services / Q000191:economics* / Q000592:standards / Q000600:supply & distribution; D019057:Quality-Adjusted Life Years; D014481:United States