Abstract
Background
The exploratory study assessed trends in the abundance of CTX-M-type extended spectrum beta-lactamase (ESBL) and vancomycin-resistance genes vanA and vanB in the stool samples of German soldiers and police officers returning from predominantly tropical deployments next to the common diarrheagenic Escherichia (E.) coli pathovars enteropathogenic Escherichia coli (EPEC), enterotoxigenic E. coli (ETEC) and enteroaggregative E. coli (EAEC)) as well as rarely imported Vibrio spp. between 2006 and 2024.
Methods
Surveillance was performed applying real-time polymerase chain reaction and results were stratified by World Health Organization region of deployment as well as by deployment period. For the latter, the study interval was divided into three pre-COVID-19-pandemic periods, the COVID-19-pandemic period and the post-COVID-19-pandemic period. Averaged prevalences were used as references.
Results
In stool samples of 1817 deployed German soldiers and 117 police officers, averaged prevalences were 47.9% and 24.8% for the ESBL-type beta-lactamase blaCTX-M, 30.2% and 14.5% for vanB, 9.0% and 17.9% for EPEC, 3.4% and 12.8% for ETEC, 4.0% and 3.4% for EAEC as well as 2.0% and 3.4% for Vibrio spp., respectively. While resistance genes peaked during early deployments, maximum prevalences for enteropathogens were seen later.
Conclusions
The assessment suggested time- and region-dependence of the assessed parameters.
Introduction
As defined by the World Health Organization (WHO) [1], antimicrobial resistance is “one of the top global health and development threats”. Historically, the military as well as military deployments have a relevant share in the acquisition, import and spread of microorganisms showing increased antimicrobial resistance (AMR) [2]. The relevance and dimension of the topic was early recognized by the US American armed forces in response to the experience of their military engagement in the Middle East and led to the implementation of a well-funded large-scale network-based multi-drug-resistance surveillance campaign called “Antimicrobial Resistance Monitoring and Research Program” more than 10 years ago [3, 4]. Also, large-scale molecular epidemiological assessments of resistant bacterial isolates based on next generation sequencing were early established by the US armed forces medical service [5].
Focusing on the situation in German soldiers, a previous culture-based AMR surveillance approach for German soldiers during post-deployment medical assessments [6, 7] has so far only shown low to moderate enteric colonization rates with ESBL-positive Escherichia coli. ESBL prevalence was unevenly distributed. In particular, deployment to tropical high endemicity settings and living under the local conditions of the deployment site, e.g., in case of United Nations (UN) observers, were observed as risk factors for increased prevalence compared to other deployment settings. Apart of ESBL-positive Enterobacterales, enteric colonization with vancomycin-resistant enterococci was found to be virtually negligible in German soldiers after their return from deployment in the culture-based approach [6].
In contrast to these previous works [6, 7], the presentation of molecular AMR surveillance results in stool samples of German deployment returnees was the aim of the here presented study. Thereby, the focus was on CTX-M-type ESBL genes, which were shown to be the major and predominant cause of third generation cephalosporin-resistance in Enterobacterales previously isolated from German deployment returnees [7], and the vanA and vanB genes as molecular markers for vancomycin-resistant enterococci. CTX-M-type ESBL beta-lactamases are the quantitatively dominating molecular determinants of ESBL-type beta-lactam resistance, which made them of interest for the here-described assessment. Thereby, the “CTX” element of the abbreviation stands for the third-generation cephalosporine ceftriaxone, which is a marker substance for ESBL-type resistance, while the letter “M” stands for the town Munich as the place of its first description [8]. The above-mentioned van genes mediate glycopeptide resistance and the acronyme “van” stands for vancomycin, a glycopeptide drug commonly used for the treatment of several severe human infections with Gram-positive bacteria. In particular, vanA and vanB genes can occur in the enterococcal species E. faecalis and E. faecium, which are the enterococci most frequently associated with infections in human patients. This is why the genes were considered as interesting for the here-provided assessment. As detailed elsewhere, both ESBL-positive Enterobacterales and vancomycin-resistant enterococci are harmless in case of mere enteric colonization but can cause difficult-to-manage infections in case of translocation to sterile compartments [7, 9]. Such infections can comprise bacteremia and even sepsis but also urinary tract infections due to both Enterobacterales and enterococci, less frequently, also infections of primarily sterile compartments like joints or cardiac valves [7, 9]. While young, healthy and immunocompetent soldiers are rarely affected apart from injury- or war wound-induced translocation risks to primarily non-sterile body compartments, increased infection risks may result from transmission events to severely ill and immunosuppressed individuals in close contact with colonized soldiers. Difficult-to-treat infections can then arise if causative bacterial agents are associated with genes associated with reduced susceptibility towards commonly used antibiotic drugs.
For beta-lactamase genes [10] and even more for vancomycin resistance-associated genes [9], previous studies had shown superior sensitivity of PCR compared to traditional culture-based detection with partly considerable influence of pre-analytic conditions. Molecular AMR detections were associated with WHO regions of deployment and colonization dynamics along the timeline were assessed. Special situations like the period of the COVID-19 pandemic were also considered, because a previous assessment by our group had suggested likely effects of associated infection prevention and control protocol enforcement on the occurrence of infectious gastroenteritis and thus on the fecal-oral transmission route in deployed soldiers [11]. From Germany as the home country of the deployed soldiers and police officers, reliable surveillance data on the abundance of CTX-M-type ESBL, vanA and vanB genes in stool samples of healthy individuals are not available. This makes specific risk assessments focusing on these parameters unfeasible so far.
For comparison purposes, results of the molecular detection dynamics of frequent and rare gastrointestinal pathogens were used. This was done to assess whether colonization with resistant bacteria shows comparable or discrepant prevalence patterns compared to infections with enteric pathogens on deployment. As pathogens frequently detected in the gut of deployed soldiers, not only in the United Kingdom (UK) [12] and in the USA [13–16] but also in Germany [17], enteropathogenic (EPEC), enterotoxigenic (ETEC) and enteroaggregative (EAEC) E. coli were chosen. EPEC and EAEC were primarily included as indicators of poor hygiene conditions. As detailed elsewhere [18], EPEC attaches on intestinal epithelial cells and causes lesions there associated with clinical diarrhea. However, EPEC-induced clinically apparent diarrhea primarily occurs in children, while adults are usually asymptomatically colonized. EAEC [18] is characterized by the expression of aggregative adherence fimbriae, facilitating aggregative adhesion on enterocytes. EAEC's etiological relevance for travelers' diarrhea has recently been controversially debated, because a large-sized epidemiological study has shown virtually indistinguishable proportions of diarrhea in individuals with and without EAEC detections in their stool samples [19]. ETEC expresses genes coding for heat-stable and heat-labile enterotoxins [18] and is considered as a major cause of travelers' diarrhea. In contrast to the frequently detected E. coli described above, Vibrio spp. were rarely reported in stool samples of soldiers apart from outbreak scenarios [20, 21] in spite of the introduction of cholera in Haiti by UN peacekeepers more than a decade ago [22] and also in spite of the undeniable historic relevance of Vibrio spp. in previous armed conflicts like, e.g., the American Civil War [23]. Only round about a dozen among about 100 described Vibrio species are of medical relevance for human individuals. Toxin-expressing Vibrio cholerae isolates causes cholera, a life-threatening disease associated with diarrhea-associated massive loss of water. Non-cholera Vibrio spp. with etiological relevance for human individuals like Vibrio haemolyticus, Vibrio alginolyticus and Vibrio vulnificus can cause diarrhea as well. In case of V. vulnificus, however, severe wound infections with the potential of progression towards life-threatening systemic infections are considered as the more troublesome consequence of an infection [24].
In Germany as the home country of the deployed soldiers and police officers, prevalence for EPEC, ETEC, EAEC is not systematically assessed by surveillance programs. For Vibrio spp., only cases of cholera are obligatorily recorded with few imported cases per year in Germany [25]. These enteric pathogen-based control groups were added to evaluate a potential differential influence of the impact of periods of aggravated infection prevention and control schemes, e.g., during the COVID-19 pandemic, on the abundance of enteric pathogens and on the enteric resistome. As mentioned above, the COVID-19-pandemic was included as a separate stratum, because a previous assessment had indicated a reduced number of diarrhea events in deployed German soldiers during this period [11]. This finding suggests a potential impact of the pandemic-related enforcement of stricter infection prevention and control procedures on fecal-oral transmission events among deployed soldiers, an effect which could be of relevance for the here-assessed enteric colonization or infections events as well. Summarized, the study was explorative and hypothesis forming, as external reliable surveillance data for comparison purposes were not available. By doing so, a data-based holistic background for infection control and prevention assessments should be provided. Without surveillance data, it is difficult to assess effects of infection control and prevention procedures, especially if colonization with microorganisms which are not necessarily associated with clinically apparent infections is affected.
Methods
Study design
The study was conducted as a longitudinal surveillance assessment on diagnostic stool samples of German military and police officers returning from deployment. The stool samples were provided starting in 2006 until the end of June 2024. Returned police officers were assessed beginning in 2015 based on German interministerial administrative assistance (“Amtshilfe”). The assessment was stratified by deployment side on WHO (World Health Organization) region level and deployment period. In deviation from the WHO region definitions, however, South Sudan was counted as part of the Eastern Mediterranean Region. The reason is that South Sudan did not yet exist as a sovereign state when the assessment was started in 2006. To avoid data inconsistency, South Sudan was assigned to the Mediterranean Region together with Sudan, to which it politically belonged until its independence in 2011. Focusing on deployment periods, the overall assessment period was sub-stratified into the period before 2010, the two five-year-periods 2010–2014 and 2015–2019, the COVID-19-pandemic period 2020–2022, and the post-pandemic period after 2022. The year 2023 was defined as part of the post-pandemic period for this assessment, because WHO declared the formal end of the pandemic at the beginning of May 2023, thus assigning the larger share of this year to the post-pandemic period. When timelines of individual deployments included time periods in two different assessment strata, the deployment was assigned to the stratum with the larger share.
Study population
The study population consisted of German soldiers and police officers providing stool samples for post-deployment diagnostic assessments between 2006 and the end of June 2024 after predominantly but not exclusively tropical deployments. Information on age, sex and geographic deployment sites were provided for the assessment.
Molecular diagnostics
The assessed stool samples were analyzed applying in-house real-time PCR for samples collected until 2022, while the diagnostic workflow was changed to commercial real-time PCR in 2022. Regarding the in-house approach, nucleic acids from the analyzed stool samples were extracted using the QIAamp stool DNA mini kit (Qiagen, Hilden, Germany) and the eluates were stored at −80 °C afterwards. Using previously published oligonucleotides for the specific PCR targets, a 7-plex real-time PCR assay run on a LightCycler Pro device (Roche, Basel, Switzerland) comprised the ESBL gene blaCTX-M [26], the vancomycin resistance-mediating genes vanA and vanB [27, 28], the diarrheagenic E. coli pathovars EPEC (enteropathogenic E. coli, eae gene and EAF plasmid sequence on the same fluorescence channel), ETEC (enterotoxigenic E. coli, eltB gene and estB genes on the same fluorescence channel) and EAEC (enteroaggregative E. coli, aatA gene) [17] as well as specific sequences of important Vibrio (V.) spp. with etiological relevance for human patients (both the sodB and ctx genes of V. cholerae, the vvha gene of V. vulnificus and the toxR gene of Vibrio parahaemolyticus on the same fluorescence channel) [29]. As detailed elsewhere, specificity estimations ranged between 94% and 100% for the applied assays [17, 24–29]. Details on the used oligonucleotides are provided in the supplementary Table 1 for readers interested in reproducing the assessments. The reaction mix consisted of HotStarTaq master mix (Qiagen, Hilden, Germany), a Mg2+ concentration of 6.0 mM, primer and probe concentrations as indicated in the supplementary Table 1 and 2 µL sample DNA eluate in total reaction volumes of 20 µL. Run conditions comprised initial denaturation at 95 °C for 15 min followed by 45 cycles of denaturation at 95°C for 15 s, annealing at 60 °C for 60 s and elongation at 72 °C for 30 s, followed by subsequent hold at 40 °C for 20 s. Typical sigmoid-shaped amplification curves were accepted as positive real-time PCR signals irrespective of the recorded cycle threshold value. In each run, quality control comprised the inclusion of plasmid-based positive controls (sequence inserts in pEX-A128 vector plasmid backbones (eurofins Genomics, Luxembourg) provided in the supplementary Table 1) and PCR-grade water-based negative controls. Ten-fold dilution steps of the positive control plasmids were also used to identify the technical detection threshold of each parameter (provided in the supplementary Table 1), which is necessary to decide how many target DNA copies will go undetected by the PCR approach. Further, PCR-inhibition in the samples was controlled with an in-house real-time PCR assay targeting a sequence fragment of Phocid Herpes Virus (PhHV) as described in detail elsewhere [30] in the diagnostic process. Starting in 2022, commercial real-time PCR assessment with assays accredited by the manufacturer for in-vitro diagnostic use in Europe replaced the in-house assays. In detail, after nucleic acid extraction using the STARMag 96 × 4 Universal Cartridge kit (SeeGene, Seoul, Republic of Korea) on a Starlet extraction automate (SeeGene) as described by the manufacturer, real-time PCR results were obtained with the SeeGene assays Allplex GI Panels 1 (for Vibrio spp.) and 2 (for EPEC, ETEC, and EAEC) as well as Allplex Entero-DR (for blaCTX-M, vanA, and vanB) on a Bio-Rad cycler CFX 96 (Bio-Rad Laboratories Inc., Hercules, CA, USA), again according to the manufacturer's instructions.
Statistics
The obtained real-time PCR surveillance data were mainly descriptively assessed. In line with the exploratory and hypothesis-forming study design, analytical statistical analyses were restricted to simple operations. In detail, averaged values over all deployment periods and sites for soldiers and police officers were used as references. Fisher's exact test or – in case of higher numbers – the Chi-squared test were used for binary results and the non-parametric Mann-Whitney U test in case of numeric results in order to compare deployment-specific findings for soldiers and police officers with those references. More complex statistical operations were not performed, keeping in mind the conditional dependence of the compared populations. P-values <0.05 were considered as significant without corrections for multiple testing with, e.g., the Holm-Bonferroni method [31].
Ethics
Anonymized retrospective assessment of surveillance data from the deployment returnees without informed consent was covered by the ethical clearance (reference number WF-019/17) from the Ethics Committee of the Medical Association of Hamburg, Germany, in line with German national laws. The assessment was performed in line with the Declaration of Helsinki and all its amendments.
Results
Population characteristics
Data from a total of 1817 soldiers and 117 police officers were included in the assessment; details are provided in the Tables 1–5. The deployed forces were unevenly distributed over the assessed deployment periods and deployment areas as indicated in Table 5. The soldiers were generally younger than the police officers with a mean age (± standard deviation (SD)) of 36.1 (±10.4) years of age compared to 46.7 (±9.8) years, respectively. Generally, there was a tendency towards higher age of soldiers in later deployment periods which became prominent in the period of the COVID-19 pandemic and did not vanish afterwards. For police forces, this trend was not detectable. Details of this shift are provided in the Table 2, the raw data are in the Table 1. African deployments were continuously associated with older deployed soldiers starting from 2010. For both the assessed soldiers and police forces, there was a quantitative dominance of male individuals on deployment, which was even more pronounced for police officers than for military personnel. The female : male-ratio was 1 : 10.8 for the assessed soldiers and 1 : 15.0 for the police forces. For soldiers, there was a particular predominance of males deployed to the Eastern Mediterranean Region until 2010, while slightly more military females were on the African deployments starting in 2015. For other deployment sites and periods as well as for police forces, no specific pattern was observed. In general, over the assessment periods, there was a tendency towards a higher proportion of deployed female individuals compared to the early assessment periods (Tables 3 and 4).
Temporal and spatial age distribution of deployed forces (raw data)
Assessment period | Population | African Region, mean value (± SD) | Region of the Americas, mean value (± SD) | South-East Asia Region, mean value (± SD) | European Region, mean value (± SD) | Eastern Mediterranean Region, mean value (± SD) | Western Pacific Region, mean value (± SD) | Multiple WHO regions, mean value (± SD) | Total, mean value (± SD) |
Age (years) | |||||||||
Before 2010 | Soldiers | 33.1 (±8.8) | 55 (−) | n.a. | 31.5 (±8.8) | 32.4 (±8.8) | n.a. | n.a. | 32.4 (±8.5) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2010–2014 | Soldiers | 37 (±11.7) | 33.2 (±13.1) | 33.1 (±16.1) | 26 (±2.2) | 37.3 (±13.3) | 27 (±8.7) | 38.3 (±18.1) | 36.7 (±12.8) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2015–2019 | Soldiers | 37.7 (±6.3) | 38.3 (±17.1) | 31.1 (±9.5) | 36.5 (±9.5) | 28.9 (±13.8) | 27 (−) | 43.2 (±14.7) | 34.4 (±13.3) |
Police officers | 41.4 (±6.1) | n.a. | n.a. | 45.9 (±8.2) | 42.3 (±11.7) | n.a. | n.a. | 43.8 (±9) | |
2020–2022 | Soldiers | 41.5 (±11.0) | 38 (±17.0) | n.a. | 45 (−) | 46.7 (±9.7) | 53 (±7.1) | n.a. | 42.6 (±10.9) |
Police officers | 48.2 (±9.5) | n.a. | n.a. | 45.5 (±10.2) | 50.5 (±0.7) | 48 (−) | n.a. | 47 (±9.5) | |
After 2022 | Soldiers | 37.6 (±8.9) | 26 (−) | n.a. | 47.3 (±12.8) | 27 (−) | 26 (−) | 46.8 (±10.4) | 40.7 (±10.8) |
Police officers | 48.2 (±10.5) | n.a. | n.a. | 47.3 (±11.8) | 48.0 (±10.5) | 57 (−) | 51.0 (±5.6) | 48.4 (±10.3) | |
Total | Soldiers | 37.9 (±10.0) | 39.1 (±19.9) | 31.9 (±12.2) | 32.1 (±9.5) | 35.4 (±9.7) | 35.5 (±15.0) | 43.8 (±13.6) | 36.1 (±10.4) |
Police officers | 47.0 (±10.1) | n.a. | n.a. | 46.0 (±9.7) | 45.9 (±10.3) | 52.5 (±6.4) | 51.0 (±5.6) | 46.7 (±9.8) |
SD = standard deviation, n.a. = not applicable.
Temporal and spatial age distribution of deployed forces. Mean values over all deployed military and police forces (reference values) are shown in yellow. Lower mean values (P < 0.05) are indicated in green, higher mean values (P < 0.05) in red. No color is used in case of lacking significance for difference or lacking values
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SD = standard deviation, n.a. = not applicable. ref. = reference. n.e. = not estimable. P = significance level.
Temporal and spatial distribution of the female: male-ratio of deployed forces (raw data)
Assessment period | Population | African Region, n : n | Region of the Americas, n : n | South-East Asia Region, n : n | European Region, n : n | Eastern Mediterranean Region, n : n | Western Pacific Region, n : n | Multiple WHO regions, n : n | Total, n : n |
Female : male-ratio | |||||||||
Before 2010 | Soldiers | 1 : 9.9 | 0 : 1.0 | n.a. | 1 : 74.0 | 1 : 25.4 | n.a. | n.a. | 1 : 22.9 |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2010–2014 | Soldiers | 1 : 14.4 | 1 : 5.5 | 1 : 4.5 | 1 : 4.0 | 1 : 54.7 | 3.0 : 0 | 1 : 4 | 1 : 14.8 |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2015–2019 | Soldiers | 1 : 5.9 | 1 : 3.8 | 1 : 1.3 | 1 : 1.7 | 1 : 27.0 | 0 : 1.0 | 0 : 5 | 1 : 7.3 |
Police officers | 1 : 9.0 | n.a. | n.a. | 1 : 14.0 | 0 : 6 | n.a. | n.a. | 1 : 14.5 | |
2020–2022 | Soldiers | 1 : 5.2 | 0 : 2.0 | n.a. | 0 : 1.0 | 1 : 10.5 | 0 : 2.0 | n.a. | 1 : 6.1 |
Police officers | 1 : 16.0 | n.a. | n.a. | 1 : 5.0 | 0 : 2 | 0 : 1.0 | n.a. | 1 : 8.5 | |
After 2022 | Soldiers | 1 : 4.6 | 1.0 : 0 | n.a. | 1 : 6.0 | 0 : 1.0 | 0 : 1.0 | 1 : 3.2 | 1 : 4.0 |
Police officers | 1 : 8.7 | n.a. | n.a. | 1 : 3.5 | 1 : 5.0 | 0 : 1.0 | 0 : 3.0 | 1 : 7.0 | |
Total | Soldiers | 1 : 7.0 | 1 : 4.1 | 1 : 1.9 | 1 : 23.4 | 1 : 27.9 | 1 : 1.3 | 1 : 4.0 | 1 : 10.8 |
Police officers | 1 : 12.8 | n.a. | n.a. | 1 : 6.0 | 1 : 13.0 | 0 : 2.0 | 0 : 3.0 | 1 : 15.0 |
n = number, n.a. = not applicable.
Temporal and spatial distribution of the likelihood of deployed forces for being female compared to the totals of deployed soldiers and police officers (references). Higher odds ratios values are indicated in green, lower ones in red. No color is used in case of lacking significance or lacking values
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n = number, n.e. = not estimable. ref. = reference. P = significance level.
Temporal and spatial distribution of deployment sites
Assessment period | Population | African Region, n/n (%) | Region of the Americas, n/n (%) | South-East Asia Region, n/n (%) | European Region, n/n (%) | Eastern Mediterranean Region, n/n (%) | Western Pacific Region, n/n (%) | Multiple WHO regions, n/n (%) | Total, n/n (%) |
Deployment sites | |||||||||
Before 2010 | Soldiers | 120/720 (16.7%) | 1/41 (2.4%) | 0/29 (0%) | 150/171 (87.7%) | 422/809 (52.2%) | 0/7 (0%) | 0/40 (0%) | 693/1817 (38.1%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2010–2014 | Soldiers | 139/720 (19.3%) | 13/41 (31.7%) | 11/29 (37.9%) | 5/171 (2.9%) | 167/809 (20.6%) | 3/7 (42.9%) | 10/40 (25.0%) | 348/1817 (19.2%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2015–2019 | Soldiers | 305/720 (42.4%) | 24/41 (58.5%) | 18/29 (62.1%) | 8/171 (4.7%) | 196/809 (24.2%) | 1/7 (14.3%) | 5/40 (12.5%) | 557/1817 (30.7%) |
Police officers | 10/56 (17.9%) | n.a. | n.a. | 15/42 (35.7%) | 6/14 (42.9%) | 0/7 (0%) | 0/3 (0%) | 31/117 (26.5%) | |
2020–2022 | Soldiers | 106/720 (14.7%) | 2/41 (4.9%) | 0/29 (0%) | 1/171 (0.6%) | 23/809 (2.8%) | 2/7 (28.6%) | 0/40 (0%) | 134/1817 (7.4%) |
Police officers | 17/56 (30.4%) | n.a. | n.a. | 18/42 (42.9%) | 2/14 (14.3%) | 1/2 (50.0%) | 0/3 (0%) | 38/117 (32.5%) | |
After 2022 | Soldiers | 50/720 (6.9%) | 1/41 (2.4%) | 0/29 (0%) | 7/171 (4.1%) | 1/809 (0.1%) | 1/7 (14.3%) | 25/40 (62.5%) | 85/1817 (4.7%) |
Police officers | 29/56 (51.8%) | n.a. | n.a. | 9/42 (21.4%) | 6/14 (42.9%) | 1/2 (50.0%) | 3/3 (100%) | 48/117 (41.0%) | |
Total | Soldiers | 720 (100%) | 41 (100%) | 29 (100%) | 171 (100%) | 809 (100%) | 7 (100%) | 40 (100%) | 1817 (100%) |
Police officers | 56 (100%) | n.a. | n.a. | 42 (100%) | 14 (100%) | 2 (100%) | 3 (100%) | 117 (100%) |
n = number, n.a. = not applicable.
Surveillance results
Focusing on the assessed resistance genes in the stool samples of the screened soldiers and police officers, CTX-M-type beta-lactamase genes (blaCTX-M) coding for enzymes which mediate the expression of ESBL were recorded in 47.9% of the stool samples collected from military personnel and in 24.8% of stool samples taken from police officers. Details are provided in the Tables 6 and 7. Noteworthy, particularly high percentages ranging between 80% and 90% were observed in samples collected from soldiers from the very early deployments collected before 2010. Afterwards, the proportions of positive samples dropped to values below or equal to the references with the exemption of police officers in Africa between 2015 and 2019, for which increased proportions were observed as well. Deployment sites from the African, the Eastern Mediterranean and the European Region associated with deployments before 2010 most prominently contributed to the high prevalence in soldiers, while deployments to the African Region between 2015 and 2019 were the only ones with significantly increased colonization rates in police forces.
Temporal and spatial distribution of CTX-M-type ESBL genes in deployed forces (raw data)
Assessment period | Population | African Region, n/n (%) | Region of the Americas, n/n (%) | South-East Asia Region, n/n (%) | European Region, n/n (%) | Eastern Mediterranean Region, n/n (%) | Western Pacific Region, n/n (%) | Multiple WHO regions, n/n (%) | Total, n (%) |
CTX-M-type ESBL genes | |||||||||
Before 2010 | Soldiers | 109/120 (90.8%) | 0/1 (0%) | n.a. | 128/150 (85.3%) | 340/422 (80.6%) | n.a. | n.a. | 577/693 (83.3%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2010–2014 | Soldiers | 36/139 (25.9%) | 7/13 (53.8%) | 2/11 (18.2%) | 1/5 (20%) | 27/167 (16.2%) | 0/3 (0%) | 1/10 (10%) | 74/348 (21.3%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2015–2019 | Soldiers | 91/305 (29.8%) | 11/24 (45.8%) | 10/18 (55.6%) | 1/8 (12.5%) | 44/196 (22.4%) | 1/1 (100%) | 4/5 (80%) | 162/557 (29.1%) |
Police officers | 6/10 (60%) | n.a. | n.a. | 3/15 (20%) | 2/6 (33.3%) | n.a. | n.a. | 11/31 (35.5%) | |
2020–2022 | Soldiers | 34/106 (32.1%) | 0/2 (0%) | n.a. | 0/1 (0%) | 9/23 (39.1%) | 1/2 (50%) | n.a. | 44/134 (32.8%) |
Police officers | 2/17 (11.8%) | n.a. | n.a. | 6/18 (33.3%) | 0/2 (0%) | 1/1 (100%) | n.a. | 9/38 (23.7%) | |
After 2022 | Soldiers | 8/50 (16.0%) | 0/1 (0%) | n.a. | 1/7 (14.3%) | 0/1 (0%) | 0/1 (0%) | 4/25 (16.0%) | 13/85 (15.3%) |
Police officers | 6/29 (20.7%) | n.a. | n.a. | 0/9 (0%) | 1/6 (16.7%)) | 1/1 (100%) | 1/3 (33.3%) | 9/48 (18.8%) | |
Total* | Soldiers | 278/720 (38.6%) | 18/41 (43.9%) | 12/29 (41.4%) | 131/171 (76.6%) | 420/809 (51.9%) | 2/7 (28.6%) | 9/40 (22.5%) | 870/1817 (47.9%) |
Police officers | 14/56 (25.0%) | n.a. | n.a. | 9/42 (21.4%) | 3/14 (21.4%) | 2/2 (100%) | 1/3 (33.3%) | 29/117 (24.8%) |
n = number. n.a. = not applicable.
Odds ratios of CTX-M-type ESBL genes in deployed forces by WHO region of deployment site and assessment period compared to the totals of deployed soldiers and police officers (references). Lower odds ratios are indicated in green, higher odds ratios in red. No color coding is applied in case of lacking significance or lacking values
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n = number. n.e. = not estimable. ref. = reference. P = significance level.
When looking at the assessed vanA and vanB genes, which mediate vancomycin resistance in enterococci, an even more differentiated picture was seen. While vanA genes were not recorded at all, vanB prevalences of 30.2% and 14.5% were seen in the collected stool samples of deployed soldiers and police officers, respectively. Similar as observed for the blaCTX-M genes, soldiers showed particularly high colonization rates in the very early deployment periods, comprising deployments to the African, the European and the Eastern Mediterranean Region, which dropped in later deployments to intermediate or even lower levels compared to the reference value. For police officers, no significances were seen. Details are provided in the Tables 8 and 9 below.
Temporal and spatial distribution of vanB-type vancomycin resistance genes in deployed forces (raw data)
Assessment period | Population | African Region, n/n (%) | Region of the Americas, n/n (%) | South-East Asia Region, n/n (%) | European Region, n/n (%) | Eastern Mediterranean Region, n/n (%) | Western Pacific Region, n/n (%) | Multiple WHO regions, n/n (%) | Total, n (%) |
vanB-type vancomycin resistance genes | |||||||||
Before 2010 | Soldiers | 55/120 (45.8%) | 1/1 (100%) | n.a. | 68/150 (45.3%) | 206/422 (48.8%) | n.a. | n.a. | 330/693 (47.6%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2010–2014 | Soldiers | 48/139 (34.5%) | 4/13 (30.8%) | 2/11 (18.2%) | 1/5 (20%) | 44/167 (26.3%) | 0/3 (0%) | 2/10 (20%) | 101/348 (29.0%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2015–2019 | Soldiers | 68/305 (22.3%) | 2/24 (8.3%) | 2/18 (11.1%) | 3/8 (37.5%) | 25/196 (12.8%) | 0/1 (0%) | 1/5 (20%) | 101/557 (18.1%) |
Police officers | 1/10 (10%) | n.a. | n.a. | 1/15 (6.7%) | 1/6 (16.7%) | n.a. | n.a. | 3/31 (9.7%) | |
2020–2022 | Soldiers | 4/106 (3.8%) | 0/2 (0%) | n.a. | 0/1 (0%) | 2/23 (8.7%) | 1/2 (50%) | n.a. | 7/134 (5.2%) |
Police officers | 3/17 (17.6%) | n.a. | n.a. | 3/18 (16.7%) | 0/2 (0%) | 0/1 (0%) | n.a. | 6/38 (15.8%) | |
After 2022 | Soldiers | 4/50 (8.0%) | 0/1 (0%) | n.a. | 0/7 (0%) | 0/1 (0%) | 0/1 (0%) | 5/25 (20.0%) | 9/85 (10.6%) |
Police officers | 4/29 (13.8%) | n.a. | n.a. | 3/9 (33.3%) | 1/6 (16.7%) | 0/1 (0%) | 0/3 (0%) | 8/48 (16.7%) | |
Total | Soldiers | 179/720 (24.9%) | 7/41 (17.1%) | 4/29 (13.8%) | 72/171 (42.1%) | 277/809 (34.2%) | 1/7 (14.3%) | 8/40 (20.0%) | 548/1817 (30.2%) |
Police officers | 8/56 (14.3%) | n.a. | n.a. | 7/42 (16.7%) | 2/14 (14.3%) | 0/2 (0%) | 0/3 (0%) | 17/117 (14.5%) |
n = number. n.a. = not applicable.
Odds ratios of vanB-type vancomycin resistance genes in deployed forces by WHO region of deployment site and assessment period compared to the totals of deployed soldiers and police officers (references). Lower odds ratios are indicated in green, higher odds ratios in red. No color coding is applied in case of lacking significance or lacking values
![]() |
n = number. n.e. = not estimable. ref. = reference. P = significance level.
Surveillance data on diarrheagenic E. coli and Vibrio spp. were included to compare resistance trends in stool samples with colonization rates of both frequently and less frequently observed bacterial enteric pathogens. Thereby, contrasting patterns were recorded as shown in the Tables 6–9. In total, recorded prevalences for soldiers and police officers were 9.0% and 17.9% for enteropathogenic EPEC, 3.4% and 12.8% for enterotoxigenic ETEC, 4.0% and 3.4% for enteroaggregative EAEC as well as 2.0% and 3.4% for Vibrio spp. in samples obtained from deployed soldiers and police officers, respectively. For EPEC and ETEC, in particular, there was a tendency for increased prevalence values in the pre-pandemic period 2015–2019 and during the COVID-19 pandemic. For EAEC and Vibrio spp., the tendencies were less obvious but pointed towards a similar direction, quite different from the pattern of assessed molecular resistance determinants. Geographically, increased prevalence rates for all assessed enteric bacterial pathogens were recorded associated with deployments to the African Region, while a pathogen-specific distribution pattern over the other WHO regions was seen. Details are provided in the Tables 10–17.
Temporal and spatial distribution of EPEC (enteropathogenic E. coli) in deployed forces (raw data)
Assessment period | Population | African Region, n/n (%) | Region of the Americas, n/n (%) | South-East Asia Region, n/n (%) | European Region, n/n (%) | Eastern Mediterranean Region, n/n (%) | Western Pacific Region, n/n (%) | Multiple WHO regions, n/n (%) | Total, n (%) |
EPEC (enteropathogenic E. coli) | |||||||||
Before 2010 | Soldiers | 7/120 (5.8%) | 0/1 (0%) | n.a. | 7/150 (4.7%) | 23/422 (5.5%) | n.a. | n.a. | 37/693 (5.3%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2010–2014 | Soldiers | 5/139 (3.6%) | 0/13 (0%) | 0/11 (0%) | 0/5 (0%) | 8/167 (4.8%) | 1/3 (33.3%) | 0/10 (0%) | 14/348 (4.0%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2015–2019 | Soldiers | 60/305 (19.7%) | 3/24 (12.5%) | 1/18 (5.6%) | 3/8 (37.5%) | 20/196 (10.2%) | 0/1 (0%) | 2/5 (40%) | 89/557 (16.0%) |
Police officers | 2/10 (20%) | n.a. | n.a. | 7/15 (46.7%) | 1/6 (16.7%) | n.a. | n.a. | 10/31 (32.3%) | |
2020–2022 | Soldiers | 17/106 (16.0%) | 0/2 (0%) | n.a. | 0/1 (0%) | 0/23 (0%) | 0/2 (0%) | n.a. | 17/134 (12.7%) |
Police officers | 2/17 (11.8%) | n.a. | n.a. | 4/18 (22.2%) | 0/2 (0%) | 1/1 (100%) | n.a. | 7/38 (18.4%) | |
After 2022 | Soldiers | 4/50 (8.0%) | 0/1 (0%) | n.a. | 0/7 (0%) | 0/1 (0%) | 0/1 (0%) | 2/25 (8.0%) | 6/85 (7.1%) |
Police officers | 4/29 (13.8%) | n.a. | n.a. | 0/9 (0%) | 0/6 (0%) | 0/1 (0%) | 0/3 (0%) | 4/48 (8.3%) | |
Total | Soldiers | 93/720 (12.9%) | 3/41 (7.3%) | 1/29 (3.4%) | 10/171 (5.8%) | 51/809 (6.3%) | 1/7 (14.3%) | 4/40 (10.0%) | 163/1817 (9.0%) |
Police officers | 8/56 (14.3%) | n.a. | n.a. | 11/42 (26.2%) | 1/14 (7.1%) | 1/2 (50.0%) | 0/3 (0%) | 21/117 (17.9%) |
n = number. n.a. = not applicable.
Odds ratios of EPEC (enteropathogenic E. coli) in deployed forces by WHO region of deployment site and assessment period compared to the totals of deployed soldiers and police officers (references). Lower odds ratios are indicated in green, higher odds ratios in red. No color coding is applied in case of lacking significance or lacking values
![]() |
n = number. n.e. = not estimable. ref. = reference. P = significance level.
Temporal and spatial distribution of ETEC (enterotoxigenic E. coli) in deployed forces (raw data)
Assessment period | Population | African Region, n/n (%) | Region of the Americas, n/n (%) | South-East Asia Region, n/n (%) | European Region, n/n (%) | Eastern Mediterranean Region, n/n (%) | Western Pacific Region, n/n (%) | Multiple WHO regions, n/n (%) | Total, n (%) |
ETEC (enterotoxigenic E. coli) | |||||||||
Before 2010 | Soldiers | 0/120 (0%) | 0/1 (0%) | n.a. | 3/150 (2%) | 3/422 (0.7%) | n.a. | n.a. | 6/693 (0.9%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2010–2014 | Soldiers | 4/139 (2.9%) | 0/13 (0%) | 1/11 (9.1%) | 0/5 (0%) | 0/167 (0%) | 1/3 (33.3%) | 0/10 (0%) | 6/348 (1.7%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2015–2019 | Soldiers | 13/305 (4.3%) | 3/24 (12.5%) | 3/18 (16.7%) | 1/8 (12.5%) | 4/196 (2%) | 0/1 (0%) | 0/5 (0%) | 24/557 (4.3%) |
Police officers | 1/10 (10%) | n.a. | n.a. | 0/15 (0%) | 0/6 (0%) | n.a. | n.a. | 1/31 (3.2%) | |
2020–2022 | Soldiers | 14/106 (13.2%) | 0/2 (0%) | n.a. | 0/1 (0%) | 1/23 (4.3%) | 0/2 (0%) | n.a. | 15/134 (11.2%) |
Police officers | 2/17 (11.8%) | n.a. | n.a. | 2/18 (11.1%) | 1/2 (50%) | 0/1 (0%) | n.a. | 5/38 (13.2%) | |
After 2022 | Soldiers | 2/50 (4.0%) | 0/1 (0%) | n.a. | 0/7 (0%) | 0/1 (0%) | 0/1 (0%) | 0/25 (0%) | 2/85 (2.4%) |
Police officers | 6/29 (20.7%) | n.a. | n.a. | 0/9 (0%) | 1/6 (16.7%)) | 1/1 (100%) | 1/3 (33.3%) | 9/48 (18.8%) | |
Total | Soldiers | 33/720 (4.6%) | 3/41 (43.9%) | 4/29 (7.3%) | 4/171 (2.3%) | 8/809 (1.0%) | 1/7 (14.3%) | 0/40 (0%) | 61/1817 (3.4%) |
Police officers | 9/56 (16.1%) | n.a. | n.a. | 2/42 (4.8%) | 2/14 (14.3%) | 1/2 (50.0%) | 1/3 (33.3%) | 15/117 (12.8%) |
n = number. n.a. = not applicable.
Odds ratios of ETEC (enterotoxigenic E. coli) in deployed forces by WHO region of deployment site and assessment period compared to the totals of deployed soldiers and police officers (references). Lower odds ratios are indicated in green, higher odds ratios in red. No color coding is applied in case of lacking significance or lacking values
![]() |
n = number. n.e. = not estimable. ref. = reference. P = significance level.
Temporal and spatial distribution of EAEC (enteroaggregative E. coli) in deployed forces (raw data)
Assessment period | Population | African Region, n/n (%) | Region of the Americas, n/n (%) | South-East Asia Region, n/n (%) | European Region, n/n (%) | Eastern Mediterranean Region, n/n (%) | Western Pacific Region, n/n (%) | Multiple WHO regions, n/n (%) | Total, n (%) |
EAEC (enteroaggregative E. coli) | |||||||||
Before 2010 | Soldiers | 8/120 (6.7%) | 0/1 (0%) | n.a. | 4/150 (2.7%) | 11/422 (2.6%) | n.a. | n.a. | 23/693 (3.3%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2010–2014 | Soldiers | 11/139 (7.9%) | 0/13 (0%) | 3/11 (27.3%) | 0/5 (0%) | 5/167 (3.0%) | 0/3 (0%) | 0/10 (0%) | 19/348 (5.5%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2015–2019 | Soldiers | 13/305 (4.3%) | 0/24 (0%) | 2/18 (11.1%) | 0/8 (0%) | 9/196 (4.6%) | 0/1 (0%) | 1/5 (20%) | 25/557 (4.5%) |
Police officers | 1/10 (10%) | n.a. | n.a. | 0/15 (0%) | 0/6 (0%) | n.a. | n.a. | 1/31 (3.2%) | |
2020–2022 | Soldiers | 2/106 (1.9%) | 0/2 (0%) | n.a. | 0/1 (0%) | 0/23 (0%) | 0/2 (0%) | n.a. | 2/134 (1.5%) |
Police officers | 2/17 (11.8%) | n.a. | n.a. | 0/18 (0%) | 0/2 (0%) | 0/1 (0%) | n.a. | 2/38 (5.3%) | |
After 2022 | Soldiers | 2/50 (4.0%) | 0/1 (0%) | n.a. | 0/7 (0%) | 0/1 (0%) | 0/1 (0%) | 1/25 (4.0%) | 3/85 (3.5%) |
Police officers | 1/29 (3.4%) | n.a. | n.a. | 0/9 (0%) | 0/6 (0%)) | 0/1 (0%) | 0/3 (0%) | 1/48 (2.1%) | |
Total | Soldiers | 36/720 (5.0%) | 0/41 (0%) | 5/29 (17.2%) | 4/171 (2.3%) | 25/809 (3.1%) | 0/7 (0%) | 2/40 (5.0%) | 72/1817 (4.0%) |
Police officers | 4/56 (7.1%) | n.a. | n.a. | 0/42 (0%) | 0/14 (0%) | 0/2 (0%) | 0/3 (0%) | 4/117 (3.4%) |
n = number. n.a. = not applicable.
Odds ratios of EAEC (enteroaggregative E. coli) in deployed forces by WHO region of deployment site and assessment period compared to the totals of deployed soldiers and police officers (references). Lower odds ratios are indicated in green, higher odds ratios in red. No color coding is applied in case of lacking significance or lacking values
![]() |
n = number. n.e. = not estimable. ref. = reference. P = significance level.
Temporal and spatial distribution of Vibrio spp. in deployed forces (raw data)
Assessment period | Population | African Region, n/n (%) | Region of the Americas, n/n (%) | South-East Asia Region, n/n (%) | European Region, n/n (%) | Eastern Mediterranean Region, n/n (%) | Western Pacific Region, n/n (%) | Multiple WHO regions, n/n (%) | Total, n (%) |
Vibrio spp. | |||||||||
Before 2010 | Soldiers | 0/120 (0%) | 0/1 (0%) | n.a. | 0/150 (0%) | 0/422 (0%) | n.a. | n.a. | 0/693 (0%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2010–2014 | Soldiers | 1/139 (0.7%) | 1/13 (7.7%) | 0/11 (0%) | 0/5 (0%) | 1/167 (0.6%) | 0/3 (0%) | 0/10 (0%) | 3/348 (0.9%) |
Police officers | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | |
2015–2019 | Soldiers | 22/305 (7.2%) | 1/24 (4.2%) | 0/18 (0%) | 1/8 (12.5%) | 4/196 (2.0%) | 0/1 (0%) | 0/5 (0%) | 28/557 (5.0%) |
Police officers | 0/10 (0%) | n.a. | n.a. | 1/15 (6.7%) | 2/6 (33.3%) | n.a. | n.a. | 3/31 (9.7%) | |
2020–2022 | Soldiers | 5/106 (4.7%) | 0/2 (0%) | n.a. | 0/1 (0%) | 0/23 (0%) | 0/2 (0%) | n.a. | 5/134 (3.7%) |
Police officers | 0/17 (0%) | n.a. | n.a. | 0/18 (0%) | 0/2 (0%) | 0/1 (0%) | n.a. | 0/38 (0%) | |
After 2022 | Soldiers | 0/50 (0%) | 0/1 (0%) | n.a. | 0/7 (0%) | 0/1 (0%) | 0/1 (0%) | 0/25 (0%) | 0/85 (0%) |
Police officers | 0/29 (0%) | n.a. | n.a. | 0/9 (0%) | 1/6 (16.7%)) | 0/1 (0%) | 0/3 (0%) | 1/48 (2.1%) | |
Total | Soldiers | 28/720 (3.9%) | 2/41 (4.9%) | 0/29 (0%) | 1/171 (0.6%) | 5/809 (0.6%) | 0/7 (0%) | 0/40 (0%) | 36/1817 (2.0%) |
Police officers | 0/56 (0%) | n.a. | n.a. | 1/42 (2.4%) | 3/14 (21.4%) | 0/2 (0%) | 0/3 (0%) | 4/117 (3.4%) |
n = number. n.a. = not applicable.
Odds ratios of Vibrio spp. in deployed forces by WHO region of deployment site and assessment period compared to the totals of deployed soldiers and police officers (references). Lower odds ratios are indicated in green, higher odds ratios in red. No color coding is applied in case of lacking significance or lacking values
![]() |
n = number. n.e. = not estimable. ref. = reference. P = significance level.
Recorded cycle threshold (Ct) values of the real-time PCRs are shown in the supplementary Tables 2–7. Obvious shifts in recorded Ct values over the assessed deployment periods and regions were not observed. Of note, Ct-values from the post-pandemic assessment period (after 2022) cannot be directly compared with the Ct values from the earlier collected samples due to a change in the applied real-time PCR approach as stated in the methods chapter.
Discussion
The assessment was conducted to investigate the temporal and spatial dynamics of blaCTX-M genes commonly mediating ESBL-type resistance in Enterobacterales and of vanA as well as vanB genes mediating vancomycin-resistance in enterococci in stool samples collected from deployed German soldiers and police officers. In comparison, respective dynamics for diarrheagenic E. coli as common bacterial pathogens and for Vibrio spp. as less common ones were co-assessed. The study provided a number of results.
First, the assessment confirmed the above-mentioned suspicion that previous merely culture-based screening approaches for resistant bacteria in stool samples of returnees from deployment [6, 7] might have underestimated the abundance of resistance-associated genes. This is well in line with previous reports on inferior sensitivity of merely culture-based assessments [9, 10]. While low colonization rates for ESBL-positive Enterobacterales and virtual non-abundance of vancomycin-resistant enterococci in stool samples of German returnees from deployment had been described previously [6], the here presented assessment provides evidence for high prevalence of associated molecular resistance determinants. Admittedly, the approach did not necessarily measure the abundance of vital resistant bacteria, because the applied molecular diagnostic approach might have resulted in the identification of DNA detritus from already dead bacteria as well [32]. Even in this case, however, the obtained data provide evidence on very high exposure rates of deployed German military and police forces compared to previously reported culture-based results [6, 7].
Second, the assessment indicates a both geographically as well as periodically uneven detection rate of molecular resistance determinants in the stool samples collected during the post-deployment investigations. In case of vanB gene assessment, for example, even contradictory tendencies for colonization rates with vancomycin-resistant enterococci in the gut of deployed soldiers and of police officers over time could be demonstrated. For CTX-M-type beta-lactamase genes, a tendency for declining prevalence over the assessment periods could be observed for deployed military personnel, although the international spread of ESBL-positive Enterobacterales is still ongoing and evolving [33]. These findings indicate that not only geographic but also mission-specific characteristics may affect colonization rates of deployed soldiers with resistant bacteria. This issue makes direct conclusions from the local epidemiology challenging and encourages mission-tailored surveillance approaches.
Third, there were marked differences in the temporal and spatial dynamics of assessed molecular resistance determinants over the various assessed deployment periods and regions compared to the co-assessed enteropathogenic bacteria. The lacking parallelism in these developments makes simple explanations like, e.g., better or worse compliance with standard infection prevention and control procedures, considerably less likely. Enteric colonization with bacterial enteric pathogens on the one hand as well as ESBL-positive Enterobacterales and vancomycin-resistant enterococci on the other hand is mediated via the fecal-oral transmission route. However, even the COVID-19-pandemic period 2020–2022, when comparably strict hygiene protocols were enforced on deployment, was not associated with a marked and stable decrease of recorded colonization rates with both enteropathogenic bacteria and resistant enteric bacterial colonizers. This finding is in line with previous observations on deployment-associated infections in German soldiers and police officers, suggesting only minor influence of the COVID-19 pandemic period [11].
Of note, the observed detection rates of Vibrio spp. in the assessed returnees from deployment were in a similar range like the EAEC detection rates. High recorded cycle threshold values mostly >35 (see also supplementary Table 7 for details) suggested most likely pathogen DNA residuals from previous, already cleared infections on deployment. Nevertheless, the findings encourage ongoing consideration of Vibrio spp. as a potential causative agent of diarrhea on deployment. Noteworthy, oral whole cell vaccines against cholera [34] contain target DNA of the Vibrio spp. PCR and so, residual DNA from very recent oral vaccination can be an alternative explanation of positive Vibrio spp. PCR rather than true infection. Within biological matrices like stool, DNA of dead microorganisms as, e.g., associated with the oral cholera vaccine, is preserved for several days [32].
From the point of view of individual medicine, the observed enteric colonization rates with ESBL-positive Enterobacterales or vancomycin-resistant enterococci are usually not harmful for healthy deployed forces. However, it can contribute to the further spread of such resistant bacteria and associated infections in more vulnerable populations. Historic investigations have indicated that at least culturally detectable quantities of ESBL-positive Enterobacterales in the human gut after travelling abroad have a good prognosis of spontaneous vanishing in the course of several months after travel [35, 36]. At least within the first months after return from deployment, thorough compliance with standard hygiene procedures is nevertheless advisable in order not to facilitate an avoidable spread of resistant bacteria, which is quite likely in the household setting as reported elsewhere [36].
Our study has several limitations. The number and composition of available samples was limited by the number of individuals who provided specimens for voluntary post-deployment assessments. Insofar, the study population is not fully representative for the total of deployed German soldiers and police officers which needs to be considered when interpreting the results. This also contributed to the above-mentioned uneven distribution of assessed specimens over the different deployment periods and regions. For the same reason, the investigation was designed as a holistic exploratory, hypothesis-forming assessment only, without elaborated statistical analyses. Future confirmatory studies should be performed with a well-defined and standardized study population.
Conclusions
In spite of the abovementioned limitations, the investigations confirmed two-digit prevalence rates for the resistance-mediating genes blaCTX-M and vanB in stool samples of deployed German soldiers and police officers. This prevalence shows a variance depending on the region and period of deployment which is not matched with detection dynamics of bacterial enteropathogenic microorganisms in spite of a comparable transmission route. Multiple factors on deployment may have affected specific resistance prevalence values, as, e.g., recently demonstrated for co-occurring travelers' diarrhea as well as its therapy with antibiotic drugs [37].
Conflicts of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Author contributions
Conceptualization, V.N., H.F.; methodology, V.N., S.D., A.P.S., U.S., H.F.; software, V.N.; validation, V.N., H.F.; formal analysis, V.N.; investigation, V.N., D.F.W., M.H., H.F.; resources, D.F.W., H.F.; data curation, V.N.; writing—original draft preparation, V.N., H.F.; writing—review and editing, V.N., D.F.W., M.H., U.M., S.D., A.P.S., U.S., H.F.; visualization, V.N., H.F.; supervision, H.F.; project administration, H.F.; funding acquisition, H.F. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Acknowledgements
Annett Michel and Simone Priesnitz are gratefully acknowledged for excellent technical assistance, Nancy Schumacher for excellent data management assistance.
Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1556/1886.2024.00093.
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