Patterns of stressful life events in pregnancy: A latent class approach linking trauma and birth outcomes

Patterns of stressful life events in pregnancy: A latent class approach linking trauma and birth outcomes | 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 Patterns of stressful life events in pregnancy: A latent class approach linking trauma and birth outcomes Gia Elise Barboza-Salerno, Taylor Harrington This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7166704/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Stressful life events during pregnancy, including relational, financial, and emotional stressors, are well-established risk factors for adverse birth outcomes. Additional exposures such as adverse childhood experiences (ACEs), intimate partner violence (IPV), material hardship, and substance use can contribute to stressful life experiences during pregnancy. However, few studies have examined how these factors cluster into distinct profiles of stressful life experiences during pregnancy, or how such profiles are associated with poor birth outcomes. Methods: Data were drawn from 5,280 pregnant individuals participating in the Kansas Pregnancy Risk Assessment Monitoring System (K-PRAMS). Latent class analysis (LCA) was performed to identify stress profiles and assess adverse birth outcomes as distal variables. Multinomial logistic regression models examined how ACEs predict latent class membership, controlling for maternal demographic characteristics. Analyses were conducted in Mplus v8.4. Results: Five distinct stress profiles were identified: (1) Multiple Overlapping Stressors (5%, n = 275), (2) Financial Insecurity (11%, n = 591), (3) Family Member Illness or Death (16%, n = 858), (4) Residential Instability and Family Conflict (9%, n = 455), and (5) Low Stress (59%, n = 3,083). Compared to the Low Stress class, individuals in all high-risk classes had significantly greater odds of reporting ACEs, IPV, material hardship during pregnancy, and substance use. Differential associations were observed between specific stress profiles and adverse birth outcomes . Conclusions: The findings highlight the heterogeneity of stress exposure during pregnancy and underscore the importance of identifying distinct antenatal stress profiles. Tailoring perinatal interventions to specific stress profiles—particularly for individuals with histories of early adversity—may improve outcomes for both mothers and infants . Stressful life experiences Latent class analysis Maternal health Neonatal health Adverse childhood experiences Figures Figure 1 Background Although often portrayed as a time of joy, pregnancy is frequently marked by stressful life events that can compromise maternal and infant health [ 1 ]. Researchers define antenatal stressful life events as emotional and social hardships that individuals face during pregnancy or before conception, including housing instability, unemployment, financial insecurity, and interpersonal conflict [ 2 , 3 ]. These stressors undermine maternal well-being and quality of life by increasing blood pressure [ 4 ], intensifying symptoms of anxiety and depression [ 3 , 5 , 6 ], and lowering parenting self-efficacy. Numerous studies have documented strong associations between antenatal stress exposure and adverse outcomes such as small for gestational age (SGA), low birth weight (LBW), and preterm birth (PTB) [ 4 , 7 , 8 ]. For instance, researchers estimate that individuals reporting high stress face a 25–60% greater risk of preterm delivery, even after controlling for a broad array of individual- and biological risk factors [ 9 ]. Psychosocial stress during pregnancy affects infant health through two primary pathways. The first pathway involves biological processes, where chronic stress activates neuroendocrine and inflammatory systems [ 1 ]. This leads to elevated cortisol levels, suppressed immune function, and increased systemic inflammation, all of which are associated with birth complications such as preeclampsia and restricted fetal growth. The second pathway involves behavioral mechanisms. Stressful life events can lead to increased smoking, substance use, and inadequate prenatal care, which in turn raise the risk of poor birth outcomes. These behaviors contribute to adverse developmental outcomes, including increased risk of morbidity and mortality, and delays in cognitive, behavioral, and emotional development [ 10 – 12 ]. Rather than arising in isolation, prenatal stress exposures frequently reflect the continuation of cumulative stress burdens that begin in childhood and endure into adulthood [ 13 ]. ACEs and IPV are interconnected forms of trauma that increase vulnerability to stressful life experiences during pregnancy [ 14 , 15 ]. ACEs, defined as exposure to household substance use, parental incarceration, foster care placement, housing or food insecurity, and the loss of a loved one, predict chronic stress reactivity and emotional dysregulation across the life course [ 16 , 17 ]. Individuals with a history of ACEs are more likely to face financial hardship, limited social support, and housing instability in adulthood—factors that increase the risk of mental health challenges and maladaptive coping behaviors. When such stressors occur during pregnancy, especially among individuals with a history of IPV, they compound antenatal stress and increase the risk of adverse outcomes [ 18 , 19 ]. Nearly one-third of pregnant individuals report experiencing early childhood adversity [ 20 ], and up to 8% experience intimate partner violence (IPV) during pregnancy [ 21 , 22 ]. Despite its high prevalence, which exceeds that of several routinely screened complications such as gestational diabetes and pregnancy-induced hypertension, IPV often goes unrecognized and unaddressed in clinical care [ 23 ]. IPV encompasses a range of harmful behaviors that can compromise the health and well-being of mother and infant, such as physical abuse, psychological aggression, coercive control, and stalking. The impact may be especially severe for individuals with a history of ACEs, who are more vulnerable to the psychological and physiological effects of ongoing stress during pregnancy [ 24 ]. Although interest in the impact of stressful life events on pregnancy has grown, few studies have explored how these events cluster together or how their cumulative and co-occurring nature shapes birth outcomes. Most existing research measures ACEs using a cumulative count of experiences, assuming all stressors contribute equally to risk, an untenable assumption that fails to capture the complexity and variability of lived experiences [ 12 , 25 ]. In reality, material stressors like eviction or job loss may affect maternal health differently than emotional or relational disruptions, such as the death of a loved one or partner separation [ 26 , 27 ]. Therefore, methods such as Latent Class Analysis (LCA), which identify distinct patterns of co-occurring stressors, better enable researchers to capture meaningful variation in how pregnant individuals experience and are affected by antenatal stress [ 26 – 28 ]. To date, only one study has applied LCA to prenatal stressful life experiences [ 29 ]. Using data from a national survey, the authors identified three latent classes of antenatal stressful life events: a low-stress group, a group characterized by illness and death, and a multiple stressors group involving financial, housing, and relational hardships. The group facing multiple stressors was more likely to be unmarried, socioeconomically disadvantaged, and racially minoritized, and they also had higher risks of adverse maternal health outcomes. While this study highlights the heterogeneity of prenatal stress and shows the usefulness of latent class analysis in identifying vulnerability to antenatal stressful experiences, significant gaps remain regarding how ACEs and IPV shape latent stressor profiles and their subsequent influence on perinatal health outcomes. Building on prior work, this study uses LCA to identify different types of antenatal stressful experiences among pregnant women. Our main research questions are: (1) What latent subgroups of pregnant women can be identified based on co-occurring antenatal stressful experiences in the year before birth? (2) Do ACEs, IPV, smoking, social support, and material hardship experienced before or during pregnancy influence latent class membership? (3) Are specific stress profiles associated with a higher risk of poor birth outcomes? Methods Data This study draws on data from the Kansas Pregnancy Risk Assessment Monitoring System (K-PRAMS), a population-based surveillance project conducted by the Kansas Department of Health and Environment in collaboration with the Centers for Disease Control and Prevention (CDC) [ 30 ]. K-PRAMS collects detailed information on maternal behaviors, experiences, and health conditions before, during, and shortly after pregnancy to inform policy and prevention efforts in maternal and child health. Kansas is one of the few states to include survey items on material hardship during pregnancy, coercive forms of IPV, and ACEs, enabling a broader examination of social and developmental risk factors. We analyzed data from the 2016–2021 survey years, the most recent years for which complete data were available for all variables of interest. After excluding cases with missing values on key study variables, the final sample included N = 5,280 individuals who had a recent live birth in Kansas. K-PRAMS is designed to be representative of all live births in the state, and the inclusion of multiple years increases the robustness and generalizability of findings across diverse geographic and sociodemographic groups across the state [ 30 ]. Survey Design K-PRAMS employs a stratified systematic sampling design, drawing from Kansas birth certificate records approximately 2 to 6 months postpartum. Each month, a stratified sample of new mothers is selected, with intentional oversampling of high-risk groups, including low birth weight births, to ensure that low birth weight infants are disproportionately represented relative to those with normal birth weight. This approach improves the representation of populations disproportionately affected by maternal and infant health disparities. Individuals who are selected to participate in the K-PRAMS receive a mailed questionnaire, with follow-up mailings and telephone interviews used to maximize response rates. The questionnaire includes variables, such as gestational diabetes, high blood pressure, anxiety, and depression, based on self-report to assess baseline pregnancy risk. Other variables measuring maternal demographic characteristics (e.g., race/ethnicity) and birth outcomes (e.g., preterm birth) are obtained from linked birth certificate data. These survey responses are integrated with birth certificate records to create a comprehensive dataset for analysis. Our analysis included data that were weighted to account for the complex sampling design, differential response rates, and noncoverage, ensuring that our estimates are generalizable to the population of women with live births in Kansas during the study period. The CDC, The Ohio State University, and the Kansas Department of Health and Environment Institutional Review Boards have approved the Kansas PRAMS protocol. Measures Latent Class Items Stressful life events. Thirteen stressful life events were assessed based on PRAMS core questions regarding maternal experiences in the 12 months before delivery. Respondents answered “yes” or “no” to whether they had experienced each of the following: (1) a close family member was very sick and hospitalized; (2) separation or divorce from a husband or partner; (3) moving to a new address; (4) experiencing homelessness; (5) husband or partner lost their job; (6) respondent lost her job despite wanting to continue working; (7) more frequent arguments with husband or partner; (8) husband or partner said they did not want the pregnancy; (9) difficulty paying bills; (10) cut in pay or hours for respondent or partner; (11) respondent went to jail; (12) someone close had a problem with alcohol or drugs; and (13) husband or partner was away due to military deployment or extended work travel. These items were used as the primary indicators in the latent class model. Independent Variables ACEs. Seven binary (yes/no) items from the K-PRAMS dataset were used to assess early traumatic or adverse experiences occurring before age 18. These measures captured whether the respondent: (1) experienced the death of someone close; (2) had parents or guardians who divorced or separated; (3) had to move due to problems paying rent or mortgage; (4) went hungry because the family could not afford enough food; (5) had a parent or guardian who was incarcerated or involved with the legal system; (6) lived with a parent or guardian who had problems with alcohol or drug use; and (7) was ever placed in foster care. These variables reflect key domains of adversity—loss, instability, deprivation, and household dysfunction—and were used to examine their contribution to antenatal stress profiles. Material hardship and social support. Material hardship was assessed using eight dichotomous items capturing access to basic needs 12 months prior to delivery. Respondents were asked whether they experienced unmet needs in the following areas: (1) affordable transportation; (2) food insecurity; (3) access to safe housing; (4) housing stability, including frequent moves or eviction risk; (5) household crowding; (6) reliable utility services, such as heat, water, or electricity; (7) consistent phone access; and (8) any other unmet basic need not otherwise specified. Each item was coded as 1 (yes, unmet need) or 0 (no), and summed to create an index showing increasing levels of material hardship. These items were included as predictors of latent class membership to assess the contribution of economic strain and unmet basic needs to stressful life experience profiles during pregnancy. Social support was assessed by asking, “During your most recent pregnancy, who would have helped you if a problem had come up?” Participants could select multiple sources. For this analysis, the focus was on partner support, with a yes/no for choosing “husband or partner.” Responses were cleaned for consistency, keeping only valid answers. Cigarette use was self-reported for the 3 months before pregnancy as a yes/no variable. IPV. Participants were asked if, during their most recent pregnancy, any of the following occurred: "My husband or partner threatened me or made me feel unsafe," "I was frightened for my safety or my family’s safety because of my husband or partner's anger or threats," “My husband or partner tried to control my daily activities, such as whom I could talk to or where I could go," or “My husband or partner forced me to participate in touching or sexual activities against my will." The responses were converted into a binary variable to indicate whether participants experienced any IPV during pregnancy. Distal Variables Adverse birth outcomes. PTB is defined as delivery before 37 completed weeks of gestation, while very preterm birth VPTB refers to delivery before 33 weeks. LBW is defined as a birth weight under 2,500 grams. Small for gestational age (SGA) is defined as a birth weight below the 10th percentile for gestational age and sex, based on national reference standards. Covariates Sociodemographic factors included maternal age, education, household income, health issues, cigarette use, and social support. Maternal race and ethnicity were categorized as: (1) Black, non-Hispanic; (2) White, non-Hispanic; (3) Hispanic; (4) American Indian/Alaska Native, non-Hispanic; (5) Asian, non-Hispanic; and (6) Other, non-Hispanic. Gestational diabetes was marked “yes” if it started during pregnancy, excluding pre-pregnancy diabetes. High blood pressure was “yes” if hypertension or preeclampsia began during pregnancy, excluding pre-existing cases. Anxiety and depression were marked if experienced during pregnancy. Prenatal care utilization was measured using the Kotelchuck Index, which assesses care timing and number of visits relative to expected. It was classified as inadequate, intermediate, adequate, or adequate plus, reflecting the adequacy and intensity of prenatal care. Pre-pregnancy BMI was calculated from self-reported height and weight, categorized as underweight (< 18.5), normal (18.5–24.9), overweight (25.0–29.9), and obese (≥ 30.0). Statistical analysis Latent Class Analysis (LCA) was conducted using Mplus Version 8.4 to identify subgroups of respondents characterized by patterns of antenatal stress exposure, early adversity, material hardship, and behavioral risk. Models specifying one through seven latent classes were estimated using robust maximum likelihood estimation (MLR) to accommodate non-normality and categorical indicators. Survey weights, strata, and cluster variables were incorporated to account for PRAMS’ complex sampling design. Model selection was based on fit indices and substantive interpretability. The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size-adjusted BIC (aBIC), entropy values, and two likelihood ratio tests—the Vuong-Lo-Mendell-Rubin (VLMR) and Lo-Mendell-Rubin adjusted test (LMR-LRT)—were used to guide model selection. The final model was chosen based on the decrease in AIC, BIC, and aBIC, significant VLMR and LMR-LRT p -values (< 0.05), and an entropy value greater than 0.70. Solutions were also evaluated for class distinctiveness, interpretability, and minimum class size (≥ 2.5% of the sample). Following class enumeration, associations between class membership and covariates or outcomes were estimated using the Bolck, Croon, and Hagenaars (BCH) three-step approach. This method preserves latent class measurement while incorporating classification error into the estimation of covariate and outcome effects. First, BCH weights were calculated using posterior probabilities. Second, covariates including ACEs, social support, and behavioral risk factors were entered into multinomial logistic regressions predicting latent class membership. Third, birth outcomes (PTB, VPTB, LBW, SGA) were modeled as distal outcomes across classes using BCH-weighted regressions, adjusting for sociodemographic covariates. All models used 100 random starts and 50 final stage optimizations to ensure model convergence and loglikelihood stability. Results The final analytic sample included 5,290 unweighted respondents, representing a weighted population of 162,290 individuals who had recently given birth. The majority identified as non-Latina White (73.2%), followed by Latina (13.9%), non-Latina Black (6.4%), and other race/ethnicity (6.5%). The average maternal age was 28.4 years. Approximately one-third of respondents (36.0%) were unmarried, and 32.7% resided in rural areas. Educational attainment varied, with 50.0% having less than a high school degree, 26.7% completing high school, and 24.0% attaining a bachelor’s degree. Nearly one-quarter (22.4%) reported annual household incomes below $19,000. In terms of perinatal health and behavior, 20.0% reported a diagnosis of perinatal depression. Smoking was reported by 8.9% of the sample, with most indicating light use (< 1 cigarette/day). According to the Kotelchuck Index, 63.9% received adequate or better prenatal care, with 21.5% classified as “adequate plus,” while 7.8% and 7.7% received intermediate or inadequate care, respectively. Regarding physical health, 13.9% reported hypertension, and 9.9% experienced gestational diabetes. Based on BMI, 39.8% had normal weight, 27.3% were overweight, and 30.3% were obese. Low birth weight (LBW; <2,500 g) and preterm birth (PTB; <37 weeks’ gestation) were observed in 5.7% and 8.6% of cases, respectively. A five-class solution was selected based on the model fit indices. While the six- and seven-class models showed slight improvements in entropy, their LMR-LRT p -values were non-significant ( p = 0.7331 and p = 0.2430, respectively) and did not justify the additional complexity (See Table S1 for a description of all LC models we estimated). Table 1 presents model fit indices for the final model, and Table 2 displays the conditional item response probabilities for each of the five latent classes, which were labeled as follows: (1) Low Stress (58.6%), (2) Family Member Illness and Death (16.3%), (3) Financial Insecurity (11.2%), (4) Residential Instability and Family Conflict (8.7%), and (5) Multiple Overlapping Stressors (5.2%). Figure 1 presents the profile plot of conditional item response probabilities for each class. Table 1 Model fit statistics for class enumeration for the K = 1 through K = 7 class models Model Loglikelihood (H0) AIC BIC Entropy VLRT K−1 LRT Adjusted Test 1 Class -25334.1 50694.12 50779.51 -- -- -- 2 Class -23457.6 46969.25 47146.6 0.774 < 0.001 < 0.001 3 Class -23080.8 46243.53 46512.83 0.727 < 0.001 < 0.001 4 Class -22890.8 45891.66 46252.91 0.709 0.0001 0.0001 5 Class -22731.8 45601.57 46054.78 0.74 0.0011 0.0013 6 Class -22684.9 45535.88 46081.05 0.746 0.7331 0.734 Note: VLRT = Vuong-Lo-Mendell-Rubin Likelihood Ratio Test; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; ABIC = Adjusted Bayesian Information Criterion. Table 2 Conditional response probabilities of maternal material hardship within classes Item Class 1: Family member illness and death N = 858 (16.31%) Class 2: Multiple overlapping stressors N = 275 (5.23%) Class 3: Financial insecurity N = 591 (11.22%) Class 4: Residential Instability and Family Conflict N = 455 (8.65%) Class 5: Low Stress 3083 (58.59%) Family Member Hospitalized 0.782 0.620 0.172 0.14 0.092 Divorce or Separation 0.042 0.371 0.041 0.286 0.009 Moved to a different address 0.434 0.812 0.504 0.526 0.281 Partner Lost Job 0.044 0.567 0.389 0.075 0.013 Lost Job 0.027 0.571 0.272 0.127 0.018 Work hours or pay cut 0.103 0.579 0.706 0.027 0.036 Problems paying rent or mortgage 0.083 0.849 0.506 0.343 0.026 Partner away 0.08 0.100 0.054 0.086 0.039 Partner arguments 0.163 0.718 0.356 0.736 0.069 Partner did not want a pregnancy 0.045 0.269 0.05 0.318 0.009 Someone close had a drug or alcohol problem 0.199 0.600 0.091 0.332 0.025 Experienced homelessness 0.001 0.296 0.017 0.034 0.002 A close person died 0.703 0.575 0.105 0.103 0.055 Notes. Bolded figures were used to help interpret the classes. Class 1 (Multiple Overlapping Stressors), the smallest class, displayed the highest and broadest pattern of adversity, with particularly high probabilities of housing instability (0.849 difficulty paying rent, 0.296 homelessness), employment disruption (0.579 reduced work hours), and relational adversity (0.718 partner arguments, 0.600 substance use in close relationships), reflecting compounded disadvantage across economic, housing, and relational domains. Class 2 (Family Member Illness and Death) showed high probabilities of having a family member hospitalized (0.782) and experiencing the death of someone close (0.703), with low endorsement of other stressors, suggesting stress defined almost exclusively by chronic illness and mortality. Class 3 (Residential Instability and Family Conflict) was characterized by high probabilities of residential moves (0.526), frequent arguments with a partner (0.736), and rejection of pregnancy by a partner (0.318), indicating stress concentrated in household disruption and interpersonal conflict. Class 4 (Financial Insecurity) exhibited elevated probabilities of reduced work hours or pay (0.706), difficulty paying rent (0.506), and partner job loss (0.389), indicating acute economic hardship along with moderate levels of relational stress. Class 5 (Low Stress) was the most prevalent and characterized by uniformly low conditional probabilities across all indicators of adversity, serving as the reference group in subsequent analyses. The pairwise odds ratios demonstrate strong class separation and within-class homogeneity for most indicators, indicating high between-class differentiation and low within-class variability (see Table S2). Some items demonstrated weaker separation, for example, "partner away" and "moved," which showed relatively low odds ratios across several class comparisons, suggesting these indicators were less effective in distinguishing between classes. Table 3 presents the results of the multinomial logistic regression models predicting latent class membership based on IPV exposure, ACEs, cigarette use, material hardship, and social support controlling for maternal demographics. Compared to the Low Stress class, individuals in the Multiple Overlapping Stressors class had significantly higher odds of experiencing IPV (OR = 5.87; 95% CI [1.04, 33.01]), reporting more ACEs (OR = 1.70; 95% CI [1.57, 1.85]), and using cigarettes during pregnancy (OR = 1.08; 95% CI [1.05, 1.11]). They were also more likely to experience severe material hardship (OR = 2.09; 95% CI [1.87, 2.33]). Social support was not a significant predictor of membership in this highest-risk class. In the Residential Instability and Family Conflict class, IPV (OR = 5.62; 95% CI [1.22, 25.93]), ACEs (OR = 1.35; 95% CI [1.26, 1.46]), cigarette use (OR = 1.06; 95% CI [1.04, 1.09]), and material hardship (OR = 1.48; 95% CI [1.33, 1.66]) were all significant predictors compared to the Low Stress class. Lower social support was also associated with increased odds of class membership (OR = 1.28; 95% CI [1.09, 1.49]). Membership in the Financial Insecurity class was significantly associated with higher ACEs (OR = 1.35; 95% CI [1.27, 1.43]), greater cigarette use (OR = 1.03; 95% CI [1.01, 1.06]), lower social support (OR = 1.22; 95% CI [1.07, 1.39]), and greater material hardship (OR = 1.33; 95% CI [1.20, 1.48]). IPV was not a significant predictor for this group. Finally, the Family Member Illness and Death class was predicted by higher ACEs (OR = 1.26; 95% CI [1.19, 1.34]) and cigarette use (OR = 1.04; 95% CI [1.02, 1.08]). IPV, material hardship, and social support were not significantly associated with membership in this class. All models were adjusted for maternal age, education, income, and race/ethnicity. Table 3 Multinomial Logistic Regression of Psychosocial Risk Factors Predicting Latent Class Membership Multiple overlapping stressors Family Member illness and death Residential instability and family conflict Financial insecurity IPV 5.871 (1.04, 33.01) 0.92 (0.08, 10.70) 5.622 (1.22, 25.93) 2.018 (0.13, 32.19) ACES 1.704 (1.567, 1.854) 1.261 (1.186, 1.341) 1.353(1.257, 1.457) 1.354 (1.271, 1.43) Cigarette Use 1.083 (1.053, 1.113) 1.044 (1.016, 1.077) 1.063 (1.035, 1.093) 1.034 (1.006, 1.063) Social Support .9023 (.7908,1.029) 1.119 (.9807, 1.277) 1.275 (1.093, 1.488) 1.218 (1.068, 1.390) Material Hardship 2.088 (1.87, 2.33) 1.064 (.0.923, 1.201) 1.481 (1.325, 1.655) 1.334 (1.200, 1.482) Note. Analysis controls for maternal age, education, income, and race/ethnicity, and physical and behavioral health problems experienced during pregnancy. Demographic characteristics varied significantly by class ( p < .001) (Table 4). The Low Stress class had the highest mean maternal age (29.4 years) and educational attainment (mean = 4.8). Financial Insecurity was associated with the youngest age (26.8 years) and lowest education (mean = 3.8). Non-Latina Black women were disproportionately represented in the Family Illness and Death class (15.3%), while Latina women were overrepresented in the Financial Insecurity and Low Stress classes. Marital status also differed: women in the Low Stress class were most likely to be married (75.0%), whereas those in the Family Illness and Death class were least likely (21.0%). Smoking during pregnancy was highest among women in the Family Illness and Death class and lowest in the Low Stress class. Depression and anxiety symptoms were significantly more prevalent among women in higher adversity classes, especially in the Residential Instability and Multiple Overlapping Stressor groups. Adverse birth outcomes varied by latent class (Table 5 ). Rates of LBW were highest in the Multiple Overlapping Stressors (52.0%), followed by Financial Insecurity (46.1%), Residential Instability (46.2%), and Family Illness and Death (40.0%), compared to 38.1% in the Low Stress class. SGA was most prevalent in the Multiple Overlapping Stressors class (28.2%), significantly higher than in the reference group (19.6%). PTB occurred most frequently among women in the Multiple Overlapping Stressors (39.1%) and Financial Insecurity (37.4%) classes, compared to 32.0% in the Low Stress group. Table 4 Sample characteristics by profiles of stressful life experiences. Dependent: Class Multiple overlapping stressors Family member illness or death Residential Instability and Family Conflict Financial insecurity Low Stress Total p Total N (%) 229 (4.5) 688 (13.6) 369 (7.3) 554 (11.0) 3205 (63.5) 5045 (100) Education (SD 3.4 (1.1) 4.5 (1.6) 3.8 (1.4) 4.1 (1.5) 4.8 (1.7) 4.6 (1.7) < 0.001 Age Mean (SD) 27.3 (5.7) 28.4 (5.6) 26.8 (5.6) 27.9 (5.5) 29.4 (5.4) 28.8 (5.5) < 0.001 Income Dependence Mean (SD) 3.1 (1.6) 3.1 (1.4) 3.1 (1.6) 3.2 (1.5) 3.1 (1.4) 3.1 (1.4) 0.729 Race/Ethnicity < 0.001 Hispanic 21 (9.2) 59 (8.6) 54 (14.6) 78 (14.1) 369 (11.5) 581 (11.5) NH Black 35 (15.3) 55 (8.0) 38 (10.3) 47 (8.5) 207 (6.5) 382 (7.6) NH White 167 (72.9) 543 (78.9) 253 (68.6) 391 (70.6) 2376 (74.1) 3730 (73.9) Other 6 (2.6) 31 (4.5) 24 (6.5) 38 (6.9) 253 (7.9) 352 (7.0) Marital Status < 0.001 Married 48 (21.0) 448 (65.1) 140 (37.9) 316 (57.0) 2405 (75.0) 3357 (66.5) Unmarried 181 (79.0) 240 (34.9) 229 (62.1) 238 (43.0) 800 (25.0) 1688 (33.5) Urbanicity Urban 153 (66.8) 470 (68.3) 234 (63.4) 395 (71.3) 2231 (69.6) 3483 (69.0) 0.093 Ruran 76 (33.2) 218 (31.7) 135 (36.6) 159 (28.7) 974 (30.4) 1562 (31.0) Cigarette Smoking Mean (SD) 4.0 (6.0) 1.1 (3.7) 2.2 (4.8) 1.4 (4.1) 0.6 (2.6) 1.0 (3.5) < 0.001 Diabetes (pre-pregnancy) No 221 (96.5) 666 (96.8) 354 (95.9) 532 (96.0) 3066 (95.7) 4839 (95.9) 0.713 Yes 8 (3.5) 22 (3.2) 15 (4.1) 22 (4.0) 139 (4.3) 206 (4.1) High Blood Pressure (pre-pregnancy) 0.890 No 211 (92.1) 637 (92.6) 343 (93.0) 520 (93.9) 2985 (93.1) 4696 (93.1) Yes 18 (7.9) 51 (7.4) 26 (7.0) 34 (6.1) 220 (6.9) 349 (6.9) Depression (pre-pregnancy) < 0.001 No 99 (43.2) 526 (76.5) 220 (59.6) 381 (68.8) 2762 (86.2) 3988 (79.0) Yes 130 (56.8) 162 (23.5) 149 (40.4) 173 (31.2) 443 (13.8) 1057 (21.0) Anxiety (pre-pregnancy) No 79 (34.5) 439 (63.8) 183 (49.6) 316 (57.0) 2472 (77.1) 3489 (69.2) < 0.001 Yes 150 (65.5) 249 (36.2) 186 (50.4) 238 (43.0) 733 (22.9) 1556 (30.8) Table 5. Distal birth outcomes by profiles of stressful life experiences Multiple overlapping stressors Family member illness and death Residential Instability and Family Conflict Financial insecurity Low Stress Low Birth Weight (LBW) (%) 52.01 a,b 39.99 a,c 46.2 d 46.13 c,e 38.05 b,d,e Small for Gestational Age (SGA) (%) 28.22 a 22.67 23.8 23.37 19.58 a Gestational Age < 37 Weeks (%) 39.08 a 32.46 36.37 37.41 b 31.96 a,b Note. The table displays the percentage of individuals in each stress profile who experienced the specified birth outcomes. Subscripted letters (e.g., a , b ) indicate significant pairwise differences between groups based on post hoc comparisons (p < .05). Groups that share the same letter are significantly different from one another for that outcome. Discussion The goal of this study was to identify latent profiles of antenatal stressful life experiences and examine how these profiles relate to early adversity, sociodemographic risk, and adverse birth outcomes. Using a person-centered latent class analysis, we identified five distinct stress subgroups: Low Stress, Family Member Illness and Death, Financial Insecurity, Residential Instability and Family Conflict, and Multiple Stressors. Each subgroup reflected unique constellations of traumatic, financial, relational, and emotional stressors in the year before giving birth [ 31 ], suggesting that stress exposure is not uniform but shaped by specific contextual vulnerabilities. Class membership was significantly associated with maternal adversity histories, behavioral health risks, and perinatal outcomes. Our findings suggest that antenatal stress is heterogeneous and may involve distinct underlying mechanisms with essential implications for infant health. Mukherjee et al. (2016) [ 29 ] used latent class analysis on PRAMS data from 2009–2011 to identify three broad stress profile groups among pregnant women: a low-stress group, a group characterized by illness and death, and a “multiple stressors” group with high levels of economic, relational, and traumatic stressors. Similarly, in our study, we identified a “multiple overlapping stressors” class, which aligns conceptually with Mukherjee et al.'s highest-stress group. However, while their multiple stressors class comprised approximately 22% of the sample, our equivalent class represented only 5.2% of participants, likely reflecting differences in sample composition. Both studies similarly found that individuals in high-stress classes were more likely to experience sociodemographic disadvantage and elevated health risks. However, our analysis differs by identifying more nuanced classes of antenatal stress, including specific subgroups marked by financial insecurity and by family illness and bereavement—patterns not separately identified in Mukherjee et al.'s three-class model. We examined links between latent class membership and adverse birth outcomes, providing insights into how different patterns of antenatal stress may influence perinatal health. Consistent with prior research, individuals reporting more ACEs were significantly more likely to belong to higher-risk classes—particularly the Multiple Overlapping Stressors class—supporting a life course framework of adversity whereby stressful life experiences during pregnancy are compounded by ACEs. Cigarette use before pregnancy was also linked to higher-risk class membership, notably in the Financial Insecurity and Residential Instability classes. Additionally, low social support significantly predicted membership in three of the four high-risk classes, emphasizing the protective role of social support for decreasing susceptibility to the adverse effects of specific social stressors and potentially reducing adverse birth outcomes [ 33 ]. Next, we assessed associations between prenatal stress exposure and adverse birth outcomes. Membership in higher-risk stress classes was significantly associated with increased likelihood of LBW, SGA, and PTB. Women in these classes exhibited the most concentrated social and health-related vulnerabilities, including the lowest average educational attainment, youngest maternal age, and highest prevalence of non-marital status. These groups also reported the highest rates of prenatal depression, anxiety, and cigarette use. Hispanic and Black individuals were disproportionately represented in the highest-risk stress classes. These findings align with previous research showing that lower income women and women from historically minoritzed groups are more likely to experience multiple, co-occurring stressors, such as adverse childhood experiences (ACEs), intimate partner violence (IPV), and inadequate social support, that increase the risk of LBW, PTB, and SGA infants [ 32 , 33 ]. Notably, Kansas is one of the few PRAMS-participating states that collects data on coercive control, IPV during pregnancy, and ACEs, providing a unique opportunity to examine the compounding effects of these exposures. Future studies should investigate the specific causal pathways through which these stressors influence maternal and infant health. Elevated risks for adverse birth outcomes were also observed in the Financial Insecurity and Residential Instability groups, indicating that chronic and cumulative stressors during pregnancy, especially those rooted in socioeconomic hardship and unstable living conditions, are connected to increased vulnerability. These groups similarly showed higher levels of psychological distress, including moderate rates of depression and anxiety. Conversely, the class characterized by family member illness or death, despite reporting high acute stress levels, did not show worse birth outcomes. Likewise, members of the Low Stress group experienced the best birth outcomes, likely due to their access to protective resources such as higher education levels, older age, economic advantages of marriage, and the lowest rates of depression, anxiety, and cigarette use. Research has identified two primary pathways through which stressful life events may influence birth outcomes [ 34 ], and our findings align with both. First, antenatal stress can lead to behavioral changes that elevate physiological risks during pregnancy. In this study, individuals in the highest-risk profiles reported elevated rates of smoking and psychological distress, including depression and anxiety. These behaviors are well-documented risk factors for complications such as gestational diabetes, hypertension, and infection, all of which can contribute to PTB and LBW. Second, psychosocial stressors can directly affect biology by disrupting the maternal hypothalamic-pituitary-adrenal (HPA) axis, with chronic or acute stress increasing cortisol and placental corticotropin-releasing hormone (CRH), both of which can impact the timing of labor. The heightened anxiety and depression observed in the Multiple Overlapping Stressors and Residential Instability groups may reflect this kind of biological dysregulation, offering a plausible mechanism linking these stress profiles to adverse perinatal outcomes. Implications for practice and policy This study has clear and actionable implications for federal and state maternal health policies, including comprehensive legislation such as the Black Maternal Health Momnibus Act [ 35 ] and related efforts to promote equity through structural and trauma-informed care. By identifying the factors that contribute to membership in high-risk antenatal stress profiles, including overlapping exposures to housing instability, food insecurity, material hardship, IPV, and ACEs, the study provides strong evidence that these structural and psychosocial stressors significantly increase the likelihood of poor birth outcomes. These findings directly support key provisions of the Social Determinants for Moms Act , one of the bills within this comprehensive legislative package designed by the Black Maternal Health Caucus to tackle key drivers of maternal health disparities, which advocates for upstream investments in housing, nutrition, and transportation as essential strategies for reducing maternal health disparities [ 35 ]. The study also identifies grief- and illness-related stress exposures that reflect the caregiving and trauma burdens emphasized in the Protecting Moms Who Served Act . While these exposures were not directly linked to adverse birth outcomes, their contribution to antenatal stress profiles underscores the need for expanded screening and trauma-informed support, especially among veterans and military-connected families. The inclusion of parental incarceration as a significant predictor of high-stress class membership further supports the need for trauma-informed perinatal care, as outlined in the Justice for Incarcerated Moms Act . Additionally, with nearly one-third of the sample residing in rural areas and the highest-risk classes exhibiting the greatest unmet behavioral health needs, our findings underscore the importance of expanding access to care through policies such as the Tech to Save Moms Act, which supports telehealth and remote screening tools to reduce barriers to care posed by antenatal stressors. Collectively, these findings support maternal health policies that confront the cumulative burden of structural adversity through sustained, integrated, and inclusive approaches that explicitly account for sociodemographic disparities and geographic residence, particularly among populations facing compounded risks due to rural isolation, incarceration, trauma exposure, and unmet behavioral health needs. Strengths and limitations This study had several strengths, including the use of a large, population-based sample, the application of a person-centered latent class framework, and the implementation of the BCH approach to preserve class structure when examining predictors and outcomes. However, several limitations should be noted. First, all measures were self-reported and may be subject to recall or social desirability bias. Second, the cross-sectional design limits causal inference, and associations between antenatal stress typologies and birth outcomes may be shaped by multiple, unmeasured mediators. Third, although the sample is representative of births in Kansas, the findings may not generalize to other populations; Kansas was selected due to the availability of detailed ACEs and material hardship modules. Finally, although the BCH method maintains class structure in covariate and outcome models, it does not account for uncertainty in class assignment. Future research should explore longitudinal designs and examine the mediating mechanisms—behavioral, psychosocial, and biological—through which early life adversity shapes antenatal stress exposure and perinatal health outcomes. Conclusions This study found five profiles of antenatal stressful life experiences shaped by early adversity and linked to birth outcome disparities. Those with overlapping material, relational, and behavioral adversities faced the highest risk of poor perinatal health. Results emphasize the need to address structural and psychosocial stressors to achieve more equitable healthcare that supports maternal and infant well-being. Declarations Conflict of interest: The authors declare that they have no conflict of interest. Ethics approval and consent to participate This study was reviewed and deemed exempt by The Ohio State University Institutional Review Board and approved by the Centers for Disease Control and Prevention under the data usage agreement for the Pregnancy Risk Assessment Monitoring System. Participants provided informed consent as part of the PRAMS data collection protocol. All procedures adhered to the ethical standards outlined in the 1964 Declaration of Helsinki and its subsequent amendments. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research was supported with funding from the Institute for Population Research at The Ohio State University. Author Contribution G.B.S. drafted the initial manuscript, conceptualized and designed the study, and conducted the statistical analyses. T.H. assisted with the conceptualization, literature review, and edited and revised the manuscript. All authors reviewed and approved the final manuscript as submitted. Acknowledgement The authors would like to thank members of the Investigating Spatial Structures in Urban Environments (ISSUES) lab for their thoughtful feedback on the manuscript. Availability of data and materials The data supporting the findings of this study can be requested at https://www.cdc.gov/prams/index.htm , but restrictions apply to their use. The data were used under a data use agreement and are not publicly available. References Coussons-Read ME. Effects of prenatal stress on pregnancy and human development: mechanisms and pathways. Obstet Med. 2013;6:52–7. Koenig MD, Crooks N, Burton T, Li Y, Hemphill NO, Erbe K, et al. Structural violence and stress experiences of young pregnant Black people. J racial ethnic health disparities. 2024;11:1918–32. Mukherjee S. Antenatal Stressful Life Events and Postpartum Depression in the United States: the Role of Women’s Socioeconomic Status at the State Level. 2016. Morgan N, Christensen K, Skedros G, Kim S, Schliep K. Life stressors, hypertensive disorders of pregnancy, and preterm birth. J Psychosom Obstet Gynecol. 2022;43:42–50. Kogler L, Müller VI, Chang A, Eickhoff SB, Fox PT, Gur RC, et al. Psychosocial versus physiological stress—Meta-analyses on deactivations and activations of the neural correlates of stress reactions. NeuroImage. 2015;119:235–51. Salm Ward T, Kanu FA, Robb SW. Prevalence of stressful life events during pregnancy and its association with postpartum depressive symptoms. Archives women’s mental health. 2017;20:161–71. Braveman PA, Heck K, Egerter S, Marchi KS, Dominguez TP, Cubbin C, et al. The Role of Socioeconomic Factors in Black–White Disparities in Preterm Birth. Am J Public Health. 2015;105:694–702. Ghimire U, Papabathini SS, Kawuki J, Obore N, Musa TH. Depression during pregnancy and the risk of low birth weight, preterm birth and intrauterine growth restriction-an updated meta-analysis. Early Hum Dev. 2021;152:105243. Wadhwa PD, Entringer S, Buss C, Lu MC. The contribution of maternal stress to preterm birth: issues and considerations. Clin Perinatol. 2011;38:351–84. Traylor CS, Johnson JD, Kimmel MC, Manuck TA. Effects of psychological stress on adverse pregnancy outcomes and nonpharmacologic approaches for reduction: an expert review. Am J Obstet Gynecol MFM. 2020;2:100229. Bussieres E-L, Tarabulsy GM, Pearson J, Tessier R, Forest J-C, Giguere Y. Maternal prenatal stress and infant birth weight and gestational age: A meta-analysis of prospective studies. Dev Rev. 2015;36:179–99. Lawrence BC, Kheyfets A, Carvalho K, Dhaurali S, Kiani M, Moky A et al. The Impact of Psychosocial Stress on Maternal Health Outcomes: A Multi-State PRAMS 8 (2016–2018). Analysis. 2022;15. Schetter CD, Tanner L. Anxiety, depression and stress in pregnancy: implications for mothers, children, research, and practice. Curr Opin Psychiatry. 2012;25:141–8. Curry MA. The Interrelationships Between Abuse Substance Use, and Psychosocial Stress During Pregnancy. J Obstetric Gynecologic Neonatal Nurs. 1998;27:692–9. Thomas SA, Clements-Nolle KD, Wagner KD, Omaye S, Lu M, Yang W. Adverse childhood experiences, antenatal stressful life events, and marijuana use during pregnancy: A population-based study. Prev Med. 2023;174:107656. Alhusen JL, Ray E, Sharps P, Bullock L. Intimate Partner Violence During Pregnancy: Maternal and Neonatal Outcomes. J Womens Health (Larchmt). 2015;24:100–6. Huber-Krum S, Miedema SS, Shortt JW, Villaveces A, Kress H. Path Analysis of Adverse Childhood Experiences, Early Marriage, Early Pregnancy, and Exposure to Intimate Partner Violence Among Young Women in Honduras. J Fam Viol. 2023. https://doi.org/10.1007/s10896-023-00520-y . Atzl VM, Narayan AJ, Rivera LM, Lieberman AF. Adverse childhood experiences and prenatal mental health: Type of ACEs and age of maltreatment onset. J Fam Psychol. 2019;33:304. Wadsworth P, Degesie K, Kothari C, Moe A. Intimate Partner Violence During the Perinatal Period. J Nurse Practitioners. 2018;14:753–9. Campbell JC. Health consequences of intimate partner violence. Lancet. 2002;359:1331–6. Hussey JM, Marshall JM, English DJ, Knight ED, Lau AS, Dubowitz H, et al. Defining maltreatment according to substantiation: Distinction without a difference? Child Abuse Negl. 2005;29:479–92. Silverman JG, Decker MR, Reed E, Raj A. Intimate partner violence victimization prior to and during pregnancy among women residing in 26 U.S. states: Associations with maternal and neonatal health. Am J Obstet Gynecol. 2006;195:140–8. Curry MA. The Interrelationships Between Abuse, Substance Use, and Psychosocial Stress During Pregnancy. J Obstetric Gynecologic Neonatal Nurs. 1998;27:692–9. CDC. About Child Abuse and Neglect. Child Abuse and Neglect Prevention. 2024. https://www.cdc.gov/child-abuse-neglect/about/index.html . Accessed 15 Dec 2024. Mehta N, Bliss L, Trolard A, Kondis JS. The Relationship Between Temperature and Temporal Patterns and Incidence of Abusive Head Trauma in a Midwest Region Hospital. Child Maltreat. 2022;27:194–201. McLaughlin KA, Sheridan MA. Beyond Cumulative Risk: A Dimensional Approach to Childhood Adversity. Curr Dir Psychol Sci. 2016;25:239–45. Barboza GE. Latent Classes and Cumulative Impacts of Adverse Childhood Experiences. Child Maltreat. 2018;23:111–25. McCutcheon AL. Latent class analysis. Sage; 1987. Mukherjee S, Coxe S, Fennie K, Madhivanan P, Trepka MJ. Stressful Life Event Experiences of Pregnant Women in the United States: A Latent Class Analysis. Women’s Health Issues. 2017;27:83–92. Kansas Department of Health and Environment, Bureau of Epidemiology & Public Health Informatics. Kansas pregnancy risk assessment monitoring system (PRAMS) 2022 surveillance report. Technical Report. Topeka, KS: Kansas Department of Health and Environment; 2025. Booth EJ, Kitsantas P, Min H, Pollack AZ. Stressful life events and postpartum depressive symptoms among women with disabilities. Womens Health (Lond Engl). 2021;17:17455065211066186. Lu MC, Chen B. Racial and ethnic disparities in preterm birth: The role of stressful life events. Am J Obstet Gynecol. 2004;191:691–9. Rosenthal L, Lobel M. Explaining racial disparities in adverse birth outcomes: Unique sources of stress for Black American women. Soc Sci Med. 2011;72:977–83. Goin DE. Community violence and pregnancy: An understudied exposure in the etiology of adverse birth outcomes. PhD Thesis. UC Berkeley; 2019. Committee on Health, Education, Labor & Pensions & Committee on Finance (Sponsor: Booker). (2023). Black Maternal Health Momnibus Act of 2023 (S. 1606). 118th Congress. Retrieved from Congress.gov. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7166704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504494920,"identity":"cd30d3ca-6f8f-4aee-9a74-c36151bea64f","order_by":0,"name":"Gia Elise Barboza-Salerno","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACAwYGNiiTuQFMsbFDaMYGwlqgavh4DpCqRU4iAb8Wc/azzx58+GPHIN9+sPFx5Q4bBjbJx4c/8zDYyG44gF2LZU+6ueEMnmQGgzOJzYZnz6QxsEmnpUnzMKQZ49JicCCNTZpHgrl+A0Nim2Rj22GglhwzZh6Gw4k4tZx/BtRiUM8g3/+w/SdYi+QZY6DD/uPWcgNkS8JhBoYbiW2MYC0SPAZAhx3AqcVyxjN2wxkHjgP1PmwGOiyNh40nLU1yjkGy8UwcWsz509iAIVYNdFjywY+NbTZy8u2HD394U2En24dDCwbggTqYSOWjYBSMglEwCrACANlRVk4LsZ8WAAAAAElFTkSuQmCC","orcid":"","institution":"The Ohio State University","correspondingAuthor":true,"prefix":"","firstName":"Gia","middleName":"Elise","lastName":"Barboza-Salerno","suffix":""},{"id":504494922,"identity":"89cb5ddf-3c6d-462e-8773-6d5c7eaf95d4","order_by":1,"name":"Taylor Harrington","email":"","orcid":"","institution":"The Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"Taylor","middleName":"","lastName":"Harrington","suffix":""}],"badges":[],"createdAt":"2025-07-19 22:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7166704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7166704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90308692,"identity":"c1669777-3eaf-4490-92cf-76c3d5dbca83","added_by":"auto","created_at":"2025-09-01 09:38:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1118514,"visible":true,"origin":"","legend":"\u003cp\u003eProfiles of Antenatal Stressful Life Events Across Latent Classes\u003c/p\u003e\n\u003cp\u003eNote.\u003cstrong\u003e \u003c/strong\u003eThis radar chart illustrates the probability of endorsing each stressful life event across the five latent classes derived from the latent class analysis (LCA): Class 1: \u003cem\u003eMultiple Overlapping Stressors\u003c/em\u003e (red), Class 2: \u003cem\u003eFamily Member Illness and Death\u003c/em\u003e (green), Class 3: \u003cem\u003eResidential Instability and Family Conflict\u003c/em\u003e (purple), Class 4: \u003cem\u003eFinancial Insecurity\u003c/em\u003e (brown), and Class 5: \u003cem\u003eLow Stress\u003c/em\u003e (blue). Each axis represents a specific stressor experienced during pregnancy or in the year prior. Class 1 exhibits elevated probabilities across nearly all stress domains, reflecting cumulative stress exposure, while Class 5 reflects low probabilities across all items, indicating minimal stress exposure. The distinct patterns across classes highlight the heterogeneity of stressful life experiences during pregnancy.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7166704/v1/4fe185e964cc7b3a353e2ff5.png"},{"id":90310834,"identity":"089fdc0d-6e99-4ffc-8690-53b92fc400ab","added_by":"auto","created_at":"2025-09-01 09:46:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2155959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7166704/v1/678f9e31-2339-4455-a287-f162e422cbec.pdf"},{"id":90308691,"identity":"b610e134-ad9c-4a31-b177-e8ffd9e75f1f","added_by":"auto","created_at":"2025-09-01 09:38:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":29318,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7166704/v1/eab64956b9f52d2e1a67836b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Patterns of stressful life events in pregnancy: A latent class approach linking trauma and birth outcomes","fulltext":[{"header":"Background","content":"\u003cp\u003eAlthough often portrayed as a time of joy, pregnancy is frequently marked by stressful life events that can compromise maternal and infant health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Researchers define antenatal stressful life events as emotional and social hardships that individuals face during pregnancy or before conception, including housing instability, unemployment, financial insecurity, and interpersonal conflict [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These stressors undermine maternal well-being and quality of life by increasing blood pressure [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], intensifying symptoms of anxiety and depression [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and lowering parenting self-efficacy. Numerous studies have documented strong associations between antenatal stress exposure and adverse outcomes such as small for gestational age (SGA), low birth weight (LBW), and preterm birth (PTB) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For instance, researchers estimate that individuals reporting high stress face a 25–60% greater risk of preterm delivery, even after controlling for a broad array of individual- and biological risk factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePsychosocial stress during pregnancy affects infant health through two primary pathways. The first pathway involves biological processes, where chronic stress activates neuroendocrine and inflammatory systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This leads to elevated cortisol levels, suppressed immune function, and increased systemic inflammation, all of which are associated with birth complications such as preeclampsia and restricted fetal growth. The second pathway involves behavioral mechanisms. Stressful life events can lead to increased smoking, substance use, and inadequate prenatal care, which in turn raise the risk of poor birth outcomes. These behaviors contribute to adverse developmental outcomes, including increased risk of morbidity and mortality, and delays in cognitive, behavioral, and emotional development [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Rather than arising in isolation, prenatal stress exposures frequently reflect the continuation of cumulative stress burdens that begin in childhood and endure into adulthood [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eACEs and IPV are interconnected forms of trauma that increase vulnerability to stressful life experiences during pregnancy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. ACEs, defined as exposure to household substance use, parental incarceration, foster care placement, housing or food insecurity, and the loss of a loved one, predict chronic stress reactivity and emotional dysregulation across the life course [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Individuals with a history of ACEs are more likely to face financial hardship, limited social support, and housing instability in adulthood—factors that increase the risk of mental health challenges and maladaptive coping behaviors. When such stressors occur during pregnancy, especially among individuals with a history of IPV, they compound antenatal stress and increase the risk of adverse outcomes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nearly one-third of pregnant individuals report experiencing early childhood adversity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and up to 8% experience intimate partner violence (IPV) during pregnancy [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite its high prevalence, which exceeds that of several routinely screened complications such as gestational diabetes and pregnancy-induced hypertension, IPV often goes unrecognized and unaddressed in clinical care [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. IPV encompasses a range of harmful behaviors that can compromise the health and well-being of mother and infant, such as physical abuse, psychological aggression, coercive control, and stalking. The impact may be especially severe for individuals with a history of ACEs, who are more vulnerable to the psychological and physiological effects of ongoing stress during pregnancy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough interest in the impact of stressful life events on pregnancy has grown, few studies have explored how these events cluster together or how their cumulative and co-occurring nature shapes birth outcomes. Most existing research measures ACEs using a cumulative count of experiences, assuming all stressors contribute equally to risk, an untenable assumption that fails to capture the complexity and variability of lived experiences [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In reality, material stressors like eviction or job loss may affect maternal health differently than emotional or relational disruptions, such as the death of a loved one or partner separation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, methods such as Latent Class Analysis (LCA), which identify distinct patterns of co-occurring stressors, better enable researchers to capture meaningful variation in how pregnant individuals experience and are affected by antenatal stress [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e–\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo date, only one study has applied LCA to prenatal stressful life experiences [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Using data from a national survey, the authors identified three latent classes of antenatal stressful life events: a low-stress group, a group characterized by illness and death, and a multiple stressors group involving financial, housing, and relational hardships. The group facing multiple stressors was more likely to be unmarried, socioeconomically disadvantaged, and racially minoritized, and they also had higher risks of adverse maternal health outcomes. While this study highlights the heterogeneity of prenatal stress and shows the usefulness of latent class analysis in identifying vulnerability to antenatal stressful experiences, significant gaps remain regarding how ACEs and IPV shape latent stressor profiles and their subsequent influence on perinatal health outcomes. Building on prior work, this study uses LCA to identify different types of antenatal stressful experiences among pregnant women. Our main research questions are: (1) What latent subgroups of pregnant women can be identified based on co-occurring antenatal stressful experiences in the year before birth? (2) Do ACEs, IPV, smoking, social support, and material hardship experienced before or during pregnancy influence latent class membership? (3) Are specific stress profiles associated with a higher risk of poor birth outcomes?\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eData\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study draws on data from the Kansas Pregnancy Risk Assessment Monitoring System (K-PRAMS), a population-based surveillance project conducted by the Kansas Department of Health and Environment in collaboration with the Centers for Disease Control and Prevention (CDC) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. K-PRAMS collects detailed information on maternal behaviors, experiences, and health conditions before, during, and shortly after pregnancy to inform policy and prevention efforts in maternal and child health. Kansas is one of the few states to include survey items on material hardship during pregnancy, coercive forms of IPV, and ACEs, enabling a broader examination of social and developmental risk factors. We analyzed data from the 2016–2021 survey years, the most recent years for which complete data were available for all variables of interest. After excluding cases with missing values on key study variables, the final sample included N = 5,280 individuals who had a recent live birth in Kansas. K-PRAMS is designed to be representative of all live births in the state, and the inclusion of multiple years increases the robustness and generalizability of findings across diverse geographic and sociodemographic groups across the state [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurvey Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eK-PRAMS employs a stratified systematic sampling design, drawing from Kansas birth certificate records approximately 2 to 6 months postpartum. Each month, a stratified sample of new mothers is selected, with intentional oversampling of high-risk groups, including low birth weight births, to ensure that low birth weight infants are disproportionately represented relative to those with normal birth weight. This approach improves the representation of populations disproportionately affected by maternal and infant health disparities.\u003c/p\u003e\u003cp\u003eIndividuals who are selected to participate in the K-PRAMS receive a mailed questionnaire, with follow-up mailings and telephone interviews used to maximize response rates. The questionnaire includes variables, such as gestational diabetes, high blood pressure, anxiety, and depression, based on self-report to assess baseline pregnancy risk. Other variables measuring maternal demographic characteristics (e.g., race/ethnicity) and birth outcomes (e.g., preterm birth) are obtained from linked birth certificate data. These survey responses are integrated with birth certificate records to create a comprehensive dataset for analysis. Our analysis included data that were weighted to account for the complex sampling design, differential response rates, and noncoverage, ensuring that our estimates are generalizable to the population of women with live births in Kansas during the study period. The CDC, The Ohio State University, and the Kansas Department of Health and Environment Institutional Review Boards have approved the Kansas PRAMS protocol.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasures\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLatent Class Items\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eStressful life events.\u003c/em\u003e Thirteen stressful life events were assessed based on PRAMS core questions regarding maternal experiences in the 12 months before delivery. Respondents answered “yes” or “no” to whether they had experienced each of the following: (1) a close family member was very sick and hospitalized; (2) separation or divorce from a husband or partner; (3) moving to a new address; (4) experiencing homelessness; (5) husband or partner lost their job; (6) respondent lost her job despite wanting to continue working; (7) more frequent arguments with husband or partner; (8) husband or partner said they did not want the pregnancy; (9) difficulty paying bills; (10) cut in pay or hours for respondent or partner; (11) respondent went to jail; (12) someone close had a problem with alcohol or drugs; and (13) husband or partner was away due to military deployment or extended work travel. These items were used as the primary indicators in the latent class model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIndependent Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eACEs.\u003c/em\u003e Seven binary (yes/no) items from the K-PRAMS dataset were used to assess early traumatic or adverse experiences occurring before age 18. These measures captured whether the respondent: (1) experienced the death of someone close; (2) had parents or guardians who divorced or separated; (3) had to move due to problems paying rent or mortgage; (4) went hungry because the family could not afford enough food; (5) had a parent or guardian who was incarcerated or involved with the legal system; (6) lived with a parent or guardian who had problems with alcohol or drug use; and (7) was ever placed in foster care. These variables reflect key domains of adversity—loss, instability, deprivation, and household dysfunction—and were used to examine their contribution to antenatal stress profiles.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMaterial hardship and social support.\u003c/em\u003e Material hardship was assessed using eight dichotomous items capturing access to basic needs 12 months prior to delivery. Respondents were asked whether they experienced unmet needs in the following areas: (1) affordable transportation; (2) food insecurity; (3) access to safe housing; (4) housing stability, including frequent moves or eviction risk; (5) household crowding; (6) reliable utility services, such as heat, water, or electricity; (7) consistent phone access; and (8) any other unmet basic need not otherwise specified. Each item was coded as 1 (yes, unmet need) or 0 (no), and summed to create an index showing increasing levels of material hardship. These items were included as predictors of latent class membership to assess the contribution of economic strain and unmet basic needs to stressful life experience profiles during pregnancy. Social support was assessed by asking, “During your most recent pregnancy, who would have helped you if a problem had come up?” Participants could select multiple sources. For this analysis, the focus was on partner support, with a yes/no for choosing “husband or partner.” Responses were cleaned for consistency, keeping only valid answers. Cigarette use was self-reported for the 3 months before pregnancy as a yes/no variable.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIPV.\u003c/em\u003e Participants were asked if, during their most recent pregnancy, any of the following occurred: \"My husband or partner threatened me or made me feel unsafe,\" \"I was frightened for my safety or my family’s safety because of my husband or partner's anger or threats,\" “My husband or partner tried to control my daily activities, such as whom I could talk to or where I could go,\" or “My husband or partner forced me to participate in touching or sexual activities against my will.\" The responses were converted into a binary variable to indicate whether participants experienced any IPV during pregnancy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDistal Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAdverse birth outcomes.\u003c/em\u003e PTB is defined as delivery before 37 completed weeks of gestation, while very preterm birth VPTB refers to delivery before 33 weeks. LBW is defined as a birth weight under 2,500 grams. Small for gestational age (SGA) is defined as a birth weight below the 10th percentile for gestational age and sex, based on national reference standards.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSociodemographic factors included maternal age, education, household income, health issues, cigarette use, and social support. Maternal race and ethnicity were categorized as: (1) Black, non-Hispanic; (2) White, non-Hispanic; (3) Hispanic; (4) American Indian/Alaska Native, non-Hispanic; (5) Asian, non-Hispanic; and (6) Other, non-Hispanic. Gestational diabetes was marked “yes” if it started during pregnancy, excluding pre-pregnancy diabetes. High blood pressure was “yes” if hypertension or preeclampsia began during pregnancy, excluding pre-existing cases. Anxiety and depression were marked if experienced during pregnancy. Prenatal care utilization was measured using the Kotelchuck Index, which assesses care timing and number of visits relative to expected. It was classified as inadequate, intermediate, adequate, or adequate plus, reflecting the adequacy and intensity of prenatal care. Pre-pregnancy BMI was calculated from self-reported height and weight, categorized as underweight (\u0026lt; 18.5), normal (18.5–24.9), overweight (25.0–29.9), and obese (≥ 30.0).\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eLatent Class Analysis (LCA) was conducted using Mplus Version 8.4 to identify subgroups of respondents characterized by patterns of antenatal stress exposure, early adversity, material hardship, and behavioral risk. Models specifying one through seven latent classes were estimated using robust maximum likelihood estimation (MLR) to accommodate non-normality and categorical indicators. Survey weights, strata, and cluster variables were incorporated to account for PRAMS’ complex sampling design.\u003c/p\u003e\u003cp\u003eModel selection was based on fit indices and substantive interpretability. The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size-adjusted BIC (aBIC), entropy values, and two likelihood ratio tests—the Vuong-Lo-Mendell-Rubin (VLMR) and Lo-Mendell-Rubin adjusted test (LMR-LRT)—were used to guide model selection. The final model was chosen based on the decrease in AIC, BIC, and aBIC, significant VLMR and LMR-LRT \u003cem\u003ep\u003c/em\u003e-values (\u0026lt; 0.05), and an entropy value greater than 0.70. Solutions were also evaluated for class distinctiveness, interpretability, and minimum class size (≥ 2.5% of the sample).\u003c/p\u003e\u003cp\u003eFollowing class enumeration, associations between class membership and covariates or outcomes were estimated using the Bolck, Croon, and Hagenaars (BCH) three-step approach. This method preserves latent class measurement while incorporating classification error into the estimation of covariate and outcome effects. First, BCH weights were calculated using posterior probabilities. Second, covariates including ACEs, social support, and behavioral risk factors were entered into multinomial logistic regressions predicting latent class membership. Third, birth outcomes (PTB, VPTB, LBW, SGA) were modeled as distal outcomes across classes using BCH-weighted regressions, adjusting for sociodemographic covariates. All models used 100 random starts and 50 final stage optimizations to ensure model convergence and loglikelihood stability.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe final analytic sample included 5,290 unweighted respondents, representing a weighted population of 162,290 individuals who had recently given birth. The majority identified as non-Latina White (73.2%), followed by Latina (13.9%), non-Latina Black (6.4%), and other race/ethnicity (6.5%). The average maternal age was 28.4 years. Approximately one-third of respondents (36.0%) were unmarried, and 32.7% resided in rural areas. Educational attainment varied, with 50.0% having less than a high school degree, 26.7% completing high school, and 24.0% attaining a bachelor\u0026rsquo;s degree. Nearly one-quarter (22.4%) reported annual household incomes below $19,000.\u003c/p\u003e\n\u003cp\u003eIn terms of perinatal health and behavior, 20.0% reported a diagnosis of perinatal depression. Smoking was reported by 8.9% of the sample, with most indicating light use (\u0026lt;\u0026thinsp;1 cigarette/day). According to the Kotelchuck Index, 63.9% received adequate or better prenatal care, with 21.5% classified as \u0026ldquo;adequate plus,\u0026rdquo; while 7.8% and 7.7% received intermediate or inadequate care, respectively. Regarding physical health, 13.9% reported hypertension, and 9.9% experienced gestational diabetes. Based on BMI, 39.8% had normal weight, 27.3% were overweight, and 30.3% were obese. Low birth weight (LBW; \u0026lt;2,500 g) and preterm birth (PTB; \u0026lt;37 weeks\u0026rsquo; gestation) were observed in 5.7% and 8.6% of cases, respectively.\u003c/p\u003e\n\u003cp\u003eA five-class solution was selected based on the model fit indices. While the six- and seven-class models showed slight improvements in entropy, their LMR-LRT \u003cem\u003ep\u003c/em\u003e-values were non-significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7331 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2430, respectively) and did not justify the additional complexity (See Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e for a description of all LC models we estimated). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents model fit indices for the final model, and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the conditional item response probabilities for each of the five latent classes, which were labeled as follows: (1) Low Stress (58.6%), (2) Family Member Illness and Death (16.3%), (3) Financial Insecurity (11.2%), (4) Residential Instability and Family Conflict (8.7%), and (5) Multiple Overlapping Stressors (5.2%). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the profile plot of conditional item response probabilities for each class.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eModel fit statistics for class enumeration for the K\u0026thinsp;=\u0026thinsp;1 through K\u0026thinsp;=\u0026thinsp;7 class models\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLoglikelihood (H0)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAIC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBIC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEntropy\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVLRT\u003csub\u003eK\u0026minus;1\u003c/sub\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLRT Adjusted Test\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 Class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-25334.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50694.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50779.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e--\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e--\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e--\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 Class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-23457.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46969.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47146.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 Class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-23080.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46243.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46512.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.727\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 Class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-22890.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45891.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46252.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 Class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-22731.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45601.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46054.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 Class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-22684.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45535.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46081.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.734\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eNote: VLRT\u0026thinsp;=\u0026thinsp;Vuong-Lo-Mendell-Rubin Likelihood Ratio Test; AIC\u0026nbsp;=\u0026nbsp;Akaike Information Criterion; BIC =\u0026nbsp;Bayesian Information Criterion; ABIC\u0026nbsp;=\u0026nbsp;Adjusted\u0026nbsp;Bayesian Information Criterion.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eConditional response probabilities of maternal material hardship within classes\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eItem\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClass 1: Family member illness and death\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;858 (16.31%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClass 2: Multiple overlapping stressors\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;275 (5.23%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClass 3: Financial insecurity\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;591 (11.22%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClass 4: Residential Instability and Family Conflict\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;455 (8.65%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClass 5: Low Stress 3083 (58.59%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily Member Hospitalized\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.782\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.620\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.092\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDivorce or Separation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.371\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMoved to a different address\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.434\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.812\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.504\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.526\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.281\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePartner Lost Job\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.567\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLost Job\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.571\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.127\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWork hours or pay cut\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.579\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.706\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProblems paying rent or mortgage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.083\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.849\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.506\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.343\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePartner away\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.086\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePartner arguments\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.718\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.736\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePartner did not want a pregnancy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.318\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSomeone close had a drug or alcohol problem\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.600\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExperienced homelessness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.296\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA close person died\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.703\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.575\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.055\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNotes. Bolded figures were used to help interpret the classes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClass 1 (Multiple Overlapping Stressors), the smallest class, displayed the highest and broadest pattern of adversity, with particularly high probabilities of housing instability (0.849 difficulty paying rent, 0.296 homelessness), employment disruption (0.579 reduced work hours), and relational adversity (0.718 partner arguments, 0.600 substance use in close relationships), reflecting compounded disadvantage across economic, housing, and relational domains. Class 2 (Family Member Illness and Death) showed high probabilities of having a family member hospitalized (0.782) and experiencing the death of someone close (0.703), with low endorsement of other stressors, suggesting stress defined almost exclusively by chronic illness and mortality. Class 3 (Residential Instability and Family Conflict) was characterized by high probabilities of residential moves (0.526), frequent arguments with a partner (0.736), and rejection of pregnancy by a partner (0.318), indicating stress concentrated in household disruption and interpersonal conflict. Class 4 (Financial Insecurity) exhibited elevated probabilities of reduced work hours or pay (0.706), difficulty paying rent (0.506), and partner job loss (0.389), indicating acute economic hardship along with moderate levels of relational stress. Class 5 (Low Stress) was the most prevalent and characterized by uniformly low conditional probabilities across all indicators of adversity, serving as the reference group in subsequent analyses.\u003c/p\u003e\n\u003cp\u003eThe pairwise odds ratios demonstrate strong class separation and within-class homogeneity for most indicators, indicating high between-class differentiation and low within-class variability (see Table S2). Some items demonstrated weaker separation, for example, \"partner away\" and \"moved,\" which showed relatively low odds ratios across several class comparisons, suggesting these indicators were less effective in distinguishing between classes.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the multinomial logistic regression models predicting latent class membership based on IPV exposure, ACEs, cigarette use, material hardship, and social support controlling for maternal demographics. Compared to the Low Stress class, individuals in the Multiple Overlapping Stressors class had significantly higher odds of experiencing IPV (OR\u0026thinsp;=\u0026thinsp;5.87; 95% CI [1.04, 33.01]), reporting more ACEs (OR\u0026thinsp;=\u0026thinsp;1.70; 95% CI [1.57, 1.85]), and using cigarettes during pregnancy (OR\u0026thinsp;=\u0026thinsp;1.08; 95% CI [1.05, 1.11]). They were also more likely to experience severe material hardship (OR\u0026thinsp;=\u0026thinsp;2.09; 95% CI [1.87, 2.33]). Social support was not a significant predictor of membership in this highest-risk class. In the Residential Instability and Family Conflict class, IPV (OR\u0026thinsp;=\u0026thinsp;5.62; 95% CI [1.22, 25.93]), ACEs (OR\u0026thinsp;=\u0026thinsp;1.35; 95% CI [1.26, 1.46]), cigarette use (OR\u0026thinsp;=\u0026thinsp;1.06; 95% CI [1.04, 1.09]), and material hardship (OR\u0026thinsp;=\u0026thinsp;1.48; 95% CI [1.33, 1.66]) were all significant predictors compared to the Low Stress class. Lower social support was also associated with increased odds of class membership (OR\u0026thinsp;=\u0026thinsp;1.28; 95% CI [1.09, 1.49]). Membership in the Financial Insecurity class was significantly associated with higher ACEs (OR\u0026thinsp;=\u0026thinsp;1.35; 95% CI [1.27, 1.43]), greater cigarette use (OR\u0026thinsp;=\u0026thinsp;1.03; 95% CI [1.01, 1.06]), lower social support (OR\u0026thinsp;=\u0026thinsp;1.22; 95% CI [1.07, 1.39]), and greater material hardship (OR\u0026thinsp;=\u0026thinsp;1.33; 95% CI [1.20, 1.48]). IPV was not a significant predictor for this group. Finally, the Family Member Illness and Death class was predicted by higher ACEs (OR\u0026thinsp;=\u0026thinsp;1.26; 95% CI [1.19, 1.34]) and cigarette use (OR\u0026thinsp;=\u0026thinsp;1.04; 95% CI [1.02, 1.08]). IPV, material hardship, and social support were not significantly associated with membership in this class. All models were adjusted for maternal age, education, income, and race/ethnicity.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultinomial Logistic Regression of Psychosocial Risk Factors Predicting Latent Class Membership\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMultiple overlapping stressors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFamily Member illness and death\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResidential instability and family conflict\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFinancial insecurity\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIPV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.871 (1.04, 33.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92 (0.08, 10.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.622 (1.22, 25.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.018 (0.13, 32.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.704 (1.567, 1.854)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.261 (1.186, 1.341)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.353(1.257, 1.457)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.354 (1.271, 1.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCigarette Use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.083 (1.053, 1.113)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.044 (1.016, 1.077)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.063 (1.035, 1.093)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.034 (1.006, 1.063)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial Support\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.9023 (.7908,1.029)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.119 (.9807, 1.277)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.275 (1.093, 1.488)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.218 (1.068, 1.390)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaterial Hardship\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.088 (1.87, 2.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.064 (.0.923, 1.201)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.481 (1.325, 1.655)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.334 (1.200, 1.482)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eNote. Analysis controls for maternal age, education, income, and race/ethnicity, and physical and behavioral health problems experienced during pregnancy.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDemographic characteristics varied significantly by class (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Table\u0026nbsp;4). The Low Stress class had the highest mean maternal age (29.4 years) and educational attainment (mean\u0026thinsp;=\u0026thinsp;4.8). Financial Insecurity was associated with the youngest age (26.8 years) and lowest education (mean\u0026thinsp;=\u0026thinsp;3.8). Non-Latina Black women were disproportionately represented in the Family Illness and Death class (15.3%), while Latina women were overrepresented in the Financial Insecurity and Low Stress classes. Marital status also differed: women in the Low Stress class were most likely to be married (75.0%), whereas those in the Family Illness and Death class were least likely (21.0%). Smoking during pregnancy was highest among women in the Family Illness and Death class and lowest in the Low Stress class. Depression and anxiety symptoms were significantly more prevalent among women in higher adversity classes, especially in the Residential Instability and Multiple Overlapping Stressor groups.\u003c/p\u003e\n\u003cp\u003eAdverse birth outcomes varied by latent class (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Rates of LBW were highest in the Multiple Overlapping Stressors (52.0%), followed by Financial Insecurity (46.1%), Residential Instability (46.2%), and Family Illness and Death (40.0%), compared to 38.1% in the Low Stress class. SGA was most prevalent in the Multiple Overlapping Stressors class (28.2%), significantly higher than in the reference group (19.6%). PTB occurred most frequently among women in the Multiple Overlapping Stressors (39.1%) and Financial Insecurity (37.4%) classes, compared to 32.0% in the Low Stress group.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable\u0026nbsp;4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSample characteristics by profiles of stressful life experiences.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDependent: Class\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple overlapping stressors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFamily member illness or death\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eResidential Instability and Family Conflict\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial insecurity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow Stress\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal N (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229 (4.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e688 (13.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e369 (7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e554 (11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3205 (63.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5045 (100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation (SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.4 (1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.5 (1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.8 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.1 (1.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.8 (1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.6 (1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge Mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.3 (5.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4 (5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.8 (5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.9 (5.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.4 (5.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.8 (5.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncome Dependence Mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.1 (1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.1 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.1 (1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.2 (1.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.1 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.1 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.729\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHispanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (9.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59 (8.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 (14.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78 (14.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e369 (11.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e581 (11.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNH Black\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (15.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55 (8.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (10.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47 (8.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e207 (6.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e382 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNH White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e167 (72.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e543 (78.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e253 (68.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e391 (70.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2376 (74.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3730 (73.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (4.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (6.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e253 (7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e352 (7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (21.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e448 (65.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140 (37.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e316 (57.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2405 (75.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3357 (66.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnmarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181 (79.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e240 (34.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229 (62.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e238 (43.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e800 (25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1688 (33.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eUrbanicity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrban\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e153 (66.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e470 (68.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234 (63.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e395 (71.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2231 (69.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3483 (69.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRuran\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76 (33.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e218 (31.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135 (36.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159 (28.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e974 (30.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1562 (31.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCigarette Smoking Mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.0 (6.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1 (3.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.2 (4.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0 (3.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetes (pre-pregnancy)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e221 (96.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e666 (96.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e354 (95.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e532 (96.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3066 (95.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4839 (95.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.713\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (3.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (3.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (4.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e139 (4.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHigh Blood Pressure (pre-pregnancy)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.890\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e211 (92.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637 (92.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e343 (93.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e520 (93.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2985 (93.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4696 (93.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51 (7.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (6.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e220 (6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e349 (6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDepression (pre-pregnancy)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99 (43.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e526 (76.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e220 (59.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e381 (68.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2762 (86.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3988 (79.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130 (56.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162 (23.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149 (40.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173 (31.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e443 (13.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1057 (21.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAnxiety (pre-pregnancy)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79 (34.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e439 (63.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e183 (49.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e316 (57.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2472 (77.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3489 (69.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150 (65.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e249 (36.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e186 (50.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e238 (43.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e733 (22.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1556 (30.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003cstrong\u003eTable 5.\u003c/strong\u003e Distal birth outcomes by profiles of stressful life experiences\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMultiple overlapping stressors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFamily member illness and death\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResidential Instability and Family Conflict\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFinancial insecurity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLow Stress\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow Birth Weight (LBW) (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.01\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.99\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.2\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.13\u003csup\u003ec,e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.05\u003csup\u003eb,d,e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmall for Gestational Age (SGA) (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.58\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGestational Age\u0026thinsp;\u0026lt;\u0026thinsp;37 Weeks (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.41\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.96\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote. The table displays the percentage of individuals in each stress profile who experienced the specified birth outcomes. Subscripted letters (e.g., \u003cem\u003ea\u003c/em\u003e, \u003cem\u003eb\u003c/em\u003e) indicate significant pairwise differences between groups based on post hoc comparisons (p\u0026thinsp;\u0026lt;\u0026thinsp;.05). Groups that share the same letter are significantly different from one another for that outcome.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe goal of this study was to identify latent profiles of antenatal stressful life experiences and examine how these profiles relate to early adversity, sociodemographic risk, and adverse birth outcomes. Using a person-centered latent class analysis, we identified five distinct stress subgroups: Low Stress, Family Member Illness and Death, Financial Insecurity, Residential Instability and Family Conflict, and Multiple Stressors. Each subgroup reflected unique constellations of traumatic, financial, relational, and emotional stressors in the year before giving birth [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], suggesting that stress exposure is not uniform but shaped by specific contextual vulnerabilities. Class membership was significantly associated with maternal adversity histories, behavioral health risks, and perinatal outcomes.\u003c/p\u003e\u003cp\u003eOur findings suggest that antenatal stress is heterogeneous and may involve distinct underlying mechanisms with essential implications for infant health. Mukherjee et al. (2016) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] used latent class analysis on PRAMS data from 2009\u0026ndash;2011 to identify three broad stress profile groups among pregnant women: a low-stress group, a group characterized by illness and death, and a \u0026ldquo;multiple stressors\u0026rdquo; group with high levels of economic, relational, and traumatic stressors. Similarly, in our study, we identified a \u0026ldquo;multiple overlapping stressors\u0026rdquo; class, which aligns conceptually with Mukherjee et al.'s highest-stress group. However, while their multiple stressors class comprised approximately 22% of the sample, our equivalent class represented only 5.2% of participants, likely reflecting differences in sample composition. Both studies similarly found that individuals in high-stress classes were more likely to experience sociodemographic disadvantage and elevated health risks. However, our analysis differs by identifying more nuanced classes of antenatal stress, including specific subgroups marked by financial insecurity and by family illness and bereavement\u0026mdash;patterns not separately identified in Mukherjee et al.'s three-class model.\u003c/p\u003e\u003cp\u003eWe examined links between latent class membership and adverse birth outcomes, providing insights into how different patterns of antenatal stress may influence perinatal health. Consistent with prior research, individuals reporting more ACEs were significantly more likely to belong to higher-risk classes\u0026mdash;particularly the Multiple Overlapping Stressors class\u0026mdash;supporting a life course framework of adversity whereby stressful life experiences during pregnancy are compounded by ACEs. Cigarette use before pregnancy was also linked to higher-risk class membership, notably in the Financial Insecurity and Residential Instability classes. Additionally, low social support significantly predicted membership in three of the four high-risk classes, emphasizing the protective role of social support for decreasing susceptibility to the adverse effects of specific social stressors and potentially reducing adverse birth outcomes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNext, we assessed associations between prenatal stress exposure and adverse birth outcomes. Membership in higher-risk stress classes was significantly associated with increased likelihood of LBW, SGA, and PTB. Women in these classes exhibited the most concentrated social and health-related vulnerabilities, including the lowest average educational attainment, youngest maternal age, and highest prevalence of non-marital status. These groups also reported the highest rates of prenatal depression, anxiety, and cigarette use. Hispanic and Black individuals were disproportionately represented in the highest-risk stress classes. These findings align with previous research showing that lower income women and women from historically minoritzed groups are more likely to experience multiple, co-occurring stressors, such as adverse childhood experiences (ACEs), intimate partner violence (IPV), and inadequate social support, that increase the risk of LBW, PTB, and SGA infants [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Notably, Kansas is one of the few PRAMS-participating states that collects data on coercive control, IPV during pregnancy, and ACEs, providing a unique opportunity to examine the compounding effects of these exposures. Future studies should investigate the specific causal pathways through which these stressors influence maternal and infant health.\u003c/p\u003e\u003cp\u003eElevated risks for adverse birth outcomes were also observed in the Financial Insecurity and Residential Instability groups, indicating that chronic and cumulative stressors during pregnancy, especially those rooted in socioeconomic hardship and unstable living conditions, are connected to increased vulnerability. These groups similarly showed higher levels of psychological distress, including moderate rates of depression and anxiety. Conversely, the class characterized by family member illness or death, despite reporting high acute stress levels, did not show worse birth outcomes. Likewise, members of the Low Stress group experienced the best birth outcomes, likely due to their access to protective resources such as higher education levels, older age, economic advantages of marriage, and the lowest rates of depression, anxiety, and cigarette use.\u003c/p\u003e\u003cp\u003eResearch has identified two primary pathways through which stressful life events may influence birth outcomes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and our findings align with both. First, antenatal stress can lead to behavioral changes that elevate physiological risks during pregnancy. In this study, individuals in the highest-risk profiles reported elevated rates of smoking and psychological distress, including depression and anxiety. These behaviors are well-documented risk factors for complications such as gestational diabetes, hypertension, and infection, all of which can contribute to PTB and LBW. Second, psychosocial stressors can directly affect biology by disrupting the maternal hypothalamic-pituitary-adrenal (HPA) axis, with chronic or acute stress increasing cortisol and placental corticotropin-releasing hormone (CRH), both of which can impact the timing of labor. The heightened anxiety and depression observed in the Multiple Overlapping Stressors and Residential Instability groups may reflect this kind of biological dysregulation, offering a plausible mechanism linking these stress profiles to adverse perinatal outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications for practice and policy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has clear and actionable implications for federal and state maternal health policies, including comprehensive legislation such as the \u003cem\u003eBlack Maternal Health Momnibus Act\u003c/em\u003e [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and related efforts to promote equity through structural and trauma-informed care. By identifying the factors that contribute to membership in high-risk antenatal stress profiles, including overlapping exposures to housing instability, food insecurity, material hardship, IPV, and ACEs, the study provides strong evidence that these structural and psychosocial stressors significantly increase the likelihood of poor birth outcomes. These findings directly support key provisions of the \u003cem\u003eSocial Determinants for Moms Act\u003c/em\u003e, one of the bills within this comprehensive legislative package designed by the Black Maternal Health Caucus to tackle key drivers of maternal health disparities, which advocates for upstream investments in housing, nutrition, and transportation as essential strategies for reducing maternal health disparities [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study also identifies grief- and illness-related stress exposures that reflect the caregiving and trauma burdens emphasized in the \u003cem\u003eProtecting Moms Who Served Act\u003c/em\u003e. While these exposures were not directly linked to adverse birth outcomes, their contribution to antenatal stress profiles underscores the need for expanded screening and trauma-informed support, especially among veterans and military-connected families. The inclusion of parental incarceration as a significant predictor of high-stress class membership further supports the need for trauma-informed perinatal care, as outlined in the \u003cem\u003eJustice for Incarcerated Moms Act\u003c/em\u003e. Additionally, with nearly one-third of the sample residing in rural areas and the highest-risk classes exhibiting the greatest unmet behavioral health needs, our findings underscore the importance of expanding access to care through policies such as the Tech to Save Moms Act, which supports telehealth and remote screening tools to reduce barriers to care posed by antenatal stressors. Collectively, these findings support maternal health policies that confront the cumulative burden of structural adversity through sustained, integrated, and inclusive approaches that explicitly account for sociodemographic disparities and geographic residence, particularly among populations facing compounded risks due to rural isolation, incarceration, trauma exposure, and unmet behavioral health needs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study had several strengths, including the use of a large, population-based sample, the application of a person-centered latent class framework, and the implementation of the BCH approach to preserve class structure when examining predictors and outcomes. However, several limitations should be noted. First, all measures were self-reported and may be subject to recall or social desirability bias. Second, the cross-sectional design limits causal inference, and associations between antenatal stress typologies and birth outcomes may be shaped by multiple, unmeasured mediators. Third, although the sample is representative of births in Kansas, the findings may not generalize to other populations; Kansas was selected due to the availability of detailed ACEs and material hardship modules. Finally, although the BCH method maintains class structure in covariate and outcome models, it does not account for uncertainty in class assignment. Future research should explore longitudinal designs and examine the mediating mechanisms\u0026mdash;behavioral, psychosocial, and biological\u0026mdash;through which early life adversity shapes antenatal stress exposure and perinatal health outcomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study found five profiles of antenatal stressful life experiences shaped by early adversity and linked to birth outcome disparities. Those with overlapping material, relational, and behavioral adversities faced the highest risk of poor perinatal health. Results emphasize the need to address structural and psychosocial stressors to achieve more equitable healthcare that supports maternal and infant well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest:\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThis study was reviewed and deemed exempt by The Ohio State University Institutional Review Board and approved by the Centers for Disease Control and Prevention under the data usage agreement for the Pregnancy Risk Assessment Monitoring System. Participants provided informed consent as part of the PRAMS data collection protocol. All procedures adhered to the ethical standards outlined in the 1964 Declaration of Helsinki and its subsequent amendments.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported with funding from the Institute for Population Research at The Ohio State University.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.B.S. drafted the initial manuscript, conceptualized and designed the study, and conducted the statistical analyses. T.H. assisted with the conceptualization, literature review, and edited and revised the manuscript. All authors reviewed and approved the final manuscript as submitted.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank members of the Investigating Spatial Structures in Urban Environments (ISSUES) lab for their thoughtful feedback on the manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study can be requested\u003c/p\u003e\u003cp\u003eat \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/prams/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/prams/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, but restrictions apply to their use. The data were used under a data use agreement and are not publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCoussons-Read ME. Effects of prenatal stress on pregnancy and human development: mechanisms and pathways. Obstet Med. 2013;6:52\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoenig MD, Crooks N, Burton T, Li Y, Hemphill NO, Erbe K, et al. Structural violence and stress experiences of young pregnant Black people. J racial ethnic health disparities. 2024;11:1918\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMukherjee S. Antenatal Stressful Life Events and Postpartum Depression in the United States: the Role of Women\u0026rsquo;s Socioeconomic Status at the State Level. 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorgan N, Christensen K, Skedros G, Kim S, Schliep K. Life stressors, hypertensive disorders of pregnancy, and preterm birth. J Psychosom Obstet Gynecol. 2022;43:42\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKogler L, M\u0026uuml;ller VI, Chang A, Eickhoff SB, Fox PT, Gur RC, et al. 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Child Maltreat. 2022;27:194\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcLaughlin KA, Sheridan MA. Beyond Cumulative Risk: A Dimensional Approach to Childhood Adversity. Curr Dir Psychol Sci. 2016;25:239\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarboza GE. Latent Classes and Cumulative Impacts of Adverse Childhood Experiences. Child Maltreat. 2018;23:111\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCutcheon AL. Latent class analysis. Sage; 1987.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMukherjee S, Coxe S, Fennie K, Madhivanan P, Trepka MJ. Stressful Life Event Experiences of Pregnant Women in the United States: A Latent Class Analysis. Women\u0026rsquo;s Health Issues. 2017;27:83\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKansas Department of Health and Environment, Bureau of Epidemiology \u0026amp; Public Health Informatics. Kansas pregnancy risk assessment monitoring system (PRAMS) 2022 surveillance report. Technical Report. Topeka, KS: Kansas Department of Health and Environment; 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBooth EJ, Kitsantas P, Min H, Pollack AZ. Stressful life events and postpartum depressive symptoms among women with disabilities. Womens Health (Lond Engl). 2021;17:17455065211066186.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu MC, Chen B. Racial and ethnic disparities in preterm birth: The role of stressful life events. Am J Obstet Gynecol. 2004;191:691\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenthal L, Lobel M. Explaining racial disparities in adverse birth outcomes: Unique sources of stress for Black American women. Soc Sci Med. 2011;72:977\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoin DE. Community violence and pregnancy: An understudied exposure in the etiology of adverse birth outcomes. PhD Thesis. UC Berkeley; 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCommittee on Health, Education, Labor \u0026amp; Pensions \u0026amp; Committee on Finance (Sponsor: Booker). (2023). \u003cem\u003eBlack Maternal Health Momnibus Act of 2023\u003c/em\u003e (S. 1606). 118th Congress. Retrieved from Congress.gov.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stressful life experiences, Latent class analysis, Maternal health, Neonatal health, Adverse childhood experiences","lastPublishedDoi":"10.21203/rs.3.rs-7166704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7166704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eStressful life events during pregnancy, including relational, financial, and emotional stressors, are well-established risk factors for adverse birth outcomes. Additional exposures such as adverse childhood experiences (ACEs), intimate partner violence (IPV), material hardship, and substance use can contribute to stressful life experiences during pregnancy. However, few studies have examined how these factors cluster into distinct profiles of stressful life experiences during pregnancy, or how such profiles are associated with poor birth outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData were drawn from 5,280 pregnant individuals participating in the Kansas Pregnancy Risk Assessment Monitoring System (K-PRAMS). Latent class analysis (LCA) was performed to identify stress profiles and assess adverse birth outcomes as distal variables. Multinomial logistic regression models examined how ACEs predict latent class membership, controlling for maternal demographic characteristics. Analyses were conducted in Mplus v8.4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFive distinct stress profiles were identified: (1) Multiple Overlapping Stressors (5%, n = 275), (2) Financial Insecurity (11%, n = 591), (3) Family Member Illness or Death (16%, n = 858), (4) Residential Instability and Family Conflict (9%, n = 455), and (5) Low Stress (59%, n = 3,083). Compared to the Low Stress class, individuals in all high-risk classes had significantly greater odds of reporting ACEs, IPV, material hardship during pregnancy, and substance use. Differential associations were observed between specific stress profiles and adverse birth outcomes\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe findings highlight the heterogeneity of stress exposure during pregnancy and underscore the importance of identifying distinct antenatal stress profiles. Tailoring perinatal interventions to specific stress profiles—particularly for individuals with histories of early adversity—may improve outcomes for both mothers and infants\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Patterns of stressful life events in pregnancy: A latent class approach linking trauma and birth outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:30:04","doi":"10.21203/rs.3.rs-7166704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-30T15:22:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T13:01:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T19:39:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271372573577444952820088768265538963858","date":"2025-08-29T15:50:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159275612841443838564663262619413876446","date":"2025-08-20T15:43:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-20T08:45:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-23T13:54:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-22T08:45:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T08:40:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-07-19T22:29:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79931cbf-a771-4eaf-9d7a-c377bb8fad9d","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T18:23:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 09:30:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7166704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7166704","identity":"rs-7166704","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}