Akhtarul Islam

Objective This study aimed to determine the factors associated with minimum dietary diversity (MDD) and estimate the socioeconomic inequalities in MDD among children from five South Asian countries. Design Cross-sectional. Setting The study used the most recent round of secondary databases of Demographic Health Survey data of Bangladesh (2017–2018), India (2019–2021), Maldives (2016–2017), Nepal (2018) and Pakistan (2017–2018). Participants This study used information on MDD and other explanatory variables from a total of 136 980 (weighted) children aged 6–23 months. Methods Multivariable logistic regression was employed to identify the factors associated with MDD and concentration index (CIX) and Lorenz curve were used to measure the socioeconomic inequalities in MDD. Results The overall weighted prevalence of MDD in South Asia was 23.37%. The highest prevalence of MDD was found among children from Maldives (70.7%), while the lowest was in Pakistan (14.2%). Living in affluent versus poor households, having a mother who is employed versus a mother who is unemployed, exposure to various forms of media (newspapers and magazines), seeking antenatal care (ANC) more than four times compared with those who sought ANC less than four times and having children older than 4 years old are the most common significant factors associated with MDD deficiency. This study found the value of the CIX for MDD (MDD: CI=0.0352; pConclusion Inequality in the prevalence of MDD favours the affluent. Health policy and intervention design should prioritise minimising socioeconomic inequalities concerning the MDD. In addition, policy-makers should prioritise the associated factors of MDD such as education, wealth status, employment, media exposure while designing intervention or policies.

Background: Bangladesh is one of the most densely populated countries in the world. This large population predominantly affects the socioeconomic development of the people living in this country. Therefore, it is essential to identify the determinants of household size in the country and suggest implications for future interventions in similar contexts. Objectives: The primary purpose of the present study was to explore the distribution of household size and its determinants based on the nationally representative Bangladesh Demographic and Health Survey (BDHS) data. Material and Methods: The data were extracted from BDHS 2017–2018, a national survey. Individual responses from the adult women of each household were considered in this nationwide survey. We applied univariate and bivariate analyses (chi-squared test) to explore the distribution of household size and different selected determinants. The multinomial logistic regression was utilized to identify the association between household size and the selected independent determinants in our study. Results: Findings of our multinomial logistic regression analysis showed that sex of the household head, division, decision about health, respondent's age, residence, respondent's education, husband's education, wealth index, religion, husband's occupation, and respondent's working status were significantly associated with the household size (P ≤ 0.05). We observed that household size tended to be more prominent in rural areas, in the Sylhet division, in families where the head of the household was a male, and in families where others except the respondents and their husbands made health-related decisions. Besides, large household size was also common among respondents aged 25 years or less, respondents and their husbands with higher education, respondents from the rich and middle class, respondents who were Muslim, respondents whose husbands were businessmen and jobholders, and respondents who were workers. Conclusions: The findings of our study suggest that household size in Bangladesh is associated with different socioeconomic factors. We recommend promoting awareness programs on family planning promotion and early marriage prevention, especially in rural areas, to prevent the rapid growth of the population. Expansion of education for both men and women and female involvement in income-generating activities should be encouraged to effectively control the household size in Bangladesh.

This study aimed to measure the socio-economic inequalities in having minimum dietary diversity (MDD) among Bangladeshi children aged 6–23 months as well as to determine the factors that potentially contribute to the inequity. The Bangladesh Demographic and Health Survey (BDHS) 2017–2018 data were used in this study. A sample of 2405 (weighted) children aged 6–23 months was included. The overall weighted prevalence of MDD was 37.47%. The concentration index (CIX) value for inequalities in MDD due to wealth status was positive and the concentration curve lay below the line of equality (CIX: 0.1211, p < 0.001), where 49.47% inequality was contributed by wealth status, 25.06% contributed by the education level of mother, and 20.41% contributed by the number of ante-natal care (ANC) visits. Similarly, the CIX value due to the education level of mothers was also positive and the concentration curve lay below the line of equality (CIX: 0.1341, p < 0.001), where 52.68% inequality was contributed by the education level of mother, 18.07% contributed by wealth status, and 14.69% contributed by the number of ANC visits. MDD was higher among higher socioeconomic status (SES) groups. Appropriate intervention design should prioritize minimizing socioeconomic inequities in MDD, especially targeting the contributing factors of these inequities.

The emergency of the pandemic and the absence of treatment have motivated researchers in all the fields to deal with the pandemic situation. In the field of computer science, major contributions include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Since the emergence of information technology, data science and machine learning have become the most widely used techniques to detect, diagnose, and predict the positive cases of COVID-19. This paper presents the prediction of confirmed cases of COVID-19 and its mortality rate and then a COVID-19 warning system is proposed based on the machine learning time series model. We have used the date and country-wise confirmed, detected, recovered, and death cases features for training of the model based on the COVID-19 dataset. Finally, we compared the performance of time series models on the current study dataset, and we observed that PROPHET and Auto-Regressive (AR) models predicted the COVID-19 positive cases with a low error rate. Moreover, death cases are positively correlated with the confirmed detected cases, mainly based on different regions’ populations. The proposed forecasting system, driven by machine learning approaches, will help the health departments of underdeveloped countries to monitor the deaths and confirm detected cases of COVID-19. It will also help make futuristic decisions on testing and developing more health facilities, mostly to avoid spreading diseases.

Internet addiction seems to have become a tremendous issue around the world during the COVID-19 pandemic situation. This study intends to investigate the underlying components and their consequences on internet addiction. Our study included about 348 participants from university students of the southern part of Bangladesh. The sample was obtained via convenience sampling strategy. Univariate analysis comprised frequencies with percentages, Chi-square test was used to determine the relationship between variables. With the help of multinomial logistic regression, the effect of various risk factors was found simultaneously. Our evaluation will be designated to indicate that majority respondents are moderate and addicted internet users. This may also reveal the significant risk factors: preferred place of using internet (RRR = 0.222, CI = [0.061,0.0811]), time since first internet use (RRR = 0.211, CI = [0.056,0.799]). Moreover, mental health condition such as depression (p-value < 0.01), anxiety (p-value = 0.006), and stress (p-value = 0.034) indicated significant relationship with internet addiction. Based on our findings, we were able to identify risk factors that could aid in the reduction of internet addiction.

Background. The most prominent form of cancer in women is breast cancer, and modifiable lifestyle risk factors, including smoking, alcohol consumption, and induced abortion, can all contribute significantly to this disease. Objectives. This study’s primary purpose was to assess the prevalence of breast cancer among women in developed and developing countries and the association between three modifiable hazard factors (induced abortion, smoking behavior, and alcohol use) and breast cancer. Methods. This study performed a systematic literature database review up to September 21, 2021. We employed meta‐analytic tools such as the random effects model, forest plot, and subgroup analysis to conduct the research. Additionally, we conducted a sensitivity analysis to assess the influence of outliers. Results. According to the random effects model, smoker women have a higher risk of developing breast cancer from different countries (OR = 1.46; 95% CI: 1.08–1.97). In the case of induced abortion, the pooled estimate (OR = 1.25; 95% CI: 1.01–1.53) indicated a significant link between abortion and breast cancer. Subgroup analysis revealed that smoking substantially influences breast cancer in developing and developed countries. Breast cancer was more common among women who smoked in developed countries than in developing nations. Conclusion. The observed findings give sufficient support for the hypothesis that smoking and abortion have a significant influence on breast cancer in different nations. Health organizations should individually design comprehensive scientific plans to raise awareness about the risks of abortion and smoking in developed and developing countries.

Background. The primary objective of this research was to investigate how socioeconomic and demographic factors influence the usage of contraceptives by women in 18 developing countries. Methods. The study used the latest DHS data from 18 developing countries in order to acquire a broad perspective of contraceptive methods. We applied meta-analysis techniques for 18 developing countries to find out the summary results. Results. The overall summary effect showed that the variable respondent education ( ; 95% CI: 1.17 to 1.65), husband education ( ; 95% CI: 1.32 to 1.93), type of place of residence ( ; 95% CI 0.78 to 0.98), current working status ( ; 95% CI 1.30 to 1.66), age of the respondent ( ; 95% CI 2.35 to 4.93), breastfeeding status ( ; 95% CI 1.11 to 1.62), and desire for more children ( ; 95% CI 0.43 to 0.65) were the significant factors for contraceptive utilization in developing countries. Conclusions. According to the findings of this descriptive study, the respondent’s age, level of education, and work status were shown to be the most significant factors that influence the usage of contraceptives in developing countries. It is necessary to take reasonable steps in order to increase the rate of utilizing methods of contraception among women of reproductive age who are uneducated, living in rural areas, and unemployed.