RESEARCH AND PRACTICE
Neighborhood Disadvantage, Preconception Stressful Life Events, and Infant Birth Weight Whitney P. Witt, PhD, MPH, Hyojun Park, MA, Lauren E. Wisk, PhD, Erika R. Cheng, PhD, MPA, Kara Mandell, PhD, Debanjana Chatterjee, PhD, and Dakota Zarak, BA
A growing body of literature emphasizes the importance of examining risk factors not only during pregnancy, but also from early life and across a woman’s life span when investigating obstetric outcomes.1 Studies have demonstrated that exposure to preconception stressful life events (PSLEs) increases the risk for adverse birth outcomes, including giving birth to very low birth weight (VLBW)2 or preterm infants.3,4 Maternal chronic conditions have also been significantly associated with an increased risk of low birth weight (LBW) and preterm birth.5 It is well documented that non-Hispanic Black women are more likely to have LBW6 or VLBW7 infants compared with their nonHispanic White counterparts. Local contexts, including neighborhoods, may influence health outcomes above and beyond individual-level characteristics.8 Disadvantaged neighborhood conditions, characterized by lower neighborhood socioeconomic status, lower neighborhood social relations and engagement, higher rates of violent crime, or higher levels of perceived social and physical disorder and discrimination, may adversely affect birth weight,8 preterm birth,9,10 or both,10---13 independent of individual risk factors. Furthermore, neighborhood conditions have also been shown to modify the effect of maternal psychosocial stress on physical health14 and the risk of having a LBW infant or preterm birth.15 Adverse neighborhood conditions may also exacerbate racial disparities in women’s health or birth outcomes. For example, compared with non-Hispanic White women, non-Hispanic Black women are more likely to experience adverse health conditions and have higher risks of having a LBW infant or preterm birth as a result of perpetual social and environmental insults or prolonged active coping with stressful circumstances that are linked with living in
Objectives. We sought to determine whether the effects of preconception stressful life events (PSLEs) on birth weight differed by neighborhood disadvantage. Methods. We drew our data from the Early Childhood Longitudinal Study, Birth Cohort (2001–2002; n = 9300). We created a neighborhood disadvantage index (NDI) using county-level data from the 2000 US Census. We grouped the NDI into tertiles that represented advantaged, middle advantaged, and disadvantaged neighborhoods. Stratified multinomial logistic regressions estimated the effect of PSLEs on birth weight, controlling for confounders. Results. We found a gradient in the relationship between women’s exposure to PSLEs and having a very low birth weight (VLBW) infant by NDI tertile; the association was strongest in disadvantaged neighborhoods (adjusted odd ratio [AOR] = 1.62; 95% confidence interval [CI] = 1.04, 2.53), followed by middle (AOR = 1.39; 95% CI = 1.00, 1.93) and advantaged (AOR = 1.29; 95% CI = 0.91, 1.82) neighborhoods. We observed a similar gradient for women with chronic conditions and among minority mothers. Conclusions. Women who experienced PSLEs, who had chronic conditions, or were racial/ethnic minorities had the greatest risk of having VLBW infants if they lived in disadvantaged neighborhoods; this suggests exacerbation of risk within disadvantaged environments. Interventions to reduce rates of VLBW should focus on reducing the deleterious effects of stressors and on improving neighborhood conditions. (Am J Public Health. 2015;105:1044–1052. doi:10. 2105/AJPH.2015.302566)
disadvantaged neighborhoods.15---17 However, to the best of our knowledge, no study has investigated the impact of neighborhood conditions on the association between stressful life events prior to pregnancy and birth weight in a national sample of women living in the United States. To address this gap in the literature, we used population-based data available from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B) to determine whether the deleterious effects of preconception factors, such as PSLEs, maternal chronic disease, or race/ethnicity, on birth weight were exacerbated by living in a disadvantaged neighborhood. An analytical model was presented to clarify this research question (Figure 1). Findings from our study provide critical information about how neighborhood conditions influence the association
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between maternal experiences before or during pregnancy and birth weight.
METHODS Data were from the ECLS-B, which is a nationally representative cohort of children born in 2001 and their parents. The ECLS-B used a clustered, list-frame design to select a probability sample of the approximately 4 million children born in 2001, with oversampling of children from minority groups, twins, and children born with VLBW and LBW.18 Children born to mothers younger than 15 years, those who were adopted after the birth certificate was issued, and those who did not survive until 9 months of age were excluded from the sampling frame.18 Registered births were sampled within primary sampling units (counties or groups of contiguous counties) from the
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6. death of a spouse, or 7. fertility problems. Neighborhood disadvantage. We collected the county of residence from the infant’s birth certificate. Using an approach from previous studies,19,20 we used 5 county-level socioeconomic measures from the 2000 US Census to construct a neighborhood disadvantage index (NDI) for each county in the United States based on the results of a principal component analysis. The measures included the percentages of 1. families in poverty, 2. households with incomes below the state median, 3. women without a bachelor’s degree, 4. single mothers, and 5. unemployed mothers of young children (Table 1).19---21
FIGURE 1—Analytical model of neighborhood disadvantage, preconception stressful life events, and infant birth weight.
National Center for Health Statistics’ vital statistics system. We obtained restricted data with permission and approval from the Institute for Education Sciences Data Security Office of the US Department of Education, National Center for Education Statistics (NCES). In accordance with NCES guidelines, all reported unweighted sample sizes were rounded to the nearest 50.18 We drew data for the present study from the first data collection wave, which occurred when the children were approximately 9 months of age (2001---2002; n = 10 700). Participants were eligible for this study if the main survey respondent was the infant’s biological mother (n = 10 550); we subsequently excluded 450 records with missing birth certificate data. 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. We subsequently excluded 50 participants with missing federal information
processing standard codes, which yielded a final study sample of 9300 mother---infant dyads.
Measures Birth weight. The infant’s birth weight in grams was derived from the birth certificate and categorized as very low (VLBW; < 1500 g), low (LBW; 1500---2499 g), normal (2500--3999 g), or high (‡ 4000 g). Our definition of PSLEs is detailed elsewhere.2,4,18a Stressful life events prior to conception. Our definition of PSLEs is detailed elsewhere.2,4 Briefly, we derived the date of conception using information from the birth certificate on the length of gestation and the infant’s date of birth, and identified women who had a PSLE if they indicated that 1 or more of the following events occurred prior to conception: 1. 2. 3. 4. 5.
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death of the respondent’s mother, death of the respondent’s father, death of a previous live born child, divorce, separation from partner,
The principal component analysis procedure created the NDI as the sum of standardized values from socioeconomic measures weighted by their respective factor loadings, which measured the strength of association between factors (e.g., NDI) and their elements (e.g., the 5 county-level measures).13,19-- 22 Higher values of the NDI indicated higher levels of neighborhood disadvantage; our results showed that the NDI explained considerable amounts of variances in each individual measure (factor loading ranged from 0.62 to 0.88). We subsequently divided the NDI into tertiles among the 700 counties in our sample, which represented advantaged (lowest NDI score), middle advantaged, and disadvantaged (highest NDI score) neighborhoods.20,21 Prenatal health and stress. Birth certificate data determined if women had experienced any of the following pregnancy complications: anemia; diabetes; (oligo) hydramnios; hypertension during pregnancy; eclampsia; incompetent cervix; Rh sensitization; uterine bleeding; premature rupture of membranes; placental abruption; or placenta previa. Data from the birth certificate also identified whether women had previously given birth to a preterm or small-for-gestational age infant and if women had the following chronic conditions: 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 the square of height in meters) from the respondent’s
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TABLE 1—Descriptive Statistics and Factor Loadings of Neighborhood Disadvantage Index: the 2000 US Census and the Early Childhood Longitudinal Study, Birth Cohort, 2001–2002 Characteristic
Range
Factor Loadingsa
10.0 (5.2)
1.7 to 35.4
0.88
56.6 (10.1)
23.7 to 81.3
0.70
700 700
72.4 (8.1) 6.5 (2.0)
43.1 to 94.2 2.7 to 15.5
0.62 0.65
700
5.1 (3.1)
0.0 to 23.6
0.71
No. of Counties
Mean (SD)
Families living in poverty, %
700
Households with incomes
700
Women without a bachelor’s degree, % Single female head of household, % Unemployed mothers of young children, %
County-level measuresb
below the state median, %
Neighborhood disadvantage index (NDI)c Overall
700
–0.12 (0.97)
–2.91 to 3.19
...
Advantaged
233
–1.13 (0.55)
–2.91 to –0.52
...
Middle advantaged
234
–0.14 (0.21)
–0.51 to 0.24
...
Disadvantaged
233
0.93 (0.59)
0.25 to 3.19
...
a
Factor loadings were derived from principal component analysis (PCA) using all 3139 counties in the US Census data. The PCA procedure created the neighborhood disadvantage index (NDI) as the sum of standardized values from socioeconomic measures weighted by their respective factor loadings, which measure the strength of association between factors (e.g., NDI) and their elements (e.g., 5 county-level measures). b Descriptive statistics for county-level measures were derived from the counties represented in the Early Childhood Longitudinal Study, Birth Cohort. c Higher NDI indicates worse neighborhood conditions.
measured height and self-reported weight before 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).23 In addition, we evaluated plurality (whether the index child was a singleton or multiple birth), parity (data from the birth certificate, which was coded as number of previous live births: nulliparous, primiparous, or multiparous), and maternal report of alcohol and tobacco use during the 3 months before pregnancy and in the final 3 months of pregnancy (never, use in the 3 months before conception only, or any use in the final 3 months of pregnancy). 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: 1. 2. 3. 4. 5.
death of the respondent’s mother, death of the respondent’s father, divorce, separation from partner, or death of a spouse.
Maternal sociodemographic factors. We examined the following maternal sociodemographic factors: race/ethnicity (nonHispanic White, non-Hispanic Black, non-Hispanic
Asian/Pacific Islanders, Hispanic, or nonHispanic other race), 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, 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), and socioeconomic status (SES). We defined SES by using a 5-category composite index (quintiles) generated by the NCES that incorporated parental education, occupation, and household income.18
Statistical Analyses We conducted the 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 we applied survey weights to produce estimates that accounted for the complex survey design, unequal probabilities of selection, and survey nonresponse. We generated summary statistics to describe sample characteristics and used the v2 test to
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determine significant differences in sample characteristics among the 3 NDI groups (e.g., advantaged, middle, and disadvantaged). We used stratified analysis to examine how the association between PSLEs and birth weight varied by NDI tertile. Within each tertile, we used multinomial logistic regression to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) comparing birth weight statuses of infants born to women exposed and not exposed to PSLEs. We made comparisons to examine how obstetric, maternal sociodemographic, and maternal health behavior factors were associated with birth weight across neighborhoods. We conducted a sensitivity analysis to examine the robustness of the estimates by using an alternative NDI categorization, whereby we created tertiles using all of the 3139 counties in the United States.
RESULTS Among 9300 mothers, 19.7% experienced any PSLE. Most mothers resided in advantaged neighborhoods (44.9%), followed by middle and disadvantaged neighborhoods (35.1% and 19.9%, respectively; Table 2). Those living in disadvantaged neighborhoods were more likely to experience stressful life events during pregnancy, be nondrinkers, and be smokers. These women were also more likely to be younger, non-Hispanic Black, Hispanic, unmarried, publicly insured, and have lower SES.
Preconception Stressful Life Events When stratified by neighborhood conditions, we found a gradient in the effect of PSLEs on the risk of having VLBW infants after adjustment for other covariates; the association between PSLEs and delivering a VLBW infant was strongest in disadvantaged neighborhoods (AOR = 1.62; 95% CI = 1.04, 2.53), followed by middle (AOR = 1.39; 95% CI = 1.00, 1.93) and advantaged (AOR = 1.29; 95% CI = 0.91, 1.82) neighborhoods (Table 3 and Figure 2). In the adjusted stratified model, mothers with chronic conditions were more likely to have VLBW infants when they lived in disadvantaged neighborhoods (AOR = 2.85; 95% CI = 1.94, 4.19) compared with middle (AOR = 1.89; 95% CI = 1.30, 2.73) or advantaged (AOR = 1.89; 95% CI = 1.34, 2.65) neighborhoods.
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TABLE 2—Descriptive Statistics by Neighborhood Disadvantage: the Early Childhood Longitudinal Study, Birth Cohort; United States; 2001–2002 Neighborhood Condition Characteristic Total, weighted no.
Total
Advantaged
Middle
Disadvantaged
3 752 600
1 686 191
1 318 375
748 034
%
100.0%
44.9%
35.1%
19.9%
Total, unweighted no.
9 300
4 250
3 100
1 950
Very low birth weight Low birth weight
1.2 5.5
1.1 5.2
1.2 5.9
1.4 5.7
Normal birth weight
83.7
83.0
84.0
85.0
9.6
10.8
9.0
7.9
Birth outcomes, %
High birth weight
P
.006
*
Stress and obstetric factors Stressful life events before conception, %
.188
None
80.3
79.6
80.0
82.1
Any
19.7
20.4
20.0
17.9
94.3
95.3
94.1
92.2
5.7
4.7
5.9
7.8
None
86.5
86.5
85.5
87.9
Any
13.5
13.5
14.5
12.1
79.5
79.8
77.4
82.7
20.5
20.2
22.6
17.3
No
99.0
99.0
98.8
99.3
Yes
1.0
1.0
1.2
0.7
3.3
3.3
3.5
3.0
18.5–24.9
49.5
51.3
48.9
46.2
25–29.9
26.8
26.0
26.7
28.6
‡ 30 Unknown
17.9 2.5
16.7 2.7
18.4 2.4
19.9 2.3
Singleton
98.3
98.2
98.3
98.5
Multiple
1.7
1.8
1.7
1.5
Nulliparous
40.7
41.6
39.0
41.4
Primaparous
32.8
33.6
32.5
31.5
Multiparous
26.5
24.8 Maternal health behaviors
28.5
27.1
Never
62.0
56.5
64.2
70.5
***
3 mo before conception only
34.7
39.4
32.8
27.3
***
3.3
4.1
3.0
2.3
Stressful life events during pregnancy, % None Any
< .001
Pregnancy complications, %
.535
Maternal chronic conditions, % None Any Previous child born preterm or SGA, %
.373
.536
Prepregnancy BMI, kg/m2, % < 18.5
.222
No. of children born, %
.161
Parity, %
.103
Alcohol use, %
Final 3 mo of pregnancy
*
* < .001
Tobacco use, %
* .013
Never
76.7
78.6
77.2
71.8
*
3 mo before conception only Final 3 mo of pregnancy
12.3 11.0
11.6 9.8
11.6 11.2
15.0 13.2
*
Continued
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TABLE 2—Continued Maternal sociodemographic factors Age, y, %
< .001
15–19
7.5
5.4
8.3
10.8
***
20–24
24.3
20.4
25.5
30.7
***
25–29
26.3
24.9
26.6
28.7
30–34 ‡ 35
25.0 17.0
28.4 20.8
24.2 15.4
18.6 11.2
Non-Hispanic White
57.4
64.9
53.1
47.9
Non-Hispanic Black
14.0
8.6
16.7
21.4
***
3.5
4.8
2.7
1.8
***
Race/ethnicity, %
Non-Hispanic Asian/Pacific Islander Non-Hispanic other Hispanic Marital status (at birth), % Married or living with partner Separated/divorced/widowed
*** *** < .001
2.5
2.5
2.6
2.3
22.7
19.2
24.9
26.6
83.5
88.3
80.8
77.1
***
< .001 ***
3.0
2.3
3.6
3.7
**
13.5
9.4
15.6
19.2
***
Private only
59.1
70.7
53.9
42.1
***
Any public
37.5
26.1
42.9
53.8
***
3.4
3.3
3.2
4.0
19.7
14.1
23.9
25.1
< .001 ***
Second quintile
20.0
15.9
21.2
27.2
***
Third quintile
20.1
19.2
20.3
21.7
Never married Health insurance status, %
None Socioeconomic status, % First quintile (lowest)
< .001
Fourth quintile
20.2
22.9
19.2
15.7
***
Fifth quintile (highest)
20.0
27.8
15.5
10.4
***
Northeast
17.2
16.6
15.9
20.8
Midwest South
22.4 36.5
27.3 26.4
21.3 42.8
13.3 48.5
**
West
23.9
29.8
20.1
17.4
*
Region of residence, %
< .001
Note. BMI = body mass index; SGA = small for gestational age. Data are weighted percentages. National Center for Education Statistics rounding rules were applied to unweighted numbers; unweighted subgroup numbers may not add to the total because of rounding error. Subgroup post hoc P values: *P < .05; **P < .01; ***P < .001.
When stratified by neighborhood disadvantage, we similarly observed a gradient in the risk of having VLBW infants among non-Hispanic Black mothers after adjusting for other risk factors; this association was strongest in disadvantaged neighborhoods (AOR = 3.75; 95% CI = 1.88, 7.48), followed by middle (AOR = 2.66; 95% CI = 1.77, 3.99) and advantaged (AOR = 2.27; 95% CI = 1.46, 3.54) neighborhoods. In addition, Hispanic women had increased odds of delivering a VLBW infant in disadvantaged neighborhoods (AOR = 1.86; 95% CI = 1.14, 3.02), but not in middle or advantaged neighborhoods.
Sensitivity Analyses
DISCUSSION
The sampling frame of the ECLS-B included 700 counties of the 3139 counties in the United States, and these 700 counties might not be necessarily representative all counties in the United States. To examine whether our findings were robust to the different stratification of counties, we ran the same models using NDI tertiles based on all 3139 counties. We identified a similar gradient across neighborhood tertiles, such that that the impact of PSLEs was largest in disadvantaged neighborhoods, followed by middle and advantaged neighborhoods.
We found a gradient in the association between experiencing any PSLE and the increased risk of having a VLBW infant by neighborhood disadvantage. The strongest association was found in the most disadvantaged neighborhoods, followed by middle and advantaged neighborhoods. Our results were consistent with previous studies that showed an association between neighborhood disadvantage24,25 and PSLEs2---4 with adverse birth outcomes. The similar gradient in the risk of having VLBW
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TABLE 3—Neighborhood Effects on the Associations Between Very Low Birth Weight and Preconception Stressful Life Events, Chronic Conditions, and Race/Ethnicity: the Early Childhood Longitudinal Study, Birth Cohort; United States; 2001–2002 Stratified Characteristic
Overall, OR (95% CI)
Advantaged, OR (95% CI)
Middle, OR (95% CI)
Disadvantaged, OR (95% CI)
Stressful life event before conception Unadjusted model None (Ref)
1.00
1.00
1.00
1.00
1.70 (1.46, 1.99)
1.80 (1.40, 2.32)
1.62 (1.28, 2.05)
1.70 (1.22, 2.36)
None (Ref)
1.00
1.00
1.00
1.00
Any
1.35 (1.10, 1.65)
1.29 (0.91, 1.82)
1.39 (1.00, 1.93)
1.62 (1.04, 2.53)
Any Adjusted modela
Maternal chronic conditions Unadjusted model None (Ref)
1.00
1.00
1.00
1.00
Any
2.80 (2.34, 3.36)
2.87 (2.16, 3.82)
2.43 (1.73, 3.40)
3.54 (2.52, 4.96)
1.00
1.00
1.00
1.00
1.98 (1.59, 2.46)
1.89 (1.34, 2.65)
1.89 (1.30, 2.73)
2.85 (1.94, 4.19)
Adjusted modela None (Ref) Any
Race/ethnicity Unadjusted model Non-Hispanic White (Ref)
1.00
1.00
1.00
1.00
Non-Hispanic Black
2.43 (2.03, 2.90)
2.49 (1.86, 3.33)
2.11 (1.65, 2.70)
2.78 (1.78, 4.35)
Non-Hispanic Asian/Pacific Islander
0.84 (0.56, 1.28)
0.99 (0.60, 1.64)
0.57 (0.26, 1.22)
0.77 (0.18, 3.36)
Non-Hispanic other Hispanic
0.93 (0.52, 1.65) 1.19 (0.98, 1.44)
0.93 (0.44, 2.00) 1.22 (0.90, 1.64)
0.78 (0.31, 2.00) 1.07 (0.78, 1.47)
1.21 (0.47, 3.13) 1.34 (0.88, 2.04)
Adjusted modela Non-Hispanic White (Ref)
1.00
1.00
1.00
1.00
Non-Hispanic Black
2.79 (2.08, 3.73)
2.27 (1.46, 3.54)
2.66 (1.77, 3.99)
3.75 (1.88, 7.48)
Non-Hispanic Asian/Pacific Islander
1.23 (0.78, 1.94)
1.23 (0.71, 2.13)
0.94 (0.42, 2.11)
0.95 (0.18, 5.02)
Non-Hispanic other
0.71 (0.35, 1.43)
0.68 (0.27, 1.71)
0.67 (0.28, 1.60)
1.08 (0.38, 3.12)
Hispanic
1.56 (1.19, 2.03)
1.46 (0.91, 2.33)
1.39 (0.91, 2.13)
1.86 (1.14, 3.02)
Note. CI = confidence interval; OR = odds ratio. a Other covariates include stressful life events during pregnancy, pregnancy complications, previous child born preterm or small for gestational age, prepregnancy body mass index, number of children born, parity, maternal age (years), race/ethnicity, marital status (at birth), health insurance status, socioeconomic status, regions of residence, tobacco use, and alcohol use.
infants by neighborhood condition was also found among mothers who experienced maternal chronic conditions 5 and among nonHispanic Black and Hispanic women. 6,7 The collective impact of disadvantage26 might explain the elevated risk of having VLBW infants among women living in disadvantaged neighborhoods and among those who experienced PSLEs, who had chronic health conditions, or were racial/ethnic minorities. This hypothesis suggests that neighborhood conditions might operate as a moderator on the effects of maternal experiences before and during pregnancy on birth outcomes.
Specifically, disadvantaged neighborhood conditions might directly affect the amount of resources and stressors that reproductive age women experience, such as limited access to prenatal care, unhealthy behaviors, and poor health conditions,8,25,27 which all eventually contribute to adverse birth outcomes. Furthermore, disadvantaged neighborhoods might be associated with increased chronic stressors that contribute to deterioration in normal physiological processes, which might be more severe among minority women compared with nonHispanic White women across the life course.1,17,24
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Our findings suggest that focusing solely on individual-level risk factors during a woman’s pregnancy might not be sufficient to prevent or reduce adverse birth outcomes. Because of persistent disparities in birth outcomes, such proximal factors might reflect only a single dimension of disadvantage, and to achieve better birth outcomes it might be necessary to address upstream health determinants before pregnancy and within women’s social contexts. For example, 1 study found that, in the city of Milwaukee, higher neighborhood SES was more protective against poor birth outcomes, including infant mortality rates, LBW, or
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Note. A = advantaged neighborhoods; D = disadvantaged neighborhoods; M = middle advantaged neighborhoods. The unlabeled point estimates reflect the overall association. Models were adjusted for stressful life events during pregnancy, pregnancy complications, previous child born preterm or small for gestational age, prepregnancy body mass index, number of children born, parity, maternal age (years), race/ethnicity, marital status (at birth), health insurance status, socioeconomic status, regions of residence, tobacco use, and alcohol use
FIGURE 2—The gradient of the risk for having very low birth weight infants associated with preconception stressful life events by neighborhood disadvantage.
preterm birth among non-Hispanic White women compared with non-Hispanic Black women.28 Although this was only 1 example of how neighborhood conditions differentially affect maternal and child health, such regional racial/ethnic disparities in birth outcomes are pervasive across the United States, and have drawn the attention of policymakers at the local, state, and federal levels.29 To address regional disparities, neighborhood- or community-level interventions are required; the Best Babies Zone initiative is an example of a community-level effort to improve maternal and child health outcomes by implementing cross-sector collaborations for better educational, economic, and community conditions.30,31 At the Best Babies Zone site in Oakland, California, community residents
have partnered with national and local organizations to develop solutions to neighborhood issues and foster new county collaborations that reduce existing health disparities between non-Hispanic Black and non-Hispanic White women and ensure a healthy future for the neighborhood’s children.31
Limitations There are some potential limitations of this work that should be acknowledged. First, because we cross-sectionally examined NDI at the infant’s birth, we could not establish a causal association among neighborhood context, PSLEs, and birth outcomes, or estimate the cumulative effect of neighborhood conditions over the life course on birth outcomes. In addition, we did not account for the length of
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time that the respondents resided in their neighborhoods before the birth of the child. Also, the ECLS-B excluded children who died before 9 months of age; this possibly resulted in a survival bias that would have likely biased our results to the null. We did not account for the timing of exposure of PSLEs; however, in previous work, we found that the effect of PSLEs that occurred 1 year or more before conception had a stronger association with VLBW than did events that occurred within 1 year prior to conception. 2 Future research should strive to explore the exposure window of PSLEs when investigating the antecedents of adverse obstetric outcomes. Neighborhood effects are often measured at the census block, tract, or local levels, 8 but census tract or block information was not available from birth certificates provided by ECLS-B data, so we could not conduct analyses using smaller areas. However, there are 2 reasons that a county might be a more useful proxy for neighborhoods. First, the potential influences of socioeconomic characteristics of neighborhoods might be more meaningful on a higher level (e.g., county) than a lower one (e.g., block or tract). 8 For example, an individual’s daily life might not be limited to a census tract or block level, but instead, it could extend to cover a larger scale, such as a city or county level. Second, counties are the primary administrative division of most states 32 and thus represent the level that policy interventions can be directly linked with. Our approach provided consistent results with previous studies that used a census tract or census block level as a proxy for neighborhood in examining health outcomes.19---21,33 Our study also had important strengths. First, we stratified our exposure by using an a priori conceptualization of exposure (i.e., NDI tertile), which is an important consideration for neighborhood studies.33 Second, the majority of previous studies examined the independent impact of either neighborhood conditions or PSLEs on birth outcomes separately, within a local context or within several states.10---13,25 We extended the findings of previous neighborhood studies by identifying a gradient in the
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association between PSLEs and birth weight using nationally representative data.
editing, and refining the article. D. Zarak assisted with writing and editing of the article.
Conclusions
Acknowledgments
We found a gradient in the association between experiencing any PSLE and the increased risk of having a VLBW infant by neighborhood disadvantage. The risk of having VLBW infants among women with PSLEs, chronic conditions, or minority women was strongest among those living in disadvantaged neighborhoods, which suggests exacerbation of risk within disadvantaged environments. To achieve better birth outcomes, it might be necessary to address upstream health determinants before pregnancy and women’s social contexts. Therefore, such population-level interventions should focus on reducing the deleterious effects of stressors and on improving neighborhood conditions to improve birth outcomes. j
About the Authors At the time of the study, Whitney P. Witt was with Maternal and Child Health Research, Truven Health Analytics, Durham, NC. Hyojun Park, Kara Mandell, and Debanjana Chatterjee were with the Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, Madison. Lauren E. Wisk was with the Center for Child Health Care Studies in the Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA. Erika R. Cheng was with Harvard Medical School and the Division of General Academic Pediatrics at the Massachusetts General Hospital for Children, Boston. Dakota Zarak was with the Department of Psychology, University of Wisconsin, Madison. Correspondence should be sent to Whitney P. Witt, PhD, MPH, Truven Health Analytics, 4819 Emperor Boulevard, Suite 125, Durham, NC 27703 (e-mail: whitney.witt@ truvenhealth.com). Reprints can be ordered at http://www. ajph.org by clicking the “Reprints” link. This article was accepted January 8, 2015.
Contributors W. P. Witt made substantial contributions to the study design, acquisition of data, interpretation of data, and drafting of the article and was ultimately responsible for overseeing the data analysis and article preparation. H. Park contributed to the study design, data preparation and analysis, interpretation of data, and drafting of the article. L. E. Wisk contributed to the study design, data preparation and analysis, interpretation of data, and drafting of the article. E. R. Cheng helped with the study design, data interpretation, and drafting of the article. K. Mandell helped with the interpretation of the study data, summarization of study results, and editing of the article. D. Chatterjee assisted in interpreting the study data, summarizing study results,
This project was made possible by a Health Resources and Services Administrative (HRSA) (W. P. Witt, L. E. Wisk, and D. Chatterjee: R40MC23625; principal investigator W. P. Witt) grant. Additional funding for this research was provided by grants from the Agency for Healthcare Research and Quality (K. Mandell T32 HS00083; principal investigator M. Smith), the 2012-2013 Herman I. Shapiro Distinguished Graduate Fellowship (L. E. Wisk), and a National Research Service Award institutional training grant (E. R. Cheng: T32-HD075727; principal investigator J. A. Finkelstein). L. E. Wisk was supported by the Thomas O. Pyle Fellowship and an Agency for Healthcare Research and Quality Postdoctoral Training Grant 2T32HS000063-20 (principal investigator J. A. Finkelstein). D. Chatterjee was additionally supported by National Research Service Award (NRSA) in Primary Medical Care Grant T32HP22239 (principal investigator: I. Borowsky). We would also like to 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 because the data had been previously collected and de-identified.
References 1. Lu MC, Halfon N. Racial and ethnic disparities in birth outcomes: a life-course perspective. Matern Child Health J. 2003;7(1):13---30. 2. Witt WP, Cheng ER, Wisk LE, et al. Maternal stressful life events prior to conception and the impact on infant birth weight in the United States. Am J Public Health. 2014;104(suppl 1):S81---S89. 3. Dole N, Savitz DA, Hertz-Picciotto I, Siega-Riz AM, McMahon MJ, Buekens P. Maternal stress and preterm birth. Am J Epidemiol. 2003;157(1):14---24. 4. Witt WP, Cheng ER, Wisk LE, et al. Preterm birth in the United States: the impact of stressful life events prior to conception and maternal age. Am J Public Health. 2014;104(suppl 1):S73---S80. 5. Graham J, Zhang L, Schwalberg R. Association of maternal chronic disease and negative birth outcomes in a non-Hispanic Black-White Mississippi birth cohort. Public Health Nurs. 2007;24(4):311---317. 6. Valero De Bernabé J, Soriano T, Albaladejo R, et al. Risk factors for low birth weight: a review. Eur J Obstet Gynecol Reprod Biol. 2004;116(1):3---15. 7. Kempe A, Wise PH, Barkan SE, et al. Clinical determinants of the racial disparity in very low birth weight. N Engl J Med. 1992;327(14):969---973. 8. Morenoff JD. Neighborhood mechanisms and the spatial dynamics of birth weight. AJS. 2003;108(5): 976---1017. 9. Giurgescu C, Zenk SN, Dancy BL, Park CG, Dieber W, Block R. Relationships among neighborhood environment, racial discrimination, psychological distress, and preterm birth in African American women. J Obstet Gynecol Neonatal Nurs. 2012;41(6):E51---E61.
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10. Luo ZC, Wilkins R, Kramer MS; Fetal and Infant Health Study Group of the Canadian Perinatal Surveillance System. Effect of neighbourhood income and maternal education on birth outcomes: a populationbased study. CMAJ. 2006;174(10):1415---1420. 11. Elo IT, Culhane JF, Kohler IV, et al. Neighbourhood deprivation and small-for-gestational-age term births in the United States. Paediatr Perinat Epidemiol. 2009;23 (1):87---96. 12. Farley TA, Mason K, Rice J, Habel JD, Scribner R, Cohen DA. The relationship between the neighbourhood environment and adverse birth outcomes. Paediatr Perinat Epidemiol. 2006;20(3):188---200. 13. Vinikoor-Imler LC, Messer LC, Evenson KR, Laraia BA. Neighborhood conditions are associated with maternal health behaviors and pregnancy outcomes. Soc Sci Med. 2011;73(9):1302---1311. 14. Boardman JD. Stress and physical health: the role of neighborhoods as mediating and moderating mechanisms. Soc Sci Med. 2004;58(12):2473---2483. 15. Nkansah-Amankra S, Luchok KJ, Hussey JR, Watkins K, Liu X. Effects of maternal stress on low birth weight and preterm birth outcomes across neighborhoods of South Carolina, 2000---2003. Matern Child Health J. 2010;14(2):215---226. 16. Geronimus AT, Bound J. Black/white differences in women’s reproductive-related health status: evidence from vital statistics. Demography. 1990;27(3):457---466. 17. Geronimus AT. Black/white differences in the relationship of maternal age to birthweight: a population-based test of the weathering hypothesis. Soc Sci Med. 1996;42(4):589---597. 18. Nord C, Edwards B, Hilpert R, Branden L, Andreassen C, Elmore A. Early Childhood Longitudinal Study, Birth Cohort (ECLS-B): User’s Manual for the ECLS-B 9-Month-2-Year Restricted-Use Data File and Electronic Code Book. Washington, DC: National Center for Education Statistics; 2006. 18a. Witt WP, Wisk LE, Cheng ER, et al. Determinants of cesarean delivery in the US: a lifecourse approach. Matern Child Health J. 2015;19(1):84---93. 19. McManus BM, Robert S, Albanese A, Sadek-Badawi M, Palta M. Racial disparities in health-related quality of life in a cohort of very-low-birth-weight 2- and 3-yearolds with and without asthma. J Epidemiol Community Health. 2012;66(7):579---585. 20. McManus BM, Robert SA, Albanese A, SadekBadawi M, Palta M. Relationship between neighborhood disadvantage and social function of Wisconsin 2- and 3-year-olds born at very low birth weight. Arch Pediatr Adolesc Med. 2011;165(2):119---125. 21. Cheng ER, Park H, Robert SA, Palta M, Witt WP. Impact of county disadvantage on behavior problems among US children with cognitive delay. Am J Public Health. 2014;104(11):2114---2121. 22. Messer LC, Laraia BA, Kaufman JS, et al. The development of a standardized neighborhood deprivation index. J Urban Health. 2006;83(6):1041---1062. 23. North American Association for the Study of Obesity; National Heart Lung and Blood Institute. The Practical Guide: Identification, Evaluation, and Treatment for Overweight and Obesity in Adults. Bethesda, MD: US Department of Health and Human Services, Public Health Service, National Institutes of Health, National Heart, Lung, and Blood Institute; 2000.
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24. Blumenshine P, Egerter S, Barclay CJ, Cubbin C, Braveman PA. Socioeconomic disparities in adverse birth outcomes: a systematic review. Am J Prev Med. 2010; 39(3):263---272. 25. Metcalfe A, Lail P, Ghali WA, Sauve RS. The association between neighbourhoods and adverse birth outcomes: a systematic review and meta-analysis of multi-level studies. Paediatr Perinat Epidemiol. 2011; 25(3):236---245. 26. Grollman EA. Multiple disadvantaged statuses and health: the role of multiple forms of discrimination. J Health Soc Behav. 2014;55(1):3---19. 27. Wakeel F, Wisk LE, Gee R, Chao SM, Witt WP. 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. Arch Womens Ment Health. 2013;16(6):435---451. 28. Ward TC, Mori N, Patrick TB, Madsen MK, Cisler RA. Influence of socioeconomic factors and race on birth outcomes in urban Milwaukee. WMJ. 2010;109(5): 254---260. 29. Lu MC, Kotelchuck M, Hogan V, Jones L, Wright K, Halfon N. Closing the Black-White gap in birth outcomes: a life-course approach. Ethn Dis. 2010;20(1 suppl 2): S2-62---S2-76. 30. Pies C, Hussey W, Merrell S, Strouse C. Best Babies Zones—a new approach to reducing infant mortality. Available at: http://www.bestbabieszone.org. Accessed August 15, 2014. 31. Shrimali BP, Luginbuhl J, Malin C, Flournoy R, Siegel A. The Building Blocks Collaborative: advancing a life course approach to health equity through multisector collaboration. Matern Child Health J. 2014; 18(2):373---379. 32. USCB. Geographic areas reference manual. 1994. Available at: https://www.census.gov/geo/reference/ pdfs/GARM/GARMcont.pdf. Accessed August 15, 2014. 33. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Choosing area based socioeconomic measures to monitor social inequalities in low birth weight and childhood lead poisoning: The Public Health Disparities Geocoding Project (US). J Epidemiol Community Health. 2003;57(3):186---199.
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Neighborhood Disadvantage, Preconception Stressful Life Events, and Infant Birth Weight Whitney P. Witt, PhD, MPH, Hyojun Park, MA, Lauren E. Wisk, PhD, Erika R. Cheng, PhD, MPA, Kara Mandell, PhD, Debanjana Chatterjee, PhD, and Dakota Zarak, BA
A growing body of literature emphasizes the importance of examining risk factors not only during pregnancy, but also from early life and across a woman’s life span when investigating obstetric outcomes.1 Studies have demonstrated that exposure to preconception stressful life events (PSLEs) increases the risk for adverse birth outcomes, including giving birth to very low birth weight (VLBW)2 or preterm infants.3,4 Maternal chronic conditions have also been significantly associated with an increased risk of low birth weight (LBW) and preterm birth.5 It is well documented that non-Hispanic Black women are more likely to have LBW6 or VLBW7 infants compared with their nonHispanic White counterparts. Local contexts, including neighborhoods, may influence health outcomes above and beyond individual-level characteristics.8 Disadvantaged neighborhood conditions, characterized by lower neighborhood socioeconomic status, lower neighborhood social relations and engagement, higher rates of violent crime, or higher levels of perceived social and physical disorder and discrimination, may adversely affect birth weight,8 preterm birth,9,10 or both,10---13 independent of individual risk factors. Furthermore, neighborhood conditions have also been shown to modify the effect of maternal psychosocial stress on physical health14 and the risk of having a LBW infant or preterm birth.15 Adverse neighborhood conditions may also exacerbate racial disparities in women’s health or birth outcomes. For example, compared with non-Hispanic White women, non-Hispanic Black women are more likely to experience adverse health conditions and have higher risks of having a LBW infant or preterm birth as a result of perpetual social and environmental insults or prolonged active coping with stressful circumstances that are linked with living in
Objectives. We sought to determine whether the effects of preconception stressful life events (PSLEs) on birth weight differed by neighborhood disadvantage. Methods. We drew our data from the Early Childhood Longitudinal Study, Birth Cohort (2001–2002; n = 9300). We created a neighborhood disadvantage index (NDI) using county-level data from the 2000 US Census. We grouped the NDI into tertiles that represented advantaged, middle advantaged, and disadvantaged neighborhoods. Stratified multinomial logistic regressions estimated the effect of PSLEs on birth weight, controlling for confounders. Results. We found a gradient in the relationship between women’s exposure to PSLEs and having a very low birth weight (VLBW) infant by NDI tertile; the association was strongest in disadvantaged neighborhoods (adjusted odd ratio [AOR] = 1.62; 95% confidence interval [CI] = 1.04, 2.53), followed by middle (AOR = 1.39; 95% CI = 1.00, 1.93) and advantaged (AOR = 1.29; 95% CI = 0.91, 1.82) neighborhoods. We observed a similar gradient for women with chronic conditions and among minority mothers. Conclusions. Women who experienced PSLEs, who had chronic conditions, or were racial/ethnic minorities had the greatest risk of having VLBW infants if they lived in disadvantaged neighborhoods; this suggests exacerbation of risk within disadvantaged environments. Interventions to reduce rates of VLBW should focus on reducing the deleterious effects of stressors and on improving neighborhood conditions. (Am J Public Health. 2015;105:1044–1052. doi:10. 2105/AJPH.2015.302566)
disadvantaged neighborhoods.15---17 However, to the best of our knowledge, no study has investigated the impact of neighborhood conditions on the association between stressful life events prior to pregnancy and birth weight in a national sample of women living in the United States. To address this gap in the literature, we used population-based data available from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B) to determine whether the deleterious effects of preconception factors, such as PSLEs, maternal chronic disease, or race/ethnicity, on birth weight were exacerbated by living in a disadvantaged neighborhood. An analytical model was presented to clarify this research question (Figure 1). Findings from our study provide critical information about how neighborhood conditions influence the association
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between maternal experiences before or during pregnancy and birth weight.
METHODS Data were from the ECLS-B, which is a nationally representative cohort of children born in 2001 and their parents. The ECLS-B used a clustered, list-frame design to select a probability sample of the approximately 4 million children born in 2001, with oversampling of children from minority groups, twins, and children born with VLBW and LBW.18 Children born to mothers younger than 15 years, those who were adopted after the birth certificate was issued, and those who did not survive until 9 months of age were excluded from the sampling frame.18 Registered births were sampled within primary sampling units (counties or groups of contiguous counties) from the
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6. death of a spouse, or 7. fertility problems. Neighborhood disadvantage. We collected the county of residence from the infant’s birth certificate. Using an approach from previous studies,19,20 we used 5 county-level socioeconomic measures from the 2000 US Census to construct a neighborhood disadvantage index (NDI) for each county in the United States based on the results of a principal component analysis. The measures included the percentages of 1. families in poverty, 2. households with incomes below the state median, 3. women without a bachelor’s degree, 4. single mothers, and 5. unemployed mothers of young children (Table 1).19---21
FIGURE 1—Analytical model of neighborhood disadvantage, preconception stressful life events, and infant birth weight.
National Center for Health Statistics’ vital statistics system. We obtained restricted data with permission and approval from the Institute for Education Sciences Data Security Office of the US Department of Education, National Center for Education Statistics (NCES). In accordance with NCES guidelines, all reported unweighted sample sizes were rounded to the nearest 50.18 We drew data for the present study from the first data collection wave, which occurred when the children were approximately 9 months of age (2001---2002; n = 10 700). Participants were eligible for this study if the main survey respondent was the infant’s biological mother (n = 10 550); we subsequently excluded 450 records with missing birth certificate data. 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. We subsequently excluded 50 participants with missing federal information
processing standard codes, which yielded a final study sample of 9300 mother---infant dyads.
Measures Birth weight. The infant’s birth weight in grams was derived from the birth certificate and categorized as very low (VLBW; < 1500 g), low (LBW; 1500---2499 g), normal (2500--3999 g), or high (‡ 4000 g). Our definition of PSLEs is detailed elsewhere.2,4,18a Stressful life events prior to conception. Our definition of PSLEs is detailed elsewhere.2,4 Briefly, we derived the date of conception using information from the birth certificate on the length of gestation and the infant’s date of birth, and identified women who had a PSLE if they indicated that 1 or more of the following events occurred prior to conception: 1. 2. 3. 4. 5.
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death of the respondent’s mother, death of the respondent’s father, death of a previous live born child, divorce, separation from partner,
The principal component analysis procedure created the NDI as the sum of standardized values from socioeconomic measures weighted by their respective factor loadings, which measured the strength of association between factors (e.g., NDI) and their elements (e.g., the 5 county-level measures).13,19-- 22 Higher values of the NDI indicated higher levels of neighborhood disadvantage; our results showed that the NDI explained considerable amounts of variances in each individual measure (factor loading ranged from 0.62 to 0.88). We subsequently divided the NDI into tertiles among the 700 counties in our sample, which represented advantaged (lowest NDI score), middle advantaged, and disadvantaged (highest NDI score) neighborhoods.20,21 Prenatal health and stress. Birth certificate data determined if women had experienced any of the following pregnancy complications: anemia; diabetes; (oligo) hydramnios; hypertension during pregnancy; eclampsia; incompetent cervix; Rh sensitization; uterine bleeding; premature rupture of membranes; placental abruption; or placenta previa. Data from the birth certificate also identified whether women had previously given birth to a preterm or small-for-gestational age infant and if women had the following chronic conditions: 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 the square of height in meters) from the respondent’s
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TABLE 1—Descriptive Statistics and Factor Loadings of Neighborhood Disadvantage Index: the 2000 US Census and the Early Childhood Longitudinal Study, Birth Cohort, 2001–2002 Characteristic
Range
Factor Loadingsa
10.0 (5.2)
1.7 to 35.4
0.88
56.6 (10.1)
23.7 to 81.3
0.70
700 700
72.4 (8.1) 6.5 (2.0)
43.1 to 94.2 2.7 to 15.5
0.62 0.65
700
5.1 (3.1)
0.0 to 23.6
0.71
No. of Counties
Mean (SD)
Families living in poverty, %
700
Households with incomes
700
Women without a bachelor’s degree, % Single female head of household, % Unemployed mothers of young children, %
County-level measuresb
below the state median, %
Neighborhood disadvantage index (NDI)c Overall
700
–0.12 (0.97)
–2.91 to 3.19
...
Advantaged
233
–1.13 (0.55)
–2.91 to –0.52
...
Middle advantaged
234
–0.14 (0.21)
–0.51 to 0.24
...
Disadvantaged
233
0.93 (0.59)
0.25 to 3.19
...
a
Factor loadings were derived from principal component analysis (PCA) using all 3139 counties in the US Census data. The PCA procedure created the neighborhood disadvantage index (NDI) as the sum of standardized values from socioeconomic measures weighted by their respective factor loadings, which measure the strength of association between factors (e.g., NDI) and their elements (e.g., 5 county-level measures). b Descriptive statistics for county-level measures were derived from the counties represented in the Early Childhood Longitudinal Study, Birth Cohort. c Higher NDI indicates worse neighborhood conditions.
measured height and self-reported weight before 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).23 In addition, we evaluated plurality (whether the index child was a singleton or multiple birth), parity (data from the birth certificate, which was coded as number of previous live births: nulliparous, primiparous, or multiparous), and maternal report of alcohol and tobacco use during the 3 months before pregnancy and in the final 3 months of pregnancy (never, use in the 3 months before conception only, or any use in the final 3 months of pregnancy). 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: 1. 2. 3. 4. 5.
death of the respondent’s mother, death of the respondent’s father, divorce, separation from partner, or death of a spouse.
Maternal sociodemographic factors. We examined the following maternal sociodemographic factors: race/ethnicity (nonHispanic White, non-Hispanic Black, non-Hispanic
Asian/Pacific Islanders, Hispanic, or nonHispanic other race), 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, 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), and socioeconomic status (SES). We defined SES by using a 5-category composite index (quintiles) generated by the NCES that incorporated parental education, occupation, and household income.18
Statistical Analyses We conducted the 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 we applied survey weights to produce estimates that accounted for the complex survey design, unequal probabilities of selection, and survey nonresponse. We generated summary statistics to describe sample characteristics and used the v2 test to
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determine significant differences in sample characteristics among the 3 NDI groups (e.g., advantaged, middle, and disadvantaged). We used stratified analysis to examine how the association between PSLEs and birth weight varied by NDI tertile. Within each tertile, we used multinomial logistic regression to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) comparing birth weight statuses of infants born to women exposed and not exposed to PSLEs. We made comparisons to examine how obstetric, maternal sociodemographic, and maternal health behavior factors were associated with birth weight across neighborhoods. We conducted a sensitivity analysis to examine the robustness of the estimates by using an alternative NDI categorization, whereby we created tertiles using all of the 3139 counties in the United States.
RESULTS Among 9300 mothers, 19.7% experienced any PSLE. Most mothers resided in advantaged neighborhoods (44.9%), followed by middle and disadvantaged neighborhoods (35.1% and 19.9%, respectively; Table 2). Those living in disadvantaged neighborhoods were more likely to experience stressful life events during pregnancy, be nondrinkers, and be smokers. These women were also more likely to be younger, non-Hispanic Black, Hispanic, unmarried, publicly insured, and have lower SES.
Preconception Stressful Life Events When stratified by neighborhood conditions, we found a gradient in the effect of PSLEs on the risk of having VLBW infants after adjustment for other covariates; the association between PSLEs and delivering a VLBW infant was strongest in disadvantaged neighborhoods (AOR = 1.62; 95% CI = 1.04, 2.53), followed by middle (AOR = 1.39; 95% CI = 1.00, 1.93) and advantaged (AOR = 1.29; 95% CI = 0.91, 1.82) neighborhoods (Table 3 and Figure 2). In the adjusted stratified model, mothers with chronic conditions were more likely to have VLBW infants when they lived in disadvantaged neighborhoods (AOR = 2.85; 95% CI = 1.94, 4.19) compared with middle (AOR = 1.89; 95% CI = 1.30, 2.73) or advantaged (AOR = 1.89; 95% CI = 1.34, 2.65) neighborhoods.
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TABLE 2—Descriptive Statistics by Neighborhood Disadvantage: the Early Childhood Longitudinal Study, Birth Cohort; United States; 2001–2002 Neighborhood Condition Characteristic Total, weighted no.
Total
Advantaged
Middle
Disadvantaged
3 752 600
1 686 191
1 318 375
748 034
%
100.0%
44.9%
35.1%
19.9%
Total, unweighted no.
9 300
4 250
3 100
1 950
Very low birth weight Low birth weight
1.2 5.5
1.1 5.2
1.2 5.9
1.4 5.7
Normal birth weight
83.7
83.0
84.0
85.0
9.6
10.8
9.0
7.9
Birth outcomes, %
High birth weight
P
.006
*
Stress and obstetric factors Stressful life events before conception, %
.188
None
80.3
79.6
80.0
82.1
Any
19.7
20.4
20.0
17.9
94.3
95.3
94.1
92.2
5.7
4.7
5.9
7.8
None
86.5
86.5
85.5
87.9
Any
13.5
13.5
14.5
12.1
79.5
79.8
77.4
82.7
20.5
20.2
22.6
17.3
No
99.0
99.0
98.8
99.3
Yes
1.0
1.0
1.2
0.7
3.3
3.3
3.5
3.0
18.5–24.9
49.5
51.3
48.9
46.2
25–29.9
26.8
26.0
26.7
28.6
‡ 30 Unknown
17.9 2.5
16.7 2.7
18.4 2.4
19.9 2.3
Singleton
98.3
98.2
98.3
98.5
Multiple
1.7
1.8
1.7
1.5
Nulliparous
40.7
41.6
39.0
41.4
Primaparous
32.8
33.6
32.5
31.5
Multiparous
26.5
24.8 Maternal health behaviors
28.5
27.1
Never
62.0
56.5
64.2
70.5
***
3 mo before conception only
34.7
39.4
32.8
27.3
***
3.3
4.1
3.0
2.3
Stressful life events during pregnancy, % None Any
< .001
Pregnancy complications, %
.535
Maternal chronic conditions, % None Any Previous child born preterm or SGA, %
.373
.536
Prepregnancy BMI, kg/m2, % < 18.5
.222
No. of children born, %
.161
Parity, %
.103
Alcohol use, %
Final 3 mo of pregnancy
*
* < .001
Tobacco use, %
* .013
Never
76.7
78.6
77.2
71.8
*
3 mo before conception only Final 3 mo of pregnancy
12.3 11.0
11.6 9.8
11.6 11.2
15.0 13.2
*
Continued
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TABLE 2—Continued Maternal sociodemographic factors Age, y, %
< .001
15–19
7.5
5.4
8.3
10.8
***
20–24
24.3
20.4
25.5
30.7
***
25–29
26.3
24.9
26.6
28.7
30–34 ‡ 35
25.0 17.0
28.4 20.8
24.2 15.4
18.6 11.2
Non-Hispanic White
57.4
64.9
53.1
47.9
Non-Hispanic Black
14.0
8.6
16.7
21.4
***
3.5
4.8
2.7
1.8
***
Race/ethnicity, %
Non-Hispanic Asian/Pacific Islander Non-Hispanic other Hispanic Marital status (at birth), % Married or living with partner Separated/divorced/widowed
*** *** < .001
2.5
2.5
2.6
2.3
22.7
19.2
24.9
26.6
83.5
88.3
80.8
77.1
***
< .001 ***
3.0
2.3
3.6
3.7
**
13.5
9.4
15.6
19.2
***
Private only
59.1
70.7
53.9
42.1
***
Any public
37.5
26.1
42.9
53.8
***
3.4
3.3
3.2
4.0
19.7
14.1
23.9
25.1
< .001 ***
Second quintile
20.0
15.9
21.2
27.2
***
Third quintile
20.1
19.2
20.3
21.7
Never married Health insurance status, %
None Socioeconomic status, % First quintile (lowest)
< .001
Fourth quintile
20.2
22.9
19.2
15.7
***
Fifth quintile (highest)
20.0
27.8
15.5
10.4
***
Northeast
17.2
16.6
15.9
20.8
Midwest South
22.4 36.5
27.3 26.4
21.3 42.8
13.3 48.5
**
West
23.9
29.8
20.1
17.4
*
Region of residence, %
< .001
Note. BMI = body mass index; SGA = small for gestational age. Data are weighted percentages. National Center for Education Statistics rounding rules were applied to unweighted numbers; unweighted subgroup numbers may not add to the total because of rounding error. Subgroup post hoc P values: *P < .05; **P < .01; ***P < .001.
When stratified by neighborhood disadvantage, we similarly observed a gradient in the risk of having VLBW infants among non-Hispanic Black mothers after adjusting for other risk factors; this association was strongest in disadvantaged neighborhoods (AOR = 3.75; 95% CI = 1.88, 7.48), followed by middle (AOR = 2.66; 95% CI = 1.77, 3.99) and advantaged (AOR = 2.27; 95% CI = 1.46, 3.54) neighborhoods. In addition, Hispanic women had increased odds of delivering a VLBW infant in disadvantaged neighborhoods (AOR = 1.86; 95% CI = 1.14, 3.02), but not in middle or advantaged neighborhoods.
Sensitivity Analyses
DISCUSSION
The sampling frame of the ECLS-B included 700 counties of the 3139 counties in the United States, and these 700 counties might not be necessarily representative all counties in the United States. To examine whether our findings were robust to the different stratification of counties, we ran the same models using NDI tertiles based on all 3139 counties. We identified a similar gradient across neighborhood tertiles, such that that the impact of PSLEs was largest in disadvantaged neighborhoods, followed by middle and advantaged neighborhoods.
We found a gradient in the association between experiencing any PSLE and the increased risk of having a VLBW infant by neighborhood disadvantage. The strongest association was found in the most disadvantaged neighborhoods, followed by middle and advantaged neighborhoods. Our results were consistent with previous studies that showed an association between neighborhood disadvantage24,25 and PSLEs2---4 with adverse birth outcomes. The similar gradient in the risk of having VLBW
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TABLE 3—Neighborhood Effects on the Associations Between Very Low Birth Weight and Preconception Stressful Life Events, Chronic Conditions, and Race/Ethnicity: the Early Childhood Longitudinal Study, Birth Cohort; United States; 2001–2002 Stratified Characteristic
Overall, OR (95% CI)
Advantaged, OR (95% CI)
Middle, OR (95% CI)
Disadvantaged, OR (95% CI)
Stressful life event before conception Unadjusted model None (Ref)
1.00
1.00
1.00
1.00
1.70 (1.46, 1.99)
1.80 (1.40, 2.32)
1.62 (1.28, 2.05)
1.70 (1.22, 2.36)
None (Ref)
1.00
1.00
1.00
1.00
Any
1.35 (1.10, 1.65)
1.29 (0.91, 1.82)
1.39 (1.00, 1.93)
1.62 (1.04, 2.53)
Any Adjusted modela
Maternal chronic conditions Unadjusted model None (Ref)
1.00
1.00
1.00
1.00
Any
2.80 (2.34, 3.36)
2.87 (2.16, 3.82)
2.43 (1.73, 3.40)
3.54 (2.52, 4.96)
1.00
1.00
1.00
1.00
1.98 (1.59, 2.46)
1.89 (1.34, 2.65)
1.89 (1.30, 2.73)
2.85 (1.94, 4.19)
Adjusted modela None (Ref) Any
Race/ethnicity Unadjusted model Non-Hispanic White (Ref)
1.00
1.00
1.00
1.00
Non-Hispanic Black
2.43 (2.03, 2.90)
2.49 (1.86, 3.33)
2.11 (1.65, 2.70)
2.78 (1.78, 4.35)
Non-Hispanic Asian/Pacific Islander
0.84 (0.56, 1.28)
0.99 (0.60, 1.64)
0.57 (0.26, 1.22)
0.77 (0.18, 3.36)
Non-Hispanic other Hispanic
0.93 (0.52, 1.65) 1.19 (0.98, 1.44)
0.93 (0.44, 2.00) 1.22 (0.90, 1.64)
0.78 (0.31, 2.00) 1.07 (0.78, 1.47)
1.21 (0.47, 3.13) 1.34 (0.88, 2.04)
Adjusted modela Non-Hispanic White (Ref)
1.00
1.00
1.00
1.00
Non-Hispanic Black
2.79 (2.08, 3.73)
2.27 (1.46, 3.54)
2.66 (1.77, 3.99)
3.75 (1.88, 7.48)
Non-Hispanic Asian/Pacific Islander
1.23 (0.78, 1.94)
1.23 (0.71, 2.13)
0.94 (0.42, 2.11)
0.95 (0.18, 5.02)
Non-Hispanic other
0.71 (0.35, 1.43)
0.68 (0.27, 1.71)
0.67 (0.28, 1.60)
1.08 (0.38, 3.12)
Hispanic
1.56 (1.19, 2.03)
1.46 (0.91, 2.33)
1.39 (0.91, 2.13)
1.86 (1.14, 3.02)
Note. CI = confidence interval; OR = odds ratio. a Other covariates include stressful life events during pregnancy, pregnancy complications, previous child born preterm or small for gestational age, prepregnancy body mass index, number of children born, parity, maternal age (years), race/ethnicity, marital status (at birth), health insurance status, socioeconomic status, regions of residence, tobacco use, and alcohol use.
infants by neighborhood condition was also found among mothers who experienced maternal chronic conditions 5 and among nonHispanic Black and Hispanic women. 6,7 The collective impact of disadvantage26 might explain the elevated risk of having VLBW infants among women living in disadvantaged neighborhoods and among those who experienced PSLEs, who had chronic health conditions, or were racial/ethnic minorities. This hypothesis suggests that neighborhood conditions might operate as a moderator on the effects of maternal experiences before and during pregnancy on birth outcomes.
Specifically, disadvantaged neighborhood conditions might directly affect the amount of resources and stressors that reproductive age women experience, such as limited access to prenatal care, unhealthy behaviors, and poor health conditions,8,25,27 which all eventually contribute to adverse birth outcomes. Furthermore, disadvantaged neighborhoods might be associated with increased chronic stressors that contribute to deterioration in normal physiological processes, which might be more severe among minority women compared with nonHispanic White women across the life course.1,17,24
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Our findings suggest that focusing solely on individual-level risk factors during a woman’s pregnancy might not be sufficient to prevent or reduce adverse birth outcomes. Because of persistent disparities in birth outcomes, such proximal factors might reflect only a single dimension of disadvantage, and to achieve better birth outcomes it might be necessary to address upstream health determinants before pregnancy and within women’s social contexts. For example, 1 study found that, in the city of Milwaukee, higher neighborhood SES was more protective against poor birth outcomes, including infant mortality rates, LBW, or
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Note. A = advantaged neighborhoods; D = disadvantaged neighborhoods; M = middle advantaged neighborhoods. The unlabeled point estimates reflect the overall association. Models were adjusted for stressful life events during pregnancy, pregnancy complications, previous child born preterm or small for gestational age, prepregnancy body mass index, number of children born, parity, maternal age (years), race/ethnicity, marital status (at birth), health insurance status, socioeconomic status, regions of residence, tobacco use, and alcohol use
FIGURE 2—The gradient of the risk for having very low birth weight infants associated with preconception stressful life events by neighborhood disadvantage.
preterm birth among non-Hispanic White women compared with non-Hispanic Black women.28 Although this was only 1 example of how neighborhood conditions differentially affect maternal and child health, such regional racial/ethnic disparities in birth outcomes are pervasive across the United States, and have drawn the attention of policymakers at the local, state, and federal levels.29 To address regional disparities, neighborhood- or community-level interventions are required; the Best Babies Zone initiative is an example of a community-level effort to improve maternal and child health outcomes by implementing cross-sector collaborations for better educational, economic, and community conditions.30,31 At the Best Babies Zone site in Oakland, California, community residents
have partnered with national and local organizations to develop solutions to neighborhood issues and foster new county collaborations that reduce existing health disparities between non-Hispanic Black and non-Hispanic White women and ensure a healthy future for the neighborhood’s children.31
Limitations There are some potential limitations of this work that should be acknowledged. First, because we cross-sectionally examined NDI at the infant’s birth, we could not establish a causal association among neighborhood context, PSLEs, and birth outcomes, or estimate the cumulative effect of neighborhood conditions over the life course on birth outcomes. In addition, we did not account for the length of
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time that the respondents resided in their neighborhoods before the birth of the child. Also, the ECLS-B excluded children who died before 9 months of age; this possibly resulted in a survival bias that would have likely biased our results to the null. We did not account for the timing of exposure of PSLEs; however, in previous work, we found that the effect of PSLEs that occurred 1 year or more before conception had a stronger association with VLBW than did events that occurred within 1 year prior to conception. 2 Future research should strive to explore the exposure window of PSLEs when investigating the antecedents of adverse obstetric outcomes. Neighborhood effects are often measured at the census block, tract, or local levels, 8 but census tract or block information was not available from birth certificates provided by ECLS-B data, so we could not conduct analyses using smaller areas. However, there are 2 reasons that a county might be a more useful proxy for neighborhoods. First, the potential influences of socioeconomic characteristics of neighborhoods might be more meaningful on a higher level (e.g., county) than a lower one (e.g., block or tract). 8 For example, an individual’s daily life might not be limited to a census tract or block level, but instead, it could extend to cover a larger scale, such as a city or county level. Second, counties are the primary administrative division of most states 32 and thus represent the level that policy interventions can be directly linked with. Our approach provided consistent results with previous studies that used a census tract or census block level as a proxy for neighborhood in examining health outcomes.19---21,33 Our study also had important strengths. First, we stratified our exposure by using an a priori conceptualization of exposure (i.e., NDI tertile), which is an important consideration for neighborhood studies.33 Second, the majority of previous studies examined the independent impact of either neighborhood conditions or PSLEs on birth outcomes separately, within a local context or within several states.10---13,25 We extended the findings of previous neighborhood studies by identifying a gradient in the
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association between PSLEs and birth weight using nationally representative data.
editing, and refining the article. D. Zarak assisted with writing and editing of the article.
Conclusions
Acknowledgments
We found a gradient in the association between experiencing any PSLE and the increased risk of having a VLBW infant by neighborhood disadvantage. The risk of having VLBW infants among women with PSLEs, chronic conditions, or minority women was strongest among those living in disadvantaged neighborhoods, which suggests exacerbation of risk within disadvantaged environments. To achieve better birth outcomes, it might be necessary to address upstream health determinants before pregnancy and women’s social contexts. Therefore, such population-level interventions should focus on reducing the deleterious effects of stressors and on improving neighborhood conditions to improve birth outcomes. j
About the Authors At the time of the study, Whitney P. Witt was with Maternal and Child Health Research, Truven Health Analytics, Durham, NC. Hyojun Park, Kara Mandell, and Debanjana Chatterjee were with the Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, Madison. Lauren E. Wisk was with the Center for Child Health Care Studies in the Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA. Erika R. Cheng was with Harvard Medical School and the Division of General Academic Pediatrics at the Massachusetts General Hospital for Children, Boston. Dakota Zarak was with the Department of Psychology, University of Wisconsin, Madison. Correspondence should be sent to Whitney P. Witt, PhD, MPH, Truven Health Analytics, 4819 Emperor Boulevard, Suite 125, Durham, NC 27703 (e-mail: whitney.witt@ truvenhealth.com). Reprints can be ordered at http://www. ajph.org by clicking the “Reprints” link. This article was accepted January 8, 2015.
Contributors W. P. Witt made substantial contributions to the study design, acquisition of data, interpretation of data, and drafting of the article and was ultimately responsible for overseeing the data analysis and article preparation. H. Park contributed to the study design, data preparation and analysis, interpretation of data, and drafting of the article. L. E. Wisk contributed to the study design, data preparation and analysis, interpretation of data, and drafting of the article. E. R. Cheng helped with the study design, data interpretation, and drafting of the article. K. Mandell helped with the interpretation of the study data, summarization of study results, and editing of the article. D. Chatterjee assisted in interpreting the study data, summarizing study results,
This project was made possible by a Health Resources and Services Administrative (HRSA) (W. P. Witt, L. E. Wisk, and D. Chatterjee: R40MC23625; principal investigator W. P. Witt) grant. Additional funding for this research was provided by grants from the Agency for Healthcare Research and Quality (K. Mandell T32 HS00083; principal investigator M. Smith), the 2012-2013 Herman I. Shapiro Distinguished Graduate Fellowship (L. E. Wisk), and a National Research Service Award institutional training grant (E. R. Cheng: T32-HD075727; principal investigator J. A. Finkelstein). L. E. Wisk was supported by the Thomas O. Pyle Fellowship and an Agency for Healthcare Research and Quality Postdoctoral Training Grant 2T32HS000063-20 (principal investigator J. A. Finkelstein). D. Chatterjee was additionally supported by National Research Service Award (NRSA) in Primary Medical Care Grant T32HP22239 (principal investigator: I. Borowsky). We would also like to 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 because the data had been previously collected and de-identified.
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