title: A model based on age, sex, and morbidity to explain variation in UK general practice prescribing: cohort study.
abstract: OBJECTIVE
  To examine whether patient level morbidity based measure of clinical case mix explains variations in prescribing in general practice.
  
  
  METHODS
  Retrospective study of a cohort of patients followed for one year.
  
  
  METHODS
  UK General Practice Research Database.
  
  
  METHODS
  129 general practices, with a total list size of 1 032 072.
  
  
  METHODS
  Each patient was assigned a morbidity group on the bases of diagnoses, age, and sex using the Johns Hopkins adjusted clinical group case mix system. Multilevel regression models were used to explain variability in prescribing, with age, sex, and morbidity as predictors.
  
  
  RESULTS
  The median number of prescriptions issued annually to a patient is 2 (90% range 0 to 18). The number of prescriptions issued to a patient increases with age and morbidity. Age and sex explained only 10% of the total variation in prescribing compared with 80% after including morbidity. When variation in prescribing was split between practices and within practices, most of the variation was at the practice level. Morbidity explained both variations well.
  
  
  CONCLUSIONS
  Inclusion of a diagnosis based patient morbidity measure in prescribing models can explain a large amount of variability, both between practices and within practices. The use of patient based case mix systems may prove useful in allocation of budgets and therefore should be investigated further when examining prescribing patterns in general practices in the UK, particularly for specific therapeutic areas.
authors.affiliation: Department of Statistical Science, University College London, London WC1E 6BT. rumana@stats.ucl.ac.uk
authors.forename: Rumana Z
authors.initials: RZ
authors.lastname: Omar
authors.forename: Caoimhe
authors.initials: C
authors.lastname: O'Sullivan
authors.forename: Irene
authors.initials: I
authors.lastname: Petersen
authors.forename: Amir
authors.initials: A
authors.lastname: Islam
authors.forename: Azeem
authors.initials: A
authors.lastname: Majeed
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D005194:Family Practice / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D009017:Morbidity; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D012044:Regression Analysis; D017678:Sex Distribution; D006113:United Kingdom