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Do doctors in dispensing practices with a financial conflict of interest prescribe more expensive drugs? A cross-sectional analysis of English primary care prescribing data
  1. Ben Goldacre1,
  2. Carl Reynolds2,
  3. Anna Powell-Smith1,
  4. Alex J Walker1,
  5. Tom A Yates3,
  6. Richard Croker1,
  7. Liam Smeeth4
  1. 1 The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Primary Care Health Sciences, Oxford, UK
  2. 2 National Heart and Lung Institute, Imperial College London, London, UK
  3. 3 Institute for Global Health, London, UK
  4. 4 Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
  1. Correspondence to Dr Ben Goldacre; ben.goldacre{at}phc.ox.ac.uk

Abstract

Objectives Approximately one in eight practices in primary care in England are ‘dispensing practices’ with an in-house dispensary providing medication directly to patients. These practices can generate additional income by negotiating lower prices on higher cost drugs, while being reimbursed at a standard rate. They, therefore, have a potential financial conflict of interest around prescribing choices. We aimed to determine whether dispensing practices are more likely to prescribe high-cost options for four commonly prescribed classes of drug where there is no evidence of superiority for high-cost options.

Design A list was generated of drugs with high acquisition costs that were no more clinically effective than those with the lowest acquisition costs, for all four classes of drug examined. Data were obtained prescribing of statins, proton pump inhibitors (PPIs), angiotensin receptor blockers (ARBs) and ACE inhibitors (ACEis). Logistic regression was used to calculate ORs for prescribing high-cost options in dispensing practices, adjusting for Index of Multiple Deprivation score, practice list size and the number of doctors at each practice.

Setting English primary care.

Participants All general practices in England.

Main outcome measures Mean cost per dose was calculated separately for dispensing and non-dispensing practices. Dispensing practices can vary in the number of patients they dispense to; we, therefore, additionally compared practices with no dispensing patients, low, medium and high proportions of dispensing patients. Total cost savings were modelled by applying the mean cost per dose from non-dispensing practices to the number of doses prescribed in dispensing practices.

Results Dispensing practices were more likely to prescribe high-cost drugs across all classes: statins adjusted OR 1.51 (95% CI 1.49 to 1.53, p<0.0001), PPIs OR 1.11 (95% CI 1.09 to 1.13, p<0.0001), ACEi OR 2.58 (95% CI 2.46 to 2.70, p<0.0001), ARB OR 5.11 (95% CI 5.02 to 5.20, p<0.0001). Mean cost per dose in pence was higher in dispensing practices (statins 7.44 vs 6.27, PPIs 5.57 vs 5.46, ACEi 4.30 vs 4.24, ARB 11.09 vs 8.19). For all drug classes, the more dispensing patients a practice had, the more likely it was to issue a prescription for a high-cost option. Total cost savings in England available from all four classes are £628 875 per month or £7 546 502 per year.

Conclusions Doctors in dispensing practices are more likely to prescribe higher cost drugs. This is the largest study ever conducted on dispensing practices, and the first contemporary research suggesting some UK doctors respond to a financial conflict of interest in treatment decisions. The reimbursement system for dispensing practices may generate unintended consequences. Robust routine audit of practices prescribing higher volumes of unnecessarily expensive drugs may help reduce costs.

  • prescribing
  • dispensing practices
  • conflict of interest

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors BG, CR and TAY conceived and designed the study. RC created the lists of high-cost and low-cost drugs used for each query. AP-S obtained, merged and cut the data before analysis. BG conducted the analysis with input from AJW, LS, TAY and CR. AP-S replicated the analysis in IPython. BG drafted the paper, all authors contributed substantial revisions. BG acts as guarantor.

  • Funding This work was supported by The Health Foundation (grant number 7599).

  • Competing interests BG, AP-S and CR have produced free open websites offering insights into NHS prescribing data. BG receives income from speaking and writing for lay audiences on problems in science and medicine, including financial conflict of interest, but not dispensing practices or prescribing data. BG and AP-S have received funding from West of England Academic Health Sciences Network, the Health Foundation and NHS England for work on UK prescribing data. BG has received funding from the Wellcome Trust and the Laura and John Arnold Foundation to work on better use of data in medicine. TAY has worked on studies that received support from Pasante, GSK and Sanofi but has not financially benefited from this support. RC is employed by a CCG to optimise prescribing including minimising use of the high-cost items analysed here, and has received income as a paid member of advisory boards for Galen Pharmaceuticals Martindale Pharma, Galderma (UK), ProStraken Group, Menarini Farmaceutica Internazionale SRL and Stirling Anglian Pharmaceuticals. BG, CR, TAY and RC have all been employed by the NHS for a large part of their career.

  • Patient consent This study exclusively uses aggregated, publicly available data and therefore does not require patient consent.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement Raw data can be accessed from NHS Digital. Stata code for statistical analysis, Python code for data extraction, data and code lists to identify drugs, are all available as online supplementary material on Figshare (https://doi.org/10.6084/m9.figshare.6510260).

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