Identifying Barriers to The Implementation of Communicating Narrative Concerns Entered by Registered Nurses, An Early Warning System SmartApp

BACKGROUND

 Nurses are at the frontline of detecting patient deterioration. We developed Communicating Narrative Concerns Entered by Registered Nurses (CONCERN), an early warning system for clinical deterioration that generates a risk prediction score utilizing nursing data. CONCERN was implemented as a randomized clinical trial at two health systems in the Northeastern United States. Following the implementation of CONCERN, our team sought to develop the CONCERN Implementation Toolkit to enable other hospital systems to adopt CONCERN.

OBJECTIVE

 The aim of this study was to identify the optimal resources needed to implement CONCERN and package these resources into the CONCERN Implementation Toolkit to enable the spread of CONCERN to other hospital sites.

METHODS

 To accomplish this aim, we conducted qualitative interviews with nurses, prescribing providers, and information technology experts in two health systems. We recruited participants from July 2022 to January 2023. We conducted thematic analysis guided by the Donabedian model. Based on the results of the thematic analysis, we updated the α version of the CONCERN Implementation Toolkit.

RESULTS

 There was a total of 32 participants included in our study. In total, 12 themes were identified, with four themes mapping to each domain in Donabedian's model (i.e., structure, process, and outcome). Eight new resources were added to the CONCERN Implementation Toolkit.

CONCLUSIONS

 This study validated the α version of the CONCERN Implementation Toolkit. Future studies will focus on returning the results of the Toolkit to the hospital sites to validate the β version of the CONCERN Implementation Toolkit. As the development of early warning systems continues to increase and clinician workflows evolve, the results of this study will provide considerations for research teams interested in implementing early warning systems in the acute care setting.

EMCDDA framework and practical guidance for naming cathinones

Cathinones are often sold as "legal" alternatives to controlled stimulants such as amphetamine, MDMA and cocaine. Cathinones are the second largest group of new psychoactive substances (NPS), with close to 170 monitored by the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA). Although all cathinones are related to the parent compound cathinone, one of the psychoactive principles in khat, assigning consistent, informative and user-friendly common names to these substances is challenging. Over time different naming approaches have been applied, leading to cathinones being known by several names. This work provides a framework and practical examples for the consistent naming of cathinones which is easy to understand and can be applied by the forensic community, researchers, clinical practitioners, and policy makers. The scope of the issue and rationale for earlier naming approaches are also discussed. The new naming framework has been developed based on established naming approaches and centered around the common "cathinone," and "phenone" motifs/scaffolds. The proposed framework establishes clear rules to derive the EMCDDA framework names for cathinones. Each name is, in turn, composed by a principal name containing a parent letter, derived after the "cathinone" or the "phenone" scaffold. Additional substitutions are prepended to the principal name. The framework also provides for exceptions for several cathinones and structural analogs scheduled under UN and EU legislation.

Artificial Intelligence Approach for Severe Dengue Early Warning System

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts. Early detection and accessibility to medical care can reduce severe Dengue mortality rate from 50% to 2%. In the previous study, both statistical and machine learning methods have the potential for predicting a Dengue outbreak, but the study is still fragmented and limited on implementing the generated model into an early warning system application. In this study, we developed an artificial intelligence model with spatiotemporal to predict Dengue outbreak and Dengue incidence case which is ready to be implemented into an early warning system application. Indonesia, especially Semarang City, has experienced an endemic Dengue. We used Semarang City spatiotemporal, meteorological, climatological, and Dengue surveillance epidemiology data from January 2014 to December 2021 in 16 districts of Semarang City. We reviewed 7208 samples from 16 districts and 1 city per week during 8 years. The entire dataset was divided into training (80%) and testing (20%) to develop a prediction model. We used machine learning and Long Short Term Memory (LSTM) to predict Dengue outbreak 1 week before the event for each district. and machine learning to predict Dengue incident cases 1 week before the event for each district. Accuracy, area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score were considered to evaluate the Dengue outbreak prediction model. The Dengue incidence cases prediction model will evaluate using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2). Extra Trees Classifier model shown outperform in Dengue outbreak prediction, with accuracy 0.8925, AUROC 0. 9529, Recall 0.6117, precision 0.8880, and F1 score 0.7238. CatBoost Regressor model is shown to outperform in Dengue incidence cases prediction, with R2 0.5621, MAE 0.6304, MSE 1.1997, and RMSE 1.0891. The study proves that Artificial Intelligence (AI) with a spatiotemporal approach can give higher performance in Dengue outbreak and incidence cases prediction. Utilization of AI approaches that are sensitive with spatiotemporal feasibility to implement in Dengue early warning system application may contribute to increase the policy makers and community attention to do accurate community-based vector control.

Expanding Critical Care Delivery beyond the Intensive Care Unit: Determining the Design and Implementation Needs for a Tele-Critical Care Consultation Service

BACKGROUND

 Unplanned intensive care unit (ICU) admissions from medical/surgical floors and increased boarding times of ICU patients in the emergency department (ED) are common; approximately half of these are associated with adverse events. We explore the potential role of a tele-critical care consult service (TC3) in managing critically ill patients outside of the ICU and potentially preventing low-acuity unplanned admissions and also investigate its design and implementation needs.

METHODS

 We conducted a qualitative study involving general observations of the units, shadowing of clinicians during patient transfers, and interviews with clinicians from the ED, medical/surgical floor units and their ICU counterparts, tele-ICU, and the rapid response team at a large academic medical center in St. Louis, Missouri, United States. We used a hybrid thematic analysis approach supported by open and structured coding using the Consolidated Framework for Implementation Research (CFIR).

RESULTS

 Over 165 hours of observations/shadowing and 26 clinician interviews were conducted. Our findings suggest that a tele-critical care consult (TC3) service can prevent avoidable, lower acuity ICU admissions by offering a second set of eyes via remote monitoring and providing guidance to bedside and rapid response teams in the care delivery of these patients on the floor/ED. CFIR-informed enablers impacting the successful implementation of the TC3 service included the optional and on-demand features of the TC3 service, around-the-clock availability, and continuous access to trained critical care clinicians for avoidable lower acuity (ALA) patients outside of the ICU, familiarity with tele-ICU staff, and a willingness to try alternative patient risk mitigation strategies for ALA patients (suggested by TC3), before transferring all unplanned admissions to ICUs. Conversely, the CFIR-informed barriers to implementation included a desire to uphold physician autonomy by floor/ED clinicians, potential role conflicts with rapid response teams, additional workload for floor/ED nurses, concerns about obstructing unavoidable, higher acuity admissions, and discomfort with audio-visual tools. To amplify these potential enablers and mitigate potential barriers to TC3 implementation, informed by this study, we propose two key characteristics-essential for extending the delivery of critical care services beyond the ICU-underlying a telemedicine critical care consultation model including its virtual footprint and on-demand and optional service features.

CONCLUSION

 Tele-critical care represents an innovative strategy for delivering safe and high-quality critical care services to lower acuity borderline patients outside the ICU setting.

Contrastive Transfer Learning for Prediction of Adverse Events in Hospitalized Patients

OBJECTIVE

Deterioration index (DI) is a computer-generated score at a specific frequency that represents the overall condition of hospitalized patients using a variety of clinical, laboratory and physiologic data. In this paper, a contrastive transfer learning method is proposed and validated for early prediction of adverse events in hospitalized patients using DI scores.

METHODS AND PROCEDURES

An unsupervised contrastive learning (CL) model with a classifier is proposed to predict adverse outcome using a single temporal variable (DI scores). The model is pretrained on an unsupervised fashion with large-scale time series data and fine-tuned with retrospective DI score data.

RESULTS

The performance of this model is compared with supervised deep learning models for time series classification. Results show that unsupervised contrastive transfer learning with a classifier outperforms supervised deep learning solutions. Pretraining of the proposed CL model with large-scale time series data and fine-tuning that with DI scores can enhance prediction accuracy.

CONCLUSION

A relationship exists between longitudinal DI scores of a patient and the corresponding outcome. DI scores and contrastive transfer learning can be used to predict and prevent adverse outcomes in hospitalized patients.

CLINICAL IMPACT

This paper successfully developed an unsupervised contrastive transfer learning algorithm for prediction of adverse events in hospitalized patients. The proposed model can be deployed in hospitals as an early warning system for preemptive intervention in hospitalized patients, which can mitigate the likelihood of adverse outcomes.

Can Citizen Science Contribute to Avian Influenza Surveillance?

Global change is an important driver of the increase in emerging infectious diseases in recent decades. In parallel, interest in nature has increased, and different citizen science platforms have been developed to record wildlife observations from the general public. Some of these platforms also allow registering the observations of dead or sick birds. Here, we test the utility of live, sick and dead observations of birds recorded on the platform Observation.org for the early detection of highly pathogenic avian influenza virus (HPAIV) outbreaks in the wild in Belgium and The Netherlands. There were no significant differences in the morbidity/mortality rate through Observation.org one to four weeks in advance. However, the results show that the HPAIV outbreaks officially reported by the World Organisation for Animal Health (WOAH) overlapped in time with sudden increases in the records of sick and dead birds in the wild. In addition, in two of the five main HPAIV outbreaks recorded between 2016 and 2021, wild Anseriformes mortality increased one to two months before outbreak declaration. Although we cannot exclude that this increase was related to other causes such as other infectious diseases, we propose that Observation.org is a useful nature platform to complement animal health surveillance in wild birds. We propose possible approaches to improve the utility of the platform for pathogen surveillance in wildlife and discuss the potential for HPAIV outbreak detection systems based on citizen science to complement current surveillance programs of health authorities.

Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework

BACKGROUND

A hallmark of unregulated drug markets is their unpredictability and constant evolution with newly introduced substances. People who use drugs and the public health workforce are often unaware of the appearance of new drugs on the unregulated market and their type, safe dosage, and potential adverse effects. This increases risks to people who use drugs, including the risk of unknown consumption and unintentional drug poisoning. Early warning systems (EWSs) can help monitor the landscape of emerging drugs in a given community by collecting and tracking up-to-date information and determining trends. However, there are currently few ways to systematically monitor the appearance and harms of new drugs on the unregulated market in Canada.

OBJECTIVE

The goal of this work is to examine how artificial intelligence can assist in identifying patterns of drug-related risks and harms, by monitoring the social media activity of public health and law enforcement groups. This information is beneficial in the form of an EWS as it can be used to identify new and emerging drug trends in various communities.

METHODS

To collect data for this study, 145 relevant Twitter accounts throughout Quebec (n=33), Ontario (n=78), and British Columbia (n=34) were manually identified. Tweets posted between August 23 and December 21, 2021, were collected via the application programming interface developed by Twitter for a total of 40,393 tweets. Next, subject matter experts (1) developed keyword filters that reduced the data set to 3746 tweets and (2) manually identified relevant tweets for monitoring and early warning efforts for a total of 464 tweets. Using this information, a zero-shot classifier was applied to tweets from step 1 with a set of keep (drug arrest, drug discovery, and drug report) and not-keep (drug addiction support, public safety report, and others) labels to see how accurately it could extract the tweets identified in step 2.

RESULTS

When looking at the accuracy in identifying relevant posts, the system extracted a total of 584 tweets and had an overlap of 392 out of 477 (specificity of ~84.5%) with the subject matter experts. Conversely, the system identified a total of 3162 irrelevant tweets and had an overlap of 3090 (sensitivity of ~94.1%) with the subject matter experts.

CONCLUSIONS

This study demonstrates the benefits of using artificial intelligence to assist in finding relevant tweets for an EWS. The results showed that it can be quite accurate in filtering out irrelevant information, which greatly reduces the amount of manual work required. Although the accuracy in retaining relevant information was observed to be lower, an analysis showed that the label definitions can impact the results significantly and would therefore be suitable for future work to refine. Nonetheless, the performance is promising and demonstrates the usefulness of artificial intelligence in this domain.

Building an Early Warning System for Depression: Rationale, Objectives, and Methods of the WARN-D Study

Background

Depression is common, debilitating, often chronic, and affects young people disproportionately. Given that only 50% of patients improve under initial treatment, experts agree that prevention is the most effective way to change depression's global disease burden. The biggest barrier to successful prevention is to identify individuals at risk for depression in the near future. To close this gap, this protocol paper introduces the WARN-D study, our effort to build a personalized early warning system for depression.

Method

To develop the system, we follow around 2,000 students over 2 years. Stage 1 comprises an extensive baseline assessment in which we collect a broad set of predictors for depression. Stage 2 lasts 3 months and zooms into participants' daily experiences that may predict depression; we use smartwatches to collect digital phenotype data such as sleep and activity, and we use a smartphone app to query participants about their experiences 4 times a day and once every Sunday. In Stage 3, we follow participants for 21 months, assessing transdiagnostic outcomes (including stress, functional impairment, anxiety, and depression) as well as additional predictors for future depression every 3 months. Collected data will be utilized to build a personalized prediction model for depression onset.

Discussion

Overall, WARN-D will function similarly to a weather forecast, with the core difference that one can only seek shelter from a thunderstorm and clean up afterwards, while depression may be successfully prevented before it occurs.

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Background

The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection.

Methods

We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients.

Results

The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients.

Conclusions

The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves.

Funding

University of Vienna.

Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students

Introduction

There is a global effort to address the school dropout phenomenon. The urgency to act on it comes from the harmful evidence that school dropout has on societal and individual levels. Early Warning Systems (EWS) for school dropout at-risk student identification have been developed to anticipate and help schools have a better chance of acting on it. However, several studies point to a doubt that Correct EWS may come too late because they use only publicly available and general student and school information. We hypothesize that having a tool to assess more subjective and inter-relational factors would help anticipate where and when to act to prevent school dropout. This study aimed to develop a multidimensional measure for assessing relational factors for predicting school dropout (SD) risk in the Brazilian context.

Methods

We performed several procedures, including (a) the specialized literature review, (b) the item development of the Relational Factors for the Risk of School Dropout Scale (IAFREE in Portuguese), (c) the content validity analysis, (d) a pilot study, and (e) the administration of the IAFREE to a large Brazilian sample of high school and middle school students (N = 15,924).

Results

After the theoretical steps, we found content validity for five relational dimensions for SD (Student-School, Student-School Professionals, Student-Family, Student-Community, and Student-Student) that include 12 facets of risk factors. At the empirical stage, confirmatory analysis corroborated the proposed theoretical model with 12 first-order risk factors and 5 s-order dimensions (36 items). Further, through the Item Response Theory analysis, we assessed the individual item parameters of the items, providing a brief measure without losing psychometric quality (IAFREE-12).

Discussion

We discuss how this model may fill gaps in Correct EWS models and how to advance it. The IAFREE is a good measure for scholars investigating the risk of SD. These results are important for implementing an early warning system for SD that looks into the complexity of the school dropout phenomenon.

Continuous Vital Signs Monitoring with a Wireless Device on a General Ward: A Survey to Explore Nurses' Experiences in a Post-Implementation Period

BACKGROUND

Nurse engagement, perceived need and usefulness affect healthcare technology use, acceptance and improvements in quality, safety and accessibility of healthcare. Nurses' opinions regarding continuous monitoring appear to be positive. However, facilitators and barriers were little studied. This study explored nurses' post-implementation experiences of the facilitators and barriers to continuously monitoring patients' vital signs using a wireless device on general hospital wards.

METHODS

This study employed a cross-sectional survey. Vocational and registered nurses from three general wards in a Dutch tertiary university hospital participated in a survey comprising open and closed questions. The data were analysed using thematic analysis and descriptive statistics.

RESULTS

Fifty-eight nurses (51.3%) completed the survey. Barriers and facilitators were identified under four key themes: (1) timely signalling and early action, (2) time savings and time consumption, (3) patient comfort and satisfaction and (4) preconditions.

CONCLUSIONS

According to nurses, early detection and intervention for deteriorating patients facilitate the use and acceptance of continuously monitoring vital signs. Barriers primarily concern difficulties connecting patients correctly to the devices and system.

Towards a leptospirosis early warning system in northeastern Argentina

Leptospirosis is a zoonotic disease with a high burden in Latin America, including northeastern Argentina, where flooding events linked to El Niño are associated with leptospirosis outbreaks. The aim of this study was to evaluate the value of using hydrometeorological indicators to predict leptospirosis outbreaks in this region. We quantified the effects of El Niño, precipitation, and river height on leptospirosis risk in Santa Fe and Entre Ríos provinces between 2009 and 2020, using a Bayesian modelling framework. Based on several goodness of fit statistics, we selected candidate models using a long-lead El Niño 3.4 index and shorter lead local climate variables. We then tested predictive performance to detect leptospirosis outbreaks using a two-stage early warning approach. Three-month lagged Niño 3.4 index and one-month lagged precipitation and river height were positively associated with an increase in leptospirosis cases in both provinces. El Niño models correctly detected 89% of outbreaks, while short-lead local models gave similar detection rates with a lower number of false positives. Our results show that climatic events are strong drivers of leptospirosis incidence in northeastern Argentina. Therefore, a leptospirosis outbreak prediction tool driven by hydrometeorological indicators could form part of an early warning and response system in the region.

Early Warning Systems for Emerging Profiles of Antimicrobial Resistance in Italy: A National Survey

Antimicrobial resistance (AMR) national surveillance systems in Italy lack alert systems for timely detection of emerging profiles of AMR with potential relevance to public health. Furthermore, the existence of early warning systems (EWS) at subnational level is unclear. This study aims at mapping and characterizing EWS for microbiological threats available at regional level in Italy, focusing on emerging AMR, and at outlining potential barriers and facilitators to their development/implementation. To this end, a three-section, web-based survey was developed and administered to all Italian regional AMR representatives from June to August 2022. Twenty out of twenty-one regions and autonomous provinces (95.2%) responded to the survey. Among these, nine (45%) reported the implementation of EWS for microbiological threats at regional level, three (15%) reported that EWS are in the process of being developed, and eight (40%) reported that EWS are not currently available. EWS characteristics varied widely among the identified systems concerning both AMR profiles reported and data flow: the microorganisms most frequently included were extensively drug-resistant (XDR) Enterobacterales, with the lack of a dedicated regional IT platform reported in most cases. The results of this study depict a highly heterogeneous scenario and suggest that more efforts aimed at strengthening national AMR surveillance systems are needed.

Non-fatal intoxications involving the novel benzodiazepine clonazolam: case series from the Emerging Drugs Network of Australia - Victoria project

INTRODUCTION

Clonazolam is an unregistered novel benzodiazepine which emerged in global illicit drug markets in 2014. We describe the clinical features of four cases of non-fatal clonazolam mono-intoxications from patients presenting to emergency departments in Australia.

CASES

Four patients aged between 16 and 19 years presented to hospital with a sedative toxidrome (Glasgow Coma Scale range 8-13) and elevated heart rate (median heart rate 100 beats per minute, range 92-105) following reported benzodiazepine exposure. Three patients reported the use of a large quantity (7-20 tablets) of Xanax^®, a brand of alprazolam not commercially available in Australia. Two patients required nasopharyngeal airway insertion following the development of airway obstruction. The median time to return of a normal conscious state (Glasgow Coma Scale 15) was 23 h (range 5-30 h). Clonazolam (range 0.2-2.1 µg/L) and its main metabolite 8-aminoclonazolam (range 5.9-19.1 µg/L) were the only substances detected by liquid chromatography-tandem mass spectrometry in blood samples of all patients.

CONCLUSION

Clonazolam intoxication resulted in sedation with mild sinus tachycardia. Three patients who reported multiple tablet exposures experienced prolonged sedation, and two of these patients developed airway obstruction. In this series, clonazolam was unknowingly ingested through possible illicit substitution within an unregulated counterfeit benzodiazepine product.

Impact of an Early Warning System Protocol, for Patients Admitted to the Medical Floors with SARS-COV2 Pneumonia, on ICU Admission

BACKGROUND

COVID-19 placed a significant burden on the global healthcare system. Strain in critical care capacity has been associated with increased COVID-19-related ICU mortality. This study evaluates the impact of an early warning system and response team implemented on medical floors to safely triage and care for critically ill patients on the floor and preserve ICU capacity.

METHODS

We conducted a multicenter, retrospective cohort study, comparing outcomes between intervention and control hospitals within a US eight-hospital urban network. Patients hospitalized with COVID-19 pneumonia between April 13^th, 2020 and June 19^th, 2020 were included in the study, which was a time of a regional surge of COVID-19 admissions. An automated, electronic early warning protocol to identify patients with moderate-severe hypoxemia on the medical floors and implement early interventions was implemented at one of the eight hospitals (“the intervention hospital”).

RESULTS

Among 1024 patients, 403 (39%) were admitted to the intervention hospital and 621 (61%) were admitted to one of the control hospitals. Adjusted for potential confounders, patients at the intervention hospital were less likely to be admitted to the ICU (HR = 0.73, 95% CI 0.53, 1.000, P = .0499) compared to the control hospitals. Patients admitted from the floors to the ICU at the intervention hospital had shorter ICU stay (HR for ICU discharge: 1.74; 95% CI 1.21, 2.51, P = .003). There was no significant difference between intervention and control hospitals in need for mechanical ventilation (OR = 0.93; 95% CI 0.38, 2.31; P = .88) or hospital mortality (OR = 0.79; 95% CI 0.52, 1.18; P = .25).

CONCLUSION

A protocol to conserve ICU beds by implementing an early warning system with a dedicated response team to manage respiratory distress on the floors reduced ICU admission and was not associated with worse outcomes compared to hospitals that managed similar levels of respiratory distress in the ICU.

EMCDDA framework and practical guidance for naming synthetic cannabinoids

Synthetic cannabinoids (SCs), often sold as "legal" replacements for cannabis, are the largest group of new psychoactive substances monitored by the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA). Currently, close to 240 structurally heterogeneous SCs are monitored through the European Union (EU) Early Warning System, and attributing consistent, informative, and user-friendly names to SCs has been a challenge in the past. Over time, several naming conventions have been employed with the aim of making SCs more easily recognizable by non-chemists, including regulators. To achieve this, the names assigned need to contain detailed information on the structural features present in the substance. This work provides a theoretical framework and a practical hands-on guideline for consistent naming of SCs, which is easy to understand and can be applied by the forensic community, researchers, clinical practitioners, and policy-makers. The proposed framework builds on the established letter code system for molecular building blocks (core, linker, linked group, and tail) implemented by the EMCDDA in 2013 and has been expanded to incorporate additional structural features through substitution. The scope of the issue of attributing semi-systematic code names is illustrated, and earlier approaches used for naming SCs are discussed. The concepts and rules of the EMCDDA framework are described through a flowchart that provides a basis for naming new SCs, a graphical overview of the chemical diversity of SCs, and a detailed list of the SCs identified in the EU by the Early Warning System of the EMCDDA for reference.

Artificial intelligence in public health: the potential of epidemic early warning systems

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Management of Climate Resilience: Exploring the Potential of Digital Twin Technology, 3D City Modelling, and Early Warning Systems

Cities, and in particular those in coastal low-lying areas, are becoming increasingly susceptible to climate change, the impact of which is worsened by the tendency for population concentration in these areas. Therefore, comprehensive early warning systems are necessary to minimize harm from extreme climate events on communities. Ideally, such a system would allow all stakeholders to acquire accurate up-to-date information and respond effectively. This paper presents a systematic review that highlights the significance, potential, and future directions of 3D city modelling, early warning systems, and digital twins in the creation of technology for building climate resilience through the effective management of smart cities. In total, 68 papers were identified through the PRISMA approach. A total of 37 case studies were included, among which (n = 10) define the framework for a digital twin technology, (n = 14) involve the design of 3D virtual city models, and (n = 13) entail the generation of early warning alerts using the real-time sensor data. This review concludes that the bidirectional flow of data between a digital model and the real physical environment is an emerging concept for enhancing climate resilience. However, the research is primarily in the phase of theoretical concepts and discussion, and numerous research gaps remain regarding the implementation and use of a bidirectional data flow in a true digital twin. Nonetheless, ongoing innovative research projects are exploring the potential of digital twin technology to address the challenges faced by communities in vulnerable areas, which will hopefully lead to practical solutions for enhancing climate resilience in the near future.

The Emerging Drugs Network of Australia - Victoria Clinical Registry: A state-wide illicit substance surveillance and alert network

OBJECTIVES

With an increasingly dynamic global illicit drug market, including the emergence of novel psychoactive substances, many jurisdictions have moved to establish toxicosurveillance systems to enable timely detection of harmful substances in the community. This paper describes the methodology for the Emerging Drugs Network of Australia - Victoria (EDNAV) project, a clinical registry focused on the collection of high-quality clinical and analytical data from ED presentations involving illicit drug intoxications. Drug intelligence collected from the project is utilised by local health authorities with the aim to identify patterns of drug use and emerging drugs of concern.

METHODS

The project involves 10 public hospital EDs in Victoria, Australia. Patients 16 years and over, presenting to a network ED with a suspected illicit drug-related toxicity and a requirement for venepuncture are eligible for inclusion in the study under a waiver of consent. Clinical and demographic parameters are documented by site-based clinicians and comprehensive toxicological analysis is conducted on patient blood samples via specialised forensic services. All data are then deidentified and compiled in a project specific database.

RESULTS

Cases are discussed in weekly multidisciplinary team meetings, with a view to identify potentially harmful substances circulating in the community. High-risk signals are escalated to key stakeholders to produce timely and proportionate public health alerts with a focus on harm minimisation.

CONCLUSIONS

The EDNAV project represents the first centralised system providing near real-time monitoring of community drug use in Victoria and is fundamental in facilitating evidence-based public health intervention.

From signal to alert: A cluster of exposures to counterfeit alprazolam tablets containing five novel benzodiazepines

OBJECTIVE

To illustrate the toxicosurveillance role of the Emerging Drugs Network of Australia - Victoria (EDNAV) project in informing timely harm minimisation interventions.

METHODS

Utilisation of an ethics approved clinical registry storing de-identified clinical and analytical data on Victorian ED illicit drug-related presentations.

RESULTS

In April 2022, six adults presented to hospital with varying levels of sedation, following the use of counterfeit benzodiazepines. Comprehensive toxicological analysis identified five separate novel benzodiazepines within blood samples from each patient. A public 'Drug Alert' was subsequently issued, and local emergency physicians were notified.

CONCLUSION

Toxicosurveillance projects, such as EDNAV, are critical to the continued monitoring and reporting of illicit substance use in the community.

COVID-19 surveillance in wastewater: An epidemiological tool for the monitoring of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic has prompted a lot of questions globally regarding the range of information about the virus's possible routes of transmission, diagnostics, and therapeutic tools. Worldwide studies have pointed out the importance of monitoring and early surveillance techniques based on the identification of viral RNA in wastewater. These studies indicated the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in human feces, which is shed via excreta including mucus, feces, saliva, and sputum. Subsequently, they get dumped into wastewater, and their presence in wastewater provides a possibility of using it as a tool to help prevent and eradicate the virus. Its monitoring is still done in many regions worldwide and serves as an early "warning signal"; however, a lot of limitations of wastewater surveillance have also been identified.

Wastewater pandemic preparedness: Toward an end-to-end pathogen monitoring program

Molecular analysis of public wastewater has great potential as a harbinger for community health and health threats. Long-used to monitor the presence of enteric viruses, in particular polio, recent successes of wastewater as a reliable lead indicator for trends in SARS-CoV-2 levels and hospital admissions has generated optimism and emerging evidence that similar science can be applied to other pathogens of pandemic potential (PPPs), especially respiratory viruses and their variants of concern (VOC). However, there are substantial challenges associated with implementation of this ideal, namely that multiple and distinct fields of inquiry must be bridged and coordinated. These include engineering, molecular sciences, temporal-geospatial analytics, epidemiology and medical, and governmental and public health messaging, all of which present their own caveats. Here, we outline a framework for an integrated, state-wide, end-to-end human pathogen monitoring program using wastewater to track viral PPPs.

Detecting Acute Deterioration in Older Adults Living in Residential Aged Care: A Scoping Review

OBJECTIVES

To explore models, processes, or tools implemented in residential aged care (RAC) to support registered nurses (RNs) to identify and respond to the acute deterioration of residents.

DESIGN

Scoping literature review of English Language articles published in peer reviewed journals.

SETTINGS AND PARTICIPANTS

Studies were conducted in RAC facilities providing long-term 24-hour medical, nursing, and social care for people age 65 years or older with age-related disability.

METHODS

We completed a MESH term and key word search of MEDLINE, Embase, CINAHL, PubMed, and Google Scholar. Included studies had (1) part of the intervention based in RAC; (2) had a direct impact on RAC day to day practice; and (3) contained or provided access to the detail of the intervention. Data was charted by author, date, country, study design and the components, genesis, and efficacy of the methods used to identify and respond to acute deterioration.

RESULTS

We found 46 studies detailing models of care, clinical patterns of acute deterioration, and deterioration detection tools. It was not possible to determine which element of the models care had the greatest impact on RN decision making. The clinical patterns of acute deterioration painted a picture of acute deterioration in the frail. There was limited evidence to support the use of existing deterioration detection tools in the RAC population.

CONCLUSION AND IMPLICATIONS

We found no straight forward systematic method to support RAC RNs to identify and respond to the acute deterioration of residents. This is an important practice gap. The clinical pattern of acute deterioration described in the literature has the potential to be used for the development of a tool to support RAC RNs to identify and respond to the acute deterioration of residents.

Urban Seismic Network Based on MEMS Sensors: The Experience of the Seismic Observatory in Camerino (Marche, Italy)

Urban seismic networks are considered very useful tools for the management of seismic emergencies. In this work, a study of the first urban seismic network in central Italy is presented. The urban seismic network, built using MEMS sensors, was implemented in the urban district of Camerino, one of the cities in central Italy with the greatest seismic vulnerability. The technological choices adopted in developing this system as well as the implemented algorithms are shown in the context of their application to the first seismic event recorded by this innovative monitoring infrastructure. This monitoring network is innovative because it implements a distributed computing and statistical earthquake detection algorithm. As such, it is not based on the traces received by the stations from the central server; rather, each station carries out the necessary checks on the signal in real time, sending brief reports to the server in case of anomalies. This approach attempts to shorten the time between event detection and alert, effectively removing the dead times in the systems currently used in the Italian national network. The only limit for an instant alarm is the latency in the tcp/ip packages used to send the short reports to the server. The presented work shows the infrastructure created; however, there is not enough data to draw conclusions on this new early warning approach in the field, as it is currently in the data collection phase.

A Flexible and Wearable Nylon Fiber Sensor Modified by Reduced Graphene Oxide and ZnO Quantum Dots for Wide-Range NO2 Gas Detection at Room Temperature

Reduced graphene oxide (rGO) fiber as a carbon-based fiber sensor has aroused widespread interest in the field of gas sensing. However, the low response value and poor flexibility of the rGO fiber sensor severely limit its application in the field of flexible wearable electronics. In this paper, a flexible and wearable nylon fiber sensor modified by rGO and ZnO quantum dots (QDs) is proposed for wide-range NO₂ gas detection at room temperature. The response value of the nylon fiber sensor to 100 ppm NO₂ gas is as high as 0.4958, and the response time and recovery time are 216.2 s and 667.9 s, respectively. The relationship between the sensor's response value and the NO₂ concentration value is linear in the range of 20-100 ppm, and the fitting coefficient is 0.998. In addition, the test results show that the sensor also has good repeatability, flexibility, and selectivity. Moreover, an early warning module was also designed and is proposed in this paper to realize the over-limit monitoring of NO₂ gas, and the flexible sensor was embedded in a mask, demonstrating its great application potential and value in the field of wearable electronics.

Early Warning System for Emergency Care: Designing a Timely Monitoring Mobile-Based System

Early Warning Scores (EWSs) systems support the timely detection of patient deterioration and rapid response of the care team. Due to the mobility nature of healthcare settings, there has been a growing tendency to use mobile-based devices in these settings. This chapter aimed to design a mobile-based EWS application (app). This was a descriptive study to design the architecture of the proposed EWS app. The design of architecture was done using the Unified Modeling Language diagrams including a class diagram, use-case diagram, and activity diagram. We evaluated the architecture using the ARID scenario-based evaluation method. The proposed EWS application (app) was the integration of three EWSs, including NEWS2, PEWS, and MEOWS. The workflow of these EWSs systems was designed and integrated into a single app. Also, the static structure of the proposed EWS app was designed by class diagram and the behavioral structure was depicted by use-case and activity diagrams. The class diagram showed the system components and their relationships. However, the use-case diagram displayed the app's interaction with its environment, and the activity diagram illustrated how the EWS app processes were carried out. Evaluation results showed the possibility of designing the architecture for the proposed EWS app. In our app, the EWSs were designed in the clinician's workflow, and it was integrated with the patient's Electronic Health Record (EHR). These factors may lead to more use of EWSs. Considering the frequency of alerts represented to clinicians and the user-friendly design of the app, some suggestions can be considered by EWS systems developers in the future.

The ADEBAR project - European and international provision of analytical data from structure elucidation and analytical characterization of NPS

Novel substances for which none or limited analytical data are available constitute a challenge for police and customs forensic laboratories. The time-consuming process of structural elucidation and acquisition of analytical data has been centralized in the ADEBAR project in Germany, co-funded since 2017 by the EU's Internal Security Fund. The project aims to comprehensively characterize substances relevant for forensic-toxicological casework within the analytical competence network. The analytical datasets are distributed digitally through European and (inter-) national channels. Additionally, pharmacological evaluation allows for estimating in vivo potency and potential harm required as scientific evidence for legislative amendments. The ADEBAR project contributes to the availability of analytical data on new substances relevant to the daily work of police and customs laboratories. Since the inception of the ADEBAR project, 549 samples have been registered, and 302 substance reports notified to the EMCDDA, including numerous spectrometric and spectroscopic data. In addition, 3619 mass spectra have been accumulated in ADEBAR mass spectra databases. A central institution for the structure elucidation and acquisition of valid, high-quality analytical data for police and customs forensic laboratories and forensic medicine institutes is important in the future because there does not seem to be an end to the dynamic of novel NPS appearing on the drug market.

Factors that influence hospital nurses' escalation of patient care in response to their early warning score: A qualitative evidence synthesis

BACKGROUND

The Early Warning Score (EWS) is a validated tool that has improved patient outcomes internationally. This scoring system is used within the hospital setting to identify potentially deteriorating patients, thus expediting referral to appropriate medical personnel. It is increasingly recognised that there are other influencing factors along with EWS, which impact on nurses' decisions to escalate care.

AIM

The aim of this review was to identify and synthesise data from qualitative studies, which examined factors influencing nurses' escalation of care in response to patients' EWS.

METHODS

The systematic search strategy and eligibility criteria were guided by the SPIDER (Sample Phenomenon of Interest Design Evaluation Type of Research) framework. Eleven databases and five grey literature databases were searched. Titles and abstracts were independently screened in line with pre-established inclusion and exclusion criteria using the cloud-based platform, Rayyan. The selected studies underwent quality appraisal using CASP (Critical Appraisal Skills Programme, 2017, https://www.casp-uk.net/casp-toolschecklists) and subsequently synthesised using Thomas and Harden's thematic analysis approach. GRADE-CERQual (Grading of Recommendations Assessment Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research) was used to assess confidence in results. The EQUATOR listed guideline ENTREQ (Tong et al., 2012, BMC Medical Research Methodology, 12) was used to synthesise and report findings.

RESULTS

Eighteen studies from seven countries including 235 nurses were identified. Following synthesis, four analytical themes were generated with eighteen derived consequent findings. The four themes identified were as follows: 1) Marrying nurses' clinical judgement with EWS 2) SMART communication 3) EWS Protocol: Blessing and a Curse 5) Hospital Domain.

CONCLUSION

Nurses strive to find balance by simultaneously navigating within the boundaries of both the EWS protocol and the hospital domain. They view the EWS as a valid essential component in the system but one that does not give a definitive answer and absolute direction. They value the protocols' ability to identify deteriorating patients and convey the seriousness of a situation to their multidisciplinary colleagues but also find it somewhat restrictive and frustrating and wish to have credence given to their own intuition and clinical judgement.

Analysis of clinical application effects and challenges of early warning system for the high-risk obstetric women of China: A scoping review

BACKGROUND

Obstetric critical illness is an important factor that leads to an increase in maternal mortality. Early warning assessment can effectively reduce maternal and neonatal mortality and morbidity. However, there are multiple early warning systems, and the effect and applicability of each system in China still need to be explored.

OBJECTIVES

To elaborate on the application, effectiveness and challenges of the existing early warning systems for high-risk obstetric women in China and to provide a reference for clinical practice.

DESIGN

A scoping review guided by the Arksey and O'Malley framework and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for scoping review (PRISMA-ScR) guidelines.

ELIGIBILITY CRITERIA

We included original studies related to early warning and excluded those that were guidelines, consensus and reviews. The included studies were published in Chinese or English by Chinese scholars as of June 2021.

DATA SOURCES

CNKI, Wanfang, VIP, Cochrane, CINAHL, Embase, PubMed and Web of Science databases were searched systematically, and the reference sections of the included papers were snowballed.

RESULTS

In total, 598 articles were identified. These articles were further refined using keyword searches and exclusion criteria, and 17 articles met the inclusion criteria. We extracted data related to each study's population, methods and results. Early warning tools, outcome indices, effects and challenges are discussed.

CONCLUSIONS

Although all studies have shown that early warning systems have good application effects, the use of early warning systems in China is still limited, with poor regional management and poor sensitivity for specific obstetric women. Future research needs to develop more targeted early warning tools for high-risk obstetric women and address the current challenges in clinical applications.

Novel Epidemic Metrics to Communicate Outbreak Risk at the Municipality Level: Dengue and Zika in the Dominican Republic

Arboviruses remain a significant cause of morbidity, mortality and economic cost across the global human population. Epidemics of arboviral disease, such as Zika and dengue, also cause significant disruption to health services at local and national levels. This study examined 2014-2016 Zika and dengue epidemic data at the sub-national level to characterise transmission across the Dominican Republic. For each municipality, spatio-temporal mapping was used to characterise disease burden, while data were age and sex standardised to quantify burden distributions among the population. In separate analyses, time-ordered data were combined with the underlying disease migration interval distribution to produce a network of likely transmission chain events, displayed using transmission chain likelihood matrices. Finally, municipal-specific reproduction numbers (R_m) were established using a Wallinga-Teunis matrix. Dengue and Zika epidemics peaked during weeks 39-52 of 2015 and weeks 14-27 of 2016, respectively. At the provincial level, dengue attack rates were high in Hermanas Mirabal and San José de Ocoa (58.1 and 49.2 cases per 10,000 population, respectively), compared with the Zika burden, which was highest in Independencia and San José de Ocoa (21.2 and 13.4 cases per 10,000 population, respectively). Across municipalities, high disease burden was observed in Cotuí (622 dengue cases per 10,000 population) and Jimani (32 Zika cases per 10,000 population). Municipal infector-infectee transmission likelihood matrices identified seven 0% likelihood transmission events throughout the dengue epidemic and two 0% likelihood transmission events during the Zika epidemic. Municipality reproduction numbers (R_m) were consistently higher, and persisted for a greater duration, during the Zika epidemic (R_m = 1.0) than during the dengue epidemic (R_m < 1.0). This research highlights the importance of disease surveillance in land border municipalities as an early warning for infectious disease transmission. It also demonstrates that a high number of importation events are required to sustain transmission in endemic settings, and vice versa for newly emerged diseases. The inception of a novel epidemiological metric, R_m, reports transmission risk using standardised spatial units, and can be used to identify high transmission risk municipalities to better focus public health interventions for dengue, Zika and other infectious diseases.

The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial

Background

Every year, hundreds of thousands of inpatients die from cardiac arrest and sepsis, which could be avoided if those patients’ risk for deterioration were detected and timely interventions were initiated. Thus, a system is needed to convert real-time, raw patient data into consumable information that clinicians can utilize to identify patients at risk of deterioration and thus prevent mortality and improve patient health outcomes. The overarching goal of the COmmunicating Narrative Concerns Entered by Registered Nurses (CONCERN) study is to implement and evaluate an early warning score system that provides clinical decision support (CDS) in electronic health record systems. With a combination of machine learning and natural language processing, the CONCERN CDS utilizes nursing documentation patterns as indicators of nurses’ increased surveillance to predict when patients are at the risk of clinical deterioration.

Objective

The objective of this cluster randomized pragmatic clinical trial is to evaluate the effectiveness and usability of the CONCERN CDS system at 2 different study sites. The specific aim is to decrease hospitalized patients’ negative health outcomes (in-hospital mortality, length of stay, cardiac arrest, unanticipated intensive care unit transfers, and 30-day hospital readmission rates).

Methods

A multiple time-series intervention consisting of 3 phases will be performed through a 1-year period during the cluster randomized pragmatic clinical trial. Phase 1 evaluates the adoption of our algorithm through pilot and trial testing, phase 2 activates optimized versions of the CONCERN CDS based on experience from phase 1, and phase 3 will be a silent release mode where no CDS is viewable to the end user. The intervention deals with a series of processes from system release to evaluation. The system release includes CONCERN CDS implementation and user training. Then, a mixed methods approach will be used with end users to assess the system and clinician perspectives.

Results

Data collection and analysis are expected to conclude by August 2022. Based on our previous work on CONCERN, we expect the system to have a positive impact on the mortality rate and length of stay.

Conclusions

The CONCERN CDS will increase team-based situational awareness and shared understanding of patients predicted to be at risk for clinical deterioration in need of intervention to prevent mortality and associated harm.

Trial Registration

ClinicalTrials.gov NCT03911687; https://clinicaltrials.gov/ct2/show/NCT03911687

International Registered Report Identifier (IRRID)

DERR1-10.2196/30238

In action-an early warning system for the detection of unexpected or novel pathogens

Proactive approaches in preventing future epidemics include pathogen discovery prior to their emergence in human and/or animal populations. Playing an important role in pathogen discovery, high-throughput sequencing (HTS) enables the characterization of microbial and viral genetic diversity within a given sample. In particular, metagenomic HTS allows the unbiased taxonomic profiling of sequences; hence, it can identify novel and highly divergent pathogens such as viruses. Newly discovered viral sequences must be further investigated using genomic characterization, molecular and serological screening, and/or invitro and invivo characterization. Several outbreak and surveillance studies apply unbiased generic HTS to characterize the whole genome sequences of suspected pathogens. In contrast, this study aimed to screen for novel and unexpected pathogens in previously generated HTS datasets and use this information as a starting point for the establishment of an early warning system (EWS). As a proof of concept, the EWS was applied to HTS datasets and archived samples from the 2018–9 West Nile virus (WNV) epidemic in Germany. A metagenomics read classifier detected sequences related to genome sequences of various members of Riboviria. We focused the further EWS investigation on viruses belonging to the families Peribunyaviridae and Reoviridae, under suspicion of causing co-infections in WNV-infected birds. Phylogenetic analyses revealed that the reovirus genome sequences clustered with sequences assigned to the species Umatilla virus (UMAV), whereas a new peribunyavirid, tentatively named ‘Hedwig virus’ (HEDV), belonged to a putative novel genus of the family Peribunyaviridae. In follow-up studies, newly developed molecular diagnostic assays detected fourteen UMAV-positive wild birds from different German cities and eight HEDV-positive captive birds from two zoological gardens. UMAV was successfully cultivated in mosquito C6/36 cells inoculated with a blackbird liver. In conclusion, this study demonstrates the power of the applied EWS for the discovery and characterization of unexpected viruses in repurposed sequence datasets, followed by virus screening and cultivation using archived sample material. The EWS enhances the strategies for pathogen recognition before causing sporadic cases and massive outbreaks and proves to be a reliable tool for modern outbreak preparedness.

Modeling Early Warning Systems: Construction and Validation of a Discrete Event Simulation Model for Heart Failure

OBJECTIVES

Developing and validating a discrete event simulation model that is able to model patients with heart failure managed with usual care or an early warning system (with or without a diagnostic algorithm) and to account for the impact of individual patient characteristics in their health outcomes.

METHODS

The model was developed using patient-level data from the Trans-European Network - Home-Care Management System study. It was coded using RStudio Version 1.3.1093 (version 3.6.2.) and validated along the lines of the Assessment of the Validation Status of Health-Economic decision models tool. The model includes 20 patient and disease characteristics and generates 8 different outcomes. Model outcomes were generated for the base-case analysis and used in the model validation.

RESULTS

Patients managed with the early warning system, compared with usual care, experienced an average increase of 2.99 outpatient visits and a decrease of 0.02 hospitalizations per year, with a gain of 0.81 life years (0.45 quality-adjusted life years) and increased average total costs of €11 249. Adding a diagnostic algorithm to the early warning system resulted in a 0.92 life year gain (0.57 quality-adjusted life years) and increased average costs of €9680. These patients experienced a decrease of 0.02 outpatient visits and 0.65 hospitalizations per year, while they avoided being hospitalized 0.93 times. The model showed robustness and validity of generated outcomes when comparing them with other models addressing the same problem and with external data.

CONCLUSIONS

This study developed and validated a unique patient-level simulation model that can be used for simulating a wide range of outcomes for different patient subgroups and treatment scenarios. It provides useful information for guiding research and for developing new treatment options by showing the hypothetical impact of these interventions on a large number of important heart failure outcomes.

Estimation of Earthquake Early Warning Parameters for Eastern Gulf of Corinth and Western Attica Region (Greece). First Results

The main goal of an Earthquake Early Warning System (EEWS) is to alert before the arrival of damaging waves using the first seismic arrival as a proxy, thus becoming an important operational tool for real-time seismic risk management on a short timescale. EEWSs are based on the use of scaling relations between parameters measured on the initial portion of the seismic signal after the arrival of the first wave. To explore the plausibility of EEWSs around the Eastern Gulf of Corinth and Western Attica, amplitude and frequency-based parameters, such as peak displacement (P_d), the integral of squared velocity (IV&nbsp;2) and the characteristic period (τ_c), were analyzed. All parameters were estimated directly from the initial 3 s, 4 s, and 5 s signal windows (t_w) after the P arrival. While further study is required on the behavior of the proxy quantities, we propose that the IV&nbsp;2 parameter and the peak amplitudes of the first seconds of the P waves present significant stability and introduce the possibility of a future on-site EEWS for areas affected by earthquakes located in the Eastern Gulf of Corinth and Western Attica. Parameters related to regional-based EEWS need to be further evaluated.

Climate change and dengue fever knowledge, attitudes and practices in Bangladesh: a social media-based cross-sectional survey

BACKGROUND

Bangladesh experienced its worst dengue fever (DF) outbreak in 2019. This study investigated the knowledge, attitudes and practices (KAP) among university students in Bangladesh and significant factors associated with their prevention practices related to climate change and DF.

METHODS

A social media-based (Facebook) cross-sectional KAP survey was conducted and secondary data of reported DF cases in 2019 extracted. Logistic regression and spatial analysis were run to examine the data.

RESULTS

Of 1500 respondents, 76% believed that climate change can affect DF transmission. However, participants reported good climate change knowledge (76.7%), attitudes (87.9%) and practices (39.1%). The corresponding figures for DF were knowledge (47.9%), attitudes (80.3%) and practices (25.9%). Good knowledge and attitudes were significantly associated with good climate change adaptation or mitigation practices (p<0.05). Good knowledge, attitudes and previous DF experiences were also found to be significantly associated with good DF prevention practices (p<0.001). There was no significant positive correlation between climate change and DF KAP scores and the number of DF cases.

CONCLUSIONS

Findings from this study provide baseline data that can be used to promote educational campaigns and intervention programs focusing on climate change adaptation and mitigation and effective DF prevention strategies among various communities in Bangladesh and similar dengue-endemic countries.

Corrigendum: Development of an Early Warning System for Owners Using a Validated Health-Related Quality of Life (HRQL) Instrument for Companion Animals and Its Use in a Large Cohort of Dogs

[This corrects the article DOI: 10.3389/fvets.2020.575795.].

Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal

STUDY OBJECTIVES

The aim of the study was to inspect acoustic properties and sleep characteristics of pre-apneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated.

METHODS

Participants with habitual snoring or heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted and snoring related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples and a machine learning algorithm was used to establish two prediction models.

RESULTS

A total of 74 eligible participants were included. Model 1 tested by five-fold cross validation achieved the accuracy of 0.92 and area under the curve of 0.94 for respiratory event prediction. model 2 with acoustic features and sleep information tested by Leave-One-Out cross validation had the accuracy of 0.78 and area under the curve of 0.80. Sleep position was found to be the most important amongst all sleep features contributing to the performance.

CONCLUSIONS

Pre-apneic sound presented unique acoustic characteristics and snoring related breathing sound could be deployed as a real-time apneic event predictor. The model combined with sleep information served as a promising tool for an early warning system to forecast apneic events.

Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems

Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation measures can be realized. To date however, LEWS have only rarely been implemented in informal settlements due to their high costs and complex operation. Based on modern Internet of Things (IoT) technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication protocol, the Inform@Risk research project is developing a cost-effective geosensor network specifically designed for use in a LEWS for informal settlements. It is currently being implemented in an informal settlement in the outskirts of Medellin, Colombia for the first time. The system, whose hardware and firmware is open source and can be replicated freely, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g., tilt sensor) on board and can be connected to various different sensors including a newly developed low cost subsurface sensor probe for the detection of ground movements and groundwater level measurements. Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed LEWS offers a good benefit-cost ratio and in the future can hopefully find application in other parts of the world.

Clinician Emotions Surrounding Pediatric Oncology Patient Deterioration

Background

Pediatric oncology patients have a high rate of clinical deterioration frequently requiring critical care. Patient deterioration events are distressing for clinicians, but little is known about how Pediatric Early Warning Systems (PEWS) impact clinicians' emotional responses to deterioration events.

Methods

Semi-structured interviews were conducted with 83 nurses, pediatricians, oncologists, and intensive care clinicians who had recently participated in a patient deterioration event at two pediatric oncology hospitals of different resource-levels: St. Jude Children's Research Hospital (N = 42 participants) in Memphis, Tennessee or Unidad Nacional de Oncología Pediátrica (N = 41 participants) in Guatemala City, Guatemala. Interviews were conducted in the participants' native language (English or Spanish), transcribed, and translated into English. Each transcript was coded by two researchers and analyzed for thematic content.

Results

Emotions around patient deterioration including concern, fear, and frustration were reported across all disciplines at both hospitals. Concern was often triggered by an elevated PEWS score and usually resulted in increased attention, which reassured bedside clinicians that patients were receiving necessary interventions. However, persistently elevated PEWS scores, particularly at St. Jude Children's Research Hospital, occasionally resulted in a false sense of relief, diminishing clinician attention and negatively impacting patient care. Nurses at both institutions described how PEWS amplified their voices, engendering confidence and empowerment, two of the only positive emotions described in the study.

Conclusion

Clinicians experienced a range of emotions while caring for high-risk patients in the setting of clinical deterioration. These emotions have the potential to contribute to compassion fatigue and burnout, or to resilience. Acknowledgment and further investigation of the complex interplay between PEWS and clinician emotions are necessary to maximize the impact of PEWS on patient safety while simultaneously supporting staff wellbeing.

Prediction of obstetrical and fetal complications using automated electronic health record data

An increasing number of delivering women experience major morbidity and mortality. Limited work has been done on automated predictive models that could be used for prevention. Using only routinely collected obstetrical data, this study aimed to develop a predictive model suitable for real-time use with an electronic medical record. We used a retrospective cohort study design with split validation. The denominator consisted of women admitted to a delivery service. The numerator consisted of women who experienced a composite outcome that included both maternal (eg, uterine rupture, postpartum hemorrhage), fetal (eg, stillbirth), and neonatal (eg, hypoxic ischemic encephalopathy) adverse events. We employed machine learning methods, assessing model performance using the area under the receiver operator characteristic curve and number needed to evaluate. A total of 303,678 deliveries took place at 15 study hospitals between January 1, 2010, and March 31, 2018, and 4130 (1.36%) had ≥1 obstetrical complication. We employed data from 209,611 randomly selected deliveries (January 1, 2010, to March 31, 2017) as a derivation dataset and validated our findings on data from 52,398 randomly selected deliveries during the same time period (validation 1 dataset). We then applied our model to data from 41,669 deliveries from the last year of the study (April 1, 2017, to March 31, 2018 [validation 2 dataset]). Our model included 35 variables (eg, demographics, vital signs, laboratory tests, progress of labor indicators). In the validation 2 dataset, a gradient boosted model (area under the receiver operating characteristic curve or c statistic, 0.786) was slightly superior to a logistic regression model (c statistic, 0.778). Using an alert threshold of 4.1%, our final model would flag 16.7% of women and detect 52% of adverse outcomes, with a number needed to evaluate of 20.9 and 0.455 first alerts per day per 1000 annual deliveries. In conclusion, electronic medical record data can be used to predict obstetrical complications. The clinical utility of these automated models has not yet been demonstrated. To conduct interventions to assess whether using these models results in patient benefit, future work will need to focus on the development of clinical protocols suitable for use in interventions.

Advances in the Detection of Toxic Algae Using Electrochemical Biosensors

Harmful algal blooms (HABs) are more frequent as climate changes and tropical toxic species move northward, especially along the Iberian Peninsula, a rich aquaculture area. Monitoring programs, detecting the presence of toxic algae before they bloom, are of paramount importance to protect ecosystems, aquaculture, human health and local economies. Rapid, reliable species identification methods using molecular barcodes coupled to biosensor detection tools have received increasing attention as an alternative to the legally required but impractical microscopic counting-based techniques. Our electrochemical detection system has improved, moving from conventional sandwich hybridization protocols using different redox mediators and signal probes with different labels to a novel strategy involving the recognition of RNA heteroduplexes by antibodies further labelled with bacterial antibody binding proteins conjugated with multiple enzyme molecules. Each change has increased sensitivity. A 150-fold signal increase has been produced with our newest protocol using magnetic microbeads (MBs) and amperometric detection at screen-printed carbon electrodes (SPCEs) to detect the target RNA of toxic species. We can detect as few as 10 cells L^-1 for some species by using a fast (~2 h), simple (PCR-free) and cheap methodology (~2 EUR/determination) that will allow this methodology to be integrated into easy-to-use portable systems.

A Multimodal Sepsis Quality-Improvement Initiative Including 24/7 Screening and a Dedicated Sepsis Response Team-Reduced Readmissions and Mortality

Objectives

To evaluate if a hospitalwide sepsis performance improvement initiative improves compliance with the Centers for Medicare and Medicaid Services-mandated sepsis bundle interventions and patient outcomes.

Study Design

Retrospective analysis comparing 6 months before and 14 months after intervention.

Setting

Tertiary teaching hospital in Washington, DC.

Subjects

Patients admitted with a diagnosis of sepsis to a tertiary hospital.

Interventions

Implementation of a multimodal quality-improvement initiative.

Measurements and Main Results

A total of 4,102 patients were diagnosed with sepsis, severe sepsis, or septic shock during the study period, 861 patients (21%) were diagnosed during a 6-month preintervention period, and 3,241 (79%) were diagnosed in a 13-month postintervention period. Adjusted for patient case-mix, the prevalence of simple sepsis increased by 12%, but it decreased for severe sepsis and septic shock by 5.3% and 6.9%, respectively. Compliance with all sepsis bundle interventions increased by 31.1 percentage points (p < 0.01). All-cause hospital readmission and readmission due to infection were both reduced by 1.6% and 1.7 percentage points (p < 0.05). Death from any sepsis diagnosis was reduced 4.5% (p < 0.01). Death from severe sepsis and septic shock both was reduced by 5% (p < 0.01) and 6.5% (p < 0.01), respectively.

Conclusions

After the implementation of multimodal sepsis performance initiatives, we observed a higher prevalence of sepsis secondary to screening but a lower prevalence of severe sepsis and septic shock, an improvement in compliance with the sepsis bundle interventions bundle, as well as reduction in hospital readmission and all- cause mortality rate.

New Psychoactive Substances: Evolution in the Exchange of Information and Innovative Legal Responses in the European Union

At the end of 2019, the European Monitoring Centre for Drugs and Drug Addiction was monitoring around 790 new psychoactive substances, more than twice the total number of controlled substances under the United Nations Conventions. These substances, which are not subject to international drug controls, include a wide range of molecules, including the assortment of drugs such as synthetic cannabinoids, stimulants, opiates, and benzodiazepines. Most of them are sold as "legal" substitutes for illicit drugs, while others are intended for small groups willing to experiment with them in order to know their possible new effects. At the national level, various measures have been taken to control new substances and many European countries have responded with specific legislation in favor of consumer safety and by extending or adapting existing drug laws to incorporate the new psychoactive substances. Moreover, since 1997, an early warning system has been created in Europe for identifying and responding quickly to the risks of new psychoactive substances. In order to establish a quicker and more effective system to address the criminal activities associated with new dangerous psychoactive substances, the European legal framework has considerably changed over the years.

Retrospective Study on the Seasonal Forecast-Based Disease Intervention of the Wheat Blast Outbreaks in Bangladesh

Seasonal disease risk prediction using disease epidemiological models and seasonal forecasts has been actively sought over the last decades, as it has been believed to be a key component in the disease early warning system for the pre-season planning of local or national level disease control. We conducted a retrospective study using the wheat blast outbreaks in Bangladesh, which occurred for the first time in Asia in 2016, to study a what-if scenario that if there was seasonal disease risk prediction at that time, the epidemics could be prevented or reduced through prediction-based interventions. Two factors govern the answer: the seasonal disease risk prediction is accurate enough to use, and there are effective and realistic control measures to be used upon the prediction. In this study, we focused on the former. To simulate the wheat blast risk and wheat yield in the target region, a high-resolution climate reanalysis product and spatiotemporally downscaled seasonal climate forecasts from eight global climate models were used as inputs for both models. The calibrated wheat blast model successfully simulated the spatial pattern of disease epidemics during the 2014-2018 seasons and was subsequently used to generate seasonal wheat blast risk prediction before each winter season starts. The predictability of the resulting predictions was evaluated against observation-based model simulations. The potential value of utilizing the seasonal wheat blast risk prediction was examined by comparing actual yields resulting from the risk-averse (proactive) and risk-disregarding (conservative) decisions. Overall, our results from this retrospective study showed the feasibility of seasonal forecast-based early warning system for the pre-season strategic interventions of forecasted wheat blast in Bangladesh.

Development of an Early Warning System for Owners Using a Validated Health-Related Quality of Life (HRQL) Instrument for Companion Animals and Its Use in a Large Cohort of Dogs

Preventive measures in human healthcare are recognized as a means of providing early detection of disease, however, the veterinary profession has not been as effective in communicating the benefits of preventive measures to pet owners. Readily available pet healthcare information on the internet, owners not understanding that regular health evaluations can ensure the well-being of their pets and owners confusing the signs of chronic disease with normal aging have contributed to declining numbers of veterinary visits. The use of web-based generic health–related quality of life (HRQL) measures to evaluate health status (wellness) remotely could facilitate veterinary preventive medicine. This publication describes the development and practical application of an integrated alert system for an online generic HRQL measurement instrument (VetMetrica™) which generates scores in four domains of HRQL—Energetic/Enthusiastic (E/E), Happy/Content (H/C), Active/Comfortable (A/C), and Calm/Relaxed (C/R)—for 2 age groups (young/middle-aged, ≤7 years and old, ≥8 years). The alert provides an early warning, via email to owners, that a potentially significant deterioration in health status has occurred. The model accurately predicted the health status of 93 and 83% of sick young/middle aged and old dogs respectively, with healthy dogs predicted with 83% accuracy. HRQL data, collected via a white-labeled veterinary clinic branded app designed to facilitate connected care between owner and veterinarian, were analyzed for 6,108 dogs, aged between 6 weeks and 16 years. Of these 5,002 were deemed to be in perfect health by their owners, yet the alert was triggered for 1,343 (27%) of these, 75% of which were young/middle-aged and 25% were old, indicating that acute injuries notwithstanding, many middle aged dogs may have been suffering from undetected chronic disease such as osteoarthritis. This work has demonstrated that the use of VetMetrica™ delivered via the PetDialog™ app, which supports 24/7 remote health monitoring is an efficient way for vets to provide all their owners with the opportunity to monitor their animal's wellness throughout their lifetime, providing the vet with a mechanism to identify health problems early while stimulating owners to be more proactive in seeking veterinary attention.

High Density Real-Time Air Quality Derived Services from IoT Networks

In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices' dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

Preliminary Study of Sars-Cov-2 Occurrence in Wastewater in the Czech Republic

The virus SARS-CoV-2, which has caused the recent COVID-19 pandemic, may be present in the stools of COVID-19 patients. Therefore, we aimed to detect SARS-CoV-2 in wastewater for surveillance of SARS-CoV-2 in the population. Samples of untreated wastewater were collected from 33 wastewater treatment plants (WWTPs) of different sizes within the Czech Republic. SARS-CoV-2 RNA was concentrated from wastewater and viral RNA was determined using real-time reverse transcription polymerase chain reaction (RT-qPCR). SARS-CoV-2 RNA was detected in 11.6% of samples and more than 27.3% of WWTPs; in some of them, SARS-CoV-2 was detected repeatedly. Our preliminary results indicate that an epidemiology approach that focuses on the determination of SARS-CoV-2 in wastewater could be suitable for SARS-CoV-2 surveillance in the population.

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017-2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

Using Passive Infrared Detectors to Record Group Activity and Activity in Certain Focus Areas in Fattening Pigs

Animal behavior is an important aspect in the assessment of animal welfare. Passive infrared detectors (PID), detecting thermal changes to measure activity, have already been used to record data on the behavior of groups of animals. Within this study, the suitability of these detectors for the collection of activity profiles for focused areas is further investigated. The aim was to record the activity of a group of eleven fattening pigs in a pen, as well as the activity in the five functional areas for resting, feeding, drinking, exploration, and elimination. In order to evaluate the data obtained, the behavior was video recorded for visual assessment. In addition, relevant indoor environment parameters were recorded (ammonia, air temperature, and relative humidity). For the measurement of activity by PID, strong correlations from up to r = 0.87 (p < 0.01) could be found compared to visual assessment. The results indicate that activity changes during the day and activity in defined functional areas can be recorded using PIDs. These data combined with data of climate-related sensors could serve the farmer as a monitoring tool for early detection of behavioral changes or serve as partial aspect within a Weak Point Analysis within external on-farm consulting.

Selective Predation by Owls on Infected Bank Voles (Myodes glareolus) as a Possible Sentinel of Tularemia Outbreaks

Tularemia is a widely spread zoonotic disease in the northern hemisphere, caused by the bacterium Francisella tularensis. In humans, tularemia is an acute febrile illness with incidence peaks in late summer to early autumn of outbreak years, but there is no early warning system in place that can reduce the impact of disease by providing timely risk information. In this study, we revisit previously unpublished data on F. tularensis in water, sediment, soil, and small mammals from 1984 in northern Sweden. In addition, we used human case data from the national surveillance system for tularemia in the same year. In the environmental and small mammal material, bank vole (Myodes glareolus) samples from urine and bladder were the only samples that tested positive for F. tularensis. The prevalence of F. tularensis among trapped bank voles was 13.5%, although all six bank voles that were retrieved from owl nest boxes in early May tested positive. Forty-two human tularemia cases were reported from August to December in 1984. Based on these results, we encourage investigating the potential role of tularemia-infected bank voles retrieved from owl nest boxes in spring as an early warning for outbreaks of tularemia among humans in summer and autumn of the same year.