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The American Journal of Managed Care August 2014
Personalized Preventive Care Reduces Healthcare Expenditures Among Medicare Advantage Beneficiaries
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Impact of Hypertension on Healthcare Costs Among Children
Todd P. Gilmer, PhD; Patrick J. O’Connor, MD, MPH; Alan R. Sinaiko, MD; Elyse O. Kharbanda, MD, MPH; David J. Magid, MD, MPH; Nancy E. Sherwood, PhD; Kenneth F. Adams, PhD; Emily D. Parker, MD, PhD;
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Impact of Hypertension on Healthcare Costs Among Children

Todd P. Gilmer, PhD; Patrick J. O’Connor, MD, MPH; Alan R. Sinaiko, MD; Elyse O. Kharbanda, MD, MPH; David J. Magid, MD, MPH; Nancy E. Sherwood, PhD; Kenneth F. Adams, PhD; Emily D. Parker, MD, PhD;
This study demonstrates a major influence of prehypertension and hypertension on healthcare costs in a large cohort of children, independent of body mass index.
Despite the significant prevalence of elevated blood pressure (BP) and body mass index (BMI) in children, few studies have assessed their combined impact on healthcare costs. This study estimates healthcare costs related to BP and BMI in children and adolescents.

Study Design
Prospective dynamic cohort study of 71,617 children aged 3 to 17 years with 208,800 child years of enrollment in integrated health systems in Colorado or Minnesota between January 1, 2007, and December 31, 2011.

Generalized linear models were used to calculate standardized annual estimates of total, inpatient, outpatient, and pharmacy costs, outpatient utilization, and receipt of diagnostic and evaluation tests associated with BP status and BMI status.

Total annual costs were significantly lower in children with normal BP ($736, SE = $15) and prehypertension ($945, SE = $10) than children with hypertension ($1972, SE = $74) (P <.001, each comparison), adjusting for BMI. Total annual cost for children below the 85th percentile of BMI ($822, SE = $8) was significantly lower than for children between the 85th and 95th percentiles ($954, SE = $45) and for children at or above the 95th percentile ($937, SE = $13) (P <.001, each), adjusting for HT.

This study shows strong associations of prehypertension and hypertension, independent of BMI, with healthcare costs in children. Although BMI status was also statistically significantly associated with costs, the major influence on cost in this large cohort of children and adolescents was BP status. Costs related to elevated BMI may be systematically overestimated in studies that do not adjust for BP status.

Am J Manag Care. 2014;20(8):622-628
Prehypertension and hypertension have strong effects on healthcare costs in children, independent of body mass index (BMI).
  • This study examines the combined impact of blood pressure (BP) and BMI on healthcare costs.
  • Although BMI status was associated with costs, the major influence on cost in this large cohort of children and adolescents was BP status.
  • Costs related to elevated BMI may be systematically overestimated in studies that do not adjust for BP status.
The potential long-term adverse outcomes associated with prehypertension (preHT) and hypertension (HT) in children and adolescents are well recognized.1-3 Blood pressure (BP) in youth correlates with BP in early adulthood and may be associated with elevated cardiovascular (CV) risk later in life.4-9 Moreover, elevated BP is associated with elevated body mass index (BMI) in children and adolescents,1-3,10-15 thus compounding potential CV risk.

Because of the acknowledged increased CV risk associated with childhood preHT or HT, it seems reasonable to suggest that healthcare costs would also increase with preHT or HT as the result of increased testing and evaluation, increased follow-up, or increased CV-related comorbidities. However, information on the cost related to elevated BP in children is scarce.16-19 Studies of obese children suggest modestly elevated costs of care, but none of these prior studies consider the fact that elevated BMI is significantly related to elevated BP.20-24 

Studies assessing the relation between BMI and healthcare costs, but failing to account for BP levels or other clinical conditions associated with elevated BMI, may overestimate the independent impact of BMI.25 In this project, we model the association of elevated BP and costs, while taking into account detailed data on BMI and other comorbid conditions (including depression and other mental health conditions). Using this broader approach permits a more accurate assessment of healthcare costs in children and adolescents with elevated BP than data currently available in the literature.


  Electronic health records (EHRs) were used to identify a dynamic, longitudinal cohort of children and adolescents who were enrolled in 2 large integrated health maintenance organizations in Colorado and Minnesota. Children and adolescents aged 3 to 17 years were included in the study if they were continuously enrolled in the health plans for 1 or more years from January 1, 2007, to December 31, 2010; had measured height, weight, and BP; and had comprehensive insurance coverage including coverage for pharmaceuticals. Only BP measurements obtained in primary care outpatient clinics were included in this study; BP measurements obtained in the emergency department (ED) or urgent care centers were excluded.

Both health plans used an EpiCare (Verona, Wisconsin) EHR. Demographics, including age, gender, and race/ethnicity, were derived from administrative eligibility data. BMI and presence of preHT and HT were ascertained from EHR vital signs data. Age and gender-specific BMI percentiles were calculated for each visit in which both height and weight were measured, and children and adolescents were assigned their maximum BMI percentile during the study period. Age, gender, and height-specific BP percentiles were calculated for both systolic BP and diastolic BP measures obtained in outpatient settings (including emergency departments and urgent care clinics)

using published equations.26

Children who had 3 or more BP measurements taken during the study period were included. Normal BP was defined as having all systolic BP and diastolic BP measurements < 90th percentile; preHT was defined as having 1 or more BP measurements ≥90th percentile or ≥120/80 mm Hg; HT was defined as either 3 consecutive BP measurements ≥95th percentile or 1 or more BP measurements ≥99th percentile. Children were also identified as having HT if they received a diagnosis of essential hypertension from their healthcare provider during the study period (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes 401.xx). To focus this study on essential hypertension, children diagnosed with secondary hypertension (ICD-9-CM codes 405.xx) or other cardiovascular conditions were excluded. Normal BMI was defined as <85th percentile, overweight as ≥85th to <95th percentile, and obesity as ≥95th percentile.

EHRs accurately measure utilization, but do not directly measure resource use or costs. Therefore, we applied standard pricing methodologies to estimate annual inpatient, outpatient, and pharmacy costs.27,28 Inpatient admissions were priced using nationally representative 2010 payment rates based on Diagnostic Related Groups. Costs for outpatient services including physician, outpatient hospital, and clinic services; costs for all other outpatient services such as nursing services, laboratory, and radiology were priced using the nationally representative 2010 payment rates per relative value unit. Costs for phar- maceuticals, including prescriptions for antihypertensive and other medications, were priced at 68% of their average wholesale prices in 2010.27

We examined utilization of outpatient services to determine whether preHT and HT might be driving utilization and costs through increased diagnostic testing and evaluation. We used procedure codes to estimate the annual number of preventive, office, or outpatient medical visits and the number of outpatient psychiatry and psychology visits. We also identified the likelihood of ED services or specialist consultations. In addition, we identified the likelihood of receiving diagnostic procedures including urinalysis, laboratory testing, chest x-ray, electrocardiogram, renal ultrasound, and echocardiogram.

We measured medical comorbidities using the Chronic Illness and Disability Payment System (CDPS).29 CDPS is a combined diagnostic and pharmacy-based risk adjustment model commonly used to measure illness burden and to adjust capitated payments to health plans. The CDPS algorithm assigns ICD-9-CM diagnosis codes to 58 diagnostic categories including selected mental health conditions, and national drug codes to 15 pharmaceutical categories, and applies severity weights developed from a national database of medical claims. The resulting CDPS score provides a measure of illness burden based on expected future healthcare expenditure conditional on each person’s diagnostic and pharmaceutical profile.

Statistical Analysis

We analyzed inpatient, outpatient, pharmacy, and total costs using generalized linear models. Generalized linear models are commonly used to estimate healthcare costs when the dependent variable is non-negative and when its distribution is noticeably skewed and kurtotic (with a heavy right-hand tail).30 Inpatient and pharmacy costs were estimated using 2-part models: logistic regression was used to estimate the probability of any use of a service, and a generalized linear model based on a gamma family with a log link function was used to estimate costs conditional on receiving at least 1 service. Total and outpatient costs was estimated using a single generalized linear model. The number of preventive, office, or outpatient visits and the number of outpatient psychiatry and psychology visits were estimated using negative binomial regression, and the probability of receipt of each of the remaining measures of outpatient utilization were estimating using logistic regression. We selected these specific distributions based on standard tests for assessing alternative generalized linear and transformed models.31-33 We assessed goodness of fit using a modified Hosmer-Lemeshow test and a Pregibon’s link test.34,35

The primary predictor variables of interest were indicator variables for BMI ≥85th to <95th percentile, BMI ≥ 95th percentile, preHT, and HT. Analyses of BMI were adjusted for HT, and analyses of HT were adjusted for BMI. Additional control covariates included age (specified as a set of indicator variables with 1 for each year), gender, race/ethnicity, site, and year. We conducted analyses with and without controlling for comorbidity using the CDPS score. Controlling for comorbidity may reduce confounding of the cost estimates if increased illness burden is correlated with BP or BMI status. However, it may be inappropriate to adjust for comorbidity if an increased illness burden is the result of BP or BMI status. Therefore, we conducted 2 sets of analysis to provide a range of estimates and to demonstrate the sensitivity of the estimates to medical comorbidities.

We calculated cost estimates associated with BMI, preHT, and HT that were adjusted for age, sex, race/ ethnicity, CDPS score, and either BMI or BP status. For example, the standardized costs of children with pediatric HT were calculated as the mean predicted values across all children assuming they all had pediatric HT. Standard errors were calculated using the nonparametric bootstrap, and P values were computed using the percentile method from the empirical distributions of the results from 1000 replicates.36 All analyses were conducted in STATA, version 12.37 This project was reviewed in advance, approved, and monitored by the HealthPartners Institute for Education and Research Institutional Review Board.


The study sample included 71,617 children aged 3 to 17 years followed for 35.0 (SD = 13.7) months and providing 208,800 child years of data. Study sample characteristics are shown in Table 1. The mean age was 10.3 (SD = 4.4) years, and 51% were female. The majority (57%) were non-Latino white, 14% were Latino, 10% were African American, and 4% were Asian. Native American, Hawaiian / Pacific Islander, or multiple races were 1% or less, and 14% were of unknown race/ethnicity. BMI percentile was normal in 64%, 18% were overweight, and 18% were obese. BP percentile was normal in 55%, 42% were prehypertensive, and 3% had hypertension. Among children identified with hypertension, 83% were identified using only BP measures. Among the 17% with an ICD-9-CM code for essential hypertension, 35% had BP measurements consistent with the diagnosis, while 65% did not have 3 consecutive BP measurements ≥95th percentile, and 31% filled prescriptions for antihypertensive medications. Among the 83% identified by BP only, 2% filled prescriptions for antihypertensive medications.

Table 2 shows the mean standardized annual costs among children by BMI and BP status without controlling for additional morbidity. Estimates of costs related to BMI were adjusted for HT, and estimates of costs related to HT were adjusted for BMI. There were no statistically significant interactions between BP and BMI status. As noted in Table 2, costs for children with preHT ($945, SE = $10) were significantly higher than for children with normal BP ($736, SE = $15), and annual costs for children with HT ($1972, SE = $74) were significantly greater than for children with preHT or normal BP (P <.001 each). Among children with hypertension, there was no difference in total costs between children who received a diagnosis of hypertension compared to children who were identified only by BP measures in the EMR review (P = .814). The largest cost component for all 3 BP groups was outpatient care, followed by pharmacy and inpatient care.

There were smaller cost differentials related to BMI. Total annual cost for children below the 85th percentile of BMI ($822, SE = $8) was significantly lower than for children between the 85th and 95th percentiles ($954, SE = $454) and for children at or above the 95th percentile ($937, SE = $13;) (P <.001 each). Total annual cost for children between the 85th and 95th percentiles of BMI was not statistically significantly different from the total cost for children above the 95th percentile (P = .754).

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