The child opportunity index and its association with resting and exercising blood pressure in children: a Chicago metro area pilot study

Abstract

Objective

To determine whether there are differences and a spatial distribution of blood pressure (BP) measures based on Child Opportunity Index (COI) score quintiles among children aged 13–18 years old who reside in IL.

Methods

A retrospective analysis of a cardiopulmonary exercise testing database was conducted to examine associations between neighborhood opportunity (measured by COI scores) and pediatric BP. BP differences across COI quintiles were analyzed using ANOVA and chi-square tests, with multivariable regressions (adjusting for age and BMI) and local bivariate analysis used to assess associations and spatial patterns.

Results

Among 1354 children, those in the lowest COI quintile were more often Black or Latinx and had the highest BMI (p < 0.01); maximal pulse pressure differed across COI quintiles (p = 0.046), and COI showed only modest independent associations with maximal diastolic BP and pulse pressure, while BMI was the strongest predictor of all BP measures (p < 0.01). Geographic Information System analysis did not yield statistically significant spatial outcomes in the data sample.

Conclusions

Findings suggest that while neighborhood opportunity shows limited independent associations with BP measures, BMI remains a dominant predictor of BP variation among children in IL, underscoring the need to address obesity-related risk in pediatric cardiovascular health.

Highlights

• •Neighborhood opportunity demonstrated limited effects on pediatric blood pressure.
• •Body Mass Index was the primary predictor of blood pressure variation.
• •First Chicago pediatric blood pressure study using exploratory GIS mapping.
• •High exercise blood pressure may predict future resting hypertension in youth.
• •Clinicians should weigh neighborhood factors in child cardiovascular health risk assessments.

1. Introduction

The current prevalence of pediatric hypertension (HTN) is estimated to be 2.3–3.8% in the US (Bell et al., 2019; Ouyang et al., 2026; Goulding et al., 2021). The 2017 American Academy of Pediatrics (AAP) Clinical Practice Guidelines update pediatric HTN criteria using data from a large population of healthy children, with additional reference to adult data (Flynn et al., 2017a; Blanchette and Flynn, 2019; Flynn et al., 2017b). The AAP provides absolute blood pressure (BP) criteria for children aged 13 years and older, while criteria for children under 13 years are based on percentiles adjusted for age, sex, and height. The 2017 CPG notably lowered the criteria for elevated BP and HTN in the pediatric population, increasing its sensitivity to identify patients at risk (Bell et al., 2019; Blanchette and Flynn, 2019). The changes of the 2017 CPG led to an increase in prevalence of HTN, especially among children 13 and older who are obese or have other cardiovascular risk factors. Pediatric patients with HTN are at risk of developing HTN as adults (Theodore et al., 2015).

Existing health disparities and inequities disproportionally impact children who are Black (Non-Latinx), with 29% increased odds of having HTN (compared to children who are White); the odds increase to 50% in boys 13–17 years of age (Chen et al., 2015; Colen et al., 2018). Nevertheless, some of these differences may be attributable to variance in social determinants of health, such as socioeconomic disadvantage, access to healthcare, and safe spaces for physical activity, as well as obesity (Chen et al., 2015; Hanevold, 2023). Latinx children have a high obesity prevalence, a documented risk factor in developing HTN, and a subsequently elevated cardiovascular health risk compared to White children (Chen et al., 2015; Hanevold, 2023). Studies that assess the Child Opportunity Index 2.0 (COI) present evidence that neighborhoods with low socioeconomic levels and other deprivation measures at the neighborhood level are strongly associated with an increased prevalence of pediatric HTN (Hanevold, 2023). The COI is a publicly available dataset that captures neighborhood conditions that influence children's development by accounting for 29 indicators of resources in three domains: education, health and environment, and social and economic. The COI ranges from 1 to 100 with 100 representing the optimal environment for children (Acevedo-Garcia et al., 2020).

To date, no studies to our best knowledge examining the relationship between neighborhood opportunity and pediatric resting or exercise BP have utilized the COI, limiting our understanding of how neighborhood factors influence key cardiovascular risk indicator. Assessing both resting and exercise BP measures in children offers a more comprehensive view of cardiovascular risk by revealing stress-related BP responses that may predict future HTN and are less influenced by short-term psychological factors (Alvarez-Pitti et al., 2022; Huang et al., 2024). Thus, the first aim of this study was to determine whether there are differences in area resting and exercising BP measures based on census tract data aggregated COI score quintiles (consisting of education, health environment, and socioeconomic components) among children aged 13–18 years old. We hypothesized that children in neighborhoods with higher COI scores have lower area resting and exercising BP measures when compared to those in neighborhoods with lower COI scores. The second aim of the study was to explore the spatial distribution of participants' COI scores and their association with area BP measures among children in the Chicago metropolitan area. We hypothesized there is an inverse association between COI scores and area BP measures, both at rest and during peak exercise.

2. Methods

2.1. Study design and population

The research team conducted a retrospective analysis of a cardiopulmonary exercise testing (CPET) database between 2004 and 2022 (Griffith et al., 2024). This study was approved by the Institutional Review Board. The CPET database is a comprehensive dataset from pediatric participants without heart disease undergoing CPET. Children aged 13–18 years were included in the analysis. Data extracted from CPET included participants' addresses, age, height, weight, body mass index (BMI), sex, and self-reported race and ethnicity (White [non-Latinx], Black [non-Latinx], and Latinx). A comprehensive range of BP measures included resting systolic blood pressure (SBP), resting diastolic blood pressure (DBP), resting pulse pressure, maximal SBP, maximal DBP, and maximal pulse pressure. COI data, including aggregated and domain-specific scores for education, health environment, and socioeconomic status, were linked to CPET data using participants' residential addresses. The study population was distributed into very low, low, medium, high, and very high COI quintiles concordant with national percentiles. We excluded those participants who were missing demographic data (age, race, and/or ethnicity), those with documented congenital or other heart disease, and any individuals with submaximal CPET results (respiratory exchange ratio < 1.10).

All participants completed a treadmill exercise using the Bruce protocol (Bruce, 1971). Manual cuff BP was obtained once at rest, at approximately minute two of each three-minute Bruce Protocol stage (minutes two, five, eight, and eleven), and once immediately post-recovery to assess maximal blood pressure. Individual BP measures were averaged by COI quintiles to calculate area BP measures. Resting SBP and DBP were obtained in the sitting position prior to exercise testing. Patients were instructed to continue exercising until volitional fatigue and holding on was actively discouraged. Resting heart rate (HR) was defined as the baseline HR measured in the supine position before exercise, while maximal HR represented the highest value achieved during CPET.

2.2. Measures

For our first aim, the dependent variables were: mean area resting SBP, area resting DBP, area maximal SBP, and area maximal DBP. Area resting and exercising pulse pressures were calculated by subtracting area DBP from area SBP. Independent variables for aim one included COI aggregate scores. For our second aim, we explored the associations of state-normed COI 2.0 scores with mean area resting SBP and mean area resting DBP. Participants were Illinois residents at the time of data collection. Participant addresses were extracted from the CPET database and geocoded using ArcGIS Pro software (version 3.3, Esri, Inc.). We then calculated the mean area resting SBP and mean area DBP for participants within each census tract. Tracts were sourced from the National Historical Geographic Information Systems (NHGIS) platform, which packages spatial and sociodemographic census data for spatial analysis. The tract-level mean area resting SBP and DBP measures were then joined with 2015 COI scores obtained from Diversity Data Kids, who maintains and publishes the composite index.

2.3. Statistical analyses

For the first aim, we used analysis of variance (ANOVA) to examine whether demographic and anthropometric continuous variables (age, height, weight, and BMI) differed across areas (COI quintiles). Chi-square analyses were conducted to assess differences of categorical variables (sex, and race/ ethnicity) across COI quintiles. To evaluate differences in BP across COI quintiles while accounting for potential confounding, we conducted an analysis of covariance (ANCOVA) adjusting for BMI. We then performed a series of simple linear regressions to estimate the unadjusted association between COI scores and each mean area BP measure, followed by multivariable linear regressions adjusting for age and BMI to further assess these relationships. Race/ ethnicity are closely tied to structural disparities in low-opportunity neighborhoods, so they were excluded from the multivariable models to avoid adjusting for factors on the causal pathway. Continuous variables are reported as mean (SD), categorical variables as n (%), and statistical significance was defined as p < 0.05 for all analyses.

For aim two, a local bivariate relationships analysis was conducted to explore the spatial associations between Illinois-only mean area resting SBP and state- normed COI scores, as well as Illinois-only mean census tract resting DBP and state- normed COI scores. State-normed COI scores are ranked from 1 to 100 and provide insight into state-specific variations related to neighborhood resources and children's level of opportunity. Local bivariate relationships analysis examines whether the values of one variable depend on or are independent of the values of another, and how this association varies across space (Cheung et al., 2017). It is well suited for identifying spatial heterogeneity in variable relationships across the study area by census tract. Statistical analyses were performed using IBM SPSS Statistics (Version 30; IBM Corp., Armonk, NY).

3. Results

3.1. Results for aim 1

The study sample population included 1354 children. Table 1 shows demographic and clinical characteristics of this study population based on national COI quintiles: very low (<20th percentile), low (20th to <40th percentile), moderate (40th to <60th percentile), high (60th to <80th percentile), and very high (> 80th percentile) based on national COI percentiles. ANOVA results indicated statistically significant differences in age (p = 0.03) and anthropometric measures (height, weight, and BMI; p < 0.01 for all). Children in the lowest COI quintile were predominantly Black (non-Latinx) and Latinx and exhibited the highest BMI values.

Table 1.

Demographic and anthropometric means of children in Illinois by Children Opportunity Index 2.0 quintiles (2004–2022).

Demographic & Anthropometric Characteristics	Children Opportunity Index 2.0 Quintiles					Total COI Sample (n = 1354)	p-value
	Very Low (n = 217)	Low (n = 153)	Moderate (n = 225)	High (n = 265)	Very High (n = 494)
Age (years)	15.4 (1.4)	15.2 (1.6)	15.3 (1.5)	15.2 (1.4)	15.0 (1.4)	15.2 (1.4)	0.03
Sex (%)  Male  Female	48.4 51.6	50.3 49.7	54.2 45.8	47.5 52.5	44.3 55.7	47.9 52.1	0.16
Race/ Ethnicity (%)  White  Black  Latinx  Other	6.0 33.2 58.1 2.8	30.1 14.4 48.4 7.2	32.4 11.6 48.4 7.6	59.6 10.6 22.6 7.2	78.3 5.1 9.5 7.1	50.0 12.8 30.7 6.5	<0.01
Height (cm)	166.0 (10.0)	164.8 (12.8)	166.8 (9.7)	168.1 (10.1)	168.2 (10.0)	167.2 (10.4)	<0.01
Weight (kg)	65.9 (16.2)	65.2 (19.4)	64.7 (16.4)	65.9 (17.3)	61.3 (15.5)	64.0 (16.7)	<0.01
BMI (kg/m2)	23.9 (5.3)	23.7 (5.7)	23.3 (5.1)	23.1 (4.9)	21.6 (4.3)	22.8 (5.9)	<0.01

COI, Children Opportunity Index; BMI, Body Mass Index; Data are mean (SD); p- values were derived from one- way ANOVA tests.

Table 2 shows the ANCOVA results examining participants' BP profiles across COI quintiles, adjusting for BMI. The BP profile included mean area resting and maximal SBP, DBP and pulse pressure, all measured in mmHg. A statistically significant difference was observed for area maximal pulse pressure across aggregate COI quintiles (p = 0.05), with the lowest quintile exhibiting the lowest mean value of 94.7 mmHg (SD 20.2). No other area BP measure differed significantly across quintiles.

Table 2.

Area Blood Pressure Measures of Children in Illinois by Children's Opportunity Index Quintiles, Adjusted for Body Mass Index (2004–2022).

Area Blood Pressure Measures (mm Hg)	Children Opportunity Index 2.0 Quintiles					Total COI Sample (n = 1354)	p-value
	Very Low (n = 217)	Low (n = 153)	Moderate (n = 225)	High (n = 265)	Very High (n = 494)
Resting SBP	111.3 (10.6)	111.2 (11.0)	111.0 (11.0)	111.2 (10.2)	109.9 (10.1)	110.6 (10.5)	0.99
Resting DBP	71.4 (8.3)	71.0 (7.8)	72.4 (8.2)	71.7 (7.3)	71.0 (7.5)	71.3 (7.8)	0.38
Resting Pulse Pressure	39.8 (9.7)	40.1 (10.6)	38.6 (10.1)	39.6 (9.2)	39.0 (9.9)	39.3 (9.8)	0.69
Maximal SBP	158.2 (19.5)	160.7 (20.6)	160.2 (19.4)	161.1 (20.1)	159.4 (21.0)	159.8 (20.2)	0.28
Maximal DBP	60.3 (9.9)	59.6 (10.3)	59.7 (10.4)	58.5 (9.5)	57.6 (9.6)	58.8 (9.9)	0.06
Maximal Pulse Pressure	94.7 (20.2)	98.7 (21.9)	98.8 (20.9)	100.1 (21.9)	99.0 (22.5)	98.6 (21.7)	0.05

COI, Children Opportunity Index; BMI, Body Mass Index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; Data are mean (SD); p- values were derived from one- way ANCOVA tests adjusted for BMI.

Table 3 presents the simple linear regression analyses in which COI was modeled as the predictor and each BP area measure is a separate outcome. In the unadjusted models, COI was not significantly associated with area resting SBP, area resting DBP, area resting pulse pressure, or area maximal SBP. COI demonstrated a statistically significant inverse association with area maximal DBP and a positive association with area maximal pulse pressure, although both effect sizes were modest. In the multivariable models adjusted for age and BMI, COI remained significantly associated only with area maximal DBP (β = −0.030, p = 0.003) and area maximal pulse pressure (β = 0.063, p = 0.005), with no significant associations observed for the other area BP outcomes. BMI emerged as a strong and consistent positive predictor across all area BP measures, whereas age was significantly associated only with area maximal SBP and area maximal DBP. Overall, BMI, more than age, accounted for substantial variation in area BP, while the independent contribution of COI was limited to area maximal diastolic BP and area maximal pulse pressure.

Table 3.

Association of Area Blood Pressure Measures and Children Opportunity Index Quintiles among Illinois Children, Adjusted by Body Mass Index and Age (2004–2022).

Unadjusted Models: Simple Linear Regression Analyses (COI as predictor)
Area Blood Pressure Measures (outcomes; mm Hg)	β for Blood Pressure Measure (95% CI)
Resting SBP	0.01 (−0.03, 0.01)
Resting DBP	−0.01 (−0.02, 0.01)
Resting Pulse Pressure	< 0.01 (−0.02, 0.012)
Maximal SBP	0.01 (−0.02, 0.05)
Maximal DBP	−0.04 (−0.05, −0.02)
Maximal Pulse Pressure	0.05 (0.01, 0.09)
Adjusted Models: Multivariable Linear Regression Analyses (COI as predictor; adjusted for Age and BMI)
Area Blood Pressure Measures (outcomes; mm Hg)	β for COI (95% CI)	β for Age (95% CI)	β for BMI (95% CI)
Resting SBP	<0.01 (−0.02, 0.02)	0.55 (0.16, 0.94)	0.59 (0.47, 0.70)
Resting DBP	0.01 (−0.01, 0.01)	0.20 (−0.10, 0.50)	0.40 (0.31, 0.48)
Resting Pulse Pressure	< −0.01 (−0.02, 0.02)	0.35 (−0.03, 0.73)	0.19 (0.08, 0.30)
Maximal SBP	0.04 (−0.01, 0.07)	1.00 (0.22, 1.76)	0.95 (0.73, 1.18)
Maximal DBP	−0.03 (−0.05, −0.010)	0.47 (0.06, 0.87)	0.13 (0.01, 0.25)
Maximal Pulse Pressure	0.06 (0.02, 0.11)	0.26 (−0.64, 1.16)	0.70 (0.44, 0.97)

COI, Children Opportunity Index; BMI, Body Mass Index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; confidence intervals derived from simple and multivariable linear regression models.

3.2. Results for aim 2

After testing the relationship between Illinois-only mean census tract resting SBP and DBP and state-normed COI scores, Geographic Information System (GIS) analysis did not yield statistically significant spatial outcomes in the data sample. To further evaluate these findings, two sensitivity analyses were conducted including 1) assigning zero values to non-reporting tracts and 2) imputing values using the average of neighboring tracts. Neither approach altered the findings, and the local bivariate analysis results remained non-significant. Spatial autocorrelation was also assessed to identify patterns of dispersion, randomness, or clustering based on feature location and input values. Results indicated a random spatial pattern (see supplemental materials, Appendix A).

4. Discussion

This study found that most area BP measures were comparable across COI quintiles; however, adjusted multivariable models indicated that COI explained modest variation in area maximal DBP (β = −0.030, p = 0.003) and area maximal pulse pressure (β = 0.063, p = 0.005). These findings are concordant with prior research linking lower neighborhood opportunity and socioeconomic disadvantage to higher pediatric HTN risk and higher BP outcomes (Aris et al., 2021). Children age was found to be another predictor of higher BP measures, similar to other studies (Bell et al., 2019; Flynn et al., 2017a; Brady et al., 2021; Azegami et al., 2021). BMI, a modifiable, individual- level factor, was the strongest determinant of pediatric BP than neighborhood opportunity, aligning with evidence that individual risks account for most variation in BP (Goulding et al., 2021). This study expanded on the emerging literature by incorporating a comprehensive set of both resting and exercise area BP measures rarely examined in the context of social drivers of health. Although current guidelines rely on resting and ambulatory BP for diagnosis, incorporating a broad set of BP measures in selected at-risk children may enhance early detection, improve risk stratification, and guide targeted prevention strategies such as fitness promotion and weight management (Flynn et al., 2017a; Alvarez-Pitti et al., 2022; Huang et al., 2024; Lurbe et al., 2016).

Pediatric research reporting differences in BP measures has frequently attributed these findings to presumed variance across racial and ethnic groups, occasionally citing disparities in body size as contributing factors (Chen et al., 2015; Colen et al., 2018; Cheung et al., 2017). These findings suggest that maximal DBP and pulse pressure, in the context of resource inequity, may contribute to pediatric cardiovascular risk, challenging conventional interpretations and supporting a multidimensional analytic approach. Moreover, prior investigations have demonstrated that reported racial and ethnic differences in other health outcomes like cardiorespiratory fitness and obesity often attenuate when environmental resources are incorporated into the analysis (Wang et al., 2024; Sharifi et al., 2016; Tylavsky et al., 2020). Abnormally high peak SBP and DBP during exercise may be associated with the future development of HTN at rest in patients who otherwise have normal resting BP (Theodore et al., 2015; Robinson et al., 2024). Since there are prior studies showing regional disparities in HTN prevalence based on SES (Liu et al., 2022), our study may suggest that some of these regional BP differences seen in adults may start in childhood via small differences in peak exercise DBP. Further research is warranted to better elucidate potential factors that influence overall cardiovascular health in children.

Access to high-quality physical environments and opportunities, like green spaces, parks, walkable neighborhoods, and safe play areas, has a significant positive impact on children's health outcomes (Fyfe-Johnson et al., 2021). Proximity to parks, recreational facilities, and sidewalks is associated with increased physical activity, reduced sedentary behavior, and better overall well-being in children, while the absence of such resources or exposure to environmental adversities can contribute to negative health outcomes and disparities (Fyfe-Johnson et al., 2021). Advocacy for equitable distribution of resources and the creation of safe, accessible spaces is crucial for promoting pediatric health and reducing disparities, as these efforts can help ensure all children can thrive regardless of their neighborhood (Fyfe-Johnson et al., 2021; Bole et al., 2024).

It's possible these study findings would differ with more data points from other regions across the US. Notably, adult HTN displays a clear spatial pattern (Weng et al., 2024). If pediatric HTN is a predictor for adult HTN, spatial analysis was expected to reflect this relationship (Hypertension rate, 2025). However, the lack of significant findings in our second aim may reflect the distinction between statistical and spatial significance. Results can be statistically significant without exhibiting a spatial pattern, clustering, or autocorrelation. The results from the annual Healthy Chicago Survey report on geographic distribution of adult HTN across the city and surrounding areas. Data collected from 2016 to 2018 shows a spatial distribution of self-reporting adults with HTN as high as 61.9% in west and south regions of the city of Chicago, compared to a range of 12.7–28.7% in downtown areas as well as the north side (Hypertension rate, 2025). Socioeconomic and neighborhood disadvantage may shape pediatric HTN through multiple pathways, underscoring the need for future studies that integrate diverse datasets and the potential spatial distribution patterns of HTN risk, despite absence of spatial cluster significance in this study.

The Social Vulnerability Index (SVI) is a similar measure to the COI that assesses the level of neighborhood vulnerability to external stresses using other domains like household composition or minority status (Zolotor et al., 2024). Current research suggests an association between higher SVI and increased cardiovascular risk factors, including HTN, at the community and adult levels; however, there is a notable gap in direct evidence linking SVI to pediatric HTN (Jain et al., 2022; Bevan et al., 2023; Mah et al., 2023). The Area Deprivation Index (ADI) is another granular measure of socioeconomic disadvantage at the neighborhood level, and evidence suggests a clear link between higher ADI values with an increase in HTN risk, though direct pediatric-specific studies are limited (Xu et al., 2022; Vintimilla et al., 2023). At least one study has suggested COI may be a more comprehensive measure to use in research on child health and health-related social needs than the SVI or ADI (Mah et al., 2023). Further investigation into the relationship between pediatric HTN and neighborhood vulnerability indicators, such as the SVI and ADI, may reveal cardiovascular disparities in this population that can guide future health policy decisions.

This is the first study of pediatric BP measures at rest and at peak exertion in the Chicago metropolitan area that included an exploratory GIS analysis to our knowledge. Another study strength includes a detailed BP measures data set of modest size (1354 children), a child- specific index highlighting neighborhood dynamics that promote healthy development, and the use of census tracts to obtain a detailed, geographical analysis. Our study had a few limitations. All our data was collected at a single, urban, large hospital in the Midwest of the US, so it may not represent a national or international sample of children. Furthermore, geospatial analysis may have been impacted by the distribution of CPETs across census tracts. BP were measured by multiple physiologists over the years, and resting BP values were collected prior to exercise testing, raising the potential of internal consistency issues and altered resting BP related to anticipatory sympathetic tone.

5. Conclusions

Neighborhood opportunity demonstrated limited independent effects on pediatric BP measures by area, whereas BMI was the primary predictor of BP variation, emphasizing the need to prioritize obesity-related risk reduction in children. Compelling evidence shows the association between pediatric HTN and the likelihood of developing cardiac disease and HTN in adulthood (Theodore et al., 2015; Azegami et al., 2021). Therefore, pediatric clinicians must consider neighborhood and influences when evaluating children's health risks, recognizing that these factors intersect with broader educational, social, and economic conditions. Strengthening the domains of community opportunity—education, health and environment, and social and economic well-being—can create more equitable foundations for healthy development. Targeted investments in these areas may help lower childhood BP and promote long-term cardiovascular and population health outcomes.

Future work includes studying the potential association between weight and BMI with COI scores by census tract, as our findings suggest a difference in COI quintiles of obesity markers. Incorporating qualitative data in future studies on the impact of safety and neighborhood resources could reveal a better contextual understanding of BP and other cardiovascular health markers in the pediatric population.

Author contribution

• •Roberto López- Rosado: conception and design of the study, analysis and interpretation of data, drafting the article, revising it for critical intellectual content, and final approval of the version submitted.
• •Garett J Griffith: conception and design of the study, acquisition of data, analysis and interpretation of data, revising article for critical intellectual content, and final approval of the version submitted.
• •Mech E Frazier: conception and design of the study, acquisition of data, analysis and interpretation of data, drafting article, revising it critically for intellectual content, final approval of the version to be submitted.
• •Jennifer M Ryan: analysis and interpretation of data, revising it critically for intellectual content, final approval of the version to be submitted.
• •Alan P Wang: conception and design of the study, analysis and interpretation of data, revising it for critical intellectual content, and final approval of the version submitted.
• •Kendra Ward: conception and design of the study, analysis and interpretation of data, revising it for critical intellectual content, and final approval of the version submitted.

CRediT authorship contribution statement

Roberto López-Rosado: Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Formal analysis, Data curation, Conceptualization. Garett J. Griffith: Writing – review & editing, Visualization, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Mech E. Frazier: Writing – review & editing, Software, Resources, Methodology, Formal analysis, Data curation, Conceptualization. Jennifer M. Ryan: Writing – review & editing, Validation, Resources. Alan P. Wang: Writing – review & editing, Resources, Project administration, Methodology, Investigation, Conceptualization. Kendra Ward: Writing – review & editing, Supervision, Project administration, Methodology, Investigation, Conceptualization.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary material: Regional Distribution of Mean Resting Systolic and Diastolic Blood Pressure in Children Across Illinios 2004-2022.

Data availability

The data used in the study were obtained from a confidential clinical database and are not publicly available due to patient privacy and institutional confidentiality restrictions.