Showing papers by "Sheikh Mohammed Shariful Islam published in 2023"
4 citations
3 citations
TL;DR: In this article , the authors conducted a systematic review according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) in 2022 and extensively reviewed six online databases (PubMed, Scopus, Medline, EMBASE, Web of Science and Google Scholar).
Abstract: Stigma affects different life aspects in people living with bipolar disorder and their families. This study aimed to examining the experience of stigma and evaluating predictors, consequences and strategies to combat stigma in people with bipolar disorder and their families.We conducted a systematic review according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) in 2022. We extensively reviewed six online databases (PubMed, Scopus, Medline, EMBASE, Web of Science and Google Scholar). Articles published in the English language about stigma in people living with bipolar disorders and their families were included.A total of 42,763 articles were retrieved, of which 40 articles from 14 countries were included in this study (n = 7417 participants). Of the 40 articles, 29 adopted quantitative methods (72.5%), two used mixed-methods (5%), eight used qualitative (20%) methods, and one was a case series (2.5%). The results of the studies were categorized into four themes: 1. Stigma experienced by people living with bipolar disorders and their families, 2. Predictors of stigma in people living with bipolar disorders and their families, 3. Consequences of stigma in people living with bipolar disorders and their families, 4. Effective interventions and strategies to reduce stigma in people living with bipolar disorders and their families.The results of this study might be useful to design psychiatric cognitive interventions to reduce stigma in people living with bipolar disorders and their families and designing community-based interventions to normalize bipolar disorder at the community level.
2 citations
TL;DR: In this article , the authors examined factors associated with psychosocial distress, fear of COVID-19 and coping strategies among the general population in Saudi Arabia, and found that people in the Kingdom of Saudi Arabia were at a higher risk of psychological distress along with medium-high resilience during the crisis.
1 citations
TL;DR: In this paper , the authors used machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features, and showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short and long-term.
Abstract: Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.
1 citations
TL;DR: In this article , Korosoglou, Alizadehsani, Islam and Rolf presented an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
Abstract: COPYRIGHT © 2023 Korosoglou, Alizadehsani, Islam and Rolf. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
TL;DR: In this paper , the authors present the design and development of a smartphone-based lifestyle app integrating a wearable device for hypertension management, which provides health education to people with hypertension related to their condition, while utilizing wearable devices to promote lifestyle modification and blood pressure management.
Abstract: Background Several research studies have demonstrated the potential of mobile health apps in supporting health management. However, the design and development process of these apps are rarely presented. Objective We present the design and development of a smartphone-based lifestyle app integrating a wearable device for hypertension management. Methods We used an intervention mapping approach for the development of theory- and evidence-based intervention in hypertension management. This consisted of six fundamental steps: needs assessment, matrices, theoretical methods and practical strategies, program design, adoption and implementation plan, and evaluation plan. To design the contents of the intervention, we performed a literature review to determine the preferences of people with hypertension (Step 1) and necessary objectives toward the promotion of self-management behaviors (Step 2). Based on these findings, we implemented theoretical and practical strategies in consultation with stakeholders and researchers (Steps 3), which was used to identify the functionality and develop an mHealth app (Step 4). The adoption (Step 5) and evaluation (Step 6) of the mHealth app will be conducted in a future study. Results Through the needs analysis, we identified that people with hypertension preferred having education, medication or treatment adherence, lifestyle modification, alcohol and smoking cessation and blood pressure monitoring support. We utilized MoSCoW analysis to consider four key elements, i.e., education, medication or treatment adherence, lifestyle modification and blood pressure support based on past experiences, and its potential benefits in hypertension management. Theoretical models such as (i) the information, motivation, and behavior skills model, and (ii) the patient health engagement model was implemented in the intervention development to ensure positive engagement and health behavior. Our app provides health education to people with hypertension related to their condition, while utilizing wearable devices to promote lifestyle modification and blood pressure management. The app also contains a clinician portal with rules and medication lists titrated by the clinician to ensure treatment adherence, with regular push notifications to prompt behavioral change. In addition, the app data can be reviewed by patients and clinicians as needed. Conclusions This is the first study describing the design and development of an app that integrates a wearable blood pressure device and provides lifestyle support and hypertension management. Our theory-driven intervention for hypertension management is founded on the critical needs of people with hypertension to ensure treatment adherence and supports medication review and titration by clinicians. The intervention will be clinically evaluated in future studies to determine its effectiveness and usability.
TL;DR: The burden of Type 2 diabetes in Australia has increased considerably over three decades as discussed by the authors , and there is an urgent need to prioritise resource allocation for prevention programs, screening initiatives to facilitate early detection, and effective and accessible management strategies for the large proportion of the population impacted by type 2 diabetes.
TL;DR: In this article , the validity, features, and clinical use of wearable cuffless BP devices were systematically reviewed and validated using MEDLINE, Embase, IEEE Xplore and the Cochrane Database till December 2019.
Abstract: Background: High blood pressure (BP) is the most common modifiable cardiovascular risk factor, yet its monitoring remains problematic. Wearable cuffless BP devices offer a potential solution for improved monitoring over time, however little is known about their validity and utility. We aimed to systematically review the validity, features, and clinical use of wearable cuffless BP devices. Methods: We searched MEDLINE, Embase, IEEE Xplore and the Cochrane Database till December 2019 for studies that reported validating cuffless BP devices. We extracted information about study characteristics, device features, validation processes, and clinical applications. Devices were classified according to their functions and features. We defined devices with a mean systolic BP (SBP) and diastolic BP (DBP) biases of < 5 mmHg as valid as a consensus. Our definition of validity did not include assessment of device measurement precision, which is assessed by standard deviation of the mean difference- a critical component of ISO protocol validation criteria. Study quality was assessed using the QUADAS-2 tool. A random-effects model meta-analysis was performed to summarise the mean biases for systolic and diastolic BP across studies. Results: Of the 430 studies identified, 16 studies (15 devices, N = 974) were selected. The majority of devices (81.3%) used photoplethysmography to estimate BP against a reference device; other technologies included tonometry, auscultation and electrocardiogram. In addition to BP and heart rate, some devices also measured night-time BP (n = 5), sleep monitoring (n = 3), oxygen saturation (n = 3), temperature (n = 2) and electrocardiogram (n = 3). Eight devices showed mean biases of < 5 mmHg for SBP and DBP compared to a reference device and three devices were commercially available. The meta-analysis showed no statistically significant differences between the wearable and reference devices for SBP (pooled mean difference = 3.42 mmHg, 95%CI −2.17, 9.01, I2 95.4%) and DBP (pooled mean = 1.16 mmHg, 95%CI -1.26, 3.58, I2 87.1%). Conclusion: Available cuffless BP devices utilise different sensing technologies, offering the potential for continuous BP monitoring. The variation in standards and validation protocols limited the comparability of findings across studies and the identification of the most accurate device. Challenges such as validation using standard protocols and in real-life settings must be overcome before they can be recommended for uptake into clinical practice.
TL;DR: In this article , the authors explored the burden of hypertensive heart disease (HHD) and high systolic blood pressure (SBP) among the Australian population over time and assessed the prevalence, mortality, disability-adjusted life-years (DALY), years lived with disability (YLD) and years of life lost (YLL) attributable to HHD and high SBP between 1990 and 2019 in Australia.
Abstract: Background: There is a dearth of comprehensive studies on the burden of hypertensive heart disease (HHD) and high systolic blood pressure (SBP) among the Australian population over time. We aimed to explore the burden of HHD and SBP, and how it changed over time from 1990 to 2019. Methods: We analysed the 2019 Global Burden of Disease data with a focus on Australia. We assessed the prevalence, mortality, disability-adjusted life-years (DALY), years lived with disability (YLD) and years of life lost (YLL) attributable to HHD and high SBP between 1990 and 2019 in Australia. GBD data sources included surveillance and survey data, published, and unpublished research articles and reports, vital registration and hospital data. Data were presented as point estimates with their corresponding 95% uncertainty intervals (UI). Results: From 1990 to 2019, the burden of HHD and SBP in Australia decreased. Age standardized prevalence rate of HHD was 119.3 cases per 100,000 people (95% UI 86.6 to 161.0) in 1990, compared to 80.1 cases (95% UI 57.4 to 108.1) in 2019. HHD death stood at 3.4 cases per 100,000 population (95% UI 2.6 to 3.8) in 1990, compared to 2.5 (95% UI 1.9 to 3.0), 32.0 cases (95% UI 26.1 to 38.8) in 2019. HHD contributed to 57.2 cases per 100,000 population (95% UI 46.6 to 64.7) of DALYs in 1990 compared to 38.4 cases per 100,000 population (95% UI 32.0 to 45.2) in 2019. Death rates per 100,000 population attributable to high SBP declined significantly over time for both sexes from 1990 (155.6 cases; 95% UI 131.2 to 177.0) to approximately one third in 2019 (53.8 cases; 95% UI 43.4 to 64.4). Conclusion: Over the past three decades, the burden of HHD and high SBP in Australia reduced, but remains relatively high, particularly HHD. The contribution of HHD and high SBP to mortality, DALYs, and YLLs reduced significantly from 1990 to 2019. Efforts to identify people with high blood pressure early and population level measures for improving the management of blood pressure should be a priority for Australia.
TL;DR: In this article , the authors used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier (SGD), Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based gradient boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension.
Abstract: Abstract We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35–70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.
TL;DR: In this paper , the authors used published user reviews of commercially available apps to determine the user experience issues to guide future app development in stroke caregiving, and highlighted critical issues that affect the usability, usefulness, desirability, findability, accessibility, credibility, and value of the app that contribute to decreased satisfaction and increased frustration.
Abstract: Background Existing research has demonstrated the potential of mHealth apps in improving the caregiving outcomes of stroke. Since most of the apps were published in commercially available app stores without explaining their design and evaluation processes, it is necessary to identify the user experience issues to promote long-term adherence and usage. Objective The purpose of this study was to utilize published user reviews of commercially available apps to determine the user experience issues to guide future app development in stroke caregiving. Methods User reviews were extracted from the previously identified 46 apps that support stroke caregiving needs using a python-scraper. The reviews were pre-processed and filtered using python scripts to consider English reviews that described issues faced by the user. The final corpus was categorized based on TF-IDF vectorization and k-means clustering technique, and the issues extracted from the various topics were classified based on the seven dimensions of user experience to highlight factors that may affect the usage of the app. Results A total of 117,364 were extracted from the two app stores. After filtration, 13,368 reviews were included and classified based on the user experience dimensions. Findings highlight critical issues that affect the usability, usefulness, desirability, findability, accessibility, credibility, and value of the app that contribute to decreased satisfaction and increased frustration. Conclusion The study identified several user experience issues due to the inability of the app developers to understand the needs of the user. Further, the study describes the inclusion of a participatory design approach to promote an improved understanding of user needs; therefore, limiting any issues and ensuring continued use.
TL;DR: In this paper , a review was conducted to determine whether various dietary factors were associated with arterial stiffness in the pediatric population, and the results indicated that some dietary factors may be associated with hypertension in pediatric populations; however, inconsistencies were observed across all study designs.
Abstract: Arterial stiffness is a risk factor for cardiovascular disease that is affected by diet. However, research understanding how these dietary risk factors are related to arterial stiffness during childhood is limited. The purpose of this review was to determine whether various dietary factors were associated with arterial stiffness in the pediatric population. Five databases were systematically searched. Intervention studies, cross-sectional and cohort studies were included that investigated nutrient or food intake and outcomes of arterial stiffness, primarily measured by pulse wave velocity (PWV) and augmentation index (AIx), in the pediatric population (aged 0–18 years). A final 19 studies (six intervention and 13 observational) were included. Only two intervention studies, including a vitamin D and omega-3 supplementation trial, found protective effects on PWV and AIx in adolescents. Findings from observational studies were overall inconsistent and varied. There was limited evidence to indicate a protective effect of a healthy dietary pattern on arterial stiffness and an adverse effect of total fat intake, sodium intake and fast-food consumption. Overall, results indicated that some dietary factors may be associated with arterial stiffness in pediatric populations; however, inconsistencies were observed across all study designs. Further longitudinal and intervention studies are warranted to confirm the potential associations found in this review.
TL;DR: In this article , a cross-sectional study was conducted to determine the correlations between household food insecurity and stress, anxiety, and depression in mothers living in Mashhad, Iran, and found that a higher level of food insecurity correlates with extreme degrees of stress and anxiety.
Abstract: Food insecurity is a public health concern with pervasive effects on numerous human biological factors. In addition to physical problems, food insecurity may have adverse social and psychological impacts. The present study aimed to determine the correlations between household food insecurity and stress, anxiety, and depression in mothers living in Mashhad, Iran. In this cross-sectional study we recruited 312 mothers. We collected data on the food insecurity status of households using the Household Food Insecurity Access Scale (HFIAS) and used the Depression Anxiety Stress Scale (DASS) to assess the levels of stress, anxiety, and depression in the subjects. The prevalence rate of food insecurity was 51.9%, and the prevalence rate of stress, anxiety, and depression was 70.2%, 70.2%, and 55.1%, respectively. In the food-insecure group, 94.3% of the mothers had stress, 91.4% had anxiety, and 87.1% had depression. While in the food-secure group, 60.7%, 61.3%, and 37.3% of the mothers had stressed, anxiety, and depression, respectively. In all the analytical models, food insecurity was significantly and positively associated with stress, anxiety, and depression (P<0.001). Our results suggested that a higher level of food insecurity correlates with extreme degrees of stress, anxiety, and depression. Therefore, the improvement of mothers' mental health in terms of stress, anxiety, and depression depends on the improvement of household food insecurity.
TL;DR: In 2019, the 2019 Global Burden of Disease (GBD) findings highlighted raised systolic blood pressure (SBP) as the leading cause of death globally as mentioned in this paper .
Abstract: Objective: The 2019 Global Burden of Disease (GBD) findings highlight raised systolic blood pressure (SBP) as the leading cause of death globally. In a high-income country, Australia, it is unclear how SBP ranks among other risk factors regarding the overall and cardiovascular disease (CVD) burden, and whether the situation has changed over time. Design and method: We analysed the 2019 GBD data with a focus on Australia. We assessed the ten leading risk factors for all-cause and CVD deaths and disability-adjusted life-years (DALYs) and compared findings with the Australia Burden of Diseases Study. Results: From 1990 to 2019, raised SBP remained the leading risk factor for attributable deaths (followed by dietary risks and tobacco use), accounting for 29,056/75,235 (39%) (95% Uncertainty Interval (UI) [24,863–32,915]) deaths in 1990; 21,845/76,893 (28%) [17,678–26,044] in 2010; and 25,498/90,393 (28%) [20,152–30,851] in 2019. From 1990 to 2019, contributions of SBP to cardiovascular deaths were 54% [46–62], 44% [37–51] and 44% [36–52]. Recently (2010 to 2019), the contribution of SBP to cardiovascular deaths and DALYs increased in males (9,361/17,551 (53%) deaths in 2010 to 11,042/20,420 (54%) in 2019; and 181,127/325,783 (56%) to 203,677/359,931 (57%) DALYs) and females (10,184/18,047 (56%) cardiovascular deaths in 2010 and 11,228/19,811(57%) in 2019; and DALYs from 140,625/240,510 (58%) to 154,013/260,348 (59%)). Conclusions: Raised SBP continues to be the leading risk factor for all-cause and cardiovascular deaths in Australia. We urge cross-disciplinary stakeholder engagement to implement effective strategies to detect, treat and control raised blood pressure as a central priority to mitigate the CVD burden.
TL;DR: In this paper , the authors performed a systematic analysis of burden of hypertensive heart disease using the Global Burden of Disease (GBD) study data from South Asia, including Bangladesh, Bhutan, India, Pakistan, and Nepal.
Abstract: Background: There is lack of comprehensive population based studies on the burden of hypertensive heart diseases (HHD) in South Asia and its trends over time. We aimed to explore the burden of HHD, and how it changed over time from 1990 to 2019 in South Asia. Methods: We performed a systematic analysis of burden of hypertensive heart disease using the Global Burden of Disease (GBD) study data from South Asia, including Bangladesh, Bhutan, India, Pakistan, and Nepal. GBD data sources included surveillance and survey data, published, and unpublished research articles and reports, vital registration and hospital data. We assessed the prevalence, mortality, disability adjusted life years (DALY), years lived with disability (YLD) and years of life lost (YLL) attributable to HHD between 1990 and 2019 in South Asia. Data were presented as point estimates with their corresponding 95% uncertainty intervals (UI). Results: From 1990 to 2019 the age standardized prevalence for HHD in South Asia increased from 110.2 (95% UI, 77.4 - 156.8) to 111.8 (78.3 - 159.0) per 100,000 population, and from 8.7 (5.2 - 13.7) to 8.8 (5.2 - 13.9) for YLDs, in both genders, respectively. The prevalence increased with age and was higher among females. The age standardized rate decreased from 334.1 (206.3 - 452.4) to 229.2 (166.1 - 296.9) for DALYs, and from 19.0 (12.1 - 26.7) to 13.2 (9.3 - 17.4) for death, respectively. In 2019, HHD prevalence was highest in Pakistan (138.6, 95% UI, 96.2 to 196.9), and lowest in Nepal (69.9, 49.5 to 98.3). Countries with the biggest increase in YLDs were Bangladesh (8.3%, 95% UI, -3.9% to 22.8%), followed by India (5.0%, 2.4% to 7.7%), Bhutan (4.0%, -8.7% to 18.3%), Pakistan (3.4%, -3.5% to 10.8%), and Nepal (1.0%, -11.5% to 15.5%). The age standardized DALYs decreased from 334.1 (95% UI, 206.3 to 452.4) in 1990 to 229.2 (95% UI, 166.1 to 296.9) in 2019 with the highest significant percentage change in the India (-34.1%, 95% UI, -48.3% to -9.5%), followed by Bangladesh, Bhutan and Nepal. Deaths due to HHD significantly declined in all countries except Pakistan with a small increase (0.3%, -24.6% to 26.8%) and in Nepal with a decrease (-8.6, -38.8% to 36.8%), respectively. Conclusion: HHD remains a significant cause of disease burden in South Asian countries. The increased prevalence amongst older people and females warrants particular attention. Policies focusing on preventing modifiable risk factors and access to essential medicines is warranted.
TL;DR: In this paper , the authors studied the associations of short-term exposure to Sulfur dioxide (SO2) and particulate matter (PM10) with the number of daily hospital admissions of hypertensive cardiovascular diseases.
Abstract: Background and aims: Air pollution is a major environmental risk factor and the leading cause of disease burden with detrimental effects on cardiovascular systems. Cardiovascular diseases are predisposed by various risk factors, including hypertension, as the most important modifiable risk factor. However, there is a lack of sufficient data concerning the impact of air pollution on hypertension. We sought to study the associations of short-term exposure to Sulfur dioxide (SO2) and particulate matter (PM10) with the number of daily hospital admissions of hypertensive cardiovascular diseases (HCD). Methods: All hospitalized patients between March 2010 to March 2012 were recruited with the final diagnosis of HCD based on the International Classification of Diseases 10 (codes: I10-I15) from 15 hospitals in Isfahan, one of the most polluted cities in Iran. The 24-hour average concentrations of pollutants were obtained from 4 monitoring stations. In addition to single- and two-pollutant models, we used Negative Binomial and Poisson models with covariates of holidays, dew point, temperature, wind speed, and extracted latent factors of other pollutants controlling for multi-collinearity to examine the risk for hospital admissions for HCD affected by SO2 and PM10 exposures in the multi-pollutant model. Results: A total of 3132 hospitalized patients (63% female) with a mean (standard deviation) age of 64.96 (13.81) were incorporated in the study. The mean concentrations of SO2 and PM10 were 37.64 μg/m3 and 139.08 μg/m3, respectively. Our findings showed that a significantly increased risk of HCD-induced hospital admission was detected for a 10 μg/m3 increase in the 6-day and 3-day moving average of SO2 and PM10 concentrations in the multi-pollutant model with a percent change of 2.11% (95% confidence interval: 0.61 to 3.63%) and 1.19% (0.33 to 2.05%), respectively. This finding was robust in all models and did not vary by gender (for SO2 and PM10) and season (for SO2). However, people aged 35-64 and 18-34 years were vulnerable to SO2 and PM10 exposure-triggered HCD risk, respectively. Conclusions: This study supports the hypothesis of the association between short-term exposure to ambient SO2 and PM10 and the number of hospital admissions due to HCD.
TL;DR: In this article , the authors applied machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables, including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF), SVM, GP, Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease.
Abstract: Abstract Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas.
TL;DR: In this article , the authors used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5-19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020.
Abstract: Abstract Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being 1–6 . Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5–19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m –2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified.
TL;DR: In this paper , the authors used 2019 Global Burden of Disease (GBD) data to report the HHD age-standardised prevalence, disability adjusted life years (DALYs), years of life lost (YLLs), and mortality, as well as HHD risk factors attribution percent with their 95% uncertainty interval (UI).
TL;DR: In this paper , a digital general practitioner (GP) model for rural Bangladesh, digital platforms and a statistical analysis of the data that was gathered from the pilot project were presented, where a total of 12,746 people were provided regular health services during a pilot project, from all genders and age groups.
Abstract: Bangladesh's health care system, particularly in rural areas, experiences enormous obstacles in providing complete preventive and primary healthcare services due to the lack of adequate healthcare facilities, resource constraints, and a non-functional referral system. To alleviate these problems, in this study, we introduce the digital general practitioner (GP) model for rural Bangladesh, digital platforms and present a statistical analysis of the data that was gathered from the pilot project. A total of 12,746 people were provided regular health services during the pilot project, from all genders and age groups, and provided their socio-demographic and healthcare-related data. We analyzed healthcare-related data by carrying out both descriptive and inferential statistics. By utilizing this digital GP model, rural residents can receive routine health screenings at their homes, identify health risks early, receive consultation and health education, and be referred to GP and upper-level health facilities as needed. We found that hypertension was more prevalent (4.84% of the served population), and cancer was the least prevalent of all the NCDs in the studied population (0.05% of the served population). The population for stroke, hypertension, diabetes increased until the 50–59 age range as age increased, following which the population proportion declined as age increased. Additionally, 3.96% of young females were severely malnourished, comparably higher proportion than young males (2.34%). NCDs such as hypertension, diabetes was prevalent among rural people. Necessary steps should be taken to raise preventive and primary healthcare awareness among rural people. The absence of proper healthcare facilities, resource constraints, and a non-functional referral system hamper Bangladesh's health care system's ability to provide comprehensive preventive and primary healthcare services in rural area. As a result, patients develop advanced ailments, including non-communicable diseases (NCDs), and must seek treatment at an expensive specialty hospital. To resolve this issue, we introduce a digital GP model for rural Bangladesh, then show digital platforms that use the concept, and lastly summarize significant findings from the piloted digital GP model. By utilizing this digital GP model, rural residents can receive routine health screenings at their homes, identify health risks early, receive consultation and health education, and be referred to GP and upper-level health facilities as need. From our data analysis, we discovered high burden of NCDs such as hypertension and diabetes in the piloted area. Necessary steps should be taken to raise preventive and primary healthcare awareness among rural people.