Socioeconomic Information Research Articles

Return to work after primary total knee replacement in patients under 55 years of age: a retrospective study of 129 cases.

Primary total knee arthroplasty (TKA) has shown excellent results in the treatment of osteoarthritis, and its indications have now been extended to younger patients of working age. Few articles in the literature have studied the return to work of young subjects, and no specific studies have been conducted in France. Therefore, we carried out a retrospective study to 1) investigate the rate and delay of return to work after primary TKA in a population under 55 years of age and 2) identify factors influencing early return to work before 3 months, the period usually used as the duration of initial work stoppage (TS). The hypothesis of this study was that the rate and time to return to work were similar to those of other Western studies. This was a single-center retrospective study including all patients under 55 years of age who underwent primary TKA between 2010 and 2019, regardless of their professional status. A questionnaire collected all socioeconomic and medical information, time to return to work, and factors influencing it. Our population consisted of 223 TKAs (201 patients), with a participation rate of 64%. Among 129 patients, with a mean age of 50.3 ± 4.6 years [29-55], 109 were working (84%). Ninety-four of 109 patients (86.2%) returned to work after surgery at a mean time of 17.3 ± 10.6 weeks [12-24]. Previous hip or knee prosthetic surgery had a significant negative influence on the rate of return to work (OR 0.08; 95% CI [0.008; 0.7] (p = 0.027)). Manual workers returned to work significantly earlier (OR 8.2; 95% CI [1.6; 51.4] (p = 0.017)). A total of 56 patients (43.4%) were off work preoperatively, for a mean time of 16.7 ± 17.8 weeks. [4-24], and 49 of these patients returned to work (87.5%) at a mean time of 18.7 ± 11 weeks [12-24] compared with patients who did not receive a preoperative work stoppage (WK) (p = 0.7). The hypothesis was only partially confirmed. Compared with patients in other Western countries, French patients under 55 years of age who underwent primary TKA surgery appeared to return to work at the same rate but with a longer delay. Patients on WT prior to surgery were more likely to return to work after the operation, although this difference did not reach the significance threshold. Retrospective observational study.

Moroccan eating habits during COVID-19 lockdown: a preliminary analysis

The present study investigated the impact of the COVID-19 pandemic on dietary habits. Data were collected from an online survey (n = 171, out of 315 initial respondents) during the lockdown period from April to June 2020. The questionnaire was provided in Arabic, French, and English language and divided into five sections: sociodemographic and socioeconomic information, eating motivations, food consumption frequency, food-related behaviors, and purchase priorities. The results showed that health and pleasure were the primary perceived changes in eating motivations caused by the pandemic. The results of cluster analysis exhibited that individuals from cluster 1 with “Good Habits” had not changed their consumption frequency of food during the lockdown. In contrast, cluster 2 with “Bad Habits” showed higher consumption of salty snacks, chocolates, cakes, cookies, and processed foods (p < 0.001) than the period before the pandemic. On the other hand, the priority to purchase frozen vegetables, chocolates, and canned beans and legumes was observed to increase in cluster 2 during the lockdown period (p = 0.02, p = 0.005, and p = 0.001, respectively). The COVID-19 pandemic had a discernible impact on Moroccan dietary habits, emphasizing the need to fully understand the long-term effects. The observed changes highlight the importance of promoting healthy eating habits, especially during times of crisis. By analyzing the shifts in food consumption and the underlying motivations, policymakers can develop targeted interventions to support healthier dietary choices among the Moroccan population, contributing to improved public health outcomes.

Socio-economic inequalities in oral health among Portuguese older adults: a cross-sectional study

BackgroundAlthough the oral health status has improved in developed countries in recent years, oral diseases are still unequally distributed across socio-economic groups. Research on the impact of socio-economic factors on oral health care among older adults in Europe, including Portugal, remains limited. The main aim of this study was to investigate the association between socio-economic factors and oral health indicators in Portuguese older adults.MethodsThis retrospective cross-sectional study analyzed data from 915 participants (aged 65 years and older) from the third wave (2015–2016) of the Epidemiology of Chronic Diseases Cohort Study (EpiDoC), a population-based study. Socio-economic and demographic information, general health-related characteristics and oral health data (prosthetic need, oral hygiene frequency, and last dental procedure) were collected by questionnaire. Multivariate logistic and multinomial regression models analyzed the associations between socio-economic factors and oral health indicators.ResultsParticipants with lower education level, perceived lower income and a fewer number of private sector appointments were more likely to have poor oral health (need for prosthetic treatment, less frequent oral hygiene, and tooth extraction as last dental procedure). Other factors found to be associated with oral health were sex, age, geographical area, smoking habits, and body mass index.ConclusionsSocio-economic factors were associated with oral health in Portuguese older adults. These findings may contribute to future national public health strategies by expanding oral health services to ensure better access and coverage for at-risk groups.

Respiratory syncytial virus vaccination among US adults aged ≥60 years.

Respiratory syncytial virus (RSV) vaccines have been recommended for US adults aged ≥60 years for nearly one year. However, the extent of vaccination coverage and the factors influencing uptake remain underexplored. This study aimed to evaluate national and state-specific RSV vaccination coverage among US adults aged ≥60 years and to identify demographic and socioeconomic factors that influence vaccination uptake. This cross-sectional study analyzed data from the US Census Household Pulse Survey, from January 9 to March 4, 2024, which included 49,322 adults aged ≥60 years. Participants self-reported their RSV vaccination status along with demographic and socioeconomic information. Multivariable Poisson regression was used to identify factors associated with vaccination uptake. As of March 4, 2024, RSV vaccination coverage among US adults aged ≥60 years was estimated at 22.2% (95% CI, 21.6%-22.7%). State-specific vaccination coverage ranged from 12.4% in Mississippi to 33.1% in Colorado. Coverage was higher in older age groups, with 21.2% among those aged 65-69 years, 28.1% among 70-74 years, 31.9% among 75-79 years, and 30.2% among those aged ≥80 years, compared to 10.8% among those aged 60-64 years. Racial/ethnicity disparities were evident, with lower coverage among Hispanics (18.3%) and non-Hispanic Blacks (17.6%) compared to non-Hispanic Whites (23.3%). Coverage was similar between males (22.6%) and females (21.8%). Factors positively associated with vaccination included older age, higher education level, higher household income, unemployment, and residing in the Midwest or West. Conversely, being non-Hispanic Black, uninsured, experiencing financial difficulties, and lack of COVID-19 vaccination were associated with reduced likelihood of receiving the RSV vaccine. During the first RSV season after the recommendations for RSV vaccination in US adults aged ≥60 years, uptake shows promise but is marked by significant disparities related to race/ethnicity, socioeconomic status, and geographic location. Efforts to address these disparities are crucial to enhance vaccination coverage and reduce the impact of RSV on this vulnerable population.

Unveiling the link between self-regulated learning and academic success: a longitudinal study on Chinese medical students.

Self-regulated learning (SRL) has been the focus of medical education, with the belief that mastering effective SRL strategies can significantly contribute to the academic achievements of medical students, thereby establishing a robust foundation for their future clinical practice. Despite the importance of SRL, empirical evidence linking SRL to academic performance has been mixed. This study aims to quantify the association between SRL and the academic performance of medical students. A one-year longitudinal study was conducted consisting of two waves separated by one year, wherein students from China Medical University who were enrolled in the clinical medicine program in 2018 were randomly selected and followed. Participants provided socioeconomic information in the first wave and completed the Self-regulated Learning Perception Scale (SRLPS) in both waves. Participants' academic performance was assessed using Grade-point Average (GPA) scores for the following year. Fixed-effects models were utilized to address potential endogeneity issues when investigating the association between SRL and academic performance based on the longitudinal data collected. The final sampled data consisted of 395 medical students who completed two rounds of questionnaires. The overall score of SRL was positively correlated with academic performance insignificantly (β = 0.007, SE = 0.004). However, the domain, Learning motivation and action, was significantly positively correlated with academic performance (β = 0.052, SE = 0.020), while the other domain, Lack of self-directedness, was significantly negatively correlated with academic performance (β=-0.037, SE = 0.009). Enhancements in certain self-regulated learning behaviors, like cultivating intrinsic motivation for learning, positively correlated with the academic performance of Chinese medical students. However, the negative correlation with self-directedness in learning highlights the multifaceted nature of SRL and the complexity of its relationship with students' educational outcomes.

The Frequency and Main Characteristics of Obesity in Undocumented Migrants Receiving Medical Assistance from a Charitable Organisation in Italy.

Obesity is continually growing not only in medium- and high-income countries but also in low-income countries, from where increasing numbers of migrants arrive in Western countries. We aimed to investigate the frequency and characteristics of obesity in a sample of undocumented migrants, a population for which official health data are not available. We collected demographic and socio-economic data and information on medical diagnoses and pharmacologic treatments for 341 undocumented migrants consecutively attending the outpatient clinic of a big non-governmental organisation in Milan, Italy, from March to July 2023. To measure obesity, we used both body mass index (BMI) and waist circumference (WC). We used multivariate robust Poisson regression models to calculate prevalence ratios (PRs) and 95% Confidence Intervals (CIs) of overweight or obesity according to socio-demographic conditions and other risk factors. Using BMI, the proportion of migrants with obesity was 28.7% (95% CI 24.0-33.0%) and those with overweight represented 32.3% (95% CI 27.3-37.5%). Obesity was more frequent among Asians (53.9%, 95% CI 37.2-69.9%), followed by Latinos (38.7%, 95% CI 29.6-48.5%) and Eastern Europeans (38.2%, 95% CI 25.4-52.3%). Using WC, 68.3% (95% CI 63.1-73.2%) of migrants had values suggestive of overweight or obesity. In the multivariate analyses, overweight and obesity were more frequent in migrants with older age, with a stable employment, and who had been present in Italy for a long time, as well as in those with CV diseases. Moreover, individuals with obesity needed more medications for the cardiovascular system and for the alimentary tract and metabolism. In our sample of undocumented migrants, overweight and obesity were frequent, representing an important public health issue, considering the difficulty experienced by such individuals in finding access to both prevention and healthcare services.

A Nutrition Education Intervention Positively Affects the Diet-Health-Related Practices and Nutritional Status of Mothers and Children in a Pulse-Growing Community in Halaba, South Ethiopia.

We conducted a six-month nutrition education intervention focused on the consumption of pulses and other foods to assess the effect on knowledge, attitude and practice (KAP) as well as the nutritional status of children and mothers from two pulse-growing communities in Halaba, south Ethiopia. About 200 mother-child pairs in each of two purposively selected communities participated in this intervention study. A six-month nutrition education programme, involving interactive monthly community meetings and home visits, was offered to one of the two communities and the other served as a control/comparison. This study incorporated the use of Health Belief Model constructs to assess the KAP/perceptions of mothers surrounding pulse and other food consumptions, as well as nutrition-related issues before and after the intervention. Objective measures included dietary diversity scores (DDSs), one-day weighed dietary intakes and nutritional status measures based on anthropometric information. Demographics and socioeconomic information were also collected at baseline and endline. Significant improvements (p < 0.05) were found in the intervention group on the KAP and perceptions of pulse nutrition benefits among mothers, DDSs and pulse and animal source food consumption indexes for mothers and children and the mean body-mass-index-for-age Z-score and wasting among children. Community-based nutrition education interventions involving monthly interactive community meetings and home visits in pulse-growing communities from a resource-poor country like Ethiopia can be effective in improving mothers' knowledge of pulse nutrition and consumption frequency, leading to improvements in the DDSs of children and mothers while decreasing child underweight and wasting.

Machine learning-based prediction of elevated N terminal pro brain natriuretic peptide among US general population.

Natriuretic peptide-based pre-heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre-heart failure, has not been well established. This study evaluated the performance of machine learning prediction models for predicting elevated N terminal pro brain natriuretic peptide (NT-proBNP) levels in the US general population. Individuals aged 20-79years without cardiovascular disease from the nationally representative National Health and Nutrition Examination Survey 1999-2004 were included. Six prediction models (two conventional regression models and four machine learning models) were trained with the 1999-2002 cohort to predict elevated NT-proBNP levels (>125pg/mL) using demographic, lifestyle, and commonly measured biochemical data. The model performance was tested using the 2003-2004 cohort. Of the 10237 individuals, 1510 (14.8%) had NT-proBNP levels >125pg/mL. The highest area under the receiver operating characteristic curve (AUC) was observed in SuperLearner (AUC [95% CI]=0.862 [0.847-0.878], P<0.001 compared with the logistic regression model). The logistic regression model with splines showed a comparable performance (AUC [95% CI]=0.857 [0.841-0.874], P=0.08). Age, albumin level, haemoglobin level, sex, estimated glomerular filtration rate, and systolic blood pressure were the most important predictors. We found a similar prediction performance even after excluding socio-economic information (marital status, family income, and education status) from the prediction models. When we used different thresholds for elevated NT-proBNP, the AUC (95% CI) in the SuperLearner models 0.846 (0.830-0.861) for NT-proBNP>100pg/mL and 0.866 (0.849-0.884) for NT-proBNP>150pg/mL. Using nationally representative data from the United States, both logistic regression and machine learning models well predicted elevated NT-proBNP. The predictive performance remained consistent even when the models incorporated only commonly available variables in daily clinical practice. Prediction models using regularly measured information would serve as a potentially useful tools for clinicians to effectively identify targets of natriuretic-peptide screening.

Socioeconomic determinants of malaria and hepatitis infections: insights from the Federal Medical Center, Makurdi, North Central, Nigeria

BackgroundMalaria and hepatitis are prevalent public health issues in Nigeria, significantly impacting health outcomes. Given the importance of the Federal Medical Center, Makurdi, as a key healthcare provider in the region, it is crucial to understand the prevalence and factors associated with these diseases within this setting. This study is designed to address this need, aiming to evaluate the prevalence and risk factors of malaria and hepatitis B and C among patients at the Federal Medical Center, Makurdi.MethodologyA cross-sectional study design was employed, involving the meticulous analysis of patient records and diagnostic data from the Federal Medical Center, Makurdi. Data on malaria and hepatitis B and C prevalence were collected from laboratory reports and patient interviews. Socioeconomic information, including income, education level, and healthcare access, was also gathered. Statistical analyses were performed with utmost care to identify associations between disease prevalence and risk factors.ResultsThe study examined 248 patients at the Federal Medical Centre, Makurdi, assessing malaria, Hepatitis B surface antigen (HBsAg), and Hepatitis C virus (HCV). Malaria prevalence was 52.4%, with higher rates in males (57.7%) than females (47.2%). HBsAg prevalence was 6.9%, and HCV was 4.8%, with no significant differences by sex or marital status. Income level impacted HCV rates, with middle-income individuals showing higher prevalence (21.4%). Malaria was most common in the 26–40 age group (35.4%).DiscussionThe findings underscore the need for targeted public health interventions. Enhanced access to preventive measures, such as insecticide-treated nets and safer blood transfusion practices, and educational disease prevention programs, are essential. Addressing socioeconomic disparities is crucial for effective disease control.ConclusionTo mitigate the burden of malaria and hepatitis B and C at the Federal Medical Center, Makurdi, a multifaceted approach is required. Improving preventive measures, healthcare access, and addressing socioeconomic determinants will reduce disease prevalence and improve patient outcomes.

Comparison of SVM, KNN, and Naïve Bayes Classification Methods in Predicting Student Transfers at BK Palu School

Student transfers are a significant issue in schools and can affect the dynamics of education and student performance. This research aims to predict student transfers using a comparative analysis of three classification methods: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. The study utilizes historical data from BK Palu School, covering the years 2022 to 2024, which includes demographic, academic, socio-economic, and student quality information. The methodology involves data collection, data preparation, algorithm selection, implementation, and evaluation of the three methods. The performance of the classification methods is assessed using metrics such as accuracy, precision, recall, and F1-score. The results indicate that SVM has the highest accuracy in predicting student transfers, followed by KNN and Naïve Bayes. This study contributes to identifying key factors influencing student transfers and offers schools a robust model to develop targeted strategies for reducing transfer rates. Ultimately, this research provides insights into optimizing student retention and improving the overall quality of education.

Socioeconomic status, demographic factors and dietary quality of Lifeways study grandparents mapped from the 1948 National Nutrition Survey: associations with growth outcomes in their grandchildren at birth, 5, and 10 years

Early-life experiences play a crucial role in shaping later-life outcomes, potentially impacting the health of multiple generations(1). The 'Developmental Origins of Health and Disease (DOHaD)' theory underscores the significance of early environmental factors in influencing immediate and future well-being(2); evidence dating back to the 1980s links exposures such as malnutrition to epigenetic changes and the development of chronic diseases(2). Despite increasing interest in multigenerational associations, further empirical evidence is needed to fully understand the connection between grandparents’ early-life experiences and the growth outcomes of their grandchildren. The objective of this research was to examine the relationship between grandparents’ occupation group, population dietary quality (PDQ), and geographical area of residence in early-life and their grandchildren’s growth outcomes at birth, 5- and 10-years, and to illustrate how the mapping of data from previous studies to recent studies based on socioeconomic status and demographic information can offer significant insights into health outcomes.Data from the Lifeways Cross-Generational Cohort Study (2001-2013)(3,4) were used to identify children with at least one grandparent born after the foundation of the Irish Free State; thus, ranging from infancy to young adulthood when the 1948 National Nutrition Survey (NNS)(5) was conducted. The data from the grandparents of these children were then linked to the NNS categories of occupation group, PDQ and geographical area, based on information collected through the Lifeways baseline questionnaire. Associations between socioeconomic factors pertaining to maternal and paternal grandparents’ and grandchildren’s growth outcomes were tested statistically.Significant associations were observed between female grandchildren’s waist-z-scores (WACZ) at age 10 and the occupational group of the paternal grandmother (p = 0.030). Female grandchildren of skilled workers exhibited larger median waist circumferences (67 cm) compared to those with professional paternal grandmothers (58.6 cm) (p = 0.049). Poor PQD in both maternal grandmothers and grandfathers was positively associated with ponderal index (PI) in their female grandchildren (p = 0.022, r = 0.169; p = 0.002, r = 0.252 respectively) but not in their male grandchildren (p = 0.176, r = 0.108; p = 0.800; r = 0.022, respectively). Conversely, good PQD in maternal grandparents was inversely associated with PI in their grandchildren (p = 0.041, r = −0.111; p = 0.035, r = −0.125). No association was observed between the early-life geographical area of residence of grandparents and their grandchildren’s growth outcomes at any age.This study contributes insights into the relationship between the socioeconomic factors and population dietary quality of grandparents and growth outcomes in their grandchildren. The study highlights the importance of grandparents’ early-life experiences in shaping some growth outcomes in their grandchildren. This research also highlights how the mapping of data from prior studies to recent studies based on socioeconomic status and demographic data can provide valuable insights into health outcomes. Further research using this approach could lead to meaningful public health findings and is required to corroborate these study findings.

The Effects of War-Related Stress on Human Development: Differences in Body Proportions of Polish Women Born Before and During World War II.

This study aims to explore the lasting effects of stress experienced by pregnant women during World War II (WWII) on body and head measurements of their adult daughters. The research sample consists of 336 female university students born in Poland between 1925 and 1951. The data include body measurements and socioeconomic information (parental occupation and number of siblings) acquired from questionnaires collected between the 1950s and 1970s. Student's t-test, Mann-Whitney test and Analysis of Variance were used to analyze differences in body measurements between groups of women born before and during the war, as well as the possible influences of socioeconomic variables. The mean measurements of body height, symphysion height, and waist circumference were lower in women conceived and born during the war compared to those born in the pre-war period. In contrast, the mean measurements of biacromial (shoulder) width, trunk length, and three head dimensions were higher in women conceived and born during the war. Additionally, the number of siblings appeared to be a significant factor that may have influenced the body measurements of women in both groups. For instance, a higher number of living siblings, particularly sisters, was associated with reduced body dimensions, such as body height and waist circumference, while a greater number of deceased siblings was linked to an increase in certain body dimensions. The results suggest that war-related prenatal conditions may have influenced the postnatal growth and development of women conceived and born during the war. Notably, the direction of these changes varied, which indicates that the growth response to the war-related conditions was a complex adaptation, reflecting both positive and negative changes in different body parts, rather than a uniform pattern of growth suppression.

Discover the Factors of Longevity by Causal Discovery with PC Algorithm and Neural Networks

The study title has been to study the multilayered factors that govern a human prospect of living through the application of the PC Algorithm (Graph-Based Algorithm), logistic regression analysis, and the neural networks in combination. To realign the research purpose, a vast array of data can be used, which included socioeconomic status information alongside health and environmental aspects carefully chosen for their likely influence on how long people can live. This article will explain the longevity determinants by examining not only the conditions but also the environmental and health indicators and their combinatory effects. The PC algorithm can produce a directed acyclic graph (DAG) to see how elements like healthcare spending, education, and CO2 emissions are correlated with human longevity and how they influence each other. Moreover, the research takes other examples by analysis: researchers can examine hypothetical events. Neural networks were employed to depict the interrelationship among these variables which subsequently provided a user-appropriate understanding of their sudden joint influence. Consequently, a hypothesize was purposed that the potential interventions for the elongation of human life. Policy-making researchers can gain a better representation of the situation of health inequality and the new recommended solutions to tackle this problem and increase life expectancy by implementing complex analysis tools to blows away the criminal aspects of human longevity.

Prescription time trends in patients with high-impact chronic pain: A National Patient Registry Study.

High-impact chronic pain (HICP), defined as chronic pain with a significant impact on daily function, affects approximately 8% of the Western population. In Denmark, HICP still remains to be described at the population level. Some patients with HICP are referred to the Danish pain centres, where they are registered with a procedural code. We conducted a nationwide registry-based study of all Danish patients registered with a visit to a pain centre from January 2005 to March 2022, to explore time trends in the prescription of analgesics and sedatives in this HICP subpopulation. Furthermore, data on socioeconomics and hospital diagnoses are reported. We used data from the Central Person Registry, the Danish National Patient Registry, the Danish National Prescription Registry, combined with data on socioeconomic information from Statistics Denmark. Data was collected on prescriptions 3 months before the first visit to a pain centre. Prescriptions were stratified into five calendar periods: before 2011, 2011-2013, 2014-2016, 2017-2019, and 2020-2022. We identified 66,577 patients referred to a pain centre. Over 15 years, prescriptions before referral declined for opioids (from 53.2% to 31.7%), NSAIDs (from 28.3% to 23.5%), antidepressants (from 20.5% to 16.8%), anxiolytics (from 12.3% to 3.2%), and sleep medication (from 15.8% to 7.6%). In contrast, prescriptions increased for paracetamol (from 31.1% to 48.9%) and gabapentinoids (from 19.2% to 27.7%). In patients with HICP visiting Danish pain centres, prescriptions before referral decreased for opioids over 15 years, with a simultaneously increased prescription of gabapentinoids (gabapentin and pregabalin). This nationwide study of 66,577 Danish patients with high-impact chronic pain reveals a significant decrease in filled opioid prescriptions over the past 15 years, with a simultaneous rise in gabapentinoid use before referral to pain centres. These findings suggest a shift in clinical practice towards alternative pain management strategies. The study underscores the need for continued research into the long-term effects of these changes and their impact on patient outcomes.

Intersectional inequalities in child social-emotional health: a case for proportionate universalism

Abstract Background Social-emotional difficulties in early childhood are associated with a range of outcomes across the life course and are related to socioeconomic factors. The aim of this study was to examine intersectional inequalities in social-emotional problems in preschool children relating to their parents’ income, education and country of birth in addition to investigating the public health implications. Methods This population-based study with a repeated cross-sectional design in the Västerbotten County of Sweden used the Ages and Stages Questionnaires: Social-Emotional (ASQ:SE) for children aged 3 in child health care services over the years of 2014-2018 and socio-economic information from national population registers. The effective sample of 8,823 individuals was analyzed using additive binomial regression in combination with an analysis of individual heterogeneity and discriminatory accuracy (AIHDA) approach to estimate risk differences for social-emotional problems across 27 intersectional groups and discriminatory accuracy. Results Average risk differences generally increased in the groups where multiple dimensions of social inequality intersected, with risk differences as high as 18% (95% CI 8 to 28%) and 25% (95% CI 14 to 37%) compared to the most advantaged category. The discriminatory accuracy of all three included regression models was estimated as moderate, but improved in a slight but statistically significant way with the addition of social inequalities. Conclusions This study increases our understanding of intersectional and social inequalities in social-emotional problems in preschool children. It supports the need for universal public health policies in addition to policies targeting more vulnerable groups when addressing this issue, consistent with the concept of proportionate universalism. An intersectional research perspective including discriminatory accuracy could increase our knowledge of health inequities and improve public health effectiveness. Key messages • Intersecting dimensions of social inequalities are associated with an increased risk of social-emotional problems in children aged 3. • Universal public health policies are needed in addition to policies targeting vulnerable population groups with regards to social-emotional problems in young children.

A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations

Student dropout rates can have a significant negative impact on both the development of educational institutions and the personal growth of students. Consequently, many institutions are focused on identifying key factors that contribute to dropout and implementing strategies to mitigate them. This study aims to predict student dropout rates using classical machine learning algorithms while analyzing the key factors influencing these outcomes in higher education. The dataset includes demographic, socioeconomic, and academic information from various sources. Additionally, the study leverages the Local Interpretable Model-Agnostic Explanations (LIME) model to provide insights into the predictions, offering a clearer understanding of the factors driving dropout decisions. This knowledge is crucial for identifying influential factors and, more importantly, enhancing early intervention strategies and policies in educational settings, ultimately reducing dropout rates.

Mapping the interplay of work-arrangement, residential location, and activity engagement within an integrated model

Virtual activities, such as teleworking have been identified as major travel demand management strategies to tackle traffic congestion and emission. However, integrated models, which are capable of testing such strategies, have not yet been properly extended to capture emerging activity patterns. This study fills-up the literature gap by implementing individuals’ work-arrangement and introducing in-home (IH) virtual activities within an integrated Transport, Land-use, and Emission (iTLE) modeling framework. First, it conducts a household travel survey, which collected socio-economic, activity-travel, and work-related information through a questionnaire survey in the Halifax Regional Municipality (HRM), Nova Scotia, Canada. Next, it develops a work-arrangement choice model based on the mixed-logit modelling (MXL) approach and implements it within iTLE. After that, individuals’ daily activity programs are generated in a sequential manner utilizing a Markov Chain Monte Carlo (MCMC) modelling approach considering their work-arrangement, employment, and vehicle ownership. To demonstrate the application of the developed tool, individuals’ socio-demographics, residential-location, work-location, and activity-participation are longitudinally simulated up to 2031. The analysis of teleworking parameters revealed distinct clusters and individual preferences, emphasizing the significance of personalized approaches in formulating teleworking strategies. Most non-teleworkers stay closer to downtown than full-teleworkers while clustered behavior is observed among hybrid workers for residential location choice. Increase is observed for shopping, dining-out, and IH maintenance/discretionary activities while decrease is observed for out-of-home work. The outcomes of this paper will be helpful for policymakers and transport researchers to understand the evolution of work-arrangement and examine subsequent impacts on transportation and land-use systems.

Socioeconomic Determinants of Household Access to HIV/AIDS Healthcare in Kibra Sub-County, Nairobi County, Kenya

This study examined the impact of social and economic factors on the uptake of HIV/AIDS care in Kibra Sub County, Nairobi. The research was grounded in Behavioral theory and focused on how household income, literacy levels, and access to health information influenced the adoption of HIV/AIDS treatment. A descriptive survey design approach was employed, involving a sample of 365 households chosen through simple random and stratified sampling techniques from target population of 61,690 households, as outlined by Krejcie and Morgan's formula. Data collection was carried out using key informant interviews and self-administered questionnaires. Descriptive techniques, such as frequency and median analysis, as well as inferential techniques, such as bivariate correlation analysis, chi-square testing, and binomial logistic regression, were used. The results were displayed using tables, graphs, and percentages. Thematic analysis was also used to examine qualitative data in order to find new topics. The analysis revealed a strong positive relationship between access to health information and the likelihood of seeking treatment. Households with higher income and literacy levels were more likely to pursue HIV/AIDS care. Quantitative results showed that access to HIV-related information increased the likelihood of treatment uptake by 6.666 times. Additionally, increased household income and higher literacy levels were linked to a 7.434-fold and 3.554-fold rise, respectively, in the probability of seeking care. The study concluded that socio-economic factors, particularly income, literacy, and information access, significantly influenced healthcare-seeking behaviors in Kibra. To enhance access to HIV/AIDS treatment, it is recommended that policymakers, NGOs, and other stakeholders implement targeted interventions, including income-generating programs and comprehensive health education, to address the socio-economic barriers that hinder healthcare uptake.

Cohort Profile: TRacing Etiology of Non-communicable Diseases (TREND): Rationale, Progress and Perspective

AbstractThe TRacing Etiology of Non-communicable Diseases (TREND) cohort is a prospective longitudinal cohort and biobank that is mainly based in Ma’anshan, Anhui Province, China. The primary aim of the study is to decipher comprehensive molecular characterization and deep phenotyping for a broad spectrum of chronic non-communicable diseases (NCDs), which focuses on providing mechanistic insights with diagnostic, prognostic and therapeutic implications. The recruitment was initiated in 2023 and is expected to complete in 2025 with 20,000 participants originated from urban and rural area. In the first phase, 3360 participants were recruited. Follow-up visits are scheduled annually and intervally for a total of 30 years. The cohort includes individuals aged over 18 years. Two participants with first-degree linkage were recruited from a household. The age distribution of recruited participants was stratified into four categories: 18–45, 45–55, 55–65, and ≥65 years, aligning with the population proportions of Ma’anshan. Meanwhile, the gender distribution was controlled by pairing men and women from the same household. Data collected at baseline includes socio-economic information, medical history, lifestyle and nutritional habits, anthropometrics, blood oxygen, electrocardiogram (ECG), heart sound, as well as blood, urine and feces tests results. Molecular profiling includes genome, proteome, metabolome, microbiome and extracellular vesicles -omics. Blood, urine and fecal samples are collected and stored at −80 °C in a storage facility for future research.

Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method

ObjectiveThis paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using machine learning techniques. By identifying the most important predictors of depressive symptoms through the analysis, the findings can have important implications for early depression detection and intervention. ParticipantsFor our cross-sectional study, we recruited a total of 976 volunteers, with a specific focus on individuals aged 50 and above. Each participant was requested to provide their demographic, socioeconomic information and undergo several physical health tests. Additionally, they were asked to respond to questionnaires that assessed their mental well-being. Furthermore, participants were requested to maintain an activity log for a continuous 14-day period, starting from the day after they signed up. They had the option to use either a provided mobile application or paper to record their activities. MethodsWe evaluated multiple machine learning models to find the best-performing one. Subsequently, we conducted post-hoc analysis to extract the variable significance from the selected model to gain deeper insights into the factors influencing depression. ResultsLogistic Regression was chosen as it exhibited superior performance across other models, with AUC of 0.807 ± 0.038, accuracy of 0.798 ± 0.048, specificity of 0.795 ± 0.061, sensitivity of 0.819 ± 0.097, NPV of 0.972 ± 0.013 and PPV of 0.359 ± 0.064. The top influential predictors identified in the model included loneliness, health indicator (i.e. frailty, eyesight, functional mobility), time spent on activities (i.e. staying home, doing exercises and visiting friends) and perceived income adequacy. ConclusionThese findings have the potential to identify individuals at risk of depression and prioritize interventions based on the influential factors. The amount of time dedicated to daily activities emerges as a significant indicator of depression risk among middle-aged and elderly individuals, along with loneliness, physical health indicators and perceived income adequacy.