Determinants of household food security and dietary diversity during the COVID-19 pandemic in Bangladesh.

OBJECTIVES: The study aimed to determine the associated factors of household food security (HFS) and household dietary diversity (HDD) during the COVID-19 pandemic in Bangladesh. DESIGN: Both online survey and face-to-face interviews were employed in this cross-sectional study. The Household Food Security Scale and Household Dietary Diversity Score were used to access HFS and HDD, respectively. The HDD scores were derived from a 24-h recall of food intake from 12 groups. SETTING: Bangladesh. PARTICIPANTS: A total sample of 1876 households were recruited. RESULTS: The overall mean scores of HFS and HDD were 31·86 (sd 2·52) and 6·22 (sd 5·49), respectively. Being a rural resident, having no formal education, occupation of household head other than government job and low monthly income were potential determinants of lower HFS and HDD. Approximately 45 % and 61 % of Bangladeshi households did not get the same quantity and same type of food, respectively, as they got before the pandemic. Over 10 % of respondents reported that they lost their job or had to close their businesses, and income reduction was reported by over 70 % of household income earners during the COVID-19 pandemic, which in turn was negatively associated with HFS and HDD. CONCLUSION: Household socio-economic variables and COVID-19 effects on occupation and income are potential predictors of lower HFS and HDD scores. HFS and HDD deserve more attention during this pandemic particularly with reference to low-earning households and the households whose earning persons' occupation has been negatively impacted during the COVID-19 pandemic.

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification.

In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods-Mutual Information, ReliefF, Chi Square, and Xvariance-and then each feature from the union set was assessed by three classification algorithms-support vector machine, naïve Bayes, and k-nearest neighbors-and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.

Prevalence and factors associated with dietary supplement use among Bangladeshi public university students: A cross-sectional study.

INTRODUCTION: The usage of dietary supplement (DS) such as vitamins, minerals, and fish oil has expanded, but there is limited data on their use by sub-populations such as university students. The study was aimed to investigate the prevalence of DS use among Bangladeshi university students and its associated factors. METHODS: A cross-sectional survey of 390 students was conducted from two public universities from Barishal Division in Bangladesh using a structured questionnaire with 72 questions divided into five sections: sociodemographic, knowledge, opinions, and attitudes, types of DS, reasons and sources for using DS, and adverse reactions after taking DS. Descriptive statistics and logistic regression were utilized to estimate the results. RESULTS: Among all the students, 15.6% students were using DS where only 7.7% of them used DS according to physicians' recommendation. Additionally, students used DS for general health and well-being, weight gaining and as a source of energy for physical and sporting activities, etc. The use of DS was significantly associated with female sex (AOR = 5.44, 95% CI: 2.18-13.52), ≥25 years age (AOR = 0.08, 95% CI: 0.01-0.67), underweight (AOR = 5.86, 95% CI: 1.95-17.62), having major illness (AOR = 6.99, 95% CI: 1.98-24.70) and good knowledge of DS (AOR = 2.64, 95% CI: 1.23-5.64). CONCLUSION: This study provides new findings on DS use and its correlates in Bangladeshi students which may be used by the policymakers to improve DS usage among students. Adaptation of an appropriate program is recommended to educate students on proper and safer ways of using DS.

Perceived Stress and Coping Behavior among Future Nurses: A Cross-Sectional Study in West Bengal, India.

BACKGROUND: Demanding clinical and academic environments have been potential sources of stress among nursing students. Inability to cope effectively often potentiates this stress. If not intervened early, this may have a detrimental effect on health and may eventually affect the future workforce in rendering care. OBJECTIVES: The objective of this study was to explore the levels and sources of perceived stress and coping behavior among undergraduate nursing students in West Bengal. MATERIALS AND METHODS: This descriptive cross-sectional study was conducted in two nursing training institutions in West Bengal, from July to September 2018, using a validated pretested self-administered questionnaire comprising demographics, Perceived Stress Scale (PSS), and coping behavior inventory (CBI). Students having at least 6 months of clinical exposure were invited to participate. Of 256 eligible students, 182 returned completed questionnaires, giving an overall response rate of 71%. Descriptive statistics, Pearson's correlation, and multiple regression analysis were performed using SPSS 16.0 software. RESULTS: "Stress from assignments and workload" and "problem-solving" was the most prominent stressor and coping behavior (Factor rank 1) respectively among students. Statistically significant correlation was observed between overall mean PSS and CBI score (r = 0.306, P < 0.01). Years of education, self-decision to join, increased screen time, staying at hostel significantly predicted stress (R 2 = 0.248, F = 9.640, P < 0.01), and coping behavior (R 2 = 0.223, F = 10.077, P < 0.01) among students. CONCLUSIONS: Stress from academics and clinics were high among nursing students. As they are the future carers, it is apt to intervene early to mitigate their stress and enhance their coping skills during professional training and practice.

Quantification of health-related quality of life among patients with rheumatoid arthritis: An institution-based study in Kolkata, West Bengal.

CONTEXT: Rheumatoid arthritis (RA) is a known chronic debilitating disease accounting for a large percentage of disability globally. Pain and stiffness, decreased work function, depression and emotional state alteration, fatigue, disability, and social handicaps are some patient reported outcomes, which if considered with priority the health-related quality of life (HRQOL) of patients with RA could improve. AIMS: This study was conducted with the aim to assess the HRQOL of the patients with RA and the determinants related to it. SETTINGS AND DESIGN: This was a cross-sectional study conducted at Rheumatology Department of a tertiary care hospital, Kolkata. SUBJECTS AND METHODS: A total of 252 patients with RA were selected in this study through systematic random sampling. STATISTICAL ANALYSIS USED: Data were analyzed using appropriate statistical measures with Statistical Package for the Social Sciences(SPSS) version 16.0 (Armonk, NY: IBM Corporation) software program, version 16.0. Univariate and multivariable logistic regression were carried out. RESULTS: In the study, the mean age of the patients was 43.1 years (mean age ±SD: 43.05±10.63 years). The proportion of female subjects was 84.5%. Unsatisfactory QOL was found in 59.9% study participants. In multivariable logistic regression unsatisfactory quality of life was significantly associated to moderate to high functional disability [AOR: 6.04, CI: 2.86, 12.78], disease activity moderate to high [AOR: 5.41, CI: 1.87, 15.69], presence of comorbidity [AOR: 2.90, CI: 1.39, 6.04], extra-articular manifestations [AOR: 3.14, CI: 1.41, 6.96] and delay in starting Disease-Modifying Anti-Rheumatoid Drugs (DMARDs) [AOR: 1.24, CI: 1.08, 1.42]. CONCLUSION: Findings of this study clearly indicate the presence of high proportion of unsatisfactory QOL among the patients with RA. Early identification and prompt referral are the key strategies to prevent any permanent damage. Regular follow-up of the patients should be carried out to prevent or delay the disability progression and provide high-quality physical and mental health.