The Impact of COVID-19 Pandemic on Weight and Body Mass Index in Saudi Arabia: A Longitudinal Study

The COVID-19 pandemic has had a major impact on various health conditions. The objective of this study was to assess the impact of the COVID-19 pandemic on body weight and body mass index (BMI) in Saudi Arabia. We used electronic health records obtained from a healthcare system representing five hospitals in three different regions in the Kingdom to examine the change in weight utilizing a longitudinal design. The study included all adults who had visited outpatient clinics in two different time points, pre-2020 (years 2018 and 2019 prior to COVID-19) and post-2020 (the year 2021). Weight and BMI changes in percentages were described. Also, bivariate chi-square test, paired t-test, and multivariable multinomial logistic regression model were used for the analyses. A total of 165,279 individuals were included in the study. On average, a significant weight gain of 0.33 kg (95% CI: 0.29–0.36) was observed in our study. Approximately 10% of the population had shifted to either overweight or obese BMI classes during the study period, as 4.8% of those with normal BMI pre-2020 had shifted to overweight or obese classes at post-2020, and 5.1% of those who were overweight had shifted to obese class. Also, 23.1% of the population had gained 5% or more of their pre-2020 weight, while 17% had lost 5% or more. Young individuals were over three times more likely to gain 5% or more than older individuals (OR: 3.34; 95% CI: 3.12–3.56). Females had 24% higher odds to gain 5% or more of their pre-2020 weight than males (OR: 1.24; 95% CI: 1.21–1.27). Diabetics were 27% more likely to lose 5% or more than non-diabetics (OR: 1.27; 95% CI: 1.23–1.31). Our findings provide insights into the impact of COVID-19 on weight and population health. Further investment in interventions for weight management is warranted during similar circumstances such as lockdowns due to infection waves or new variants. Future studies are also needed to explore the modifications that have occurred during the pandemic in the weight-related lifestyle factors such as dietary choices and physical activity levels.

Is Leave for Fathers Pronatalist? A Mixed-Methods Study of the Impact of Fathers’ Uptake of Parental Leave on Couples’ Childbearing Intentions in South Korea

While many countries with low birth rates have implemented policies incentivizing fathers to take parental leave with the anticipation that it will contribute to raising birth rates, there is scant research empirically testing whether fathers’ uptake of leave is pronatalist. Existing research is limited to a few European (mostly Nordic) countries, and it is unclear whether there exists a positive causal relationship. Using mixed methods, this paper seeks to explore the processes and mechanisms by which fathers’ uptake of parental leave impacts intentions for additional children in South Korea, a country characterized by lowest-low fertility and low but rapidly expanding uptake of leave by fathers. Results based on multinomial logistic regression models suggest that in comparison to fathers who expect to take their first leave shortly, fathers with leave experience are less likely to report couple-level intentions for another child, significantly so at parity two. Interviews of fathers with parental leave experience confirm that fathers attenuate their fertility intentions downwards in light of the difficulties of childcare during their leave. While these intentions may change further down the line and/or couples may decide to continue an unplanned pregnancy, results suggest that fathers’ parental leave has an anti- rather than pronatalist effect in South Korea. This study demonstrates that in countries with poor support for the reconciliation of employment and childcare, equalizing the gendered division of parental leave may not be sufficient to see a reversal in its fertility trends.

Linked Lives: Does Disability and Marital Quality Influence Risk of Marital Dissolution among Older Couples?

Using fourteen waves of data from the Health and Retirement Study (HRS), a longitudinal panel survey with respondents in the United States, this research explores whether marital quality—as measured by reports of enjoyment of time together—influences risk of divorce or separation when either spouse acquires basic care disability. Discrete-time event history models with multiple competing events were estimated using multinomial logistic regression. Respondents were followed until they experienced the focal event (i.e., divorce or separation) or right-hand censoring (i.e., a competing event or were still married at the end of observation). Disability among wives was predictive of divorce/separation in the main effects model. Low levels of marital quality (i.e., enjoy time together) were associated with marital dissolution. An interaction between marital quality and disability yielded a significant association among couples where at least one spouse acquired basic care disability. For couples who acquired disability, those who reported low enjoyment were more likely to divorce/separate than those with high enjoyment; however, the group with the highest predicted probability were couples with low enjoyment, but no acquired disability.

KLASIFIKASI REGRESI LOGISTIK MULTINOMIAL DAN FUZZY K-NEAREST NEIGHBOR (FK-NN) DALAM PEMILIHAN METODE KONTRASEPSI DI KECAMATAN BULAKAMBA, KABUPATEN BREBES, JAWA TENGAH

The characteristics of society in choosing contraceptive methods are also the crucial factors for the government to prepare the family planning services needed at Bulakamba District, Brebes Regency, Central Java. In this case, a classification process needs to be done to assist the process of classifying the characteristics of society in the selection of contraceptive methods. Multinomial Logistic Regression classification is good in exploring data information meanwhile Fuzzy K Nearest Neighbor (FK-NN) classification is good for handling big data and noise. These two methods used in this study because they are relevant to the data applied and will be compared their classification accuracy through APER and Press's Q calculations.The classification accuracy results obtained based on the APER calculation for Multinomial Logistic Regression is 88,25% and Fuzzy K Nearest Neighbor (FK-NN) is 88,92%. Meanwhile, the Press's Q value of both methods are 9600,945 and 9518,014 greater than χ 2𝛼,1 which is 3,841, so that it is statistically accurate. Based on the results obtained, it can be concluded that Multinomial Logistic Regression classification method has a better classification accuracy than Fuzzy K Nearest Neighbor (FK-NN) method.

A robust event-driven approach to always-on object recognition

<div> <div> <div> <p>We propose a neuromimetic architecture able to perform always-on pattern recognition. To achieve this, we extended an existing event-based algorithm [1], which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events acquired by a neuromorphic camera, these time surfaces allow to code the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we extended this method to increase its performance. Our first contribution was to add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns [2]. A second contribution is to draw an analogy between the HOTS algorithm and Spiking Neural Networks (SNN). Following that analogy, our last contribution is to modify the classification layer and remodel the offline pattern categorization method previously used into an online and event-driven one. This classifier uses the spiking output of the network to define novel time surfaces and we then perform online classification with a neuromimetic implementation of a multinomial logistic regression. Not only do these improvements increase consistently the performances of the network, they also make this event-driven pattern recognition algorithm online and bio-realistic. Results were validated on different datasets: DVS barrel [3], Poker-DVS [4] and N-MNIST [5]. We foresee to develop the SNN version of the method and to extend this fully event-driven approach to more naturalistic tasks, notably for always-on, ultra-fast object categorization. </p> </div> </div> </div>

Transitions in Social Network Types over Time among Older Adults

<b><i>Objective:</i></b> Network typology studies have identified heterogeneous types of older adults’ social networks. However, little is known about stability and change in social network types over time. We investigate transitions in social network types among older adults, aged 60 years and older, and factors associated with such transitions. <b><i>Methods:</i></b> We used data on 1,305 older adults, participating in 2 waves of a national, longitudinal survey, conducted in 2016–2017 and 2019, in Singapore. Latent transition analysis identified the distinct types of social networks and their transition patterns between the waves. Multinomial logistic regression examined the association of baseline and change in physical, functional, and mental health and baseline sociodemographic characteristics with network transitions into more diverse or less diverse types. <b><i>Results:</i></b> We found 5 social network types at both waves, representing the most to the least diverse types – diverse, unmarried and diverse, extended family, immediate family, and restricted. Between waves, about 57% of respondents retained their social network type, whereas 24% transitioned into more diverse types and 19% into less diverse types. Those who were older and less educated and those with worsening functional and mental health were more likely to transition into less diverse types versus remaining in the same type. <b><i>Discussion:</i></b> The findings capture the dynamics in social network composition among older adults in the contemporary aging society. We highlight sociodemographic and health disparities contributing to later life social network diversity.

A robust event-driven approach to always-on object recognition

<div> <div> <div> <p>We propose a neuromimetic architecture able to perform always-on pattern recognition. To achieve this, we extended an existing event-based algorithm [1], which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events acquired by a neuromorphic camera, these time surfaces allow to code the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we extended this method to increase its performance. Our first contribution was to add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns [2]. A second contribution is to draw an analogy between the HOTS algorithm and Spiking Neural Networks (SNN). Following that analogy, our last contribution is to modify the classification layer and remodel the offline pattern categorization method previously used into an online and event-driven one. This classifier uses the spiking output of the network to define novel time surfaces and we then perform online classification with a neuromimetic implementation of a multinomial logistic regression. Not only do these improvements increase consistently the performances of the network, they also make this event-driven pattern recognition algorithm online and bio-realistic. Results were validated on different datasets: DVS barrel [3], Poker-DVS [4] and N-MNIST [5]. We foresee to develop the SNN version of the method and to extend this fully event-driven approach to more naturalistic tasks, notably for always-on, ultra-fast object categorization. </p> </div> </div> </div>

Perceived social support is greater among food secure Iranian households during the COVID-19 outbreak

Purpose Coronavirus Disease 2019 (COVID-19) pandemic has affected many different aspects of people’s lives around the world, including household food security. This study aims to investigate the food security status and its determinants, with emphasis on perceived social support among the Iranian population during the epidemic.. Design/methodology/approach This cross-sectional study was conducted among 2,871 Iranian adults by social media in all provinces of the country. Demographic and socioeconomic information, household food security status and perceived social support status were assessed by the validated questionnaires. Data was analyzed by statistical package for the social sciences version 22.0, with one-way ANOVA, Chi-square and multinomial logistic regression tests. Findings The prevalence of food security among the studied population was 55.2%. The results indicated that perceived social support plays a protective role on food security [odds ratio (OR) = 1.07, confidence interval (CI) = 1.06, 1.09, P-value < 0.001]. Reduced income during the epidemic [OR = 0.29, CI = 0.17, 0.47, P-value < 0.001] and presence of an old person (>65 years old) in the household [OR = 1.72, CI = 1.14, 2.60, P-value < 0.05], were significantly higher among moderate to severe food insecure group than food-secure group. More monthly income [OR = 0.28, CI = 0.13, 0.57, P-value < 0.001] and homeownership [OR = 1.83, CI = 1.22, 2.75, P-value < 0.05] were also predictors of food security status. Originality/value The development of supportive strategies which act immediately can protect vulnerable people against the consequences of the epidemic, including food insecurity. Long-term planning should also be considered to improve society’s resistance against such disasters.

THE INFLUENCE OF SOCIOECONOMIC STATUS ON THE MODES CHOICE OF TRANSPORTATION IN MANADO CITY

Good infrastructure and transportation facilities move people and goods take place safely and economically in terms of time and cost. The trips made by people on weekdays or weekends affect environmental conditions in the area. The purpose of this paper is to find out the influence of socioeconomic status on modes choice of transportation both on weekdays and weekends. The study location is in Manado Municipality. There are 3 (three) modes of transportation reviewed, i.e. private cars, motorcycles, and public transportation. Indicators of socioeconomics status of transportation users are age, education, occupation, income, number of family members, and vehicle ownership. Data regarding the modes of transportation and socioeconomic status of travelers were obtained through questionnaire surveys. SEM-AMOS was used to measure the validity and reliability of the data. The probability of the mode choice on weekdays and weekends was analyzed using multinomial logistic regression analysis. The results showed that the socioeconomic status of the traveler has an influence on the mode choice of transportation by 49.2% on weekends and 49.5% on weekdays. Furthermore, the probability of transportation mode choice on weekends is the car by 88.4%, and on weekdays is motorcycles by 71.6%.

Impact of Crop Diversification on Household Food and Nutrition Security in Southern and Central Mali

Many African countries, including Mali, depend on the production of a single or a limited range of crops for national food security. In Mali, this heavy reliance on a range of basic commodities or staple crops, or even just one, exacerbates multiple risks to agricultural production, rural livelihoods, and nutrition. With this in mind, the smart food campaign was initiated to strengthen the resilience and nutritional situation of households and peasant communities where the diet is mainly cereal-based and remains very undiversified and poor in essential micronutrients. As part of the campaign, our study aims to analyze the impact of agricultural diversification on food consumption and household nutritional security. The analysis uses survey data from 332 individuals randomly selected. Multinomial logistic regression and the Simpson diversity index were used to determine the index and estimate the determinants of crop diversification. The consumption score index weighted by consumption frequency and anthropometric indices (for children) were used to assess the nutritional status of households. The results show four types of strategies of diversification: 7.55% are cereals only, 5.66% combine millet–sorghum–groundnut, 41.51% combine millet–sorghum–groundnut–cowpea, and 45.28% combine millet–sorghum–groundnut–cowpea–maize. The estimation of the regression model shows that socioeconomic factors have a positive influence. With a consumption score index of 34 in the villages and 40.5 in Bamako, based on eight food groups, we find that the quality of food is insufficient in rural areas, but it is acceptable in the urban center of Bamako. Analysis of the nutritional status of children aged 6–48 months reveals that 30% of the surveyed population is in a situation of nutritional insecurity (all forms combined). To help improve crop diversification and the nutritional quality of foods, we suggest, among other things, subsidies and public spending to facilitate access to inputs that allow the acquisition of a wider range of inputs and services, intensification of nutrition awareness, and education programs to maximize the incentive to consume nutritious foods from self-production and market purchases. Finally, we propose to facilitate access to technologies promoting food diversification and improving food and nutritional security, particularly in rural areas.