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Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 90
International Journal of Medical and Health Sciences
Journal Home Page: ht...
Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 91
MATERIALS AND METHODS
Literature search and data extraction
There were thr...
Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 92
Statistical analysis
Resistance data for all antibiotics were used to calc...
Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 93
Nitrofurantoin Anti-infective 37 -0.5106 74.51 M
Azithromycin Macrolides 3...
Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 94
Figure:2 Comparison of highly resistant antibiotics with their correspondi...
Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 95
DISCUSSION
In this study, the data extraction process selected total 35
an...
Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 96
doctors, nurses, hospital interns, chemists, medical
promotion officers of...
Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 97
CONCLUSION
There are some limitations of this study as meta analysis
appro...
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Identifying Antibiotics posing potential Health Risk: Microbial Resistance Scenario in Bangladesh

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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.

Publicada em: Saúde e medicina
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Identifying Antibiotics posing potential Health Risk: Microbial Resistance Scenario in Bangladesh

  1. 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
  2. 2. Int J Med Health Sci. Jan 2015,Vol-4;Issue-1 91 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. 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. 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. 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. 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. 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. 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. REFERENCES 1. Ambrus JL and Ambrus JR, Nutrition and infectious diseases in developing countries and problems of acquired immunodeficiency syndrome. Exp Biol Med 2004; 229(6): 464-72. 2. Goossens H, Antibiotic consumption and link to resistance. Clin Microbiol Infect 2009; 15 Suppl 3:12- 5. 3. Kariuki S, Situation Analysis and Recommendations: Antibiotic Use and Resistance in Kenya. CDDEP 2011;14-27 4. Howard DH, etal. The global impact of drug resistance. Clin Infect Dis 2003; 36(Suppl 1): S4-10. 5. Halpern MT, etal. Meta-analysis of bacterial resistance to macrolides. J Antimicrob Chemother 2005; 55(5): 748-57. 6. Terr D. Weighted Mean. A Wolfram Web Resource, created by Eric W. Weisstein. Available from: http://mathworld.wolfram.com/WeightedMean.html. 7. Morgan WT. A Review of Eight Statistics Software Packages for General Use. The American Statistician 1998; 52(1): 70-82. 8. Forgy E. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 1965; 21: 768--780. 9. MacQueen JB. Some Methods for Classification and Analysis of MultiVariate Observations. in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability. 1967. University of California Press. 10. Hartigan MAW. A K-Means Clustering Algorithm. Applied Statistics 1979; 28: 100--108. 11. Kruskal WH. Historical Notes on the Wilcoxon Unpaired Two-Sample Test. Journal of the American Statistical Association 1957; 52(279):356-360. 12. Cizman M. The use and resistance to antibiotics in the community. Int J Antimicrob Agents 2003; 21(4): 297- 307. 13. Essack SY, Schellack N, Pople T, Merwe L. Situation Analysis: Antibiotic Use and Resistance in South Africa, in South African Medical Journal 2011; 549- 596. 14. Alam I. Antibiotic Policy: An Essential, Time Demanded but Ignored Reality in Treating Infectious Diseases in Bangladesh. Bangladesh J Med Microbiol 2008; 2(2). 15. Hasan MH. Pattern of Antibiotics Use at the Primary Health Care Level of Bangladesh: Survey Report-1. S J Pharm Sci 2009; 2(1). 16. Hutchinson JM, etal. Measurement of antibiotic consumption: A practical guide to the use of the Anatomical Thgerapeutic Chemical classification and Definied Daily Dose system methodology in Canada. Can J Infect Dis 2004; 15(1):29-35. 17. IMS Health (Bangladesh). Available from: http://www.imshealth.com/portal/site/imshealth?CUR RENT_LOCALE=bn_bd. 18. Ganguly NK. Situation Analysis: Antibiotic Use and Resistance in India. CDDEP 2011; 1-74. 19. Mittmann N, etal. Oral fluoroquinolones in the treatment of pneumonia, bronchitis and sinusitis. Can J Infect Dis 2002;13(5): 293-300. 20. Shimada K, etal. Clinical studies on ceftriaxone in respiratory tract infections.. Jpn J Antibiot 1993;46(2):184-91. 21. Quintiliani R. Cefixime in the treatment of patients with lower respiratory tract infections: results of US clinical trials. Clin Ther 1996;18(3): 373-90; discussion 372. 22. Lalla F. Cefixime in the treatment of upper respiratory tract infections and otitis media. Chemotherapy 1998;44 Suppl 1: 19-23. 23. Struelens MJ. The epidemiology of antimicrobial resistance in hospital acquired infections: problems and possible solutions. BMJ 1998;317(7159): 652-4. 24. Cosgrove SE. The Relationship between Antimicrobial Resistance and Patient Outcomes: Mortality, Length of Hospital Stay, and Health Care Costs. Clin Infec Dis 2006. 42(Supplement 2): S82-S89. _______________________________________________ *Corresponding author: Atai Rabby E-Mail:bdrabby@gmail.com

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