Presence of bacteriuria caused by trimethoprim resistant bacteria in patients prescribed antibiotics: Multilevel model with practice and individual patient data

ArticleinBMJ (online) 328(7451):1297 · May 2004with20 Reads
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Abstract

To look for evidence of a relation between antibiotic resistance and prescribing by general practitioners by analysis of prescribing at both practice and individual patient level. Repeated cross-sectional study in 1995 and 1996. 28 general practices in the Ninewells Hospital laboratory catchment area, Tayside, Scotland. SUBJECTS REVIEWED: 8833 patients registered with the 28 practices who submitted urine samples for analysis. Resistance to trimethoprim in bacteria isolated from urine samples at practice and individual level simultaneously in a multilevel model. Practices showed considerable variation in both the prevalence of trimethoprim resistance (26-50% of bacteria isolated) and trimethoprim prescribing (67-357 prescriptions per 100 practice patients). Although variation in prescribing showed no association with resistance at the practice level after adjustment for other factors (P = 0.101), in the multilevel model resistance to trimethoprim was significantly associated with age, sex, and individual-level exposure to trimethoprim (P < 0.001) or to other antibiotics (P = 0.002). The association with trimethoprim resistance was strongest for people recently exposed to trimethoprim, and there was no association for people with trimethoprim exposure more than six months before the date of the urine sample. Analysis of practice level data obscured important associations between antibiotic prescribing and resistance. The results support efforts to reduce unnecessary prescribing of antibiotics in the community and show the added value of individual patient data for research on the outcomes of prescribing.

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Information in practice
Presence of bacteriuria caused by trimethoprim resistant bacteria in
patients prescribed antibiotics: multilevel model with practice and
individual patient data
P T Donnan, L Wei, D T Steinke, G Phillips, R Clarke, A Noone, F M Sullivan, T M MacDonald, P G Davey
Abstract
Objective To look for evidence of a relation between antibiotic
resistance and prescribing by general practitioners by analysis
of prescribing at both practice and individual patient level.
Design Repeated cross-sectional study in 1995 and 1996.
Setting 28 general practices in the Ninewells Hospital
laborator y catchment area, Tayside, Scotland.
Subjects reviewed 8833 patients registered with the 28
practices who submitted urine samples for analysis.
Main outcome measures Resistance to trimethoprim in
bacteria isolated from urine samples at practice and individual
level simultaneously in a multilevel model.
Results Practices showed considerable variation in both the
prevalence of trimethoprim resistance (26-50% of bacteria
isolated) and trimethoprim prescribing (67-357 prescriptions
per 100 practice patients). Although variation in prescribing
showed no association with resistance at the practice level after
adjustment for other factors (P = 0.101), in the multilevel model
resistance to trimethopr im was significantly associated with age,
sex, and individual-level exposure to trimethoprim (P < 0.001)
or to other antibiotics (P = 0.002). The association with
trimethoprim resistance was strongest for people recently
exposed to trimethopr im, and there was no association for
people with trimethopr im exposure more than six months
before the date of the urine sample.
Discussion Analysis of practice level data obscured important
associations between antibiotic prescr ibing and resistance. The
results support efforts to reduce unnecessary prescribing of
antibiotics in the community and show the added value of
individual patient data for research on the outcomes of
prescribing.
Introduction
The increasing prevalence of drug resistant bacteria is a major
public health problem throughout the world.
1
Prescribers and
policy makers require more precise information about the rela-
tion between prescribing and resistance in order to analyse the
consequences of prescribing decisions.
2
In Britain national prescribing data are currently available
only at the general practice level, and analyses of these data have
shown a weak relation between trimethoprim prescribing and
resistance.
34
Collection of data about individual patients is tech-
nically achievable but is more expensive to collect and analyse
than practice level data. In addition, linking of information from
multiple data sources and creation of databases containing
patient specific information raises important issues of confiden-
tiality.
5
The Copenhagen Recommendations identified the need
for research to establish the added value of person specific data-
bases that link prescribing to other clinical information such as
antibiotic resistance in order to meet the challenges of data pro-
tection legislation in Europe.
6
We recently reviewed the literature supporting an association
between prescribing in primary care and drug resistance but
have found no studies that directly compared the results of prac-
tice level and patient level data.
7
The aim of this study was to test
the hypothesis that, in compar ison with practice level data, analy-
sis of individual patient data would reveal a much stronger asso-
ciation between antibiotic exposure and resistance.
Methods
Study population
The study population was drawn from adults resident in the Tay-
side region aged 35 years and registered with a general practi-
tioner in the catchment area for Ninewells Hospital laboratory
from January 1995 to December 1996 inclusive. The subjects
were still alive in December 1996 or had died in Tayside during
this period. The final study population was 166 000 subjects
from 28 practices, which is roughly 44% of the population of the
Tayside region.
Ethics and data protection
This study was done under a set of standard operating
procedures governing the use of personal data for research in
the Medicines Monitoring Unit (MEMO) of Dundee University
and written after the Data Protection Act in 1998 and the recom-
mendations of the Caldicott report for implementation of the act
in the NHS. They have been reviewed by an external privacy
advisory committee, established in 1999 and chaired by
Professor Elizabeth Russell from Aberdeen. The procedures
have been developed in partnership with NHS Tayside and
approved by the three NHS Tayside Caldicott Guardians.
Responsibility for university staff compliance with the proce-
dures lies with the University Caldicott Guardian, appointed on
the advice of the NHS Guardians in 2000. Our compliance has
been externally audited twice, in 2001 and 2003.
Patient data were anonymised electronically with programs
written by MEMO. Firstly, each patient’s unique community
health index (CHI) number was changed to a new unique
number that did not include any identifiable data, such as date of
birth. Secondly, identifiable variables were changed (for example,
full date of birth changed to age to nearest two months, postcode
changed to Carstairs deprivation code). Thirdly, any identifying
page 1 of 5BMJ VOLUME 328 29 MAY 2004 bmj.com
Page 1
textual data (name, address, etc) and any other identifying codes
such as general practice or pharmacy codes were removed or
changed to an anonymised mapped code. Finally, the study pro-
tocol and all other associated documents were entered within a
project management system. The study protocol was also
registered with NHS Tayside’s data protection officer and
included in a list of studies for external audit. Once registered on
the project management system, the protocol cannot be altered
without approval by Tayside Research Ethics Committee and
registration of the new protocol with the data protection officer.
Once the data are anonymised, legally they are not covered
by the requirements of the Data Protection Act. However, it has
to be possible to re-identify an individual for the purposes of
research governance and feedback of important results to
professionals and patients. Exemption from the need for written
consent for studies that follow agreed standard operating proce-
dures was given by the Tayside Research Ethics Committee and
the NHS Caldicott Guardians. In addition, all citizens of Tayside
are informed that their electronic health records may be used in
teaching, audit, or research through a leaflet distributed to all
general practices by NHS Tayside. The leaflet says that individu-
als can request in writing that their records are not used in this
way and that their objections will be respected.
Additional information about MEMO’s standard operating
procedures, external audits, and anonymisation software is avail-
able on our website (www.dundee.ac.uk/memo) by following the
link to “Confidentiality/Advisory Issues.
Antibiotic resistance
The Ninewells Hospital medical laboratory receives about
45 000 urine samples for analysis from general practices each
year. We obtained data on culture and sensitivity tests of these
samples electronically. We included samples from midstream
urine collection but excluded catheter specimens. We identified
samples with and without trimethoprim resistant Gram negative
bacteria and linked this information to patient records by means
of the CHI number
8
and so to our information on practice and
patient characteristics.
Outcomes
At the practice level, the outcome was the proportion of patients
within the practice with urine samples containing trimethoprim
resistant bacteria, whereas we coded individuals as having either
resistant or sensitive bacteria.
Practice characteristics
We obtained prescribing information from the prescription
database of MEMO, as described in detail elsewhere.
8
Briefly, this
database contains prospectively gathered information on all dis-
pensed community prescr iptions since 1 January 1993 and diag-
nostic and demographic data on all patients admitted to hospital
in Tayside since 1980 (Scottish morbidity record 1 (SMR1)).
Hence, it does not include hospital or dental prescribed antibiot-
ics. After their encashment at Tayside pharmacies, the
prescriptions are sent by the Common Services Agency to
MEMO. There, menu driven software determines the CHI
number from the details on the prescription. The date the
prescription was written is recorded, and the individual drug
code entered from a drug dictionary developed by the Prescrip-
tion Services Division and mapped to the British National
Formulary.
9
We recorded other characteristics about each practice,
including the number of general practitioners, the ratio of male
to female doctors, the number of patients, fundholding status,
number of urine samples sent for analysis, and the distance from
Ninewells Hospital calculated from the practice postcode. We
extracted the age and sex distributions for each practice from the
MEMO database, and the distr ibutions of Carstairs codes of
social deprivation.
10
Patient specific characteristics
These included age, sex, and Carstairs social deprivation
categor y, number of urine samples sent, and prescribing of
trimethoprim, other antibiotics, and a selection of other drugs as
general prescribing indicator s (hormone replacement therapy,
oral contraceptives, benzodiazepines, and selective serotonin
reuptake inhibitors).
Statistical methods
Practice level analysis
We analysed the practice level data using multiple logistic
regression. The equation allowed for confounding and
interaction to be examined among the independent variables.
The variables considered were number of urine samples;
number of general practitioners; proportion of male general
practitioners; fundholding status (yes or no); distance of practice
from Ninewells Hospital; proportions of patients prescribed tri-
methoprim, other antibiotics, benzodiazepines, hormone
replacement therapy, oral contraceptives, and selective serotonin
receptor inhibitors; proportions of population who were men,
aged 60 year s or more, and had high social deprivation scores.
We fitted a full model allowing for all significant covariates along
with trimethoprim prescribing. In order to combine 1995 and
1996 data, we used a generalised estimating equation (GEE)
model with year as the unit of repeated measures (using SAS,
version 8). We assessed the robustness of practice ranking by
prevalence of trimethoprim resistance by means of a simulation
method described by Marshall and Spiegelhalter.
11
Simultaneous practice and person specific analysis
We used MLwiN software for analysis of resistance and prescrib-
ing in relation to practice level and patient level factors simulta-
neously. We tested for a quadratic relationship with age because
antibiotic prescribing is g reater in young children and elderly
people.
12
We performed the analyses for the whole dataset with
year as a fixed covariate to allow for possible practice and popu-
lation changes over time. Only practices that existed in both
years were included in these analyses.
Results
Practice level results
Of the 28 practices, 11 (39%) were fund holders in 1995 and 17
(61%) in 1996, and six (21%) had only male doctors. The
practices’ list sizes ranged from 1342 to 10 653. None of these
differences in practice characteristics affected the results.
The total number of patients who submitted urine samples
was 8833 (fig 1). There was considerable variation between prac-
tices in the prevalence of trimethoprim resistance in Gram nega-
tive bacteria isolated from urine specimens (from 26% to 50%).
There was similar variation in prescribing of trimethoprim (from
67 to 357 prescriptions per 100 practice patients) and of other
antibiotics (from 2099 to 6352). However, apparent differences
between practices and between years were not statistically signifi-
cant, with considerable overlap of 95% confidence intervals (fig
2). Formal statistical analysis of practice rankings in 1995 and
1996 revealed that the 95% confidence intervals for most
practices extended from fir st to last place. There was no relation
Information in practice
page 2 of 5 BMJ VOLUME 328 29 MAY 2004 bmj.com
Page 2
between the number of urine samples sent by a practice and dis-
pensing of trimethoprim or of other antibiotics.
The crude Spearman rank correlation between practice pre-
scribing of trimethoprim and resistance was 0.039. In the
multiple logistic adjusted analyses of practice level data from
1995 and 1996, the number of urine samples sent for analysis
was positively associated with resistance (table 1). In addition, a
high percentage of male general practitioners and high rates of
prescribing oral contraceptives, hormone replacement therapy,
and selective serotonin reuptake inhibitors were negatively asso-
ciated with resistance. In the adjusted analysis trimethoprim pre-
scribing was not significantly associated with trimethoprim
resistance. No other variables were independently associated
with trimethoprim resistance after adjustment, either with a
stepwise procedure or with fitting a full model (table 1).
Multilevel modelling
In contrast to the practice level analysis, the simultaneous
practice and individual level analysis showed many var iables to
be associated with trimethoprim resistance after adjustment,
some highly significant. Older age and being female were both
significantly associated with higher prevalence of resistance
(table 2). Importantly, exposure to trimethoprim was strongly
associated with trimethoprim resistance (odds ratio 1.22 (95%
confidence interval 1.16 to 1.28)). Exposure to other antibiotics
was less strongly, but still highly significantly, associated with tri-
methoprim resistance (odds ratio 1.18 (1.06 to 1.32)). There were
no significant associations for practice level variables with resist-
ance after allowing for patient level factors; in fact, the variability
of resistance due to practice level factors was negligible. There
were some differences in the results for 1995 and 1996, resulting
in significant interactions by year. No other variable was
significantly associated with trimethoprim resistance in the com-
bined data (table 2), but hormone replacement therapy was
weakly associated with trimethoprim resistance (P = 0.028).
In our analysis, exposure to antibiotics always preceded
resistance. In case-control studies this can lead to sampling bias
due to sending only urine sample from patients who have not
responded to initial antibiotic treatment. Although not a
case-control design, we analysed the odds ratio of trimethoprim
Initial urine samples (n=28 461)
With CHI number (n=26 023)
From within the fixed population (n=20 680)
Samples in 1995
(n=7824)
Samples in 1994
(n=4382)
Samples in 1996
(n=8474)
People with
complete data
(n=4004)
People with
complete data
(n=4829)
Without CHI number (n=2438)
People within Ninewells
hospital catchment
area (n=4090)
People within Ninewells
hospital catchment
area (n=4916)
Fig 1 Flowchart for identification of individual subjects for patient level analyses
from among those patients who had urine samples sent to Ninewells Hospital for
analysis
Individual general practices
Resistance to trimethoprim (%)
0
20
30
40
50
60
70
10
1996
1995
Fig 2 Prevalence of trimethoprim resistant bacteria in patients’ urine samples by
general practice (n=28). Practices are ranked in order of prevalence of resistance
in 1995. (Error bars show 95% confidence intervals)
Table 1 Adjusted repeated measures model of the relation between
prevalence of trimethoprim resistant bacteria in patients’ urine samples and
drug prescribing and other variables at the practice level (1995-6)
Variables Odds ratio (95% CI) P value
More trimethoprim prescriptions
(+1000 scripts)
1.01 (0.99 to 1.02) 0.101
Greater percentage of male GPs
(+10%)
0.95 (0.92 to 0.97) <0.001
More urine samples sent for
analysis (+100)
1.34 (1.26 to 1.40) <0.001
More SSRI prescriptions (+1000
scripts)
0.67 (0.61 to 0.82) <0.001
More HRT prescriptions (+1000
scripts)*
0.61 (0.50 to 0.82) 0.001
More oral contraceptive
prescriptions (+1000 scripts)*
0.74 (0.61 to 0.99) 0.022
SSRI=selective serotonin reuptake inhibitor. HRT=hormone replacement therapy.
*Among female patients only.
Table 2 Multilevel model of the relation between presence of trimethoprim
resistant bacteria in individual patients’ urine samples and drug prescribing
and other variables at the practice level and the patient level (1995-6)
Variables Odds ratio (95% CI) P value
Patient level factors
Older age (+10 years) 1.15 (1.13 to 1.17) <0.001
Year (1996 v 1995) 2.68 (2.14 to 3.35) <0.001
Sex (male v female) 0.70 (0.59 to 0.82) <0.001
Sex × year 1.42 (1.11 to 1.82) 0.005
Carstairs deprivation score 0.99 (0.97 to 1.00) 0.096
More urine samples sent for
analysis (+1)
1.04 (0.99 to 1.10) 0.106
More trimethoprim prescriptions
(+1)
1.22 (1.16 to 1.28) <0.001
More prescriptions of other
antibiotics (+1)
1.18 (1.06 to 1.32) 0.002
Benzodiazepines prescriptions (yes
v no)
1.12 (0.68 to 1.83) 0.658
SSRI prescriptions (yes v no) 0.67 (0.34 to 1.32) 0.249
HRT prescriptions (yes v no)* 0.79 (0.64 to 0.97) 0.028
Oral contraceptive prescriptions
(yes v no)*
1.01 (0.82 to 1.23) 0.954
Practice level factors†
Medium practice size (v small) 1.05 (0.90 to 1.22) 0.552
Large practice size (v small) 1.09 (0.93 to 1.27) 0.282
Greater percentage of male GPs
(+10%)
0.87 (0.65 to 1.18) 0.381
Fundholding practice (yes v no) 0.96 (0.84 to 1.09) 0.545
SSRI=selective serotonin reuptake inhibitor. HRT=hormone replacement therapy.
*Among female patients only.
†Practice effect
1j
=0.007 (SE 0.006)
Information in practice
page 3 of 5BMJ VOLUME 328 29 MAY 2004 bmj.com
Page 3
exposure for patients with trimethoprim resistant bacteria by the
number of days between trimethoprim exposure and submission
of the urine sample (fig 3). This analysis showed that patients
with trimethoprim resistant bacteria were more likely to have
been exposed to trimethoprim up to six months before the date
of the urine sample: odds ratio 9.19 (6.35 to 13.3) for trimetho-
prim exposure 8-15 days before the urine sample date, declining
sharply to 2.93 (2.20 to 3.89) for exposure 16-30 days before and
then steadily to 1.45 (1.03 to 2.05) for exposure 121-180 days
(4-6 months) before. There was no association between
trimethoprim resistance and exposure to trimethoprim more
than 180 days (6 months) previously (odds ratio 1.00 (0.82 to
1.23)).
Discussion
We found a strong association between antibiotic prescribing
and resistance at the individual patient level that was obscured by
analysis of aggregate level data from the same population. A key
conclusion is that aggregate level studies should not be used to
assess the impact of changes in prescribing on resistance.
Secondly, every time an antibiotic is prescribed in the
community it increases the risk to the individual patient of colo-
nisation by drug resistant bacteria. Thirdly, our results provide
important supporting evidence for initiatives to achieve
universal electronic prescribing in the NHS, showing the added
value of analysis of prescribing data at the individual level.
Comparison with other studies
We found no relation between antibiotic prescribing and
resistance at the general practice level. A systematic review of the
literature about trimethoprim resistance and primary care
prescribing from 1980 to 2000 identified five studies with area
level data, of which only one found a significant relation.
13
A sub-
sequent study of 371 practices in England reported a weak rela-
tion between trimethoprim prescr ibing and resistance (Spear-
man’s rank correlation 0.24).
4
Six case-control studies published
before 2001 all showed a strong relation between trimethoprim
prescribing and resistance but had inadequate control for popu-
lation differences in demographics.
13
This review did not include
the results of a large case-control study in the Tayside population
from 1993 to 1994, which confirmed a strong relation between
trimethoprim resistance and prior exposure to trimethoprim or
other antibiotics after controlling for other variables.
14
We found only one other example of a parallel analysis of
individual level data and aggregated data about prescribing and
resistance.
15
This study, of 35 423 hospital inpatients, reported
significant increases in prescribing of antibiotics over a four year
period without any apparent change in the prevalence of resist-
ance. However, multiple proportional hazards regression analy-
sis revealed that exposure to a fluoroquinolone, third generation
cephalosporin, ampicillin-sulbactam, or imipenem was a strong
risk factor for colonisation with bacteria resistant to these
drugs.
15
Implications of results
The discrepancy between results with individual level data and
aggregated data about antibiotic prescribing and resistance is
probably largely due to the ecological fallacy.
16
Ecological studies
about exposure and outcome are valid only if differences in
exposure at the population level accurately reflect differences in
exposure to all of the individuals within the populations. With
respect to prescribing, large variations in the average consump-
tion of drugs by populations are the product of much greater
variation in exposure within the population, and ecological stud-
ies of exposure and outcome are therefore fundamentally
flawed. Additional problems with aggregated data on prescribing
and resistance include sampling bias and inability to control for
confounding.
7
Nonetheless, very large ecological studies may
reveal associations between antibiotic prescribing and resistance
at the population level,
17
and one study suggests that historical
antibiotic use in a hospital department and exposure of
individual patients to antibiotics are both independent risk
factors for infection by drug resistant bacteria.
18
Our results show that isolation of trimethopr im resistant
bacteria from urinary samples was not associated with trimetho-
prim exposure more than six months before the sample was
taken. There is relatively little published information about the
persistence of antibiotic resistant bacteria in the human
intestinal flora.
19 20
Our cross sectional study suggests that the
influence of trimethoprim prescribing reduces with time, but this
needs to be confirmed in longitudinal cohort studies. Most of the
available evidence comes from animal studies, which show that
resistance persists long after exposure to the antibiotic has
ceased, in par t because of selective pressure exerted by
completely unrelated antibiotics.
21 22
Human cohort studies have
shown associations between antibiotic exposure and colonisa-
tion or infection by drug resistant bacteria, both in the
community
23–25
and in hospitals.
26 27
At the population level we
know that resistance to individual antibiotics persists in humans
long after withdrawal of these drugs from clinical practice,
28 29
but
these results are explained by selection by related antibiotics that
are still in use. We need more information from longitudinal
human studies at the individual level
30
to understand how the
process of intestinal colonisation and persistence can be
influenced by antibiotic control and by other measures.
31
The
ability of primary care computing systems to produce high qual-
ity, patient specific data is increasing in Britain and elsewhere,
32
though the use of data may be restricted by legislation.
33
Conclusion
Our results support efforts to reduce unnecessary prescribing of
antibiotics in the community.
34
Clear demonstration of the added
value of individual data will be important in the debate about
practical and reasonable methods for obtaining consent for
record linkage research.
35 36
Days after exposure to trimethoprim
Odds ratio
0
0
4
6
8
10
12
14
2
4-7
8-15
16-30
31-45
46-60
61-90
91-120
121-180
>180
Fig 3 Odds ratio (95% confidence interval) of trimethoprim exposure in patients
with trimethoprim resistant bacteria in urine versus those with sensitive bacteria
by days after exposure to trimethoprim
Information in practice
page 4 of 5 BMJ VOLUME 328 29 MAY 2004 bmj.com
Page 4
MEMO is a member of the MRC Health Services Research Collaboration.
Participators: PTD designed the statistical analysis, wrote the grant applica-
tion, supervised LW, and wrote the first draft of the paper. LW carried out
the statistical analysis and commented on the paper. DTS, GP, RC, AN, FMS,
and TMM were involved in designing the study, collecting data, interpreting
results, and re-drafting the paper. PGD designed the study, cowrote the
grant application, and paper and is guarantor for the study.
Funding: The study was funded by a grant from the Chief Scientist Office.
Competing interests: TMM serves on advisory boards for Pfizer, Pharmacia,
and Novartis but none relating to the current topic. PGD serves on advisory
boards about antibiotic prescribing and resistance for Aventis and Pharma-
cia.
Ethical approval: The study protocol was approved by Tayside Research
Ethics Committee.
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(Accepted 12 March 2004)
bmj.com 2004;328:1297
Tayside Centre for General Practice, Community Health Sciences, University of
Dundee, Dundee DD2 4BF
P T Donnan senior lecturer in medical statistics
F M Sullivan professor of primary care research and development
Medicines Monitoring Unit (MEMO), University of Dundee
LWeistatistician
T M MacDonald professor of clinical pharmacology and pharmacoepidemiology
PGDaveyprofessor of pharmacoeconomics
Primary Care Information Unit, Information and Statistics Division, Edinburgh
D T Steinke research pharmacist
Department of Medical Microbiology, Ninewells Hospital, Dundee
G Phillips consultant
Information and Statistics Division, Edinburgh
R Clarke principal pharmacist
Scottish Centre for Infection and Environmental Health, Glasgow
A Noone consultant epidemiologist
Correspondence to: P T Donnan p.t.donnan@dundee.ac.uk
What is already known on this topic
UK national prescribing data are usually available only at
the practice level, and analysis of these data has shown a
weak relation between antibiotic prescribing and drug
resistance in infective organisms
Such analyses probably suffer from ecological bias, which
arises when area level data obscure important associations
between individual exposure and risk
What this study adds
At the practice level, there was no association between
variation in antibiotic prescribing and resistance after
adjustment for practice level factors such as fundholding
status
Inclusion of individual prescribing data in a multilevel
model revealed a highly significant association between
exposure to trimethoprim or other antibiotics, particularly
in the previous six months, and resistance to trimethoprim
These results show that recent antibiotic exposure increases
the risk of colonisation or infection by drug resistant
bacteria and show the added value of analysis of data about
individual patient prescribing
Information in practice
page 5 of 5BMJ VOLUME 328 29 MAY 2004 bmj.com
Page 5
    • "For instance, Harbarth et al. demonstrate this in a study reporting a significant association between antibiotic exposure and resistance at the individual level but not at the group level [102]. Similarly, Donnan et al. studied the association between trimethoprim resistance in urinary bacteria and antibiotic exposure and found no association at the practice level, whereas individual exposure to antibiotics was significantly associated with trimethoprim resistance in the multi-level model [99]. The main feature of cluster-randomised trials is the same as that of a randomised trial, except that investigators control the intervention by assigning it randomly to groups of patients rather than individuals104105106 . "
    [Show abstract] [Hide abstract] ABSTRACT: Antibiotics are essential agents that have greatly reduced human mortality due to infectious diseases. Their use, and sometimes overuse, have increased over the past several decades in humans, veterinary medicine and agriculture. However, the emergence of resistant pathogens is becoming an increasing problem that could result in the re-emergence of infectious diseases. Antibiotic prescription in human medicine plays a key role in this phenomenon. Under selection pressure, resistance can emerge in the commensal flora of treated individuals and disseminate to others. However, even if the effects of antimicrobial use on resistance is intuitively accepted, scientific rationales are required to convince physicians, legislators and public opinion to adopt appropriate behaviours and policies. With this review, we aim to provide an overview of different epidemiological study designs that are used to study the relationship between antibiotic use and the emergence and spread of resistance, as well as highlight their main strengths and weaknesses.
    Preview · Article · Dec 2012 · European Journal of Clinical Microbiology
    0Comments 14Citations
    • "@BULLET Prescribing database: The Health Informatics Centre (HIC) has person-specific dispensing information for the whole of Tayside [17], @BULLET Hospitalisation, Scottish Morbidity Records (SMR1 – general admissions, SMR4 – alcohol related psychiatric admissions and SMR6 – cancer admissions), @BULLET Death registry from the General Registry Office, @BULLET Carstairs categories for social deprivation based on the decennial census [18], @BULLET Endoscopy, @BULLET Regional biochemistry, @BULLET Pathology, @BULLET Virology, and @BULLET Immunology databases. Diagnostic algorithms for liver diseases have been created and this database has already been used to assess the epidemiology and economic burden of viral hepatitis [19] and other liver diseases. "
    [Show abstract] [Hide abstract] ABSTRACT: Liver function tests (LFTs) are routinely performed in primary care, and are often the gateway to further invasive and/or expensive investigations. Little is known of the consequences in people with an initial abnormal liver function (ALF) test in primary care and with no obvious liver disease. Further investigations may be dangerous for the patient and expensive for Health Services. The aims of this study are to determine the natural history of abnormalities in LFTs before overt liver disease presents in the population and identify those who require minimal further investigations with the potential for reduction in NHS costs. A population-based retrospective cohort study will follow up all those who have had an incident liver function test (LFT) in primary care to subsequent liver disease or mortality over a period of 15 years (approx. 2.3 million tests in 99,000 people). The study is set in Primary Care in the region of Tayside, Scotland (pop approx. 429,000) between 1989 and 2003. The target population consists of patients with no recorded clinical signs or symptoms of liver disease and registered with a GP. The health technologies being assessed are LFTs, viral and auto-antibody tests, ultrasound, CT, MRI and liver biopsy. The study will utilise the Epidemiology of Liver Disease In Tayside (ELDIT) database to determine the outcomes of liver disease. These are based on hospital admission data (Scottish Morbidity Record 1), dispensed medication records, death certificates, and examination of medical records from Tayside hospitals. A sample of patients (n = 150) with recent initial ALF tests or invitation to biopsy will complete questionnaires to obtain quality of life data and anxiety measures. Cost-effectiveness and cost utility Markov model analyses will be performed from health service and patient perspectives using standard NHS costs. The findings will also be used to develop a computerised clinical decision support tool. The results of this study will be widely disseminated to primary care, as well as G.I. hospital specialists through publications and presentations at local and national meetings and the project website. This will facilitate optimal decision-making both for the benefit of the patient and the National Health Service.
    Full-text · Article · Feb 2007 · BMC Health Services Research
    0Comments 6Citations
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