Journal of Preventive Medicine and Care

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ISSN: 2474-3585
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    Antimicrobial Resistance: A Situational Analysis in the Deido Health District, Douala, Cameroon

    Charles Njumkeng 1 4   Elvis T. AMIN 1   Denis Zofou 3   Jane-Francis K. T. Akoachere 2   Patrick A. Njukeng 1 2  

    1Global Health Systems Solutions, Douala, Cameroon

    2Department of Microbiology and Parasitology, University of Buea, Cameroon

    3Department of Biochemistry and Molecular Biology, University of Buea, Cameroon

    4Department of Public Health and Hygiene, University of Buea, Cameroon

    Abstract

    Background

    The rapid and ongoing spread of antimicrobial-resistant organisms threatens the ability to successfully prevent, control, or treat a growing number of infectious diseases in developed and developing countries. This study was designed to convey more insight on the profile of antimicrobial resistance and the capacity of laboratories conducting antimicrobial susceptibility testing in Cameroon.

    Methods

    A multicentre cross-sectional study was conducted from October 2019 to March 2020 in the Deido Health District. Laboratories that carry out culture and sensitivity testing within the Deido Health District were identified and assessed to determine their capacity as well as the quality of results from microbiological investigations. Information on antimicrobial susceptibility of various isolates was collected using tablet phones in which the study questionnaires had been incorporated.

    Results

    Gaps identified in antimicrobial susceptibility testing that cut across laboratories included; insufficient standard operating procedures, inadequate records on personnel training and competency assessment, lack of safety equipment such as biosafety cabinet, stock out and non-participation in external quality assurance program. The turnaround time for antimicrobial susceptibility testing ranged from 3 – 7 days. Out of the 1797 samples cultured, 437(24.3%) had at least one isolate. A total of 15 different isolates were identified with Candida albicans being the most frequent 178 (40.7%), followed by Escherichia coli 80(18.3%). Among the 15 classes of antimicrobial drugs used in this study, the overall resistance of the isolates showed that five classes had class median resistance above 40% (Cephalosporins, Penicillins, Beta-lactam, Macrolides, and Polyenes).

    Conclusion

    This study has shown the need to develop a coordinated national approach to fight antimicrobial resistance. Scaling-up of antimicrobial susceptibility testing will, therefore, require strengthening the microbiology units of laboratory systems as well as ensuring the use of laboratory data for decision making.

     

    Author Contributions
    Received 27 May 2021; Accepted 08 Jun 2021; Published 09 Jun 2021;

    Academic Editor: Padmavathi Kora, Gokaraju Rangaraju Institute of Engineering & Technology, Hyderabad.

    Checked for plagiarism: Yes

    Review by: Single-blind

    Copyright ©  2021 Charles Njumkeng, et al.

    License
    Creative Commons License     This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Competing interests

    The authors have declared that no competing interests exist.

    Citation:

    Charles Njumkeng, Elvis T. AMIN, Denis Zofou, Jane-Francis K. T. Akoachere, Patrick A. Njukeng (2021) Antimicrobial Resistance: A Situational Analysis in the Deido Health District, Douala, Cameroon. Journal of Preventive Medicine And Care - 3(2):31-46.

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    DOI 10.14302/issn.2474-3585.jpmc-21-3851

    Introduction

    Antimicrobial resistance is a major public health problem. The rapid and ongoing spread of antimicrobial-resistant organisms threatens the ability to successfully prevent, control, or treat a growing number of infectious diseases in developed and developing countries 1, 2. Studies have projected that if the current trend continues, by 2050 an estimated 10 million deaths will occur annually as a result of antimicrobial resistance (AMR) 3, 4, 5.

    WHO underscored in the global action plan the need to continue to raise awareness of AMR through research, surveillance, and monitoring in different countries 6, 7, 8. This is critical for the AMR response system as it: provides data on antimicrobial resistance rate, information to guide clinicians as well as a platform from which AMR reduction strategy can build on 6, 9.

    The impact of AMR is already overwhelming on several health systems worldwide. In the USA, AMR is estimated to be responsible for more than 2 million of infectious diseases and accounts for about 23,000 annual deaths 10. Understanding the real situation in Africa has been challenging due to the limited availability of data at the country level 11, 12. The unavailability of data presents significant challenges in the fight against AMR as it creates gaps in the effective surveillance of AMR, standardization of methodologies, and effective data sharing 11, 13, 14. The existence of gaps in public health information on AMR is more worrisome given that alteration in resistance mechanisms, the emergence of new resistance, and multidrug-resistant pathogens can only be detected through continuous information gathering. It is, therefore, very certain that the fight against AMR should be founded on the field realities given that accurate data is highly dependent on quality-assured microbiology laboratories 13, 15.

    Studies on antimicrobial resistance in different parts of Cameroon indicate that antimicrobial drugs top the list of commonly prescribed drugs in hospitals following the high burden of infectious diseases 16, 17. Mindful of the fact that their use has been identified as an important factor for developing and propagating resistance 18, it is important to update the situation of the resistance profile over time to better inform decision-makers. This study was designed to convey more insight on the profile of AMR resistance and the capacity of laboratories conducting antimicrobial susceptibility testing in Cameroon.

    Methods

    Study Design and Study population

    This was a multicentre cross-sectional study conducted from October 2019 to March 2020 in the Deido Health District. Laboratories that carry out culture and antimicrobial susceptibility testing (AST) within the Deido Health District were identified and assessed to determine their capacity as well as the quality of results from microbiological investigations using the Modified WHO Antimicrobial Resistance Surveillance Questionnaire 16 and Stepwise Laboratory Quality Improvement Process Towards Accreditation (‎SLIPTA) Checklist 19.

    The laboratory staff working on the microbiology bench were trained on how to collect information using a structured questionnaire incorporated in tablet phones. Information on antimicrobial susceptibility of various isolates was collected using tablet phones in which the study questionnaires had been incorporated.

    Participants included in this study were individuals of all ages and sex who visited any of the three (Deido District Hospital, St Padre Pio Hospital and Daniel Muna Memorial Clinic) main hospitals in the Deido Health District for culture and sensitivity tests between October 2019 and March 2020.

    Antimicrobial Susceptibility Testing

    Antimicrobial susceptibility of the isolates was determined using the disc diffusion technique on Mueller Hinton agar for bacteria isolates and Sabouraud dextrose agar for fungi isolates as described in the guidelines of the Clinical and Laboratory Standard Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints20, 21

    After inoculating the isolates and placing the antimicrobial discs, the plates were incubated for 24h against Staphylococcus isolates and 16–18h for other isolates. The diameters of the zones of complete inhibition (as qualified with the eye) were measured, the diameter of the antimicrobial disk was also measured. The measured diameter was compared to the critical values of each antimicrobial disc to determine whether the target isolate was sensitive, resistant or intermediate. Mindful of the difficulty in obtaining commercial control strains in the country to be used for daily routine, the laboratories had established in house controls that are derived from commercial controls. Control tests were performed with Staphylococcus aureus and E. coli in house derived strains.

    For mycoplasma susceptibility testing, the specimen was inoculated into the Mycoplasma Susceptibility kit (Autobio Diagnostics Co.,Ltd., Zhengzhou, China) within an hour as described by the manufacturer's guidelines. If there is a growth, Urealpasmaurealyticum and Mycoplasma hominis metabolizes urea and arginine, respectively. This changes the colour of the culture medium (from yellow to red). The kit was incubated at 37˚C for 48 h after which susceptibility or resistance bacterial were determined with the aid guidelines of Clinical and Laboratory Standards Institute.

    Data Collection

    Data on the laboratory capacity and quality of culture and antimicrobial susceptibility testing was collected with the modified WHO Antimicrobial Resistance Surveillance Questionnaire and the SLIPTA ‎ Checklist. A structured questionnaire was used to collect demographic information, patient history concerning the usage of antimicrobials. Furthermore, the questionnaire was also designed to capture information on the type of specimen that was cultured, the isolate, and the drugs to which the microbe was resistant as well as the turnaround time for culture and antimicrobial susceptibility testing. Since the questionnaire was incorporated into a tablet phone, an email address was created which was used for weekly backup of the data set.

    Data Analysis

    Data was collected with a questionnaire designed using Epi Info Data software. The data set was exported from Epi Info to excel spreadsheet. Missing variables or discrepancies in data were corrected from the medical records of patient. The data was then exported and analyzed using SPSS version 20 (IBM, Chicago, IL). Descriptive statistics such as the number of microbes isolated, type of specimen cultured were expressed as proportions. The overall resistance of the isolates to a class of antimicrobial agents was calculated as median resistance and inter quarter range. The resistance rate of a specific group of the isolate to the antimicrobial agent was also calculated as median resistance. Comparison of the proportion of antimicrobial resistance between groups was assessed with the chi-square test and the threshold for statistical significance set at p < 0.05.

    Ethical Considerations

    Ethical approval was obtained from the Faculty of Health Sciences Institutional review board of the University of Buea (N0: 2019/941-01/UB.SG.IRB.FHS). Administrative authorization was obtained from the Littoral Regional Delegation of Public Health and the Deido Health District. Written consent was obtained from the participants after the purpose of the study was orally explained to them. Since the questionnaire was incorporated into the tablet phones, passwords were given for each tablet to avoid unauthorized accessto the database

    Results

    Laboratory Antimicrobial Susceptibility Testing Capacity

    From the audit results using WHO Antimicrobial Resistance Surveillance and SLIPTA checklist, the methods in use for culture, identification, and antimicrobial susceptibility testing in the laboratories were kit-based (only for mycoplasma) and conventional methods. With respect to personnel, the laboratories were headed by qualified microbiologists and Quality Assurance Officers were present in all the laboratories. The laboratories reported not to be using control strains but they had established in-house quality control isolates. All the laboratories had a backup system for power. The laboratories were using either the “CLSI or the EUCAST” guide for the interpretation of AST.

    On the other hand, gaps identified which cut across the three laboratories included; insufficient standard operating procedures, inadequate records on personnel training and competency assessment, lack of safety equipment such as biosafety cabinet, stock out, non-participation in external quality assurance programs, and audits were not regularly performed. The study findings also showed that there are no structures in place to oversee AMR activities in the facilities and at district levels, hence non-utilization of laboratory AST data to inform authorities. Another gap that cut across these laboratories was the non-usage of quality indicators to measure performance.

    Characteristics of the Study Population

    A total of 1797 samples were cultured in the three laboratories during the study period among which, 424 (23.6%) were female samples. The samples were collected from individuals aged 1 to 94 years, with a mean age of 19.84 years (SD 14.9). The majority of the participants 1199 (66.7) were within the age group 20 – 40 years. Most of the specimens cultured were, urine 731(40.7%) follow by vagina smear 642(35.7%) while the CSF was 2(0.1%). It took the laboratories 3 to 7 days to give out culture and susceptibility testing results. It is important to note that 1164(64.7%) of results were given within 3 working days while 16(0.9%) took more than 5 days (Table 1).

    Out of the 1797 samples cultured, 437(24.3) had at least one isolate. A total of 15 different isolates were identified with Candida albicans being the most frequent 178 (40.7%), mainly isolated from virginal smears followed by Escherichia coli 80 (18.3%) isolated predominantly from urine cultures. Only one Neisseria gonorrhoeae was isolated from a urethra smear during the study period. Overall the greatest number of isolates was from virginal smears 198 (45.3%) followed by urine 116 (26.5%) and stool 81(18.5%) (Table 2).

    Overall Antimicrobial Resistance in the Study Population

    Among the 15 classes of antimicrobial drugs used in this study, the overall resistance of the isolates showed that 5 classes had class median resistance above 40% (Cephalosporins, Penicillins, Beta-lactam, Macrolides and Polyenes). Polyenes had the highest median resistance with Amphotericin B having an overall resistance rate of 89.6 (81.7 – 94.9). The median resistance for Beta-lactam 62.2% with oxacillin being the most resistant in the group 73.0% (95% CI 60.3 – 83.4). On the other hand, the least resistance was observed among the aminoglycosides with a class median of 12.8% with amikacin having the lowest resistance in the group (6.5%, 95% CI 0.8 – 21.4) (Table 3).

    For the resistance of the various isolates to the different antimicrobials tested against, coagulase-negative Staphylococcus (CoNS), Streptococcus spp., Pseudomonas aeruginosa and Serratia liquefaciens had median resistance rate ≥50%. The highest median resistance rate was observed for Pseudomonas aeruginosa with a median resistance rate of 71.0% (IQR, 45.6 – 89.5). The least overall resistance was observed among Citrobacter isolates 17.0% (IQR 0.0 – 61.8), followed by Ureaplasmaurealyticum and Klebsiella species each with a median resistance rate of 33.0% (Table 4).

    Table 1. Description of the study participants according to sex, age, and specimen and turnaround time in Deido Health District
    Variable Category Frequent (%)N= 1797
    Sex Male 424(23.6)
    Female 1,373(76.4)
      <5 92(5.1)
    5-10 88(4.9)
    11-19 126 (7.0)
    20-40 1199 (66.7)
    41-60 205(11.4)
    >60 87(4.8)
    Mean 29.84 (SD,14.9, )
    Range 94
    Specimen US 55(3.1)
    VS 642(35.7)
    Urine 731(40.7)
    Stool 301(16.8)
    Semen 11(0.6)
    Wound 20(1.1)
    CSF 2(0.1)
    Vulva 24(1.3)
    Others body fluid 11(0.6)
    Duration of Culture results 3days 1164(64.7)
    4-5day 617(34.3)
    >5days 16(0.9)
    Mean 3.54 (SD 0.8)
    Range 3- 7

    Table 2. Distribution of Clinical specimen and isolated pathogens in the Deido Health District
    Isolate  Frequency (%) N = 437 Specimen
    Staphylococcus aureus 26(5.9) US (8), VS (5), Urine (9), Semen (1), Wound (3)
    Enterococcus spp. 10(2.3) Urine (10)
    Enterobacter spp 11(2.5) Vulva (1), wound (2) Urine (4) VS (4),
    Neisseria gonorrhoeae 1(.2) US (1)
    Escherichia coli 80(18.3) VS (17), wound (2) Urine (58) Stool (1), Vulva (1),
    Klebsiella spp. 22(5.0) VS (8), Urine (14)
    Salmonella spp. 2(.5) Stool (2)
    Pseudomonas aeruginosa 8(1.8) Urine (2), Wound (6)
    Ureaplasma urealyticum 16(3.7) VS (13), US (2) Semen (1)
    Mycoplasmas hominis 24(5.5) VS (4), US (19) Urine (1)
    Serratia liquefaciens 12(2.7) VS (1), wound (1) Urine (9), Other body fluid (1),
    Citrobacter 11(2.5) VS (6), Urine (5)
    Stretococcus 12(2.7) VS (1), US (1) Semen (1) Urine (1)
    CoNS 24(5.5) VS (19), US (1) Semen (1) Urine (3)
    Candida albicans 178(40.7) VS (97), Stool (79), Vulva (2),
    Total 437(100.0) VS (198), US (17), Urine (116), Stool (81), Semen (5), Wound (12), Valve (5) other body fluid (1)

    Table 3. Overall activity of antimicrobial to the isolates in the study Deido Health District
    Antimicrobial Class Antimicrobial Resistance (95%CI) Class Median Resistance (IQR)
    Cephalosporins Cefuroxime 50.0 (23.0 – 77.0) 51(40-53)
    Cefotaxime 50.6 (39.1 – 62.1)
    Ceftazidime 50.8 (37.5 – 64.1)
    Ceftriaxone 30.1 (20.5 - 41.2)
    Cefixime 54.7(41.7 – 67.2)
    Penicillins Amoxicillin 37.5(24.9 – 51.5) 49(39- 68)
    Ampicllin 65.7(55.6 – 74.8)
    Piperacillin 40.5(29.6 – 52.1)
    Cloxacillin 48.6(36.4 – 60.8)
    Amoxiclav 71.0 (61.5 – 79.4)
    Beta-lactam Aztreonam 51.4 (34.0 – 68.6) 62.2
    oxacillin 73.0 (60.3 – 83.4)
    Quinolones Ciprofloxacin 37.7(26.3 – 50.2) 39(36.8- 49.3)
    Norfloxacin 39.2 (25.8 – 53.9)
    Ofloxacin 55.9(45.2 – 66.2)
    Nalidixic acid 42.9 (24.5 – 62.8)
    Levofloxacin 32.0(19 – 46.7)
    Perfloxacin 33.3 (23.2 – 44.7)
    Macrolides Josamycin 31.6 (12.6 – 56.6) 42(33 - 52)
    Erythromycin 33.9 (22.1 – 47.4)
    Clarithromycin 42.2 (29.9 – 55.2)
    Roxithromycin 57.9(33.5 – 79.7)
    Azithromycin 62.1 (42.3 – 79.3)
    Aminoglycosides Gentamicin 17.6 (10.4 – 27.0) 12.8(ð)
    Amikacin 6.5 (0.8 – 21.4)
    Netilmicin 12.8 (6.8 – 21.2)
    Glycopeptide Vacomycin 48.4 (30.2 – 66.9) -
    Tetracyclines Tetracycline 31.3(11.0 – 58.7) 27.1
    Doxycycline 27.1 (15.3 – 41.8)
    Monocycline 38.5 (23.4 55.4)
    Antifolate Trimethoprim 75.0 (53.3 – 90.2) -
    Nitrofurans Nitrofurantoin 49.3 (36.8 – 1.8) -
    Antimycobacterial Rifampicin 59.4 (46.4 - -71.5) -
      Chloramphenicol 23.8 (8.2 – 47.2) -
    Antifungals
    Azoles Fluconazole 56.4 (46.2 – 66.3) 31(16 - 62)
    Itraconazole 9.5 (1.2 – 30.4)
    Econazol 21.5 (15.4 – 28.8)
    Ketoconazole 55.8 (47.6 – 63.7)
    Miconazole 10.4(6.2 – 16.1)
    Clotrimazole 41.0(30.0- 52.7)
    Polyenes Nystatin 68.7 (57.6 – 78.4) 79.2 (ð)
    Amphotericin B 89.6 (81.7 – 94.9)
    Allyl amines Terbinafine 50.0 (29.1 – 70.9) -
    Others Grisefulvin 31.3 (`6.1 – 50.0) -

    Table 4. Overall isolates resistance to the various antimicrobials they were tested against in Deido Health District
    Isolate Median resistance (IQR)
    Staphylococcus aureus 41.5 (12.8 – 80.0)
    CoNS 50 (11.0 – 73.0)
    Enterococcus spp. 29(0.0 – 57.5)
    Streptococcus spp. 61.0( 2.75 – 82.3)
    Escherichia coli 43.5(29 – 54.8)
    Klebsiella spp. 33.0 (0.0 – 60.0)
    Pseudomonas aeruginosa 71.0(45.6 – 89.5)
    Serratia liquefaciens 50.0(14.0 - 100)
    Citrobacter 17.0(0.0 – 61.8)
    Urealpasma urealyticum 33.0(0.0 – 51.0)
    Mycoplasma hominis 40.0(20.3 – 59.0)
    Candida albicans 43.50(19.75 – 70.0)

    Resistance Rates of Gram-Positive Bacteria to Antimicrobials

    Among the cephalosporins, cefixime showed 100% resistance to Staphylococcus aureus and CoNSwhile cefuroxime showed no resistance to these two groups of bacterial. Among penicillins, ampicillin showed a resistance rate of at least 50% to all gram-positive isolates while cloxacillin (9.1%), amoxicillin-clavulanic acid (33.3%) piperacillin (21.4%) and amoxicillin (11.1%) were the resistance to Staphylococcus aureus, CoNS, Enterococcus spp and Streptococcus respectively. Among the Beta-lactam, Staphylococcus aureus and Enterococcusspp showed 100% resistance to oxacillin. With respect to the Quinolones, Staphylococcus aureus, Enterococcus spp, and Streptococcus showed 100% resistance to nalidixic acid while perfloxacin was the least resistant to Staphylococcus aureus, and Streptococcus with a resistance of 11.1% and 28.6% respectively. Streptococcus also had 100% resistance to ciprofloxacin. Most of the tetracyclines showed 100% resistance.

    Generally, among the 10 classes of drugs tested against the gram-positive bacteria in the study, only clarithromycin (a quinolone) and rifampicin (an antimycobacterial) showed resistance rate with a significant difference across the different category of gram-positive bacteria ( p-value 0.044 and 0.0001 respectively) (Table 5)

    Resistance Rates of Gram-Negative Bacteria to Antimicrobials

    Escherichia coli showed high resistance of above 70% to ampicillin, amoxicillin-clavulanic acid, oxacillin, nalidixic acid, vancomycin, and trimethoprim while it was least resistant to netilmicin (4.2%) followed by ceftriaxone and perfloxacin 11.4% and 11.8% respectively. Klebsiella spp was less resistant to netilmicin (5.9%) and pefloxacin (16.7%) while it showed resistance above 70% to cefuroxime, cefotaxime, amoxicillin-clavulanic acid, oxacillin and rifampicin.

    Pseudomonas aeruginosa showed high resistance of above 70% to cefuroxime, cefotaxime, cefixime, piperacillin, cloxacillin, amoxicillin-clavulanic acid, oxacillin, ofloxacin, azithromycin, antifolate and chloramphenicol, whereas, its lowest resistance wastto ceftriaxone (20.0%) while it showed no resistance to ampicillin, pefloxacin and amikacin (Table 6).

    Serratia liquefaciens resistance to ceftazidime, amoxicillin-clavulanic acid, oxacillin, ofloxacin, nalidixic acid, perfloxacin, trimethoprim, and rifampicin was above 70% while it was not resistant to aztreonam, levofloxacin, gentamicin, amikacin, and nitrofurantoin. Citrobacter showed 100% resistance to trimethoprim, oxacillin, nalidixic acid, and perfloxacin. (Table 6).

    Mycoplasma Resistance to Antimicrobial Drug

    For the resistance of Ureaplasmaurealyticum and Mycoplasma hominis to the various antimicrobials, no statistical significance difference was noted. Ureaplasmaurealyticum showed no resistance to oxacillin, perfloxacin, erythromycin, vancomycin, and nitrofurantoin while Mycoplasma hominis showed no resistance to only ciprofloxacin. Ureaplasmaurealyticum highest resistance was to Clarithromycin (63.6%) while Mycoplasma hominis showed 100% resistance to trimethoprim (Table 7).

    Table 5. Antimicrobial resistance among gram-positive bacteria in Deido Health District
    Antimicrobial Class Antimicrobial Isolated bacteriaResistance (%) P Value
    Staphylococcus aureus CoNS Enterococcus spp. Streptococcus spp  
    Cephalosporins Cefuroxime 0 0 -    
    Cefotaxime 18.2 66.7 -   0.227
    Ceftazidime - - 50    
    Ceftriaxone 75 33.3 33.3   0.510
    Cefixime 100 - 100 - -
    Penicillin Amoxicillin 33.3   0 11.1 0.411
    Ampicllin 50.0 60.0 50.0 100 0.895
    Piperacillin 21.4 0 40 0 0.234
    Cloxacillin 9.1   50.0 66.7 0.131
    Amoxicillin clavulanic acid 50.0 33.3 60 60 0.125
    Beta-lactam Aztreonam 80   66.7   0.315
    oxacillin 100 68.8 100 62.5 0.418
    Quinolones Ciprofloxacin 0 75 0 100 0.282
    Norfloxacin 100 0 0    
    Ofloxacin 80.0 87.5 0 83.3 0.071
    Nalidixic acid 100 73.3 100 100  
    Levofloxacin - 0 - - -
    Perfloxacin 11.1 50 0 28.6 0.063
    Macrolides Erythromycin 55.6 35.3 - 50.0 0.351
    Clarithromycin 33.3 60.0 0 80 0.044
    Azithromycin 100 80.0 0 100 0.145
    Aminoglycosides Gentamicin 20.0 50 0 66.7 0.199
    Amikacin 50.0 50.0 0 - 0.287
    Netilmicin 22.2 11.1 33.3 14.3 0.741
    Glycopeptide Vacomycin 25 - - - -
    Tetracyclines Tetracycline 10.0 100 0 0 0.348
    Doxycycline 0 0 100 0 0.122
    Monocycline          
    Antifolate Trimethoprim 0 35.7 - 0 0.298
    Nitrofurans Nitrofurantoin 57.1 73.3 0.0 62.5 0.133
    Antimycobacterial Rifampicin 91.7 - 25.0 0.0 0.00001

    Table 6. Antimicrobial resistance among isolated gram-negative bacteria in Deido Health District
    Antimicrobial Class Antimicrobial Isolated bacteriaResistance (%) P-Value
    Escherichia coli Klebsiella spp. Pseudomonas aeruginosa Serratia liquefaciens Citrobacter  
    Cephalosporins Cefuroxime 42.9 100 100 - - 0.582
    Cefotaxime 50.0 71.4 85.7 40.0 25.0 0.158
    Ceftazidime 48.7 0 50.0 80.0 50.0 0.277
    Ceftriaxone 11.4 57.1 20.0 33.3 0 0.025
    Cefixime 43.9 50.0 85.7 50.0 66.7 0.420
    Penicillins Amoxicillin 33.3 60.0 - 66.7 60.0 0.796
    Ampicllin 74.5 25.0 0.0 14.3 16.7 0.011
    Piperacillin 40.0 62.5 80.0 40.0 16.7 0.111
    Cloxacillin 55.0 45.5 100 66.7 42.9 0.977
    Amoxiclav 81.2 100 83.3 88.9 50.0 0.705
    Beta-lactam Aztreonam 53.8 50.0 66.7 0 0 0.192
    oxacillin 100 100 100 100 100  
    Quinolones Ciprofloxacin 38.7 0 33.3 20.0 0 0.183
    Norfloxacin 28.6 25.0 50.0 60.0 42.9 0.846
    Ofloxacin 48.4 50.0 100 100 0 0.462
    Nalidixic acid 71.4 0 - 100 100 0.362
    Levofloxacin 40.0 0.0 60.0 0.0 0.0 0.249
    Perfloxacin 11.8 16.7 0 100 100 0.111
     Macrolides Erythromycin 50.0 0.0 - - 0.0 0.415
    Clarithromycin 0 0 - - 0 -
    Azithromycin - - 100 - -  
    Aminoglycosides Gentamicin 10.9 33.3 50.0 0 0 0.052
    Amikacin 0 0 0 0 - 0.755
    Netilmicin 4.2 5.9 50.0 25.0 0 0.200
    Glycopeptide Vacomycin 100 40.0 - - - 0.090
    Tetracyclines Tetracycline           -
    Doxycycline 40.0 0.0 - - 66.7 0.090
    Antifolate Trimethoprim 79.6 - 80.0 100 100 0.853
    Nitrofurans Nitrofurantoin 28.6 0.0 - 0.0 0.0 0.094
    Antimycobacterial Rifampicin 54.2 71.4 - 100 0.0 0.016
      Chloramphenicol - - 75.0 - 0.0 0.171

    Table 7. Mycoplasma resistance to the antimicrobial drug in Deido Health District.
    Antimicrobial Class Antimicrobial Isolated bacteriaResistance (%) P Value
    Urealpasma urealyticum Mycoplasma hominis  
    Beta-lactam Aztreonam      
    oxacillin 0.0 56.2 0.471
    Quinolones Ciprofloxacin 50.0 0.0 0.264
    Ofloxacin 33.3 40.0 0.554
    Levofloxacin 28.6 40.0 0.647
    Perfloxacin 0.0 63.2 0.376
    Macrolides Josamycin 35.7 20.0 0.516
    Erythromycin 0.0 21.1 0.798
    Clarithromycin 63.6 27.3 0.128
    Roxithromycin 57.1 60.0 0.912
    Azithromycin 53.8 60.0 0.895
    Glycopeptide Vacomycin 0.0 47.1 0.516
      Doxycycline 33.3 20.0 0.675
    Monocycline 37.5 39.1 0.499
    Antifolate Trimethoprim - 100  
    Nitrofurans Nitrofurantoin 0.0 52.6 0.376

    Among the antifungal agents, the highest resistance of Candida albicans was observed against Amphotericin B (88.8%). Fluconazole, ketoconazole, and nystatin showed resistance above 50%. Candida albicans showed the least resistance to Miconazole (9.8%)

    Discussion

    This study was designed to provide insight on the profile of AMR resistance and the capacity of antimicrobial susceptibility testing laboratories in the Deido Health District. It is worth noting that, the laboratories involved in this study are located within an urban setting were more the 60% of the Cameroon health workforce is concentrated. It has been proven that Antimicrobial resistance is a great threat in treating infectious diseases and it is increasing the cost of medical care 22, 23However, the lack of information on the situation has been a setback in steaming the fight against this threat. Therefore, understanding the true picture of AMR is very critical and important in a country like Cameroon and other developing countries where there are weak or no systematic guidelines for antibiotic usage.

    Quality laboratory diagnosis is paramount for containing and enhancing the appropriate usage of antimicrobials 24, 25. Some of the gaps identify in the laboratory testing included insufficient standard operating procedures, inadequate records on personnel training and competency assessment, lack of safety equipment such as biosafety cabinet, stock out, non-participation in external quality assurance program and audits were not regularly performed. These gaps epitomize the fact that little attention has been put in place to support testing. Funding to support the fight against AMR is insufficient, especially in developing countries. Previous publications have attributed the scarcity information on AMR in the Central Africa Region to lack of established national or regional AMR surveillance systems, inadequate laboratory capacity, insufficient resources, weak infrastructures and insufficient standard operating procedures 3, 4.

    This study showed that it took at least 3days for a culture result to be released. This can be justified by the fact that the laboratories only use conventional identification and AST techniques. The automated methods and point of care kit that provide faster results are not in use in these laboratories.

    The findings in this study indicate that there is a need for more commitment to improving laboratory capacities of hospitals in Cameroon. There have been limited commitment by the Government on health care in Cameroon over the past years with only 4.7% of the national GDP currently allocated to health care with just about 8% of this budget allocated to improving health infrastructure and laboratory capacity 26.

    In this study, a total of 1797 samples were received by the laboratories for culture from the three hospitals within the study period. This number of cultures done within 5months seems small because these health facilities are secondary level health facilities in the most populated town in Cameroon They can be partially accounted for by the fact that national and local treatment guidelines in many resources limited countries still emphasize on empirical treatment as reported by a study in Tanzania with similar findings 27. However, ensuring adherence to antimicrobial therapy guidelines formulated using evidence-based generated data will go a long way to reducing the burden of antimicrobial resistance. Urine specimens contributed to over 40% of the specimen cultured and this could be attributed to a previously reported high prevalence of urinary tract infections of over 54% in some parts of Cameroon 28

    The most frequently isolated pathogens were Candida albicans (40%) predominantly from vagina smear. This was followed by E. coli isolated from urine culture. These findings could be explained by the fact that most of the samples were from women with a majority within the reproductive age. Candida vaginitis has also been reported to be common among women within the reproductive age 29. With regards to the resistance pattern to antimicrobials, the highest resistance of Candida albicans was noted with Amphotericin B while miconazole had the least resistance. Even though Amphotericin B resistance is unexpectedly high and contrary to many other studies, the susceptibility of Candida albicans to miconazole has been widely reported despite the rising trend of resistance to all Azoles 30, 31.

    Among the different pathogens isolated, Pseudomonas aeuroginosa demonstrated the highest resistance to most antimicrobials, followed by streptococcus spp and Escherichia coli. This was similar to previous reports 28, 32 as these infections are frequently treated with cephalosporins and penicillins that also demonstrated the highest level of resistance. Cephalosporins and penicillins are the most common antimicrobials sold over the counter in most pharmacies as well as by roadside vendors without any formal quality control 33. For the Mycoplasmas, both Ureaplasmaurealyticum and Mycoplasma hominis demonstrated increasing resistance to Clarithromycin and Trimethoprim. However, this is explained by the fact that the biological characteristic of Mycoplasmas have been reported to result in the ineffectiveness of several Other substances (sulfonamides, trimethoprim, rifampin, polymyxin, nalidixic acid, linezolid, and some others) 34. Furthermore, despite fluoroquinolones, and macrolides being considered the most effective anti-mycoplasma agents, there have also been recent reports of treatment failure, rising resistance rates due to repeated mutations 29.

    On the other hand, it was also found that, among the gram-positive bacteria isolates, Staphylococcus aureus showed the highest resistance to the cephalosporins and Beta-lactam and quinolones antimicrobials included in the study. Methicillin-resistant Staphylococcus aureus has also been among the frequently reported gram-positive isolate with resistance to common antimicrobials and has been designated a public health threat 10. The high resistance rate could be because gram-positive pathogens generally exhibit an immense genetic repertoire to adapt and develop resistance to virtually all antimicrobials clinically available. Furthermore, Methicillin-resistant Staphylococcus aureus has been reported to be resulting from nosocomial infections exposed to a variety of antimicrobials 35.

    Conclusion

    The antimicrobial resistance situation in the Deido Health District is preoccupying as is the case with other developing countries. Apart from aminoglycosides, pathogens showed an antibiotic class median resistance over 25% of the various antimicrobial agents. Despite the importance of testing in the fight against AMR, the laboratory turnaround time remains long and the laboratories are under-equipped and the quality of AST is seriously affected.

    This study has shown there is a need to develop a coordinated national approach by Cameroon's ministry of public health to fight AMR with much priority on antimicrobial susceptibility testing. Scaling-up AST testing will, therefore, require strengthening the microbiology units of laboratory systems and ensuring the use of laboratory data for clinical decision-making.

    Declarations

    Acknowledgments

    The authors express sincere gratitude to the Faculty of Science University of Buea, Cameroon, the directors and laboratory staff of the hospitals involved, all the study participants and the littoral Regional Delegation of Public Health

    Funding Sources

    This study was funded by the corresponding author and also received support from the faculty of Science University of Buea, Cameroon. However, the results and conclusions made in this publication are made by the authors and may not represent the official position of the faculty of Science University of Buea, Cameroon 

    Abbreviations

    AMR: antimicrobial resistance

    AST: antimicrobial susceptibility testing

    CLSI: Clinical and Laboratory Standard Institute

    CSF: Cerebrospinal fluid

    EUCAST: European Committee on Antimicrobial Susceptibility Testing

    IRB: Institutional review board

    SLIPTA: Stepwise Laboratory Quality Improvement Process towards Accreditation

    US: Urethral Smear

    VS: Vagina Smear

    WHO: World Health Organization

    Availability of Data and Materials

    All relevant data are included in this manuscript

    Ethics Approval

    This study protocol was reviewed and approved by the Faculty of Health Sciences Institutional Review Board (IRB) of the University of Buea, Cameroon (N0: 2019/941-01/UB.SG.IRB.FHS).

    Consent for Publication

    Not applicable

    Author’s Contributions

    PAN conceived, designed and supervised the study implementation, CN conceived, designed, coordinated the study, analyzed the data and drafted the paper, ETA designed, coordinated the study and participated in drafted the paper, JKTA reviewed and corrected the study proposal and the final manuscript write up and DZ contributed in developing the manuscript. All authors read and approved the final manuscript

    References

    1.WHO. (2014) Antimicrobial resistance: global report on surveillance 2014 [Internet]. Antimicrobial resistance: global report on surveillance. Available from: http://www.who.int/antimicrobial-resistance/publications/surveillancereport/en/%0Ahttp://www.who.int/drugresistance/documents/surveillancereport/en/
    2.Hillier S, Roberts Z, Dunstan F, Butler C, Howard A et al. (2007) Prior antibiotics and risk of antibiotic-resistant community-acquired urinary tract infection: A case-control study. , Journal of Antimicrobial Chemotherapy 60, 92-9.
    3. (2018) Africa Centre for Disease Control. Africa CDC Framework for Antimicrobial Resistance,2018-2023. Africa Centre for Disease Control. 1-24.
    4.Achiangia Njukeng P, Ako-Arrey Ebot, D Tajoache Amin E, Njumkeng C, Sevidzem Wirsiy F. (2019) Antimicrobial Resistance in the Central African Region: A Review. , Journal of Environmental Science and Public Health 03.
    5.WHO. (2014) Antimicrobial resistance in SEARO [Internet]. Available from: https://www.who.int/southeastasia/health-topics/antimicrobial-resistance
    6.World Health Organization (WHO). Global Antimicrobial Resistance Surveillance System. Who. 2015;36
    7.Tsutsui A, Suzuki S. (2018) Japan nosocomial infections surveillance (JANIS): A model of sustainable national antimicrobial resistance surveillance based on hospital diagnostic microbiology laboratories. , BMC Health Services Research 18.
    8.WHO. (2016) Global action plan on AMR [Internet]. Available from: http://www.who.int/antimicrobial-resistance/global-action-plan/en/ , Who
    9.Jorak A, Keihanian F, Saeidinia A, Heidarzadeh A, Saeidinia F. (2014) A cross sectional study on knowledge, Attitude and practice of medical students toward antibiotic resistance and its prescription. Advances in Environmental Biology , Iran 8, 675-81.
    10.CDC. (2019) Biggest Threats and Data | Antibiotic/Antimicrobial Resistance | CDC [Internet]. p. 1. Available from: https://www.cdc.gov/drugresistance/biggest-threats.html%0Ahttps://www.cdc.gov/drugresistance/biggest_threats.html
    11.Wangai F K, Masika M M, Lule G N, Karari E M, Maritim M C et al. (2019) Bridging antimicrobial resistance knowledge gaps: The East African perspective on a global problem. 14, PLoS ONE..
    12.Tadesse B T, Ashley E A, Ongarello S, Havumaki J, Wijegoonewardena M et al. (2017) Antimicrobial resistance in Africa: A systematic review. , BMC Infect Dis 17(1), 1-17.
    13.O ’neill J. (2016) Tackling Drug-Resistant Infections Globally: an Overview of Our Work the Review on Antimicrobial Resistance.
    14.Liu Y Y, Wang Y, Walsh T R, Yi L X, Zhang R et al. (2016) Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: A microbiological and molecular biological study. 16, 161-8.
    15.Xavier B B, Lammens C, Ruhal R, Malhotra-Kumar S, Butaye P et al. (2016) Identification of a novel plasmid-mediated colistinresistance gene, mcr-2, in Escherichia coli. , Belgium 21.
    16.Cookson B D. (1999) Nosocomial antimicrobial resistance surveillance. , J Hosp Infect.43(SUPPL.1)
    17.E D C, D N A, J-F K T A. (2018) Prescribing patterns and associated factors of antibiotic prescription in primary health care facilities of Kumbo East and Kumbo West Health Districts. Available from: http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L620962927%0Ahttp://dx.doi.org/10.1371/journal.pone.0193353 , North West Cameroon [Internet] 13, 196861.
    18.Malik B, Bhattacharyya S. (2019) Antibiotic drug-resistance as a complex system driven by socio-economic growth and antibiotic misuse. Vol. 9, Scientific Reports.
    19.Clinical F, Laboratories P H. (2015) S tepwise L aboratory Quality I mprovement P rocess T owards A ccreditation ( SLIPTA ) Checklist For Clinical and Public Health Laboratories.
    20.DFJ Brown, Wootton M, Howe R A. (2016) Antimicrobial susceptibility testing breakpoints and methods from. , BSAC to EUCAST 71, 3-5.
    21. (2012) Clinical and Laboratory Standards Institute. Performance standards for antimicrobial disk susceptibility tests: Approved standard - Eleventh edition. 32, 1-58.
    22.Laxminarayan R, Chaudhury R R. (2016) Antibiotic Resistance in India: Drivers and Opportunities for Action. 13, PLoS Medicine..
    23.Dadgostar P. (2019) Antimicrobial resistance: implications and costs. , Infection and Drug Resistance 12, 3903-10.
    24.Okeke I N, Peeling R W, Goossens H, Auckenthaler R, Olmsted S S et al. (2011) Diagnostics as essential tools for containing antibacterial resistance. Vol. 14, Drug Resistance Updates. 95-106.
    25.Mendelson M, Røttingen J A, Gopinathan U, Hamer D H, Wertheim H et al. (2016) Maximising access to achieve appropriate human antimicrobial use in low-income and middle-income countries. Vol. 387, The Lancet. 188-98.
    26.Nkoutchou H, Arrey W H. (2019) Report on Cameroon’s.
    27.Antimicrobial resistance pattern: a report of microbiological cultures at a tertiary hospital in Tanzania |. , BMC Infectious Diseases
    28.JFTK Akoachere, Yvonne S, Akum N H, Seraphine E N. (2012) Etiologic profile and antimicrobial susceptibility of community-acquired urinary tract infection in two Cameroonian towns. , BMC Research Notes 5.
    29.Muller E E, Mahlangu M P, Lewis D A, Kularatne R S. (2019) Macrolide and fluoroquinolone resistance-associated mutations in Mycoplasma genitalium in Johannesburg, South Africa,2007-2014.Vol.19,BMC Infectious Diseases.
    30.Ghaddar N, Anastasiadis E, Halimeh R, Ghaddar A, Dhar R et al. (2020) Prevalence and antifungal susceptibility of Candida albicans causing vaginal discharge among pregnant women in Lebanon. Vol. 20, BMC Infectious Diseases.
    31.Zaidi K U, Mani A, Thawani V, Mehra A. (2016) . Total Protein Profile and Drug Resistance in Candida albicans Isolated from Clinical Samples . Vol. 2016, Molecular Biology International 1-6.
    32.Richard P, Flochl R Le, Chamoux C, Pannier M, Espaze E et al. (1994) Pseudomonas aeruginosa outbreak in a burn unit: Role of antimicrobials in the mergence of multiply resistant strains. , Journal of Infectious Diseases 170, 377-83.
    33.Knowledge. (2019) practices and attitudes on antibiotics use in Cameroon: Self-medication and prescription survey among children, adolescents and adults in private pharmacies. 14, PLoS ONE..
    34.Chernova O A, Medvedeva E S, Mouzykantov A A, Baranova N B, Chernov V M. (2016) Mycoplasmas and their antibiotic resistance: The problems and prospects in controlling infections. , Acta Naturae 8, 24-34.
    35.Rice L B. (2006) Antimicrobial resistance in gram-positive bacteria. , Am J Med.119(6Suppl1): 11-9.