Translation and Validation of the Indicators of Quality Nursing Work Environments in the Portuguese Cultural Context

The Indicators of Quality Nursing Work Environment scale (IQN-WE) with 65 items, developed by Chiou-Fen Lin, Meei-Shiow Lu, and Hsiu-Ying Huang in 2016, aimed to create a set of quality indicators of the nursing work environment. The translation and validation for the Portuguese cultural context of the IQN-WE scale was performed in this study.

OBJECTIVES

culturally and linguistically adapt the IQN-WE scale, originating from the Portuguese version, and evaluate its psychometric characteristics.

METHODS

it is a descriptive, cross-sectional, observational and quantitative study. The IQN-WE scale was validated in a sample of 542 nurses belonging to a hospital center in Portugal. The study obtained a response of 21.69%, of whom 78.0% were women and 22% men. The mean age of the sample was 39 years and a standard deviation of 8.1 years. All nurses who work have a bachelor's degree, and 13.5% have a master's degree.

RESULTS

the study obtained an instrument with a total explained variance of 52.67% and KMO = 0.843. It found a strong-to-moderate linear correlation matrix between the dimensions. The pre-test and the team of experts ensured the content validity. The determination of internal consistency guaranteed reliability, with 0.95. Confirmatory factor analysis validated the construct. The factorial model presented a goodness of fit index, with five factors.

CONCLUSION

the study achieved an instrument with 50 items in five dimensions: Team Support and Professional Development; Team Organization and Management; Safe Nursing Work Environment; Information Systems and Risk Control; Salary and Welfare. The IQN-WE-PT scale proved to be an appropriate instrument to be applied in health organizations in Portugal.

Meta-Analysis Addressing the Implications of Model Uncertainty in Understanding the Persistence of Indicators and Pathogens in Natural Surface Waters

This study evaluates the impact persistence model selection has on the prediction of persistence values of interest and the identification of influential water quality and environmental factors for microorganisms in natural surface waters. Five persistence models representing first-order decay and nonlinear decay profiles were fit to a comprehensive database of 629 data sets for fecal indicator bacteria (FIB), bacteriophages, bacteria, viruses, and protozoa mined from the literature. Initial periods of minimal decay and decay rates tapering off over time were often observed, and a two-parameter model, based on the logistic probability distribution, provided the best fit to the data most frequently. First-order decay kinetics provided the best fit to less than 20% of the analyzed data. Using the best fitting models in this analysis, T90 and T99 metrics were calculated for each data set and used as the dependent variable in a variety of exploratory factor analyses. Random forest methods identified temperature and predation as some of the most important water quality factors influencing persistence, and the protozoa target type differed the most from FIB. This analysis further confirmed the interactions between temperature and predation and suggests that pH and turbidity be more frequently documented in persistence studies to further elucidate their impact on target persistence. The findings from this analysis and the calculated persistence metrics can be used to better inform quantitative microbial risk assessments and may lead to improved predictions of human health risks and water management decisions.

Development and Validation of Indicators for Population Injury Surveillance in Hong Kong: Development and Usability Study

BACKGROUND

Injury is an increasingly pressing global health issue. An effective surveillance system is required to monitor the trends and burden of injuries.

OBJECTIVE

This study aimed to identify a set of valid and context-specific injury indicators to facilitate the establishment of an injury surveillance program in Hong Kong.

METHODS

This development of indicators adopted a multiphased modified Delphi research design. A literature search was conducted on academic databases using injury-related search terms in various combinations. A list of potential indicators was sent to a panel of experts from various backgrounds to rate the validity and context-specificity of these indicators. Local hospital data on the selected core indicators were used to examine their applicability in the context of Hong Kong.

RESULTS

We reviewed 142 articles and identified 55 indicators, which were classified into 4 domains. On the basis of the ratings by the expert panel, 13 indicators were selected as core indicators because of their good validity and high relevance to the local context. Among these indicators, 10 were from the construct of health care service use, and 3 were from the construct of postdischarge outcomes. Regression analyses of local hospitalization data showed that the Hong Kong Safe Community certification status had no association with 5 core indicators (admission to intensive care unit, mortality rate, length of intensive care unit stay, need for a rehabilitation facility, and long-term behavioral and emotional outcomes), negative associations with 4 core indicators (operative intervention, infection rate, length of hospitalization, and disability-adjusted life years), and positive associations with the remaining 4 core indicators (attendance to accident and emergency department, discharge rate, suicide rate, and hospitalization rate after attending the accident and emergency department). These results confirmed the validity of the selected core indicators for the quantification of injury burden and evaluation of injury-related services, although some indicators may better measure the consequences of severe injuries.

CONCLUSIONS

This study developed a set of injury outcome indicators that would be useful for monitoring injury trends and burdens in Hong Kong.

Definition of Optimized Indicators from Sensors Data for Damage Detection of Instrumented Roadways

Pavement instrumentation sensors have become a common practice for structural monitoring. Data processing often consists of extracting different values generally representative of variations in the whole pavement, but this makes it more difficult to follow a specific characteristic of the structure. To overcome this limitation, this paper aimed to transpose an original method, labeled the Optimized Indicators Method (OIM), which consists of finding some weighting functions of a signal to evaluate some indicators linked to a physical characteristic of the structure. The main advantage of this method is that the inferred indicators are particularly sensitive to a specific characteristic of the structure without being sensitive to the others. This research work consisted of analyzing, conventionally and with the OIM, the signals of strain gauges, which were recorded during an experimental campaign carried out on a circular instrumented pavement and submitted to an accelerated fatigue testing. The OIM, calibrated through an experimental reference signal, makes it possible to evaluate independently, through weighting functions, some specific physical characteristics of the pavement. It showed a two-stepped degradation, starting with a damaging of the bituminous layer, followed by an alteration of the base layer, which could not be easily deduced from a conventional processing method.

Correlation between pathological features and protein expressions of TfR1, VEGF and MMP-9 in patients with osteosarcoma

OBJECTIVE

To analyze the pathological features of patients with osteosarcoma and the correlation of expressions of transferrin receptor 1 (TfR1), vascular endothelial growth factor (VEGF) and matrix metalloproteinase (MMP)-9 in tumor tissue with the pathological features.

METHODS

The tumor tissue and paracancerous tissue samples from 31 patients with osteosarcoma were collected before treatment to measure the expressions of TfR1, VEGF and MMP-9. Kaplan-meier curve was used to analyze the relationship of TfR1, VEGF and MMP-9 with the survival time of patients. Log-rank test was used to test the difference, and Spearman test was used to test the correlation.

RESULTS

Among the 31 osteosarcoma samples, 23 (74.19%), 24 (77.42%) and 17 (54.84%) samples showed medium-high expressions of VEGF, TfR1 and MMP-9, respectively, which were significantly higher than those in the paracancerous samples (P<0.05). Furthemore, the medium-high expression rates of VEGF, TfR1 and MMP-9 were significantly different in patients with different Enneking stages, different histological differentiation degrees, and with or without distant metastasis. Besides, the expression of VEGF was also significantly different in different-size osteosarcoma samples (P<0.05). The survival time of patients with low expressions of TfR1, VEGF and MMP-9 was significantly longer than that of patients with medium-high expressions (P<0.05). Additionally, in osteosarcoma samples, significant positive correlations were shown between the expressions of TfR1 and VEGF, as well as between TfR1 and MMP-9 (P<0.05), but there was no significant correlation between VEGF and MMP-9 (P>0.05).

CONCLUSION

TfR1, VEGF and MMP-9 are abnormally expressed in osteosarcoma lesions, suggesting that they all play a role in the occurrence and development of osteosarcoma.

ROBITT: A tool for assessing the risk-of-bias in studies of temporal trends in ecology

Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete 'risk-of-bias' assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.We introduce ROBITT, a structured tool for assessing the 'Risk-Of-Bias In studies of Temporal Trends in ecology'. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken.Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis.We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence-based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality.

In-hospital admissions and deaths in the context of the COVID-19 pandemic, in Romania

Introduction

The COVID-19 pandemic context led to a relevant burden on essential sectors of society; hospital sector capacity is tested in this period.

Methods

A cross-sectional study of admissions in hospitals reporting DRG data for 2018-2020. Trend analysis of admissions and deaths in hospitals was carried out for identifying annual patterns and deviations from the 2010-2020 trend. Data aggregated by year, month, diagnosis, death in hospital. Graph analysis for time and diagnosis comparisons and correlation identifying associations.

Results

There is an annual change in admission and death patterns recorded in Romanian hospitals. An important contraction in number of acute hospital admissions was recording during the COVID-19 pandemic; patients' hesitancy to seek healthcare and limited capacity to treat patients other than COVID-19 patients due to legal regulations limiting the admissions number could explain this pattern of admission only for serious condition or emergency surgery. In Romania excess deaths in second half 2020 was generally greater than COVID-19 deaths. Overall, excess mortality between March and December 2020 was more than double than reported COVID-19 deaths. The same mortality pattern persists, but with significant decreases for some diagnoses.

Conclusions

The Romanian healthcare system has been challenged due to COVID-19 pandemic, leading to temporary reorganization of hospitals with consequences on all acute care diagnostics and therapeutic pathways. It is a challenge to identify causes of reduced inpatient treatment rates and to provide evidence on hospital activity for understanding future optimal management of patients with COVID-19, but also with other acute and chronic conditions.

Exploring Environmental Health Inequalities: A Scientometric Analysis of Global Research Trends (1970-2020)

Environmental health inequalities (EHI), understood as differences in environmental health factors and in health outcomes caused by environmental conditions, are studied by a wide range of disciplines. This results in challenges to both synthesizing key knowledge domains of the field. This study aims to uncover the global research status and trends in EHI research, and to derive a conceptual framework for the underlying mechanisms of EHI. In total, 12,320 EHI publications were compiled from the Web of Science core collection from 1970 to 2020. Scientometric analysis was adopted to characterize the research activity, distribution, focus, and trends. Content analysis was conducted for the highlight work identified from network analysis. Keyword co-occurrence and cluster analysis were applied to identify the knowledge domain and develop the EHI framework. The results show that there has been a steady increase in numbers of EHI publications, active journals, and involved disciplines, countries, and institutions since the 2000s, with marked differences between countries in the number of published articles and active institutions. In the recent decade, environment-related disciplines have gained importance in addition to social and health sciences. This study proposes a framework to conceptualize the multi-facetted issues in EHI research referring to existing key concepts.

A Theoretically Derived Approach to Impact: Implementing Policy Influence Strategies

Advocacy is an intentional act of influencing government and an important precondition for successful policy change in society. Drawing from an existing framework on policy influence, we propose an approach to quantifying the impact of policy influence efforts, specifically within the context of European Public Health (EPH) advocacy. The analysis hinges on the article "Moving from tokenism" which provides a starting point to conceptualize strategies to quantify impact. An exploratory case study approach allowed to integrate literature on advocacy evaluation in parallel with the internal documentation of a EPH advocacy organization We provide recommendations to advocacy organizations that aim to create an infrastructure towards quantifying the impact of their efforts. The framework is mostly tailored to the needs of EPH advocacy, but it can also have resonance beyond the scope of a specific sector.

Exploring indicators of extremity pain of spinal source as identified by Mechanical Diagnosis and Therapy (MDT): a secondary analysis of a prospective cohort study

OBJECTIVE

To explore indicators that predict whether patients with extremity pain have a spinal or extremity source of pain.

METHODS

The data were from a prospective cohort study (n = 369). Potential indicators were gathered from a typical Mechanical Diagnosis and Therapy (MDT) history and examination. A stepwise logistic regression with a backward elimination was performed to determine which indicators predict classification into spinal or extremity source groups. A Receiver Operating Characteristic (ROC) curve was constructed to examine the number of significant indicators that could predict group classification.

RESULTS

Five indicators were identified to predict group classification. Classification into the spinal group was associated with the presence of paresthesia [odds ratio (OR) 1.984], change in symptoms with sitting/neck or trunk flexion/turning neck/when still (OR 2.642), change in symptoms with posture change (OR 3.956), restrictions in spinal movements (OR 2.633), and no restrictions in extremity movements (OR 2.241). The optimal number of indicators for classification was two (sensitivity = 0.638, specificity = 0.807).

DISCUSSION

This study provides guidance on clinical indicators that predict the source of symptoms for isolated extremity pain. The clinical indicators will allow clinicians to supplement their decision-making process in regard to spinal and extremity differentiation so as to appropriately target their examinations and interventions.

Nurse-sensitive indicators during COVID-19

Purpose

Nurses are on the forefront of delivering care to patients hospitalized with COVID‐19. Nurses’ impact on patient care can be discerned through assessment and documentation strategies, including structured and unstructured narratives, clinical pathways, flowsheets, and problem‐based approaches. To date, there are no published reports regarding nursing assessment and documentation during the COVID‐19 pandemic using an assessment framework to capture clinical decision making, nursing diagnoses, and key social determinant of health (SDoH) data. Hence, the purpose of this investigation was to conduct an exploratory nursing documentation audit of patients hospitalized with COVID‐19 during the first surge to identify types and frequency of nurse‐sensitive indicators, including SDoH.

Method

This pilot study utilized a retrospective chart review design at a single academic medical center, utilizing Gordon's Eleven Functional Health Patterns (FHP) framework to extract clinical, social, and nursing assessment data for patients hospitalized with COVID‐19. Descriptive statistics were computed for continuous variables and counts/percentages for categorical variables.

Findings

Data from 94 patient records were analyzed. Most patients were male (59.6%), with a mean age of 58 years. Nearly 15% of patients were Black and 12.8% were Hispanic, most residing in four geographic areas. Nine of the 11 FHPs were reflected in nurse‐sensitive indicators documented in the electronic health record. SDoH data were inconsistently documented, including race, education, history of neglect/abuse, and occupation.

Conclusion

The FHP framework captured many nurse‐sensitive indicators during the first COVID‐19 surge, although screening for and documenting SDoH data were limited.

Implications for nursing practice

Findings can influence the development of nursing assessment and documentation during crisis care delivery that are inclusive of distinct sociodemographic factors, in addition to clinical factors, to provide comprehensive, culturally sensitive care. Such documentation will enhance the use of nursing knowledge guided by a nursing framework to make visible the essential contributions of nurses to healthcare delivery.

Data to Decisions: Methods to Create Neighbourhood Built Environment Indicators Relevant for Early Childhood Development

Healthy development in the early years lays the foundations for children’s ongoing physical, emotional, and social development. Children develop in multiple contexts, including their local neighbourhood. Neighbourhood-built environment characteristics, such as housing, walkability, traffic exposure, availability of services, facilities, and parks, are associated with a range of health and wellbeing outcomes across the life course, but evidence with early years’ outcomes is still emerging. Data linkage techniques were used to assemble a dataset of spatial (objectively-measured) neighbourhood-built environment (BE) measures linked to participant addresses in the 2015 Australian Early Development Census (AEDC) for children living in the 21 most populous urban and regional Australian cities (n = 235,655) to help address this gap. This paper describes the methods used to develop this dataset. This linked dataset (AEDC-BE) is the first of its kind worldwide, enabling opportunities for identifying which features of the built environment are associated with ECD across Australia at scale, allow comparisons between diverse contexts, and the identification of where best to intervene. National data coverage provides statistical power to model real-world complexities, such as differences by city, state/territory, and remoteness. The neighbourhood-built environment can be modified by policy and practice at scale, and has been identified as a way to help reduce inequitable early childhood development outcomes.

Designing Clinical Indicators for Common Residential Aged Care Conditions and Processes of Care: The CareTrack Aged Development and Validation Study

BACKGROUND

People who live in aged care homes have high rates of illness and frailty. Providing evidence-based care to this population is vital to ensure the highest possible quality of life. This study (CareTrack Aged, CT Aged) aimed to develop a comprehensive set of clinical indicators for guideline-adherent, appropriate care of commonly managed conditions and processes of care in aged care.

METHODS

Indicators were formulated from recommendations found through systematic searches of Australian and international clinical practice guidelines (CPGs). Experts reviewed the indicators using a multi-round modified Delphi process to develop consensus on what constitutes appropriate care.

RESULTS

From 139 CPGs, 5,609 recommendations were used to draft 630 indicators. Clinical experts (n=41) reviewed the indicators over two rounds. A final set of 236 indicators resulted, mapped to 16 conditions and processes of care. The conditions and processes were admission assessment; bladder and bowel problems; cognitive impairment; depression; dysphagia and aspiration; end of life/palliative care; hearing and vision; infection; medication; mobility and falls; nutrition and hydration; oral and dental care; pain; restraint use; skin integrity; and sleep.

CONCLUSIONS

The suite of CT Aged clinical indicators can be used for research, assessment of quality of care in individual facilities and across organisations to guide improvement, and to supplement regulation or accreditation of the aged care sector. They are a step forward for Australian and international aged care sectors, helping to improve transparency, so that the level of care delivered to aged care consumers can be rigorously monitored and continuously improved.

Global genetic diversity status and trends: towards a suite of Essential Biodiversity Variables (EBVs) for genetic composition

Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well‐being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within‐species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (N_e). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large‐scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long‐term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally.

Characterising and monitoring preconception health in England: a review of national population-level indicators and core data sources

Initiatives to optimise preconception health are emerging following growing recognition that this may improve the health and well-being of women and men of reproductive age and optimise health in their children. To inform and evaluate such initiatives, guidance is required on indicators that describe and monitor population-level preconception health. We searched relevant databases and websites (March 2021) to identify national and international preconception guidelines, recommendations and policy reports. These were reviewed to identify preconception indicators. Indicators were aligned with a measure describing the prevalence of the indicator as recorded in national population-based data sources in England. From 22 documents reviewed, we identified 66 indicators across 12 domains. Domains included wider (social/economic) determinants of health; health care; reproductive health and family planning; health behaviours; environmental exposures; cervical screening; immunisation and infections; mental health, physical health; medication and genetic risk. Sixty-five of the 66 indicators were reported in at least one national routine health data set, survey or cohort study. A measure of preconception health assessment and care was not identified in any current national data source. Perspectives from three (healthcare) professionals described how indicator assessment and monitoring may influence patient care and inform awareness campaign development. This review forms the foundation for developing a national surveillance system for preconception health in England. The identified indicators can be assessed using national data sources to determine the population's preconception needs, improve patient care, inform and evaluate new campaigns and interventions and enhance accountability from responsible agencies to improve preconception health.

National Radon Action Plans in Europe and Need of Effectiveness Indicators: An Overview of HERCA Activities

Protection of the population and of workers from exposure to radon is a unique challenge in radiation protection. Many coordinated actions and a variety of expertise are needed. Initially, a National Radon Action Plan (NRAP) has been developed and implemented by some countries, while it is currently recommended by international organizations (e.g., World Health Organization) and required by international regulations, such as the European Council Directive 2013/59/Euratom and the International Basic Safety Standards on Radiation Protection and Safety of Radiation Sources, cosponsored by eight international organizations. Within this framework, the Heads of the European Radiological Protection Competent Authorities (HERCA) have organized activities aimed at sharing experiences to contribute toward the development and implementation of effective NRAPs. Two workshops were held in 2014 and 2015, the latter on radon in workplaces. As a follow-up to these, an online event took place in March 2021, and a second specific workshop on NRAP is planned for June 2022. These workshops were attended by experts from the competent authorities of European countries, relevant national and international organizations. The experience of several countries and the outcomes from these workshops have highlighted the need for adequate indicators of the effectiveness and progress of the actions of NRAPs, which could also be useful to implement the principle of optimization and the graded approach in NRAPs. In this paper, the activities of HERCA to support the development and implementation of effective NRAPs are described and some examples of effectiveness indicators are reported, including those already included in the NRAP of some European countries.

Indicators and predictors modifiable by the nursing department during the preoperative period: A scoping review

AIM AND OBJECTIVES

The aim of this study is to identify preoperative indicators and/or predictors of complications or inefficiencies in the surgical process that can be modified within nursing practice.

BACKGROUND

Due to rapid sociodemographic and technological change, the global demand for surgical attention is rising exponentially, requiring new strategies for optimisation and sustainability in perioperative care.

DESIGN

We conduced the scoping review using the methodology recommended by the Joanna Briggs Institute supported with The PAGER framework and guided by the PRISMA-ScR Checklist.

METHODS

Four databases (CINAHL, MEDLINE, SCOPUS and PUBMED) were examined to extract relevant published results for elective surgery on adult patients during the period 2011-2021. This process identified 609 records. Exclusion criteria were applied, and the sample was then evaluated with the Quality Assessment Tool for Studies with Diverse Designs (QATSDD), after which 15 studies remained.

RESULTS

The following preoperative indicators and/or predictors were considered: (1) Anxiety; (2) Pain; (3) Health education, knowledge and training; (4) Satisfaction; (5) Management/organisation (including costs, resources used/available, organisational issues, hospital stay (preoperative), standardisation and protocolisation.

CONCLUSION

The identification of five indicators and/or predictors of complications or inefficiencies in the surgical process, which can be modified by nursing, allows the effective application of interventions in the preoperative phase, optimising care and improving health outcomes.

RELEVANCE TO CLINICAL PRACTICE

The development and implementation of specific nursing skills in the preoperative phase are essential to optimise the surgical process.

Auditing completion of nursing records as an outcome indicator for identifying patients at risk of developing pressure ulcers, falling, and social vulnerability: An observational study

AIM

To evaluate the completion of nursing records through scheduled audits to analyse risk outcome indicators.

BACKGROUND

Nursing records support clinical decision-making and encourage continuity of care, hence the importance of auditing their completion in order to take corrective action where necessary.

METHOD

This was an observational descriptive study carried out from February to November 2020 with a sample of 1131 electronic health records belonging to patients admitted to COVID-19 hospital units during three observation periods: pre-pandemic, first wave, and second wave.

RESULTS

A significant reduction in nursing record completion rates was observed between pre-pandemic period and first and second waves: Braden scale 40.97%, 28.02%, and 30.99%; Downton scale: 43.74%, 22.34%, and 33.91%; Gijón scale: 40.12%, 26.23%, and 33.64% (p < 0.001). There was an increase in the number of records completed between the first and second waves following the measures adopted after the quality audit.

CONCLUSIONS

The use of scheduled audits of nursing records as quality indicators facilitated the detection of areas for improvement, allowing timely corrective actions.

IMPLICATIONS FOR NURSING MANAGEMENT

Support from nursing managers at health care facilities to implement quality assessment programmes encompassing audits of clinical record completion will encourage the adoption of measures for corrective action.

Integrating Urban Land Tenure Security in Health Determinants: The Design of Indicators for Measuring Land Tenure Security and Health Relationships in Developing Country Contexts

Both urban land tenure insecurity and poor urban health outcomes are research topics of urban geographers and health experts. However, health outcomes or patterns are hardly measured in relation to land tenure security. There are no clear measures or indicators of if and how these two issues interrelate and which type of land tenure deficiency is likely to lead to which kind of health outcomes or patterns. To address this knowledge quandary, we reviewed literature to identify which characteristics of land tenure could relate to which types of health outcomes. The review found four specific land tenure security pathways which significantly influence health outcomes. For each of these, it is possible to identify a set of indicators which could measure the extent of interrelation between land tenure security and health. The result of this process is the design of a list of 46 land tenure-enabled indicators that can be applied empirically. The indicators demonstrate how to design a transdisciplinary approach that connects land management and global urban health knowledge spaces.

[Epidemiological indicators and correlations of adolescent aggression in an urban student population]

Adolescent aggression has received a wide and longtime attention in scientific research, because of the extent of the phenomenon in this age group and of the negative consequences it inflicts on affected adolescents, and their human environments. The aim of this cross-sectional study was to determine the proportion (of high levels) of aggressive behaviors (physical, verbal, and direct aggression, anger, and hostility) in an urban sample of adolescent students, as well as to investigate associations between the occurrence of these behaviors, and adolescents' characteristics and mental health problems. The sample consisted of 2050 students attending the second grade of 49 random selected High Schools and Senior High Schools of the Regional Unit of the Central Sector of Attica and Piraeus. The Buss- Perry Aggression Questionnaire was administered to measure participants' aggression behaviors, while the Strength and Difficulties Questionnaire was also used to estimate their mental health and behavioral difficulties. Information about adolescents' individual, family, and school characteristics, was also collected. Results of the statistical analysis showed that the occurrence rates of high levels of participants' aggressive behaviors ranged between 2.2 (for total aggression) and 10.5% (for anger). Among individual characteristics, gender (with boys predominating in physical and direct aggression and girls in anger), (older) age, and sports activity (to direct aggression) were related to participants' aggressive behaviors. On the other hand, non-intact family structure and household insecurity food intake were positive correlated with specific aggressive behaviors, while pocket money allowance was positive associated with all of them. Concerning participants' mental health and behavioral issues, conduct problems and hyperactivity/ inattention were positive correlated with all investigated aggressive behaviors. In conclusion, the vast majority of the Central Sector of Attica and Piraeus adolescents did not seem to show high levels of aggressive behaviors (except anger). Nevertheless, considering this study outcomes (such as the "aggressive" burden of older adolescents, the role of family structure and pocket money allowance, as well as the co-occurrence with mental and behavioral problems), further longitudinal study is required to better understand the mechanisms that facilitate adolescent aggression.

Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City

Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback–Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.

Elliott M, A. Abel G. The underlying structure of the English Cancer Patient Experience Survey: Factor analysis to support survey reporting and design

BACKGROUND

The English Cancer Patient Experience Survey (CPES) is a regularly conducted survey measuring the experience of cancer patients. We studied the survey's underlying structure using factor analysis to identify potential for improvements in reporting or questionnaire design.

METHODS

Cancer Patient Experience Survey 2015 respondents (n = 71,186, response rate 66%) were split into two random subgroups. Using exploratory factor analysis (EFA) on the first subgroup, we identified the survey's latent structure. EFA was then applied to 12 sets of items. A first ("core") set was formed by questions that applied to all participants. The subsequent sets contained the "core set" plus questions corresponding to specific care pathways/patient groups. We used confirmatory factor analysis (CFA) on the second data subgroup for cross-validation.

RESULTS

The EFA suggested that five latent factors underlie the survey's core questions. Analysis on the remaining 11 care pathway/patient group items also indicated the same five latent factors, although additional factors were present for questions applicable to patients with an overnight stay or those accessing specialist nursing. The five factors models had an excellent fit (comparative fit index = 0.95, root mean square error of approximation = 0.045 for core set of questions). Items loading on each factor generally corresponded to a specific section or subsection of the questionnaire. CFA findings were concordant with the EFA patterns.

CONCLUSION

The findings suggest five coherent underlying sub-constructs relating to different aspects of cancer health care. The findings support the construction of evidence-based composite indicators for different domains of experience and provide options for survey re-design.

Indicators and ICTs application for municipal waste management

The worldwide populace is rising steadily. Urbanization is likewise expanding quickly with the rising populace. Fast urbanization has considerably increased the generation of municipal solid waste (MSW). The MSW management issues have recently been analyzed through various assessment indicators and information and communication technologies (ICTs). This article provides an overview of applications of assessment indicators and ICTs for addressing the environmental issues of waste disposal and management in municipalities. The selection of indicators mainly depends on the stakeholders' specific requirements, such as waste management strategies, urban planning and development, human health, and energy generation. The literature analysis revealed that collection, sorting, recycling, cost efficiency, and environmental aspect were the leading indicators used in waste management studies. And these indicators reduce the complexity of systems and formulate evaluations easier for the decision-maker. Moreover, these are also helpful in assessing the improvement and reporting the waste condition to the expert. These analysis further revealed that information and communication technology is a requirement in the planning and managing of current solid waste disposal problems. The use of ICTs in waste management systems mitigates possible constraints regarding spot selection, inept waste disposal, waste collection monitoring, and proper recycling.

Monitoring the Dutch Solid Start Program: Developing an Indicator Set for Municipalities to Monitor their First Thousand Days-Approach

INTRODUCTION

The Dutch Solid Start program aims to improve the collaboration between the medical and social sector to offer every child the best start in life. Municipalities form local coalitions of partners within the medical and social sector to support parents and children during the first thousand days. The aim of this study was to develop an indicator set for coalitions to monitor their local Solid Start program.

METHODS

A modified Delphi study with three rounds was carried out among Dutch experts in Solid Start practice, policy and research (n = 39) to reach consensus.

RESULTS

The indicator set included 19 indicators covering the three phases of the Solid Start program: preconception, pregnancy and after birth (up to two years). Prioritized indicators included both social and medical topics, among which poverty, psychological/psychiatric problems, stress, smoking, cumulation of risk factors, preconception care, low literacy, premature birth, and intellectual disability. Additionally, a development agenda was established with topics and indicators that lacked data or clear operationalization (e.g. stress, unintended pregnancy, loneliness).

DISCUSSION AND CONCLUSION

The developed indicator set enhances the conversation between policymakers, managers, professionals and other stakeholders about the local situation and developments in order to prioritize interventions and policies. Next, the indicator set needs evaluation to assess its usefulness.

Severe Type of COVID-19: Pathogenesis, Warning Indicators and Treatment

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, is a major public health issue. The epidemic is unlikely to be contained until the global launch of safe and effective vaccines that could prevent serious illnesses and provide herd immunity. Although most patients have mild flu-like symptoms, some develop severe illnesses accompanied by multiple organ dysfunction. The identification of pathophysiology and early warning biomarkers of a severe type of COVID-19 contribute to the treatment and prevention of serious complications. Here, we review the pathophysiology, early warning indicators, and effective treatment of Chinese and Western Medicine for patients with a severe type of COVID-19.