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Off-label drug use in hospitalized children: a prospective observational study at Gondar University Referral Hospital, Northwestern Ethiopia.

Yonas Getaye TeferaBegashaw M GebresillassieAbebe B MekuriaTamrat B AbebeDaniel A ErkuNurahmed SeidHabiba B Beshir
Published in: Pharmacology research & perspectives (2017)
Most of the medications which are currently used for the treatment of childhood diseases are either not licensed or being prescribed outside the terms of the product license (off-label prescribing). This study aimed at determining the extent of unlicensed and off-label drug uses and associated factors in children hospitalized in Gondar University Referral Hospital, Northwest Ethiopia. An institution-based prospective cross-sectional study was employed from April 15 to July 15, 2016. A total of 243 pediatric patients admitted to Gondar university referral hospital were included in the study using simple random sampling method. Data were collected using structured questionnaire, and the data collected were entered and analyzed using Statistical Packages for Social Sciences (SPSS) version 20. From the total of 800 drugs prescribed, 607 (75.8%) were off-label. Off-label medicine use was frequently observed in antimicrobials (60.6%) followed by central nervous system drugs (14.3%). The extent off-label prescribing was highest in age group of 6-13 years (30%). Inappropriate dosing and frequency (42.3%) were the most common reason for off-label medicine use. Having other variables controlled, age group and undergoing surgical procedure remained to be significant predictors of off-label prescribing in the multivariate regression analysis. Implementing evidence-based approach in prescribing by generating more quality literatures on the safety profile and effectiveness of off-label would improve the injudicious use of drugs in pediatric population.
Keyphrases
  • primary care
  • adverse drug
  • healthcare
  • randomized controlled trial
  • electronic health record
  • mental health
  • drug induced
  • data analysis
  • machine learning
  • quality improvement
  • combination therapy
  • replacement therapy