Patterns of Antimicrobial Resistance Among Major Bacterial Pathogens Isolated from Clinical Samples in Bangladesh (2017–2020): A Nationwide Cross-Sectional Study

Abstract

Antimicrobial resistance (AMR) is a critical public health issue in Bangladesh, where antibiotic use is widespread but often unregulated. This nationwide cross-sectional study (2017–2020) analyzed data from 26 public and private laboratories across all divisions of the country. Standardized data on antimicrobial susceptibility testing (AST) were collected, curated, and analyzed using WHONET, QAAPT, and R software to assess resistance patterns in 232,329 bacterial isolates from various clinical specimens. Escherichia coli was the most common pathogen (32.5%), followed by Klebsiella sp. (15.5%) and Pseudomonas sp. (10.6%). Urine specimens comprised 50.3% of the tested samples, while blood and soft tissue/body fluids accounted for 12.1% and 24.8%, respectively. Patients aged 55 years and older represented the largest group (36.3%), highlighting their vulnerability to drug-resistant infections. Resistance to third-generation cephalosporins was alarmingly high in Escherichia coli (62.9% resistant to ceftriaxone), whereas carbapenem resistance remained relatively low (5.3% and 6.8% to imipenem and meropenem, respectively). Klebsiella sp. showed widespread resistance, though carbapenems remained relatively effective (imipenem resistance 20.3%, meropenem 21.7%). In contrast, Salmonella sp. remained largely sensitive to third-generation cephalosporins. However, 42% of Staphylococcus aureus isolates were methicillin-resistant (MRSA). This study underscores the urgent need for improved antibiotic stewardship, enhanced diagnostic capacity, and strengthened AMR surveillance to preserve treatment options in Bangladesh.

1. Introduction

Antimicrobial resistance (AMR) is a global public health crisis which will claim 8.22 million lives annually by 2050 if left unchecked [1]. Low- and middle-income countries in Africa and South Asia bear a disproportionate burden of AMR [2]. For example, in Bangladesh, infections due to antibiotic-resistant microorganisms accounted for over 98,800 deaths in 2019 [2,3] and are estimated to increase more than USD 500 million per year for AMR-related healthcare costs. Despite the recognition of AMR as a significant public health issue in Bangladesh, there remains a lack of comprehensive data on bacterial drug-resistance patterns in community-acquired infections over an extended period [4].

Current AMR surveillance in Bangladesh primarily focuses on a limited number of patients based on specific case definitions, often overlooking findings from private laboratories [5]. This surveillance has 11 sentinel sites (all tertiary hospitals), with only one site from the private sector. This practice poses challenges in producing representative antibiograms and hinders evidence-based decision-making for AMR containment and developing clinical guidelines for infectious diseases. Furthermore, the absence of a centralized laboratory information-sharing system impedes effective antimicrobial surveillance. It compromises patient safety, particularly in a setting where most of the population lacks access to microbiology laboratory services [6].

In response to these critical gaps, we conducted a nationwide cross-sectional study to assess the status of AMR in Bangladesh between 2017 and 2020. Data were collected from all available clinical samples obtained from 34 public and private microbiology laboratories across the country, with 26 ultimately meeting quality inclusion criteria. Previous studies in Bangladesh have largely focused on isolated facilities, a limited number of pathogens, or shorter observation periods [7,8,9,10,11], and have not provided comprehensive nationwide data integrating both public and private sector laboratories. By applying standardized tools such as the Rapid Laboratory Quality Assessment (RLQA) [Supplementary Materials] and WHONET across a diverse range of laboratories, our study was designed to generate robust microbiology evidence and derive reliable insights into the current landscape of AMR in Bangladesh.

2. Materials and Methods

2.1. Study Design and Setting

A retrospective cross-sectional analysis was conducted between 2020 and 2022, using collected laboratory data from 2017 to 2020, involving public and private microbiology laboratories across different regions of Bangladesh (Figure 1). The study aimed to obtain data on antimicrobial susceptibility testing (AST) for four years between January 2017 and December 2020. The study was conducted in collaboration with the Communicable Disease Control (CDC) division of the Ministry of Health and Family Welfare (MoHFW) of Bangladesh, the Institute of Epidemiology, Disease Control and Research (IEDCR) in Bangladesh, and the International Vaccine Institute (IVI) in South Korea, and represented the activity of ‘Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA)’ project, a regional initiative supported by the Fleming Fund [12].

2.2. Inclusion, Exclusion Criteria

To collect AST data, the Directorate General of Health Services (DGHS) and the CAPTURA consortium identified 46 public and private medical colleges, hospitals, and diagnostic centers across Bangladesh based on their capacity to perform antimicrobial susceptibility testing. A predesigned questionnaire, incorporating the RLQA tool version 2.0, was used to assess the quality of microbiology testing at each facility. The RLQA evaluated seven key components: equipment, staffing, media, pathogen identification, AST, Internal Quality Control (IQC), and External Quality Assurance (EQA).

This tool, developed specifically for the CAPTURA project, aimed to assess AST practices in microbiology laboratories. However, it is not a fully comprehensive or validated laboratory evaluation. A cutoff score of 60 was established for inclusion, based on discussions among the project team. Laboratories were excluded if they lacked adequate data storage or did not follow internationally recognized standards, such as those set by the Clinical and Laboratory Standards Institute (CLSI) or the European Committee for Antimicrobial Susceptibility Testing (EUCAST).

2.3. Data Collection

Following the RLQA assessment, we observed significant variability in data collection practices and recording platforms across different laboratories. To address this inconsistency, we introduced a standardized method to unify data from multiple sources into a consistent format. Prior to data collection, we conducted over 32 in-person and virtual training sessions on AMR data collection, standardization, analysis, and reporting. These sessions engaged more than 160 microbiologists, technologists, software developers, and key decision-makers at the ministry and facility levels. A master trainer pool facilitated the training to ensure widespread adoption of best practices.

To streamline data management, we introduced WHONET version 25.4.5 [13], a widely used, free desktop software developed by the WHO Collaborating Center for Surveillance of Antimicrobial Resistance. This tool enabled laboratories to convert data from Excel spreadsheets and Laboratory Information Systems (LIS) using WHONET’s BacLink feature. Additionally, facilities relying on manual registers were able to input their data directly into WHONET, ensuring a standardized and structured format for all collected data.

Data management, data quality, and statistical analysis: Since the datasets were collected using different data management systems, a tailored approach for data curation was necessary. Beyond the cleaning via WHONET, each dataset required closer examination and hands-on curation conducted by the CAPTURA team. The team found additional outliers, incorrect organisms, null specimen dates, and incorrect AST results using the SQLite Database Browser software version 3.13.1. Following this additional curation, a combined dataset was assembled using the WHONET data combination and encryption functionality. We utilized a combination of robust tools to process and analyze the data effectively. WHONET was employed for data unification, ensuring standardization and consistency across diverse datasets. For analytical preparation and visualization, we leveraged the Quick Analysis of Antimicrobial Patterns and Trends (QAAPT) [14], a versatile, free web-based and user-friendly platform specifically designed for AMR data analysis and reporting. Additionally, R software version 4.5.0, a powerful tool for statistical computing and data visualization, was employed to develop the country-level map [15]. Statistical analyses were performed using the WHONET, QAAPT, and R software (with the following packages: ozmaps, sf, ggplot2, and mapdata).

3. Results

3.1. Data Collection

A total of 1,037,013 unique records were collected from 34 labs, excluding 11 facilities due to inadequate data storage platforms (manual or digital). Furthermore, data from nine labs were excluded due to a <60 RLQA score. We included data from all organisms with more than 200 isolates and excluded contaminants, normal flora, oral flora, and mixed material pathogens. For patients with multiple isolates from the same infection episode, only the first isolate was considered, following the CLSI M100 [16] and M39 [17] guidelines for calculating trends and resistance patterns. Most laboratories used the disc diffusion (DD) method for AST and maintained results in manual registers or electronic databases. However, some laboratories used VITEK 2, and data from these labs could not be analyzed due to frequent database erasures. Ultimately, 973,278 records from 26 laboratories across eight divisions in Bangladesh were selected for downstream analysis (Figure 2).

3.2. Geographical, Temporal, and Demographic Distribution

Among these AST records, 73.5% (n = 715,721) were clinical specimens with no microorganism growth, and 23.4% (n = 257,557) were specimens with microbial growth in bacterial culture medium. After excluding data with missing information (gender, specimen identification, age, specimen date, organism) (9.8%, n = 25,228), 90.2% (n = 232,329) isolate records were selected for the final analysis (Figure 2). The laboratories were geographically distributed across the country; however, most were in the capital city, Dhaka (Figure 1). The majority (67.2%, n = 156,117) of the data were collected between 2018 and 2019. The monthly distribution of isolates indicates a decrease in culture positive cases during the winter (January–February) and an increase during the summer (July–September) season (Figure 3).

In terms of gender-wise distribution, 44.6% of samples out of 232,329 positive isolates were from male patients, and 55.4% were from females. The majority of the samples were from the 55 and above age group (36.3%, n = 84,296) in both genders (Figure 4). Most of the samples were from urine specimens (50.3%), followed by soft tissue or body fluid specimens (24.8%) and blood specimens (12.1%).

3.3. Most Commonly Found Microorganisms

The most frequently isolated organisms among all samples were Escherichia coli (32.5%), Klebsiella sp. (15.5%), Pseudomonas sp. (10.6%), Staphylococcus aureus (7.8%), and Enterococcus sp. (7.7%), respectively (

Table 1. Baseline characteristics of major variables of positive growth isolates.

Characteristics	Frequency n = 232,329	Percentage (%)
Gender
Male	103,647	44.6
Female	128,682	55.4
Age group
<1	7527	3.2
1–4 Years	11,270	4.9
5–14 Years	15,774	6.8
15–24 Years	24,772	10.7
25–34 Years	29,574	12.7
35–44 Years	25,682	11.1
45–54 Years	33,434	14.4
55–69 Years	54,098	23.3
70+ Years	30,198	13
Number of AST records analyzed per year
2017	46,233	19.9
2018	72,573	31.2
2019	83,544	36.0
2020	29,979	12.9
Specimen type
Urine	116,837	50.3
Soft tissue and body fluid	57,596	24.8
Blood	28,002	12.1
Respiratory	24,668	10.6
Genital	3453	1.5
Stool	1773	0.8
The most common organisms found (top seven)
Escherichia coli	75,472	32.5
Klebsiella sp.	36,012	15.5
Pseudomonas sp.	24,638	10.6
Staphylococcus aureus	18,018	7.8
Enterococcus sp.	17,868	7.7
Staphylococcus, coagulase negative	10,695	4.6
Acinetobacter sp.	9489	4.1

). Figure 5 shows the distribution of the most common organisms according to specimen type.

Of the top five pathogens found in urine specimens, Escherichia coli was frequently (52.0%, n = 60,790) reported, followed by Klebsiella sp. (15.0%, n = 17,487), Enterococcus sp. (13.1%, n = 15,339) Pseudomonas sp. (4.3%, n = 5062), and Staphylococcus, coagulase negative (3.0%, n = 3385). Pseudomonas sp. (24.9%, n = 14,348) and Staphylococcus aureus (19.4%, n = 11,146) were the major isolated pathogens from soft tissue and body fluid samples, followed by other Gram-negative bacteria. The blood culture data showed Salmonella typhi (32.5%, n = 9101) as the primary pathogen causing bloodstream infections (BSI). Other frequently reported pathogens from BSI included Staphylococcus, coagulase negative (13.4%, n = 3763), Escherichia coli (10.8%, n = 3026), Staphylococcus aureus (6.9%, n = 1955), and Salmonella paratyphi (6.3%, n = 1767). In respiratory tract infections, Klebsiella sp. (32.2%, n = 7950) was a commonly reported pathogen. Similarly, Candida sp. (32.9%, n = 1138) predominated in genital specimens while Escherichia coli (71.1%, n = 1261) was the most common in stool samples.

3.4. Patterns of Antibiotic Resistance Among Common Gram-Negative Bacteria

There were considerable variations in antibiotic susceptibility among different bacterial species. Escherichia coli was relatively more susceptible to imipenem, meropenem, amikacin, netilmicin, nitrofurantoin, and piperacillin–tazobactam (resistance proportion, 5.3%, 6.8%, 13.6%, 14.7%, 14.4%, and 19.7% respectively) and commonly resistant to amoxicillin (84.6%) and ampicillin (91.5%). More than 50% of isolates of Klebsiella sp. were resistant to most antibiotics except amikacin (30.2%), gentamicin (35.6%), netilmicin (34.1%) and piperacillin–tazobactam (38.5%). Mecillinam showed better susceptibility against both urinary isolates. Overall, compared to Escherichia coli, Klebsiella sp. was more resistant to antibiotics. However, most isolates remain susceptible to meropenem and imipenem (

Table 2. Proportion of antimicrobial-resistant Gram-negative microorganisms.

Antibiotics/Organisms	Gram-Negative
	Escherichia coli % (n1/n2) *	Klebsiella sp. % (n1/n2)	Pseudomonas sp. % (n1/n2)	Salmonella sp. % (n1/n2)	Acinetobacter sp. % (n1/n2)
Amikacin	13.6 (8690/63,938)	30.2 (9107/30,143)	52.2 (11,466/21,983)	-	62.0 (4727/7619)
Amoxicillin	84.6 (9691/11,455)	-	-	29.3 (1080/3681)	- -
Amoxicillin–Clavulanate	58.1 (26,484/45,552)	66.1 (16,703/25,276)	90.6 (7427/8196)	-	92.3 (4406/4772)
Ampicillin	91.5 (4687/5123)	-	-	22.4 (1207/5387)	-
Aztreonam	56.9 (18,743/32,949)	55.3 (8797/15,899)	61.3 (1082/1766)	-	-
Azithromycin	-	-	-	28.8 (2516/8751)	-
Cefepime	43.1 (15,683/36,359)	44.7 (6549/14,663)	92.2 (3478/3772)	-	65.1 (3216/4942)
Ceftazidime	53.8 (28,949/53,787)	53.5 (12,850/24,030)	53.7 (9166/17,062)	-	78.2 (6226/7958)
Ceftriaxone	62.9 (41,573/66,106)	59.2 (18,142/30,626)	-	1.4 (155/10,968)	82.6 (5833/7066)
Cefoxitin	31.1 (1157/3723)	50.1 (663/1324)	-	-	-
Cefuroxime	68.1 (32,081/47,085)	65.2 (11,810/18,107)	-	-	-
Chloramphenicol	-	-	-	17.5 (1055/6019)	-
Ciprofloxacin	65.6 (43,970/67,037)	52.4 (16,469/31,417)	58.9 (12,345/20,958)	28.7 (3175/11,074)	72.1 (5818/8067)
Doxycycline	56.8 (11,117/19,559)	48.7 (2817/5784)	-	-	48.4 (1105/2283)
Gentamicin	24.4 (16,440/67,358)	35.6 (11,295/31,748)	57.9 (12,994/22,435)	-	67.0 (5838/8720)
Imipenem	5.3 (2661/50,154)	20.3 (4482/22,097)	37.2 (5310/14,289)	-	57.4 (3744/6521)
Mecillinam (only Urine)	22.1 (3225/14,570)	33.1 (1250/3774)	-	-	-
Meropenem	6.8 (3384/49,699)	21.7 (4781/22,047)	38.8 (4863/12,545)	-	55.8 (3320/5952)
Nalidixic acid	-	-	-	92.9 (8335/8972)	-
Netilmycin	14.7 (4637/31,602)	34.1 (5358/15,700)	56.2 (7935/14,124)	-	47.5 (2610/5496)
Nitrofurantoin (only urine)	14.4 (8209/56,957)	40.8 (6568/16,090)	-	-	64.9 (998/1538)
Piperacillin–Tazobactam	19.7 (6847/34,698)	38.5 (7811/20,291)	35.0 (6887/19,671)	-	68.9 (5092/7392)
Tetracycline	52.2 (1603/3069)	55.0 (16,724/30,430)	-	-	-
Sulfamethoxazole–Trimethoprim	53.9 (34,493/64,028)	50.8 (1515/2984)	-		58.6 (4928/8406)

* Percentages in the table indicate the proportion of isolates from each bacterial strain resistant to each antibiotic. Dash (-) within the table indicates either antibiotics that are not recommended or have not been tested for the specific pathogen. Numbers in parentheses (n1/n2) represent n1 as the number of isolates tested against a particular antibiotic for a given microorganism and n2 as the total number of isolates tested for that microorganism.

).

Pseudomonas sp. exhibited alarming levels of resistance to a wide range of antibiotics, with the exception of imipenem (37.2%), meropenem (38.8%), and piperacillin–tazobactam (35.0%). In contrast, Salmonella sp. demonstrated lower resistance to most antibiotic groups, particularly third-generation cephalosporins such as ceftriaxone (1.4%) and cefixime (2.9%). A large majority (92.9%) of Salmonella sp. isolates were found to be resistant to nalidixic acid. Acinetobacter sp. displayed an extremely high level of resistance to most antibiotics, with only netilmicin and doxycycline showing susceptibility in 50% of the isolates (Table 2).

3.5. The Pattern of Antibiotic Resistance Among Common Gram-Positive Pathogens

The results indicate that Staphylococcus aureus showed the least resistance to linezolid (5.3%) and gentamicin (20.2%), but high resistance to penicillin G (80.1%) and azithromycin (80.1%). Chloramphenicol (12.4%) was found to be more effective for the BSI isolates. Approximately 42% of the isolates were resistant to cefoxitin, indicating a high prevalence of methicillin-resistant Staphylococcus aureus (MRSA). Enterococcus demonstrated the highest resistance to tetracycline (76.7%) and ciprofloxacin (71.4%), while showing the least resistance to linezolid (3.3%), penicillin (26.8%), and ampicillin (27.7%). Similarly, Streptococcus sp. was least resistant to linezolid (2.0%) and penicillin (7.8%), but most resistant to trimethoprim/sulfamethoxazole (69.7%), tetracycline (57.1%), and erythromycin (56.9%). Coagulase-negative Staphylococcus was least resistant to linezolid (6.4%) and gentamicin (34.6%), but most resistant to azithromycin (85.1%) and penicillin G (77.6%). In addition, urinary isolates of all four pathogens showed a better susceptibility to nitrofurantoin (

Table 3. Antimicrobial resistance pattern for Gram-positive organisms.

Antibiotics	Gram-Positive
	Staphylococcus aureus % (n1/n2) *	Enterococcus sp. % (n1/n2)	Streptococcus sp. % (n1/n2)	CoNS % (n1/n2)
Ampicillin	-	27.7 (2377/8588)	-	-
Azithromycin	80.1 (4338/5417)	-	-	85.1 (2318/2724)
Cefoxitin	41.7 (2017/4837)	-	-	-
Chloramphenicol (blood)	12.4 (213/1725)	-	9.3 (54/578)	-
Ciprofloxacin	62.2 (8949/14,382)	71.4 (11,988/16,796)	-	-
Doxycycline	27.2 (1564/5742)	59.0 (3640/6168)	28.3 (100/353)	34.6 (1122/3244)
Erythromycin	-	-	56.9 (1222/2146)	-
Gentamicin	20.2 (3380/16,708)	-	-	32.1 (2725/8485)
Linezolid	5.3 (476/8999)	3.3 (329/10,082)	2.1 (70/3414)	6.4 (310/4817)
Nitrofurantoin (Urine)	14.8 (549/3712)	11.0 (1599/14,575)	5.6 (95/1688)	16.6 (531/3194)
Oxacillin	35.5 (2134/6019)	-	-	-
Penicillin G	80.1 (2708/3379)	26.8 (2686/10,011)	7.8 (191/2439)	77.6 (2953/3807)
Tetracycline	21.8 (748/3435)	76.7 (1629/2123)	57.1 (887/1554)	26.9 (831/3088)
Trimethoprim/Sulfamethoxazole	37.6 (5786/15,384)	-	69.9 (2351/3363)	43.3 (3504/8090)

* Percentages in the table indicate the proportion of isolates from each bacterial strain resistant to each antibiotic. Dash (-) within the table indicates either antibiotics that are not recommended or have not been tested for the specific pathogen. Numbers in parentheses (n1/n2) represent n1 as the number of isolates tested against a particular antibiotic for a given microorganism and n2 as the total number of isolates tested for that microorganism.

).

3.6. Susceptibility Patterns of Escherichia coli in Urine and Blood Specimens

The antimicrobial susceptibility of Escherichia coli was assessed using isolates from urine and blood specimens (Figure 6). A total of 63,816 isolates were analyzed, comprising 95.3% from urine (n = 60,790) and 4.7% from blood (n = 3026) sources. Susceptibility rates for 14 antibiotics were compared between the two specimen types. Among urine isolates, the highest susceptibility was observed for nitrofurantoin (76.9%), amikacin (73.3%), and imipenem (64.5%). In contrast, the lowest susceptibility was found for tetracycline (1.6%), followed by aztreonam (17.6%) and ciprofloxacin (29.9%).

In blood isolates, the highest susceptibility was seen for amikacin (79.2%), meropenem (67.6%), and imipenem (66.3%). Susceptibility to piperacillin/tazobactam (60.1%) and trimethoprim/sulfamethoxazole (46.2%) was also notably higher in blood isolates than in urine. However, nitrofurantoin, which showed the highest effectiveness in urine isolates, was significantly less effective against blood isolates (7.8%), indicating its limited utility for bloodstream infections.

4. Discussion

We analyzed 232,329 AST records from 26 public and private laboratories across Bangladesh, spanning the period from 2017 to 2020. These records were retrospectively collected from microbiology culture and AST datasets to assess resistance patterns and trends. The results highlighted significant antibiotic resistance among common bacterial species, impacting the Bangladeshi population. Our analysis included samples from inpatient and outpatient records, and we implemented several measures to ensure the accuracy and quality of the data.

Most of our samples were collected in 2018 and 2019, with a noticeably lower number of samples from 2017, possibly due to many laboratories not retaining data older than three years. The period from April to June 2020 saw a significant reduction in bacteriological testing, which coincided with the onset of the COVID-19 pandemic, leading to a decline in data for that year. Higher temperature and humidity, increased prevalence of waterborne diseases, poor personal hygiene, vector-borne infections, and dehydration-related complications may have contributed to higher infection rates during summer [18]. Regarding gender distribution, the number of samples from female patients was notably higher than that from male patients. This could be attributed to the fact that the majority of the samples were derived from urine specimens (50.3%) commonly submitted to rule out urinary tract infection (UTI). Furthermore, across both genders, the largest proportion (36.3%) of samples came from patients aged 55 years and older. This observation was anticipated for several reasons. Elderly individuals may have compromised immune systems due to various underlying health conditions. Moreover, elderly patients may be more susceptible to hospital-acquired infections while receiving treatment for non-infectious conditions [19].

The organisms frequently isolated among our samples (Escherichia coli, Klebsiella sp., Pseudomonas sp., Staphylococcus aureus, and Enterococcus sp.) are commonly found pathogens in clinical samples such as urine, soft tissues, and body fluids [4,6,7]. The blood culture data identified Salmonella typhi as the major pathogen causing BSI. This finding is consistent with the high incidence of Salmonella infections in Bangladesh and neighboring countries [9,10,11]. Lower susceptibility rates were observed in non-urine Escherichia coli isolates compared to urine isolates. The high effectiveness of nitrofurantoin in urine isolates, alongside its poor performance in non-urine samples, suggests its limited use for non-urinary infections. These results emphasize the importance of specimen-specific antimicrobial stewardship to optimize treatment outcomes.

The antibiotic resistance patterns identified in our study closely mirror those found in similar studies conducted in Bangladesh. A recent systematic review analyzed resistance pattern data from 46 local studies and found a pattern that aligns with our findings [4]. Escherichia coli was the most common causative organism and displayed high resistance to ampicillin, amoxicillin/clavulanic acid, trimethoprim/sulfamethoxazole, and third-generation cephalosporins. Our study also revealed a similar pattern in other organisms causing urinary tract infections, supporting the existing patterns. Enterococcus sp. exhibited high-level resistance to ceftriaxone, a finding consistent with our analysis. Additionally, our study found a comparable resistance level against ceftriaxone (2–3%) among Salmonella sp. samples, further validating the review’s findings. The fact that our study predominantly involved hospital-admitted patients may account for some variations in the observed resistance values compared to the studies included in the review.

We also compared our results with the national AMR surveillance findings [8]. Since our database included data from the national AMR surveillance, we separated the two sources of data (surveillance vs. non-surveillance data). We compared the resistance patterns of Escherichia coli and Staphylococcus aureus. We observed similar resistance patterns between the two databases, and that result was published in another paper [5]. Our study has allowed comparison with regional AMR pattern data as well. Our limited article search found no extensive laboratory data review of India or Pakistan’s settings. However, we could compare our studies’ results with those countries’ national AMR surveillance system reports and systematic reviews (mainly of hospital data). In Pakistan, an alarming level of resistance in Salmonella species has been found [20], which differs from our findings. Resistance among Escherichia coli, Staphylococcus aureus, and Acinetobacter sp. were also a little higher than the result found in our study, which can be expected due to the nature of the sample origin (hospital setting). In India, a higher level of resistance compared to our findings was found among Escherichia coli samples, especially against the carbapenem group of antibiotics [21]. Other organisms have shown a similar level of resistance to our results.

The CAPTURA study conducted in Nepal has allowed us to directly compare our results with theirs, as both studies followed a similar methodology [22]. In Nepal, similar to ours, urine samples were tested at a higher rate than other samples (51.3%). They also found Escherichia coli (37.5% of positive records) as the most common bacteria isolated in the obtained dataset, followed by Staphylococcus aureus, Klebsiella sp., Pseudomonas sp., Acinetobacter sp., coagulase-negative Staphylococci, and Enterococcus sp. (14.1%, 12.3%, 6.2%, 5.9%, 5.7%, 2.7%, respectively). We observe a similar resistance pattern between the two countries for Klebsiella sp. and Salmonella sp. Escherichia coli was more resistant to imipenem (22%) in Nepal; however, it had a similar resistance level against third-generation cephalosporins. Resistance against azithromycin and linezolid was higher in our Staphylococcus aureus isolates than theirs.

Our study’s main strength lies in the comprehensive nature of the dataset used for the AST. This dataset was gathered from a wide range of public and private laboratories across the country, providing a thorough and representative picture of antimicrobial resistance patterns in Bangladesh. We actively engaged with major stakeholders, including the Ministry of Health’s Communicable Disease Control unit, and sought input from national experts to ensure that local issues in lab assessments and data collection were effectively addressed. We employed rigorous techniques for data collection and cleaning to ensure that the data were comparable and reliable. Importantly, our study included clinical samples from both hospital and community (doctors’ private chambers) patients, which is unique compared to other studies that tend to focus solely on hospital settings. This approach helped to prevent the skewing of resistance profiles. Furthermore, we adhered to the latest CLSI guidelines [17] on antibiograms, which significantly enhanced the accuracy and relevance of our study.

Our study had several limitations that may have impacted the robustness of our findings. One significant constraint was the potential variation in data quality produced by different laboratories. Despite our efforts to ensure adherence to international standards, we anticipated variations in lab techniques and quality control practices. Furthermore, we observed disparities in the use of modern equipment among the participating laboratories. Most laboratories used disc diffusion methods for AST, but this approach has limitations in accurately reporting results for certain critical antibiotics, such as colistin and vancomycin. Another noteworthy limitation was the incomplete collection of patient information by some laboratories, which hindered further exploratory and comparative subgroup analysis. Additionally, it is important to note that many laboratories reported organisms only up to the genus level, which may have impacted the depth of our analysis. In addition to the above points, our analysis is subject to the inherent limitation of using routine data, including the lack of control over sampling practices.

5. Conclusions

This study provides a comprehensive analysis of AMR patterns in Bangladesh from 2017 to 2020, revealing a high prevalence of multidrug-resistant bacterial pathogens in clinical settings. Our findings highlight significant resistance to third-generation cephalosporins among key bacterial species, underscoring the urgent need for strategic interventions. The increasing resistance trends limit treatment options and pose a major public health challenge, reinforcing the necessity for immediate action.

To effectively address AMR, a multifaceted approach is essential. Strengthening antimicrobial stewardship programs, enforcing stricter regulations on antibiotic use, and raising public awareness are critical steps in curbing resistance. Additionally, robust infection prevention and control (IPC) measures, improvements in water, sanitation, and hygiene (WASH) practices, and the integration of AMR data across healthcare sectors are crucial in mitigating the spread of resistant infections. Enhancing national surveillance systems will also play a pivotal role in tracking resistance trends and informing policy decisions.

By implementing these measures, Bangladesh can make significant progress in containing AMR and safeguarding the effectiveness of antibiotics for future generations. Sustained efforts and collaborative action at national and global levels are imperative to combat this growing threat to public health.