Objectives. We determined whether and to what extent a woman’s exposure to stressful life events prior to conception (PSLEs) was associated with preterm birth and whether maternal age modified this relationship.
Methods. We examined 9350 mothers and infants participating in the first wave of the Early Childhood Longitudinal Study, Birth Cohort, a nationally representative sample of US women and children born in 2001, to investigate the impact of PSLEs on preterm birth in the United States. We estimated the effect of exposure on preterm birth with weighted logistic regression, adjusting for maternal sociodemographic and health factors and stress during pregnancy.
Results. Of the women examined, 10.9% had a preterm birth. In adjusted analyses, women aged 15 to 19 years who experienced any PSLE had over a 4-fold increased risk for having a preterm birth. This association differed on the basis of the timing of the PSLE.
Conclusions. Findings suggest that adolescence may be a sensitive period for the risk of preterm birth among adolescents exposed to PSLEs. Clinical, programmatic, and policy interventions should address upstream PSLEs, especially for adolescents, to reduce the prevalence of preterm birth and improve maternal and child health.
Preterm birth occurs in approximately 12% of all births in the United States.1 Preterm birth is a leading cause of neonatal death in the United States2 and contributes substantially to childhood and adult morbidity and mortality.3–6 Reducing the prevalence of preterm birth has significant implications for the future health and well-being of children and families and accordingly is a national health priority7 and the focus of numerous public health efforts. However, despite extensive research, practice, and policy devoted to reducing the number of children born preterm, the prevalence of preterm birth in the United States remains unacceptably high, suggesting that additional risk factors must be identified for outcomes to improve.
Maternal exposure to stress during pregnancy is an important contributor to preterm birth, primarily through the neuroendocrine, immune, and inflammatory processes.8,9 In addition, European population-based evidence has suggested that exposure to stressors before pregnancy (i.e., severe life events such as death or serious illness of a relative) may also be associated with preterm birth.10 However, to our knowledge, no study has investigated the independent effects of events prior to conception and during pregnancy that are critical for isolating the effects of stress at these different time periods. A study by Khashan et al.11 examined these periods as mutually exclusive categories compared with an unexposed group. Specifically, women were recorded as having a stressful life event: (1) before pregnancy, (2) during the first trimester, (3) during the second trimester, or (4) during the third trimester. Women who experienced a stressful life event in more than 1 time period were categorized into 1 of these groups on the basis of this a priori hierarchy. Similarly, in a study by Class et al.12 women who experienced both preconception and prenatal stress were removed from analyses.
In addition, a well-known U-shaped relationship exists between maternal age and the risk of preterm birth, such that adolescents and older women have a higher risk.13–15 However, whether this effect varies by women’s exposure to stress is not known.
We capitalized on population-based data available from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B), to accomplish 2 specific aims. First, we sought to determine whether and to what extent a woman’s exposure to stressful life events prior to conception (PSLEs) was associated with experiencing a preterm birth. Second, we sought to identify whether the association between PSLEs and preterm birth was modified by maternal age. Findings from this national study provide critical evidence about preconception predictors of preterm birth and therefore have significant implications for approaches to preconception, interconception, and primary care, as well as for policy and programmatic efforts to improve birth outcomes.
Data were from the first wave of the ECLS-B, a nationally representative cohort of children born in 2001 and their parents. The ECLS-B used a clustered, list frame design to select a nationally representative probability sample of the approximately 4 million children born in 2001, with oversampling of children from minority groups (specifically, Chinese, other Asian/Pacific Islander, American Indian, and Alaska Native), twins, and children born with very low and low birth weights.16 Children born to mothers younger than 15 years, those who were adopted after their birth certificates were issued, and those who did not survive until 9 months of age were excluded from the sampling frame.17 Registered births were sampled within primary sampling units (counties or groups of contiguous counties) from the National Center for Health Statistics vital statistics system. More than 14 000 births were sampled and contacted; from these sampled births, the final study cohort (consisting of completed 9-month interviews) of 10 700 was formed when the children were aged approximately 9 months.
We obtained restricted data for this study by permission and with approval from the Institute for Education Sciences Data Security Office of the US Department of Education, National Center for Education Statistics. In accordance with center guidelines, all reported unweighted sample sizes were rounded to the nearest 50.16
Participants were eligible for this study if the main survey respondent was the infant’s biological mother (n = 10 550); we subsequently excluded an additional 450 records with missing birth certificate data. The ECLS-B included individual records for each child within twin pairs identified through oversampling; for this analysis, we randomly selected 1 twin from each pair to retain in the sample. For other multiples in the sample (i.e., not explicitly recruited as part of the oversampling), only 1 infant from the household was surveyed. Our final sample contained 9350 mother–child dyads.
The first wave of the study occurred when the child was aged approximately 9 months. Data were collected from the infant’s birth certificate, computer-assisted personal interviews, and parental self-administered questionnaires.
We categorized the child’s gestational age at birth (reported on the birth certificate in clinical weeks) as preterm (< 37 weeks gestation) and term (≥ 37 weeks gestation).
We derived the date of conception using information from the birth certificate on the length of gestation and date of birth of the index child. We coded women as having experienced a PSLE if they indicated that 1 or more of the following events occurred prior to conception: death of the respondent’s mother, death of the respondent’s father, death of a previous live-born child, divorce, separation from partner, death of a spouse, or fertility problems.
death of the respondent’s mother,
death of the respondent’s father,
death of a previous live-born child,
separation from partner,
death of a spouse, or
All of these experiences are considered stressful life events or have been operationalized as such in previous research.18–21 Death of a previous live-born child was collected from birth certificate data and was assumed to have occurred prior to conception. To examine this assumption, we tested alternate specifications of our PSLE measure, with death of a child removed and with death of a child included as a pregnancy event. These modifications did not substantially change our findings; therefore, we present the results from the model including death of a child as a PSLE.
We used data from the birth certificate to determine whether women had experienced any of the following pregnancy complications: anemia, diabetes, oligohydramnios or hydramnios, hypertension during pregnancy, eclampsia or preeclampsia, incompetent cervix, Rh sensitization, uterine bleeding, premature rupture of membranes, placental abruption, or placenta previa. Birth certificate data were from the 1989 revision of the US Standard Certificate of Live Birth (see http://www.cdc.gov/nchs/data/techap99.pdf).
We also used data from the birth certificate to determine whether women had previously given birth to a preterm or small for gestational age (SGA) infant and to identify women with chronic conditions, including cardiac disease, lung disease, genital herpes, hemoglobinopathy, chronic hypertension, renal disease, or other medical risk factors. We calculated prepregnancy body mass index (BMI; defined as weight in kilograms divided by height in meters squared) from the respondent’s measured height and self-report of weight prior to pregnancy (< 18.5 kg/m2 [underweight], 18.5–24.9 kg/m2 [normal], 25–29.9 kg/m2 [overweight], ≥ 30 kg/m2 [obese], and unknown).22 In addition, we evaluated timing of initiation of prenatal care (in the first trimester, in the second or third trimester, or did not receive prenatal care), whether the index child was a singleton or multiple birth, and parity (data from the birth certificate, coded as number of prior live births: 0, 1, or ≥ 2). Finally, we coded women as having experienced a stressful life event during pregnancy if they indicated that 1 or more of the following events occurred during their pregnancy: death of the respondent’s mother, death of the respondent’s father, divorce, separation from partner, or death of a spouse.
death of the respondent’s mother,
death of the respondent’s father,
separation from partner, or
death of a spouse.
Maternal sociodemographic factors included race/ethnicity (White [non-Hispanic], Black [non-Hispanic], Asian/Pacific Islander [non-Hispanic], Hispanic, or other race [non-Hispanic]), age (15–19, 20–24, 25–29, 30–34, or ≥ 35 years), marital status at the infant’s birth (married or living with partner; separated, divorced, or widowed; or never married), health insurance coverage during pregnancy (no health insurance, any publicly funded insurance, or private health insurance coverage only), US region of residence (Northeast, Midwest, South, or West),23 and socioeconomic status (SES). We defined SES using a 5-category composite index (quintiles) generated by the National Center for Education Statistics that incorporated: father's or male guardian’s education, mother's or female guardian’s education, father's or male guardian’s occupation, mother's or female guardian’s occupation, and household income.16
father's or male guardian’s education,
mother's or female guardian’s education,
father's or male guardian’s occupation,
mother's or female guardian’s occupation, and
We conducted analyses using survey procedures from SAS version 9.2 (SAS Institute, Cary, NC). We corrected the standard errors for clustering within strata and the primary sampling unit and used applied survey weights to produce estimates that account for the complex survey design, unequal probabilities of selection, and survey nonresponse. All results are based on weighted counts.
We generated summary statistics to describe the sample characteristics and used the χ2 test to determine significant differences in sociodemographic characteristics between women who did and did not experience any PSLE and by infant term status.
We used staged multivariate logistic regression models to examine the impact of exposure to PSLEs on the infant’s term status. Model 1 adjusted for exposure to any stressful life event during pregnancy, maternal chronic conditions, having a prior preterm or SGA baby, prepregnancy BMI, initiation of prenatal care, plurality, parity, maternal age, maternal race/ethnicity, marital status at birth, health insurance coverage, SES, and region of residence; model 2 added pregnancy complications. We estimated adjusted odds ratios (AORs) and 95% confidence intervals (CIs) comparing the term status of infants born to women exposed and not exposed to PSLEs from the multivariate models. In addition, we tested the models with multiples removed from the sample; because this did not influence our findings, we present results from the full sample.
Given the established U-shaped relationship between age and term status,13–15 we initially chose to examine the interaction between continuous maternal age and PSLEs with a quadratic functional form (P = .038). However, we present the relationship with categorical age for ease of interpretation.
Sensitivity analyses examined the effect of exposure to PSLEs on infant term status within 3 nonmutually exclusive time frames: PSLEs that occurred (1) within 1 year prior to conception, (2) 1 year or more prior to conception, and (3) prior to conception without a definite time window.
Of mothers, 19.7% experienced any PSLE (Table 1). Mean length of gestation was 38.8 weeks; 10.9% of women delivered a preterm infant (Table 2). Exposure to PSLEs was more common among women with preterm infants than among women with term infants (preterm, 24.2%; term, 19.2%; P < .01). Compared with mothers of children born at term, mothers of children born preterm were more likely to have experienced a pregnancy complication or a chronic condition or to have had a prior preterm or SGA baby; they were also more likely to deliver multiples, to initiate prenatal care after the first trimester, and to be Black (Non-Hispanic), never married, publicly insured, or of low SES. Mothers who gave birth to a preterm infant were more likely to be adolescents (aged 15–19 years) and less likely to be aged 25 to 29 years than were mothers who gave birth to a term infant.
Type and Timing of Stressful Life Events Prior to Conception: US Early Childhood Longitudinal Study, Birth Cohort, 2001
|Stressful life events prior to conception|
|Any stressful life event prior to conception||19.7|
|≥ 3 events||0.2|
|Type of stressful life events prior to conception|
|Experienced fertility problems|
|Death of mother prior to conception|
|Death of father prior to conception|
|Death of a child|
|Divorced prior to conception|
|Separated prior to conception|
|Widowed prior to conception|
|Timing of stressful life events prior to conception|
|< 1 y prior to conceptionb|
|≥ 1 y prior to conceptionb|
Note. Data are weighted percentages. National Center for Education Statistics rounding rules applied to unweighted numbers. Weighted total: n = 3 774 441; unweighted total: n = 9350.
aStressful life events for which an exact event date was not available, including fertility problems or death of a child.
bStressful life events for which an exact event date was available, including death of mother or father, divorce, marital separation or widowed.
Descriptive Statistics by Maternal Stressful Life Events Prior to Conception and Preterm Birth Status: US Early Childhood Longitudinal Study, Birth Cohort, 2001
|Stressful Life Events Prior to Conception||Birth Status|
|Total weighted no. (%)||3 774 441 (100.0)||3 029 554 (80.3)||744 887 (19.7)||412 176 (10.9)||3 362 265 (89.1)|
|Total unweighted no.||9350||7350||1950||2250||7100|
|Weeks gestation, mean (SD), median||38.80 (2.45), 38.52||38.86 (2.38), 38.56||38.56 (2.70), 38.36||≤ .001||33.94 (4.08), 34.44||39.40 (1.47), 38.74||≤ .001|
|Stress and obstetric factors|
|Stressful life events prior to conception||.003|
|Mean (SD)||0.22 (0.48)||0.00 (…)||1.13 (0.38)||≤ .001||0.28 (0.80)||0.22 (0.44)||.003|
|Stressful life events during pregnancy, %||.001||.761|
|Pregnancy complications, %||.026||≤ .001|
|Maternal chronic conditions, %||.098||≤ .001|
|Prior child born preterm or SGA, %||≤ .001||≤ .001|
|Prepregnancy BMI, kg/m2, %||.369||.038|
|Initiation of prenatal care, %||.246||.011|
|In the first trimester||95.5||95.5||95.8||93.5||95.8||*|
|In the second or third trimester||4.2||4.3||3.7||5.8||4.0||*|
|Did not receive prenatal care||0.3||0.3||0.5||0.6||0.3||*|
|No. of children born, %||≤ .001||≤ .001|
|Parity,a %||≤ .001||.068|
|Maternal sociodemographic factors|
|Age, y, %||≤ .001||.02|
|Race/ethnicity, %||.001||≤ .001|
|Asian/Pacific Islander (non-Hispanic)||3.5||3.6||3.0||*||3.0||3.5|
|Marital status at delivery, %||≤ .001||≤ .001|
|Married or living with partner||83.4||82.9||85.5||*||78.0||84.1||***|
|Separated, divorced, widowed||3.1||2.5||5.3||***||3.9||2.9|
|Health insurance status, %||.008||≤ .001|
|Socioeconomic status, %||.001||≤ .001|
|First quintile (lowest)||19.7||20.6||15.8||***||24.7||19.1||***|
|Fifth quintile (highest)||20.1||20.2||19.7||15.6||20.6||***|
|Region of residence, %||.208||.520|
Note. BMI = body mass index; SGA = small for gestational age. Preterm defined as < 37-wk gestation; term defined as ≥ 37-wk gestation. National Center for Educational Statistics rounding rules applied to unweighted numbers; unweighted subgroup numbers may not add to the total because of rounding error.
aParity of the mother not including her most recent live birth.
*P < .05; **P < .01; ***P < .001.
Adjusted analyses revealed a statistically significant interaction between any PSLEs and continuous age (P = .038; data not shown). When we examined this interaction categorically, we found that the effect of PSLEs on preterm birth was strongest for women aged 15 to 19 years (AOR = 4.32; 95% CI = 1.48, 12.61), and this effect diminished as women increased in age (Figure 1; Table 3). Women aged 35 years and older had higher odds of delivering a preterm infant than did women aged 25 to 29 years, regardless of their exposure to PSLEs (Table 3). Pregnancy complications were significantly and independently associated with increased odds of preterm birth (AOR = 2.33; 95% CI = 1.93, 2.82). In the fully adjusted model, having a prior child born preterm or SGA, delivering multiples, Black (non-Hispanic) race, Hispanic ethnicity, and low SES were significantly associated with increased odds of having a preterm birth (Appendix A, available as a supplement to the online version of this article at http://www.ajph.org).
Staged Multivariable Logistic Regression Models Predicting Preterm Birth: US Early Childhood Longitudinal Study, Birth Cohort, 2001
|Adjusted Odds of Preterm Birtha (< 37 Wk Gestation)|
|Variable||Model 1, AOR (95% CI)||Model 2, AOR (95% CI)|
|No stressful life events prior to conception|
|15–19 y||1.15 (0.82, 1.61)||1.12 (0.79, 1.60)|
|20–24 y||1.08 (0.86, 1.36)||1.07 (0.85, 1.34)|
|25–29 y (Ref)||1.00||1.00|
|30–34 y||1.28 (0.97, 1.69)||1.24 (0.94, 1.65)|
|≥ 35 y||1.66 (1.23, 2.23)||1.59 (1.18, 2.14)|
|Any stressful life events prior to conception|
|15–19 y||3.83 (1.29, 11.36)||4.32 (1.48, 12.61)|
|20–24 y||1.45 (0.98, 2.14)||1.43 (0.97, 2.11)|
|25–29 y||1.29 (0.87, 1.92)||1.24 (0.82, 1.87)|
|30–34 y||1.35 (0.95, 1.92)||1.30 (0.91, 1.85)|
|≥ 35 y||1.59 (1.08, 2.34)||1.49 (1.02, 2.18)|
|Stressful life events during pregnancy|
|Any||0.79 (0.53, 1.17)||0.77 (0.52, 1.16)|
|Any||2.33 (1.93, 2.82)|
Note. AOR = adjusted odds ratio; CI = confidence interval. Models also control for maternal chronic conditions, having a prior preterm or small-for-gestational-age baby, prepregnancy body mass index, initiation of prenatal care, plurality, parity, maternal race/ethnicity, marital status, health insurance coverage, socioeconomic status, and region of residence. Full regression models are available in the Appendix (available as a supplement to the online version of this article at http://www.ajph.org). All models account for complex sampling design of the Early Childhood Longitudinal Study, Birth Cohort.
When we examined the timing of women’s exposure to PSLEs, we found that the interaction between PSLEs and younger age (15–19 years) was observed for events occurring within 1 year prior to conception (AOR = 10.97; 95% CI = 1.93, 62.21; data not shown). Women who were exposed to any PSLEs 1 year or more prior to conception and were aged 20 to 24 or 30 years or older also had greater risk of preterm birth than did women aged 25 to 29 years without such an event (for women aged 20–24 years: AOR = 1.83; 95% CI = 1.00, 3.34; for women aged 30–34 years: AOR = 1.44; 95% CI = 0.98, 2.12; for women aged ≥ 35 years: AOR = 1.62; 95% CI = 1.08, 2.43; data not shown).
As the first population-based study to our knowledge to investigate the relationship between exposure to PSLEs and preterm birth in the United States, this study contributes 3 significant findings to the literature. First, our findings suggest that the magnitude of the effect of PSLEs on preterm birth varies by maternal age. This effect heterogeneity may partially explain the established U-shaped relationship between maternal age and preterm birth. Second, the findings indicate that the association between PSLEs and preterm birth was strongest among women aged 15 to 19 years, suggesting that adolescence may be a sensitive period for the risk of preterm births for adolescents who have been exposed to PSLEs and highlighting a potentially important period for interventions aimed at reducing preterm birth. Finally, we found that racial and ethnic disparities in preterm birth persisted even after accounting for PSLEs and a host of covariates, informing future research regarding racial/ethnic disparities in preterm birth. Overall, these novel findings have significant implications for research, policy, and practice surrounding the risk of preterm birth in the United States.
Our findings are consistent with previous research showing a U-shaped relationship between maternal age and risk of preterm birth such that adolescents and older women have a higher risk.13–15 Though this relationship is well established, the pathways by which it occurs remain equivocal.24 Other studies have found that stressful life events during and before pregnancy have been associated with preterm birth.25,26 However, to our knowledge, we are the first to investigate whether the relationship between exposure to PSLEs and preterm birth varied by maternal age. More important, in the adjusted model we did not find evidence to support the persistence of a U-shaped relationship between maternal age and risk of preterm birth in the absence of PSLEs; instead, only women older than 35 years displayed higher odds of preterm birth, compared with women aged 25–29 years. We did, however, observe the U-shaped relationship among women with any exposure to PSLEs. Because previous studies adjusting for various sociodemographic factors have failed to explain the mechanisms leading to this U shape, we suggest that adolescent exposure to PSLEs may account for a large portion of the established increased risk of preterm birth among adolescent mothers.
On a molecular level, younger women may be more vulnerable to the pathophysiological repercussions of exposure to severe PSLEs because the adolescent brain has been shown to have a heightened response to stress. Specifically, adolescents tend to experience an overactivation of their stress hormone receptors, such as dopamine, serotonin, and adrenaline.27,28 Additionally, on a macro level, expectations and available personal resources may partly explain why younger mothers are more susceptible to PSLEs. Because death of a parent was the most frequently experienced PSLE for women aged 15 to 19 years, such an unexpected event may be particularly traumatic for younger women,29 whereas the increased likelihood, and thus anticipation, of parental death as women age may lessen the negative psychopathological toll of such an event. Moreover, because adolescent mothers may be more emotionally and financially reliant on their parents30 or partners (separation from a partner was the next most common event among these women) than older women, losing a parent or partner may substantially lessen their available resources, thereby encumbering their ability to cope with traumatic experiences and intensifying the adverse reaction to such stressors.
Interestingly, the effect of PSLEs among women aged 15 to 19 years was substantially heightened when the exposure occurred within 1 year prior to conception; women aged 20 to 24 years who were exposed to PSLEs a year or more prior to conception also had increased odds of preterm birth. Taken together with literature on adverse childhood events and childhood disadvantage,31,32 these results suggest that adolescence and early adulthood may be a particularly sensitive period that has implications for reproductive health. Although based on a small number of exposed cases (e.g., women aged 15 to 19 years with a PSLE in the year prior to conception), these results provide compelling evidence of the potential importance of PSLEs, and the timing of PSLEs, among adolescent and young adult women. It is encouraging that, because the magnitude of this effect was less for older women, over time women may be able to rebound from negative events experienced in adolescence. More research is warranted that adopts a life course approach and investigates adolescence as a sensitive period for the effect of PSLEs on the occurrence of preterm birth. Such future research is necessary to pinpoint new avenues for public health efforts to improve birth outcomes.
Although we found that PSLEs were associated with an increased risk of preterm birth among adolescent mothers, this association did not explain existing racial disparities in preterm birth. Racial disparities in preterm birth rates in the United States have been well documented, with Black women experiencing preterm birth at much higher rates than White women.1 Some studies have suggested that stress may be a contributor to racial disparities in preterm birth through a “weathering” effect, in which exposure to chronic and acute stress among Black women across the life course is posited to lead to more rapid biological aging.14,25,33 Among the nationally representative sample used in this study, though, race remained an important risk factor for preterm birth even after controlling for stressful life events prior to and during pregnancy. This finding should, however, be interpreted cautiously. Specifically, the ECLS-B collected limited data about stressful life events, and the included events may be less salient for Black and other minority mothers who may be less likely to be married and more likely to experience poverty-related stressors. A more culturally appropriate measure of PSLEs may yield different results, and future work is needed to replicate our findings.
Several potential limitations should be considered when interpreting our results. First, children who died before 9 months of age were not eligible to participate in the ECLS-B. Our study, therefore, likely excluded children with the worst birth outcomes (e.g., stillbirth, neonatal death), a potential survival bias leading to conservative estimates of the effect of PSLEs on preterm birth. Second, birth certificate data may underreport or incorrectly report some information (e.g., pregnancy complications).1 However, underreporting these data would bias our results toward the null, leading to conservative estimates. Similarly, we relied on self-reported data for factors such as prepregnancy BMI, which may have biased our estimates in an unknown direction. The ECLS-B collected limited data on stressful life events; failing to capture additional events may have resulted in misclassification. Moreover, the number of individuals who endorsed specific events was small; therefore, we were not able to conduct analyses examining the independent effect of each type of event (e.g., death of a spouse or partner) on preterm birth. Finally, our operationalization of PSLEs may not have comprehensively captured the spectrum of stressors that some women experience.34–38
The results from this and other studies investigating the role of PSLEs for obstetric outcomes26,39 advocate for increased attention to more upstream factors, such as stress and stressors, to combat downstream outcomes such as preterm birth. Moreover, treating stress and stressors as public health problems in and of themselves may be an effective way to prevent multiple adverse obstetric outcomes simultaneously. Similarly, because stress has been implicated as a risk factor for many other public health concerns, including cardiovascular disease40 and all-cause mortality,41 providing individuals with the resources to cope with stress may help prevent multiple poor health outcomes across the life course.
More importantly, our findings have salient implications for clinical practice. First, clinical interventions designed to improve birth outcomes may be most effective if administered before pregnancy. The preconception period is increasingly being acknowledged as an important area for women’s health, and strategies to reduce general mental distress and improve health behaviors, for example, have been recognized as important avenues for women’s preconception health care.42 Future research should test whether screening and counseling women of reproductive age about stress is associated with reductions in preterm birth. Second, findings from this study also emphasize the importance of providing care to pregnant adolescents, especially those who have experienced a PSLE. Along these lines, pediatricians and obstetricians–gynecologists should strive to administer preconception care to women across the life course in an effort to improve obstetric outcomes. For these recommendations to be fully realized, though, changes are needed across multiple domains in clinical practice, public health support, and health care coverage to provide appropriate services for women and adolescents and to overcome barriers to preconception care delivery (e.g., fragmented provision of services, lack of available treatment services, inadequate reimbursement of risk assessment, and health promotion services).43 Future work is needed to examine the effectiveness of preconception women’s health services on improving birth outcomes and subsequent child health outcomes over the life course.
In this national, population-based study, adolsecents who were exposed to any PSLE had over a 4-fold increased risk for having a preterm birth. This finding suggests that adolescence may be a sensitive period for the risk of preterm birth for adolescents who have been exposed to PSLEs. Although much remains to be learned about the mechanisms underlying this relationship, our results indicate that clinical, programmatic, and policy interventions may need to address upstream stressful life events prior to conception, especially for adolescents, to reduce the prevalence of preterm birth and improve maternal and child health.
This work was supported in whole or in part by federal funds from the US Department of Health and Human Services, Health Resources and Services Administration (grant R40MC23625; PI, W. P. W.). Additional funding for this research was provided by a grant from the Health Disparities Research Scholars Program to F. Wakeel (T32HD049302; PI, G. S.).
We also thank the anonymous reviewers for their helpful comments and suggestions.
Human Participant Protection
The University of Wisconsin–Madison Health Sciences institutional review board considered this study exempt from review.