Bayesian spatial analysis of socio-demographic factors influencing pregnancy termination and its residual geographic variation among ever-married women of reproductive age in Bangladesh

Exploring the Link Between Parental Sociodemographic Characteristics and Multiple Births: Insights from National Birth Data in Japan, 1995-2020

This present study investigated the parental characteristics of multiple births using national birth data in Japan. This study included birth data from Vital Statistics: Occupational and Industrial Aspects every five fiscal years from 1995 to 2020. The multiple birth rates were defined as the number of live-birth deliveries with multiple fetuses (e.g., twins, triplets) per total live-birth deliveries. Parental ages, nationalities, occupations and household occupation (occupation of the top earner of the household) were considered as parental characteristics. The multiple birth rates were calculated based on parental characteristics for each year, and a log-binomial regression model was used to assess the association between parental characteristics and multiple births. The multiple birth rate for Japanese mothers consistently exceeded that for non-Japanese mothers over the years, and the rate increased progressively from manual workers to lower non-manual workers and then to upper non-manual workers for both maternal and paternal occupations. The regression results indicated that the risk ratio (RR) for multiple births among non-Japanese mothers was significantly lower than that among Japanese mothers. Moreover, concerning household occupation, the RRs of self-employed individuals, full-time employees at smaller companies, others, and the unemployed were significantly lower than those of full-time employees at larger companies. Furthermore, the RRs of lower non-manual and manual workers were significantly lower than those of upper non-manual workers in maternal and paternal occupations. The results suggested an association between multiple births and parental socioeconomic status in Japan.

Understanding and predicting pregnancy termination in Bangladesh: A comprehensive analysis using a hybrid machine learning approach

Reproductive health issues, including unsafe pregnancy termination, remain a significant concern for women in developing nations. This study focused on investigating and predicting pregnancy termination in Bangladesh by employing a hybrid machine learning approach. The analysis used data from the Bangladesh Demographic and Health Surveys conducted in 2011, 2014, and 2017 to 2018. Ten independent variables, encompassing factors such as age, residence, division, wealth index, working status, BMI, total number of children ever born, recent births, and number of living children, were examined for their potential associations with pregnancy termination. The dataset undergoes preprocessing, addressing missing values and balancing class distributions. To predict pregnancy termination, 8 classical machine learning models and hybrid models were used in this study. The models' performance was evaluated based on the area under the curve, precision, recall, and F1 score. The results highlighted the effectiveness of the hybrid models, particularly the Voting hybrid model (area under the curve: 91.97; precision: 84.14; recall: 83.87; F1 score: 83.84), in accurately predicting pregnancy termination. Notable predictors include age, division, and wealth index. These findings hold significance for policy interventions aiming to reduce pregnancy termination rates, emphasizing the necessity for tailored approaches that consider regional disparities and socioeconomic factors. Overall, the study demonstrates the efficacy of hybrid machine learning models in comprehending and forecasting pregnancy termination, offering valuable insights for reproductive health initiatives in Bangladesh and similar contexts.

Predictors of Pregnancy Termination among Young Women in Ghana: Empirical Evidence from the 2014 Demographic and Health Survey Data

Pregnancy termination remains a delicate and contentious reproductive health issue because of a variety of political, economic, religious, and social reasons. The present study examined the associations between demographic and socio-economic factors and pregnancy termination among young Ghanaian women. This study used data from the 2014 Demographic and Health Survey of Ghana. A sample size of 2114 young women (15-24 years) was considered for the study. Both descriptive (frequency, percentages, and chi-square tests) and inferential (binary logistic regression) analyses were carried out in this study. Statistical significance was pegged at p < 0.05. Young women aged 20-24 were more likely to have a pregnancy terminated compared to those aged 15-19 (AOR = 3.81, CI = 2.62-5.54). The likelihood of having a pregnancy terminated was high among young women who were working compared to those who were not working (AOR = 1.60, CI = 1.19-2.14). Young women who had their first sex at the age of 20-24 (AOR = 0.19, CI = 0.10-0.39) and those whose first sex occurred at first union (AOR = 0.57, CI = 0.34-0.96) had lower odds of having a pregnancy terminated compared to those whose first sex happened when they were less than 15 years. Young women with parity of three or more had the lowest odds of having a pregnancy terminated compared to those with no births (AOR = 0.39, CI = 0.21-0.75). The likelihood of pregnancy termination was lower among young women who lived in rural areas (AOR = 0.65, CI = 0.46-0.92) and those in the Upper East region (AOR = 0.18, CI = 0.08-0.39). The findings indicate the importance of socio-demographic factors in pregnancy termination among young women in Ghana. Government and non-governmental organizations in Ghana should help develop programs (e.g., sexuality education) and strategies (e.g., regular sensitization programs) that reduce unintended pregnancies which often result in pregnancy termination. These programs and strategies should include easy access to contraceptives and comprehensive sexual and reproductive health education. These interventions should be designed considering the socio-demographic characteristics of young women. Such interventions will help to achieve Sustainable Development Goal 3.1 that seeks to reduce the global maternal mortality ratio to fewer than 70 per 100,000 live births by 2030.