Desired Numbers of Children, Fertility Preferences and Related Factors among Couples Who Referred to Pre-Marriage Counseling in Alborz Province, Iran

Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques

The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniques to predict the desire to have children can offer a promising solution to address these challenges. Therefore, this study aimed to predict the childbearing tendency in women on the verge of marriage using ML techniques. Data from 252 participants (203 expressing a "desire to have children" and 49 indicating "reluctance to have children") in Abadan, and Khorramshahr cities (Khuzestan Province, Iran) was analyzed. Seven ML algorithms, including multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and K-nearest neighbors (KNN), were employed. The performance of these algorithms was assessed using metrics derived from the confusion matrix. The RF algorithm showed superior performance, with the highest sensitivity (99.5%), specificity (95.6%), and receiver operating characteristic curve (90.1%) values. Meanwhile, MLP emerged as the top-performing algorithm, showcasing the best overall performance in accuracy (77.75%) and precision (81.8%) compared to other algorithms. Factors such as age of marriage, place of residence, and strength of the family center with the birth of a child were the most effective predictors of a woman's desire to have children. Conversely, the number of daughters, the wife's ethnicity, and the spouse's ownership of assets such as cars and houses were among the least important factors in predicting this desire. ML algorithms exhibit excellent predictive capabilities for childbearing tendencies in women on the verge of marriage, highlighting their remarkable effectiveness. This capacity to offer accurate prognoses holds significant promise for advancing research in this field.

Principal Factors Affecting Couples' Childbearing Policies: A Roadmap for Policymaking

BACKGROUND

The concept of negative population growth, population aging, and the need to implement child-encouraging policies is an important concern in many countries. As this issue is completely cultural and country-based, this study is designed to assess and prioritize the perception of newly married couples to the policies that may have a crucial role in the childbearing intention around the world.

MATERIALS AND METHODS

Through a descriptive cross-sectional study, 300 couples were selected by a simple random sampling method. Multilevel binary logistic regression was used for investigating the relationships among dimensions of family policies, socio-demographic variables, and childbearing intention.

RESULTS

Childbearing perception positively correlated with education and permanent job in both genders, maternal age range of 25-35, the higher length of marriage, having more children, and living in a government settlement. The most important family policies that couples preferred were contextual requirements (mean rank of 4.50%). Positive childbearing perception negatively correlated with higher age categories in women, the number of children, rental housing status, no insurance access, higher educational attainment, and low employment ranks in both men and women.

CONCLUSIONS

This study cleared that family policies affect the childbearing intention of young couples. Polices involved contextual requirements, supporting couples to integrate work and home, health promotion plans, child-centered social support, and promoting the level of social and cultural relations.

Investigating changes in unintended pregnancies before and after the changes in the family planning policies in Iran: A multivariate decomposition analysis

Background: Unintended pregnancy is a global public health problem that has adverse health, economic, and social consequences for families and societies. This study aimed to measure levels of and analyze changes in unintended pregnancies before and after the changes in the family planning policies in Iran.

Methods: Data were extracted from Iran's Demographic and Health Surveys in 2000, 2010, and 2015. The study population consisted of married pregnant women aged 15-49 years. In this study, the data of 112 400, 29 609, and 32 264 households and 4976, 1123, and 900 married pregnant women in 2000, 2010 and 2015, respectively, were used. Logistic regression model was applied to estimate effect of the study explanatory variables on unintended pregnancy in each year, and contribution of different factors to the changes in unintended pregnancies was investigated using multivariate decomposition method. All the study analyses were performed using the Stata software, with the statistical significance level of 0.05.

Results: The rate of unintended pregnancies decreased by 13% in 2000-2010 and by 1.3% in 2010-2015. Changes in women's likelihood of experiencing unintended pregnancy positively contributed to unintended pregnancy reduction in 2000-2010 and 2010- 2015; however, the change patterns were different. Changes in the distribution of women's characteristics had positive and negative contributions in 2000-10 and 2010-15, respectively. An increase in pregnant women's parity was the main factor with counteracting effect on unintended pregnancy reduction in 2010-2015.

Conclusion: Unintended pregnancies decreased among Iranian married women over the study years, but its reduction was slowed down after the changes in the family planning policies. Policy actions in health and other socioeconomic sectors aiming to encourage nulliparous women to become pregnant and help nulliparous women, women with the parity of more than 2, and women in the middle and late reproductive ages to reduce their risk of unintended pregnancy, can accelerate the trend of unintended pregnancy reduction in the future.