Current Health Condition Research Articles

Logistic Regression With Machine Learning Sheds Light on the Problematic Sexual Behavior Phenotype.

There has been a longstanding debate about whether the mechanisms involved in problematic sexual behavior (PSB) are similar to those observed in addictive disorders, or related to impulse control or to compulsivity. The aim of this report was to contribute to this debate by investigating the association between PSB, addictive disorders (internet addiction, compulsive buying), measures associated with the construct known as reward deficiency (RDS), and obsessive-compulsive disorder (OCD). A Canadian university Office of the Registrar invited 68,846 eligible students and postdoctoral fellows. Of 4710 expressing interest in participating, 3359 completed online questionnaires, and 1801 completed the Mini-International Neuropsychiatric Interview. PSB was measured by combining those screening positive (score at least 6) on the Sexual Addiction Screening Test-Revised Core with those self-reporting PSB. Current mental health condition(s) and childhood trauma were measured by self-report. OCD was assessed by a combination of self-report and Mini-International Neuropsychiatric Interview data. Of 3341 participants, 407 (12.18%) screened positive on the Sexual Addiction Screening Test-Revised Core. On logistic regression, OCD, attention deficit, internet addiction, a family history of PSB, childhood trauma, compulsive buying, and male gender were associated with PSB. On multiple correspondence analysis, OCD appeared to cluster separately from the other measures, and the pattern of data differed by gender. In our sample, factors that have previously been associated with RDS and OCD are both associated with increased odds of PSB. The factors associated with RDS appear to contribute to a separate data cluster from OCD and to lie closer to PSB.

Suicides Among American Indian or Alaska Native Persons - National Violent Death Reporting System, United States, 2015-2020.

Compared with the general U.S. population, American Indian or Alaska Native (AI/AN) persons, particularly those who are not Hispanic or Latino (Hispanic) AI/AN, are disproportionately affected by suicide; rates among this group consistently surpass those among all other racial and ethnic groups (1). Suicide rates among non-Hispanic AI/AN persons increased nearly 20% from 2015 (20.0 per 100,000) to 2020 (23.9), compared with a <1% increase among the overall U.S. population (13.3 and 13.5, respectively) (1). Understanding characteristics of suicide among AI/AN persons is critical to developing and implementing effective prevention strategies. A 2018 report described suicides in 18 states among non-Hispanic AI/AN persons only (2). The current study used 2015-2020 National Violent Death Reporting System (NVDRS) data among 49 states, Puerto Rico, and the District of Columbia to examine differences in suicide characteristics and contributing circumstances among Hispanic and non-Hispanic AI/AN populations, including multiracial AI/AN. Results indicated higher odds across a range of circumstances, including 10 of 14 relationship problems (adjusted odds ratio [aOR] range=1.2-3.8; 95% CI range=1.0-5.3) and six of seven substance use problems (aOR range=1.2-2.3; 95% CI range=1.1-2.5), compared with non-AI/AN persons. Conversely, AI/AN decedents had reduced odds of having any current known mental health condition, any history of mental health or substance use treatment, and other common risk factors (aOR range=0.6-0.8; 95% CI=0.2-0.9). Suicide is preventable. Communities can implement a comprehensive public health approach to suicide prevention that addresses long-standing inequities affecting AI/AN populations (3).

Intelligent analysis of irregular physical factors for panic disorder using quantum probability

ABSTRACT Panic disorder (PD) is considered one of the destructive ailments, with various individuals experiencing a critical functional disorder. As the range of remission for PD is achieved only between 20% and 50% with the help of regular pharmacotherapy, modern solutions are expected to deal with this issue. By taking the advantage of Internet of Things (IoT), a novel IoT-inspired behaviour monitoring framework is proposed for the analysis of panic disorder in a particular context. A quantum probability-inspired quantification measure is calculated to determine the scale of health irregularity. In addition, Temporal Data Mining (TDM) is performed for the formulation of temporal data granules to measure Individual Health Index (IHI) by utilising the Multi-scaled Gated Recurrent Unit (M-GRU) technique of deep learning. Moreover, a two-phased alert generation approach is proposed for notifying the current health condition of an individual to the concerned caretaker or medical specialist for assistive or medical services. In the comparative analysis, the proposed framework has outperformed the state-of-the-art approaches by achieving a considerable classification accuracy of 96.89% for event determination and 94.14% accuracy for health severity determination. Similarly, a considerable improvement with respect to Specificity, Sensitivity, and F-measure has been observed for the proposed framework.

Which consumers are willing to pay for smart car healthcare services? A discrete choice experiment approach

The technology adoption lifecycle model categorizes consumer groups in the new concept products and services market, based on different characteristics and needs, into innovators, early adopters, early majority, late majority, and laggards, in order of adopting new products and services. This study aims to estimate heterogeneous consumer preferences and willingness to pay for a new concept convergence service, the smart car healthcare service, using a choice experiment questionnaire and a hierarchical Bayesian mixed logit. We found that consumers were willing to pay an additional 3000 to 6000 KRW/month (2.65–5.29 USD/month) for a service that measured both physical movement and vital signs compared to either the former or the latter. It was also found that they were willing to pay about 3000 KRW/month (2.65 USD/month) more for a service that provides health condition predictions compared to one that provides only the current health condition. In addition, customers who were young, innovative, and favored the utility of healthcare services showed a lower sensitivity to service fees than those who were not. Thus, they were found to have a greater willingness to pay for advanced smart car healthcare services.

Attitudes of Patients With Chronic Heart Failure Toward Digital Device Data for Self-documentation and Research in Germany: Cross-sectional Survey Study.

BackgroundIn recent years, the use of digital mobile measurement devices (DMMDs) for self-documentation in cardiovascular care in Western industrialized health care systems has increased. For patients with chronic heart failure (cHF), digital self-documentation plays an increasingly important role in self-management. Data from DMMDs can also be integrated into telemonitoring programs or data-intensive medical research to collect and evaluate patient-reported outcome measures through data sharing. However, the implementation of data-intensive devices and data sharing poses several challenges for doctors and patients as well as for the ethical governance of data-driven medical research.ObjectiveThis study aims to explore the potential and challenges of digital device data in cardiology research from patients’ perspectives. Leading research questions of the study concerned the attitudes of patients with cHF toward health-related data collected in the use of digital devices for self-documentation as well as sharing these data and consenting to data sharing for research purposes.MethodsA cross-sectional survey of patients of a research in cardiology was conducted at a German university medical center (N=159) in 2020 (March to July). Eligible participants were German-speaking adult patients with cHF at that center. A pen-and-pencil questionnaire was sent by mail.ResultsMost participants (77/105, 73.3%) approved digital documentation, as they expected the device data to help them observe their body and its functions more objectively. Digital device data were believed to provide cognitive support, both for patients’ self-assessment and doctors’ evaluation of their patients’ current health condition. Interestingly, positive attitudes toward DMMD data providing cognitive support were, in particular, voiced by older patients aged >65 years. However, approximately half of the participants (56/105, 53.3%) also reported difficulty in dealing with self-documented data that lay outside the optimal medical target range. Furthermore, our findings revealed preferences for the self-management of DMMD data disclosed for data-intensive medical research among German patients with cHF, which are best implemented with a dynamic consent model.ConclusionsOur findings provide potentially valuable insights for introducing DMMD in cardiovascular research in the German context. They have several practical implications, such as a high divergence in attitudes among patients with cHF toward different data-receiving organizations as well as a large variance in preferences for the modes of receiving information included in the consenting procedure for data sharing for research. We suggest addressing patients’ multiple views on consenting and data sharing in institutional normative governance frameworks for data-intensive medical research.

Integrating digital twins and deep learning for medical image analysis in the era of COVID-19

BackgroundDigital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and technologies. Digital twins may allow healthcare organizations to determine methods of improving medical processes, enhancing patient experience, lowering operating expenses, and extending the value of care. During the present COVID-19 pandemic, various medical devices, such as X-rays and CT scan machines and processes, are constantly being used to collect and analyze medical images. When collecting and processing an extensive volume of data in the form of images, machines and processes sometimes suffer from system failures, creating critical issues for hospitals and patients. MethodsTo address this, we introduce a digital-twin-based smart healthcare system integrated with medical devices to collect information regarding the current health condition, configuration, and maintenance history of the device/machine/system. Furthermore, medical images, that is, X-rays, are analyzed by using a deep-learning model to detect the infection of COVID-19. The designed system is based on the cascade recurrent convolution neural network (RCNN) architecture. In this architecture, the detector stages are deeper and more sequentially selective against small and close false positives. This architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the other. At each stage, the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different stages. In this manner, the arrangement of detectors is adjusted to increase the intersection over union, overcoming the problem of overfitting. We train the model by using X-ray images as the model was previously trained on another dataset. ResultsThe developed system achieves good accuracy during the detection phase of COVID-19. The experimental outcomes reveal the efficiency of the detection architecture, which yields a mean average precision rate of 0.94.

Awareness, Knowledge, and Acceptance of Dental Implants among the Geriatric Population of Jeddah, Saudi Arabia.

Background:Dental implants (DI) are gaining popularity among the general population, and special consideration has been given to the geriatric population in the replacement of missing teeth subject to their medical stability. The study aimed to assess the awareness, knowledge, and acceptance regarding DI among the geriatric population of Jeddah, Saudi Arabia.Methodology:A cross-sectional survey was carried out through face-to-face interviews using a pretested and standardized questionnaire by calibrated interviewers. Participants of age >58 years who have not undergone any implant or bone graft procedures attending different prosthodontic clinics in Jeddah were recruited according to availability after obtaining consent. Responses received were converted into digital format and subjected to statistical analysis.Results:The knowledge related to DI was not satisfactory, and only 4.5% showed good knowledge. The willingness to replace the current prosthesis with DI was found in 49.6%, and it was also observed that 40% of the participants were using some tooth-supported fixed dental prosthesis. Only 41.3% and 31% were aware of bone graft and sinus lift procedures related to DI.Conclusion:Even though most participants were aware of DI, the knowledge related to it was little satisfactory. Older people should be appropriately explained about the best treatment choice for teeth replacement by connecting to their current systemic health condition with its benefits and long-term effects. Unexpectedly, the awareness related to bone graft and sinus lift procedures related to DI was found to be relatively acceptable as well as the willingness to accept bone graft if needed for DI placement was observed.

Under-nutrition and associated factors among pregnant women in public health care hospitals of Gedeo Zone, southern Ethiopia: A cross-sectional study

BackgroundBalanced and adequate nutritious food during pregnancy helps to improve maternal weight and for the healthy growth of the fetus. There has been little progress in reducing pregnant undernutrition in Ethiopia; it has been too slow. ObjectiveThe objective of this study was to assess the prevalence of undernutrition and associated factors among pregnant women in public health care hospitals of Gedeo Zone, Southern Ethiopia. MethodA cross-sectional study design was used in public hospitals of Gedeo Zone, Southern Ethiopia from February 01 to March 01, 2019. Pretested structure questionnaire was used to collect data on the socio-economic and demographic characteristics of the study participants. Epi-data was used to code and enter the data and SPSS-25 was used to analyse and interpret the data. To assess the relationship between the dependent and the independent variables, bi-variable and multivariable logistic regression analysis was done. ResultsIn this study, the prevalence of undernutrition among pregnant women was 21% (95% CI: 20.8–21.2). After controlling other co-variables, the multivariable logistic regression model revealed that average monthly income, women's educational status, nutrition education and counseling, and parity were found to have a significant association with pregnant women's nutritional status. The odds of under-nutrition among pregnant women whose monthly income is <800 ETB were 2.8 times higher than those whose monthly incomes were >1500 (AOR: 2.89; 95%CI: 1.49–5.6). ConclusionIn this study the magnitude of undernutrition among pregnant women was found to be higher than the previously reported findings. Average household monthly income, family size, mother's educational status, nutrition education and counseling, current health condition of the mother, and parity were factors significantly associated with undernutrition of pregnant mothers. Therefore, Interventions should be initiated in earlier stages of pregnancy to prevent the high level of undernutrition during the second and third trimester in this study area.

Factors associated with patient experiences of the burden of using medicines and health-related quality of life: A cross-sectional study.

Polypharmacy, defined as the concurrent use of multiple medications, is a growing concern globally. This study aimed to identify the significant factors that predict the perceived burden of medication and health-related quality of life. Adults, aged 18 years and above who have used at least two regular medicines, were invited to complete the study questionnaires between June and October 2019. Multiple linear regression analysis was conducted to identify significant predictors for perceived burden of medication and health-related quality of life. A total of 119 participants completed this study. The average age of the participants was 63 years (SD±16 years). Factors significantly predicting perceived burden of medication were participants' current health condition (p = 0.001), overall burden of treatment (p<0.001) and being hypertensive (p = 0.037). Similarly, participants' current health condition (p<0.001) and overall burden of treatment (p = 0.086) were significant predictors for perceived health-related quality of life. This study revealed that hypertensive participants in poor health tended to experience higher perceived burden of medication, which in turn was found to be correlated with lower perceived health-related quality of life.

Influence of sociodemographic and lifestyle factors on taxed sugar-sweetened beverage consumption in Thailand

Globally, the factors influencing sugar-sweetened beverage (SSB) consumption have been extensively studied in many countries. However, there is no research investigating these factors at the population level. This study analyzed the major factors influencing taxed SSB consumption in Thailand. The Thai SSB tax applies to energy drinks (ED), carbonated drinks (CD) and non-100% fruit juice drinks (FD), and came into effect in 2017. This study used data from the second wave of the 'Longitudinal Monitoring Survey of Sugar-Sweetened Beverage Consumption' survey conducted in Thailand in 2019. A total of 4,736 Thai residents aged 15 years or older from four geographic regions and Bangkok in Thailand participated in the study. Data on self-reported frequency and quantity of consumption of taxed SSB were collected using semi-quantitative food frequency questionnaire. The data also include sociodemographic characteristics, lifestyle behaviors (physical activity, smoking, and alcohol drinking), current health condition, and knowledge of sugar recommendations. Binary and multinomial logistic regression and generalized linear model analyses were conducted. CD was the most consumed taxed SSB (34.2%) among the sample, followed by FD and ED, respectively. The findings suggest that certain sociodemographic characteristics (male, residing in Bangkok or Central region, lower educational attainment), physical inactivity, and smoking were associated with a higher level of SSB consumption. Consumption was lower for every year increase in age. We also observed a statistically-significant association between lack of knowledge of recommended limit on sugar intake and higher level of CD consumption. In order to further reduce SSB consumption in Thailand, more comprehensive, multifaceted policies and strategies are needed to complement the existing SSB taxation policy in Thailand, with a particular focus on addressing particular sociodemographic groups and other health-related behaviors.

Abstract P3-16-04: Estimation of willingness-to-pay for breast cancer treatments through contingent valuation method in Japanese breast cancer patients (JCOG1709A): The main study findings

Abstract Background In 2019, the Japanese government has introduced the economic valuation to healthcare system. However, the threshold for determining cost-effectiveness-analysis has yet to be adequately studied. Therefore, we have designed a study to examine the financial value of “life” and “health” based on willingness-to-pay (WTP) considered by patients with primary and metastatic breast cancer (PBC and MBC) in Japan. Methods This study is conducted by forty-five hospitals under the Breast Cancer Study Group of Japan Clinical Oncology Group (JCOG). In the preliminary study, 168 patients (84 patients with PBC and 84 patients with MBC) from 20 to 79 years of age paying medical costs were examined their WTP for setting up the dichotomous-choice method survey form for using in the main study. Virtual scenario-specific treatments to avoid the recurrence and death of breast cancer for one year for PBC patients, and to prolong the survival period for one year for the patients with MBC were presented. The patients were evaluated how much money would pay to receive the treatment in a self-written answer and the amount of WTP that would be presented in the main study was determined. In the main study, 1,620 patients (810 patients with PBC and 810 patients with MBC) were surveyed to their WTP for hypothetical scenarios using the dichotomous-choice method survey form. With the two-step dichotomous-choice method, if the first stage response was yes, a higher amount was displayed for the second stage. If the first stage response was no, a lower amount was shown for the second stage. For the main analysis, the acceptance probability curve was calculated using the parametric method. The calculation was based on the obtained responses and median WTP, which was determined by calculating the amount at which the acceptance probability curve is at 50%. The obtained amount was converted to WTP per one QALY based on the displayed question content. Results Completed surveys were collected from 716 PBC patients (88.4%) and 702 patients (86.7%). Based on the preliminary study, the amount of WTP offering in the first stage was set 5 million yen (JPY) (50,000 USD), and offering in the second stage was 7 million JPY (70,000 USD) for those who responded accepting the scenario treatment and 3 million JPY (30,000 USD) for those who responded refusing. We obtained point estimates of WTP for each virtual scenario treatments. The WTP of the two hypothetical scenarios for PBC patients were as follows; scenario 1) the treatment to gain one year without recurrence: 2.06 million JPY (20,600 USD), scenario 2) the treatment that reduces risk of recurrence by 40%: 2.40 million JPY (24,000 USD). The WTP of the two hypothetical scenarios for MBC patients were as follows; scenario 3) the treatment that can prolong survival for one year in current health condition: 2.81 million JPY (28,100 USD), scenario 4) the treatment that can prolong survival by one year in the health condition before having breast cancer: 2.92 million JPY (29,200 USD). Conclusion We created hypothetical 1-quality-adjusted survival year (QALY) scenarios for breast cancer patients with different disease status and validated them using the contingent valuation method. Our results demonstrate that WTP per 1QALY varies with the disease status of breast cancer. Therefore, we conclude that we cannot simply use a uniform threshold when examining the cost-effectiveness of medical interventions in breast cancer as a unit of cost/QALY. Citation Format: Tsuguo Iwatani, Fumikata Hara, Tadahiko Shien, Masato Takahashi, Norikazu Masuda, Yasuaki Sagara, Keita Sasaki, Junki Mizusawa, Haruhiko Fukuda, Takaru Shiroiwa, Hiroji Iwata. Estimation of willingness-to-pay for breast cancer treatments through contingent valuation method in Japanese breast cancer patients (JCOG1709A): The main study findings [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-16-04.

An Intelligent Coaching Prototype for Elderly Care

The world ageing problem is prompting new sustainable ways to support elderly people. As such, it is important to promote personalized and intelligent ways to assure the active and healthy ageing of the population. Technological breakthroughs have led to the development of personalized healthcare systems, capable of monitoring and providing feedback on different aspects that can improve the health of the elderly person. Furthermore, defining motivational strategies to persuade the elderly person to be healthier and stay connected to such systems is also fundamental. In this work, a coaching system is presented, especially designed to support elderly people and motivate them to pursue healthier ways of living. To do this, a coaching application is developed using both a cognitive virtual assistant to directly interact with the elderly person and provide feedback on his/her current health condition, and several gamification techniques to motivate the elderly person to stay engaged with the application. Additionally, a set of simulations were performed to validate the proposed system in terms of the support and feedback provided to the user according to his progress, and through interactions with the cognitive assistant.

Relationship Between Support for Workers With Illness and Work Functioning Impairment in Japan During the COVID-19 Pandemic.

This study examined the relationship between job accommodations for workers with poor health and work functioning impairment during the COVID-19 pandemic. An internet survey was conducted in December 2020. We included 24,429 subjects for analysis. One question was used to determine whether subjects needed job accommodations from their company to continue working in their current health condition. The odds ratios (ORs) of the necessity of job accommodations for sick workers associated with work functioning impairment were estimated using multilevel logistic regression analysis. The OR of work functioning impairment among sick workers not receiving job accommodations was 5.75 (95% confidence interval (CI): 5.34 to 6.20, P < 0.001) and those receiving job accommodations was 1.88 (95% CI: 1.69 to 2.08, P < 0.001) compared to healthy workers. This study suggests that providing job accommodations to workers with poor health may improve their work functioning impairment.

The Association between Parenting Confidence and Later Child Mental Health in the Area Affected by the Fukushima Nuclear Disaster: The Fukushima Health Management Survey.

After the 2011 Fukushima Daiichi Nuclear Power Station accident, the Fukushima Health Management Survey was conducted to assess children’s lifestyle and mental health conditions. The participants in this study were 1126 children, aged 0 to 3 years, living in the evacuation zone at the time of the disaster. The parenting confidence of their mothers was assessed using a self-administered questionnaire as a baseline in 2013. We examined the association of parenting confidence level at baseline, using a total difficulty score of the Strengths and Difficulties Questionnaire (SDQ) and reluctance to attend school among children in a follow-up study in 2016 and 2017. As a result, no confidence was reported by 178 (15.8%) mothers, while 477 (42.4%) responded with “not sure” and 471 (41.8%) were confident. In the multiple logistic analysis, after adjusting for covariates such as the child’s sex, age, and current health condition, the group lacking parenting confidence demonstrated a significantly higher risk level for SDQ total difficulties (OR, 2.8; 95% CI, 1.59–4.93) and reluctance to attend school (OR = 1.98, 95% CI: 1.24–3.18) than the confident mothers. After a major disaster, which can have long-term effects on communities, intensive psychological care for mothers with young children is needed to prevent various mental health problems in their children.

Gambaran Kualitas Hidup Pekerja Pra Lanjut Usia (45-56 Tahun) Dengan Sindrom Metabolik Di PT. X Tahun 2020

Quality of life can be determined by the individual's perception of his current life, including his health condition. Among occupational diseases, degenerative diseases are the first cause of death, and metabolic syndrome is one of them. This study aims to describe the quality of life of pre-elderly workers (45-56 years) with the risk of metabolic syndrome (obesity, hypertension, hypercholesterolemia and hyperglycemia) at PT. X. The research was conducted with a qualitative descriptive method. Data was collected by interview method and the results of medical check-up documents. The results showed that the respondents rated their quality of life quite well. They judged that his current physical condition was quite good. Respondents do not know that their current health condition can trigger degenerative diseases in the future if sedentary behavior is still ignored. Five of the seven respondents still do not exercise regularly and three of the seven respondents are active smokers with cigarette consumption of 3-20 cigarettes/day. The seven respondents did not pay enough attention to their food intake. Psychologically, respondents perceive their mental condition is very good, they perceive the work environment as a very comfortable place to build social relationships. More metabolic syndrome related support programs at PT X need to be offered to increase workers' awareness of their health status.

A Real Time Implementation of an IOT based Vehicle Health Monitoring System

This paper approaches an IoT based vehicle health monitoring system that is embedded for detecting the condition of a vehicle by monitoring the internal parameters such as heating rate, engine oil level and status of the CO of the vehicle. It is a real time vehicle health monitoring system is designed and developed to detect and identify the actuator and sensor faults created by automatically or manually by the user of the vehicle. Actually, Vehicles need repair after a certain interval of time and if are not repaired at fixed intervals, it can lead to loss of life of the persons travelling on it and there are many key issues which can affect the vehicle. So, the primary objective of this system is developing an IoT based embedded system that can detect the internal condition of a vehicle by evaluating the various parameters that are used to examine in the vehicle’s current health condition. In fact, this is a real time evaluation system that can be used for rapid condition screening. As a result, it provides all reliable information about the vehicle conditions. This IoT based system claims that it can detect and identify actuator and sensor faults with almost minimal detection latency even after lots of disturbances and uncertainties.

AUTOMOTIVE HEALTH TRACKING USING IOT AND ARDUINO

This paper is concerned with the development of an embedded system for detecting the vehicle condition by monitoring the internal parameters used in evaluating the vehicle's current health condition. An in-vehicle embedded system is being developed in this project to generate a vehicle health report (VHR) whenever the user requires it. It predicts future errors, allowing the driver to travel without interruption and avoid accidents. As a result, it warns the driver of potential errors and assists him in driving safely. The data needed to generate the health report is made up of parameter values (outputs of in-built sensors) from various systems inside the vehicle. Our framework is based on the Arduino and IoT stages, which are used to separate various parameters, for example, motor warming and fuel pipe blockage, for safe and cautious driving. The data is sent to IoT, where it can be checked by both the vehicle manufacturer via distributed computing and the vehicle owner via the Android application. The equipment unit consists of an Arduino, a WI-FI module, a portable Androidbased device, and a unique parameter checking sensors module. The ESP8266 WiFi Module is a self-contained SOC with integrated TCP/IP protocol stack that can give any microcontroller access to your WiFi network, which is used in current generation automotive.

Under-Nutrition and Associated Factors Among Pregnant Women in Public Health Care Hospitals of Gedeo Zone, SNNPR, Ethiopia: A Cross-Sectional Study

Background: Balanced and Adequate nutritious food during pregnancy helps to improve maternal weight and for the healthy growth of the fetus. There has been little progress in reducing pregnant under nutrition in Ethiopia; it has been too slow. Objective: The objective of this study was to assess the prevalence of under-nutrition and determinants amongst pregnant mothers in public health care hospitals of Gedeo Zone, SNNPR, Ethiopia, from Feb, 1 to March, 1- 2019. Method: A cross-sectional study design was used in public hospitals of Gedeo Zone. Pretested structure questionnaire was used to collect data on socio-economic and demographic characteristics of the study participant, while a height measuring board and a digital bath balance were used to collect anthropometric measurements such as MUAC, height, and weight. Epi-data was used to code and enter the data into a computer, and SPSS-25 was used to analyse and interpret the data. To evaluate the relationship between the dependent variable and the independent variables, univariable and multivariable logistic regression analysis was done. Results: In this study, the proportion of pregnant women who were undernourished was 21% (95%CI: 20.8–21.2).After controlling other co-variables, average monthly income, women's educational status, nutrition education and counselling, and parity were found to have a significant relationship with pregnant mothers' nutritional status in multivariable logistic regression analysis. Conclusion: The magnitude of under nutrition in pregnant women in this study was found to be very high; average household monthly income, family size, mother’s educational status, nutrition education and counselling, current health condition of the mother and parity were factors significantly associated with malnourished pregnant mothers. Funding Information: The study was funded by Dilla University, Ethiopia. Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The study was reviewed and approved by the Institutional Review Board (IRB) of Dilla University College of Medicine and Health Science. The study purpose, procedures, possible risks, and benefits were explained to participants in local languages. Informed written consent was obtained and signed.

Significance Of Multilayer Perceptron Model For Early Detection Of Diabetes Over Ml Methods

Diabetes is one of the chronicdiseases in the world. Millions of people are suffering with several other health issues caused by diabetes, every year. Diabetes has got three stages such as type2, type1 and insulin. Curing of diabetes disease at later stages is practically difficult. Here in this paper, we proposed a DNN model and its performance comparison with some of the machine learning models to predict the disease at an earlystage based on the current health condition of the patient. An artificial neural network (ANN) is a predictive model designed to work the same way a human brain does and works better with larger datasets. Having the concept of hidden layers, neural networks work better at predictive analytics and can make predictions with more accuracy. Novelty of this work lies in integration of feature selection method used to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. The results achieved using this method and several conventional machines learning approaches such as Logistic Regression, Random Forest Classifier (RFC) are compared. The proposed DNN method is proved to show better accuracy than Machine learning models for early stage detection of diabetes. This paper work is applicable to clinical support as a tool for making predecisions by the doctors and physicians.

Mother’s Health Seeking Behavior During Childhood illness in Northeast India: Findings of National Family Health Survey 4 (2015-16)

The latest National Family Health Survey (NFHS-4) report reflects the current health condition of the countryand its states as well as.Based on these findings this review carried out to highlight the mother’s healthseeking behaviours during childhood illness for the North-eastern states of India along with existing healthprograms catering to maternal and child health.Overall 60 percent of the children aged under five (U-5) werereceived any advice or treatment for Acute Respiratory Infection (ARI) from a health provider in NortheastIndia. Nearly 20 percent of the children with fever were given antibiotic drug and only 1 percent of themwere given antimalarial drug. Meghalaya is the only North-eastern state were the maximum incidence (12%)of diarrhoea has been observed while least was seen in Sikkim (2%). The lack of basic amenities is directlyor indirectly responsible for the high burden of childhood morbidities in this region. Also, poor and unequaldistribution of maternal health seeking behaviours for their children were observed to all the North-easternstates of India. A huge proportion of children are still lagging behind from the adequate advices or treatmentsdue to various reasons like, poor quality of care and the lake of government health facility in the area, healthpersonnel were often absent from government health facilities and the major problem was the distance to ahealth facility specially in rural areas.