Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance.

BACKGROUND: European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens. METHODS: We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software. RESULTS: Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics. CONCLUSIONS: The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study.

Epidemic Intelligence (EI) encompasses all activities related to early identification, verification, analysis, assessment, and investigation of health threats. It integrates an indicator-based (IBS) component using systematically collected surveillance data, and an event-based component (EBS), using non-official, non-verified, non-structured data from multiple sources. We described current EI practices in Europe by conducting a survey of national Public Health (PH) and Animal Health (AH) agencies. We included generic questions on the structure, mandate and scope of the institute, on the existence and coordination of EI activities, followed by a section where respondents provided a description of EI activities for three diseases out of seven disease models. Out of 81 gatekeeper agencies from 41 countries contacted, 34 agencies (42%) from 26 (63%) different countries responded, out of which, 32 conducted EI activities. Less than half (15/32; 47%) had teams dedicated to EI activities and 56% (18/34) had Standard Operating Procedures (SOPs) in place. On a national level, a combination of IBS and EBS was the most common data source. Most respondents monitored the epidemiological situation in bordering countries, the rest of Europe and the world. EI systems were heterogeneous across countries and diseases. National IBS activities strongly relied on mandatory laboratory-based surveillance systems. The collection, analysis and interpretation of IBS information was performed manually for most disease models. Depending on the disease, some respondents did not have any EBS activity. Most respondents conducted signal assessment manually through expert review. Cross-sectoral collaboration was heterogeneous. More than half of the responding institutes collaborated on various levels (data sharing, communication, etc.) with neighbouring countries and/or international structures, across most disease models. Our findings emphasise a notable engagement in EI activities across PH and AH institutes of Europe, but opportunities exist for better integration, standardisation, and automatization of these efforts. A strong reliance on traditional IBS and laboratory-based surveillance systems, emphasises the key role of in-country laboratories networks. EI activities may benefit particularly from investments in cross-border collaboration, the development of methods that can automatise signal assessment in both IBS and EBS data, as well as further investments in the collection of EBS data beyond scientific literature and mainstream media.

National point prevalence study on carriage of multidrug-resistant microorganisms in Dutch long-term care facilities in 2018.

OBJECTIVES: Long-term care facilities (LTCFs) may act as a reservoir of ESBL-producing Enterobacterales (ESBL-E) and carbapenemase-producing Enterobacterales (CPE) for hospitals and the general population. In this study, we estimated the prevalence and molecular epidemiology of rectal carriage with ESBL-E and CPE in residents of Dutch LTCFs between March 2018 and December 2018. METHODS: LTCFs were geographically selected across the country. For each LTCF, a random sample of residents were tested for ESBL-E and CPE in 2018. To identify risk factors for high carriage prevalence and/or individual carriage, characteristics of LTCFs and of a subset of the tested residents were collected. WGS was conducted on isolates from LTCFs with an ESBL-E prevalence of >10% and all CPE isolates to identify institutional clonal transmission. RESULTS: A total of 4420 residents of 159 LTCFs were included. The weighted mean ESBL-E prevalence was 8.3% (95% CI: 6.8-10.0) and no CPE were found. In 53 LTCFs (33%), where ESBL-E prevalence was >10%, MLST using WGS (wgMLST) was performed. This included 264 isolates, the majority being Escherichia coli (n = 224) followed by Klebsiella pneumoniae (n = 30). Genetic clusters were identified in more than half (30/53; 57%) of high ESBL-positive LTCFs. Among the E. coli isolates, blaCTX-M-15 (92/224; 41%) and blaCTX-M-27 (40/224; 18%) were the most prevalent ESBL-encoding genes. For K. pneumoniae isolates, the most common was blaCTX-M-15 (23/30; 80%). CONCLUSIONS: The estimated prevalence of ESBL-E rectal carriage in Dutch LTCFs is 8.3% and resistance is observed mainly in E. coli with predominance of blaCTX-M-15 and blaCTX-M-27. ESBL-E prevalence in LTCFs seems comparable to previously reported prevalence in hospitals and the general population.

Reducing contacts to stop SARS-CoV-2 transmission during the second pandemic wave in Brussels, Belgium, August to November 2020.

To evaluate the effect of physical distancing and school reopening in Brussels between August and November 2020, we monitored changes in the number of reported contacts per SARS-CoV-2 case and associated SARS-CoV-2 transmission. The second COVID-19 pandemic wave in Brussels was the result of increased social contact across all ages following school reopening. Physical distancing measures including closure of bars and restaurants, and limiting close contacts, while primary and secondary schools remained open, reduced social mixing and controlled SARS-CoV-2 transmission.

Modelling the transmission of healthcare associated infections: a systematic review.

BACKGROUND: Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time. METHODS: MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings. RESULTS: In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data. CONCLUSIONS: Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models.

An annotated dataset for event-based surveillance of antimicrobial resistance.

This paper presents an annotated dataset used in the MOOD Antimicrobial Resistance (AMR) hackathon, hosted in Montpellier, June 2022. The collected data concerns unstructured data from news items, scientific publications and national or international reports, collected from four event-based surveillance (EBS) Systems, i.e. ProMED, PADI-web, HealthMap and MedISys. Data was annotated by relevance for epidemic intelligence (EI) purposes with the help of AMR experts and an annotation guideline. Extracted data were intended to include relevant events on the emergence and spread of AMR such as reports on AMR trends, discovery of new drug-bug resistances, or new AMR genes in human, animal or environmental reservoirs. This dataset can be used to train or evaluate classification approaches to automatically identify written text on AMR events across the different reservoirs and sectors of One Health (i.e. human, animal, food, environmental sources, such as soil and waste water) in unstructured data (e.g. news, tweets) and classify these events by relevance for EI purposes.

The values and risks of an Intergovernmental Panel for One Health to strengthen pandemic prevention, preparedness, and response.

The COVID-19 pandemic has shown the need for better global governance of pandemic prevention, preparedness, and response (PPR) and has emphasised the importance of organised knowledge production and uptake. In this Health Policy, we assess the potential values and risks of establishing an Intergovernmental Panel for One Health (IPOH). Similar to the Intergovernmental Panel on Climate Change, an IPOH would facilitate knowledge uptake in policy making via a multisectoral approach, and hence support the addressing of infectious disease emergence and re-emergence at the human-animal-environment interface. The potential benefits to pandemic PPR include a clear, unified, and authoritative voice from the scientific community, support to help donors and institutions to prioritise their investments, evidence-based policies for implementation, and guidance on defragmenting the global health system. Potential risks include a scope not encompassing all pandemic origins, unclear efficacy in fostering knowledge uptake by policy makers, potentially inadequate speed in facilitating response efforts, and coordination challenges among an already dense set of stakeholders. We recommend weighing these factors when designing institutional reforms for a more effective global health system.

Impact of the COVID-19 pandemic and response on the utilisation of health services in public facilities during the first wave in Kinshasa, the Democratic Republic of the Congo.

INTRODUCTION: Health service use among the public can decline during outbreaks and had been predicted among low and middle-income countries during the COVID-19 pandemic. In March 2020, the government of the Democratic Republic of the Congo (DRC) started implementing public health measures across Kinshasa, including strict lockdown measures in the Gombe health zone. METHODS: Using monthly time series data from the DRC Health Management Information System (January 2018 to December 2020) and interrupted time series with mixed effects segmented Poisson regression models, we evaluated the impact of the pandemic on the use of essential health services (outpatient visits, maternal health, vaccinations, visits for common infectious diseases and non-communicable diseases) during the first wave of the pandemic in Kinshasa. Analyses were stratified by age, sex, health facility and lockdown policy (ie, Gombe vs other health zones). RESULTS: Health service use dropped rapidly following the start of the pandemic and ranged from 16% for visits for hypertension to 39% for visits for diabetes. However, reductions were highly concentrated in Gombe (81% decline in outpatient visits) relative to other health zones. When the lockdown was lifted, total visits and visits for infectious diseases and non-communicable diseases increased approximately twofold. Hospitals were more affected than health centres. Overall, the use of maternal health services and vaccinations was not significantly affected. CONCLUSION: The COVID-19 pandemic resulted in important reductions in health service utilisation in Kinshasa, particularly Gombe. Lifting of lockdown led to a rebound in the level of health service use but it remained lower than prepandemic levels.

Antimicrobial resistance and COVID-19: Intersections and implications.

Before the coronavirus 2019 (COVID-19) pandemic began, antimicrobial resistance (AMR) was among the top priorities for global public health. Already a complex challenge, AMR now needs to be addressed in a changing healthcare landscape. Here, we analyse how changes due to COVID-19 in terms of antimicrobial usage, infection prevention, and health systems affect the emergence, transmission, and burden of AMR. Increased hand hygiene, decreased international travel, and decreased elective hospital procedures may reduce AMR pathogen selection and spread in the short term. However, the opposite effects may be seen if antibiotics are more widely used as standard healthcare pathways break down. Over 6 months into the COVID-19 pandemic, the dynamics of AMR remain uncertain. We call for the AMR community to keep a global perspective while designing finely tuned surveillance and research to continue to improve our preparedness and response to these intersecting public health challenges.

How to improve outbreak response: a case study of integrated outbreak analytics from Ebola in Eastern Democratic Republic of the Congo.

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018-2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d'Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks.

Quantifying antibiotic impact on within-patient dynamics of extended-spectrum beta-lactamase resistance.

Antibiotic-induced perturbation of the human gut flora is expected to play an important role in mediating the relationship between antibiotic use and the population prevalence of antibiotic resistance in bacteria, but little is known about how antibiotics affect within-host resistance dynamics. Here we develop a data-driven model of the within-host dynamics of extended-spectrum beta-lactamase (ESBL) producing Enterobacteriaceae. We use blaCTX-M (the most widespread ESBL gene family) and 16S rRNA (a proxy for bacterial load) abundance data from 833 rectal swabs from 133 ESBL-positive patients followed up in a prospective cohort study in three European hospitals. We find that cefuroxime and ceftriaxone are associated with increased blaCTX-M abundance during treatment (21% and 10% daily increase, respectively), while treatment with meropenem, piperacillin-tazobactam, and oral ciprofloxacin is associated with decreased blaCTX-M (8% daily decrease for all). The model predicts that typical antibiotic exposures can have substantial long-term effects on blaCTX-M carriage duration.

Bacteria that are resistant to antibiotics are a growing global health crisis. One type of antibiotic resistance arises when certain bacteria that can produce enzymes called extended-spectrum beta-lactamases (or ESBLs for short) become more common in the gut. These enzymes stop important antibiotics, like penicillin, from working. However, exactly which antibiotics and treatment durations contribute to the emergence of this antibiotic resistance remain unknown. Now, Niehus et al. find certain antibiotics that are associated with an increase in the number of gut bacteria carrying antibiotic resistance genes for ESBL enzymes. First, rectal swabs collected from 133 patients from three European hospitals were analysed to measure the total gut bacteria and the number of genes for ESBL enzymes. These samples had been collected at several time points including when the patient was first admitted to hospital, then every two to three days during their stay, and finally when they were discharged. Combining the analysis of the samples with details of the patients' charts showed that treatment with two antibiotics: cefuroxime and ceftriaxone, was linked to an increase in ESBL genes in the gut bacteria. Other antibiotics ­ namely, meropenem, piperacillin-tazobactam and oral ciprofloxacin ­ were associated with a decrease in the number of bacteria with ESBL genes. Niehus et al. then performed further analysis to see if different treatment regimens affected how long patients were carrying gut bacteria with ESBL genes. This predicted that a longer course of meropenem, 14 days rather than 5 days, would shorten the length of time patients carried ESBL-resistant bacteria in their guts by 70%, although this effect will likely depend on the location of the hospital and the local prevalence of other types of antibiotic resistance. This analysis reveals new details about how antibiotic treatment can affect ESBL resistance genes. More studies are needed to understand how antibiotics affect other antibiotic resistance genes and how resistant bacteria spread. This will help scientists understand how much specific antibiotic regimens contribute to antibiotic resistance. It may also help scientists develop new antibiotic treatment strategies that reduce antibiotic resistance.

Transmission Dynamics of Hyper-Endemic Multi-Drug Resistant Klebsiella pneumoniae in a Southeast Asian Neonatal Unit: A Longitudinal Study With Whole Genome Sequencing.

Background:Klebsiella pneumoniae is an important and increasing cause of life-threatening disease in hospitalized neonates. Third generation cephalosporin resistance (3GC-R) is frequently a marker of multi-drug resistance, and can complicate management of infections. 3GC-R K. pneumoniae is hyper-endemic in many developing country settings, but its epidemiology is poorly understood and prospective studies of endemic transmission are lacking. We aimed to determine the transmission dynamics of 3GC-R K. pneumoniae in a newly opened neonatal unit (NU) in Cambodia and to address the following questions: what is the diversity of 3GC-R K. pneumoniae both within- and between-host; to what extent is high carriage prevalence driven by ward-based transmission; and to what extent can environmental contamination explain patterns of patient acquisition. Methods: We performed a prospective longitudinal study between September and November 2013. Rectal swabs from consented patients were collected upon NU admission and every 3 days thereafter. Morphologically different colonies from swabs growing cefpodoxime-resistant K. pneumoniae were selected for whole-genome sequencing (WGS). Results: One hundred and fifty-eight samples from 37 patients and 7 environmental sites were collected. 32/37 (86%) patients screened positive for 3GC-R K. pneumoniae and 93 colonies from 119 swabs were successfully sequenced. Isolates were resistant to a median of six (range 3-9) antimicrobials. WGS revealed high diversity; pairwise distances between isolates from the same patient were either 0-1 SNV or >1,000 SNVs; 19/32 colonized patients harbored K. pneumoniae colonies differing by >1000 SNVs. Diverse lineages accounted for 18 probable importations to the NU and nine probable transmission clusters involving 19/37 (51%) of screened patients. Median cluster size was five patients (range 3-9). Seven out of 46 environmental swabs (15%) were positive for 3GC-R K. pneumoniae. Environmental sources were plausible sources for acquisitions in 2/9 transmission clusters, though in both cases other patients were also plausible sources. Conclusion: The epidemiology of 3GC-R K. pneumoniae was characterized by multiple introductions, high within- and between host diversity and a dense network of cross-infection, with half of screened neonates part of a transmission cluster. We found no evidence to suggest that environmental contamination was playing a dominant role in transmission.

Why sensitive bacteria are resistant to hospital infection control.

Background: Large reductions in the incidence of antibiotic-resistant strains of Staphylococcus aureus and Clostridium difficile have been observed in response to multifaceted hospital-based interventions. Reductions in antibiotic-sensitive strains have been smaller or non-existent. It has been argued that since infection control measures, such as hand hygiene, should affect resistant and sensitive strains equally, observed changes must have largely resulted from other factors, including changes in antibiotic use. We used a mathematical model to test the validity of this reasoning. Methods: We developed a mechanistic model of resistant and sensitive strains in a hospital and its catchment area. We assumed the resistant strain had a competitive advantage in the hospital and the sensitive strain an advantage in the community. We simulated a hospital hand hygiene intervention that directly affected resistant and sensitive strains equally. The annual incidence rate ratio ( IRR) associated with the intervention was calculated for hospital- and community-acquired infections of both strains. Results: For the resistant strain, there were large reductions in hospital-acquired infections (0.1 ≤ IRR ≤ 0.6) and smaller reductions in community-acquired infections (0.2 ≤ IRR ≤  0.9). These reductions increased in line with increasing importance of nosocomial transmission of the strain. For the sensitive strain, reductions in hospital acquisitions were much smaller (0.6 ≤ IRR ≤ 0.9), while communityacquisitions could increase or decrease (0.9 ≤ IRR ≤ 1.2). The greater the importance of the community environment for the transmission of the sensitive strain, the smaller the reductions. Conclusions: Counter-intuitively, infection control interventions, including hand hygiene, can have strikingly discordant effects on resistant and sensitive strains even though they target them equally, following differences in their adaptation to hospital and community-based transmission. Observed lack of effectiveness of control measures for sensitive strains does not provide evidence that infection control interventions have been ineffective in reducing resistant strains.

The projected effectiveness of Clostridium difficile vaccination as part of an integrated infection control strategy.

BACKGROUND: Early clinical trials of a Clostridium difficile toxoid vaccine show efficacy in preventing C. difficile infection (CDI). The optimal patient group to target for vaccination programmes remains unexplored. This study performed a model-based evaluation of the effectiveness of different CDI vaccination strategies, within the context of existing infection prevention and control strategies such as antimicrobial stewardship. METHODS: An individual-based transmission model of CDI in a high-risk hospital setting was developed. The model incorporated data on patient movements between the hospital, and catchment populations from the community and long-term care facilities (LTCF), using English national and local level data for model-parameterisation. We evaluated vaccination of: (1) discharged patients who had an CDI-occurrence in the ward; (2) LTCF-residents; (3) Planned elective surgical admissions and (4) All three strategies combined. RESULTS: Without vaccination, 10.9 [Interquartile range: 10.0-11.8] patients per 1000 ward admissions developed CDI, of which 31% were ward-acquired. Immunising all three patient groups resulted in a 43% [42-44], reduction of ward-onset CDI on average. Among the strategies restricting vaccination to one target group, vaccinating elective surgical patients proved most effective (35% [34-36] reduction), but least efficient, requiring 146 [133-162] courses to prevent one ICU-onset case. Immunising LTCF residents was most efficient, requiring just 13 [11-16] courses to prevent one case, but considering this only comprised a small group of our hospital population, it only reduced ICU-onset CDI by 9% [8-11]. Vaccination proved most efficient when ward-based transmission rates and antimicrobial consumption were high. CONCLUSIONS: Strategy success depends on the interaction between hospital and catchment populations, and importantly, consideration of importations of CDI from outside the hospital which we found to substantially impact hospital dynamics. Vaccination may be most desirable in settings or patient groups where levels of broad-spectrum antimicrobial use are high and difficult to reduce.

Contamination of the Hospital Environment From Potential Clostridium difficile Excretors Without Active Infection.

Clostridium difficile was recovered from 33 (34%) of 98 rooms of patients who were excretors compared with 36 (49%) of 73 rooms of patients with active infection. Not all laboratory algorithms can distinguish between these 2 groups, yet both may be a significant source for ongoing transmission.

Nosocomial transmission of C. difficile in English hospitals from patients with symptomatic infection.

BACKGROUND: Recent evidence suggests that less than one-quarter of patients with symptomatic nosocomial Clostridium difficile infections (CDI) are linked to other in-patients. However, this evidence was limited to one geographic area. We aimed to investigate the level of symptomatic CDI transmission in hospitals located across England from 2008 to 2012. METHODS: A generalized additive mixed-effects Poisson model was fitted to English hospital-surveillance data. After adjusting for seasonal fluctuations and between-hospital variation in reported CDI over time, possible clustering (transmission between symptomatic in-patients) of CDI cases was identified. We hypothesised that a temporal proximity would be reflected in the degree of correlation between in-hospital CDI cases per week. This correlation was modelled through a latent autoregressive structure of order 1 (AR(1)). FINDINGS: Forty-six hospitals (33 general, seven specialist, and six teaching hospitals) located in all English regions met our criteria. In total, 12,717 CDI cases were identified; seventy-five per cent of these occurred >48 hours after admission. There were slight increases in reports during winter months. We found a low, but statistically significant, correlation between successive weekly CDI case incidences (phi = 0.029, 95%CI: 0.009-0.049). This correlation was five times stronger in a subgroup analysis restricted to teaching hospitals (phi = 0.104, 95%CI: 0.048-0.159). CONCLUSIONS: The results suggest that symptomatic patient-to-patient transmission has been a source of CDI-acquisition in English hospitals in recent years, and that this might be a more important transmission route in teaching hospitals. Nonetheless, the weak correlation indicates that, in line with recent evidence, symptomatic cases might not be the primary source of nosocomial CDI in England.