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; and Karen L. Margolis, MD, MPH
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. METHODS
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). Table 3 shows that the standardized cost differentials between categories narrowed when controlling for additional comorbidity using the CDPS score. The differential in total costs between HT and normotensive children declined by $383 (SE = $90) to $852 (SE = $53), and the cost differential between preHT and normotensive children declined by $67 (SE = $18) to $141 (SE = $9) (P <.001 each) when adjusting for additional comorbidity using CDPS. The differentials in total cost for children above the 95th percentile of BMI and children between the 85th and 95th percentiles compared to children below the 85th percentile declined by $67 (SE = $19, P <.001) and $64 (SE = $48, P = .192), respectively, to $48 (SE = $11, P <.001) and $68 (SE = $15, P <.001) when adjusting for additional comorbidity using CDPS. Table 4 shows estimates of outpatient utilization associated with preHT and HT. There were statistically significant increases in all measures of utilization except for laboratory testing. Utilization was at least 50% higher in HT than normal BP children in all categories, except for laboratory testing (P <.001 each), with the large percentage increases observed for renal ultrasound, echocardiogram, and chest x-ray. However, these procedures were performed at a relatively low rate, and all diagnostic and evaluation procedures with the exception of urinalysis were conducted in fewer than 10% of children with HT. We reviewed the types of specialist consultations, but could not identify any specific pattern to differentiate visits among the BP categories.
The present study shows the strong incremental effects of both preHT and HT, independent of BMI and comorbidity, on healthcare costs in children. Although BMI status also was significantly associated with cost, the major influence on cost in this large cohort of children was BP status. It should be noted that the percent of children with preHT was higher than previously reported.38 Because the definition for preHT requires only a single BP measurement, the requirement of 3 separate clinic BP measurements for inclusion in this study increased the number with preHT.
An important factor in this analysis is that hypertension was identified primarily from review of EHRs, with only 17% recognized by the clinic physicians. The lack of recognition of HT by providers using computerized medical record systems has been reported previously,39 as has the extent of the underrecognition.40 However, there was no significant difference in cost associated with the EHR method, as opposed to physician recognition and diagnosis. Thus, there may be something inherent in the presence of hypertension or prehypertension that leads to increased outpatient and emergency department visits, specialist consultations, increased diagnostic and evaluation procedures, and their associated costs; or medical issues resulting in more frequent clinic visits with greater number of BP measurements resulting in a larger number of diagnoses of preHT or HT. This may account for the significantly greater use of cardiac studies and chest x-rays in the subjects with hypertension.
Elevated BP in adults has been shown to be associated with substantial increased costs in 3 national data sets.41-43 Studies of adults have further shown that both obesity and hypertension are independent determinants of costs.44 In contrast, little attention has been devoted to the costs or to strategies to address elevated BP in children or adolescents. Given the strong relationship between elevated BP and healthcare costs, a case could be made to health plans and payers to consider implementing strategies to manage healthcare utilization of children with elevated BP. These strategies might include improved primary care, case management, health behavior interventions, family education, or other interventions.45-47
In contrast to prior studies, costs of healthcare services in this study were not significantly different between overweight (BMI ≥85th percentile to < 95th percentile) and obese (BMI ≥95th percentile) children controlling for BP status. Studies assessing healthcare utilization and cost related to overweight or obesity previously have not been able to adjust for BP status due to lack of BP data. By including both BMI and BP status in the analyses, this study has been able to extend previous findings by showing that the effect of BMI is lower than previously reported and is confounded by the associated impact of preHT and HT.
These findings are relevant to ongoing clinical and public policy discussions. First, they suggest that greater attention should be paid to elevated BP in children and adolescents as a driver of healthcare costs. Second, elevated BP has a significant effect on cost independent from elevated BMI and other comorbid conditions. Third, studies that do not account for the impact of BP level on utilization may overestimate the effect of BMI on utilization and costs.
There are limitations that should be considered in the interpretation of these data. First, despite the large, racially and ethnically diverse study population, these results may not be generalizable to costs of care in non–insurance-driven healthcare systems. However, it is currently challenging to conduct this type of study and capture detailed clinical and cost data outside the highly integrated healthcare systems included in this study. Second, it is possible that some allopathic care was obtained outside the insurance system, although prior studies suggest that this is well under 5% of total allopathic care. Third, although we adjusted for comorbidity using CDPS, there may be some residual differences in comorbidities by BP status. Finally, the results from this study do not imply causality between hypertension and the increased cost of patient care. However, the data clearly show an association between BP and healthcare costs in youth.
In summary, this study provides for the first time an estimate of the impact of BP and BMI status on healthcare utilization and cost in children and adolescents. Results suggest that costs attributable to overweight or obesity may be systematically overestimated in studies that do not adjust for BP status, and that BP status has a major independent association with utilization and costs of healthcare in youth. While we do not have the data to examine whether lowering BP in those with elevated BP would reduce their healthcare cost, it seems reasonable to suggest this may occur.