Effects of duplicate and screening isolates on surveillance of community and hospital antibiotic resistance

J. T. Magee* on behalf of the Welsh Antibiotic Study Group{dagger}

Communicable Diseases Surveillance Centre, Abton House, Wedal Road, Cardiff CF4 3QX, UK

Received 22 January 2004; returned 18 March 2004; revised 19 April 2004; accepted 23 April 2004


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Footnotes
 Acknowledgements
 References
 
Objectives: To investigate common contentions that duplicate and screening isolates consistently show marked excess resistance, and that inclusion of such isolates significantly distorts regional resistance estimates.

Methods: Two Welsh surveys of antibiotic resistance for routine diagnostic isolates were analysed, comprising 309 129 isolates of six common community pathogens and 85 061 ward isolates of 11 common hospital pathogens. Duplicate isolates were defined as isolates from the same patient of the same pathogen with an indistinguishable susceptibility pattern, excluding the initial isolate. Significance was assessed from 95% confidence limits of the difference between resistance estimates.

Results: Duplicate isolates comprised ~20% of total isolates. For the 195 antibiotic–pathogen combinations investigated, differences in resistance between duplicate and non-duplicate isolates were statistically significant for 93. Only 54 combinations showed significantly increased resistance amongst duplicates, and only 30 of these showed a difference >5%. Comparisons of de-duplicated with un-de-duplicated regional resistance estimates showed significant differences for only 18 of 195 antibiotic–pathogen combinations; none were sufficient to alter judgement on clinical use. Screening isolates produced little disturbance of resistance estimates for Staphylococcus aureus, with the exception of flucloxacillin resistance, where inclusion of screening and duplicate isolates resulted in an increase of 4.4% for both community and hospital resistance estimates.

Conclusions: The contentions were incorrect for these regional surveys. However, the proportion (and so effects) of screening and duplicate isolates may be greater in surveys of units with frequent repetitive sampling practice (burns, ITU, cystic fibrosis), or pathogens subjected to unusually intensive infection control sampling.

Keywords: duplicate isolates , screening isolates , antibiotic resistance surveillance


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Footnotes
 Acknowledgements
 References
 
Increasing antibiotic resistance has prompted concern about our continuing ability to treat bacterial infections,1 and calls for increased UK surveillance.2 Targeted surveillance studies are an important research approach to the problem, but are limited in geographic and temporal scope, and are not an economically viable option for continuing comprehensive surveillance. Routine diagnostic data are available in bulk, on a continuous and geographically comprehensive basis, and collection of these data, at least from computerized laboratories, is a realistic economic option. However, much of the basic information required to design a satisfactory resistance surveillance system based upon these data remains unknown.2,3 The possible effects of various potential biasing factors on observed resistance levels are often discussed with vigour. However, there is little published information on these factors that might inform discussions, with the exception of standardization of methodology and interpretation of susceptibility tests. The effects of other factors, e.g. duplicate isolates and testing policies, remain largely unknown.

The process of specimen submission and laboratory processing is directed to the immediate, important primary purposes of diagnosis, guidance of treatment and infection control. Repeat isolates of the same organism from a single infection may occur when efficacy of treatment is monitored. Here, excess resistance is possible because initial blind therapy may fail to eradicate resistant organisms, or because some infections with persistent multi-resistant strains resolve slowly despite appropriate therapy. A patient may suffer repeated infections with the same organism, yielding multiple indistinguishable isolates. For common pathogens, these infections are often endogenous, and specimens taken to assess carriage during or between infection episodes may yield further indistinguishable isolates from the commensal flora. It is widely believed that specimen submission strategies are biased, with more specimens submitted from patients with resistant pathogens. If so, inclusion of these duplicate isolates might bias resistance estimates. Further, isolates from screening specimens may reflect commensal carriage, rather than infection. Screening activity is primarily aimed at infection control, often of resistant pathogens, e.g. methicillin-resistant Staphylococcus aureus (MRSA) and may be carried out by methods that selectively isolate resistant organisms, and again might bias resistance estimates by loading data with resistant commensal isolates.

Resistance surveillance is directed to monitoring changes in resistance, and to producing current estimates of local resistance in infection. Ideally, both should be assessed from comprehensive data, with a single isolate representing each clone involved in an infection. Data pre-processing to mark duplicate and screening isolates requires complex programming. However, once programmed, modern computational power allows rapid processing.

It is commonly contended that duplicate and screening isolates are more resistant than other strains, and that this produces a significant bias to excess resistance in surveillance estimates when these isolates are not removed. This paper examines these contentions, in an attempt to produce a generalized strategy to deal with this problem.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Footnotes
 Acknowledgements
 References
 
Community susceptibility data

These were for Welsh community isolates from April 1996 to March 2001 for 14 of the 18 laboratories providing diagnostic services to Welsh community practices. Data were collected for isolates of six common community pathogens [urinary coliforms, Haemophilus influenzae, methicillin-susceptible S. aureus (MSSA), MRSA, Streptococcus pneumoniae, and Streptococcus pyogenes]. These comprised, for each isolate: date specimen received; specimen type; practice address code; patient date of birth, sex and unique identifier code; specimen laboratory number; and isolate susceptibility, where tested, to 13 antibiotics [penicillin, ampicillin, co-amoxiclav, cefuroxime, cefalexin, methicillin (usually reported as flucloxacillin), erythromycin, tetracycline, trimethoprim, nalidixic acid, ciprofloxacin, norfloxacin and nitrofurantoin].

Hospital susceptibility data

These data from April 2000 to March 2003 were for wards and other units dealing with inpatients at two teaching and five district general hospitals in Wales. These were collected for common hospital pathogens [Acinetobacter spp., undifferentiated coliforms from all sites, enterococci, coagulase-negative staphylococci (CoNS), Enterobacter spp., Escherichia coli, Klebsiella spp., MSSA, MRSA, Pseudomonas spp. and Serratia spp.] Data collection was as for community isolates with the following modifications: ward codes rather than practice codes were collected, and susceptibilities, where tested, for 16 additional antibiotics (amikacin, aztreonam, ceftazidime, cefotaxime, colistin, fusidic acid, gentamicin, imipenem, linezolid, mupirocin, neomycin, rifampicin, quinupristin–dalfopristin, teicoplanin, piperacillin–tazobactam and vancomycin). Nalidixic acid, nitrofurantoin and norfloxacin susceptibilities were not collected for hospital isolates.

Laboratories contributing data to these surveys utilized various disc-diffusion susceptibility testing methods, mostly based on the Stokes comparative method, and attained acceptable results in the UK National External Quality Assessment Service scheme. Standardization on the BSAC protocol commenced in 2000, and most participating laboratories had completed this change by 2003.

Data pre-processing and analysis

The data were downloaded from the laboratory computers via the TelePath list generator or Microbiology DataStore. Formatting and marking of duplicate and screening isolates were carried out via Excel Visual Basic macros. For patients with multiple isolates of the same pathogen, the susceptibility patterns of the isolates were compared. The first isolate representing each distinct susceptibility pattern was included in the analysis but further isolates of the same pathogen with indistinguishable antibiograms from that patient were marked as duplicates, and the period in days from the initial isolate noted. Antibiogram matching was based on comparison of susceptible (S) or resistant (R) results, excluding mismatches with intermediate (I) results, or where the antibiotic was not tested (N ‘result’), e.g. the pattern RINRNSS would be interpreted as distinct from the pattern SRSRNSS, but indistinguishable from the pattern RSSINSS. The term ‘non-duplicate isolates’ is used throughout to refer to all unique isolates plus the initial isolate from each patient duplicate series. Analyses were carried out in Excel or Statistical Programs for the Social Sciences (SPSS Inc., Chicago, IL, USA); 95% confidence limits for individual resistance estimates and differences were calculated by Wilson's method4,5 and method 10 in the paper by Newcombe,6 respectively. If the 95% confidence interval for the difference between two estimates did not enclose zero, the difference was regarded as statistically significant. There are complex technical issues involved in comparisons of the estimates based upon all isolates with those excluding duplicate, screening, or duplicate and screening isolates, and the approach is not precise in this case. However, in view of the lack of clinical significance (see Results) for those differences deemed statistically significant, the approach was considered sufficiently sensitive for the purpose, and was readily computed in Excel spreadsheets.

Data exclusions and fusion

Resistance estimates were excluded from analysis where these were based upon tests for <30 duplicate isolates, and so could not be analysed,3 or resistance among non-duplicate isolates was >98%. This step removed irrelevant pathogen/antibiotic combinations, e.g. penicillin susceptibility in E. coli. MRSA and MSSA were regarded as distinct pathogen groups. Previous work3 had shown that these species subgroups were subject to distinct laboratory testing policies, and they showed clear differences in frequency of duplicate isolates indicating parallel differences in sampling policy (Table 1). The exception was for estimates of flucloxacillin resistance, which were calculated for S. aureus rather than these subgroups. This reduced the number of antibiotic–organism combinations by one in both the community and hospital pathogen series. Intermediate susceptibility results were regarded as resistant in calculation of resistance estimates.


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Table 1. Variation in the frequency of duplicate isolates between pathogens for the two surveys analysed

 
Duplicate isolate marking

This was carried out by four distinct strategies.

  1. Minimum processing strategy: all isolates were included.
  2. Duplicate isolate elimination strategy: all duplicate isolates were excluded.
  3. Infection threshold duplicate isolate elimination: for all patients with a series of indistinguishable isolates, data on the first isolate of the series were included. Subsequent indistinguishable organisms isolated less than n days (where n is the threshold interval) from the first isolate were excluded. The first indistinguishable organism isolated beyond the threshold was regarded as the first isolate of a new infection episode, all duplicates found within n days of this first new infection isolate were excluded, and so on.
  4. Clearance threshold duplicate isolate elimination: for all patients with multiple isolates, and for each series of indistinguishable isolates, data on the first isolate of the series were included. Subsequent isolates within n days of the last previous isolate were regarded as representing continuing infection or colonization, and excluded. Where a gap of more than n days occurred, this was regarded as clearance of the organism; the isolate after this gap was regarded as the initial isolate of a new infection, and so on.

Screening isolates

These were marked for MRSA and MSSA when overtly identified as such in the specimen type, and for sites commonly sampled in screening, but rarely involved in infection — isolates from axilla, nose, throat, perineum, umbilicus and groin swabs were regarded as commensal screening, rather than infection isolates.


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Footnotes
 Acknowledgements
 References
 
For most pathogens, duplicate isolates comprised ~10–20% of isolates (Table 1). Lower proportions tended to be found for pathogens with low isolation rates; the lowest level of duplicates was for S. pyogenes (6.5%), where high treatment efficacy probably discourages submission of follow-up swabs. Higher proportions of duplicate isolates were found for: Pseudomonas spp., originating largely from units involved in treatment of burns or cystic fibrosis, where duplicates comprised 40–70% of isolates; enterococci, Enterobacter, and Serratia, where duplicates originated mainly from intensive care units; and among community MRSA.

The interval between the initial and subsequent duplicate isolates showed an inverse exponential frequency distribution for each pathogen investigated (Figure 1). However, the fine structure of this distribution often showed an overlying 7 day periodicity for duplicates isolated within 100 days of the initial isolate; this was most marked for community isolates (Figure 2), and may represent literal interpretation of instructions to return for further specimens in n weeks by the patient. This effect was much less marked for hospital isolates, but a 7 day periodicity was detectable out to 42 days from the initial isolate for some hospital pathogens. There was no clear break in the frequency distributions that might guide selection of a threshold beyond which isolates should be considered as representing a new episode of infection for any of the pathogens.



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Figure 1. Distribution of duplicate isolate frequency with time elapsed from the initial isolate. Frequencies are shown for 10 day intervals. This inverse exponential curve for hospital MRSA isolates is typical of the distributions found for each of the hospital and community pathogens investigated.

 


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Figure 2. Distribution of duplicate isolates with time elapsed from the initial isolate for the first 100 days. Frequencies are shown for 1 day intervals. The 7 day periodicity, deficit of isolates from 1 to 14 days, and excess of isolates from 14 to 42 days were most marked for community urinary coliform isolates, as illustrated here. These effects were less marked for other community pathogens, and often only barely discernable for the hospital pathogens investigated.

 
Differences in resistance between duplicate and non-duplicate isolates

The definition is given in Materials and methods. Table 2 shows details for the 362 antibiotic–pathogen combinations. Briefly, 46% of these combinations were eliminated as irrelevant or not analysable by the rules outlined in Materials and methods; these included combinations such as penicillin, flucloxacillin, fusidic acid, erythromycin, vancomycin and teicoplanin resistance in the Gram-negative pathogens, and nalidixic acid resistance in the Gram-positive pathogens. For the 195 combinations remaining, there was little evidence of consistently elevated resistance in duplicate isolates. About half (52%) showed no significant difference between resistance in duplicate and non-duplicate isolates. Significantly increased resistance among duplicate isolates was found for about a quarter (28%) of the 195 combinations, but for most of these the difference in resistance was small (≤5%). Only 30 of 195 (15%) combinations showed resistance among duplicate isolates that exceeded resistance among non-duplicate isolates by >5%. This 5% threshold delimits those areas where inclusion of duplicate data in surveillance estimates might lead to ≥1% overestimation of resistance given a proportion of duplicates of about 20%, as found here. These 30 combinations were mostly from the hospital survey, and included 12 of the 17 antibiotics investigated for CoNS (see Discussion).


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Table 2. Details of the differences in resistance between duplicate and non-duplicate isolates for the 362 available antibiotic–pathogen combinations, showing their assignment to various categories

 
Differences between resistance estimates calculated including or excluding duplicate isolates

These were much smaller than the differences between duplicate and non-duplicate isolates, because duplicate isolates composed a small proportion of total isolates (Table 1). The two threshold de-duplication strategies yielded essentially identical results, and results for the minimal, duplicate elimination and clearance (with an arbitrary threshold of 91 days, adopted because this threshold is recommended for distinction of continuing from repeat infection for infectious disease surveillance in Wales) strategies are discussed as examples. When resistance estimates that were calculated excluding duplicates were compared with estimates based on all isolates, only 10 antibiotic–organism combinations showed significant differences for community pathogens, and only eight for hospital pathogens. In all 18 instances, the difference in resistance was considered insufficient to be of clinical significance, as it would not affect judgement on suitability for use in treatment (Table 3).


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Table 3. Details of those antibiotic-pathogen combinations showing significant differences (assessed from 95% confidence limits for the difference)5 between resistance estimates based upon all isolates, and data excluding duplicate isolatesa,b

 
Effects of screening isolate removal

Comparisons of resistance estimates based upon all isolates, and upon data excluding duplicate isolates, screening isolates, and both duplicate and screening isolates are shown in Table 4. Of the 10 significant differences, only those related to flucloxacillin resistance appeared sufficiently large to be of possible clinical significance. Vancomycin resistance amongst enterococci was low (~1%) in the units surveyed, and the low numbers of screening isolates precluded analysis of their effects on resistance estimates for enterococci.


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Table 4. Details of the antibiotic–pathogen combinations showing significant differences (assessed from 95% confidence limits for the difference)5 between resistance estimates calculated for all isolates, and excluding duplicate and screening isolates

 
Effects of duplicate isolates at ward level

Table 5 shows ciprofloxacin resistance estimates for a burns ward where 56% of Pseudomonas spp. isolates were duplicates. Comparisons of resistance estimates calculated with and without duplicate isolates, and with the regional estimate of resistance (Table 3) could not be interpreted with confidence, as the small number of ward isolates left wide 95% limits.


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Table 5. An example of the effect of duplicate isolates on resistance estimates at ward level

 
Details of all analyses and of the Excel Visual Basic macros are available from the author in Excel workbook format.


    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Footnotes
 Acknowledgements
 References
 
A variety of time thresholds have been employed for de-duplication in resistance surveillance, from 3 days to 1 year,7 but none appear to have been validated. None of the pathogens studied here showed a break in the histogram of duplicate frequency that might be interpreted as a real separation of continuing from repeat infection. Shannon & French7 reported closely similar results for duplicate isolates from blood cultures concluding, as here, that these thresholds are arbitrary. The concept of a threshold that would reliably distinguish between isolates from continuing or new infection did not emerge from the data for any of the pathogens investigated. Further investigation to validate the threshold concept would require extensive, costly typing studies that would be difficult to justify, given the minimal pragmatic effects on resistance estimates, and the lack of any evidence for a bimodal distribution in duplicate intervals for a broad selection of common hospital and community pathogens.

Equally, the concept does not fit well with reality. Most common pathogens are implicated in a spectrum of disease, from short-term acute infections such as minor septic cuts, to chronic infections, e.g. leg ulcers, and chronic obstructive airways disease. Further, these pathogens often readily integrate into the commensal flora, and so might well be encountered as duplicate isolates from screening swabs, or in re-emergent endogenous infection. An alternative concept of applying a threshold by disease type, rather than pathogen, might fit better to reality, but would be beyond the resources of anything other than well-financed research surveys with good clinical input, given the limited clinical information normally encountered on specimen request forms.

If, despite the problems discussed above, the concept of arbitrary threshold de-duplication is applied in resistance surveillance, this study suggests that the threshold is better set long (>42 days) and to an interval that corresponds to a whole number of weeks plus 2 or 5 days to minimize periodicity effects. In the author's opinion, series of indistinguishable isolates from the same patient are best regarded as mostly representing presumptive continuing clinical/sub-clinical infection or colonization, and only the first isolate should be considered in disease or susceptibility surveillance, provided there is confidence in the reproducibility of susceptibility patterns. If there is little confidence in test reproducibility for a specific antibiotic-species combination, then it might be best to exclude that antibiotic in de-duplication for the species and to develop a more reliable test protocol. These views largely concur with the NCCLS recommendation on calculation of oxacillin resistance estimates for S. aureus8 validated by Shannon & French.9 However, differences between resistance estimates based upon all isolate, de-duplicated and threshold de-duplicated data were generally small, and the argument has little pragmatic significance, with specific exceptions outlined below. Further efforts to establish confidence in resistance surveillance from routine data would be best directed to resolving the more significant effects of laboratory testing policies,3 and controversy on the effects of specimen submission practice.

The contention that duplicate isolates are generally and significantly more resistant than non-duplicate isolates was untrue for these surveys, and the corollary that this hypothetical excess resistance produces strong bias towards increased resistance in estimates based on raw isolate data was also incorrect. The proportion of duplicate isolates was low, their difference in resistance was small, and their contribution to resistance estimates calculated across all isolates was minor. The net result was that few resistance estimates based upon de-duplicated data showed significant deviation from ‘raw data’-based estimates, and these differences were too small to be clinically significant. Most published work on this topic involves local surveys. Shannon & French7,9 reported largely similar results, but found clinically significant differences for gentamicin resistance in Klebsiella spp. Bennett et al.10 reported no differences of practical significance for a variety of hospital pathogens. Horvat et al.11 reported significant differences for de-duplicated methicillin resistance estimates for S. aureus, but with a proportion of MRSA duplicate isolates of ~50%.

Equally, screening isolates appeared to have only minor effects on resistance estimates. The sole exception was for estimates of methicillin resistance in S. aureus, where removal of duplicate and screening isolates resulted in a decrease of 4.4% for both hospital and community resistance estimates. The difference is possibly sufficient to justify more elaborate pre-processing for estimates of this important resistance. Shannon & French also found a moderate decrease (8%) in methicillin resistance estimates for S. aureus when duplicate and screening isolates were eliminated from data.9 Small differences for in-patient estimates of vancomycin resistance in enterococci and larger differences in gentamicin resistance for Klebsiella spp. were noted in the latter study, but were not evident in Welsh data. This might reflect differences in local endemicity, or repetitive sampling and infection-control sampling practice; neither of these organisms was considered a major infection-control problem in the hospitals surveyed here.

The grossly aberrant proportion (12/17) of antibiotics showing >5% excess resistance among duplicate isolates of CoNS deserves comment. Repetitive sampling of multiple sites (e.g. peripheral and central blood) is often used to distinguish between infection and contamination for this organism. Where the patient yields multiple indistinguishable isolates, this is taken as indicative of infection, whereas isolation of CoNS with distinct antibiograms, or from a single specimen tends to be interpreted as commensal contamination. Paradoxically, in this case, resistance estimates for duplicate isolates may be more relevant to infection than de-duplicated estimates, which may well be biased by inclusion of large numbers of commensal isolates.

Clearly, the proportion of duplicate and screening isolates will be affected by local sampling practice. For species where abnormally large numbers of screening, infection-monitoring or colonization-clearance specimens are submitted, a disproportionate level of screening and duplicate isolates is likely to occur, and this may perturb resistance estimates. These situations can be readily identified with current local knowledge of infection control and ward submission practice. Infection control activity centres on local problem organisms, often MRSA and vancomycin-resistant enterococci, but certainly including other organisms from time to time. Some types of unit tend to have policies of frequent repetitive sampling (e.g. intensive care, burns and cystic fibrosis units). Resistance estimates in these cases should be treated with some suspicion, particularly when high cross-infection activity is superimposed upon a repetitive sampling policy. Equally, where screening methods involve selective isolation of resistant organisms, exclusion of screening isolates should be considered.

The conclusion was that removal of duplicate and screening isolates was not necessary in calculating regional resistance estimates in these surveys, with the exception of methicillin resistance in S. aureus. This is not entirely firm; practice on submission of repeat specimens or screening swabs may vary between units, hospitals, regions or countries. It would be wise to carry out a similar analysis on local data, with periodic checks to ensure that these conclusions remained valid, particularly for organisms subject to high levels of infection-control activity or units with repetitive sampling. However, a large increase (two-fold or more) in the proportion of duplicate and screening isolates would be required to invalidate the conclusion, and the appropriate action might well be to investigate sampling practice on economic grounds, rather than to alter data processing strategies.

These analyses centred on effects upon resistance estimates calculated at regional level. Local variations in sampling and screening practice may produce greater effects for small geographic units. This was encountered with the effects of laboratory testing policies, which were small for regional estimates, but much greater for local estimates.3 Where resistance estimates for progressively smaller units are calculated, the effects from localized variations in practice become greater, and the reduction in sample size produces successively wider confidence limits. Resistance estimates at, e.g. ward level, should be interpreted with caution, bearing in mind the 95% confidence interval for the estimate and knowledge of local practice.


    Acknowledgements
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Footnotes
 Acknowledgements
 References
 
In memory of Dr Phil Thomas, Director of Swansea Public Health Laboratory and a member of the Welsh Antibiotic Study Group who died on the 8th November 2003. My thanks to those who helped with the paper, particularly the staff of the Welsh and Border laboratories that provided the data, Dr Maggie Heginbothom, Jo Bell, David Williams, Mark Thomas and Dr Brendan Mason at Abton House, and Daffyd Williams and his ICN colleagues throughout Wales. The work was supported by grants from the Department of Health (Project Ref. No. 120) and the Wales Office of Research and Development for Health and Social Care (Project R00/1/027), and received ethical approval from the Multi-Centre Research Ethics Committee for Wales (MREC 03/09/052 and MREC 00/9/39).

The Welsh Antibiotic Study Group comprises: Dr K. Al Shafi, Royal Gwent Hospital, Newport; Dr C. Banerjee, Princess of Wales Hospital, Bridgend; Dr N. J. B. Carbarns, Nevill Hall Hospital, Abergavenny; Dr C. Cefai, Wrexham Maelor Hospital; Dr K. El-Bouri, Prince Charles Hospital, Merthyr Tydfil; Professor F. Dunstan, Department of Epidemiology, University of Wales Medical School, Cardiff; Dr K. A. Fitzgerald, National Public Health Service for Wales (NPHSW), Cardiff; Dr G. Harrison, Prince Phillip Hospital, NPHSW, Carmarthenshire; Dr A. Howard, NPHSW, Cardiff; Dr S. Hillier, Department of Epidemiology, University of Wales Medical School, Cardiff; Dr D. N. Looker, NPHSW, Rhyl; Dr L. Macfarlane, NPHSW, Aberystwyth; Professor S. Palmer, Department of Epidemiology, University of Wales Medical School, Cardiff; Dr P. Thomas, NPHSW, Swansea; Dr R. Salmon, CDSC Wales; Dr M. Sheppard, Withybush Hospital, Haverfordwest; Dr M. Simmons, Welsh Assembly; Dr M. Walker, NPHSW, Bangor; Dr D. White, Royal Glamorgan Hospital, Pontypridd; and the author.


    Footnotes
 
* Tel: +44-29-2052-1997; Fax: +44-29-2052-1987; Email: john.magee{at}nphs.wales.nhs.uk

{dagger} Members of the Welsh Antibiotic Study Group are listed in the Acknowledgements. Back


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Footnotes
 Acknowledgements
 References
 
1 . Cohen, M. L. (1992). Epidemiology of drug resistance: implications for a post-antimicrobial era. Science 257, 1050–5.[ISI][Medline]

2 . Standing Medical Advisory Committee Subgroup on Antimicrobial Resistance (1998). The Path of Least Resistance. Department of Health, London.

3 . Heginbothom, M. L., Magee, J. T., Bell, J. L. et al. (2004). Laboratory testing policies and their effects on routine surveillance of community antimicrobial resistance. Journal of Antimicrobial Chemotherapy 53, 1010–17.[Abstract/Free Full Text]

4 . Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association 22, 209–12.

5 . Newcombe, R. G. (1998). Two-sided confidence intervals for the single proportion: comparison of seven methods. Statistics in Medicine 17, 857–72.[CrossRef][ISI][Medline]

6 . Newcombe, R. G. (1998). Interval estimation for the difference between independent proportions: comparison of eleven methods. Statistics in Medicine 17, 873–90.[CrossRef][ISI][Medline]

7 . Shannon, K. P. & French, G. L. (2002). Antibiotic resistance: effect of different criteria for classifying isolates as duplicates on apparent resistance frequencies. Journal of Antimicrobial Chemotherapy 49, 201–4.[Abstract/Free Full Text]

8 . National Committee for Clinical Laboratory Standards (2000). Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data—Proposed Guideline M39-P. NCCLS, Wayne, PA, USA.

9 . Shannon, K. P. & French, G. L. (2002). Validation of the NCCLS proposal to use results only from the first isolate of a species per patient in the calculation of susceptibility frequencies. Journal of Antimicrobial Chemotherapy 50, 965–9.[Abstract/Free Full Text]

10 . Bennett, W. P., O'Connor, M. L. & Wasilauskas, B. L. (1985). A comparison of antibiotic susceptibility profiles using single and multiple isolates per patient. Infection Control 6, 157–60.[ISI][Medline]

11 . Horvat, R. T., Klutman, N. E., Lacy, M. K. et al. (2003). Effect of duplicate isolates of methicillin-susceptible and methicillin-resistant Staphylococcus aureus on antibiogram data. Journal of Clinical Microbiology 41, 4611–6.[Abstract/Free Full Text]