We aimed to examine whether an association exists between maternal high-risk fertility behavior and chronic undernutrition among children under 5 y of age. In addition, we explored the relationship ...between poverty and high-risk fertility behavior and the relative roles they play as obstacles in the reduction of the risk of undernutrition among children.
The analysis was based on responses from married women ages 15 to 49 who lived with at least one child under the age of 5; and three cross-sectional, nationally representative samples from India, Bangladesh, and Nepal were considered.
Maternal high-risk fertility behavior was associated with an increased risk of chronic undernutrition among children in India, Bangladesh, and Nepal. Multiple high-risk categories appeared to have more profound consequences on the outcomes measured. Findings also demonstrated that with regard to the risk of undernutrition, children of mothers who were either poor or who experienced high-risk fertility were not uniquely disadvantaged.
The results suggest that with regard to the risk of chronic undernutrition, the negative effect of high-risk fertility behavior extends across all economic backgrounds and is not limited to children of mothers who were either poor or who experienced high-risk fertility.
•Maternal high-risk fertility behavior was associated with an increased risk of chronic undernutrition among children under 5 y of age.•Multiple high-risk categories appeared to have more profound consequences on the outcomes measured.•With regard to the risk of undernutrition, children of mothers who were either poor or who experienced high-risk fertility were not uniquely disadvantaged.
This study aimed to explore the association between high–risk fertility behaviors and neonatal mortality in Ethiopia.
A community-based cross-sectional study was conducted using data from the 2019 ...Ethiopian Mini-Demographic and Health Survey.
Mixed-effects logit regression models were fitted to 5527 children nested within 305 clusters. The definition of high-risk fertility behavior was adopted from the 2019 EMDHS. The fixed effects (the association between the outcome variable and the explanatory variables) were expressed as adjusted odds ratios (ORs) with 95 % confidence intervals and measures of variation explained by intra-class correlation coefficients, median odds ratio, and proportional change invariance.
The presence of births with any multiple high-risk fertility behaviors was associated with a 70 % higher risk of neonatal mortality (AOR = 1.7, (95 % CI: 1.2, 2.3) than those with no high-risk fertility behavior. From the combined risks of high-risk fertility behaviors, the combination of preceding birth interval <24 months and birth order four or higher had an 80 % increased risk of neonatal mortality (AOR = 1.8, (95 % CI, 1.2, 2.7) as compared to those who did not have either of the two. The 3-way risks (combination of preceding birth interval <24 months, birth order 4+, and mother's age at birth 34+) were associated with approximately four times increased odds of neonatal mortality (AOR (95 % CI:3.9 (2.1, 7.4).
High-risk fertility behavior is a critical predictor of neonatal mortality in Ethiopia, with three-way high-risk fertility behaviors increasing the risk of neonatal mortality fourfold. In addition, antenatal follow-up was the only non-high fertility behavioral factor significantly associated with the risk of neonatal mortality in Ethiopia.
The floating population has been growing rapidly in China, and their fertility behaviors do affect urban management and development. Based on the data set of the China Migrants Dynamic Survey in ...2016, the logistic regression model and multiple linear regression model were used to explore the related factors of fertility behaviors among the floating populace. The artificial neural network model, the naive Bayes model, and the logistic regression model were used for prediction. The findings showed that age, gender, ethnic, household registration, education level, occupation, duration of residence, scope of migration, housing, economic conditions, and health services all affected the reproductive behavior of the floating population. Among them, the improvement duration of post-migration residence and family economic conditions positively impacted their fertility behavior. Non-agricultural new industry workers with college degrees or above living in first-tier cities were less likely to have children and more likely to delay childbearing. Among the prediction models, both the artificial neural network model and logistic regression model had better prediction effects. Improving the employment and income of new industry workers, and introducing preferential housing policies might improve their probability of bearing children. The artificial neural network and logistic regression model could predict individual fertility behavior and provide a scientific basis for the urban population management.
Fertility rate in Iran has decreased by more than 70% in the last three decades. Continuous decrease in fertility rate will create socioeconomic crises for the country in a near future. A significant ...factor behind fertility behaviors is women's attitudes towards maternal and spousal roles. Such attitudes are mainly determined by social capital. This study aims to determine and explore of relationship between social capital and fertility behavior among female healthcare workers.
This sequential explanatory mixed methods study will be conducted using the follow-up explanations model in two phases. In the first phase, a population-based cross-sectional survey will be conducted on 500 female workers recruited through multistage cluster sampling from healthcare settings located in Babol, Iran, and the relationship of social capital with fertility behaviors will be assessed. In the second phase, a qualitative study will be done to explain the findings of the first phase. Finally, the findings of the first phase will be explained using the findings of the second phase.
Understanding the relationship of social capital with fertility behaviors is essential to effective planning for the management of population decline. The findings of the present study will provide population policy-makers with helpful information.
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