Disparities in Receipt of Guideline-Adherent Blood Pressure Screening: An Observational Examination of Electronic Health Record Data from a Massachusetts Healthcare System

Abstract

Objective:

To describe the prevalence of blood pressure (BP) screening according to the 2017 American Academy of Pediatrics (AAP) guidelines and differences according to social vulnerability indicators.

Study Design:

We extracted electronic health record data from January 1, 2018, through December 31, 2018 from the largest healthcare system in Central Massachusetts. Outpatient visits for children 3 to 17 years without a prior hypertension diagnosis were included. Adherence was defined by the AAP guideline (≥1 BP screening for children with a body mass index [BMI] <95%) and at every encounter for children with a BMI ≥95%). Independent variables included social vulnerability indicators at the patient level (insurance type, language, Child Opportunity Index, race/ethnicity) and clinic level (location, Medicaid population). Covariates included child’s age, sex, and BMI status, and clinic specialty, patient panel size, and number of healthcare providers. We used direct estimation to calculate prevalence estimates and multivariable mixed effects logistic regression to determine the odds of receiving guideline adherent BP screening.

Results:

Our sample comprised 19,695 children (median age 11 years, 48% female) from 7 pediatric and 20 family medicine clinics. The prevalence of guideline adherent BP screening was 89%. In our adjusted model, children with a BMI ≥95%, with public insurance, and who were patients at clinics with larger Medicaid populations and larger patient panels had lower odds of receiving guideline adherent BP screening.

Conclusion:

Despite overall high adherence to BP screening guidelines, patient- and clinic-level disparities were identified.

Although hypertension was once considered an adult disorder, the effect of childhood high blood pressure (BP) on adult cardiovascular disease is increasingly acknowledged as a critical public health issue.(1,2) Recent prevalence estimates show that approximately 10% of American children aged 8–17 years old have BP in the hypertensive or elevated range as defined by the 2017 American Academy of Pediatrics (AAP) guidelines, and that Black and Hispanic children are disproportionately affected.(3–5) Given this, the importance of prevention and early detection of pediatric hypertension is clear.(6,7)

Clinical practice guidelines have the potential to improve patient outcomes and address health disparities if properly implemented.(8,9) The AAP released clinical practice guidelines for BP screening and management in 2017 as an update to the National Heart Lung and Blood Institutes’ Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children.(4,10) In the 2017 guidelines, the AAP continued the recommendation of regular BP screening in all children 3–17 years of age, introduced a new simplified classification system for children aged ≥13 years, and emphasized the important role of obesity.(4) Examination of adherence to these guidelines has been limited to a single multisite investigation of children with high BP, and, to our knowledge, no contemporary studies have examined disparities in receipt of guideline adherent screening.(11) An equity focused approach is necessary as data increasingly indicate disparities in the burden of pediatric hypertension and call attention to the influence of social risk factors on pediatric cardiovascular health.(3–5,12,13)

Based on a review of electronic health records, the present study examines the prevalence of adherence to the 2017 AAP guidelines for BP screening and differences according to social vulnerability indicators. We hypothesized that the prevalence of guideline adherent BP screening would be higher than past estimates though still suboptimal; and that there would be an inverse relationship between receipt of guideline adherent screening and social vulnerability.

Methods

This cross-sectional study was conducted through a retrospective review of electronic health record (EHR) data from the UMass Memorial Health (UMMH) system, the largest not-for-profit, healthcare system in Central Massachusetts. We extracted all patient-level variables from EHR data and obtained clinic level variables from the UMMH Office of Clinical Integration. This study was approved by the Institutional Review Board at UMass Chan Medical School.

Study Sample

Children aged 3–17 years who had been seen for any outpatient primary care visit at one of the 27 pediatric or family medicine outpatient practices within the UMMH system between January 1, 2018 and December 31, 2018, were included. Children with a diagnosis of hypertension (ICD-10 code I10 or I15) prior to this time and those with a documented pregnancy during this time were excluded. We used a complete case approach such that patients with missing social vulnerability indicators or covariates were excluded as shown in Figure 1. The final study sample consisted of 19,695 children.

Figure 1: Cohort Identification.

This figure describes the selection of the cohort.

Primary Study Outcome

The primary outcome of interest was receipt of AAP guideline adherent BP screening during the calendar year. This outcome was operationalized in terms of frequency of screening by the presence of ≥1 documented BP screening for a child with body mass index (BMI) <95^th percentile at any healthcare encounter during 2018 and a documented BP screening at every encounter during that year for children with obesity (i.e., BMI ≥95^th percentile) in accordance with AAP guidelines.(4)

Indicators of Social Vulnerability

We examined several factors related to children’s social vulnerability to assess differences in the receipt of guideline adherent BP screening. Patient-level indicators of social vulnerability included: (A) race/ethnicity categorized as American Indian/Alaska Native, Asian, Black/African American, Hispanic/Latino, Native Hawaiian/Pacific Islander, White, other race Non-Hispanic/Latino, (B) primary language categorized as English or non-English, (C) insurance type categorized as public or private used at the index BP screening visit, and (D) the Child Opportunity Index. It is the UMMH system’s protocol that race/ethnicity and primary language are self (or parent) reported and recorded in the EHR. The Child Opportunity Index is a census tract based measure of social determinants of health and includes 29 indicators (education, health, environment, social, and economic) that describe and proxy the resources and conditions of a child’s neighborhood which may affect healthy development.(14–16) The Child Opportunity Index was obtained by linking the child’s address as recorded at the time of data extraction (June 2021) at the census tract level to the 2015 Child Opportunity Index using geographic information system mapping.(14,15) The scores from the Child Opportunity Index are categorized at the census tract level based on the tracts relative rank to all other tracts in the metro area (Worcester MA). The categories are very low, low, moderate, high, and very high, and each category contains 20% of the child population from the metro area (Worcester MA). Prior research has linked very low Child Opportunity Index to several negative outcomes including increased acute care visits, increased ED visits, increased hospitalizations, and greater 30-day readmission rates.(17–20)

Clinic-level measures related to social vulnerability were chosen based on the available clinic level data, and prior literature. These factors included rural location (due to well-known urban-rural differences in health and in primary care),(21,22) and the percentage of the clinic population with Medicaid coverage in 2022 (due to potential differences in concentration of Medicaid population across clinics).(23,24) Clinics were designated as rural based on the Massachusetts State Office of Rural Health’s definition, and the Medicaid population was drawn from patient panel size and claims rosters provided by insurance payors.(25)

Covariates

Patient-level covariates included age at the date of index healthcare visit (during 2018), sex, and BMI status determined by BMI percentile and categorized according to Centers for Disease Control and Prevention guidelines.(26,27) Since BMI was not recorded at all index visits, the BMI category which was documented at the highest frequency during the 2018 year was used.

Clinic-level covariates included the 2018 patient panel size (number of patients under the clinic’s care associated with primary care providers with ≥1 encounter in the previous 3 years, including both children and adults for family medicine clinics), number of providers employed by the clinic, and clinic specialty (pediatrics or family medicine).

Statistical Analysis

Proportions with 95% confidence intervals (CI) were calculated to describe categorical variables and medians with interquartile ranges (IQR) were calculated to describe the distribution of continuous variables. We used direct estimation to determine the overall prevalence of AAP guideline adherent BP screening as well as the prevalence of guideline adherent BP screening stratified by BMI status because of differences in screening recommendations based on this variable.

To examine possible associations between patient- and clinic-level social vulnerability factors with guideline adherent care, and to account for the hierarchical data structure (children nested within clinics), we used multilevel mixed effects logistic regression analysis. First, to assess variation in guideline adherent care at the clinic-level, we ran a null multilevel mixed effects model containing only random effects for clinic and calculated the interclass correlation coefficient (ICC). Next, we ran unadjusted logistic regressions separately for each patient-(accounting for clustering by clinic) and clinic-level factor to identify bivariate associations between social vulnerability factors and receipt of guideline adherent screening. Lastly, we built a fully adjusted multilevel mixed effects logistic regression model to determine associations of social vulnerability indicators with receipt of guideline adherent screening after adjusting for all covariates. Our model building process used likelihood ratio tests to compare the fit of the null model to one containing all patient-level social vulnerability indicators (adjusting for all patient-level covariates found to be associated with guideline adherent care at the bivariate level (p ≤0.2)) and then to compare that patient-level model to one which additionally included all clinic-level social vulnerability factors (adjusting for all clinic-level covariates found to be associated with guideline adherent care at the bivariate level (p ≤0.2)). We choose 0.2 as a cut point a priori because it has been shown to be an acceptable method and is commonly used. (28) However, since all variables met this cut point and were included in the final model, this method was equivalent to model selection by a prior selection of variables based on existing literature and study hypothesis. During the model building process, we also explored correlations across all patient- and clinic-level variables and multicollinearity was not detected in the models. A priori power calculations based on the use of logistic regression in observational studies of clinical data indicated sufficient power and supported the use of these methods.(29,30)

Finally, to assess if there were significant differences between those with and without data on the Child Opportunity Index (n=2,358 missing address) and BMI (n=2,295 with no measurement), and to assess if the use of a complete case analysis biased our results, we conducted a sensitivity analysis. For this analysis, we created a missing category for the Child Opportunity Index and BMI variables. First, we ran the full analysis described above with adherence based on all BMIs assumed to be <95^th percentile, then we ran it again with adherence based on all BMIs assumed to be ≥95^th percentile and compared all results. All analyses were conducted in STATA 15.

Results

Study Sample Characteristics

Our sample consisted of 19,695 children aged 3–17 years with a median of 2 healthcare encounters per child (IQR 1–3). The median age of the study sample was 11 years (IQR 7–14 years), 48% were female, and 19% had a BMI percentile of ≥95. Most were White (66%), spoke English (94%), and had private insurance (55%). The clinics from which this sample was drawn had specialties of Pediatrics (n=7), and Family Medicine (n=20), with 30% of children cared for in Family Medicine clinics. The median clinic patient panel size was 6,676 (IQR 3,051–8,488) for a median of 5 providers (IQR 3–12), and the median percentage of the panel covered by Medicaid was 30% (IQR 25–56).

Receipt of Guideline Adherent Blood Pressure Screening

Overall, 89% of children in our sample received guideline adherent BP screening. This prevalence varied significantly across BMI status, such that only 57% of children with a BMI percentile of ≥95% received guideline adherent screening compared with 97% of children with a BMI percentile <95%. As shown in Table 1, children who identified as Black or African American and Hispanic or Latino, who were non-English speaking, who were publicly insured, who had a BMI ≥95%, and who had a very low Child Opportunity Index were overrepresented in the group that did not receive guideline adherent screening as compared with their prevalence in the overall study population.

Table 1:

Demographic Characteristics of Study Cohort (N=19,695).

Characteristic	Overall (n=19,695)	Received Guideline Adherent Screening (n=17,615)	Did Not Receive Guideline Adherent Screening (n=2,080)
Median # of encounters	2 (IQR 1–3)	2 (IQR 1–3)	3 (IQR 2–4)
Median # of encounters with a BP screening	1 (IQR 1–2)	1 (IQR 1–2)	1 (IQR 0–1)
Age (median, years)	11 (IQR 7–14)	11 (IQR 7–14)	8 (IQR 4–13)
Sex
Female	48.4%	48.8%	48.4%
Race/Ethnicity
American Indian/ Alaska Native	0.2%	0.2%	0.2%
Asian	4.0%	4.1%	3.1%
Black or African American	7.6%	7.5%	8.1%
Hispanic or Latino	18.3%	17.5%	24.3%
Native Hawaiian or Pacific Islander	0.03%	0.02%	0.09%
White	66.5%	67.3%	60.3%
Other Race Non-Hispanic or Latino	3.4%	3.3%	3.8%
Primary Language
English	94.3%	94.7%	91.6%
Body Mass Index Percentile
≥85th to 94th percentile	17.2%	18.6%	4.9%
≥95th percentile	18.7%	11.9%	76.7%
Insurance Type
Public	45.1%	43.6%	58.1%
Child Opportunity Index
Very High	22.6%	23.4%	15.8%
High	21.6%	21.8%	20.2%
Moderate	19.6%	19.4%	20.7%
Low	17.0%	17.0%	15.8%
Very Low	19.2%	18.4%	25.9%

Bivariate Associations of Social Vulnerability Indicators with Guideline Adherent BP Screening

In unadjusted bivariate analyses, we found each of the patient-level social vulnerability indicators (non-English speaking, public insurance, lower Child Opportunity Index, and Hispanic/Latino race/ethnicity) to be associated with a lower odds of receiving guideline adherent BP screening (Table 2). With respect to clinic characteristics, children belonging to clinics with a higher proportion of Medicaid patients, with larger patient panels, and with more providers also had a lower odds of receiving guideline adherent BP screening (Table 2).

Table 2:

Odds of Receiving AAP CPG Adherent BP Screening

Covariate	Unadjusted Odds Ratio* (95% CI)	Fully Adjusted Odds Ratio** (95% CI)
Child-Level Social Vulnerability Indicators
Child Opportunity Index
Very High	Ref	Ref
High	0.74 (0.64–0.86)	0.95 (0.79–1.16)
Moderate	0.70 (0.60–0.81)	0.88 (0.72–1.07)
Low	0.69 (0.59–0.80)	1.01 (0.82–1.24)
Very Low	0.52 (0.45–0.60)	0.87 (0.70–1.09)
Race/Ethnicity
white	Ref	Ref
American Indian/Alaska Native	0.64 (0.24–1.66)	0.39 (0.11–1.36)
Asian	1.27 (0.98–1.65)	0.91 (0.65–1.27)
Black or African American	0.88 (0.74–1.05)	1.31 (1.04–1.66)
Hispanic or Latino	0.71 (0.63–0.79)	1.21 (1.01–1.44)
Native Hawaiian or Pacific Islander	0.17 (0.03–1.04)	0.12 (0.01–1.70)
Other Race Non-Hispanic	0.87 (0.68–1.11)	1.01 (0.73–1.38)
Insurance Type
Private	Ref	Ref
Public	0.58 (0.52–0.63)	0.84 (0.74–0.96)
Language
English	Ref	Ref
Non-English	0.69 (0.58–0.82)	1.00 (0.79–1.26)
Clinic-Level Social Vulnerability Indicators
Clinic Rurality
Non-rural	Ref	Ref
Rural	1.29 (1.13–1.47)	0.39 (0.14–1.08)
% Clinic Population on Medicaid (continuous per 10% increase)	0.87 (0.84–0.89)	0.66 (0.49–0.89)
Child-Level Covariates
BMI Status
BMI <95th percentile	Ref	Ref
BMI >95* percentile (indicative of obesity)	0.04 (0.03–0.04)	0.03 (0.02–0.03)
Sex
Male	Ref	Ref
Female	1.16 (1.06–1.28)	1.07 (0.95–1.20)
Age (continuous in years)	1.10 (1.09–1.12)	1.20 (1.19–1.22)
Clinic-Level Covariates
Patient Panel Size (continuous per 1,000 additional patients)	0.97 (0.96–0.99)	0.72 (0.52–0.99)
Number of Providers	0.99 (0.98–0.99)	1.42 (1.11–1.80)
Clinic Type
Pediatrics	Ref	Ref
Family Medicine	1.59 (1.43–1.78)	0.73 (0.28–1.96)

^*Child-level variables are adjusted for clustering within clinics;

^**Fully adjusted model includes all variables

Adjusted Multilevel Mixed Effects Logistic Regression Analysis

The results of the final fully adjusted model are presented in Table 2. This model included all patient- and clinic-level social vulnerability indicators and covariates while also adjusting for clustering of children within clinics. The fully adjusted model showed a lower odds of receiving guideline adherent BP screening in children with public insurance (16% lower odds compared with private insurance), and in those with a BMI ≥95th percentile (97% lower odds compared with those with a BMI<95th percentile). Ancillary data (sub-population proportions) in Table 3 support these adjusted multilevel mixed effects logistic regression model findings. In the model, the odds of receiving guideline adherent BP screening were additionally found to be higher in older children (20% higher odds per 1 year increase in age), and children of Black or African American and Hispanic or Latino race/ethnicity (31% and 21% increased odds respectively compared with White counterparts). Children belonging to clinics with a higher proportion of Medicaid patients, with larger patient panel sizes and with fewer providers also had lower odds of receiving guideline adherent BP screening. The associations between language and Child Opportunity Index with the receipt of guideline adherent BP screening were not significant in the adjusted model. These bivariate associations were attenuated by the inclusion of the BMI ≥95% indicator in the adjusted model. The inclusion of BMI≥95% in the model also changed the direction of the association with Hispanic or Latino race/ethnicity (from negative to positive) and brought the relationship with Black or African American race/ethnicity to statistical significance such that these children had higher odds of receiving guideline adherent BP screening. Correlations between the patient-level variables and receipt of guideline adherent screening support the differences seen between bivariate and multivariable adjusted associations and are shown in Table 4; online.

Table 3:

Data Supporting Multivariable Adjusted Multilevel Mixed Effects Logistic Regression Findings

	Overall Population		Sub-population with BMI ≥95% (indicative of obesity)		Sub-population with Public Insurance
Race/Ethnicity	Total # of children	# of children who did not receive guideline adherent screening (% of total within race/ethnicity population)	# of children (% of total within race/ethnicity population)	# of children who did not receive guideline adherent screening (% of within race/ethnicity population who did not received guideline adherent screening)	# of children (% of total within race/ethnicity population)	# of children who did not receive guideline adherent screening (% of within race/ethnicity population who did not received guideline adherent screening)
white	13,107	1,255 (9.6%)	2,185 (16.7%)	980 (78.2%)	4,189 (32%)	555 (44.2%)
Asian	796	65 (8.2%)	96 (12%)	30 (46.2%)	283 (35.6%)	35 (53.8%)
Black or African American	1,491	169 (11.3%)	350 (23.5%)	126 (74.6%)	1,054 (70.7%)	116 (68.6%)
Hispanic or Latino	3,597	506 (14.1%)	933 (25.9%)	399 (78.9%)	2,893 (80.4%)	437 (86.4%)
Other Race Non-Hispanic	664	78 (11.7%)	127 (19.1%)	56 (71.8%)	442 (66.6%)	60 (76.9%)

^*American Indian/Alaska Native and Native Hawaiian or Pacific Islander groups redacted from ancillary data due to cell sizes <5.

Table 4:

Correlations Between Child-level Variables and Receipt of Guideline Adherent Screening

Variable	Correlation with receipt of guideline adherent screening
Child Opportunity Index	−0.02
Race/ethnicity	0.03
Insurance	0.09
Language	0.04
BMI Status (BMI ≥95th percentile, indicative of obesity)	−0.51
Sex	−0.03
Age	0.14

During the model building process, our null model showed an ICC of 0.19, supporting the use of multilevel modeling to account for the clustering of children within clinics. The addition of the patient-level variables which had bivariate associations with guideline adherent screening (p<0.2) significantly improved model fit as shown by a statistically significant likelihood ratio test. The addition of the clinic-level variables to the model also resulted in a statistically significant improvement in fit; as such this model containing patient- and clinic-level social vulnerability indicators comprised the final regression model.

Sensitivity Analysis

In our sensitivity analysis, we found that including children who did not have a Child Opportunity Index (due to their home address not being documented), and who had no documented BMI measurement, did not meaningfully change the principal study results.

Discussion

This study observed a high prevalence of adherence to the AAP’s BP screening guidelines in the largest healthcare system in Central Massachusetts. However, disparities in receipt of guideline adherent BP screening related to social vulnerability were found. Specifically, we found that children with obesity, with public insurance, and those who belonged to clinics with a larger proportion of Medicaid patients, larger patient panels, and fewer providers had lower odds of receiving guideline adherent BP screening.

During 2018, 89% of children in our study received BP screening at the frequency recommended by the AAP. To our knowledge, the only other investigation of the 2017 AAP guidelines assessed adherence to measurement techniques (i.e., 3 measures taken and averaged) and found very low adherence (2%).(11) Our findings inherently differ from these prior findings as our focus was on the frequency of screening in all children rather than the methods used to screen children with high BPs.

Our findings also differ from other prior investigations into BP screening in our use of contemporary data (after 2017) and our focus specifically on adherence to screening guidelines.(31–33) Prior investigations reporting on the prevalence of any screening (not guideline adherent screening) include: only half of children with abnormal BP levels from the AAP Comparative Effectiveness Research through Collaborative Electronic Reporting Consortium data (1999–2016) had yearly BP screening documented over 3 consecutive years; one-third of visits in 2000–2009 from the National Hospital Ambulatory Medical Care Survey had documented BP measurement; and three-quarters of preventive care visits in 2009 from the National Ambulatory Medical Care Survey had BP measurement documented.(31–33) Our prevalence estimate of guideline adherence is considerably higher and suggests increased recognition of the importance of pediatric hypertension by providers, likely due at least in part to increased focus on pediatric hypertension by national academies in recent years. This is particularly interesting given that the US Preventive Service Task Force maintains an “I” recommendation for BP screening in children citing insufficient evidence to weigh potential benefits and harms.(34,35) Prevalent screening despite contradictory recommendations highlights providers’ beliefs regarding the importance of pediatric BP screening.

Despite the high frequency of guideline adherent BP screening that was observed in our sample, we found that children with obesity who are at increased risk for hypertension are not being screened at every visit as recommended by the AAP. Although past studies have found obesity to be positively associated with any BP screening and with diagnosis of hypertension, again these studies inherently differ from the present study in our focus specifically on guideline adherent screening (in which screening is recommended at a higher frequency for children with obesity).(31,36,37) Our finding that many children with obesity are not screened at the guideline recommended frequency underscores a need to improve the frequency of BP screening in these high-risk children. Consensus among important national bodies regarding recommendations for BP screening in children (especially those with obesity), as well as the use of evidence-based implementation strategies to understand and intervene on barriers and facilitators to adherence, could support increased frequency of BP screening in this at-risk group.

In our sample, publicly insured children had a far lower odds of receiving guideline adherent BP screening compared with privately insured children, highlighting the important influence of insurance type on the receipt of preventive care. This finding is also in line with the previously mentioned National Ambulatory Medical Care 2009 study which found preventive care visits covered by public insurance were less likely to include blood pressure documentation when compared with those covered by private insurance.(32) While insurance coverage is an important determinant of healthcare access and may improve diagnosis of hypertension, our findings suggest that the type of insurance coverage may also be important.(38)

Additional social vulnerability factors, including non-English language and lower Child Opportunity Index were associated with a lack of guideline adherent BP screening in our bivariate analysis. These relationships, however, were attenuated with the addition of the child’s BMI status (obesity indicator) to the multivariable adjusted regression model. The inclusion of the obesity indicator in the model also resulted in a reversal of the bivariate relationship between Hispanic or Latino race/ethnicity (from negative to positive) as well as the emergence of a positive relationship between Black or African American race/ethnicity and guideline adherence. These findings highlight the important and complex relationship between obesity and social vulnerability in children.(39) Given this relationship, and since these factors are difficult to disentangle, it is of great importance that children with obesity have their BP screened at every healthcare encounter. Since children with obesity are at increased risk of hypertension in patient- and adulthood, lower receipt of guideline adherent BP screening could exacerbate and perpetuate disparities in the burden of pediatric and adult hypertension.

Lower adherence to recommended BP screening among children with obesity is likely due at least in part to the more frequent screening recommendation in this population, inclusive of all visit types as well as a potential lack of provider knowledge of this aspect of the guidelines. This is supported by our data, which show that while those who did not receive guideline adherent screening have a higher median number of encounters, they have the same median number of encounters in which their BP was screened (compared with those who received guideline adherent screening). Whereas BP screening at child-well visits (yearly physical) may be routine, screening at other types of visits (e.g., nurse visits, sick visits) may be less common. In our cohort, the median number of encounters with a BP screening was 1, suggesting that BP is only screened yearly, likely during the yearly physical. Implementation of strategies to support providers and staff in screening BP beyond the yearly physical are needed. Potential solutions include integration of BP screening recommendations into clinic workflows and implementation of reminders within the EHR for all visits.(9) Additionally, recommendations related to the importance of social vulnerability could be added to the AAP’s guidelines.(9,38)

The Importance of Clinic Context in Guideline Adherent BP Screening

We also identified several clinic-level factors associated with receipt of guideline adherent BP screening. Children belonging to clinics with larger Medicaid populations, larger patient panel sizes, and fewer providers were less likely to receive guideline adherent blood pressure screening. These findings suggest that provider time is a key factor in adherence to clinical practice guidelines for BP screening since Medicaid administrative logistics, larger patient panel sizes, and fewer providers are all related to decreased provider time.(40,41) Systems level interventions which aim to decrease provider burden and increase time available to deliver recommended preventive care such as regular BP screening are warranted.

The primary strength of the present study is its novel focus on screening for hypertension among children, a condition that tracks across the life course. This research extends previous work in this field by investigating differences in social vulnerability indicators and potential root causes contributing to known racial, ethnic, and socioeconomic disparities in pediatric hypertension. Additionally, this work uses contemporary data from a large healthcare system and is among the first, to our knowledge, to examine provider adherence to current pediatric BP screening guidelines.

This study does, however, have several limitations that should be considered in the interpretation of its results. Despite the data being recent and representing an important population, it is limited as it is cross-sectional, from one year, and one healthcare system. Due to limitations in our data, we were unable to assess adherence to BP screening in groups of children the AAP recommends more frequent screening, including children taking medications known to increase BP, those with kidney disease, aortic arch obstruction or coarctation, or diabetes. Also due to the nature of EHR data, we were only able to assess whether screening occurred, we were unable to assess who measured the BP, if multiple measures were taken, what techniques were used to ensure accurate measurement and at what type of visits the BP measurements were taken during. Nor were we able to confirm that race/ethnicity and primary language data were based on self (or parent) report.

Conclusions

Despite finding a high level of provider adherence to AAP guidelines for pediatric BP screening in patients aged 3–17 years living in Central Massachusetts, we found important disparities in relation to which children are less likely to receive the recommended screening. Our findings highlight the important role of insurance type, social vulnerability indicators related to obesity, and clinic context in relation to adherence to BP screening guidelines. The present work calls attention to how inequitable receipt of guideline adherent BP screening may be contributing to and perpetuating known disparities in pediatric hypertension and how quality improvement efforts related to screening may help to improve health equity through prevention and lifestyle management of pediatric hypertension.

Abbreviations:

BMI

Body Mass Index

BP

Blood Pressure

AAP

American Academy of Pediatrics

HER

Electronic Health Record

UMMH

UMass Memorial Health

CI

Confidence Interval

IQR

Inter Quartile Range

ICC

Interclass Correlation Coefficient