RESUMEN

This study focuses on the importance of early and regular Antenatal Care (ANC) visits in reducing maternal and child mortality rates in Bangladesh, a country where such health indicators are a concern. The research utilized data from the Bangladesh Demographic and Health Survey (BDHS) conducted in 2017-18 and employed the Cox proportional hazard model to identify factors influencing women's intention of ANC services. The results revealed that 40.4% of women engaged in at least one ANC activity during the first trimester, which, although higher than in other countries, falls below the global average. Notably, women between the aged of 25 and 29 years took 15% less time for their first ANC visit compared to their younger counterparts, suggesting higher awareness and preparedness in this age group. Education, both for women and their partners, had a significant influence on the intention to visit ANC early. Women in the poor wealth quantile exhibited lower odds of seeking timely ANC, whereas those with a planned pregnancy were more likely to do so. Moreover, access to mass media decreased the timing of ANC visits by 26% compared to women who were not exposed. Moreover, living in rural areas was linked to a 17% delay in the timing of the first ANC visit compared to urban areas. These findings underscore the importance of addressing these determinants to improve the timeliness and accessibility of ANC services, thereby enhancing maternal and child health outcomes in Bangladesh.

Asunto(s)

RESUMEN

Intended pregnancy is one of the significant indicators of women's well-being. Globally, 74 million women become pregnant every year without planning. Unintended pregnancies account for 28% of all pregnancies among married women in Bangladesh. This study aimed to investigate the performance of six different machine learning (ML) algorithms applied to predict unintended pregnancies among married women in Bangladesh. From BDHS 2017-18, only 1129 pregnant women aged 15-49 were eligible for this study. An independent χ 2 test had performed before we considered six popular ML algorithms, such as logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), naïve Bayes (NB), and elastic net regression (ENR) to predict the unintended pregnancy. Accuracy, sensitivity, specificity, Cohen's Kappa statistic, and area under curve (AUC) value were used as model evaluation. The bivariate analysis result showed that women aged 30-49 years, poor, not educated, and living in male-headed households had a higher percentage of unintended pregnancy. We found various performance parameters for the classification of unintended pregnancy: LR accuracy = 79.29%, LR AUC = 72.12%; RF accuracy = 77.81%, RF AUC = 72.17%; SVM accuracy = 76.92%, SVM AUC = 70.90%; KNN accuracy = 77.22%, KNN AUC = 70.27%; NB accuracy = 78%, NB AUC = 73.06%; and ENR accuracy = 77.51%, ENR AUC = 74.67%. Based on the AUC value, we can conclude that of all the ML algorithms we investigated, the ENR algorithm provides the most accurate classification for predicting unwanted pregnancy among Bangladeshi women. Our findings contribute to a better understanding of how to categorize pregnancy intentions among Bangladeshi women. As a result, the government can initiate an effective campaign to raise contraception awareness.

Asunto(s)

RESUMEN

The outbreak of COVID-19 is a global problem today, and, to reduce infectious cases and increase recovered cases, it is relevant to estimate the future movement and pattern of the disease. To identify the hotspot for COVID-19 in Bangladesh, we performed a cluster analysis based on the hierarchical k-means approach. A well-known epidemiological model named "susceptible-infectious-recovered (SIR)" and an additive regression model named "Facebook PROPHET Procedure" were used to predict the future direction of COVID-19 using data from IEDCR. Here we compare the results of the optimized SIR model and a well-known machine learning algorithm (PROPHET algorithm) for the forecasting trend of the COVID-19 pandemic. The result of the cluster analysis demonstrates that Dhaka city is now a hotspot for the COVID-19 pandemic. The basic reproduction ratio value was 2.1, which indicates that the infection rate would be greater than the recovery rate. In terms of the SIR model, the result showed that the virus might be slightly under control only after August 2022. Furthermore, the PROPHET algorithm observed an altered result from SIR, implying that all confirmed, death, and recovered cases in Bangladesh are increasing on a daily basis. As a result, it appears that the PROPHET algorithm is appropriate for pandemic data with a growing trend. Based on the findings, the study recommended that the pandemic is not under control and ensured that if Bangladesh continues the current pattern of infectious rate, the spread of the pandemic in Bangladesh next year will increase.

RESUMEN

BACKGROUND: The present study aimed to identify factors that are associated with puberty knowledge among school-going rural adolescents in Bangladesh. METHODS: This cross-sectional study was conducted on 2724 school-going (grades VI-IX) adolescents who were aged between 10-24 years. The adolescents resided only in rural areas of Bangladesh. In this study, relationship between socio-demographic factors and controlling behaviour was assessed considering Bronfenbrenner's bioecological model. Considering the complex nature of Bronfenbrenner's bioecological model the structural equation model to explore factors related to the Adolescents' knowledge of pubertal changes. RESULTS: The structural equation model result showed a significant association among gender, education, age, and parental limit setting on daily activities with student's knowledge on pubertal changes. peer connection, and peer regulation were associated with adolescent knowledge on puberty directly as well as through the mediator variables year of schooling, academic performance and, parental behavioural control. CONCLUSION: Adolescents Age, years of schooling, and teachers concerns are positively associated with adolescents' knowledge on puberty. Whereas, parents' and peers' controlling behaviors are negatively associated with adolescents' understanding of pubertal changes. Therefore, there is needed an effective plan to raise the attention of parents and teachers on adolescents' pubertal issues to ensure adolescents' informed pubertal period.

Asunto(s)

RESUMEN

Early marriage is a form of violation of child rights to grow and develop. The Sustainable Development Goals had included early marriage in target 5.3, aiming to eliminate by 2030. This study examines the socio-demographic factors associated with women's early marriage in Bangladesh, Ghana, and Iraq using information extracted from 2019, 2017-2018, and 2018 Multiple Indicator Cluster Surveys (MICSs) of Bangladesh, Ghana, and Iraq, respectively. The chi-square test examined the association between socio-demographic factors and early marriage separately in all three countries. In logistic regression, key factors were primarily evaluated for determining effects on early marriage separately in all three countries. The mean age of the mother at first marriage was found to be 16.86, 20.23, and 20.05 years in Bangladesh, Ghana, and Iraq successively. According to surveys conducted in Bangladesh, Ghana, and Iraq, education levels of household heads and women, wealth status, mass media, number of household members, and residence were significant factors linked to early marriage. The odds of getting married early were significantly higher among women with no formal education and primary education than women with secondary or higher education in all three countries. In terms of economic status, a negative association was found between wealth status and early marriage in both Bangladesh and Ghana. Based on the findings, the study recommended that government take the necessary steps to reduce child marriage in all three countries by raising women's education and campaigning women by media to harmful effects of early marriage, particularly women from low-income families.