Insights from social-ecological systems thinking for understanding and preventing famine.

The risk of famine is rising in many countries today. Bold changes to famine information and response systems are urgently needed to improve capacities to prevent famine. To this end, the paper identifies six insights from social-ecological systems (SES) thinking for understanding and preventing famine. It argues that a state of famine emerges from human-environment interdependencies, complex causality, and non-linear system dynamics, shaped by history and context. The likelihood of famine can be reduced by strengthening resilience to the diverse stresses and shocks that drive destitution, food insecurity, undernutrition, morbidity, and mortality. SES thinking offers new opportunities to understand the dynamics of famine, diagnose lesser-known drivers, pinpoint new metrics, ascertain leverage points for intervention, and develop conceptual frameworks to inform policy. SES concepts and methods could also support the development of practical analytical tools to guide decisionmakers on how, where, and when to intervene most effectively and efficiently to strengthen resilience to the drivers of famine.

The impact of mandated use early warning system tools on the development of nurses' higher-order thinking: A systematic review.

AIM: Ascertain the impact of mandated use of early warning systems (EWSs) on the development of registered nurses' higher-order thinking. DESIGN: A systematic literature review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklist (Page et al., 2021). DATA SOURCES: CINAHL, Medline, Embase, PyscInfo. REVIEW METHODS: Eligible articles were quality appraised using the MMAT tool. Data extraction was conducted independently by four reviewers. Three investigators thematically analysed the data. RESULTS: Our review found that EWSs can support or suppress the development of nurses' higher-order thinking. EWS supports the development of higher-order thinking in two ways; by confirming nurses' subjective clinical assessment of patients and/or by providing a rationale for the escalation of care. Of note, more experienced nurses expressed their view that junior nurses are inhibited from developing effective higher-order thinking due to reliance on the tool. CONCLUSION: EWSs facilitate early identification of clinical deterioration in hospitalised patients. The impact of EWSs on the development of nurses' higher-order thinking is under-explored. We found that EWSs can support and suppress nurses' higher-order thinking. EWS as a supportive factor reinforces the development of nurses' heuristics, the mental shortcuts experienced clinicians call on when interpreting their subjective clinical assessment of patients. Conversely, EWS as a suppressive factor inhibits the development of nurses' higher-order thinking and heuristics, restricting the development of muscle memory regarding similar presentations they may encounter in the future. Clinicians' ability to refine and expand on their catalogue of heuristics is important as it endorses the future provision of safe and effective care for patients who present with similar physiological signs and symptoms. IMPACT: This research impacts health services and education providers as EWS and nurses' development of higher-order thinking skills are essential aspects of delivering safe, quality care. NO PATIENT OR PUBLIC CONTRIBUTION: This is a systematic review, and therefore, comprises no contribution from patients or the public.

Food prices and the wages of the poor: A cost-effective addition to high-frequency food security monitoring.

The affordability of nutritious food for "all people, at all times" is a critically important dimension of food security. Yet surprisingly, timely high-frequency indicators of food affordability are rarely collected in any systematic fashion despite price volatility emerging as major source of food insecurity in the 21st Century. The 2008 global food crisis prompted international agencies to invest heavily in monitoring domestic food prices in low and middle income countries (LMICs). However, food price monitoring is not sufficient for measuring changes in diet affordability; for that, one must also measure changes either in income or in an income proxy. We propose using the wages of unskilled workers as a cheap and sufficiently accurate income proxy, especially for the urban and rural non-farm poor. We first outline alternative measures of "food wage" indices, defined as wages deflated either by consumer food price indices or novel healthy diet cost indices. We then discuss the conceptual strengths and limitations of food wages. Finally, we examine patterns and trends in different types of real food wage series during well-known food price crises in Ethiopia (2008, 2011 and 2022), Sri Lanka (2022) and Myanmar (2022). In all these instances, food wages declined by 20-30%, often in the space of a few months. In Myanmar, the decline in real wages during 2022 closely matches declines in household disposable income. We strongly advocate tracking the wages of the poor as a timely, accurate and cost-effective means of monitoring food affordability for important segments of the world's poor.

'White lies and safety nets': The perceptions of nurses on the use of early warning systems and the development of higher-order thinking skills.

BACKGROUND: Algorithmic tools such as early warning systems (EWSs) have been embedded into clinical practice globally to facilitate the early recognition of patient deterioration and to guide the escalation of care. Concerns have been raised that the mandated use of these EWS tools may impact the development of nurses' higher-order thinking. However, the relationship between EWS tools and the development of higher-order thinking is poorly understood. OBJECTIVES: This paper provides the qualitative results of a larger study that sought to explore the impact of EWS tools on the development of nurses' higher-order thinking. The objective of this component of the study was to ascertain the thoughts and perceptions of nurses on the use of EWSs and how this related to the development of higher-order thinking skills. METHODS: A mixed-method, concurrent study design was used to explore the concept of the development of nurses' higher-order thinking in the context of EWS tools. The qualitative responses from a Qualtrics survey were thematically analysed and presented. FINDINGS: Two major themes were uncovered: White Lies and Safety Nets. Our analysis of the data suggested that some nurses amend their documentation practice to accommodate the EWS's escalation process, uncovering a view that the tool did not account for clinical reasoning. Parallel to this, some nurses found that these systems supported clinical decision-making and helped to build confidence, thus acting as a safety net for their practice. CONCLUSION: Reliance on EWSs can both hinder and/or support the development of higher-order thinking. Early warning systems are useful tools in ensuring patient safety but should be used in conjunction with nurses' higher-order thinking.

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.

Early warning systems and end-user decision-making: A risk formalism tool to aid communication and understanding.

In this work, we introduce a formalism to highlight the role of decision-making implicit in the setup of early warning systems (EWSs) and its consequences with respect to loss avoidance for end users. The formalism, a close relative of the cost/loss approach, combines EWS verification scores with traditional expressions of risk from the point of view of the user. This formalism articulates in mathematical format many well-known issues surrounding EWS usage, offering a conceptual anchor for concepts that otherwise may seem to wobble among the multidisciplinary perspectives participating in the EWS chain. This decision model is visually represented in a variation of the popular "performance diagram" used in forecast and warning verification. Our diagram adds to this the perspective of a generic user, in an effort to gain insight into how choices made regarding EWS settings may determine which users benefit from warnings and which do not. Although these results are based on a conceptual model, they are useful to better understand the actual benefits experienced by users and to highlight aspects that may temper unrealistic expectations on EWSs. The recent United Nations initiative to extend EWSs for natural hazards to all nations within 5 years will make EWSs more common and more public. The approach proposed here can be a tool to promote greater transparency and improve the necessary dialog between warning issuers and users in order to reduce loss.

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 Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning.

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave "picking" directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.

Prototype of an IoT-Based Low-Cost Sensor Network for the Hydrological Monitoring of Landslide-Prone Areas.

Steep slopes covered by loose unsaturated pyroclastic deposits widely dispersed in Campania, Southern Italy, are often subjected to shallow landslides that turn into fast debris flows causing a large amount of damage and many casualties, triggered by heavy and persistent precipitation. The slope of Cervinara, located around 40 km northeast of Naples, was involved in a destructive flowslide between 15 and 16 December 1999, triggered by a rain event of 325 mm in 48 h. Hydrometeorological monitoring activities have been carried out near the landslide scarp of 1999 since 2017 to assess the water balance and to identify major hydrological processes involving the cover and the shallow groundwater system developing in the upper part of the underlying limestone fractured bedrock. Since 1 December 2022, a remotely accessible low-cost network has been installed to expand the field hydrological monitoring. The use of a network of low-cost capacitive sensors, communicating within the domain of Internet of Things (IoT) technology, aiming at dispersed monitoring of soil moisture, has been tested. Specifically, the tested prototype network allows measurements of the soil water content at two different points, communicating through a Wi-Fi-based IoT system using ESP32 boards. The ThingSpeakTM IoT platform has been used for remote field data visualization. Based on the obtained results, the prototype of this IoT-based low-cost network shows the potential to expand the amount of hydrological data, suitable for setting up early warning systems in landslide-prone areas.

Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning.

The use of bivalve mollusks as bioindicators in automated monitoring systems can provide real-time detection of emergency situations associated with the pollution of aquatic environments. The behavioral reactions of Unio pictorum (Linnaeus, 1758) were employed in the development of a comprehensive automated monitoring system for aquatic environments by the authors. The study used experimental data obtained by an automated system from the Chernaya River in the Sevastopol region of the Crimean Peninsula. Four traditional unsupervised machine learning techniques were implemented to detect emergency signals in the activity of bivalves: elliptic envelope, isolation forest (iForest), one-class support vector machine (SVM), and local outlier factor (LOF). The results showed that the use of the elliptic envelope, iForest, and LOF methods with proper hyperparameter tuning can detect anomalies in mollusk activity data without false alarms, with an F1 score of 1. A comparison of anomaly detection times revealed that the iForest method is the most efficient. These findings demonstrate the potential of using bivalve mollusks as bioindicators in automated monitoring systems for the early detection of pollution in aquatic environments.

How registered nurses are measuring respiratory rates in adult acute care health settings: An integrative review.

AIMS AND OBJECTIVES: This integrative review aimed to draw conclusions from evidence on how registered nurses are measuring respiratory rates for acute care patients. BACKGROUND: Despite the growing research supporting respiratory rate as an early indicator for clinical deterioration, respiratory rate has consistently been the least frequently measured and accurately documented vital sign. DESIGN: An integrative review. METHODS: A systematic literature search was conducted in June 2022 in four databases: CINAHL, PubMed, Medline and Scopus. Quality appraisal was undertaken using the Joanna Briggs Institute's Checklist. PRISMA guidelines were followed to ensure explicit reporting and reported in the PRISMA checklist. RESULTS: Overall, 9915 records were identified, and 19 met the inclusion criteria. Of these 19 articles, seven themes emerged: estimation and digit preference, lack of understanding and knowledge, not valuing the clinical significance of respiratory rate, oxygen saturation substitute, interobserver agreement, subjective concern and count duration. A high prevalence of bias, estimation and incorrect technique was evident. A total of 15 articles reported specifically on how registered nurses are measuring respiratory rates on general medical and surgical wards. CONCLUSIONS: Despite its importance, this integrative review has determined that respiratory rates are not being assessed correctly by nursing staff in the acute care environment. Evidence of using estimation, value bias or quick count and multiply techniques are emerging themes which urgently require further research. No patient or public contribution.

How do nurses use early warning system vital signs observation charts in rural, remote and regional health care facilities: A scoping review.

INTRODUCTION: Physiological signs of clinical deterioration are known to occur in the hours preceding a serious adverse event. As a result, track and trigger systems known as early warning systems (EWS) were introduced and routinely implemented as patient observation tools to trigger an alert in the presence of abnormal vital signs. OBJECTIVE: The objective aimed to explore the literature pertaining to EWS and their utilisation in rural, remote and regional health care facilities. DESIGN: The Arksey and O'Malley's methodological framework was used to guide the scoping review. Only studies reporting on rural, remote and regional health care settings were included. All four authors participated in the screening, data extraction and analysis process. FINDINGS: Our search strategy yielded 3869 peer-reviewed articles published between 2012 and 2022, with six studies ultimately included. Collectively, the studies included in this scoping review examined the complex interaction between patient vital signs observation charts and recognition of patient deterioration. DISCUSSION: Whilst rural, remote and regional clinicians use EWS to recognise and respond to clinical deterioration, noncompliance dilutes the tool's effectiveness. This overarching finding is informed by three contributing factors: documentation, communication and challenges specific to the rural context. CONCLUSION: The success of EWS relies on accurate documentation and effective communication within the interdisciplinary team to support appropriate responses to clinical patient decline. More research is required to understand the nuances and complexities of rural and remote nursing and to address challenges associated with the use of EWS in rural health care settings.

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.

Development, implementation and evaluation of an evidence-based paediatric early warning system improvement programme: the PUMA mixed methods study.

BACKGROUND: Paediatric mortality rates in the United Kingdom are amongst the highest in Europe. Clinically missed deterioration is a contributory factor. Evidence to support any single intervention to address this problem is limited, but a cumulative body of research highlights the need for a systems approach. METHODS: An evidence-based, theoretically informed, paediatric early warning system improvement programme (PUMA Programme) was developed and implemented in two general hospitals (no onsite Paediatric Intensive Care Unit) and two tertiary hospitals (with onsite Paediatric Intensive Care Unit) in the United Kingdom. Designed to harness local expertise to implement contextually appropriate improvement initiatives, the PUMA Programme includes a propositional model of a paediatric early warning system, system assessment tools, guidance to support improvement initiatives and structured facilitation and support. Each hospital was evaluated using interrupted time series and qualitative case studies. The primary quantitative outcome was a composite metric (adverse events), representing the number of children monthly that experienced one of the following: mortality, cardiac arrest, respiratory arrest, unplanned admission to Paediatric Intensive Care Unit, or unplanned admission to Higher Dependency Unit. System changes were assessed qualitatively through observations of clinical practice and interviews with staff and parents. A qualitative evaluation of implementation processes was undertaken. RESULTS: All sites assessed their paediatric early warning systems and identified areas for improvement. All made contextually appropriate system changes, despite implementation challenges. There was a decline in the adverse event rate trend in three sites; in one site where system wide changes were organisationally supported, the decline was significant (ß = -0.09 (95% CI: - 0.15, - 0.05); p = < 0.001). Changes in trends coincided with implementation of site-specific changes. CONCLUSIONS: System level change to improve paediatric early warning systems can bring about positive impacts on clinical outcomes, but in paediatric practice, where the patient population is smaller and clinical outcomes event rates are low, alternative outcome measures are required to support research and quality improvement beyond large specialist centres, and methodological work on rare events is indicated. With investment in the development of alternative outcome measures and methodologies, programmes like PUMA could improve mortality and morbidity in paediatrics and other patient populations.

Assessment of extreme heat and hospitalizations to inform early warning systems.

Heat early warning systems and action plans use temperature thresholds to trigger warnings and risk communication. In this study, we conduct multistate analyses, exploring associations between heat and all-cause and cause-specific hospitalizations, to inform the design and development of heat-health early warning systems. We used a two-stage analysis to estimate heat-health risk relationships between heat index and hospitalizations in 1,617 counties in the United States for 2003-2012. The first stage involved a county-level time series quasi-Poisson regression, using a distributed lag nonlinear model, to estimate heat-health associations. The second stage involved a multivariate random-effects meta-analysis to pool county-specific exposure-response associations across larger geographic scales, such as by state or climate region. Using results from this two-stage analysis, we identified heat index ranges that correspond with significant heat-attributable burden. We then compared those with the National Oceanic and Atmospheric Administration National Weather Service (NWS) heat alert criteria used during the same time period. Associations between heat index and cause-specific hospitalizations vary widely by geography and health outcome. Heat-attributable burden starts to occur at moderately hot heat index values, which in some regions are below the alert ranges used by the NWS during the study time period. Locally specific health evidence can beneficially inform and calibrate heat alert criteria. A synchronization of health findings with traditional weather forecasting efforts could be critical in the development of effective heat-health early warning systems.

The effectiveness of forecast-based humanitarian assistance in anticipation of extreme winters: a case study of vulnerable herders in Mongolia.

This paper presents evidence relating to a forecast-based cash and non-food item distribution among vulnerable herder households during the 2017-18 dzud (extreme winter) season in Mongolia, and analyses the results of a quasi-experimental study evaluating its impacts. An innovative approach in disaster risk reduction, forecast-based financing (FbF) can have short- and long-term benefits to vulnerable households but remains understudied. The paper contributes information on a multimodal FbF programme offering one-off cash grants and in-kind veterinary kits. The data found significant effects of reduced mortality and increased offspring survival in some types of livestock, and that the timing of FbF assistance is crucial, as reported early assistance correlated to positive outcomes in terms of reduced animal mortality. These findings can be used to design more effective FbF interventions, to understand better the appropriateness of FbF designs, and to use early warnings and early actions to help people prepare and withstand disasters such as dzuds.

HERO: Hybrid Effortless Resilient Operation Stations for Flash Flood Early Warning Systems.

Floods are the most frequent type of natural disaster. Flash floods are one of the most common types of floods, caused by rapid and excessive rainfall. Normally, when a flash flood occurs, the water of the upstream river increases rapidly and flows to the downstream watersheds. The overflow of water increasingly submerges villages in the drainage basins. Flash flood early warning systems are required to mitigate losses. Water level monitoring stations can be installed at upstream river areas. However, telemetry stations face several challenges because the upstream river areas are far away and lack of public utilities (e.g., electric power and telephone lines). This research proposes hybrid effortless resilient operation stations, named HERO stations, in the flash flood early warning system. The HERO station was designed and developed with a modular design concept to be effortlessly customized and maintained. The HERO station adapts its working operation against the environmental changes to maintain a long working period with high data sensing accuracy. Moreover, the HERO station can switch its communication mode between the centralized and decentralized communication modes to increase availability. The network of the HERO stations has already been deployed in the northern part of Thailand. It results in improvements of the telemetry station's availability. The HERO stations can adapt to environmental changes. The flash flood early warning messages can be disseminated to the villagers to increase the flood preparation time and to reduce flash flood damage.

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review.

Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.

Assessing the effect of spatial-temporal droughts on dominant crop yield changes in Central Malawi.

Central Malawi has intensely been subjected to different climate-related shocks such as floods, dry spells, and droughts, resulting in decreases in crop yields. Due to their recurrence arising from the effects of climate change, drought characterization, monitoring, and prediction are crucial in guiding agriculture-water users and planners to prepare drought risk management plans and early warning systems. This research analyzed droughts, using multiple drought indices and their impacts on dominant crops over Central Malawi. Forty years of hydro-meteorological data (1977-2017) from nine rain-gauging stations and crop yield data from 1983 to 2017 from four districts were analyzed. The study discovered that drought events in the Agricultural Development Division (ADD) are highly a function of rainfall deficit and high temperatures. The results highlighted that the rainfall patterns in the area are not dependable, calling for the utilization of climate-smart irrigation systems such as drip irrigation and rainwater harvesting technologies. Furthermore, we achieved that crops such as cassava and groundnuts must be promoted to withstand the long water stress duration. These crops also have a multiplier effect; hence, they can enhance food security in the region. This study recommends that using more robust variables in drought analysis studies is necessary for effective drought monitoring and early warning systems. In corroboration with disaster management NGOs, it is recommended that the government should be proactive in developing integrated drought management policies and planning strategies for drought adaptation and mitigation.

Clinical and organizational risk factors for mortality during deterioration events among pediatric oncology patients in Latin America: A multicenter prospective cohort.

BACKGROUND: Hospitalized pediatric hematology-oncology (PHO) patients have frequent clinical deterioration events (CDE) requiring intensive care unit (ICU) admission, particularly in resource-limited settings. The objective of this study was to describe CDEs in hospitalized PHO patients in Latin America and to identify event-level and center-level risk factors for mortality. METHODS: In 2017, the authors implemented a prospective registry of CDEs, defined as unplanned transfers to a higher level of care, use of ICU-level interventions on the floor, or nonpalliative floor deaths, in 16 PHO centers in 10 countries. PHO hospital admissions and hospital inpatient days were also reported. This study analyzes the first year of registry data (June 2017 to May 2018). RESULTS: Among 16 centers, 553 CDEs were reported in PHO patients during 11,536 admissions and 119,414 inpatient days (4.63 per 1000 inpatient days). Event mortality was 29% (1.33 per 1000 inpatient days) but ranged widely across centers (11%-79% or 0.36-5.80 per 1000 inpatient days). Significant risk factors for event mortality included requiring any ICU-level intervention on the floor and not being transferred to a higher level of care. Events with organ dysfunction, a higher severity of illness, and a requirement for ICU intervention had higher mortality. In center-level analysis, hospitals with a higher volume of PHO patients, less floor use of ICU intervention, lower severity of illness on transfer, and lower rates of floor cardiopulmonary arrest had lower event mortality. CONCLUSIONS: Hospitalized PHO patients who experience CDEs in resource-limited settings frequently require floor-based ICU interventions and have high mortality. Modifiable hospital practices around the escalation of care for these high-risk patients may contribute to poor outcomes. Earlier recognition of critical illness and timely ICU transfer may improve survival in hospitalized children with cancer.