Archives of Women's Mental Health

, Volume 16, Issue 6, pp 435–451

The balance between stress and personal capital during pregnancy and the relationship with adverse obstetric outcomes: findings from the 2007 Los Angeles Mommy and Baby (LAMB) study

Authors

    • Department of Obstetrics and Gynecology, School of Medicine and Public HealthUniversity of Wisconsin—Madison
    • Department of Population Health Sciences, School of Medicine and Public HealthUniversity of Wisconsin—Madison
  • Lauren E. Wisk
    • Department of Population Health Sciences, School of Medicine and Public HealthUniversity of Wisconsin—Madison
  • Rebekah Gee
    • Department of Health Policy and Systems Management, School of Public HealthLouisiana State University Health Sciences Center
  • Shin M. Chao
    • Child and Adolescent Health ProgramsLos Angeles County Department of Maternal
  • Whitney P. Witt
    • Department of Population Health Sciences, School of Medicine and Public HealthUniversity of Wisconsin—Madison
Original Article

DOI: 10.1007/s00737-013-0367-6

Cite this article as:
Wakeel, F., Wisk, L.E., Gee, R. et al. Arch Womens Ment Health (2013) 16: 435. doi:10.1007/s00737-013-0367-6

Abstract

Stress during pregnancy is a salient risk factor for adverse obstetric outcomes. Personal capital during pregnancy, defined as internal and social resources that help women cope with or decrease their exposure to stress, may reduce the risk of poor obstetric outcomes. Using data from the 2007 Los Angeles Mommy and Baby study (N = 3,353), we examined the relationships between the balance of stress and personal capital during pregnancy, or the stress-to-capital ratio (SCR), and adverse obstetric outcomes (i.e., pregnancy complications, preterm birth (PTB), low birth weight (LBW), and small for gestational age (SGA)). Women with a higher SCR (i.e., greater stress relative to personal capital during pregnancy) were significantly more likely to experience at least one pregnancy complication, PTB, and lower gestational age, but not LBW or SGA. Accounting for pregnancy complications completely mediated the association between the SCR and PTB. Our findings indicate that experiencing greater stress relative to personal capital during pregnancy is associated with an increased risk for pregnancy complications, PTB, and lower gestational age and that pregnancy complications may be a mechanism by which the SCR is related to adverse obstetric outcomes.

Keywords

Personal capitalMaternal stressObstetric outcomesPreterm birthPregnancy complications

Introduction

In the USA, adverse obstetric outcomes, including pregnancy complications, preterm birth (PTB), low birth weight (LBW), and small for gestational age (SGA), account for over 40 % of all neonatal deaths (Heron 2012) and have contributed to significant increases in childhood morbidity in recent decades (Hack et al. 1995; Kramer 2003; McCormick 1985). Mounting evidence suggests that stress during pregnancy is a salient risk factor for poor obstetric outcomes (Dole et al. 2003; Rondo et al. 2003). However, studies have largely employed uni-dimensional stress measures, such as perceived stress or stressful life events (Sable and Wilkinson 2000; Zhu et al. 2010). Furthermore, very few studies have examined the components of personal capital during pregnancy, or the resources that may help women cope with or decrease their exposure to stress during pregnancy (Wakeel et al. 2013), and none has explored personal capital as a composite construct that is related to obstetric outcomes. Importantly, no study has examined the relationship between stress, personal capital, and obstetric outcomes. Therefore, we created and employed a conceptually based ratio of stress and personal capital, or the stress-to-capital ratio (SCR), to determine if and how the balance between these two factors during pregnancy was associated with obstetric outcomes.

Theory suggests that stress is a process by which environmental stimuli (e.g., stressful life events) may overwhelm an individual’s coping resources (Cohen and Williamson 1987), leading to the perception of stress and subsequent physiological responses that attempt to gain homeostasis (McEwen 1998). Physiological responses to stress that have been shown to directly impact obstetric outcomes primarily entail the neuroendocrine and immune/inflammatory processes (Coussons-Read et al. 2005, 2007; Dunkel Schetter 2011; Gennaro and Hennessy 2003; Steer 2005; Wadhwa et al. 1998, 2001, 2004). Additionally, the two pathways most likely interact with each other in that the combination of chronic stress and infectious pathogens has an additive impact, resulting in shortened gestational length (Wadhwa et al. 2001).

Personal capital is a multidimensional set of resources that may help women cope with or reduce their exposure to stress during pregnancy (Wakeel et al. 2013), yet as defined, it has been under-explored in the literature and has not been tied to obstetric outcomes. We posit that personal capital may operate on multiple levels (Bronfenbrenner 1977, 1979) to increase the likelihood of favorable obstetric outcomes. At the individual level, internal resources (i.e., self-esteem and mastery) have been associated with higher infant birth weight (Rini et al. 1999) and decreased risk of intrauterine growth retardation (Goldenberg et al. 1991). At the interpersonal level, social resources, including partner support and social network support, have been linked to increased fetal growth, better labor progress, higher birth weight, and higher infant APGAR scores (Collin et al. 1993; Feldman et al. 2000). At the community level, neighborhood support may play an indirect role in lowering women’s risk of adverse obstetric outcomes (Bell et al. 2006). As previous studies have provided evidence of the independent effects of personal capital components on obstetric outcomes, we expand this work by incorporating these critical resources into a unified construct to comprehensively capture the available resources that women may garner during pregnancy.

Although stress and components of personal capital have been shown to be associated with obstetric outcomes, there is a paucity of research examining the relationships among stress, personal capital, and outcomes. To our knowledge, our study is the first to examine the balance of stress and personal capital during pregnancy in relation to subsequent obstetric outcomes. Our research draws upon the framework of perinatal health of Misra et al. (2003), which integrates the life-course model (Halfon and Hochstein 2002) with the multiple determinants of health theory (Evans and Stoddart 2003), as well as stress theory (House 1974). The life-course model posits that perinatal health is influenced by cumulative effects of events across the lifespan as well as intergenerational effects, while the multiple determinants of heath theory proposes that a combination of interrelated biological, psychological, behavioral, social, and environmental factors contributes to health outcomes. Additionally, stress theory (Lazarus and Folkman 1984) contends that both maternal risks (e.g., stress) and resources (e.g., personal capital) likely influence obstetric outcomes and that greater risks relative to resources may lead to deleterious obstetric outcomes. Overall, we posit that stress and personal capital likely accumulate over the life course from multiple sources and modify each other in impacting obstetric outcomes. Consequently, we operationalized the relationship between stress and personal capital as a ratio that captures the continuum of the relative levels of these two factors during pregnancy, rather than examining the interaction between stress and personal capital, which ignores stress relative to personal capital and instead only captures concurrent increases or decreases in these two constructs. Therefore, the aims of this paper were to: (1) create a SCR which empirically represents the level of balance between stress and personal capital during pregnancy and (2) examine the relationships between the SCR and the risk of adverse obstetric outcomes, specifically pregnancy complications, PTB, LBW, and SGA.

Materials and methods

Sample

Data are from 3,353 women who participated in the 2007 Los Angeles Mommy and Baby (LAMB) study, a cross-sectional, population-based study that examined preconception, prenatal, and postpartum correlates of adverse maternal and child health outcomes. Eligible mothers were Los Angeles County residents who had given birth to a live-born infant in Los Angeles County in 2007.

The 2007 LAMB study was a collaboration between the University of California, Los Angeles (UCLA) and the Los Angeles County Department of Maternal, Child and Adolescent Health. They employed a stratified random sample, selected by sampling neighborhoods based on census tracts and then sampling births within the neighborhoods, resulting in a sample of 4,518 women. The response rate for the Los Angeles County sample was 56 %, after adjusting for faulty addresses, language issues, maternal deaths, and loss to follow-up due to inability to locate the respondent.

Respondents with missing values for personal capital (n = 762), stress (n = 467), and prenatal depressive symptoms (n = 113) variables were removed by listwise deletion, resulting in a sample size of 3,411 women. Furthermore, as 99 of these mothers had gestational age data that were either missing (n = 90) or deemed illogical (n = 9) (i.e., values <100 or >365 days), we imputed for this variable based on estimated dates of conception and infant birth data. We used only estimated gestational age values that were logical (100–365 days), resulting in a final sample size of 3,353 women for the present analyses.

We compared the sociodemographic characteristics and obstetric outcomes of women with missing data (n = 985) and women with complete data (n = 3,353). Women with missing data were significantly more likely to be Hispanic, unmarried, low income, or less educated; there were no significant differences in obstetric outcomes among the two groups.

Procedures

In 2007, over 10,000 surveys were mailed to eligible women within 6 months after delivery. The recruitment process entailed a(n): (1) mailed preletter 4 months after childbirth, (2) initial mailed questionnaire 1 week later, (3) mailed reminder 1 week later, (4) second mailed questionnaire 2 weeks later (if needed), and (5) telephone follow-up 2 weeks later (if needed). Finally, the data from the completed surveys were linked to corresponding California birth certificates prior to analyses. The survey was approved in 2007 by both the Los Angeles County Department of Public Health and UCLA Institutional Review Boards and therefore has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Measures

Outcome variables

Poor obstetric outcomes included pregnancy complications, PTB, LBW, and SGA. Pregnancy complications, which included pregnancy-induced hypertension, gestational diabetes, premature labor (i.e., labor that began <37 weeks gestation), premature rupture of membranes (i.e., membrane rupture occurring <37 weeks gestation), bacterial vaginosis, and kidney or bladder infection during pregnancy, were dichotomized as either experiencing any versus no complication. Three gestational age categories were used, including PTB (i.e., delivery occurring before 259 days of gestation), early-term birth (i.e., delivery occurring between 259 and 272 days), and late-term birth (i.e., delivery occurring at or after 273 days). Preterm and early-term births were compared with late-term births. Infants weighing less than 2,500 g at birth were considered to be LBW and were compared with normal-weight infants (i.e., weighing 2,500–4,000 g). Using the gender-specific growth curves of Olsen et al. (2010), we defined SGA infants as those weighing less than 10 % of the birth weight of infants of their gender and gestational age and compared them with appropriate-for-age infants (i.e., weighing above the 10th percentile for their age and gender). Infant birth weight, gestational age, and gender were obtained from birth records; pregnancy complications were self-reported in the LAMB survey.

Stress and personal capital measures

Stress was operationalized as perceived stress and severe stressful life events during pregnancy (“Appendix”). Perceived stress items (e.g., feeling sad) assessed psychological stress during pregnancy and inquired about the degree to which the respondent appraised her life as stressful (Cohen et al. 1983). Severe stressful life events measured environmental stress and included events (e.g., death of a loved one) that have been shown to impact obstetric outcomes (Newton and Hunt 1984).

Personal capital was operationalized as internal resources, partner support, social network support, and neighborhood support during pregnancy (“Appendix”). Internal resources included self-esteem and mastery. Self-esteem (e.g., overall satisfaction with self) was defined as a “positive or negative orientation toward oneself or an overall evaluation of one’s worth or value” (Jesse et al. 2006, p. 70). Mastery (e.g., being able to do anything she sets her mind to do) embodied the belief that one has control over her life (Rini et al. 1999). Partner support encompassed instrumental (e.g., financial support) and emotional support from the baby’s father (Fragile Families and Child Wellbeing Study 2003). Social network support included support (e.g., having someone help with childcare) from kin and close friends (Adams et al. 1991). The neighborhood support items concerned neighborhood social cohesion (e.g., being a close-knit neighborhood) and reciprocal exchange (e.g., neighbors visiting each other’s homes) (Buka et al. 2003).

Stress-to-capital ratio

The relationship between stress and personal capital was operationalized as a ratio in order to capture the continuum of women’s relative levels of stress and personal capital. Testing the interaction between stress and personal capital was not performed as an interaction term ignores stress relative to personal capital and instead only captures concurrent increases or decreases in these two constructs. Therefore, we argue that an interaction between these constructs is neither practically relevant nor a sufficient answer to our research question; as such, only the method of the SCR was employed in this study.

Prior to the creation of the SCR, items within the stress and personal capital measures were summed to create raw overall scores for each measure, with higher scores reflecting greater levels of that measure. Based on theory (Fig. 1), weighting schemes were applied to the variables that contributed to the composite measures of stress and personal capital. For the composite stress measure, perceived stress was weighted twice as heavily (67 %) as severe life events (33 %) because while life events may not render the same impact on all individuals, the subjective appraisal of stress arguably has a more direct effect on mental health during pregnancy as well as the behavioral and biological pathways leading to adverse obstetric outcomes (Ayers 2001; Dole et al. 2003; Hedegaard et al. 1996; Lazarus and Folkman 1984, Lobel et al. 1992). Additionally, severe life events are rare shocks, whereas perceived stress is likely to be persistent and chronic (Lobel and Dunkel-Schetter 1990). Furthermore, for the composite personal capital measure, proximal (i.e., internal) resources were weighed more heavily than distal (i.e., social) resources, as they were posited to have a more direct impact on the individual (Cohen and Wills 1985; Hobfoll and Leiberman 1987; Hobfoll 2002). As such, internal resources, partner support, social network support, and neighborhood support comprised 32.5, 27.5, 22.5, and 17.5 %, respectively, of the overall personal capital score. The weighted composite measures for stress and personal capital were constructed such that the scores ranged from 1 to 100.
https://static-content.springer.com/image/art%3A10.1007%2Fs00737-013-0367-6/MediaObjects/737_2013_367_Fig1_HTML.gif
Fig. 1

A conceptual framework for the relationship between the balance of personal capital and stress during pregnancy and adverse obstetric outcomes. The figure illustrates the balance between personal capital and stress during pregnancy. The personal capital score was created using a weighting scheme based on Bronfenbrenner’s ecological model and comprised internal resources, partner support, social network support, and neighborhood support. Proximal resources were weighed more heavily than distal resources as they were posited to have a larger and more direct impact on the individual. As such, internal resources, partner support, social network support, and neighborhood support comprised 32.5, 27.5, 22.5, and 17.5 %, respectively, of the overall score. Further, for the composite stress measure, perceived stress was weighted twice as heavily (67 %) as severe life events (33 %) because while life events may not render the same impact on all individuals, the subjective appraisal of stress arguably has a more direct effect on mental health during pregnancy as well as the behavioral and biological pathways leading to adverse obstetric outcomes. Additionally, severe life events are rare shocks, whereas perceived stress is more likely to be persistent and chronic. Therefore, the figure graphically depicts how each component was weighted and then summed to create the composite measures of personal capital and stress. Finally, the level of balance between personal capital and stress (empirically captured by the stress-to-capital ratio) is hypothesized to be associated with adverse obstetric outcomes

Two sensitivity analyses were performed to examine the assumptions used in the weighting schemes for personal capital and stress. First, 20 weighting schemes (based on five schemes for stress and four schemes for personal capital) were empirically tested by evaluating the predictive ability of each scheme for the outcomes under investigation. This analysis showed that the results did not substantially vary across these weighting schemes. Second, we conducted confirmatory factor analysis (CFA) using Mplus 7 (Muthen and Muthen, Los Angeles, CA, USA). The standardized factor loadings from our CFA, which included personal capital and stress as two latent factors with their respective components, confirmed our chosen weighting schemes for personal capital and stress (data available upon request). Based on the results from these analyses, the weighting scheme with the best predictive ability was chosen as the final scheme.

In order to create the SCR, the finalized weighted composite measures of stress and personal capital were standardized. First, various cumulative distribution functions for the stress and personal capital measures were examined for the best possible fit. The weighted composite score for stress followed a Gamma distribution, and the weighted composite score for personal capital followed a Beta distribution. The probability of each weighted score was calculated based on the appropriate distribution, and this probability was applied to a normal/Gaussian distribution (mean of 50, standard deviation of 10) to determine the standardized score. The standardized stress score was then divided by the standardized personal capital score to derive the raw SCR. Finally, the raw SCR was natural log-transformed in order to ensure a symmetric distribution for the final SCR (Fig. 2). An SCR greater than zero indicated greater stress relative to personal capital during pregnancy.
https://static-content.springer.com/image/art%3A10.1007%2Fs00737-013-0367-6/MediaObjects/737_2013_367_Fig2_HTML.gif
Fig. 2

Distribution of the stress-to-capital ratio (SCR) for 2007 LAMB study respondents. The histogram illustrates the distribution of the SCR scores for the 2007 LAMB study respondents. The blue curve represents a normal distribution fit to the mean and standard deviation of the SCR. The mean SCR for the sample was −0.09, with a standard deviation of 0.45

Control variables

The multivariate analyses controlled for maternal race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian/Pacific Islander, Native American), age (continuous), education (≥12 versus <12 years of schooling), annual household income (≤$19,000, $20,000–39,000, $40,000–59,000, $60,000–99,000, ≥$100,000), marital status (married, single/never married, separated/divorced/widowed, cohabiting), prenatal depressive symptoms (yes versus no), the number of prior children ≤5 years old (continuous), parity (primaparous versus multiparous), and pre-pregnancy health conditions (i.e., hypertension and diabetes) (yes versus no) and depression (yes versus no). Further, to capture contextual neighborhood effects, we adjusted for geographic region of residence within Los Angeles County, or service planning area (SPA; Antelope Valley, San Fernando Valley, San Gabriel Valley, Metro Los Angeles, West, South, East, and South Bay). Data for race/ethnicity, age, education, and SPA were obtained from birth certificates, whereas data for the other control variables were self-reported in the survey.

Analytic approach

Analyses were conducted using SAS 9.3 (SAS Institute Inc, Cary, NC, USA) and were weighted to account for sampling design and survey non-response. First, in order to determine the levels of stress and personal capital within the sample, the means of the raw, weighted, and standardized scores for each stress and personal capital measure as well as the raw and log-transformed final SCRs were calculated; reliability statistics (i.e., Cronbach’s alphas) were also computed for the overall measures and components of stress and personal capital. Second, bivariate comparisons were made between sociodemographic, health, and obstetric characteristics and the SCR. Third, multivariable logistic regression models were developed to examine the relationships between the SCR and pregnancy complications, LBW, and SGA. Multinomial logistic regression models were employed to investigate the relationship between the SCR and the gestational age categories (i.e., PTB versus late-term birth; early-term birth versus late-term birth). Additionally, Sobel tests (Baron and Kenny 1986) were performed to test for the potential mediating role of pregnancy complications in the associations between the SCR and PTB, LBW, and SGA. Finally, we conducted multivariable linear regression to examine the relationships between the SCR and gestational age and birth weight as continuous outcomes.

Results

Distribution of stress, personal capital, and stress-to-capital ratio scores

Overall, the composite stress and personal capital measures and the stressful life events measure had low reliability, whereas all of the personal capital components and the perceived stress measure had high reliability (Cronbach’s alphas ≥0.80) (Table 1). Respondents, on average, had relatively low weighted stress scores (mean = 24) and high weighted personal capital scores (mean = 72) (Table 1). The final SCR ranged from −2.1 to 1.6, with a mean of −0.09 (Fig. 2; Table 1).
Table 1

Distribution of stress, personal capital, and the stress-to-capital ratio scores, 2007 LAMB study

 

Raw (unweighted) score

Weighted score

Standardized score

Cronbach's alpha

Mean (SD)

Range

Mean (SD)

Range

Mean (SD)

Range

Stress score

8.87 (5.29)

0.0–31.0

23.91 (14.03)

1.0–85.9

46.64 (11.81)

9.8–78.8

0.523

  Perceived stress

8.33 (4.91)

0.0–27.0

    

0.849

  Severe stressful life events

0.54 (0.89)

0.0–7.0

    

0.472

Personal capital score

63.01 (14.46)

19.0–98.0

71.85 (13.87)

12.1–98.3

50.00 (10.00)

14.3–81.6

0.477

  Internal resources

20.88 (4.67)

0.0–28.0

    

0.795

  Partner support

5.35 (1.35)

0.0–6.0

    

0.831

  Social network support

18.35 (8.86)

0.0–28.0

    

0.919

  Neighborhood support

18.42 (6.82)

0.0–40.0

    

0.813

Stress-to-Capital Ratio

Raw (untransformed)

Log transformed

Absolute difference

 

Mean (SD)

Range

Mean (SD)

Range

Mean (SD)

Range

 

1.00 (0.43)

0.1–4.9

−0.09 (0.45)

−2.1–1.6

−3.36 (18.69)

−71.8–56.1

 

Descriptive comparison of maternal characteristics by stress-to-capital ratio scores

The majority of respondents were 19–34 years old, Hispanic, multiparous, or had incomes ≤$39,000 or ≥12 years of schooling (Table 2). Over half of the women were married or had no prior children ≤5 years old. Approximately 15 % of women reported pre-pregnancy depression, 20 % of women reported prenatal depressive symptoms, and 5 % of women reported having a pre-pregnancy health condition. Additionally, 44, 13, 8, and 11 % of women had pregnancy complications, PTB, a LBW infant, and a SGA infant, respectively.

Overall, respondents who were ≤18 years old, non-White, unmarried, or low-income had, on average, higher SCR (i.e., greater stress relative to personal capital) scores (Table 2). Women who lived in SPA 1, had at least two prior children ≤5 years old, had lower education, or had experienced pre-pregnancy health conditions or depression, prenatal depressive symptoms, pregnancy complications or PTB also had higher SCR scores.
Table 2

Weighted means of stress-to-capital ratio scores by selected maternal sociodemographic and health characteristics, 2007 LAMB study

 

Stress-to-capital ratio

  Total

Mean/%

Mean

SD

p value

Unweighted n = 3,353

 

−0.09

0.45

 

Weighted n = 246,286

 

−0.07

0.46

 

Sociodemographic factors

  Age

27.9

   

    ≤18

7.4 %

0.07

0.34

***

    19–34

75.8 %

−0.07

0.46

Ref

    ≥35

16.8 %

−0.16

0.49

***

  Race/ethnicity

    Non-Hispanic White

13.1 %

−0.18

0.49

Ref

    Non-Hispanic Black

4.7 %

0.02

0.54

***

    Hispanic

71.9 %

−0.06

0.44

***

    Asian/Pacific Islander

9.6 %

−0.10

0.44

*

    Native American

0.7 %

−0.04

0.52

NS

  Annual household income

    <$20,000

42.8 %

0.05

0.42

***

    $20,000–39,000

23.0 %

−0.12

0.45

**

    $40,000–59,000

9.3 %

−0.12

0.42

*

    $60,000–99,000

10.4 %

−0.21

0.44

Ref

    ≥$100,000

9.4 %

−0.30

0.48

**

    Unknown

5.1 %

−0.06

0.51

***

  Education

    ≤12 years (no HS diploma/GED)

33.0 %

−0.04

0.42

*

    ≥HS diploma/GED

65.0 %

−0.09

0.47

Ref

    Unknown

2.0 %

−0.06

0.42

NS

  Marital status

    Married

54.5 %

−0.19

0.47

Ref

    Separated/divorced/widowed

2.9 %

0.37

0.40

***

    Single/never married

11.2 %

0.23

0.43

***

    Cohabiting

30.6 %

−0.01

0.38

***

    Unknown

0.8 %

0.05

0.47

**

  Service planning area

    1 (Antelope Valley)

2.5 %

0.08

0.56

***

    2 (San Fernando Valley)

21.0 %

−0.13

0.47

NS

    3 (San Gabriel Valley)

18.8 %

−0.07

0.44

NS

    4 (Metro Los Angeles)

9.9 %

−0.07

0.46

NS

    5 (West)

2.5 %

−0.16

0.58

Ref

    6 (South)

15.0 %

−0.02

0.42

**

    7 (East)

14.0 %

−0.05

0.43

*

    8 (South Bay)

16.3 %

−0.09

0.48

NS

Health factors

  Pre-pregnancy depression

   

***

    Yes

14.9 %

0.26

0.39

 

    No

85.1 %

−0.13

0.45

 

  Pre-pregnancy health conditions (hypertension or diabetes)

***

    Yes

5.1 %

0.05

0.40

 

    No

94.9 %

−0.08

0.47

 

  Prenatal depressive symptoms

   

***

    Yes

20.0 %

0.30

0.34

 

    No

80.0 %

−0.17

0.44

 

Other control variables

  Parity

   

NS

    Primaparous (no prior births)

37.7 %

−0.08

0.45

 

    Multiparous (≥1 prior birth)

62.3 %

−0.07

0.47

 

  Number of prior children under age 5

    0

58.8 %

−0.08

0.46

Ref

    1

35.1 %

−0.08

0.47

NS

    ≥2

6.1 %

0.04

0.47

***

Obstetric outcomes

  Pregnancy complications

   

***

    Any (≥1)

44.3 %

−0.02

0.45

 

    None (0)

55.7 %

−0.12

0.47

 

  Gestational age

272.8 days

   

    Preterm (<259 days)

12.7 %

0.00

0.48

***

    Early term (259–272 days)

29.4 %

−0.07

0.45

NS

    Late term (>273 days)

57.9 %

−0.09

0.47

Ref

  Birth weight

3,284.9 g

   

    Low (<2,500 g)

7.5 %

−0.05

0.51

NS

    Normal (2,500–4,000 g)

84.1 %

−0.08

0.46

Ref

    High (<4,000 g)

8.3 %

−0.06

0.41

NS

  Small for gestational age

   

NS

    Yes

10.5 %

−0.07

0.48

 

    No

89.5 %

−0.07

0.46

 

Weighted means and percentages accounted for sampling design and survey non-response.

Ref reference category, NS not significant

(p ≥ 0.05); *p < 0.05; **p < 0.01; ***p < 0.001

Pregnancy complications

Women who experienced at least one pregnancy complication had significantly higher mean SCR scores than women who did not experience any complications (Table 3). Black, single/never married women, as well as women with unknown educational attainment, prenatal depressive symptoms, or pre-pregnancy health conditions or depression were also significantly more likely to report at least one pregnancy complication (Table 3).
Table 3

Characteristics of 2007 LAMB study respondents by pregnancy complications and multivariate analysis of the odds of any complications

 

Total

Any complications

No complications

Any complication (vs. none)

AOR

95 % CI

Mean/%

Mean/%

Total

  Weighted

246,286

109,218

137,068

  

  Weighted %

100 %

44.3 %

55.7 %

  

Total

  Unweighted

3,353

1,497

1,856

  

  Unweighted %

100 %

44.6 %

55.4 %

  

Stress-to-capital ratio***

 

−0.02

−0.12

1.35

1.10, 1.65

Sociodemographic characteristics

  Age

 

27.9

27.9

1.01

0.99, 1.03

  Race/ethnicity***

    Non-Hispanic White**

 

11.3 %

14.5 %

1.00

Reference

    Non-Hispanic Black**

 

5.8 %

3.8 %

1.79

1.24, 2.58

    Hispanic

 

73.3 %

70.8 %

1.22

0.97, 1.53

    Asian/Pacific Islander

 

8.8 %

10.3 %

1.05

0.80, 1.39

    Native American

 

0.8 %

0.6 %

1.01

0.45, 2.25

  Annual household income*

    <$20,000

 

44.5 %

41.5 %

1.23

0.93, 1.63

    $20,000–39,000

 

23.4 %

22.7 %

1.28

0.97, 1.68

    $40,000–59,000

 

9.5 %

9.2 %

1.28

0.94, 1.73

    $60,000–99,000*

 

9.1 %

11.4 %

1.00

Reference

    >$100,000**

 

8.0 %

10.6 %

0.99

0.74, 1.33

   Unknown

 

5.5 %

4.6 %

1.45

0.92, 2.26

  Education*

    ≤12 years (no HS diploma/GED)

 

33.0 %

33.0 %

0.93

0.76, 1.14

    ≥HS diploma/GED

 

64.1 %

65.7 %

1.00

Reference

    Unknown**

 

2.9 %

1.3 %

2.19

1.21, 3.95

  Marital status*

    Married*

 

52.2 %

56.4 %

1.00

Reference

    Separated/divorced/widowed

 

3.4 %

2.5 %

1.09

0.64, 1.85

    Single/never married*

 

12.9 %

9.9 %

1.06

0.80, 1.41

    Cohabiting

 

30.8 %

30.4 %

0.96

0.78, 1.17

    Unknown

 

0.7 %

0.9 %

0.76

0.30, 1.93

  Service planning area

    1 (Antelope Valley)

 

3.0 %

2.2 %

1.39

0.83, 2.35

    2 (San Fernando Valley)

 

21.6 %

20.5 %

1.29

0.88, 1.90

    3 (San Gabriel Valley)

 

18.0 %

19.4 %

1.10

0.74, 1.64

    4 (Metro Los Angeles)

 

9.3 %

10.3 %

1.04

0.68, 1.61

    5 (West)

 

2.1 %

2.8 %

1.00

Reference

    6 (South)

 

14.8 %

15.2 %

1.04

0.68, 1.60

    7 (East)

 

13.5 %

14.4 %

1.10

0.73, 1.66

    8 (South Bay)

 

17.7 %

15.2 %

1.31

0.88, 1.94

Other control variables

  Pre-pregnancy depression***

    Yes

 

18.0 %

12.5 %

1.19

0.94, 1.52

    No

 

82.0 %

87.5 %

1.00

Reference

  Pre-pregnancy health conditions

   (hypertension or diabetes)***

    Yes

 

9.3 %

1.9 %

4.76

3.13, 7.30

    No

 

90.7 %

98.2 %

1.00

Reference

  Prenatal depressive symptoms***

    Yes

 

24.1 %

16.8 %

1.23

0.98, 1.54

    No

 

75.9 %

83.2 %

1.00

Reference

  Parity

    Primaparous (no prior births)

 

37.5 %

37.9 %

0.96

0.77, 1.20

    Multiparous (≥1 prior birth)

 

62.5 %

62.1 %

1.00

Reference

  Number of prior children under age 5

 

0.46

0.49

0.90

0.76, 1.06

Data are weighted to account for sampling design and survey non-response. The multivariate model included all variables listed in the table.

*p < 0.05; **p < 0.01; ***p < 0.001 (for univariate analysis)

Multivariate analysis indicated that women with higher SCR’s were significantly more likely to experience at least one pregnancy complication (odds ratio (OR) = 1.35; p = 0.004) (Table 3). Additionally, a higher SCR was most significantly associated with hypertension (OR = 1.54; p = 0.03), diabetes (OR = 1.45; p = 0.02), and premature labor (OR = 1.36; p = 0.03) during pregnancy.

Preterm birth and gestational age

Bivariate comparisons indicated that women who experienced PTB had significantly higher mean SCR scores than women who experienced a late-term birth (Table 4). Also, women who were Hispanic, multiparous, had low or unknown income, cohabited with their partners, or experienced prenatal depressive symptoms or pre-pregnancy health conditions or depression were more likely to experience PTB (Table 4). Notably, among women who experienced PTB, 70 % of them also reported at least one pregnancy complication. Furthermore, women who were older, multiparous, or experienced at least one pregnancy complication were more likely to have an early-term birth, as opposed to a late-term birth (Table 4).
Table 4

Characteristics of 2007 LAMB study respondents by infant gestational age and multinomial logistic regression analysis of the odds of preterm birth and early-term birth

 

Total

Preterm birth

Early-term birth

Late-term birth

Preterm birth (vs. late-term birth)

Early-term birth (vs. late-term birth)

Model 1 AOR (95 % CI)

Model 2 AOR (95 % CI)

Model 1 AOR (95 % CI)

Model 2 AOR (95 % CI)

Mean/%

Mean/%

Mean/%

Total

  Weighted

246,286

31,229

72,394

142,663

    

  Weighted %

100 %

12.7 %

29.4 %

57.9 %

    

Total

  Unweighted

3,353

499

956

1,898

    

  Unweighted %

100 %

14.9 %

28.5 %

56.6 %

    

Stress-to-capital ratio

 

0.00***

−0.07

−0.09

1.44 (1.07, 1.93)

1.33 (0.98, 1.80)

1.18 (0.94, 1.47)

1.15 (0.92, 1.44)

Sociodemographic characteristics

  Age

 

27.9

28.4**

27.6

1.02 (1.00, 1.04)

1.02 (1.00, 1.04)

1.01 (1.00, 1.03)

1.01 (1.00, 1.03)

    Race/ethnicity

    Non-Hispanic White

 

9.5 %

12.2 %

14.4 %

1.00 (ref)

1.00 (ref)

1.00(ref)

1.00 (ref)

    Non-Hispanic Black

 

4.5 %

5.1 %

4.5 %

1.26 (0.72, 2.18)

1.07 (0.61, 1.88)

1.43 (0.96, 2.13)

1.36 (0.91, 2.04)

    Hispanic

 

76.9%**

72.1 %

70.7 %

1.41 (0.97, 2.03)

1.35 (0.93, 1.95)

1.23 (0.96, 1.58)

1.22 (0.95, 1.56)

    Asian/Pacific Islander

 

8.1 %

10.0 %

9.7 %

1.19 (0.78, 1.81)

1.20 (0.78, 1.83)

1.18 (0.88, 1.58)

1.17 (0.87, 1.58)

    Native American

 

1.0 %

0.6 %

0.7 %

1.44 (0.52, 3.94)

1.48 (0.53, 4.15)

1.17 (0.46, 2.97)

1.18 (0.47, 2.97)

  Annual household income

    <$20,000

 

45.3%*

42.1 %

42.6 %

1.33 (0.89, 2.00)

1.26 (0.83, 1.90)

0.91 (0.67, 1.25)

0.90 (0.66, 1.23)

    $20,000–39,000

 

26.0%**

23.8 %

22.0 %

1.55 (1.05, 2.30)

1.46 (0.97, 2.18)

1.00 (0.74, 1.35)

0.99 (0.73, 1.33)

    $40,000–59,000

 

8.3 %

9.1 %

9.7 %

1.16 (0.73, 1.84)

1.06 (0.66, 1.70)

0.88 (0.63, 1.24)

0.87 (0.62, 1.22)

    $60,000–99,000

 

7.5 %

11.1 %

10.7 %

1.00 (ref)

1.00 (ref)

1.00 (ref)

1.00 (ref)

    >$100,000

 

6.5 %

9.5 %

10.0 %

1.00 (0.63, 1.60)

1.00 (0.63, 1.59)

0.94 (0.69, 1.29)

0.94 (0.69, 1.29)

    Unknown

 

6.4%*

4.5 %

4.9 %

1.83 (1.01, 3.34)

1.65 (0.89, 3.09)

0.88 (0.53, 1.46)

0.86 (0.52, 1.42)

  Education

    ≤12 years (no HS diploma/GED)

 

33.7 %

33.6 %

32.5 %

0.88 (0.66, 116)

0.89 (0.67, 1.19)

1.03 (0.82, 1.30)

1.04 (0.83, 1.30)

    ≥HS diploma/GED

 

62.9 %

64.7 %

65.6 %

1.00 (ref)

1.00 (ref)

1.00 (ref)

1.00 (ref)

    Unknown

 

3.4 %

1.6 %

1.9 %

1.70 (0.82, 3.55)

1.37 (0.65, 2.90)

0.81 (0.39, 1.69)

0.77 (0.37, 1.59)

  Marital status

    Married

 

49.1 %

56.0 %

55.0 %

1.00 (ref)

1.00 (ref)

1.00 (ref)

1.00 (ref)

    Separated/divorced/widowed

 

3.4 %

3.0 %

2.7 %

1.15 (0.59, 2.24)

1.16 (0.58, 2.32)

1.19 (0.68, 2.08)

1.18 (0.68, 2.08)

    Single/never married

 

12.6 %

9.7 %

11.7 %

1.04 (0.70, 1.55)

1.04 (0.69, 1.56)

0.85 (0.61, 1.18)

0.84 (0.61, 1.18)

    Cohabiting

 

34.7%*

29.8 %

30.1 %

1.18 (0.90, 1.56)

1.22 (0.92, 1.61)

1.02 (0.81, 1.27)

1.02 (0.81, 1.28)

    Unknown

 

0.2 %

1.5%*

0.6 %

0.37 (0.05, 2.82)

0.40 (0.06, 2.95)

2.62 (1.09, 6.31)

2.67 (1.13, 6.29)

  Service planning area

    1 (Antelope Valley)

 

2.8 %

2.7 %

2.4 %

1.36 (0.62, 2.97)

1.23 (0.55, 2.76)

1.33 (0.75, 2.36)

1.30 (0.73, 2.31)

    2 (San Fernando Valley)

 

19.0 %

21.5 %

21.2 %

1.23 (0.67, 2.27)

1.14 (0.61, 2.12)

1.28 (0.84, 1.97)

1.26 (0.82, 1.93)

    3 (San Gabriel Valley)

 

17.6 %

20.7 %

18.1 %

1.31 (0.70, 2.43)

1.26 (0.67, 2.38)

1.43 (0.93, 2.21)

1.42 (0.92, 2.20)

    4 (Metro Los Angeles)

 

10.3 %

9.2 %

10.2 %

1.33 (0.68, 2.58)

1.30 (0.66, 2.58)

1.12 (0.70, 1.81)

1.12 (0.69, 1.81)

    5 (West)

 

1.8 %

2.1 %

2.8 %

1.00 (ref)

1.00 (ref)

1.00 (ref)

1.00 (ref)

    6 (South)

 

15.7 %

14.7 %

15.0 %

1.25 (0.65, 2.41)

1.22 (0.62, 2.39)

1.15 (0.72, 1.85)

1.15 (0.71, 1.85)

    7 (East)

 

15.0 %

11.7 %

15.0 %

1.26 (0.67, 2.38)

1.21 (0.64, 2.32)

0.94 (0.59, 1.49)

0.93 (0.59, 1.48)

    8 (South Bay)

 

17.8 %

17.3 %

15.5 %

1.49 (0.81, 2.78)

1.37 (0.73, 2.58)

1.35 (0.87, 2.10)

1.33 (0.86, 2.07)

Other control variables

  Pre-pregnancy depression

    Yes

 

18.3%*

14.7 %

14.3 %

1.11 (0.80, 1.55)

1.06 (0.75, 1.49)

1.02 (0.78, 1.34)

1.01 (0.77, 1.32)

    No

 

81.7 %

85.3 %

85.7 %

1.00 (ref)

1.00 (ref)

1.00 (ref)

1.00 (ref)

  Pre-pregnancy health conditions (hypertension or diabetes)

    Yes

 

7.1%*

5.4 %

4.6 %

1.35 (0.87, 2.11)

0.93 (0.60, 1.44)

1.13 (0.76, 1.67)

1.00 (0.68, 1.49)

    No

 

92.9 %

94.6 %

95.4 %

1.00 (ref)

1.00 (ref)

1.00 (ref)

1.00 (ref)

  Prenatal depressive symptoms

    Yes

 

24.2%*

20.1 %

19.1 %

1.02 (0.75, 1.40)

0.95 (0.68, 1.33)

0.98 (0.76, 1.26)

0.96 (0.74, 1.24)

    No

 

75.8 %

79.9 %

80.9 %

1.00 (ref)

1.00 (ref)

1.00 (ref)

1.00 (ref)

  Parity

    Primaparous (no prior births)

 

34.3%**

32.3%***

41.2 %

0.87 (0.64, 1.19)

0.87 (0.64, 1.19)

0.71 (0.56, 0.90)

0.71 (0.56, 0.90)

    Multiparous (≥1 prior birth)

 

65.7 %

67.7 %

58.8 %

1.00 (ref)

1.00 (ref)

1.00 (ref)

1.00 (ref)

  Number of prior children under age 5

 

0.52

0.50

0.46

1.08 (0.86, 1.35)

1.12 (0.89, 1.40)

0.95 (0.80, 1.13)

0.96 (0.81, 1.15)

Obstetric outcomes

  Pregnancy complications

    Any

 

70.4%***

46.2%***

37.7 %

 

3.77 (2.96, 4.80)

 

1.40 (1.18, 1.67)

    None

 

29.6 %

53.8 %

62.3 %

 

1.00 (ref)

 

1.00 (ref)

Data are weighted to account for sampling design and survey non-response. Model 1 included all variables listed in the table except pregnancy complications. Model 2 included all variables in the table.

*p < 0.05; **p < 0.01; ***p < 0.001 (for univariate analysis)

Further, the SCR was significantly associated with PTB (OR = 1.36; p = 0.03), but not early-term birth, in the multivariate analysis (Table 4 model 1). Accounting for pregnancy complications completely mediated this association for PTB (Table 4 model 2). Sobel test findings further confirmed that pregnancy complications mediated the association between the SCR and PTB (Z value = −5.82, p < 0.001). We then conducted supplementary logistic regression analyses to individually account for each pregnancy complication and found that premature labor appeared to be the largest contributor to the mediating effect of pregnancy complications on the risk of PTB (data not shown). Furthermore, using multivariable linear regression to examine the association between the SCR and gestational age, we found that a one-unit increase in the SCR was associated with a decrease of 1.8 gestational days, even after accounting for pregnancy complications (Table 5 model 2).
Table 5

Multivariate linear regression analysis of infant gestational age among 2007 LAMB study respondents

 

Model 1

Model 2

Coefficient

95 % CI

Coefficient

95 % CI

Stress-to-capital ratio

−2.21**

−3.84, −0.59

−1.78*

−3.37, −0.20

Sociodemographic characteristics

  Age

−0.20***

−0.32, −0.08

−0.19**

−0.31, −0.07

  Race/ethnicity

    Non-Hispanic White

1.00

Reference

1.00

Reference

    Non-Hispanic Black

−0.37

−3.36, 2.61

0.48

−2.52, 3.47

    Hispanic

−1.48

−3.25, 0.29

−1.20

−2.96, 0.57

    Asian/Pacific Islander

−0.52

−2.51, 1.47

−0.44

−2.40, 1.51

    Native American

−2.22

−7.42, 2.97

−2.22

−7.42, 2.99

  Annual household income

    <$20,000

−1.34

−3.26, 0.59

−1.04

−2.94, 0.86

    $20,000–39,000

−2.20*

−4.06, −0.35

−1.85*

−3.67, −0.03

    $40,000–59,000

−0.37

−2.34, 1.60

−0.02

−1.96, 1.93

    $60,000–99,000

1.00

Reference

1.00

Reference

    >$100,000

−0.23

−2.11, 1.65

−0.24

−2.08, 1.59

    Unknown

−4.29*

−8.46, −0.11

−3.75

−7.90, 0.39

  Education

    ≤12 years (no HS diploma/GED)

1.40

−0.20, 3.00

1.30

−0.30, 2.90

    ≥HS diploma/GED

1.00

Reference

1.00

Reference

    Unknown

−1.14

−4.86, 2.58

−0.02

−3.62, 3.59

  Marital status

    Married

1.00

Reference

1.00

Reference

    Separated/divorced/widowed

−1.03

−5.06, 2.99

−0.91

−4.93, 3.10

    Single/never married

−1.45

−3.86, 0.96

−1.36

−3.74, 1.03

    Cohabiting

−1.39

−2.95, 0.17

−1.46

−2.99, 0.08

    Unknown

2.00

−3.72, 7.71

1.59

−3.56, 6.73

  Service planning area

    1 (Antelope Valley)

−0.51

−4.56, 3.54

−0.05

−4.09, 3.99

    2 (San Fernando Valley)

−0.99

−3.63, 1.65

−0.61

−3.18, 1.96

    3 (San Gabriel Valley)

−1.87

−4.52, 0.79

−1.72

−4.32, 0.87

    4 (Metro Los Angeles)

−1.13

−4.15, 1.88

−1.07

−4.04, 1.90

    5 (West)

1.00

Reference

1.00

Reference

    6 (South)

−1.21

−4.26, 1.83

−1.15

−4.12, 1.81

    7 (East)

−0.77

−3.64, 2.10

−0.64

−3.45, 2.17

    8 (South Bay)

−2.67

−5.50, 0.16

−2.28

−5.03, 0.46

Other control variables

  Pre-pregnancy depression

    Yes

−0.33

−2.40, 1.75

−0.07

−2.12, 1.99

    No

1.00

Reference

1.00

Reference

  Pre-pregnancy health conditions (hypertension or diabetes)

    Yes

−2.62

−5.45, 0.21

−0.54

−3.33, 2.25

    No

1.00

Reference

1.00

Reference

  Prenatal depressive symptoms

    Yes

0.22

−1.74, 2.19

0.53

−1.42, 2.48

    No

1.00

Reference

1.00

Reference

  Parity

    Primaparous (no prior births)

1.02

−0.71, 2.76

0.97

−0.74, 2.68

    Multiparous (≥1 prior birth)

1.00

Reference

1.00

Reference

  Number of prior children under age 5

−0.66

−2.11, 0.79

−0.82

−2.26, 0.63

Obstetric outcomes

  Pregnancy complications

    Any

  

−6.22***

−7.43, −5.01

    None

1.00

Reference

1.00

Reference

Data are weighted to account for sampling design and survey non-response. Model 1 included all variables listed in the table except pregnancy complications. Model 2 included all variables listed in the table.

*p < 0.05; **p < 0.01; ***p < 0.001

Infant birth weight and small for gestational age

Overall, the mean SCR scores did not significantly differ by LBW or SGA statuses (data available upon request). Further, there were no significant associations between the SCR and LBW, SGA, or continuous infant birth weight, even in the unadjusted models (data available upon request).

Supplementary analyses of relationships between the individual personal capital and stress measures and obstetric outcomes

In order to compare the individual associations between the SCR, standardized personal capital, and standardized stress measures with obstetric outcomes, multivariable logistic regression analyses were conducted. In the final models, none of the personal capital or stress components or composite scores was independently associated with adverse obstetric outcomes (data available upon request).

Discussion

Our study contributes to the literature in three important ways. First, our findings provide evidence that experiencing greater stress relative to personal capital during pregnancy is significantly associated with the risk of pregnancy complications, PTB, and lower gestational age; further, pregnancy complications completely mediate the relationship between the SCR and PTB. Second, most studies have explored individual dimensions of stress and personal capital, whereas we developed conceptually based, multi-dimensional stress and personal capital measures. This contribution is particularly significant in terms of personal capital as it has been previously under-explored. Third, to our knowledge, our study is the first to examine the balance between stress and personal capital during pregnancy and its relationship with adverse outcomes, as previous work has only considered the independent effects of stress and individual components of personal capital. Expanding the theoretical approach to incorporate both risks and resources during pregnancy is imperative because these factors do not exist independently of one another; rather, they coexist to determine women’s resiliency (i.e., the ability to bounce back after facing adversity due to having adequate personal capital relative to stress) and ultimately impact their risk of adverse obstetric outcomes.

Our finding regarding the significant association between the SCR and pregnancy complications is important, as there is a dearth in the literature on the relationships between stress, personal capital, and pregnancy complications. A prospective study by Roy-Matton et al. (2011), which operationalized pregnancy complications similar to our study, revealed that women who had higher perceived stress at 10–20 weeks of pregnancy were more likely than their counterparts to experience subsequent pregnancy complications. Our findings expand on this research by supporting the theoretically justified link between higher stress and lower personal capital taxing the female body to the extent that pregnancy complications may result. Although we cannot definitively establish that the pregnancy complications occurred after experiencing a high SCR, the proposed mechanism is biologically plausible. As this association has important implications for reducing the risk of pregnancy complications, future prospective work should focus on this pathway.

We also found that experiencing pregnancy complications, especially premature labor, mediated the risk of PTB for women with higher SCRs. This finding is unsurprising, as spontaneous premature labor is a primary symptom of PTB (Steer 2005). This finding is also important, as it indicates that targeting efforts to decrease women’s risk of pregnancy complications, especially premature labor, may help prevent PTB. Additionally, our finding regarding the significant negative linear relationship between the SCR and gestational age, even after accounting for pregnancy complications, underscores the critical role that the SCR plays in determining infant gestational age, which may not be best captured by the use of cut-points (i.e., PTB vs. not PTB). Each day in the womb has critical long-term medical and social implications for child health (Moster et al. 2008), and if higher stress relative to personal capital can decrease gestation by several days, then reducing women’s stress and increasing their resources will play an important role in improving birth outcomes and subsequent child health.

Furthermore, we found that the SCR was not significantly associated with LBW, continuous birth weight, or SGA in our study. Theory and empirical evidence suggest that the biological pathways to LBW and SGA can differ from the pathways to PTB (Coussons-Read et al. 2005, 2007; Dunkel Schetter 2011; Gennaro and Hennessy 2003; Steer 2005; Wadhwa et al. 1998, 2001, 2004). The mechanisms contributing to LBW and/or SGA may involve more prolonged exposures or exposures that occur further upstream, such as the preconception period (Precht et al. 2007); therefore, future research should investigate the SCR both during and before pregnancy in relation to infant growth and birth weight.

Our findings have important implications for clinical and public health interventions to reduce women’s risk for experiencing adverse obstetric outcomes. In clinical settings, routinely assessing the life stressors, psychological stress, and available internal and social resources among women who are pregnant or planning pregnancy would enable healthcare providers to offer the most appropriate support (e.g., referrals to community resources) to women with high levels of stress or low personal capital. Moreover, though life stressors may be unavoidable, there are multiple public health opportunities at the individual, interpersonal, and community levels to increase personal capital and potentially counteract perceived stress for pregnant and preconception women. We posit that programs that offer comprehensive resources (e.g., individual, group, and couples counseling, individual and group prenatal care) would be more likely to increase personal capital relative to stress and, subsequently, resiliency, among pregnant women.

Several potential limitations should be considered when interpreting the findings of this study. First, as the survey was administered postpartum and many of the variables in this study were self-reported, responses may be vulnerable to bias. Second, women with missing data for the stress, personal capital, and prenatal depressive symptoms variables were removed from the analyses; nonetheless, as these women were likely to have the lowest personal capital and highest stress, our findings were likely conservative estimates of the examined associations. Third, the weighting schemes for the stress and personal capital measures, though conceptually based, may not have accurately captured the relative importance of the individual components and may not be applicable to all populations. However, our sensitivity analysis found very similar associations among the different SCRs and obstetric outcomes. Fourth, the measurement of stress and personal capital was limited to the available variables in the LAMB survey; therefore, we were unable to incorporate pregnancy-specific measures of stress or other potential components of personal capital (e.g., spirituality).

The LAMB study had some potential limitations as well. Its cross-sectional nature precluded the assumption of causality between predictors and outcomes. Also, it had a relatively low response rate, though we argue that non-respondents were at higher risk for having low personal capital and greater stress and that our findings were thus conservative estimates of the associations examined. Finally, though the study sample was diverse and representative of the LA population, the findings may not be generalizable to other populations.

Based on the findings and potential limitations, this study highlights the need for further areas of research. First, future studies need to assess the reliability and validity of the SCR in relation to obstetric outcomes to test the generalizability of our measure and our findings in other populations. Second, in order to inform effective clinical and public health interventions, further longitudinal research is warranted to examine the potential causal mechanisms in which the SCR may be associated with pregnancy complications. Third, we recommend that longitudinal studies incorporate life course measures of stress (e.g., early childhood trauma, pregnancy-specific stress) and personal capital (e.g., childhood family stability) in order to identify effective points of intervention throughout the life course and ultimately improve maternal and child health outcomes.

Conclusion

Our study examined the relationship between the balance of personal capital and stress during pregnancy, operationalized as the SCR, and adverse obstetric outcomes, including pregnancy complications, PTB, LBW, and SGA. Our findings indicate that women’s relative level of personal capital and stress during pregnancy is significantly associated with the risk of experiencing at least one pregnancy complication, PTB, and lower gestational age; further, pregnancy complications mediated the associated between the SCR and PTB, thereby suggesting that pregnancy complications may be a mechanism linking the SCR to adverse obstetric outcomes. Most importantly, our work underscores the importance of recognizing that risks and resources coexist in women’s lives and likely impact each other; therefore, we need to address both women’s stress and personal capital during pregnancy in order to improve obstetric outcomes and long-term maternal and child health outcomes.

Acknowledgments

This project was made possible by the Health Resources and Services Administration grant # R40MC06635, the Los Angeles County Productivity and Investment fund, and the Los Angeles County Department of Maternal, Child and Adolescent Health Programs general grants and was completed in collaboration with 2007 LAMB Principal Investigator M.C. Lu. FW was supported by a grant from the Health Disparities Research Training Program (T32 HD049302; Principal Investigator G. Sarto). LW was supported by a grant from the Agency for Healthcare Research and Quality (T32 HS000083; Principal Investigator M. Smith). We would like to thank the Los Angeles Mommy and Baby (LAMB) study team (Chandra Higgins, Diana Liu, Marian Eldahaby, Carmen Gutierrez, Yvornia Horton, and Martha Martinez) for their dedicated work in the design and implementation of the 2007 LAMB project. We would also like to thank the Lifecourse Epidemiology and Family Health team for their incredible support with the editing of the manuscript.

Copyright information

© Springer-Verlag Wien 2013