The impact of working hours on pregnancy intention in childbearing-age women in Korea, the country with the world’s lowest fertility rate

Abstract

This study aimed to assess factors affecting pregnancy intention among women of reproductive age in Korea. We analyzed data from the Korean National Health and Nutrition Examination Survey (KNHANES), a population-based survey that included 22,731 women aged 15–49. As age was associated with birth year and was found to be a confounding factor in the analysis of participants’ characteristics, we used propensity score matching to assess the characteristics of pregnant women compared with non-pregnant women of the same age and birth year. We also employed the XGBoost machine learning model to identify the most important factors related to pregnancy intentions. Our feature importance analysis showed that weekly working hours were the most significant factor affecting pregnancy intentions. Additionally, we performed cluster analysis and logistic regression models to determine optimal weekly working hours. Cluster analysis identified participants into three distinct groups based on their characteristics, indicating that the group with an average of 34.4±12.9 hours per week had the highest likelihood of becoming pregnant. Logistic regression was used to analyze the odds of pregnancy for every 5-hour increase in weekly working hours. The results of logistic regression indicated that women who worked between 35–45 hours per week had higher odds of pregnancy, with significant odds ratios of 2.009 (95% confidence interval: 1.581–2.547, p < .001) for 40–45 hours per week and 1.450 (95% confidence interval: 1.001–2.040, p < .05) for 35–40 hours per week, compared to women working other hours. In Korea, the standard workweek is typically 40 hours; however, Koreans often work considerably longer hours, with the second-highest number of working hours among OECD countries in 2022. This study suggests that strict monitoring of working hours and expansion of telecommuting for childbearing-age women are important factors in increasing the fertility rate in Korea.

Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning

“…PS is the conditional probability of an individual being in a certain group, based on covariates or predictor variables in the model [32]. Many researchers have used PS matching in medical and social science studies to reduce selection bias [33,34]. The nearest neighbor (NN) with a PS caliper, which imposed a tolerance level on the maximum PS distance, was used to match the two groups to reduce the risk of bad matches when the NN was located far away [35].…”

“…Because XGBoost, similar to many other machine learning algorithms, employs random initialization during tree model construction, these random processes can result in relatively small variations in the model's performance. As suggested by previous studies utilizing XGBoost in prediction models [34,42], we conducted analyses 100 times, allowing us to reduce these effects and provide more accurate estimates of 95% CIs.…”

Hybrid operating rooms and the risk of postoperative hypothermia in pregnant women with placenta previa: A retrospective cohort study

“…A noticeable increase has been observed in births among women of advanced maternal age (≥ 35 years) [ 1 , 2 ]. In South Korea, 35.7% of pregnancies were among women in this age group in 2022 [ 3 ]. This increase in births at advanced maternal age is associated with the increased incidence of placenta previa [ 4 ], which is associated with an increased risk of bleeding [ 5 ].…”