World Health Organizations launched a global action plan on antimicrobial resistance since 2015. Along with other objectives, the plan was aimed to strengthen knowledge of the spread of antimicrobial resistance through surveillance and research. Given their high bacterial densities and that they receive antibiotics, metals, and other selective agents, wastewater systems are a logical hotspot for antibiotic resistance surveillance. The current study reports on the result of antibiotic resistance surveillance conducted in selected wastewater systems of Eastern Ethiopia from Feb. 2018 to Oct. 2019. We monitored three wastewater systems in Eastern Ethiopia, such as the activated sludge system of Dire Dawa University, waste stabilization pond of Haramaya University, and a septic tank of Hiwot Fana Specialized University Hospital for 18 months period. We collected 66 wastewater samples from 11 sampling locations and isolated 722 bacteria using selective culture media and biochemical tests. We tested their antibiotic susceptibility using the standard Kirby-Bauer disk diffusion method on the surface of the Mueller-Hinton agar and interpreted the result according to EUCAST guidelines. The result shows the highest percentage of resistance for ampicillin among isolates of hospital wastewater effluent which is 36 (94.7%), 33 (91.7%), and 32 (88.9%) for
Increased resistance of microorganisms to commonly prescribed antibiotics has become a major challenge in the current medical practice. Considering the threat it poses on global public health, the World Health Organization (WHO) declares that antibiotic resistance is a “major threat to public health” [
Harboring a large number of commensal human and animal bacteria along with antibiotic resistance determinants, wastewater systems are antibiotic resistance hotspots, where antibiotic resistance develops, proliferates, and discharges into the environment [
Despite the efforts to elucidate the role of wastewater treatment plants (WWTPs) in relation to antibiotic resistance, there is still no clear evidence that WWTPs, especially the biological treatment processes, are contributing to the proliferation of antibiotic resistance. Some studies suggest that WWTPs achieve a significant reduction in the number of ARBs [
Antibiotic resistance monitoring was conducted at selected wastewater systems (activated sludge system, waste stabilization pond, and septic tank system) in Eastern Ethiopia from October 2018 to April 2019. The activated sludge system and waste stabilization pond were full-scale plants receiving sewage from dormitories, cafeteria, animal farms, and laboratories at Dire Dawa University and Haramaya University, respectively (Figure
Schematic diagram of unit operations and unit processes and sampling locations. (a) Activated sludge system at Dire Dawa University with the five sampling locations. (b) Waste stabilization pond at Haramaya University with the four sampling locations.
Wastewater samples were collected on a quarterly basis in October 2018–April 2019 from the specified sampling locations in the wastewater system. Plastic containers sterilized with 70% (v/v) alcohol were used to collect samples. During sampling, sample containers were rinsed three times with sample water before filling with the sample. To obtain a flow representative sample, the actual samples were obtained by integrating grab samples collected in a 30-minute interval in the morning hours at 8–11 am. After collection, the samples were protected from direct sunlight and transported in a cooler box containing ice packs to the laboratory for analyses. All samples were stored at 4◦C and analyzed within 24 h of sample collection.
Wastewater samples were analyzed for pH on-site using a digital pH meter. Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), and total suspended solid were analyzed in the laboratory according to standard methods [
Water samples were analyzed for the target bacterial using standard methods for the examination of water and wastewater [
R2A agar was used for the enumeration of total heterotrophic bacteria after incubation at 37°C for 24 hours. mEndo-LES agar was used for total coliform and mFC agar for the fecal coliform count after incubation at 37°C and 44.5°C for 24 hours, respectively. m-TEC agar was used for the enumeration of Thermotolerant
Media and incubation conditions used for the enumeration, and primary isolation of the indicated bacteria from wastewater samples.
Bacteria | Media | Incubation conditions |
---|---|---|
Total heterotrophic count | R2A agar | 37°C; 24 h |
Total coliforms | mEndo-LES agar | 37°C; 24 h |
Fecal coliforms | mFC agar | 44.5°C; 24 h |
mEnterococcus agar + TITG agar base | 37°C; 48 h ← 35–37°C for 18–24 hours | |
m-TEC agar | 35–37°C for 2 hours and at 44.5 ± 0.5°C for 22 hours | |
mADA-V agar | 37°C; 24 h | |
Cetrimide agar | 35°C for 18 h |
Two isolates per sample of each bacterial species were collected to perform antimicrobial susceptibility testing (AST) except for hospital wastewater, for which three isolates were collected. The standard Kirby-Bauer disk diffusion method was used to determine the antimicrobial susceptibility profiles of the isolates [
The susceptibility test was performed by placing paper disks impregnated with specific amounts of antibiotics on a lawn of bacteria grown on agar and aerobically incubated at 35 + 1°C for 18–24 hours. After an incubation period, the diameter for the zone of inhibition, the area around the disk without bacterial growth, was measured.
Phenotypic resistance is often interpreted based on clinical standards and recommended breakpoints. A more reliable alternative for the interpretation of the antibiotic resistance of environmental bacteria may be the epidemiological cut-off (ECOFF) value developed by the European Committee on Antimicrobial Susceptibility Testing (EUCAST), which, in a given taxonomic group, separates the populations with acquired resistance mechanisms (non-wild-type) from the wild-type populations that have no resistance. In contrast to clinical breakpoints, the ECOFF values are epidemiologically based, do not relate to the therapeutic efficiency, and do not differ among different committees [
Disk content and EUCAST breaking points of each antibiotic tested for specific indicator bacteria.
Antibiotic class | Antibiotic | Code | Content ( | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
<R | ≥S | <R | ≥S | <R | ≥S | R | S | ||||
Ampicillin | AMP | 10|2 | 14 | 14 | 8 | 10 | IR | NR | |||
Amoxicillin/Clav | AMC30 | 20/10 | 19 | 19 | IR | NR | |||||
Cephalosporin | Ceftazidime | CAZ30 | 10 | 19 | 22 | IR | 17 | 17 | 21 | 24 | |
Cefepime | CFP | 30 | 24 | 27 | IR | 21 | 21 | 24 | 27 | ||
Aminoglycosides | Gentamicin | GEN10 | 10|30 | 14 | 17 | 8 | 8 | 15 | 15 | NR | |
Amikacin | AMIK | 30 | 15 | 18 | 15 | 18 | NR | ||||
Fluoroquinolone | Levofloxacin | LVL5 | 5 | 23 | 19 | 15 | 15 | 22 | 22 | 24 | 27 |
Ciprofloxacin | CIP5 | 5 | 24 | 26 | 15 | 15 | 26 | 26 | 24 | 27 | |
Carbapenem | Meropenem | MRP10 | 10 | 16 | 22 | NR | 18 | 24 | |||
Sulfonamides | Co-Trimoxazole | SxT25 | 1.25/23.75 | 11 | 14 | 23 | 23 | NR | 16 | 19 |
IR: intrinsically resistant, NR: not recommended.
MARI was determined for each isolate by using the formula
Data analysis was done using descriptive and inferential statistical tools in the R programming environment. A
In the specified monitoring period, 66 samples were collected from 11 sampling locations in the three monitoring sites in six monitoring rounds sampled quarterly from Feb. 2018 to Oct. 2019 (Table
Number of samples and bacterial isolates obtained per monitoring sites.
Site | No. of sampling points | No. of sample | Number of isolate | ||||
---|---|---|---|---|---|---|---|
E. faecium | |||||||
ASS | 5 | 30 | 61 | 60 | 58 | 59 | 58 |
WSP | 4 | 24 | 52 | 48 | 48 | 48 | 48 |
STS | 2 | 12 | 38 | 36 | 36 | 36 | 36 |
Total | 11 | 66 | 151 | 144 | 142 | 143 | 142 |
ASS: activated sludge system, WSP: waste stabilization pond, STS: septic tank system.
Selected physicochemical and biological characteristics of wastewater analyzed were presented in Table
Mean value of selected biological and physicochemical characteristics of raw and effluent wastewater in the three monitoring sites and removal capacity of wastewater treatment facilities.
Site | Characteristics | Total coliform cfu/100 ml | Fecal coliform cfu/100 ml | BOD mg/L | COD mg/L | TSS mg/L | pH | ||
---|---|---|---|---|---|---|---|---|---|
ASS | Raw | 5.14 | 2.45 | 1.31 | 1.17 | 737.67 | 931.33 | 707.67 | 7.23 |
Effluent | 3.18 | 5.12 | 3.93 | 2.75 | 73.17 | 119.33 | 63.50 | 9.45 | |
Log reduction | 3.21 | 2.68 | 2.52 | 2.62 | 1.003 | 0.89 | 1.04 | ||
WSP | Raw | 5.55 | 2.74 | 1.13 | 9.87 | 1108.33 | 1275.33 | 951.67 | 7.45 |
Effluent | 9.9 | 7.28 | 3.33 | 5.36 | 91.17 | 187.67 | 60.67 | 9.25 | |
Log reduction | 1.74 | 2.57 | 2.53 | 2.26 | 1.08 | 0.83 | 1.19 | ||
STS | Raw | 1.39 | 7.93 | 9.3 | 1.53 | ||||
Effluent | 3.5 | 3.9 | 4.28 | 4.06 | |||||
Log reduction | 1.59 | 1.3 | 1.33 | 1.57 |
ASS: activated sludge system, WSP: waste stabilization pond, STS: septic tank system, BOD; biochemical oxygen demand, COD: chemical oxygen demand, TSS: total suspended solid.
BOD to COD ratio is an important aggregate measure of wastewater characteristics, indicating the biodegradability of wastewater [
This study evaluated ten commonly prescribed antibiotics against five groups of bacteria proposed to indicate antibiotic resistance level in the environment and the results are presented in Table
Antibiotic resistance among isolates of environmental resistance indicator bacterial species by monitoring site.
Site | Resistance phenotype | No. tested | Number and (%) resistant to antibiotic tested | MARI mean (SD) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AMP | AMC30 | CAZ30 | CFP | GEN10 | AMIK | LVL5 | CIP5 | MRP10 | SxT25 | ||||
ASS | 61 | 29 (47.5) | 28 (45.9) | 30 (49.2) | 19 (31.2) | 10 (16.4) | 13 (21.3) | 14 (22.9) | 17 (27.87) | 11 (18) | 15 (24.6) | 0.30 (0.03) | |
60 | 26 (43.3) | – | – | – | 8 (13.3) | – | 13 (21.6) | 15 (25) | – | 16 (26.7) | 0.26 (0.03) | ||
58 | 25 (43.1) | – | – | – | 11 (18.9) | – | 12 (20.7) | 15 (25.9) | – | 18 (31) | 0.28 (0.04) | ||
59 | – | – | 25 (42.37) | 23 (39) | 12 (20.3) | 12 (20.3) | 12 (20.3) | 16 (27.1) | 11 (18.6) | – | 0.27 (0.03) | ||
58 | – | – | 19 (32.8) | 21 (36.2) | – | – | 12 (20.7) | 17 (29.31) | – | 24 (41.4) | 0.32 (0.04) | ||
WSP | 52 | 28 (53.8) | 25 (48.1) | 27 (51.9) | 19 (36.5) | 13 (25) | 14 (26.9) | 17 (32.7) | 19 (36.54) | 15 (28.9) | 15 (28.9) | 0.37 (0.03) | |
48 | 26 (54.2) | – | – | 11 (22.9) | – | 14 (29.2) | 17 (35.4) | – | 17 (35.4) | 0.35 (0.03) | |||
48 | 23 (47.9) | – | – | 11 (22.9) | – | 13 (27.1) | 17 (35.4) | – | 21 (43.7) | 0.35 (0.03) | |||
48 | – | – | 24 (50) | 20 (41.7) | 9 (18.75) | 48 (29.2) | 14 (29.2) | 15 (31.3) | 13 (27.1) | – | 0.32 (0.04) | ||
48 | – | – | 16 (33.3) | 19 (39.6) | – | – | 13 (27.1) | 18 (37.5) | – | 29 (60.4) | 0.40 (0.04) | ||
STS | 38 | 36 (94.7) | 30 (78.9) | 33 (86.8) | 31 (81.6) | 15 (39.5) | 17 (44.7) | 21 (55.26) | 19 (50) | 16 (42.1) | 29 (76.32) | 0.65 (0.03) | |
36 | 33 (91.7) | – | – | 13 (36.1) | – | 19 (52.8) | 21 (58.3) | – | 22 (61.1) | 0.60 (0.04) | |||
36 | 32 (88.9) | – | – | 18 (50) | – | 23 (63.9) | 23 (63.9) | – | 27 (75) | 0.68 (0.04) | |||
36 | – | – | 28 (77.8) | 28 (77.8) | 16 (44.4) | 17 (47.2) | 17 (47.22) | 21 (58.3) | 17 (47.2) | – | 0.57 (0.03) | ||
36 | – | – | 26 (72.2) | 29 (80.6) | – | – | 19 (52.8) | 25 (69.4) | – | 25 (69.4) | 0.69 (0.4) |
ASS: activated sludge system, WSP: waste stabilization pond, STS: septic tank system, AMP: ampicillin, AMC30: amoxicillin/clav, CAZ30: ceftazidime, CFP, cefepime, GEN10: gentamicin, AMIK: amikacin, LVL5: levofloxacin, CIP5: ciprofloxacin, MRP10: meropenem, SxT25: Co-Trimoxazole.
Level of antibiotic susceptibility among
Isolates have shown reduced susceptibility for
From the three monitored sites, a total of 286
Level of antibiotic susceptibility among
A total of 143
Level of antibiotic susceptibility among
Form 66 samples collected from the three sites, 142
Based on the result of the susceptibility test shown in Table
Accordingly, isolates from hospital wastewater have shown elevated resistance characteristics for all isolates and drugs tested while isolates of ASS expressed lower resistance for all isolates and tested drugs. The highest percentage resistance across all isolates was for ampicillin resistance for hospital wastewater which is 36 (94.7), 33 (91.7), and 32 (88.9) for
The rate of isolation of resistant bacteria in the hospital wastewater was higher than that in the nonhospital environment for all indicator variables; this was statistically significant (
The difference in antibiotic resistance level among the three monitored sites and its change in the course of the wastewater treatment process was shown in box plots of Figures
Box plot of mean MARI value in the three wastewater systems.
Change in MARI value in the course of wastewater treatment in the three studied sites.
Multidrug resistance level as measured by the mean value of MERI has also shown clear variation at each stage in the three monitored sites. The bar graph in Figure
MARI has been used to estimate the health risks associated with the spread of drug resistance in an environment. A MARI value of 0.2 (arbitrary) is used to differentiate between low and high health risks, and MARI greater than 0.2 suggests that strain(s) of bacteria originate from an environment with high contamination or antibiotics usage [
This study showed that the intensity of resistance increases in the course of the wastewater treatment process. There is a clear increase in the measure of multidrug resistance profile of isolates in the course of the wastewater treatment process. This can be taken as an indication of the propensity of wastewater systems to intensify antibiotic resistance. Currently, there is no clear evidence of whether resistance may develop in wastewater treatment plants (WWTPs) [
This report has numerous strengths; to mention some, we have tried to avoid the wrong “high resistance” alarm by excluding bacteria/antibiotic combination that leads to intrinsic resistance. We have significantly reduced redundant testing in the case where cross-resistance is a rule. Although this study addresses important environmental health issues, it is not free from limitations. We are unable to identify the genes responsible for expressed resistance. We are also unable to determine the level of antibiotic resistance determinants such as antibiotic residue, heavy metal concentration, and antibiotic resistance genes in the wastewater systems. In addition, carbapenemase and extended-spectrum beta-lactamase pattern of isolated bacterial species were not determined.
Our antibiotic resistance surveillance program has shown the role of wastewater systems in the proliferation of antibiotic resistance in the wastewater systems. The study has found a high level of environmental antibiotic resistance indicator bacteria thrive in the wastewater systems. Multiresistance patterns to antibiotics were common among the isolates. The percentages of resistance in the wastewater treatment plant were increased through the course of treatment. Hospital wastewater exhibited higher resistance to tested antibiotics than the other two wastewater systems. The multidrug resistance index has significantly increased in the advancement of the wastewater treatment process for all wastewater treatment plants. This may indicate the proliferation of resistance in the wastewater treatment system.
The presence of antibiotic-resistant organisms in these wastewater systems should not be overlooked. For the future, wastewater systems should be designed to control the dissemination of antibiotic-resistant bacteria. Further disinfection or other advanced treatment processes have to be included in the treatment design. It is also imperative that wastewater discharge compliance monitoring should determine antibiotic susceptibility/resistance patterns of isolated microbes beyond traditional efficiency measures. Further studies should be conducted in the region to determine antibiotic-resistant determinants in the wastewater system such as antibiotic residue and resistance genes.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
AT involved in raising the initial idea, proposal development, the collection of samples, processing of samples in the laboratory, analysis and interpretation of data, and writing the manuscript. YA, TA, BS, and DM were involved in reviewing the proposal, commenting on the design of the method, and reviewing drafts of the analysis. All authors read and approved the final manuscript.
The authors would like to thank the Department of Environmental Health Sciences, College of Health Sciences, and Haramaya University for their logistic and material support. The authors would also like to thank Mr. Wegene Deriba, laboratory coordinator, for his valuable cooperation in the permission of space and instruments. The source of funding for this research was Haramaya University, with grant ID HURG-02-01.