Application of a Tool to Identify Undiagnosed Hypertension — United States, 2016

Approximately 11 million U.S. adults with a usual source of health care have undiagnosed hypertension, placing them at increased risk for cardiovascular events (1–3). Using data from the National Health and Nutrition Examination Survey (NHANES), CDC developed the Million Hearts Hypertension Prevalence Estimator Tool, which allows health care delivery organizations (organizations) to predict their patient population’s hypertension prevalence based on demographic and comorbidity characteristics (2). Organizations can use this tool to compare predicted prevalence with their observed prevalence to identify potential underdiagnosed hypertension. This study applied the tool using medical billing data alone and in combination with clinical data collected among 8.92 million patients from 25 organizations participating in American Medical Group Association (AMGA) national learning collaborative* to calculate and compare predicted and observed adult hypertension prevalence. Using billing data alone revealed that up to one in eight cases of hypertension might be undiagnosed. However, estimates varied when clinical data were included to identify comorbidities used to predict hypertension prevalence or describe observed hypertension prevalence. These findings demonstrate the tool’s potential use in improving identification of hypertension and the likely importance of using both billing and clinical data to establish hypertension and comorbidity prevalence estimates and to support clinical quality improvement efforts.

This study used medical billing^† and electronic health record (EHR) clinical data collected among 8.92 million patients, aged 18–85 years, who had ≥1 ambulatory office visit for evaluation and management in 2016 within one of 25 AMGA-member organizations. These organizations use the Optum One population health analytic tool^§ and pool billing and clinical data as part of a national learning collaborative.

Observed hypertension prevalence was defined using three case definitions that use increasing amounts of billing and clinical data collected during the observation year. The first hypertension case definition included patients with at least one diagnosis code for hypertension^¶ on a billing claim. Patients without a diagnosis code on a billing claim, but who had a diagnosis code for “hypertension” on their EHR problem list** met the hypertension criteria for the second case definition. Additional patients were added who did not meet criteria for the first two case definitions, but who had elevated in-office blood pressure (BP) readings, defined as a single reading ≥160/100 mm Hg or two readings on different days ≥140/90 mm Hg. The first and second case definitions reflect documented diagnoses of hypertension. The BP criteria in the third case definition align with national recommendations for diagnosing hypertension^††; however, patients who meet this definition alone are not considered to have a hypertension diagnosis and might not have hypertension upon further assessment.

Predicted hypertension prevalence was determined by applying the Hypertension Prevalence Estimator Tool to the organizations’ data; development and validation of the tool are described elsewhere (2). The tool requires input of the patient population’s demographic characteristics (i.e., distribution by sex, race/ethnicity, and age group) with the option^§§ of providing the prevalence of three comorbidities within the patient population that aid in predicting hypertension prevalence (i.e., the presence of none, one, or two or more of the following conditions: obesity, diabetes, and chronic kidney disease). Similar to identifying hypertension, comorbidities were identified during the observation year using 1) medical billing claims only^¶¶; 2) problem list diagnosis codes; or 3) other clinical data.***

The observed hypertension prevalence and the 95% confidence intervals of the predicted hypertension prevalence, calculated with and without use of organization-specific information on comorbidity prevalence, were compared overall and by organization using each case definition.

A total of 8.92 million patient records were included, with patient populations ranging from 50,000 to 1.02 million across the 25 organizations. Nearly 40% of patients were aged 45–64 years; 57% were female, and 74% were non-Hispanic white (range = 47%–90%) (Table 1). Overall, 5.9% of patients with ≥1 office visit during 2016 had no BP reading recorded (range = 0.3%–15.9%) (Supplementary Figure, https://stacks.cdc.gov/view/cdc/54153).

Comorbidity prevalence and predicted and observed hypertension prevalence varied overall and by organization depending on the evidence used (Table 2) (Table 3) (Supplementary Figure, https://stacks.cdc.gov/view/cdc/54153). Overall obesity prevalence increased from 10.7% using billing data alone to 45.0% using all three data sources (Table 2). Use of billing data alone indicated that 4.4% of patients had 2–3 comorbidities; the addition of problem list data alone and in combination with other clinical data increased detection of 2–3 comorbidities to 5.7% and 14.3%, respectively. Prevalence of 2–3 comorbidities ranged from 8.3% to 18.1% across organizations using all three data sources.

With the addition of each data source to identify hypertension and the comorbidities, overall observed hypertension prevalence increased from 29.1% to 30.0% to 36.0% (range = 2.60–3.21 million patients), and overall predicted hypertension prevalence increased from 33.2% to 33.9% to 39.5% (range = 2.96–3.52 million patients), respectively (Table 2) (Table 3) (Supplementary Figure, https://stacks.cdc.gov/view/cdc/54153). Differences between the estimates for observed and predicted hypertension prevalence ranged from 3.5 to 4.1 percentage points, representing a range of 312,000 to 366,000, or one in eight to one in 11 patients who potentially have undiagnosed hypertension. Across the 25 organizations, observed hypertension prevalence ranged from 24.2% to 46.1%, predicted hypertension prevalence ranged from 35.5% to 47.6%, and the difference between the two ranged from 1.0 to 13.8 percentage points, with predicted prevalence always higher than observed prevalence.

Removing organization-specific comorbidity data from the information used to predict hypertension prevalence and relying on the NHANES-based comorbidity estimates provided in the Estimator Tool resulted in an overall predicted hypertension prevalence of 38.5% and increased the difference between observed and predicted prevalence from 2.5 to 9.4 percentage points, depending on the data sources used to identify hypertension (Table 2).

Corresponding author: Elizabeth L. Ciemins, eciemins@amga.org, 406-281-3275.

¹American Medical Group Association (AMGA), Alexandria, Virginia; ²Division for Heart Disease and Stroke Prevention, CDC.

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*Aged 18–85 years with a least one ambulatory care visit during 2016.

Abbreviation: CI = confidence interval.
* Range of values calculated across the 25 health care delivery organizations participating in the American Medical Group Association’s national learning collaborative; 95% CIs are not provided for the predicted hypertension prevalence estimates.
^† Based on Million Hearts Hypertension Prevalence Estimator Tool.
^§ Compared with observed prevalence. Observed prevalence was always less than predicted prevalence.
^¶ The comorbidity profile of the health care delivery organization’s patient population is estimated using National Health and Nutrition Examination Survey databased on the organization’s patient population’s age, gender, and race/ethnicity characteristics.

* Observed prevalence of the three comorbidities within the organizations’ patient population is used to predict hypertension prevalence. Comorbidities were identified based on: 1) “medical claims only”: at least one diagnosis code for the condition on an outbound billing claim (International Classification of Disease, Tenth Revision, Clinical Modification [ICD-10-CM] code of E66.09, E66.1, E66.8, E66.9, E66.01, E66.2, Z68.3X, Z68.4X, Z68.54, or R93.9 for obesity; E10.X or E11.X for diabetes; and I12.X, I13.X, or N18.X for chronic kidney disease); 2) “medical claims plus problem list”: adds additional patients who had a diagnosis code for obesity, diabetes, or chronic kidney disease on their electronic health record (EHR) problem list (same codes as designated for claims); and 3) “medical claims plus problem list & clinical data”: adds additional patients who had a body mass index ≥30 kg/m² for obesity; hemoglobin A1c of ≥6.5%, plasma glucose of ≥126 mg/dL, fasting plasma glucose of ≥126 mg/dL, or a glucose tolerance test of ≥200 mg/dL for diabetes; and an estimated glomerular filtration rate of <60 mL/min per 1.73 m² for chronic kidney disease.
^† Predicted prevalence of the three comorbidities within the organizations’ patient population is used to predict hypertension prevalence. Predicted comorbidity prevalence is estimated based on the organization population prevalence of age, gender, and race/ethnicity characteristics and use of National Health and Nutrition Examination Survey data. Using this method does not affect the observed hypertension prevalence; therefore, no observed prevalence values are provided.
^§ Defined using: 1) “medical claims only”: at least one diagnosis code for hypertension on an outbound billing claim ( ICD-10-CM code of I10, I11.X, I12.X, or I13.X); 2) “medical claims plus problem list”: adds additional patients who had a diagnosis code for “hypertension” on their EHR problem list (same codes as designated for claims); and 3) “medical claims plus problem list & clinical data”: adds additional patients who had elevated in-office blood pressure readings, defined as a single reading ≥160/100 mm Hg or two readings on different days ≥140/90 mm Hg.
^¶ Determined by applying the Million Hearts Hypertension Prevalence Estimator Tool to the organizations’ data. The predicted hypertension prevalence is estimated based on the distribution of patients by age, gender, race/ethnicity, and predicted or diagnosed comorbidity prevalence (presence of 0, 1, or 2–3 of the following conditions: obesity, diabetes and chronic kidney disease).