When Marriage Is Not Enough: Unequal Benefits of Educational Homogamy for Birthweight by Marital Status

When Marriage Is Not Enough: Unequal Benefits of Educational Homogamy for Birthweight by Marital Status | 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 When Marriage Is Not Enough: Unequal Benefits of Educational Homogamy for Birthweight by Marital Status John Whesu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9142248/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines how educational assortative mating (EAM) and marital status jointly shape risks of low birthweight (LBW) and macrosomia using 2024 National Vital Statistics System natality data (N = 2,906,124). Birthweight reflects cumulative social, behavioral, and biological processes during pregnancy. Prior research documents strong educational gradients but typically treats parental education and marital status as independent predictors. Guided by family systems and resource multiplication theories, this study investigates how joint parental educational pairing – and its interaction with marital status – structures risk at both ends of the birthweight distribution. Multinomial logistic regression models estimate relative risks of LBW and macrosomia versus normal birthweight as functions of EAM, marital status, and their interaction with adjustments. Results show that among unmarried women, relative to heterogamy, medium- and high-educated homogamy are associated with lower LBW risk and higher macrosomia risk, whereas low-educated homogamy shows little difference in LBW risk. Among heterogamous couples, marriage is protective against LBW but increases macrosomia risk; within marriage, however, educational homogamy attenuates this risk. Marital advantages are increasingly concentrated among medium- and high-educated homogamous unions and are substantially weaker among low-educated homogamous couples. In sum, marriage amplifies advantages among medium- and high-educated homogamous couples but does not offset disadvantage among low-educated homogamous families, underscoring assortative mating as a mechanism of early-life health inequality. Findings position marriage as a conditional – rather than universal – health-protective institution, highlighting the need for policies that move beyond marriage promotion to address structural educational and economic disadvantages that shape unequal birth outcomes. Educational assortative mating educational homogamy infant health birthweight macrosomia health inequality Introduction Prior research documents a robust association between infant health and later-life outcomes. Infant health is among the most important predictors of childhood development and adult life chances in education, health, income and wealth (Ahmed et al., 2024; Aizer & Currie, 2014; Bilgin et al., 2018; Conley & Bennett, 2000; Jańczewska et al., 2023). In the United States (U.S.), extreme birthweight – a critical indicator of infant health – remains prevalent (Centers for Diseases Control and Prevention, 2024). Although the majority of infants are born healthy, a substantial share are born either with low birth weight (hereafter LBW) (Donahue et al., 2010; Osterman et al., 2025) or with macrosomia (Akanmode & Mahdy, 2025; Pillai et al., 2020). However, LBW – defined as birthweight less than 2,500 grams – has been more extensively studied, whereas macrosomia – clinically defined as birthweight greater than 4,000 grams – has received comparatively less attention (Fishman, 2020; Koyanagi et al., 2013; Yang et al., 2006). Extreme birthweight outcomes are not only biomedical conditions, but are also early markers of social inequality, reflecting how family resources, structure, and social conditions become biologically embedded before birth (Almond et al., 2018). Evidence suggests that marriage and parental educational attainment are independently associated with birthweight. Married mothers tend to experience more favorable birthweight outcomes than their unmarried counterparts (Shah et al., 2011; Yan, 2025). Marriage may improve birthweight outcomes by promoting healthier behaviors, facilitating resource pooling and reducing family stress (Song, 2021). Similarly, higher maternal education is associated with better infant health (Godah et al., 2021; Pikhartova & Shelton, 2022; Shrestha, 2020), and paternal education also exerts an independent influence (Meng & Groth, 2018; Nicolaidis et al., 2004). Integrating maternal and paternal education, emerging research on educational assortative mating (hereafter EAM) suggests that educational homogamy – when parents have similar levels of education – is more beneficial for birthweight than educational heterogamy, in which parents have dissimilar levels of education (Abufhele et al., 2022; Rauscher, 2020). However, educational homogamy is not a uniform category; low-, medium-, and high-educated homogamy represents distinct socioeconomic contexts that may produce divergent fetal health outcomes (Rauscher, 2020). Educational homogamy may confer advantages by enhancing parental agreement regarding the organization of family life, facilitating coordinated health behaviors, and reducing maternal stress (Beck & González-Sancho, 2009). Collectively, prior research suggests that marriage and educational homogamy independently shape birthweight outcomes. Yet, important gaps remain. First, existing EAM studies focus almost exclusively on LBW, overlooking macrosomia and the broader birthweight distribution. Second, prior work has not systematically compared different levels of educational homogamy (low, medium, and high education) across marital status. Third, it remains unclear whether the protective associations of educational homogamy for LBW extend to macrosomia, or whether these associations vary by marital status. These gaps are consequential because both marriage and EAM are central mechanisms through which socioeconomic advantage is consolidated within families. As the “diverging destinies” framework argues, family structure increasingly interacts with socioeconomic resources to produce stratified child outcomes (McLanahan, 2004). Understanding how EAM and marriage jointly shape birthweight therefore illuminates how inequality is reproduced at the very start of life. This study addresses these gaps by examining the joint effects of EAM and marital status on birthweight outcomes using 2024 National Vital Statistics System (NVSS) natality data. It contributes to research on family structure, assortative mating, infant health, and early-life inequality by: (1) extending research on EAM beyond LBW to include macrosomia; (2) modeling the full birthweight distribution as a product of the intersecting effects of EAM and marital status and; (3) demonstrating how varying levels of educational homogamy interact with marriage to structure risks of LBW and macrosomia relative to unmarried heterogamous unions. By doing so, this study shows that marriage operates as a conditional stratifying institution, amplifying the advantages of high-educated homogamy while failing to offset disadvantage among low-educated homogamous unions (McLanahan and Percheski 2008). Background and Theoretical Frameworks Educational assortative mating (EAM), marital status and birthweight Parental educational attainment is strongly associated with birth-related and infant health outcomes (Balaj et al., 2021 ; Buciu et al., 2025 ; Cantarutti et al., 2017 ; Ruiz et al., 2015 ). Maternal education is consistently linked to improved birthweight (Currie & Moretti, 2003 ; Godah et al., 2021 ; Pikhartova & Shelton, 2022 ; Shrestha, 2020 ). Although comparatively less studied, paternal education also exerts an independent influence on birthweight and infant health outcomes (Meng & Groth, 2018 ; Parker & Schoendorf, 1992 ). Focusing solely on individual parental characteristics may obscure the broader familial and relational contexts in which pregnancies occur (Abufhele et al., 2022 ). Recent work emphasizes the importance of “bringing the father back in”, arguing that the combined educational resources of both parents better capture variation in infant health (Rangel & Rauscher, 2024 ; Swaminathan et al., 2022 ). EAM studies indicate that educational homogamy – particularly among highly educated couples – is associated with more favorable birthweight outcomes than heterogamy (Abufhele et al., 2022 ; Rauscher, 2020 ). These patterns suggest that parental educational similarity may foster shared health-promoting behaviors, greater accumulation of socioeconomic resources, and reduced maternal stress. However, homogamy among low-educated couples may reflect constrained socioeconomic conditions rather than resource consolidation, potentially producing different birthweight patterns than medium- or high-educated homogamy. Parallel evidence highlights the role of marital status. Infants born to married mothers consistently exhibit better health outcomes than those born to unmarried mothers (Shah et al., 2011 ; Yan, 2025 ). Marriage is often understood to confer advantages through economic stability, social support, reduced stress, and healthier behaviors (Frimmel & Pruckner, 2014 ; Song, 2021 ; Yan, 2025 ). Yet the benefits of marriage may not be uniform across socioeconomic contexts. Marriage may amplify existing socioeconomic advantages rather than compensate for their absence, suggesting that marital benefits depend on underlying parental resources – including education. This aligns with the “diverging destinies” framework (McLanahan, 2004 ), which posits that the effects of family structure are increasingly conditioned by parental socioeconomic resources, producing unequal child outcomes. Theoretical perspectives and potential mechanisms This study draws on Family Systems Theory and Resource Multiplication Theory to interpret intersecting patterns of EAM and marital status. Family systems theory emphasizes couple-level dynamics and interdependencies of partners’ attributes (Cox & Paley, 2003 ; Minuchin, 1985 ). Partner similarities can promote agreement on health behaviors – such as diet and prenatal care – during pregnancy (Beck & González-Sancho, 2009 ; Rauscher, 2020 ). In high-resource context – such as in high-educated homogamous settings – homogamous unions may facilitate behavioral regulation where shared educational values promote behaviors that limit excessive fetal growth. Conversely, disparities between partners can produce conflict, negatively affecting maternal and infant health. Joint parental education and marital status therefore shape maternal stress, health behaviors, and access to resources during pregnancy. Resource multiplication theory posits that when partners are both socioeconomically advantaged, their combined resources amplify health benefits (Ross & Mirowsky, 2006 ). This theory is often contrasted with “resource substitution,” which suggests that marriage should matter most for the least advantaged (the low-educated) because it provides a necessary buffer. In contrast, multiplication theory proposes that institutional advantages such as marriage amplify preexisting resources rather than compensate for their absence (McLanahan & Percheski, 2008 ; Schwartz, 2013 ). Under this framework, high-educated homogamous marriages may produce the most favorable birth environments – reducing LBW risk while potentially shifting fetal growth toward the upper end of the distribution through greater pooled resources. Conversely, low-educated unions may lack sufficient resource depth, contributing to persistent disparities in infant health (McLanahan & Percheski, 2008 ). Together, these frameworks suggest that EAM and marital status operate jointly as family-level mechanisms through which social inequality becomes biologically embedded at birth, shaping both fetal growth restriction and excessive fetal growth across the full birthweight spectrum. Collectively, they explain both how partner similarity shapes prenatal behaviors (family systems) and why advantaged couples benefit disproportionately from marriage (resource multiplication). Based on these theoretical and empirical considerations, this study examines the following research questions: How do EAM and marital status independently structure risks of LBW and macrosomia? To what extent does the protective association between marriage and LBW depend on the level of parental educational homogamy? Does educational homogamy moderate the risks of LBW and macrosomia associated with marriage? Do high-educated homogamous marriages simultaneously reduce LBW risk while increasing excessive fetal growth? Data and Methods Sample This study uses the 2024 National Vital Statistics System (NVSS) natality file, which provides comprehensive, high-quality information on a large annual population of live births in the U.S. and its territories. The data are deidentified and publicly available. Consistent with prior research (Barreto et al., 2024 ; Rauscher, 2020 ; Song, 2021 ), the analytic sample is restricted to live singleton births. For parsimony, the sample is further limited to women identified as White, Black, Asian, or Hispanic, excluding American Indian or Alaska Native (AIAN), Native Hawaiian and Other Pacific Islander (NHOPI), and multiracial women. Missing data on most variables were minimal, ranging from 0.09% for birthweight to 1.86% for number of prenatal visits. Larger proportions of missingness (2.5%) were observed for body mass index (BMI), father’s education (12.6%), and mother’s education (1.9%). Multiple imputation (MI) was initially considered to address missingness in BMI and parental education. However, MI proved computationally infeasible given the exceptionally large sample size and the complexity of multinomial models with multiple interaction terms. To avoid selection bias while retaining the full analytic sample, a missing-indicator approach was used. For parental education, cases with missing information for either parent or both (11.73% of the final sample) were retained by creating a distinct “missing parental education” category. This category is included in the multinomial logistic regression and interacted with marital status to assess whether the absence of parental education reporting moderates the association between marriage and birthweight. Missing BMI values were addressed using mean substitution paired with a binary missingness indicator. Missing BMI values were replaced with the sample mean calculated from non-missing cases, and a dummy variable was included in all models to account for potential bias associated with non-reporting of maternal weight. After addressing missingness and dropping negligible missing values via listwise deletion (birthweight has the highest [1.86%]), the final analytic sample consists of 2,906,124 live singleton births. Measures Outcome Variable The outcome variable, birthweight, Birthweight is measured as a nominal categorical variable with three mutually exclusive categories: low birthweight (LBW), normal birthweight, and macrosomia. LBW is defined as a birthweight of less than 2,500 grams, normal birthweight ranges from 2,500 to 4,000 grams, and macrosomia is defined as a birthweight greater than 4,000 grams. Predictors The key independent variables are educational assortative mating (EAM) and marital status. EAM is constructed by combining maternal and paternal educational attainment. Maternal and paternal education are first coded as categorical variables (1 = less than high school, 2 = high school diploma or GED, 3 = some college, 4 = bachelor’s degree or higher; 5 = missing or unreported). These measures are then combined to create a categorical indicator of educational heterogamy and graded educational homogamy with five categories: 0 = heterogamy (reference), 1 = low-educated homogamy, 2 = medium-educated homogamy, 3 = high-educated homogamy, 4 = missing or unreported parental education. Educational heterogamy refers to parents with dissimilar levels of educational attainment, whereas educational homogamy captures parental educational similarity and is further differentiated by level. Low-educated homogamy includes unions in which both parents have less than a high school education or a high school diploma/GED; medium-educated homogamy includes unions in which both parents have some college education but no bachelor’s degree; and high-educated homogamy includes captures unions in which both parents have attained at least a bachelor’s degree. Modeling EAM with heterogamy as the reference category preserves the full analytic sample and allows direct comparison across graded levels of educational homogamy. Marital status is measured as a binary indicator (1 = married, 0 = unmarried) Covariates The model adjusts for a set of demographics, socioeconomic and clinical variables. These include maternal age (1 = 12–19 [under 20], 2 = 20–29, 3 = 30+), race/ethnicity (1 = White, 2 = Black, 3 = Asian, 4 = Hispanic), maternal smoking (1 = yes, 0 = no), nativity (1 = US-born, 0 = foreign-born), parity (continuous), number of prenatal visits (continuous), source of payment for delivery (1 = private insurance, 2 = Medicaid, 3 = self-payment, 4 = others), BMI (continuous), unreported BMI (1 = yes, 0 = no), sex of infant (1 = male, 0 = female), gestational age at birth (1 = preterm, 2 = full term), pre-pregnancy diabetes (1 = yes, 0 = no) and pre-pregnancy hypertension (1 = yes, 0 = no). Analytic Strategy I employ multinomial logistic regression to estimate the relative risk of LBW and macrosomia, using normal birthweight as the reference category. This approach – unlike binary logistic regression – allows for the simultaneous estimation of risks at both tails of the birthweight distribution. Analyses proceed in two stages. Model 1 estimates the unadjusted associations between EAM and marital status and birthweight outcomes, without additional covariates. Model 2 introduces interaction terms between EAM and marital status and adjusts for maternal age, race/ethnicity, nativity, maternal smoking, parity, number of prenatal visits, source of payment for delivery, body mass index (BMI), an indicator for missing BMI, gestational age at birth, infant sex, and pre-pregnancy diabetes and hypertension. This modeling strategy allows for a comparison of crude associations with fully adjusted estimates and facilitates assessment of whether the joint effects of EAM and marital status on birthweight outcomes persist net of sociodemographic, behavioral, and pregnancy-related factors. In all models, unmarried heterogamous unions serve as the reference group for the primary predictors, and normal birthweight (2,500–4,000g) serves as the reference for the outcome categories. This allows for a direct assessment of how various forms of educational homogamy and the transition into marriage deviate from a baseline of non-marital educational discordance Given the complexity of interpreting interaction terms in multinomial models, I calculate predicted probabilities to facilitate interpretation. Predicted probabilities illustrate absolute differences in the likelihood of LBW and macrosomia by EAM categories and marital status, providing an intuitive understanding of disparities and allowing for direct comparisons across groups. Standard errors are estimated using the robust Huber-White sandwich estimator to account for potential heteroscedasticity. Because the NVSS provides a near-universal census of all births in the U.S., sampling weights are not required for population-level representation. All analyses are conducted using Stata 19.0 SE. Results Descriptive statistics Table 1 presents sociodemographic, behavioral, and clinical characteristics by educational assortative mating (heterogamy, low-, medium-, and high-educated homogamy, and missing education). Birthweight distributions varied across educational pairings. Low birthweight was most prevalent in the missing education group (11.3%) and lowest among high-educated homogamy births (5.0%), with intermediate levels in heterogamy (6.9%), low-educated homogamy (7.7%), and medium-educated homogamy (6.7%). In contrast, macrosomia was most common among high-educated homogamy births (8.5%) and least common in the missing (4.7%) and low-educated homogamy group (6.1%). Marital status differed sharply by educational pairing. Marriage was nearly universal among high-educated homogamy births (92.7%) but substantially lower in heterogamy (60.2%), medium-educated homogamy (63.0%), and especially low-educated homogamy (43.6%), while the missing group was overwhelmingly unmarried (90.5%). Racial/ethnic composition also varies. High-educated homogamy births were predominantly White (70.8%) and Asian (11.5%), whereas low-educated homogamy births included larger shares of Hispanic (42.2%) and Black (15.6%) mothers. Maternal age and smoking followed clear educational gradients. Mothers aged 30 + were most common in high-educated homogamy births (74.3%) and least common in low-educated homogamy births (32.6%), while teenage motherhood was concentrated in the low-educated (8.2%) and missing (11.0%) groups. Smoking during pregnancy was rare overall but highest in the missing group (7.1%) and lowest in high-educated homogamy births (0.2%). Infant sex was evenly distributed across educational pairings. Clinical and healthcare indicators showed similar stratification. Preterm birth was least common in high-educated homogamy births (7.9%) and highest in the missing group (15.3%). Medicaid coverage predominated in low-educated (63.8%) and missing (70.4%) groups, whereas private insurance was most common in high-educated homogamy births (85.3%). Diabetes and hypertension were uncommon across all categories (< 2% and < 5%, respectively). See Appendix A for a complete overview of sample characteristics without crosstabulation. Multivariable analyses Using unmarried educationally heterogamous women as reference, Table 2 presents unadjusted and adjusted relative risk ratios (RRRs) from multinomial logistic regression models predicting birthweight using EAM and marital status (Model 1), and their interaction, adjusting for maternal age, race/ethnicity, nativity, maternal smoking, parity, number of prenatal visits, payment source, gestational age at birth, sex of infant, pre-pregnancy diabetes and hypertension (Model 2). LBW and macrosomia are interpreted in reference to normal birthweight. Low birthweight (LBW) vs Normal birthweight Model 1 shows clear educational and marital gradients in LBW risk with higher parental educational attainment and being married exhibiting protective advantage against LBW risk. Relative to heterogamous unions, high-educated homogamy (RRR = 0.80, 95% CI: 0.79–0.81, p < 0.05) and medium-educated homogamy (RRR = 0.98, 95% CI: 0.95–0.99, p < 0.05) are associated with lower risk of LBW. In contrast, low-educated homogamy (RRR = 1.06, 95% CI: 1.05–1.07) as well as births with missing parental education (RRR = 1.44, 95% CI: 1.42–1.46) exhibit markedly elevated LBW risk. Marriage offers strong protection against LBW risk, with married mothers experiencing a 27% lower risk (RRR = 0.73, 95% CI: 0.72–0.74, p < 0.05) compared with unmarried mothers. Model 2, which includes interaction terms and full adjustment, suggests educational gradient in LBW risks across marital status net of demographic, socioeconomic and clinical factors, as indicated by the main effects for EAM and marital status. Among unmarried women, relative to heterogamous unions, medium-educated homogamy (RRR = 0.95, 95% CI: 0.91–0.98, p < 0.05) and high-educated homogamy (RRR = 0.86, 95% CI: 0.83–0.90, p 0.05). Missing parental education remains associated with higher LBW risks (RRR = 1.09, 95% CI: 1.07–1.11, p < 0.05). Among heterogamous couples, marriage is associated with a lower risk of LBW (RRR = 0.90, 95% CI: 0.89–0.92, p < 0.05). The interaction between EAM and marital status reveals heterogeneity in marital advantage across educational pairings. Relative to unmarried heterogamous unions, marriage is associated with higher LBW risk among low-educated homogamous couples (RRR = 1.08, 95% CI: 1.05–1.11, p 0.05), and lower LBW risk among high-educated homogamous couples (RRR = 0.94, 95% CI: 0.90–0.99, p < 0.05). Marriage is also associated with elevated LBW risk among births with missing parental education (RRR = 1.07, 95% CI: 1.01–1.12, p < 0.05). Macrosomia vs Normal birthweight Model 1 indicates clear educational and marital gradients in the risk of macrosomia. Relative to heterogamous unions, medium-educated homogamy (RRR = 1.02, 95% CI: 1.00-1.04, p < 0.05) and high-educated homogamy (RRR = 1.03, 95% CI: 1.01–1.04, p < 0.05) are associated with higher risks of macrosomia, whereas low-educated homogamy (RRR = 0.87, 95% CI: 0.86–0.88, p < 0.05) and births with missing parental education (RRR = 0.78, 95% CI: 0.77–0.80, p < 0.05) exhibit significantly lower risks compared with heterogamous unions. Marriage is associated with a substantially elevated risk of macrosomia, with married mothers experiencing a 45% higher risk relative to unmarried mothers (RRR = 1.45, 95% CI: 1.43–1.46, p < 0.05). Model 2, which incorporates interaction terms and full adjustment, suggests that educational gradients in macrosomia risk persist across marital status net of covariates. Among unmarried women, relative to heterogamous unions, medium-educated homogamy (RRR = 1.07, 95% CI: 1.03–1.11, p < 0.05) and high-educated homogamy (RRR = 1.16, 95% CI: 1.12–1.20, p < 0.05) are associated with significantly higher risks of macrosomia, whereas low-educated homogamy (RRR = 0.92, 95% CI: 0.90–0.94, p < 0.05) and missing parental education (RRR = 0.87, 95% CI: 0.85–0.89, p < 0.05) are associated with lower risks. Among heterogamous couples, marriage is associated with elevated macrosomia risk (RRR = 1.17, 95% CI: 1.15–1.19, p < 0.05). The interaction between EAM and marital status reveals heterogeneity in macrosomia risks by marital status across educational pairings. Relative to unmarried heterogamous unions, marriage is associated with lower risks of macrosomia among low-educated homogamous couples (RRR = 0.95, 95% CI: 0.92–0.98, p < 0.05), medium-educated homogamous couples (RRR = 0.93, 95% CI: 0.89–0.98, p < 0.05), and high-educated homogamous couples (RRR = 0.95, 95% CI: 0.92–0.99, p < 0.05). Thus, although high-educated homogamy is associated with elevated macrosomia risk among unmarried women and marriage is associated with higher risk among heterogamous unions, the interaction estimates indicate that marriage attenuates the excess macrosomia risk observed among high-educated homogamous couples. In contrast, marriage is associated with a modestly higher risk of macrosomia among births with missing parental education (RRR = 1.06, 95% CI: 1.00-1.11, p < 0.05). Predicted Probabilities of LBW and macrosomia Table 3 presents predicted probabilities of low birthweight (LBW) and macrosomia by educational assortative mating (EAM) and marital status, based on the fully adjusted multinomial logistic regression model. Overall, predicted probabilities of LBW vary modestly across EAM and marital status groups, ranging between 6% and 8%. Among heterogamous unions, predicted probabilities of LBW are similar for unmarried and married women (0.07 in both groups). Slightly higher probabilities are observed among unmarried low-educated homogamous unions (0.08), whereas probabilities among married low-educated, medium-educated, and high-educated homogamous unions cluster around 7%. The lowest predicted probability of LBW is observed among married high-educated homogamous couples (0.06). Births with missing parental education exhibit comparatively higher predicted probabilities of LBW regardless of marital status (0.08). Predicted probabilities of macrosomia also show limited but systematic variation across EAM–marital status categories, ranging from 6% to 8%. Among heterogamous unions, macrosomia is more common among married women (0.07) than unmarried women (0.06). For homogamous unions, predicted probabilities of macrosomia are similar across educational levels among unmarried women (approximately 0.06–0.07). In contrast, married high-educated homogamous couples exhibit the highest predicted probability of macrosomia (0.08), while married low- and medium-educated homogamous couples show probabilities around 7%. As with LBW, births with missing parental education display modestly elevated probabilities across marital statuses. Robustness Check As a robustness check, additional analyses were conducted using binary indicators of low birthweight (LBW; 4,000 g). Each outcome was estimated as a function of educational assortative mating (EAM), marital status, and their interaction, adjusting for the same covariates included in the multinomial models. Overall, results closely mirrored those from the primary multinomial analyses, yielding substantively similar conclusions. For LBW, marriage was associated with significantly lower risk among mothers in high-educated homogamous unions (OR = 0.86, 95% CI: 0.83–0.90, p < 0.05) and medium-educated homogamous unions (OR = 0.94, 95% CI: 0.91–0.98, p 0.05). These findings indicate that although marriage is generally associated with reduced LBW risk, its protective effect is concentrated among medium- and high-educated homogamous unions and does not offset disadvantage among low-educated homogamous couples. Results for macrosomia similarly confirmed the multinomial findings. Marriage was associated with higher macrosomia risk among women in educationally heterogamous unions (OR = 1.18, 95% CI: 1.16–1.20, p < 0.05), whereas interaction estimates indicated that educational homogamy modestly attenuated this excess risk across low-educated (OR = 0.95, 95% CI: 0.92–0.97, p < 0.05), medium-educated (OR = 0.93, 95% CI: 0.89–0.97, p < 0.05), and high-educated homogamous unions (OR = 0.95, 95% CI: 0.92–0.99, p < 0.05). Thus, while marriage elevates macrosomia risk in heterogamous unions, educational similarity within marriage partially mitigates this risk, consistent with the main analysis (see Appendix B). To ensure that findings were not driven by categorical birthweight thresholds, Appendix C presents fully adjusted linear regression models predicting continuous birthweight (grams). Although linear models cannot capture excessive fetal growth at the upper tail, they provide a complementary assessment of average birthweight differences across EAM pairings. Interaction results indicate that all educational homogamous unions exhibit declines in mean birthweight relative to the reference group. However, the magnitude of decline is largest among low-educated (− 13.11 g) and medium-educated (− 13.86 g) homogamous unions and smallest among high-educated homogamous unions (− 7.83 g). These patterns further substantiate that married women in high-educated homogamous unions experience the greatest relative birthweight advantage, reinforcing conclusions from the multinomial and binary models. Additional multinomial models adjusting for father’s race/ethnicity and age yield substantively identical conclusions to the main analyses (Appendix D). High-educated homogamy remains strongly protective against LBW while associated with elevated macrosomia risk, and marriage continues to reduce LBW risk but increase macrosomia. Interaction patterns are likewise unchanged, with marital advantages concentrated among high-educated homogamous unions and attenuated among low-educated homogamy. These findings indicate that observed EAM-marriage associations are not attributable to compositional differences in paternal demographic characteristics. Discussion This study examines how educational assortative mating (EAM) and marital status jointly structure birthweight outcomes across the full distribution using recent US natality data. Focusing on the two extremes of birthweight – low birthweight (LBW) and macrosomia – the findings indicate that although marriage is generally associated with more favorable birthweight outcomes, its advantages are not uniform, as the protective association of marriage is systematically conditioned on parental educational pairing, with meaningful reductions in extreme birthweight risks concentrated among medium- and high-educated homogamous unions and substantially attenuated benefits among low-educated homogamous families. First, the unadjusted model shows that EAM and marriage are independently protective against LBW risk. Consistent with prior studies (Abufhele et al., 2022 ; Rauscher, 2020 ), high-educated homogamy is significantly associated with lower LBW risk, whereas low-educated homogamy elevates risk. Likewise, and in line with prior research (Shah et al., 2011 ; Song, 2021 ), marriage is protective, with married women exhibiting significantly lower LBW risk than their unmarried counterparts. Interaction models show that high-educated homogamy reduces LBW risk even among unmarried parents and that marriage lowers LBW risk for educationally heterogamous couples. However, marital protection is uneven across parental educational pairings, concentrated among medium- and high-educated homogamous unions, with low-educated homogamous couples deriving minimal or no protection. Prior research consistently documents lower LBW risk among married mothers, often attributing this advantage to pooled resources, social support, and healthier prenatal environments (Shah et al., 2011 ; Song, 2021 ; Yan, 2025 ). The present findings refine and stratify this narrative by demonstrating that marital protection varies by joint parental education. Only medium- and high-educated homogamous unions experience meaningful reductions in LBW risk, whereas low-educated homogamous couples do not. These results extend resource multiplication theory by showing that marriage does not merely amplify individual-level socioeconomic resources but also magnify dyadic educational configurations. In this sense, marriage functions less as a universal protective institution and more as a mechanism of inequality amplification in early-life health across parental education pairings (McLanahan & Percheski, 2008 ; Ross & Mirowsky, 2006 ). This dyadic perspective advances research on family inequality by showing that marriage benefits depend not only on who marries, but also on the educational pairing of the couple (Rauscher, 2020 ). This pattern aligns with the “diverging destinies” framework (McLanahan, 2004 ), which contends that growing interactions between family structure and parental socioeconomic resources contribute to widening stratification in child outcomes. The present findings show that these diverging destinies emerge even at birth, with advantaged unions (medium- and high-educated homogamy) consolidating protective factors and disadvantaged unions (low-educated homogamy) unable to leverage marriage into comparable health benefits. Second, regarding macrosomia, both unadjusted and adjusted models show that high-educated homogamy is associated with elevated macrosomia risk among unmarried women relative to unmarried educationally heterogamous parents. Among heterogamous parents, marriage is likewise associated with higher macrosomia risk. However, within marriage, educational homogamy attenuates the elevated macrosomia risks observed among married heterogamous couples, as reflected in significantly lower macrosomia risks across all educationally homogamous unions. These patterns suggest that the relationship between socioeconomic advantage – proxied here by medium- and high-educated homogamy – and excessive fetal growth may be contingent on marital context and family-level organization. One interpretation, consistent with family systems theory, is that educationally homogamous marriages may facilitate greater coordination of health behaviors, prenatal decision-making, and healthcare utilization. Socioeconomic advantages associated with medium- and high-educated homogamy may shift fetal growth upward through improved nutrition or overnutrition. At the same time, marital coordination regulates behaviors in ways that preclude excessive fetal growth. In this sense, marriage may operate not only as a resource-pooling institution but also as a behavior-structuring context that moderates risks associated with excessive fetal growth. At the lower end of the educational distribution, reduced macrosomia risk among (married) low-educated homogamous couples may reflect constrained material conditions associated with socioeconomic disadvantage, including limited nutritional resources. Although this interpretation remains necessarily cautious, the pattern aligns with extensive evidence linking socioeconomic deprivation and maternal undernutrition to restricted fetal growth and LBW (Aizer & Currie, 2014 ; Blumenshine et al., 2010 ; Kramer, 1987 ; Sau et al., 2025 ). From this perspective, lower macrosomia prevalence among low-educated homogamous couples likely reflects structural constraints on fetal growth rather than a true health advantage. Births with missing parental education exhibit consistently higher LBW and macrosomia risks, and comparatively weaker benefits from marriage, indicating that data missingness itself may signal structural marginalization. This group likely captures socially disadvantaged or unstable family contexts insufficiently measured by conventional socioeconomic indicators. Recognizing missing parental education as substantively meaningful, rather than purely methodological, broadens the interpretive scope of demographic and health research. Taken together, these findings underscore the importance of family-level configurations of education and marital status in understanding birthweight disparities. They illustrate how advantaged unions consolidate resources and generate protective effects, whereas disadvantaged or resource-deprived family formations fail to produce comparable benefits. This relational perspective bridges individual-level socioeconomic research with broader sociological theories of stratification, demonstrating that inequality in early-life health is produced not only through parental characteristics but through the joint distribution of resources within families. The findings show that marriage operates as a conditional stratifying institution whose benefits accrue disproportionately to socioeconomically advantaged couples, thereby reinforcing inequality at the very start of the life course. Several limitations, however, merit consideration. First, marital status is measured dichotomously as married versus unmarried, excluding other forms of family formation in a society that has undergone profound demographic changes in recent decades (A. Cherlin, 2010 ), particularly in family structure (A. J. Cherlin, 2004 ). Critically, the 'unmarried' category in birth certificate data is heterogeneous, likely containing a high proportion of cohabiting unions which now characterize a majority of nonmarital births (Guzzo, 2006 ; Lichter et al., 2014 ; Manning & Smock, 2005 ). While cohabiting partners often pool resources and provide social support similar to married spouses, prior research suggests these unions often lack the same level of institutionalized 'multiplication' of resources found in marriage (Song, 2021 ). Moreover, given that prior research links nontraditional family structures to infant health (Dello Iacono et al., 2022 ; Heiland & Liu, 2006 ; Martín, 2010 ; Schmeer, 2011 ), future work using data that distinguishes between cohabitation and single parenthood should examine how these specific family forms moderate relationships between parental education and infant health. Second, both parents’ educational attainment is reported by the mother, introducing potential measurement error, particularly among unmarried births where contact with the father is less frequent. Although maternal reporting of paternal attributes is generally reliable (Reichman et al. 2001 ), dyadic survey data would allow further validation (Reichman et al., 2001 ). Third, although models adjust for extensive maternal, behavioral, and clinical factors, unmeasured socioeconomic or paternal characteristics may persist. Finally, the large NVSS sample increases statistical power, potentially yielding statistically significant yet substantively modest differences. However, even modest shifts in birthweight distributions can have meaningful population-level implications for long-term public health and socioeconomic inequality. Despite these limitations, this study contributes to research on family structure, assortative mating and infant health in three ways. First, it demonstrates that EAM structures inequality in infant health at birth, shaping both restricted fetal growth and distributional shifts toward excessive birthweight. Second, it reconceptualizes marriage as a conditional stratifying institution rather than a universal health resource that uniformly confers health advantage. Third, while prior work has focused almost exclusively on LBW, this study incorporates macrosomia, demonstrating that educational stratification and marital context shape the entire birthweight distribution. Conclusion Marriage does not confer equal benefits for infant health across parental educational pairings. Instead, its protective and risk-producing effects depend fundamentally on parental educational similarity and underlying socioeconomic advantage. Among married births, marriage provides the greatest protection against LBW within high-educated homogamous unions while failing to offset disadvantage among low-educated homogamous families. At the same time, although marriage elevates macrosomia risk among educationally heterogamous couples, educational homogamy moderates this excess risk across all homogamous unions. Understanding disparities in infant health therefore requires moving beyond individual characteristics or marital status alone toward a family-level, distributional perspective on early-life inequality. These findings contribute to sociological theory by demonstrating that marriage functions as a conditional stratifying institution whose benefits are contingent on dyadic resource configurations, thereby reinforcing diverging destinies from the earliest stages of life. Policies that promote marriage in isolation are unlikely to reduce infant health disparities without addressing underlying socioeconomic inequality, particularly educational disadvantages. Interventions targeting education, economic stability, and prenatal health resources – particularly for low-educated and structurally marginalized families – are more likely to yield meaningful improvements. Simultaneously, patterns of elevated macrosomia risk in specific socioeconomic and marital contexts underscore the need for balanced prenatal care strategies that address both insufficient and excessive fetal growth. Table 1 Demographic, socioeconomic and clinical characteristics by educational assortative mating. N = 2,906,124 Educational Homogamy Characteristics Heterogamy (n = 979433) Low-educated (n = 565268) Medium-educated (n = 145841) High-educated (n = 874703) Missing (n = 340879) Birthweight Low 67269 (6.87) 43510 (7.70) 9692 (6.65) 43520 (4.98) 38361 (11.25) Normal 839792 (85.74) 487289 (86.20) 125038 (85.74) 756696 (86.51) 286600 (84.04) Macrosomia 72372 (7.39) 34469 (6.10) 11111 (7.62) 74487 (8.52) 15918 (4.67) Marital status Unmarried 389707 (39.79) 318884 (56.41) 54084 (37.08) 63870 (7.30) 308386 (90.47) Married 589726 (60.21) 246384 (43.59) 91757 (62.96) 810833 (92.70) 32493 (9.53) Race/ethnicity White 539975 (55.13) 225246 (39.85) 80277 (55.04) 619157 (70.78) 115764 (33.96) Black 134801 (13.76) 88093 (15.58) 23503 (16.12) 57094 (6.53) 116429 (34.16) Asian 33819 (3.45) 13636 (2.41) 4243 (2.91) 100445 (11.48) 5986 (1.76) Hispanic 270838 (27.65) 238293 (42.16) 37818 (25.93) 98007 (11.20) 102700 (30.13) Nativity Foreign-born 204933 (20.92) 194850 (34.47) 23681 (16.24) 195121 (22.31) 76175 (22.35) US-born 774500 (79.08) 370418 (65.53) 122160 (83.76) 679582 (77.69) 264704 (77.65) Maternal age Under 20 27914 (2.85) 46150 (8.16) 1698 (1.16) 109 (0.01) 37331 (10.95) 20–29 493029 (50.34) 334885 (59.24) 78200 (53.62) 225072 (25.73) 176999 (51.92) 30+ 458490 (46.81) 184233 (32.59) 65943 (45.22) 649522 (74.26) 126549 (37.12) Maternal smoking No 953462 (97.35) 546535 (96.69) 142954 (98.02) 873405 (99.85) 316723 (92.91) Yes 25971 (2.65) 18733 (3.31) 2887 (1.98) 1298 (0.15) 24156 (7.09) Gestational age at birth Full term 875981 (89.44) 479840 (88.07) 130746 (89.65) 805921 (92.14) 288597 (84.66) Preterm 103452 (10.56) 67428 (11.93) 15095 (10.35) 68782 (7.86) 52282 (15.34) Payment source Medicaid 415030 (42.37) 360554 (63.78) 58413 (40.05) 73453 (8.40) 239918 (70.38) Private insurance 487762 (49.80) 135966 (24.05) 74092 (50.80) 745988 (85.28) 74737 (21.92) Out of pocket 42518 (4.34) 53068 (9.39) 6133 (4.21) 30630 (3.50) 18744 (5.50) Others a 34123 (3.48) 15680 (2.77) 7203 (4.94) 24632 (2.82) 7480 (2.19) Infant sex Female 478175 (48.82) 276711 (48.95) 71043 (48.71) 427925 (48.92) 167068 (48.89) Male 501258 (51.18) 288557 (51.05) 74798 (51.29) 446778 (51.08) 173811 (51.08) Diabetes b No 964832 (98.51) 557094 (98.55) 143728 (98.55) 866907 (99.11) 335655 (98.47) Yes 14601 (1.49) 8174 (1.45) 2113 (1.45) 7796 (0.89) 5224 (1.53) Hypertension b No 941313 (96.11) 547465 (96.85) 140195 (96.13) 850685 (97.25) 326175 (95.69) Yes 38120 (3.89) 17803 (3.15) 5646 (3.87) 24018 (2.75) 14704 (4.31) a Includes women who used Indian Health Service, CHAMPUS/TRICARE, other government and other. b Diagnosed before pregnancy. Table 2 Multinomial Logistic Regression Predicting Low Birthweight and Macrosomia versus Normal Birthweight. N = 2,906,124 Relative Risk Ratio (95% CI) Low birthweight vs Normal birthweight Macrosomia vs Normal birthweight Predictors Model 1 uRRR Model 2 aRRR Model 1 uRRR Model 2 aRRR Educational Assortative Mating Heterogamy (reference) Low-educated homogamy 1.06*** (1.05–1.07) 1.02 (.99-1.04) .87*** (.86–.88) .92*** (.90–.94) Medium-educated homogamy .98* (.95–.99) .95** (.91 – .98) 1.02* (1.00–1.04) 1.07*** (1.03–1.11) High-educated homogamy .80*** (.79 – .81) .86*** (.83 – .90) 1.03*** (1.01–1.04) 1.16*** (1.12–1.20) Missing parental education 1.44*** (1.42–1.46) 1.09*** (1.07–1.11) .78*** (.77–.80) .87*** (.85–.89) Marital status Unmarried (reference) Married .73*** (.72–.74) .90*** (.89–.92) 1.45*** (1.43–1.46) 1.17*** (1.15–1.19) Educational Assortative Mating X Marital status Unmarried heterogamy (reference) Married low-educated homogamy 1.08*** (1.05–1.11) .95*** (.92–.98) Married medium-educated homogamy 1.03 (.98–1.08) .93** (.89–.98) Married high-educated homogamy .94** (.90–.99) .95** (.92–.99) Missing parental education 1.07* (1.01–1.12) 1.06* (1.00–1.11) Maternal age Under 20 (reference) 20–29 1.04*** (1.02–1.08) 1.32*** (1.28–1.39) 30+ 1.21*** (1.18–1.24) 1.43*** (1.38–1.48) Race/ethnicity White (reference) Black 1.95*** (1.92–1.98) .48*** (.47–.49) Asian 2.04*** (1.99–2.08) .40*** (.39–.41) Hispanic 1.13*** (1.11–1.15) .69*** (.68–.70) Nativity Foreign-born (reference) US-born 1.32*** (1.30–1.34) .94*** (.93–.95) Maternal smoking No (reference) Yes 1.83*** (1.79–1.88) .53*** (.51–.56) Parity .85*** (.85–.86) 1.12*** (1.11–1.13) Number of prenatal visits .94*** (.93–.94) 1.02*** (1.02–1.02) Gestational age Full term (reference) Preterm 15.92*** (15.76–16.10) .35*** (.34–.36) Infant sex Female (reference) Male .74*** (.74–.75) 1.82*** (1.81 − 1.84) Payment source Medicaid (reference) Private insurance .94*** (.93–.96) 1.08*** (1.07–1.09) Self-pay .81*** (.71–.83) 1.55*** (1.52–1.59) Others .95** (.92–98) 1.17*** (1.14–1.20) Body mass index .98*** (.98–.99) 1.04*** (1.04–1.04) Missing 1.08*** (1.04–1.12) 1.05* (1.00–1.08) Pre-pregnancy diabetes No (reference) Yes 1.00 (.96–1.03) 2.06*** (1.99–2.12) Pre-pregnancy hypertension No (reference) Yes 2.07*** (2.02–2.11) .47*** (.46–.49) Note : Confidence Interval (CI) 95% in parentheses. RRR is Relative Risk Ratio where uRRR is unadjusted and aRRR is adjusted. *p<.05; **p<.01; ***p<.001. Table 3 Predicted Probabilities of Birthweight by Educational Assortative Mating and Marital Status. Predictors Low birthweight Macrosomia Unmarried heterogamy 0.07*** (0.07–0.07) 0.06*** (0.06–0.07) Married heterogamy 0.07*** (0.07–0.07) 0.07*** (0.07–0.08) Unmarried low-educated homogamy 0.08*** (0.07–0.08) 0.06*** (0.06–0.06) Married low-educated homogamy 0.07*** (0.07–0.07 0.07*** (0.07–0.07) Unmarried medium-educated homogamy 0.07*** (0.06–0.07) 0.07*** (0.07–0.07) Married medium-educated homogamy 0.07*** (0.07–0.07) 0.07*** (0.07–0.08) Unmarried high-educated homogamy 0.07*** (0.06–0.07) 0.07*** (0.07–0.08 Married high-educated homogamy 0.06*** (0.06–0.06) 0.08*** (0.08–0.08) Unmarried missing parental education 0.08*** (0.08–0.08) 0.06*** (0.06–0.06) Married missing parental education 0.08*** (0.07–0.08) 0.07*** (0.07–0.07) Note : Based on fully adjusted multinomial logistic regression model. Confidence interval (95%) in parentheses. *p<.05; **p<.01; ***p<.001. Declarations Funding The author received no funding for this research. Note: 1.The terms educational homogamy and educational similarity are used interchangeably throughout this manuscript. 2. References to homogamy and heterogamy pertain specifically to educational assortative mating. 3. Heterogamy is modeled as a single reference category to preserve the analytic sample and to evaluate how graded levels of educational homogamy (low, medium, high) compare to a common heterogamous baseline. This approach aligns with the study’s focus on stratification within homogamy, recognizing that some forms – particularly low-educated homogamy – may reflect concentrated disadvantage rather than benefit. Future research will disaggregate heterogamy into educational hypogamy and hypergamy. 4. Parental education missingness is treated as a substantive category representing households with high levels of social and institutional disconnection. 5. Although fathers’ characteristics may influence birth outcomes, the present analyses focus on paternal education because the study conceptualizes educational assortative mating as a couple-level socioeconomic structure. Including additional paternal socioeconomic indicators could introduce overcontrol bias by absorbing variation central to EAM, obscuring the relational mechanism under investigation. To evaluate the robustness of this specification, supplementary models additionally adjust for father’s age and race/ethnicity. These fully adjusted results are substantively unchanged in direction, magnitude, and statistical significance relative to the main models, supporting the stability of the reported associations. Detailed estimates are presented in Appendix D. Author Contribution This article is sole authored. Every part of the article (conceptualization, research design, methods, analysis and writing) was undertaken solely by the author, John Whesu. 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Social Science & Medicine , 63 (5), 1400–1413. https://doi.org/10.1016/j.socscimed.2006.03.013 Ruiz, M., Goldblatt, P., Morrison, J., Kukla, L., Švancara, J., Riitta-Järvelin, M., Taanila, A., Saurel-Cubizolles, M. J., Lioret, S., Bakoula, C., Veltsista, A., Porta, D., Forastiere, F., van Eijsden, M., Vrijkotte, T. G. M., Eggesbø, M., White, R. A., Barros, H., Correia, S., & Pikhart, H. (2015). Mother’s education and the risk of preterm and small for gestational age birth: A DRIVERS meta-analysis of 12 European cohorts. Journal of Epidemiology and Community Health , 69 (9), 826–833. https://doi.org/10.1136/jech-2014-205387 Sau, S., Pal, P., Dey, A., Pahari, S., Chakraborty, E., Maity, K. K., Tamili, D. K., Pal, A., & Das, B. (2025). Influence of maternal and socioeconomic factors as determinants of low birth weight among hospital delivery mothers of rural West Bengal, India. Clinical Epidemiology and Global Health , 36 , 102225. https://doi.org/10.1016/j.cegh.2025.102225 Schmeer, K. K. (2011). The Child Health Disadvantage of Parental Cohabitation. Journal of Marriage and Family , 73 (1), 181–193. https://doi.org/10.1111/j.1741-3737.2010.00797.x Schwartz, C. R. (2013). Trends and Variation in Assortative Mating: Causes and Consequences. Annual Review of Sociology , 39 (Volume 39, 2013), 451–470. https://doi.org/10.1146/annurev-soc-071312-145544 Shah, P. S., Zao, J., & Ali, S. (2011). Maternal marital status and birth outcomes: A systematic review and meta-analyses. Maternal and Child Health Journal , 15 (7), 1097–1109. https://doi.org/10.1007/s10995-010-0654-z . & Knowledge Synthesis Group of Determinants of preterm/LBW births Shrestha, V. (2020). Maternal education and infant health gradient: New answers to old questions. Economics and Human Biology , 39 , 100894. https://doi.org/10.1016/j.ehb.2020.100894 Song, H. (2021). The unequal consequences of family structures for infant health. Social Science Research , 100 , 102604. https://doi.org/10.1016/j.ssresearch.2021.102604 Swaminathan, A., Lahaie Luna, M., Rennicks White, R., Smith, G., Rodger, M., Wen, S. W., Walker, M., & Corsi, D. J. (2022). The influence of maternal and paternal education on birth outcomes: An analysis of the Ottawa and Kingston (OaK) birth cohort. The Journal of Maternal-Fetal & Neonatal Medicine: The Official Journal of the European Association of Perinatal Medicine the Federation of Asia and Oceania Perinatal Societies the International Society of Perinatal Obstetricians , 35 (25), 9631–9638. https://doi.org/10.1080/14767058.2022.2049751 Yan, J. (2025). Does Marriage Still Make a Difference in Infant Health? Eastern Economic Journal , 51 (2), 246–267. https://doi.org/10.1057/s41302-024-00284-3 Yang, Q., Greenland, S., & Flanders, W. D. (2006). Associations of Maternal Age- and Parity-Related Factors With Trends in Low-Birthweight Rates: United States, 1980 Through 2000. American Journal of Public Health , 96 (5), 856–861. https://doi.org/10.2105/AJPH.2004.049312 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9142248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623910200,"identity":"9a6fa124-604b-4538-be4f-737e8d9ff30b","order_by":0,"name":"John Whesu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACxgYGhoMfKmygXDZitLQxMB6WOJNGghagIuYDvG2HSdDCPL/H4IBk23l5/v7DBxg+lB0mrIWxjcfgQMG524YzbqQlMM44R5QW3g0HJMpuJzDc4DFgRriQkBYetnMJ8ufPf2D+S7yWtgMJBgdyGJgZidOS/wEYyMmGG2+kGRzsOZdOWIth87Hkjx8q7OTlzh9++OBHmTURWhqQOAcIqwcCeaJUjYJRMApGwcgGAOrbQKH3QD2HAAAAAElFTkSuQmCC","orcid":"","institution":"Case Western Reserve University","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"","lastName":"Whesu","suffix":""}],"badges":[],"createdAt":"2026-03-16 23:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9142248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9142248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707011,"identity":"61a1f3d7-4317-494c-ab14-c1382b1df492","added_by":"auto","created_at":"2026-04-24 09:19:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":743646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9142248/v1/b62cf42b-1f7f-4be0-bd4b-174f1ec8eb77.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eWhen Marriage Is Not Enough: Unequal Benefits of Educational Homogamy for Birthweight by Marital Status\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrior research documents a robust association between infant health and later-life outcomes. Infant health is among the most important predictors of childhood development and adult life chances in education, health, income and wealth\u0026nbsp;(Ahmed et al., 2024; Aizer \u0026amp; Currie, 2014; Bilgin et al., 2018; Conley \u0026amp; Bennett, 2000; Jańczewska et al., 2023). In the United States (U.S.), extreme birthweight \u0026ndash; a critical indicator of infant health \u0026ndash; remains prevalent\u0026nbsp;(Centers for Diseases Control and Prevention, 2024). \u0026nbsp;Although the majority of infants are \u0026nbsp;born healthy, a substantial share are born either with low birth weight (hereafter LBW)\u0026nbsp;(Donahue et al., 2010; Osterman et al., 2025)\u0026nbsp;or with macrosomia\u0026nbsp;(Akanmode \u0026amp; Mahdy, 2025; Pillai et al., 2020). However, LBW \u0026ndash; defined as birthweight less than 2,500 grams \u0026ndash; has been more extensively studied, whereas macrosomia \u0026ndash; clinically defined as birthweight greater than 4,000 grams \u0026ndash; has received comparatively less attention\u0026nbsp;(Fishman, 2020; Koyanagi et al., 2013; Yang et al., 2006). Extreme birthweight outcomes are not only biomedical conditions, but are also early markers of social inequality, reflecting how family resources, structure, and social conditions become biologically embedded before birth\u0026nbsp;(Almond et al., 2018).\u003c/p\u003e\n\u003cp\u003eEvidence suggests that marriage and parental educational attainment are independently associated with birthweight. Married mothers tend to experience more favorable birthweight outcomes than their unmarried counterparts\u0026nbsp;(Shah et al., 2011; Yan, 2025). Marriage may improve birthweight outcomes by promoting healthier behaviors, facilitating resource pooling and reducing family stress\u0026nbsp;(Song, 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, higher maternal education is associated with better infant health\u0026nbsp;(Godah et al., 2021; Pikhartova \u0026amp; Shelton, 2022; Shrestha, 2020), and paternal education also exerts an independent influence\u0026nbsp;(Meng \u0026amp; Groth, 2018; Nicolaidis et al., 2004). Integrating maternal and paternal education, emerging research on educational assortative mating (hereafter EAM) suggests that educational homogamy \u0026ndash; when parents have similar levels of education \u0026ndash; is more beneficial for birthweight than educational heterogamy, in which parents have dissimilar levels of education\u0026nbsp;(Abufhele et al., 2022; Rauscher, 2020). However, educational homogamy is not a uniform category; low-, medium-, and high-educated homogamy represents distinct socioeconomic contexts that may produce divergent fetal health outcomes\u0026nbsp;(Rauscher, 2020). Educational homogamy may confer advantages by enhancing parental agreement regarding the organization of family life, facilitating coordinated health behaviors, and reducing maternal stress\u0026nbsp;(Beck \u0026amp; Gonz\u0026aacute;lez-Sancho, 2009).\u003c/p\u003e\n\u003cp\u003eCollectively, prior research suggests that marriage and educational homogamy independently shape birthweight outcomes. Yet, important gaps remain. First, existing EAM studies focus almost exclusively on LBW, overlooking macrosomia and the broader birthweight distribution. Second, prior work has not systematically compared different levels of educational homogamy (low, medium, and high education) across marital status. Third, it remains unclear whether the protective associations of educational homogamy for LBW extend to macrosomia, or whether these associations vary by marital status. These gaps are consequential because both marriage and EAM are central mechanisms through which socioeconomic advantage is consolidated within families. As the \u0026ldquo;diverging destinies\u0026rdquo; framework argues, family structure increasingly interacts with socioeconomic resources to produce stratified child outcomes\u0026nbsp;(McLanahan, 2004). Understanding how EAM and marriage jointly shape birthweight therefore illuminates how inequality is reproduced at the very start of life.\u003c/p\u003e\n\u003cp\u003eThis study addresses these gaps by examining the joint effects of EAM and marital status on birthweight outcomes using 2024 National Vital Statistics System (NVSS) natality data. It contributes to research on family structure, assortative mating, infant health, and early-life inequality by: (1) extending research on EAM beyond LBW to include macrosomia; (2) modeling the full birthweight distribution as a product of the intersecting effects of EAM and marital status and; (3) demonstrating how varying levels of educational homogamy interact with marriage to structure risks of LBW and macrosomia relative to unmarried heterogamous unions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy doing so, this study shows that marriage operates as a conditional stratifying institution, amplifying the advantages of high-educated homogamy while failing to offset disadvantage among low-educated homogamous unions (McLanahan and Percheski 2008).\u003c/p\u003e"},{"header":"Background and Theoretical Frameworks","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eEducational assortative mating (EAM), marital status and birthweight\u003c/h2\u003e \u003cp\u003eParental educational attainment is strongly associated with birth-related and infant health outcomes (Balaj et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Buciu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cantarutti et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ruiz et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Maternal education is consistently linked to improved birthweight (Currie \u0026amp; Moretti, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Godah et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pikhartova \u0026amp; Shelton, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shrestha, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although comparatively less studied, paternal education also exerts an independent influence on birthweight and infant health outcomes (Meng \u0026amp; Groth, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Parker \u0026amp; Schoendorf, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFocusing solely on individual parental characteristics may obscure the broader familial and relational contexts in which pregnancies occur (Abufhele et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recent work emphasizes the importance of \u0026ldquo;bringing the father back in\u0026rdquo;, arguing that the combined educational resources of both parents better capture variation in infant health (Rangel \u0026amp; Rauscher, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Swaminathan et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). EAM studies indicate that educational homogamy \u0026ndash; particularly among highly educated couples \u0026ndash; is associated with more favorable birthweight outcomes than heterogamy (Abufhele et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rauscher, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These patterns suggest that parental educational similarity may foster shared health-promoting behaviors, greater accumulation of socioeconomic resources, and reduced maternal stress. However, homogamy among low-educated couples may reflect constrained socioeconomic conditions rather than resource consolidation, potentially producing different birthweight patterns than medium- or high-educated homogamy.\u003c/p\u003e \u003cp\u003eParallel evidence highlights the role of marital status. Infants born to married mothers consistently exhibit better health outcomes than those born to unmarried mothers (Shah et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yan, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Marriage is often understood to confer advantages through economic stability, social support, reduced stress, and healthier behaviors (Frimmel \u0026amp; Pruckner, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Song, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yan, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet the benefits of marriage may not be uniform across socioeconomic contexts. Marriage may amplify existing socioeconomic advantages rather than compensate for their absence, suggesting that marital benefits depend on underlying parental resources \u0026ndash; including education. This aligns with the \u0026ldquo;diverging destinies\u0026rdquo; framework (McLanahan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which posits that the effects of family structure are increasingly conditioned by parental socioeconomic resources, producing unequal child outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical perspectives and potential mechanisms\u003c/h2\u003e \u003cp\u003eThis study draws on Family Systems Theory and Resource Multiplication Theory to interpret intersecting patterns of EAM and marital status.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFamily systems theory\u003c/em\u003e emphasizes couple-level dynamics and interdependencies of partners\u0026rsquo; attributes (Cox \u0026amp; Paley, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Minuchin, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Partner similarities can promote agreement on health behaviors \u0026ndash; such as diet and prenatal care \u0026ndash; during pregnancy (Beck \u0026amp; Gonz\u0026aacute;lez-Sancho, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rauscher, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In high-resource context \u0026ndash; such as in high-educated homogamous settings \u0026ndash; homogamous unions may facilitate behavioral regulation where shared educational values promote behaviors that limit excessive fetal growth. Conversely, disparities between partners can produce conflict, negatively affecting maternal and infant health. Joint parental education and marital status therefore shape maternal stress, health behaviors, and access to resources during pregnancy.\u003c/p\u003e \u003cp\u003e \u003cem\u003eResource multiplication theory\u003c/em\u003e posits that when partners are both socioeconomically advantaged, their combined resources amplify health benefits (Ross \u0026amp; Mirowsky, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This theory is often contrasted with \u0026ldquo;resource substitution,\u0026rdquo; which suggests that marriage should matter most for the least advantaged (the low-educated) because it provides a necessary buffer. In contrast, multiplication theory proposes that institutional advantages such as marriage amplify preexisting resources rather than compensate for their absence (McLanahan \u0026amp; Percheski, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schwartz, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Under this framework, high-educated homogamous marriages may produce the most favorable birth environments \u0026ndash; reducing LBW risk while potentially shifting fetal growth toward the upper end of the distribution through greater pooled resources. Conversely, low-educated unions may lack sufficient resource depth, contributing to persistent disparities in infant health (McLanahan \u0026amp; Percheski, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTogether, these frameworks suggest that EAM and marital status operate jointly as family-level mechanisms through which social inequality becomes biologically embedded at birth, shaping both fetal growth restriction and excessive fetal growth across the full birthweight spectrum. Collectively, they explain both how partner similarity shapes prenatal behaviors (family systems) and why advantaged couples benefit disproportionately from marriage (resource multiplication). Based on these theoretical and empirical considerations, this study examines the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow do EAM and marital status independently structure risks of LBW and macrosomia?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo what extent does the protective association between marriage and LBW depend on the level of parental educational homogamy?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes educational homogamy moderate the risks of LBW and macrosomia associated with marriage?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDo high-educated homogamous marriages simultaneously reduce LBW risk while increasing excessive fetal growth?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Data and Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample\u003c/h2\u003e \u003cp\u003eThis study uses the 2024 National Vital Statistics System (NVSS) natality file, which provides comprehensive, high-quality information on a large annual population of live births in the U.S. and its territories. The data are deidentified and publicly available. Consistent with prior research (Barreto et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rauscher, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Song, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the analytic sample is restricted to live singleton births. For parsimony, the sample is further limited to women identified as White, Black, Asian, or Hispanic, excluding American Indian or Alaska Native (AIAN), Native Hawaiian and Other Pacific Islander (NHOPI), and multiracial women. Missing data on most variables were minimal, ranging from 0.09% for birthweight to 1.86% for number of prenatal visits. Larger proportions of missingness (2.5%) were observed for body mass index (BMI), father\u0026rsquo;s education (12.6%), and mother\u0026rsquo;s education (1.9%).\u003c/p\u003e \u003cp\u003eMultiple imputation (MI) was initially considered to address missingness in BMI and parental education. However, MI proved computationally infeasible given the exceptionally large sample size and the complexity of multinomial models with multiple interaction terms. To avoid selection bias while retaining the full analytic sample, a missing-indicator approach was used.\u003c/p\u003e \u003cp\u003eFor parental education, cases with missing information for either parent or both (11.73% of the final sample) were retained by creating a distinct \u0026ldquo;missing parental education\u0026rdquo; category. This category is included in the multinomial logistic regression and interacted with marital status to assess whether the absence of parental education reporting moderates the association between marriage and birthweight.\u003c/p\u003e \u003cp\u003eMissing BMI values were addressed using mean substitution paired with a binary missingness indicator. Missing BMI values were replaced with the sample mean calculated from non-missing cases, and a dummy variable was included in all models to account for potential bias associated with non-reporting of maternal weight. After addressing missingness and dropping negligible missing values via listwise deletion (birthweight has the highest [1.86%]), the final analytic sample consists of 2,906,124 live singleton births.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Variable\u003c/h2\u003e \u003cp\u003eThe outcome variable, birthweight, Birthweight is measured as a nominal categorical variable with three mutually exclusive categories: low birthweight (LBW), normal birthweight, and macrosomia. LBW is defined as a birthweight of less than 2,500 grams, normal birthweight ranges from 2,500 to 4,000 grams, and macrosomia is defined as a birthweight greater than 4,000 grams.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePredictors\u003c/h2\u003e \u003cp\u003eThe key independent variables are educational assortative mating (EAM) and marital status. EAM is constructed by combining maternal and paternal educational attainment. Maternal and paternal education are first coded as categorical variables (1\u0026thinsp;=\u0026thinsp;less than high school, 2\u0026thinsp;=\u0026thinsp;high school diploma or GED, 3\u0026thinsp;=\u0026thinsp;some college, 4\u0026thinsp;=\u0026thinsp;bachelor\u0026rsquo;s degree or higher; 5\u0026thinsp;=\u0026thinsp;missing or unreported). These measures are then combined to create a categorical indicator of educational heterogamy and graded educational homogamy with five categories: 0\u0026thinsp;=\u0026thinsp;heterogamy (reference), 1\u0026thinsp;=\u0026thinsp;low-educated homogamy, 2\u0026thinsp;=\u0026thinsp;medium-educated homogamy, 3\u0026thinsp;=\u0026thinsp;high-educated homogamy, 4\u0026thinsp;=\u0026thinsp;missing or unreported parental education.\u003c/p\u003e \u003cp\u003eEducational heterogamy refers to parents with dissimilar levels of educational attainment, whereas educational homogamy captures parental educational similarity and is further differentiated by level. Low-educated homogamy includes unions in which both parents have less than a high school education or a high school diploma/GED; medium-educated homogamy includes unions in which both parents have some college education but no bachelor\u0026rsquo;s degree; and high-educated homogamy includes captures unions in which both parents have attained at least a bachelor\u0026rsquo;s degree.\u003c/p\u003e \u003cp\u003eModeling EAM with heterogamy as the reference category preserves the full analytic sample and allows direct comparison across graded levels of educational homogamy.\u003c/p\u003e \u003cp\u003eMarital status is measured as a binary indicator (1\u0026thinsp;=\u0026thinsp;married, 0\u0026thinsp;=\u0026thinsp;unmarried)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe model adjusts for a set of demographics, socioeconomic and clinical variables. These include maternal age (1\u0026thinsp;=\u0026thinsp;12\u0026ndash;19 [under 20], 2\u0026thinsp;=\u0026thinsp;20\u0026ndash;29, 3\u0026thinsp;=\u0026thinsp;30+), race/ethnicity (1\u0026thinsp;=\u0026thinsp;White, 2\u0026thinsp;=\u0026thinsp;Black, 3\u0026thinsp;=\u0026thinsp;Asian, 4\u0026thinsp;=\u0026thinsp;Hispanic), maternal smoking (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no), nativity (1\u0026thinsp;=\u0026thinsp;US-born, 0\u0026thinsp;=\u0026thinsp;foreign-born), parity (continuous), number of prenatal visits (continuous), source of payment for delivery (1\u0026thinsp;=\u0026thinsp;private insurance, 2\u0026thinsp;=\u0026thinsp;Medicaid, 3\u0026thinsp;=\u0026thinsp;self-payment, 4\u0026thinsp;=\u0026thinsp;others), BMI (continuous), unreported BMI (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no), sex of infant (1\u0026thinsp;=\u0026thinsp;male, 0\u0026thinsp;=\u0026thinsp;female), gestational age at birth (1\u0026thinsp;=\u0026thinsp;preterm, 2\u0026thinsp;=\u0026thinsp;full term), pre-pregnancy diabetes (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no) and pre-pregnancy hypertension (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no).\u003c/p\u003e\n\u003ch3\u003eAnalytic Strategy\u003c/h3\u003e\n\u003cp\u003eI employ multinomial logistic regression to estimate the relative risk of LBW and macrosomia, using normal birthweight as the reference category. This approach \u0026ndash; unlike binary logistic regression \u0026ndash; allows for the simultaneous estimation of risks at both tails of the birthweight distribution.\u003c/p\u003e \u003cp\u003eAnalyses proceed in two stages. Model 1 estimates the unadjusted associations between EAM and marital status and birthweight outcomes, without additional covariates. Model 2 introduces interaction terms between EAM and marital status and adjusts for maternal age, race/ethnicity, nativity, maternal smoking, parity, number of prenatal visits, source of payment for delivery, body mass index (BMI), an indicator for missing BMI, gestational age at birth, infant sex, and pre-pregnancy diabetes and hypertension. This modeling strategy allows for a comparison of crude associations with fully adjusted estimates and facilitates assessment of whether the joint effects of EAM and marital status on birthweight outcomes persist net of sociodemographic, behavioral, and pregnancy-related factors. In all models, unmarried heterogamous unions serve as the reference group for the primary predictors, and normal birthweight (2,500\u0026ndash;4,000g) serves as the reference for the outcome categories. This allows for a direct assessment of how various forms of educational homogamy and the transition into marriage deviate from a baseline of non-marital educational discordance\u003c/p\u003e \u003cp\u003eGiven the complexity of interpreting interaction terms in multinomial models, I calculate predicted probabilities to facilitate interpretation. Predicted probabilities illustrate absolute differences in the likelihood of LBW and macrosomia by EAM categories and marital status, providing an intuitive understanding of disparities and allowing for direct comparisons across groups. Standard errors are estimated using the robust Huber-White sandwich estimator to account for potential heteroscedasticity. Because the NVSS provides a near-universal census of all births in the U.S., sampling weights are not required for population-level representation. All analyses are conducted using Stata 19.0 SE.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents sociodemographic, behavioral, and clinical characteristics by educational assortative mating (heterogamy, low-, medium-, and high-educated homogamy, and missing education). Birthweight distributions varied across educational pairings. Low birthweight was most prevalent in the missing education group (11.3%) and lowest among high-educated homogamy births (5.0%), with intermediate levels in heterogamy (6.9%), low-educated homogamy (7.7%), and medium-educated homogamy (6.7%). In contrast, macrosomia was most common among high-educated homogamy births (8.5%) and least common in the missing (4.7%) and low-educated homogamy group (6.1%).\u003c/p\u003e \u003cp\u003eMarital status differed sharply by educational pairing. Marriage was nearly universal among high-educated homogamy births (92.7%) but substantially lower in heterogamy (60.2%), medium-educated homogamy (63.0%), and especially low-educated homogamy (43.6%), while the missing group was overwhelmingly unmarried (90.5%). Racial/ethnic composition also varies. High-educated homogamy births were predominantly White (70.8%) and Asian (11.5%), whereas low-educated homogamy births included larger shares of Hispanic (42.2%) and Black (15.6%) mothers.\u003c/p\u003e \u003cp\u003eMaternal age and smoking followed clear educational gradients. Mothers aged 30\u0026thinsp;+\u0026thinsp;were most common in high-educated homogamy births (74.3%) and least common in low-educated homogamy births (32.6%), while teenage motherhood was concentrated in the low-educated (8.2%) and missing (11.0%) groups. Smoking during pregnancy was rare overall but highest in the missing group (7.1%) and lowest in high-educated homogamy births (0.2%). Infant sex was evenly distributed across educational pairings.\u003c/p\u003e \u003cp\u003eClinical and healthcare indicators showed similar stratification. Preterm birth was least common in high-educated homogamy births (7.9%) and highest in the missing group (15.3%). Medicaid coverage predominated in low-educated (63.8%) and missing (70.4%) groups, whereas private insurance was most common in high-educated homogamy births (85.3%). Diabetes and hypertension were uncommon across all categories (\u0026lt;\u0026thinsp;2% and \u0026lt;\u0026thinsp;5%, respectively). See Appendix A for a complete overview of sample characteristics without crosstabulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable analyses\u003c/h2\u003e \u003cp\u003eUsing unmarried educationally heterogamous women as reference, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents unadjusted and adjusted relative risk ratios (RRRs) from multinomial logistic regression models predicting birthweight using EAM and marital status (Model 1), and their interaction, adjusting for maternal age, race/ethnicity, nativity, maternal smoking, parity, number of prenatal visits, payment source, gestational age at birth, sex of infant, pre-pregnancy diabetes and hypertension (Model 2). LBW and macrosomia are interpreted in reference to normal birthweight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLow birthweight (LBW) vs Normal birthweight\u003c/h2\u003e \u003cp\u003eModel 1 shows clear educational and marital gradients in LBW risk with higher parental educational attainment and being married exhibiting protective advantage against LBW risk. Relative to heterogamous unions, high-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.79\u0026ndash;0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and medium-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;0.98, 95% CI: 0.95\u0026ndash;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are associated with lower risk of LBW. In contrast, low-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 1.05\u0026ndash;1.07) as well as births with missing parental education (RRR\u0026thinsp;=\u0026thinsp;1.44, 95% CI: 1.42\u0026ndash;1.46) exhibit markedly elevated LBW risk. Marriage offers strong protection against LBW risk, with married mothers experiencing a 27% lower risk (RRR\u0026thinsp;=\u0026thinsp;0.73, 95% CI: 0.72\u0026ndash;0.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared with unmarried mothers.\u003c/p\u003e \u003cp\u003eModel 2, which includes interaction terms and full adjustment, suggests educational gradient in LBW risks across marital status net of demographic, socioeconomic and clinical factors, as indicated by the main effects for EAM and marital status. Among unmarried women, relative to heterogamous unions, medium-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.91\u0026ndash;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and high-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;0.86, 95% CI: 0.83\u0026ndash;0.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are associated with significantly lower risks of LBW, whereas low-educated homogamy does not differ significantly from heterogamy (RRR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: .99-1.02, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Missing parental education remains associated with higher LBW risks (RRR\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 1.07\u0026ndash;1.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among heterogamous couples, marriage is associated with a lower risk of LBW (RRR\u0026thinsp;=\u0026thinsp;0.90, 95% CI: 0.89\u0026ndash;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe interaction between EAM and marital status reveals heterogeneity in marital advantage across educational pairings. Relative to unmarried heterogamous unions, marriage is associated with higher LBW risk among low-educated homogamous couples (RRR\u0026thinsp;=\u0026thinsp;1.08, 95% CI: 1.05\u0026ndash;1.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), no significant difference among medium-educated homogamous couples (RRR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: .98-1.18, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and lower LBW risk among high-educated homogamous couples (RRR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.90\u0026ndash;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Marriage is also associated with elevated LBW risk among births with missing parental education (RRR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.01\u0026ndash;1.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMacrosomia vs Normal birthweight\u003c/h2\u003e \u003cp\u003eModel 1 indicates clear educational and marital gradients in the risk of macrosomia. Relative to heterogamous unions, medium-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.00-1.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and high-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.01\u0026ndash;1.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are associated with higher risks of macrosomia, whereas low-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.86\u0026ndash;0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and births with missing parental education (RRR\u0026thinsp;=\u0026thinsp;0.78, 95% CI: 0.77\u0026ndash;0.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) exhibit significantly lower risks compared with heterogamous unions. Marriage is associated with a substantially elevated risk of macrosomia, with married mothers experiencing a 45% higher risk relative to unmarried mothers (RRR\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.43\u0026ndash;1.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eModel 2, which incorporates interaction terms and full adjustment, suggests that educational gradients in macrosomia risk persist across marital status net of covariates. Among unmarried women, relative to heterogamous unions, medium-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.03\u0026ndash;1.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and high-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;1.16, 95% CI: 1.12\u0026ndash;1.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are associated with significantly higher risks of macrosomia, whereas low-educated homogamy (RRR\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.90\u0026ndash;0.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and missing parental education (RRR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.85\u0026ndash;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are associated with lower risks. Among heterogamous couples, marriage is associated with elevated macrosomia risk (RRR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.15\u0026ndash;1.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe interaction between EAM and marital status reveals heterogeneity in macrosomia risks by marital status across educational pairings. Relative to unmarried heterogamous unions, marriage is associated with lower risks of macrosomia among low-educated homogamous couples (RRR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.92\u0026ndash;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), medium-educated homogamous couples (RRR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.89\u0026ndash;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and high-educated homogamous couples (RRR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.92\u0026ndash;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Thus, although high-educated homogamy is associated with elevated macrosomia risk among unmarried women and marriage is associated with higher risk among heterogamous unions, the interaction estimates indicate that marriage attenuates the excess macrosomia risk observed among high-educated homogamous couples. In contrast, marriage is associated with a modestly higher risk of macrosomia among births with missing parental education (RRR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 1.00-1.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePredicted Probabilities of LBW and macrosomia\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents predicted probabilities of low birthweight (LBW) and macrosomia by educational assortative mating (EAM) and marital status, based on the fully adjusted multinomial logistic regression model. Overall, predicted probabilities of LBW vary modestly across EAM and marital status groups, ranging between 6% and 8%. Among heterogamous unions, predicted probabilities of LBW are similar for unmarried and married women (0.07 in both groups). Slightly higher probabilities are observed among unmarried low-educated homogamous unions (0.08), whereas probabilities among married low-educated, medium-educated, and high-educated homogamous unions cluster around 7%. The lowest predicted probability of LBW is observed among married high-educated homogamous couples (0.06). Births with missing parental education exhibit comparatively higher predicted probabilities of LBW regardless of marital status (0.08).\u003c/p\u003e \u003cp\u003ePredicted probabilities of macrosomia also show limited but systematic variation across EAM\u0026ndash;marital status categories, ranging from 6% to 8%. Among heterogamous unions, macrosomia is more common among married women (0.07) than unmarried women (0.06). For homogamous unions, predicted probabilities of macrosomia are similar across educational levels among unmarried women (approximately 0.06\u0026ndash;0.07). In contrast, married high-educated homogamous couples exhibit the highest predicted probability of macrosomia (0.08), while married low- and medium-educated homogamous couples show probabilities around 7%. As with LBW, births with missing parental education display modestly elevated probabilities across marital statuses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRobustness Check\u003c/h2\u003e \u003cp\u003eAs a robustness check, additional analyses were conducted using binary indicators of low birthweight (LBW; \u0026lt; 2,500 g) and macrosomia (\u0026gt;\u0026thinsp;4,000 g). Each outcome was estimated as a function of educational assortative mating (EAM), marital status, and their interaction, adjusting for the same covariates included in the multinomial models. Overall, results closely mirrored those from the primary multinomial analyses, yielding substantively similar conclusions.\u003c/p\u003e \u003cp\u003eFor LBW, marriage was associated with significantly lower risk among mothers in high-educated homogamous unions (OR\u0026thinsp;=\u0026thinsp;0.86, 95% CI: 0.83\u0026ndash;0.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and medium-educated homogamous unions (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.91\u0026ndash;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, mothers in low-educated homogamous unions did not experience comparable marital protection (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 0.99\u0026ndash;1.04, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings indicate that although marriage is generally associated with reduced LBW risk, its protective effect is concentrated among medium- and high-educated homogamous unions and does not offset disadvantage among low-educated homogamous couples.\u003c/p\u003e \u003cp\u003eResults for macrosomia similarly confirmed the multinomial findings. Marriage was associated with higher macrosomia risk among women in educationally heterogamous unions (OR\u0026thinsp;=\u0026thinsp;1.18, 95% CI: 1.16\u0026ndash;1.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas interaction estimates indicated that educational homogamy modestly attenuated this excess risk across low-educated (OR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.92\u0026ndash;0.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), medium-educated (OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.89\u0026ndash;0.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and high-educated homogamous unions (OR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.92\u0026ndash;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Thus, while marriage elevates macrosomia risk in heterogamous unions, educational similarity within marriage partially mitigates this risk, consistent with the main analysis (see Appendix B).\u003c/p\u003e \u003cp\u003eTo ensure that findings were not driven by categorical birthweight thresholds, Appendix C presents fully adjusted linear regression models predicting continuous birthweight (grams). Although linear models cannot capture excessive fetal growth at the upper tail, they provide a complementary assessment of average birthweight differences across EAM pairings. Interaction results indicate that all educational homogamous unions exhibit declines in mean birthweight relative to the reference group. However, the magnitude of decline is largest among low-educated (\u0026minus;\u0026thinsp;13.11 g) and medium-educated (\u0026minus;\u0026thinsp;13.86 g) homogamous unions and smallest among high-educated homogamous unions (\u0026minus;\u0026thinsp;7.83 g). These patterns further substantiate that married women in high-educated homogamous unions experience the greatest relative birthweight advantage, reinforcing conclusions from the multinomial and binary models.\u003c/p\u003e \u003cp\u003eAdditional multinomial models adjusting for father\u0026rsquo;s race/ethnicity and age yield substantively identical conclusions to the main analyses (Appendix D). High-educated homogamy remains strongly protective against LBW while associated with elevated macrosomia risk, and marriage continues to reduce LBW risk but increase macrosomia. Interaction patterns are likewise unchanged, with marital advantages concentrated among high-educated homogamous unions and attenuated among low-educated homogamy. These findings indicate that observed EAM-marriage associations are not attributable to compositional differences in paternal demographic characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examines how educational assortative mating (EAM) and marital status jointly structure birthweight outcomes across the full distribution using recent US natality data. Focusing on the two extremes of birthweight \u0026ndash; low birthweight (LBW) and macrosomia \u0026ndash; the findings indicate that although marriage is generally associated with more favorable birthweight outcomes, its advantages are not uniform, as the protective association of marriage is systematically conditioned on parental educational pairing, with meaningful reductions in extreme birthweight risks concentrated among medium- and high-educated homogamous unions and substantially attenuated benefits among low-educated homogamous families.\u003c/p\u003e \u003cp\u003eFirst, the unadjusted model shows that EAM and marriage are independently protective against LBW risk. Consistent with prior studies (Abufhele et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rauscher, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), high-educated homogamy is significantly associated with lower LBW risk, whereas low-educated homogamy elevates risk. Likewise, and in line with prior research (Shah et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Song, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), marriage is protective, with married women exhibiting significantly lower LBW risk than their unmarried counterparts. Interaction models show that high-educated homogamy reduces LBW risk even among unmarried parents and that marriage lowers LBW risk for educationally heterogamous couples. However, marital protection is uneven across parental educational pairings, concentrated among medium- and high-educated homogamous unions, with low-educated homogamous couples deriving minimal or no protection.\u003c/p\u003e \u003cp\u003ePrior research consistently documents lower LBW risk among married mothers, often attributing this advantage to pooled resources, social support, and healthier prenatal environments (Shah et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Song, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yan, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The present findings refine and stratify this narrative by demonstrating that marital protection varies by joint parental education. Only medium- and high-educated homogamous unions experience meaningful reductions in LBW risk, whereas low-educated homogamous couples do not. These results extend resource multiplication theory by showing that marriage does not merely amplify individual-level socioeconomic resources but also magnify dyadic educational configurations. In this sense, marriage functions less as a universal protective institution and more as a mechanism of inequality amplification in early-life health across parental education pairings (McLanahan \u0026amp; Percheski, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ross \u0026amp; Mirowsky, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This dyadic perspective advances research on family inequality by showing that marriage benefits depend not only on who marries, but also on the educational pairing of the couple (Rauscher, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This pattern aligns with the \u0026ldquo;diverging destinies\u0026rdquo; framework (McLanahan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which contends that growing interactions between family structure and parental socioeconomic resources contribute to widening stratification in child outcomes. The present findings show that these diverging destinies emerge even at birth, with advantaged unions (medium- and high-educated homogamy) consolidating protective factors and disadvantaged unions (low-educated homogamy) unable to leverage marriage into comparable health benefits.\u003c/p\u003e \u003cp\u003eSecond, regarding macrosomia, both unadjusted and adjusted models show that high-educated homogamy is associated with elevated macrosomia risk among unmarried women relative to unmarried educationally heterogamous parents. Among heterogamous parents, marriage is likewise associated with higher macrosomia risk. However, within marriage, educational homogamy attenuates the elevated macrosomia risks observed among married heterogamous couples, as reflected in significantly lower macrosomia risks across all educationally homogamous unions. These patterns suggest that the relationship between socioeconomic advantage \u0026ndash; proxied here by medium- and high-educated homogamy \u0026ndash; and excessive fetal growth may be contingent on marital context and family-level organization.\u003c/p\u003e \u003cp\u003eOne interpretation, consistent with family systems theory, is that educationally homogamous marriages may facilitate greater coordination of health behaviors, prenatal decision-making, and healthcare utilization. Socioeconomic advantages associated with medium- and high-educated homogamy may shift fetal growth upward through improved nutrition or overnutrition. At the same time, marital coordination regulates behaviors in ways that preclude excessive fetal growth. In this sense, marriage may operate not only as a resource-pooling institution but also as a behavior-structuring context that moderates risks associated with excessive fetal growth.\u003c/p\u003e \u003cp\u003eAt the lower end of the educational distribution, reduced macrosomia risk among (married) low-educated homogamous couples may reflect constrained material conditions associated with socioeconomic disadvantage, including limited nutritional resources. Although this interpretation remains necessarily cautious, the pattern aligns with extensive evidence linking socioeconomic deprivation and maternal undernutrition to restricted fetal growth and LBW (Aizer \u0026amp; Currie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Blumenshine et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kramer, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Sau et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). From this perspective, lower macrosomia prevalence among low-educated homogamous couples likely reflects structural constraints on fetal growth rather than a true health advantage.\u003c/p\u003e \u003cp\u003eBirths with missing parental education exhibit consistently higher LBW and macrosomia risks, and comparatively weaker benefits from marriage, indicating that data missingness itself may signal structural marginalization. This group likely captures socially disadvantaged or unstable family contexts insufficiently measured by conventional socioeconomic indicators. Recognizing missing parental education as substantively meaningful, rather than purely methodological, broadens the interpretive scope of demographic and health research.\u003c/p\u003e \u003cp\u003eTaken together, these findings underscore the importance of family-level configurations of education and marital status in understanding birthweight disparities. They illustrate how advantaged unions consolidate resources and generate protective effects, whereas disadvantaged or resource-deprived family formations fail to produce comparable benefits. This relational perspective bridges individual-level socioeconomic research with broader sociological theories of stratification, demonstrating that inequality in early-life health is produced not only through parental characteristics but through the joint distribution of resources within families. The findings show that marriage operates as a conditional stratifying institution whose benefits accrue disproportionately to socioeconomically advantaged couples, thereby reinforcing inequality at the very start of the life course.\u003c/p\u003e \u003cp\u003eSeveral limitations, however, merit consideration. First, marital status is measured dichotomously as married versus unmarried, excluding other forms of family formation in a society that has undergone profound demographic changes in recent decades (A. Cherlin, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), particularly in family structure (A. J. Cherlin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Critically, the 'unmarried' category in birth certificate data is heterogeneous, likely containing a high proportion of cohabiting unions which now characterize a majority of nonmarital births (Guzzo, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lichter et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Manning \u0026amp; Smock, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While cohabiting partners often pool resources and provide social support similar to married spouses, prior research suggests these unions often lack the same level of institutionalized 'multiplication' of resources found in marriage (Song, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, given that prior research links nontraditional family structures to infant health (Dello Iacono et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Heiland \u0026amp; Liu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mart\u0026iacute;n, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Schmeer, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), future work using data that distinguishes between cohabitation and single parenthood should examine how these specific family forms moderate relationships between parental education and infant health. Second, both parents\u0026rsquo; educational attainment is reported by the mother, introducing potential measurement error, particularly among unmarried births where contact with the father is less frequent. Although maternal reporting of paternal attributes is generally reliable (Reichman et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), dyadic survey data would allow further validation (Reichman et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Third, although models adjust for extensive maternal, behavioral, and clinical factors, unmeasured socioeconomic or paternal characteristics may persist. Finally, the large NVSS sample increases statistical power, potentially yielding statistically significant yet substantively modest differences. However, even modest shifts in birthweight distributions can have meaningful population-level implications for long-term public health and socioeconomic inequality.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study contributes to research on family structure, assortative mating and infant health in three ways. First, it demonstrates that EAM structures inequality in infant health at birth, shaping both restricted fetal growth and distributional shifts toward excessive birthweight. Second, it reconceptualizes marriage as a conditional stratifying institution rather than a universal health resource that uniformly confers health advantage. Third, while prior work has focused almost exclusively on LBW, this study incorporates macrosomia, demonstrating that educational stratification and marital context shape the entire birthweight distribution.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMarriage does not confer equal benefits for infant health across parental educational pairings. Instead, its protective and risk-producing effects depend fundamentally on parental educational similarity and underlying socioeconomic advantage. Among married births, marriage provides the greatest protection against LBW within high-educated homogamous unions while failing to offset disadvantage among low-educated homogamous families. At the same time, although marriage elevates macrosomia risk among educationally heterogamous couples, educational homogamy moderates this excess risk across all homogamous unions.\u003c/p\u003e \u003cp\u003eUnderstanding disparities in infant health therefore requires moving beyond individual characteristics or marital status alone toward a family-level, distributional perspective on early-life inequality. These findings contribute to sociological theory by demonstrating that marriage functions as a conditional stratifying institution whose benefits are contingent on dyadic resource configurations, thereby reinforcing diverging destinies from the earliest stages of life.\u003c/p\u003e \u003cp\u003ePolicies that promote marriage in isolation are unlikely to reduce infant health disparities without addressing underlying socioeconomic inequality, particularly educational disadvantages. Interventions targeting education, economic stability, and prenatal health resources \u0026ndash; particularly for low-educated and structurally marginalized families \u0026ndash; are more likely to yield meaningful improvements. Simultaneously, patterns of elevated macrosomia risk in specific socioeconomic and marital contexts underscore the need for balanced prenatal care strategies that address both insufficient and excessive fetal growth.\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\u003eDemographic, socioeconomic and clinical characteristics by educational assortative mating.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,906,124\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eEducational Homogamy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeterogamy\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;979433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-educated\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;565268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedium-educated\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;145841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh-educated\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;874703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;340879)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirthweight\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67269\u003c/p\u003e \u003cp\u003e(6.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43510\u003c/p\u003e \u003cp\u003e(7.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9692\u003c/p\u003e \u003cp\u003e(6.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43520\u003c/p\u003e \u003cp\u003e(4.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38361\u003c/p\u003e \u003cp\u003e(11.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e839792 (85.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e487289\u003c/p\u003e \u003cp\u003e(86.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125038\u003c/p\u003e \u003cp\u003e(85.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e756696\u003c/p\u003e \u003cp\u003e(86.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e286600\u003c/p\u003e \u003cp\u003e(84.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrosomia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72372\u003c/p\u003e \u003cp\u003e(7.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34469\u003c/p\u003e \u003cp\u003e(6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11111\u003c/p\u003e \u003cp\u003e(7.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74487\u003c/p\u003e \u003cp\u003e(8.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15918\u003c/p\u003e \u003cp\u003e(4.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital 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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e389707\u003c/p\u003e \u003cp\u003e(39.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e318884\u003c/p\u003e \u003cp\u003e(56.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54084\u003c/p\u003e \u003cp\u003e(37.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63870\u003c/p\u003e \u003cp\u003e(7.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e308386\u003c/p\u003e \u003cp\u003e(90.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e589726\u003c/p\u003e \u003cp\u003e(60.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246384\u003c/p\u003e \u003cp\u003e(43.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91757\u003c/p\u003e \u003cp\u003e(62.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e810833\u003c/p\u003e \u003cp\u003e(92.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32493\u003c/p\u003e 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colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225246\u003c/p\u003e \u003cp\u003e(39.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80277\u003c/p\u003e \u003cp\u003e(55.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e619157\u003c/p\u003e \u003cp\u003e(70.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e115764\u003c/p\u003e \u003cp\u003e(33.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134801\u003c/p\u003e \u003cp\u003e(13.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88093\u003c/p\u003e \u003cp\u003e(15.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23503\u003c/p\u003e \u003cp\u003e(16.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57094\u003c/p\u003e \u003cp\u003e(6.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e116429\u003c/p\u003e \u003cp\u003e(34.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33819\u003c/p\u003e \u003cp\u003e(3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13636\u003c/p\u003e \u003cp\u003e(2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4243\u003c/p\u003e \u003cp\u003e(2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100445\u003c/p\u003e \u003cp\u003e(11.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5986\u003c/p\u003e \u003cp\u003e(1.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270838\u003c/p\u003e \u003cp\u003e(27.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238293\u003c/p\u003e \u003cp\u003e(42.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37818\u003c/p\u003e \u003cp\u003e(25.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98007\u003c/p\u003e \u003cp\u003e(11.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e102700\u003c/p\u003e \u003cp\u003e(30.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNativity\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign-born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204933\u003c/p\u003e \u003cp\u003e(20.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194850\u003c/p\u003e \u003cp\u003e(34.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23681\u003c/p\u003e \u003cp\u003e(16.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e195121\u003c/p\u003e \u003cp\u003e(22.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76175\u003c/p\u003e \u003cp\u003e(22.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUS-born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e774500\u003c/p\u003e \u003cp\u003e(79.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e370418\u003c/p\u003e \u003cp\u003e(65.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122160\u003c/p\u003e \u003cp\u003e(83.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e679582\u003c/p\u003e \u003cp\u003e(77.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e264704\u003c/p\u003e \u003cp\u003e(77.65)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27914\u003c/p\u003e \u003cp\u003e(2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46150\u003c/p\u003e \u003cp\u003e(8.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1698\u003c/p\u003e \u003cp\u003e(1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37331\u003c/p\u003e \u003cp\u003e(10.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e493029\u003c/p\u003e \u003cp\u003e(50.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e334885\u003c/p\u003e \u003cp\u003e(59.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78200\u003c/p\u003e \u003cp\u003e(53.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e225072\u003c/p\u003e \u003cp\u003e(25.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e176999\u003c/p\u003e \u003cp\u003e(51.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e458490\u003c/p\u003e \u003cp\u003e(46.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184233\u003c/p\u003e \u003cp\u003e(32.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65943\u003c/p\u003e \u003cp\u003e(45.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e649522\u003c/p\u003e \u003cp\u003e(74.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e126549\u003c/p\u003e \u003cp\u003e(37.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal smoking\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e953462\u003c/p\u003e \u003cp\u003e(97.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e546535\u003c/p\u003e \u003cp\u003e(96.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142954\u003c/p\u003e \u003cp\u003e(98.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e873405\u003c/p\u003e \u003cp\u003e(99.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e316723\u003c/p\u003e \u003cp\u003e(92.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25971\u003c/p\u003e \u003cp\u003e(2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18733\u003c/p\u003e \u003cp\u003e(3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2887\u003c/p\u003e \u003cp\u003e(1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1298\u003c/p\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24156\u003c/p\u003e \u003cp\u003e(7.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at birth\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e875981\u003c/p\u003e \u003cp\u003e(89.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e479840\u003c/p\u003e \u003cp\u003e(88.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130746\u003c/p\u003e \u003cp\u003e(89.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e805921\u003c/p\u003e \u003cp\u003e(92.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e288597\u003c/p\u003e \u003cp\u003e(84.66)\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 \u003cp\u003e103452\u003c/p\u003e \u003cp\u003e(10.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67428\u003c/p\u003e \u003cp\u003e(11.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15095\u003c/p\u003e \u003cp\u003e(10.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68782\u003c/p\u003e \u003cp\u003e(7.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52282\u003c/p\u003e \u003cp\u003e(15.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePayment source\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415030\u003c/p\u003e \u003cp\u003e(42.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360554\u003c/p\u003e \u003cp\u003e(63.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58413\u003c/p\u003e \u003cp\u003e(40.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73453\u003c/p\u003e \u003cp\u003e(8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e239918\u003c/p\u003e \u003cp\u003e(70.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e487762\u003c/p\u003e \u003cp\u003e(49.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135966\u003c/p\u003e \u003cp\u003e(24.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74092\u003c/p\u003e \u003cp\u003e(50.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e745988\u003c/p\u003e \u003cp\u003e(85.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74737\u003c/p\u003e \u003cp\u003e(21.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut of pocket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42518\u003c/p\u003e \u003cp\u003e(4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53068\u003c/p\u003e \u003cp\u003e(9.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6133\u003c/p\u003e \u003cp\u003e(4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30630\u003c/p\u003e \u003cp\u003e(3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18744\u003c/p\u003e \u003cp\u003e(5.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34123\u003c/p\u003e \u003cp\u003e(3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15680\u003c/p\u003e \u003cp\u003e(2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7203\u003c/p\u003e \u003cp\u003e(4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24632\u003c/p\u003e \u003cp\u003e(2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7480\u003c/p\u003e \u003cp\u003e(2.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfant sex\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e478175\u003c/p\u003e \u003cp\u003e(48.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276711\u003c/p\u003e \u003cp\u003e(48.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71043\u003c/p\u003e \u003cp\u003e(48.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e427925\u003c/p\u003e \u003cp\u003e(48.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e167068\u003c/p\u003e \u003cp\u003e(48.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e501258\u003c/p\u003e \u003cp\u003e(51.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288557\u003c/p\u003e \u003cp\u003e(51.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74798\u003c/p\u003e \u003cp\u003e(51.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e446778\u003c/p\u003e \u003cp\u003e(51.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e173811\u003c/p\u003e \u003cp\u003e(51.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003csup\u003eb\u003c/sup\u003e\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e964832\u003c/p\u003e \u003cp\u003e(98.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e557094\u003c/p\u003e \u003cp\u003e(98.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143728\u003c/p\u003e \u003cp\u003e(98.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e866907\u003c/p\u003e \u003cp\u003e(99.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e335655\u003c/p\u003e \u003cp\u003e(98.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14601\u003c/p\u003e \u003cp\u003e(1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8174\u003c/p\u003e \u003cp\u003e(1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2113\u003c/p\u003e \u003cp\u003e(1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7796\u003c/p\u003e \u003cp\u003e(0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5224\u003c/p\u003e \u003cp\u003e(1.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003csup\u003eb\u003c/sup\u003e\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e941313\u003c/p\u003e \u003cp\u003e(96.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e547465\u003c/p\u003e \u003cp\u003e(96.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140195\u003c/p\u003e \u003cp\u003e(96.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e850685\u003c/p\u003e \u003cp\u003e(97.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e326175\u003c/p\u003e \u003cp\u003e(95.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38120\u003c/p\u003e \u003cp\u003e(3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17803\u003c/p\u003e \u003cp\u003e(3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5646\u003c/p\u003e \u003cp\u003e(3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24018\u003c/p\u003e \u003cp\u003e(2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14704\u003c/p\u003e \u003cp\u003e(4.31)\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 \u003csup\u003ea\u003c/sup\u003e Includes women who used Indian Health Service, CHAMPUS/TRICARE, other government and other.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e Diagnosed before pregnancy.\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\u003e\u003cem\u003eMultinomial Logistic Regression Predicting Low Birthweight and Macrosomia versus Normal Birthweight.\u003c/em\u003e\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\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,906,124\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eRelative Risk Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLow birthweight\u003c/p\u003e \u003cp\u003evs\u003c/p\u003e \u003cp\u003eNormal birthweight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMacrosomia\u003c/p\u003e \u003cp\u003evs\u003c/p\u003e \u003cp\u003eNormal birthweight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003euRRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eaRRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003euRRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eaRRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Assortative Mating\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeterogamy (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06***\u003c/p\u003e \u003cp\u003e(1.05\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(.99-1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.87***\u003c/p\u003e \u003cp\u003e(.86\u0026ndash;.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.92***\u003c/p\u003e \u003cp\u003e(.90\u0026ndash;.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.98*\u003c/p\u003e \u003cp\u003e(.95\u0026ndash;.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.95**\u003c/p\u003e \u003cp\u003e(.91 \u0026ndash; .98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02*\u003c/p\u003e \u003cp\u003e(1.00\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07*** (1.03\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.80***\u003c/p\u003e \u003cp\u003e(.79 \u0026ndash; .81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.86***\u003c/p\u003e \u003cp\u003e(.83 \u0026ndash; .90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03***\u003c/p\u003e \u003cp\u003e(1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16*** (1.12\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing parental education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44***\u003c/p\u003e \u003cp\u003e(1.42\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09*** (1.07\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.78***\u003c/p\u003e \u003cp\u003e(.77\u0026ndash;.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.87***\u003c/p\u003e \u003cp\u003e(.85\u0026ndash;.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.73***\u003c/p\u003e \u003cp\u003e(.72\u0026ndash;.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.90***\u003c/p\u003e \u003cp\u003e(.89\u0026ndash;.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45***\u003c/p\u003e \u003cp\u003e(1.43\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17*** (1.15\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Assortative Mating X Marital 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried heterogamy (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried low-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08*** (1.05\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.95***\u003c/p\u003e \u003cp\u003e(.92\u0026ndash;.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried medium-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(.98\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.93**\u003c/p\u003e \u003cp\u003e(.89\u0026ndash;.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried high-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.94**\u003c/p\u003e \u003cp\u003e(.90\u0026ndash;.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.95**\u003c/p\u003e \u003cp\u003e(.92\u0026ndash;.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing parental education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07*\u003c/p\u003e \u003cp\u003e(1.01\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06*\u003c/p\u003e \u003cp\u003e(1.00\u0026ndash;1.11)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder 20 (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04*** (1.02\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32*** (1.28\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21*** (1.18\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43*** (1.38\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95*** (1.92\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.48***\u003c/p\u003e \u003cp\u003e(.47\u0026ndash;.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04*** (1.99\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.40***\u003c/p\u003e \u003cp\u003e(.39\u0026ndash;.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13*** (1.11\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.69***\u003c/p\u003e \u003cp\u003e(.68\u0026ndash;.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNativity\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign-born (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUS-born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32*** (1.30\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.94***\u003c/p\u003e \u003cp\u003e(.93\u0026ndash;.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal smoking\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83*** (1.79\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.53***\u003c/p\u003e \u003cp\u003e(.51\u0026ndash;.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.85***\u003c/p\u003e \u003cp\u003e(.85\u0026ndash;.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12*** (1.11\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of prenatal visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.94***\u003c/p\u003e \u003cp\u003e(.93\u0026ndash;.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02*** (1.02\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull term (reference)\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 \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 \u003cp\u003e15.92*** (15.76\u0026ndash;16.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.35***\u003c/p\u003e \u003cp\u003e(.34\u0026ndash;.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfant sex\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.74***\u003c/p\u003e \u003cp\u003e(.74\u0026ndash;.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82***\u003c/p\u003e \u003cp\u003e(1.81 \u0026minus;\u0026thinsp;1.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePayment source\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.94***\u003c/p\u003e \u003cp\u003e(.93\u0026ndash;.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08*** (1.07\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-pay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.81***\u003c/p\u003e \u003cp\u003e(.71\u0026ndash;.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55*** (1.52\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.95**\u003c/p\u003e \u003cp\u003e(.92\u0026ndash;98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17*** (1.14\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.98***\u003c/p\u003e \u003cp\u003e(.98\u0026ndash;.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04*** (1.04\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08*** (1.04\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05*\u003c/p\u003e \u003cp\u003e(1.00\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy diabetes\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(.96\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06*** (1.99\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy hypertension\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.07*** (2.02\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.47***\u003c/p\u003e \u003cp\u003e(.46\u0026ndash;.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e: Confidence Interval (CI) 95% in parentheses. RRR is Relative Risk Ratio where uRRR is unadjusted and aRRR is adjusted. *p\u0026lt;.05; **p\u0026lt;.01; ***p\u0026lt;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003e\u003cem\u003ePredicted Probabilities of Birthweight by Educational Assortative Mating and Marital Status.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow birthweight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMacrosomia\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried heterogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06*** (0.06\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried heterogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried low-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08*** (0.07\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06*** (0.06\u0026ndash;0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried low-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried medium-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07*** (0.06\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried medium-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried high-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07*** (0.06\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried high-educated homogamy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06*** (0.06\u0026ndash;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08*** (0.08\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried missing parental education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08*** (0.08\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06*** (0.06\u0026ndash;0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried missing parental education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08*** (0.07\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07*** (0.07\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote\u003c/em\u003e: Based on fully adjusted multinomial logistic regression model. Confidence interval (95%) in parentheses. *p\u0026lt;.05; **p\u0026lt;.01; ***p\u0026lt;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author received no funding for this research.\u003c/p\u003e \u003cp\u003eNote:\u003c/p\u003e \u003cp\u003e1.The terms \u003cem\u003eeducational homogamy\u003c/em\u003e and \u003cem\u003eeducational similarity\u003c/em\u003e are used interchangeably throughout this manuscript.\u003c/p\u003e \u003cp\u003e2. References to \u003cem\u003ehomogamy\u003c/em\u003e and \u003cem\u003eheterogamy\u003c/em\u003e pertain specifically to educational assortative mating.\u003c/p\u003e \u003cp\u003e3. Heterogamy is modeled as a single reference category to preserve the analytic sample and to evaluate how graded levels of educational homogamy (low, medium, high) compare to a common heterogamous baseline. This approach aligns with the study\u0026rsquo;s focus on stratification within homogamy, recognizing that some forms \u0026ndash; particularly low-educated homogamy \u0026ndash; may reflect concentrated disadvantage rather than benefit. Future research will disaggregate heterogamy into educational hypogamy and hypergamy.\u003c/p\u003e \u003cp\u003e4. Parental education missingness is treated as a substantive category representing households with high levels of social and institutional disconnection.\u003c/p\u003e \u003cp\u003e5. Although fathers\u0026rsquo; characteristics may influence birth outcomes, the present analyses focus on paternal education because the study conceptualizes educational assortative mating as a couple-level socioeconomic structure. Including additional paternal socioeconomic indicators could introduce overcontrol bias by absorbing variation central to EAM, obscuring the relational mechanism under investigation. To evaluate the robustness of this specification, supplementary models additionally adjust for father\u0026rsquo;s age and race/ethnicity. These fully adjusted results are substantively unchanged in direction, magnitude, and statistical significance relative to the main models, supporting the stability of the reported associations. Detailed estimates are presented in Appendix D.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis article is sole authored. Every part of the article (conceptualization, research design, methods, analysis and writing) was undertaken solely by the author, John Whesu.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePublicly available National Vital Statistics System (NVSS) 2024 natality data. 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Associations of Maternal Age- and Parity-Related Factors With Trends in Low-Birthweight Rates: United States, 1980 Through 2000. \u003cem\u003eAmerican Journal of Public Health\u003c/em\u003e, \u003cem\u003e96\u003c/em\u003e(5), 856\u0026ndash;861. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2105/AJPH.2004.049312\u003c/span\u003e\u003cspan address=\"10.2105/AJPH.2004.049312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Educational assortative mating, educational homogamy, infant health, birthweight, macrosomia, health inequality","lastPublishedDoi":"10.21203/rs.3.rs-9142248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9142248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines how educational assortative mating (EAM) and marital status jointly shape risks of low birthweight (LBW) and macrosomia using 2024 National Vital Statistics System natality data (N\u0026thinsp;=\u0026thinsp;2,906,124). Birthweight reflects cumulative social, behavioral, and biological processes during pregnancy. Prior research documents strong educational gradients but typically treats parental education and marital status as independent predictors. Guided by family systems and resource multiplication theories, this study investigates how joint parental educational pairing \u0026ndash; and its interaction with marital status \u0026ndash; structures risk at both ends of the birthweight distribution. Multinomial logistic regression models estimate relative risks of LBW and macrosomia versus normal birthweight as functions of EAM, marital status, and their interaction with adjustments.\u003c/p\u003e \u003cp\u003eResults show that among unmarried women, relative to heterogamy, medium- and high-educated homogamy are associated with lower LBW risk and higher macrosomia risk, whereas low-educated homogamy shows little difference in LBW risk. Among heterogamous couples, marriage is protective against LBW but increases macrosomia risk; within marriage, however, educational homogamy attenuates this risk. Marital advantages are increasingly concentrated among medium- and high-educated homogamous unions and are substantially weaker among low-educated homogamous couples. In sum, marriage amplifies advantages among medium- and high-educated homogamous couples but does not offset disadvantage among low-educated homogamous families, underscoring assortative mating as a mechanism of early-life health inequality. Findings position marriage as a conditional \u0026ndash; rather than universal \u0026ndash; health-protective institution, highlighting the need for policies that move beyond marriage promotion to address structural educational and economic disadvantages that shape unequal birth outcomes.\u003c/p\u003e","manuscriptTitle":"When Marriage Is Not Enough: Unequal Benefits of Educational Homogamy for Birthweight by Marital Status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 13:04:00","doi":"10.21203/rs.3.rs-9142248/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85ff9ee1-ff4b-435f-8787-e048bbe8094f","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T13:04:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 13:04:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9142248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9142248","identity":"rs-9142248","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}