PURPOSE: When a Medication Guide (MG) is part of Risk Evaluation and Mitigation Strategy (REMS), manufacturers assess the effectiveness of MGs through patient surveys, which have not undergone systematic evaluation. We aimed to characterize knowledge rates from these patient surveys, describe their design and respondent characteristics, and explore predictors of acceptable knowledge rates. METHODS: We analyzed MG assessments submitted to the Food and Drug Administration from September 2008 through June 2012. We evaluated the prevalence of specific characteristics, and calculated knowledge rates, whereby we defined "acceptable knowledge" when ≥ 80% of respondents correctly answered questions about the primary drug risk. Univariate logistic models were used to investigate the predictors of acceptable knowledge rates. RESULTS: We analyzed the first completed MG assessment for each drug with a patient survey, resulting in 66 unique MG assessments. The mean knowledge rate was 63.8%, with 20 MG assessments (30.3%) achieving the 80% threshold. Compared to assessments that did not reach acceptable knowledge rates, those that did were more likely associated with additional REMS elements (e.g. Elements to Assure Safe Use or Communication Plans). Other factors, including mean age, reading or understanding the MG, and being offered or accepting counseling were not associated with knowledge rates. There was considerable variation in the design of MG assessments. CONCLUSIONS: Most MG assessments did not reach the 80% knowledge threshold, but those associated with additional interventions were more likely to achieve it. Our study highlights the need to improve patient-directed information and the methods of assessing it.
PURPOSE: When a Medication Guide (MG) is part of Risk Evaluation and Mitigation Strategy (REMS), manufacturers assess the effectiveness of MGs through patient surveys, which have not undergone systematic evaluation. We aimed to characterize knowledge rates from these patient surveys, describe their design and respondent characteristics, and explore predictors of acceptable knowledge rates. METHODS: We analyzed MG assessments submitted to the Food and Drug Administration from September 2008 through June 2012. We evaluated the prevalence of specific characteristics, and calculated knowledge rates, whereby we defined "acceptable knowledge" when ≥ 80% of respondents correctly answered questions about the primary drug risk. Univariate logistic models were used to investigate the predictors of acceptable knowledge rates. RESULTS: We analyzed the first completed MG assessment for each drug with a patient survey, resulting in 66 unique MG assessments. The mean knowledge rate was 63.8%, with 20 MG assessments (30.3%) achieving the 80% threshold. Compared to assessments that did not reach acceptable knowledge rates, those that did were more likely associated with additional REMS elements (e.g. Elements to Assure Safe Use or Communication Plans). Other factors, including mean age, reading or understanding the MG, and being offered or accepting counseling were not associated with knowledge rates. There was considerable variation in the design of MG assessments. CONCLUSIONS: Most MG assessments did not reach the 80% knowledge threshold, but those associated with additional interventions were more likely to achieve it. Our study highlights the need to improve patient-directed information and the methods of assessing it.
Authors: Terry C Davis; Michael S Wolf; Pat F Bass; Jason A Thompson; Hugh H Tilson; Marolee Neuberger; Ruth M Parker Journal: Ann Intern Med Date: 2006-11-29 Impact factor: 25.391
Authors: Cheryl Enger; Muhammad Younus; Kenneth R Petronis; Jingping Mo; Robert Gately; John D Seeger Journal: Pharmacoepidemiol Drug Saf Date: 2013-01-24 Impact factor: 2.890
Authors: Michael S Wolf; Jennifer King; Elizabeth A H Wilson; Laura M Curtis; Stacy Cooper Bailey; James Duhig; Allison Russell; Ashley Bergeron; Amanda Daly; Ruth M Parker; Terry C Davis; William H Shrank; Bruce Lambert Journal: J Gen Intern Med Date: 2012-05-08 Impact factor: 5.128
Authors: Laurie J Zografos; Elizabeth Andrews; Daniel L Wolin; Brian Calingaert; Eric K Davenport; Kelly A Hollis; Ursula Maria Schmidt-Ott; Paul Petraro; Zdravko P Vassilev Journal: Pharmaceut Med Date: 2019-06
Authors: Aaron S Kesselheim; Sarah A McGraw; Sara Z Dejene; Paula Rausch; Gerald J Dal Pan; Brian M Lappin; Esther H Zhou; Jerry Avorn; Eric G Campbell Journal: Drug Saf Date: 2017-06 Impact factor: 5.606
Authors: Nancy A Brandenburg; Robert Bwire; John Freeman; Florence Houn; Paul Sheehan; Jerome B Zeldis Journal: Drug Saf Date: 2017-04 Impact factor: 5.606
Authors: Daina B Esposito; Vibha C A Desai; Judith J Stephenson; M Soledad Cepeda; Jennifer G Lyons; Crystal N Holick; Gregory P Wedin; Stephan Lanes Journal: Patient Prefer Adherence Date: 2021-02-24 Impact factor: 2.711