The present study was undertaken to investigate the trends of antimicrobial resistance and identify antibiotics that are posing public health risk due to resistant microbes in Bangladesh. Antimicrobial resistance data of Bangladesh for last 10 years were searched out and compared with corresponding antibiotic consumption rates. In this study, a factor is introduced to identify the therapeutic sub-class of antibiotics that are mostly threatened by growing antimicrobial resistance. Highly resistance trend against several antibiotics such as cloxacillin, ampicillin, metronidazole, oxacillin, amoxicillin, tetracycline, cotrimoxazole, penicillin etc. were also indentified. Heat map analysis of this study revealed that nine antimicrobial agents: metronidazole, amoxicillin, tetracycline, cotrimoxazole, cephadine, penicillin, ciprofloxacin, doxycycline and nalidixic acid are associated with public health risk due to growing bacterial resistance. This study would significantly contribute in minimizing development and spread of antibiotic resistance by revealing the microbial resistance scenario and aid the effective antibiotic treatment options in Bangladesh.

1. Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 90
International Journal of Medical and Health Sciences
Journal Home Page: http://www.ijmhs.net ISSN:2277-4505
Identifying Antibiotics posing potential Health Risk: Microbial Resistance Scenario
in Bangladesh
Atai Rabby1*
, Rasel Al Mahmud2
, Towhidul MM Islam3
, Yearul Kabir4
, Md. Rakibul Islam5
1
Research Associate, 3
Lecturer, 4
Professor, 5
Associate Professor, Department of Biochemistry and Molecular Biology,
Faculty of Biological Sciences, University of Dhaka, Dhaka-1000, Bangladesh.
2
Lecturer, Department of Biochemistry, Primeasia University, Banani, Dhaka, Bangladesh.
ABSTRACT
The present study was undertaken to investigate the trends of antimicrobial resistance and identify antibiotics that are posing
public health risk due to resistant microbes in Bangladesh. Antimicrobial resistance data of Bangladesh for last 13 years were
searched out and compared with corresponding antibiotic consumption rates. In this study, a factor is introduced to identify the
therapeutic subclass of antibiotics that are mostly threatened by growing antimicrobial resistance. Highly resistance trend against
several antibiotics such as cloxacillin, ampicillin, metronidazole, oxacillin, amoxicillin, tetracycline, cotrimoxazole, penicillin etc.
were also indentified. Heat map analysis of this study revealed that nine antimicrobial agents: metronidazole, amoxicillin,
tetracycline, cotrimoxazole, cephadine, penicillin, ciprofloxacin, doxycycline and nalidixic acid are associated with public health
risk due to growing bacterial resistance. This study would significantly contribute in minimizing development and spread of
antibiotic resistance by revealing the microbial resistance scenario and aid the effective antibiotic treatment options in
Bangladesh.
KEYWORDS: Antibiotics, Resistance, Bacteria, Microbial Drug Resistance, Public health
INTRODUCTION
Infectious diseases remain among the leading causes of
morbidity and mortality of human[1]. For decades it seemed
as if modern medicine had conquered many of the infectious
diseases that once threatened human and animal health.
Antibiotics have been considered to be an inexhaustible
common, both for medical practitioner and general people,
and the resulting over-consumption has produced a net
increase in antibiotic resistance and a likely reduction in the
therapeutic efficacy of the drugs[2]. Although antibiotics are
effective in treating many cases, but years of use, misuse
and overuse of antibiotics and other antimicrobial drugs
have led to the emergence of drug-resistant pathogens[3].
There are also host and environmental factors associated
with these phenomena. Treatments for these drug-resistant
pathogens are less effective, more expensive, and more toxic
to the patient than antibiotics are for drug-susceptible
pathogen[4]. Some strains of bacteria are now resistant to all
but a single drug, while others have no effective treatment at
all. Therapeutic options for these community-acquired
pathogens are extremely limited, as are prospects for the
development of the next generation antimicrobial drugs. So
there is an immediate urgency to find the causal events
responsible for this behavior of pathogens to deal with
antibiotic resistance.
In this study we have used a meta analysis approach
described by Michael T. Halpern for Meta-analysis of
bacterial resistance to macrolides[5]. The primary objectives
of this study were (i) to determine the quantity and pattern
of antibiotic resistance in Bangladesh between 2000 and
2012 (ii) to analyze antibiotic resistance rates in relation to
antibiotic consumption and (iii) to identify antibiotics
implying potential health risk due to higher consumption
with higher microbial resistance in order to provide data for
empirical therapeutic regimens for key indications. The
scope of this study is further extended by relating the
resistance data with antibiotics price and hospital popularity
and how these factors intensify the emergence of
antimicrobial resistance.
Original article

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MATERIALS AND METHODS
Literature search and data extraction
There were three stages of this study: Literature search and
article inclusion, data abstraction and analysis. PubMed,
Bangladesh online journal system and Google were used as
the sources for literature search to identify articles that are
eligible for review. In each search step we discarded the
articles that are present in another source, thus one article
had been included only once even though it was found in
several searching sources. Finally, 29 articles were included
for data abstraction process Fig. 1. Inclusion criteria used to
select the eligible articles are listed in Table 1.
Table 1: Criterias For Articles Identification & Data Abstraction
(a) Inclusion criteria for articles
Publication year from 2000 to 2012
Presents primary results (excluded review articles and meta analyses)
Sample size and resistance measuring methods clearly indicated
Presents bacterial resistance results of Bangladesh only
Indentified bacterial isolates
Published in English
(b) Inclusion criteria for data abstraction
Presentation of separate resistance values for each antibiotic
Presentation of results by bacterial species
Specified the place of sample collection
Figure:1 Identification and review of articles. There were 439 articles identified in the literature searche. Among these 439
articles 29 were included in this study that fulfill certain inclusion criteria.
If data were imprecise in any article or abstract, it was
discarded from our analysis. Patients age group, places and
sample source (e.g. environmental sample or blood culture)
were not considered in the inclusion criteria during the
article review process. Two independent reviewers reviewed
each article. Any differences for inclusion or in data
abstraction were discussed among the authors. All articles
that were evaluated for inclusion were also subjected to a
review of references. In this manner, all publications and
reports that were referenced in the retrieved articles were
also appraised for potential inclusion in this analysis. Data
abstracted from each article included the study population
characteristics, the sample size for each treatment group,
and the percent resistance for the overall population.

3. Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 92
Statistical analysis
Resistance data for all antibiotics were used to calculate
their weighted mean of resistance by Graph pad prism
implemented column statistics in 95% confidence interval[6,
7]. K-means unsupervised clustering was performed to
classify antibiotics based on resistance percentage into high,
medium and low[8-10]. Column graph was used to relate
resistance of antibiotics with their corresponding
consumption rate and price. Mann-Whitney test was done to
identify significant price difference and resistance rate
between antibiotics developing high resistance and
antibiotics developing low resistance[11]. No heterogeneity
test was performed on the experimental data therefore it
could be possible that some ambiguous data was extracted
during the inclusion process.
RESULTS
By using data extraction process, it was found that a total of
35 antibiotics were assessed for their resistance (Table 2).
Among all the antibiotics analyzed, resistance to cloxacillin
was found to be maximum (100%) however, it was not
included in the present study as there was only one report on
this antibiotic. When the remaining antibiotics were
considered, it was found that the resistance to ampicillin was
highest [80% (95% CI(64.89 – 94.81)]; and resistance to
imipenem and linezolid were the least (5% and 4%
respectively). Resistance data from a single study and
antibiotics without availability of consumption data were
excluded from further analysis.
As no heterogeneity was evaluated for the studies included,
the analysis was focused on the comparative resistance
presentation. To identify antimicrobials against which high
level of resistance was noted K-means unsupervised
clustering was performed on their resistance data and
classified into three categories: high, medium and low.
From this analysis, resistance to 13 antibiotics found to be
high, among which six belong to penicillins group (Table 2).
Table 2: Antibiotics with their corresponding therapeutic subclass and calculated mean resistance.
Antibiotic Therapeutic subclass Mean* LM UM Class
Cloxacillin Penicillins 100 0 0 H
Ampicillin Penicillins 80 64.89 94.71 H
Metronidazole Antiprotozoal 78 0 0 H
Oxacillin Penicillins 78 -201.5 357.5 H
Amoxicillin Penicillins 77 58.46 96.38 H
Tetracycline Tetracyclines 73 54.37 91.17 H
Cotrimoxazole Sulfanilamides 71 61.51 79.59 H
Cephalexin Cephalosporins 66 48.48 84.06 H
Penicillin Penicillins 59 13.29 105 H
Ciprofloxacin Quinolones 58 45.74 70.63 H
Gentamycin Amino glycosides 57 44.82 69.5 H
Nalidixic Acid Quinolones 56 41.57 70.88 H
Cefixime Cephalosporins 49 29.77 67.73 M
Doxycycline Tetracyclines 46 20.79 71.21 M
Ceftazidime Cephalosporins 45 29.19 60.61 M
Cephradine Cephalosporins 42 30.6 53.18 M
Cefepime Cephalosporins 42 29.54 53.96 M
Erythromycin Macrolides 40 22.12 58.77 M
Ceftriaxone Cephalosporins 40 29.57 49.86 M
Amikacin Amino glycosides 39 -418.4 496.4 M

4. Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 93
Nitrofurantoin Anti-infective 37 -0.5106 74.51 M
Azithromycin Macrolides 35 0 0 M
Chloramphenicol Anti-infective 34 4.13 63.07 M
Streptomycin Antitubercular 32 14.43 48.57 M
Fusidic Acid Amino glycosides 28 -42.38 97.38 M
Cefuroxime Quinolones 20 -12.66 51.99 L
Isoniazide Antitubercular 18 10.2 25.8 L
Cefotaxime Cephalosporins 14 0 0 L
Clarithromycin Macrolides 10 0 0 L
Etahmbutol Antitubercular 10 1.718 17.48 L
Meropenem Carbapenems 8 -27.52 44.19 L
Rifampicin Antitubercular 6 0.1545 12.65 L
Azteonam Monobactam 6 -6.706 18.71 L
Imepenem Carbapenems 5 0 0 L
Linezolid Oxazolidinone 4 0 0 L
Note: UM: Upper Mean; LM: Lower Mean; * mean with 95% confidence interval (CI)
Consumption rate is one of the indicators, which give us the
usage statistics of antibiotics[12, 13]. While many reports
described serious misuse or overuse of antibiotics[14] and
the need of rational antibiotic prescribing practices, but there
are only few published comparisons of different antibiotic
consumption in Bangladesh[15]. To estimate standard
antibiotic consumption, the Anatomical Therapeutic
Chemical (ATC) Classification System and the Defined
Daily Dose (DDD) measurement units (ATC/DDD version
2007) were assigned[16] to the antibiotic sales data and the
consumption data in DDDs per 1000 inhabitants per day
(DID) was calculated by the following formula:
𝐷𝐼𝐷𝑗 =
𝑆𝑖
𝑃𝑖
× 𝑈𝑖
1
𝑖
𝐷𝐷𝐷𝑗
1000
Where 𝐷𝐼𝐷𝑗 is the consumption data in DDDs per 1000
inhabitants per day for 𝑗 antibiotic, 𝑆𝑖 is Sales per year for 𝑖
dosage form, 𝑃𝑖 is Price of the 𝑖 dosage form, 𝑈𝑖 is Unit of 𝑖
dosage form inmilligram and 𝐷𝐷𝐷𝑗 is defined daily dose of
𝑗 antibiotic. The sales data was collected from
Intercontinental Marketing Services (IMS) last quarter
report of 2011[17]. It should be clearly indicated that
consumption rate of antibiotics has been estimated
from
𝑆 𝑖
𝑃 𝑖
× 𝑈𝑖
1
𝑖 .
When consumption rate of antibiotics were evaluated with
their corresponding resistance data for different years, it
appeared that the antibiotics to which high level of
resistance was exhibited are still being extensively used by
the patients (Fig. 2). The consumption of substances within
2007 to 2011, measured in DID, increased for metronidazole
(+25.99%), amoxicillin (+5.66%), cotrimoxazole
(+45.41%), cephalexin (+88.93%), ciprofloxacin (+19.17%),
gentamycin (+12.99%), cefixime (+155.96%), doxycycline
(+8.02%), ceftazidime (+37.27%), cefepime(+170.07%),
ceftriaxone (+43.19%), amikacin (+47.13%), azithromycin
(+195.86%), cefuroxime (214.27%), cefotaxime (0.58%),
clarithromycin (102.03%) and linezolid(69.39%). On the
other hand, DIDs decreased for ampicillin (-55.16%),
tetracycline (-2.91%), penicillin (-63.48%), nalidixic acid (-
38.05%), cephradine (-10.60%), erythromycin (-20.18%),
nitrofurantoin (-89.99%), chloramphenicol (-12.14%).

5. Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 94
Figure:2 Comparison of highly resistant antibiotics with their corresponding consumption rate in Bangladesh. The
consumption rate is calculated using Defined Daily Dose (DDD) per 1000 inhabitants per day (DID) in milligram. The gray
and black color bars indicate consumption rate of year 2007 and 2011 respectively
When therapeutic subclass of antibiotics were investigated,
development of high level of resistance was found in first
generation cephalosporins, penicillins, tetracyclines,
quinolones, amino glycosides, third generation
cephalosporins, sulfonamides and broad spectrum
antibiotics (Table 3). An algorithm was developed to
evaluate these therapeutic groups as following:
𝐹𝑇 =
𝐻 𝑎
𝐼𝑎
× 100 ×
𝐼𝑎
𝑇𝑎
× 100
Here 𝐹𝑇 represents resistance factor of a therapeutic group,
𝐻 𝑎 is indicating highly resistance antibiotic noted in the
study of this therapeutic group, 𝐼𝑎 is included antibiotics in
the study and 𝑇𝑎 is total antibiotic found in relevant country.
The factor considers both identified high resistance that are
experimentally proved and antibiotics that are not included
in study due to no experimental data. Therefore, high value
𝐹𝑇 indicates higher probability of that therapeutic subclass.
Five therapeutic subclasses were found using 𝐹𝑇value,
against which remarkably enhanced resistance was
identified (Table 3). These groups are first generation
cephalosporins, penicillins, tetracyclines, quinolones, amino
glycosides, third generation cephalosporins and
sulfonamides. No subclass with highly resistant antibiotics
was found for antitubercular, carbapenems, second-
generation cephalosporins, fourth generation
cephalosporins, macrolides, oxazolidinone and tricyclic
glycopeptides.
Table 3: Antimicrobial resistance pattern in therapeutic subclasses
Therapeutic Class
Total
Antibiotics
available in
Bangladesh
Antibiotics
included in
this analysis
Antibiotics
found as
highly
resistant
Percentage
of highly
resistant
antibiotics
Percentage
of included
antibiotics
among total
Factor*
Cephalosporin’s (First generation) 4 2 2 100 50 5000
Penicillin’s 16 7 7 100 44 4375
Tetracycline’s 5 2 1 50 40 2000
Quinolones 13 2 2 100 15 1538
Amino glycosides 7 2 1 50 29 1429
Cephalosporin’s (Third generation) 9 3 1 33 33 1111
Sulfonamides 11 1 1 100 9 909
Broad -spectrum antibiotics 14 5 1 20 36 714
* Factor = Percentage of highly resistant antibiotics x percentage of included antibiotics among total antibiotics available in Bangladesh

6. Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 95
DISCUSSION
In this study, the data extraction process selected total 35
antibiotics that meet the criteria for the analysis, among
them 13 were noted to which high level of antimicrobial
resistance was found (Table 2). Antibiotics such as
ampicillin, metronidazole, amoxicillin, tetracycline,
cotrimoxazole, penicillin and ciprofloxacin are most popular
in Bangladesh. These antibiotics are cheaper as well as
effective; therefore rising high level of resistance against
these drugs has raised an alarming situation because this
would ultimately limit the treatment options for poor people,
as they cannot afford costly treatment. Moreover, low priced
antibiotics are used extensively and always popular to the
consumers (patients) due to limited purchasing power of
high priced drugs in developing countries like
Bangladesh[3, 13, 18]. When antibiotic resistance and price
were compared, it was found that price is certainly related to
antibiotic consumption hence in the development of
resistance (Fig. 3). Probably, misuse and overuse of the
cheaper antibiotics are higher than the costly antibiotics. To
investigate the price factor further, we conducted a survey
on the chemists selling the antibiotics. Surprisingly, it was
found that only 30-40% patients buy full course of
antibiotics, and among the remaining 60-70% patients, only
5-10% comes again to buy remaining of the course (data not
presented). In most cases (~65%) patient could not afford
the cost of full course antibiotics. In Bangladesh, other
cheaper antibiotics as noted moderately resistant in this
study are cefixime, doxycycline, cephradine, nitrofurantoin
and chloramphenicol. According to our analysis, as these
antibiotics are comparatively cheaper and effective, they
would be the next target of antimicrobial resistance.
Figure:3 Socioeconomic status, in other words price factor of drugs are presented here with their resistance rate. Price
difference between these two groups was evaluated by Mann-Whitney test and was statistically significant with p value
0.0046. Gray and black color indicates antibiotics classified as low and highly resistant respectively.
High consumption rate per 1000 inhabitants (DIDs) for
metronidazole, cotrimoxazole, cephalexin, amoxicillin,
ciprofloxacin and gentamycin indicates a health risk threat
of using these antibiotics as high resistance has been
developed against them, thus cure rate will decrease and
patient will need to change the course of antibiotic. This
could be life threatening if prognosis is not assessed in
proper time. Although, DIDs for ampicillin, tetracycline,
penicillin and nalidixic acid is decreased over time but
extreme increment of DIDs of cefixime, cefepime,
cefuroxime, azithromycin and clarithromycin clearly
indicates that the pressure of antimicrobial resistance is
going to be more complex as these drugs are being
extensively used as alternative treatment options and could
become next target of high microbial resistance.
Development of high-level resistance in the therapeutic
subclass of first generation cephalosporin will limit the
treatment option for gram-positive bacteria. Third
generation cephalosporins and quinolones are greatly used
in respiratory tract infections[19-22] therefore, development
of resistance in quinolones and third generation
cephalosporins will limit the treatment options for
respiratory infections (Table 3). Moreover, development of
high level of resistance in penicillins and tetracyclines will
limit cost effective treatment options. In brief, these
observations signify that antibiotics resistance in
Bangladesh should be a sound concern or this will
ultimately margin our major treatment options as well as
cost effective treatments.
In Bangladesh, hospitals are the breeding area for
development of antimicrobial resistance[23, 24] as no
proper disposal system available in the hospitals. Therefore,
antibiotic popularity in hospitals was assessed and the most
popular antibiotics were noted based on discussion with

7. Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 96
doctors, nurses, hospital interns, chemists, medical
promotion officers of pharmaceutical and hospital
procurement report. It was found that metronidazole,
amoxicillin, tetracycline, cotrimoxazole, penicillin,
ciprofloxacin, nalidixic acid, cefixime, doxycycline,
cephradine, ceftriaxone, azithromycin and chloramphenicol
are the most popular antibiotics and extensively used in
hospitals. From these popular antibiotics high level of
resistance was noted against amoxicillin, tetracycline,
cotrimoxazole, ciprofloxacin and nalidixic acid and
moderate level of resistance was noted against cefixime,
doxycycline, cephradine, ceftriaxone, azithromycin and
chloramphenicol.
Finally, all the factors discussed above were used to produce
a heat map (Fig. 4). In the heat map we assumed that a
antibiotic encompassing at least three dark squares should
be considered to pose potential health risk. It was found that
metronidazole, cotrimoxazole and ciprofloxacin are in the
extreme line of health risk and amoxicillin, tetracycline,
penicillin, nalidixic acid, doxycycline and cephradine are in
major line of health risk due to bacterial resistance (Fig. 4).
Since the consumption and hospital popularity of ampicillin
is low thus the use of this antibiotic is decreasing gradually,
therefore ampicillin was not considered as potential health
risk although it was classified as highly resistant antibiotic.
Gentamycin is another drug with higher resistance and
consumption rate but due to the high price and lower
hospital popularity consumption of gentamycin will fall
sooner. So, gentamycin will not pose health risk of
microbial resistance.
Figure:4 Heat map of antibiotics with their respective risk factors to public health. The heat map is of black color with
three saturation values (dark, light and white). Darker color indicating higher value for consumption rate, hospital
popularity and antibiotic resistance but lower value for price.

8. Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 97
CONCLUSION
There are some limitations of this study as meta analysis
approach cannot determine the exact antibiotic resistance
rate. Furthermore, the lack of consumption data from the
hospital setting neglects the possible influence of hospital
prescribing on the evolution of resistance. But from this
study it is clear that bacteria have already developed high
level of resistance against major antibiotics like amoxicillin,
tetracycline, cotrimoxazole, cephalexin, penicillin and
ciprofloxacin, which confined the scopes of cheaper
treatment. Microbial species have not been included this
analysis but has been noted and will be available upon
request. We have also identified antibiotics that have been
greatly threaten by microbial resistance therefore are
subjected to prescribe carefully. Therefore, if a national
guideline of antibiotics use along with the current antibiotic
resistance scenario would available to the health
professionals then that might significantly contribute in
minimizing development and spread of antibiotic resistance
in Bangladesh.
Acknowledgement
We thank Mahmuda Khatun and Sajib Chakrabarty for their
help during data mining and statistical analysis. We also
thank Professor Syed Saleheen Qadri for his inspiration to
us all.
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_______________________________________________
*Corresponding author: Atai Rabby
E-Mail:bdrabby@gmail.com