Neighborhood conditions and birth outcomes: Understanding the role of perceived and extrinsic measures of neighborhood quality : Environmental Epidemiology

Secondary Logo

Journal Logo

Original Research Article

Neighborhood conditions and birth outcomes

Understanding the role of perceived and extrinsic measures of neighborhood quality

Eick, Stephanie M.a,*; Cushing, Larab; Goin, Dana E.c; Padula, Amy M.c; Andrade, Aileenc; DeMicco, Erinc; Woodruff, Tracey J.c; Morello-Frosch, Rachelc,d

Author Information
Environmental Epidemiology: October 2022 - Volume 6 - Issue 5 - p e224
doi: 10.1097/EE9.0000000000000224

Abstract

What this study adds

Living in a disadvantaged neighborhood has been associated with adverse birth outcomes. However, most prior studies have conceptualized neighborhoods using census boundaries, which may not always correlate with how individuals classify their neighborhoods. We observed that those who lived in an extrinsically disadvantaged neighborhood and who had poor neighborhood perceptions had modestly higher birthweight z-scores. This is one of few studies examining neighborhood perceptions in conjunction with extrinsic measures of neighborhood quality, defined using census block group indicators. Our findings indicate that neighborhood factors are not consistently associated with adverse birth outcomes.

Introduction

Preterm birth and low birthweight, two of the most common adverse birth outcomes, affect between 8% and 10% of all livebirths in the United States.1 Over the lifespan, these infants suffer from chronic health conditions and neurodevelopmental delays at higher rates relative to those infants born at term, at a normal birthweight, and not growth restricted.2–4 Furthermore, racial and ethnic disparities in adverse birth outcomes have been well-documented, with Blacks and Latinx consistently experiencing the highest rates of adverse birth outcomes.1 Despite their high prevalence, the etiology of these adverse birth outcomes remains poorly understood. Known individual-level risk factors do not wholly explain racial/ethnic disparities in adverse birth outcomes,5–7 leading to calls for more research on the role of neighborhood environments. It is becoming increasingly apparent that individual-level risk factors are not evenly distributed across geographic regions and social classes. Thus, there is a need to better understand the role that neighborhood social factors play in health outcomes.

Even after controlling for individual-level socioeconomic factors that influence which neighborhoods people live in, neighborhood inequalities may be driving disparities observed in adverse birth outcomes.8 Living in a disadvantaged neighborhood can restrict access to healthy foods options9 and educational opportunities,10 increase exposure to community violence,11 and may be associated with reduced physical activity because of limited greenspace.12 Neighborhood inequities are attributable to structural discrimination, such as racial residential segregation and housing discrimination, which ultimately influenced land use decisions, such as where to build highways (e.g., a source of emissions), and prevented communities from building wealth through homeownership.13 Together with more overt forms of discrimination, these factors can limit economic mobility and produce health disparities. Extrinsic measures of neighborhood disadvantage capture distinct aspects of the physical environment within a well-defined physical area (i.e., census units) and are often defined using a variety of indicators comprised of poverty, deprivation, racial residential segregation or racial composition, police violence, and crime.14 Studies have shown that neighborhood disadvantage, defined using extrinsic measures, may be particularly deleterious during pregnancy, as pregnant people who live in the most deprived neighborhoods are at the highest risk for preterm birth and low birthweight,14 with the strongest association observed among Blacks and Latinx.15 Further, our prior work has shown that perceived neighborhood quality, assessed via in-person interview questionnaires, is associated with experiences of stressful life events during pregnancy, and that experiencing stressful life events is associated with reduced fetal growth.16,17

While studies have examined extrinsic measures of neighborhood disadvantage in relation to birth outcomes, we have a limited understanding of how perceived neighborhood quality may influence birth outcomes and if there is a joint effect of living in an objectively deprived neighborhood and perceiving it as such. This may be particularly important, as census tract boundaries, defined as statistical subdivisions of a county encompassing between 1,200 and 8,000 residents, do not always correlate with how individuals define their neighborhoods and spend their time,18 suggesting that extrinsic measures of neighborhood disadvantage may be subject to exposure misclassification. Additionally, extrinsic measures do not fully capture collective efficacy or social cohesion, which reflect perceived willingness of residents to improve their neighborhoods and provide help to one another19 and may buffer against harmful effects.20 The health effects associated with neighborhood economic transitions (i.e., gentrification) are also under explored and studies indicate the effects of gentrification on the risk of preterm birth vary across racial and ethnic groups.21

In the present study, our study team leveraged an ongoing birth cohort in the San Francisco Bay Area of California with information on multiple extrinsic indicators of neighborhood disadvantage, as well as individual-level information about neighborhood perceptions, assessed via interview questionnaire. We examined extrinsic and perceived neighborhood quality measures in relation to gestational age and birthweight for gestational age z-scores, hypothesizing that worse extrinsic and perceived neighborhood quality would be associated with shorter gestational age and birthweight z-scores. Extrinsic measures were defined based on secondary data linked to geocoded residential addresses and perceived measures were assessed via an interview questionnaire at the second trimester.

Methods

Study population

Participants were enrolled in the Chemicals in Our Bodies (CIOB) study, an ongoing prospective birth cohort which has previously been described in detail elsewhere.22 Participants included in the present analysis delivered between 2014 and 2020 and included all individuals with completed medical record abstraction at the time of our analysis (N = 817). CIOB was designed to examine the cumulative effects of chemical and nonchemical stressors on fetal growth and offspring neurodevelopment. Pregnant people were recruited during the second trimester of pregnancy from three hospitals affiliated with the University of California, San Francisco (UCSF). Those recruited from Moffitt Long and Mission Bay were economically and ethnically diverse and were primarily privately insured, whereas the Zuckerberg San Francisco General Hospital serves predominantly low-income people of color without private health insurance. Eligibility criteria for CIOB included >18 years of age, singleton pregnancy, and English or Spanish speakers. As part of the study, participants consented to study staff accessing their medical records. The Institutional Review Boards at the UCSF (10-00861) and the University of California, Berkeley (2010-05-04) approved the study and all participants provided written, informed consent.

Perceived neighborhood measures

Perceived neighborhood quality was assessed during the second trimester using a self-administered interview questionnaire. The validated questionnaire included 15 questions regarding four subscale measures: collective efficacy, neighborhood safety, neighborhood satisfaction, and neighborhood physical order (Table S1; https://links.lww.com/EE/A195).23–25 Participants were classified as having experienced poor perceived neighborhood quality if they reported that their neighborhood lacked any of the four components.26 For all questions, answer options ranged from strongly disagree (a score of one) to strongly agree (a score of five) and positively worded statements were reverse coded so that higher scores always corresponded to poorer perceived neighborhood quality.

To assess collective efficacy, participants were asked how strongly they agreed with the statements “people around here are willing to help their neighbors,” “this is a close-knit neighborhood,” “people in this neighborhood can be trusted,” “people in this neighborhood generally don’t get along with each other,” “people in this neighborhood do not share the same values,” “children were skipping school and hanging out on a street corner,” “children were spray-painting graffiti on a local building,” “children were showing disrespect to an adult,” and “a fight broke out in front of their house.” Participants experienced low collective efficacy if their average score was ≥4.

Participants who strongly disagreed or disagreed to the statement “I feel safe in this neighborhood” were considered to perceive their neighborhood as unsafe.

Participants were considered to experience neighborhood dissatisfaction if they strongly disagreed or disagreed with the statement “I think this neighborhood is a good place for me to live” or strongly agreed or agreed with the statement “I would move out of this neighborhood if I could.”

Neighborhood physical order was assessed using three questions: “there is a lot of loud noise from cars, motorcycles, music, neighbors, or airplanes in my neighborhood,” “my neighborhood has a lot of vacant lots or vacant houses,” “there is heavy car or truck traffic in this neighborhood.” Participants were classified as living in a disorderly neighborhood if their average score was ≥4.

Extrinsic neighborhood measures

Maternal addresses during pregnancy were linked to census block group measures of extrinsic neighborhood quality. Addresses were geocoded using the Decentralized Geomarker Assessment for Multi-Site Studies (DeGAUSS) geocoding package. For addresses that could not be successfully geocoded with DeGAUSS, we used Google API.

Index of concentration at the extremes—Income

The Index of Concentration at the Extremes (ICE) captures the extent to which the disadvantaged and privileged populations are concentrated within a specific geographic area.27,28 We focused on ICE for income and defined advantaged individuals as those with an annual household income of >$200,000 and disadvantaged individuals as those with annual household income of <$40,000, representing the 20th versus 80th percentile of household income in the San Francisco Bay Area. We calculated ICE using 2014 to 2018 US American Community Survey (ACS) 5-year block group estimates.29 ICE is a continuous variable with scores ranging from negative one to one. We created tertiles for ICE based on all block groups in the CIOB study population, where the lowest tertile was considered the most disadvantaged and the highest tertile was considered the least disadvantaged.

Area Deprivation Index

We included the Area Deprivation Index (ADI) as an extrinsic measure of neighborhood disadvantage. The ADI is publicly available through the Neighborhood Atlas and is derived from 2014 to 2018 US ACS data.30 The 2018 ADI is a composite ranked index of 17 census block group factors encompassing a variety of social determinants of health, such as housing, income, employment, transportation, and education. State level ADI decile rankings range from 1 to 10, where one signifies the lowest level of neighborhood deprivation and a score of 10 signifies the highest level of deprivation. Tertiles of the ADI were created based on the distribution in the CIOB study population.

Gentrification

Information on displacement and gentrification typologies was obtained from the Urban Displacement Project,31 which provides a nuanced view of the stages of gentrification for a given metropolitan region. The typology classifies a metropolitan area’s census block groups into eight distinct categories using housing and demographic data obtained from the 1990, 2000, and 2010 US Decennial Census, 2013–2018 US ACS, and real estate market data from Zillow. Due to the small sample size across some categories, we collapsed the eight categories into three groups. Ongoing gentrification included those block groups classified as “low-income/susceptible to displacement,” “ongoing displacement of low-income households,” “at risk of gentrification,” and “early/ongoing gentrification.” “Advanced gentrification” and “stable moderate/mixed income” were considered to be stable. Finally, we considered block groups to be exclusive if they were classified as “at risk of being exclusive,” “becoming exclusive,” or “stable/advanced exclusive.”

Demographic characteristics and birth outcomes

Maternal age, maternal education, marital status, current smoking status, maternal race/ethnicity, and maternal nativity were self-reported on an interview questionnaire administered during the second trimester. Participants were classified as experiencing financial strain if their annual household income was below the 2017 San Francisco county poverty line or reported finding it difficult to pay for food, housing, medical care, utilities, or other basic necessities.32 Information regarding parity and prepregnancy body mass index (BMI; kg/m2) was abstracted from the participant’s medical record. Covariates were defined based on their presentation in Table 1.

Table 1. - Demographics characteristics in the chemicals in our bodies study population (N = 817).
N (%)
Maternal age, years
 18–24 81 (10%)
 25–29 108 (13%)
 30–34 297 (36%)
 >35 317 (39%)
 Missing 14 (1.7%)
Maternal education
 <High school 84 (10%)
 High school degree or some college 204 (25%)
 College degree 195 (24%)
 Graduate degree 294 (36%)
 Missing 40 (4.9%)
Maternal race/ethnicity
 White 309 (38%)
 Black 49 (6%)
 Asian/Pacific Islander 141 (17%)
 Latina 279 (34%)
 Other/multiracial 26 (3%)
 Missing 13 (1.6%)
Prepregnancy body mass index
 Underweight (<18.5 kg/m2) 23 (3%)
 Normal (18.5–24.9 kg/m2) 376 (46%)
 Overweight (25–29.9 kg/m2) 179 (22%)
 Obese (>30 kg/m2) 119 (15%)
 Missing 120 (14.7%)
Parity
 No prior births 385 (47%)
 One or more prior births 385 (47%)
 Missing 47 (5.8)
Financial strain
 Yes 224 (27%)
 No 374 (46%)
 Missing 219 (26.8%)
Marital status
 Married 507 (67%)
 Living together 145 (18%)
 Single 78 (10%)
 Missing 87 (10.6%)
Infant sex
 Male 391 (48%)
 Female 399 (49%)
 Missing 27 (3.3%)
Nativity
 Foreign born 313 (38%)
 US born 401 (49%)
 Missing 103 (12.6%)
Gestational age (weeks)
 Mean (SD) 39 (2.0)
 Missing 55 (6.7%)
Birthweight (g)
 Mean (SD) 3345 (578.7)
 Missing 34 (4.2%)
Birthweight z-score
 Mean (SD) 0.10 (0.99)
 Missing 62 (7.6%)
SD, standard deviation.

Gestational age and infant birthweight were similarly abstracted from the medical record. Gestational age was estimated using the clinician’s best estimation of chronological gestational age based on last menstrual period, early ultrasound, or in vitro fertilization date. To disentangle the effects of gestational age on fetal growth, we calculated birthweight for gestational age z-scores. Birthweight z-scores were sex specific and calculated using a US population based reference.33

Statistical analysis

We examined the distribution of extrinsic neighborhood measures across perceived measures of neighborhood, as well as the distribution of extrinsic and perceived neighborhood quality measures across racial and ethnic groups (white versus person of color [POC]) and nativity status (foreign versus US born). Unadjusted and adjusted linear regression models were used to examine associations between objective and perceived neighborhood quality measures, and birth outcomes (e.g., gestational age and birthweight z-scores). Extrinsic and perceived neighborhood quality measures were treated as individual exposures in separate models. In models which included extrinsic neighborhood quality measures as the exposure of interest, data were organized in a hierarchical fashion with individual participants (level-1 units) nested within block groups (level-2 units). Due to limitations associated with multilevel modeling in this setting (ie, unbalanced data with many small clusters), we accounted for the nonindependence and clustering of individuals within block groups using the Huber-White cluster sandwich estimator of variance.34 We observed no evidence of nonlinearity was using loess curves (data not shown).

Maternal age, education, and marital status were retained as covariates in adjusted models. These covariates were chosen via a Directed Acyclic Graph (DAG; Figure S1; https://links.lww.com/EE/A195) that was informed via a literature review and associations between exposures and outcomes in our study population.35 We did not adjust for smoking status due to the small number of participants in our study population who reported being a current smoker (<2%), and because we thought it was likely to be a mediator rather than a confounder. We conceptualized race/ethnicity and nativity as social factors that may be proxies for experiences of racism and other forms of discrimination. We did not adjust for race/ethnicity and nativity in our primary models as we hypothesized that they would modify the neighborhood quality and birth outcomes associations.14,36 We hypothesized that financial strain may be acting as both a confounder and effect modifier, thus we conducted sensitivity analyses where we additionally adjusted for financial strain, as well as stratified by financial strain. To further examine effect modification, we examined the relationships between extrinsic and perceived neighborhood quality measures and birth outcomes using linear regression models stratified by race/ethnicity and nativity status. Additionally, we estimated the joint effects of poor perceived neighborhood quality and extrinsic neighborhood quality. In these analyses, we examined the association between extrinsic neighborhood quality indicators and birth outcomes stratified by overall poor perceived neighborhood quality (yes versus no).

We did not examine preterm birth (N = 63; 8.3%), low birthweight (N = 46; 5.9%), and small for gestational age (N = 70; 7.3%) due to the small number of participants who experienced these outcomes. Further, we did not adjust for multiple comparisons, as it is not always necessary in observational epidemiologic studies and may increase the probability of type II error due to low statistical power.37 A complete case analysis was used for all models and all analyses were conducted in R Version 4.0.1.

Results

At the time of our analysis, there were 817 birth parent-child pairs enrolled in CIOB. Of this group, roughly 75% were at least 30 years of age (N = 614) and over 50% had a college or graduate degree (N = 489). Approximately 38% of participants self-identified as White (N = 309), 34% as Latina (N = 279), and 38% of participants were born outside of the United States (N = 313) (Table 1). The mean gestational age at delivery was 39 weeks and the mean birthweight was 3,345 g.

Approximately half of participants lived in a neighborhood classified as “stable,” as defined by the measure of gentrification (N = 375; 46%) (Table S2; https://links.lww.com/EE/A195). US born and white participants were more likely to live in the least disadvantaged neighborhoods according to ICE income and the ADI, while over 50% of participants who were people of color (N = 384) or foreign-born (N = 242) lived in areas that were stable or experiencing ongoing gentrification (Table S2; https://links.lww.com/EE/A195). In the overall study population, 17% (N = 141) reported poor perceived neighborhood quality and these individuals were also more likely to live in the most disadvantaged areas according to all extrinsic measures (Table 2). Few participants experienced poor collective efficacy (2%); therefore, we did not include it as an exposure in subsequent analyses. Block groups with at least 40% of participants reporting poor perceived neighborhood quality were clustered around the Bayview District and just north of the Mission District (Figure 1). Block groups classified as experiencing early or ongoing gentrification and were the most disadvantaged according to ICE income and were also clustered around these areas (Figure 1).

Table 2. - Distribution of perceived neighborhood measures across extrinsic neighborhood measures.
Poor neighborhood quality Dissatisfied with neighborhood Disorderly neighborhood Unsafe neighborhood
No
(N = 531)
Yes
(N = 141)
No
(N = 668)
Yes
(N = 98)
No
(N = 718)
Yes
(N = 48)
No
(N = 656)
Yes
(N = 110)
N (%) N (%) N (%) N (%) N (%) N (%) N (%) N (%)
ICE income
 Low (most disadvantaged) 143 (27%) 75 (53%) 185 (28%) 65 (66%) 224 (31%) 25 (52%) 58 (53%) 191 (29%)
 Medium 189 (36%) 32 (23%) 233 (35%) 20 (20%) 242 (34%) 12 (25%) 21 (19%) 233 (36%)
 High (least disadvantaged) 199 (37%) 34 (24%) 250 (37%) 13 (13%) 252 (35%) 11 (23%) 31 (28%) 232 (35%)
Area Deprivation Index
 Low (least disadvantaged) 252 (47%) 50 (35%) 308 (46%) 31 (32%) 326 (45%) 14 (29%) 302 (46%) 38 (35%)
 Medium 122 (23%) 29 (21%) 153 (23%) 18 (18%) 159 (22%) 12 (25%) 149 (23%) 22 (20%)
 High (most disadvantaged) 153 (29%) 62 (44%) 202 (30%) 49 (50%) 228 (32%) 22 (46%) 200 (30%) 50 (45%)
Urban displacement
 Exclusive 175 (86.6%) 27 (13.4%) 211 (94.2%) 13 (5.8%) 218 (97.3%) 6 (2.7%) 201 (89.7%) 23 (10.3%)
 Stable 250 (82.2%) 54 (17.8%) 320 (91.4%) 30 (8.6%) 332 (94.6%) 19 (5.4%) 309 (88.0%) 42 (12.0%)
 Ongoing gentrification 94 (6.2%) 58 (32.8%) 124 (70.1%) 53 (29.9%) 155 (88.1%) 21 (11.9%) 131 (74.4%) 45 (25.6%)
Percentages may not sum to 100 due to rounding. Perceived neighborhood quality is a composite measure of neighborhood dissatisfaction, disorderly neighborhood, unsafe neighborhood, and collective efficacy.

F1
Figure 1.:
Distributions of poor perceived neighborhood quality, and tertiles of the ADI, ICE income, and gentrification across San Francisco, CA block groups. To protect confidentiality and avoid displaying unstable estimates, maps were restricted block groups in San Francisco with >2 participants (N = 683).

In unadjusted models, compared to the least disadvantaged tertile, living in a neighborhood in the most disadvantaged tertiles of ICE income was associated with shorter gestational age in weeks (Table 3) (β = –0.49, 95% confidence interval [CI] = –0.84, –0.15). The association with ICE was attenuated after adjustment for maternal age, education, and marital status, with maternal age being the strongest driver. In adjusted models and relative to the least disadvantaged tertile, living in the most disadvantaged tertile of the ADI was similarly associated with a reduction in gestational age (β = –0.35, 95% CI = –0.67, –0.02). Gentrification and perceived indicators of neighborhood quality were not strongly associated with gestational age in unadjusted or adjusted models (Table 3).

Table 3. - Linear regression estimates and 95% confidence intervals for the relationship between perceived and extrinsic neighborhood measures and birth outcomes.
Gestational age (weeks) Birthweight z-scores
Unadjusted Adjusted1 Unadjusted Adjusted1
N Beta 95% CI N Beta 95% CI N Beta 95% CI N Beta 95% CI
Extrinsic
ICE Income
 Low (Most Disadvantaged) 241 –0.49 (–0.84, –0.15) 221 –0.14 (–0.53, 0.25) 239 0.11 (–0.07, 0.29) 219 0.19 (–0.01, 0.38)
 Medium 255 –0.21 (–0.57, 0.14) 232 0.02 (–0.32, 0.35) 253 0.02 (–0.15, 0.19) 230 0.06 (–0.11, 0.23)
 High (Least Disadvantaged) 266 Ref Ref 245 Ref Ref 263 Ref Ref 242 Ref Ref
Area Deprivation Index
 Low (Least Disadvantaged) 338 Ref Ref 310 Ref Ref 332 Ref Ref 304 Ref Ref
 Medium 176 –0.46 (–0.85, –0.07) 154 –0.32 (–0.67, 0.03) 176 –0.02 (–0.19, 0.15) 154 0.01 (–0.17, 0.18)
 High (Most Disadvantaged) 243 –0.38 (–0.71, –0.06) 231 –0.35 (–0.67, –0.02) 242 –0.04 (–0.21, 0.13) 230 –0.05 (–0.23, 0.14)
Urban displacement
 Exclusive 228 Ref Ref 211 Ref Ref 225 Ref Ref 208 Ref Ref
 Stable 350 0.25 (–0.11, 0.61) 318 0.32 (–0.02, 0.65) 348 0.02 (–0.14, 0.18) 316 0.07 (–0.09, 0.24)
 Ongoing Gentrification 168 –0.2 (–0.63, 0.24) 156 0.19 (–0.26, 0.64) 166 0.1 (–0.1, 0.3) 154 0.22 (–0.01, 0.44)
Perceived
Poor neighborhood quality
 No 511 Ref Ref 486 Ref Ref 509 Ref Ref 484 Ref Ref
 Yes 130 –0.25 (–0.61, 0.1) 123 –0.1 (–0.46, 0.27) 128 0.14 (–0.06, 0.33) 121 0.21 (0.01, 0.42)
Dissatisfied with neighborhood
 No 640 Ref Ref 605 Ref Ref 635 Ref Ref 600 Ref Ref
 Yes 89 –0.33 (–0.75, 0.09) 87 0.05 (–0.39, 0.5) 87 0.16 (–0.06, 0.38) 85 0.22 (–0.02, 0.45)
Disorderly neighborhood
 No 685 Ref Ref 649 Ref Ref 678 Ref Ref 642 Ref Ref
 Yes 45 0.2 (–0.37, 0.78) 43 0.43 (–0.16, 1.01) 45 0.11 (–0.19, 0.41) 43 0.18 (–0.13, 0.49)
Unsafe neighborhood
 No 628 Ref Ref 594 Ref Ref 622 Ref Ref 588 Ref Ref
 Yes 102 –0.27 (–0.67, 0.12) 98 –0.14 (–0.55, 0.26) 101 0.05 (–0.15, 0.26) 97 0.12 (–0.1, 0.33)
1Models adjusted for age, education, and marital status.
Perceived neighborhood quality is a composite measure of neighborhood dissatisfaction, disorderly neighborhood, unsafe neighborhood, and collective efficacy.

After adjustment for maternal age, education, and marital status, having poor perceived neighborhood quality, being dissatisfied with one’s neighborhood and living in a neighborhood experiencing ongoing gentrification were associated with higher birthweight z-scores (Table 3) (β = 0.21, 95% CI = 0.01, 0.42; β = 0.22, 95% CI = –0.02, 0.45; β = 0.22, 95% CI = –0.01, 0.44, respectively). This corresponds to an increase of 91 g and 95 g for poor perceived neighborhood quality and neighborhood dissatisfaction, respectively, for a 40-week gestation birth. Associations between extrinsic and perceived neighborhood quality measures and adverse birth outcomes were similar when financial strain was added as a covariate in adjusted models, and CIs overlapped with our primary results (Table S3; https://links.lww.com/EE/A195). When stratifying by race/ethnicity, nativity, and financial strain, these unintuitive associations between neighborhood perceptions and birthweight z-scores generally persisted among US born, white participants, and those who did not experience financial strain only (Tables S4-S6; https://links.lww.com/EE/A195).

In models examining the joint effect of living in an extrinsic disadvantaged neighborhood and perceiving it as such, we observed that those who reported poor perceived neighborhood quality and lived in two most disadvantaged tertiles of the ADI compared to the most advantaged had lower birthweight z-scores (Table 4).

Table 4. - Adjusted linear regression estimates and 95% confidence intervals for the relationship between extrinsic neighborhood measures and birth outcomes stratified by perceived poor neighborhood quality.
Gestational age (weeks) Birthweight z-scores
Poor neighborhood quality—Yes Poor neighborhood quality—No Poor neighborhood quality—Yes Poor neighborhood quality—No
N Beta 95% CI N Beta 95% CI N Beta 95% CI N Beta 95% CI
Extrinsic
ICE income
 Low (most disadvantaged) 66 –0.13 (–0.94, 0.69) 130 –0.38 (–0.83, 0.08) 65 0.17 (–0.31, 0.64) 129 0.18 (–0.05, 0.42)
 Medium 27 –0.34 (–1.2, 0.52) 172 –0.03 (–0.38, 0.33) 26 0.19 (–0.29, 0.67) 172 0.06 (–0.14, 0.26)
 High (least disadvantaged) 30 Ref Ref 184 Ref Ref 30 Ref Ref 183 Ref Ref
Area Deprivation Index
 Low (least disadvantaged) 44 Ref Ref 231 Ref Ref 42 Ref Ref 230 Ref Ref
 Medium 25 –0.02 (–0.78, 0.74) 111 –0.3 (–0.67, 0.07) 25 –0.31 (–0.86, 0.25) 111 0.04 (–0.16, 0.23)
 High (most disadvantaged) 54 –0.07 (–0.91, 0.77) 142 –0.34 (–0.71, 0.02) 54 –0.67 (–1.2, –0.13) 141 0.04 (–0.17, 0.26)
Urban displacement
 Exclusive 25 Ref Ref 161 Ref Ref 25 Ref Ref 160 Ref Ref
 Stable 45 –0.56 (–1.31, 0.19) 229 0.33 (–0.02, 0.69) 44 0.15 (–0.3, 0.59) 229 0.05 (–0.15, 0.24)
 Ongoing gentrification 51 0.1 (–0.8, 1.01) 86 –0.09 (–0.63, 0.45) 50 0.35 (–0.17, 0.87) 85 0.17 (–0.12, 0.45)
Models adjusted for age, education, and marital status.
Perceived neighborhood quality is a composite measure of neighborhood dissatisfaction, disorderly neighborhood, unsafe neighborhood, and collective efficacy.

Discussion

Among a diverse cohort of pregnant people in the San Francisco Bay Area, we observed that those who perceived their neighborhood as poor quality were also more likely to live in extrinsically disadvantaged neighborhoods and reside in areas experiencing ongoing gentrification. Living in an extrinsically disadvantaged neighborhood, according to the ADI and ICE for income, and reporting poor neighborhood quality or feeling unsafe in one’s neighborhood were also associated with shorter gestational age, although associations were not statistically significant. In contrast, we observed that living in a disadvantaged neighborhood, according to both extrinsic and perceived factors, was associated with higher birthweight for gestational age z-scores, an indicator of fetal growth. Our findings provide important information on the role of neighborhood perceptions, which contributes to the growing body of literature highlighting neighborhood social factors as contributors to birth outcomes.

Our finding that living in the most deprived tertiles of ICE for income was modestly associated with shorter gestational age at birth is consistent with past research examining ICE in relation to adverse maternal and child health outcomes, such as infant mortality, which occurs more frequently among those born preterm.8,38–42 For example, a study using intergenerationally linked California birth records found that living in neighborhoods with the greatest concentration of poverty according to ICE for income both in early childhood and adulthood was associated with an increased risk of preterm birth.8 Among a study of very preterm infants (<32 weeks gestation) in New York City, living in the lowest quintile (greatest concentration of poverty) relative to the highest was associated with a 40% increased risk of neonatal death.40 Similar associations were also observed in Chicago and California, where communities in the lower quintiles had higher infant mortality rates relative to the those in the most advantaged quintile of ICE for income.38,39

Using the ADI, we also observed that neighborhood deprivation was associated with a slight reduction in gestational age. Prior studies assessing neighborhood deprivation and gestational age have observed similar relationships,14 although to our knowledge none have used the ADI. For example, a study of eight metropolitan cities in the United States found that those in the most deprived quintile relative to the least deprived had increased odds of delivering preterm.43 Living in a disadvantaged neighborhood (operationalized by the ADI), was also associated with worse outcomes in terms of desired postpartum sterilization.44 Other factors that may contribute to neighborhood disadvantage, including fatal police violence, have also been linked to adverse birth outcomes.45

A unique aspect of our study was that we also had detailed information on neighborhood perceptions, which provides information about how individuals feel about their neighborhoods, as opposed to solely focusing on extrinsic measures, which may not reflect where individuals spend their time. We found that those who reported living in a poor quality or unsafe neighborhood had moderately shorter gestational age relative to those with better neighborhood perceptions. These unadjusted findings support what was observed with the Los Angeles Mommy and Baby surveys, which showed that worsening economic hardship and poor perceived neighborhood quality were associated with increased odds of preterm birth.46 However, associations between neighborhood perceptions and gestational age were further attenuated after adjusting for covariates in our study population. One explanation for these findings could be that neighborhood perceptions differ across racial and ethnic groups, which could be due to experiences of discrimination. Prior research using the California Behavioral Risk Factor Surveillance System (BRFSS) indicates that Latinos and Blacks report worse perceived neighborhood disorder relative to whites.47 In stratified analyses, we observed that poor perceived neighborhood quality was associated with a reduction in gestational age among POC only, although CIs were wide. While our fully adjusted models did not include race/ethnicity, we did include education, and marital status as indicators of socioeconomic status. In our study, non-White participants tended to be younger, unmarried and have lower education attainment, which is likely reflective of structural barriers and discrimination that disproportionally influence POC.

Contrary to our hypothesis, we observed that those who lived in an area experiencing ongoing gentrification and who reported poor perceived neighborhood quality and neighborhood dissatisfaction had higher birthweight z-scores. These inverse associations may be reflective of the uniqueness of our cohort. For example, participants living in San Francisco may be more likely to report their neighborhood as poor quality, even if they have a relatively high income, as San Francisco experienced an affordable housing shortage during the timeframe of our study. It is possible that neighborhood perceptions may change over time, and could vary based on how long an individual has lived in their neighborhood. Prior evidence also suggests that the neighborhood environment does not strongly influence birthweight among immigrants, of which we have many in our study.48 When stratifying by race/ethnicity and nativity status, the positive associations between neighborhood perceptions and higher birthweight z-scores persisted primarily among white and US born participants. However, the sample size for these analyses was small and this imprecision is reflected in our wide CIs. While a small percentage of white participants perceived their neighborhood as being of poor quality (<10%), the majority of white, US born participants in our study lived in exclusive and advantaged neighborhoods according to our extrinsic measures. Neighborhood affluence has been shown to be protective against adverse birth outcomes,49 which may suggest that other socioeconomic factors are more strongly tied to birthweight relative to neighborhood perceptions.

Our study has many strengths. We had detailed information on both perceived neighborhood quality and extrinsic indicators of neighborhood disadvantage, representing an advancement over prior studies as extrinsic neighborhood measures may not be truly reflective of where individuals interact and spend their time. We also included a measure of gentrification, that has not been as extensively studied in relation to birth outcomes and may be an important contributor to health disparities. We also acknowledge our limitations. First, we were not able to assess how social support modifies the relationship between objective and perceived neighborhood quality. Prior work suggests that social relationships and personal contacts buffer the negative effects of neighborhood deprivation on health outcomes.50 Second, we were unable to further stratify our results beyond white versus POC due to the sample size restrictions. It is highly likely that the relationship between perceived neighborhood quality and birth outcomes would vary across individual non-White racial and ethnic groups, as this has been observed previously.47 Third, we did not have information on paternal characteristics, which may have an impact on the birth outcomes examined here. We additionally did not have information on maternal exposure to smoking prior to pregnancy. Finally, our results may not be generalizable beyond the San Francisco Bay Area and larger studies are needed to confirm these findings.

Conclusions

In our study population, we observed that living in the most extrinsically disadvantaged neighborhoods and having poor neighborhood perceptions were both associated with a modest increase in birthweight z-scores, while associations with gestational age were less consistent. Our findings indicate that the neighborhood environment is inconsistently associated with adverse birth outcomes, which contributes to a growing body of literature exploring the role of neighborhood inequalities on health outcomes. Future studies are needed to further disentangle the effects of objective and perceived neighborhood quality on additional maternal and child health outcomes, such as offspring neurodevelopment.

Conflict of interest statement

The authors declare that they have no financial conflict of interest with regard to the content of this report.

References

1. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK. Births: final data for 2019. Natl Vital Stat Rep. 2021;70:1–51.
2. Belbasis L, Savvidou MD, Kanu C, Evangelou E, Tzoulaki I. Birth weight in relation to health and disease in later life: an umbrella review of systematic reviews and meta-analyses. BMC Med. 2016;14:147.
3. Luu TM, Katz SL, Leeson P, Thébaud B, Nuyt AM. Preterm birth: risk factor for early-onset chronic diseases. CMAJ. 2016;188:736–746.
4. Cheong JL, Doyle LW, Burnett AC, et al. Association between moderate and late preterm birth and neurodevelopment and social-emotional development at age 2 years. JAMA Pediatr. 2017;171:e164805.
5. Goldenberg RL, Cliver SP, Mulvihill FX, et al. Medical, psychosocial, and behavioral risk factors do not explain the increased risk for low birth weight among black women. Am J Obstet Gynecol. 1996;175:1317–1324.
6. McGrady GA, Sung JF, Rowley DL, Hogue CJ. Preterm delivery and low birth weight among first-born infants of black and white college graduates. Am J Epidemiol. 1992;136:266–276.
7. Kramer MR, Hogue CR. What causes racial disparities in very preterm birth? A biosocial perspective. Epidemiol Rev. 2009;31:84–98.
8. Shrimali BP, Pearl M, Karasek D, Reid C, Abrams B, Mujahid M. Neighborhood privilege, preterm delivery, and related racial/ethnic disparities: an intergenerational application of the index of concentration at the extremes. Am J Epidemiol. 2020;189:412–421.
9. Hilmers A, Hilmers DC, Dave J. Neighborhood disparities in access to healthy foods and their effects on environmental justice. Am J Public Health. 2012;102:1644–1654.
10. Nieuwenhuis J, Kleinepier T, van Ham M. The role of exposure to neighborhood and school poverty in understanding educational attainment. J Youth Adolesc. 2021;50:872–892.
11. Goin DE, Rudolph KE, Ahern J. Predictors of firearm violence in urban communities: a machine-learning approach. Health Place. 2018;51:61–67.
12. De la Fuente F, Saldías MA, Cubillos C, et al. Green space exposure association with type 2 diabetes mellitus, physical activity, and obesity: a systematic review. Int J Environ Res Public Health. 2020;18:97.
13. Self RO. American Babylon. STU-Student edition. Princeton University Press; 2003. Available at: http://www.jstor.org/stable/j.ctt5hhq2x. Accessed 23 June 2022.
14. Ncube CN, Enquobahrie DA, Albert SM, Herrick AL, Burke JG. Association of neighborhood context with offspring risk of preterm birth and low birthweight: a systematic review and meta-analysis of population-based studies. Soc Sci Med. 2016;153:156–164.
15. Janevic T, Stein CR, Savitz DA, Kaufman JS, Mason SM, Herring AH. Neighborhood deprivation and adverse birth outcomes among diverse ethnic groups. Ann Epidemiol. 2010;20:445–451.
16. Eick SM, Goin DE, Izano MA, et al. Relationships between psychosocial stressors among pregnant women in San Francisco: A path analysis. PLoS One. 2020;15:e0234579.
17. Goin DE, Izano MA, Eick SM, et al. Maternal experience of multiple hardships and fetal growth: extending environmental mixtures methodology to social exposures. Epidemiology. 2021;32:18–26.
18. Basta LA, Richmond TS, Wiebe DJ. Neighborhoods, daily activities, and measuring health risks experienced in urban environments. Soc Sci Med. 2010;71:1943–1950.
19. Fone D, White J, Farewell D, et al. Effect of neighbourhood deprivation and social cohesion on mental health inequality: a multilevel population-based longitudinal study. Psychol Med. 2014;44:2449–2460.
20. Kingsbury M, Clayborne Z, Colman I, Kirkbride JB. The protective effect of neighbourhood social cohesion on adolescent mental health following stressful life events. Psychol Med. 2020;50:1292–1299.
21. Huynh M, Maroko AR. Gentrification and preterm birth in New York City, 2008–2010. J Urban Health. 2014;91:211–220.
22. Eick SM, Enright EA, Geiger SD, et al. Associations of maternal stress, prenatal exposure to per- and polyfluoroalkyl substances (PFAS), and demographic risk factors with birth outcomes and offspring neurodevelopment: an overview of the ECHO.CA.IL prospective birth cohorts. Int J Environ Res Public Health. 2021;18:E742.
23. Schulz AJ, Kannan S, Dvonch JT, et al. Social and physical environments and disparities in risk for cardiovascular disease: the healthy environments partnership conceptual model. Environ Health Perspect. 2005;113:1817–1825.
24. Parker EA, Lichtenstein RL, Schulz AJ, et al. Disentangling measures of individual perceptions of community social dynamics: results of a community survey. Health Educ Behav. 2001;28:462–486.
25. Sampson RJ, Raudenbush SW, Earls F. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science. 1997;277:918–924.
26. Izano MA, Cushing LJ, Lin J, et al. The association of maternal psychosocial stress with newborn telomere length. PLoS One. 2020;15:e0242064.
27. Krieger N, Waterman PD, Spasojevic J, Li W, Maduro G, Van Wye G. Public health monitoring of privilege and deprivation with the index of concentration at the extremes. Am J Public Health. 2016;106:256–263.
28. Krieger N, Kim R, Feldman J, Waterman PD. Using the index of concentration at the extremes at multiple geographical levels to monitor health inequities in an era of growing spatial social polarization: Massachusetts, USA (2010-14). Int J Epidemiol. 2018;47:788–819.
29. The United States Census Burea. American Community Survey 5-Year Data. Available at: https://www.census.gov/programs-surveys/acs/data.html. Access date June 1, 2021.
30. Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible - the neighborhood atlas. N Engl J Med. 2018;378:2456–2458.
31. Thomas T, Driscoll A, Picado Aguilar G, et al. Urban-displacement/displacement-typologies: Release 1.1. doi:10.5281/zenodo.4356684. Available at: https://github.com/urban-displacement/displacement-typologies. Accessed May 21, 2021.
32. Kahn JR, Pearlin LI. Financial strain over the life course and health among older adults. J Health Soc Behav. 2006;47:17–31.
33. Talge NM, Mudd LM, Sikorskii A, Basso O. United States birth weight reference corrected for implausible gestational age estimates. Pediatrics. 2014;133:844–853.
34. Harrell FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Vol 608. Springer; 2001.
35. Dunlop AL, Essalmi AG, Alvalos L, et al.; program collaborators for Environmental Influences on Child Health Outcomes. Racial and geographic variation in effects of maternal education and neighborhood-level measures of socioeconomic status on gestational age at birth: Findings from the ECHO cohorts. PLoS One. 2021;16:e0245064.
36. Scott KA, Chambers BD, Baer RJ, Ryckman KK, McLemore MR, Jelliffe-Pawlowski LL. Preterm birth and nativity among Black women with gestational diabetes in California, 2013-2017: a population-based retrospective cohort study. BMC Pregnancy Childbirth. 2020;20:593.
37. Rothman KJ. No adjustments are needed for multiple comparisons. Epidemiology. 1990;1:43–46.
38. Bishop-Royse J, Lange-Maia B, Murray L, Shah RC, DeMaio F. Structural racism, socio-economic marginalization, and infant mortality. Public Health. 2021;190:55–61.
39. Chambers BD, Baer RJ, McLemore MR, Jelliffe-Pawlowski LL. Using index of concentration at the extremes as indicators of structural racism to evaluate the association with preterm birth and infant Mortality-California, 2011-2012. J Urban Health. 2019;96:159–170.
40. Janevic T, Zeitlin J, Egorova NN, et al. Racial and economic neighborhood segregation, site of delivery, and morbidity and mortality in neonates born very preterm. J Pediatr. 2021;235:116–123.
41. Wallace ME, Crear-Perry J, Green C, Felker-Kantor E, Theall K. Privilege and deprivation in Detroit: infant mortality and the Index of Concentration at the Extremes. Int J Epidemiol. 2019;48:207–216.
42. Dyer L, Chambers BD, Crear-Perry J, Theall KP, Wallace M. The Index of concentration at the extremes (ICE) and pregnancy-associated mortality in Louisiana, 2016-2017. Matern Child Health J. 2022;26:814–822.
43. O’Campo P, Burke JG, Culhane J, et al. Neighborhood deprivation and preterm birth among non-Hispanic Black and White women in eight geographic areas in the United States. Am J Epidemiol. 2008;167:155–163.
44. Arora KS, Ascha M, Wilkinson B, et al. Association between neighborhood disadvantage and fulfillment of desired postpartum sterilization. BMC Public Health. 2020;20:1440.
45. Goin DE, Gomez AM, Farkas K, et al. Occurrence of fatal police violence during pregnancy and hazard of preterm birth in California. Paediatr Perinat Epidemiol. 2021;35:469–478.
46. Bhatia N, Chao SM, Higgins C, Patel S, Crespi CM. Association of mothers’ perception of neighborhood quality and maternal resilience with risk of preterm birth. Int J Environ Res Public Health. 2015;12:9427–9443.
47. Plascak JJ, Hohl B, Barrington WE, Beresford SA. Perceived neighborhood disorder, racial-ethnic discrimination and leading risk factors for chronic disease among women: California Behavioral Risk Factor Surveillance System, 2013. SSM Popul Health. 2018;5:227–238.
48. Urquia ML, Frank JW, Glazier RH, Moineddin R, Matheson FI, Gagnon AJ. Neighborhood context and infant birthweight among recent immigrant mothers: a multilevel analysis. Am J Public Health. 2009;99:285–293.
49. Kane JB, Miles G, Yourkavitch J, King K. Neighborhood context and birth outcomes: going beyond neighborhood disadvantage, incorporating affluence. SSM Popul Health. 2017;3:699–712.
50. Klijs B, Mendes de Leon CF, Kibele EUB, Smidt N. Do social relations buffer the effect of neighborhood deprivation on health-related quality of life? Results from the LifeLines Cohort Study. Health Place. 2017;44:43–51.
Keywords:

neighborhood; built environment; birth outcomes; pregnancy

Supplemental Digital Content

Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.