Practice factors that influence antibiotic prescribing in general practice in Tayside

D. T. Steinkea,*, D. J. G. Bainb, T. M. MacDonalda and P. G. Daveya

a Medicines Monitoring Unit and b Tayside Centre for General Practice, Ninewells Hospital, Dundee DD1 9SY, UK

Abstract

A cohort design was used to evaluate antibiotic prescribing in relation to patient and general practice characteristics. The study included prescribing to all subjects resident in Tayside, from January to December 1994 and found 215217 antibiotic prescriptions dispensed to 118596 people. Training status of general practitioners (GPs) was found to be the characteristic most associated with prescribing. Adjusting for other GP characteristics had little effect on these results. Training practice status was the dominant factor associated with significant differences in rates of antibiotic prescribing, in class of antibiotic prescribed and in performance indicators of antibiotic prescribing.

Introduction

Prescribing is an activity central to general practice.1 The average general practitioner (GP) in Scotland prescribes over 15000 items each year at a cost of £130000.1 General practice prescribing makes up 82% of all prescribing costs, with hospital prescribing accounting for the rest.1

Within general practice, infections are common presenting problems and antibiotics are among the most frequently prescribed drugs,1 accounting for 11% of total prescribing in Scotland.2 Antibiotics have long been considered an important target for analysis of prescribing.1 However, the increasing prevalence of antibiotic resistance has resulted in the governments of the European Union issuing statements about the need for more prudent prescribing.3

The extension of the teaching of medical students into general practice has resulted in large numbers of GPs becoming involved in undergraduate and postgraduate teaching. In the postgraduate field, criteria are established for formal approval as a training practice, with accreditation of these training practices for undergraduate teaching being of a more informal nature. General practice trainers are expected to take part in a series of educational workshops and seminars that focus on developing methods of providing high quality care. There is evidence that professional education and peer review can reduce prescribing rates.1,2 Prescribing is also influenced by the introduction of audit groups and by educational material and attendance at postgraduate meetings.1,2 The aim of this study was to evaluate the importance of practice characteristics, including training status and patient demographics, on antibiotic prescribing. In addition, six prescribing performance indicators were used, based on previously published suggestions.1

Materials and methods

Data sources

The study used the Medicines Monitoring Unit (MEMO) record-linkage database for prescribing.4 This is a detailed GP-specific, patient-specific computerized record of all prescriptions dispensed in Tayside. Linked to this database are patient demographics, drug and dose information and prescribing GP. Deprivation category for each patient was estimated from Carstairs scores, a method of quantifying relative deprivation or affluence in different localities. Carstairs scores are applied to postcode sectors and are derived by manipulating selected census variables (overcrowding, male unemployment, type of work and availability of a car) in order to create a composite score.5

Study population

The study population comprised individuals who were alive, resident in the Tayside region of Scotland, and registered with a GP between January 1994 and December 1994 inclusive. The analysis excluded temporary residents or people who had moved into or out of practices during the year of study.

Information about general practices

Training practices were identified through the list of practices formally approved for the Vocational Training Scheme for Registrars in the Tayside region and practices accredited for the teaching of medical students. Details of GP list size, time for which the physician has been at the same practice and fundholding status were obtained through Tayside Health Board.

The unique GP number on each prescription was used to determine whether the drug had been prescribed from a training or non-training practice. GPs who started practice or retired from practice within the study period were excluded and any prescriptions ascribed to their numbers were removed from the database. GP registrars in general practice were identified by their unique GP number and analysed separately.

Analysis of prescribing

Antibiotics were identified from the British National Formulary6 (BNF), Chapters 5.1.1–5.1.3. They were analysed by drug class (BNF chapter heading) and by individual drugs within a class. Six performance indicators for prescribing were based on previously published indicators:1 (i) ratio of broad-spectrum penicillins (BNF, Chapter 5.1.1.3) to narrow-spectrum penicillins (BNF, Chapters 5.1.1.1–2); (ii) ratio of co-amoxiclav to amoxycillin; (iii) ratio of co-trimoxazole to trimethoprim; (iv) ratio of trade name to generic name prescribing; (v) ratio of prescribing of nine newer antibiotics (cefaclor, cefuroxime axetil, cefixime, ceftibuten, cefpodoxime proxetil, azithromycin, clarithromycin, ciprofloxacin, ofloxacin and norfloxacin) compared with other antibiotics; (vi) mean number of different antibiotics prescribed by each doctor.

Statistical analysis

Patients for whom an antibiotic was dispensed were compared with those for whom an antibiotic was not dispensed in a logistic regression model.7 Odds ratios and 95% confidence intervals were calculated for the differences between the patients. The Wilcoxon–Mann–Whitney sum rank test was used to analyse the differences in prescribing indicators. P values of <0.05 were interpreted as indicating statistical significance. The logistic regression analysis and Wilcoxon–Mann–Whitney sum rank test were performed with the SAS (version 6.12) statistical package.

Results

There were 138 GPs in non-training practices and 93 in training practices in the study, with a total population of 354819. During the study, 215217 antibiotic prescriptions were given to 118596 people (33% of the resident population of Tayside). After exclusion of 2834 prescriptions that could not be linked to practices with known list sizes, 16774 prescriptions written by doctors outside the Tayside area and 12713 issued by GP registrars, there were 182896 antibiotic prescriptions dispensed to 103946 people.

Non-training practices prescribed significantly more antibiotics (539 prescriptions/1000 population) than training practices (402 prescriptions/1000 population). Regression analysis showed that training status was the most influential variable for antibiotic prescribing (Table IGo). The increased risk was not eliminated when adjusted for variables that might explain differences between practices. Inclusion of fundholding status or deprivation category slightly reduced the odds ratio, but the rate of prescribing by non-training practices was still significantly higher (Table IGo). Inclusion of GP registrars in the regression analysis reduced the odds ratio for antibiotic prescribing, but non-training practices still had significantly higher rates of prescribing (Table IGo).


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Table I. Odds ratios, unadjusted and adjusted by variable, of receiving an antibiotic prescription from a non-training practice versus a training practice
 
Analysis of prescribing performance indicators

Analysis by class of antibiotic revealed striking differences (Table IGo). The greatest difference between non-training and training practices was seen with broad-spectrum antibiotics (cephalosporins, broad-spectrum penicillins and quinolones). There was less difference between the practice types in prescribing for narrow-spectrum drugs (sulphonamides/trimethoprim, tetracylines and narrow-spectrum penicillins).

There were small differences in performance indicators of prescribing (Table IIGo). Three of the differences were statistically significant: non-training practices prescribed a higher proportion of broad-spectrum penicillins, a higher proportion of newer antibiotics and a greater number of different antibiotics per doctor.


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Table II. Performance indicators of prescribing
 
Discussion

We have shown that training status of a practice is associated with clinically and statistically significant differences in prescribing, both in terms of overall rate and in proposed indicators of prescribing performance.

When GP registrars were added to the cohort of training practice doctors, the differences between training and non-training practices diminished. This suggests that the GP registrars are more like the non-training GPs. The GP registrars may be seeing more patients who require an antibiotic, which may explain why their prescribing is higher. However, they may also be prescribing inappropriately to avoid patient–doctor conflict. More research in this area is needed.

Fundholding status did not explain much of the differences in prescribing between non-training and training practices. Other studies have focused on the impact of fundholding on prescribing costs and have also shown a modest or transient effect.8 Fundholding practices are able to reallocate money saved from changes in prescribing to purchase other services for their patients. Non-fundholding practices have less incentive to alter their prescribing habits, because the opportunity costs of prescribing are less obvious.1

Excess prescribing of antibiotics for non-bacterial infections and inappropriate usage of the newer antibiotics in general practice both need to be tackled if we are to avoid emergence of resistant strains of bacteria.1 The present study did not identify the reason for an antibiotic prescription; none the less, the finding that training practices prescribe significantly less antibiotics per patient population size than non-training practices suggests that regular review of prescribing may slow or reverse the trend towards increased prescribing.1 Non-training practices prescribed more new antibiotics than training practices (2.3% and 1.3%, respectively; Table IIGo), but these drugs only made up a small proportion of antibiotic prescribing .

Differences in the population characteristics may explain the difference in prescribing. The training practices may have a ‘healthier’ population requiring fewer antibiotic prescriptions. The outcome was adjusted by age, gender and deprivation category to account for possible differences in population characteristics between training and non-training practices. These confounding variables did not significantly affect the risk of receiving an antibiotic prescription from a non-training practice (Table IGo).

Much of the discussion about prescribing in general practice has focused on costs, but there is increasing interest in devising indicators of prescribing quality.1 However, it should be acknowledged that these are crude.1 The current study shows that prescribing in training practices is different from that in non-training practices and that, in general, the prescribing in training practices is more conservative. It is possible that these differences arise from the training practices' participation in a series of staff development exercises promoting rational prescribing and appropriate use of medication. Educational conferences, outreach and audits have modest effects on prescribing behaviour, but the most successful educational interventions involve repeated peer review of prescribing.8 It is also possible that prescribers who are inherently self-critical are more likely to work in training practices. None the less, our data suggest that the training scheme does influence prescribing and that its influence should be evaluated more formally.

In conclusion, we have found that training status was the dominant factor determining the amount of antibiotic prescribing in general practice. Training practices also had more favourable performance indicators of prescribing.

Acknowledgments

MEMO is part of the MRC Health Services Research Collaboration. Douglas Steinke was supported by a Bayer Fellowship in Pharmacoepidemiology and Pharmacoeconomics at the time of the study.

Notes

* Corresponding author. Tel: +44-1382-632575; Fax: +44-1382-642637; E-mail: doug{at}memo.dundee.ac.uk Back

References

1 . Rimmer, B. & Ross, S. (1997). Perspectives on primary care prescribing. Health Bulletin 55, 243–62.

2 . Chew, R. (1992). Compendium of Health Statistics, 8th edn. Office of Health Economics, London.

3 . Hart, M., Livermore, D. M. & Weinberg, J. R. (Eds). (1998). The Path of Least Resistance. Standing Medical Advisory Committee Sub-Group on Antimicrobial Resistance. Department of Health, London.

4 . Evans, J., McDevitt, D. & MacDonald, T. (1995). The Tayside Medicines Monitoring Unit (MEMO): a record-linkage system for pharmacovigilance. Pharmaceutical Medicine 9, 177–84.

5 . McLoone, P. (1994). Carstairs Codes for Scottish Postcode Sectors from the 1991 Census. Glasgow Public Health Research Unit, University of Glasgow.

6 . British Medical Association and Pharmaceutical Society of Great Britain. (1994). The British National Formulary, number 27. British Medical Association, London.

7 . Hosmer, D. & Lemeshow, S. (1989). Applied Logistic Regression. John Wiley, New York, NY.

8 . Wilson, R. P., Buchan, I. & Walley, T. (1995). Alterations in prescribing by general practitioner fundholders: an observational study. British Medical Journal 311, 1347–50.[Abstract/Free Full Text]

Received 9 November 1999; returned 18 February 2000; revised 30 March 2000; accepted 22 May 2000