Prior trimethoprim use and trimethoprim-resistant urinary tract infection: a nested case–control study with multivariate analysis for other risk factors

Douglas T. Steinkea,1, R. Andrew Seatonb, Gabrielle Phillipsb, Thomas M. MacDonalda and Peter G. Daveya

a Medicines Monitoring Unit (MEMO), Department of Clinical Pharmacology and Therapeutics, Level 7, Ninewells Hospital, Dundee DD1 9SY; b Department of Medical Microbiology, Ninewells Hospital, Dundee DD1 9SY, UK


    Abstract
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Trimethoprim resistance is increasingly prevalent in community-acquired urinary infections. The objective of this study was to evaluate the association between exposure to community-prescribed trimethoprim and other risk factors in subjects and subsequent trimethoprim-resistant urinary tract infection. The design was a nested case–control study using a record-linkage database. Study subjects submitted a urine sample to the Ninewells Hospital Laboratory between July 1993 and December 1995. Antibiotic exposure in subjects with trimethoprim-resistant isolates (cases) was compared with antibiotic exposure in subjects with trimethoprim-susceptible isolates (controls). Study subjects were drawn from the catchment area of a large teaching hospital in Tayside, Scotland. There were 13765 males and females aged 1–106 years who submitted their first urine sample for culture during the study period. After adjustment for significant risk factors and confounding variables, logistic regression analysis showed exposure to trimethoprim [odds ratio (OR) 4.35] or any antibiotic other than trimethoprim (OR 1.32) to be predictive of resistance. The growth of Proteus spp. (OR 115.14) and bacterial growth other than Escherichia coli and Proteus spp. (OR 2.83) were also predictor variables. Hospitalization in the previous 6 months was not independently associated with trimethoprim resistance. In conclusion, trimethoprim resistance was independently associated with exposure to trimethoprim and to antibiotics other than trimethoprim. Reduction in trimethoprim prescribing alone may not reduce the prevalence of trimethoprim resistance.


    Introduction
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
The increasing prevalence of infections caused by antibiotic-resistant bacteria is a major public health problem.1 The UK guidance to medical practitioners stresses the importance of prudent prescribing of antimicrobials in hospitals or the community.2 Recommendations to primary care doctors include limiting prescribing to 3 days treatment for uncomplicated cystitis.2

Trimethoprim or trimethoprim/sulphamethoxazole are the drugs most commonly recommended as first-line treatment for uncomplicated cystitis.35 In developing countries >80% of bacteria causing urinary tract infections are now resistant to these drugs.69 In developed countries <30% of urinary isolates are resistant to trimethoprim, but rates of resistance have been increasing in these and other human pathogens.10,11 Information linking rates of prescribing of trimethoprim to the prevalence of trimethoprim resistance are inconsistent,1215 and it has been suggested that many of the community isolates may have originated from a hospital source.16

We performed a pilot study that linked individual patient data about dispensed prescribing from community pharmacies to the results of microbiology tests on samples submitted from the community.17 This showed that trimethoprim resistance was associated with previous exposure to trimethoprim, but was also associated with previous hospitalization and exposure to oestrogen. We now report the results of a much larger study that was designed to allow multivariate analysis of the relative contribution of multiple risk factors and to exclude patients with multiple urine samples, who are an important source of bias in studies of the prevalence of antibiotic resistance.18


    Patients and methods
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
The study utilized the unique record-linkage database at the Medicines Monitoring Unit (MEMO), University of Dundee, which is described in detail elsewhere.19 In brief, patient-specific prescription, hospital discharge and microbiology laboratory data are record linked using a unique anonymous patient identifier. Population-based observational studies can thus be conducted.

Study design

The study compared exposure to antibiotics and other risk factors in a cohort of subjects who submitted urine cultures with significant growth (>104–105 cfu/mL) of Gram-negative trimethoprim-resistant organisms with a cohort of subjects who submitted urine cultures with significant growth of Gram-negative trimethoprim-susceptible organisms.

The primary exposure variables, prior exposure to trimethoprim and prior exposure to any other antibiotic, were adjusted for other possible risk factors. These were available for all subjects in the analysis and included age, sex, socio-economic status, type of bacteria isolated, diabetes mellitus,20 corticosteroid21 and oestrogen21 therapy. In addition, data on prior hospitalizations were available.

Study population

The study population comprised individuals from the Tayside region of Scotland who were resident in Tayside and registered with a general practitioner between January 1993 and December 1995. Urine sample data were obtained from the Ninewells Hospital catchment area with a total study population of approximately 163 000 people.

Study subjects

All subjects in Tayside who submitted urine samples or dip slides for culture and susceptibility (disc susceptibility test using Stokes' method) in the study period of January 1993 to December 1995 were possible study subjects. Subjects with incident urine samples were determined by eliminating the subjects who submitted samples in the first 6 months of the study period (January 1993 to June 1993, inclusive). The first sample submitted after 30 June 1993 was considered a subject's incident sample and was used in the analysis of exposure and risk factors. Subjects were then divided into those whose urinary isolates were resistant to trimethoprim (the resistant cohort) and those whose urinary isolates were susceptible to trimethoprim (the susceptible cohort) on the basis of a disc test. Subjects with no significant growth in their urine sample were not included. Catheter samples and samples not tested for trimethoprim were also excluded from the study.

Definition of risk factors

Community exposure to dispensed drugs.
Subjects were defined as exposed to an antimicrobial drug if they were dispensed any antibiotics listed in the British National Formulary (BNF)22 in Chapters 5.1.1–5.1.13 in the 180 days preceding the date of submission of the urine sample. Exposure to trimethoprim was analysed separately. Exposure to an antimicrobial drug between 0 and 3 days before the submission of a urine culture was considered non-exposed, because it was unclear whether the sample was collected before consumption of antibiotics or vice versa.

Since the use of certain other drugs has been associated with an antimicrobial, data on these other drugs were extracted from the databases and used in the analysis as possible risk factors. These were: exposure to oral corticosteroids defined as a drug listed in the BNF in Chapter 6.3, but excluded inhaled corticosteroids; exposure to hormone replacement therapy (HRT) defined as a drug listed in the BNF in Chapter 6.4.1; and exposure to oral contraceptives (OCP) defined as a drug listed in the BNF in Chapter 7.3. Subjects were considered exposed to the above drug groups if a prescription was dispensed at any time in the 180 days before urine sample submission.

Hospitalizations.
Hospitalization for any reason in the 180 days before submission of urine sample was defined as a risk factor.

Socio-economic status.
The socio-economic status of subjects was determined by small area postcodes derived from census data and analysed as Carstairs deprivation categories.23 The seven unequal deprivation categories range from deprivation category 1 (most affluent) to deprivation category 7 (least affluent). Deprivation categories 6 and 7 were combined because of low numbers of subjects.

Diabetes mellitus.
Patients with diabetes mellitus were identified using DARTS, a validated register of all patients with diabetes in Tayside, Scotland.24

Data analysis

Cohort risk factors were assembled and odds ratios were calculated with 95% confidence intervals (CIs) using the statistical package SAS (version 6.12, SAS Institute Inc., Cary, NC, USA). Logistic regression fitted continuous and categorical data to find significant predictor variables associated with an increased likelihood of infection with trimethoprim-resistant bacteria.25 The logistic model was calculated in the forward direction with variables entering the model at <5% significance and exiting the model when significance was >5%.


    Results
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
There were 27512 urine samples submitted by 17015 people during the study period; 13765 subjects met the inclusion criteria for the study. Of these, 10330 urine samples had no bacterial growth and were excluded, leaving samples from 3435 subjects. Descriptions of the variables for these study subjects are given in Table IGo.


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Table I.  Description and exposure variables for the study cohorts
 
Table IIGo gives the unadjusted and adjusted odds ratios (ORs) calculated from logistic regression for the comparison of the trimethoprim-resistant cohort with the trimethoprim-susceptible cohort. The predictor variables increasing age, the type of bacteria and deprivation category 4 were significantly associated with trimethoprim resistance. Infection with Proteus spp. was highly associated with trimethoprim-resistant infection (OR 115.14 compared with Escherichia coli). An analysis that excluded this confounder produced similar findings for trimethoprim and any antibiotic; however, hospitalization in the previous 180 days was also significant. Subjects with trimethoprim-resistant infection were more likely to have prior hospital exposure and a Proteus spp. culture (OR 1.64, 95% CI 1.05–2.57).


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Table II.  Unadjusted ORs and model selection using logistic regression for the study cohorts for trimethoprim-resistant versus trimethoprim-susceptible infection
 

    Discussion
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Previous studies have produced inconsistent results with respect to the relationship between resistance to trimethoprim and exposure to trimethoprim and co-trimoxazole in the community.1215,26 Two studies showed no relationship between risk of infection or colonization by trimethoprim-resistant bacteria and exposure to trimethoprim.12,14 One study of carriage of trimethoprim-resistant bacteria by children in a day care centre showed that cross infection during nappy changing was responsible for transfer of resistance.14 Huovinen et al.12 found no relationship between rates of trimethoprim prescribing and resistance in different districts of Finland. In contrast, comparison of prescribing rates of co-trimoxazole in two areas of Israel with markedly different rates of resistance to co-trimoxazole showed that the area with the higher rate of resistance also had a significantly higher rate of consumption of co-trimoxazole.13 In a study of patients hospitalized with community-acquired bacteraemia it was found that previous exposure to ampicillin, sulphonamides and trimethoprim was strongly associated with resistance in E. coli, but not in other enteric bacilli.15 A recent study of the prescribing of 190 general practitioners in Wales found a significant association between rates of antibiotic prescribing and the prevalence of trimethoprim resistance.26

Hospitalized patients are much more likely to be colonized with trimethoprim-resistant bacteria than subjects in the community.27,28 Because of the close genetic homology between resistant isolates from hospital and community, it has been suggested that many of the community isolates may have originated from a hospital source.16

Within-hospital studies of the relationship between antibiotic prescribing and resistance have been hindered by difficulties in defining terms, selection biases, artefacts produced by study methods and failure to control for confounding variables.29,30 Previous studies of the relationship between trimethoprim prescribing and resistance in the community have suffered from similar methodological problems. Some studies of prescribing within whole populations12,13 have not had access to data about individual patients, and have therefore been unable to exclude patients with multiple urine samples, who are an important source of bias.18 The most recent population-based study was able to exclude multiple urine samples;26 however, the association between antibiotic prescribing and resistance may be confounded by other variables that cannot be investigated without additional data about individual patients.30 The only previous studies that did have information about prescribing and resistance within individual patients suffered from selection bias, being confined to children attending a day care centre14 or patients hospitalized with bacteraemia.15 Our study is the first multivariate analysis of potential risk factors for trimethoprim resistance using data about individual patients from an entire population. We have shown that trimethoprim resistance is strongly associated with prior exposure to trimethoprim in the community, as well as with exposure to other antibiotics. Prior exposure to hospital was significantly associated with trimethoprim resistance in a univariate analysis but not in the multivariate analysis, indicating that hospitalization was confounded by other risk factors and should not be considered independent.

In our study, 35% of patients with infections caused by trimethoprim-resistant bacteria had not been exposed to trimethoprim, other antibiotics or hospitalization in the previous 6 months, indicating that resistance can emerge or persist independently of these risk factors. Family members of subjects infected with trimethoprim-resistant bacteria have a higher risk of colonization by trimethoprim-resistant bacteria than family members of uninfected control subjects.31 Intrafamilial spread of drug resistance has also been demonstrated for Streptococcus pyogenes32 and coagulase-negative staphylococci.33 Intrafamilial spread of drug resistance could be due to transfer of genetic information between bacteria as well as cross infection of household contacts by drug-resistant bacteria.34 Trimethoprim resistance is encoded by plasmids, which are highly transferable between bacteria.35,36

Travel is an additional potential mechanism for acquisition of drug-resistant bacteria or genetic determinants of resistance.37 Stool samples were obtained from 13 individuals before, during and after travel to Mexico. None of these individuals took prophylactic antibiotics, although four of 13 took short courses of an antimicrobial agent for therapy of traveller's diarrhoea. No trimethoprim-resistant E. coli isolates were found by this method before travel, whereas 57% of the individuals had trimethoprim- and co-trimoxazole-resistant E. coli by the final week in Mexico. This increase in resistance occurred regardless of whether an individual took a short course of antimicrobial therapy. The most likely source of drug-resistant bacteria is food, which may contain strains of E. coli that are multiply drug resistant but are not enterotoxigenic.38

We have shown that infection with trimethoprimresistant bacteria is strongly associated with prior exposure to trimethoprim. However, exposure to antibiotics other than trimethoprim was also independently associated with trimethoprim resistance in our study and in a recent study from Wales.26 This suggests that a reduction in the use of trimethoprim alone may not reduce the prevalence of trimethoprim resistance. Streptomycin resistance has persisted in hospitals, despite the fact that streptomycin has been virtually unused for over 20 years, probably because streptomycin resistance is linked to other genes that have different selective pressures.39 It is also likely that decay in the prevalence of drug-resistant bacteria will occur slowly after reduction in overall use of antibiotics.40 Nonetheless, we have provided prescribers and the public with further evidence to show that exposure to antibiotics in the community does increase the prevalence of drug-resistant bacteria. This new information endorses the recommendation that every effort should be made to minimize unnecessary use of antibiotics in the community.2


    Acknowledgments
 
MEMO is part of the MRC Health Services Research Collaboration. D. S. 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
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 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
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Received 22 November 1999; returned 23 March 2000; revised 7 June 2000; accepted 19 March 2001