ABSTRACT
Background
Before the COVID‐19 pandemic, Northern Ghana reported high resistance to third‐generation cephalosporins, but most bacteria remained susceptible to amikacin and meropenem. Increased antimicrobial use during the early COVID‐19 pandemic likely heightened antimicrobial resistance. This study compared antimicrobial resistance before (October 2018–March 2020) and during (April 2020–December 2022) the COVID‐19 pandemic in Ghana's Northern Region.
Methods
A retrospective study was conducted at the Tamale Public Health Reference Laboratory. After the exclusion of incomplete records, 3677 culture cases were selected and analyzed. Antimicrobial susceptibility test results for all pathogenic bacteria were included.
Results
Of the 3677 cultures analysed, the majority were sputum samples (1792, 48.7%). From these, 1485 bacterial isolates covering 14 species underwent antimicrobial susceptibility testing. The predominant isolates were Klebsiella spp. (26%), Moraxella catarrhalis (20.7%), and Escherichia coli (18.4%). The majority of the participants were between the ages of (21–30) years. Antimicrobial resistance increased steadily above 50% from the third‐quarter of 2021 to the second‐quarter 2022. In a chi‐square comparison, the susceptibility of bacteria to amoxiclav (p = 0.027), ceftazidime (p = 0.002), ciprofloxacin (p = 0.001), gentamycin (p = 0.035), and meropenem (p < 0.001) was significantly decreased during than before‐COVID‐19. The percentage increase in resistance observed was amoxiclav (8.8%), ceftazidime (7.9%), ciprofloxacin (9.2%), gentamicin (4.4%), and meropenem (16.7%).
Conclusion
Our findings reveal an increase in bacterial resistance to antibiotics during the COVID‐19 pandemic, reinforcing the need for public health measures to optimize antibiotic use. We recommend further research to elucidate the molecular mechanisms underlying this bacterial resistance to antibiotics.
Keywords: antibiotic resistance, antibiotics, bacterial pathogens, COVID‐19 impact, Ghana, susceptibility
This retrospective study analyzed and compared antimicrobial resistance patterns of common bacterial pathogens before (October 2018–March 2020) and during (April 2020–December 2022) the COVID‐19 pandemic in Ghana's Northern Region. Amoxiclav (p = 0.027), ceftazidime (p = 0.002), ciprofloxacin (p = 0.001), gentamycin (p = 0.035), and meropenem (p < 0.001) were significantly decreased in terms of microbial susceptibility, during than the before‐COVID‐19. The findings of this study reveal an increase in bacterial resistance to antibiotics during the COVID‐19 pandemic, reinforcing the need for public health measures to optimize antibiotic use and combat the growing threat of antimicrobial resistance.
1. Background
More than three years have passed since the coronavirus disease 2019 (COVID‐19) pandemic hit the world in December 2019. Since the initial outbreak of the disease in Wuhan, China, over 770 million people have been infected worldwide with more than 171,000 confirmed cases in Ghana as of December 24, 2023 WHO [1]. In the early days of the pandemic, no specific treatment or vaccine was available for the management of the disease. However, several antibiotics including azithromycin, doxycycline, clarithromycin, ceftriaxone, erythromycin, amoxicillin, amoxicillin‐clavulanic acid, ampicillin, gentamicin, benzylpenicillin, piperacillin/tazobactam, ciprofloxacin, ceftazidime, cefepime, vancomycin, meropenem, and cefuroxime were recommended for the management of COVID‐19 patients [2].
It was well established that antibiotics were not effective against viral infections but were used in the case of COVID‐19 to treat bacterial co‐infections in order to prevent complications [3]. Treatment of co‐infections was thought to give an advantage to the immune system and not to treat the viral infection itself, since viral infections alone are generally self‐limiting. However, not all COVID‐19 patients may have bacterial co‐infection [4]. Owing to the fear of being quarantined and the stigma attached to COVID‐19 during the height of the pandemic, many individuals preferred self‐treatment via the use of antibiotics purchased at over‐the‐counter pharmacy shops rather than reporting to the hospital [5]. In Ghana and most sub‐Saharan African countries, it is easier to purchase antibiotics from pharmacies without a prescription unlike in other developed countries [6]. Hence, antibiotic resistance remains a significant contributor to health challenges in most developing countries including Ghana. A study by Forson et al., 2018 in Ghana reported high resistance of one of the commonest pathogens, Escherichia coli to ampicillin and tetracycline (over 70%), cotrimoxazole (59.8%), and cefuroxime (32.9%) [7]. In the Northern part of Ghana, between 2017 and 2018, there was over 50% resistance of Acinetobacter spp., Klebsiella pneumoniae , Escherichia coli , and Proteus spp. to third generation cephalosporins (ceftazidime and ceftriaxone) [8]. Also, a separate study conducted between 2018 and 2019 in the Northern region reported high resistance of Klebsiella spp. (40%), Acinetobacter spp. (62.5%), and Escherichia coli (61.1%) to ceftriaxone, and low resistance to amikacin and meropenem (less than 10%) [9]. The antimicrobial resistance (AMR) expressed by the commonly isolated bacterial pathogens in the Northern region is feared to worsen due to the outbreak of COVID‐19. The speculation regarding the worsening state of antimicrobial resistance in the locality is borne from the fact that clinical management of COVID‐19 infection involved the usage of antibiotics in our healthcare settings.
The increased use of antibiotics in the management of COVID‐19 patients and the inappropriate use of antibiotics during the pandemic may have further led to increased AMR among various bacterial pathogens. Additionally, increased adoption of COVID‐19 precautionary measures such as regular hand washing with soap under running water and frequent use of hand sanitizers may lead to the accumulation of antimicrobials in gutters and stagnant water which could influence the selection of antimicrobial resistance genes among bacteria [10]. The management of COVID‐19 and its related activities including the use of antibiotics for the treatment of superinfections may or may not have an effect on the AMR. The global crises of AMR appears to have been further fueled by the COVID‐19 pandemic. Recent published data across different geographical areas of the world describe an increase in multidrug‐resistant pathogens during the era of the COVID‐19 pandemic [11, 12]. Very high incidence of carbapenem‐resistant Enterobacteriaceae infection from 6.7% in 2019 to 50% in 2020 was reported among intensive care unit patients [13]. In North‐Africa, precisely in Egypt, bacterial pathogens such as Escherichia coli , Acinetobacter baumannii and Klebsiella pneumoniae saw a significant increase in antimicrobial resistance to multiple antibiotics post than pre COVID‐19 pandemic [14]. Limited studies in Sub‐Saharan Africa have also speculated rise in antimicrobial resistance due to excessive prescription of antibiotics during the COVID‐19 pandemic [10, 11, 12, 13, 14, 15]. However, there is a dearth of information regarding patterns of antimicrobial resistance before and during the COVID‐19 pandemic in Ghana and most sub‐Saharan African countries. Therefore, this study aims to (1) analyse and compare laboratory data on antimicrobial resistance and susceptibility patterns before and during the COVID‐19 pandemic in the Northern Region of Ghana, and (2) describe the demographic characteristics of patients and determine the prevalent microbial isolates from culture results.
2. Methods
2.1. Study Area
The study was conducted at the Tamale Public Health Reference Laboratory (TPHRL) in the Northern Region of Ghana. The Northern Region currently shares borders with the North East Region to the north, the Oti Region to the south, the Savannah Region to the west and on the east with Togo. The Northern Region has a total land size of 25,448 km2 with a population of 2.3 million inhabitants [16]. The capital is Tamale where the Tamale Public Health Reference Laboratory is located. The TPHRL is situated on the premises of the Tamale Teaching Hospital and serves as the referral laboratory for the Northern Region, North‐East Region, Savannah Region, Upper West Region, and Upper East Region.
2.2. Research Design and Study Population/Sample Selection
This was a retrospective cross‐sectional study conducted from October 2018 to December 2022. The study population included patients of all ages and genders who were tested for microbial infections using culture and antimicrobial susceptibility tests. Records from all types of clinical samples in the Tamale Public Health Reference Laboratory during the study period were analysed. A total of 3867 culture case records were retrieved from the laboratory information system. Of these, 190 cases were excluded due to incomplete records. Consequently, a total of 3677 culture cases were included in the final analysis. (Figure 1). Patient samples at the TPHRL were collected and processed according to established protocols. Laboratory procedures included bacteriological culture and antimicrobial susceptibility testing to identify and analyse microbial infections (Appendix S1).
FIGURE 1.
Flowchart of sample selection and analysis. The chart includes the total number of cultures performed, stratified before and during the COVID‐19 pandemic.
2.3. Data Collection
Data on bacteriological culture and susceptibility testing were retrieved from the archived records of all clinical samples from the laboratory information system and records. Biodata of patients such as gender, age, and sample type were inferred from comprehensive patient laboratory records. The clinical diagnosis (provisional diagnosis) for the cultured clinical specimens included upper and lower respiratory tract infections, urinary tract infections, sepsis, and wound infections, among others. All the data obtained from the laboratory records were verified by comparing the data to the main databases through double data checks by two independent individuals, and discrepancies were then resolved by a third‐party check. Missing and inconsistent data were removed during data cleaning.
2.4. Data Analysis
Data were entered and cleaned in Microsoft Excel 2013 (Microsoft Inc., USA). For analysis purposes, the data were categorized into the period before COVID‐19 pandemic and during the COVID‐19 pandemic taking into consideration the date on which the first case of COVID‐19 was reported in Ghana. The data collected before the COVID‐19 pandemic included data from October 2018 to March 2020 while those collected during the COVID‐19 pandemic included data from April 2020 to December 2022. In computing descriptive statistics, categorical variables were summarized as frequencies and percentages. Chi‐square tests were used to compare before COVID‐19 pandemic and during COVID‐19 pandemic antimicrobial resistance rates. The statistical analyses and generation of graphs were performed using International Business Machines Statistical Package for the Social Sciences (IBM SPSS) Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism version 8.0 (GraphPad Software Inc., San Diego, CA, USA) where necessary. P‐values less than 0.05 were considered statistically significant.
3. Results
3.1. Demographic Characteristics and Distribution of Isolates Recovered From Patients
Within the five‐year study period, a total of 3677 patient records were reviewed and included in the final analysis. From the records, seven sample types including sputum (n = 1792, 48.7%), urine (n = 601, 16.3%), blood (n = 509, 13.8%), high vaginal swabs (n = 335, 9.1%), stool (n = 154, 4.2%), aspirate (n = 150, 4.1%), and wound (n = 136, 3.7%) were identified, and a total of 1786 microbial organisms (bacteria and fungi) were isolated. Of the 1786 microbial isolates, 1485 (83.1%) were bacteria, while 301 (16.9%) were fungi (Candida spp). A chi‐square test was used to compare the number of bacteria recovered before and during the COVID‐19 pandemic. Significantly higher proportions of the bacteria were recovered during the COVID‐19 period, 1499 (50.5%, p < 0.001). The majority of the study participants were between the ages of 21–30 years (Table 1). Klebsiella spp., 392 (26.4%), Moraxella catarrhalis, 308 (20.7%), Escherichia coli, 273 (18.4%), and Pseudomonas spp., 181 (12.2%) were the most common among the isolated bacterial species. Pseudomonas spp., Staphylococcus spp., Acinetobacter spp., Enterobacter spp., and Streptococcus spp. were more frequently identified before the onset of COVID‐19, whereas Klebsiella spp., Moraxella catarrhalis and Escherichia coli were more prevalent during the COVID‐19 period (Table 2).
TABLE 1.
Demographic characteristics and microbial cultures of patients.
| Variables | Before COVID‐19 Total (n)=707 Frequency (%) | During COVID‐19 Total (n)=2970 Frequency (%) | p |
|---|---|---|---|
| Gender | |||
| Male | 335 (47.4) | 1424 (47.9) | 0.802 |
| Female | 372 (52.6) | 1546 (52.1) | |
| Age categories | |||
| ≤ 20 | 138 (19.5) | 543 (18.3) | |
| 21–30 | 124 (17.5) | 734 (24.7) | |
| 31–40 | 125 (17.7) | 546 (18.4) | < 0.001 |
| 41–50 | 88 (12.4) | 343 (11.5) | |
| 51–60 | 70 (9.9) | 287 (9.7) | |
| ≥ 61 | 162 (22.9) | 517 (17.4) | |
| Culture results | |||
| Negative | 420 (59.4) | 1471 (49.5) | |
| Positive | 287 (40.6) | 1499 (50.5) | < 0.001 |
Note: Continuous variables are presented in frequencies and percentages, p‐values were computed using Chi‐square/Fisher's exact test. p < 0.001 (significant association).
TABLE 2.
Isolated microbial pathogens.
| Microbial pathogens | Before COVID‐19 n = 371 Frequency (%) | During COVID‐1 n = 1415 Frequency (%) | Total n = 1786 Frequency (%) |
|---|---|---|---|
| Fungi (Candida spp) | 4 (1.1) | 297 (21.0) | 301 (16.9) |
| Bacteria | 367 (98.9) | 1118 (79.0) | 1485 (83.1) |
| Acinetobacter spp | 33 (9.0) | 69 (6.2) | 102 (6.9) |
| Citrobacter spp | 3 (0.8) | 2 (0.2) | 5 (0.3) |
| Escherichia coli | 46 (12.5) | 227 (20.3) | 273 (18.4) |
| Enterobacter spp | 33 (9.0) | 44 (3.9) | 77 (5.2) |
| Gardernella vaginalis | 1 (0.3) | 1 (0.1) | 2 (0.1) |
| Klebsiella spp | 96 (26.2) | 296 (26.5) | 392 (26.4) |
| Staphylococcus spp | 31 (8.4) | 68 (6.1) | 99 (6.7) |
| Moraxella catarrhalis | 49 (13.4) | 259 (23.2) | 308 (20.7) |
| Proteus spp | 9 (2.5) | 10 (0.9) | 19 (1.3) |
| Pseudomonas spp | 53 (14.4) | 128 (11.4) | 181 (12.2) |
| Raoutella ornithinolytica | 2 (0.5) | 1 (0.1) | 3 (0.2) |
| Salmonella spp | 3 (0.8) | 4 (0.4) | 7 (0.5) |
| Serratia spp | 0 (0.0) | 3 (0.3) | 3 (0.2) |
| Streptococcus spp | 8 (2.2) | 6 (0.5) | 14 (0.9) |
Note: Continuous variables are presented in frequencies and percentages.
3.2. Quarterly Antimicrobial Resistance Patterns of the Isolates
Although the antimicrobial resistance pattern generally showed an irregular trend from the third quarter of 2018 through to the third quarter of 2019, a gradual continuous increase in antimicrobial resistance above 50% was observed from the third quarter of 2021 to the second quarter of 2022 and in the last quarter of 2022. The lowest antimicrobial resistance was observed in the fourth quarter of 2019 followed by that observed in the third quarter of 2018, with the highest resistance rate observed in the second quarter of 2019 (Figure 2).
FIGURE 2.
Quarterly antimicrobial resistance pattern.
3.3. Antimicrobial Resistance and the COVID‐19 Pandemic Situation
In order to determine the statistical differences in bacterial susceptibility to antibiotics before and during the COVID‐19 pandemic, a chi‐square test was employed. With the exception of cefotaxime, ceftriaxone, chloramphenicol, and tetracycline, all the antibiotics tested against the bacterial isolates in this study showed reduced susceptibility in the COVID‐19 pandemic era. A significantly lower susceptibility of bacteria to amoxiclav, ceftazidime, ciprofloxacin, gentamycin, and meropenem was observed in the COVID‐19 pandemic period than in the pre‐COVID‐19 period with p‐values of 0.027, 0.002, 0.001, < 0.035, and < 0.001, respectively (Figure 3).
FIGURE 3.
Trend analysis of antibiotic resistance before and during the COVID‐19 pandemic. p‐values were computed using the Chi‐square test.
4. Discussion
This study provides data on antibiotic resistance patterns of bacteria isolated from clinical samples before and during the COVID‐19 pandemic in the Northern Region of Ghana. We recovered 1485 bacterial isolates from seven clinical specimen types at the Tamale Public Health Reference Laboratory. Most of the isolates recovered were from sputum and urine samples. This finding is consistent with that of Gnimatin et al. who found bacterial respiratory infections to be common in the Northern Region of Ghana [17]. The high number of bacteria isolated from sputum could be due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) induced respiratory bacterial infections. This is explained by Hughes et al. who noted that SARS‐CoV‐2 infection could predispose the respiratory tract to bacterial co‐infection [18, 19].
Klebsiella spp. were the most common bacteria recovered followed by Moraxella catarrhalis and Escherichia coli . This finding is consistent with those of multiple studies conducted at the same site [9, 10, 11, 12, 13, 14, 15, 16, 17]. High rates of infection due to Klebsiella spp. are not surprising, because the isolate is a common opportunistic pathogen and an ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) member known to cause respiratory, urinary tract, and bloodstream infections in clinical settings and the community [20, 21]. Although Moraxella catarrhalis is a commensal of the upper respiratory tract, it is now recognised as a significant pathogen that causes respiratory tract infection and acute otitis media [22, 23]. The pathogenic significance of Moraxella catarrhalis is more pronounced in individuals with reduced immunity, such as the elderly. The predominance of the older age in our study may explain why Moraxella catarrhalis is among the common bacterial isolates. Escherichia coli , although primarily a gut commensal, is also a common cause of community‐acquired urinary tract infections and a major cause of nosocomial infections, including pneumonia and bacteraemia. These infections are prevalent in developing countries like Ghana, which could be a contributory factor to the predominance of Escherichia coli in our study. While the majority of participants fell within the 21–30 years age range, a notably greater proportion of bacteria were recovered from individuals aged above 50 years (Table S1), which aligns with the findings reported by Agyepong et al. in Ghana [24]. This observation explains the potential impact of age‐related factors on bacterial prevalence. The increased recovery of bacteria among older individuals may be attributed to the waning immunity associated with aging, rendering them more susceptible to infections. Additionally, the decline in immune function in this age group often coincides with prevalent comorbidities, such as hypertension, diabetes, and lung impairment, as documented earlier [25, 26]. This collective insight suggests a complex relationship between age, immunity, and underlying health conditions, contributing to the observed variations in bacterial recovery among the different age groups in our study. We observed that between the third quarter of 2018 and the third quarter of 2019 there was no discernible pattern of antimicrobial resistance; however, a gradual continuous increase of resistance above 50% was observed from the third quarter of 2021 to the second quarter of 2022. This particular period in Northern Ghana is characterised by dry season with abundant atmospheric dust, potentially fostering an environment conducive to respiratory tract bacterial infections [27]. This period also falls within the COVID‐19 era, and the increased trend of bacteria resistance to antibiotics could also be attributed to a possible increased usage of antibiotics in the management of COVID‐19 related diseases. This could be due to the earlier speculated effectiveness of antibiotics in treating SARS‐CoV‐2 infection when used in combination with hydroxychloroquine, which was later proven to be ineffective [28, 29]. Despite several studies have reported increased antibiotic consumption during the COVID‐19 period [30, 31].
Additionally, significantly higher proportions of bacteria with reduced susceptibility to amoxiclav, ceftazidime, ciprofloxacin, gentamycin, and meropenem (p < 0.05) were observed during the COVID‐19 pandemic than before the COVID‐19 pandemic. Ghana was one of the 10 African countries that recommended the use of antibiotics in the management of confirmed cases of COVID‐19, be it asymptomatic, mild, or moderate symptoms [2]. This may be a contributory factor to the increased bacterial resistance to antibiotics noted in our study. Other drivers of AMR are the pandemic effect and some environmental factors peculiar to developing countries like Ghana [1]. The pandemic gave birth to lockdown in the country, social distancing protocols, and reliance on telemedicine over clinical visits. Misinformation about the management of COVID‐19 abounds on social media, and restricted access to healthcare services significantly affected routine medical examination of patients and led to the unjudicial misuse of antibiotics. In Ghana, treatment of some medical wastewater does not comply with appropriate laid down protocols, posing a dire challenge to environmental safety. The wastewater eventually becomes a hotspot for antimicrobial resistance; a mixture of antiseptics, antibacterial medicated soap, remnants of antibiotics, and high levels of bacterial loads [2]. The combination of antibiotics and antiseptics in the presence of bacteria in the environment can induce mutation in bacterial receptors and pathways causing the emergence of drug‐resistant microbial species [3]. Likewise, there was also a high resistance of bacteria to chloramphenicol, ceftriaxone, cefotaxime, and tetracycline recorded in the pre‐COVID‐19 period than during the COVID‐19 period, but there was no significant difference between them. Our finding of reduced bacterial susceptibility to antibiotics is consistent with multiple reports and studies across the globe [11, 12, 13, 32, 33]. However, some studies reported increased susceptibility of bacterial strains to antibiotics in a fluctuating trend during the pandemic [34]. These highlight the complex association existing between indiscriminate and abusive use of antibiotics, as well as the delicate balance between susceptibility and resistance in the population studied.
An increasing trend of reduced susceptibility to ciprofloxacin, co‐amoxiclav, and gentamycin was equally observed by [35] and was largely attributed to their overall use for prophylaxis and treatment. Amoxiclav and ciprofloxacin are among the low‐cost and commonly used oral antibiotics in Ghana. The increased resistance of bacteria to these antibiotics, as observed in this study, is a public health threat to their future clinical usefulness. Gentamycin is one of the least expensive antibiotics among the aminoglycosides family, and it is widely used in Ghana for the presumptive management of febrile illness in children [36]. Continuous active antibiotic resistance surveillance and appropriate antibiotic usage are encouraged to prevent future increases in resistance. The increase in resistance of bacteria to these antibiotics has detrimental implications for public health. This may increase the risk of treatment failure, leading to prolonged illness, facilitation of the spread of resistant bacteria, and healthcare‐associated outbreaks. It is, therefore, very prudent to ensure judicious use of antibiotics, enhance infection control practices, and continuous monitoring of resistance patterns. Also, healthcare systems can help alleviate AMR by ensuring they work in clean and safe healthcare facilities supported by effective infection prevention and control procedures such as good sanitation practices, constant water supply, as well as enhanced training of healthcare providers in various health facilities [37].
The significant increase in bacterial resistance to meropenem during the COVID‐19 pandemic compared with the pre‐pandemic period is alarming. Meropenem is a restricted antibiotic for the treatment of multidrug‐resistant bacteria in the absence of other alternatives [38]. A similar study conducted in Mexico reported a significant increase in bacterial resistance to meropenem from 11.2% to 21.4% during the pandemic as compared to the period before the pandemic [39]. Though resistance to meropenem and other carbapenems has been reported in other parts of the world [40, 41], no resistance was reported by Obeng‐Nkrumah et al. and only 5% resistance by Agyepong et al. in Ghana, indicating that the resistance is likely to be a recent phenomenon [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]. The increasing rise in multidrug‐resistant bacteria globally could be a contributory factor to the recent rise of bacterial resistance to meropenem as meropenem is mostly used for the treatment of multidrug‐resistant bacteria infection.
According to the World Health Organisation, there have been a worsened spread of antimicrobial resistance during and after the COVID‐19 pandemic which resulted from extensive overuse of antibiotics in the pandemic era. Many studies have reported a conspicuous difference in antibiotic usage pre, during and post COVID‐19 pandemic [43]. Africa in particular has had a significant upshot on the trend of AMR during the COVID‐19 pandemic. Studies in Nigeria and South Africa have reported a surge in multidrug resistant pathogens including Staphylococcus aureus and Escherichia coli during the pandemic [44]. In Kenya and Uganda, the COVID‐19 pandemic has caused diversion of healthcare resources and attention from AMR leading to collateral disruption of health delivery and monitoring of AMR [45]. Remarkably, the rise in AMR during the COVID‐19 pandemic has awakened the healthcare institutions in the low‐ and middle‐income countries to improve the surveillance systems and implement antibiotic stewardship programs. These systems and approaches have yielded notable results. Countries such as Ethiopia and Tanzania conducted a national survey post the COVID‐19 pandemic and reported elevation of drug‐resistant bacteria to commonly used antibiotics [46]. This underscores the importance of championing the campaign against the misuse of antibiotics and instituting proper stewardship of antimicrobial resistance programs while recovering from the COVID‐19 pandemic.
The findings of this work support the global concern of a potential long‐term rise and propagation of AMR at the beginning of the COVID‐19 pandemic due to inappropriate usage of antimicrobial agents and self‐medication [47, 48]. With self‐medication practices, the likelihood of using antibiotics for the wrong purpose is high and the possibility of emergence of multidrug‐resistant bacterial infections is also high. The impact of self‐medication or misuse of drugs is higher in low‐to‐middle‐income countries as it often leads to disease complications, treatment failures, long stays in hospitals, and subsequent increase in hospital bills [49]. The pooled prevalence of self‐medications, including antibiotics during the COVID‐19 period reported by Kazemioula et al. in Africa was 41.5%, and that reported by Opoku et al. in Ghana was 53.7% [50, 51]. It is therefore evident that the increased proportion of antimicrobial resistance in the COVID‐19 period observed in this study could be linked to the indiscriminate use of antibiotics.
5. Conclusion
The findings of this study highlight a significant increase in bacterial resistance to commonly used antimicrobials including amoxiclav, ceftazidime, ciprofloxacin, gentamycin, and meropenem during the COVID‐19 pandemic compared with before the pandemic. The study revealed Klebsiella spp. as the predominant bacterial isolate, with a higher prevalence of bacteria isolated among patients aged above 50 years. It is imperative therefore to implement effective public health measures to ensure appropriate antibiotic use moving forward to help prevent any future AMR. These measures should focus on optimizing antibiotic use and curbing the escalating threat of AMR in the region to safeguard public health in the post‐pandemic era.
5.1. Recommendations
Public health and social care systems should work together to effectively educate the general populace on the appropriate utilization of antimicrobials.
At the national level, laboratory diagnostics need to be strengthened and decentralized to assist in the discrimination of viral respiratory infections and bacterial infections, ensuring improved public health responses to future outbreaks.
6. Limitations
Data on antibiotics consumption rate over the period of the study would have provided a relationship between antibiotic consumption and bacteria resistance; however, such data was not available for comparison.
Supplementary table: Distribution of culture result among gender and age categories.
Within the study period, 1786 positive culture cases were recorded out of a total of 3677 cases. A significantly higher proportion of study participants older than 60 years of age was associated with positive cultures. Participants within the age category of 21–30 years were also significantly associated with negative cultures (p < 0.0001). Female participants (n = 973, 54.5%) were significantly associated with a higher rate of culture positivity as compared to male participants (n = 813, 45.5%; p = 0.006).
Author Contributions
O. N. D and H. A. O.: Conceptualization, O. N. D, A. A, G. A and H .A. O.: Methodology, E. W. W, A. A. K, S. A, D. D. A, S. A. L, V. C. K, H. Y and B. D. N.: Data collection and data cleaning, O. N. D, H. A. O, A. A and K. M. O: Data analysis and results interpretation, O. N. D, H. A. O and A. A.: Writing ‐original draft preparation, G. A, A. A. K, D. D. A, E. W. W, S. O. A and E. K. A.: Editing of original draft, O. N. D, H. A. O, S. A. L and V. C. K.: Discussion of results, H. Y, E. K. A, E. K. A, S. O. A, G. A.: Visualization. All authors: Reviewed and final manuscript.
Funding
The authors have nothing to report.
Ethics Statement
The protocol used in this study was reviewed and approved by the Ethical Review Committee (ERC) of the Ghana Health Service (approval number: GHS‐ERC 026/12/20). Consent to participate was secured from all participants prior to their involvement in the study.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
AppendixS1: jcla70191‐sup‐0001‐AppendixS1.docx.
TableS1: Distribution of culture result among gender and age categories.
Acknowledgements
The authors are grateful to all staff of the Tamale Public Health Laboratory and Tamale Teaching Hospital who contributed to generating these data.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- 1. WHO , “Coronavirus Disease (COVID‐19),” (2023), https://www.who.int/news‐room/fact‐sheets/detail/coronavirus‐disease‐%28covid‐19%29.
- 2. Adebisi Y. A., Jimoh N. D., Ogunkola I. O., et al., “The Use of Antibiotics in COVID‐19 Management: A Rapid Review of National Treatment Guidelines in 10 African Countries,” Tropical Medicine and Health 49, no. 1 (2021): 51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Sampson V., Kamona N., and Sampson A., “Could There Be a Link Between Oral Hygiene and the Severity of SARS‐CoV‐2 Infections?,” British Dental Journal 228, no. 12 (2020): 971–975. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Nicolson G. L. and de Mattos G. F., “COVID‐19 Coronavirus: Is Infection Along With Mycoplasma or Other Bacteria Linked to Progression to a Lethal Outcome?,” International Journal of Clinical Medicine 11, no. 5 (2020): 282–302. [Google Scholar]
- 5. Mushkat M. and Mushkat R., “The Challenge of COVID‐19 and the World Health Organization's Response: The Principal‐Agent Model Revisited,” Am. U. Int'l L. Rev 36 (2020): 487. [Google Scholar]
- 6. Do N. T., Vu H. T. L., Nguyen C. T. K., et al., “Community‐Based Antibiotic Access and Use in Six Low‐Income and Middle‐Income Countries: A Mixed‐Method Approach,” Lancet Global Health 9, no. 5 (2021): e610–e619. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Forson A. O., Tsidi W. B., Nana‐Adjei D., Quarchie M. N., and Obeng‐Nkrumah N., “ Escherichia coli Bacteriuria in Pregnant Women in Ghana: Antibiotic Resistance Patterns and Virulence Factors,” BMC Research Notes 11, no. 1 (2018): 901. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Vicar E. K., Acquah S. E. K., Williams W., Kuugbee E. D., Saba C. K. S., and Mensah G. I., “Antibiotic Resistant Bacteria Infecting Wounds of Rural Community Dwellers in Northern Ghana,” European Journal of Medical and Health Sciences 3, no. 1 (2021): 112–117. [Google Scholar]
- 9. Deberu O., Nkrumah B., Sylverken A. A., et al., “Common Bacteria in Sputum or Gastric Lavage of Patients Presenting With Signs and Symptoms of Lower Respiratory Tract Infections,” Pan African Medical Journal 38, no. 1 (2021): 383. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Egyir B., Obeng‐Nkrumah N., and Kyei G. B., “COVID‐19 Pandemic and Antimicrobial Resistance: Another Call to Strengthen Laboratory Diagnostic Capacity in Africa,” African Journal of Laboratory Medicine 9, no. 1 (2020): 1–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Lai C.‐C., Chen S. Y., Ko W. C., and Hsueh P. R., “Increased Antimicrobial Resistance During the COVID‐19 Pandemic,” International Journal of Antimicrobial Agents 57, no. 4 (2021): 106324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Petrakis V., Panopoulou M., Rafailidis P., et al., “The Impact of the COVID‐19 Pandemic on Antimicrobial Resistance and Management of Bloodstream Infections,” Pathogens 12, no. 6 (2023): 780. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Tiri B., Sensi E., Marsiliani V., et al., “Antimicrobial Stewardship Program, COVID‐19, and Infection Control: Spread of Carbapenem‐Resistant Klebsiella Pneumoniae Colonization in ICU COVID‐19 Patients. What Did Not Work? ,” Journal of Clinical Medicine 9, no. 9 (2020) 2744. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Abdelaziz Abdelmoneim S., Mohamed Ghazy R., Anwar Sultan E., Hassaan M. A., and Anwar Mahgoub M., “Antimicrobial Resistance Burden Pre and Post‐COVID‐19 Pandemic With Mapping the Multidrug Resistance in Egypt: A Comparative Cross‐Sectional Study,” Scientific Reports 14, no. 1 (2024): 7176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Iwu C. J., Jordan P., Jaja I. F., Iwu C. D., and Wiysonge C. S., “Treatment of COVID‐19: Implications for Antimicrobial Resistance in Africa,” Pan African Medical Journal 35, no. Suppl 2 (2020): 119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. GSS , “Ghana 2021 Population and Housing Census,” in Population and Housing Census, G.S. Service, Editor (Ghana Statistical Service: Ghana, 2021), 1–128. [Google Scholar]
- 17. Gnimatin J.‐P., Weyori E. W., Agossou S. M., and Adokiya M. N., “Bacterial Infections Epidemiology and Factors Associated With Multidrug Resistance in the Northern Region of Ghana,” Scientific Reports 12, no. 1 (2022): 22069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Hughes S., Troise O., Donaldson H., Mughal N., and Moore L. S. P., “Bacterial and Fungal Coinfection Among Hospitalized Patients With COVID‐19: A Retrospective Cohort Study in a UK Secondary‐Care Setting,” Clinical Microbiology and Infection 26, no. 10 (2020): 1395–1399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Zhou F., Yu T., du R., et al., “Clinical Course and Risk Factors for Mortality of Adult Inpatients With COVID‐19 in Wuhan, China: A Retrospective Cohort Study,” Lancet 395, no. 10229 (2020): 1054–1062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. De Oliveira D. M., Forde B. M., Kidd T. J., et al., “Antimicrobial Resistance in ESKAPE Pathogens,” Clinical Microbiology Reviews 33, no. 3 (2020): 10–1128, 10.1128/cmr.00181-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Martin R. M. and Bachman M. A., “Colonization, Infection, and the Accessory Genome of Klebsiella Pneumoniae,” Frontiers in Cellular and Infection Microbiology 8 (2018): 4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Boschi C., Hoang V. T., Giraud‐Gatineau A., et al., “Coinfections With SARS‐CoV‐2 and Other Respiratory Viruses in Southeastern France: A Matter of Sampling Time,” Journal of Medical Virology 93, no. 4 (2021): 1878–1881. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Monasta L., Ronfani L., Marchetti F., et al., “Burden of Disease Caused by Otitis Media: Systematic Review and Global Estimates,” PLoS One 7, no. 4 (2012): e36226. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Agyepong N., Govinden U., Owusu‐Ofori A., and Essack S. Y., “Multidrug‐Resistant Gram‐Negative Bacterial Infections in a Teaching Hospital in Ghana,” Antimicrobial Resistance & Infection Control 7, no. 1 (2018): 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Chang Y.‐J., Yeh M. L., Li Y. C., et al., “Predicting Hospital‐Acquired Infections by Scoring System With Simple Parameters,” PLoS One 6, no. 8 (2011): e23137. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Simonetti A. F., Viasus D., Garcia‐Vidal C., and Carratalà J., “Management of Community‐Acquired Pneumonia in Older Adults,” Therapeutic Advances in Infectious Disease 2, no. 1 (2014): 3–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Smets W., Moretti S., Denys S., and Lebeer S., “Airborne Bacteria in the Atmosphere: Presence, Purpose, and Potential,” Atmospheric Environment 139 (2016): 214–221. [Google Scholar]
- 28. Cavalcanti A. B., Zampieri F. G., Rosa R. G., et al., “Hydroxychloroquine With or Without Azithromycin in Mild‐To‐Moderate Covid‐19,” New England Journal of Medicine 383, no. 21 (2020): 2041–2052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Furtado R. H., Berwanger O., Fonseca A. B., et al., “Azithromycin in Addition to Standard of Care Versus Standard of Care Alone in the Treatment of Patients Admitted to the Hospital With Severe COVID‐19 in Brazil (COALITION II): A Randomised Clinical Trial,” Lancet 396, no. 10256 (2020): 959–967. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Grau Cerrato S., Ferrández‐Ocaña R., Grau C., Echeverría‐Esnal S., Gómez‐Zorrilla C., and Sorli A., “Evolution of Antimicrobial Consumption During the First Wave of COVID‐19 Pandemic,” Antibiotics (Basel) 10, no. 2 (2021): 132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Mustafa L., Tolaj I., Baftiu N., and Fejza H., “Use of Antibiotics in COVID‐19 ICU Patients,” Journal of Infection in Developing Countries 15, no. 4 (2021): 501–505. [DOI] [PubMed] [Google Scholar]
- 32. Bongiovanni M., Barilaro G., Zanini U., and Giuliani G., “Impact of the COVID‐19 Pandemic on Multidrug‐Resistant Hospital‐Acquired Bacterial Infections,” Journal of Hospital Infection 123 (2022): 191–192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Meng Q., Li W., Jiang H., et al., “Comparison of the Distribution and Changes in the Antibiotic Resistance of Clinical Bacterial Isolates From the Lower Respiratory Tract of Children in Shenzhen Before the Epidemic, During the Epidemic, and During the Period of Normalized Prevention and Control of COVID‐19,” Infectious Diseases and Therapy 12, no. 2 (2023): 563–575. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Fu P., Xu H., Jing C., et al., “Bacterial Epidemiology and Antimicrobial Resistance Profiles in Children Reported by the ISPED Program in China, 2016 to 2020,” Microbiology Spectrum 9, no. 3 (2021): e00283‐21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Ong A., Ahmed H., Hayward S. S., Yap J. J., Patel P., and Dasgupta P., “Trends in Antibiotic Resistance for Over 700,000 Escherichia Coli Positive Urinary Tract Infections Over Six Years (2014–2019) From a University Teaching Hospital,” Central European Journal of Urology 74, no. 2 (2021): 249. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Omenako K. A., Enimil A., Marfo A. F. A., et al., “Pattern of Antimicrobial Susceptibility and Antimicrobial Treatment of Neonates Admitted With Suspected Sepsis in a Teaching Hospital in Ghana, 2021,” International Journal of Environmental Research and Public Health 19, no. 19 (2022): 12968. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Organization, W.H , “WHO Operational Handbook on Tuberculosis. Module 1: Prevention‐Infection Prevention and Control,” (2023), World Health Organization.
- 38. Sistanizad M., Kouchek M., Miri M., et al., “Carbapenem Restriction and Its Effect on Bacterial Resistance in an Intensive Care Unit of a Teaching Hospital,” Iranian Journal of Pharmaceutical Research: IJPR 12, no. 3 (2013): 503–509. [PMC free article] [PubMed] [Google Scholar]
- 39. López‐Jácome L. E., Fernández‐Rodríguez D., Franco‐Cendejas R., et al., “Increment Antimicrobial Resistance During the COVID‐19 Pandemic: Results From the Invifar Network,” Microbial Drug Resistance 28, no. 3 (2022): 338–345. [DOI] [PubMed] [Google Scholar]
- 40. Agodi A., Barchitta M., Quattrocchi C., Maugeri S., Aldisio C., and Marchese C., “Antibiotic Trends of Klebsiella Pneumoniae and Acinetobacter Baumannii Resistance Indicators in an Intensive Care Unit of Southern Italy, 2008–2013,” Antimicrobial Resistance and Infection Control 4, no. 1 (2015): 1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Mao T., Zhai H. U. I. J. U. A. N., Duan G. U. A. N. G. C. A. I., and Yang H. A. I. Y. A. N., “Patterns of Drug‐Resistant Bacteria in a General Hospital, China, 2011–2016,” Polish Journal of Microbiology 68, no. 2 (2019): 225–232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Obeng‐Nkrumah N., Twum‐Danso K., Krogfelt K. A., and Newman M. J., “High Levels of Extended‐Spectrum Beta‐Lactamases in a Major Teaching Hospital in Ghana: The Need for Regular Monitoring and Evaluation of Antibiotic Resistance,” American Journal of Tropical Medicine and Hygiene 89, no. 5 (2013): 960–964. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Romaszko‐Wojtowicz A., Tokarczyk‐Malesa K., Doboszyńska A., and Glińska‐Lewczuk K., “Impact of COVID‐19 on Antibiotic Usage in Primary Care: A Retrospective Analysis,” Scientific Reports 14, no. 1 (2024): 4798. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Gutema G. and Homa G., “Cropping Up Crisis at the Nexus Between COVID‐19 and Antimicrobial Resistance (AMR) in Africa: A Scoping Review and Synthesis of Early Evidence,” Cureus 14, no. 1 (2022): e21035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Murungi M., Ndagije H. B., Kiggundu R., et al., “Antimicrobial Consumption Surveillance in Uganda: Results From an Analysis of National Import Data for the Human Health Sector, 2018–2021,” Journal of Infection and Public Health 16 (2023): 45–51. [DOI] [PubMed] [Google Scholar]
- 46. Mazumder P. and Kumar M., Antimicrobial Resistance Ignited by COVID‐19 Pandemic: SOS for Antimicrobial Stewardship (Springer, 2023). [Google Scholar]
- 47. Lucien M. A. B., Canarie M. F., Kilgore P. E., et al., “Antibiotics and Antimicrobial Resistance in the COVID‐19 Era: Perspective From Resource‐Limited Settings,” International Journal of Infectious Diseases 104 (2021): 250–254. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Rawson T. M., Ming D., Ahmad R., Moore L. S. P., and Holmes A. H., “Antimicrobial Use, Drug‐Resistant Infections and COVID‐19,” Nature Reviews Microbiology 18, no. 8 (2020): 409–410. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Aslam A., Gajdács M., Zin C. S., et al., “Evidence of the Practice of Self‐Medication With Antibiotics Among the Lay Public in Low‐and Middle‐Income Countries: A Scoping Review,” Antibiotics 9, no. 9 (2020): 597. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Kazemioula G., Golestani S., Alavi S. M. A., Taheri F., Gheshlagh R. G., and Lotfalizadeh M. H., “Prevalence of Self‐Medication During COVID‐19 Pandemic: A Systematic Review and Meta‐Analysis,” Frontiers in Public Health 10 (2022): 1041695. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Opoku R., Dwumfour‐Asare B., Agrey‐Bluwey L., et al., “Prevalence of Self‐Medication in Ghana: A Systematic Review and Meta‐Analysis,” BMJ Open 13, no. 3 (2023): e064627. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
AppendixS1: jcla70191‐sup‐0001‐AppendixS1.docx.
TableS1: Distribution of culture result among gender and age categories.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.