Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Dec 12.
Published in final edited form as: J Youth Adolesc. 2022 Jul 19;51(11):2146–2160. doi: 10.1007/s10964-022-01658-6

Neighborhood Socioeconomic Deprivation in Early Childhood Mediates Racial Disparities in Blood Pressure in a College Student Sample

Olivia Nichols a, Thomas Fuller-Rowell a, Austin T Robinson b, DaJuandra Eugene c, Lydia Homandberg d
PMCID: PMC9744188  NIHMSID: NIHMS1851907  PMID: 35852667

Abstract

The influence of childhood contexts on adult blood pressure is an important yet understudied topic. Using a developmental perspective, this study examines the association between neighborhood socioeconomic disadvantage in early childhood (0–5yrs), middle childhood (6–12yrs) and adolescence (13–18yrs) on subsequent blood pressure in young adulthood. Data were from 263 college students (52% Black; Mage = 19.21 years) and neighborhood socioeconomic disadvantage was measured using a tract-level Area Deprivation Index. Neighborhood disadvantage in early childhood was significantly associated with diastolic blood pressure and explained 22% of the race difference between Black and White adults. The findings are consistent with the notion that early childhood may be a sensitive period for the effects of neighborhood disadvantage on blood pressure.

Keywords: Neighborhood socioeconomic disadvantage, child development, health disparities, blood pressure, early childhood

Introduction

High blood pressure is a major risk factor of cardiovascular disease, the leading cause of death in the United States (Virani et al., 2021). Recent estimates suggest nearly half of adults living in the United States have high blood pressure, resulting in over 116 million adults living with increased risk for cardiovascular disease and stroke (Center for Disease Control, 2021). In 2019 alone, nearly 517 thousand deaths were attributed to high blood pressure (Center for Disease Control, 2021). Associated with numerous social determinants of health, the prevalence of high blood pressure is unevenly distributed in the United States and a major contributor to racial health disparities (Balfour et al., 2015). Black Americans have the highest hypertension rates of any race or ethnicity (Virani et al., 2021, and among the highest rates in the world (Cooper et al., 2005). Numerous studies have shown that blood pressure disparities emerge during childhood and track into adulthood (Chen & Wang, 2008, Yang et al., 2020). Though childhood experiences are known to play a key role in shaping health outcomes and health disparities (Braveman & Barclay, 2009; Fuller-Rowell et al., 2021b), few studies have considered the importance of childhood contexts in predicting adult blood pressure. To address this knowledge gap, the current study considers the degree to which neighborhood socioeconomic disadvantage during three childhood periods—early childhood, middle childhood, and adolescence—is associated with blood pressure, and mediates disparities in blood pressure between Black and White college students.

Neighborhood and Health Inequity

In the United States, neighborhood environments have been profoundly shaped by institutional and structural racism (Bailey et al., 2021; Williams & Collins, 2001). Enacted through policy, law, and violence, racial residential segregation intentionally sorted the spatial distribution of economic, educational, and environmental characteristics to advantage White families and disadvantage Black families (Rothstein, 2018). The legacies and adaptations of this practice have contributed to significant racial disparities in neighborhood environments and resources that persist today. Racial disparities are still evident in access to primary care physicians (Gaskin et al., 2012), pharmaceuticals (Guadamuz et al., 2021), healthful food options (Morland et al., 2002), public parks (Abercrombie et al., 2008) recreational facilities (Moore et al., 2008), and safe outdoor space for physical activity (Franzini et al., 2010). Black families are also still more likely to experience housing discrimination and poorer socioeconomic conditions such as overcrowded housing, proximity to environmental hazards, and higher rates of evictions than White families (Greenberg et al., 2016; Pais et al., 2014). Among children, recent data suggests 76% of poor Black children are living in neighborhoods with higher levels of poverty than the poorest 25% of White children (Mcardle & Acevedo-Garcia, 2019). Importantly, numerous studies have found neighborhood racial disparities to be substantial, even after adjusting for individual or family socioeconomic status (Fuller-Rowell et al., 2016a; Morenoff et al., 2007). Black families with relatively high incomes are still disproportionately exposed to neighborhood disadvantage compared to White families with similar incomes and socioeconomic status (Charles, 2003; Sharkey, 2014). These spatial relations and structural forms of racism are fundamental determinants of racial disparities in health-promoting opportunities, behaviors, indicators, and outcomes (Bailey et al., 2021; Schulz et al., 2002).

By shaping exposure to physical and social risk, educational resources and economic opportunities, the neighborhood environment is both directly and indirectly associated with health behaviors and conditions, including blood pressure (Diez Roux et al., 2001). Though most research examining the association between neighborhood characteristics and health is cross sectional (based on a single time point) and focused on adult samples (Diez Roux et al., 2016, Mujahid et al., 2008), childhood samples have provided significant insight into the association between neighborhood environments and cardiovascular health outcomes including high blood pressure, obesity and metabolic syndrome (Alvarado, 2019; Lippert et al., 2017; Martin et al., 2019). Few reports, however, have considered childhood experiences of neighborhood socioeconomic disadvantage beyond a singular point in time when examining the consequences of exposure to neighborhood disadvantage. Recently, studies have called for such greater attention to specific developmental periods within childhood using both defined age categories among young children (p. 124, Shonkoff et al., 2021), and evaluations of disadvantage that extend into and evaluate the adolescent period (p. 105, Gilman et al., 2019). To meet these recommendations and coincide with age categories used in national studies (e.g., McGonagle & Freedman, 2015), the current study considers the role of developmental timing to neighborhood socioeconomic disadvantage on young adult blood pressure using three developmental periods: early childhood (0–5 years), middle childhood (6–12 years) and adolescence (13–18 years). Consistent with prior work, all analyses adjust for family SES to examine the role of neighborhood SES independent of family SES (Jimenez et al., 2019), and sex to account for differences in blood pressure between males and females (Virani et al., 2021).

Life-Course Perspective to Neighborhood Exposure

Life-course or developmental approaches to health stratification consider how socially patterned exposure to physical, environmental, and socioeconomic risk throughout development may shape health outcomes and health disparities (Ben-Shlomo & Kuh, 2002; Jones et al., 2019). Early developmental periods (e.g., prenatal, infancy and early childhood) are commonly studied sensitive time periods due to high levels of plasticity (i.e., potential for change in response to environmental stimuli; Kuh et al., 2003) and the malleability of developing biological systems. There is broad consensus that early life experiences are critical for healthy development and that opportunities for safe and supportive early life experiences (or lack thereof) are rooted in a child’s early environment (Ferraro et al., 2016). Recently, Gilman et al (2019) and Jimenez et al (2019) tested the role of early exposure to family and neighborhood socioeconomic disadvantage among middle-aged adults in the New England Family Study (M = 44.2 years). Both studies found exposure to socioeconomic disadvantage (family or neighborhood) at birth was more strongly associated with measures of adiposity and blood pressure than exposure to disadvantage at age 7 years or in adulthood. Specifically, after adjusting for individual and parent SES, Jimenez et al (2019) found higher neighborhood socioeconomic status at birth was associated with a 1.9 mmHg lower systolic blood pressure and 1.3 mmHg lower diastolic blood pressure in adulthood.

Early exposure to neighborhood socioeconomic disadvantage is thought to be associated with later health through at least three pathways. First, early environmental stress (e.g., proximity to environmental health hazards like air pollution, major freeways or industrial centers, dilapidated housing), can disrupt development directly by increasing exposure to toxicants (e.g., lead, air pollution), which alter and impede immune, reproductive, respiratory, and cardiovascular function (Barker, 2004). Second, early neighborhood disadvantage can indirectly influence later health through the development and persistence of health behaviors. While most research has focused on family socioeconomic disadvantage and later health behaviors (Non et al., 2016), some studies also suggest the influence of neighborhood disadvantage on health behavior can independently influence certain health outcomes, such as substance use (Wardle et al., 2003) and diet quality (Keita et al., 2009). Lastly, indirect effects between early neighborhood socioeconomic status and later health may also result from higher maternal stress, violence, or exposures to adverse childhood experiences (Harding, 2009; Kohen et al., 2008, Wang et al., 2020).

Notably absent from prior investigations is a measure of adolescent neighborhood disadvantage. Sensitive periods are known to extend beyond the early childhood years and into adolescence, often marked by transitions or developmental change (Bishop et al., 2020). Puberty, for example, has shown to be a sensitive period of development and a “crucial period for the development of hypertension in later life” (p. 66, Shen et al., 2017). Moreover, compared to middle childhood (ages 8–12), adolescence (ages 13–17) is when racial disparities in blood pressure are most prominent in childhood (Chen et al., 2015). Cross-sectional studies further suggest that neighborhood socioeconomic disadvantage in adolescence may explain racial disparities in adolescent blood pressure (McGrath et al., 2006).

During adolescence, social connections outside of the family continue to expand, and adolescents spend large amounts of time engaging with their neighborhood environment directly, both with increasing levels of independence and within social groups and local institutions. With increased independence and embeddedness within the larger social institutions and ecologies of their neighborhoods, adolescent behavior and identity can become shaped by the social and economic setting. Deviant health behaviors such as smoking, alcohol, and illicit drug use (Huang et al., 2020), as well as sleep problems (Troxel et al., 2017) and more poor diet (Lee & Cubbin, 2002) occur more frequently among adolescents living in more socioeconomically disadvantaged neighborhoods. Given the temporal proximity to young adulthood, health behaviors and patterns that become more prominent in adolescence can endure into the transition to young adulthood and persist into adulthood (Bishop et al., 2020; Lawrence et al., 2017). As the individual relationship with the neighborhood changes from early childhood through adolescence, identifying the unique role of each period becomes critical in building more targeted and effective interventions informed from life-course perspectives.

Current Study

Building from prior research, the current study examines patterns of exposure to neighborhood socioeconomic disadvantage during three developmental periods—early childhood (0–5 years), middle childhood (6–12 years), and adolescence (13–18 years)—in relation to blood pressure in young adulthood. By considering the unique effects of exposure to neighborhood socioeconomic disadvantage at each developmental period, this study is positioned to further clarify and understand the relationship between neighborhood socioeconomic status in childhood and cardiovascular risk in young adulthood. Health behaviors (i.e., physical activity, diet, smoking, alcohol use and sleep) are also considered to examine whether proximal health behaviors attenuate the relationship between neighborhood disadvantage and later blood pressure. Consistent with the salience of early life experiences for subsequent health, it is expected that exposure to neighborhood disadvantage in early childhood will more strongly predict college student blood pressure than middle childhood and adolescent exposure. Racial disparities in blood pressure and childhood exposure to neighborhood socioeconomic disadvantage are also expected. Thus, it is expected that exposure to neighborhood socioeconomic disadvantage in early childhood will mediate a significant portion of the Black-White racial disparity in blood pressure among college students.

Methods

Design and Participants

Data were from the first wave of a study examining social determinants of health and health disparities among college students attending a large research university in the southeastern United States. Using student records, equal numbers of first- and second-year Black and White students were invited to participate between September 2018 to April 2019. At the time of recruitment, the University student body was 86% White, 4% Black, 3% Hispanic, 2% Asian and 5% other or multiracial according to student records. Additional description of the study design is provided elsewhere (Fuller-Rowell et al., 2021a). The final sample included 263 undergraduate students (53% female; Mage = 19.21 years, SD = 1.01). The sample was approximately half African American or Black (N = 137, 58% female) and half European-American or White (N = 126, 48% female). First generation White students (those who did not have a parent who had graduated from a four-year college) were over-sampled to reduce potential confounding by socioeconomic status. As expected in a college sample, the socioeconomic background of participants was above the national average with 59% of participants having at least one parent with a four-year degree, and 40% growing up with a household income of above $120,000.

Measures

Residential address.

Residential addresses for each participant were collected during a laboratory visit. Participants identified their residential address, or closest geographical unit (e.g., city, county), for each year while growing up. To help identify the correct residential address, research assistants and participants used Google Maps to visually confirm each address and participants were encouraged to contact family members or friends to assist in recalling each address. Based on the number of years the participant lived in the neighborhood, primary residential addresses were identified to reflect three developmental periods: early childhood (0–5 years), middle childhood (6–12 years), and adolescence (13–18 years). If the participant lived in two addresses for a similar amount of time within the same period, the participant chose which address they felt was most reflective of that developmental period. The same residential address could be used as the primary residence for multiple developmental periods, if applicable.

Neighborhood socioeconomic disadvantage.

Census-tract boundaries were used to define each residential neighborhood and capture an aggregate index of disadvantage (Singh, 2003). Census tracts are developed by the US Census Bureau to have an optimal population size of 4000 persons, with boundaries defined to follow visible or identifiable features (e.g., residential clusters or legal boundaries) that are intended to be maintained over time (U.S. Census Bureau, 2020). For each period, neighborhood characteristics were geocoded by matching the primary period address to census tract-level data from the corresponding year of the US Census or American Community Survey (ACS) (2000, 2010, 2015). These Census/ACS survey years were chosen so that the residential history data, collected from participants in 2018–2019 (Mean age = 19.21, SD = 1.01), closely corresponded to the three developmental periods. Variables from the 2000 Census (SF3) were measured from the decennial collection while variables from the 2010 and 2015 ACS were measured based on non-overlapping 5-year estimates.

The Area Disadvantage Index (ADI; Singh, 2003), composed of 17 poverty, education, housing, and employment census-tract indicators was scored for each developmental period and tract. For comparability with prior work, each indicator was independently weighed by Singh’s (2003) factor scores, which were developed in a nationally representative sample. Similar to prior studies (Kind et al., 2014), the composite score was standardized to have a mean of 100 and a SD of 20 to assist interpretation. The descriptions of each indicator and corresponding factor score can be found in online supplemental material, Table S1.

Blood pressure.

Resting systolic blood pressure and diastolic blood pressure were calculated from the average of three readings, taken every 2 minutes, while participants were awake and in a seated position. Participants were seated for at least 45 minutes completing questionnaires and given at least 2 minutes of quiet rest prior to beginning the readings. Readings were taken using the oscillometric method on the participant’s non-dominant arm using a non-invasive Welch Allyn Connex Vital Signs Monitor (VSM) 6400 point of care device (Jones et al., 2001; Welch Allyn, Inc, Skaneateles Falls, New York).

Health covariates.

Physical activity, diet, tobacco use, and alcohol use were assessed through participant reports (Gilman et al., 2019; Steptoe & Feldman, 2001) and sleep was assessed using wrist actigraphy (Fuller-Rowell et al., 2021a).

Physical activity.

Physical activity was measured through participant report of average physical activity duration (minutes) and intensity in a typical week and converted into an average metabolic equivalent of task (MET) spent engaging in moderate or vigorous physical activity (Mendes et al., 2018).

Diet.

A modified Dietary Fat and Free Sugar - Short Questionnaire (DFS; Francis & Stevenson, 2013) was used to consider the role of diet. Participants reported how frequently they consumed 26 dietary items from 1 (less than 1 per month) to 5 (more than 5 per week), which were then summed to create an overall DFS score.

Tobacco use.

Tobacco consumption was measured by a single item question: how often in the last three months participants “used tobacco products (cigarettes, chewing tobacco, cigars, e-cigarettes, etc.)” on a scale from 1 (never) to 6 (daily).

Alcohol use.

Similar to tobacco use, alcohol use was measured by a single item question referring to how often in the last three months participants “consumed alcoholic beverages (beer, wine, spirits, etc.)” on a scale from 1 (never) to 6 (daily).

Sleep.

Following the laboratory visit, participants received an Actiwatch 2 activity monitor (Respironics, Inc.) to wear for eight continuous nights (M = 7.61, SD = .76). Sleep time (i.e., total number of minutes scored as sleep) derived from Actigraphy data was averaged over the eight nights to measure sleep duration (see Fuller-Rowell et al., 2021a for additional detail).

Demographic and Family Socioeconomic Covariates.

Race and sex were coded using university records and participant reports while measures of family socioeconomic status were collected through participant reports.

Race.

Participant race was collected from student records obtained from the university for participant recruitment and confirmed through participant reports. Race was then coded such that 0 = White and 1 = Black/African American.

Biological Sex.

Participant sex was also collected from student records obtained from the university and confirmed through participant report. Biological sex was coded so that 0 = male and 1 = female.

Parent education.

Highest parent education was coded from participant reports of mother and fathers’ level of formal education. Response options were on an eleven-point scale from “no school or some grade school” (coded as 1) to “PH.D., ED.D., MD, DDS, LLB, LLD, JD, or other professional degree” (coded as 11). The highest score of the two parents was used.

Household income.

Household income was calculated by summing participant estimates of parent income in the primary household. Participants reported each parent’s income in the primary household on a scale from 1 (< $5,000) to 32 (> $500,000) with each interval split by five thousand-dollar increments (e.g., 16 = $75,000 to $79,999). The interval mid-point was used to create an estimated income for each parent and the incomes were summed where applicable to create an estimated household income. Household income was divided by the corresponding U.S. Census 2010 poverty line based on family size to calculate a family adjusted measure of household income using an income-to-needs ratio.

Analysis Plan

Independent sample t-tests and chi-squared tests were used to examine mean differences between Black and White students. Cohen’s d was used to calculate the effect size of mean differences (Lakens, 2013). To examine the effects of neighborhood socioeconomic disadvantage on blood pressure, a series of regression models were estimated in Mplus, Edition 8.4 using the maximum likelihood estimation with robust standard errors (Muthen & Muthen, 1998-2012). Linear regression models were used instead of multilevel models due to very low levels of clustering in the data (i.e., majority of census tracts contained only one participant). On average there was 1.12 participants within each neighborhood for early childhood, 1.18 for middle childhood, and 1.26 for adolescence. Sensitivity analyses were conducted to examine whether the small amount of clustering in the data had any impact on the pattern of inference reported. Analyses showed no substantive differences between linear regression models and models which account for clustering of individuals within neighborhoods (see Table S2 of supplemental materials).

Model 1 considered the magnitude of race differences in systolic and diastolic blood pressure adjusting for sex and family socioeconomic indicators. In line with prior research (Boylan & Robert, 2017; Chen & Paterson, 2006), parent education and income were included to consider the role of neighborhood socioeconomic status after adjusting for family socioeconomic status.

Measures of neighborhood disadvantage in early childhood (Model 2), middle childhood (Model 3) and adolescence (Model 4) were then added as independent predictors of blood pressure and race disparities in blood pressure. Like Gilman et al. (2019), Jimenez et al. (2019), Kravitz-Wirtz (2016) and Martin et al (2019), sensitive period hypotheses were then tested by including all three developmental periods in the same model (Model 5) to examine whether neighborhood exposures remained predictive after adjusting for later exposures. Lastly, an overall indicator of exposure to childhood neighborhood socioeconomic disadvantage, computed as the mean of the three developmental periods, was considered (Model 6). In both Models 5 and 6, the degree to which exposure attenuated racial disparities in blood pressure was also considered. A second model series then added physical activity, diet, substance use, and sleep to each model to consider whether proximal health behaviors attenuated the relationship between childhood periods of neighborhood disadvantage and young adult blood pressure. These two-model series are presented separately for systolic (Table 3) and diastolic (Table 4) blood pressure.

Table 3.

Results from multiple regression models examining predictors of systolic blood pressure

Model 1
 Model 2
 Model 3
 Model 4
 Model 5
 Model 6
Variables B SE B SE B SE B SE B SE B SE
Intercept 111.73*** 2.42 107.08*** 4.62 111.99*** 4.43 111.45*** 4.41 108.93*** 4.90 109.85*** 4.84
 Race (B=1) 3.89** 1.20 3.46** 1.25 3.91** 1.25 3.86** 1.26 3.66** 1.26 3.71** 1.26
 Sex (F=1) −10.11*** 1.16 −10.03*** 1.16 −10.11*** 1.16 −10.12*** 1.16 −9.96*** 1.16 −10.11*** 1.16
 Parent Edu. 0.34 0.28 0.40 0.29 0.34 0.29 0.34 0.29 0.38 0.29 0.36 0.29
 Income-to-needs −0.11 0.21 − 0.04 0.22 −0.12 0.22 −0.11 0.22 −0.07 0.22 −0.08 0.22
ADI
 Early Childhood -- -- 0.04 0.03 -- -- -- -- 0.08 0.05 -- --
 Middle Childhood -- -- -- -- −0.02 0.03 -- -- −0.05 0.05 -- --
 Adolescence -- -- -- -- -- -- 0.01 0.03 −0.01 0.05 -- --
 Average -- -- -- -- -- -- -- -- -- -- 0.02 0.04
R2 24.5% 24.9% 24.5% 24.5% 25.3% 24.5%
Intercept 116.67*** 6.25 112.14*** 7.30 117.07*** 7.17 116.62*** 7.16 114.09*** 7.43 115.09*** 7.42
 Race 3.43** 1.32 2.99* 1.36 3.47* 1.36 3.43* 1.37 3.20* 1.37 3.28* 1.37
 Sex −9.84*** 1.21 −9.75*** 1.21 −9.84*** 1.21 −9.84*** 1.22 −9.65*** 1.22 −9.84*** 1.21
 Parent Edu. 0.40 0.28 0.46 0.29 0.39 0.29 0.40 0.29 0.44 0.29 0.41 0.29
 Income-to-needs −0.11 0.21 −0.04 0.22 −0.12 0.22 −0.11 0.22 −0.06 0.22 −0.08 0.22
 Tobacco Use 0.35 0.51 0.35 0.51 0.35 0.51 0.35 0.51 0.37 0.51 0.34 0.51
 Alcohol Use −0.38 0.58 −0.38 0.57 −0.38 0.58 −0.38 0.58 −0.42 0.57 −0.37 0.58
 Physical Activity −0.35 0.37 −0.34 0.37 −0.35 0.37 −0.35 0.37 −0.34 0.37 −0.35 0.37
 Diet 0.01 0.04 0.01 0.04 0.01 0.04 0.01 0.04 0.01 0.04 0.01 0.04
 Sleep −0.01 0.01 −0.02 0.01 −0.01 0.01 −0.01 0.01 −0.01 0.01 −0.01 0.01
ADI
 Early Childhood -- -- 0.04 0.03 -- -- -- -- 0.08 0.05 -- --
 Middle Childhood -- -- -- -- −0.01 0.03 -- -- −0.05 0.05 -- --
 Adolescence -- -- -- -- -- -- 0.00 0.03 −0.01 0.05 -- --
 Average -- -- -- -- -- -- -- -- -- -- 0.02 0.04
Open in a new tab

Table 4.

Results from multiple regression models examining predictors of diastolic blood pressure

Model 1
 Model 2
 Model 3
 Model 4
 Model 5
 Model 6
Variables B SE B SE B SE B SE B SE B SE
Intercept 69.60*** 1.62 63.75*** 3.05 66.66*** 2.95 66.95*** 2.95 64.31*** 3.26 64.86*** 3.22
 Race (B=1) 2.72** 0.80 2.18** 0.83 2.44** 0.83 2.45** 0.84 2.25** 0.84 2.27** 0.84
 Sex (F=1) −2.33** 0.77 −2.23** 0.77 −2.33** 0.77 −2.37** 0.77 −2.21** 0.77 −2.33** 0.77
 Parent Edu. 0.15 0.19 0.23 0.19 0.19 0.19 0.19 0.19 0.22 0.19 0.21 0.19
 Income-to-needs −0.13 0.14 −0.05 0.15 −0.09 0.15 −0.10 0.14 −0.05 0.15 −0.06 0.15
ADI
 Early Childhood -- -- 0.05* 0.02 -- -- -- -- 0.06* 0.03 -- --
 Middle Childhood -- -- -- -- 0.03 0.02 -- -- −0.01 0.04 -- --
 Adolescence -- -- -- -- -- -- 0.02 0.02 −0.01 0.03 -- --
 Average -- -- -- -- -- -- -- -- -- -- 0.04 0.02
R2 7.90% 9.80% 8.40% 8.30% 9.90% 9.0%
Intercept 70.95*** 4.09 65.41*** 4.75 68.34*** 4.69 68.72*** 4.68 66.11*** 4.85 66.76*** 4.84
 Race 2.90** 0.86 2.37** 0.88 2.65** 0.89 2.67** 0.89 2.45** 0.89 2.49** 0.89
 Sex −2.24** 0.79 −2.13** 0.79 −2.41** 0.79 −2.28** 0.79 −2.11** 0.79 −2.25** 0.79
 Parent Edu. 0.22 0.19 0.30 0.19 0.25 0.19 0.25 0.19 0.29 0.19 0.27 0.19
 Income-to-needs −0.14 0.14 −0.05 0.14 −0.09 0.14 −0.11 0.14 −0.06 0.14 −0.08 0.14
 Tobacco Use 0.49 0.33 0.49 0.33 0.47 0.33 0.47 0.33 0.48 0.33 0.46 0.33
 Alcohol Use 0.04 0.38 0.05 0.37 0.07 0.38 0.06 0.38 0.04 0.37 0.07 0.37
 Physical Activity −0.78** 0.24 −0.76** 0.24 −0.76** 0.24 −0.78** 0.24 −0.77** 0.24 −0.77** 0.24
 Diet 0.01 0.03 0.01 0.03 0.01 0.03 0.01 0.03 0.01 0.03 0.01 0.03
 Sleep −0.01 0.01 −0.01 0.01 −0.01 0.01 −0.01 0.01 −0.01 0.01 −0.01 0.01
ADI
 Early Childhood -- -- 0.05* 0.02 -- -- -- -- 0.06* 0.03 -- --
 Middle Childhood -- -- -- -- 0.02 0.02 -- -- −0.01 0.04 -- --
 Adolescence -- -- -- -- -- -- 0.02 0.02 −0.01 0.03 -- --
 Average -- -- -- -- -- -- -- -- -- -- 0.04 0.02
R2 13.0% 14.8% 13.4% 13.3% 14.8% 13.8%
Open in a new tab

Note. All coefficients are unstandardized estimates. B = Black, F = Female, ADI= Area Disadvantage Index.

+

p < .10

*

p < .05.

**

p < .01.

***

p < .001.

To examine whether neighborhood disadvantage mediated race differences in blood pressure, indirect effects were then assessed using path analyses in Mplus (MacKinnon et al., 2007). Full information maximum likelihood (FIML) estimation was used to manage missing data, allowing the sample size to be consistent across all estimated models. Of the 263 participants, 19 (7.2%) had missing residential address data for early childhood, 5 (2.0%) had missing residential data for middle childhood and 20 (7.6%) had missing sleep time data due to Actiwatch malfunctions. All other variables had < 1% missing data. Females were more likely to have missing early childhood (t = −2.31, p = .021) and middle childhood (t = −2.11, p = .039) data than males. Frequent moving (measured as the total number of residential addresses during childhood) was also associated with missing early childhood data (r = .18, p = .003). Sensitivity analyses were therefore added to adjust for the total number of moves experienced during childhood. No other missing data trends –including race differences– were noted for participants with missing residential data. Analyses using only participants with complete neighborhood data showed no differences in the model results.

Results

Descriptive statistics and correlations among primary study variables are shown in Table 1. Mean blood pressure for the full sample was 110.6 mmHg/70.4 mmHg (SD = 10.7/6.5) (systolic blood pressure/diastolic blood pressure). Males had higher systolic blood pressure (M = 115.8, SD = 11.1) and diastolic blood pressure (M = 71.5, SD = 6.9) than females (systolic: M = 106.12, SD = 8.1, t = 8.17, p < .001; diastolic: M = 69.5, SD = 6.0, t = 2.54, p = .012). Over 60% of students in this sample had different residential addresses in early childhood and middle childhood; 73% of students had different addresses for early childhood and adolescence; and 52% of students had different addresses for middle childhood and adolescence. Higher parent education and income to needs were associated with living in neighborhoods with lower area deprivation index scores for each developmental period (p < .001 for all).

Table 1.

Bivariate Correlations and Descriptive Statistics Among Study Variables

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
1. Sex (F = 1) -
2. Parent Ed. −0.01 -
3. Income: Needs −0.07 0.29 -
4. EC ADI −0.02 −0.23 −0.38 -
5. MC ADI 0.03 −0.21 −0.36 0.75 -
6. A ADI 0.08 −0.23 −0.36 0.68 0.81 -
7. Mean ADI 0.04 −0.24 −0.40 0.88 0.93 0.91 -
8. SBP −0.45 0.07 −0.03 0.12 0.03 0.02 0.06 -
9. DBP −0.16 0.04 −0.09 0.20 0.13 0.12 0.17 0.80 -
10. Tobacco −0.23 0.01 0.20 −0.10 −0.09 −0.08 −0.09 0.09 0.09 -
11. Alcohol −0.09 0.07 0.16 −0.12 −0.15 −0.15 −0.15 −0.02 −0.01 0.39 -
12. Phys. Activity −0.13 0.11 0.06 −0.03 −0.04 −0.04 −0.04 0.03 −0.16 −0.06 0.01 -
13. Diet −0.12 −0.09 −0.07 0.08 0.11 0.10 0.11 0.10 0.10 0.08 0.02 −0.09 -
14. Sleep Time 0.14 0.01 0.08 −0.10 −0.09 −0.09 −0.10 −0.17 −0.11 0.03 0.03 −0.15 −0.08 -
15. Total Moves −0.05 −0.07 −0.10 0.15 0.14 0.03 0.11 0.14 0.11 −0.02 −0.05 0.06 0.05 −0.05 -
Mean (no. %) (53.6) 8.10 4.54 100.0 100.0 100.0 100.0 110.61 70.41 1.59 2.43 1.67 65.22 380.26 2.36
SD 2.15 3.03 20.0 20.0 20.0 18.1 10.72 6.48 1.28 1.10 1.61 14.15 54.08 2.14
Open in a new tab

Note. EC = early childhood, MC = middle childhood, A = adolescence, ADI = area disadvantage index, SBP = systolic blood pressure, DBP = diastolic blood pressure. Bolded coefficients indicate statistical significance (p < .05).

Race differences across study variables are shown in Table 2. Average systolic blood pressure and diastolic blood pressure for Black students was 112.1 mmHg (SD =11.3) and 71.7 mmHg (SD = 6.5) compared to 109.0 mmHg (SD = 9.8) and 69.0 mmHg (SD = 6.2) for White students (systolic d = .29, diastolic d = .43). Of the 17% of students with elevated blood pressure (>120 systolic and/or >80 diastolic), 68% (n = 30) were Black and 32% (n = 14) were White (p = 0.021, Fisher’s exact test). Effect sizes (Cohen’s d) suggest dietary fat and sugar consumption was higher among Black students than White students (d = .26), while tobacco (d = .47) and alcohol (d = .49) use was higher among White students than Black students. Actigraphy data showed Black students averaged significantly less sleep than White students per night (d = .67). Race differences were also identified in family income with Black students reporting a lower income to needs ratio than White students (d = .52). No differences were found in parent education or participants’ physical activity.

Table 2.

Mean differences among study variables by racial group

White (n = 126)
 Black (n = 137
Variables M ± SD (%) M ± SD (%) p
Sex (Female = 1) (48.4) (58.4) .106
Age (years) 19.15 ± .96 19.26 ± 1.06 .370
Parent Education 8.02 ± 2.18 8.18 ± 2.13 .552
Income to Needs Ratio 5.37 ± 3.66 3.81 ± 2.12 <.001
Physical Activity 1.56 ± 1.44 1.77 ± 1.76 .294
Dietary Fat & Sugar 63.32 ± 12.44 67.04 ± 15.44 .035
Tobacco Use 1.90 ± 1.54 1.31 ± 0.91 <.001
Alcohol Use 2.71 ± 1.19 2.18 ± 0.94 <.001
Sleep Time (minutes) 397.51 ± 41.09 363.15 ± 59.85 <.001
Number of Moves 1.75 ± 1.75 2.92 ± 2.30 <.001
Area Disadvantage Index
 Early Childhood 93.56 ± 22.82 105.99 ± 14.60 <.001
 Middle Childhood 93.44 ± 23.88 106.12 ± 12.94 <.001
 Adolescence 92.93 ± 23.07 106.41 ± 13.88 <.001
 Mean ADI 93.36 ± 21.34 106.25 ± 11.57 <.001
Blood Pressure (mmHg)
 Resting Systolic 108.99 ± 9.80 112.10 ± 11.33 .019
 Resting Diastolic 68.99 ± 6.21 71.70 ± 6.48 .001
Open in a new tab

Note. Parent Education is scored from 1 (less than 8th grade) to 11 (professional). Independent sample t-tests and chi-squared tests were used to determine statistical significance of differences between Black and White samples. Cohen’s d was used to calculate effect size of mean differences. A commonly used interpretation is to refer to effect sizes as small (d = 0.2), medium (d = 0.5), and large (d = 0.8). For reference, a d of 0.5 indicates that the two group means differ by half a standard deviation.

Neighborhood indices also varied as a function of race. At each developmental period, Black students lived in census tracts with higher levels of socioeconomic disadvantage than White students, with a difference more than half a standard deviation in magnitude (12.4 in early childhood, 12.7 in middle childhood, 13.5 in adolescence and 12.9 for lifetime). Effect sizes suggest substantial race differences in early childhood (d = .65), middle childhood (d = .66), adolescence (d = .71) and lifetime (d = .75) neighborhood socioeconomic disadvantage. Black students also had more frequent moves during childhood than White students (d = .57). On average, Black students lived in 2.9 neighborhoods (SD = 2.3), with 16.8% of Black students living in the same neighborhood until college. In comparison, White students averaged 1.8 neighborhoods (SD = 1.8), with 30.2% of White students living in the same neighborhood until college.

Developmental Exposure to Neighborhood Disadvantage

Tables 3 and 4 show the model results for systolic blood pressure (Table 3) and diastolic blood pressure (Table 4). Adjusting for sex and family SES, Model 1 demonstrated that Black students had 3.89 mmHg (SE = 1.2, p = 0.001) higher systolic and 2.72 mmHg (SE = .80, p = 0.001) higher diastolic blood pressure than White students, equivalent to an effect size of .36 SD units for systolic (SE = .11) and .42 SD units for diastolic (SE = .12). Male students had 10.11 mmHg (SE = 1.16, p < 0.001) higher systolic and 2.33 mmHg (SE = .77, p = 0.003) higher diastolic blood pressure than female students, equivalent to an effect size of .94 SD units for systolic (SE = .11) and .36 SD units for diastolic (SE = .19) blood pressure. Though neither parent education nor income to needs were significant predictors of systolic (p = .224, p = .596) or diastolic (p = .426 and p = .362) blood pressure, together, family SES accounted for 5.5% of the race difference in systolic blood pressure and 7.6% of the race difference in diastolic blood pressure.

Models 2–4 show the effect of neighborhood disadvantage in early childhood, middle childhood, and adolescence in predicting blood pressure and attenuating race differences, adjusting for family SES. Neighborhood disadvantage in early childhood (Model 2) exhibited the strongest association with diastolic blood pressure. A one-point increase in neighborhood socioeconomic disadvantage in early childhood was associated with a .05 mmHg (SE = .02, p = .023) higher diastolic blood pressure in young adulthood. Therefore, each 20-point increase (one standard deviation), is associated with 1 mmHg increase in diastolic blood pressure. Path analyses further show early neighborhood disadvantage explained 22% of the total race differences in diastolic blood pressure through indirect effects (β = .62, SE = .30, p = .039), adjusting for sex, parent education and income-to-needs. Early neighborhood socioeconomic disadvantage attenuated the race difference in systolic blood pressure by 11% but was not a statistically significant predictor of systolic blood pressure (p = .231).

No associations were found between middle childhood neighborhood socioeconomic disadvantage scores (Model 3) and systolic (p = .949) or diastolic (p = .229) blood pressure, or between adolescence neighborhood socioeconomic disadvantage scores (Model 4) and systolic (p = .931) and diastolic (p = .273) blood pressure. Middle childhood and adolescence neighborhood socioeconomic disadvantage had no effect on the race difference in systolic blood pressure but attenuated 8.6% and 7.9% of the race difference in diastolic blood pressure, respectively. Shown in Model 5, the association between neighborhood disadvantage in early childhood and diastolic blood pressure persisted after adjusting for later exposure to neighborhood disadvantage. Specifically, a one unit increase in early childhood neighborhood socioeconomic disadvantage was associated with a .06 mmHg (SE = .03, p = .049) higher diastolic blood pressure. After adjusting for neighborhood disadvantage in middle childhood and adolescence, early childhood neighborhood disadvantage increased but did not significantly predict systolic blood pressure (p = .087) In Model 6, a general measure of neighborhood disadvantage in childhood (i.e., mean neighborhood socioeconomic disadvantage score of the three developmental periods) did not significantly predict systolic (p = .647) or diastolic (p = .086) blood pressure. Average exposure to neighborhood disadvantage during childhood explained 4.6% and 16.5% of the race difference in systolic and diastolic blood pressure, respectively.

Adjusting for proximal health behaviors had little to no effect on the model results. In Model 1, health behaviors attenuated race differences in systolic blood pressure by 11.1% to 3.43 mmHg (SE = 1.32) and increased race differences in diastolic blood pressure by 6.6% to 2.90 mmHg (SE = .86). Physical activity was the only health behavior to significantly predict blood pressure. A one-unit increase in average MET expenditure in moderate or vigorous physical activity was associated with a .78 mmHg (SE = .24, p = .001) decrease in diastolic blood pressure. In Model 2, neighborhood disadvantage in early childhood continued to be a significant predictor of diastolic blood pressure after adjusting for health behaviors. With health behaviors included in the model, a one-point increase in neighborhood disadvantage in early childhood was still associated with a .05 mmHg (SE = .02, p = .023) higher diastolic blood pressure in young adulthood. Similarly, early neighborhood disadvantage continued to explain 20% the race differences in diastolic blood pressure through indirect effects (β = .59, SE = .29, p = .041), after adjusting for health covariates. Shown in Model 5, the association between neighborhood disadvantage in early childhood and blood pressure also persisted after adjusting for full model covariates. More specifically, a one unit increase in neighborhood disadvantage in early childhood was associated with a .06 mmHg (SE = .03, p = .042) higher diastolic blood pressure after adjusting for health behaviors and neighborhood disadvantage in middle childhood and adolescence.

Sensitivity Analyses

Because the area deprivation index of neighborhood disadvantage measure used weights derived from prior research for each indicator, additional analyses were conducted to consider whether the results differed when an unweighted version of the measure was used (i.e., with each indicator weighted equally). Results of these analyses yielded the same substantive findings, with slightly stronger associations between neighborhood disadvantage in early childhood and diastolic blood pressure (p = .020). Full results of these analyses are presented in Table S3 of the online supplemental materials. Finally, models tested whether the total number of moves during childhood may predict blood pressure or attenuate race differences in blood pressure. Total moves did not significantly predict systolic (β = .38, SE = .28, p = .172) or diastolic (β = .12, SE = .19, p = .551) blood pressure, but did explain an additional 15.2% of the race difference in systolic and 7.0% in diastolic blood pressure in Model 1. Adjusting for total number of moves had no effect on the association between early childhood neighborhood socioeconomic disadvantage and blood pressure. Early childhood neighborhood socioeconomic disadvantage continued to significantly predict diastolic blood pressure in Model 2 (β = .05, SE = .02, p = .022) and Model 5 (β = .06, SE = .03, p = .046).

Discussion

Understanding the role of early life experiences on later health is essential to improve population health and reduce health disparities. While recent studies have used a developmental or life-course perspective to evaluate contextual influences on cardiovascular health, few have taken the same approach to evaluate racial disparities in these outcomes. Using a developmental framework, the aim of this study was to test whether exposure to neighborhood socioeconomic disadvantage during specific periods of childhood would be associated with blood pressure and mediate race differences in blood pressure.

Consistent with the study hypothesis, higher levels of neighborhood socioeconomic disadvantage during early childhood (0–5 years) were more strongly associated with later blood pressure than middle childhood (6–12 years) or adolescence (13–18 years). Adding to the findings from the New England Family Study (Gilman et al., 2019; Jimenez et al., 2019) these results suggest early childhood may be a sensitive period for exposure to neighborhood disadvantage relative to middle childhood and now, adolescence, while also contributing to a larger body of research suggesting early environments are associated with subsequent health (Braveman & Barclay, 2009; Ferraro et al., 2016). Moreover, this study is the first to document that unequal exposure to neighborhood socioeconomic disadvantage in early childhood also explains racial disparities in blood pressure among young adult college students.

Exposure to neighborhood disadvantage in early childhood—independent of middle childhood or adolescence—accounted for 20% of the difference between Black and White college students’ diastolic blood pressure, after adjusting for more proximal health behaviors. Notably, the magnitude of the race difference in neighborhood disadvantage was similar across the three developmental periods. This suggests the developmental findings were not driven by a larger race difference in exposure to neighborhood disadvantage in early childhood as compared to middle childhood and adolescence. This also builds upon prior studies that have found racial disparities in body mass, depression, and metabolic syndrome in college or among high-achieving high school students are, in part, explained by unequal exposure to childhood disadvantage (Gaydosh et al., 2018; Gaydosh & McLanahan, 2021).

While previous work identified adolescence as a sensitive period for neighborhood socioeconomic disadvantage and obesity in young adults (Alvardo, 2016; Kravitz-Wirtz, 2016), this study suggests that, for blood pressure, early childhood exposure to neighborhood disadvantage may be most salient. Although the prevalence of high blood pressure rises with increases in obesity, approximately 45% of the population considered normal weight (body mass index below 25) still has high blood pressure in the United States (Landi et al., 2018). More specifically, the results suggest neighborhood socioeconomic disadvantage is especially predictive of diastolic blood pressure. In populations under the age of 50, diastolic blood pressure is considered a more prognostic cardiovascular risk factor than systolic blood pressure and after age 50, systolic blood pressure is more important (p. 1212, Chobanian et al., 2003).

In this sample of college students, there was found no evidence that proximal health behaviors—physical activity, diet, substance use (tobacco and alcohol) or sleep—substantially attenuates the relationship between early neighborhood disadvantage and later blood pressure. While many adult health behaviors, particularly substance use, have been linked to early socioeconomic disadvantage (Melchior et al., 2007; Non et al., 2016), the findings add to a body of literature suggesting the association between experiences of socioeconomic disadvantage and health outcomes persists after adjusting for proximal health behaviors (Lantz et al., 1998; Non et al., 2014, Gilman et al., 2019). Direct mechanisms associated with air pollution or exposure to toxins that disrupt the more malleable biological system (Salvi, 2007) or indirect mechanisms associated with psychological or physiological stress responses caused by greater exposure to early adversity (Bunea et al., 2017, Sulgia et al., 2020) or parenting stress (Wang et al., 2020) may be more likely mechanisms that may be driving the association between neighborhood socioeconomic disadvantage in early childhood and higher blood pressure in young adulthood. Because children from low socioeconomic backgrounds experience multiple types of adversity (Melchior et al., 2007), assessing a range of biological, behavioral, and psychosocial mechanisms will be essential next steps to understand how early neighborhood environments are associated with later cardiovascular risk.

Young adulthood is a particularly important transitional period in the lifespan that holds significant ties to both childhood experiences and later health outcomes. As described in Schwartz (2016), young adulthood is considered a “gap” between the structured nature of childhood, and the realities of adulthood (p. 311). Yet the health and well-being of young adults is quite consequential (Harris, 2010). Results from the Coronary Artery Risk Development in Young Adults study found, among young adults ages 18 to 30, participants with elevated blood pressure during young adulthood were significantly more likely to be experience cardiovascular disease events (e.g., stroke, ischemic attack) nearly 19 years later than young adults with lower blood pressure (Yano et al., 2018). Similarly, results from a cohort of over 18 thousand male university students showed, after adjusting for age, body mass, smoking and physical activity while attending college, blood pressure measured at age 18 was associated with the likelihood of coronary heart disease in adulthood. Even in models that adjusted for middle-age hypertension, high blood pressure during college was still associated with higher risk of all-cause mortality, cardiovascular disease, and coronary heart disease several decades later (Gray et al., 2011). Thus, while the current study findings for young adult blood pressure suggest modest effects, with age, could become quite significant.

The results presented are also specific to the college student context of young adulthood, a unique social context and period of change. While upward mobility through college education can lead to healthier and longer lives, studies have shown that racial health disparities persist—and even widen—at higher levels of education (Williams & Sternthal, 2010). Studies have also suggested childhood exposure to socioeconomic disadvantage explains some of the racial disparities in physical health gains received through higher education (Gaydosh et al., 2018, Gaydosh & McLanahan, 2021); suggesting the findings may persist after graduation from college. While prevention efforts that target the social structures upholding racial disparities in neighborhood socioeconomic status should be prioritized, study results also suggest interventions during young adulthood to improve blood pressure should go beyond targeting proximal health behaviors like diet, sleep time, and substance use.

This study is not without limitations. First, residential addresses were collected through retrospective recall. While participant recall is more susceptible to error, residential addresses are less likely to be prone to recall bias. Studies comparing participant recall of residential addresses to historical records have found reported 84% accuracy, over 50 years later (Berney & Blane, 1997). Even greater accuracy is likely in a young adult sample due to the temporal proximity of the recall period. To aid participant recall, visual cues (i.e., use of Google Maps Street view) were provided to assist with participant memory and for the participant to provide visual confirmation of the reported addresses.

Another limitation to retrospective data collection is the inability to capture blood pressure at the time of each residency. Without evaluating change in blood pressure, this study is not able to evaluate a direction of causal effects. A more rigorous assessments of vascular health, such as endothelial function or arterial stiffness, would also provide further insight into the potential mechanism and biological imprints of early exposure to neighborhood disadvantage. Prospective longitudinal studies that measure both residency and health at different points in development will be an important next step to bolster confidence in the effects of early exposure on later blood pressure and to better investigate when and how the effects of early environments emerge on later health.

Similarly, additional prospective longitudinal studies that examine profiles and trajectories of change and length of stay in neighborhood context (e.g., upward and downward neighborhood mobility) are also needed to further examine how changes in neighborhood context impacts health outcomes. The analytic framework and design for this study was based on the work of Jimenez et al (2019), Gilman et al (2019), Kravitz-Wirtz (2016) and Martin et al (2019) and expands upon on their models assessing sensitive periods in exposure to socioeconomic disadvantage, including a model with all three neighborhood measures of neighborhood socioeconomic disadvantage (Model 7). Although most participants lived in different neighborhoods for each period, the three periods were highly correlated across developmental periods with coefficients ranging from .68 (early childhood and adolescence, p < .001) to .81 (middle childhood and adolescence, p < .001) which can suggest high levels of multicollinearity. However, Variance Inflation Factors scores (VIFs) were moderate for each neighborhood disadvantage indicator with scores of 2.36 for early childhood, 3.63 for middle childhood and 3.11 for adolescence. While VIFs closest to 1 are ideal, the most common indication of problematic collinearity are VIFs greater than 10, with some more conservative estimates suggesting cut off values of 5 (p. 101, James et al., 2013; p. 142, Allison, 1999) and 2.5 (p. 1958, Johnson et al., 2018). VIF estimates for the models used in this study indicated that the key measure of neighborhood disadvantage in early childhood had a VIF below 2.5 (2.36). Based on this evidence, shared variance is likely not driving the substantive findings nor is one time point is determined by another.

The fourth limitation relates to the assessment of neighborhood socioeconomic disadvantage. Though the ADI has shown good reliability as a composite measure to track neighborhood-level disparities (Durfey et al., 2019) and an objective measure of neighborhood is a notable strength, census tract measures of neighborhood socioeconomic disadvantage are crude proxies for a range of social and physical environment factors that do not always reflect an individual’s lived experience. Examining subjective reports of the neighborhood will be important to consider how the perception of, and interaction with, the neighborhood may influence or moderate health outcomes in future investigations. Additionally, observational techniques that capture the true physical environment and social infrastructure of the community will be important to delineate more specific determinants of health. Lastly, results of this study should be interpreted within the college student context. With greater variability in income, education and neighborhood socioeconomic conditions, community samples are also needed to understand the larger implications of early exposure to neighborhood disadvantage on health and may show even larger effects.

Conclusion

Childhood experiences play a central role in shaping health outcomes and health disparities, yet the role of developmental timing and importance of neighborhood contexts are often overlooked. By considering the unique effects of exposure to neighborhood socioeconomic disadvantage during early childhood (0–5 years), middle childhood (6–12 years), and adolescence (13–18 years) on young adult blood pressure, this study expands upon the contributions of prior studies and provides insight into how childhood exposures to neighborhood socioeconomic disadvantage, and subsequent health consequences, are stratified. The study results indicate that early childhood neighborhood socioeconomic disadvantage is associated with later blood pressure and due to unequal exposure to neighborhood socioeconomic disadvantage, mediates a portion of race differences in blood pressure between Black and White college students. Combined with other work, the results of this study suggest that early environments play an important role in shaping young adult cardiovascular health and that efforts to end enduring neighborhood socioeconomic disparities are critical to eliminating racial inequities in health.

Supplementary Material

Supplementary Material

References

  1. Abercrombie L, Sallis J, Conway T, Frank L, Saelens B, & Chapman J (2008). Income and Racial Disparities in Access to Public Parks and Private Recreation Facilities. American Journal of Preventive Medicine, 34(1), 9–15. [DOI] [PubMed] [Google Scholar]
  2. Allison P (1999). Multiple Regression: A Primer. Thousand Oaks: Pine Forge Press. [Google Scholar]
  3. Alvarado SE (2019). The indelible weight of place: Childhood neighborhood disadvantage, timing of exposure, and obesity across adulthood. Health & Place, 58, 102159. [DOI] [PubMed] [Google Scholar]
  4. Bailey ZD, Feldman JM, & Bassett MT (2021). How Structural Racism Works—Racist Policies as a Root Cause of U.S. Racial Health Inequities. New England Journal of Medicine, 384(8), 768–773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Balfour P, Rodriguez C, & Ferdinand K (2015). The Role of Hypertension in Race-Ethnic Disparities in Cardiovascular Disease. Current Cardiovascular Risk Reports, 9(4). [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Barker D (2004). The Developmental Origins of Adult Disease. Journal of the American College of Nutrition, 23(sup6), 588S–595S. [DOI] [PubMed] [Google Scholar]
  7. Berney LR, & Blane DB (1997). Collecting retrospective data: Accuracy of recall after 50 years judged against historical records. Social Science & Medicine, 45(10), 1519–1525. 10.1016/S0277-9536(97)00088-9 [DOI] [PubMed] [Google Scholar]
  8. Ben-Shlomo Y, & Kuh D (2002). A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31(2), 285–293. 10.1093/ije/31.2.285 [DOI] [PubMed] [Google Scholar]
  9. Bishop AS, Walker SC, Herting JR, & Hill KG (2020). Neighborhoods and health during the transition to adulthood: A scoping review. Health & Place, 63, 102336. [DOI] [PubMed] [Google Scholar]
  10. Boylan JM, & Robert SA (2017). Neighborhood SES is particularly important to the cardiovascular health of low SES individuals. Social Science & Medicine, 188, 60–68. 10.1016/j.socscimed.2017.07.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Braveman P, & Barclay C (2009). Health Disparities Beginning in Childhood: A Life-Course Perspective. Pediatrics, 124(3), S163–S175. [DOI] [PubMed] [Google Scholar]
  12. Bunea IM, Szentágotai-Tătar A, & Miu AC (2017). Early-life adversity and cortisol response to social stress: A meta-analysis. Translational Psychiatry, 7, 1274. 10.1038/s41398-017-0032-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Carnethon M, Pu J, Howard G, Albert M, Anderson C, Bertoni A, Mujahid M, Palaniappan L, Taylor H, Willis M, & Yancy C (2017). Cardiovascular Health in African Americans: A Scientific Statement from the American Heart Association. Circulation, 136(21), e393–423. [DOI] [PubMed] [Google Scholar]
  14. Center of Disease Control (2021). Facts about Hypertension. Retrieved December 10, 2021, from https://www.cdc.gov/bloodpressure/facts.htm
  15. Charles C (2003). The Dynamics of Racial Residential Segregation. Annual Review of Sociology, 29(1), 167–207. 10.1146/annurev.soc.29.010202.100002 [DOI] [Google Scholar]
  16. Chen E, & Paterson LQ (2006). Neighborhood, family, and subjective socioeconomic status: How do they relate to adolescent health? Health Psychology, 25(6), 704–714. 10.1037/0278-6133.25.6.704 [DOI] [PubMed] [Google Scholar]
  17. Chen L, Simonsen N, & Liu L (2015). Racial Differences of Pediatric Hypertension in Relation to Birth Weight and Body Size in the United States. PLoS ONE, 10(7), e0132606. 10.1371/journal.pone.0132606 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Chen X, & Wang Y (2008). Tracking of blood pressure from childhood to adulthood: A systematic review and meta-regression analysis. Circulation, 117(25), 3171–3180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Joseph L Izzo J, Jones DW, Materson B, Oparil S, Jackson T Wright J, & Roccella E (2003). The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 Report. JAMA, 289(19), 2560–2571. [DOI] [PubMed] [Google Scholar]
  20. Cooper R, Wolf-Maier K, Luke A, Adeyemo A, Banegas JR, Forrester T, Giampaoli S, Joffres M, Kastarinen M, Primatesta P, Stegmayr B, & Thamm M (2005). An international comparative study of blood pressure in populations of European vs. African descent. BMC Medicine, 3(1), 2. 10.1186/1741-7015-3-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Diez Roux A (2001). Investigating Neighborhood and Area Effects on Health. American Journal of Public Health, 91(11), 1783–1789. 10.2105/AJPH.91.11.1783 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Diez Roux AV, Mujahid MS, Hirsch JA, Moore K, & Moore LV (2016). The Impact of Neighborhoods on CV Risk. Global Heart, 11(3), 353–363. 10.1016/j.gheart.2016.08.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Durfey S, Kind A, Buckingham W, DuGoff E & Trivedi A (2019). Neighborhood disadvantage and chronic disease management. Health Services Research, 54, 206–216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Ferraro K, Schafer M, & Wilkinson L (2016). Childhood Disadvantage and Health Problems in Middle and Later Life: Early Imprints on Physical Health? American Sociological Review, 81(1), 107–133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Francis H, & Stevenson R (2013). Validity and test–retest reliability of a short dietary questionnaire to assess intake of saturated fat and free sugars: A preliminary study. Journal of Human Nutrition and Dietetics, 26(3), 234–242. [DOI] [PubMed] [Google Scholar]
  26. Franzini L, Taylor W, Elliott MN, Cuccaro P, Tortolero SR, Janice Gilliland M, Grunbaum J, & Schuster MA (2010). Neighborhood characteristics favorable to outdoor physical activity: Disparities by socioeconomic and racial/ethnic composition. Health & Place, 16(2), 267–274. [DOI] [PubMed] [Google Scholar]
  27. Fuller-Rowell T, Curtis D, El-Sheikh M, Chae D, Boylan J, & Ryff C (2016). Racial disparities in sleep: The role of neighborhood disadvantage. Sleep Medicine, 27–28, 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Fuller-Rowell TE, Nichols OI, Robinson AT, Boylan JM, Chae DH, & El-Sheikh M (2021a). Racial disparities in sleep health between Black and White young adults: The role of neighborhood safety in childhood. Sleep Medicine, 81, 341–349 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Fuller-Rowell TE, Nichols OI, Jokela M, Kim ES, Yildirim ED, & Ryff CD (2021b). A Changing Landscape of Health Opportunity in the United States: Increases in the Strength of Association Between Childhood Socioeconomic Disadvantage and Adult Health Between the 1990s and the 2010s. American Journal of Epidemiology. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Gaskin D, Dinwiddie G, Chan K, & McCleary R (2012). Residential segregation and the availability of primary care physicians. Health Services Research, 47(6), 2353–2376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Gaydosh L, Schorpp KM, Chen E, Miller GE, & Harris KM (2018). College completion predicts lower depression but higher metabolic syndrome among disadvantaged minorities in young adulthood. Proceedings of the National Academy of Sciences, 115(1), 109–114. 10.1073/pnas.1714616114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Gaydosh L, & McLanahan S (2021). Youth academic achievement, social context, and body mass index. SSM - Population Health, 13, 100708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Gilman S, Huang Y, Jimenez M, Agha G, Chu S, Eaton C, Goldstein R, Kelsey K, Buka S, & Loucks E (2019). Early life disadvantage and adult adiposity: Tests of sensitive periods during childhood and behavioral mediation in adulthood. International Journal of Epidemiology, 48(1), 98–107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Gray L, Lee I-M, Sesso HD, & Batty GD (2011). Blood Pressure in Early Adulthood, Hypertension in Middle Age, and Future Cardiovascular Disease Mortality: HAHS (Harvard Alumni Health Study). Journal of the American College of Cardiology, 58(23), 2396–2403. 10.1016/j.jacc.2011.07.045 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Greenberg D, Gershenson C, & Desmond M (2016). Discrimination in Evictions: Empirical Evidence and Legal Challenges. Harvard Civil Rights-Civil Liberties Law Review, 51(1), 115–158. [Google Scholar]
  36. Guadamuz JS, Wilder JR, Mouslim MC, Zenk SN, Alexander GC, & Qato DM (2021). Fewer Pharmacies in Black and Hispanic/Latino Neighborhoods Compared with White or Diverse Neighborhoods, 2007–15. Health Affairs, 40(5), 802–811. 10.1377/hlthaff.2020.01699 [DOI] [PubMed] [Google Scholar]
  37. Harding DJ (2009). Collateral Consequences of Violence in Disadvantaged Neighborhoods. Social Forces, 88(2), 757–784. 10.1353/sof.0.0281 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Harris KM (2010). An Integrative Approach to Health. Demography, 47(1), 1–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Huang Y, Edwards J, & Laurel-Wilson M (2020). The shadow of context: Neighborhood and school socioeconomic disadvantage, perceived social integration, and the mental and behavioral health of adolescents. Health & Place, 66, 102425. 10.1016/j.healthplace.2020.102425 [DOI] [PubMed] [Google Scholar]
  40. James G, Witten D, Hastie T, & Tibshirani R (2013). An Introduction to Statistical Learning (Vol. 103). Springer; New York. 10.1007/978-1-4614-7138-7 [DOI] [Google Scholar]
  41. Jimenez M, Wellenius G, Subramanian S, Buka S, Eaton C, Gilman S, & Loucks E (2019). Longitudinal associations of neighborhood socioeconomic status with cardiovascular risk factors: A 46-year follow-up study. Social Science & Medicine, 241, 112574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Johnston R, Jones K, & Manley D (2018). Confounding and collinearity in regression analysis: A cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour. Quality & Quantity, 52(4), 1957–1976. 10.1007/s11135-017-0584-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Jones C, Taylor K, Poston L, & Shennan A (2001). Validation of the Welch Allyn ‘Vital Signs’ oscillometric blood pressure monitor. Journal of Human Hypertension, 15, 191–195. [DOI] [PubMed] [Google Scholar]
  44. Jones N, Gilman S, Cheng T, Drury S, Hill C, & Geronimus A (2019). Life Course Approaches to the Causes of Health Disparities. American Journal of Public Health, 109(S1), S48–S55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Keita AD, Casazza K, Thomas O, & Fernandez JR (2009). Neighborhood-Level Disadvantage Is Associated with Reduced Dietary Quality in Children. Journal of the American Dietetic Association, 109(9), 1612–1616. 10.1016/j.jada.2009.06.373 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Kind A, Jencks S, Brock J, Yu M, Bartels C, Ehlenbach W, Greenberg C, & Smith M (2014). Neighborhood Socioeconomic Disadvantage and 30 Day Rehospitalizations: An Analysis of Medicare Data. Annals of Internal Medicine, 161(11), 765–774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Kohen DE, Leventhal T, Dahinten VS, & McIntosh CN (2008). Neighborhood disadvantage: Pathways of effects for young children. Child development, 79(1), 156–169. [DOI] [PubMed] [Google Scholar]
  48. Kravitz-Wirtz N (2016). Temporal Effects of Child and Adolescent Exposure to Neighborhood Disadvantage on Black/White Disparities in Young Adult Obesity. Journal of Adolescent Health, 58(5), 551–557. 10.1016/j.jadohealth.2016.01.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, & Power C (2003). Life course epidemiology. Journal of Epidemiology and Community Health, 57(10), 778–783. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Lakens D (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. 10.3389/fpsyg.2013.00863 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Landi F, Calvani R, Picca A, Tosato M, Martone AM, Ortolani E, Sisto A, D’Angelo E, Serafini E, Desideri G, Fuga MT, & Marzetti E (2018). Body Mass Index is Strongly Associated with Hypertension: Results from the Longevity Check-Up 7+ Study. Nutrients, 10(12), 1976. 10.3390/nu10121976 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Lantz PM, House JS, Lepkowski JM, Williams DR, Mero RP, & Chen J (1998). Socioeconomic factors, health behaviors, and mortality: Results from a nationally representative prospective study of US adults. JAMA, 279(21), 1703–1708. 10.1001/jama.279.21.1703 [DOI] [PubMed] [Google Scholar]
  53. Lawrence E, Mollborn S, & Hummer R (2017). Health Lifestyles across the Transition to Adulthood: Implications for Health. Social Science & Medicine (1982), 193, 23–32. 10.1016/j.socscimed.2017.09.041 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Lee RE, & Cubbin C (2002). Neighborhood Context and Youth Cardiovascular Health Behaviors. American Journal of Public Health, 92(3), 428–436. 10.2105/AJPH.92.3.428 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Lippert A, Evans C, Razak F, & Subramanian S (2017). Associations of Continuity and Change in Early Neighborhood Poverty with Adult Cardiometabolic Biomarkers in the United States: Results from the National Longitudinal Study of Adolescent to Adult Health, 1995–2008. American Journal of Epidemiology, 185(9), 765–776. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. MacKinnon D, Fairchild A, & Fritz M (2007). Mediation analysis. Annual Review of Psychology, 58, 593. 10.1146/annurev.psych.58.110405.085542 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Martin CL, Kane JB, Miles GL, Aiello AE, & Harris KM (2019). Neighborhood disadvantage across the transition from adolescence to adulthood and risk of metabolic syndrome. Health & Place, 57, 131–138. 10.1016/j.healthplace.2019.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Mcardle N, & Acevedo-Garcia D (2019). Consequences of Segregation for Children’s Opportunity and Wellbeing. 16.
  59. McGonagle K & Freedman V 2015. The Panel Study of Income Dynamics’ Childhood Retrospective Circumstances Study (PSID-CRCS) User Guide: Final Release. Institute for Social Research, University of Michigan. Accessed at https://psidonline.isr.umich.edu/CRCS/2014UserGuide.pdf [Google Scholar]
  60. McGrath J, Matthews K, & Brady S (2006). Individual versus neighborhood socioeconomic status and race as predictors of adolescent ambulatory blood pressure and heart rate. Social Science & Medicine, 63(6), 1442–1453. [DOI] [PubMed] [Google Scholar]
  61. Melchior M, Moffitt T, Milne B, Poulton R, & Caspi A (2007). Why Do Children from Socioeconomically Disadvantaged Families Suffer from Poor Health When They Reach Adulthood? A Life-Course Study. American Journal of Epidemiology, 166(8), 966–974. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Mendes M de A, Silva I. da, Ramires V, Reichert F, Martins R, Ferreira R, & Tomasi E (2018). Metabolic equivalent of task (METs) thresholds as an indicator of physical activity intensity. PLOS ONE, 13(7), e0200701. 10.1371/journal.pone.0200701 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Moore LV, Diez Roux AV, Evenson KR, McGinn AP, & Brines SJ (2008). Availability of Recreational Resources in Minority and Low Socioeconomic Status Areas. American Journal of Preventive Medicine, 34(1), 16–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Morenoff J, House J, Hansen B, Williams D, Kaplan G, & Hunte H (2007). Understanding social disparities in hypertension prevalence, awareness, treatment, and control: The role of neighborhood context. Social Science & Medicine, 65(9), 1853–1866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Morland K, Wing S, Diez Roux A, & Poole C (2002). Neighborhood characteristics associated with the location of food stores and food service places. American Journal of Preventive Medicine, 22(1), 23–29. 10.1016/S0749-3797(01)00403-2 [DOI] [PubMed] [Google Scholar]
  66. Mujahid M, Diez Roux A, Morenoff J, Raghunathan T, Cooper R, Ni H, & Shea S (2008). Neighborhood Characteristics and Hypertension: Epidemiology, 19(4), 590–598. [DOI] [PubMed] [Google Scholar]
  67. Muthen L & Muthen B (1998). Mplus User’s Guide: Seventh Edition. Muthen & Muthen. [Google Scholar]
  68. Non AL, Rewak M, Kawachi I, Gilman SE, Loucks EB, Appleton AA, Román JC, Buka SL, & Kubzansky LD (2014). Childhood Social Disadvantage, Cardiometabolic Risk, and Chronic Disease in Adulthood. American Journal of Epidemiology, 180(3), 263–271. 10.1093/aje/kwu127 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Non A, Román J, Gross C, Gilman S, Loucks E, Buka S, & Kubzansky L (2016). Early childhood social disadvantage is associated with poor health behaviours in adulthood. Annals of Human Biology, 43(2), 144–153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Pais J, Crowder K, & Downey L (2014). Unequal Trajectories: Racial and Class Differences in Residential Exposure to Industrial Hazard. Social Forces, 92(3), 1189–1215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Raymond C, Marin M-F, Majeur D, & Lupien S (2018). Early child adversity and psychopathology in adulthood: HPA axis and cognitive dysregulations as potential mechanisms. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 85, 152–160. 10.1016/j.pnpbp.2017.07.015 [DOI] [PubMed] [Google Scholar]
  72. Rothstein R (2018). The color of law. Liveright Publishing Corporation. [Google Scholar]
  73. Salvi S (2007). Health effects of ambient air pollution in children. Paediatric Respiratory Reviews, 8(4), 275–280. 10.1016/j.prrv.2007.08.008 [DOI] [PubMed] [Google Scholar]
  74. Schwartz SJ (2016). Turning Point for a Turning Point: Advancing Emerging Adulthood Theory and Research. Emerging Adulthood, 4(5), 307–317. 10.1177/2167696815624640 [DOI] [Google Scholar]
  75. Schulz AJ, Williams DR, Israel BA, & Lempert LB (2002). Racial and Spatial Relations as Fundamental Determinants of Health in Detroit. The Milbank Quarterly, 80(4), 677–707. 10.1111/1468-0009.00028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Sharkey P (2014). Spatial Segmentation and the Black Middle Class. American Journal of Sociology, 119(4), 903–954. 10.1086/674561 [DOI] [PubMed] [Google Scholar]
  77. Shen W, Zhang T, Li S, Zhang H, Xi B, Shen H, Fernandez C, Bazzano L, He J, & Chen W (2017). Race and Sex Differences of Long-Term Blood Pressure Profiles from Childhood and Adult Hypertension. Hypertension, 70(1), 66–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Shonkoff JP, Slopen N, & Williams DR (2021). Early childhood adversity, toxic stress, and the impacts of racism on the foundations of health. Annual Review of Public Health, 42, 115–134. [DOI] [PubMed] [Google Scholar]
  79. Singh G (2003). Area Disadvantage and Widening Inequalities in US Mortality, 1969–1998. American Journal of Public Health, 93(7), 1137–1143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Steptoe A, & Feldman P (2001). Neighborhood problems as sources of chronic stress: Development of a measure of neighborhood problems, and associations with socioeconomic status and health. Annals of Behavioral Medicine, 23(3), 177–185. [DOI] [PubMed] [Google Scholar]
  81. Suglia S, Campo R, Brown A, Stoney C, Boyce C, Appleton A, Bleil M, Boynton-Jarrett R, Dube S, Dunn E, Ellis B, Fagundes C, Heard-Garris N, Jaffee S, Johnson S, Mujahid M, Slopen N, Su S, & Watamura S (2020). Social Determinants of Cardiovascular Health: Early Life Adversity as a Contributor to Disparities in Cardiovascular Diseases. The Journal of Pediatrics, 219, 267–273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Troxel WM, Shih RA, Ewing B, Tucker JS, Nugroho A, & D’Amico EJ (2017). Examination of neighborhood disadvantage and sleep in a multi-ethnic cohort of adolescents. Health & Place, 45, 39–45. 10.1016/j.healthplace.2017.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, Elkind MSV, Evenson KR, Ferguson JF, Gupta DK, Khan SS, Kissela BM, Knutson KL, Lee CD, Lewis TT, …Tsao C (2021). Heart Disease and Stroke Statistics—2021 Update. Circulation, 143(8), e254–e743. 10.1161/CIR.0000000000000950 [DOI] [PubMed] [Google Scholar]
  84. Wang D, Choi JK. & Shin J (2020) Long-term Neighborhood Effects on Adolescent Outcomes: Mediated through Adverse Childhood Experiences and Parenting Stress. J Youth Adolescence 49, 2160–2173. 10.1007/s10964-020-01305-y [DOI] [PubMed] [Google Scholar]
  85. Wardle J, Jarvis MJ, Steggles N, Sutton S, Williamson S, Farrimond H, Cartwright M, & Simon AE (2003). Socioeconomic disparities in cancer-risk behaviors in adolescence: Baseline results from the Health and Behaviour in Teenagers Study (HABITS). Preventive Medicine, 36(6), 721–730. 10.1016/S0091-7435(03)00047-1 [DOI] [PubMed] [Google Scholar]
  86. Williams D & Collins C (2001). Racial residential segregation: A fundamental cause of racial disparities in health. Public Health Reports (Washington, D.C.: 1974), 116(5), 404–416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Williams DR, & Sternthal M (2010). Understanding racial-ethnic disparities in health: sociological contributions. Journal of health and social behavior, 51(1_suppl), S15–S27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. U.S. Census Bureau (2020). Census Tract. Glossary. Retrieved from: www.census.gov/programs-surveys/geography/about/glossary.html
  89. Yang L, Magnussen CG, Yang L, Bovet P, & Xi B (2020). Elevated Blood Pressure in Childhood or Adolescence and Cardiovascular Outcomes in Adulthood: A Systematic Review. Hypertension, 75(4), 948–955. [DOI] [PubMed] [Google Scholar]
  90. Yano Y, Reis JP, Colangelo LA, Shimbo D, Viera AJ, Allen NB, Gidding SS, Bress AP, Greenland P, Muntner P, & Lloyd-Jones DM (2018). Association of Blood Pressure Classification in Young Adults Using the 2017 American College of Cardiology/American Heart Association Blood Pressure Guideline with Cardiovascular Events Later in Life. JAMA, 320(17), 1774–1782. 10.1001/jama.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material
NIHMS1851907-supplement-Supplementary_Material.docx (77.8KB, docx)

RESOURCES