title: Is the choice of the statistical model relevant in the cost estimation of patients with chronic diseases? An empirical approach by the Piedmont Diabetes Registry.
abstract: BACKGROUND
  Chronic diseases impose large economic burdens. Cost analysis is not straightforward, particularly when the goal is to relate costs to specific patterns of covariates, and to compare costs between diseased and healthy populations. Using different statistical methods this study describes the impact on results and conclusions of analyzing health care costs in a population with diabetes.
  
  
  METHODS
  Direct health care costs of people living in Turin were estimated from administrative databases of the Regional Health System. Patients with diabetes were identified through the Piedmont Diabetes Registry. The effect of diabetes on mean annual expenditure was analyzed using the following multivariable models: 1) an ordinary least squares regression (OLS); 2) a lognormal linear regression model; 3) a generalized linear model (GLM) with gamma distribution. Presence of zero cost observation was handled by means of a two part model.
  
  
  RESULTS
  The OLS provides the effect of covariates in terms of absolute additive costs due to the presence of diabetes (€ 1,832). Lognormal and GLM provide relative estimates of the effect: the cost for diabetes would be six fold that for non diabetes patients calculated with the lognormal. The same data give a 2.6-fold increase if calculated with the GLM. Different methods provide quite different estimated costs for patients with and without diabetes, and different costs ratios between them, ranging from 3.2 to 5.6.
  
  
  CONCLUSIONS
  Costs estimates of a chronic disease vary considerably depending on the statistical method employed; therefore a careful choice of methods to analyze data is required before inferring results.
authors.affiliation: Unit of Cancer Epidemiology, "Città della Salute e della Scienza" Hospital and CPO Piemonte, Turin, Italy. eva.pagano@cpo.it.
authors.forename: Eva
authors.initials: E
authors.lastname: Pagano
authors.affiliation: Epidemiology Unit, ASL 5, Piedmont Region, Grugliasco, Turin, Italy. petrelli@inmp.it.|National Institute for Health, Migration and Poverty (INMP), Rome, Italy. petrelli@inmp.it.
authors.forename: Alessio
authors.initials: A
authors.lastname: Petrelli
authors.affiliation: Epidemiology Unit, ASL 5, Piedmont Region, Grugliasco, Turin, Italy. roberta.picariello@epi.piemonte.it.
authors.forename: Roberta
authors.initials: R
authors.lastname: Picariello
authors.affiliation: Unit of Cancer Epidemiology, "Città della Salute e della Scienza" Hospital and CPO Piemonte, Turin, Italy. franco.merletti@unito.it.|Department of Medical Sciences, University of Turin, corso Dogliotti 14, 10126, Turin, Italy. franco.merletti@unito.it.
authors.forename: Franco
authors.initials: F
authors.lastname: Merletti
authors.affiliation: Epidemiology Unit, ASL 5, Piedmont Region, Grugliasco, Turin, Italy. roberto.gnavi@epi.piemonte.it.
authors.forename: Roberto
authors.initials: R
authors.lastname: Gnavi
authors.affiliation: Department of Medical Sciences, University of Turin, corso Dogliotti 14, 10126, Turin, Italy. graziella.bruno@unito.it.
authors.forename: Graziella
authors.initials: G
authors.lastname: Bruno
mesh_terms: D000328:Adult; D000368:Aged; D002755:Choice Behavior; D002908:Chronic Disease; D003922:Diabetes Mellitus, Type 1 / Q000191:economics* / Q000628:therapy; D003924:Diabetes Mellitus, Type 2 / Q000191:economics* / Q000628:therapy; D005260:Female; D017048:Health Care Costs; D005102:Health Expenditures; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012042:Registries