title: Temporal and within practice variability in the health improvement network.
abstract: BACKGROUND
  The Health Improvement Network (THIN) database is a primary care electronic medical record database in the UK designed for pharmacoepidemiologic research. Matching on practice and calendar year often is used to account for secular trends in time and differences across practices. However, little is known about the consistency within practices across observation years and among practices within a given year, in THIN or other large medical record databases.
  
  
  METHODS
  We analyzed mortality rates, cancer incidence rates, prescribing rates, and encounter rates across 415 practices from 2000 to 2007 using a practice-year as the unit of observation in separate random and fixed effects longitudinal Poisson regression models. Adjusted models accounted for aggregate practice-level characteristics (smoking, obesity, age, and Vision software experience).
  
  
  RESULTS
  In adjusted models, subsequent calendar years were associated with lower reported mortality rates, increasing cancer reporting rates, increasing prescriptions per patient, and decreasing encounters per patient, with a corresponding linear trend (p < 0.001 for all analyses). For calendar year 2007, the ratio of the 75th percentile to the 25th percentile for crude rate of cancer, mortality, prescriptions, and encounters was 1.63, 1.63, 1.45, and 1.42, respectively. Adjusting for practice characteristics reduced the among-practice variation by approximately 40%.
  
  
  CONCLUSIONS
  THIN data are characterized by secular trends and among-practice variation, both of which should be considered in the design of pharmacoepidemiology studies. Whether these are trends in data quality or true secular trends could not be definitively differentiated.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA. khaynes@mail.med.upenn.edu
authors.forename: Kevin
authors.initials: K
authors.lastname: Haynes
authors.forename: Warren B
authors.initials: WB
authors.lastname: Bilker
authors.forename: Tom R
authors.initials: TR
authors.lastname: Tenhave
authors.forename: Brian L
authors.initials: BL
authors.lastname: Strom
authors.forename: James D
authors.initials: JD
authors.lastname: Lewis
mesh_terms: D016208:Databases, Factual; D004812:Epidemiologic Methods; D005194:Family Practice; D006801:Humans; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D008962:Models, Theoretical; D017891:Pharmacoepidemiology / Q000379:methods*; D010818:Practice Patterns, Physicians'; D055656:Prescriptions / Q000706:statistics & numerical data; D013997:Time Factors; D006113:United Kingdom / Q000453:epidemiology