title: The effect of identifying macroprolactinemia on health-care utilization and costs in patients with elevated serum prolactin levels.
abstract: OBJECTIVE
  The routine screening for macroprolactin of all hyperprolactinemic patients may avoid unnecessary imaging procedures and medication prescription. The study described the frequency and types of tests requested after a diagnosis of high serum prolactin concentration, and assessed whether the diagnosis of macroprolactinemia resulted in lower downstream utilization and costs compared with hyperprolactinemic patients.
  
  
  METHODS
  A cost analysis was conducted using a decision tree to model the health-care utilization of the two groups. The database of the Fleury Medicina e Saúde provided the tests and medication of patients with a prolactin value >or=30 microg/L for a period of 6 months.
  
  
  RESULTS
  Six hundred fifty-four of 1793 patients (36.5%) had hyperprolactinemia because of macroprolactin. The average number of tests per individual was higher (P = 0.001) in the patients with true hyperprolactinemia (3.07) than in patients with macroprolactinemia (2.51).The average cost in the hyperprolactinemic group (R$425 or euro 162) was significantly higher (P < 0.001) than the macroprolactinemic group (R$340 or euro 130), an incremental cost 25% higher.
  
  
  CONCLUSIONS
  The macroprolactin screening did not completely avoid inappropriate clinical investigation or associated health-care costs. Our results demonstrate the importance of proper medical education and knowledge diffusion of the meaning of macroprolactinemia.
authors.affiliation: São Paulo Center for Health Economics-GRIDES-UNIFESP, São Paulo, SP, Brazil. patricia.soarez@cpes.org.br
authors.forename: Patrícia Coelho
authors.initials: PC
authors.lastname: de Soárez
authors.forename: Silvia Corral de Arêa Leão
authors.initials: SC
authors.lastname: Souza
authors.forename: José Gilberto Henriques
authors.initials: JG
authors.lastname: Vieira
authors.forename: Marcos Bosi
authors.initials: MB
authors.lastname: Ferraz
mesh_terms: D000328:Adult; D001938:Brazil / Q000453:epidemiology; D003365:Costs and Cost Analysis; D016208:Databases, Factual; D003663:Decision Trees; D003955:Diagnostic Tests, Routine / Q000191:economics / Q000706:statistics & numerical data; D005260:Female; D005455:Fluorescent Antibody Technique; D017048:Health Care Costs / Q000706:statistics & numerical data; D006296:Health Services / Q000191:economics* / Q000706:statistics & numerical data; D006801:Humans; D006966:Hyperprolactinemia / Q000097:blood / Q000175:diagnosis* / Q000191:economics* / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D010901:Pituitary Function Tests / Q000191:economics* / Q000706:statistics & numerical data; D010902:Pituitary Gland / Q000000981:diagnostic imaging; D011388:Prolactin / Q000097:blood / Q000191:economics*; D011859:Radiography
chemical_list: D011388:Prolactin