Prevalence of AmpC beta-lactamase producing Escherichia coli and Klebsiella pneumoniae in Africa: a systematic review and meta-analysis

Abstract

Background

The emergence of AmpC beta-lactamase (AmpC) poses a significant challenge in the context of antimicrobial resistance (AMR). AmpC confers resistance to narrow- and broad- spectrum cephalosporins, beta-lactam/beta-lactamase inhibitor combinations and aztreonam making it clinically relevant and presenting a formidable threat to effective therapeutic interventions. Thus, the aim of this study was to assess magnitude of AmpC producing Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) in Africa.

Methods

Articles were extensively searched in bibliographic databases and grey literature using entry terms and combinations key words. Electronic databases such as PubMed, Scopus, Science Direct, Embase, and other online sources such as African Journal Online, Google Scholar, and ResearchGate were used to find relevant articles. Furthermore, the Joanna Briggs Institute quality appraisal tool was used to assess the quality of the included studies. Studies meeting eligibility criteria were extracted in MS Excel and exported to STATA version 14 software for statistical analysis. A random-effects model was used to compute the pooled prevalence of AmpC producing E. coli and K. pneumoniae. Heterogeneity was quantified by using the Cochrane Q test and I² statistics. Publication bias was assessed using a funnel plot and Egger’s test. Additionally, sensitivity analysis was conducted to assess the impact of a single study on the pooled effect size.

Result

Of the 2,619 studies identified, 25 studies were eligible for quantitative analysis, involving a total of 11,908 E. coli, and 4,654 K. pneumoniae isolates. The overall pooled prevalence of AmpC producing E. coli and K. pneumoniae in Africa was 18.79% (95% CI: 15.00, 22.58). The pooled prevalence of AmpC producing E. coli and K. pneumoniae were 15.32% (95% CI: 12.03, 18.61) and 17.25% (95% CI: 13.18, 21.32), respectively. There was significant heterogeneity among studies (I² = 99.0%, p < 0.001). Our study depicted that Egypt had the highest pooled prevalence of AmpC producing E. coli and K. pneumoniae with 28.91% (14.74, 43.08) and 27.84% (8.47, 47.21) respectively. Furthermore, studies conducted after 2020 showed the highest pooled prevalence of AmpC producing E. coli 28.29% (11.78, 44.80) and K. pneumoniae 29.04% (13.13, 44.85) while lowest pooled prevalence of AmpC producing E. coli 6.28% (95% CI: 2.99, 9.58) and K. pneumoniae 5.41% (95% CI: 1.73, 9.09) was observed among studies conducted before the year 2016.

Conclusion

This study showed an increase in AmpC producing E. coli and K. pneumoniae in Africa over the past 20 years. Therefore, regular identification of AmpC, infection prevention control, strengthening of the antimicrobial resistance surveillance system and an effective antibiotic policy are required to combat the antibiotics resistance in Africa.

PROSPERO registration identification number

CRD42024501640

Supplementary Information

The online version contains supplementary material available at 10.1186/s13756-025-01578-7.

Introduction

Drug resistant bacteria are serious global public health threats due to their emergence and spread. Antimicrobial resistance (AMR) causes an estimated 4.95 million deaths in 2019, of which 1.27 million were directly caused by MDR bacteria. Escherichia coli and Klebsiella pneumoniae were among the six most common pathogens associated with resistance-related deaths. The highest burden is in Western sub-Saharan Africa, with 27.3 per 100,000 AMR-attributable and 114.8 per 100,000 AMR-associated deaths [1]; therefore, if no action is taken against AMR, by 2050 this number could rise to 10 million per year [2, 3].

According to Africa CDC report, AMR poses a growing and urgent threat in Africa. With nearly 1.3 million deaths attributed to AMR in Africa in 2019, inadequate monitoring and control measures by governments have worsened the situation, hindering the prevention of resistant micro-organisms. AMR stands as one of the leading public health challenges of the 21st century, with Africa having the world’s highest mortality rate from AMR infections, resulting in 27.3 deaths per 100,000 attributable to AMR [4].

The loss of susceptibility to beta-lactam antibiotics in Gram negative bacteria is an emerging problem worldwide, primarily caused by the production of beta-lactamases, particularly carbapenemases, extended-spectrum (ESBL) and AmpC beta-lactamases [5].

Beta-lactamases are produced by a number of bacteria and confer resistance to beta-lactam antibiotics, such as cephalosporins, carbapenems, penicillins, and monobactams. One of the most important mechanisms underlying Enterobacteriaceae resistance to beta-lactam antibiotics is the activity of AmpC beta-lactamases. With the exception of carbapenems and fourth-generation cephalosporins, these enzymes confer resistance to most beta-lactam antibiotics [6] and commonly found in Gram negative bacteria predominantly in K. pneumoniae and E. coli [7].

AmpC beta-lactamases may be encoded in the chromosomes or plasmids. Among Gram negative bacteria, most Enterobacterales have chromosomally encoded AmpC beta-lactamases. Due to the mobilization of certain AmpC genes from their chromosomal origin, plasmids spread the gene to other strains through transformation and conjugation, resulting in widespread drug resistance attributable to plasmids-mediated AmpC beta-lactamase (pAmpC) [8–10]. In particular, E. coli and K. pneumoniae have acquired pAmpC beta-lactamases [11].

Plasmid-mediated AmpC variants are categorized into five evolutionary groups: the CIT variants (CMY-2 types), EBC variants (ACT-1 type, MIR-1), DHA variants, ACC variants, and FOX and MOX variants, based on sequence similarities with species specific AmpC enzymes [12, 13]. Though several AmpC genes are detected in Enterobacteriaceae, particularly blaCMY-2 and blaDHA-1 are the most common in E. coli and K. pneumoniae strains, respectively [14–17].

Bacteria producing AmpC act as a covert reservoir for ESBLs. The co-existence of these enzymes makes treatment more difficult because AmpC can obscure the identification of ESBLs [18].

Third-generation cephalosporins are empirically used to treat infections in low-income countries; nevertheless, poor therapeutic outcomes may result from failure to detect AmpC related resistance. Additionally, AmpC are frequently associated with other multiple resistance genes including those of resistance to quinolones and other beta lactamase genes, leaving the clinician with few therapeutic options [12, 19]. Therefore, AmpC identification is essential for improving the therapeutic management of infected individuals and providing epidemiological data to the setting [20]. Studies on Gram negative bacteria’s susceptibility to antibiotics have shown that over time, the hyper production of ESBL and AmpC enzymes has led to an increased resistance [21–23].

Considerable studies in Africa have focused on epidemiology and resistant traits of ESBL and Carbapenemase producing bacteria but information regarding AmpC producers is still scarce. Furthermore, a more comprehensive study is essential to gain a better understanding of the burden of AmpC producing pathogens in the low-income settings such as Africa. Thus, this systematic review and meta-analysis aimed to assess the magnitude of AmpC producing E. coli and K. pneumoniae in Africa.

Methods

Protocol registration and guidelines

This systematic review and meta-analysis was designed to estimate the pooled prevalence of AmpC producing E. coli and K. pneumoniae in Africa. The results was reported based on Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA) [24]. The review protocol was registered in the International Prospective Register of Systematic Review (PROSPERO) under registration number CRD42024501640.

Search strategy and selection of studies

A thorough bibliographic database search was conducted to retrieve studies published/ reported the prevalence of AmpC producing E. coli and K. pneumoniae anywhere in the African region between January 1, 2000 and January 2, 2024 in PubMed, Scopus, Embase and Science Direct. Furthermore, grey literatures such as African Journal Online (AJOL), Google Scholar and ResearchGate websites were screened. A direct Google search was also carried out using the reference lists of the included studies to incorporate further relevant studies that were missed during electronic database searches.

An exhaustive search strategy was employed using the condition, context, population, and outcome of interest (CoCoPop) framework to formulate questions, and all eligible studies were accessed by employing Medical Subject Headings (MeSH) terms and combination key words including AmpC beta-lactamase”, “AmpC beta lactamase”, “AmpC β-lactamase”, “β-lactamases”, “beta lactamases”, “beta-lactamases”, “Klebsiella pneumoniae”, “K. pneumoniae”, “Escherichia coli”, “E. coli”, “Gram negative bacilli”, “Gram negative rods”, Enterobacteriaceae, “Africa”, “Algeria”, “Angola”, “Benin”, “Botswana”, “Burkina Faso”, “Burkina Fasso”, “Upper Volta”, “Burundi”, “Cameroon”, “Cape Verde”, “Central African Republic”, “Chad”, “Comoros”, “Comoro Islands”, “Congo”, “Democratic Republic of the Congo”, “Zaire”, “Djibouti”, “Egypt”, “Equatorial Guinea”, “Eritrea”, “Ethiopia”, “Eswatini”,“Gabon”, “Gambia”, “Ghana”, “Guinea”, “Guinea Bissau”, “Ivory Coast”, “Cote d’Ivoire”, “Kenya”, “Lesotho”, “Liberia”, “Libya”, “Libia”, “Jamahiriya”, “Madagascar”, “Malawi”, “Mali,“Mauritania”, “Mauritius”, “Morocco”, “Mozambique”, “Namibia”, “Niger”, “Nigeria”, “Rwanda”, “Sao Tome”, “Senegal”, “Seychelles”, “Sierra Leone”, “Somalia”, “South Africa”. “Sudan”, “South Sudan”, “Swaziland”, “Tanzania, “Tanganyika”, “Zanzibar”, “Togo”, “Tunisia”, “Uganda”, “Western Sahara”, “Zambia”, “Zimbabwe”, “Southern Africa”, “West Africa”, “Western Africa”, “East Africa”, “Eastern Africa”, “North Africa”, “Northern Africa”, “Subsaharan Africa”, “Sub-Saharan Africa”, “Central Africa”. Boolean operators (“OR” and “AND”) were used as necessary in the advanced searching of articles. Moreover, the bibliographies of included studies were reviewed for additional articles, and authors were contacted to obtain any missing papers.

Eligibility criteria

Inclusion criteria

Articles that fulfilled the following criteria were included:

Original articles that reported AmpC producing E. coli and K. pneumoniae in all age populations of Africa, laboratory-based observational studies (cross-sectional studies, case-control and cohort studies), studies conducted on human/clinical specimens. Studies conducted in Africa, studies available online from 2000 to 2023, studies published in English language, utilizing AmpC detection methods such as polymerase chain reaction (PCR) and disk diffusion method.

Exclusion criteria

No confirmation of AmpC production using phenotypic and/ or genotypic methods such as the Cloxacillin agar dilution test, Cloxacillin Disc Tests, Boronic acid Disc Tests, AmpC Disc Test, the epsilometric test (E-test) and molecular methods, qualitative studies, studies conducted on environmental samples, animal samples, review articles, case reports, and letters to the editor, studies with outcomes of interest are missing or vague were excluded.

Study selection

All articles that were found through searching electronic databases of identified studies were imported using EndNote version 20 software (Clarivate Analytics USA) and duplicates were removed. Three independent reviewers (SG, MAB and EA) identified the articles from databases and other sources. Four independent reviewers (DG, MT, AG and HD) thoroughly vetted each article’s title, abstract, and full text based on the eligibility criteria.

The full texts of potentially eligible studies were then evaluated in detail against the inclusion criteria by two reviewers (SG and MAB), double-checked by a third reviewer (DG), and added to the extraction collection. Any disagreements among reviewers throughout each stage of screening were unraveled through discussion or with the intruding of a third reviewer (DG).

Quality assessment for risk of bias

Three authors (SG, MAB, and EA) carefully evaluated the papers’ quality. The Joanna Brigg Institute (JBI) quality appraisal tool was used to assess the quality of the included studies [25]. During the assessment, the critical appraisal checklist was utilized. The checklist comprised of a set of 9 questions regarding sampling frame, sampling technique, sample size, study setting description, validity of study methods, and data analysis, which were cumulatively scored out of 100. Research with an average quality score of 50% or more were classified as high quality (low risk of bias) whereas studies pertaining score below 50% were deemed low quality (high risk of bias) [25]. Therefore, articles having high quality were included in this study.

Data extraction

Three reviewers (SG, MAB, and EA) extracted data including (author(s)’ names, publication year, country, study period, setting, study design, study population type, laboratory diagnosis method, sample size, number of bacterial isolates, number of AmpC producing isolates, and prevalence of AmpC producing isolates) from the eligible studies using Microsoft Excel sheets.

Data synthesis and statistical analysis

The extracted data was analyzed by using stata version 14.0 software (Stata Corp.,College Station, TX). Logit transformation was applied for pooling proportions. The Cochrane Q test and I² statistics with the corresponding p-value of < 0.05 were used to assess heterogeneity between studies and I² test statistic values greater than 75% indicating the presence of heterogeneity. The pooled prevalence of AmpC producing E. coli and K. pneumoniae was estimated using a random effect model with a 95% confidence interval [26].

Additionally, a predetermined subgroup analysis was carried out according to the diagnostic method, study design, country, and year of publication. Furthermore, a leave-one-out approach sensitivity analysis was conducted to assess the influence of individual studies on the total pooled estimate. Meta-regression was also used to identifying potential sources of heterogeneity among the included studies by examining of the effect of study characteristics on the observed variations in AmpC prevalence. Publication bias was assessed using inspection of funnel plot symmetry and Egger’s test statistics [27, 28]. When there is an asymmetric funnel, missing studies that provide information on the accuracy of the estimate of publication bias are incorporated using the Trim-and-Fill technique.

Finally, the results were displayed in tables along with a pooled prevalence, a 95% confidence interval, a corresponding p-value, and forest plots.

Result

Selection of studies

A total of 2619 articles were retrieved from bibliographic database searches and other sources. From those articles, duplication resulted in the removal of 1318 articles. Following the removal of duplicates, 1301 articles were screened and 1100 articles were removed using the title and 201 using abstract review. Finally, a total of 104 full-text articles were thoroughly examined based on the eligibility criteria, and only 25 articles were considered possibly eligible and included in the meta-analysis and systematic review (Fig. 1).

Fig. 1.

PRISMA flow diagram of the included studies for the systematic review and meta-analysis

Characteristics of the included studies

As summarized in Table 1, 25 original studies from different African countries were included in this systematic review and meta-analysis. The sample size of the studies ranged from 80 to 11,393. Of the 20,612 reported isolates, E. coli and K. pneumoniae accounted 11,908 and 4,654 respectively. From these isolates 693 (E. coli) and 353 (K. pneumoniae) were AmpC producer. Out of 25 studies included in this systematic review and meta-analysis, 23 studies employed a cross-sectional study design.

Table 1.

Characteristics of studies on AmpC producing E. coli and K. pneumoniae in Africa

Author/ Reference	Pub. year	Country	Study design	Study population	Age group	AmpC BL detection method	Sample size	Total isolate	No of EC isolates	No of KP isolates	Prevalence; N (%)			Detected AmpC genes
											EC	KP	Total AmpC prevalence
											AmpC	AmpC		EC	KP
Ebongue et al., [29]	2018	Cameroon	CS	IP and OP	All age	Phenotyping	195	195	96	61	11 (11.5)	8 (13.1)	12.1	NT	NT
El-Hady and Adel, [30]	2015	Egypt	CS	ICU admitted patients	NI	Molecular	148	148	42	85	16 (35.7)	22 (25.9)	29.9	CMY-1, CMY-2,	CMY-1, CMY-2
Yakubu et al., [31]	2022	Nigeria	CS	Diabetic patients	All age	Phenotyping	196	74	41	23	22 (53.7)	3 (13.0)	39.1	NA	NA
Legese et al., [32]	2022	Ethiopia	CS	Sepsis patients	All age	Molecular	1416	301	53	103	5 (9.4)	9 (8.7)	8.9	CMY-2, CMY-42, CMY-148	CMY-6, DHA-1
Dirar et al., [33]	2020	Sudan	CS	IP and OP	All age	Phenotyping	168	168	81	52	9 (11.1)	11 (21.2)	15.0	NT	NT
Shuaibu et al., [34]	2021	Nigeria	CS	IP and OP	All age	Phenotyping	1000	1000	390	167	248 (63.6)	111 (66.5)	64.4	NT	NT
Yusuf et al., [35]	2013	Nigeria	CS	IP and OP	All age	Phenotyping	500	500	247	50	7 (2.8)	2 (2.0)	3.0	NT	NT
Owusu et al., [36]	2023	Ghana	CS	IP and OP	NI	Molecular	181	181	83	30	4 (4.8)	4 (13.3)	7.1	FOX, DHAM	FOX
Tekele et al., [37]	2020	Ethiopia	CS	IP and OP	All age	Phenotyping	338	338	224	41	4 (1.8)	3 (7.3)	2.6	NT	NT
Iabadene et al., [38]	2009	Algiers	CS	IP and OP	NI	Molecular	505	505	223	112	3 (1.3)	1 (0.9)	1.2	CMY-2, DHA-1	CMY-2
Rensing et al., [39]	2019	Egypt	CS	BSI and Gastroenteritis patients	NI	Molecular	325	206	127	38	3 (2.4)	4 (10.5)	4.2	CIT	CIT, DHA-1
Mohamed et al., [40]	2020	Egypt	PT	UTI patients	All age	Molecular	705	440	303	71	9 (2.97)	9 (12.7)	4.8	MOX, DHA, CIT	DHA
Helmy and Wasfi, [41]	2014	Egypt	CS	UTI patients	NI	Molecular	143	143	102	30	19 (18.6)	2 (6.7)	15.9	-	CIT, EBC, MOX
Yengui et al., [42]	2022	Tunisia	CS	IP and OP	Adult	Molecular	2000	500	44	36	8 (18.2)	21 (58.3)	80.5	CMY-2	CMY-2
Ben Hassena et al., [43]	2022	Tunisia	CS	IP and OP	All age	Molecular	1220	195	106	31	2 (1.9)	1 (3.2)	2.2	ACC, FOX	ACC, FOX
Najjuka et al., [44]	2020	Uganda	CS	Outpatient	All age	Molecular	1448	985	930	55	157 (16.9)	23 (41.8)	18.3	CIT, DHA, EBC	CIT, DHA, EBC
Cherif et al., [45]	2016	Tunisia	CS	IP and OP	NI	Molecular	11,393	11,393	7504	2905	5 (0.1)	4 (0.1)	0.08	FOX-3, MOX-2, CMY-4, CMY-16	FOX-3, MOX-2, CMY-4, CMY-16
Amadi et al., [46]	2023	Nigeria	CS	Suppurative otitis media patients	All age	Molecular	300	300	41	67	1 (2.4)	11 (1.5)	11.1	FOX	FOX
Zorgani et al., [47]	2017	Libya	CS	Inpatient	All age	Molecular	151	151	75	76	6 (8.0)	3 (3.9)	5.9	AmpC, CMY, MOX	AmpC, CMY, MOX, DHA, EBC
Khalifa et al., [48]	2021	Egypt	CS	IP and OP	All age	Molecular	80	80	45	35	38 (84.4)	29 (82.8)	83.7	AmpC	AmpC
Sultan et al., [49]	2019	Egypt	PT	Patients in ICU	Adult	Phenotyping	240	240	62	46	22 (35.5)	14 (30.4)	33.3	NT	NT
Akinjogunla et al., [50]	2023	Nigeria	CS	IP and OP	Adult	Phenotyping	414	414	174	126	42 (24.1)	28 (22.2)	23.3	NT	NT
Maduakor et al., [51]	2022	Nigeria	CS	IP and OP	NI	Phenotyping	600	600	85	65	20 (23.5)	16 (24.6)	24.0	NT	NT
Gharout-Sait et al., [52]	2015	Algeria	CS	IP and OP	NI	Molecular	922	922	551	221	9 (1.6)	5 (2.3)	1.8	CMY-4	CMY-4, DHA
Yusuf et al., [53]	2014	Nigeria	CS	IP and OP	NI	Phenotyping	633	633	278	128	23 (8.3)	9 (7.0)	7.9	NT	NT
								20,612	11,908	4654	693	353

EC = E. coli; KP = K. pneumoniae; BSI = Blood Stream Infection; CI = Clinical Isolates; NI = Not indicated; NT = Not Tested; OP = Out Patient; IP = Inpatient; CS = Cross-sectional

Regarding sources of studies, (7 out of 25) were reported from Nigeria, followed by Egypt (4 out of 25). Majority of studies (60%) included study participants from both inpatient and outpatient. Among the eligible studies, 15 (60%) used molecular method for the detection of AmpC beta-lactamase producing isolates (Table 1).

Prevalence of AmpC producing E. coli and K. pneumoniae in Africa

Among the eligible studies included in this systematic review and meta-analysis, the pooled prevalence of AmpC producing E. coli and K. pneumoniae was 15.32% (95% CI: 12.03, 18.61) and 17.25% (95% CI: 13.18, 21.32), respectively (Table 2).

Table 2.

Pooled prevalence of AmpC producing E. coli and K. pneumoniae in Africa

Author	E. coli Effect [95% CI]	K. pneumoniae Effect [95% CI]
Ebongue et al. (2018)	11.5 (5.12–17.88)	13.1(4.63- 21.57)
El-Hady and Adel (2015)	35.7 (21.21–50.19)	25.9(16.59–35.21)
Yakubu et al. (2022)	53.7(38.44–68.96)	13.0(-0.74- 26.74)
Legese et al. (2022)	9.4(1.54–17.26)	8.7(3.26–14.14)
Dirar et al. (2020)	11.1( 4.26–17.94)	21.2(10.09–32.31)
Shuaibu et al. (2021)	63.6(58.82–68.38)	66.5(59.34–73.66)
Yusuf et al. (2013)	2.8(0.74–4.86)	2.0(-1.88- 5.88)
Owusu et al. (2023)	4.8(0.20–9.40)	13.3(1.15–25.45)
Tekele et al. (2020)	1.8(0.06–3.54)	7.3(-0.66- 15.26)
Iabadene et al. (2009)	1.3(-0.19- 2.79)	0.9(-0.85- 2.65)
Rensing et al. (2019)	2.4( -0.26- 5.06)	10.5(0.75–20.25)
Mohamed et al. (2020)	2.97(1.059–4.88)	12.7(4.95–20.44)
Helmy and Wasfi (2014)	18.6(11.05–26.15)	6.7(-2.25- 15.65)
Yengui et al. (2022)	18.2(6.79–29.60)	58.3(42.19–74.41)
Hassena et al. (2022)	1.9(-0.70- 4.50)	3.2(-3.0-9.40)
Najjuka et al. (2020)	16.9(14.49–19.31)	41.8(28.76–54.83)
Cherif et al. (2016)	0.1(0.028- 0.17)	0.1(-0.01- 0.21)
Amadi et al. (2023)	2.4(-2.29- 7.08)	1.5(-1.41- 4.41)
Zorgani et al. (2017)	8.0(1.86–14.14)	3.9(-0.45- 8.25)
Khalifa et al. (2021)	84.4(73.79- 95.0)	82.8(70.30–95.30)
Sultan et al. (2019)	35.5(23.59–47.41)	30.4(17.11–43.69)
Akinjogunla et al. (2023)	24.1(17.74- 30.45)	22.2(14.94–29.46)
Maduakor et al. (2022)	23.5(14.49- 32.51)	24.6(14.13–35.07)
Gharout-Sait et al. (2015)	1.6(0.55–2.65)	2.3(0.32–4.28)
Yusuf et al. (2014)	8.3(5.06–11.54)	7.0(2.58–11.42)
Overall	15.32(12.03–18.61)	17.25(13.18–21.32)
I² (%)	98.3%	96.9%
P value	< 0.001	< 0.001

The overall pooled prevalence of AmpC producing E. coli and K. pneumoniae in Africa was 18.79% (95% CI: 15.00, 22.576). The included studies showed a significant variation in the prevalence of AmpC producing E. coli and K. pneumoniae, ranging from 0.08% (95% CI: 0.03, 0.13) in Tunisia to 83.7% (95% CI: 75.61, 91.79) in Egypt. There was significant heterogeneity between the pooled prevalence of AmpC producing E. coli and K. pneumoniae with an I² of 99.0%, p < 0.001 (Fig. 2).

Fig. 2.

Forest plot showing overall pooled prevalence of AmpC producing E. coli and K. pneumoniae in Africa

Subgroup analysis of AmpC producing E. coli and K. pneumoniae in Africa

We conducted a subgroup analysis taking into account a number of variables, such as the country, publication year, study design and AmpC detection method. The subgroup analysis based on country, showed that Egypt had the highest pooled prevalence of AmpC producing E. coli 28.91% (14.74, 43.08) and AmpC producing K. pneumoniae 27.84% (8.47, 47.21). There was high level of heterogeneity among studies reporting AmpC producing E. coli and K. pneumoniae in almost all countries with the exception of studies reporting AmpC producing K. pneumoniae in Ethiopia, where no heterogeneity was observed (I² = 0.0%, p = 0.7).

Based on the AmpC detection method, the pooled prevalence of AmpC producing E. coli and K. pneumoniae as determined by the phenotypic method was 22.97% (11.87, 34.07) and 20.65% (8.04, 33.26) respectively. Moreover, there was a high level of heterogeneity among these studies. In terms of publication year, research conducted after 2020 showed the highest pooled prevalence of AmpC producing E. coli 28.29% (11.78, 44.80) and K. pneumoniae 29.04% (13.13, 44.85) In contrast, the lowest pooled prevalence of AmpC producing E. coli and K. pneumoniae was 6.28% (2.99, 9.58) and 5.41% (1.73, 9.09) respectively among studies conducted before 2016 (Table 3).

Table 3.

Subgroup analysis of AmpC producing E. coli and K. pneumoniae by different categories of studies

Isolates	Subgroup	Category	Number of studies	Pooled prevalence (95% CI)	Heterogeneity test (I2)	P-value	Heterogeneity between groups (P-value)
E. coli	Country	Nigeria	7	25.04 (8.36, 41.73)	99.0%	< 0.001	0.001
		Egypt	6	28.91 (14.74, 43.08)	98.2%	< 0.001
		Tunisia	3	2.55 (-1.35, 6.45)	82.6%	0.003
		Ethiopia	2	4.59 (-2.59, 11.78)	70.8%	0.064
		Total pooled	18	19.71 (14.59, 24.83)	98.6%	< 0.001
	AmpC detection method	Phenotypic	10	22.97 (11.87, 34.07)	98.7%	< 0.001	0.029
		Molecular	15	10.12 (7.03, 13.21)	97.3%	< 0.001
		Total pooled	25	15.32 (12.03, 18.61)	98.3%	< 0.001
	Publication year	< 2016	6	6.28 (2.99, 9.58)	90.9%	< 0.001	0.034
		2016–2020	9	8.31 (4.20, 12.43)	97.0%	< 0.001
		> 2020	10	28.29 (11.78, 44.80)	98.8%	< 0.001
		Total pooled	25	15.32 (12.03, 18.61)	98.3%	< 0.001
	Study design	Cross-sectional	23	15.41 (11.89, 18.93)	98.4%	< 0.001	0.841
		Prospective	2	18.68 (-13.18, 50.54)	96.4%	< 0.001
		Total pooled	25	15.32 (12.03, 18.61)	98.3%	< 0.001
Total pooled				15.32(12.03–18.61)
K. pneumoniae	Country	Nigeria	7	19.41 (4.87, 33.95)	98.1%	< 0.001	0.001
		Egypt	6	27.84 (8.47,47.21)	95.7%	< 0.001
		Tunisia	3	17.48 (0.09, 34.87)	96.1%	< 0.001
		Ethiopia	2	8.25 (3.76, 12.75)	0.0%	0.776
		Total pooled		20.29 (13.33, 27.24)	97.6%	< 0.001
	AmpC detection method	Phenotypic	10	20.65 (8.04, 33.26)	96.7%	< 0.001	0.296
		Molecular	15	13.60 (9.62, 17.57)	95.7%	< 0.001
		Total pooled	25	17.25 (13.18, 21.32)	96.9%	< 0.001
	Publication year	< 2016	6	5.41 (1.73, 9.09)	84.6%	< 0.001	0.003
		2016–2020	9	14.21 (7.18, 21.25)	92.2%	< 0.001
		> 2020	10	29.04 (13.13, 44.85)	98.0%	< 0.001
		Total pooled	25	17.25 (13.18, 21.32)	96.9%	< 0.001
	Study design	Cross-sectional	23	16.97 (12.78, 21.17)	97.0%	< 0.001	0.682
		Prospective	2	20.69 (3.43, 37.96)	80.3%	0.024
		Total pooled	25	17.25 (13.18, 21.32)	96.9%	< 0.001
Total pooled				17.25(13.18–21.32)

Meta-regression

A meta-regression analysis was conducted to investigate possible sources of heterogeneity among the studies included in the meta-analysis. To assess study variables possible influence on the overall effect size, country, publication year, sample size, population type and AmpC detection methods were included as covariates in the meta-regression model. The results of the meta-regression analysis showed that the observed heterogeneity was not caused by any of the computed factors (Table 4).

Table 4.

Meta-regression of AmpC production of E. coli and K. pneumoniae by different categories of studies included in the systematic review and meta-analysis

	Type of variables	Exp(b)	SE	t	P	95% CI
AmpC Production	Sample size	0.99	0.00	-0.61	0.549	0.99, 1.00
	Total isolates	0.99	0.00	-0.82	0.420	0.99, 1.00
	Publication year	9.59	11.82	1.83	0.080	0.75, 122.79
	Country	3.53	6.24	0.71	0.482	0.09, 136.53

*= Significant causes of heterogeneity

Publication bias

The asymmetry of the funnel plot in this study indicated the presence of publication bias. The funnel plot graph appears asymmetrical, suggesting the existence of publication bias (Fig. 3). We used Egger’s test to investigate this finding further, and the results clearly showed publication bias (P < 0.05) as shown in Table 5. According to our findings, statistically significant p-value of < 0.001 were obtained for both AmpC producing E. coli and K. pneumoniae.

Fig. 3.

Funnel plot of AmpC producing E. coli and K. pneumoniae in Africa

Table 5.

Egger’s test statistics summary of AmpC producing E. coli and K. pneumoniae in Africa

Category	Std-Eff	Coef.	Std. Err.	T	P	95% CI
AmpC PEC	Slope	-0.11	0.21	-0.53	0.598	-0.56, 0.33
	Bias	5.45	1.19	4.59	0.000	2.99,7.91
AmpC PKP	Slope	-0.17	0.24	-0.70	0.489	-0.68, 0.33
	Bias	4.19	0.84	4.99	0.000	2.45, 5.93

AmpC PEC = AmpC producing E. coli; AmpC PKP = AmpC producing K. pneumoniae

Trim and fill analysis of pooled prevalence of AmpC producing E. coli and K. pneumoniae in Africa

Because of the existence of publication bias, a trim and fill analysis was carried out. The pooled prevalence of AmpC producing E. coli and K. pneumoniae in Africa was 1.43% with the addition of 13 extra studies (95% CI: -2.569, 5.429) (Table 6).

Table 6.

Trim and fill analysis of the prevalence of AmpC producing E. coli and K. pneumoniae in Africa

Method	Pooled est.	95% CI		Asymptotic		No. of studies
		Lower	Upper	z-value	p-value
Fixed	0.145	0.091	0.199	5.248	< 0.001	25
Random	18.789	15.003	22.576	9.726	< 0.001
Test for heterogeneity: Q = 2453.107 on 24 degrees of freedom (p = < 0.001)
Moment-based estimate of between studies variance = 86.209
Trimming estimator: Linear
Meta-analysis type: Fixed-effects model
Iteration	Estimate	Tn	# To trim		Diff
1	0.145	324	13		325
2	0.104	324	13		0
Filled
Meta-analysis
Method	Pooled est.	95% CI		Asymptotic		No. of studies
		Lower	Upper	z-value	p-value
Fixed	0.104	0.050	0.158	3.790	< 0.001	38
Random	1.430	-2.569	5.429	0.701	0.483

Test for heterogeneity: Q = 4784.650 on 37 degrees of freedom (p = 0.000)

Moment-based estimate of between studies variance = 149.065

Sensitivity analysis

Results of the sensitivity analysis showed that upon the exclusion of individual studies, the pooled effect size continuously stayed within the 95% CI of the overall pooled effect size. This confirmed that no single study had any appreciable impact on the overall pooled prevalence of E. coli and K. pneumoniae that produce AmpC beta-lactamase in Africa (Table 7).

Table 7.

Sensitivity analysis of the included studies

S No.	Study omitted	Estimate	95% CI
1	Ebongue et al., [29]	19.07	15.20, 22.94
2	El-Hady and Adel, [30]	18.35	14.53, 22.18
3	Yakubu et al., [31]	18.14	14.32, 21.96
4	Legese et al., [32]	19.22	15.34, 23.09
5	Dirar et al., [33]	18.94	15.08, 22.80
6	Shuaibu et al., [34]	16.12	13.07, 19.18
7	Yusuf et al., [35]	19.57	15.57, 23.57
8	Owusu et al., [36]	19.29	15.41, 23.17
9	Tekele et al., [37]	19.59	15.58, 23.59
10	Iabadene et al., [38]	19.79	15.50, 24.08
11	Rensing et al., [39]	19.46	15.54, 23.38
12	Mohamed et al., [40]	19.47	15.50, 23.43
13	Helmy and Wasfi, [41]	18.90	15.04, 22.76
14	Yengui et al., [42]	16.41	12.80, 20.02
15	Ben Hassena et al., [43]	19.57	15.62, 23.52
16	Najjuka et al., [44]	18.76	14.97, 22.54
17	Cherif et al., [45]	20.15	14.95, 25.34
18	Amadi et al., [46]	19.10	15.23, 22.97
19	Zorgani et al., [47]	19.36	15.47, 23.26
20	Khalifa et al., [48]	16.19	12.65, 19.74
21	Sultan et al., [49]	18.24	14.42, 22.07
22	Akinjogunla et al., [50]	18.57	14.75, 22.39
23	Maduakor et al., [51]	18.56	14.73, 22.41
24	Gharout-Sait et al., [52]	19.88	15.29, 24.47
25	Yusuf et al., [53]	19.29	15.38, 23.22
	Combined	18.79	15.00, 22.57

Discussion

Drug resistance presents a treatment challenge in both hospital settings and in the community as most of the bacteria have acquired resistance to multiple antibiotics. AmpC beta-lactamase is one of the commonly detected enzymes causing resistance in the clinical laboratory settings. The fact that AmpC confer resistance to both narrow and broad spectrum cephalosporins, beta-lactam/beta-lactamase inhibitor combinations and aztreonam makes them clinically relevant [54]. The aim of the current systematic review and meta-analysis was to determine the pooled prevalence of AmpC producing E. coli and K. pneumoniae in Africa from various studies and assess the distribution patterns of AmpC beta-lactamase across the continent.

The overall pooled prevalence estimate of AmpC producing E. coli and K. pneumoniae in Africa was found to be 18.79% (95% CI: 15.00, 22.58) with individual prevalence ranging from 0.08 to 83.7%. This finding is supported by a recent systematic review that report a global increase in the prevalence of AmpC producing E. coli and K. pneumoniae over the past ten years [7]. Similarly, other studies also reported that K. pneumoniae and E. coli were the major species producing AmpC beta-lactamase [55, 56].The current study indicated that the pooled prevalence of AmpC producing K. pneumoniae was 17.25% (95% CI: 13.18, 21.32) which was in accordance with previous study 16.7% [57]. On the other hand, in our study CMY is the frequently detected gene in K. pneumoniae which is in agreement with a study that reported CMY and DHA as the most common genes in Korea [58]. Likewise, another study in South Korea showed that the first transmissible CMY gene was identified in a K. pneumoniae isolate [59]. However, it should be well-known that sometimes specific genes are prevalent in certain areas [60, 61].

Based on our subgroup analysis, the pooled prevalence rates based on country were assessed and the results showed significant variations between African countries. However, it should be taken into account that some studies may have overestimated the prevalence of plasmid mediated AmpC beta-lactamase since they did not differentiate between chromosomes and plasmids mediated AmpC beta-lactamases. Prevalence data might vary markedly according to the methodology applied, sample size, geographical region, setting, patient type and varied infection prevention and control policies. Furthermore, the number of relevant studies in countries such as Ethiopia and Tunisia were quite limited; and as a result, these results should be interpreted cautiously.

Moreover, our pooled prevalence revealed the prevalence of AmpC producing E. coli and K. pneumoniae detected using phenotypic method was higher compared to molecular methods. The possible reason for the difference might be the fact that phenotypic method is less sensitive, that means the sensitivity of the phenotypic tests was negatively influenced by the co-presence of ESBL enzyme in the test isolate or phenotypic tests’ inhibitory effect was concealed by the ESBL activity [18, 62]. Furthermore, it should be noted that there are no CLSI guideline for the detection of AmpC mediated resistance in Gram negative clinical isolates. As a result, this can lead to misleading results, particularly in phenotypic tests [63].

In this systematic review and meta-analysis, a noteworthy trend was observed between the years ≤ 2016 (6.28%) and > 2020 (28.29%), revealing a significant increase in the pooled prevalence of AmpC producing E. coli. Similarly, the pooled prevalence of AmpC producing K. pneumoniae exhibited a marked escalation, transitioning from 5.41% in the period ≤ 2016 to 29.04% in > 2020. According to Antimicrobial Resistance Surveillance in Europe 2022 report, there was an uptick in the percentage of AMR in E. coli rising from 14.9% in 2016 to 15.1% in 2020. The report also included the AMR rates in K. pneumoniae which increased from 31.4% in 2016 to 33.9% in 2020. Percentages of AMR reported for 2020 nevertheless remain at a high level, highlighting the need for further efforts to improve antimicrobial stewardship and IPC [64]. The important point we understood from this finding is that antimicrobial resistant bacteria are increasing from time to time and still a public health concern.

The pooled prevalence of AmpC producing E. coli is 15.32% (95% CI: 12.03, 18.61) with I² = 98.3%, P < 0.001. This finding is different from study conducted in Calgary in which about 96% of E. coli was positive for AmpC [65]. The wide variation might be due to the detection of AmpC producer using phenotypic method, seasonal variability in the occurrence of AmpC producing E. coli isolates (they collected isolates in different season). Seasonal variation is one of the factors affecting the spread of drug resistant bacteria including E. coli and K. pneumoniae which has been shown in different studies [66, 67]. MacFadden et al. showed that there was a temporal distribution in beta-lactamase carriage with a decrease in the prevalence in May and an increase in July [66]. Another study also assessed the incidence of carriage of beta-lactamase producing E. coli and K. pneumoniae in hospitalized patients and found an increasing trend in the incidence of beta-lactamase carriage during summer compared to winter [67]. The other reason for variation of pooled prevalence of AmpC producing E. coli might be the fact that majority of the study participants (83%) were from the community who may have been prone to contact with animals. Several studies have shown that animals may represent a source for dissemination of AmpC encoding genes of E. coli and humans may acquire these genes from animals. Concern arises from evidence of CMY-2–producing isolates in cattle [68], pork [69], poultry [70, 71] and dogs and cats [72]. This is because household pets and animals that provide food can serve as reservoirs for resistant organisms. In contrast, some studies have recognized a role of E. coli that produces AmpC in nosocomial infections [12, 73, 74].

In our study, the predominantly present gene in E. coli was CMY (46.7%). This finding is comparable with previous population-based laboratory surveillance for AmpC producing E. coli in Calgary reported to be (34%) [65]. On the contrary, our finding is higher compared to study done in France (0.09%) [74]. This wide variation might be due to differences in study population, type of specimen, sample size, AmpC detection method, geographic area and the extent of antibiotic use. Among the AmpC beta-lactamase genes, particularly, blaCMY is the most prevalent AmpC gene in isolates of E. coli from companion animals and human [75, 76]. Studies done in Canada, Europe, and America also confirmed that, CMY is the most frequent plasmidic cephalosporinase in E. coli [77, 78].

This is the first systematic review and meta-analysis reporting the pooled prevalence of AmpC producing E. coli and K. pneumoniae in Africa. Though, there is no adequate study on AmpC producers like other beta-lactamases (ESBL, Carbapenemase) in Africa, this study for the first time indicated that AmpC beta-lactamase contributes for the spread of antimicrobial resistance and showed predominant genes in Africa which helps for policy makers and stakeholders to implement strategies for antimicrobial resistance. Additional strength of the study is that it includes various studies from different countries in Africa, diverse target populations, various specimens, alternative detection methods and predominant AmpC genes in Africa. Furthermore, using sensitivity analysis, meta-regression, and subgroup analysis, a detailed summary is provided. The methodological quality of over 90% of the studies was rated as having low risk of bias, ensuring the quality of our findings. The included studies were highly heterogeneous; therefore the results should be interpreted with caution. This is probably not only the result of publication bias, but it might possibly be the result of variations in methodological issues, like variations in sample size, target population, detection method and study settings.

This review has limitations such as inability to assess factors associated with the pooled prevalence of AmpC producing E. coli and K. pneumoniae, heterogeneity of studies included for the estimation of pooled prevalence. Therefore, the random-effects model of Sidik- Jonkman analysis was implemented in the meta-analyses to reduce the influence of study heterogeneity, and subgroup analyses, sensitivity analysis and meta-regression were also performed. Some African countries were not represented in our analysis since there were no eligible studies available.

Conclusion

This study showed a notable increase in the prevalence of AmpC producing E. coli and K. pneumoniae in Africa over the past two decades. Consistent identification of AmpC beta-lactamase is crucial in averting therapeutic failures, assisting the physicians in prescribing the most appropriate antibiotics and contributing to hospital infection control. Sequencing and typing the strains may be required to better understand the genetic relatedness and the molecular epidemiology underlying this resistance mechanism. Furthermore, it is imperative to strengthen the antimicrobial resistance surveillance system and implement an effective antibiotic policy to combat the escalating threat of antibiotic resistance in Africa.

Electronic supplementary material

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Abbreviations

AmpC

AmpC beta-lactamase

AMR

Antimicrobial resistance

CI

confidence interval

CLSI

Clinical Laboratory Standards Institute

CDC

Center for Disease Control

ESBL

Extended Spectrum Beta-Lactamases

ICU

Intensive Care Unit

MDR

Multidrug resistance

MIC

Minimum inhibitory concentration

pAmpC

Plasmid mediated AmpC

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Stata

statistics and data

WHO

World Health Organization

Author contributions

SG conceived and designed the study. SG, EA, and MAB participated in article search, and data extraction. SG, MT, and DG conduct a quality assessment of the included studies and perform the statistical analysis and interpretation of the data. SG drafts manuscript. SG, AG and EA check the validity and monitor the overall process. DG, MT, and HD critically reviewed the manuscript. ZM, HE, BE, OM and AS contributed by reviewing and editing the manuscript. All the authors read and approved the final manuscript.

Funding

There is no specific funding for this research.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.