Abstract
Differential rates of diagnosis and treatment by hypertension (HTN) type may contribute to poor HTN control in young adults. The objective of this study was to compare rates of receiving a hypertension diagnosis and antihypertensive agent among young adults with (1) isolated systolic, (2) isolated diastolic, and (3) combined systolic/diastolic HTN. A retrospective analysis was conducted in patients aged 18 to 39 years (n=3003) with incident HTN. Kaplan–Meier survival and Cox proportional hazards analyses were performed. Only 56% with isolated systolic HTN received a diagnosis compared with 63% (systolic/diastolic); 32% with isolated systolic HTN received an initial antihypertensive compared with 52% (systolic/diastolic). Compared with patients with systolic/diastolic HTN, those with isolated systolic HTN had a 50% slower diagnosis rate (hazard ratio [HR], 0.50; 95% confidence interval [CI], 0.41–0.60) and those with isolated diastolic HTN had a 26% slower rate (HR, 0.74; CI, 0.60–0.92). Patients with isolated systolic HTN had 58% slower medication initiation (HR, 0.42; CI, 0.34–0.51) and those with isolated diastolic HTN had 31% slower rates (HR, 0.69; CI, 0.55–0.86). Young adults with isolated systolic HTN have lower diagnosis and treatment rates.
Hypertension (HTN) remains an underrecognized cardiovascular risk factor among young adults (aged 18 to 39 years).1 Uncontrolled HTN in young adults contributes to premature target organ damage and an increased risk of cardiovascular morbidity and mortality.2, 3, 4 Unfortunately, young adults with HTN in the United States have the lowest awareness, treatment, and control rates compared with middle‐aged and older adults.1 Sociodemographic factors and comorbid conditions that contribute to poor diagnosis and treatment rates in this population have been previously identified.5 It is unclear whether the type of HTN (eg, isolated systolic, isolated diastolic, combined systolic/diastolic)6, 7 also contributes to low rates of diagnosis and treatment among young adults.
According to the National Health and Nutrition Examination Survey (NHANES), isolated diastolic HTN is the most common HTN subtype among untreated young adults (<40 years).6 It has been established that both systolic and diastolic blood pressure (BP) confer an increased risk for cardiovascular events. Since the report of the Fifth Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure (JNC),8 there has been equal emphasis on the control of systolic and diastolic BP; therefore, consistently identifying elevations in either may be an important intervention point to improve HTN control among young adults. Previous research in older adults demonstrated that clinicians were more likely to titrate antihypertensive medication and control diastolic than systolic HTN.9, 10 In addition, NHANES III (1991–1994) demonstrated that poor HTN control rates among middle‐aged and older adults were caused by lack of systolic BP control.8 However, variations in rates of diagnosis and treatment by type of HTN among young adults with incident HTN are unknown. Therefore, the objectives of our study were to compare rates and predictors by type of incident HTN (isolated systolic, isolated diastolic, and combined systolic/diastolic) of receiving an initial HTN diagnosis and initial antihypertensive medication prescription in a large young adult population with regular primary care.
Patients and Methods
Sample
This study was approved by the University of Wisconsin‐Madison Health Sciences institutional review board with a waiver of consent. We performed a secondary retrospective analysis of electronic medical record data from a large, Midwestern, multidisciplinary academic group practice. To construct the sample, we first identified all patients 18 years and older who met established criteria from the Wisconsin Collaborative for Healthcare Quality (WCHQ)11 for being “currently managed” (ie, medically homed) in the medical practice between January 1, 2008, and December 31, 2011. WCHQ is a multi‐stakeholder, voluntary consortium of Wisconsin organizations committed to publicly reporting performance measures of quality of healthcare services.12, 13 Patients were defined as receiving regular primary care contact if they met criteria for being currently managed. Per WCHQ, patients are defined as currently managed by a primary care practice if they had two or more billable office encounters in an outpatient, non‐urgent primary care setting, or one primary care encounter and one office encounter in an urgent care setting (regardless of diagnosis code), within 3 years, with at least one visit occurring in the prior 2 years.14 Electronic health records were then assessed for the first date a patient met criteria for a new HTN diagnosis (incident HTN). A patient met BP eligibility criteria based on: (1) ≥3 outpatient BP measurements from three separate dates, ≥30 days apart but within a 2‐year span (systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg),15 or (2) two elevated BPs5, 16 (systolic BP ≥160 mm Hg or diastolic BP ≥100 mm Hg), ≥30 days apart but within a 2‐year period. A greater number of measurements were required for stage 1 BP elevation, given the high variability of BP and possible white‐coat effect (ie, alerting reaction) in young people. The BPs did not need to be from billable encounters. If more than one BP was taken at a visit, the average was used.15 A total of 33,974 patients met both BP eligibility criteria for incident HTN and WCHQ “currently managed” criteria (Figure 1).
Figure 1.
Study Sample: enrollment and analysis. WCHQ indicates Wisconsin Collaborative for Healthcare Quality; JNC 7, the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.
For each patient meeting WCHQ criteria and BP eligibility criteria, an “index date” was assigned, equal to the first date on which both of these criteria were met. A 365‐day period prior to this index date was used as the “baseline period” to assess patients' baseline comorbidities and healthcare utilization. To achieve a sample with incident HTN, patients were excluded if they had an antihypertensive medication prescription or a prior HTN diagnosis in the baseline period per the Tu criteria (Figure 1, n=19,004).17 The Tu administrative algorithm defines patients who have been diagnosed with HTN using the following International Classification of Diseases, Ninth Revision (ICD‐9) codes:17 401.x (essential HTN), 402.x (hypertensive heart disease), 403.x (hypertensive chronic kidney disease), 404.x (hypertensive heart and chronic kidney disease), and 405.x (secondary HTN). Among patients with incident HTN, all patients 40 years and older were excluded (n=11,967). Our final sample included 3003 young adult patients (18–39 years) meeting clinical criteria for incident HTN.
Eligible patients were enrolled in the study from the first date they met criteria for being both currently managed and having incident HTN (index date). The mean of the last two BPs prior to study entry were used to diagnose the patients with one of three types of HTN: (1) isolated systolic HTN (systolic BP ≥140 mm Hg and diastolic BP <90 mm Hg), (2) isolated diastolic HTN (systolic BP <140 mm Hg and diastolic BP ≥90 mm Hg), and (3) combined systolic/diastolic HTN (systolic BP ≥140 mm Hg and diastolic BP ≥90 mm Hg). Patients continued to accrue time in this study until: (1) the study ended, (2) they achieved BP control before antihypertensive medication initiation, or (3) censoring. Therefore, all of the BPs were elevated during the time the patient was followed in the study (until the outcome was achieved or censoring). BP control was defined per JNC 7 criteria as three consecutive normal BPs on three separate dates (<130/80 mm Hg if diagnosed with diabetes mellitus or chronic kidney disease; otherwise <140/90 mm Hg).15 JNC 7 criteria were the established US HTN guidelines during the reporting period. Patients were censored if they died (censored day of death) or were no longer currently managed (censored at end of the calendar year). Pregnant patients were censored 1 year before, during, and 1 year after pregnancy using a modified Manson approach.18 The end date for the study was December 31, 2011.
Primary Outcome Variables
There were two primary outcomes. The first was time (days) from the index date to the initial HTN diagnosis, defined as the date of the first outpatient electronic health record documentation of an ICD‐9 code for HTN per the Tu criteria17 or ICD‐9 code 796.2 (“elevated BP without a diagnosis of HTN”). The second outcome was time from the index date to an initial outpatient electronic medical record entry of an antihypertensive medication prescription. Results are reported in months.
Explanatory Variables
Patient and provider explanatory variables to identify barriers to an initial HTN diagnosis and antihypertensive medication initiation were selected based on an established conceptual model for clinical inertia.19 Patient‐related factors included sociodemographics (age, sex, race/ethnicity, marital status, and Medicaid use during the baseline or study period), behavioral risk factors (baseline tobacco use and body mass index), and comorbidities. Patients' race/ethnicity was included because of the increased prevalence of HTN among young African Americans.20 Patients self‐classified their race/ethnicity in response to electronic health record entry options (white, black, Asian, Hispanic/Latino, other [Native Hawaiian, Pacific Islander, multi‐racial]). Patient comorbidities were assessed at baseline using established algorithms for hyperlipidemia (ICD‐9 codes: 272.0–272.4),21 diabetes mellitus, with/without complications (ICD‐9 codes: 250.00–250.93, 357.2, 362.0‐362.02, 366.41),22 congestive heart failure (ICD‐9 codes: 398.91, 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 414.8, 428.x),21 chronic kidney disease (ICD‐9 codes: 016.0, 095.4,189.0, 189.9, 223.0, 236.91, 250.4, 271.4, 274.1, 283.11, 403.X1, 404.X2, 404.X3, 440.1, 442.1, 447.3, 572.4, 580‐588, 591, 642.1, 646.2, 753.12‐753.17, 753.19, 753.2, 794.4),23 depression (ICD‐9 codes: 296.2X, 296.3X, 300.4X),24 and anxiety (ICD‐9 codes: 300.0‐300.02, 300.09, 300.21‐300.23, 300.3, 309.24, 309.81).24 Elixhauser and the Medicare Chronic Condition Data Warehouse Administrative algorithms were used to identify atrial fibrillation,25 chronic pulmonary disease,26 stroke/transient ischemic attack,25 collagen vascular disease,26 and deficiency anemias.26 Because of low prevalence, we created an indicator variable for the presence of any of the following comorbidities: atrial fibrillation,25 chronic pulmonary disease,26 stroke/transient ischemic attack,25 collagen vascular disease,26 deficiency anemias,26 congestive heart failure,21 and chronic kidney disease.23
Patients' morbidity burden can predict healthcare utilization, which may influence diagnosis and antihypertensive medication initiation rates.27, 28 Therefore, we used the Johns Hopkins Adjusted Clinical Group (ACG) Case‐Mix System (version 10.0), which assesses morbidity burden to predict future healthcare utilization.28, 29 The ACG risk score was selected because our population sample contains a diverse mix of government‐insured patients and typically younger and healthier privately insured patients. An ACG risk score of 1.0 represents expected healthcare utilization on an individual level according to the patient's age and sex.29 In addition, the number of baseline primary care, specialty, and urgent care visits were measured based on a full 365‐day period. Primary care visits included family medicine, internal medicine, and a combination of lower prevalence primary care specialties (obstetrics/gynecology, pediatrics/adolescent medicine) based on physician (faculty, resident, fellow), nurse practitioner, and physician assistant visits.
Patients were assigned to the primary care provider they saw most frequently in outpatient face‐to‐face evaluation and management visits, as reported in professional service claims.14 Models additionally controlled for providers' age, specialty (internal medicine, family medicine/family practice, other), and sex, which were obtained from the provider group's human resource office and/or the American Medical Association 2011 Masterfile data.
Statistical Analysis
Analyses were conducted using SAS 9.1.3 (SAS Institute, Inc, Cary, NC) and Stata 13.1 (StataCorp, College Station, TX). Baseline comparisons between individuals with various types of HTN were performed using analysis of variance and chi‐square descriptive statistics. Univariate Kaplan–Meier survival curves30 were computed by type of HTN (isolated systolic, isolated diastolic, or systolic/diastolic) to evaluate the probability of receiving an initial HTN diagnosis and initial antihypertensive medication, as a function of time since meeting criteria for incident HTN. Multivariate Cox proportional hazards regression analyses were conducted to obtain adjusted hazard ratios (HRs) and 95% confidence intervals (95% CIs) for receiving an initial HTN diagnosis and HTN treatment. Robust estimates of the variance were used to account for within‐cluster correlation.31, 32 Explanatory variables used in the Cox regression models include patient sociodemographics (age, sex, race, Medicaid use), baseline comorbidities (dyslipidemia, diabetes mellitus, anxiety and/or depression, low prevalence conditions), healthcare utilization (ACG risk score, baseline visit count), behavioral risk factors (body mass index, tobacco status), and provider characteristics (specialty). Tests were considered significant at P<.05. The proportional hazards assumption for each model was tested using a generalized linear regression of the scaled Schoenfeld residuals on functions of time.33 A subsequent sensitivity analysis was performed, limited to patients with stage 2 HTN.
Results
Descriptive Data
Overall, 3003 young adults met the study criteria (Figure 1); 45% (n=1345) had isolated systolic HTN, 21% (n=633) isolated diastolic HTN, and 34% (n=1025) systolic/diastolic HTN (Table 1). After study entry, there was a mean (standard deviation) of 16 (13) months of follow‐up (interquartile range, 5.8–23.8 months). Patients with systolic/diastolic HTN had the longest mean follow‐up at 18 (13) months, patients with isolated systolic HTN had a follow‐up duration of 15 (12) months, and patients with isolated diastolic HTN had a follow‐up duration of 15 (12) months. Patients with systolic/diastolic HTN were less likely to be white and had fewer baseline urgent care visits within the healthcare system. A total of 1257 patients (42%) were censored within the first 36 months of follow‐up, prior to achieving the primary outcomes, as a result of no longer meeting “currently managed” criteria (n=1222), pregnancy (n=29), or death (n=6).
Table 1.
Baseline Demographics by Type of Incident Hypertension
| Total Population (N=3003) | Type of Hypertension | P Value | |||
|---|---|---|---|---|---|
| Isolated Systolic Hypertension (n=1345) | Isolated Diastolic Hypertension (n=633) | Combined Systolic/Diastolic Hypertension (n=1025) | |||
| Patient characteristics | |||||
| Age, No. (%), y | |||||
| 18–29 | 1128 (38) | 585 (43) | 208 (33) | 335 (33) | <.001 |
| 30–39 | 1875 (62) | 760 (57) | 425 (67) | 690 (67) | |
| Male sex, No. (%) | 1819 (61) | 835 (62) | 337 (53) | 647 (63) | <.001 |
| Race/ethnicity, No. (%) | |||||
| White | 2477 (82) | 1122 (83) | 524 (83) | 831 (81) | .32 |
| Non‐white | 526 (18) | 223 (17) | 109 (17) | 194 (19) | |
| Marital status, No. (%) | |||||
| Married | 1273 (42) | 555 (41) | 275 (43) | 443 (43) | .53 |
| Not married | 1730 (58) | 790 (59) | 358 (57) | 582 (57) | |
| Tobacco use, No. (%) | |||||
| Current tobacco use | 713 (24) | 320 (24) | 157 (25) | 236 (23) | .92 |
| Former smoker | 581 (19) | 261 (19) | 124 (20) | 196 (19) | |
| Never used tobacco | 1709 (57) | 764 (57) | 352 (56) | 593 (58) | |
| BMI, kg/m2, No. (%) | |||||
| Not obese (BMI ≤29.9 kg/m2) | 1148 (38) | 571 (42) | 213 (34) | 364 (36) | <.001 |
| Obese/morbidly obese (BMI ≥30 kg/m2) | 1855 (62) | 774 (58) | 420 (66) | 661 (64) | |
| On Medicaid ever,a No. (%) | 533 (18) | 235 (17) | 125 (20) | 173 (17) | .31 |
| Systolic blood pressure, mean (SD) | 143 (9.0) | 144 (6.8) | 134 (6.4) | 147 (9.2) | .044 |
| Diastolic blood pressure, mean (SD) | 90 (8.6) | 83 (6.6) | 94 (4.6) | 96 (6.0) | <.001 |
| Baseline comorbid conditions, No. (%) | |||||
| Hyperlipidemia | 231 (7.7) | 109 (8.1) | 56 (8.9) | 66 (6.4) | .15 |
| Diabetes mellitus | 96 (3.2) | 42 (3.1) | 25 (4.0) | 29 (2.8) | .44 |
| Anxiety and/or depression | 682 (23) | 312 (23) | 183 (29) | 187 (18) | <.001 |
| Low prevalence conditionb | 198 (6.6) | 91 (6.7) | 54 (8.5) | 53 (5.2) | .026 |
| ACG risk score, mean (SD) | 1.0 (1.2) | 1.1 (1.2) | 1.2 (1.3) | 0.95 (1.1) | .07 |
| Baseline ambulatory visits, mean (SD) | |||||
| Primary care visits | 2.6 (2.6) | 2.8 (2.7) | 3.0 (2.9) | 2.2 (2.2) | .42 |
| Urgent care visits | 0.88 (1.4) | 0.91 (1.5) | 0.99 (1.4) | 0.78 (1.3) | <.001 |
| Provider factors | |||||
| Provider specialty, No. (%) | |||||
| Primary care | 2612 (87) | 1168 (87) | 545 (86) | 899 (88) | .63 |
| Subspecialty | 391 (13) | 177 (13) | 88 (14) | 126 (12) | |
Abbreviations: ACG, adjusted clinical group case‐mix assessment system; BMI, body mass index; SD, standard deviation. Bold values indicate significance. aOn Medicaid during the baseline or study period. bBecause of low prevalence, an indicator variable was created for the presence of any of the following: atrial fibrillation, chronic pulmonary disease, stroke/transient ischemic attack, collagen vascular disease, deficiency anemias, congestive heart failure, and chronic kidney disease.
HTN Diagnosis Rates by Type of HTN
Among all patients aged 18 to 39 years, 1059 received an initial HTN diagnosis after meeting criteria for incident HTN. The Kaplan–Meier curve demonstrated that young adults with isolated systolic HTN had the slowest diagnosis rate, and those with systolic/diastolic HTN had the fastest diagnosis rate (Figure 2). After censoring, only 56% of young adults with isolated systolic HTN received a diagnosis, compared with 58% (isolated diastolic HTN) and 63% (systolic/diastolic HTN). Young adults with isolated systolic HTN were diagnosed after a mean of 6.4 (9.7) months, isolated diastolic HTN after 5.3 (8.6) months, and systolic/diastolic HTN after 4.0 (7.2) months. The likelihood of receiving an initial diagnosis increased over time for all HTN types (Figure 2).
Figure 2.
Probability of receiving an initial hypertension (HTN) diagnosis by type of HTN.
Predictors of an Initial HTN Diagnosis by Type of HTN
Unadjusted Cox proportional hazards models (Table 2) demonstrated that young adults with isolated systolic HTN had a significantly slower rate of receiving an HTN diagnosis. After adjusting for patient demographic, comorbidity, and provider factors, young adults with isolated systolic HTN had a 50% slower diagnosis rate (HR, 0.50; 95% CI, 0.41–0.60) and those with isolated diastolic HTN had a 26% slower diagnosis rate (HR, 0.74; 95% CI, 0.60–0.92) compared with young adults with systolic/diastolic HTN.
Table 2.
Receiving an Initial Diagnosis by Type of Incident Hypertension Among Young Adults (Aged 18–39 Years)
| Variable | Unadjusted HR | 95% CI | P Value | Adjusted HR | 95% CI | P Valuea |
|---|---|---|---|---|---|---|
| Patient characteristics | ||||||
| Type of hypertension | ||||||
| Isolated systolic hypertension | 0.48 | 0.40–0.58 | <.001 | 0.50 | 0.41–0.60 | <.001 |
| Isolated diastolic hypertension | 0.73 | 0.59–0.90 | .003 | 0.74 | 0.60–0.92 | .006 |
| Combined systolic/diastolic hypertension (reference) | 1.00 | – | – | 1.00 | – | – |
| Patient age, y | ||||||
| 18–29 | 0.83 | 0.70–0.99 | .039 | |||
| 30–39 (reference) | 1.00 | – | – | |||
| Male sex | 0.76 | 0.64–0.91 | .002 | |||
| Race/ethnicity | ||||||
| White | 0.76 | 0.61–0.94 | .011 | |||
| Non‐white (reference) | 1.00 | – | – | |||
| Tobacco use | ||||||
| Current tobacco use | 0.71 | 0.57–0.88 | .002 | |||
| Former smoker | 0.96 | 0.78–1.19 | .73 | |||
| Never used tobacco (reference) | 1.00 | – | – | |||
| BMI, kg/m2 | ||||||
| Not obese (BMI ≤29.9 kg/m2) | 0.93 | 0.79–1.11 | .42 | |||
| Obese/morbidly obese (BMI ≥30 kg/m2) (reference) | 1.00 | – | – | |||
| On Medicaid everb | 1.09 | 0.87–1.37 | .46 | |||
| Baseline comorbid conditions | ||||||
| Hyperlipidemia | 1.23 | 0.89–1.68 | .21 | |||
| Diabetes mellitus | 1.11 | 0.69–1.79 | .66 | |||
| Anxiety and/or depression | 1.06 | 0.86–1.31 | .60 | |||
| Low prevalence conditionsc | 0.97 | 0.68–1.38 | .87 | |||
| ACG risk score | 0.999 | 0.92–1.08 | .99 | |||
| Baseline ambulatory visits | ||||||
| Primary care visits | 0.96 | 0.92–0.99 | .049 | |||
| Urgent care visits | 0.94 | 0.88–1.01 | .11 | |||
| Provider factors | ||||||
| Provider specialty | ||||||
| Primary care (reference) | 1.00 | – | – | |||
| Specialty care | 0.98 | 0.77–1.24 | .86 | |||
Abbreviations: ACG, adjusted clinical group case‐mix assessment system; BMI, body mass index; CI, confidence interval; HR, hazard ratio. Bold values indicate significance. aGlobal P value for proportional hazards assumption P=.48. bOn Medicaid during the baseline or study period. cBecause of low prevalence, an indicator variable was created for the presence of any of the following: atrial fibrillation, chronic pulmonary disease, stroke/transient ischemic attack, collagen vascular disease, deficiency anemias, congestive heart failure, and chronic kidney disease.
Younger patients (aged 18 to 29 years) had a slower diagnosis rate (HR, 0.83; 95% CI, 0.70–0.99) compared with patients aged 30 to 39 years. In addition, men (HR, 0.76; 95% CI, 0.64–0.91), patients of white race (HR, 0.76; 95% CI, 0.61–0.94), patients with a fewer number of primary care visits (HR, 0.96; 95% CI, 0.92–0.99), and current tobacco users (HR, 0.71; 95% CI, 0.57–0.88) had slower diagnosis rates. Provider factors did not significantly predict diagnosis rates by type of HTN. A sensitivity analysis limited to patients with stage 2 HTN demonstrated a similar pattern with patients with isolated systolic HTN having a 50% slower diagnosis rate (HR, 0.50; 95% CI, 0.27–0.90, P=.022) compared with young adults with systolic/diastolic HTN (full model not shown).
Antihypertensive Medication Initiation Rates by Type of HTN
Overall, 687 young adults received an initial antihypertensive prescription after meeting JNC 7 clinical criteria for incident HTN; 143 (21%) received the prescription before a documented HTN diagnosis. Similar to diagnosis rates, the Kaplan–Meier curve (Figure 3) demonstrated that young adults with isolated systolic HTN had the slowest rates of receiving antihypertensive medication, and young adults with systolic/diastolic HTN had the fastest medication initiation rates. Only 32% of young adults with isolated systolic HTN received an initial antihypertensive medication, compared with 46% (isolated diastolic) and 52% (systolic/diastolic). Young adults with isolated systolic HTN received an antihypertensive prescription after a mean of 8.6 (10.0) months, isolated diastolic HTN of 7.3 (9.4) months, and systolic/diastolic HTN of 5.9 (8.7) months. The likelihood of receiving an initial antihypertensive medication prescription increased over time for all HTN subgroups (Figure 3).
Figure 3.
Probability of receiving an initial antihypertensive medication prescription by type of hypertension (HTN).
Predictors of an Initial Antihypertensive Prescription by Type of HTN
Unadjusted Cox proportional hazards models for receiving an initial antihypertensive prescription (Table 3) demonstrated that young adults with isolated systolic HTN had a significantly slower rate of receiving antihypertensive medication. After adjustment, young adults with isolated systolic HTN had a 58% slower medication initiation rate (HR, 0.42; 95% CI, 0.34–0.51) and those with isolated diastolic HTN had a 31% slower rate (HR, 0.69; 95% CI, 0.55–0.86) compared with young adults with systolic/diastolic HTN.
Table 3.
Receiving an Initial Antihypertensive Prescription by Type of Incident Hypertension Among Young Adults (Aged 18–39 Years)
| Variable | Unadjusted HR | 95% CI | P Value | Adjusted HR | 95% CI | P Valuea |
|---|---|---|---|---|---|---|
| Patient characteristics | ||||||
| Type of hypertension | ||||||
| Isolated systolic hypertension | 0.41 | 0.33–0.50 | <.001 | 0.42 | 0.34–0.51 | <.001 |
| Isolated diastolic hypertension | 0.71 | 0.57–0.88 | .002 | 0.69 | 0.55–0.86 | .001 |
| Combined systolic/diastolic hypertension (reference) | 1.00 | – | – | 1.00 | – | – |
| Patient age, y | ||||||
| 18–29 | 0.74 | 0.61–0.89 | .001 | |||
| 30–39 (reference) | 1.00 | – | – | |||
| Male sex | 0.69 | 0.57–0.83 | <.001 | |||
| Race/ethnicity | ||||||
| White | 0.81 | 0.65–1.01 | .07 | |||
| Non‐white (reference) | 1.00 | – | – | |||
| Tobacco use | ||||||
| Current tobacco use | 0.94 | 0.72–1.22 | .65 | |||
| Never used tobacco (reference) | 1.00 | – | – | |||
| Former smoker | 0.96 | 0.77–1.20 | .72 | |||
| BMI, kg/m2 | ||||||
| Not obese (BMI ≤29.9 kg/m2) | 0.87 | 0.72–1.05 | .14 | |||
| Obese/morbidly obese (BMI ≥30 kg/m2) (reference) | 1.00 | – | – | |||
| On Medicaid everb | 1.58 | 1.27–1.97 | <.001 | |||
| Baseline comorbid conditions | ||||||
| Hyperlipidemia | 1.06 | 0.76–1.48 | .73 | |||
| Diabetes mellitus | 2.05 | 1.36–3.11 | .001 | |||
| Anxiety and/or depression | 1.12 | 0.91–1.38 | .28 | |||
| Low prevalence conditionsc | 0.70 | 0.47–1.05 | .08 | |||
| ACG risk score | 0.99 | 0.91–1.08 | .85 | |||
| Baseline ambulatory visits | ||||||
| Primary care visits | 0.97 | 0.93–1.02 | .25 | |||
| Urgent care visits | 0.96 | 0.89–1.04 | .31 | |||
| Provider factors | ||||||
| Provider specialty | ||||||
| Primary care | 0.88 | 0.70–1.10 | .27 | |||
| Specialty care (reference) | 1.00 | – | – | |||
Abbreviations: ACG, adjusted clinical group case‐mix assessment system; BMI, body mass index; CI, confidence interval; HR, hazard ratio. Bold values indicate significance. aGlobal P value for proportional hazards assumption P=.32. bOn Medicaid during the baseline or study period. cBecause of low prevalence, an indicator variable was created for the presence of any of the following: atrial fibrillation, chronic pulmonary disease, stroke/transient ischemic attack, collagen vascular disease, deficiency anemias, congestive heart failure, and chronic kidney disease.
Younger patients (aged 18 to 29 years) had a slower medication initiation rate (HR, 0.74; CI 95%, 0.61–0.89) compared with those aged 30 to 39 years. In addition, men (HR, 0.69; 95% CI, 0.57–0.83) had slower rates of medication initiation. Patients with a history of Medicaid (HR, 1.58; 95% CI, 1.27–1.97) or diabetes (HR, 2.05; 95% CI, 1.36–3.11) had faster medication initiation rates. Provider factors did not significantly predict medication initiation rates. A sensitivity analysis limited to patients with stage 2 HTN did not demonstrate a significant difference in treatment rates among young adults by type of HTN (data not shown).
Discussion
Our findings demonstrate significant differences in rates of HTN diagnosis and medication initiation among young adults according to HTN subtype. Young adults with isolated systolic HTN had the slowest rates of receiving an initial HTN diagnosis and/or initial antihypertensive medication compared with adults with isolated diastolic or combined systolic/diastolic HTN. The systolic/diastolic population differed by length of follow‐up, race/ethnicity, and urgent care use compared with the other groups. However, the type of HTN remained a significant predictor of diagnosis and antihypertensive medication initiation even after adjusting for patient and provider factors. Low HTN diagnosis and treatment rates have been previously documented in young adult populations;34, 35, 36 however, this study highlights that the HTN subtype is an independent contributor to low HTN diagnosis and treatment rates among young adults.
Our data are in agreement with that from previous studies in middle‐aged and older adults that HTN treatment rates in patients with isolated systolic HTN lagged compared with that in patients with isolated diastolic and systolic/diastolic HTN.6 This result may be due to concern from healthcare providers about a white‐coat (ie, alert) reaction. In addition, it may reflect attempts to try lifestyle modifications, which may delay pharmacotherapy initiation. This may also reflect providers' responses to previously published data in young men, which showed that coronary heart disease was related more to elevated diastolic BP than systolic pressure.37 The topic remains controversial, although others have also suggested that diastolic BP is more strongly related to cardiovascular disease before the age of 45 years.7, 37 Prior research demonstrates that, for all levels of diastolic BP, an increase in systolic BP confers an additional cardiovascular disease risk.38 Our finding that young adults with combined systolic/diastolic HTN had the fastest rates of diagnosis and treatment likely reflects providers' perceptions of these individuals being at increased cardiovascular risk. In addition, combined systolic and diastolic elevation may increase diagnostic certainty to overcome clinical inertia.19 Even with more severe (stage 2) HTN, young adults with isolated systolic HTN had slower diagnosis rates. A significant lag was not seen with treatment of stage 2 HTN. This may represent statistical power limitations or may reflect prompt treatment for patients with more severe BP elevation once the diagnosis is established.
Understanding disparate rates of diagnosis and treatment within young adult populations can inform the development of interventions to improve HTN control. Provider factors did not independently predict differences in practice patterns, highlighting the need for healthcare system interventions, including improving awareness that systolic HTN also promotes target organ damage and is associated with an elevated cardiovascular risk profile.39
In addition to HTN subtype, other factors predicted slower HTN diagnosis and treatment rates among young adults. In this study, patients aged 18 to 29 years had slower HTN diagnosis and treatment rates compared with patients aged 30 to 39 years, consistent with previous research.5 This may reflect a greater emphasis of lifestyle counseling in the youngest adult population prior to definitive diagnosis and pharmacotherapy, which overlaps with the American Heart Association adolescent HTN guidelines.40 Although the majority of hypertensive young adults were male in our study, consistent with national rates,1 men had a slower HTN diagnosis and treatment rate compared with women. This has been previously demonstrated in young adult populations,35, 36 including NHANES data showing that women are more likely than men to be treated for HTN.1 Also in our study, white patients were less likely to receive a diagnosis and antihypertensive medication, which may reflect providers responding to the known increased risk of target organ damage specifically among African Americans.1
It was concerning that current tobacco users had slower rates of receiving an initial HTN diagnosis. This finding confirms previous work,5, 35 and may reflect a concern about BP variability, since cigarette smoking can increase BP measurements.1, 41 However, given the additional cardiovascular risk conferred by smoking, further studies are needed to understand slower HTN diagnosis and treatment rates in tobacco users.
Study Strengths and Limitations
The primary strength of this study was the ability to analyze young adults receiving regular primary care in a large multispecialty group practice. One limitation is the use of data from a single healthcare system, which limits the generalizability of the findings in that treatment patterns may differ among other systems and regions. However, this healthcare system is one of the 10 largest physician practices in the United States, including over 300 primary care physicians and 43 primary care clinics. We were not able to analyze practice type (academic/nonacademic) or setting (rural/urban); yet, the inclusion of patient demographic, comorbidity, and utilization data with provider data improves the generalizability and clinical applicability of our study. Another limitation is the retrospective use of administrative data, which reduces generalizability and raises the potential for misclassification of diagnoses or lack of documentation in the health record. However, previously established algorithms were used to identify HTN and other comorbidities. There are limitations of using a 365‐day baseline period to assess comorbidities, but our goal was to include patients who were currently managed by the healthcare system prior to assessing comorbidities. In addition, in a previous study, we manually abstracted the electronic health record data of 500 randomly selected young adults to evaluate physician documentation of recurring elevated BPs, use of HTN diagnostic codes, and content/frequency of lifestyle education within 1 year of young adults developing incident HTN.42 There may be a bias using a different number of BP measurements according to stage of HTN; however, our goal was to reduce misclassification from BP variability. An additional limitation is the accuracy of office‐based BP readings, which may alter the classification of the type of HTN. However, in our manual abstraction study, a subset of patients' electronic health record ambulatory flowsheets was reviewed to confirm elevated BPs across multiple clinical settings and over an extended time. Unfortunately, we could not assess for masked HTN or visit adherence.
Conclusions
Young adults with isolated systolic HTN have the slowest HTN diagnosis and treatment rates compared with young adults with isolated diastolic and combined systolic/diastolic HTN. This is likely an independent contributor to young adults having the lowest HTN awareness and treatment rates compared with middle‐aged and older adults. Younger patient age, male sex, white race, and current smoking also contributed to slower rates of HTN diagnosis and treatment. Healthcare system interventions aimed at reducing practice variation by HTN subtype may improve HTN diagnosis and treatment among young adults.
Disclosures
This original research was supported by the Clinical and Translational Science Award program, previously through the National Center for Research Resources (NCRRUL1RR025011), and now by the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH) under award number UL1TR000427. Heather Johnson is supported by the National Heart, Lung, and Blood Institute of the NIH (K23HL112907), and also by the University of Wisconsin (UW) Centennial Scholars Program of the University of Wisconsin School of Medicine and Public Health. Christie Bartels is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the NIH (K23AR062381). Nancy Pandhi is supported by the National Institute on Aging of the NIH (K08AG029527). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders did not play any role in the study design; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to submit the article for publication. Additional funding for this project was provided by the UW Health Innovation Program and the UW School of Medicine and Public Health from The Wisconsin Partnership Program. H. Johnson, C. Bartels, and N. Pandhi have clinical appointments with the academic group practice that has a financial interest in delivering care to the general population from which study participants were drawn. For the remaining authors, none were declared.
Author Contributions
H. Johnson had full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis. H. Johnson designed the study, performed the statistical analysis and interpretation of the data, and drafted the manuscript. C. Bartels, C. Thorpe, J. Schumacher, and N. Pandhi assisted in the interpretation of data, critical revision of the manuscript for important intellectual content, and provided final approval of the version to be published. M. Smith assisted with the design of the study, statistical analysis, interpretation of data, critical manuscript revisions, and provided final approval of the version to be published.
Acknowledgments
The authors gratefully acknowledge Katie Ronk, BS, and Patrick Ferguson, MPH, for data preparation, and Jamie LaMantia, BS, and Ruth Gebremedhin, undergraduate research assistant, for manuscript preparation. Each of these individuals works for the University of Wisconsin‐Madison Health Innovation Program and did not receive additional compensation above their normal salaries for their contributions.
J Clin Hypertens (Greenwich). 2015;17:885–894. DOI: 10.1111/jch.12596. © 2015 Wiley Periodicals, Inc.
References
- 1. Centers for Disease Control and Prevention . Vital signs: prevalence, treatment, and control of hypertension–United States, 1999‐2002 and 2005‐2008. MMWR Morb Mortal Wkly Rep. 2011;60:103–108. [PubMed] [Google Scholar]
- 2. McCarron P, Smith GD, Okasha M, McEwen J. Blood pressure in young adulthood and mortality from cardiovascular disease. Lancet. 2000;355:1430–1431. [DOI] [PubMed] [Google Scholar]
- 3. Stamler J, Stamler R, Neaton JD. Blood pressure, systolic and diastolic, and cardiovascular risks. US population data. Arch Intern Med. 1993;153:598–615. [DOI] [PubMed] [Google Scholar]
- 4. Vos LE, Oren A, Bots ML, et al. Does a routinely measured blood pressure in young adolescence accurately predict hypertension and total cardiovascular risk in young adulthood? J Hypertens. 2003;21:2027–2034. [DOI] [PubMed] [Google Scholar]
- 5. Schmittdiel J, Selby JV, Swain B, et al. Missed opportunities in cardiovascular disease prevention?: low rates of hypertension recognition for women at medicine and obstetrics‐gynecology clinics. Hypertension. 2011;57:717–722. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Franklin SS, Jacobs MJ, Wong ND, et al. Predominance of isolated systolic hypertension among middle‐aged and elderly US hypertensives: analysis based on National Health and Nutrition Examination Survey (NHANES) III. Hypertension. 2001;37:869–874. [DOI] [PubMed] [Google Scholar]
- 7. Pickering TG, Hall JE, Appel LJ, et al. Recommendations for blood pressure measurement in humans and experimental animals: Part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Hypertension. 2005;45:142–161. [DOI] [PubMed] [Google Scholar]
- 8. Lloyd‐Jones DM, Evans JC, Larson MG, et al. Differential control of systolic and diastolic blood pressure: factors associated with lack of blood pressure control in the community. Hypertension. 2000;36:594–599. [DOI] [PubMed] [Google Scholar]
- 9. Berlowitz DR, Ash AS, Hickey EC, et al. Inadequate management of blood pressure in a hypertensive population. N Engl J Med. 1998;339:1957–1963. [DOI] [PubMed] [Google Scholar]
- 10. Banegas JR, de la Cruz JJ, Rodriguez‐Artalejo F, et al. Systolic vs diastolic blood pressure: community burden and impact on blood pressure staging. J Hum Hypertens. 2002;16:163–167. [DOI] [PubMed] [Google Scholar]
- 11. Hatahet MA, Bowhan J, Clough EA. Wisconsin Collaborative for Healthcare Quality (WCHQ): lessons learned. WMJ. 2004;103:45–48. [PubMed] [Google Scholar]
- 12. Sheehy A, Pandhi N, Coursin DB, et al. Minority status and diabetes screening in an ambulatory population. Diabetes Care. 2011;34:1289–1294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Wisconsin collaborative for healthcare quality. WCHQ website. http://www.wchq.org/. Accessed March 10, 2015.
- 14. Thorpe CT, Flood GE, Kraft SA, et al. Effect of patient selection method on provider group performance estimates. Med Care. 2011;49:780–785. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289:2560–2572. [DOI] [PubMed] [Google Scholar]
- 16. Myers MG, Tobe SW, McKay DW, et al. New algorithm for the diagnosis of hypertension. Am J Hypertens. 2005;18:1369–1374. [DOI] [PubMed] [Google Scholar]
- 17. Tu K, Campbell NR, Chen ZL, et al. Accuracy of administrative databases in identifying patients with hypertension. Open Med. 2007;1:e18–e26. [PMC free article] [PubMed] [Google Scholar]
- 18. Manson JM, McFarland B, Weiss S. Use of an automated database to evaluate markers for early detection of pregnancy. Am J Epidemiol. 2001;154:180–187. [DOI] [PubMed] [Google Scholar]
- 19. O'Connor PJ. Overcome clinical inertia to control systolic blood pressure. Arch Intern Med. 2003;163:2677–2678. [DOI] [PubMed] [Google Scholar]
- 20. Mozaffarian D, Benjamin EJ, Go AS, et al. Heart disease and stroke statistics—2015 update: a report from the American Heart Association. Circulation. 2015;131:e29–e322. [DOI] [PubMed] [Google Scholar]
- 21. Borzecki AM, Wong AT, Hickey EC, et al. Identifying hypertension‐related comorbidities from administrative data: what's the optimal approach? Am J Med Qual. 2004;19:201–206. [DOI] [PubMed] [Google Scholar]
- 22. Hebert PL, Geiss LS, Tierney EF, et al. Identifying persons with diabetes using Medicare claims data. Am J Med Qual. 1999;14:270–277. [DOI] [PubMed] [Google Scholar]
- 23. Foley RN, Murray AM, Li S, et al. Chronic kidney disease and the risk for cardiovascular disease, renal replacement, and death in the United States Medicare population, 1998 to 1999. J Am Soc Nephrol. 2005;16:489–495. [DOI] [PubMed] [Google Scholar]
- 24. Marciniak MD, Lage MJ, Dunayevich E, et al. The cost of treating anxiety: the medical and demographic correlates that impact total medical costs. Depress Anxiety. 2005;21:178–184. [DOI] [PubMed] [Google Scholar]
- 25. Chronic Conditions Data Warehouse . 2011 Chronic condition reference list. pdf file. 2011. http://www.ccwdata.org/cs/groups/public/documents/document/ccw_conditionreferencelist2011.pdf. Accessed April 8, 2015.
- 26. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36:8–27. [DOI] [PubMed] [Google Scholar]
- 27. Campbell NR, So L, Amankwah E, et al. Characteristics of hypertensive Canadians not receiving drug therapy. Can J Cardiol. 2008;24:485–490. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Starfield B, Weiner J, Mumford L, Steinwachs D. Ambulatory care groups: a categorization of diagnoses for research and management. Health Serv Res. 1991;26:53–74. [PMC free article] [PubMed] [Google Scholar]
- 29. Weiner JP, Starfield BH, Steinwachs DM, Mumford LM. Development and application of a population‐oriented measure of ambulatory care case‐mix. Med Care. 1991;29:452–472. [DOI] [PubMed] [Google Scholar]
- 30. StataCorp . STATA Survival Analysis and Epidemiological Tables Reference Manual: Release 12. College Station, TX: StataCorp LP; 2011. [Google Scholar]
- 31. Rogers W. Regression standard errors in clustered samples. Stata Tech Bull. 1993;3:19–23. [Google Scholar]
- 32. Williams RL. A note on robust variance estimation for cluster‐correlated data. Biometrics. 2000;56:645–646. [DOI] [PubMed] [Google Scholar]
- 33. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81:515–526. [Google Scholar]
- 34. Egan BM, Zhao Y, Axon RN. US trends in prevalence, awareness, treatment, and control of hypertension, 1988‐2008. JAMA. 2010;303:2043–2050. [DOI] [PubMed] [Google Scholar]
- 35. Johnson HM, Thorpe CT, Bartels CM, et al. Undiagnosed hypertension among young adults with regular primary care use. J Hypertens. 2014;32:65–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Johnson HM, Thorpe CT, Bartels CM, et al. Antihypertensive medication initiation among young adults with regular primary care use. J Gen Intern Med. 2014;29:723–731. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Bulpitt CJ. Is systolic pressure more important than diastolic pressure? J Hum Hypertens. 1990;4:471–476. [PubMed] [Google Scholar]
- 38. Rutan GH, Kuller LH, Neaton JD, et al. Mortality associated with diastolic hypertension and isolated systolic hypertension among men screened for the Multiple Risk Factor Intervention Trial. Circulation. 1988;77:504–514. [DOI] [PubMed] [Google Scholar]
- 39. Drukteinis JS, Roman MJ, Fabsitz RR, et al. Cardiac and systemic hemodynamic characteristics of hypertension and prehypertension in adolescents and young adults: the Strong Heart Study. Circulation. 2007;115:221–227. [DOI] [PubMed] [Google Scholar]
- 40. US Department of Health and Human Services . The Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents. Bethesda, MD: National Institutes of Health – National Heart, Lung, and Blood Institute; 2005. NIH Publication No. 05‐5267. [Google Scholar]
- 41. Vidt DG, Lang RS, Seballos RJ, et al. Taking blood pressure: too important to trust to humans? Cleve Clin J Med. 2010;77:683–688. [DOI] [PubMed] [Google Scholar]
- 42. Johnson HM, Olson AG, LaMantia JN, et al. Documented lifestyle education among young adults with incident hypertension. J Gen Intern Med. 2015;30:556–564. [DOI] [PMC free article] [PubMed] [Google Scholar]