Underprivileged Countries Research Articles

Analysing Amitav Ghosh’s <i>Gun Island</i> as a Climate Fiction: Transgressing ‘Borders’ and ‘Orders’ by the Humans and Nonhumans

Amitav Ghosh has a tendency to write literary pieces focusing on climate issues. This aspiration is also manifested in his novel Gun Island (2019). The author allegorizes the myth of Manasa Devi, which creates a wonderful connection between humans and natural environment in this novel. Gun Island (2019) explores the conviction of diversified environmental issues, such as environmental injustice, migrant ecologies, and climate refugees. Although natural disasters occur more or less everywhere in the world, the poor pay the highest price. The harsh reality is that the most affected are the most marginalized regions of underprivileged countries. Developed countries can still cope with its effects, but people in poor countries are persistently being displaced. The Sundarbans is one such magnificent instance in Gun Island. Overall, Ghosh has shown that migration of humans and nonhumans occurs simultaneously as a result of climate change. Humans and nonhumans transgress the precincts of ‘border’ as well as ‘order’ to migrate from one place to another eco-friendly place. His perplexing story of Gun Island is inexplicably mythical but contextually practical because it resonates with a group of Asian and African people’s own experiences, emotions and their yearning for migration to Europe due to climate change. This borderless migration cannot be stopped by any means of order.

Current Status of Machine Learning and Artificial Intelligence in Cervical Cancer Screening and Diagnosis: A Systematic Review

Background: Cervical cancer poses a substantial global health challenge, predominantly affecting underprivileged countries. The limitations of current screening methods, such as cytology and visual examination, underscore the need for improved techniques. Artificial intelligence (AI) and machine learning (ML), particularly convolutional neural networks, offer promising solutions in this regard. Methodology: Fifteen studies meeting the inclusion criteria were examined. The PRISMA criteria guided the exploration of cervical cancer screening studies employing AI, ML, and deep learning on PubMed/MEDLINE and Google Scholar. The search focused on "artificial intelligence" and "Pap smear." The investigation specifically delves into English-language studies post-2019 that pertain to the machine learning and deep learning classification of cervical cancer using mobile devices. Histology, animal research, and pre-2019 investigations are excluded. Titles and abstracts were carefully reviewed for any discrepancies and subsequently discussed. The process of data extraction involved compiling information from the selected articles. Result: The systematic review investigates the impact of AI and ML on cervical cancer detection, screening, and diagnosis. Our review reveals enhanced accuracy and efficiency in innovative technologies such as CytoBrain and computer-aided diagnostic systems employing Compact VGG and ResNet101. ML techniques like logistic regression, MLP, SVM, KNN, and naive Bayes prove beneficial for managing complex datasets, particularly when combined with class-balancing procedures. The promising aspects include the application of deep learning for automation and AI-assisted digital microscopy. These findings signify a transformative shift in cervical cancer screening, underscoring the potential of ML and AI technologies to elevate diagnostic accuracy and accessibility. Conclusion: Our study demonstrates advancements in both accuracy and responsiveness. Despite recognizing scientific and ethical considerations, the study underscores the potential of AI to enhance cervical cancer care. This systematic review advocates for policymakers and healthcare practitioners to use ongoing research for informed decision-making in this rapidly evolving field.

Effects of Stakeholder’s Management in Public Private Partnership on Implementation of Affordable Housing Projects in Meru County, Kenya

More than half of the world's population of over six billion people reside in urban areas, placing increasing responsibility on governments to provide fundamental human necessities, including housing. This challenge is particularly pronounced in Kenya, where many County Government PPP affordable housing initiatives have either failed or been stalled. Both National and County Governments have experienced difficulties in executing these projects, reflecting the nascent stage of PPP application in many underprivileged countries. In Meru County, the Affordable Housing Project (AHP) employs PPP, primarily in the form of joint ventures. This approach distinguishes it from other existing projects and generates a knowledge gap regarding the assessment of the impact of public-private partnerships on the implementation of affordable housing initiatives in this region. The aim of this research was to explore the effects of stakeholder’s management in public private partnership on implementation of affordable housing projects in Meru County, Kenya. Guided by stakeholder theory, the study employed a descriptive research design, collecting data through a structured questionnaire. A proportionate stratified random sampling method was used to select the study sample from County officials, non-governmental organization managers, development partners' managers, and household heads from selected study areas where the projects have been initiated, notably in the Buri, Maweni, Mjini, and Makandara wards. A total of 395 questionnaires were issued to the sampled respondents from a total target of 31978 who included the stakeholders in affordable housing project estates in Meru County that included county official managers in DoLPH (15), NGO’s managers (25), development partners’ managers (5) engaged in affordable housing and household heads (350). The collected data was analyzed and results presented in tables and figures, the qualitative information was presented in prose.

Global risk factor analysis of myopia onset in children: A systematic review and meta-analysis.

This work aimed to comprehensively assess the risk factors affecting myopia in children to develop more effective prevention and treatment strategies. To this end, data from database were employed to assess the relationship between the incidence of myopia and its risk factors. We searched eight databases online in June 2022. Cohort studies were included that measured the connection between risk factors and myopia. Eligibility was not restricted by language. The Newcastle-Ottawa Scale (NOS) was used to measure the risk of bias and conducted GRADE evaluation to determine the certainty of evidence. Potential risk factors with positive or negative results were seen. Inplasy Registration: https://inplasy.com/inplasy-2022-4-0109/. Evidence that risk factors for myopia are mixed, comprising both positive (20) and null (17) findings. In 19 cohort studies on 3578 children, girls were more likely to develop myopia (RR: 1.28 [1.22-1.35]). Myopia can occur at any age, from early childhood to late adulthood. Children whose parents had myopia were more likely to develop myopia. Longer outdoor activities time (RR: 0.97 [0.95-0.98]) and less near-work time (RR: 1.05 [1.02-1.07]) appeared to be significantly decrease the incidence of myopia. Children with lower SE, longer AL, a lower magnitude of positive relative accommodation, worse presenting visual acuity, deeper anterior chamber, and thinner crystalline lens may be related to myopia onset. The burden of myopia in underprivileged countries is higher than in developed countries (RR: 5.28 [2.06-13.48]). The quality of evidence for the evaluated factors was moderate to low or very low. Genetic factors, environmental factors (such as excessive use of electronic products, and poor study habits) and lifestyle factors (such as lack of outdoor activities, poor nutrition, etc.) are the main risk factors for myopia in children. Myopia prevention strategies should be designed based on environmental factors, gender, parental myopia and eye indicators in order to explore a lifestyle that is more conducive to the eye health of children.

On the Impact of Synchronous Electrocardiogram Signals for Heart Sounds Segmentation.

In this paper we study the heart sound segmentation problem using Deep Neural Networks. The impact of available electrocardiogram (ECG) signals in addition to phonocardiogram (PCG) signals is evaluated. To incorporate ECG, two different models considered, which are built upon a 1D U-net - an early fusion one that fuses ECG in an early processing stage, and a late fusion one that averages the probabilities obtained by two networks applied independently on PCG and ECG data. Results show that, in contrast with traditional uses of ECG for PCG gating, early fusion of PCG and ECG information can provide more robust heart sound segmentation. As a proof of concept, we use the publicly available PhysioNet dataset. Validation results provide, on average, a sensitivity of 97.2%, 94.5%, and 95.6% and a Positive Predictive Value of 97.5%, 96.2%, and 96.1% for Early-fusion, Late-fusion, and unimodal (PCG only) models, respectively, showing the advantages of combining both signals at early stages to segment heart sounds.Clinical relevance- Cardiac auscultation is the first line of screening for cardiovascular diseases. Its low cost and simplicity are especially suitable for screening large populations in underprivileged countries. The proposed analysis and algorithm show the potential of effectively including electrocardiogram information to improve heart sound segmentation performance, thus enhancing the capacity of extracting useful information from heart sound recordings.

Blended Learning in Algeria: Assessing Students' Satisfaction and Future Preferences Using SEM and Sentiment Analysis.

Given the still existing restrictions of COVID-19, blended learning is undoubtedly becoming a better-fitting strategy for higher education institutions in underprivileged countries. Acknowledging the current changes in higher education, this study aims to investigate the elements that influence students' satisfaction and their future preferences regarding blended learning in Algeria. A total of 782 questionnaires were collected from different Algerian universities. A structural Equation Modeling (SEM) analysis was conducted to investigate the relationship among the latent variables of the proposed theoretical model. Moreover, an unsupervised sentiment analysis approach was applied to analyze the qualitative data received in the form of feedback from the participants. The results show that students' perceived ease of use and perceived usefulness of blended learning had a significant positive impact on their satisfaction. Similarly, satisfaction had a positive influence on students' future preferences regarding blended learning. In turn, students' perceived ease of use and usefulness had an indirect effect on their future preferences, mediated by satisfaction. Additionally, qualitative data echoed students' eagerness to adopt more advanced learning technologies and what obstacles currently stand in their way. The contribution of this study is to reflect the current situation of blended learning adoption in developing countries and to support future curriculum planning and development. It can also help teachers, students, and policymakers to make better decisions and recommendations for an improved and more sustainable learning and teaching environment in the future.

A novel framework based on deep learning for COVID-19 diagnosis from X-ray images.

The coronavirus infection has endangered human health because of the high speed of the outbreak. A rapid and accurate diagnosis of the infection is essential to avoid further spread. Due to the cost of diagnostic kits and the availability of radiology equipment in most parts of the world, the COVID-19 detection method using X-ray images is still used in underprivileged countries. However, they are challenging due to being prone to human error, time-consuming, and demanding. The success of deep learning (DL) in automatic COVID-19 diagnosis systems has necessitated a detection system using these techniques. The most critical challenge in using deep learning techniques in diagnosing COVID-19 is accuracy because it plays an essential role in controlling the spread of the disease. This article presents a new framework for detecting COVID-19 using X-ray images. The model uses a modified version of DenseNet-121 for the network layer, an image data loader to separate images in batches, a loss function to reduce the prediction error, and a weighted random sampler to balance the training phase. Finally, an optimizer changes the attributes of the neural networks. Extensive experiments using different types of pneumonia expresses satisfactory diagnosis performance with an accuracy of 99.81%. This work aims to design a new deep neural network for highly accurate online recognition of medical images. The evaluation results show that the proposed framework can be considered an auxiliary device to help radiologists accurately confirm initial screening.

SARS-CoV-2 seroprevalence in children worldwide: a systematic review and meta-analysis

The higher hospitalisation rates of those aged 0-19 years (referred to herein as 'children') observed since the emergence of the immune-evasive SARS-CoV-2 Omicron variant and subvariants, along with the persisting vaccination disparities highlighted a need for in-depth knowledge of SARS-CoV-2 sero-epidemiology in children. Here, we conducted this systematic review to assess SARS-CoV-2 seroprevalence and determinants in children worldwide. In this systematic review and meta-analysis study, we searched international and preprinted scientific databases from December 1, 2019 to July 10, 2022. Pooled seroprevalences were estimated according to World Health Organization (WHO) regions (at 95% confidence intervals, CIs) using random-effects meta-analyses. Associations with SARS-CoV-2 seroprevalence and sources of heterogeneity were investigated using sub-group and meta-regression analyses. The protocol used in this study has been registered in PROSPERO (CRD42022350833). We included 247 studies involving 757,075 children from 70 countries. Seroprevalence estimates varied from 7.3% (5.8-9.1%) in the first wave of the COVID-19 pandemic to 37.6% (18.1-59.4%) in the fifth wave and 56.6% (52.8-60.5%) in the sixth wave. The highest seroprevalences in different pandemic waves were estimated for South-East Asia (17.9-81.8%) and African (17.2-66.1%) regions; while the lowest seroprevalence was estimated for the Western Pacific region (0.01-1.01%). Seroprevalence estimates were higher in children at older ages, in those living in underprivileged countries or regions, and in those of minority ethnic backgrounds. Our findings indicate that, by the end of 2021 and before the Omicron wave, around 50-70% of children globally were still susceptible to SARS-CoV-2 infection, clearly emphasising the need for more effective vaccines and better vaccination coverage among children and adolescents, particularly in developing countries and minority ethnic groups. None.

ACCESS TO MEDICINE

Availability and access to the health care facility is a fundamental right of human, it is the timely used of services according to the needs. It is included in International treaties and is recognized by authorities and various Governments throughout the world. What does it means the access to medicine? According to WHO (World Health Organization) access to medicines is “having medicines continuously available and affordable at public or private health facilities or medicine outlets that are within one hour’s walk from the homes of the population”. The affordability and availability of medicines is directly related to the strength of health care system, but the high prices of the medicines, prevalence of Non-communicable diseases (diabetes, hypertension) and increasing demand of patients are the major obstacles in this regard. According to Forbes “at least 2 billion people don’t have access to medicines and 1 billion people live in extreme poverty (less than $1.25 per day) and 2.2 billion live on less than $2 per day which is quite disturbing situation”. The World Health Organization (WHO) analyzed the global production of pharmaceutical products and it was concluded that 15% of the world’s population consumes over 90% of them. And by World Health Organization it is also estimated that one-third of the developing world population is unable to receive or purchase essential medicine on a regular basis, especially those which are on high demand. The situation is even worst in the under-privileged countries like Africa and Asia, where approximately 50% of the total population does not have sufficient medicines access and healthcare resources and the remaining 50% are using medication incorrectly. However about 10 million lives a year could be saved out of which 4 million in Africa and South-East Asia alone, if we improve the availability, balanced distribution and rational use of medicines. The overall situation of global access to essential medicines is still considered critical. In the privileged countries the budget for healthcare facilities corresponds to 24-66% of National health expenses, which indicates that there is need to monitor the availability and affordability of medicines in these countries. National policies, economical cost of medicine and strategies of procurement are required to ensure the availability of medicines cost-effectively. The people with low income and in-equal health can’t afford costly services of healthcare system. The policies for the improvement of infrastructure, financing of medical system and the rational use of medicines are formulated and followed on primarily basis. And Governments need reliable information in order to evaluate the impact of policy implementation. In addition to the cost and availability of medicines, it is mandatory to assess the quality of use and various aspects, such as rational medicine use, adherence of patients and appropriate prescription practices. Thus the access to healthcare system can be made effective, by the assessment of practices in facilities like record reviews and National expert reports. That’s why we are providing healthcare facilities to each person with the help of balanced geographical distribution. The 2016 Index showed that there is moderate progress by the pharmaceutical industries to improve medicines access. Besides these Pharmaceutical industries should play their role to perk up the medicines access. There is not any particular strategy that suits all systems, but all systems need to promote the provision of new medicinal products, by ensuring medical advancement, improving communication barriers and affordable treatment. And a Reliable Health and Supply system must provide all aspects of health system like procurement, supply and regulation of the medicines.

Exploiting Machine Learning Models for Identification of Heart Diseases

Cardiovascular illnesses have surpassed disease as the leading cause of death in industrialized, emerging, and underprivileged countries in recent decades. The mortality rate can be lowered through early detection and effective care of cardiac diseases. However, reliable detection of heart disorders in all conditions and serving doctors with 24-hour medical consultations are not possible since they require more intelligence, time, and talent to train the computer for a specific duty. Artificial intelligence and machine learning algorithms may have a substantial impact on the lives of those who are afflicted with chronic diseases. In this work, we used artificial intelligence to create a hybrid model for the prediction of cardiac disease and flags for prevention. The goal of this research was to develop a prediction model that can identify and characterize a patient's illness. We used data of 68975 patients from the University of California, Irvine's repository and trained several algorithms for CVD illness prediction. In our simulation maximum classifiers have achieved the greatest accuracy, which is a huge accomplishment for our research team. Our proposed hybrid ML algorithm framework improved CVD prediction performance when compared to previously offered prediction models. The proposed methods are decision tree and random forest models, which have a 99.98% accuracy for training, followed by support vector machine, which has a 99.30% accuracy followed by other classifiers which yield higher results in comparison. Pre-existing CVD history was considered the most important factor determining the prediction model's accuracy. Our findings reveal that our CVD prediction model based on machine learning techniques developed for health screening datasets is simple to apply and more accurate. Our proposed classifiers have the best accuracy rate.

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools.

Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based ‘dietary protein’ biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.

Abstract #1185166: Is hyperglycemia a risk factor associated with death in COVID patients everywhere in the world? Report from Honduras, Central America

To determine if hyperglycemia at admission is a risk factor for death in diabetic and non-diabetic patients hospitalized for COVID 19 in patients in a third level hospital in Central America. Descriptive cross sectional study that included patients hospitalized for COVID 19 during 2020 in Hospital Escuela, Honduras. We revised information for 1690 patients divided in three categories according to their glycemic values at admission. They were classified as ˂140 mg/dl category 1, 140-180 mg/dl category 2 and ˃180 mg/dl and category 3; we excluded 352 patients with no recorded glycemic value at admission. A total of 1357 patients were included, 644 for category 1 with 240 (37.3%) deaths out of which 58 (24.2%) were known for diabetes and 182 (75.8%) with no previous diabetes diagnosis. In category 2 out of 261 included patients there were 111 (42.5%) deaths with 40(36.0%) and 71(64.0%) previously known and not known for diabetes respectively. In category 3 out of 452 patients 194 (42.9%) deaths were confirmed with 142 (73.2%) in previously known for diabetes and 52 (26.8%) not previously known for diabetes. As reported previously in other parts of the world hyperglycemia proved to be a risk factor associated with death in patients known or not previously for diabetes. Even though it is not the only factor to be taken into consideration it has proven undoubtedly to be something that needs to be addressed upon admission for COVID 19 patients. A basal glucose measurement upon admission is fundamental in every patient and easily accessible even in underprivileged countries like ours. We found an ascending trend in increased mortality as glucose values at admission grow higher; this was true even for patients with no previous diabetes diagnosis. The registered mortality in this group of patients was about 35 to 47% higher compared to normoglycemic patients in the lowest glycemic tertile as has been found in previous publications. In the highest glycemic tertile (category 3) the mortality in diabetic patients found was 73% vrs 26% in non-diabetics that presented with extreme hyperglycemia which also suggests that chronic hyperglycemia is an important determinant.

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique.

The complete blood count (CBC) is one of the most important clinical steps in clinical diagnosis. The instruments used for CBC are usually expensive and bulky and require well-trained operators. Therefore, it is difficult for medical institutions below the tertiary level to provide these instruments, especially in underprivileged countries. Several reported on-chip blood cell tests are still in their infancy and do not deviate from conventional microscopic or impedance measurement methods. In this study, we (i) combined magnetically activated cell sorting and the differential density method to develop a method to selectively isolate three types of leukocytes from blood and obtain samples with high purity and concentration for portable leukocyte classification using the lens-free shadow imaging technique (LSIT), and (ii) established several shadow parameters to identify the type of leukocytes in a complete leukocyte shadow image by shadow image analysis. The purity of the separated leukocytes was confirmed by flow cytometry. Several shadow parameters such as the “order ratio” and “minimum ratio” were developed to classify the three types of leukocytes. A shadow image library corresponding to each type of leukocyte was created from the tested samples. Compared with clinical reference data, a correlation index of 0.98 was obtained with an average error of 6% and a confidence level of 95%. This technique offers great potential for biological, pharmaceutical, environmental, and clinical applications, especially where point-of-care detection of rare cells is required.

Prevalence and correlates of Helicobacter pylori infection among under-five children, adolescent and non-pregnant women in Nepal: Further analysis of Nepal national micronutrient status survey 2016.

Most of the Helicobacter pylori infections occur in developing countries. The risk factors for H. pylori infections are poverty, overcrowding, and unhygienic conditions, which are common problems in under-privileged countries such as Nepal. Despite having a high risk of H. pylori infections, no national level study has been conducted to assess prevalence and correlates of H. pylori infection in Nepal. Therefore, we hypothesized that micronutrients such as iron, vitamin B12 deficiency, socio-economic status, and nutritional status correlate with the prevalence of H. pylori infection in Nepal. We studied prevalence and correlates of H. pylori infection among under-five children, adolescents aged 10-19 years and married non-pregnant women aged 20-49 years using data from the Nepal National Micronutrient Status Survey 2016 (NNMSS-2016). H. pylori infection was examined in stool of 6-59 months old children and 20-49 years old non-pregnant women whereas the rapid diagnostic kit using blood sample was used among adolescent boys and girls. Prevalence of H. pylori infection was 18.2% among 6-59 months old children, 14% among adolescent boys and 16% among adolescent girls aged 10-19 years; and 40% among 20-49 years non-pregnant women. Poor socioeconomic status, crowding, and unhygienic condition were found to be positively associated with higher incidence of H. pylori infections. No significant correlation was observed between nutritional and micronutrients status (iron or risk of folate deficiency) with H. pylori infection. Findings from this study suggest that poverty-associated markers are primary contributors of H. pylori infections in Nepalese communities. To control acquisition and persistence of H. pylori infection in Nepal, we suggest improved management of safe drinking water and implementation of sanitation and hygiene programs, with a focus on those of lower socioeconomic status.

Corporate disclosures on curbing bribery and the UK Bribery Act 2010: evidence from UK companies

PurposeThis study aims to investigate whether United Kingdom (UK)-based companies have changed their voluntary disclosures on curbing the bribery of foreign officials in response to the UK Bribery Act 2010, and if so whether and how such disclosure changes substantively reflected allegations of bribery of foreign officials by news media.Design/methodology/approachBy using the notions of institutional pressure and decoupling and applying content and thematic analysis, the authors examined, in particular, disclosures on curbing bribery by the largest 100 companies listed on the London Stock Exchange in periods before and after the Bribery Act (2007–2012). News media reports covering incidents of bribery of foreign officials and related corporate disclosures before and after the Act were thoroughly examined to problematise corporate anti-bribery disclosure practices.FindingsThe study finds a significant change in disclosure on curbing bribery before and after the enactment of the UK Bribery Act, consistent with the notion of institutional coercive pressure. However, decoupling is also found: organisations' disclosures did not substantively reflect incidents of bribing foreign public officials, mostly from underprivileged developing nations.Research limitations/implicationsThis study acknowledges a limitation stemming from using media reports that focus on bribery incidents in identifying actual cases or incidents of bribery. As some of the incidents identified from news media reports appeared to be allegations, not convictions for bribery, companies could have defensible reasons for not disclosing some aspects of them.Practical implicationsRegulators should think why new or more regulations without substantive requirement are not helpful to curb corporate decoupling and injustice. The regulators should address the crisis that multinational companies (MNCs) being suppliers of bribery are much more harmful for the underprivileged communities in developing nations. Accordingly, this paper provides practical insights into how stakeholders ought to critically interpret MNCs' accounts of their involvement in bribery.Originality/valueThis study contributes to the accounting literature by problematising MNCs' operations in underprivileged countries. The findings suggest that not only public officials in developing countries as creators of bribery but also Western-based MNCs as the suppliers of bribery contribute to perpetuating unethical practices and injustices to the underprivileged communities in developing countries. This research is imperative as this is one of the first known studies that provides evidence of the actions including disclosure-related actions companies have taken in response to the UK Bribery Act.

Exploring readiness for implementing best practices: A mixed methods study.

Evidence-based practice (EBP) can improve health care in underprivileged countries. Bolivia's EBP movement is nascent and the factors contributing to better implementation in nursing are unknown. To explore Bolivian nurses' readiness to engage in EBP while highlighting the facilitators and barriers for pursuing EBP. The study used a sequential explanatory mixed methods study. First, general trends were disclosed via a survey of 170 nurses in La Paz, Bolivia, holding at least a baccalaureate regarding their perceived beliefs about EBP. The survey identified facilitators and barriers for implementing EBP in acute and ambulatory settings. Second, qualitative data was gathered via a focus group of nine nurses with the purpose of enhancing the survey results. The survey results showed that nurses believe that engaging in EBP can improve their clinical practice. However, the nurses' research behaviors were found to be infrequent. Lack of support from the nurses' clinics and hospitals and from non-nursing professionals were identified as barriers for engaging in EBP. The qualitative results revealed underlying limitations to nurses' clinical practice, including "feeling undervalued". There is a dearth of EBP knowledge among Bolivian nurses stemming from a lack of preparation in EBP environments, including EBP training opportunities. This situation affects nurses' professional dimensions of relational work, power, and collaboration. Collaborative research among educators, professional nursing societies, and local and international organizations could provide initiatives for implementing EBP, based on local health profiles.

A New Insight to Vibration Characteristics of Spans under Random Moving Load: Case Study of 38 Bridges in Ho Chi Minh City, Vietnam

We propose a novel representative power spectrum density as a specific characteristic for showing responses of spans during a long operational period. The idea behind this method is to use the representative power spectrum density as a powerful tool to evaluate the stiffness decline of spans during their operation period. In addition, a new measurement method has been introduced to replace the traditional method of monitoring the health conditions of bridges through a periodic measurement technique. This helps to reduce costs when carrying out testing bridges. Besides, the proposed approach can be widely applied not only in Vietnam but also in many other underprivileged countries around the world. Obtained results show that, during the operational process of spans, there is not only a pure vibration evaluation such as bending vibration and torsion vibration tests but also a combination of various vibration types including bending-torsion vibration or high-level vibrations like first-mode bending and first-mode torsion. Depending on each type of structure and material properties, different types of vibrations will appear more or less during the operational process of spans under a random moving load. Furthermore, the representative power spectrum density is also suitable for evaluating and determining many different fundamental vibrations through the same measurement time as well as various measurement times.

Identification and Diagnosis of Risk Factors and Symptoms for Rheumatic Heart Disease

Rheumatic heart disease (RHD) is one of the common heart disease acquired in children in many regions of the world, mainly in developing and underprivileged countries. This heart condition occurs following rheumatic fever caused by streptococcal infection. It affects the functionality of heart valves. When untreated it may lead to medical complications and even death. The disease mainly targeting children and young adults is responsible for cardiovascular morbidity and mortality in people. Prevention and treatment of acute rheumatic fever plays an important role in controlling the disease. Diagnosis of the disease is critically important because misdiagnosis of acute rheumatic fever can lead to worsening of damage caused to heart valves and may also lead to premature death. This being a preventable problem, it can be managed through surgeries to repair functions of cardiac valves. Alcohol consumption and tobacco smoking has shown synergistic effect which may prove detrimental to cardiovascular health.

O12.8. LOWER FAMILY INCOME PREDICTS PSYCHOTIC EXPERIENCES IN A COMMUNITY SAMPLE OF YOUTHS IN BRAZIL: RESULTS FROM A 3-YEAR FOLLOW-UP STUDY

BackgroundUp to 17% of community youths, from 9 to 12 years old, report subthreshold psychotic experiences (PE). Besides increasing conversion risk to psychotic disorders, PE also predicted suicide attempts and were associated with more severe general psychopathology. Understanding predisposing factors to PE during development can inform on risk to mental disorders, a crucial step to future prevention interventions. Adversity in early life has been associated with psychotic symptoms, and low socioeconomic status (SES) is an established environmental risk factor for several mental disorders. However, few studies investigated the effect of low SES on PE risk in youths, prospectively. This topic is highly relevant in underprivileged countries like Brazil, where large populations are exposed to poverty. We hypothesized that low income at baseline would predict later report of PEs.MethodsWe analyzed data from the Brazilian High Risk Cohort Study for Psychiatric Disorders (HRC), in which 2,512 youths (6–12 years old, mean age at baseline 9.7 years, SD = 1.92; 53,1% male) completed the baseline assessment and 2,012, the 3-year follow-up (T1). PE were assessed at each time-point through two sources of information: parental report, using the Child Behavior Checklist (CBCL), and youth self-report, using the positive dimension of the Community Assessment of Psychic Experiences (CAPE). A single latent variable for each time-point was created to encompass both sources of information using Confirmatory Factor Analysis, yielding good model fit. Total family income (T0) was correlated to the psychotic latent variable (T1), controlling for age, gender and baseline report of PE. Then, we investigated how any mental disorder diagnosis and exposure to trauma, two possible confounding factors, affected the results. The Development and Well-Being Assessment (DAWBA) was used to investigate mental disorders diagnoses. No conversion to psychotic disorder was observed at the follow-up. A latent variable encompassing parental and youths reports, measured by Childhood Trauma Questionnaire (CTQ), was used as trauma exposure.ResultsThe mean family income reported was approximately US$ 394 per month (p25 = US$ 195, p50 = US$ 307, p75 = US$ 516). Income (baseline) and PE (T1) had a negative significant correlation (pcorr = -0.064; df = 2004; p = 0.004). Subjects with any mental disorder reported PE more frequently, either at baseline or at the follow-up. Low income inversely correlated with trauma, whereas trauma was strongly associated with PE and having any mental disorder at both time points. The correlation between income and PE did not remain significant, after controlling for any mental disorder (T1) (pcorr = -0.049; df = 1597; p = 0.050) or trauma exposure (T0) (pcorr = -0.043; df = 2003; p = 0.053).DiscussionOther studies have previously reported a positive correlation between adversity and PE, but few have described a specific association between family income and psychotic experiences. Our additional analyses suggest that this is not specific, considering the influence of non-psychotic mental disorders, and mediated by low-income related exposures, i.e. childhood trauma. Even not surviving multivariate analyses, income could work as a proxy for adversity exposures and may be useful as a tool to define populations at risk for psychotic and non-psychotic mental disorders. We expect that the longer follow-up assessments help us answer the questions raised.

A Retrospective Study of Directed Blood Donations in the Kasungu District of Malawi

Since the discovery of blood circulation and transfusion, there has been an insatiable demand for voluntary blood donations throughout the world. However, gathering blood donors has never been easy because eligible donors constitute only a fraction of the general population and are often reluctant to donate. This is especially challenging in underprivileged countries of sub-Saharan Africa such as Malawi whose nationally run blood transfusion service struggles to maintain hospital blood banks. As a result, hospitals turn to their local communities for directed donations. A retrospective analysis from January 2014 to June 2016 of directed blood donor data from two hospitals in the Kasungu District of Malawi was conducted. The analysis of 2134 donations was carried out with respect to sex, age, hemoglobin concentration, blood group, and presence of transfusion-transmissible infections. On average, donors were 30 years of age and predominately male. Blood group O+ constituted more than half of all directed blood donations. Ultimately, about one third of donations were unable to be utilized for transfusion.