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Since Strongyloides can persist in its host for decades, and cause life threatening infections data on prevalence, the burden and risk factors for infection is crucial in migrant populations. In this observational retrospective cohort study, we describe the epidemiological, clinical, and microbiological characteristics of imported strongyloidiasis diagnosed at the Karolinska University Hospital, Stockholm, Sweden, during 2010–2021. We identified 98 individuals with strongyloidiasis, 89 (90.8%) born in endemic and 9 (9.2%) in non-endemic countries. Sub-Saharan Africa was the most common origin among the group born in endemic countries (62, 69.7%), (p < 0.005). There were 22 individuals with an underlying immunosuppressive condition. Gastrointestinal symptoms (53/98, 54.1%) were the symptoms most frequently described, and were more frequent in adults (57.0%) vs children (0%) (p = 0.013). Eosinophilia was detected in 74 (75.5%), being more frequent in the endemic-borne group (79.8% vs 33.3%, p = 0.002). Eight persons developed complications of strongyloidiasis because of either hyperinfection or disseminated disease. No people living with HIV with CD4 <500/mm3 (n = 6) developed severe strongyloidiasis. A limited number of strongyloidiasis cases was identified, with few complicated cases in immunosuppressed patients. Further studies focusing on identifying and exploring the risk of complicated strongyloidiasis in immunosuppressed patients are needed.

Background Studies elucidating the risk factors for nosocomial SARS-CoV-2 infections or assessing effectiveness of vaccination on acquisition prevention throughout the pandemic period are scarce. Here, we aimed to identify individual- and care-related risk factors and study the association between vaccination and risk of infection for nosocomial SARS-CoV-2 infections. Methods We performed a nested case-control study of patients aged ≥ 18 years hospitalised in Region Stockholm, between 1 March 2020 -15 November 2023. Each case was matched to up to 10 controls, on admission date, time period, and length of stay. Individual factors of sex, age, region of birth, education level, comorbidities and care-related factors such as number of transfers during care episode, surgery status, type of hospital unit and hospitals in the region were assessed. Vaccine effectiveness was assessed for number of doses and time since last vaccination. Conditional logistic regression was used to calculate odds ratio for risk factors and vaccine effectiveness. Results Among 2711 cases and 27,065 matched controls, older age (90 + years: 4.11 [2.71–6.23]), male sex (1.11 [1.02–1.21]) and chronic lung disease (1.25 [1.12 to 1.40]) were associated with increased odds of nosocomial infection. Among care-related factors, admission to geriatric hospital units (1.54 [1.26–1.89]) and increased number of transfers (2 + transfers: 2.48 [1.89–2.34]) were associated with higher odds. Overall, while vaccination with 2 or more doses and any time since last dose compared to being unvaccinated was associated with lower odds of infections, we observed that vaccination with 3 doses (aOR: 0.55, 95% CI: 0.46–0.67), and those with their latest dose administered within the last 3 months had the lowest odds of infection (aOR: 0.48, 95% CI: 0.39 to 0.59). Conclusions We demonstrated that vaccination is effective in reducing the risk of nosocomial SARS-CoV-2 infection, and preventive measures during early phases of pandemics should focus on high-risk patient groups. Risk of infection can be further reduced by focusing on high-risk areas within hospital settings and by reducing patient transfers during their care episode.

Background Involving link nurses in infection prevention and control is a strategy to improve clinical practice that has been implemented in hospitals worldwide. However, little is known about the use, the range and benefits of this strategy. We aimed to identify key concepts of infection control link nurses (ICLN) and ICLN programs, to evaluate the effect of such programs, and to identify gaps in the evidence base. Methods In a scoping review, we searched PubMed, CINAHL, Google and Google Scholar for manuscripts on ICLN in acute care hospitals. We included research- and opinion-based papers, abstracts, reports and guidelines. Results We included 29 publications and identified three key concepts: the profile of ICLN, strategies to support ICLN, and the implementation of ICLN programs. The majority of included studies delineates the ICLN profile with accompanying roles, tasks and strategies to support ICLN, without a thorough evaluation of the implementation process or effects. Few studies report on the effect of ICLN programs in terms of patient outcomes or guideline adherence, with positive short term effects. Conclusion This scoping review reveals a lack of robust evidence on the effectiveness of ICLN programs. Current best practice for an ICLN program includes a clear description of the ICLN profile, education on infection prevention topics as well as training in implementation skills, and support from the management at the ward and hospital level. Future research is needed to evaluate the effects of ICLN on clinical practice and to further develop ICLN programs for maximal impact.

Background In patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR). Methods Retrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated. Results From the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5–99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm. Conclusions The addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4–12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.

Background Automated surveillance methods that re-use electronic health record data are considered an attractive alternative to traditional manual surveillance. However, surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting. With semi-automated surveillance patients are classified as low or high probability of having developed infection, and only high probability patients subsequently undergo manual record review. The aim of this study was to externally validate two existing semi-automated surveillance algorithms for deep SSI after colorectal surgery, developed on Spanish and Dutch data, in a Swedish setting. Methods The algorithms were validated in 225 randomly selected surgeries from Karolinska University Hospital from the period January 1, 2015 until August 31, 2020. Both algorithms were based on (re)admission and discharge data, mortality, reoperations, radiology orders, and antibiotic prescriptions, while one additionally used microbiology cultures. SSI was based on ECDC definitions. Sensitivity, specificity, positive predictive value, negative predictive value, and workload reduction were assessed compared to manual surveillance. Results Both algorithms performed well, yet the algorithm not relying on microbiological culture data had highest sensitivity (97.6, 95%CI: 87.4–99.6), which was comparable to previously published results. The latter algorithm aligned best with clinical practice and would lead to 57% records less to review. Conclusions The results highlight the importance of thorough validation before implementation in other clinical settings than in which algorithms were originally developed: the algorithm excluding microbiology cultures had highest sensitivity in this new setting and has the potential to support large-scale semi-automated surveillance of SSI after colorectal surgery.

Automation of surveillance of infectious diseases—where algorithms are applied to routine care data to replace manual decisions—likely reduces workload and improves quality of surveillance. However, various barriers limit large-scale implementation of automated surveillance (AS). Current implementation strategies for AS in surveillance networks include central implementation (i.e. collecting all data centrally, and central algorithm application for case ascertainment) or local implementation (i.e. local algorithm application and sharing surveillance results with the network coordinating center). In this perspective, we explore whether current challenges can be solved by federated AS. In federated AS, scripts for analyses are developed centrally and applied locally. We focus on the potential of federated AS in the context of healthcare associated infections (AS-HAI) and of severe acute respiratory illness (AS-SARI). AS-HAI and AS-SARI have common and specific requirements, but both would benefit from decreased local surveillance burden, alignment of AS and increased central and local oversight, and improved access to data while preserving privacy. Federated AS combines some benefits of a centrally implemented system, such as standardization and alignment of an easily scalable methodology, with some of the benefits of a locally implemented system including (near) real-time access to data and flexibility in algorithms, meeting different information needs and improving sustainability, and allowance of a broader range of clinically relevant case-definitions. From a global perspective, it can promote the development of automated surveillance where it is not currently possible and foster international collaboration.The necessary transformation of source data likely will place a significant burden on healthcare facilities. However, this may be outweighed by the potential benefits: improved comparability of surveillance results, flexibility and reuse of data for multiple purposes. Governance and stakeholder agreement to address accuracy, accountability, transparency, digital literacy, and data protection, warrants clear attention to create acceptance of the methodology. In conclusion, federated automated surveillance seems a potential solution for current barriers of large-scale implementation of AS-HAI and AS-SARI. Prerequisites for successful implementation include validation of results and evaluation requirements of network participants to govern understanding and acceptance of the methodology.

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections. Funding: Sweden’s Innovation Agency and Stockholm County Council Disclosures: None

Objective The distribution of Extended-Spectrum Beta-Lactamase-producing Gram-negative bacteria (ESBL-GNB) colonization sites is relevant for infection control guidelines on detection and follow-up of colonization. We questioned whether it is possible to rely solely on rectal swab culture for follow-up of ESBL-GNB colonization. Methods We retrospectively assessed ESBL-GNB colonization sites in patients in a tertiary hospital in the Netherlands. The Laboratory Information Management System was queried for all bacterial cultures obtained between January 2012 and August 2016. All patients with one or more cultures positive for ESBL-GNB were identified and the distribution of ESBL-GNB positive sample sites was assessed. A subgroup analysis was performed on patients for whom at least one rectal swab specimen was available. Results We identified 1011 ESBL-GNB carriers with 16,578 specimens for analysis. ESBL-GNB were most frequently isolated from the rectum (506/1011), followed by the urogenital (414/1011) and respiratory tract (142/1011), and pus (136/1011). For 588 patients at least one rectal swab specimen was available. In this subgroup, ESBL-GNB colonization was detected only in the rectum in 55.4% (326/588) of patients, in 30.6% (180/588) in the rectum and a different culture site, and in 13.9% (82/588) no rectal colonization was detected. Conclusions Rectal colonization with ESBL-GNB was detected in 86% of ESBL-GNB carriers. However, in 14% of ESBL-GNB carriers we did not detect rectal colonization. Therefore, samples taken for follow-up of colonization with multi-drug resistant Gram-negative bacteria (MDR-GNB) should ideally also include samples from the site where the MDR-GNB was initially found.

Children are especially vulnerable during periods of resource shortage such as economic embargoes. They are likely to suffer most from poor nutrition, infectious diseases, and other ensuing short-term threats. Moreover, early life circumstances can have important consequences for long-term health. We examined the relationship between early childhood exposure to the Cuban economic situation in the nineties and the occurrence of atopic diseases later in childhood. A cross-sectional study of 1321 primary schoolchildren aged 4-14 was conducted in two Cuban municipalities. Asthma, allergic rhinoconjunctivitis and atopic dermatitis were diagnosed using the International Study of Asthma and Allergies in Childhood questionnaire. Children were divided into three groups of exposure to the economic situation in the nineties according to birth date: (1) unexposed; (2) exposed during infancy; (3) exposed during infancy and early childhood. Associations were assessed using multiple logistic regression models. Exposure during infancy had a significant inverse association with the occurrence of asthma (OR 0.56, 95%CI 0.33-0.94) and allergic rhinoconjunctivitis (OR 0.46, 95%CI 0.25-0.85). The associations were stronger after longer exposure, i.e. during infancy and early childhood, for asthma (OR 0.40, 95% CI 0.17-0.95) and allergic rhinoconjunctivitis (OR 0.29, 95%CI 0.11-0.77). No significant associations were found for atopic dermatitis. Exposure to the economic situation in the nineties during infancy and early childhood was inversely associated with asthma and allergic rhinoconjunctivitis occurrence later in childhood. We hypothesize that factors related to this period, such as infectious diseases and undernutrition, may have an attenuating effect on atopic disease development. The exact cause and underlying mechanisms need to be further elucidated.

Background This work aims at providing practical recommendations for implementing automated surveillance (AS) of surgical site infections (SSI) in hospitals and surveillance networks. It also provides an overview of the steps, choices, and obstacles that need to be taken into consideration when implementing such surveillance. Hands-on experience with existing automated surveillance systems of SSI (AS SSI systems) in Denmark, France, the Netherlands and Spain is described regarding trend monitoring, benchmarking, quality control, and research for surveillance purposes. Methods Between April and October 2023, specific aspects/options of various surveillance purposes for AS SSI were identified during regular meetings of the SSI working group in the PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network. Expert discussions provided the basis for this perspective article. Results Decisions for implementation of AS SSI systems highly depend on the purpose of the surveillance. AS SSI systems presented here differ according to study population, setting, central or local implementation; the level of automation, design, and the data sources used. However, similarities were found for the rationales of automation, design principles and obstacles that were identified. There was consensus among all the experts that shortcomings in interoperability of databases, limited time, a want of commitment on the part of stakeholders, and a shortage of resources for information technology (IT) specialists represent the main obstacles for implementing AS SSI. To overcome obstacles, various solutions were reported, including training in the development of AS systems and the interpretation of AS SSI results, early consultation of end-users, and regular exchanges between management levels, IT departments, infection prevention and control (IPC) teams, and clinicians. Conclusion Clarity on the intended application (e.g. purpose of surveillance) and information on the availability of electronic and structured data are crucial first steps necessary for guiding decisions on the design of AS systems. Adequate resources for IT specialists and regular communication between management, IT departments, IPC teams, and clinicians were identified as essential for successful implementation. This perspective article may be helpful for a wider implementation of more homogeneous AS SSI systems in Europe.