title: Risk adjustment and outcome research. Part I.
abstract: OBJECTIVE
  The increasing demand for comparative evaluation of outcomes requires the development and diffusion of epidemiologic research, the ability to correctly formulate hypotheses, to conduct analyses and to interpret the results. The purpose of this paper is to provide a detailed but easy-reading review of epidemiologic methods to compare healthcare outcomes, particularly risk-adjustment methods.
  
  
  METHODS
  The paper is divided into three parts. Part I describes confounding in observational studies, the ways confounding is identified and controlled (propensity adjustment and risk adjustment), and the methods for constructing the severity measures in risk-adjustment procedures.
  
  
  CONCLUSIONS
  It is becoming increasingly important for policy makers and planners to identify which factors may improve or worsen the effectiveness of treatments and services and to compare the performances of providers. Politicians, managers, epidemiologists, and clinicians should make their decisions based on the validity and precision of study results, by using the best scientific knowledge available. The statistical methods described in this review cannot measure 'reality' as it 'truly' is, but can produce 'images' of it, defining limits and uncertainties in terms of validity and precision. Studies that use credible risk-adjustment strategies are more likely to yield reliable and applicable findings.
authors.affiliation: Department of Epidemiology, Local Health Authority Rome E, Rome, Italy.
authors.forename: Massimo
authors.initials: M
authors.lastname: Arcà
authors.forename: Danilo
authors.initials: D
authors.lastname: Fusco
authors.forename: Anna P
authors.initials: AP
authors.lastname: Barone
authors.forename: Carlo A
authors.initials: CA
authors.lastname: Perucci
mesh_terms: D015986:Confounding Factors, Epidemiologic; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008297:Male; D016017:Odds Ratio; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D011379:Prognosis; D012372:ROC Curve; D015203:Reproducibility of Results; D020379:Risk Adjustment; D012307:Risk Factors; D012720:Severity of Illness Index