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General Practitioners reduced benzodiazepine prescriptions in an intervention study: a multilevel application
Roy Stewart1, Wim J M Niessen, Jan Broer
1University Medical Center Groningen, University of Groningen, Department of Public Health, 9713 AV Groningen, Netherlands. r.e.stewart@med.umcg.nl
Journal of Clinical Epidemiology
|September 22, 2007
Summary
A letter intervention significantly reduced long-term benzodiazepine prescribing in chronic users by 16% at 6 months and 14% at 12 months. Patient characteristics also influenced prescribing reduction.
Area of Science:
- General Practice
- Pharmacology
- Health Services Research
Background:
- Long-term benzodiazepine prescribing is a significant issue in primary care.
- Interventions targeting general practitioners (GPs) and patients are needed to reduce reliance on these medications.
Purpose of the Study:
- To evaluate the effectiveness of a letter intervention aimed at reducing long-term benzodiazepine prescribing among chronic users.
- To identify patient and prescription characteristics influencing the reduction of benzodiazepine use.
Main Methods:
- A multilevel regression model was used to analyze data from 8,170 chronic benzodiazepine users across 147 general practices.
- An intervention group (1,256 users in 19 practices) received a letter advising reduction after GP training, compared to a control group.
Main Results:
- The intervention group showed a significantly greater reduction in benzodiazepine prescribing (16% at 6 months, 14% at 12 months) compared to the control group.
- Patient age, gender, and the interaction between age and gender were significant predictors of prescribing reduction.
- Benzodiazepine type (anxiolytic/hypnotic) and duration (long/short-acting) were important baseline covariates.
Conclusions:
- The letter intervention was the primary driver for reducing long-term benzodiazepine prescribing.
- Individual patient characteristics played a notable role in the observed reductions.
- Multilevel analysis proved effective for analyzing the complex, unbalanced data structure in this study.
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