title: Backdating of events in electronic primary health care data: should one censor at the date of last data collection.
abstract: OBJECTIVE
  Studies using primary care databases often censor follow-up at the date data are last collected from clinical computer systems (last collection date (LCD)). We explored whether this results in the selective exclusion of events entered in the electronic health records after their date of occurrence, that is, backdated events.
  
  
  METHODS
  We used data from The Health Improvement Network (THIN). Using two versions of the database, we identified events that were entered into a later (THIN14) but not an earlier version of the database (THIN13) and investigated how the number of entries changed as a function of time since LCD. Times between events and the dates they were recorded were plotted as a function of time since the LCD in an effort to determine appropriate points at which to censor follow-up.
  
  
  RESULTS
  There were 356 million eligible events in THIN14 and 355 million eligible events in THIN13. When comparing the two data sets, the proportion of missing events in THIN13 was highest in the month prior to the LCD (9.6%), decreasing to 5.2% at 6 months and 3.4% at 12 months. The proportion of missing events was largest for events typically diagnosed in secondary care such as neoplasms (28% in the month prior to LCD) and negligible for events typically diagnosed in primary care such as respiratory events (2% in the month prior to LCD).
  
  
  CONCLUSIONS
  Studies using primary care databases, particularly those investigating events typically diagnosed outside primary care, should censor follow-up prior to the LCD to avoid underestimation of event rates.
authors.affiliation: Department of Primary Care and Population Health, University College London, London, UK.
authors.forename: Cormac J
authors.initials: CJ
authors.lastname: Sammon
authors.affiliation: Department of Primary Care and Population Health, University College London, London, UK.|Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark.
authors.forename: Irene
authors.initials: I
authors.lastname: Petersen
mesh_terms: D003625:Data Collection / Q000379:methods*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D006801:Humans; D011320:Primary Health Care / Q000706:statistics & numerical data*; D013997:Time Factors
keywords: CPRD; THIN; censoring; database; epidemiology; pharmacoepidemiology; primary care