Search results for: big health data

Authors: Qian Mao, Xiaozan Wang, Jiarong Zhong, Xiaolin Zou

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Introduction: As a result of the decline of students' physical health level and the increase of study pressure, students’ academic performance is not so good. Objective: This study aims to verify whether the Brain-Break intervention in the fourth-grade classroom of primary school can improve students' physical health and academic performance. Methods: According to the principle of no difference in pre-test data, students from two classes of grade four in Fuhai Road Primary School, Fushan district, Yantai city, Shandong province, were selected as experimental subjects, including 50 students in the experimental class (25 males and 25 females) and 50 students in the control class (24 males and 26 females). The content of the experiment was that the students were asked to perform a 4-minute Brain-Berak program designed by the researcher in the second class in the morning and the afternoon, and the intervention lasted for 12 weeks. In addition, the lung capacity, 50-meter run, sitting body forward bend, one-minute jumping rope and one-minute sit-ups stipulated in the national standards for physical fitness of students (revised in 2014) were selected as the indicators of physical health. The scores of Chinese, Mathematics, and English in the unified academic test of the municipal education bureau were selected as the indicators of academic performance. The independent-sample t-test was used to compare and analyze the data of each index between the two classes. The paired-sample t-test was used to compare and analyze the data of each index in the two classes. This paper presents only results with significant differences. Results: in terms of physical health, lung capacity (P=0.002, T= -2.254), one-minute rope skipping (P=0.000, T=3.043), and one-minute sit-ups (P=0.045, T=6.153) were significantly different between the experimental class and the control class. In terms of academic performance, there is a significant difference between the Chinese performance of the experimental class and the control class (P=0.009, T=4.833). Conclusion: Adding Brain-Berak intervention in the classroom can effectively improve the cardiorespiratory endurance (lung capacity), coordination (jumping rope), and abdominal strength (sit-ups) of fourth-grade students. At the same time, it can also effectively improve their Chinese performance. Therefore, it is suggested to promote micro-sports in the classroom of primary schools throughout the country so as to help students improve their physical health and academic performance.

Keywords: academic performance, brain break, fourth grade, physical health

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Authors: Ildikó Balatoni, Nikolett Kosztin

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In this study the importance of maintaining the mental and physical health of employees was examined from the perspective of the employers. To this end companies in Hajdú-Bihar county of Hungary that are within in the TOP 100 based on their net revenue were interviewed. Economic sectors that were represented the most in this survey were processing, services, trade, agriculture, and construction. We examined whether or not companies provided any benefits to their employees concerning health awareness. Among respondents those who offered various services of medical specialists and/or discounted gym or swim passes in addition to compulsory medical examinations were hard to find, however more employers organize health and sports days. Nevertheless, a significant albeit very shallow positive correlation were found between the number of offered benefits vs. total gross income and vs. number of employees (r2=0.2555, p<0.001 and r2=0.1196 and p<0.05, respectively). In conclusion, while workplace health promotion is necessary it requires a change in employers’attitudes.

Keywords: corporate health promotion, employees, employers, health

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Authors: Gabriel G. Gaft, Xayvinay Xiong, Amanda Sunday

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The emergent pandemic from COVID-19 virus has forced people to adjust to major changes. These changes include all elements of family and work life and required people to engage in novel behaviors. For many people, the social norms to which they have been accustomed no longer prevail. Not surprisingly, such enormous changes in daily life have been associated with greater problems in mental health; and research regarding ways in which mental health professionals can support people is more necessary than ever before. It is often useful to assess people’s reactions through surveys and utilize quantitative data to answer questions about coping strategies etc. It is also likely, however, that a host of individual factors are going to contribute to what might be considered 'good' or 'bad' coping mechanisms to a worldwide pandemic. To this end, qualitative studies—where the individual’s subjective experience is highlighted—are likely to provide more vital information for mental health professionals interested in supporting the particular person in front of them. This study reports on qualitative data, where X participants were asked questions about social distancing, coping strategies, and general attitudes towards social changes resulting from the COVID-19 pandemic. Informal interviews were conducted during the months of June-July 2020. Data were analyzed using Interpretative Phenomenological Analyses. Themes were identified first for each participant and then compared across different individual participants. Several findings emerged. First, all participants understood major health messages being imparted by governing bodies such as the CDC and WHO. The researchers feel this finding is important as it suggests health messages are at least being effectively communicated. Second, there was a clear trend for themes which highlighted the conflicting emotions participants felt about the changes they were expected to endure: positive and negative elements were identified, although a participant who had pre-existing conditions placed greater emphasis on the negative elements. One participant who was particularly interested in impression management also exclusively emphasized negative emotions. Third, participants who were able to reevaluate priorities—what Lazarus might call secondary appraisals—experienced social distancing as a positive rather than negative phenomenon. Finally, participants who were able to develop specific strategies—such as boundaries for work and self-care—reported themes of adjustment and contentment. Taken together, these findings suggest mental health practitioners can assist people to adjust more positively through specific techniques focusing on re-evaluation of life priorities and strategic coping skills.

Keywords: COVID-19, pandemic, phenomenology, virus

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Authors: A. Calabrese, V. F. Uricchio, D. di Noia, S. Favale, C. Caiati, G. P. Maggi, G. Donvito, D. Diacono, S. Tangaro, A. Italiano, E. Riezzo, M. Zippitelli, M. Toriello, E. Celiberti, D. Festa, A. Colaianni

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Climate change has a severe impact on human health. There is a vast literature demonstrating temperature increase is causally related to cardiovascular problem and represents a high risk for human health, but there are not study that improve a solution. In this work, it is studied how the clime influenced the human parameter through the analysis of climatic conditions in an area of the Apulia Region: Capurso Municipality. At the same time, medical personnel involved identified a set of variables useful to define an index describing health condition. These scientific studies are the base of an innovative alert system, IPPOCRATE, whose aim is to asses climate risk and share information to population at risk to support prevention and mitigation actions. IPPOCRATE is an e-health system, it is designed to provide technological support to analysis of health risk related to climate and provide tools for prevention and management of critical events. It is the first integrated system of prevention of human risk caused by climate change. IPPOCRATE calculates risk weighting meteorological data with the vulnerability of monitored subjects and uses mobile and cloud technologies to acquire and share information on different data channels. It is composed of four components: Multichannel Hub. Multichannel Hub is the ICT infrastructure used to feed IPPOCRATE cloud with a different type of data coming from remote monitoring devices, or imported from meteorological databases. Such data are ingested, transformed and elaborated in order to be dispatched towards mobile app and VoIP phone systems. IPPOCRATE Multichannel Hub uses open communication protocols to create a set of APIs useful to interface IPPOCRATE with 3rd party applications. Internally, it uses non-relational paradigm to create flexible and highly scalable database. WeHeart and Smart Application The wearable device WeHeart is equipped with sensors designed to measure following biometric variables: heart rate, systolic blood pressure and diastolic blood pressure, blood oxygen saturation, body temperature and blood glucose for diabetic subjects. WeHeart is designed to be easy of use and non-invasive. For data acquisition, users need only to wear it and connect it to Smart Application by Bluetooth protocol. Easy Box was designed to take advantage from new technologies related to e-health care. EasyBox allows user to fully exploit all IPPOCRATE features. Its name, Easy Box, reveals its purpose of container for various devices that may be included depending on user needs. Territorial Registry is the IPPOCRATE web module reserved to medical personnel for monitoring, research and analysis activities. Territorial Registry allows to access to all information gathered by IPPOCRATE using GIS system in order to execute spatial analysis combining geographical data (climatological information and monitored data) with information regarding the clinical history of users and their personal details. Territorial Registry was designed for different type of users: control rooms managed by wide area health facilities, single health care center or single doctor. Territorial registry manages such hierarchy diversifying the access to system functionalities. IPPOCRATE is the first e-Health system focused on climate risk prevention.

Keywords: climate change, health risk, new technological system

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Authors: Roshani Gusain, Deep Sinha, Karan Nayal, Anmol Kumar Mishra, Manav Singh

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In this research paper, we introduce "Mind Care Assistant - Companion App", which is a Flutter and Firebase-based mental health monitor. The app wants to improve and monitor the mental health of its users, it uses noninvasive ways to check for a change in their emotional state. By responding to questions, the app will provide individualized suggestions ᅳ tasks and mindfulness exercises ᅳ for users who are depressed or anxious. The app features a chat-bot that incorporates cognitive behavioural therapy (CBT) principles and combines natural language processing with machine learning to develop personalised responses. The feature of the app that makes it easy for us to choose between iOS and Android is cross-platform, which allows users from both mobile systems to experience almost no changes in their interfaces. With Firebase integration synchronized and real-time data storage, security is easily possible. The paper covers the architecture of the app, how it was developed and some important features. The primary research result presents the promise of a "Mind Care Assistant" in mental health care using new wait-for-health technology, proposing a full stack application to be able to manage depression/anxiety and overall Mental well-being very effectively.

Keywords: mental health, mobile application, flutter, firebase, Depression, Anxiety

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Authors: Elissar Mansour, Emily Chen, Tracee Fernandez, Mariam Basheti, Christopher Gordon, Bandana Saini

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Sleep is an essential process for the maintenance of several neurobiological processes such as memory consolidation, mood, and metabolic processes. It is known that sleep patterns vary with age and is affected by multiple factors. While non-pharmacological strategies are generally considered first-line, sedatives are excessively used in the older population. This study aimed to explore the management of sleep in residential aged care facilities (RACFs) by nurse professionals and to identify the key factors that impact provision of optimal sleep health care. An inductive thematic qualitative research method was employed to analyse the data collected from semi-structured interviews with registered nurses working in RACF. Seventeen interviews were conducted, and the data yielded three themes: 1) the nurses’ observations and knowledge of sleep health, 2) the strategies employed in RACF for the management of sleep disturbances, 3) the organizational barriers to evidence-based sleep health management. Nurse participants reported the use of both non-pharmacological and pharmacological interventions. Sedatives were commonly prescribed due to their fast action and accessibility despite the guidelines indicating their use in later stages. Although benzodiazepines are known for their many side effects, such as drowsiness and oversedation, temazepam was the most commonly administered drug. Sleep in RACF was affected by several factors such as aging and comorbidities (e.g., dementia, pain, anxiety). However, the were also many modifiable factors that negatively impacted sleep management in RACF. These include staffing ratios, nursing duties, medication side effects, and lack of training and involvement of allied health professionals. This study highlighted the importance of involving a multidisciplinary team and the urge to develop guidelines and training programs for healthcare professionals to improve sleep health management in RACF.

Keywords: registered nurses, residential aged care facilities, sedative use, sleep

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Authors: Sara Herrero Jaén, José María Santamaría García, María Lourdes Jiménez Rodríguez, Jorge Luis Gómez González, Adriana Cercas Duque, Alexandra González Aguna

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Background: Health concept has evolved throughout history. The health level is determined by the own individual perception. It is a dynamic process over time so that you can see variations from one moment to the next. In this way, knowing the health of the patients you care for, will facilitate decision making in the treatment of care. Objective: To design a technological tool that calculates the people health level in a sequential way over time. Material and Methods: Deductive methodology through text analysis, extraction and logical knowledge formalization and education with expert group. Studying time: September 2015- actually. Results: A computational tool for the use of health personnel has been designed. It has 11 variables. Each variable can be given a value from 1 to 5, with 1 being the minimum value and 5 being the maximum value. By adding the result of the 11 variables we obtain a magnitude in a certain time, the health level of the person. The health calculator allows to represent people health level at a time, establishing temporal cuts being useful to determine the evolution of the individual over time. Conclusion: The Information and Communication Technologies (ICT) allow training and help in various disciplinary areas. It is important to highlight their relevance in the field of health. Based on the health formalization, care acts can be directed towards some of the propositional elements of the concept above. The care acts will modify the people health level. The health calculator allows the prioritization and prediction of different strategies of health care in hospital units.

Keywords: calculator, care, eHealth, health

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Authors: Karina Guimaraes, Lilian Santos

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This study has the purpose of planning and instituting monitoring actions as a way of knowing the scenario of assistance to the patient with stoma, treated in the public health network in Brazil, from January to November of the year 2016, from the elaboration of a technical document containing the survey of the number of procedures offered and the value of the ostomy services, accredited in the Unified Health System-SUS. The purpose of this document is to improve the quality of these services in the efficient management of available financial resources, making it indispensable for the creation of strategies for the implementation and implementation of care services for people with stomata as a strategic tool in the promotion, prevention, qualification and efficiency in health care.

Keywords: health economic, management, ostomy, unified health system

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Authors: Engida Yisma, Berhanu Dessalegn, Ayalew Astatkie, Nebreed Fesseha

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Background: The use of the partograph is a well-known best practice for quality monitoring of labour and subsequent prevention of obstructed and prolonged labour. However, a number of cases of obstructed labour do happen in health facilities due to poor quality of intrapartum care. Methods: A cross-sectional quantitative study assessed knowledge and utilization of partograph among obstetric care givers in public health institutions of Addis Ababa, Ethiopia using a structured interviewer administered questionnaire. The collected data was analyzed using SPSS version 16.0. Logistic regression analysis was used to identify factors associated with knowledge and use of partograph among obstetric care givers. Results: Knowledge about the partograph was fair: 189 (96.6%) of all the respondents correctly mentioned at least one component of the partograph, 104 (53.3%) correctly explained the function of alert line and 161 (82.6%) correctly explained the function of action line. The study showed that 112 (57.3%) of the obstetric care givers at public health institutions reportedly utilized partograph to monitor mothers in labour. The utilization of the partograph was significantly higher among obstetric care givers working in health centres (67.9%) compared to those working in hospitals (34.4%) [Adjusted OR = 3.63(95%CI: 1.81, 7.28)]. Conclusions: A significant percentage of obstetric care givers had fair knowledge of the partograph and why it is necessary to use it in the management of labour and over half of obstetric care givers reported use of the partograph to monitor mothers in labour. Pre-service and on-job training of obstetric care givers on the use of the partograph should be given emphasis. Mandatory health facility policy is also recommended to ensure safety of women in labour in public health facilities in Addis Ababa, Ethiopia.

Keywords: partograph, knowledge, utilization, obstetric care givers, public health institutions

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Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

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In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

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Authors: Shishen Xie, Yingda L. Xie

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Tuberculosis (TB) is a potentially serious infectious disease that remains a health concern. The Interferon Gamma Release Assay (IGRA) is a blood test to find out if an individual is tuberculous positive or negative. This study applies statistical analysis to the clinical data of interferon-gamma levels of seventy-three subjects who diagnosed pulmonary TB in an anti-tuberculous treatment. Data analysis is performed to determine if there is a significant decline in interferon-gamma levels for the subjects during a period of six months, and to infer if the anti-tuberculous treatment is effective.

Keywords: data analysis, interferon gamma release assay, statistical methods, tuberculosis infection

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Authors: Jessica Hemberg, Anna K. Forsman, Johanna Nordmyr

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With a considerable increase in the aging population in the Nordic countries there is a call for a deeper understanding of healthy aging and its underlying mechanisms. The aim of this study is to uncover health and well-being for older adults according to their own views and understand what role the existential dimension play? The study uses a hermeneutical approach. Material was collected through focus group interviews with 18 older adults. The texts were interpreted through hermeneutical reading. The underlying mechanisms of health among older adults are described, illustrating the key prerequisites for health as being in the present. This implies ‘living on the continuums of life and death’ and in this field of forces also ‘living on the continuum of the past and the future’. Important aspects for being in the present was balancing ambivalent emotions, considering existential issues, and being in connectedness. Health for older adults may be understood in the light of the metaphor of taking it one day at a time. Being in the present was emphasized as a health potential for older adults highlighting the existential health dimension. From a societal point of view, this implies that health promotion should focus on highlighting the importance of the existential dimension of health since it holds health-promoting potentials for older adults. Optimal health for older adults requires awareness of one’s attitude to life through being in the present as a basis for a positive and healthy outlook on life.

Keywords: focus group interviews, hermeneutics, life experiences, older adults

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Authors: Farida Habib, Haya Alfozan, Eman Miligi, Najla Alotaibi

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Background: Women’s satisfaction with maternity services, especially care during labor and birth, has become highly significant to healthcare providers, administrators, and policymakers. Purpose: The aims of this study were to assess maternal satisfaction regarding the quality of care during labor and to compare the level of maternal satisfaction between women who delivered by physicians and those delivered by midwives. Methodology: A descriptive, cross-sectional, correlational design was used. A convenient sample of 180 low-risk cases of immediate postpartum women who delivered at King Abdul-Aziz medical city was recruited. Women whose babies were diagnosed with serious health problems were excluded from the study. Data were collected using a self-administered questionnaire. The validity and reliability of the questionnaire were ensured. The questionnaire included three parts, namely: demographics data, medical history, and obstetrical history, and the last part is the satisfaction assessment tool. Ethical confederations were ensured. Maternal satisfaction during labor was classified in terms of health care, health workers' communication, and the environment. Results: Regarding health care, women were highly satisfied with care received from nurse (M = 4.21 + 0.88), medical care received (M = 4.17 + 0.79), and comfort techniques (M = 4.04 + 0.91). Regarding health workers' communication, women were highly satisfied with the provider to treat with dignity and respect (M = 4.03 + 0.91) and orientation to the toilet, bathroom, washing area (M = 4.00 + 0.93). Regarding the environment, women were highly satisfied with the experience of their baby's birth (M = 4.18 + 0.98) and supplies with drugs and supplies (M = 4.09 + 0.97). There was no statistically significant difference in maternal satisfaction between women who delivered by physicians and those delivered by midwives. Conclusion: Women were generally satisfied with their labor and delivery experience. There was no difference in maternal satisfaction on the labor process between women who delivered by physicians and those delivered by midwives.

Keywords: maternity, satisfaction, labor, delivery

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Authors: Julio Albuja, David Zaldumbide

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Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

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Authors: Chiao-Wen Lan, David Gere

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Background and Significance: Performance has a rich history of imparting information and encouraging reflection, yet there is a paucity of literature on applying performance as a method of analysis and not as a medium for health education. This study aimed to apply ethnodrama strategies to the issue of HIV-related stigma in rural China and to use a critical performance as a vehicle for communication of health research. Methods: The program, titled 'STOP STIGMA,' included dance, narratives and original quotes from people living with HIV/AIDS in China, and spectacle such as photographs, set, and props corresponding to the history of HIV in rural China. Results: The performance represented a step away from a completely textual interpretation of data towards a theatrical style that begins to privilege what arts-based research scholars Rossiter and colleagues have termed 'an embodied, theatrical representation of data.' It offered an opportunity to deliver individual and collective stories that represent how HIV-positive people experience living with HIV/AIDS in China, which could play an integral part in the formulation of actions to effect change. Discussion: This method of communicating health research has implications for fostering dialogue among researchers, community members, and medical practitioners. Although arts-based approaches are not new to the scientific community, the integration of dance, video, ethnodrama, and sciences provides opportunities to innovate in non-traditional research dissemination and communication.

Keywords: health communication, HIV/AIDS, stigma, vulnerable populations

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Authors: Ramesh Adsul, Vikas Minchekar

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The present study attempts to explore the differences in academic achievement and psychological well being and its components – satisfaction, efficiency, sociability, mental health, interpersonal relations in low and moderate level of hemoglobin of college female students. It also tries to find out how hemoglobin, psychological well –being and academic achievement correlate to each other. For this study 200 (100 low hemoglobin level and 100 moderate hemoglobin level) college female students were selected by random sampling method. This sample is collected from the project ‘Health awareness and hemoglobin improvement programme’, which is being collaboratively conducted by ‘Akshyabhasha, MESA, U.S.A. and Smt. M.G. Kanya Mahavidyalaya, Sangli, Maharashtra, India. Psychological Well-Being Scale was used to collect the data. Students’ academic achievement was collected through college record, and hemoglobin level of female students was collected from project record. Data was analyzed by using independent ‘t’ test and Pearson’s correlation coefficient. The finding of the study revealed significant differences between low hemoglobin and moderate hemoglobin groups regarding efficiency and mental health. No significant difference was observed on satisfaction, sociability and interpersonal relations. It is also found that there is significant difference between low hemoglobin and moderate hemoglobin groups on academic achievement. The study revealed positive correlation between hemoglobin and academic achievement and psychological well-being and academic achievement. Moderate hemoglobin level create more efficiency, better mental health and good academic achievement in female students. One could say that there is significant role hemoglobin plays in psychological well being and academic achievement of college female students. Anemia is widely prevalent in all the states if India among all age groups. In India, college girls contribute major portion of population. It has been reported that 80% female population has hemoglobin deficiency, due to illiteracy of female, family structure, status of women, diet habits, gender discrimination and various superstitions. The deficiency of hemoglobin affects physical and mental health, general behavior and academic performance of students. This study is useful to educational managements, counselors, parents, students and Government also. In the development of personality physical as well as psychological health is essential. This research findings will create awareness about physical and mental health among people and society.

Keywords: academic achievement, college female students, hemoglobin, psychological well-being

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Authors: Chelly Souhir, Harizi Chahida, Hachaichi Aicha, Aissaoui Sihem, Chahed Mohamed Kouni

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Introduction: In Tunisia, few studies have attempted to describe the demand for primary care in a standardized and systematic way. The purpose of this study is to describe the main reasons for demand for care in primary health care, through a survey of the Ariana Governorate PHC and to identify their evolutionary trend compared to last 30 years, reported by studies of the same type. Materials and methods: This is a cross-sectional descriptive study which concerns the study of consultants in the first line of the governorate of Ariana and their use of care recorded during 2 days in the same week during the month of May 2016, in each of these PHC. The same data collection sheet was used in all CSBs. The coding of the information was done according to the International Classification of Primary Care (ICPC). The data was entered and analyzed by the EPI Info 7 software. Results: Our study found that the most common ICPC chapters are respiratory (42%) and digestive (13.2%). In 1996 were the respiratory (43.5%) and circulatory (7.8%). In 2000, we found also the respiratory (39,6%) and circulatory (10,9%). In 2002, respiratory (43%) and digestive (10.1%) motives were the most frequent. According to the ICPC, the pathologies in our study were acute angina (19%), acute bronchitis and bronchiolitis (8%). In 1996, it was tonsillitis ( 21.6%) and acute bronchitis (7.2%). For Ben Abdelaziz in 2000, tonsillitis (14.5%) follow by acute bronchitis (8.3%). In 2002, acute angina (15.7%), acute bronchitis and bronchiolitis (11.2%) were the most common. Conclusion: Acute angina and tonsillitis are the most common in all studies conducted in Tunisia.

Keywords: acute angina, classification of primary care, primary health care, tonsillitis, Tunisia

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Authors: Mengdi Zhang, Zhenji Gao, Ning Kang, Rongmei Liu

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Management and application of geological big data is an important part of China's national big data strategy. With the implementation of a national big data strategy, geological big data management becomes more and more critical. At present, there are still a lot of technology barriers as well as cognition chaos in many aspects of geological big data management and application, such as data sharing, intellectual property protection, and application technology. Therefore, it’s a key task to make better use of new technologies for deeper delving and wider application of geological big data. In this paper, we briefly introduce the basic principle of blockchain technology at the beginning and then make an analysis of the application dilemma of geological data. Based on the current analysis, we bring forward some feasible patterns and scenarios for the blockchain application in geological big data and put forward serval suggestions for future work in geological big data management.

Keywords: blockchain, intellectual property protection, geological data, big data management

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Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

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Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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Authors: Muhammad Saqlain, Nazar Abbas Saqib, Muazzam A. Khan

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Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF.

Keywords: decision tree, heart failure, data mining, classification model

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

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Authors: Fairuz Fadzilah Rahim

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Introduction: Indigenous adolescents are at higher risk of dog associated zoonotic infections (DAZI) as they live closely with free-roaming dogs and have limited access to veterinary care. This study aims to determine the effectiveness of health education interventions towards knowledge, attitudes, and preventive practices (KAP) of adolescents on DAZI. Methods: This one-group pre-and post-intervention study in 5 months period was conducted among Jahai adolescents aged 12 years and above. Jahai is one of the three major tribes of indigenous people in Peninsular Malaysia. Health education intervention programs using posters, slide presentations, comics, video clips, and discussion on DAZI were employed. Repeated measures of within-subjects analysis were used to identify the pre- and post- KAP of the adolescents. Results: There were 54 adolescents participated in this study with a mean age of 15.72 (SD: 2.49) and equal proportions of males (50%) and females (50%). Among the adolescents, 22.2% were married, 5.6% were illiterate, and 44.4% not continuing education at the time of data collection. The majority of them keep dogs as pets (64.8%), and few used dogs for hunting (11.1%). There was significant increase in mean scores of knowledge (F = 40.92, p < 0.001) and attitudes (F = 6.43, p = 0.014) of the adolescents. However, the preventive practices towards DAZI showed non-significant improvement on the intervention. Conclusions: The health education intervention programs showed to be effective in improving the attitudes and practices related to dog associated zoonotic infections. Emphasis on sustained health education programs is important to foster good health and wellbeing of the indigenous community.

Keywords: adolescent health, dog associated infection, zoonotic, KAP, indigenous

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Authors: Milena M. Melo, Jane M. Santos, Severine Frere, Valderio A. Reisen, Neyval C. Reis Jr., Mariade Fátima S. Leite

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Although there had been a many studies that shows the impact of air pollution on physical health, comparatively less was known of human behavioral responses and annoyance impacts. Annoyance caused by air pollution is a public health problem because it can be an ambient stressor causing stress and disease and can affect quality of life. The objective of this work is to evaluate the annoyance caused by air pollution in two different industrialized urban areas, Dunkirk (France) and Vitoria (Brazil). The populations of these cities often report feeling annoyed by dust. Surveys were conducted, and the collected data were analyzed using statistical analyses. The results show that sociodemographic variables, importance of air quality, perceived industrial risk, perceived air pollution and occurrence of health problems play important roles in the perceived annoyance. These results show the existence of a common problem in geographically distant areas and allow stakeholders to develop prevention strategies.

Keywords: air pollution, annoyance, industrial risks, public health, perception of pollution, settled dust

Procedia PDF Downloads 691

Authors: Farina Maqbool

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Background: It is well said that children are considered the future masons of the country and a healthy brain is found in a healthy body. Therefore, a healthy generation can be produced by giving knowledge of immunization to mothers. Immunization is the most lucrative public health intrusion that has placed the greatest effect on the health of the people. The main objective of the present study was to find out the mother’s knowledge, attitude, and practices towards childhood immunization. Methods: Multistage sampling technique was used. One hundred and sixty mothers were selected conveniently who have at least one child up to two years. Data were collected through the face to face interview. The chi-square test was used to test the significance of the association between independent and dependent variables. Data were analyzed using the Statistical Package for Social Science. Results: A higher percentage of mothers (85.0%) knew vaccine-preventable diseases. Major proportion (82.5%) of the mothers had thought that immunization is important for their child’s health. A majority (66.3%) of the respondents’ children were fully immunized, whereas 26.3 percent of them were replied negatively. Remaining 7.5 percent of the respondents’ child un-immunized Chi-square value (39.14) shows a highly significant association between the education of the respondents and receiving of all recommended vaccines for children. Gamma value shows a strong positive relationship between the variables.

Keywords: attitude, childhood, immunization, knowledge, practices

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Authors: Hamda Ajmal, Karen Young, Ruth Melia, John Bogue, Mary O'Sullivan, Jim Duggan, Hannah Wood

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The Covid-19 pandemic led to a range of public health mitigation strategies in order to suppress the SARS-CoV-2 virus. The drastic changes in everyday life due to lockdowns had the potential for a significant negative impact on public mental health, and a key public health goal is to now assess the evidence from available Irish datasets to provide useful insights on this issue. Text-50808 is an online text-based mental health support service, established in Ireland in 2020, and can provide a measure of revealed distress and mental health concerns across the population. The aim of this study is to explore statistical associations between public mental health in Ireland and the Covid-19 pandemic. Uniquely, this study combines two measures of emotional wellbeing in Ireland: (1) weekly text volume at Text-50808, and (2) emotional wellbeing indicators reported by respondents of the Amárach public opinion survey, carried out on behalf of the Department of Health, Ireland. For this analysis, a multivariate graphical exploratory data analysis (EDA) was performed on the Text-50808 dataset dated from 15th June 2020 to 30th June 2021. This was followed by time-series analysis of key mental health indicators including: (1) the percentage of daily/weekly texts at Text-50808 that mention Covid-19 related issues; (2) the weekly percentage of people experiencing anxiety, boredom, enjoyment, happiness, worry, fear and stress in Amárach survey; and Covid-19 related factors: (3) daily new Covid-19 case numbers; (4) daily stringency index capturing the effect of government non-pharmaceutical interventions (NPIs) in Ireland. The cross-correlation function was applied to measure the relationship between the different time series. EDA of the Text-50808 dataset reveals significant peaks in the volume of texts on days prior to level 3 lockdown and level 5 lockdown in October 2020, and full level 5 lockdown in December 2020. A significantly high positive correlation was observed between the percentage of texts at Text-50808 that reported Covid-19 related issues and the percentage of respondents experiencing anxiety, worry and boredom (at a lag of 1 week) in Amárach survey data. There is a significant negative correlation between percentage of texts with Covid-19 related issues and percentage of respondents experiencing happiness in Amárach survey. Daily percentage of texts at Text-50808 that reported Covid-19 related issues to have a weak positive correlation with daily new Covid-19 cases in Ireland at a lag of 10 days and with daily stringency index of NPIs in Ireland at a lag of 2 days. The sudden peaks in text volume at Text-50808 immediately prior to new restrictions in Ireland indicate an association between a rise in mental health concerns following the announcement of new restrictions. There is also a high correlation between emotional wellbeing variables in the Amárach dataset and the number of weekly texts at Text-50808, and this confirms that Text-50808 reflects overall public sentiment. This analysis confirms the benefits of the texting service as a community surveillance tool for mental health in the population. This initial EDA will be extended to use multivariate modeling to predict the effect of additional Covid-19 related factors on public mental health in Ireland.

Keywords: COVID-19 pandemic, data analysis, digital health, mental health, public health, digital health

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Authors: Vidhya Venugopal, Jeremiah Chinnadurai, Rebekah A. I. Lucas, Tord Kjellstrom, Bruno Lemke

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Introduction: The effects of climate change (CC) are largely discussed across the globe in terms of impacts on the environment and the general population, but the impacts on workers remain largely unexplored. The predicted rise in temperatures and heat events in the CC scenario have health implications on millions of workers in physically exerting jobs. The current health and productivity risks associated with heat exposures are characterized, future risk estimates as temperature rises and recommendations towards developing protective and preventive occupational health and safety guidelines for India are discussed. Methodology: Cross-sectional studies were conducted in several occupational sectors with workers engaged in moderate to heavy labor (n=1580). Quantitative data on heat exposures (WBGT°C), physiological heat strain indicators viz., Core temperature (CBT), Urine specific gravity (USG), Sweat rate (SwR) and qualitative data on heat-related health symptoms and productivity losses were collected. Data were analyzed for associations between heat exposures, health and productivity outcomes related to heat stress. Findings: Heat conditions exceeded the Threshold Limit Value (TLV) for safe manual work in 66% of the workers across several sectors (Avg.WBGT of 28.7°C±3.1°C). Widespread concerns about heat-related health outcomes (86%) were prevalent among workers exposed to high TLVs, with excessive sweating, fatigue and tiredness being commonly reported by workers. The heat stress indicators, core temperature (14%), Sweat rate (8%) and USG (9%), were above normal levels in the study population. A significant association was found between rise in Core Temperatures and WBGT exposures (p=0.000179) Elevated USG and SwR in the worker population indicate moderate dehydration, with potential risks of developing heat-related illnesses. In a steel industry with high heat exposures, an alarming 9% prevalence of kidney/urogenital anomalies was observed in a young workforce. Heat exposures above TLVs were associated with significantly increased odds of various adverse health outcomes (OR=2.43, 95% CI 1.88 to 3.13, p-value = <0.0001) and productivity losses (OR=1.79, 95% CI 1.32 to 2.4, p-value = 0.0002). Rough estimates for the number of workers who would be subjected to higher than TLV levels in the various RCP scenarios are RCP2.6 =79%, RCP4.5 & RCP6 = 81% and at RCP 8.5 = 85%. Rising temperatures due to CC has the capacity to further reduce already compromised health and productivity by subjecting the workers to increased heat exposures in the RCP scenarios are of concern for the country’s occupational health and economy. Conclusion: The findings of this study clearly identify that health protection from hot weather will become increasingly necessary in the Indian subcontinent and understanding the various adaptation techniques needs urgent attention. Further research with a multi-targeted approach to develop strategies for implementing interventions to protect the millions of workers is imperative. Approaches to include health aspects of climate change within sectoral and climate change specific policies should be encouraged, via a number of mechanisms, such as the “Health in All Policies” approach to avert adverse health and productivity consequences as climate change proceeds.

Keywords: heat stress, occupational health, productivity loss, heat strain, adverse health outcomes

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Authors: Joseph Omeiza Alao

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The wobbly state of waste management and the high level of environmental irresponsibility is a threat to environmental security, which invariably endangered public health, regional groundwater systems and atmospheric condition. The dumping of waste materials in water bodies and gutters and the frequent burning of waste materials heaped at dumpsites as well depict the highest level of environmental indiscipline. These unruly human factors have compelled this study to apply four different techniques for environmental impact assessment and the possible public health risks of poor waste management in Nigeria. The techniques include a geophysical survey (resistivity data acquisition), dispatched questionnaire surveys, physiochemical water analysis and a physical survey of several dumpsites. While the resistivity data indicates high-level dumpsite leachate invading the ground soil down to the water table, the physiochemical water analysis depicts high content of BOD (401 – 711) mg/l, COD (731 – 1312) mg/l, TDS (419 – 1871) mg/l and heavy metals (0.014 – 1.971) mg/l present in the regional groundwater systems, which have altered the chemistry of the regional groundwater. The resistivity data shows that the overburdened soil layer overlaying the regional groundwater systems was very low (4.5 Ωm – 151 Ωm) as against the existing data (180 Ωm – 3500 Ωm). However, the physical surveys and the dispatched questionnaire surveys explore the depth of environmental irresponsibility among the citizen. While the imprints of gross environmental indiscipline may be absolutely irreversible, adequate knowledge of the environmental implications of careless waste disposal. After a critical examination of the current waste management strategies in Nigeria, the study suggests a future direction for environmental security and sustainability. Several influential regional factors, such as geology, climatic conditions, and hydrology, were also discussed.

Keywords: groundwater, environmental indiscipline, waste management, water analysis, leachate plumes, public health

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Authors: Shreya Goyal

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The study aims to build user-friendly software to understand patient arrival patterns and compute the number of potential patients who will visit a particular health facility for a given period by using a machine learning algorithm. The underlying machine learning algorithm used in this study is the Support Vector Machine (SVM). Accurate prediction of patient arrival allows hospitals to operate more effectively, providing timely and efficient care while optimizing resources and improving patient experience. It allows for better allocation of staff, equipment, and other resources. If there's a projected surge in patients, additional staff or resources can be allocated to handle the influx, preventing bottlenecks or delays in care. Understanding patient arrival patterns can also help streamline processes to minimize waiting times for patients and ensure timely access to care for patients in need. Another big advantage of using this software is adhering to strict data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States as the hospital will not have to share the data with any third party or upload it to the cloud because the software can read data locally from the machine. The data needs to be arranged in. a particular format and the software will be able to read the data and provide meaningful output. Using software that operates locally can facilitate compliance with these regulations by minimizing data exposure. Keeping patient data within the hospital's local systems reduces the risk of unauthorized access or breaches associated with transmitting data over networks or storing it in external servers. This can help maintain the confidentiality and integrity of sensitive patient information. Historical patient data is used in this study. The input variables used to train the model include patient age, time of day, day of the week, seasonal variations, and local events. The algorithm uses a Supervised learning method to optimize the objective function and find the global minima. The algorithm stores the values of the local minima after each iteration and at the end compares all the local minima to find the global minima. The strength of this study is the transfer function used to calculate the number of patients. The model has an output accuracy of >95%. The method proposed in this study could be used for better management planning of personnel and medical resources.

Keywords: machine learning, SVM, HIPAA, data

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

Procedia PDF Downloads 83