Factors associated with antibiotic resistance in coliform organisms from community urinary tract infection in Wales

A. J. Howarda,*, J. T. Mageea, Karen A. Fitzgeraldb and F. D. J. Dunstanc,{dagger}

a Department of Medical Microbiology and Public Health Laboratory, University of Wales College of Medicine, Cardiff CF14 4XW; b Bro Taf Health Authority, Churchill House, Churchill Way, Cardiff CF1 4TW; c Department of Medical Computing and Statistics, University of Wales College of Medicine, Cardiff CF14 4XW, UK


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Routine susceptibility data for urinary coliform isolates from community practice were analysed in comparison with dispensed antibiotic prescriptions for all conditions and social deprivation data for Bro Taf and North Wales Health Authorities for financial years 1996–1998. Prescribing rates and resistance rates varied widely between practices. Among isolates from practices with high usage of an antibiotic, rates of resistance to that antibiotic tended to be high, and usage correlated significantly with resistance between practice population units. Cross-correlations were found between usage of one antibiotic and resistance to another, particularly for trimethoprim and ampicillin. Usage, particularly of trimethoprim, was associated with multi-resistance to up to four antibiotics. Resistance was more frequent in isolates from males, children and the elderly. Ampicillin resistance correlated with social deprivation. Analyses including or excluding potential repeat isolates yielded closely similar results. Indices reflecting sampling behaviour (laboratory coliform positivity rates, positivity per 1000 registered patients, specimens submitted per 1000 registered patients) varied widely between surgeries, suggesting lack of consensus on urine sampling policies. These indices showed only weak correlations with usage or resistance. Associations between resistance and usage were compared for isolates from two patient subsets that were likely to differ in their proportions of non-Escherichia coli isolates: female patients aged 16–55 years; and males, children and patients aged >55 years. The latter showed higher base levels of resistance, but the associations of resistance with usage were statistically indistinguishable for the two populations. The results suggest that usage of antibiotics in a practice population may affect the rate of urinary infection caused by resistant coliform organisms in that population.


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Antibiotics, public health measures and immunization are three tools that have been effective in the control of infectious disease. The continued contribution of antibiotics is, however, threatened by the emergence and spread of antimicrobial resistance. Growing concern about the potential impact of this on human health has prompted two UK government reports on the problem.1,2

It is the general opinion that increasing resistance is related to selection pressure and, therefore, to antibiotic usage. Associations between antibacterial use and resistance have been well studied in the hospital setting,37 but there is a paucity of information from the community. At national level, wide variations have been observed in relation to antibacterial usage in different countries8 and a broad association with antibacterial resistance has been inferred. Countries with the highest per capita antibiotic consumption tend to have the highest levels of resistance. This is seen particularly in relation to penicillin resistance in pneumococci, macrolide resistance in pneumococci and Streptococcus pyogenes and ampicillin resistance in Haemophilus influenzae.9 Further, changes in rates of resistance have been observed to follow changes in prescribing. For example, increased usage of penicillins and macrolides has coincided with increasing resistance in pneumococci,10 and reductions in prescribing of macrolides have been associated with declining rates of resistance in S. pyogenes.11,12 However, few studies offer statistical evidence for an association between antibiotic usage and resistance. Most relate to infection in the hospital setting,4,1319 with a few in the context of community carriage in commensal flora.2023

The Welsh Antibiotic Study Group instituted a survey of community antibiotic resistance to determine the current situation and investigate any association with antibiotic prescribing at practice level. The good inter-laboratory and inter-agency infrastructure in Wales provided an excellent venue to explore whether routine susceptibility data and other readily available information from established databases could provide a positive contribution to this important question. Results for the initial analyses of resistance in urinary coliform isolates and their association with antibiotic prescribing were published elsewhere.24 Here, we give a more detailed account of these results and of further analyses.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Susceptibility data

Susceptibility data for the period April 1996 to May 1998 were retrieved as electronic files from the microbiology department TelePath computers at Bangor, Cardiff (Llandough and University Hospital of Wales sites) and Rhyl Public Health Laboratories, and East Glamorgan, Prince Charles (Merthyr Tydfil) and Wrexham Maelor Hospitals. Details of individual isolates were obtained for community specimens of urine yielding Escherichia coli or coliform organisms [lactose-fermenting, oxidase-negative, Gram-negative bacilli isolated on cystine/lactose electrolytedeficient (CLED) or MacConkey agar]. The files contained date of isolation, surgery address, specimen type, hospital number, age and gender of patient, specimen number, organism isolated and susceptibility results for ampicillin, co-amoxiclav, an oral cephalosporin (cephalexin for six laboratories, cephradine for the other), trimethoprim and ciprofloxacin. The total number of urine specimens submitted by each surgery over the study period was also recorded. Selective susceptibility testing was practised in one laboratory for co-amoxiclav and in two laboratories for ciprofloxacin; these results were excluded from the analyses to avoid potential bias. Methods varied in detail between laboratories, but all used Stokes' disc diffusion technique.25

The files were transferred to Excel workbooks, where a set of Visual Basic macro routines produced spreadsheets containing percentage susceptibility rates for individual surgeries. Potential repeat isolates—multiple isolates of the same organism with the same susceptibility pattern from the same patient—were marked by a macro routine. A further macro marked isolates showing concurrent resistance to ampicillin and trimethoprim; and recorded the multiplicity of resistance, i.e. resistance to one, two, three, four or five of the recorded antibiotics. Two susceptibility rates were calculated, one excluding potential repeat isolates and the other including all isolates.

Prescription data were obtained from the Welsh Prescription Pricing Authority in Excel spreadsheets. Data for each practice comprised dispensed prescriptions per thousand registered patients per annum (pptpa) during the financial years 1996–1997 and 1997–1998 for all antibiotics, broad-spectrum penicillins, co-amoxiclav, cephalosporins, trimethoprim, co-trimoxazole and quinolone antibiotics. Usage of amoxycillin/ampicillin (for brevity, this term is used here to refer to the summed use of amoxycillin, ampicillin and the various formulations of either of these with an isoxazolyl penicillin) was estimated by subtracting co-amoxiclav usage from broad-spectrum penicillin usage. Mean usage per annum was calculated for each practice over the study period. These data were manually integrated into the susceptibility spreadsheets, linking usage rates with susceptibility rates for each surgery. Susceptibility, prescribing and profile data for two practices were excluded because they were not in operation throughout the study period.

Practice profile data were obtained for practices in the Bro Taf Health Authority. These comprised total number and age distribution of registered patients, and simple (e.g. car ownership) or complex calculated (e.g. Townsend score) variables indicating social deprivation, derived from the 1991 census.

Statistical analyses

Statistical analyses were performed in SPSS for Windows v.6.0 after transfer of the data as comma-delimited ASCII files. Susceptibility rates based on tests for <50 isolates were judged to be unreliable and were excluded from the analysis. Spearman correlation coefficients were calculated for relevant mean annual usage and susceptibility rates. This non-parametric test was thought to be appropriate as the usage data showed some evidence of skewed distribution. Logistic regression results were included as this approach removes bias resulting from variation in numbers of isolates between surgeries and allowed valid inclusion of data for practices submitting small numbers of positive specimens. These analyses were performed on susceptibility data, including or excluding repeat isolates, for the data from individual laboratories and for the combined data from all laboratories.


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The data comprised susceptibility results for 38 343 urinary coliform isolates, of which 5486 were potential repeat isolates, leaving 32 857 isolates for analysis in logistic regression. After exclusion of (i) practices yielding <50 non-repeat coliform isolates during the study period and (ii) repeat isolates, 30 798 isolates from 175 practices remained for analysis in Spearman correlation. Exact numbers of isolates entering the analyses varied somewhat below these maxima, as results for individual antibiotics were sometimes not available. The antibiotic usage data comprised dispensed prescriptions for all conditions.

Prescribing and resistance rates are summarized in Table IGo. Total antibiotic prescribing varied more than fourfold between surgeries; for individual antibiotics, prescribing varied over an 11- to 36-fold range. Rates of resistance among urinary coliforms also varied markedly, with 10–33% differences in rates between surgeries.


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Table I. Rates of resistance and antibiotic usage for surgeries submitting >=50 isolates
 
Correlations between antibiotic prescribing and resistance are shown in Table IIGo. Briefly, resistance to an antibiotic appeared to be associated with prescribing of that antibiotic, giving significant correlations along the diagonal of isologous comparisons. Heterologous comparisons (resistance to one agent compared with prescribing of another) tended to yield non-significant associations, with the exception of comparisons with trimethoprim prescribing, where significant associations with ampicillin, co-amoxiclav and cephalexin/cephradine resistance were found. This pattern of significant associations was also found in analyses of subset data for individual laboratories and health authorities, with the sole exception of the comparison of co-amoxiclav resistance and prescribing, where P values ranged from <0.001 to 0.59. Ampicillin resistance showed a significant correlation with total antibiotic usage. This was expected, as use of amoxycillin/ampicillin constituted a large portion of total antibiotic prescribing.


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Table II. Correlations between antibiotic usage and resistance rates
 
Correlations between prescribing and multiple resistance were also found. The significant cross-associations between ampicillin and trimethoprim prescribing and resistance suggested a non-random association of resistance for these agents (Table IIGo). Co-resistance to ampicillin and trimethoprim occurred in 20.3% (6653 of 32 857) non-repeat isolates. If carriage of resistance to the two agents was independent, one would expect 26.3% (trimethoprim resistance rate, Table IGo) of the 53.2% ampicillin-resistant strains (13% of all isolates) to show resistance to both agents. This anticipated co-resistance rate of 13% contrasts markedly with the observed rate of 20.3%, indicating highly significant linkage of resistance to the two agents (P < 0.001; {chi}2). After removal of data for all isolates showing resistance to both ampicillin and trimethoprim, the heterologous correlations between trimethoprim prescribing and ampicillin resistance and vice versa became non-significant, while the isologous correlations remained significant. This indicates that single resistance to these antibiotics is only associated with prescribing of the homologous antibiotic, whereas co-resistance is associated with prescribing of either (or both) antibiotics. Of all non-repeat isolates, 28.3% showed resistance to two or more of the five antibiotics investigated, 20.3% showed co-resistance to ampicillin and trimethoprim and 16.5% showed coresistance to ampicillin and trimethoprim alone.

Isolates that showed concurrent resistance to two, three, four or five of the recorded antibiotics comprised, respectively, c. 21, 5, 2 and 0.3% of the sample. Resistance to two antibiotics correlated with overall antibiotic, amoxycillin/ ampicillin and trimethoprim usage. For concurrent resistance to three antibiotics, significant correlation was found for overall antibiotic, trimethoprim and cephalosporin usage (Table IIIGo). A marginally significant correlation with quinolone usage was seen for concurrent resistance to four antibiotics. Concurrent resistance to all five antibiotics was rare and did not correlate with any of the prescribing rates examined. The broad association of trimethoprim prescribing with multiple resistance may explain the significant associations of trimethoprim prescribing with resistance to other antibiotics (Table IIGo). The significance, breadth and strength of correlations with prescribing decrease with increasing multiplicity of resistance as shown in Table IIIGo. This may well reflect a decrease in resolution with the marked decrease in numbers of isolates showing high multiplicity of resistance.


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Table III. Correlations between antibiotic usage and resistance to multiple antibiotics
 
The distribution of resistance with age and gender is illustrated in Figures 1–3GoGoGo. Figure 1Go shows the age and gender distribution of patients for all non-repeat isolates. Figure 2Go shows the age and gender distribution for patients with ampicillin-resistant isolates and is typical of the corresponding curves for the other antibiotics. Resistance rates decreased rapidly in isolates from patients aged between 0 and 15–20 years. There was no significant trend in resistance in isolates from females aged between 20 and 65 years, indicating that there is no increase in the risk of infection with resistant isolates with cumulative antibiotic exposure at current prescribing rates. In isolates from patients aged >65 years, resistance levels rose markedly. Resistance rates were significantly higher in males than females for four of the antibiotics, but not for trimethoprim (Table IVGo). The influence of differences in prescribing rates on the shape of the age/resistance curve is shown in Figure 3Go. Lines representing sub-populations from high (>500 pptpa) and low (<250 pptpa) prescribing surgeries showed high or low levels of resistance, as anticipated, but were closely similar in shape. Age and gender distributions for community antibiotic prescribing in the study population were not available from the Welsh Prescription Pricing Unit. However, data for a group of sentinel UK surgeries (provided by Mr D. C. Lloyd of the Prescribing Support Unit, Leeds) show high usage (c. 1400 pptpa) in young children, falling to c. 600 pptpa in adults and rising beyond the age of 60 years to c. 800 pptpa in the over-80s.



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Figure 1. Age and gender distribution for patients with non-repeat coliform isolates. •, males; {circ}, females.

 


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Figure 2. Age and gender distribution for patients with ampicillin- resistant isolates. Similar patterns were found for co-amoxiclav, cephalexin/cephradine, trimethoprim and ciprofloxacin resistance. Error bars represent ± 1 S.E.M. •, males; {circ}, females.

 


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Figure 3. Comparison of age distributions of patients with ampicillin-resistant isolates for high and low prescribing populations. Error bars represent ± 1 S.E.M. •, resistance in isolates from patients registered with practices with >500 prescriptions of amoxycillin/ampicillin per 1000 registered patients per year; {circ}, resistance in isolates from patients registered with practices with <250 prescriptions of amoxycillin/ampicillin per 1000 registered patients per year.

 

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Table IV. Antibiotic resistance rates in isolates from males and females
 
Associations of resistance with social deprivation were investigated for surgeries in the Bro Taf Health Authority by logistic regression analysis. A highly significant association was found between Townsend social deprivation scores (and all other simple and complex deprivation indices recorded) and rates of resistance to ampicillin (P < 0.0001; Wald), but not those to other antibiotics. Increased antibiotic usage was significantly associated with social deprivation, as described elsewhere for paediatric prescribing,26 but the association between prescribing and resistance was not completely explained by deprivation. In logistic regression, having accounted for deprivation, there was still a highly significant association between resistance and amoxycillin/ampicillin usage (P < 0.0001; Wald).

The influence of inclusion of repeat isolates on results was negligible. Table VGo shows that differences in susceptibility rates calculated including or excluding repeat isolates were <1% and showed no trend to increased resistance. The significance and strength of correlations between prescribing and resistance showed minimal differences when calculated including or excluding repeat isolates.


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Table V. Effect of including potential repeat isolates on resistance rate estimates
 
Practice resistance rates for the five antibiotics were examined in relation to the proportion of registered practice patients aged <15 and >=60 years. Ampicillin resistance rates were associated with the proportion of registered patients aged <15 years (Spearman coefficient 0.2518; P = 0.019), rising with increasing proportions of children in the practice population. Note, however, that amoxycillin/ ampicillin usage showed a parallel rise in practices with a high proportion of children (Spearman coefficient 0.2381; P = 0.027). Regression analysis indicated that the increased resistance was accounted for by this increased prescribing and there was no unaccounted further contribution to resistance from differences in child population demographics (P > 0.10; Wald). Associations with the proportion of patients >=60 years old, and with resistance to other antibiotics, were not significant (P > 0.10; Spearman).

Inclusion of coliforms other than E. coli also appeared to have little influence on the correlations. To analyse any effects, the data were divided into two subsets. One group comprised isolates from a sub-population known to show a low frequency of infection with coliforms other than E. coli, namely women aged 16–55 years. The other group comprised isolates from sub-populations that tend to show higher frequencies of complicated urinary infection (and, therefore, a greater proportion of coliforms other than E. coli27,28), i.e. the elderly (>55 years), the young (<16 years) and males. Logistic regression for association between prescribing and resistance gave similar results for all antibiotics (Table VIGo). There were clear, highly significant (P < 0.001; Wald) differences in baseline resistance between the two groups (Table VIGo), suggesting unequal partitioning of coliforms other than E. coli, which often show broad resistance. However, the association between prescribing and resistance did not differ (P > 0.10; Wald) between the groups, indicating close similarity of prescribing effects. The pattern of significant associations between prescribing and resistance found in logistic regression analysis for all non-duplicate isolates (Table VIGo), including those from surgeries submitting <50 isolates, closely mirrored that found with Spearman correlation (Table IIGo).


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Table VI. Association (logistic regression analysis) between prescribing and resistance and differences in this association between two patient groups, one comprising females of child-bearing age and the other comprising males, children and the elderly
 
Sampling bias effects were sought with three indices calculated for surgeries of the Bro Taf Health Authority. These were: laboratory positivity (the number of coliform-positive specimens per 1000 submitted urine specimens), population positivity (the number of coliform-positive urine specimens per 1000 registered patients) and population sampling rate (the number of urine specimens submitted per 1000 registered patients). All three indices varied markedly: population sampling—mean 72.8 specimens/ year/1000 patients, range 0.6–237.2, S.D. 43.9; laboratory positivity—mean 18.8%, range 8.6–50.0%, S.D. 6.0; population positivity—mean 13.1 positive specimens/year/1000 patients, range 0.1–43.2, S.D. 8.0. This might be partially explained by demographic effects, such as differing age distribution, but such wide variation clearly implies a lack of consensus on sampling strategies. This variation provided a tool for analysis of the effects of differing sampling rates (and, therefore, probable differences in sampling strategies) on perceived rates of resistance.

Of the correlations between resistance rates or prescribing and the population-based indices, only two were significant, i.e. those between (i) ampicillin resistance and per capita positivity (Spearman coefficient –0.34, P < 0.001) and (ii) ampicillin resistance and per capita sampling (Spearman coefficient 0.32, P < 0.001). They showed decreasing resistance with increasing per capita positivity and with decreasing per capita sampling, the converse effects to those anticipated for selective sampling of treatment failures. Curiously, laboratory positivity rates also showed decreasing resistance rates with increasing positivity, at highly significant P values, for ampicillin (Spearman coefficient –0.23, P < 0.001), co-amoxiclav (Spearman coefficient –0.36, P < 0.001) and trimethoprim (Spearman coefficient –0.34, P < 0.001).


    Discussion
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The breadth of variation in prescribing rates and resistance levels between practices was not anticipated, but gave a data range that was most amenable to analysis. The statistical analysis shows a link between resistance to an antibiotic and concurrent usage of that antibiotic, confirming a large mass of anecdotal evidence from hospital and community practice. Although the results do not prove a causal relationship, Darwinian selection provides a plausible mechanism to explain these observations. The links were not strong, explaining up to c. 20–30% of the observed variation in resistance between practices. However, numerous other factors may well have intervened to increase the scatter of data and obscure the associations. Examples include the following: (i) data on dental prescribing and usage associated with hospital inpatient and outpatient visits were not available and could not be apportioned to practices; (ii) dispensed prescriptions are not necessarily consumed; (iii) there may be some variation in susceptibility testing results; and (iv) there are likely to be delays between changes in prescribing and changes in resistance.

A notable finding was that the resistance–prescribing association operated between such small population units as individual practices. Surveillance of bacterial resistance in the community is commonly performed for population units at national or regional level and this structuring may have led to a widespread unwarranted perception that factors affecting resistance act over broad geographical units. For example, community practitioners tend to perceive resistance as a national problem, essentially unaffected by their individual prescribing behaviour.29 The results indicate that this is a mistaken impression. Practice antibiotic usage does have significant consequences on local levels of resistance in urinary tract infection, with the plausible implication that this may also be true of other infections. This information could be useful in altering perceptions and so could aid interventions intended to reduce unnecessary antibiotic prescribing.

The correlation between trimethoprim usage and ampicillin resistance and vice versa suggested selection of linked resistance to both antibiotics, an impression supported by the highly significant excess of isolates showing concurrent resistance to these antibiotics. Carriage of linked resistance to these two antibiotics on transmissible plasmids is widespread and well documented in faecal commensal E. coli.30,31 The results indicate the expected consequences—coselection of this multiple resistance pattern and its occurrence in autogenous urinary E. coli infections. A further notable observation was the breadth of correlation between trimethoprim usage and multiple resistance. Trimethoprim is a first-line agent for treatment of many community infections and the possibility that it may select multiple resistance is a cause for concern.

The findings on age and gender distribution of patients with resistant organisms are less certain. It is probable that coliform species other than E. coli contributed to the increased resistance rates in males and in the young and elderly. This reaffirms a widely accepted priority for bacteriological investigation of urinary infections in these higher risk groups. However, the findings in the context of an association between resistance and usage are novel. The shapes of the age–resistance curves for the male and female patient subsets (Figure 2Go) and for high and low prescribing surgeries (Figure 3Go) were consistent, and similar for all antibiotics. This suggests that the correlation between resistance and prescribing is maintained over a wide range of patient, antibiotic and isolate factors. There are, as far as we can see, no theoretical reasons to believe that antibiotic selection pressure should act differently between E. coli and other coliform species, and the evidence from Figures 2 and 3GoGo does not suggest any differential effects. This conclusion is also supported by the patient-group analysis. However, these results are indirect evidence and require confirmation in a survey with full identification of isolates.

Association between antibiotic resistance and social deprivation has not been described before. Deprivation is linked with an increased prevalence of many diseases and there are several reasons why such populations might receive more antibiotics. However, increased prescribing alone did not account for the observed excess of ampicillin-resistant isolates from socially deprived practices. Confirmatory studies would be helpful, particularly if their design included an investigation of possible mechanisms. Perhaps this effect could be due to increased person-to-person transmission of resistance plasmids or resistant strains in commensal faecal coliforms, or to poor patient compliance with prescribing instructions.

Analyses of retrospective routine diagnostic data are subject to numerous potential biases. Considerable efforts were made to investigate factors that might have acted with sufficient bias and strength to yield misleading correlations between usage and resistance. These investigations form the bulk of new material presented here. The results provide no support for the hypothesis that the core findings could be explained by patterned bias.

There was no evidence of any significant change in estimates of resistance rates, or in any other results of the statistical analyses, when potential duplicate isolates were included in the data. This agrees with Huovinen's study in Finland,32 where repeat isolates significantly affected estimates of resistance rates for inpatients, but had little influence for non-catheter outpatient specimens. These results may be helpful to laboratories that wish to issue reports outlining local community susceptibility rates in urinary infection.

Inclusion of species other than E. coli under the blanket identification of coliforms may have increased baseline estimates of resistance, but indirect evidence suggested that this did not affect the core finding that correlations existed between antibiotic resistance and usage. Variation in age demographics also proved an inadequate explanation of the observed correlations.

The most elusive of the potential biases was the effect of selective urine sampling. Correlations between resistance or prescribing and three sampling indices were mostly non-significant. Where links were found, they were weak and inconsistent with the hypothesis that the observed correlation between resistance and prescribing was an artefact of any simple relationship with selective sampling.

In conclusion, the study has provided evidence to support current interventions aimed at reducing antibiotic prescribing in the community. In contrast to the extensive knowledge of the biochemistry and molecular biology of resistance, little is known of the ecological and epidemiological mechanisms that control levels of resistance in infections. The problem of growing resistance may well encourage greater support for this important but neglected area in antibiotic research. Work on the relationship between changing levels of prescribing and changes in resistance over time may be particularly useful in assessing the need for, and likely outcome of, future interventions.


    Acknowledgments
 
Assembling the data required the assistance of so many people that it is difficult to list them individually. We thank: the staff of the seven laboratories involved, particularly those who produced the data files and linked senior partner names with practice addresses; the staff of the Welsh Prescription Pricing Unit; the Local Medical Committees of the Health Authorities who granted permission to access data; Nigel Moss of Bro Taf Health Authority for the practice profile information; Mr D. C. Lloyd of the Prescribing Support Unit, Leeds, for data on the age demographics of antibiotic prescribing in the UK; and David Myles of the University of Wales College of Medicine for computing support. The Welsh Antibiotic Study Group comprises: Dr K. Alshafi, Royal Gwent Hospital, Newport; Dr C. Banerjee, Princess of Wales Hospital, Bridgend; Dr N. J. B. Carbarns, Neville Hall Hospital, Abergavenny; Dr C. Cefai, Wrexham Maelor Hospital; Dr K. El-Bouri, Prince Charles Hospital, Merthyr Tydfil; Dr K. A. Fitzgerald, Bro Taf Health Authority, Cardiff; Dr G. Harrison, Prince Phillip Hospital, Llanelli; Dr A. J. Howard, Cardiff PHL; F. Johnstone, North Wales Health Authority, Bangor; Dr D. H. M. Joynson, Swansea PHL; Dr D. N. Looker, Rhyl PHL; Dr L. Macfarlane, Aberystwyth PHL; Professor S. R. Palmer, Department of Epidemiology, University of Wales College of Medicine; Dr R. Salmon, CDSC Wales; Dr M. Sheppard, Withybush Hospital, Haverfordwest; Dr M. D. Simmons, Carmarthen PHL; Dr M. Walker, Bangor PHL; and Dr D. White, Royal Glamorgan Hospital, Pontypridd.


    Notes
 
* Corresponding author. +44-2920-744515; Fax: +44-2920-74603; E-mail: tony.howard{at}phls.wales.nhs.uk Back

{dagger} A report written on behalf of the Welsh Antibiotic Study Group. Group members are listed in the Acknowledgements. Back


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
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Received 9 May 2000; returned 19 August 2000; revised 28 September 2000; accepted 2 November 2000