Construction of drug treatment episodes from drug-dispensing histories is influenced by the gap length

J Clin Epidemiol. 2010 Apr;63(4):422-7. doi: 10.1016/j.jclinepi.2009.07.001. Epub 2009 Oct 31.

Abstract

Objectives: When constructing drug treatment episodes using drug-dispensing databases, duration and the number of prescriptions belonging to a single treatment episode need to be defined. We investigated how different methods used to construct antidepressant treatment episodes influence their median estimated length.

Study design and setting: A follow-up study among adult antidepressant drug users, identified from the Dutch PHARMO RLS, starting selective serotonin reuptake inhibitor (SSRI) use in 2001 was conducted. The influence of varying lengths of the prescription overlap and the gap between prescriptions (number of days or percentage of prescription duration) on the median antidepressant treatment episode length were investigated.

Results: Of the 16,053 SSRI starters, 65.1% were female and mean age was 45.7 (SD: 17.2) years. Median antidepressant treatment episode length doubled when the gap length was expanded from 0 to 10 days. For short gap lengths the episode interquartile range was 40% to 200% larger when overlap was accounted for and when percentage of prescription duration gap length was used.

Conclusion: Differences in median episode length exist between methods that account for or disregard prescription overlap. These differences are of importance for studies that focus on drug exposure-outcome relationships and could have consequences for epidemiological analysis.

MeSH terms

  • Adolescent
  • Adult
  • Antidepressive Agents / administration & dosage*
  • Depressive Disorder / drug therapy*
  • Depressive Disorder / epidemiology
  • Drug Administration Schedule
  • Drug Utilization Review / methods*
  • Electronic Prescribing*
  • Female
  • Follow-Up Studies
  • Humans
  • Male
  • Medication Adherence
  • Middle Aged
  • Pharmacoepidemiology*
  • Young Adult

Substances

  • Antidepressive Agents