R E S E A R C H A R T I C L E Open Access
A systematic review and meta-analysis of the
effects of antibiotic consumption on antibiotic
Brian G Bell
, Francois Schellevis
, Ellen Stobberingh
, Herman Goossens
and Mike Pringle
Background: Greater use of antibiotics during the past 50 years has exerted selective pressure on susceptible
bacteria and may have favoured the survival of resistant strains. Existing information on antibiotic resistance
patterns from pathogens circulating among community-based patients is substantially less than from hospitalized
patients on whom guidelines are often based. We therefore chose to assess the relationship between the antibiotic
resistance pattern of bacteria circulating in the community and the consumption of antibiotics in the community.
Methods: Both gray literature and published scientific literature in English and other European languages was
examined. Multiple regression analysis was used to analyse whether studies found a positive relationship between
antibiotic consumption and resistance. A subsequent meta-analysis and meta-regression was conducted for studies
for which a common effect size measure (odds ratio) could be calculated.
Results: Electronic searches identified 974 studies but only 243 studies were considered eligible for inclusion by the
two independent reviewers who extracted the data. A binomial test revealed a positive relationship between
antibiotic consumption and resistance (p < .001) but multiple regression modelling did not produce any significant
predictors of study outcome. The meta-analysis generated a significant pooled odds ratio of 2.3 (95% confidence
interval 2.2 to 2.5) with a meta-regression producing several significant predictors (F(10,77) = 5.82, p < .01). Countries
in southern Europe produced a stronger link between consumption and resistance than other regions.
Conclusions: Using a large set of studies we found that antibiotic consumption is associated with the
development of antibiotic resistance. A subsequent meta-analysis, with a subsample of the studies, generated
several significant predictors. Countries in southern Europe produced a stronger link between consumption and
resistance than other regions so efforts at reducing antibiotic consumption may need to be strengthened in this
area. Increased consumption of antibiotics may not only produce greater resistance at the individual patient level
but may also produce greater resistance at the community, country, and regional levels, which can harm individual
Keywords: Antibiotic resistance, Antibiotic usage, Community-acquired infections, Meta-analysis
* Correspondence: firstname.lastname@example.org
Division of Primary Care, University of Nottingham, University Park,
Nottingham NG7 2RD, UK
Full list of author information is available at the end of the article
© 2014 Bell et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Bell et al. BMC Infectious Diseases 2014, 14:13
In the absence of the development of new generations of
antibiotic drugs, appropriate use of existing antibiotics is
needed to ensure the long term availability of effective
treatment for bacterial infections . If antibiotics be-
come ineffective, then established and newly emerging
infectious diseases, which are becoming an increasing
threat, may lead to increased morbidity, health care util-
isation and premature mortality [2-4].
Unfortunately, greater use of antibiotics during the
past 50 years has exerted selective pressure on suscep-
tible bacteria and may have favoured the survival of re-
sistant strains , some of which are resistant to more
than one antibiotic. If excessive antibiotic use can be re-
duced, the expectation is that resistant bacteria may be
replaced by susceptible bacteria because resistant bac-
teria may be less ‘fit’than susceptible bacteria .
More than 90% of antibiotics for medical use in
Europe are prescribed to non-hospitalized patients .
However, existing information on antibiotic resistance
patterns from pathogens circulating among community-
based patients is substantially less than from hospitalized
patients on whom guidelines are often based. We there-
fore chose to assess the relationship between the anti-
biotic resistance pattern of bacteria circulating in the
community and the consumption of antibiotics in the
community. Although Costelloe  et al. studied the re-
lationship between consumption and resistance in pri-
mary care, they examined studies at the individual
patient level only, omitting ecological studies (those con-
ducted at the supra-individual level) which are included
in this review. In this paper, we present a systematic re-
view and meta-analysis of the literature on the relation-
ship between antibiotic consumption in outpatient
settings and antibiotic resistance of pathogens circulat-
ing in the community.
We searched both the English and non-English language
literature for studies that looked at the relationship be-
tween antibiotic resistance and human antibiotic con-
sumption in the community. An attempt was made to
find the grey literature and published scientific literature
in English, Spanish, French, German, Hungarian, Dutch,
Swedish, and Croatian. These languages were chosen be-
cause the corresponding European countries were in-
volved in the current project (Appropriateness of
Prescribing Antibiotics in Primary Health Care with re-
spect to Antibiotic Resistance). Search engines, such as
Google Scholar, Embase, and Medline, were used to find
published literature with reference lists of relevant arti-
cles searched by hand. Medline was searched from 1950
to late 2010 while Embase was searched from 1980 to
late 2010. A list of search terms and connectors, which
were used for both the English and non-English
searches, can be found in Appendix 1.
We selected studies where a) the bacteria were acquired,
and antibiotic consumption was measured, in the com-
munity b) the majority of the participants did not have a
serious illness such as HIV or cancer c) the interval be-
tween consumption and resistance was one month or
greater because we wanted to examine whether there
was an enduring association between consumption and
resistance and d) a statistical link between consumption
and resistance was tested. Studies that presented de-
scriptive information and made no attempt to establish a
statistical connection between consumption and resist-
ance were not included in the analysis. No limits were
placed on when the study was published and few restric-
tions were placed on the type of study methodology, so
both observational and experimental studies were
The level of analysis for any given study ranged from
the individual patient (or individual bacterial isolate)
level to the country (or groups of countries) level. Stud-
ies examined either children or adults or both, there
were no restrictions on which body sites were sampled
for establishing bacterial resistance or how antibiotic
consumption was measured, and all bacteria and all anti-
biotics were considered relevant. Studies undertaken
anywhere in the world were included with both English
and non-English articles retrieved for our review. The
list of the criteria we used to exclude studies is provided
in Appendix 2.
To minimise the tendency to include studies that only
reported significant results, we systematically searched
the grey literature and included studies in which the pri-
mary focus of the article was not on the relationship be-
tween antibiotic consumption and antibiotic resistance.
Abstracts were examined with full articles obtained and
translated if the study looked relevant. When questions
arose in applying the exclusion criteria, the authors re-
solved any disagreements through discussion.
Full articles were examined for quality and data were in-
dependently extracted by the authors using purpose-
built forms that listed the relevant variables. Any dis-
agreements were resolved by discussing the articles and
reaching consensus. Explanatory variables that were ex-
tracted from the studies are listed in Table 1.
The main dependent variable was a dichotomous cod-
ing of study outcome based on whether or not the art-
icle supported a positive relationship between antibiotic
consumption and antibiotic resistance. A positive
Bell et al. BMC Infectious Diseases 2014, 14:13 Page 2 of 25
Table 1 Explanatory variables
Variable Number (percentages in parenthesis) total sample = 243
Outcome Positive 164 (67%)
Negative or equivocal 79 (33%)
Level of sampling Individual 72 (30%)
Region/Country 124 (51%)
Other 47 (19%)
Level of analysis Individual 178 (73%)
Region/Country 53 (22%)
Other 12 (5%)
Children/Adults Children 88 (36%)
Adults 62 (26%)
Both 93 (38%)
Bacteria* Streptococcus 132 (54%)
Staphylococcus 50 (21%)
Enteric Bacteria 69 (28%)
Haemophilus 24 (10%)
Other 17 (7%)
Most common bacteria/Drug combinations** B-lactam resistant S pneumonia 104 (43%)
Macrolide resistant S pneumonia 56 (23%)
Quinolone resistant E coli 41 (17%)
B-lactam resistant E coli 35 (14%)
Sulphonamide resistant E coli 31 (13%)
Methicillin-resistant S aureus 38 (16%)
Most common antibiotics consumed*** B-lactams 132 (54%)
Macrolides 93 (38%)
Sulphonamides 59 (24%)
Quinolones 52 (21%)
Antibiotic not specified 65 (27%)
Time between consumption and resistance^ Six months or less 129 (53%)
More than 6 months 57 (23%)
Same time 43 (18%)
Not specified 14 (6%)
How antibiotic consumption was assessed# Self report 99 (41%)
Medical records 92 (38%)
Sales/Prescriptions 65 (27%)
Direct application of antibiotic 14 (6%)
Region where study was conducted## Northern Europe 66 (27%)
Southern Europe 45 (18%)
US 67 (28%)
Other 61 (25%)
Bell et al. BMC Infectious Diseases 2014, 14:13 Page 3 of 25
relationship could be represented by either increased
consumption associated with increased resistance or de-
creased consumption associated with decreased resist-
ance. A negative relationship could be represented by
either the absence of a significant relationship between
consumption and resistance or, in rare instances, a truly
negative relationship, such as increased consumption as-
sociated with decreased resistance. Studies that did not
clearly provide either positive or negative evidence were
combined with studies that produced a negative relation-
ship to form a not-positive category which was com-
pared with the positive studies in the data analysis.
Studies were classified as positive or negative based on a
preponderance of the evidence with a positive outcome
recorded when an increase in antibiotic consumption
was associated with either an increase in resistant bac-
teria or a decrease in susceptible bacteria. For the meta-
analysis the dependent variable was the odds ratio for
the study based on the standardized effect size.
We wanted to use all of the studies in our analysis in
order to improve power and examine whether a relation-
ship between antibiotic consumption and antibiotic re-
sistance existed for the complete set of data, so we first
ran simple correlations and a logistic regression because
the data did not generally lend themselves to a meta-
analytic approach. The studies were heterogeneous with
study design and effect size measures varying greatly be-
tween studies. Furthermore, a single effect size could
not be calculated for many studies because numerous
associations between consumption and resistance were
reported and simple averaging of effect sizes for any
given study was not appropriate. For those studies (N =
88) for which a single effect size measure could be ob-
tained a meta-analysis and meta-regression analysis were
We ran a series of correlations between each of the ex-
planatory variables and the dichotomous outcome
variable in order to reduce the number of predictors in
the subsequent regression and ensure that the number
of cases (studies) relative to the number of predictors
was adequate. Variables that were significantly correlated
at this level were used in a logistic regression and en-
tered in a single step to predict whether a positive or
negative relationship was found between consumption
and resistance. A binomial test was also run to see
whether a significantly greater number of positive associ-
ations between consumption and resistance were found
in the studies. The binomial test examined whether the
proportion of studies with a positive association between
consumption and resistance significantly differed from
the proportion of studies with a positive association that
would be expected under the null hypothesis. The pro-
portion under the null hypothesis (no association be-
tween resistance and consumption) was 50% (i.e., the
probability of a positive association was equal to the
probability of a non-positive association). For a subset of
the studies, a meta-analysis and subsequent meta-
regression analysis were run. Analyses were conducted
in SPSS version 16 and Stata version 11.2.
Application of exclusion criteria
To arrive at the studies that formed the basis of our ana-
lysis (see Figure 1), we searched the English language lit-
erature and identified 745 studies that appeared relevant.
However, 503 studies (68%) did not meet our inclusion
criteria. Most of the excluded studies (over 95%) were
eliminated for one of the following five reasons: meth-
odological weaknesses (N = 213) such as no attempt to
statistically analyse the data, review article or editorial
(N = 82), study did not measure either resistance or con-
sumption (N = 62), hospital study or the patients were
too ill (N = 80), and short interval (less than a month)
between the measurement of consumption and resist-
ance (N = 54). Altogether 242 English-language studies
or 32% (239 published studies and 3 grey literature
Table 1 Explanatory variables (Continued)
Type of study Cross-sectional 101 (42%)
Ecological 56 (23%)
Case–control 35 (14%)
Quasi-experiment 21 (9%)
Other 30 (12%)
*Percentages do not equal 100% as any given study may have examined more than one type of bacteria.
**Percentage do not equal 100% as any given study may have examined more than one combination; resistant and non-susceptible strains of S pneumonia are
combined under the resistant label for this bacterium.
***Percentages do not equal 100% as any given study may have examined more than one antibiotic.
^This variable represents the maximum time interval in any given study between when consumption occurred and resistance was measured. Studies classified as
‘same time’tended to be ecological studies where the precise interval separating consumption and resistance could not be determined, these studies often
simply reported consumption and resistance occurring together over some multi-year interval.
#Percentages do not equal 100% as any given study may have used more than one method.
##The total equals 239 as three studies were conducted in both southern Europe and northern Europe and one study was coded as not applicable.
Bell et al. BMC Infectious Diseases 2014, 14:13 Page 4 of 25
documents) of the 745 originally identified studies were
considered relevant. A list of excluded studies is avail-
able from the authors upon request.
Searches conducted by colleagues in other countries
revealed 229 studies that appeared relevant, but only
one of these studies was included in our analysis. Non-
English articles were excluded, with three exceptions, be-
cause either a version of the study had already been ob-
tained in English or the study did not provide statistical
analysis of a link between antibiotic consumption and
resistance in the community. The relevant non-English
language article (Appendix 3: Dellamonica et al 2002)
along with the 242 English language articles provided us
with a total of 243 studies that formed the basis of our
Table 1 shows the number of studies that fell into vari-
ous categories for the 243 studies included in the final
analysis. More than two-thirds of the studies found a
positive relationship between antibiotic resistance and
antibiotic consumption. Most of the studies (51%) sam-
pled data at the regional or country level but analysed
data at the individual level (73%) which means that data
from individual participants were often sampled from
some larger unit, such as an entire country.
With few exceptions, the bacteria that were studied fell
into one of three classes (Streptococcus,Staphylococcus,
or Enteric bacteria, such as E coli). The most common
classes of antibiotics for which consumption was mea-
sured included B-lactams, macrolides, sulphonamides,
and quinolones although more than a quarter of the
studies did not specify which antibiotics had been
The studies in our sample were usually conducted in
either Europe or the US. More studies took place in
northern Europe than in southern Europe although both
regions were well represented. Almost 80% of the studies
used one of three study designs: cross-sectional, eco-
logical, or case–control.
Predicting study outcome
We used the binomial test to examine whether there
were more studies that produced a positive outcome in
which either increased consumption was associated with
increased resistance or decreased consumption associ-
ated with decreased resistance. The binomial test re-
vealed that the probability of a positive outcome was
significantly greater than the probability of an outcome
that was not positive (p < .001).
We next examined the correlations between the pre-
dictor variables listed in Table 1 and our dichotomous
outcome measure. Each of the categorical variables listed
in Table 1 was turned into a set of dichotomous vari-
ables for this purpose. For example, the adult/child vari-
able was recoded into three dichotomous variables, one
Figure 1 How studies were selected.
Bell et al. BMC Infectious Diseases 2014, 14:13 Page 5 of 25
representing children, another representing adults, and a
third representing both children and adults.
Three positive correlations were found: studies that
included both adults and children were more likely to
find a positive association between antibiotic consump-
tion and resistance (r = 0.17, p < .01) as were studies that
examined tetracycline-resistant Spneumonia(r = 0.14,
p < .05) and those that looked at quinolone consump-
tion (r = 0.15, P < .05). Three negative correlations were
also produced: for studies that only contained children
(r = −0.13, p < .05), those conducted in the US (r = −0.16,
p < .05) and cross-sectional studies (r = −0.15, p < .05).
Studies which contained children, those conducted in
the US, and cross-sectional studies were more likely to
find a small, either negative or not positive, associ-
ation between antibiotic consumption and antibiotic
All of the variables for which significant correlations
were found were simultaneously entered into a logistic
regression equation to predict study outcome. None of
the variables that produced significant bivariate correla-
tions were significant predictors in the logistic regression
equation, although as a set, they did significantly predict
the outcome variable (chi-square = 22.81, df = 6, p < .01)
thereby distinguishing between studies with a positive
association and other studies. However, only a little
more than 10% of the variance in the outcome variable
was explained by these predictors (Nagelkerke R
We noticed that the relationship between consump-
tion and resistance often varied within a study depend-
ing on which bacteria were being considered. So, a study
that looked at multiple types of bacteria may have found
a positive relationship between consumption and resist-
ance for one but a negative relationship for another. Our
global outcome measure did not allow us to investigate
these differences because it was based on the preponder-
ance of evidence across bacterial classes for any given
study. Therefore, we decided to conduct a separate
analysis for each bacterial category (Streptococcus,
Staphylococcus, Enteric Bacteria, and Haemophilus/
Other). We recoded the outcome measure for those
studies that contained more than one type of bacteria so
that the outcome measure in any given analysis was
based on the relationship between consumption and re-
sistance for a single type of bacteria.
First, looking at the results from the binomial tests,
we found that only enteric bacteria and streptococcus
produced significantly morepositiveoutcomesthan
negative outcomes (p < .01 for enteric bacteria and
p < .001 for streptococcus). For Staphylococcus and
Haemophilus/Other, the results from the binomial
tests were not significant. Therefore, we decided
to focus on enteric bacteria and streptococcus in
When correlations were run for the streptococcus bac-
teria, only two variables out of 48 were significantly
correlated with outcome. Tetracycline resistant S
pneumonia was significantly correlated with the out-
come variable (r = 0.19, p < .05) and so was trimethoprim
consumption (r = −0.23, p < .01). However, this is prob-
ably due to chance as the number of significant correla-
tions was less than 5% of the total number of
correlations that were run, so a logistic regression was
not conducted. In a similar vein, only two correlations
were significant for enteric bacteria. Patients who were
children (r = −0.28, p < .05) and quinolone consumption
(r = 0.33, p < .01) were significantly correlated with out-
come. Again, with only two significant correlations, we
can conclude that the results are probably due to
chance, so a logistic regression was not run.
We conducted a fixed-effects meta-analysis of a subset
of studies for which a common effect size could be ob-
tained. A fixed effects meta-analysis was more appropri-
ate than a random effects meta-analysis because we
wanted to determine whether heterogeneity between
the studies could be explained by our predictors .
Our meta-analysis focused on the most common
study designs and included articles that used cross-
sectional, case–control, cohort, non-randomised quasi-
experimental and randomized controlled trial designs.
We ran a separate meta-analysis for each of the most
common study designs (53 cross-sectional studies, 21
case–control studies, and 8 cohort studies) to see
whether the results varied by study design. Ecological
studies, which were conducted at the regional or country
level, were excluded due to much larger sample sizes
that would not allow comparisons with the much
smaller studies that were used in this analysis. We de-
cided against conducting a separate meta-analysis of the
ecological studies because there were only 17 such stud-
ies for which a common effect size measure could be
calculated. More studies than this would have been
needed to explore relationships between predictors and
effect size in any subsequent meta-regression. For the 88
studies that were included we were able to either use the
odds ratio that was reported in the study or calculate
one from the available data.
Our meta-analysis revealed that there was a significant
positive relationship between antibiotic consumption
and resistance with a pooled effect size (odds ratio) of
2.33 (z = 25.71, p < .01, 95% confidence interval 2.19 to
2.49). The results were similar for each of the most com-
mon study designs including cross-sectional (OR = 2.46,
z = 17.06, p < .01, 95% CI = 2.22 to 2.73), cohort (OR =
2.93, z = 7.02, p < .01, 95% CI = 2.17 to 3.96) and case–
control studies (OR = 2.26, z = 17.41, p < .01, 95% CI =
Bell et al. BMC Infectious Diseases 2014, 14:13 Page 6 of 25
2.07 to 2.48) so further analyses were conducted with
all 88 studies. The forest plot in Figure 2 displays the
odds ratio and weight for each study. One of the
studies (Appendix 3: Schneider-Lindner et al 2007) was
weighted more heavily than the other studies due to a very
small standard error for the odds ratio but removing this
study did not change our findings (OR = 2.74, z = 25.86,
p < .01).
Figure 2 Forest plot showing odds ratio and 95% confidence interval for each study along with study weight.
Bell et al. BMC Infectious Diseases 2014, 14:13 Page 7 of 25
We should also mention, as can be seen from the fun-
nel plot in Figure 3, that there was some evidence of po-
tential publication bias in our sample of studies. The
absence of small studies in the lower left hand side of
the plot suggests that small studies that did not find a
strong association between antibiotic consumption and
resistance may not have been published, although as
Moller and Jennions  note, publication bias is not
the only explanation for a skewed funnel plot.
Significant heterogeneity was observed with a hetero-
geneity chi-squared value of 329.75 (p < .01). The percent-
age of variation between studies due to heterogeneity was
high (73.6%) so we decided to examine whether our in-
dependent variables could explain differences between
studies in the odds ratios. Using weighted correlations,
in which the correlation between the independent
variable and the odds ratio was weighted by the corre-
sponding standard error, we found 11 significant
correlations (see Table 2) and then conducted a meta-
regression to determine the independent effect of each
variable on the odds ratio. We excluded the variable
staphylococcus because it was highly correlated with
Methicillin-Resistant Saureus(MRSA (r = 0.97)).
The meta-regression showed that our set of independ-
ent variables significantly predicted the odds ratios
(F (10, 77) = 5.82 p < .01) although significant residual
variability remained (residual sum of squares = 187.78,
df = 77, p< .01). Five variables were significant independ-
ent predictors: ‘both children and adults’z = 3.90 < .01,
‘southern Europe’z = 2.25 p < .05, ‘B-lactam consump-
tion’z=−3.15 p < .01, ‘MRSA’z=−4.58 p < .01, and
‘quinolone-resistant E coli’(QREC) z = 2.57 p < .05. We
can conclude that studies which contained both adults
and children, those conducted in southern Europe, and
a strong positive relationship between antibiotic
consumption and resistance. Studies that examined B-
lactam consumption or MRSA tended to find a weaker
relationship between consumption and resistance. The
finding that southern European countries produced a
much stronger link between resistance and consumption
confirms previous observations that antibiotic resistance
due to the consumption of antibiotics may be a greater
problem in southern Europe than in northern Europe
(Appendix 3: Goossens et al 2005).
As noted earlier, there was some evidence of publication
bias in the subsample of studies that formed our meta-
analysis. Turning to the issue of publication bias for the
full set of 243 studies, a large number of these studies (a
third of our sample) did not find a positive association
between antibiotic consumption and antibiotic resist-
ance, which reduces the likelihood of publication bias
and lessens concerns that we chose studies for inclusion
simply because they supported our hypothesis. Concerns
with publication bias are also reduced by our contention
that studies which reported negative evidence probably
would be published since studies that found no relation-
ship between consumption and resistance would still
generate a great deal of interest. To examine potential
publication bias further, we looked at 92 studies that
were primarily concerned with something other than the
relationship between consumption and resistance. These
studies, which should be less prone to the publication
bias that we are concerned with, found a roughly similar
split in the ratio of positive to not positive outcomes that
we found with the larger sample, 60% of the studies
found a positive relationship and 40% did not find a
Our initial analysis with the entire sample of 243 studies
revealed that there was a positive association between
bacterial resistance and antibiotic consumption in the
community, which means that either increased con-
sumption was associated with increased resistance or de-
creased consumption was associated with decreased
resistance. Using a much larger sample of studies we
found support for Costelloe’s  conclusion that anti-
biotic prescribing is associated with the development of
antibiotic resistance. We also extended Costelloe’s work
by including studies that were conducted at both the
ecological level and the individual level and finding that
level of analysis did not affect the relationship between
consumption and resistance.
Although several study variables were significantly cor-
related with study outcome (whether or not a positive
association was obtained between resistance and con-
sumption), all of the correlation coefficients were small
Figure 3 Funnel plot showing effect size (odds ratio) as a
function of sample size (natural log of standard error).
Bell et al. BMC Infectious Diseases 2014, 14:13 Page 8 of 25
(0.20 or less), and none of these variables were signifi-
cant predictors of study outcome when they were en-
tered into a logistic regression equation. A lack of
significant predictors means that the association between
antibiotic consumption and bacterial resistance does not
depend on any of the demographic variables that we in-
vestigated or on other variables of interest, such as the
level at which the data were sampled or analysed. Al-
though no significant predictors were found when indi-
vidual classes of bacteria were examined either, the
binomial test showed that the positive relationship be-
tween consumption and resistance was only obtained for
enteric bacteria and streptococcus so efforts at control-
ling resistance needs to focus on these bacteria.
When the 88 studies in our meta-analysis were exam-
ined, we found that there was a significant positive rela-
tionship between consumption and resistance and
several variables that were significant predictors. Studies
that contained both adults and children, those con-
ducted in southern Europe, and studies that looked at
QREC were more likely to find a strong positive rela-
tionship between antibiotic consumption and resistance
whereas studies that examined B-lactam consumption or
MRSA tended to find a weaker relationship between
consumption and resistance. The finding that southern
European countries produced a much stronger link be-
tween resistance and consumption confirms previous
observations that antibiotic resistance due to the con-
sumption of antibiotics may be a greater problem in
southern Europe than in northern Europe (Appendix 3:
Goossens et al 2005). Also, the results from the meta-
analysis indicate that the use of quinolones may need to
be reduced when treating E Coli infections.
One of the major strengths of our review is the large
number of studies that contributed to our analysis,
which means that we had plenty of power to detect an
effect. We placed few restrictions on the types of studies
that we considered relevant, which make our findings
applicable to a wide range of settings thereby increasing
ecological validity and also reducing publication bias in
which non-published studies that do not produce signifi-
cant findings are overlooked. Our review included
English-language studies and studies published in other
languages, observational and experimental studies, grey
literature as well as published scientific literature, with
no geographical restrictions placed on where the study
was conducted. For any given study the level of analysis
ranged from the individual patient (or individual isolate)
level to the country (or groups of countries) level and
both children and adults were well represented. There
were no restrictions on which body sites were sampled
for establishing bacterial resistance, how antibiotic con-
sumption was measured, which bacteria were isolated,
or which antibiotics were consumed. In spite of the
comprehensive nature of our review, a final strength is
our targeted approach to answering the primary ques-
tion, is there a relationship between antibiotic consump-
tion and antibiotic resistance in the community, by the
use of a clear set of exclusion criteria.
Potential limitations should also be noted with our re-
view. First, our primary outcome measure was based on
a dichotomous coding of whether or not a study sup-
ported a positive relationship between antibiotic con-
sumption and antibiotic resistance so it ignored possible
differences within studies because it was based on the
preponderance of evidence for any given study. However,
we believe that using a global measure of outcome to
represent the relationship between antibiotic consump-
tion and resistance was justified as a common effect size
measure could not be calculated for most of the studies
that were used in our review. Another limitation con-
cerns poor reporting of important information and the
use of weak measures in many studies. More than a
quarter of the included studies did not specify which
Table 2 Weighted correlations between odds ratios and independent variables
Variable Correlation Significance Sample size*
Both children and adults 0.32 p < .01 20
Enteric bacteria 0.45 p < .01 20
Staphylococcus −0.44 p < .01 30
MRSA −0.48 p < .01 29
QREC 0.37 p < .01 10
B-lactam consumption −0.31 p < .01 41
Sulphonamides consumption −0.28 p < .01 20
Macrolide consumption −0.24 p < .05 16
Quinolones consumption −0.22 p < .05 12
Northern Europe −0.22 p < .05 14
Southern Europe 0.24 p < .05 15
*Number of cases in each category out of 88 cases total.
Bell et al. BMC Infectious Diseases 2014, 14:13 Page 9 of 25
antibiotic had been consumed and almost half (41%) of
the studies used self-reports, which may be unreliable, to
assess antibiotic consumption. The most serious limitation
in this regard concerned inadequate reporting of how
much time had elapsed between antibiotic consumption
and the measurement of bacterial resistance. Most studies
did not provide the precise interval between when antibi-
otics were consumed and when resistance was measured,
which hindered our attempts to determine whether resist-
ance endured over time as Costelloe  had done. A final
limitation concerns some evidence of potential publication
bias in our meta-analysis in which smaller studies that did
not find a strong positive association between antibiotic
consumption and resistance may not have been published.
To conclude, our literature review reveals the following.
First, there is an association between antibiotic con-
sumption and the subsequent development of bacterial
resistance at both the individual and community level.
For clinicians this is important because our findings do
not just apply at the individual patient level but also at
the community, country and regional levels. As Bergman
(Appendix 3: Bergman et al 2006) noted, antibiotic pres-
sure at the population level may be more important than
the individual’s use of antibiotics in determining that in-
dividual’s risk of harbouring resistant bacteria. Both re-
sponsible prescribing at the individual level as well as
public policy that addresses the problem at the national
or regional level are critical components of any strategy
to reduce bacterial resistance, which supports the
current efforts of many countries, including the UK, to
ensure that antibiotics are only used when indicated and
that the most appropriate antibiotic (often an older
established antibiotic) is used. We also found that the
link between antibiotic consumption and resistance does
not depend on any of the demographic variables that we
investigated which means that antibiotic consumption
may lead to resistance for diverse groups of people in
various settings, although we would hasten to add that
several important predictors were identified in the meta-
analysis including a stronger link between resistance and
consumption in southern Europe than in other regions.
This discrepancy may have been due to the outcome
measure that was used or the particular mix of studies
that was examined in the meta-analysis. More work
needs to be done because significant residual variability
remained that could not be accounted for by the vari-
ables that were used in our meta-analysis.
Future work should also address the issue of co-
selection in which the use of one antibiotic produces
resistance to another antibiotic. If co-selection is wide-
spread, then resistance to one antibiotic could be due
to the use of another antibiotic that was not measured
in the study under investigation, in which case the conclu-
sion that there was no association between consumption
and resistance would be misleading. Unfortunately, the
studies that we examined rarely looked at this issue. More
thought also needs to be given to improving measures of
antibiotic consumption. Proxy measures, such as patient
self-report of antibiotic use, do not directly assess con-
sumption and therefore may be of limited utility.
Appendix 1 Search terms and connectors (AND/
OR) for literature search
‘Drug Resistance, Microbial’
‘Drug Resistance, Bacterial’
‘Bacterial Resistan*’(which captures terms such as
‘Antibiotic Prescri*’which includes terms such as
‘Primary Health Care’
Appendix 2 Exclusion criteria
A). Some studies were excluded because the infection
was not community acquired or the setting was
1) Studies where more than half of the resistant
bacteria were acquired in a hospital setting or
Bell et al. BMC Infectious Diseases 2014, 14:13 Page 10 of 25
in another institutional setting such as a nursing
2) Studies where antibiotic consumption was only
measured in a hospital or nursing home, not in
3) In vitro studies unless in vivo resistance was also
4) Studies that examined the topical use of
antibiotics for skin infections, dental use of
antibiotics, veterinary studies, and those that
looked at the use of antibiotics in agriculture or
5) Studies that looked at the effects of bacteria
from waste or industry on soil, air and water
6) Studies that looked at viruses, parasites or fungi
7) Studies that only looked at treatment failure or
eradication of bacteria unless resistance was also
8) Studies where most of the patients were
seriously ill (such as, when over half the patients
were on immunosuppressive therapy, were HIV
positive, had meningitis or end-stage cancer,
were using catheters, or were recruited in tertiary
centres). Those studies that looked at urinary
tract infection were only included when at least
2/3 of the sample contained women (not girls)
with an uncomplicated urinary tract infection.
B) Some studies conducted at the level of the
individual patient (as opposed to country or region)
were excluded because the temporal relationship
between consumption and resistance was not clear
or the interval between consumption and resistance
was too short (we wanted evidence of an enduring
9) Studies in which consumption was measured
after resistance or where the time between
consumption and resistance was exclusively less
than one month. Patients could be currently
taking antibiotics at the time that resistance was
measured if the long term use of antibiotics was
investigated (such as the effect of antibiotics
received in the past 6 months on resistance to
current treatment with antibiotics)
C) Some studies were given less weight than others:
10) We focused on newer studies (those conducted
since 1990) although some older studies from
the 1970s and 1980s were also included.
[Older studies tended to be methodologically
inferior (in reporting of statistics) and resistance
tends to be greater in newer studies, which
is an important consideration as the current
relationship between resistance and consumption
is more important than the relationship from 40
or 50 years ago].
11) Review articles were not included (except as a
source of references) unless the study combined
previous work in a meta-analysis or systematic
12) Studies that simply looked at the biological
mechanisms which produced resistance were
excluded (However, some studies examined
bacteria that produced resistance in a particular
way, such as b-lactamase-producing bacteria.
These studies were included because they looked
at the effects of antibiotic consumption on the
production of a particular type of resistant
13) Studies that looked at treatment failure or bacterial
eradication were excluded unless resistance was also
measured. For example, some studies examined C
Difficile, which is difficult to eradicate, but these
articles were excluded unless resistance was also
D) Studies were generally classified as weak, and
therefore excluded, when:
14) The authors acknowledged that there was
insufficient power to detect an effect
15) No statistical analysis was conducted. In some
cases, conclusions were based on graphs and
figures with no statistical results provided, for
other studies a single participant or a handful of
participants was studied so no statistical tests
16) Poor measures were used, usually poor
consumption measures (such as relying on
participant self-report when recall was poor).
Appendix 3 Included studies
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Antibiotic selection pressure and resistance in
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The authors declare that they have no competing interests.
BB performed the searches, analysed the data, and drafted first sections of
the text. MP designed the study and formulated the hypotheses. FS led the
APRES team. HG and ES provided their expertise in microbiology. MP and BB
are the guarantors. All authors had full access to all of the data (including
statistical reports and tables) in the study and can take responsibility for the
integrity of the data and the accuracy of the data analysis. All authors read
and approved the final manuscript.
Carol Coupland provided statistical advice. We also wish to thank our
colleagues in other countries who helped us search the non-English lan-
guage literature: Kathryn Hoffman and Manfred Maier (Austria), Dragan Soldo
(Croatia), Didier Duhot, Gilles Hebbrecht and Andrea Poppelier (France), Joke
Korevaar (Holland), Laszlo Kolozsvari (Hungary), Albert Boada and Boni Bolibar
(Spain), and Sigvard Molstad (Sweden). We want to acknowledge comments
made on an earlier draft by Casper den Heijer (University of Maastricht), Gilles
Hebbrecht (Société Française de Médecine Générale), Samuel Coenen (ESAC)
John Paget (NIVEL), and Evelien van Bijnen (NIVEL).
This research was funded through the Seventh Framework Programme by
the European Commission as part of the APRES (The Appropriateness of
Prescribing Antibiotics in Primary Health Care with respect to Antibiotic
Resistance) project, led by NIVEL in Utrecht, Holland (project number
223083). The work reported in this paper was led by the University of
Nottingham as part of the APRES project, an EU project investigating
antibiotic resistance in Europe. The views expressed in this publication are
those of the authors and not necessarily those of the University of
Nottingham. The funder had no role in the study design, data collection,
data analysis, interpretation of the data, writing the report, or in the decision
to submit the article for publication. The researchers were independent of
Limited to other researchers on personal request.
Division of Primary Care, University of Nottingham, University Park,
Nottingham NG7 2RD, UK.
NIVEL (Netherlands Institute for Health Services
Research), PO Box 1568, 3500 BN Utrecht, the Netherlands.
General Practice and Elderly Care Medicine/EMGO Institute for Health and
Care Research, VU University Medical Centre, Amsterdam, the Netherlands.
Caphri University of Maastricht/Maastricht, University Hospital, Medical
Microbiology, P.Debyelaan 25, 6229 HX Maastricht, the Netherlands.
University of Antwerpen, Universitair Ziekenhuis Antwerpen, Laboratory of
Medical Microbiology, Wilrijkstraat 10, B-2650 Edegem, Belgium.
Received: 10 June 2013 Accepted: 19 December 2013
Published: 9 January 2014
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Cite this article as: Bell et al.:A systematic review and meta-analysis
of the effects of antibiotic consumption on antibiotic resistance.
BMC Infectious Diseases 2014 14:13.
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