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
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Abstract |
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Introduction |
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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.
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Materials and methods |
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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 isolatesmultiple isolates of the same organism with the same susceptibility pattern from the same patientwere 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 19961997 and 19971998 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.
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Results |
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Prescribing and resistance rates are summarized in Table I. 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 1033% differences in rates between surgeries.
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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 III). 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 II
). The significance, breadth and strength of correlations with prescribing decrease with increasing multiplicity of resistance as shown in Table III
. 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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The influence of inclusion of repeat isolates on results was negligible. Table V 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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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 1655 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 VI). There were clear, highly significant (P < 0.001; Wald) differences in baseline resistance between the two groups (Table VI
), 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 VI
), including those from surgeries submitting <50 isolates, closely mirrored that found with Spearman correlation (Table II
).
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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).
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Discussion |
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A notable finding was that the resistanceprescribing 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 consequencescoselection 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 ageresistance curves for the male and female patient subsets (Figure 2) and for high and low prescribing surgeries (Figure 3
) 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 3
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.
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Acknowledgments |
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Notes |
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A report written on behalf of the Welsh Antibiotic Study Group. Group members are listed in the Acknowledgements.
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References |
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Received 9 May 2000; returned 19 August 2000; revised 28 September 2000; accepted 2 November 2000