Urgent Need For Effective Strategies Research Articles

Processing imbalanced medical data at the data level with assisted-reproduction data as an example

ObjectiveData imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification models. We focus on quantifying the effects of different imbalance degrees and sample sizes on model performance, identifying optimal cut-off values, and evaluating the efficacy of various methods to enhance model accuracy in highly imbalanced and small sample size scenarios.MethodsWe collected medical records of patients receiving assisted reproductive treatment in a reproductive medicine center. Random forest was used to screen the key variables for the prediction target. Various datasets with different imbalance degrees and sample sizes were constructed to compare the classification performance of logistic regression models. Metrics such as AUC, G-mean, F1-Score, Accuracy, Recall, and Precision were used for evaluation. Four imbalance treatment methods (SMOTE, ADASYN, OSS, and CNN) were applied to datasets with low positive rates and small sample sizes to assess their effectiveness.ResultsThe logistic model’s performance was low when the positive rate was below 10% but stabilized beyond this threshold. Similarly, sample sizes below 1200 yielded poor results, with improvement seen above this threshold. For robustness, the optimal cut-offs for positive rate and sample size were identified as 15% and 1500, respectively. SMOTE and ADASYN oversampling significantly improved classification performance in datasets with low positive rates and small sample sizes.ConclusionsThe study identifies a positive rate of 15% and a sample size of 1500 as optimal cut-offs for stable logistic model performance. For datasets with low positive rates and small sample sizes, SMOTE and ADASYN are recommended to improve balance and model accuracy.

Antimicrobial Peptides and Their Biomedical Applications: A Review.

The emergence of drug resistance genes and the detrimental health effects caused by the overuse of antibiotics are increasingly prominent problems. There is an urgent need for effective strategies to antibiotics or antimicrobial resistance in the fields of biomedicine and therapeutics. The pathogen-killing ability of antimicrobial peptides (AMPs) is linked to their structure and physicochemical properties, including their conformation, electrical charges, hydrophilicity, and hydrophobicity. AMPs are a form of innate immune protection found in all life forms. A key aspect of the application of AMPs involves their potential to combat emerging antibiotic resistance; certain AMPs are effective against resistant microbial strains and can be modified through peptide engineering. This review summarizes the various strategies used to tackle antibiotic resistance, with a particular focus on the role of AMPs as effective antibiotic agents that enhance the host's immunological functions. Most of the recent studies on the properties and impregnation methods of AMPs, along with their biomedical applications, are discussed. This review provides researchers with insights into the latest advancements in AMP research, highlighting compelling evidence for the effectiveness of AMPs as antimicrobial agents.

Dynamic Simulation of Energy Scenarios in the Transition to Sustainable Mobility in the Ecuadorian Transport Sector

In Ecuador, the growth of the transportation sector has significantly increased greenhouse gas emissions. According to experts, this sector currently contributes to 49.8% of total greenhouse gas emissions in this country. This poses significant challenges for environmental sustainability, emphasizing the urgent need for effective strategies to mitigate these emissions and promote environmentally friendly practices. Therefore, this study focuses on developing a dynamic simulation of energy scenarios for the year 2035 in the transportation sector, with the goal of transitioning to sustainable mobility, as fossil fuels are the main pollutants in the country. This study proposes system dynamics models using VENSIM 6.0b software to estimate the total energy demand and pollutant emissions in the transportation sector. The results suggest that if Ecuador aligns with global mobility trends and implements public policies promoting the use of electric vehicles, total CO2 emissions could potentially decrease from 50,161,432 kilobarrels of oil equivalent (kBOE) to 20,589,720 kBOE by the year 2035.

Intrinsic Elastomer with Remarkable Dielectric Constant via Elastification of Relaxor Ferroelectric Polymer.

High-dielectric-constant elastomers always play a critical role in the development of wearable electronics for actuation, energy storage, and sensing; therefore, there is an urgent need for effective strategies to enhance dielectric constants. The present methods mainly involve adding inorganic or conductive fillers to the polymer elastomers, however, the addition of fillers causes a series of problems, such as large dielectric loss, increased modulus, and deteriorating interface conditions. Here, the elastification of relaxor ferroelectric polymers is investigated through slight cross-linking, aiming to obtain intrinsic elastomers with high-dielectric constants. By cross-linking of the relaxor ferroelectric polymer poly(vinylidene fluoride-ter-trifluoroethylene-ter-chlorofluoroethylene) with a long soft chain cross-linker, a relaxor ferroelectric elastomer with an enhanced dielectric constant is obtained, twice that of the pristine relaxor ferroelectric polymer and surpassing all reported intrinsic elastomers. This elastomer maintains its high-dielectric constant over a wide temperature range and exhibits robust mechanical fatigue resistance, chemical stability, and thermal stability. Moreover, the ferroelectricity of the elastomer remains stable under strains up to 80%. This study offers a simple and effective way to enhance the dielectric constant of intrinsic elastomers, thus facilitating advancements in soft robots, biosensors, and wearable electronics.

Characterization and distribution of macroplastics along the coastal municipalities of Partido district, Camarines Sur, Philippines

This study was conducted to characterize and investigate the distribution of macroplastics in the nine coastal municipalities of Partido district, Camarines Sur, Philippines. The study aimed to quantify and categorize the macroplastics along the coastal areas of the study site. The results reveal variations in the type, color, size, category, and amount of plastic waste among different municipalities. Notably, plastic fragments, cigarette butt, food packaging, and disposable utensils emerged as the predominant forms of macroplastics within the study area. The majority of the collected plastic particles fell within the size range of 0.5–1.00 cm, while transparent, white, and green were identified as the prevailing colors in the samples of plastic waste. Furthermore, both hard plastic and soft plastic fragments were frequently encountered, accounting for 35% and 31% of the total plastic, respectively. Sagňay, Tigaon, and Caramoan exhibited the highest densities of macroplastics, with values of 0.62±0.11, 0.60±0.05, and 0.59±0.06 pcs/m2, respectively. In contrast, Garchitorena displayed the lowest density at 0.26±0.04. The remaining municipalities exhibited densities ranging from 0.4 to 0.5 pcs/m2. The elevated macroplastics density observed can be attributed to factors such as their degree of urbanization, tourist activity, and population density. The findings of this study provide valuable insights into the composition and characteristics of macroplastics found along the coastal municipalities of Partido district, emphasizing the urgent need for effective strategies and policies to mitigate the possible impact to the environment.

Molecular characterization of Aeromonas hydrophila detected in Channa marulius and Sperata sarwari sampled from rivers of Punjab in Pakistan.

Aeromonas hydrophila is one of the major pathogenic bacteria responsible for causing severe outbreaks at fish farms and is also a major global public health concern. This bacterium harbors many virulence genes. The current study was designed to evaluate the antidrug and virulence potential of A. hydrophila by amplifying its antimicrobial resistance and virulence genes using PCR and examining their effects on fish tissues and organs. A total of 960 fish samples of Channa marulius and Sperata sarwari were collected from four sites of the rivers of the Punjab, Pakistan. A. hydrophila isolates were subjected to biochemical identification and detection of virulence and antimicrobial resistance (AMR) genes by PCR. We retrieved 181 (6.46%) A. hydrophila isolates from C. marulius and 177 (6.25%) isolates from S. sarwari. Amplification through PCR revealed the incidence of virulence genes in 95.7% of isolates in C. marulius and 94.4% in S. sarwari. Similarly, amplification through PCR also revealed occurrence of AMR genes in 87.1% of isolates in C. marulius and 83.9% in S. sarwari. Histopathological examination revealed congestion (5.2%) and hepatocyte necrosis (4.6%) in liver, lamellar fusion (3.3%) and the presence of bacterial colonies (3.7%) in gills, fin erosion (6%), and the presence of biofilms (3.5%) in tail fins of infected fish. Phylogenetic tree analysis of 16S rRNA and gyrB gene of A. hydrophila revealed 100% and 97% similarity, respectively, with 16S rRNA gene and gyrB of A. hydrophila isolated in previous studies. The results of antimicrobial susceptibility testing showed that all isolates demonstrated resistance to sulfamethoxazole, ampicillin, neomycin, and norfloxacin, while susceptibility to gentamicin, chloramphenicol, and tetracycline, and intermediate resistance was observed against cefotaxime. The results concluded that examined fish samples were markedly contaminated with virulent and multidrug strains of A. hydrophila which may be of a potential health risk. The study emphasizes the responsible antimicrobial use in aquaculture and the urgent need for effective strategies to control the spread of virulence and antimicrobial resistance genes in A. hydrophila.

EfficientPolypSeg: Efficient Polyp Segmentation in colonoscopy images using EfficientNet-B5 with dilated blocks and attention mechanisms

Background:Colorectal cancer ranks as the second leading cause of cancer-related death worldwide, prompting the need for effective strategies to combat this disease. The urgent need for effective strategies to combat this disease has led to the exploration of innovative approaches in medical imaging and diagnosis. Automatic segmentation and detection of colorectal polyps in colonoscopy images have emerged as a promising approach, enabling timely identification and diagnosis of colorectal diseases. Methods:This paper introduces EfficientPolypSeg, an advanced segmentation architecture that leverages the capabilities of EfficientNet-B5 as the encoder, enhanced by two dilated blocks, a Channel Attention Block (CAB), a Spatial Attention Block (SAB), and a Feature Fusion Block (FFB). Notably, our model exhibits superior performance compared to state-of-the-art methods, achieving exceptional accuracy and segmentation quality. Results:To validate the effectiveness of EfficientPolypSeg, extensive experiments were conducted on four publicly available datasets. The results demonstrate the remarkable performance of our model, showcasing its ability to outperform existing approaches in terms of accuracy and segmentation quality. Our model achieved an impressive mean Dice score of 0.92 and a mean Intersection over Union (IoU) of 0.86 on these datasets. These findings highlight the potential impact of EfficientPolypSeg in improving the detection and diagnosis of colorectal polyps. Conclusion:EfficientPolypSeg, the segmentation architecture proposed in this study, exhibits remarkable capabilities in the automatic detection and segmentation of colorectal polyps. The model’s superior performance, as evidenced by extensive experimentation on diverse datasets, emphasizes its potential impact on improving the detection and diagnosis of colorectal diseases.

Characterization and risk assessment of novel SXT/R391 integrative and conjugative elements with multidrug resistance in Proteus mirabilis isolated from China, 2018-2020.

Proteus mirabilis can transfer transposons, insertion sequences, and gene cassettes to the chromosomes of other hosts through SXT/R391 integrative and conjugative elements (ICEs), significantly increasing the possibility of antibiotic resistance gene (ARG) evolution and expanding the risk of ARGs transmission among bacteria. A total of 103 strains of P. mirabilis were isolated from 25 farms in China from 2018 to 2020. The positive detection rate of SXT/R391 ICEs was 25.2% (26/103). All SXT/R391 ICEs positive P. mirabilis exhibited a high level of overall drug resistance. Conjugation experiments showed that all 26 SXT/R391 ICEs could efficiently transfer to Escherichia coli EC600 with a frequency of 2.0 × 10-7 to 6.0 × 10-5. The acquired ARGs, genetic structures, homology relationships, and conservation sequences of 26 (19 different subtypes) SXT/R391 ICEs were investigated by high-throughput sequencing, whole-genome typing, and phylogenetic tree construction. ICEPmiChnHBRJC2 carries erm (42), which have never been found within an SXT/R391 ICE in P. mirabilis, and ICEPmiChnSC1111 carries 19 ARGs, including clinically important cfr, blaCTX-M-65, and aac(6')-Ib-cr, making it the ICE with the most ARGs reported to date. Through genetic stability, growth curve, and competition experiments, it was found that the transconjugant of ICEPmiChnSCNNC12 did not have a significant fitness cost on the recipient bacterium EC600 and may have a higher risk of transmission and dissemination. Although the transconjugant of ICEPmiChnSCSZC20 had a relatively obvious fitness cost on EC600, long-term resistance selection pressure may improve bacterial fitness through compensatory adaptation, providing scientific evidence for risk assessment of horizontal transfer and dissemination of SXT/R391 ICEs in P. mirabilis.IMPORTANCEThe spread of antibiotic resistance genes (ARGs) is a major public health concern. The study investigated the prevalence and genetic diversity of integrative and conjugative elements (ICEs) in Proteus mirabilis, which can transfer ARGs to other hosts. The study found that all of the P. mirabilis strains carrying ICEs exhibited a high level of drug resistance and a higher risk of transmission and dissemination of ARGs. The analysis of novel multidrug-resistant ICEs highlighted the potential for the evolution and spread of novel resistance mechanisms. These findings emphasize the importance of monitoring the spread of ICEs carrying ARGs and the urgent need for effective strategies to combat antibiotic resistance. Understanding the genetic diversity and potential for transmission of ARGs among bacteria is crucial for developing targeted interventions to mitigate the threat of antibiotic resistance.

Automatic Detection of Feral Pigeons in Urban Environments Using Deep Learning.

The overpopulation of feral pigeons in Hong Kong has significantly disrupted the urban ecosystem, highlighting the urgent need for effective strategies to control their population. In general, control measures should be implemented and re-evaluated periodically following accurate estimations of the feral pigeon population in the concerned regions, which, however, is very difficult in urban environments due to the concealment and mobility of pigeons within complex building structures. With the advances in deep learning, computer vision can be a promising tool for pigeon monitoring and population estimation but has not been well investigated so far. Therefore, we propose an improved deep learning model (Swin-Mask R-CNN with SAHI) for feral pigeon detection. Our model consists of three parts. Firstly, the Swin Transformer network (STN) extracts deep feature information. Secondly, the Feature Pyramid Network (FPN) fuses multi-scale features to learn at different scales. Lastly, the model's three head branches are responsible for classification, best bounding box prediction, and segmentation. During the prediction phase, we utilize a Slicing-Aided Hyper Inference (SAHI) tool to focus on the feature information of small feral pigeon targets. Experiments were conducted on a feral pigeon dataset to evaluate model performance. The results reveal that our model achieves excellent recognition performance for feral pigeons.

Urgent imperatives and perspectives of Antimicrobial resistance

Antimicrobial resistance (AMR) is a pressing global health issue that threatens the effectiveness of antibiotics in treating infectious diseases. This study examines the current landscape of AMR with a specific focus on the emergence and spread of multidrug-resistant bacteria. The factors contributing to the development and spread of AMR including the overuse and misuse of antibiotics in human and animal health sectors are discussed. The mutations of bacterial genes causing AMR, highlighting their role in promoting the survival and proliferation of drug-resistant bacteria are analyzed. The consequences of AMR are far-reaching and include increased morbidity, mortality and healthcare costs. The assessment highlights the impact of medical wastes such as patient samples, clinical and drug wastes and agro-based wastes such as surface runoff of poultry and cattle medication on AMR. To address the challenge of AMR, the analysis emphasizes the need for novel antimicrobial agents. The importance of public awareness, awareness among decision makers and appropriate attention and schemes is also discussed as a promising approach to combat AMR. In conclusion, this appraisal sheds light on the escalating problem of AMR and the urgent need for effective strategies to wrestle it. The findings underscore the importance of a multidisciplinary approach involving healthcare professionals, researchers, policymakers and the public to mitigate the impact of AMR and to ensure the availability of effective treatments for infectious diseases in the future. By addressing the challenges posed by AMR, we can safeguard public health and promote sustainable use of antibiotics.

Trends in Antibiotic Resistance Patterns and Burden of Escherichia Coli Infections in Young Children: A Retrospective Cross-Sectional Study in Shenzhen, China from 2014-2018.

The emergence of multi-drug resistant ESBL-producing E. coli poses a global health problem. In this study, we aimed to investigate the prevalence of E. coli infections and their antibiotic susceptibility profiles in paediatric clinical cases in Shenzhen, China from Jan 1, 2014, to Jan 30, 2019, while also determining temporal trends, identifying ESBL-producing strains, and recommending potential empirical antibiotic therapy options. We isolated a total of 4148 E. coli from different specimens from a single paediatric healthcare centre. Additionally, we obtained relevant demographic data from the hospital's electronic health records. Subsequently, we performed antimicrobial susceptibility testing for 8 classes of antibiotics and assessed ESBL production. Out of the 4148 isolates, 2645 were from males. The highest burden of E. coli was observed in the age group of 0-1 years, which gradually declined over the five-year study period. Antimicrobial susceptibility results indicated that 82% of E. coli isolates were highly resistant to ampicillin, followed by 52.36% resistant to cefazolin and 47.46% resistant to trimethoprim/sulfamethoxazole. Notably, a high prevalence of ESBL production (49.54%) was observed among the E. coli isolates, with 60% of them displaying a multi-drug resistance phenotype. However, it is worth mentioning that a majority of the isolates remained susceptible to ertapenem and imipenem. Our findings also highlighted a decrease in E. coli infections in Shenzhen, primarily among hospitalized patients in the 0-1 year age group. However, this decline was accompanied by a considerably high rate of ESBL production and increasing resistance to multiple antibiotics. Our study underscores the urgent need for effective strategies to combat multi-drug resistant ESBL-producing E. coli Infections.

Nanocatalyst-triggered cascade immunoassay: Multi-model immunochromatography assay for sensitive detection of Salmonella typhimurium

The Salmonella-caused food safety problem is a potentially serious threat to public health and economic development, which stresses an urgent need for effective strategies to control the spread of food-borne pathogenic bacteria. In this work, a nanocatalyst-triggered cascade immunoassay (NTCI) combined with colorimetric-catalytic-photothermal multi-model signals has been developed for the detection of Salmonella typhimurium (S. Typhimurium). PtNiCo@TA trimetallic nanoflower was employed as the signal label of the NTCI, due to its excellent peroxidase-mimicking properties, colorimetric performance, biocompatibility, and stability. Here, the multi-model NTCI consists of three parts: (i) colorimetric immunochromatography assay (ICA) with a double antibody sandwich system, (ii) catalytic amplification system triggered by 3, 3′, 5, 5′ -tetramethylbenzidine (TMB) and hydrogen peroxide (H2O2), and (iii) photothermal analysis achieved by the near-infrared (NIR) laser-driven photothermal properties of oxidized TMB (oxTMB). Based on this multi-model cascade reaction, both specific qualitative and quantitative analyses of pathogenic bacteria are realized. The limit of detection (LOD) of multi-model NTCI is 500 cfu·mL−1, which is 20-fold more sensitive than that of the colorimetric analysis alone. Moreover, the NTCI system shows preeminent applicability and flexibility for S. Typhimurium detection in milk samples. Therefore, this work suggests an innovative direction for the design of nanocatalyst-triggered photothermal multi-model immunoassays in the detection of food-borne pathogens.

Games in Times of a Pandemic: Structured Overview of COVID-19 Serious Games.

The COVID-19 pandemic introduced an urgent need for effective strategies to disseminate crucial knowledge and improve people's subjective well-being. Complementing more conventional approaches to knowledge dissemination, game-based interventions were developed to create awareness and educate people about the pandemic, hoping to change their attitudes and behavior. This study provided an overview and analysis of digital and analog game-based interventions in the context of the COVID-19 pandemic. As major pandemics and other large-scale disruptive events are expected to increase in frequency in the coming decades, this analysis aimed to inform the design, uptake, and effects of similar future interventions. From November 2021 to April 2022, Scopus, Google, and YouTube were searched for articles and videos describing COVID-19-themed game-based interventions. Information regarding authorship, year of development or launch, country of origin, license, deployment, genre or type, target audience, player interaction, in-game goal, and intended transfer effects was extracted. Information regarding intervention effectiveness was retrieved where possible. A diverse assortment of 23 analog and 43 digital serious games was identified, approximately one-third of them (25/66, 38%) through scientific articles. Most of these games were developed by research institutions in 2020 (13/66, 20%) and originated in Europe and North America (38/66, 58%). A limited number (20/66, 30%) were tested on relatively small samples, using a diversity of research methods to assess the potential changes in participants' knowledge, attitudes, and behaviors as well as their gameplay experience. Although most of the evaluated games (11/20, 55%) effectively engaged and motivated the players, increased awareness, and improved their understanding of COVID-19-related issues, the games' success in influencing people's behavior was often unclear or limited. To increase the impact of similar future interventions aimed at disseminating knowledge and influencing people's attitudes and behaviors during a large-scale crisis, some considerations are suggested. On the basis of the study results and informed by existing game theories, recommendations are made in relation to game development, deployment, and distribution; game users, design, and use; game design terminology; and effectiveness testing for serious games.

Medical errors and patient safety: Strategies for reducing errors using artificial intelligence

Background: Medical errors remain a significant challenge in healthcare, contributing to adverse patient outcomes, increased costs, and extended hospitalizations. These errors encompass diagnostic inaccuracies, medication mistakes, surgical errors, and communication breakdowns. The global prevalence of medical errors underscores the urgent need for effective strategies to enhance patient safety. Aim: This article explores the role of Artificial Intelligence (AI) in reducing medical errors and improving patient safety. It aims to evaluate how AI technologies can mitigate various types of medical errors, and the challenges associated with their implementation. Methods: The study reviews current literature on AI applications in healthcare, focusing on diagnostic support, medication safety, surgical precision, and patient monitoring. It analyzes the effectiveness of AI-driven systems in reducing errors across different medical disciplines and examines the integration challenges, including ethical and regulatory concerns. Results: AI technologies, including machine learning algorithms and decision support systems, have demonstrated significant potential in enhancing diagnostic accuracy, preventing medication errors, and improving surgical outcomes. AI-driven systems have shown promising results in real-time patient monitoring, early detection of adverse events, and optimizing healthcare management. However, challenges related to data privacy, algorithm transparency, and integration into clinical workflows persist.

AI-based carbon emission forecast and mitigation framework using recycled concrete aggregates: A sustainable approach for the construction industry

<abstract> <p>The cement industry's carbon emissions present a major global challenge, particularly the increase in atmospheric carbon dioxide (CO<sub>2</sub>) levels. The concrete industry is responsible for a significant portion of these emissions, accounting for approximately 5–9% of the total emissions. This underscores the urgent need for effective strategies to curb carbon emissions. In this work, we propose to use artificial intelligence (AI) to predict future emission trends by performing a detailed analysis of cement industry's CO<sub>2</sub> emissions data. The AI predictive model shows a significant increase in overall carbon emissions from the cement sector which is attributed to population growth and increased demand for housing and infrastructure. To address this issue, we propose a framework that emphasizes on implementing carbon sequestration through reuse of construction and demolition (C & D) waste by using recycled aggregates. The paper proposes a framework addressing carbon sequestration through use of C & D waste. The framework is applied specifically to Maharashtra State in India to calculate the potential reduction in carbon emissions by construction industry resulting from recycled aggregates. The study reveals a projected saving of 24% in carbon emissions by adopting the suggested framework. The process and outcomes of the study aim to address the concerns of climate change through reduced carbon emissions in the construction industry promoting recycle and reuse of construction waste.</p> </abstract>

Psychiatric comorbidities and breast cancer: A study from Jammu region of UT of J&K, India.

Breast cancer is the most common cancer among females of all age groups. The diagnosis of cancer itself has a menacing impact on the psychosocial health of an individual leading to various psychiatric comorbidities. This can impact the overall prognosis of cancer patients. To study the prevalence of psychiatric comorbidities among female breast cancer patients and to assess its association with sociodemographic and clinical factors among these. A cross-sectional hospital-based study was conducted for a period of 1 year in the Regional Cancer Centre. All females in the age group of 18 years and above, who were confirmed to have breast cancer were included in the study. A self-designed proforma was administered to elicit information about sociodemographic and clinical details of the study subjects. Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS) were used to assess the prevalence of psychiatric comorbidities like depression, anxiety, and stress among these subjects. Data thus obtained were analyzed using the Statistical Package for the Social Sciences (SPSS) Software. A total of 205 subjects were included in the study. The mean age was reported as 49.25 ± 11.44 years, most of these were married homemakers residing in nuclear families in rural areas. Prevalence of depression, anxiety, and stress was found to be 60%, 73.17%, and 83.41%, and these were significantly associated (P < 0.05) with residence, number of living children, visits per month to hospitals, time since diagnosis, and type of treatment received. There is a high risk of psychiatric comorbidities among breast cancer female patients and an urgent need for effective strategies to prevent these.

Burden and Trends of Cancer in Bangladesh: Insights into Breast, Lung, Cervical, and Liver Cancers and Implications for Public Health Interventions

Burden and Trends of Cancer in Bangladesh: Insights into Breast, Lung, Cervical, and Liver Cancers and Implications for Public Health Interventions

Components of interventions to reduce restrictive practices with children and young people in institutional settings: the Contrast systematic mapping review

Background Incidents in which children or young people experience severe distress or harm or cause distress or harm to others occur frequently in children and young people’s institutional settings. These incidents are often managed using restrictive practices, such as restraint, seclusion, sedation or constant observation; however, these also present significant risks of physical and psychological harm to children and young people as well as staff. Numerous interventions aim to reduce the use of restrictive techniques, but research is hampered by limited attention to specific intervention components. The behavior change technique taxonomy may improve reporting by providing a common language for specifying the content and mechanisms of behaviour change. This study aimed to identify, standardise and report the effectiveness of components of interventions to reduce restrictive practices in children and young people’s institutional settings. Objectives To map interventions aimed at reducing restrictive practices in children and young people’s institutional settings internationally, to conduct behaviour change technique analysis of intervention components, to identify process elements, and to explore effectiveness evidence to identify promising behaviour change techniques and compare the results with those found in adult psychiatric inpatient settings in a companion review. Design Systematic mapping review with programme content coding using the behavior change technique taxonomy. Review methods Eleven relevant English-language health and social care research databases 1989–2019 [including Applied Social Sciences Index (ASSIA), Criminal Justice Abstracts, Educational Resources Information Center (ERIC), MEDLINE and PsycInfo®], grey literature and social media were searched during 2019 (updated January 2020). Data extraction, guided by Workgroup for Intervention Development and Evaluation Research (WIDER), Cochrane Library and theory coding scheme recommendations, included intervention characteristics and study design and reporting. Screening and quality appraisal used the Mixed Methods Appraisal Tool. The behavior change technique taxonomy was applied systematically, and interventions were coded for behaviour change technique components. Outcomes data were then related back to these components. Results There were 121 records, including 76 evaluations. Eighty-two interventions, mostly multicomponent, were identified. Evaluation approaches commonly used a non-randomised design. There were no randomised controlled trials. Behaviour change techniques from 14 out of a possible 16 clusters were detected. Four clusters (i.e. goals and planning, antecedents, shaping knowledge, and feedback and monitoring) contained the majority of identified behaviour change techniques and were detected in over half of all interventions. Two clusters (i.e. self-belief and covert learning) contained no identified behaviour change techniques. The most common setting in which behaviour change techniques were found was ‘mental health’. The most common procedure focused on staff training. The two most common behaviour change techniques were instruction on how to perform the behaviour and restructuring the social environment. Promising behaviour change techniques included instruction on how to perform the behaviour, restructuring the social environment, feedback on outcomes of behaviour and problem-solving. Compared with the companion review, service user perspectives were more sparse and there was more interest in trauma-informed approaches. Effectiveness evidence, range of interventions and reporting were broadly similar. Limitations Poor reporting may have prevented detection of some behaviour change techniques. The finding that the evidence was weak restricted the feasibility of examining behaviour change technique effectiveness. Literature searches were restricted to English-language sources. Conclusions This study generated, to our knowledge, the first review of evidence on the content and effectiveness of interventions to reduce restrictive practices in children and young people’s institutional settings. Interventions tend to be complex, reporting is inconsistent and robust evaluation data are limited, but some behaviour change techniques seem promising. Future work Promising behaviour change techniques could be further explored. Better evidence could help address the urgent need for effective strategies. Study registration This study is registered as PROSPERO CRD42019124730. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 8. See the NIHR Journals Library website for further project information.

Food Insecurity Is Associated with Diarrhea, Respiratory Illness, and Stunting but Not Underweight or Obesity in Low-Resource New Delhi Households

ABSTRACT We conducted a cross-sectional study to investigate the association between household food insecurity (HFI) and prevalence of underweight, stunting, diarrhea and respiratory illness (RI) in school-age children in New Delhi, India. Mothers of 191 children aged 8–12 years were interviewed to collect HFI data using the Food Insecurity Experience Scale. Moderate-severe HFI was identified in 43.6% of the households. The odds for diarrhea, RI and stunting were increased by 4.7, 3.3 and 2.3 times respectively in food insecure children than food secure counterparts. HFI was not associated with underweight. There is an urgent need for effective strategies to mitigate HFI.

Non-pharmacological interventions to reduce restrictive practices in adult mental health inpatient settings: the COMPARE systematic mapping review

ObjectivesThe study aimed to provide a mapping review of non-pharmacological interventions to reduce restrictive practices in adult mental health inpatient settings; classify intervention components using the behaviour change technique taxonomy; explore evidence of behaviour change techniques and interventions; and identify the behaviour change techniques that show most effectiveness and those that require further testing.BackgroundIncidents involving violence and aggression occur frequently in adult mental health inpatient settings. They often result in restrictive practices such as restraint and seclusion. These practices carry significant risks, including physical and psychological harm to service users and staff, and costs to the NHS. A number of interventions aim to reduce the use of restrictive practices by using behaviour change techniques to modify practice. Some interventions have been evaluated, but effectiveness research is hampered by limited attention to the specific components. The behaviour change technique taxonomy provides a common language with which to specify intervention content.DesignSystematic mapping study and analysis.Data sourcesEnglish-language health and social care research databases, and grey literature, including social media. The databases searched included British Nursing Index (BNI), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central Register of Controlled Trials (CCRCT), Cochrane Database of Systematic Reviews (CDSR), Database of Abstracts of Reviews of Effects (DARE), EMBASE, Health Technology Assessment (HTA) Database, HTA Canadian and International, Ovid MEDLINE®, NHS Economic Evaluation Database (NHS EED), PsycInfo®and PubMed. Databases were searched from 1999 to 2019.Review methodsBroad literature search; identification, description and classification of interventions using the behaviour change technique taxonomy; and quality appraisal of reports. Records of interventions to reduce any form of restrictive practice used with adults in mental health services were retrieved and subject to scrutiny of content, to identify interventions; quality appraisal, using the Mixed Methods Appraisal Tool; and data extraction, regarding whether participants were staff or service users, number of participants, study setting, intervention type, procedures and fidelity. The resulting data set for extraction was guided by the Workgroup for Intervention Development and Evaluation Research, Cochrane and theory coding scheme recommendations. The behaviour change technique taxonomy was applied systematically to each identified intervention. Intervention data were examined for overarching patterns, range and frequency. Overall percentages of behaviour change techniques by behaviour change technique cluster were reported. Procedures used within interventions, for example staff training, were described using the behaviour change technique taxonomy.ResultsThe final data set comprised 221 records reporting 150 interventions, 109 of which had been evaluated. The most common evaluation approach was a non-randomised design. There were six randomised controlled trials. Behaviour change techniques from 14 out of a possible 16 clusters were detected. Behaviour change techniques found in the interventions were most likely to be those that demonstrated statistically significant effects. The most common intervention target was seclusion and restraint reduction. The most common strategy was staff training. Over two-thirds of the behaviour change techniques mapped onto four clusters, that is ‘goals and planning’, ‘antecedents’, ‘shaping knowledge’ and ‘feedback and monitoring’. The number of behaviour change techniques identified per intervention ranged from 1 to 33 (mean 8 techniques).LimitationsMany interventions were poorly described and might have contained additional behaviour change techniques that were not detected. The finding that the evidence was weak restricted the study’s scope for examining behaviour change technique effectiveness. The literature search was restricted to English-language records.ConclusionsStudies on interventions to reduce restrictive practices appear to be diverse and poor. Interventions tend to contain multiple procedures delivered in multiple ways.Future workPrior to future commissioning decisions, further research to enhance the evidence base could help address the urgent need for effective strategies. Testing individual procedures, for example, audit and feedback, could ascertain which are the most effective intervention components. Separate testing of individual components could improve understanding of content and delivery.Study registrationThe study is registered as PROSPERO CRD42018086985.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full inHealth Services and Delivery Research; Vol. 9, No. 5. See the NIHR Journals Library website for further project information.