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Assessing the effect of a guideline change on drug use prevalence by including the birth cohort dimension: the case of benzodiazepines
Author(s) -
Bijlsma Maarten J.,
Hak Eelko,
Bos Jens,
De Jongvan den Berg Lolkje T. W.,
Janssen Fanny
Publication year - 2013
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.3466
Subject(s) - medicine , demography , guideline , cohort , population , cohort study , cohort effect , medical prescription , pharmacoepidemiology , pediatrics , environmental health , pathology , sociology , pharmacology
Purpose The aim of this study was to investigate whether including the birth cohort dimension in time series analysis leads to a more accurate estimation of the (long‐term) effect of a guideline change on the trend of benzodiazepine use. Methods We calculated age‐specific (20–84 years) and sex‐specific prevalence of benzodiazepine use per 1000 population per quarter year (1998 to 2008) using a prescription database set in the Netherlands. We studied the prevalence over time by age group and within birth cohorts through interrupted time series analyses to estimate the effect of the guideline change in 2001. Results From 1998 to 2008, the overall age‐standardized prevalence of benzodiazepine use per 1000 population declined from ~54 for men and ~107 for women to ~45 for men and ~85 for women. The relative change increased significantly after 2001 for both sexes and for the majority of age groups. Within birth cohorts, the prevalence increased with age until the year 2001 and leveled thereafter. The age–period approach overall had worse model fit indicators than the within‐cohort approach and predicted larger long‐term effects than the within‐cohort approach. The age–period projection estimated 36% decline in benzodiazepine use relative to 2008, whereas the birth‐cohort projection estimated 8% decline. Conclusion Explicitly following birth cohort trajectories led to models with better fit; the conventional approach estimated a stronger long‐term guideline effect. This has important implications for professional practice. Copyright © 2013 John Wiley & Sons, Ltd.