Abstract
Background
Campylobacter spp. is a significant etiological agent of bacterial gastroenteritis globally. In Burkina Faso (BFA), the actual impact of this pathogen on gastroenteritis is considerably underestimated, primarily due to inadequate surveillance systems.
Objectives
This study aimed to investigate the proportion of Campylobacter species responsible for acute gastroenteritis among patients of all ages in urban and rural areas of BFA, using molecular biology techniques.
Study design & methods
Between 2018 and 2021, faecal specimens were obtained from 1,295 individuals presenting with acute gastroenteritis. These samples underwent screening for the Campylobacter coli/jejuni/lari complex utilizing real-time polymerase chain reaction (PCR) assays. Subsequently, positive samples were subjected to species-level differentiation through the application of species-specific primers.
Results
Campylobacter spp. was detected in 25.0% (324/1,295) of the samples analysed. The majority of positive samples (95%, 308/324) were obtained from children under 5 years of age. Species identification was performed on a subset of 114 isolates, revealing 51 Campylobacter jejuni, 10 Campylobacter coli, and 53 Campylobacter isolates that remained unspeciated.
Conclusions
This study reveals a significant prevalence of Campylobacter species among patients with acute gastroenteritis, with a particularly high incidence observed in children under 5 years of age. Based on these findings, the implementation of routine Campylobacter surveillance in public health laboratories is strongly recommended to better monitor and address this health concern.
Keywords: Bacterial gastroenteritis, Zoonosis, Campylobacter, Burkina Faso, Sub-saharan Africa
Background
Campylobacter is an emerging zoonotic bacterium that has been identified as the leading cause of bacterial gastrointestinal infections in high-income countries and a significant contributor to diarrheal diseases in children under five years old in low- and middle-income countries (LMICs) [1]. Annually, it is estimated to cause between 400 million and 500 million cases of diarrhea worldwide, affecting both developed countries and LMICs [2].
Campylobacter is a spiral-shaped and microaerophilic bacterium that thrives in low-oxygen environments [3, 4]. The Campylobacter genus comprises 18 species, but only a few are significant to human health. Notably, Campylobacter coli (C. coli) and Campylobacter jejuni (C. jejuni) are responsible for over 95% of human Campylobacteriosis cases [1]. Campylobacter spreads to humans primarily through zoonotic transmission, either by direct contact with animal faces or indirectly via contaminated food and water. While animals often carry the pathogen asymptomatically, it causes illness in humans upon ingestion, facilitated by its widespread presence in the environment and food chain [1].
Campylobacter infection is usually mild in adults but can be severe in young children, the elderly, pregnant women, and immunocompromised individuals (AIDS and Cancer), requiring antibiotic treatment [5, 6].
Stool culture is the primary diagnostic method for Campylobacter and antibacterial resistance in LMICs, but it takes 48–72 h, delaying timely antibiotic therapy. Remarkably, Campylobacter is a fastidious bacterium and can enter a Viable But Non-Culturable (VBNC) state under stress, evading detection by standard culture method [7]. Consequently, stool cultures often fail to detect this bacterium, likely underestimating its prevalence [8–10]. Due to culture’s limitations as the gold standard for diagnosing these infections, alternative methods like PCR and Enzyme-Linked Immunosorbent Assays (ELISA) have emerged. Real-time PCR offers highly sensitive detection of Campylobacter species [7, 11].
Studies in West Africa have found Campylobacter prevalence rates between 2.3% and 20.3% among diarrheal outpatients using bacterial culture methods [8, 12, 13]. However, in Burkina Faso (BFA) and many other LMICs, the true incidence of Campylobacter infection in acute diarrhea cases remains undetermined. Unlike Salmonella and Shigella, which are actively monitored as part of surveillance programs for potentially epidemic diarrheal diseases, the national public health laboratories do not routinely test for or diagnose this zoonotic infection. Studies conducted between 2009 and 2019 on outpatients with acute diarrhea in Ouagadougou, BFA, reported prevalences ranging from 0.1 to 2% using culture, PCR, or both methods [8, 14, 15].
As part of the African Network for Improved Diagnosis, Epidemiology and Management of Common Infectious Agents (ANDEMIA) [16], we aimed to investigate the burden of Campylobacter species causing acute gastroenteritis in patients of all ages in urban and rural sentinel sites in BFA. We are also examining demographic factors that contributing to Campylobacter infection.
Methods
We conducted a cross-sectional study in Dano and Bobo-Dioulasso, BFA, between February 2018 to December 2021.
Study population
As part of ANDEMIA, this study examined patients of all ages presenting with acute gastroenteritis at urban and rural sentinel sites in BFA [16]. The primary symptom was acute diarrhea, characterized by the passage of three or more loose or watery stools within a 24-hour period. Patients were not eligible for inclusion if they had chronic diarrhea (persisting beyond 4 weeks) or had been hospitalized for more than 48 h. A total of 1,295 patients were enrolled at Souro Sanou University Hospital in Bobo Dioulasso in the “Hauts-Bassins” region and at Dano, Dissin and surrounding health centers in the “Sud-Ouest” region.
Biological analyses
Nucleic acids were extracted from stool samples or rectal swabs using IndiSpin Pathogen Kit (INDICAL BIOSCIENCE, Germany) according to the manufacturer´s instructions. The purified nucleic acid were then stored at -80 °C. Multiplex real-time PCR was performed to detect the Campylobacter jejuni/coli/lari complex using the FTLyo Bacterial gastroenteritis amplification kit (Siemens Health Care, Luxembourg) on an CFX 96 real-time PCR system (BIO-Rad Laboratories, USA) following the manufacturer’s protocol. The screening PCR was unable to differentiate between the 3 species.
From the 324 Campylobacter-positive samples, a random selection of 114 specimens was chosen for typing. We carried out a multiplex PCR targeting the 16 S rDNA as a control for the Campylobacter genus. For the identification of C. jejuni and C. coli, we used specific hipO and asp primers, respectively, as detailed in Table 1 and previously described [17].The PCR was performed using the QIAGEN Multiplex PCR Kit ( QIAGEN, Germany) in a 25 µL reaction volume containing 12.5 µL 2x Master Mix, 2.5 µL Q-Solution, 1.75 µL RNase-free water, primers (1 µL hipO, 2 µL asp, 0.25 µL 16 S rDNA; all 10 µM) and 5 µL template DNA. Cycling conditions were: 95 °C for 15 min; 35 cycles of 94 °C for 50 s, 57 °C for 90 s, 72 °C for 1 min; final extension at 72 °C for 3 min. PCR products were analyzed by 1.5% agarose gel electrophoresis.
Table 1.
Oligonucleotide primers for C. Coli and C. Jejuni typing
| Primer | Sequence | Expected length | Concentration | Reference |
|---|---|---|---|---|
| hipO-F | GACTTCGTGCAGATATGGATGCTT | 344pb | 0.2µM | [17] |
| hipO-R | GCTATAAC TATCCGAAGAAGCCATCA | |||
| asp-F (CC18F) | GGTATGATTTCTACAAAGCGAG | 500pb | 0.4µM | [17] |
| asp-R (CC519R) | ATAAAAGACTATCGTCGCGTG | |||
| 16 S-F | GGAGGCAGCAGTAGGGAATA | 1062pb | 0.5µM | [17] |
| 16 S-R | TGACGGGCG GTGAGTACAAG |
Statistical analyses
Statistical analyses were conducted using Stata/MP 15.1 (StataCorp, Texas, USA). Proportions of Campylobacter positivity, defined as positive PCR result, were compared using the Pearson’s Chi-square or Fisher’s exact tests. Logistic regression models were used to investigate factors associated with Campylobacter positivity. Variables with p-values < 0.2 in the bivariable models were included in the full multivariable model. A top-down approach was then used to construct the final multivariable model. P-values < 0.05 were considered statistically significant.
Results
In this study, males comprised 55.3% (716/1,295) of the participants, while children under 5 years old accounted for 88.7% (1,148/1,295). The majority of participants (75.7%; 980/1,295) resided in rural areas. Out of 1,295 samples tested, 324 (25.0%) were positive for Campylobacter spp. Among these Campylobacter cases, 95% (308/324) were children under 5 years old, 59.2% (192/324) primarily drank well water and 88.2% (286/324) lived in rural areas (Table 2).
Table 2.
Demographic and clinical characteristics of study participants according to Campylobacter spp. detection in stool samples
| Symptomatic patients | Campylobacter’s status | ||
|---|---|---|---|
| Positive N (%) | Negative N (%) | p-value* | |
| Gender ( n = 1,293)** | |||
| Male | 181 (25) | 535 (75) | 0.838 |
| Female | 143 (25) | 434 (75) | |
| Age groups (n = 1,295) | |||
| < 5 years | 308 (27) | 840 (73) | < 0.001* |
| 5–15 years | 6 (13) | 41 (87) | |
| > 15 years | 10 (10) | 90 (90) | |
| Water type (n = 1,295) | |||
| tap or mineral water | 84 (24) | 268 (76) | < 0.001* |
| Well water | 192 (30.5) | 437 (69.4) | |
| Other*** | 48 (15%) | 266 (85) | |
| Residency (n = 1,295) | |||
| Urban | 38 (12) | 277 (88) | < 0.001* |
| Rural | 286 (29) | 694 (71) | |
| Fever (n = 1,295) | |||
| Yes | 219 (22.5) | 752 (77.4) | < 0.001* |
| No | 105 (32.4) | 219 (67.6) | |
| Nausea or vomiting (n = 1,290)** | |||
| Yes | 111 (20) | 441 (80) | < 0.001* |
| No | 212 (29) | 526 (71) | |
| Diarrhea (n = 1,295) | |||
| Yes, with blood (dysentery) | 27 (26.4) | 75 (73.5) | 0.724 |
| Yes, without blood | 297 (25) | 896 (75) | |
| Weight loss (n = 1,281)** | |||
| Yes | 156 (25) | 462 (75) | 0.691 |
| No | 161 (24) | 502 (76) | |
| Comorbidities (n = 1,282)** | |||
| Yes | 0 (0) | 15 (100) | 0.024* |
| No | 323 (25.5) | 944 (74.5) | |
| Rapid malaria test (n = 1,180)** | |||
| Negative | 255 (25) | 758 (75) | 0.074 |
| Positive | 53 (32) | 114 (68) | |
| Current hospitalisation (n = 1,291)** | |||
| Yes | 134 (20) | 541 (80) | < 0.001* |
| No | 189 (31) | 427 (69) | |
| Contact with animal (n = 1,295) | |||
| Yes | 133 (26) | 372 (74) | 0.382 |
| No | 191 (24) | 599 (76) | |
*p-value < 0.05 (Pearson χ2 test); **less than 1,295 mean that for some patients there were no information; ***all types of water, including river water and rainwater; Percentage (%) are calculated per row.
After conducting multivariable analysis, several factors remained statistically significant in their association with Campylobacter detection. These key factors included the patient’s place of residence, age group, and whether they had a history of fever (Table 3). Patients from rural areas had twice (OR = 2; CI95% = [1.25–3.19]; p = 0.004) the odds of having Campylobacter detected than those living in urban areas. In addition, children under 5 years old had nearly three times (OR = 2.96; CI95% = [1.39–6.30]; p = 0.005) the odds of having Campylobacter detected compared to individuals aged 15 years old or older.
Table 3.
Sociodemographic and clinical factors associated with Campylobacter infections
| Characteristics | Bivariable | Multivariable | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P-value* | aOR | 95% CI | p-value* | |
| Residence | ||||||
| Urbain | Reference | Reference | ||||
| Rural | 3 | 2.08–4.33 | < 0.001* | 2.00 | 1.25–3.19 | 0.004* |
| Gender | ||||||
| Male | Reference | |||||
| Female | 0.97 | 0.75–1.25 | 0.838 | |||
| Age (years) | ||||||
| ≥ 15 | Reference | Reference | ||||
| 5–15 | 1.31 | 0.44–3.86 | 0.616 | 1.11 | 0.33–3.72 | 0.859 |
| < 5 | 3.3 | 1.69–6.42 | < 0.001* | 2.96 | 1.39–6.30 | 0.005* |
| Water type | ||||||
| tap or mineral water | Reference | Reference | ||||
| Well water | 1.4 | 1.04–1.88 | 0.02 | 1.25 | 0.88–1.77 | 0.209 |
| Other | 0.57 | 0.38–0.85 | 0.006 | 0.90 | 0.56–1.45 | 0.684 |
| Ongoing fever | ||||||
| No | Reference | |||||
| Yes | 0.93 | 0.66–1.31 | 0.696 | |||
| History of fever | ||||||
| No | Reference | Reference | ||||
| Yes | 0.61 | 0.46–0.80 | < 0.001* | 0.68 | 0.49– 0.96 | 0.030* |
| Abdominal pain | ||||||
| No | Reference | |||||
| Yes | 0.94 | 0.70–1.25 | 0.681 | |||
| Nausea/vomiting | ||||||
| No | Reference | Reference | ||||
| Yes | 0.62 | 0.48–0.81 | < 0.001* | 0.81 | 0.60–1.09 | 0.181 |
| Diarrhea | ||||||
| Without blood | Reference | |||||
| With blood | 1.08 | 0.68–1.71 | 0.724 | |||
| Weight loss | ||||||
| No | Reference | |||||
| Yes | 1.05 | 0.81–1.35 | 0.691 | |||
| Rapid malaria test | ||||||
| Negative | Reference | Reference | ||||
| Positive | 1.38 | 0.96–1.97 | 0.074 | 1.15 | 0.79–1.68 | 0.456 |
| Current hospitalization | ||||||
| No | Reference | Reference | ||||
| Yes | 0.55 | 0.43–0.72 | < 0.001* | 0.97 | 0.69–1.38 | 0.903 |
| Contact with animal | ||||||
| No | Reference | Reference | ||||
| Yes | 1.12 | 0.86–1.44 | 0.382 | |||
*p-value < 0.05 (Wald’s test); OR: Odds Ratio; aOR: adjusted Odds Ratio; CI: Confidence Interval
Of the 324 positive samples, 114 (35% = 114/324) was randomly selected for species identification. Among these, 106 were from children under 5 years old and 102 were from patients living in rural areas. Our analysis revealed 10 C. coli (8.8%), 51 C. jejuni (44.7%), and 53 non-typable samples (46.5%), with the latter requiring further investigation. All C. coli cases and 90.2% of C. jejuni cases were found in rural patients.
Discussion
This study identified the presence of Campylobacter, predominantly linked to human illness in both urban and rural areas of BFA. Throughout the study period, we observed a Campylobacter spp. infection prevalence of 25.0%. The age group most affected was children under five years old. This result reveals the importance of the circulation of this bacterium in BFA, especially among children under 5 years old. A systematic review and meta-analysis conducted in sub-Saharan Africa in 2021 reported a cumulative prevalence of Campylobacter spp. at 10.2% in patients suffering from diarrhoea with a high prevalence observed in children under 15 years. However, this finding was not statistically significant [18]. In our study, we reported a significant positive correlation between rural residence and Campylobacter positivity. This association may be attributed to several factors including the close proximity to livestock; the limited access to clean drinking water and food hygiene in these regions [19]. However, our data did not establish a link between positive Campylobacter results and the presence of domestic animals.
Previous studies carried out in BFA using culture as a detection technique, reported that the prevalences of Campylobacter infection ranged from 1 to 2% [8, 10]. In contrast, our study found a significantly higher prevalence of Campylobacter compared to these earlier findings in BFA. This could be attributed to the differences in the study populations and techniques used to identify Campylobacter. Sangaré et al. reported a prevalence of 2.3% among outpatients in urban areas from 2006 to 2008, using culture technique for the Campylobacter identification [8]. Also, a study carried out by Sawadogo et al., from February 5th to March 9th 2013, reported a prevalence of 1% from outpatients in Ouagadougou (urban area) using multiplex Real-Time PCR. However, it is important to note that there were differences in the detection kits utilized in their study compared to ours [14]. The application of molecular biology techniques for identifying Campylobacter allows for a more accurate assessment of infection prevalence due to its high sensitivity, as demonstrated in previous studies, when compared to traditional culture method [11, 20]. C. jejuni emerged as the most prevalent species identified, accounting for 51 out of 114 cases, followed by C. coli, which was identified in 10 out of 114 cases. These findings are consistent with previous studies conducted in humans [6, 8, 21, 22]. Nevertheless, 53 out of 114 Campylobacter species (46%) remained unidentified. This finding is consistent with the results of Sangaré et al., who, after identifying the species C. jejuni, C. coli, and C. upsaliensis, reported that 30.9% of their samples were also unidentified [8].
Considering the zoonotic nature of Campylobacter infections and the significantly higher prevalence observed in our study compared to existing literature from BFA, it is crucial to implement monitoring measures for this bacterium and its antibiotic resistance. In many regions globally where monitoring of Campylobacter resistance to antibiotics has been implemented, significant resistance levels to erythromycin and fluoroquinolones, common antibiotics for treating Campylobacteriosis have been observed [20, 22]. However, in BFA, despite the widespread consumption of poultry, laboratories do not routinely test for Campylobacter in cases of diarrhea, which could hinder effective monitoring and treatment strategies.
The limitation of relying exclusively on molecular detection methods is that they do not offer a comprehensive profile of antibiotic resistance. However, the rapid identification of the bacterium can significantly assist clinicians by enabling them to initiate targeted treatment with a specific antibiotic. This approach not only helps prevent the spread of the bacterium but also reduces the unnecessary use of antibiotics.
Conclusion
Acute diarrhoea is a significant public health concern, with Campylobacter being one of the most prevalent bacterial causes of this condition globally. Our research highlights the epidemiological burden of Campylobacter in Burkina Faso, a region with limited existing data. The findings indicate that Campylobacter infections are particularly prevalent among children and in rural areas. Further investigation is required to explore the specific rural conditions that promote infection, particularly regarding interactions with animal reservoirs. Although molecular techniques are effective for diagnosing Campylobacter infections, comprehensive studies, including high-throughput sequencing, are essential for characterizing non-typable strains and evaluating their antimicrobial resistance profiles.These findings point out the critical importance of incorporating both phenotypic and molecular detection methods for Campylobacter into the national infectious disease surveillance system. Additionally, given the widespread use of antibiotics in various animal production systems, it is essential to remain vigilant about the potential for Campylobacter to develop resistance to these antibiotics.
Acknowledgements
We sincerely acknowledge the contributions of all members of the ANDEMIA consortium and the dedicated healthcare workers at the sentinel sites. We grateful to the funding body that facilitated, the German Federal Ministry of Education and Research (BMBF; Grant Number 01KA1606 and Grant Number 01KI2047). We extend our heartfelt thanks to all individuals who contributed to the development of this work but are not listed as authors. Your invaluable support and insights have greatly enriched this project.
Abbreviations
- 16S-F
16 S-forward
- 16S-R
16 S-Reverse
- ANDEMIA
African Network for the Improvement of Diagnosis, Epidemiology and Management of Common Infectious Agents
- asp-F
asp-forward
- asp-R
asp-Reverse
- BFA
Burkina Faso
- bp
Base Pair
- C. coli
Campylobacter coli
- C. jejuni
Campylobacter jejuni
- C. upsaliensis
Campylobacter upsaliensis
- CHUSS
University Hospital of Souro Sanou
- CI
Confidence Interval
- ELISA
Enzyme-Linked Immunosorbent Assays
- FTD
Fast Track Diagnostics
- GI
Gastrointestinal Infection
- hipO-F
hipO-forward
- hipO-R
hipO-Reverse
- LMIC
Low and Middle Income Countries
- OR
Odd Ratio
- p
p-value
- PCR
Polymerase Chain Reaction
- VBNC
Viable But Non-Culturable
Author contributions
Conceptualization: SO, GS, TE, FHL, ASO, AP; Methodology: AORB, AZ, SO, GS, TE, FHL, ASO, EB; Formal analysis: AORB, AZ, GK, EB; Investigation: AORB, AZ, NFK, EB; Resources: AP, SAS, AO, MM, ASO; Data curation: AORB, NFK, EB; Writing – Original Draft: AORB, NFK, AZ, ASO; Writing – Review & Editing: AORB, NFK, AZ, GS, TE, AO, GK, MM, EB, ASO; Visualisation: AORB, NFK, AZ, GS, TE, EB, ASO; Supervision: AZ, EB, ASO; Funding acquisition: GS, TE, FHL, ASO.
Funding
This study was funded by the German Federal Ministry of Education and Research (BMBF; grant number 01KA1606) and supported by the German Federal Ministry of Health through the Partnership in Postgraduate Education initiative of Global Health Protection Programme (https://ghpp.de/en/projects/ppe/).
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study adheres to the tenets of the Declaration of Helsinki, as well as national legislation and ethical standards. This study was Approved by the Burkina Faso’ National Health Research Ethics Committee (Approval Decision No.2017-5-057). All Participants, parents or guardians of children enrolled, provided informed consent.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.