Early Warning Systems for Critical Illness Outside the Intensive Care Unit

Early warning systems (EWSs) are designed and deployed to create a rapid assessment and response for patients with clinical deterioration outside the intensive care unit (ICU). These models incorporate patient-level data such as vital signs and laboratory values to detect or prevent adverse clinical events, such as vital signs and laboratories to allow detection and prevention of adverse clinical events such as cardiac arrest, intensive care transfer, or sepsis. The applicability, development, clinical utility, and general perception of EWS in clinical practice vary widely. Here, we review the field as it has grown from early vital sign-based scoring systems to contemporary multidimensional algorithms and predictive technologies for clinical decompensation outside the ICU.

Synoptic-scale drivers of fire weather in Greece

The identification of the large-scale atmospheric circulation patterns which are associated with extreme fire weather is of great importance for developing early warning systems, management strategies, and for increasing awareness and preparedness of all the involved entities, including both the public and practitioners. Such a forecasting approach is currently missing in Greece and many other countries. Furthermore, considering climate projections over the Mediterranean, which indicate an environment more conducive to wildfire activity, the need for timely forecasting of extreme fire weather becomes increasingly urgent. Here, we present an alternative fire weather forecasting framework using ERA5 reanalysis data of atmospheric variables and fire weather indices of the Canadian Forest Fire Weather Index System (CFFWIS) during the period June-October from 1979 to 2019. Within this framework, we define the critical fire weather patterns (CFWPs) of Greece associated with different levels of fire weather severity by applying Self-Organizing-Maps (SOMs) on mid-tropospheric geopotential height. We quantify the fire weather conditions associated with each CFWP. Using a set of CFFWIS indices and key fire weather variables, our SOM-based analysis reveals five distinct CFWPs linked to different levels and characteristics of fire weather severity. The lowest fire weather severity is associated with lower than average geopotential heights, and anomalous cold and moist weather. The highest fire weather severity is associated with higher than average geopotential heights, and anomalous hot, dry, and windy conditions, suggesting the potential for wind-driven wildfires. Our analysis yields elevated fire weather severity linked to a CFWP, when hot and dry conditions are accompanied by atmospheric instability, suggesting the potential for plume-driven wildfires and the potential for pyroconvection. The main advantage of this forecasting framework is that it could be used for providing valuable information regarding the upcoming fire weather conditions even up to 7-12 days in advance depending on the atmospheric predictability.

Evaluating event-based surveillance capacity in Africa: Use of the Africa CDC scorecard, 2022-2023

Introduction

Event-based surveillance (EBS) is a critical component of Early Warning, Alert and Response (EWAR) capacity needed for outbreak prevention and control. To better understand existing EBS and monitor the progress of capacity-building efforts over time, Africa CDC developed an EBS scorecard as part of a revision to the EBS Framework.

Methods

We distributed the scorecard to African Union (AU) Member States (MSs). Survey responses from the MSs' human health sector were aggregated, cleaned, and analysed. MS, regional, and continental EBS capacity was assessed.

Results

Between 21 July 2022 and 4 April 2023, a total of 63 respondents representing 49 (89%) of 55 MSs completed the survey. Given Africa CDC's public health mandate, we acknowledged the importance of One Health collaboration in MSs but focused on and analysed only the human health sector responses. Thirty-four (71%) MSs stated having EBS in place; hotline was the most common type of EBS implemented (76%). Seventeen (50%) MSs reported multisectoral, One Health collaboration as part of EBS implementation. Scorecard outcomes showed a minimal (score of <60%) to average (score between 60-80%) level of EBS capacity in 29 and five (5) MSs respectively.

Discussion

Current EBS capacity levels need to be strengthened in Africa to ensure the continent remains prepared for future public health threats. The Africa CDC EBS scorecard provides a useful way to measure and track this capacity over time. Results can be used to advocate for and target resources for capacity building to foster public health emergency preparedness efforts.

Forecasting algorithms in the ICU

Despite significant advances in modeling methods and access to large datasets, there are very few real-time forecasting systems deployed in highly monitored environment such as the intensive care unit. Forecasting models may be developed as classification, regression or time-to-event tasks; each could be using a variety of machine learning algorithms. An accurate and useful forecasting systems include several components beyond a forecasting model, and its performance is assessed using end-user-centered metrics. Several barriers to implementation and acceptance persist and clinicians will play an active role in the successful deployment of this promising technology.

Capturing the SARS-CoV-2 infection pyramid within the municipality of Rotterdam using longitudinal sewage surveillance

Despite high vaccination rates in the Netherlands, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate. Longitudinal sewage surveillance was implemented along with the notification of cases as two parts of the surveillance pyramid to validate the use of sewage for surveillance, as an early warning tool, and to measure the effect of interventions. Sewage samples were collected from nine neighborhoods between September 2020 and November 2021. Comparative analysis and modeling were performed to understand the correlation between wastewater and case trends. Using high resolution sampling, normalization of wastewater SARS-CoV-2 concentrations, and 'normalization' of reported positive tests for testing delay and intensity, the incidence of reported positive tests could be modeled based on sewage data, and trends in both surveillance systems coincided. The high collinearity implied that high levels of viral shedding around the onset of disease largely determined SARS-CoV-2 levels in wastewater, and that the observed relationship was independent of variants of concern and vaccination levels. Sewage surveillance alongside a large-scale testing effort where 58 % of a municipality was tested, indicated a five-fold difference in the number of SARS-CoV-2-positive individuals and reported cases through standard testing. Where trends in reported positive cases were biased due to testing delay and testing behavior, wastewater surveillance can objectively display SARS-CoV-2 dynamics for both small and large locations and is sensitive enough to measure small variations in the number of infected individuals within or between neighborhoods. With the transition to a post-acute phase of the pandemic, sewage surveillance can help to keep track of re-emergence, but continued validation studies are needed to assess the predictive value of sewage surveillance with new variants. Our findings and model aid in interpreting SARS-CoV-2 surveillance data for public health decision-making and show its potential as one of the pillars of future surveillance of (re)emerging viruses.

Co-designing drug alerts for health and community workers for an emerging early warning system in Victoria, Australia

BACKGROUND

Alerts about changes in unregulated drug markets may be useful for supporting health and community workers to anticipate, prevent, and respond to unexpected adverse drug events. This study aimed to establish factors influencing the successful design and implementation of drug alerts for use in clinical and community service settings in Victoria, Australia.

METHODS

An iterative mixed methods design was used to co-produce drug alert prototypes with practitioners and managers working across various alcohol and other drug services and emergency medicine settings. A quantitative needs-analysis survey (n = 184) informed five qualitative co-design workshops (n = 31). Alert prototypes were drafted based on findings and tested for utility and acceptability. Applicable constructs from the Consolidated Framework for Implementation Research helped to conceptualise factors that impact successful alert system design.

RESULTS

Timely and reliable alerts about unexpected drug market changes were important to nearly all workers (98%) yet many reported insufficient access to this kind of information (64%). Workers considered themselves 'conduits' for information-sharing and valued alerts for increasing exposure to drug market intelligence; facilitating communication about potential threats and trends; and improving capacity for effective responding to drug-related harm. Alerts should be 'shareable' across a range of clinical and community settings and audiences. To maximise engagement and impact, alerts must command attention, be easily recognisable, be available on multiple platforms (electronic and printable formats) in varying levels of detail, and be disseminated via appropriate notification mechanisms to meet the needs of diverse stakeholder groups. Three drug alert prototypes (SMS prompt, summary flyer, and a detailed poster) were endorsed by workers as useful for supporting their work responding to unexpected drug-related harms.

DISCUSSION

Alerts informed by coordinated early warning networks that offer close to real-time detection of unexpected substances can provide rapid, evidence-based drug market intelligence to inform preventive and responsive action to drug-related harm. The success of alert systems requires adequate planning and resourcing to support design, implementation, and evaluation, which includes consultation with all relevant audiences to understand how to maximise engagement with information, recommendations, and advice. Our findings about factors impacting successful alert design have utility to inform the development of local early warning systems.

A review of synthetic cathinones emerging in recent years (2019-2022)

Purpose

The emergence of novel psychoactive substances (NPS) has been being a continuous and evolving problem for more than a decade. Every year, dozens of new, previously unknown drugs appear on the illegal market, posing a significant threat to the health and lives of their users. Synthetic cathinones are one of the most numerous and widespread groups among NPS. The purpose of this work was to identify and summarize available data on newly emerging cathinones in very recent years.

Methods

Various online databases such as PubMed, Google Scholar, but also databases of government agencies including those involved in early warning systems, were used in search of reports on the identification of newly emerging synthetic cathinones. In addition, threads on various forums created by users of these drugs were searched for reports on the effects of these new substances.

Results

We have identified 29 synthetic cathinones that have been detected for the first time from early 2019 to mid-2022. We described their structures, known intoxication symptoms, detected concentrations in biological material in poisoning cases, as well as the countries and dates of their first appearance. Due to the lack of studies on the properties of the novel compounds, we compared data on the pharmacological profiles of the better-known synthetic cathinones with available information on the newly emerged ones. Some of these new agents already posed a threat, as the first cases of poisonings, including fatal ones, have been reported.

Conclusions

Most of the newly developed synthetic cathinones can be seen as analogs and replacements for once-popular compounds that have been declining in popularity as a result of legislative efforts. Although it appears that some of the newly emerging cathinones are not widely used, they may become more popular in the future and could become a significant threat to health and life. Therefore, it is important to continue developing early warning systems and identifying new compounds so that their widespread can be prevented.

Early warning system to predict energy prices: the role of artificial intelligence and machine learning

The COVID-19 pandemic has inflicted the global economy and caused substantial financial losses. The energy sector was heavily affected and resulted in energy prices massively tumbling. The Russian invasion of Ukraine has fueled the energy maker more volatile. In such uncertain contexts, an Early Warning System (EWS) would efficiently contribute to stabilizing market swings. It will leverage the ability to control operating costs and pave the way for smooth economic recovery. Within this framework, we deploy Machine Learning (ML) models to forecast energy equity prices by employing uncertainty indices as a proxy for predicting energy market volatility. We empirically examine the comparative effectiveness of prevalent ML models and conventional approaches (regression) to forecast the energy equity prices by utilizing the daily data from 1/6/2011 to 18/1/2022 for four US uncertainty and eight energy equity indices. Results show that the Nonlinear Autoregressive with External (Exogenous) parameters (NARX) of Neural Networks (NN) scored significantly better accuracy than all other (25) ML models and conventional approaches. The study outcomes are beneficial for policymakers, governments, market regulators, investors, hedge and mutual funds, and corporations. They improve stakeholders' resilience to exogenous shocks, blaze the recovery path, and provide evidence-based for assets allocation strategies.

A Review on Applications of Time-Lapse Electrical Resistivity Tomography Over the Last 30 Years : Perspectives for Mining Waste Monitoring

Mining operations generate large amounts of wastes which are usually stored into large-scale storage facilities which pose major environmental concerns and must be properly monitored to manage the risk of catastrophic failures and also to control the generation of contaminated mine drainage. In this context, non-invasive monitoring techniques such as time-lapse electrical resistivity tomography (TL-ERT) are promising since they provide large-scale subsurface information that complements surface observations (walkover, aerial photogrammetry or remote sensing) and traditional monitoring tools, which often sample a tiny proportion of the mining waste storage facilities. The purposes of this review are as follows: (i) to understand the current state of research on TL-ERT for various applications; (ii) to create a reference library for future research on TL-ERT and geoelectrical monitoring mining waste; and (iii) to identify promising areas of development and future research needs on this issue according to our experience. This review describes the theoretical basis of geoelectrical monitoring and provides an overview of TL-ERT applications and developments over the last 30 years from a database of over 650 case studies, not limited to mining operations (e.g., landslide, permafrost). In particular, the review focuses on the applications of ERT for mining waste characterization and monitoring and a database of 150 case studies is used to identify promising applications for long-term autonomous geoelectrical monitoring of the geotechnical and geochemical stability of mining wastes. Potential challenges that could emerge from a broader adoption of TL-ERT monitoring for mining wastes are discussed. The review also considers recent advances in instrumentation, data acquisition, processing and interpretation for long-term monitoring and draws future research perspectives and promising avenues which could help improve the design and accuracy of future geoelectric monitoring programs in mining wastes.

Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients

BACKGROUND

Early warning scores are designed to identify hospitalized patients who are at high risk of clinical deterioration. Although many general scores have been developed for the medical-surgical wards, specific scores have also been developed for obstetric patients due to differences in normal vital sign ranges and potential complications in this unique population. The comparative performance of general and obstetric early warning scores for predicting deterioration and infection on the maternal wards is not known.

METHODS

This was an observational cohort study at the University of Chicago that included patients hospitalized on obstetric wards from November 2008 to December 2018. Obstetric scores (modified early obstetric warning system (MEOWS), maternal early warning criteria (MEWC), and maternal early warning trigger (MEWT)), paper-based general scores (Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), and a general score developed using machine learning (electronic Cardiac Arrest Risk Triage (eCART) score) were compared using the area under the receiver operating characteristic score (AUC) for predicting ward to intensive care unit (ICU) transfer and/or death and new infection.

RESULTS

A total of 19,611 patients were included, with 43 (0.2%) experiencing deterioration (ICU transfer and/or death) and 88 (0.4%) experiencing an infection. eCART had the highest discrimination for deterioration (p < 0.05 for all comparisons), with an AUC of 0.86, followed by MEOWS (0.74), NEWS (0.72), MEWC (0.71), MEWS (0.70), and MEWT (0.65). MEWC, MEWT, and MEOWS had higher accuracy than MEWS and NEWS but lower accuracy than eCART at specific cut-off thresholds. For predicting infection, eCART (AUC 0.77) had the highest discrimination.

CONCLUSIONS

Within the limitations of our retrospective study, eCART had the highest accuracy for predicting deterioration and infection in our ante- and postpartum patient population. Maternal early warning scores were more accurate than MEWS and NEWS. While institutional choice of an early warning system is complex, our results have important implications for the risk stratification of maternal ward patients, especially since the low prevalence of events means that small improvements in accuracy can lead to large decreases in false alarms.

Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania

BACKGROUND

Neonatal mortality remains high in Tanzania at approximately 20 deaths per 1000 live births. Low birthweight, prematurity, and asphyxia are associated with neonatal mortality; however, no studies have assessed the value of combining underlying conditions and vital signs to provide clinicians with early warning of infants at risk of mortality. The aim of this study was to identify risk factors (including vital signs) associated with neonatal mortality in the neonatal intensive care unit (NICU) in Bugando Medical Centre (BMC), Mwanza, Tanzania; to identify the most accurate generalised linear model (GLM) or decision tree for predicting mortality; and to provide a tool that provides clinically relevant cut-offs for predicting mortality that is easily used by clinicians in a low-resource setting.

METHODS

In total, 165 neonates were enrolled between November 2019 and March 2020, of whom 80 (48.5%) died. We competed the performance of GLMs and decision trees by resampling the data to create training and test datasets and comparing their accuracy at correctly predicting mortality.

RESULTS

GLMs always outperformed decision trees. The best fitting GLM showed that (for standardised risk factors) temperature (OR 0.61, 95% CI 0.40-0.90), birthweight (OR 0.33, 95% CI 0.20-0.52), and oxygen saturation (OR 0.66, 95% CI 0.45-0.94) were negatively associated with mortality, while heart rate (OR 1.59, 95% CI 1.10-2.35) and asphyxia (OR 3.23, 95% 1.25-8.91) were risk factors. To identify the tool that balances accuracy and with ease of use in a low-resource clinical setting, we compared the best fitting GLM with simpler versions, and identified the three-variable GLM with temperature, heart rate, and birth weight as the best candidate. For this tool, cut-offs were identified using receiver operator characteristic (ROC) curves with the optimal cut-off for mortality prediction corresponding to 76.3% sensitivity and 68.2% specificity. The final tool is graphical, showing cut-offs that depend on birthweight, heart rate, and temperature.

CONCLUSIONS

Underlying conditions and vital signs can be combined into simple graphical tools that improve upon the current guidelines and are straightforward to use by clinicians in a low-resource setting.

The effects of inflow of agricultural biogas digestate on bivalves' behavior

This study focused on the reaction of bivalve molluscs to biogas digestate, which is a waste product of an increasingly developing biogas production in rural areas worldwide. The effects of biogas digestate on aquatic organisms are not fully known, and neither this substance nor any types of manure were tested in the monitoring based on valvometry, which is a biomonitoring method based on bivalve behavior. The change in bivalves functioning in biogas digestate inflow was studied using three different diluted digestate concentrations. Exposure to the highest concentration of digestate induced a decline of mean shell opening and activity time of Unio tumidus species. A significant difference in behavioral patterns was recorded during the first 10 min after exposure to the digestate. A Gradual decreasing tendency of shell opening levels was apparent under the highest concentration reaching 55% compared to the pretreatment value. Also, a decreasing tendency was observed under the medium concentration (82.4% of initial level) after 2 h, while an increase in shell opening levels was recorded in the most diluted digestate. This research work proved that the inflow of biogas digestate has significant impact on bivalves' behavior. Unio tumidus is a sensitive indicator of biogas digestate inflow in the aquatic environment. Moreover, it proved that the opening and closing activities over time depend on the concentration of the digestate. Therefore, the mollusk bivalves might be utilized in early warning systems to detect organic pollutants in water.

Heat warnings, mortality, and hospital admissions among older adults in the United States

BACKGROUND

Heat warnings are issued in advance of forecast extreme heat events, yet little evidence is available regarding their effectiveness in reducing heat-related illness and death. We estimated the association of heat warnings and advisories (collectively, "alerts") issued by the United States National Weather Service with all-cause mortality and cause-specific hospitalizations among Medicare beneficiaries aged 65 years and older in 2,817 counties, 2006-2016.

METHODS

In each county, we compared days with heat alerts to days without heat alerts, matched on daily maximum heat index and month. We used conditional Poisson regression models stratified on county, adjusting for year, day of week, federal holidays, and lagged daily maximum heat index.

RESULTS

We identified a matched non-heat alert day for 92,029 heat alert days in 2,817 counties, or 54.6% of all heat alert days during the study period. Contrary to expectations, heat alerts were not associated with lower risk of mortality (RR: 1.005 [95% CI: 0.997, 1.013]). However, heat alerts were associated with higher risk of hospitalization for fluid and electrolyte disorders (RR: 1.040 [95% CI: 1.015, 1.065]) and heat stroke (RR: 1.094 [95% CI: 1.038, 1.152]). Results were similar in sensitivity analyses additionally adjusting for same-day heat index, ozone, and PM2.5.

CONCLUSIONS

Our results suggest that heat alerts are not associated with lower risk of mortality but may be associated with higher rates of hospitalization for fluid and electrolyte disorders and heat stroke, potentially suggesting that heat alerts lead more individuals to seek or access care.

Forecasting transitions in the state of food security with machine learning using transferable features

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.

Sepsis scoring systems and use of the Sepsis six care bundle in maternity hospitals

BACKGROUND

This study aimed to assess the predictive power of three different Sepsis Scoring Systems (SSSs), namely maternity Systematic Inflammatory Response Syndrome (mSIRS), quick Sepsis-related Organ Failure Assessment (qSOFA) and Modified Early Warning System (MEWS) in identifying sepsis by comparing them with positive culture. This study also sought to evaluate compliance with using the Sepsis Six Care Bundle (SSCB) operated in an individual health board.

METHODS

A retrospective cohort study was conducted in 3 maternity hospitals of a single Scottish health board that admitted 2690 pregnancies in a 12 weeks period in 2016. Data for study was obtained from medical notes, handheld and electronic health records for women who were prescribed antibiotics with a confirmed or suspected diagnosis of sepsis. Data on clinical parameters was used to classify women according to mSIRS, qSOFA and MEWS as having sepsis or not and this was compared to results of positive culture to obtain sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under Receiver Operating Characteristic curve (AUROC) along with their 95% confidence intervals. Data was also obtained on SSCB compliance.

RESULTS

A total of 89 women were diagnosed with sepsis, of which 14 had missing data, leaving 75 for final analysis. Sensitivity, specificity, PPV, NPV and AUROC of mSIRS and MEWS were almost similar with AUROC of both being around 50%. Only 33 (37.1%) had identifiable sepsis six sticker displayed on medical notes and only 2 (2.2%) had all elements of SSCB delivered within the recommended one-hour post-diagnosis period. Blood culture and full blood count with other lab tests had been performed for most women (97%) followed by intravenous antibiotics and fluids (93.9%).

CONCLUSIONS

mSIRS and MEWS were quite similar in detecting sepsis when compared to positive culture, with their ability to detect sepsis being close to chance. This underlines the need for creating a valid SSS with high sensitivity and specificity for clinical use in obstetric settings. Clinical use of SSCB was limited despite it being a health board policy, although there is considerable possibility of improvement following detailed audits and removal of barriers for implementing SSCB.

Early warning systems in maternity care: protocol for a qualitative evidence synthesis of maternity care providers' views and experiences

Background: Early warning systems (EWS) have been widely adopted for use in maternity settings internationally. The idea in using these systems is early recognition of potential or actual clinical deterioration in pregnant or postpartum women, and escalation of care. Barriers to successful implementation and use of EWS, however, have been identified. If EWS are to be applied consistently, a greater understanding of the views and experiences of EWS from the perspectives of those using and applying EWS in maternity practice is needed. This protocol describes a qualitative evidence synthesis of maternity care providers' (midwives, obstetricians, and allied maternity care professionals) views and experiences of EWS use and application in practice. Methods: Studies will be included in the review if they report on maternity care providers use and application of EWS in any birth setting. Qualitative studies and studies of mixed methods design, where qualitative data can be extracted separately, will be included. To source relevant literature the electronic databases of MEDLINE, CINHAL, Web of Science Core Collection (incorporating Social Science Citation Index) and Maternity and Infant Care (MIDIRS), from date of inception, will be searched. The methodological quality of the included studies will be appraised using the 12-criteria of the assessment tool developed by the Evidence for Policy and Practice Information and Co-ordinating Centre. Thematic synthesis will be used for synthesising the qualitative data from included studies. The confidence in the findings will be assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research. Conclusions: The findings of this qualitative evidence synthesis may provide valuable information on the barriers, challenges, and facilitators for EWS use based on the experiences of those directly involved in EWS application in maternity care provision. PROSPERO registration: CRD42021235137 (08/04/2021).

Escalation triggers and expected responses in obstetric early warning systems used in UK consultant-led maternity units

Background

The use of obstetric early warning systems (OEWS) are recommended as an adjunct to reduce maternal morbidity and mortality. The aim of this review was to document the variation in OEWS trigger thresholds and the quality of information included within accompanying escalation protocols.

Methods

A review of OEWS charts and escalation policies across consultant-led maternity units in the UK (n = 147) was conducted. OEWS charts were analysed for variation in the values of physiological parameters triggering different levels of clinical escalation. Relevant data within the escalation protocols were also searched for: urgency of clinical response; seniority of responder; frequency of on-going clinical monitoring; and clinical setting recommended for on-going care.

Results

The values of physiological parameters triggering specific clinical responses varied significantly between OEWS. Only 99 OEWS charts (67.3%) had an escalation protocol as part of the chart. For 29 charts (19.7%), the only escalation information included was generic, for example to “contact a doctor if triggers”. Only 76 (51.7%) charts detailed the required seniority of responder, 37 (25.2%) the frequency for on-going clinical monitoring, eight (5.4%) the urgency of clinical response and two (1.4%) the recommended clinical setting for on-going care.

Conclusion

The observed variations in the trigger thresholds used in OEWS charts and the quality of information included within the accompanying escalation protocols is likely to lead to suboptimal detection and response to clinical deterioration during pregnancy and the post-partum period. The development of a national OEWS and escalation protocol would help to standardise care across obstetric units.

Developing a sociocultural framework of compliance: an exploration of factors related to the use of early warning systems among acute care clinicians

Background

Early warning systems (EWS) are most effective when clinicians monitor patients’ vital signs and comply with the recommended escalation of care protocols once deterioration is recognised.

Objectives

To explore sociocultural factors influencing acute care clinicians’ compliance with an early warning system commonly used in Queensland public hospitals in Australia.

Methods

This interpretative qualitative study utilised inductive thematic analysis to analyse data collected from semi-structured interviews conducted with 30 acute care clinicians from Queensland, Australia.

Results

This study identified that individuals and teams approached compliance with EWS in the context of 1) the use of EWS for patient monitoring; and 2) the use of EWS for the escalation of patient care. Individual and team compliance with monitoring and escalation processes is facilitated by intra and inter-professional factors such as acceptance and support, clear instruction, inter-disciplinary collaboration and good communication. Noncompliance with EWS can be attributed to intra and inter-professional hierarchy and poor communication.

Conclusions

The overarching organisational context including the hospital’s embedded quality improvement and administrative protocols (training, resources and staffing) impact hospital-wide culture and influence clinicians’ and teams’ compliance or non-compliance with early warning system’s monitoring and escalation processes. Successful adoption of EWS relies on effective and meaningful interactions among multidisciplinary staff.

Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: A novel simulation and solution

When using tree-based methods to develop predictive analytics and early warning systems for preventive healthcare, it is important to use an appropriate imputation method to prevent learning the missingness pattern. To demonstrate this, we developed a novel simulation that generated synthetic electronic health record data using a variational autoencoder with a custom loss function, which took into account the high missing rate of electronic health data. We showed that when tree-based methods learn missingness patterns (correlated with adverse events) in electronic health record data, this leads to decreased performance if the system is used in a new setting that has different missingness patterns. Performance is worst in this scenario when the missing rate between those with and without an adverse event is the greatest. We found that randomized and Bayesian regression imputation methods mitigate the issue of learning the missingness pattern for tree-based methods. We used this information to build a novel early warning system for predicting patient deterioration in general wards and telemetry units: PICTURE (Predicting Intensive Care Transfers and other UnfoReseen Events). To develop, tune, and test PICTURE, we used labs and vital signs from electronic health records of adult patients over four years (n = 133,089 encounters). We analyzed primary outcomes of unplanned intensive care unit transfer, emergency vasoactive medication administration, cardiac arrest, and death. We compared PICTURE with existing early warning systems and logistic regression at multiple levels of granularity. When analyzing PICTURE on the testing set using all observations within a hospital encounter (event rate = 3.4%), PICTURE had an area under the receiver operating characteristic curve (AUROC) of 0.83 and an adjusted (event rate = 4%) area under the precision-recall curve (AUPR) of 0.27, while the next best tested method-regularized logistic regression-had an AUROC of 0.80 and an adjusted AUPR of 0.22. To ensure system interpretability, we applied a state-of-the-art prediction explainer that provided a ranked list of features contributing most to the prediction. Though it is currently difficult to compare machine learning-based early warning systems, a rudimentary comparison with published scores demonstrated that PICTURE is on par with state-of-the-art machine learning systems. To facilitate more robust comparisons and development of early warning systems in the future, we have released our variational autoencoder's code and weights so researchers can (a) test their models on data similar to our institution and (b) make their own synthetic datasets.

A new integrative perspective on early warning systems for health in the context of climate change

Climate change causes or aggravates a wide range of exposures with multiple impacts on health, both direct and indirect. Early warning systems have been established to act on the risks posed by these exposures, permitting the timely activation of action plans to minimize health effects. These plans are usually activated individually. Although they show good results from the point of view of minimizing health impacts, such as in the case of high temperature plans, they commonly fail to address the synergies across various climate-related or climate-aggravated exposures. Since several of those exposures tend to occur concurrently, failure to integrate them in prevention efforts could affect their effectiveness and reach. Thus, there is a need to carry out an integrative approach for the multiple effects that climate change has on population health. This article presents a proposal for how these plans should be articulated. The proposed integrated plan would consist of four phases. The first phase, based on early warning systems, would be the activation of different existing individual plans related to the health effects that can be caused by certain circumstances and when possible corrective measures would be implemented. The second phase would attempt to quantify the health impact foreseen by the event in terms of the different health indicators selected. The third phase would be to activate measures to minimize the impact on health, via population alerts and advisories, and additional social and health services, based on the provisions in phase two. Phase four would be related to epidemiological surveillance that permits evaluation of the effects of activating the plan. We believe that this integrative approach should be extended to all of the public health interventions related to climate change.

Defining heat waves and extreme heat events using sub-regional meteorological data to maximize benefits of early warning systems to population health

BACKGROUND

Extreme heat events have been consistently associated with an increased risk of hospitalization for various hospital diagnoses. Classifying heat events is particularly relevant for identifying the criteria to activate early warning systems. Heat event classifications may also differ due to heterogeneity in climates among different geographic regions, which may occur at a small scale. Using local meteorological data, we identified heat waves and extreme heat events that were associated with the highest burden of excess hospitalizations within the County of San Diego and quantified discrepancies using county-level meteorological criteria.

METHODS

Eighteen event classifications were created using various combinations of temperature metric, percentile, and duration for both county-level and climate zone level meteorological data within San Diego County. Propensity score matching and Poisson regressions were utilized to ascertain the association between heat wave exposure and risk of hospitalization for heat-related illness and dehydration for the 1999-2013 period. We estimated both relative and absolute risks for each heat event classification in order to identify optimal definitions of heat waves and extreme heat events for the whole city and in each climate zone to target health impacts.

RESULTS

Heat-related illness differs vastly by level (county or zone-specific), definition, and risk measure. We found the county-level definitions to be systematically biased when compared to climate zone definitions with the largest discrepancy of 56 attributable hospitalizations. The relative and attributable risks were often minimally correlated, which exemplified that relative risks alone are not adequate to optimize heat waves definitions.

CONCLUSIONS

Definitions based on county-level defined thresholds do not provide an accurate picture of the observed health effects and will fail to maximize the potential effectiveness of heat warning systems. Absolute rather than relative risks are a more appropriate measure to define the set of criteria to activate early warnings systems and thus maximize public health benefits.

Tools to combat food fraud - A gap analysis

A complex legal and institutional framework exists in the EU to ensure the safety of the feed-food chain, while such an integrated system for combating food fraud is under development. The European Commission (EC) Knowledge Centre for Food Fraud and Quality is charged with the provision of scientific insight for the policy making of EC services dealing with food fraud, and the creation of expert networks with the competent authorities of the EU Member States. To flag gaps in the existing infrastructure needed for effectively and efficiently fighting food fraud, the Centre together with the competent authorities and several EC services undertook a stocktaking exercise of what works well and which areas will need improvement. Out of several focus areas, (i) the development of early warning systems, (ii) the availability of compositional databases of vulnerable foods, and (iii) the creation of centres of competence were prioritised for further action.

Regional rainfall threshold maps drawn through multivariate geostatistical techniques for shallow landslide hazard zonation

The Empirical Rainfall Thresholds (ERTs) for shallow landslide initiation are commonly devised worldwide mostly to be implemented within landslide early warning systems. Nonetheless, since the pioneering works on ERTs in the 1980s, only meteorological variables - that are cumulated E or intensity I and duration D values of rainfalls that are likely to trigger landslides - have been used to predict landslide occurrence, even though they are characterized by a large uncertainty. Over time, many efforts have been devoted to constrain ERT to geo-morphological characters of the landslide locations but, since nowadays, they did not get to a sound new method to derive ERT and strengthen its ability to forecast future rainfall-induced landslide. In this study, local geo-morphological characters have been taken into account by means of the co-kriging technique to constrain the E and D mean values of a regional ERT and their confidence intervals. The study area, where the proposed method was trained, is the hilly side of the Abruzzo region (Italy). Here, 62 shallow landslides have been analyzed in the time span of 2013-2017 by collecting 62 (D,E) pairs related to the rainfalls that were likely to trigger them. The relevant geo-morphological features for the considered territory have been selected through the principal component analysis. Then, the Multi-Collocated Co-Kriging technique, through ISATIS Geovariances software, has been applied to derive the spatial variability structures of E and D values conditioned by the selected geo-morphological parameters. Therefore, threshold values of E and D and their confidence intervals have been calculated generating a new shape of regional ERT, consisting of maps of continuous estimated threshold values of (D,E) and confidence interval values suitable for being used in early warning systems for shallow landslide initiation.

Applying Patient Safety to Reduce Maternal Mortality

Maternal morbidity and mortality is on the rise in the United States. Several local, state, and nationwide organizations have worked toward reducing maternal mortality by improving patient safety. Early warning systems unique to the obstetric population have been developed to provide early intervention and to prevent patients from decompensating. Patient care bundles, supported by the American College of Obstetricians and Gynecologists, as well as The Council on Patient Safety, provide a standardized approach to obstetric care. Monitoring outcomes through root cause analysis is key to improving patient safety and outcomes.

The influence of weather and weather variability on mosquito abundance and infection with West Nile virus in Harris County, Texas, USA

Early warning systems for vector-borne diseases (VBDs) prediction are an ecological application where data from the interface of several environmental components can be used to predict future VBD transmission. In general, models for early warning systems only consider average environmental conditions ignoring variation in weather variables, despite the prediction from Schmalhausen's law about the importance of environmental variability for biological systems. We present results from a long-term mosquito surveillance program from Harris County, Texas, USA, where we use time series analysis techniques to study the abundance and West Nile virus (WNV) infection patterns in the local primary vector, Culex quinquefasciatus Say. We found that, as predicted by Schmalhausen's law, mosquito abundance was associated with the standard deviation and kurtosis of environmental variables. By contrast, WNV infection rates were associated with 8-month lagged temperature, suggesting environmental conditions during overwintering might be key for WNV amplification during summer outbreaks. Finally, model validation showed that seasonal autoregressive models successfully predicted mosquito WNV infection rates up to 2 months ahead, but did rather poorly at predicting mosquito abundance, a result that might reflect impacts of vector control for mosquito population reduction, geographic scale, and other artifacts generated by operational constraints of mosquito surveillance systems.

Unexplored Opportunities: Use of Climate- and Weather-Driven Early Warning Systems to Reduce the Burden of Infectious Diseases

PURPOSE OF REVIEW

Weather and climate influence multiple aspects of infectious disease ecology. Creating and applying early warning systems based on temperature, precipitation, and other environmental data can identify where and when outbreaks of climate-sensitive infectious diseases could occur and can be used by decision makers to allocate resources. Whether an outbreak actually occurs depends heavily on other social, political, and institutional factors.

RECENT FINDINGS

Improving the timing and confidence of seasonal climate forecasting, coupled with knowledge of exposure-response relationships, can identify prior conditions conducive to disease outbreaks weeks to months in advance of outbreaks. This information could then be used by public health professionals to improve surveillance in the most likely areas for threats. Early warning systems are well established for drought and famine. And while weather- and climate-driven early warning systems for certain diseases, such as dengue fever and cholera, are employed in some regions, this area of research is underdeveloped. Early warning systems based on temperature, precipitation, and other environmental data provide an opportunity for early detection leading to early action and response to potential pathogen threats, thereby reducing the burden of disease when compared with passive health indicator-based surveillance systems.

The efficacy of twelve early warning systems for potential use in regional medical facilities in Queensland, Australia

AIM

Early warning system (EWS) validation studies are conducted predominantly in tertiary metropolitan facilities and are not necessarily applicable to regional hospitals. This study evaluates 12 EWSs for use in regional subcritical hospitals.

METHOD

This is a retrospective case-control study of patients who experienced severe adverse events (SAEs) in two regional private hospitals. Vital signs collected over 72 h preceding the SAE were applied to 12 EWSs representing three classes of EWSs. The EWS area under the receiver operator characteristic curve (AUROC), sensitivity, specificity, and number of alerts were calculated.

RESULTS

Data from 159 index and 172 control patients showed no significant differences in demographics, length of stay, and level of comorbidities. Only half of index patients achieved a medical emergency alert threshold score. On average, index patients triggered alerts 20.06 (22.67) hours preceding the SAE and alerted 2.25 (3.87) times over 72 h. The AUROC ranged from 0.628 to 0.747, with a single-parameter EWS having the lowest AUROC and an aggregated weighted EWS, the highest. The sensitivity of the EWS ranges from 0.359 to 0.692. The specificity was greater than 0.9 for all the EWSs tested.

CONCLUSIONS

Based on the EWS sensitivity and AUROC, there is a lack of conclusive evidence of the efficacy of the 12 EWSs tested. However, because the adoption of the EWS in Australian hospitals is mandatory, the implementation of an aggregated weighted EWS, such as Compass, should be considered in subcritical regional private hospitals. Given that only half of SAE achieved an EWS medical alert threshold score, it is important that good clinical judgement be used with EWS.

Prognostic accuracy of the Hamilton Early Warning Score (HEWS) and the National Early Warning Score 2 (NEWS2) among hospitalized patients assessed by a rapid response team

Background

Rapid response teams (RRTs) respond to hospitalized patients experiencing clinical deterioration and help determine subsequent management and disposition. We sought to evaluate and compare the prognostic accuracy of the Hamilton Early Warning Score (HEWS) and the National Early Warning Score 2 (NEWS2) for prediction of in-hospital mortality following RRT activation. We secondarily evaluated a subgroup of patients with suspected infection.

Methods

We retrospectively analyzed prospectively collected data (2012–2016) of consecutive RRT patients from two hospitals. The primary outcome was in-hospital mortality. We calculated the number needed to examine (NNE), which indicates the number of patients that need to be evaluated in order to detect one future death.

Results

Five thousand four hundred ninety-one patients were included, of whom 1837 (33.5%) died in-hospital. Mean age was 67.4 years, and 51.6% were male. A HEWS above the low-risk threshold (≥ 5) had a sensitivity of 75.9% (95% confidence interval (CI) 73.9–77.9) and specificity of 67.6% (95% CI 66.1–69.1) for mortality, with a NNE of 1.84. A NEWS2 above the low-risk threshold (≥ 5) had a sensitivity of 84.5% (95% CI 82.8–86.2), and specificity of 49.0% (95% CI: 47.4–50.7), with a NNE of 2.20. The area under the receiver operating characteristic curve (AUROC) was 0.76 (95% CI 0.75–0.77) for HEWS and 0.72 (95% CI: 0.71–0.74) for NEWS2. Among suspected infection patients (n = 1708), AUROC for HEWS was 0.79 (95% CI 0.76–0.81) and for NEWS2, 0.75 (95% CI 0.73–0.78).

Conclusions

The HEWS has comparable clinical accuracy to NEWS2 for prediction of in-hospital mortality among RRT patients.

Electronic supplementary material

The online version of this article (10.1186/s13054-019-2355-3) contains supplementary material, which is available to authorized users.

Effectiveness of National Weather Service heat alerts in preventing mortality in 20 US cities

BACKGROUND

Extreme heat is a well-documented public health threat. The US National Weather Service (NWS) issues heat advisories and warnings (collectively, "heat alerts") in advance of forecast extreme heat events. The effectiveness of these alerts in preventing deaths remains largely unknown.

OBJECTIVES

To quantify the change in mortality rates associated with heat alerts in 20 US cities between 2001 and 2006.

METHODS

Because NWS heat alerts are issued based on forecast weather and these forecasts are imperfect, in any given location there exists a set of days of similar observed heat index in which heat alerts have been issued for some days but not others. We used a case-crossover design and conditional logistic regression to compare mortality rates on days with versus without heat alerts among such eligible days, adjusting for maximum daily heat index and temporal factors. We combined city-specific estimates into a summary measure using standard random-effects meta-analytic techniques.

RESULTS

Overall, NWS heat alerts were not associated with lower mortality rates (percent change in rate: -0.5% [95% CI: -2.8, 1.9]). In Philadelphia, heat alerts were associated with a 4.4% (95% CI: -8.3, -0.3) lower mortality rate or an estimated 45.1 (95% empirical CI: 3.1, 84.1) deaths averted per year if this association is assumed to be causal. No statistically significant beneficial association was observed in other individual cities.

CONCLUSIONS

Our results suggest that between 2001 and 2006, NWS heat alerts were not associated with lower mortality in most cities studied, potentially missing a valuable opportunity to avert a substantial number of heat-related deaths. These results highlight the need to better link alerts to effective communication and intervention strategies to reduce heat-related mortality.

Responding to New Psychoactive Substances in the European Union: Early Warning, Risk Assessment, and Control Measures

New psychoactive substances (NPS) are drugs that are not controlled by the United Nations international drug control conventions of 1961 and 1971 but that may pose similar threats to public health. Many of them are traded as "legal" replacements to controlled drugs such as cannabis, heroin, benzodiazepines, cocaine, amphetamines, and 3,4-methylenedioxymethamphetamine (MDMA). Driven by globalization, there has been a large increase in the availability and, subsequently, harms caused by these substances over the last decade in Europe. The European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) is monitoring more than 670 NPS that have appeared on Europe's drug market in the last 20 years, of which almost 90% have appeared in the last decade. While some recent policy responses have been successful in reducing availability and sales of these substances in some settings - such as "legal highs" and "research chemicals" sold openly in the high street and online - and there are signs that growth in the market is slowing, new challenges have emerged. This includes monitoring a growing number of highly potent substances - including 179 synthetic cannabinoid receptor agonists and 28 fentanils - that can pose a high risk of life-threatening poisoning to users and can cause explosive outbreaks. This chapter briefly traces the origins of NPS, provides an overview of the situation in Europe, and discusses the work of the EMCDDA as part of a legal framework of early warning, risk assessment, and control measures that allows the European Union to rapidly detect, assess, and respond to public health and social threats caused by these substances.

The effectiveness of physiologically based early warning or track and trigger systems after triage in adult patients presenting to emergency departments: a systematic review

BACKGROUND

Changes to physiological parameters precede deterioration of ill patients. Early warning and track and trigger systems (TTS) use routine physiological measurements with pre-specified thresholds to identify deteriorating patients and trigger appropriate and timely escalation of care. Patients presenting to the emergency department (ED) are undiagnosed, undifferentiated and of varying acuity, yet the effectiveness and cost-effectiveness of using early warning systems and TTS in this setting is unclear. We aimed to systematically review the evidence on the use, development/validation, clinical effectiveness and cost-effectiveness of physiologically based early warning systems and TTS for the detection of deterioration in adult patients presenting to EDs.

METHODS

We searched for any study design in scientific databases and grey literature resources up to March 2016. Two reviewers independently screened results and conducted quality assessment. One reviewer extracted data with independent verification of 50% by a second reviewer. Only information available in English was included. Due to the heterogeneity of reporting across studies, results were synthesised narratively and in evidence tables.

RESULTS

We identified 6397 citations of which 47 studies and 1 clinical trial registration were included. Although early warning systems are increasingly used in EDs, compliance varies. One non-randomised controlled trial found that using an early warning system in the ED may lead to a change in patient management but may not reduce adverse events; however, this is uncertain, considering the very low quality of evidence. Twenty-eight different early warning systems were developed/validated in 36 studies. There is relatively good evidence on the predictive ability of certain early warning systems on mortality and ICU/hospital admission. No health economic data were identified.

CONCLUSIONS

Early warning systems seem to predict adverse outcomes in adult patients of varying acuity presenting to the ED but there is a lack of high quality comparative studies to examine the effect of using early warning systems on patient outcomes. Such studies should include health economics assessments.

Early Deterioration Indicator: Data-driven approach to detecting deterioration in general ward

INTRODUCTION

Early detection of deterioration could facilitate more timely interventions which are instrumental in reducing transfer to higher levels of care such as Intensive Care Unit (ICU) and mortality [1,2].

METHODS AND RESULTS

We developed the Early Deterioration Indicator (EDI) which uses log likelihood risk of vital signs to calculate continuous risk scores. EDI was developed using data from 11,864 general ward admissions. To validate EDI, we calculated EDI scores on an additional 2418 general ward stays and compared it to the Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS). EDI was trained using the most significant variables in predicting deterioration by leveraging the knowledge from a large dataset through data mining. It was implemented electronically for continuous automatic computation. The discriminative performance of EDI, MEWS, and NEWS was calculated before deterioration using the area under the receiver operating characteristic curve (AUROC). Additionally, the performance of the 3 scores for 24h prior to deterioration were computed. EDI was a better discriminator of deterioration than MEWS or NEWS; AUROC values for the validation dataset were: EDI - 0.7655, NEWS - 0.6569, MEWS - 0.6487. EDI also identified more patients likely to deteriorate for the same specificity as NEWS or MEWS. EDI had the best performance among the 3 scores for the last 24h of the patient stay.

CONCLUSION

EDI detects more deteriorations for the same specificity as the other two scores. Our results show that EDI performs better at predicting deterioration than commonly used NEWS and MEWS.

Indigenous environmental indicators for malaria: A district study in Zimbabwe

This paper discusses indigenous environmental indicators for the occurrence of malaria in ward 11, 15 and 18 of Gwanda district, Zimbabwe. The study was inspired by the successes of use of indigenous knowledge systems in community based early warning systems for natural disasters. To our knowledge, no study has examined the relationship between malaria epidemics and climatic factors in Gwanda district. The aim of the study was to determine the environmental indicators for the occurrence of malaria. Twenty eight key informants from the 3 wards were studied. Questionnaires, focus group discussions and PRA sessions were used to collect data. Content analysis was used to analyse the data. The local name for malaria was 'uqhuqho' literally meaning a fever. The disease is also called, "umkhuhlane wemiyane" and is derived from the association between malaria and mosquitoes. The findings of our study reveal that trends in malaria incidence are perceived to positively correlate with variations in both temperature and rainfall, although factors other than climate seem to play an important role too. Plant phenology and insects are the commonly used indicators in malaria prediction in the study villages. Other indicators for malaria prediction included the perceived noise emanating from mountains, referred to as "roaring of mountains" and certain behaviours exhibited by ostriches. The results of the present study highlight the importance of using climatic information in the analysis of malaria surveillance data, and this knowledge can be integrated into the conventional health system to develop a community based malaria forecasting system.

Past, present and future of the climate and human health commission

The following paper presents the history of the Climate and Human Health Commission at the International Society of Biometeorology after more than one decade since its creation. A brief history of the origins of the human biometeorology is revealed through some of the main research topics and publications of the founders of the society in this field. Secondly, it is presented as a brief review of the activities of the commission in the last 10 years, based on the reports that have periodically been submitted by members of the commission to the Bulletin of the society. A summary of the topics of interest on human biometeorology and the most frequent research topics are also described. Thirdly, the need of adapting human biometeorology contents, methods and techniques to a changing world is articulated according to some of the new environmental threats in the XXI century. Finally, a list of future actions and research lines collected through a form from members of the commission is presented. The paper concludes with the existence of great challenge for human biometeorology in order to transform biometeorological knowledge into specific services to improve the wellbeing of human beings.

Scoping review: The use of early warning systems for the identification of in-hospital patients at risk of deterioration

INTRODUCTION

Early warning systems (EWS) were developed as a means of alerting medical staff to patient clinical decline. Since 85% of severe adverse events are preceded by abnormal physiological signs, the patient bed-side vital signs observation chart has emerged as an EWS tool to help staff identify and quantify deteriorating patients. There are three broad categories of patient observation chart EWS: single or multiple parameter systems; aggregated weighted scoring systems; or combinations of single or multiple parameter and aggregated weighted scoring systems.

OBJECTIVE

This scoping review is an overview of quantitative studies and systematic reviews examining the efficiency of the adult EWS charts in the recognition of in-hospital patient deterioration.

METHOD

A broad search was undertaken of peer-reviewed publications, official government websites and databases housing research theses, using combinations of keywords and phrases.

DATA SOURCES

CINAHL with full text; MedLine, PsycINFO, MasterFILE Premier, GreenFILE and ScienceDirect. Also, the Cochrane Library database, Department of Health government websites and Ethos, ProQuest and Trove databases were searched.

EXCLUSIONS

Paediatric, obstetric and intensive care studies, studies undertaken at the point of hospital admission or pre-admission, non-English publications and editorials.

RESULTS

Five hundred and sixty five publications, government documents, reports and theses were located of which 91 were considered and 21 were included in the scoping review. Of the 21 publications eight studies compared the efficacy of various EWS and 13 publications validated specific EWS.

CONCLUSIONS

There is low level quantitative evidence that EWS improve patient outcomes and strong anecdotal evidence that they augment the ability of the clinical staff to recognise and respond to patient decline, thus reducing the incidence of severe adverse events. Although aggregated weighted scoring systems are most frequently used, the efficiency of the specific EWS appears to be dependent on the patient cohort, facilities available and staff training and attitude. While the review demonstrates support for EWS, researchers caution that given the contribution of human factors to the EWS decision-making process, patient EWS charts alone cannot replace good clinical judgment.

Technology and the issues facing nursing assessment

This article describes an investigation into the use of technology and the issues nurses face undertaking nursing assessment. It reports qualitative, descriptive research involving interviews with ten ward nurses from three hospitals in New Zealand. Thematic analysis of the data revealed three key issues: the impact of technology, the influence of early warning systems and nurse autonomy. Results show how clinical decision making around nursing assessment is influenced by technology and the Early Warning Score. These clinical decisions may not always be informed by critical thinking in complex healthcare environments. The article concludes that nurse autonomy, while supported and endorsed in theory, is frequently in conflict with hospital risk-management policies and the use of prescriptive algorithms.

Response to an emerging vector-borne disease: surveillance and preparedness for Schmallenberg virus

Surveillance for new emerging animal diseases from a European perspective is complicated by the non-harmonised approach across Member States for data capture, recording livestock populations and case definitions. In the summer of 2011, a new vector-borne Orthobunyavirus emerged in Northern Europe and for the first time, a coordinated approach to horizon scanning, risk communication, data and diagnostic test sharing allowed EU Member States to develop early predictions of the disease, its impact and risk management options. There are many different systems in place across the EU for syndromic and scanning surveillance and the differences in these systems have presented epidemiologists and risk assessors with concerns about their combined use in early identification of an emerging disease. The emergence of a new disease always will raise challenging issues around lack of capability and lack of knowledge; however, Schmallenberg virus (SBV) gave veterinary authorities an additional complex problem: the infection caused few clinical signs in adult animals, with no indication of the possible source and little evidence about its spread or means of transmission. This paper documents the different systems in place in some of the countries (Germany and the Netherlands) which detected disease initially and predicted its spread (to the UK) and how information sharing helped to inform early warning and risk assessment for Member States. Microarray technology was used to identify SBV as a new pathogen and data from the automated cattle milking systems coupled with farmer-derived data on reporting non-specific clinical signs gave the first indications of a widespread issue while the UK used meteorological modelling to map disease incursion. The coordinating role of both EFSA and the European Commission were vital as are the opportunities presented by web-based publishing for disseminating information to industry and the public. The future of detecting emerging disease looks more positive in the light of this combined approach in the EU.

Estimating the risk of cyanobacterial occurrence using an index integrating meteorological factors: application to drinking water production

The sudden appearance of toxic cyanobacteria (CB) blooms is still largely unpredictable in waters worldwide. Many post-hoc explanations for CB bloom occurrence relating to physical and biochemical conditions in lakes have been developed. As potentially toxic CB can accumulate in drinking water treatment plants and disrupt water treatment, there is a need for water treatment operators to determine whether conditions are favourable for the proliferation and accumulation of CB in source waters in order to adjust drinking water treatment accordingly. Thus, a new methodology with locally adaptable variables is proposed in order to have a single index, f(p), related to various environmental factors such as temperature, wind speed and direction. The index is used in conjunction with real time monitoring data to determine the probability of CB occurrence in relation to meteorological factors, and was tested at a drinking water intake in Missisquoi Bay, a shallow transboundary bay in Lake Champlain, Québec, Canada. These environmental factors alone were able to explain a maximum probability of 68% that a CB bloom would occur at the drinking water treatment plant. Nutrient limitation also influences CB blooms and intense blooms only occurred when the dissolved inorganic nitrogen (DIN) to total phosphorus (TP) mass ratio was below 3. Additional monitoring of DIN and TP could be considered for these source waters prone to cyanobacterial blooms to determine periods of favourable growth. Real time monitoring and the use of the index could permit an adequate and timely response to CB blooms in drinking water sources.

Early warning of illegal development for protected areas by integrating cellular automata with neural networks

Ecological security has become a major issue under fast urbanization in China. As the first two cities in this country, Shenzhen and Dongguan issued the ordinance of Eco-designated Line of Control (ELC) to "wire" ecologically important areas for strict protection in 2005 and 2009 respectively. Early warning systems (EWS) are a useful tool for assisting the implementation ELC. In this study, a multi-model approach is proposed for the early warning of illegal development by integrating cellular automata (CA) and artificial neural networks (ANN). The objective is to prevent the ecological risks or catastrophe caused by such development at an early stage. The integrated model is calibrated by using the empirical information from both remote sensing and handheld GPS (global positioning systems). The MAR indicator which is the ratio of missing alarms to all the warnings is proposed for better assessment of the model performance. It is found that the fast urban development has caused significant threats to natural-area protection in the study area. The integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms. The comparison shows that this multi-model approach has much better performances than the single-model approach for the early warning. Compared with the single models of CA and ANN, this integrated multi-model can improve the value of MAR by 65.48% and 5.17% respectively.