Declining birth outcomes during the COVID-19 pandemic: a nationwide study in South Korea

Declining birth outcomes during the COVID-19 pandemic: a nationwide study in South Korea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Declining birth outcomes during the COVID-19 pandemic: a nationwide study in South Korea Yoonkyoung Lee, Wankyo Chung This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7351864/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective: This study aimed to investigate the impact of the coronavirus disease (COVID-19) pandemic on birth outcomes in South Korea, particularly in the context of exceptionally low birth rates. Methods: Using nationwide birth certificate data up to 2022, an interrupted time-series analysis, linear probability model, and subgroup analyses were conducted to assess the influence of the pandemic on perinatal outcomes. Results: The analysis revealed a transient increase in preterm births of 0.15% (95% CI: 0.00%, 0.03%) and a sustained rise of 0.01% (95% CI: 0.00%, 0.02%) following the onset of the pandemic. In contrast, low birth weight initially showed a short-term decline of -0.11% (95% CI: -0.24%, -0.02%), followed by an intermediate-term increase of 0.02% (95% CI: 0.01%, 0.03%). Subgroup analyses indicated that these adverse birth outcomes were more pronounced among women under 35 years old, those with higher education levels, and those who were employed. Conclusions: These findings suggest that the COVID-19 pandemic may have exacerbated pre-existing adverse trends in birth outcomes in South Korea. The results highlight the need to strengthen not only healthcare and social support for pregnant women, but also protective policies for those in the workforce particularly during large-scale public health crises. COVID-19 birth outcome preterm low birth weight Figures Figure 1 Significance Previous studies focused mainly on the short-term effects of COVID-19 on birth outcomes. By using national data up to 2022, our study captures both immediate and intermediate impacts, including those during the Omicron wave. Unlike earlier findings, we identified sustained increases in preterm births and low birth weight even after controlling for parent’s socioeconomic factors. Notably, these adverse outcomes were more pronounced among younger, highly educated, and employed women, underscoring the need for targeted healthcare and policy interventions to support these vulnerable groups during prolonged public health crises. 1. Introduction South Korea is currently experiencing an unprecedented low birth rate, both domestically and globally. The birth rate declined to 0.72 in 2023, marking the first time when live births fell below 240,000 [1]. In 2024, the total fertility rate was 0.75, remaining at an exceptionally low level by both domestic and international standards [2]. However, the qualitative change in newborns’ health is as pivotal as the quantitative decline in newborns. The gestational age and birth weight of newborns significantly impact their future health and intelligence [3–5]. Statistical data from the Korea Institute for Health and Social Affairs indicate a notable increase in the proportion of preterm births (< 37 gestational weeks), from 6% in 2011 to 9.2% in 2021. Similarly, the proportion of babies born with low birth weight (LBW, < 2500 g) also significantly rose, from 5.2% in 2011 to 7.2% in 2021. These changes can be attributed to several factors, including increased proportion of women employed, higher maternal age, and the differential use of maternity care. Furthermore, the impact of the coronavirus disease (COVID-19) pandemic on birth outcomes is likely significant [6–16]. The available evidence on the impact of the COVID-19 pandemic on birth outcomes is inconclusive [17, 18]. A systematic review and meta-analysis conducted by Chmielewska et al. ( 2021 ) revealed no statistically significant overall decline in the incidence of preterm birth. However, a significant decline was observed in high-income countries [18]. Another systemic review and meta-analysis by Yao et al. ( 2022 ) identified a significant increase in average birth weight but no overall impact on LBW, macrosomia, small for gestational age (SGA), or large for gestational age (LGA) [17]. Studies conducted in Korea have yielded comparable results regarding birth outcomes, although the findings were inconsistent [9, 10, 19]. Lee et al. ( 2023 ) observed an increase in the incidence of preterm births during the global pandemic caused by SARS-CoV-2 in 2020 [19]. However, Hwang et al. ( 2022 ) demonstrated a reduction in the prevalence of preterm birth, LBW, macrosomia, SGA, LGA, and inappropriate birth weight during the same period [10]. These disparate findings can be attributed to a confluence of factors pertaining to the specific characteristics of SARS-CoV-2 and alterations in maternal and fetal attributes that have occurred during the pandemic. This association has been explored in a few studies that sought to elucidate its underlying mechanisms by examining variations in physical activity [20], conception rates [21], caesarean section rates [12, 22, 23], and clinical factors [24, 25]. Therefore, it is crucial to control for alterations in maternal and fetal characteristics throughout the pandemic, where data are accessible, to inform the formulation of appropriate policy responses to changes in birth outcomes. The most recent nationwide birth records from South Korea, extending up to 2022, were used to answer the following questions: (1) Were there significant changes in birth outcomes during the global pandemic caused by the novel coronavirus, SARS-CoV-2? (2) After controlling for parental and fetal attributes, did these significant changes persist? (3) Do these changes vary according to maternal characteristics? 2. Materials and Methods This study used nationwide complete birth certificate data provided by the Microdata Integrated Service of Statistics Korea over 13 years before the pandemic (2010–2019) and 3 years during the pandemic (2020–2022), incorporating the most recent data available at the time of analysis. The data provided information on the birth weight and gestational age of all newborns in Korea each year, as well as various details about the parents, including their occupation, age, educational level, and birth region. Among a total of 4,911,893 births during the study period, 4,719,620 were singleton births. Of these, 316,379 cases (6.7%) with missing values for parental occupation, parental age, parental educational level, birth weight, or gestational age were excluded. As a result, 4,403,241 singleton infants were included in the final analysis. In Korea, the first COVID-19 case was reported in January 2020. However, the number of confirmed cases began to surge in February 2020 owing to a cluster infection originating from a religious group [26]. However, if the period of exposure to the novel COVID-19 was defined as January or February 2020, parents of newborns during that period would have experienced the pandemic only during the late stages of pregnancy. Accordingly, to assess the influence of exposure to the virus during the early, mid, and late stages of pregnancy, we delineated the period of exposure as December 2020. This timeframe encompassed a 10-month gestational period, commencing with a notable outbreak in February 2020. This study examined the impact of the COVID-19 pandemic on birth outcomes. The primary outcome was preterm birth, defined as a gestational age of less than 37 weeks. Additionally, we examined LBW, defined as less than 2,500 g. The analysis included confounding factors based on those identified in the scientific literature as related to preterm birth and LBW. These include sociodemographic characteristics (region, mother's age, mother's education level, and parental occupation) and obstetric factors (whether pregnancy involved multiple births or maternal parity) [11, 27]. Descriptive analysis was conducted to examine the characteristics of mothers and newborns from 2010 to 2022. A chi-square test was conducted to ascertain whether there were any significant differences in the characteristics of mothers and newborns before and after the influence point in December 2020. To investigate the influence of the COVID-19 pandemic on perinatal outcomes at the population level, we employed a single-group interrupted time-series analysis (ITSA). This approach was used to ascertain whether there were discernible differences in the incidences of preterm births and LBW infants before and after the onset of the crisis. The single-group ITSA regression model is based on the following equation: $$\:{Y}_{t}={\beta\:}_{0}+\:{\beta\:}_{1}{T}_{t}+\:{\beta\:}_{2}{COVID}_{t}+{\beta\:}_{3}{T}_{t}{COVID}_{t}+{seasonality}_{t}+\:{ϵ}_{t}$$ where \(\:{Y}_{t}\) is the monthly rate of the birth outcome variables of interest, \(\:{T}_{t}\) is the time elapsed since the commencement of the study, and \(\:{COVID}_{t}\) is a dummy variable defined as 0 for the period from January 2010 to November 2020 and 1 for the period from December 2020 to December 2022. \(\:{T}_{t}{X}_{t}\:\) is an interaction term. To account for seasonal effects, we used year and month variables as categorical variables. Hence, \(\:{\beta\:}_{0}\) measures the baseline level of birth outcome, \(\:{\beta\:}_{1}\:\) the trend in the pre-intervention phase, \(\:{\beta\:}_{2}\:\) the level change following the COVID-19, and \(\:{\beta\:}_{3}\:\) the trend change following the COVID-19. To account for serial correlation in the residuals, we employed Newey–West heteroskedasticity and autocorrelation-consistent standard errors. When analyzing data on a monthly basis, it is important to note that individual-level variables cannot be controlled, which represents a limitation of such an approach. To address this issue, we conducted further investigations into the impact of the COVID-19 pandemic on birth outcomes using an individual-level dataset. A linear probability model was employed to the individual-level birth data. We examine both level and trend changes in the two birth outcomes. In the analysis, we controlled for the infant's sex, maternal education level, parental occupation, maternal age, and infant parity as well as seasonality. To ascertain whether the impact of the pandemic differed according to maternal characteristics, additional subgroup analyses were conducted based on maternal age, occupation, and educational level. The regression model is specified as follows: $$\:{Y}_{it}=\alpha\:+{\delta\:}_{1}{Post}_{t}+{\delta\:}_{2}{Trend}_{t}+{\delta\:}_{3}\left({Post}_{t}\times\:{Trend}_{t}\right)+{X}_{it}\gamma\:+{Seasonality}_{t}+{\epsilon\:}_{it}$$ where \(\:{Y}_{it}\) ​ is a binary indicator equal to 1 if infant i born at time t experienced the adverse birth outcome and 0 otherwise. \(\:{Post}_{t}\) ​ is a dummy variable indicating the post-pandemic period (defined as 1 for births between December 2020 and December 2022, and 0 otherwise), \(\:{Trend}_{t}\) ​ is a continuous variable indicating time elapsed since the beginning of the observation period, and \(\:{Post}_{t}\times\:{Trend}_{t}\) ​ is an interaction term capturing changes in trend following the onset of the pandemic. \(\:{X}_{it}\) is a vector of individual-level covariates including infant’s sex, maternal education, parental occupation, maternal age, and infant parity. We also included fixed effects for month and year of birth to account for seasonality. Robust standard errors clustered at the monthly level were used to correct for heteroskedasticity and serial correlation. The significance level was set at p < 0.05. All statistical analyses were performed using Stata version 18. 3. Results 3.1. Trends and Level Changes in Birth Outcomes Table 1 presents the ITSA model results for birth outcomes. As shown in Table 1 and Fig. 1 , the preterm birth rate increased by 0.06% (95% CI = 0.05–0.07%) during the pre-pandemic period. In the first month of the COVID-19 period, there was an immediate increase in the preterm birth rate of 0.33% (95% CI = 0.06–0.60%), followed by a slope change of 0.01% percentage points per month (95% CI = − 0.01–0.03%), compared to the pre-pandemic trend. For LBW, the pre-pandemic slope was 0.05% (95% CI = 0.04–0.06%) per month, and this increased further by 0.02% percentage points per month (95% CI = 0.01–0.03%) following the onset of the pandemic. No statistically significant level change was observed for LBW. Table 1 Changes in birth outcomes during COVID-19 pandemic (interrupted time-series analysis) Coef. p-value 95% CI Preterm Baseline slope 0.06 < 0.001 0.05 ~ 0.07 Level change 0.33 0.018 0.06 ~ 0.60 Slope change 0.01 0.353 -0.01 ~ 0.03 Low birth weight Baseline slope 0.05 < 0.001 0.04 ~ 0.06 Level change 0.07 0.495 -0.13 ~ 0.26 Slope change 0.02 0.006 0.01 ~ 0.03 **[Table 1 here]** **[Figure 1 here]** 3.2. Maternal sociodemographic characteristics before and during COVID-19 During the pre-COVID-19 period (January 1, 2010, to November 30, 2020), 3,965,710 singleton infants were delivered. In contrast, during the post-COVID-19 period (December 1, 2020, to December 31, 2022), this figure decreased to 437,531. Table 2 illustrates the maternal sociodemographic characteristics and the frequencies and proportions of preterm births and LBW, along with the results of the chi-square tests. Table 2 Summary of maternal demographics and birth outcomes in the Pre- and Post-COVID-19 Periods (N = 4,403,241) Variables Pre-COVID 19 Period COVID-19 Period p-value Jan 2010 - Nov 2020 Dec 2020 - Dec 2022 Total, n (%) 3,965,710 (100.00) 437,531 (100.00) Maternal age < 0.000 Mean (SD) 31.63 (4.18) 33.40 (4.17) < 35 3,030,253 (76.41) 267,602 (61.16) ≥ 35 935,457 (23.59) 169,929 (38.84) Educational Attainment < 0.000 Less than middle school 68,707 (1.73) 5,060 (1.16) High school 934,698 (23.57) 68,546 (15.67) Bachelor’s degree 2,674,159 (67.43) 323,947 (79.04) Master’s degree or higher 288,146 (7.27) 39,978 (9.14) Having job < 0.000 Yes Professional 629,234 (15.87) 104,907 (23.98) Not professional 960,902 (24.23) 173,233 (39.59) No 2,375,574 (59.90) 159,391 (36.43) The number of babies < 0.000 1 2,087,167 (52.63) 255,112 (58.31) 2 1,494,856 (37.69) 148,592 (33.96) More than 3 383,687 (9.68) 33,827 (7.73) Gestational Age < 0.000 Mean (SD) 38.68 (1.50) 38.32 (1.45) < 37 190,108 (4.79) 25,973 (5.94) ≥ 37 3,775,602 (95.21) 411,558 (94.06) Birth Weight < 0.000 Mean (SD) 3.24 (0.43) 3.19 (0.43) < 2.5 146,778 (3.70) 18,746 (4.28) ≥ 2.5 3,818,932 (96.30) 418,785 (95.72) **[Table 2 here]** In the pre-COVID-19 period, 23.59% of the mothers were aged 35 years or older. This figure increased to 38.84% during the pandemic period. The chi-square test yielded statistically significant results, indicating a notable increase in the proportion of mothers aged 35 years or older compared to the pre-pandemic period. The mean gestational age was 38.68 weeks during the pre-pandemic period and 38.32 weeks during the pandemic period, indicating a downward trend. Upon examination of preterm birth rates with a cutoff of 37 weeks, the proportion of preterm births was 4.79% before the pandemic and 5.94% during the pandemic, indicating an increasing trend. The chi-square test yielded significantly different results for the comparison of preterm birth rates before and during the pandemic. A tendency towards improvement in maternal socioeconomic status was observed following the pandemic compared to the period preceding it. Compared to the period preceding the pandemic, there was a notable increase in the proportion of mothers with bachelor's, master's, or advanced degrees after the pandemic. An examination of maternal occupations revealed a notable decline in the proportion of mothers without employment, from 59.90% before the pandemic to 36.43% during the pandemic. Conversely, the proportion of mothers in professional occupations increased significantly from 15.87% before the pandemic to 23.98% during the pandemic. Examination of the variables related to birth weight revealed that the average birth weight was 3.24 kg before the pandemic and 3.19 kg during the pandemic. Regarding the criterion for LBW, defined as less than 2.5 kg, there was a notable increase in the proportion of LBW infants from 3.70% before the pandemic to 4.28%. The ITSA results are based on monthly aggregated data, limiting the ability to control for individual-level variables. To address this, we employed a linear probability model using individual-level birth data (Table 3 ). After controlling for seasonality and individual-level covariates, the change in slope following COVID-19 remained statistically significant for both preterm birth (0.01%, 95% CI = − 0.00–0.02%) and LBW (0.02%, 95% CI = 0.01–0.03%). These results indicate modest but persistent increases in both outcomes. The immediate level change in preterm birth remained significant (0.15%, 95% CI = − 0.00–0.30%), while the level change in LBW (–0.11%, 95% CI = − 0.24–0.02%) was not significant in Model 1 but became marginally significant at the 10% level after full adjustment in Model 2. Table 3 Changes in birth outcomes during the COVID-19 pandemic (Linear Probability Model) Model 1 Model 2 Coef. p-value 95% CI Coef. p-value 95% CI Preterm Baseline slope 0.07 < 0.001 0.06 ~ 0.08 0.07 < 0.001 0.06 ~ 0.08 Level change 0.22 0.003 0.07 ~ 0.38 0.15 0.050 0.00 ~ 0.30 Slope change 0.01 0.010 0.00 ~ 0.02 0.01 0.010 0.00 ~ 0.02 Low birth weight Baseline slope 0.05 < 0.001 0.05 ~ 0.06 0.05 < 0.001 0.04 ~ 0.05 Level change -0.03 0.642 -0.15 ~ 0.10 -0.11 0.091 -0.24~-0.02 Slope change 0.02 < 0.001 0.01 ~ 0.03 0.02 < 0.001 0.01 ~ 0.03 * Model 1: adjusted for trends (year, month) * Model 2: adjusted for parental age, sex, region, parental job, parental educational attainment, parity, month, and year. **[Table 3 here]** 3.3. Subgroup analysis by maternal characteristic Table 4 presents the findings of subgroup analysis based on maternal characteristics. A significant increase in the incidence of preterm births (level change) was observed in the immediate post-pandemic period compared with the pre-pandemic period among women of working age (0.16%, 95% CI = − 0.03–0.36%), those under 35 years of age (0.19%, 95% CI = 0.01–0.37%), and those with university education or above (0.19%, 95% CI = 0.03–0.35%). These represent absolute changes in probability. Moreover, these groups demonstrated a pronounced and persistent increase in preterm birth rates (slope change) relative to pre-pandemic levels. Similarly, an increased incidence of LBW (slope change) has been documented in younger, highly educated, and working women. Table 4 Subgroup analyses based on maternal characteristics Preterm Low birth weight Coef. p-value 95% CI Coef. p-value 95% CI Baseline Slope Working status Stay-at-home 0.07 < 0.001 0.06 ~ 0.08 0.05 < 0.001 0.04 ~ 0.06 Working 0.08 < 0.001 0.07 ~ 0.09 0.05 < 0.001 0.04 ~ 0.06 Maternal Age < 35 0.07 < 0.001 0.06 ~ 0.08 0.04 < 0.001 0.03 ~ 0.05 ≥ 35 0.08 < 0.001 0.06 ~ 0.10 0.07 < 0.001 0.05 ~ 0.09 Educational level high school or lower 0.09 < 0.001 0.07 ~ 0.11 0.06 < 0.001 0.04 ~ 0.08 university or higher 0.07 < 0.001 0.06 ~ 0.08 0.05 < 0.001 0.04 ~ 0.06 Level Change Working status Stay-at-home 0.15 0.242 -0.10 ~ 0.39 -0.09 0.369 -0.30 ~ 0.11 Working 0.16 0.101 -0.03 ~ 0.35 -0.12 0.167 -0.28 ~ 0.05 Maternal Age < 35 0.19 0.039 0.01 ~ 0.37 -0.01 0.931 -0.16 ~ 0.15 ≥ 35 0.06 0.629 -0.19 ~ 0.33 -0.30 0.009 -0.52~-0.07 Educational level high school or lower 0.08 0.681 -0.31 ~ 0.47 -0.24 0.140 -0.57 ~ 0.08 university or higher 0.19 0.023 0.03 ~ 0.35 -0.08 0.257 -0.22 ~ 0.06 Slope Change Working status Stay-at-home 0.01 0.273 -0.01 ~ 0.03 0.01 0.129 -0.00 ~ 0.03 Working 0.02 0.011 0.00 ~ 0.03 0.02 < 0.001 0.01 ~ 0.04 Maternal Age < 35 0.02 0.008 0.00 ~ 0.03 0.02 < 0.001 0.01 ~ 0.03 ≥ 35 0.01 0.314 -0.01 ~ 0.03 0.02 0.008 0.01 ~ 0.03 Educational level high school or lower 0.02 0.258 -0.01 ~ 0.04 0.03 0.016 0.01 ~ 0.05 university or higher 0.01 0.015 0.00 ~ 0.02 0.02 < 0.001 0.01 ~ 0.03 Significant values are shown in bold. **[Table 4 here]** 4. Discussion In the context of the global pandemic caused by the SARS-CoV-2 virus, we employed national representative singleton birth data to assess immediate level and trend changes in preterm and LBW outcomes following the onset of the pandemic in South Korea. This study defined the exposure timeframe to SARS-CoV-2 as December 2020, encompassing the 10-month pregnancy period starting from a notable outbreak in February 2020 in Korea. This study aimed to assess its impact on pregnant women at all stages of pregnancy. Descriptive analysis revealed significant differences in the socioeconomic status of mothers who gave birth before and after the onset of the global pandemic. There was an increase in the numbers of older mothers, professional mothers, and mothers with higher educational levels. However, even after including these parental socioeconomic factors in the analysis, a significant increase in preterm births and LBW was observed following the onset of the pandemic. This suggests that factors other than changes in maternal characteristics also played a role in the relationship between the pandemic and birth outcomes. Specifically, after controlling for individual characteristics, the long-standing trend of increasing rates of preterm birth and LBW persisted. These findings diverge from those of Hwang et al. ( 2022 ), who analyzed the impact of the COVID-19 pandemic on birth outcomes using the same dataset [10]. Their findings indicate a negative correlation between the pandemic and the incidence of preterm birth, LBW, and SGA. In light of these considerations, it seems reasonable to posit that Hwang et al. ( 2022 ) concentrated primarily on the initial stages of the pandemic, given that the data analyzed spanned only up to 2020. As previously noted, the number of pregnant women infected with SARS-CoV-2 was relatively low by 2020, with approximately 700 documented cases [10, 28]. In contrast, our study included birth data until 2022, which included the period during which the Omicron variant was the most prevalent, with the highest number of confirmed cases [29]. This discrepancy in timeframes may contribute to the observed disparate outcomes. Furthermore, these disparate results indirectly indicate that SARS-CoV-2 infection may contribute to the relationship between COVID-19 and adverse birth outcomes. The existing literature supports the hypothesis that there is an association between SARS-CoV-2 infection and increased risk of preterm birth [24, 30]. For LBW, there was a tendency for a short-term decrease but a more pronounced increase in the positive trend than that observed in previous periods. The initial decline in LBW infants following the onset of the global pandemic may have been due to factors such as increased rest, reduced stress as people stayed at home, and relatively mild non-pharmaceutical interventions. Unlike other countries, South Korea did not impose market closures or lockdowns, which could have affected lifestyle, physical activity, and access to healthcare for pregnant women [10]. However, the increasing trend in LBW may be attributed to prolonged economic adversity, constrained access to healthcare, and persistent stress, which have deleterious effects on maternal and fetal health. The observed upward trend in the incidence of preterm births and the associated level changes can be attributed to an increase in the number of caesarean sections. Generally, there is a correlation between caesarean sections and an elevated risk of preterm birth (PTB) as well as subsequent adverse neurological outcomes in infants [31–33]. As reported by Kim et al. ( 2023 ), the prevalence of caesarean sections in South Korea has notably increased, from 26.9% in 2012 to 58.7% in 2021. Furthermore, the data indicate that the rate of caesarean sections increased more rapidly in younger age groups (i.e., those under 25 years and between 25 and 34 years) than in the group aged 35 years and above, following the onset of the pandemic [34]. This trend aligns with the findings of our study, which revealed a significant level and trend change in preterm births exclusively among women aged < 35 years. Subgroup analysis was conducted, which revealed that adverse birth outcomes following the onset of the pandemic were disproportionately concentrated among women under the age of 35, those with a college education or higher, and those who were employed. This pattern stands in contrast to the conventional view that socio-demographically disadvantaged women – typically older, less educated, or economically marginalized – are the most vulnerable during public health crises. The findings of this study indicate the emergence of novel forms of vulnerability, arising from occupational exposure and work-related stressors, affecting pregnant women who are not traditionally considered to be at risk. These findings are consistent with those of previous studies, which have emphasized the heightened infection risks faced by pregnant women in the workforce [37–41]. The findings of this study carry significant policy implications. Current maternal protection policies frequently adopt a universal approach, focusing on clinical risk or socioeconomic disadvantage, without adequately accounting for occupational health vulnerabilities among pregnant women. In order to address this gap, it is essential that pandemic preparedness and labor policy include proactive safeguards for pregnant workers. Such safeguards should include early eligibility for remote work, flexible sick leave, job protection during quarantine, and priority access to vaccination and prenatal services. In the absence of targeted interventions, pregnant women in the workforce, particularly those engaged in essential or high-contact roles, may persistently encounter avoidable risks to maternal and neonatal health in future public health emergencies. The present study has several key strengths. We conducted an in-depth examination of the impact of the COVID-19 pandemic on various birth outcomes using nationally representative birth data up to the year 2022. By employing an interrupted time series (ITSA) model that accounts for temporal trends and a linear probability model that adjusts for parental characteristics, we were able to comprehensively consider a range of risk factors relevant to the pandemic’s effects on birth outcomes. This dual approach allowed for both population-level and individual-level insights. In addition, subgroup analyses based on maternal characteristics were conducted to identify groups that were more vulnerable to the effects of COVID-19. However, the study has several limitations. First, it lacked data on important clinical covariates such as the indications for caesarean sections, pregnancy complications, and maternal SARS-CoV-2 infection status, which could have further explained the observed associations. Second, the statistical methods used rely on certain assumptions. The ITSA model assumes a stable pre-pandemic trend and no time-varying unmeasured confounding, while the linear probability model assumes a linear relationship between predictors and outcomes and may produce biased estimates when probabilities are near 0 or 1. These limitations should be considered when interpreting the results. Nonetheless, the findings contribute valuable evidence on how the pandemic may have affected maternal and child health over an extended period. These results underscore the importance of continuously monitoring birth outcomes during public health emergencies and tailoring interventions to support vulnerable subgroups. 5. Conclusions The COVID-19 pandemic appears to have contributed to worsening birth outcomes, both immediately and in the later phases. This pattern persisted even after accounting for parental socioeconomic characteristics. Interestingly, women who were younger, educated, and employed—groups not typically considered vulnerable—were more adversely affected, suggesting that infection risk and work-related stress may have introduced new forms of vulnerability. These findings underscore the need to strengthen not only healthcare and social support systems for pregnant women, but also protective policies for those in the workforce during public health crises. Declarations a. Ethics approval and consent to participate This study analyzed publicly available and routinely collected secondary data; therefore, ethical approval was not required. Clinical trial number: not applicable b. Consent for publication Not applicable c. Availability of data and materials These data are available from a public open-access repository. All data are publicly available from the MDIS (https://mdis.kostat.go.kr/index.do). d. Competing interests None declared e. Funding None declared f. Acknowledgments None Author Contribution YK contributed to the conceptualization and design of the study, data collection and analysis, visualization, drafting of the original manuscript, and participated in the revision and editing of the manuscript. WK contributed to the conceptualization and methodological development, supervised the overall study process, secured funding, managed the project administration, and participated in the revision and editing of the manuscript. Data Availability These data are available from a public open-access repository. All data are publicly available from the MDIS ( [https://mdis.kostat.go.kr/index.do](https:/mdis.kostat.go.kr/index.do) ). References Statistics K. Birth and Death Statistics 2023. Sejong: Statistics Korea; 2023. Statistics K. Birth and Death Statistics 2024. Sejong: Statistics Korea; 2024. Eves R, Wolke D, Spiegler J, Lemola S. Association of Birth Weight Centiles and Gestational age with Cognitive Performance at age 5 years. JAMA Netw Open. 2023;6(8):e2331815–e. Merid SK, Novoloaca A, Sharp GC, Küpers LK, Kho AT, Roy R, et al. Epigenome-wide meta-analysis of blood DNA methylation in newborns and children identifies numerous loci related to gestational age. Genome Med. 2020;12:1–17. Ahmed A, Kramer MS, Bernard JY, Perez Trejo ME, Martin RM, Oken E, et al. Early childhood growth trajectory and later cognitive ability: evidence from a large prospective birth cohort of healthy term-born children. Int J Epidemiol. 2020;49(6):1998–2009. Been JV, Ochoa LB, Bertens LC, Schoenmakers S, Steegers EA, Reiss IK. Impact of COVID-19 mitigation measures on the incidence of preterm birth: a national quasi-experimental study. Lancet Public Health. 2020;5(11):e604–11. Calvert C, Brockway M, Zoega H, Miller JE, Been JV, Amegah AK, et al. Changes in preterm birth and stillbirth during COVID-19 lockdowns in 26 countries. Nat Hum Behav. 2023;7(4):529–44. Harvey EM, McNeer E, McDonald MF, Shapiro-Mendoza CK, Dupont WD, Barfield W, et al. Association of preterm birth rate with COVID-19 statewide stay-at-home orders in Tennessee. JAMA Pediatr. 2021;175(6):635–7. Kim S-Y, Kim S-Y, Kil K, Lee Y. Impact of COVID-19 mitigation policy in South Korea on the reduction of preterm or low birth weight birth rate: a single center experience. Children. 2021;8(5):332. Hwang J, Moon S, Cho K-D, Oh M-J, Hong SJ, Cho GJ. Changes in preterm birth and birthweight during the SARS-CoV-2 pandemic: a nationwide study in South Korea. Sci Rep. 2022;12(1):16288. Xie Y, Mu Y, Chen P, Liu Z, Wang Y, Li Q, et al. Interrupted-time-series analysis of the immediate impact of COVID-19 mitigation measures on preterm birth in China. Nat Commun. 2022;13(1):5190. Yalçin SS, Boran P, Tezel B, Şahlar TE, Özdemir P, Keskinkiliç B, et al. Effects of the COVID-19 pandemic on perinatal outcomes: a retrospective cohort study from Turkey. BMC Pregnancy Childbirth. 2022;22(1):51. Handley SC, Mullin AM, Elovitz MA, Gerson KD, Montoya-Williams D, Lorch SA, et al. Changes in preterm birth phenotypes and stillbirth at 2 Philadelphia hospitals during the SARS-CoV-2 pandemic, March-June 2020. JAMA. 2021;325(1):87–9. Le Vu M, Matthes KL, Brabec M, Riou J, Skrivankova VW, Hösli I, et al. Health of singleton neonates in Switzerland through time and crises: a cross-sectional study at the population level, 2007–2022. BMC Pregnancy Childbirth. 2024;24(1):218. Molina RL, Tsai TC, Dai D, Soto M, Rosenthal N, Orav EJ, et al. Comparison of pregnancy and birth outcomes before vs during the COVID-19 pandemic. JAMA Netw open. 2022;5(8):e2226531–e. Ahmed T, Roberton T, Vergeer P, Hansen PM, Peters MA, Ofosu AA, et al. Healthcare utilization and maternal and child mortality during the COVID-19 pandemic in 18 low-and middle-income countries: An interrupted time-series analysis with mathematical modeling of administrative data. PLoS Med. 2022;19(8):e1004070. Yao X, Zhu L, Yin J, Wen J. Impacts of COVID-19 pandemic on preterm birth: a systematic review and meta-analysis. Public Health. 2022;213:127–34. Chmielewska B, Barratt I, Townsend R, Kalafat E, van der Meulen J, Gurol-Urganci I, et al. Effects of the COVID-19 pandemic on maternal and perinatal outcomes: a systematic review and meta-analysis. Lancet Global Health. 2021;9(6):e759–72. Lee JY, Park J, Lee M, Han M, Jung I, Lim SM, et al. The impact of non-pharmaceutical interventions on premature births during the COVID-19 pandemic: A nationwide observational study in Korea. Front Pead. 2023;11:1140556. Delius M, Kolben T, Nußbaum C, Bogner-Flatz V, Delius A, Hahn L, et al. Changes in the rate of preterm infants during the COVID-19 pandemic Lockdown Period—Data from a large tertiary German University Center. Arch Gynecol Obstet. 2024;309(5):1925–33. Fallesen P, Oberndorfer M, Cozzani M. Changes in conception rates, not in pregnancy-related behaviour, likely caused decline in preterm births during the first year of the COVID‐19 pandemic. BJOG. 2023;130(10):1153. Gharacheh M, Kalan ME, Khalili N, Ranjbar F. An increase in cesarean section rate during the first wave of COVID-19 pandemic in Iran. BMC Public Health. 2023;23(1):936. Carvalho-Sauer R, Costa MCN, Teixeira MG, Flores-Ortiz R, Leal JTFM, Saavedra R et al. Maternal and perinatal health indicators in Brazil over a decade: assessing the impact of the COVID-19 pandemic and SARS-CoV-2 vaccination through interrupted time series analysis. Lancet Reg Health–Americas. 2024;35. Bahado-Singh R, Tarca AL, Hasbini YG, Sokol RJ, Keerthy M, Goyert G, et al. Maternal SARS-COV-2 infection and prematurity: the Southern Michigan COVID-19 collaborative. J Maternal-Fetal Neonatal Med. 2023;36(1):2199343. Alberton M, Rosa VM, Iser BPM. Prevalência e tendência temporal da prematuridade no Brasil antes e durante a pandemia de covid-19: análise da série histórica 2011–2021. Epidemiologia e Serviços de Saúde. 2023;32:e2022603. Diseases KSoI. Report on the epidemiological features of coronavirus disease 2019 (COVID-19) outbreak in the Republic of Korea from January 19 to March 2, 2020. J Korean Med Sci. 2020;35(10). Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. lancet. 2008;371(9606):75–84. Regular Briefing: Children, adolescents and pregnant women vaccinations start today. 2021 [Available from: http://ncov.mohw.go.kr / tcmBo ardVi ew. do? brdId = 3& brdGu bun = 31& dataG ubun= & ncvCo ntSeq = 6016& contS eq = 6016& board_ id = 312& gubun = BDJ. Kim D, Ali ST, Kim S, Jo J, Lim J-S, Lee S, et al. Estimation of serial interval and reproduction number to quantify the transmissibility of SARS-CoV-2 omicron variant in South Korea. Viruses. 2022;14(3):533. Klein AZ, Kunatharaju S, Golder S, Levine LD, Figueiredo JC, Gonzalez-Hernandez G. Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: A Retrospective Cohort Study Using Longitudinal Social Media Data. medRxiv. 2023. Morin C, Bokobza C, Fleiss B, Hill-Yardin EL, Van Steenwinckel J, Gressens P. Preterm birth by cesarean section: the gut-brain axis, a key regulator of brain development. Dev Neurosci. 2024;46(3):179–87. Sihombing JA, Miqbel M, Sirait BI. Relationship between Premature Rupture of the Membrane and Cesarean Delivery: Case from Jakarta, Indonesia. Asian J Res Infect Dis. 2023;12(4):41–51. Eriksson C, Jonsson M, Högberg U, Hesselman S. Fetal station at caesarean section and risk of subsequent preterm birth-A cohort study. Eur J Obstet Gynecol Reproductive Biology. 2022;275:18–23. Kim S, Oh J-W, Yun J-W. Narrative Review on the Trend of Childbirth in South Korea and Feasible Intervention to Reduce Cesarean Section Rate. J Korean Soc Matern Child Health. 2023;247(1):13. Ahmed AM, Pullenayegum E, McDonald SD, Beltempo M, Premji SS, Pole JD, Pechlivanoglou P. Association between preterm birth and economic and educational outcomes in adulthood: A population-based matched cohort study. PLoS ONE. 2024;19(11):e0311895. Islam MM. The effects of low birth weight on school performance and behavioral outcomes of elementary school children in Oman. Oman Med J. 2015;30(4):241. Corral-Gudino L, Del-Amo-Merino MP, Abadía-Otero J, Merino-Velasco I, Lorenzo-Fernández Y, García-Cruces-Méndez J et al. Impact of age on the transmission of SARS-CoV-2 in healthcare workers: Influence of nonoccupational risk factors. Wiener klinische Wochenschrift. 2024:1–10. Nguyen LH, Drew DA, Graham MS, Joshi AD, Guo C-G, Ma W, et al. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. Lancet Public Health. 2020;5(9):e475–83. Koh D. Occupational risks for COVID-19 infection. Oxford University Press UK; 2020. pp. 3–5. Miracle JE, Ganesh PR, Rose J, Terebuh P, Stange KC, Wolfe HM, Pope R. COVID-19 in pregnancy: occupations with higher density of population exposure associated with more severe disease. J Occup Environ Med. 2021;63(12):1024–8. Belingheri M, Paladino ME, Riva MA. (2020). Risk exposure to coronavirus disease 2019 in pregnant healthcare workers. J Occup Environ Med, 62(7), e370. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7351864","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514922878,"identity":"d59472d7-8736-4661-b63a-d8ed495934a9","order_by":0,"name":"Yoonkyoung Lee","email":"","orcid":"","institution":"Daejin University","correspondingAuthor":false,"prefix":"","firstName":"Yoonkyoung","middleName":"","lastName":"Lee","suffix":""},{"id":514922879,"identity":"46a2f538-b92f-4962-84e3-199c4ec4cd1f","order_by":1,"name":"Wankyo Chung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYHACZiC2gXESiNaSRrqWwyRoMTh+9rAxT8V5e3OJBMYPPxjS8glrOZOXnMxz5nbizhkJzJI9DDmWDYS0mB3IMT7M23Y7weBGAoM0A0OFAUFbzM6/AWk5Zw/UwvybOC03coyTedsOMG64kcAGtCWHsBb7G2+MDeecSU7ccOZhm2WPQRphLZL9OcYSbyrs7A2OJx++8aMimbAWEGDiAVOMDcAAJEoDUO0PIhWOglEwCkbBCAUAfVI46lyXN44AAAAASUVORK5CYII=","orcid":"","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Wankyo","middleName":"","lastName":"Chung","suffix":""}],"badges":[],"createdAt":"2025-08-12 06:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7351864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7351864/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91650525,"identity":"ebe6f93b-eda7-4e0a-8000-67dd3c3eed61","added_by":"auto","created_at":"2025-09-18 17:00:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96888,"visible":true,"origin":"","legend":"\u003cp\u003eInterrupted time-series analysis (ITSA) of birth outcomes\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7351864/v1/853b2eef05ba501da0bc9884.png"},{"id":91652101,"identity":"38c09d1b-55f7-47f5-85e3-a2ee1bce8bae","added_by":"auto","created_at":"2025-09-18 17:32:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1143637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7351864/v1/0f801470-8c46-45ec-9c5f-3abc40d38005.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Declining birth outcomes during the COVID-19 pandemic: a nationwide study in South Korea","fulltext":[{"header":"Significance","content":"\u003cp\u003ePrevious studies focused mainly on the short-term effects of COVID-19 on birth outcomes. By using national data up to 2022, our study captures both immediate and intermediate impacts, including those during the Omicron wave. Unlike earlier findings, we identified sustained increases in preterm births and low birth weight even after controlling for parent\u0026rsquo;s socioeconomic factors. Notably, these adverse outcomes were more pronounced among younger, highly educated, and employed women, underscoring the need for targeted healthcare and policy interventions to support these vulnerable groups during prolonged public health crises.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eSouth Korea is currently experiencing an unprecedented low birth rate, both domestically and globally. The birth rate declined to 0.72 in 2023, marking the first time when live births fell below 240,000 [1]. In 2024, the total fertility rate was 0.75, remaining at an exceptionally low level by both domestic and international standards [2]. However, the qualitative change in newborns\u0026rsquo; health is as pivotal as the quantitative decline in newborns. The gestational age and birth weight of newborns significantly impact their future health and intelligence [3\u0026ndash;5].\u003c/p\u003e\u003cp\u003eStatistical data from the Korea Institute for Health and Social Affairs indicate a notable increase in the proportion of preterm births (\u0026lt;\u0026thinsp;37 gestational weeks), from 6% in 2011 to 9.2% in 2021. Similarly, the proportion of babies born with low birth weight (LBW, \u0026lt;\u0026thinsp;2500 g) also significantly rose, from 5.2% in 2011 to 7.2% in 2021. These changes can be attributed to several factors, including increased proportion of women employed, higher maternal age, and the differential use of maternity care. Furthermore, the impact of the coronavirus disease (COVID-19) pandemic on birth outcomes is likely significant [6\u0026ndash;16].\u003c/p\u003e\u003cp\u003eThe available evidence on the impact of the COVID-19 pandemic on birth outcomes is inconclusive [17, 18]. A systematic review and meta-analysis conducted by Chmielewska et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) revealed no statistically significant overall decline in the incidence of preterm birth. However, a significant decline was observed in high-income countries [18]. Another systemic review and meta-analysis by Yao et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified a significant increase in average birth weight but no overall impact on LBW, macrosomia, small for gestational age (SGA), or large for gestational age (LGA) [17].\u003c/p\u003e\u003cp\u003eStudies conducted in Korea have yielded comparable results regarding birth outcomes, although the findings were inconsistent [9, 10, 19]. Lee et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed an increase in the incidence of preterm births during the global pandemic caused by SARS-CoV-2 in 2020 [19]. However, Hwang et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated a reduction in the prevalence of preterm birth, LBW, macrosomia, SGA, LGA, and inappropriate birth weight during the same period [10].\u003c/p\u003e\u003cp\u003eThese disparate findings can be attributed to a confluence of factors pertaining to the specific characteristics of SARS-CoV-2 and alterations in maternal and fetal attributes that have occurred during the pandemic. This association has been explored in a few studies that sought to elucidate its underlying mechanisms by examining variations in physical activity [20], conception rates [21], caesarean section rates [12, 22, 23], and clinical factors [24, 25]. Therefore, it is crucial to control for alterations in maternal and fetal characteristics throughout the pandemic, where data are accessible, to inform the formulation of appropriate policy responses to changes in birth outcomes.\u003c/p\u003e\u003cp\u003eThe most recent nationwide birth records from South Korea, extending up to 2022, were used to answer the following questions: (1) Were there significant changes in birth outcomes during the global pandemic caused by the novel coronavirus, SARS-CoV-2? (2) After controlling for parental and fetal attributes, did these significant changes persist? (3) Do these changes vary according to maternal characteristics?\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis study used nationwide complete birth certificate data provided by the Microdata Integrated Service of Statistics Korea over 13 years before the pandemic (2010\u0026ndash;2019) and 3 years during the pandemic (2020\u0026ndash;2022), incorporating the most recent data available at the time of analysis. The data provided information on the birth weight and gestational age of all newborns in Korea each year, as well as various details about the parents, including their occupation, age, educational level, and birth region. Among a total of 4,911,893 births during the study period, 4,719,620 were singleton births. Of these, 316,379 cases (6.7%) with missing values for parental occupation, parental age, parental educational level, birth weight, or gestational age were excluded. As a result, 4,403,241 singleton infants were included in the final analysis.\u003c/p\u003e\u003cp\u003eIn Korea, the first COVID-19 case was reported in January 2020. However, the number of confirmed cases began to surge in February 2020 owing to a cluster infection originating from a religious group [26]. However, if the period of exposure to the novel COVID-19 was defined as January or February 2020, parents of newborns during that period would have experienced the pandemic only during the late stages of pregnancy. Accordingly, to assess the influence of exposure to the virus during the early, mid, and late stages of pregnancy, we delineated the period of exposure as December 2020. This timeframe encompassed a 10-month gestational period, commencing with a notable outbreak in February 2020.\u003c/p\u003e\u003cp\u003eThis study examined the impact of the COVID-19 pandemic on birth outcomes. The primary outcome was preterm birth, defined as a gestational age of less than 37 weeks. Additionally, we examined LBW, defined as less than 2,500 g. The analysis included confounding factors based on those identified in the scientific literature as related to preterm birth and LBW. These include sociodemographic characteristics (region, mother's age, mother's education level, and parental occupation) and obstetric factors (whether pregnancy involved multiple births or maternal parity) [11, 27].\u003c/p\u003e\u003cp\u003eDescriptive analysis was conducted to examine the characteristics of mothers and newborns from 2010 to 2022. A chi-square test was conducted to ascertain whether there were any significant differences in the characteristics of mothers and newborns before and after the influence point in December 2020.\u003c/p\u003e\u003cp\u003eTo investigate the influence of the COVID-19 pandemic on perinatal outcomes at the population level, we employed a single-group interrupted time-series analysis (ITSA). This approach was used to ascertain whether there were discernible differences in the incidences of preterm births and LBW infants before and after the onset of the crisis.\u003c/p\u003e\u003cp\u003eThe single-group ITSA regression model is based on the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{t}={\\beta\\:}_{0}+\\:{\\beta\\:}_{1}{T}_{t}+\\:{\\beta\\:}_{2}{COVID}_{t}+{\\beta\\:}_{3}{T}_{t}{COVID}_{t}+{seasonality}_{t}+\\:{ϵ}_{t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the monthly rate of the birth outcome variables of interest, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the time elapsed since the commencement of the study, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{COVID}_{t}\\)\u003c/span\u003e\u003c/span\u003e is a dummy variable defined as 0 for the period from January 2010 to November 2020 and 1 for the period from December 2020 to December 2022. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{t}{X}_{t}\\:\\)\u003c/span\u003e\u003c/span\u003eis an interaction term. To account for seasonal effects, we used year and month variables as categorical variables. Hence, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e measures the baseline level of birth outcome, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\:\\)\u003c/span\u003e\u003c/span\u003ethe trend in the pre-intervention phase, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\:\\)\u003c/span\u003e\u003c/span\u003ethe level change following the COVID-19, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{3}\\:\\)\u003c/span\u003e\u003c/span\u003ethe trend change following the COVID-19. To account for serial correlation in the residuals, we employed Newey\u0026ndash;West heteroskedasticity and autocorrelation-consistent standard errors.\u003c/p\u003e\u003cp\u003eWhen analyzing data on a monthly basis, it is important to note that individual-level variables cannot be controlled, which represents a limitation of such an approach. To address this issue, we conducted further investigations into the impact of the COVID-19 pandemic on birth outcomes using an individual-level dataset. A linear probability model was employed to the individual-level birth data. We examine both level and trend changes in the two birth outcomes. In the analysis, we controlled for the infant's sex, maternal education level, parental occupation, maternal age, and infant parity as well as seasonality. To ascertain whether the impact of the pandemic differed according to maternal characteristics, additional subgroup analyses were conducted based on maternal age, occupation, and educational level.\u003c/p\u003e\u003cp\u003eThe regression model is specified as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{it}=\\alpha\\:+{\\delta\\:}_{1}{Post}_{t}+{\\delta\\:}_{2}{Trend}_{t}+{\\delta\\:}_{3}\\left({Post}_{t}\\times\\:{Trend}_{t}\\right)+{X}_{it}\\gamma\\:+{Seasonality}_{t}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{it}\\)\u003c/span\u003e\u003c/span\u003e​ is a binary indicator equal to 1 if infant \u003cem\u003ei\u003c/em\u003e born at time \u003cem\u003et\u003c/em\u003e experienced the adverse birth outcome and 0 otherwise. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Post}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ is a dummy variable indicating the post-pandemic period (defined as 1 for births between December 2020 and December 2022, and 0 otherwise), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Trend}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ is a continuous variable indicating time elapsed since the beginning of the observation period, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Post}_{t}\\times\\:{Trend}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ is an interaction term capturing changes in trend following the onset of the pandemic. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a vector of individual-level covariates including infant\u0026rsquo;s sex, maternal education, parental occupation, maternal age, and infant parity. We also included fixed effects for month and year of birth to account for seasonality. Robust standard errors clustered at the monthly level were used to correct for heteroskedasticity and serial correlation.\u003c/p\u003e\u003cp\u003eThe significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All statistical analyses were performed using Stata version 18.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Trends and Level Changes in Birth Outcomes\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the ITSA model results for birth outcomes. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the preterm birth rate increased by 0.06% (95% CI\u0026thinsp;=\u0026thinsp;0.05\u0026ndash;0.07%) during the pre-pandemic period. In the first month of the COVID-19 period, there was an immediate increase in the preterm birth rate of 0.33% (95% CI\u0026thinsp;=\u0026thinsp;0.06\u0026ndash;0.60%), followed by a slope change of 0.01% percentage points per month (95% CI = \u0026minus;\u0026thinsp;0.01\u0026ndash;0.03%), compared to the pre-pandemic trend. For LBW, the pre-pandemic slope was 0.05% (95% CI\u0026thinsp;=\u0026thinsp;0.04\u0026ndash;0.06%) per month, and this increased further by 0.02% percentage points per month (95% CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.03%) following the onset of the pandemic. No statistically significant level change was observed for LBW.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChanges in birth outcomes during COVID-19 pandemic (interrupted time-series analysis)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreterm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u0026thinsp;~\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u0026thinsp;~\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;~\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow birth weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u0026thinsp;~\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.13\u0026thinsp;~\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u0026thinsp;~\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e**[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]**\u003c/p\u003e\u003cp\u003e**[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]**\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Maternal sociodemographic characteristics before and during COVID-19\u003c/h2\u003e\u003cp\u003eDuring the pre-COVID-19 period (January 1, 2010, to November 30, 2020), 3,965,710 singleton infants were delivered. In contrast, during the post-COVID-19 period (December 1, 2020, to December 31, 2022), this figure decreased to 437,531. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the maternal sociodemographic characteristics and the frequencies and proportions of preterm births and LBW, along with the results of the chi-square tests.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of maternal demographics and birth outcomes in the Pre- and Post-COVID-19 Periods (N\u0026thinsp;=\u0026thinsp;4,403,241)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-COVID 19 Period\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCOVID-19 Period\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJan 2010\u003c/p\u003e\u003cp\u003e- Nov 2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDec 2020\u003c/p\u003e\u003cp\u003e- Dec 2022\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,965,710 (100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e437,531 (100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMaternal age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.63 (4.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.40 (4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,030,253 (76.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e267,602 (61.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e935,457 (23.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e169,929 (38.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eEducational Attainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLess than middle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68,707 (1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,060 (1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e934,698 (23.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68,546 (15.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,674,159 (67.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e323,947 (79.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMaster\u0026rsquo;s degree or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e288,146 (7.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39,978 (9.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHaving job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e629,234 (15.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104,907 (23.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot professional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e960,902 (24.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e173,233 (39.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,375,574 (59.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e159,391 (36.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eThe number of babies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,087,167 (52.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e255,112 (58.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,494,856 (37.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e148,592 (33.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMore than 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e383,687 (9.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33,827 (7.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGestational Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.68 (1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.32 (1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e190,108 (4.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25,973 (5.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,775,602 (95.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e411,558 (94.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBirth Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.24 (0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.19 (0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146,778 (3.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18,746 (4.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,818,932 (96.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e418,785 (95.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e**[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]**\u003c/p\u003e\u003cp\u003eIn the pre-COVID-19 period, 23.59% of the mothers were aged 35 years or older. This figure increased to 38.84% during the pandemic period. The chi-square test yielded statistically significant results, indicating a notable increase in the proportion of mothers aged 35 years or older compared to the pre-pandemic period. The mean gestational age was 38.68 weeks during the pre-pandemic period and 38.32 weeks during the pandemic period, indicating a downward trend. Upon examination of preterm birth rates with a cutoff of 37 weeks, the proportion of preterm births was 4.79% before the pandemic and 5.94% during the pandemic, indicating an increasing trend. The chi-square test yielded significantly different results for the comparison of preterm birth rates before and during the pandemic.\u003c/p\u003e\u003cp\u003eA tendency towards improvement in maternal socioeconomic status was observed following the pandemic compared to the period preceding it. Compared to the period preceding the pandemic, there was a notable increase in the proportion of mothers with bachelor's, master's, or advanced degrees after the pandemic. An examination of maternal occupations revealed a notable decline in the proportion of mothers without employment, from 59.90% before the pandemic to 36.43% during the pandemic. Conversely, the proportion of mothers in professional occupations increased significantly from 15.87% before the pandemic to 23.98% during the pandemic.\u003c/p\u003e\u003cp\u003eExamination of the variables related to birth weight revealed that the average birth weight was 3.24 kg before the pandemic and 3.19 kg during the pandemic. Regarding the criterion for LBW, defined as less than 2.5 kg, there was a notable increase in the proportion of LBW infants from 3.70% before the pandemic to 4.28%.\u003c/p\u003e\u003cp\u003eThe ITSA results are based on monthly aggregated data, limiting the ability to control for individual-level variables. To address this, we employed a linear probability model using individual-level birth data (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After controlling for seasonality and individual-level covariates, the change in slope following COVID-19 remained statistically significant for both preterm birth (0.01%, 95% CI = \u0026minus;\u0026thinsp;0.00\u0026ndash;0.02%) and LBW (0.02%, 95% CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.03%). These results indicate modest but persistent increases in both outcomes. The immediate level change in preterm birth remained significant (0.15%, 95% CI = \u0026minus;\u0026thinsp;0.00\u0026ndash;0.30%), while the level change in LBW (\u0026ndash;0.11%, 95% CI = \u0026minus;\u0026thinsp;0.24\u0026ndash;0.02%) was not significant in Model 1 but became marginally significant at the 10% level after full adjustment in Model 2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChanges in birth outcomes during the COVID-19 pandemic (Linear Probability Model)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreterm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u0026thinsp;~\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u0026thinsp;~\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u0026thinsp;~\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u0026thinsp;~\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u0026thinsp;~\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u0026thinsp;~\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow birth weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u0026thinsp;~\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.04\u0026thinsp;~\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.15\u0026thinsp;~\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.24~-0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u0026thinsp;~\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u0026thinsp;~\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Model 1: adjusted for trends (year, month)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Model 2: adjusted for parental age, sex, region, parental job, parental educational attainment, parity, month, and year.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e**[Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]**\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Subgroup analysis by maternal characteristic\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the findings of subgroup analysis based on maternal characteristics. A significant increase in the incidence of preterm births (level change) was observed in the immediate post-pandemic period compared with the pre-pandemic period among women of working age (0.16%, 95% CI = \u0026minus;\u0026thinsp;0.03\u0026ndash;0.36%), those under 35 years of age (0.19%, 95% CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.37%), and those with university education or above (0.19%, 95% CI\u0026thinsp;=\u0026thinsp;0.03\u0026ndash;0.35%). These represent absolute changes in probability. Moreover, these groups demonstrated a pronounced and persistent increase in preterm birth rates (slope change) relative to pre-pandemic levels. Similarly, an increased incidence of LBW (slope change) has been documented in younger, highly educated, and working women.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analyses based on maternal characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePreterm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eLow birth weight\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline Slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStay-at-home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.06\u0026thinsp;~\u0026thinsp;0.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.04\u0026thinsp;~\u0026thinsp;0.06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.07\u0026thinsp;~\u0026thinsp;0.09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.04\u0026thinsp;~\u0026thinsp;0.06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.06\u0026thinsp;~\u0026thinsp;0.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.03\u0026thinsp;~\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.06\u0026thinsp;~\u0026thinsp;0.10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.05\u0026thinsp;~\u0026thinsp;0.09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh school or lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.07\u0026thinsp;~\u0026thinsp;0.11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.04\u0026thinsp;~\u0026thinsp;0.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003euniversity or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.06\u0026thinsp;~\u0026thinsp;0.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.04\u0026thinsp;~\u0026thinsp;0.06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel Change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStay-at-home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.10\u0026thinsp;~\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.30\u0026thinsp;~\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u0026thinsp;~\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.28\u0026thinsp;~\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.01\u0026thinsp;~\u0026thinsp;0.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.16\u0026thinsp;~\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.19\u0026thinsp;~\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-0.52~-0.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh school or lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.31\u0026thinsp;~\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.57\u0026thinsp;~\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003euniversity or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.03\u0026thinsp;~\u0026thinsp;0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.22\u0026thinsp;~\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope Change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStay-at-home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;~\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.00\u0026thinsp;~\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.00\u0026thinsp;~\u0026thinsp;0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.01\u0026thinsp;~\u0026thinsp;0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.00\u0026thinsp;~\u0026thinsp;0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u0026thinsp;~\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;~\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.01\u0026thinsp;~\u0026thinsp;0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh school or lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;~\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.01\u0026thinsp;~\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003euniversity or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.00\u0026thinsp;~\u0026thinsp;0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.01\u0026thinsp;~\u0026thinsp;0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignificant values are shown in bold.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e**[Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]**\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the context of the global pandemic caused by the SARS-CoV-2 virus, we employed national representative singleton birth data to assess immediate level and trend changes in preterm and LBW outcomes following the onset of the pandemic in South Korea. This study defined the exposure timeframe to SARS-CoV-2 as December 2020, encompassing the 10-month pregnancy period starting from a notable outbreak in February 2020 in Korea. This study aimed to assess its impact on pregnant women at all stages of pregnancy.\u003c/p\u003e\u003cp\u003eDescriptive analysis revealed significant differences in the socioeconomic status of mothers who gave birth before and after the onset of the global pandemic. There was an increase in the numbers of older mothers, professional mothers, and mothers with higher educational levels. However, even after including these parental socioeconomic factors in the analysis, a significant increase in preterm births and LBW was observed following the onset of the pandemic. This suggests that factors other than changes in maternal characteristics also played a role in the relationship between the pandemic and birth outcomes. Specifically, after controlling for individual characteristics, the long-standing trend of increasing rates of preterm birth and LBW persisted. These findings diverge from those of Hwang et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who analyzed the impact of the COVID-19 pandemic on birth outcomes using the same dataset [10]. Their findings indicate a negative correlation between the pandemic and the incidence of preterm birth, LBW, and SGA. In light of these considerations, it seems reasonable to posit that Hwang et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) concentrated primarily on the initial stages of the pandemic, given that the data analyzed spanned only up to 2020. As previously noted, the number of pregnant women infected with SARS-CoV-2 was relatively low by 2020, with approximately 700 documented cases [10, 28]. In contrast, our study included birth data until 2022, which included the period during which the Omicron variant was the most prevalent, with the highest number of confirmed cases [29]. This discrepancy in timeframes may contribute to the observed disparate outcomes. Furthermore, these disparate results indirectly indicate that SARS-CoV-2 infection may contribute to the relationship between COVID-19 and adverse birth outcomes. The existing literature supports the hypothesis that there is an association between SARS-CoV-2 infection and increased risk of preterm birth [24, 30].\u003c/p\u003e\u003cp\u003eFor LBW, there was a tendency for a short-term decrease but a more pronounced increase in the positive trend than that observed in previous periods. The initial decline in LBW infants following the onset of the global pandemic may have been due to factors such as increased rest, reduced stress as people stayed at home, and relatively mild non-pharmaceutical interventions. Unlike other countries, South Korea did not impose market closures or lockdowns, which could have affected lifestyle, physical activity, and access to healthcare for pregnant women [10]. However, the increasing trend in LBW may be attributed to prolonged economic adversity, constrained access to healthcare, and persistent stress, which have deleterious effects on maternal and fetal health.\u003c/p\u003e\u003cp\u003eThe observed upward trend in the incidence of preterm births and the associated level changes can be attributed to an increase in the number of caesarean sections. Generally, there is a correlation between caesarean sections and an elevated risk of preterm birth (PTB) as well as subsequent adverse neurological outcomes in infants [31\u0026ndash;33]. As reported by Kim et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the prevalence of caesarean sections in South Korea has notably increased, from 26.9% in 2012 to 58.7% in 2021. Furthermore, the data indicate that the rate of caesarean sections increased more rapidly in younger age groups (i.e., those under 25 years and between 25 and 34 years) than in the group aged 35 years and above, following the onset of the pandemic [34]. This trend aligns with the findings of our study, which revealed a significant level and trend change in preterm births exclusively among women aged\u0026thinsp;\u0026lt;\u0026thinsp;35 years.\u003c/p\u003e\u003cp\u003eSubgroup analysis was conducted, which revealed that adverse birth outcomes following the onset of the pandemic were disproportionately concentrated among women under the age of 35, those with a college education or higher, and those who were employed. This pattern stands in contrast to the conventional view that socio-demographically disadvantaged women \u0026ndash; typically older, less educated, or economically marginalized \u0026ndash; are the most vulnerable during public health crises. The findings of this study indicate the emergence of novel forms of vulnerability, arising from occupational exposure and work-related stressors, affecting pregnant women who are not traditionally considered to be at risk. These findings are consistent with those of previous studies, which have emphasized the heightened infection risks faced by pregnant women in the workforce [37\u0026ndash;41].\u003c/p\u003e\u003cp\u003eThe findings of this study carry significant policy implications. Current maternal protection policies frequently adopt a universal approach, focusing on clinical risk or socioeconomic disadvantage, without adequately accounting for occupational health vulnerabilities among pregnant women. In order to address this gap, it is essential that pandemic preparedness and labor policy include proactive safeguards for pregnant workers. Such safeguards should include early eligibility for remote work, flexible sick leave, job protection during quarantine, and priority access to vaccination and prenatal services. In the absence of targeted interventions, pregnant women in the workforce, particularly those engaged in essential or high-contact roles, may persistently encounter avoidable risks to maternal and neonatal health in future public health emergencies.\u003c/p\u003e\u003cp\u003eThe present study has several key strengths. We conducted an in-depth examination of the impact of the COVID-19 pandemic on various birth outcomes using nationally representative birth data up to the year 2022. By employing an interrupted time series (ITSA) model that accounts for temporal trends and a linear probability model that adjusts for parental characteristics, we were able to comprehensively consider a range of risk factors relevant to the pandemic\u0026rsquo;s effects on birth outcomes. This dual approach allowed for both population-level and individual-level insights. In addition, subgroup analyses based on maternal characteristics were conducted to identify groups that were more vulnerable to the effects of COVID-19. However, the study has several limitations. First, it lacked data on important clinical covariates such as the indications for caesarean sections, pregnancy complications, and maternal SARS-CoV-2 infection status, which could have further explained the observed associations. Second, the statistical methods used rely on certain assumptions. The ITSA model assumes a stable pre-pandemic trend and no time-varying unmeasured confounding, while the linear probability model assumes a linear relationship between predictors and outcomes and may produce biased estimates when probabilities are near 0 or 1. These limitations should be considered when interpreting the results.\u003c/p\u003e\u003cp\u003eNonetheless, the findings contribute valuable evidence on how the pandemic may have affected maternal and child health over an extended period. These results underscore the importance of continuously monitoring birth outcomes during public health emergencies and tailoring interventions to support vulnerable subgroups.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe COVID-19 pandemic appears to have contributed to worsening birth outcomes, both immediately and in the later phases. This pattern persisted even after accounting for parental socioeconomic characteristics. Interestingly, women who were younger, educated, and employed\u0026mdash;groups not typically considered vulnerable\u0026mdash;were more adversely affected, suggesting that infection risk and work-related stress may have introduced new forms of vulnerability. These findings underscore the need to strengthen not only healthcare and social support systems for pregnant women, but also protective policies for those in the workforce during public health crises.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003ea. Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed publicly available and routinely collected secondary data; therefore, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Clinical trial number: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese data are available from a public open-access repository. All data are publicly available from the MDIS (https://mdis.kostat.go.kr/index.do).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed. Competing interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef. Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYK contributed to the conceptualization and design of the study, data collection and analysis, visualization, drafting of the original manuscript, and participated in the revision and editing of the manuscript. WK contributed to the conceptualization and methodological development, supervised the overall study process, secured funding, managed the project administration, and participated in the revision and editing of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThese data are available from a public open-access repository. All data are publicly available from the MDIS ( [https://mdis.kostat.go.kr/index.do](https:/mdis.kostat.go.kr/index.do) ).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStatistics K. Birth and Death Statistics 2023. Sejong: Statistics Korea; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStatistics K. Birth and Death Statistics 2024. Sejong: Statistics Korea; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEves R, Wolke D, Spiegler J, Lemola S. Association of Birth Weight Centiles and Gestational age with Cognitive Performance at age 5 years. JAMA Netw Open. 2023;6(8):e2331815\u0026ndash;e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMerid SK, Novoloaca A, Sharp GC, K\u0026uuml;pers LK, Kho AT, Roy R, et al. Epigenome-wide meta-analysis of blood DNA methylation in newborns and children identifies numerous loci related to gestational age. Genome Med. 2020;12:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed A, Kramer MS, Bernard JY, Perez Trejo ME, Martin RM, Oken E, et al. Early childhood growth trajectory and later cognitive ability: evidence from a large prospective birth cohort of healthy term-born children. Int J Epidemiol. 2020;49(6):1998\u0026ndash;2009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeen JV, Ochoa LB, Bertens LC, Schoenmakers S, Steegers EA, Reiss IK. Impact of COVID-19 mitigation measures on the incidence of preterm birth: a national quasi-experimental study. Lancet Public Health. 2020;5(11):e604\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalvert C, Brockway M, Zoega H, Miller JE, Been JV, Amegah AK, et al. Changes in preterm birth and stillbirth during COVID-19 lockdowns in 26 countries. Nat Hum Behav. 2023;7(4):529\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarvey EM, McNeer E, McDonald MF, Shapiro-Mendoza CK, Dupont WD, Barfield W, et al. Association of preterm birth rate with COVID-19 statewide stay-at-home orders in Tennessee. JAMA Pediatr. 2021;175(6):635\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim S-Y, Kim S-Y, Kil K, Lee Y. Impact of COVID-19 mitigation policy in South Korea on the reduction of preterm or low birth weight birth rate: a single center experience. Children. 2021;8(5):332.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHwang J, Moon S, Cho K-D, Oh M-J, Hong SJ, Cho GJ. Changes in preterm birth and birthweight during the SARS-CoV-2 pandemic: a nationwide study in South Korea. Sci Rep. 2022;12(1):16288.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie Y, Mu Y, Chen P, Liu Z, Wang Y, Li Q, et al. Interrupted-time-series analysis of the immediate impact of COVID-19 mitigation measures on preterm birth in China. Nat Commun. 2022;13(1):5190.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYal\u0026ccedil;in SS, Boran P, Tezel B, Şahlar TE, \u0026Ouml;zdemir P, Keskinkili\u0026ccedil; B, et al. Effects of the COVID-19 pandemic on perinatal outcomes: a retrospective cohort study from Turkey. BMC Pregnancy Childbirth. 2022;22(1):51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHandley SC, Mullin AM, Elovitz MA, Gerson KD, Montoya-Williams D, Lorch SA, et al. Changes in preterm birth phenotypes and stillbirth at 2 Philadelphia hospitals during the SARS-CoV-2 pandemic, March-June 2020. JAMA. 2021;325(1):87\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLe Vu M, Matthes KL, Brabec M, Riou J, Skrivankova VW, H\u0026ouml;sli I, et al. Health of singleton neonates in Switzerland through time and crises: a cross-sectional study at the population level, 2007\u0026ndash;2022. BMC Pregnancy Childbirth. 2024;24(1):218.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMolina RL, Tsai TC, Dai D, Soto M, Rosenthal N, Orav EJ, et al. Comparison of pregnancy and birth outcomes before vs during the COVID-19 pandemic. JAMA Netw open. 2022;5(8):e2226531\u0026ndash;e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed T, Roberton T, Vergeer P, Hansen PM, Peters MA, Ofosu AA, et al. Healthcare utilization and maternal and child mortality during the COVID-19 pandemic in 18 low-and middle-income countries: An interrupted time-series analysis with mathematical modeling of administrative data. PLoS Med. 2022;19(8):e1004070.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao X, Zhu L, Yin J, Wen J. Impacts of COVID-19 pandemic on preterm birth: a systematic review and meta-analysis. Public Health. 2022;213:127\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChmielewska B, Barratt I, Townsend R, Kalafat E, van der Meulen J, Gurol-Urganci I, et al. Effects of the COVID-19 pandemic on maternal and perinatal outcomes: a systematic review and meta-analysis. Lancet Global Health. 2021;9(6):e759\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee JY, Park J, Lee M, Han M, Jung I, Lim SM, et al. The impact of non-pharmaceutical interventions on premature births during the COVID-19 pandemic: A nationwide observational study in Korea. Front Pead. 2023;11:1140556.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelius M, Kolben T, Nu\u0026szlig;baum C, Bogner-Flatz V, Delius A, Hahn L, et al. Changes in the rate of preterm infants during the COVID-19 pandemic Lockdown Period\u0026mdash;Data from a large tertiary German University Center. Arch Gynecol Obstet. 2024;309(5):1925\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFallesen P, Oberndorfer M, Cozzani M. Changes in conception rates, not in pregnancy-related behaviour, likely caused decline in preterm births during the first year of the COVID‐19 pandemic. BJOG. 2023;130(10):1153.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGharacheh M, Kalan ME, Khalili N, Ranjbar F. An increase in cesarean section rate during the first wave of COVID-19 pandemic in Iran. BMC Public Health. 2023;23(1):936.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarvalho-Sauer R, Costa MCN, Teixeira MG, Flores-Ortiz R, Leal JTFM, Saavedra R et al. Maternal and perinatal health indicators in Brazil over a decade: assessing the impact of the COVID-19 pandemic and SARS-CoV-2 vaccination through interrupted time series analysis. Lancet Reg Health\u0026ndash;Americas. 2024;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBahado-Singh R, Tarca AL, Hasbini YG, Sokol RJ, Keerthy M, Goyert G, et al. Maternal SARS-COV-2 infection and prematurity: the Southern Michigan COVID-19 collaborative. J Maternal-Fetal Neonatal Med. 2023;36(1):2199343.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlberton M, Rosa VM, Iser BPM. Preval\u0026ecirc;ncia e tend\u0026ecirc;ncia temporal da prematuridade no Brasil antes e durante a pandemia de covid-19: an\u0026aacute;lise da s\u0026eacute;rie hist\u0026oacute;rica 2011\u0026ndash;2021. Epidemiologia e Servi\u0026ccedil;os de Sa\u0026uacute;de. 2023;32:e2022603.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiseases KSoI. Report on the epidemiological features of coronavirus disease 2019 (COVID-19) outbreak in the Republic of Korea from January 19 to March 2, 2020. J Korean Med Sci. 2020;35(10).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. lancet. 2008;371(9606):75\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRegular Briefing: Children, adolescents and pregnant women vaccinations start today. 2021 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ncov.mohw.go.kr\u003c/span\u003e\u003cspan address=\"http://ncov.mohw.go.kr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e/ tcmBo ardVi ew. do? brdId\u0026thinsp;=\u0026thinsp;3\u0026amp; brdGu bun\u0026thinsp;=\u0026thinsp;31\u0026amp; dataG ubun= \u0026amp; ncvCo ntSeq\u0026thinsp;=\u0026thinsp;6016\u0026amp; contS eq\u0026thinsp;=\u0026thinsp;6016\u0026amp; board_ id\u0026thinsp;=\u0026thinsp;312\u0026amp; gubun\u0026thinsp;=\u0026thinsp;BDJ.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim D, Ali ST, Kim S, Jo J, Lim J-S, Lee S, et al. Estimation of serial interval and reproduction number to quantify the transmissibility of SARS-CoV-2 omicron variant in South Korea. Viruses. 2022;14(3):533.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlein AZ, Kunatharaju S, Golder S, Levine LD, Figueiredo JC, Gonzalez-Hernandez G. Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: A Retrospective Cohort Study Using Longitudinal Social Media Data. medRxiv. 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorin C, Bokobza C, Fleiss B, Hill-Yardin EL, Van Steenwinckel J, Gressens P. Preterm birth by cesarean section: the gut-brain axis, a key regulator of brain development. Dev Neurosci. 2024;46(3):179\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSihombing JA, Miqbel M, Sirait BI. Relationship between Premature Rupture of the Membrane and Cesarean Delivery: Case from Jakarta, Indonesia. Asian J Res Infect Dis. 2023;12(4):41\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEriksson C, Jonsson M, H\u0026ouml;gberg U, Hesselman S. Fetal station at caesarean section and risk of subsequent preterm birth-A cohort study. Eur J Obstet Gynecol Reproductive Biology. 2022;275:18\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim S, Oh J-W, Yun J-W. Narrative Review on the Trend of Childbirth in South Korea and Feasible Intervention to Reduce Cesarean Section Rate. J Korean Soc Matern Child Health. 2023;247(1):13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed AM, Pullenayegum E, McDonald SD, Beltempo M, Premji SS, Pole JD, Pechlivanoglou P. Association between preterm birth and economic and educational outcomes in adulthood: A population-based matched cohort study. PLoS ONE. 2024;19(11):e0311895.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIslam MM. The effects of low birth weight on school performance and behavioral outcomes of elementary school children in Oman. Oman Med J. 2015;30(4):241.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorral-Gudino L, Del-Amo-Merino MP, Abad\u0026iacute;a-Otero J, Merino-Velasco I, Lorenzo-Fern\u0026aacute;ndez Y, Garc\u0026iacute;a-Cruces-M\u0026eacute;ndez J et al. Impact of age on the transmission of SARS-CoV-2 in healthcare workers: Influence of nonoccupational risk factors. Wiener klinische Wochenschrift. 2024:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen LH, Drew DA, Graham MS, Joshi AD, Guo C-G, Ma W, et al. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. Lancet Public Health. 2020;5(9):e475\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoh D. Occupational risks for COVID-19 infection. Oxford University Press UK; 2020. pp. 3\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiracle JE, Ganesh PR, Rose J, Terebuh P, Stange KC, Wolfe HM, Pope R. COVID-19 in pregnancy: occupations with higher density of population exposure associated with more severe disease. J Occup Environ Med. 2021;63(12):1024\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelingheri M, Paladino ME, Riva MA. (2020). Risk exposure to coronavirus disease 2019 in pregnant healthcare workers. J Occup Environ Med, 62(7), e370.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, birth outcome, preterm, low birth weight","lastPublishedDoi":"10.21203/rs.3.rs-7351864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7351864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e\u003cp\u003eThis study aimed to investigate the impact of the coronavirus disease (COVID-19) pandemic on birth outcomes in South Korea, particularly in the context of exceptionally low birth rates.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eUsing nationwide birth certificate data up to 2022, an interrupted time-series analysis, linear probability model, and subgroup analyses were conducted to assess the influence of the pandemic on perinatal outcomes.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eThe analysis revealed a transient increase in preterm births of 0.15% (95% CI: 0.00%, 0.03%) and a sustained rise of 0.01% (95% CI: 0.00%, 0.02%) following the onset of the pandemic. In contrast, low birth weight initially showed a short-term decline of -0.11% (95% CI: -0.24%, -0.02%), followed by an intermediate-term increase of 0.02% (95% CI: 0.01%, 0.03%). Subgroup analyses indicated that these adverse birth outcomes were more pronounced among women under 35 years old, those with higher education levels, and those who were employed.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eThese findings suggest that the COVID-19 pandemic may have exacerbated pre-existing adverse trends in birth outcomes in South Korea. The results highlight the need to strengthen not only healthcare and social support for pregnant women, but also protective policies for those in the workforce particularly during large-scale public health crises.\u003c/p\u003e","manuscriptTitle":"Declining birth outcomes during the COVID-19 pandemic: a nationwide study in South Korea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 17:00:19","doi":"10.21203/rs.3.rs-7351864/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-11T16:19:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-14T15:24:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T01:03:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-14T01:02:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-08-12T06:05:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6841ce3-f880-4df9-b9bc-ac7c9f42c7ba","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T17:00:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 17:00:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7351864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7351864","identity":"rs-7351864","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}