Transmission routes of antibiotic resistant bacteria : a systematic review

Background: Quantification of acquisition routes of antibiotic resistant bacteria (ARB) is pivotal for understanding transmission dynamics and designing cost-effective interventions. Different methods have been used to quantify the importance of transmission routes, such as relative risks, odds ratios (OR), genomic comparisons and basic reproduction numbers. We systematically reviewed reported estimates on acquisition routes’ contributions of ARB in humans, animals, water and the environment and assessed the methods used to quantify the importance of transmission routes. Methods: PubMed and EMBASE were searched, resulting in 6054 articles published up until January 1st, 2019. Full text screening was performed on 525 articles and 277 are included. Results: We extracted 718 estimates with S. aureus (n = 273), E. coli (n = 157) and Enterobacteriaceae (n = 99) being studied most frequently. Most estimates were derived from statistical methods (n = 560), mainly expressed as risks (n = 246) and ORs (n = 239), followed by genetic comparisons (n = 85), modelling (n = 62) and dosage of ARB ingested (n = 17). Transmission routes analysed most frequently were occupational exposure (n = 157), travelling (n = 110) and contacts with carriers (n = 83). Studies were mostly performed in the United States (n = 142), the Netherlands (n = 87) and Germany (n = 60). Comparison of methods was not possible as studies using different methods to estimate the same route were lacking. Due to study heterogeneity not all estimates by the same method could be pooled. Conclusion: Despite an abundance of published data the relative importance of transmission routes of ARB has not been accurately quantified. Links between exposure and acquisition are often present, but the frequency of exposure is missing, which disables estimation of transmission routes’ importance. To create effective policies reducing ARB, estimates of transmission should be weighed by the frequency of exposure occurrence

Does plasmid-based beta-lactam resistance increase E. coli infections: Modelling addition and replacement mechanisms

Infections caused by antibiotic-resistant bacteria have become more prevalent during past decades. Yet, it is unknown whether such infections occur in addition to infections with antibiotic-susceptible bacteria, thereby increasing the incidence of infections, or whether they replace such infections, leaving the total incidence unaffected. Observational longitudinal studies cannot separate both mechanisms. Using plasmid-based beta-lactam resistant E. coli as example we applied mathematical modelling to investigate whether seven biological mechanisms would lead to replacement or addition of infections. We use a mathematical neutral null model of individuals colonized with susceptible and/or resistant E. coli, with two mechanisms implying a fitness cost, i.e., increased clearance and decreased growth of resistant strains, and five mechanisms benefitting resistance, i.e., 1) increased virulence, 2) increased transmission, 3) decreased clearance of resistant strains, 4) increased rate of horizontal plasmid transfer, and 5) increased clearance of susceptible E. coli due to antibiotics. Each mechanism is modelled separately to estimate addition to or replacement of antibiotic-susceptible infections. Fitness costs cause resistant strains to die out if other strain characteristics are maintained equal. Under the assumptions tested, increased virulence is the only mechanism that increases the total number of infections. Other benefits of resistance lead to replacement of susceptible infections without changing the total number of infections. As there is no biological evidence that plasmid-based beta-lactam resistance increases virulence, these findings suggest that the burden of disease is determined by attributable effects of resistance rather than by an increase in the number of infections

Routes of transmission of VIM-positive Pseudomonas aeruginosa in the adult intensive care unit-analysis of 9 years of surveillance at a university hospital using a mathematical model

Background: Hospital outbreaks of multidrug resistant Pseudomonas aeruginosa are often caused by Pseudomonas aeruginosa clones which produce metallo-β-lactamases, such as Verona Integron-encoded Metallo-β-lactamase (VIM). Although different sources have been identified, the exact transmission routes often remain unknown. However, quantifying the role of different transmission routes of VIM-PA is important for tailoring infection prevention and control measures. The aim of this study is to quantify the relative importance of different transmission routes by applying a mathematical transmission model using admission and discharge dates as well as surveillance culture data of patients. Methods: We analyzed VIM-PA surveillance data collected between 2010 and 2018 of two intensive-care unit (ICU) wards for adult patients of the Erasmus University Medical Center Rotterdam using a mathematical transmission model. We distinguished two transmission routes: direct cross-transmission and a persistent environmental route. Based on admission, discharge dates, and surveillance cultures, we estimated the proportion of transmissions assigned to each of the routes. Results: Our study shows that only 13.7% (95% CI 1.4%, 29%) of the transmissions that occurred in these two ICU wards were likely caused by cross-transmission, leaving the vast majority of transmissions (86.3%, 95% CI 71%, 98.6%) due to persistent environmental contamination. Conclusions: Our results emphasize that persistent contamination of the environment may be an important driver of nosocomial transmissions of VIM-PA in ICUs. To minimize the transmission risk from the environment, potential reservoirs should be regularly and thoroughly cleaned and disinfected, or redesigned

Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic

Background: In autumn 2020, many countries, including the Netherlands, are experiencing a second wave of the COVID-19 pandemic. Health policymakers are struggling with choosing the right mix of measures to keep the COVID-19 case numbers under control, but still allow a minimum of social and economic activity. The priority to keep schools open is high, but the role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We used a transmission model to estimate the impact of school contacts on the transmission of SARS-CoV-2 and to assess the effects of school-based measures, including school closure, on controlling the pandemic at different time points during the pandemic. Methods and Findings: The age-structured model was fitted to age-specific seroprevalence and hospital admission data from the Netherlands during spring 2020. Compared to adults older than 60 years, the estimated susceptibility was 23% (95%CrI 20-28%) for children aged 0 to 20 years and 61% (95%CrI 50%-72%) for the age group of 20 to 60 years. The time points considered in the analyses were (i) August 2020 when the effective reproduction number (R_e) was estimated to be 1.31 (95%CrI 1.15-2.07), schools just opened after the summer holidays and measures were reinforced with the aim to reduce R_e to a value below 1, and (ii) November 2020 when measures had reduced R_e to 1.00 (95%CrI 0.94-1.33). In this period schools remained open. Our model predicts that keeping schools closed after the summer holidays, in the absence of other measures, would have reduced R_e by 10% (from 1.31 to 1.18 (95%CrI 1.04-1.83)) and thus would not have prevented the second wave in autumn 2020. Reducing non-school-based contacts in August 2020 to the level observed during the first wave of the pandemic would have reduced R_e to 0.83 (95%CrI 0.75-1.10). Yet, this reduction was not achieved and the observed R_e in November was 1.00. Our model predicts that closing schools in November 2020 could reduce R_e from the observed value of 1.00 to 0.84 (95%CrI 0.81-0.90), with unchanged non-school based contacts. Reductions in R_e due to closing schools in November 2020 were 8% for 10 to 20 years old children, 5% for 5 to 10 years old children and negligible for 0 to 5 years old children. Conclusions: The impact of measures reducing school-based contacts, including school closure, depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce R_e with non-school-based measures are exhausted or undesired and R_e is still close to 1, the additional benefit of school-based measures may be considerable, particularly among the older school children.</jats:p

Attributable sources of community-acquired carriage of Escherichia coli containing β-lactam antibiotic resistance genes: a population-based modelling study

Background: Extended-spectrum β-lactamase-producing Escherichia coli (ESBL-EC), plasmid-mediated AmpC-producing E coli (pAmpC-EC), and other bacteria are resistant to important β-lactam antibiotics. ESBL-EC and pAmpC-EC are increasingly reported in animals, food, the environment, and community-acquired and health-care-associated human infections. These infections are usually preceded by asymptomatic carriage, for which attributions to animal, food, environmental, and human sources remain unquantified. Methods: In this population-based modelling study, we collected ESBL and pAmpC gene data on the Netherlands population for 2005–17 from published datasets of gene occurrences in E coli isolates from different sources, and from partners of the ESBL Attribution Consortium and the Dutch National Antimicrobial Surveillance System. Using these data, we applied an established source attribution model based on ESBL-EC and pAmpC-EC prevalence and gene data for humans, including high-risk populations (ie, returning travellers, clinical patients, farmers), farm and companion animals, food, surface freshwater, and wild birds, and human exposure data, to quantify the overall and gene-specific attributable sources of community-acquired ESBL-EC and pAmpC-EC intestinal carriage. We also used a simple transmission model to determine the basic reproduction number (R0) in the open community. Findings: We identified 1220 occurrences of ESBL-EC and pAmpC-EC genes in humans, of which 478 were in clinical patients, 454 were from asymptomatic carriers in the open community, 103 were in poultry and pig farmers, and 185 were in people who had travelled out of the region. We also identified 6275 occurrences in non-human sources, including 479 in companion animals, 4026 in farm animals, 66 in wild birds, 1430 from food products, and 274 from surface freshwater. Most community-acquired ESBL-EC and pAmpC-EC carriage was attributed to human-to-human transmission within or between households in the open community (60·1%, 95% credible interval 40·0–73·5), and to secondary transmission from high-risk groups (6·9%, 4·1–9·2). Food accounted for 18·9% (7·0–38·3) of carriage, companion animals for 7·9% (1·4–19·9), farm animals (non-occupational contact) for 3·6% (0·6–9·9), and swimming in freshwater and wild birds (ie, environmental contact) for 2·6% (0·2–8·7). We derived an R0 of 0·63 (95% CI 0·42–0·77) for intracommunity transmission. Interpretation: Although humans are the main source of community-acquired ESBL-EC and pAmpC-EC carriage, the attributable non-human sources underpin the need for longitudinal studies and continuous monitoring, because intracommunity ESBL-EC and pAmpC-EC spread alone is unlikely to be self-maintaining without transmission to and from non-human sources. Funding: 1Health4Food, Dutch Ministry of Economic Affairs, and the EU's Horizon-2020 through One-Health European Joint Programme.</p

The discrete-time Kermack-McKendrick model: A versatile and computationally attractive framework for modeling epidemics

The COVID-19 pandemic has led to numerous mathematical models for the spread of infection, the majority of which are large compartmental models that implicitly constrain the generation-time distribution. On the other hand, the continuoustime Kermack-McKendrick epidemic model of 1927 (KM27) allows an arbitrary generation-time distribution, but it suffers from the drawback that its numerical implementation is rather cumbersome. Here, we introduce a discrete-time version of KM27 that is as general and flexible, and yet is very easy to implement computationally. Thus, it promises to become a very powerful tool for exploring control scenarios for specific infectious diseases such as COVID-19. To demonstrate this potential, we investigate numerically how the incidence-peak size depends on model ingredients. We find that, with the same reproduction number and the same initial growth rate, compartmental models systematically predict lower peak sizes than models in which the latent and the infectious period have fixed duration

Transmission routes of antibiotic resistant bacteria: a systematic review

Background: Quantification of acquisition routes of antibiotic resistant bacteria (ARB) is pivotal for understanding transmission dynamics and designing cost-effective interventions. Different methods have been used to quantify the importance of transmission routes, such as relative risks, odds ratios (OR), genomic comparisons and basic reproduction numbers. We systematically reviewed reported estimates on acquisition routes’ contributions of ARB in humans, animals, water and the environment and assessed the methods used to quantify the importance of transmission routes. Methods: PubMed and EMBASE were searched, resulting in 6054 articles published up until January 1st, 2019. Full text screening was performed on 525 articles and 277 are included. Results: We extracted 718 estimates with S. aureus (n = 273), E. coli (n = 157) and Enterobacteriaceae (n = 99) being studied most frequently. Most estimates were derived from statistical methods (n = 560), mainly expressed as risks (n = 246) and ORs (n = 239), followed by genetic comparisons (n = 85), modelling (n = 62) and dosage of ARB ingested (n = 17). Transmission routes analysed most frequently were occupational exposure (n = 157), travelling (n = 110) and contacts with carriers (n = 83). Studies were mostly performed in the United States (n = 142), the Netherlands (n = 87) and Germany (n = 60). Comparison of methods was not possible as studies using different methods to estimate the same route were lacking. Due to study heterogeneity not all estimates by the same method could be pooled. Conclusion: Despite an abundance of published data the relative importance of transmission routes of ARB has not been accurately quantified. Links between exposure and acquisition are often present, but the frequency of exposure is missing, which disables estimation of transmission routes’ importance. To create effective policies reducing ARB, estimates of transmission should be weighed by the frequency of exposure occurrence

Digital tools for the fight against COVID-19: Can a second wave be avoided?

Sinds het begin van de covid-19-epidemie zijn verschillende digitale middelen ontwikkeld, zoals apps die kunnen helpen bij de bestrijding van SARS-CoV-2. Met een app kunnen GGD’en de contacten van mensen die positief zijn getest op SARS-CoV-2 sneller traceren en daarmee verdere verspreiding van het virus beperken. Is een tweede golf hiermee te voorkomen

High prevalence of intra-familial co-colonization by extended-spectrum cephalosporin resistant Enterobacteriaceae in preschool children and their parents in Dutch households

Extended-spectrum cephalosporin-resistant (ESCR) Enterobacteriaceae pose a serious infection control challenge for public health. The emergence of the ESCR phenotype is mostly facilitated by plasmid-mediated horizontal extended-spectrum β-lactamases (ESBLs) and AmpC gene transfer within Enterobacteriaceae. Current data regarding the plasmid contribution to this emergence within the Dutch human population is limited. Hence, the aim of this study was to gain insight into the role of plasmids in the dissemination of ESBL/AmpC genes inside Dutch households with preschool children and precisely delineate co-colonization. In 87 ESCR Enterobacteriaceae from fecal samples of parents and preschool children within 66 Dutch households, genomic localization, plasmid type and insertion sequences linked to ESBL/AmpC genes were determined. Chromosomal location of ESBL/AmpC genes was confirmed when needed. An epidemiologically relevant subset of the isolates based on household co-carriage was assessed by Multilocus Sequence Typing and Pulsed-Field Gel Electrophoresis for genetic relatedness. The narrow-host range I1a and F plasmids were the major facilitators of ESBL/AmpC-gene dissemination. Interestingly, we documented a relatively high occurrence of chromosomal integration of typically plasmid-encoded ESBL/AmpC-genes. A high diversity of non-epidemic Escherichia coli sequence types (STs) was revealed; the predominant STs belonged to the pandemic lineages of extraintestinal pathogenic E. coli ST131 and ST69. Intra-familiar co-carriage by identical ESCR Enterobacteriaceae was documented in 7 households compared to 14 based on sole gene typing, as previously reported. Co-carriage was more frequent than expected based on pure chance, suggesting clonal transmission between children and parents within the household