Intended for healthcare professionals

CCBYNC Open access

High risk prescribing in primary care patients particularly vulnerable to adverse drug events: cross sectional population database analysis in Scottish general practice

BMJ 2011; 342 doi: (Published 21 June 2011) Cite this as: BMJ 2011;342:d3514
  1. Bruce Guthrie, professor of primary care medicine1,
  2. Colin McCowan, lecturer in health informatics1,
  3. Peter Davey, lead clinician for undergraduate clinical quality improvement1,
  4. Colin R Simpson, senior research fellow2,
  5. Tobias Dreischulte, research pharmacist3,
  6. Karen Barnett, research assistant1
  1. 1Quality, Safety and Informatics Research Group, Centre for Primary Care and Population Research, University of Dundee, Kirsty Semple Way, Dundee DD2 4BF, UK
  2. 2Centre for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, UK
  3. 3Tayside Medicines Unit, NHS Tayside, Dundee, UK
  1. Correspondence to: B Guthrie b.guthrie{at}
  • Accepted 7 April 2011


Objective To examine the prevalence and patterns of high risk prescribing, defined as potentially inappropriate prescribing of drugs to primary care patients particularly vulnerable to adverse drug events.

Design Cross sectional population database analysis.

Setting General practices in Scotland.

Participants 315 Scottish general practices with 1.76 million registered patients, 139 404 (7.9%) of whom were defined as particularly vulnerable to adverse drug events because of age, comorbidity, or co-prescription.

Main outcome measures How reliably each of 15 indicators—four each for non-steroidal anti-inflammatory drugs, co-prescription with warfarin, and prescribing in heart failure, two for dose instructions for methotrexate, and one for antipsychotic prescribing in dementia—and a composite of all 15 could distinguish practices in terms of their rates of high risk prescribing; and characteristics of patients and practices associated with high risk prescribing in a multilevel model.

Results 19 308 of 139 404 (13.9%, 95% confidence interval 13.7% to 14.0%) patients had received at least one high risk prescription in the past year. This composite indicator was a reasonably reliable measure of practice rates of high risk prescribing (reliability >0.7 for 95.6% of practices, >0.8 for 88.2%). The patient characteristic most strongly associated with high risk prescribing was the number of drugs prescribed (>11 long term prescribed drugs v 0; odds ratio 7.90, 95% confidence interval 7.19 to 8.68). After adjustment for patient characteristics, rates of high risk prescribing varied by fourfold between practices, which was not explained by structural characteristics of the practices.

Conclusions Almost 14% of patients defined as particularly vulnerable to adverse drug events were prescribed one or more high risk drugs. The composite indicator of high risk prescribing used could identify practices as having above average or below average high risk prescribing rates with reasonable confidence. After adjustment, only the number of drugs prescribed long term to patients was strongly associated with high risk prescribing, and considerable unexplained variation existed between practices. High risk prescribing will often be appropriate, but the large variation between practices suggests opportunities for improvement.


  • We thank the practices who contributed data to the Scottish programme for improving clinical effectiveness in primary care and allowed the anonymised data to be used for research; staff at the Primary Care Clinical Information Unit at University of Aberdeen who carried out the initial data extraction and management, particularly Katie Wilde and Fiona Chaloner; and project advisory group members Lorna Scahill and Mairi Scott.

  • Contributors: BG conceived the study and planned it with CMcC, PD, and CRS, with TD leading the literature review. KB and BG carried out the analysis. All authors contributed to the writing of the paper. BG is the guarantor.

  • Funding: This work was supported by NHS Quality Improvement Scotland (Better Measures project grant supporting KB) and the Scottish Government Chief Scientist Office (applied research programme grant 07/02 supporting TD, and Health Services and Health of the Public postdoctoral fellowship PDF/08/02 supporting CRS), but study design, data analysis, interpretation and publication were the responsibility of the research team, who had sole access to the data.

  • Competing interests: All authors have completed the ICMJE uniform disclosure form at (available on request from the corresponding author) and declare: that KB was supported by a project grant (Better Measures) from NHS Quality Improvement Scotland, TD was supported by an applied research programme grant (07/02) from the Scottish Government Chief Scientist Office, and CRS was supported by a health services and health of the public postdoctoral fellowship (PDF/08/02); no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

  • Ethical approval: Not required; all data were fully anonymised and data use was compliant with the Primary Care Clinical Informatics Unit research governance process.

  • Data sharing: No additional data available.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: and

View Full Text