title: Using pharmacy data to identify those with chronic conditions in Emilia Romagna, Italy.
abstract: OBJECTIVE
  Automated pharmacy data have been used to develop a measure of chronic disease status in the general population. The objectives of this project were to refine and apply a model of chronic disease identification using Italian automated pharmacy data; to describe how this model may identify patterns of morbidity in Emilia Romagna, a large Italian region; and to compare estimated prevalence rates using pharmacy data with those available from a 2000 Emilia Romagna disease surveillance study.
  
  
  METHODS
  Using the Chronic Disease Score, a list of chronic conditions related to the consumption of drugs under the Italian pharmaceutical dispensing system was created. Clinical review identified medication classes within the Italian National Therapeutic Formulary that were linked to the management of each chronic condition. Algorithms were then tested on pharmaceutical claims data from Emilia Romagna for 2001 to verify the applicability of the classification scheme.
  
  
  RESULTS
  Thirty-one chronic condition drug groups (CCDGs) were identified. Applying the model to the pharmacy data, approximately 1.5 million individuals (37.1%) of the population were identified as having one or more of the 31 CCDGs. The 31 CCDGs accounted for 77% (E556 million) of 2001 pharmaceutical expenditures. Cardiovascular diseases, rheumatological conditions, chronic respiratory illness, gastrointestinal diseases and psychiatric diseases were the most frequent chronic conditions. External validation comparing rates of the diseases found through using pharmacy data with those of a 2000 Emilia Romagna disease surveillance study showed similar prevalence of illness.
  
  
  CONCLUSIONS
  Using Italian automated pharmacy data, a measure of population-based chronic disease status was developed. Applying the model to pharmaceutical claims from Emilia Romagna 2001, a large proportion of the population was identified as having chronic conditions. Pharmacy data may be a valuable alternative to survey data to assess the extent to which large populations are affected by chronic conditions.
authors.affiliation: Department of Health Policy, Jefferson Medical College, Philadelphia 19107, USA. vittorio.maio@jefferson.edu
authors.forename: Vittorio
authors.initials: V
authors.lastname: Maio
authors.forename: Elaine
authors.initials: E
authors.lastname: Yuen
authors.forename: Carol
authors.initials: C
authors.lastname: Rabinowitz
authors.forename: Daniel
authors.initials: D
authors.lastname: Louis
authors.forename: Masahito
authors.initials: M
authors.lastname: Jimbo
authors.forename: Andrea
authors.initials: A
authors.lastname: Donatini
authors.forename: Sabine
authors.initials: S
authors.lastname: Mall
authors.forename: Francesco
authors.initials: F
authors.lastname: Taroni
mesh_terms: D002908:Chronic Disease / Q000145:classification / Q000453:epidemiology*; D006801:Humans; D007558:Italy / Q000453:epidemiology; D008485:Medical Audit; D009313:National Health Programs; D010593:Pharmaceutical Services / Q000706:statistics & numerical data*