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Patient-shared electronic health records provide opportunities for care outside of office visits. However, those who might benefit may be unable to or choose not to use these resources, while others might not need them.
Electronic Communications and Home Blood Pressure Monitoring (e-BP) was a randomized trial that demonstrated that Web-based pharmacist care led to improved blood pressure (BP) control. During recruitment we attempted to contact all patients with hypertension from 10 clinics to determine whether they were eligible and willing to participate. We wanted to know whether particular subgroups, particularly those from vulnerable populations, were less willing to participate or unable to because they lacked computer access.
From 2005 to 2006, we sent invitation letters to and attempted to recruit 9298 patients with hypertension. Eligibility to participate in the trial included access to a computer and the Internet, an email address, and uncontrolled BP (BP ≥ 140/90 mmHg). Generalized linear models within a modified Poisson regression framework were used to estimate the relative risk (RR) of ineligibility due to lack of computer access and of having uncontrolled BP.
We were able to contact 95.1% (8840/9298) of patients. Those refusing participation (3032/8840, 34.3%) were significantly more likely (
Older age, lower socioeconomic status, and lower levels of education were associated with decreased access to and willingness to participate in a Web-based intervention to improve hypertension control. Failure to ameliorate this may worsen health care disparities.
Clinicaltrials.gov NCT00158639; http://www.clinicaltrials.gov/ct2/show/NCT00158639 (Archived by WebCite at http://www.webcitation.org/5v1jnHaeo)
There is increasing evidence that patient access to practice-based electronic health records (defined here as patient-shared electronic health records) [
The Electronic Communications and Home Blood Pressure Monitoring (e-BP) study was a randomized controlled trial designed to test whether use of home blood pressure (BP) monitoring, use of an existing patient Web portal with a patient-shared electronic health record and secure email, and Web-based pharmacist-assisted care led to hypertension control. During recruitment we attempted to contact all patients with hypertension from 10 clinics to determine whether they were eligible and willing to participate. Patients randomized to home BP monitoring and Web-based collaborative care with a pharmacist were almost twice as likely as those in usual care to have controlled BP at the 12-month follow-up visit (adjusted relative risk [RR] 1.84, 95% CI 1.48-2.29) [
We attempted to contact all patients with a diagnosis of hypertension and taking medications for this from 10 primary care clinics to invite them to participate in the e-BP trial. During recruitment patients could refuse to participate (either actively or passively, by not responding) or be ineligible to participate because of lack of computer access, having controlled BP, or having other ineligibility medical conditions. We attempted to survey all patients contacted regardless of their willingness and eligibility to participate in the e-BP trial. Eligible patients who provided consent were randomly assigned to (1) usual care, (2) receive a home BP monitor and training to use it, and training to use an existing patient Web portal with secure messaging and other Web services, or (3) group 2 interventions plus collaborative pharmacist care management delivered via Web communications. The study design was based on the chronic care model [
We recruited participants between June 2005 and December 2006 at 10 primary care medical centers within Group Health, a nonprofit, integrated group practice that provides both medical coverage and care to more than 600,000 residents of Washington State and Idaho. Group Health Research Institute’s Institutional Review Board reviewed and approved this study.
Group Health has a comprehensive electronic health record system, EpicCare (Epic Systems Corporation, Verona, Wisconsin, USA), which integrates clinical communication and information processes into a single interface that includes physician order entry (eg, laboratory tests, prescriptions, and referrals), documentation of all patient encounters, clinical decision support, clinical messaging between physicians, secure online messaging with patients, and automated reminders at the point of care. Group Health provides patients with access to the electronic health record via a patient Web site (myGroupHealth), which they can use to refill medications, make appointments, view portions of their medical record (current health conditions, laboratory test results, after-visit summaries, allergies, and medications), and send secure messages to their health care team. Detailed description of the patient Website and its integration into overall access to care at Group Health is described elsewhere [
We used clinical and administrative data routinely collected and maintained by Group Health to identify all patients age 25-75 years with a diagnosis of hypertension and taking antihypertensive medications, with no diagnoses of diabetes, cardiovascular or renal disease, or other serious conditions (such as dementia or active treatment of cancer). Research assistants telephoned potential participants to confirm eligibility, including computer access (defined as access to a computer, the Internet, and an email address), and willingness to attend screening visits. All patients surveyed by telephone, including those ineligible or refusing to participate in the study, were asked to answer several demographic questions (race and ethnicity, education level, occupation), computer access questions, and whether they owned a home BP monitor.
Patients with a hypertension diagnosis, computer access, and no other exclusions were invited to an in-person screening visit at their primary care medical center to obtain BP measurements. Patients who had not previously signed up to use the myGroupHealth patient Website secure services were assisted in doing so and given Group Health pamphlets on the various functionalities of the Web portal. Patients were eligible to participate in the trial if their BP was elevated at both of two in-person screening visits. BP was measured three times at each visit using a validated Omron Hem-705CP automated monitor (OMRON Corporation, Schaumburg, IL, USA) with a cuff fitted for the patient’s upper arm circumference [
We used automated databases to obtain sex, age, insurance plan type (commercial, Medicare, Medicaid, or state-subsidized basic health), prior use of secure messaging, and body mass index (BMI) using the most recently recorded weight and height. The Johns Hopkins Adjusted Clinical Group’s case-mix system was used to measure each individual’s overall level of morbidity burden. Their software assigns each individual a level of overall morbidity depending on age, sex, and number and types
We present frequencies of patient characteristics by four recruitment outcomes (unable to contact, refused, ineligible, and randomized) and applied Pearson chi-square tests to assess any differences between groups. To evaluate factors related to computer access we used generalized linear models with a log link and robust sandwich variance estimator using a modified Poisson regression framework to estimate RR of not having computer access [
Modified Poisson regression models were also used to estimate the RR of uncontrolled BP among participants attending the screening visits. We present two sets of adjusted RRs for uncontrolled BP: (1) adjusted for age and sex only, and (2) adjusted for age, sex, education, race, and BMI. In our full model, we adjusted only for covariates that were associated with the uncontrolled BP in the first model. The primary analysis defined BP control based on the BP measurement from the first screening visit. A sensitivity analysis was also performed using a more conservative definition of uncontrolled BP based on study recruitment guidelines requiring uncontrolled BP at both screening visits.
Medicare insurance was omitted from multivariable models including both insurance type and age because of the significant overlap with the age category 65-75 years. In models estimating the RR of uncontrolled BP, the employment categories disabled, unemployed, and other were combined due to small sample sizes.
All analyses were performed using Stata version 11.0 statistical software (StataCorp LP, College Station, TX, USA). All reported
Our recruitment sample (N = 9298) included all patients age 25-74 years from 10 primary care medical centers with administrative data indicating they had a diagnosis of hypertension, were taking antihypertensive medications, and had no exclusionary conditions (
Recruitment Flow
Of those we attempted to contact, 32.6% (3032/9298) refused participation, 2598 at the time of the telephone survey and 434 after agreeing to attend a screening visit (either by not attending or by refusing at the time of the screening visit) (
Over half of the sampled patients (5030/9298, 54%) were ineligible for the study. The most common reasons for ineligibility were lack of computer access (n = 1673), and controlled BP at either the first or second screening visit (n = 1563). If patients lacked computer access they were not invited to have screening BP visits. Thus, patients might have had more than one reason for exclusion that was not ascertained. The demographic characteristics of ineligible patients differed by reason for ineligibility; therefore, we separately examined characteristics associated with the two most common reasons for eligibility, lack of computer access and controlled BP.
Demographic characteristics by recruitment outcome (N = 9298)
Unable to contact | Refused | Ineligible | Randomized | ||||||
n = 458 | n = 3032 | n = 5030 | n = 778 | ||||||
n | % | n | % | n | % | n | % | ||
Sex (% female) | 246 | 53.7 | 1638 | 54.0a | 3040 | 60.4a | 372 | 47.8 | |
|
|||||||||
25-39 | 22 | 4.8a | 109 | 3.6a | 137 | 2.7a | 13 | 1.7 | |
40-54 | 191 | 41.7 | 906 | 29.9 | 1322 | 26.3 | 214 | 27.5 | |
55-64 | 169 | 36.9 | 1319 | 43.5 | 1951 | 38.8 | 334 | 42.9 | |
65-75 | 76 | 16.6 | 698 | 23.0 | 1620 | 32.2 | 217 | 27.9 | |
|
|||||||||
Normal/low (<25) | 58 | 17.0a | 430 | 16.7a | 728 | 17.6a | 67 | 9.5 | |
Overweight (25-30) | 122 | 35.8 | 885 | 34.3 | 1375 | 33.3 | 227 | 32.1 | |
Obese (≥30) | 161 | 47.2 | 1268 | 49.1 | 2026 | 49.1 | 414 | 58.5 | |
Missingc | 117 | (25.6) | 449 | (14.8) | 901 | (17.9) | 70 | (9.0) | |
|
|||||||||
Commercial | 379 | 82.8a | 2328 | 76.8a | 3362 | 66.8a | 574 | 73.8 | |
Medicare | 66 | 14.4 | 661 | 21.8 | 1529 | 30.4 | 200 | 25.7 | |
Basic health/Medicaid | 13 | 2.8 | 43 | 1.4 | 139 | 2.8 | 4 | 0.5 | |
|
|||||||||
Low | 156 | 35.1a | 632 | 21.1 | 809 | 17.4a | 145 | 18.7 | |
Medium | 232 | 52.3 | 1819 | 60.7 | 2803 | 60.4 | 507 | 65.3 | |
High | 56 | 12.6 | 544 | 18.2 | 1032 | 22.2 | 124 | 16.0 | |
Missingc | 14 | (3.1) | 37 | (1.2) | 386 | (7.7) | 2 | (0.3) | |
|
|||||||||
(% yes) | 101 | 22.1a | 1063 | 35.1a | 1430 | 28.4a | 338 | 43.4 | |
|
NAd | ||||||||
White, non-Hispanic | 1592 | 74.6a | 3335 | 77.3a | 637 | 82.0 | |||
Black, non-Hispanic | 178 | 8.3 | 297 | 6.9 | 60 | 7.7 | |||
Hispanic | 59 | 2.8 | 122 | 2.8 | 16 | 2.1 | |||
Asian | 159 | 7.5 | 294 | 6.8 | 28 | 3.6 | |||
Other | 147 | 6.9 | 264 | 6.1 | 36 | 4.6 | |||
Missingc | 897 | (29.6) | 718 | (14.3) | 1 | (0.1) | |||
|
NAd | ||||||||
<HSe graduate | 24 | 1.1a | 129 | 3.0a | 5 | 0.6 | |||
HS graduate/GEDf | 257 | 12.1 | 672 | 15.6 | 57 | 7.3 | |||
Some post-HS | 855 | 40.1 | 1723 | 39.9 | 324 | 41.7 | |||
College graduate | 511 | 24.0 | 961 | 22.3 | 195 | 25.1 | |||
Postgraduate | 485 | 22.8 | 834 | 19.3 | 197 | 25.3 | |||
Missingc | 900 | (29.7) | 711 | (14.1) | 0 | (0.0) | |||
|
NAd | ||||||||
Full-time | 1268 | 59.4 | 2050 | 47.4 a | 435 | 56.0 | |||
Retired | 624 | 29.2 | 1686 | 39.0 | 270 | 34.8 | |||
Part-time | 153 | 7.2 | 324 | 7.5 | 51 | 6.6 | |||
Disabled | 23 | 1.1 | 68 | 1.6 | 4 | 0.5 | |||
Unemployed | 22 | 1.0 | 63 | 1.5 | 7 | 0.9 | |||
Other | 44 | 2.1 | 133 | 3.1 | 10 | 1.3 | |||
Missingc | 898 | (29.6) | 706 | (14.0) | 1 | (0.1) | |||
|
N/Ad | ||||||||
Yes | 1452 | 67.6a | 2533 | 58.4 | 437 | 56.2 | |||
No | 697 | 32.4 | 1808 | 41.7 | 341 | 43.8 | |||
Missingc | 883 | (29.1) | 689 | (13.7) | 0 | (0.0) |
a
b BMI: body mass index.
c Percentages with missing data (in parentheses) are not included in column percentages.
d NA: not available – survey data not collected from patients we were unable to contact.
e HS: high school.
f GED: general equivalency diploma.
The majority (7354/8840, 83.2%) of patients we contacted were willing to answer questions on computer access, even those who refused to participate in the study (2153/3032, 71%). Of those answering the computer questions, 22.8% (1673/7354) lacked computer access (no access to a computer, the Web, or email) (
Having computer access did not guarantee participation. Almost 40% (2152/5681, 37.9%) of patients with computer access refused participation. Similar to those who refused overall, computer-able refusers were significantly more likely to be female (
Adjusted relative risk (RR) of not having computer access by demographic characteristics among patients for whom computer access was ascertained during the telephone screening survey (n = 7354)
Access | No Access | Adjusted for age and sex | Adjusted for all variablesa | ||||||
n | Row % | n | Row % | RR | 95% CI | RR | 95% CI | ||
Total | 5681 | 77.3 | 1673 | 22.8 | |||||
|
|||||||||
Female | 3207 | 75.5 | 1042 | 24.5 | 1.00 | Referent | 1.00 | Referent | |
Male | 2474 | 79.7 | 631 | 20.3 | 0.85 | 0.78-0.92 | 1.01 | 0.91-1.11 | |
|
|||||||||
25-39 | 168 | 87.1 | 25 | 13.0 | 0.87 | 0.59-1.26 | 0.89 | 0.58-1.36 | |
40-54 | 1675 | 84.8 | 300 | 15.2 | 1.00 | Referent | 1.00 | Referent | |
55-64 | 2437 | 81.4 | 557 | 18.6 | 1.23 | 1.08-1.40 | 1.33 | 1.15-1.54 | |
65-75 | 1401 | 63.9 | 791 | 36.1 | 2.37 | 2.11-2.67 | 2.27 | 1.92-2.67 | |
|
|||||||||
Normal/low (<25) | 750 | 73.8 | 266 | 26.2 | 1.00 | Referent | 1.00 | Referent | |
Overweight (25-30) | 1646 | 77.5 | 478 | 22.5 | 0.93 | 0.81-1.05 | 0.92 | 0.81-1.04 | |
Obese (≥30) | 2543 | 78.7 | 687 | 21.3 | 0.96 | 0.85-1.08 | 0.91 | 0.80-1.03 | |
|
|||||||||
Commercial | 4292 | 83.1 | 872 | 16.9 | 1.00 | Referent | 1.00 | Referent | |
Basic health/Medicaid | 58 | 52.7 | 52 | 47.3 | 2.74 | 2.24-3.35 | 1.98 | 1.52-2.59 | |
|
|||||||||
Low | 1006 | 77.1 | 299 | 22.9 | 1.00 | Referent | 1.00 | Referent | |
Medium | 3456 | 78.9 | 924 | 21.1 | 0.83 | 0.74-0.93 | 0.83 | 0.73-0.95 | |
High | 1073 | 73.2 | 394 | 26.8 | 0.96 | 0.85-1.09 | 0.94 | 0.82-1.09 | |
|
|||||||||
White, non-Hispanic | 4483 | 80.5 | 1085 | 19.5 | 1.00 | Referent | 1.00 | Referent | |
Black, non-Hispanic | 398 | 74.4 | 137 | 25.6 | 1.56 | 1.34-1.81 | 1.38 | 1.17-1.62 | |
Hispanic | 138 | 70.1 | 59 | 30.0 | 1.81 | 1.47-2.23 | 1.58 | 1.26-1.99 | |
Asian | 320 | 66.5 | 161 | 33.5 | 1.86 | 1.63-2.12 | 1.96 | 1.70-2.27 | |
Other | 315 | 70.2 | 134 | 29.8 | 1.60 | 1.38-1.85 | 1.42 | 1.22-1.66 | |
|
|||||||||
<HSc graduate | 57 | 35.9 | 102 | 64.2 | 3.62 | 3.05-4.29 | 3.22 | 2.67-3.87 | |
HS graduate/GEDd | 560 | 56.7 | 427 | 43.3 | 2.63 | 2.30-3.01 | 2.53 | 2.18-2.93 | |
Some post-HS | 2222 | 76.5 | 681 | 23.5 | 1.55 | 1.36-1.77 | 1.56 | 1.36-1.80 | |
College graduate | 1421 | 85.2 | 247 | 14.8 | 1.00 | Referent | 1.00 | Referent | |
Postgraduate | 1401 | 92.9 | 117 | 7.7 | 0.51 | 0.41-0.62 | 0.53 | 0.42-0.66 | |
|
|||||||||
Full-time | 3184 | 84.8 | 571 | 15.2 | 1.00 | Referent | 1.00 | Referent | |
Retired | 1806 | 70.0 | 776 | 30.1 | 1.26 | 1.11-1.42 | 1.18 | 1.04-1.34 | |
Part-time | 423 | 80.0 | 106 | 20.0 | 1.14 | 0.95-1.37 | 1.12 | 0.92-1.37 | |
Disabled | 56 | 59.0 | 39 | 41.1 | 2.60 | 2.03-3.33 | 1.84 | 1.41-2.40 | |
Unemployed | 65 | 69.9 | 28 | 30.1 | 1.95 | 1.44-2.66 | 1.41 | 1.00-1.99 | |
Other | 129 | 69.0 | 58 | 31.0 | 1.72 | 1.38-2.16 | 1.22 | 0.95-1.56 | |
|
|||||||||
Yes | 3527 | 79.7 | 897 | 20.3 | 1.00 | Referent | 1.00 | Referent | |
No | 2150 | 75.4 | 700 | 24.6 | 1.32 | 1.21-1.44 | 1.26 | 1.15-1.38 |
a All variables shown in this table are included in the model.
b BMI: body mass index.
c HS: high school.
d GED: general equivalency diploma.
After the telephone survey, 2937 hypertensive patients with computer access agreed to attend a screening visit to have their BP measured to verify eligibility (uncontrolled BP). Of these, 2505 patients attended the first screening visit (
Male sex, non-Hispanic black race, and being overweight or obese were risk factors for uncontrolled BP regardless of whether uncontrolled BP was defined based on a single screening visit (
Among patients attending at least one screening visit, 44 had severe hypertension with BP too high to be eligible to participate in the trial (defined as an average systolic BP ≥ 200 mmHg or diastolic BP ≥ 110 mmHg; data not shown). Compared to those enrolled with uncontrolled BP, ineligible patients with very high BPs were significantly more likely to be less than age 55 years (61.4% [27/44] vs 34.6% [269/778],
Adjusted relative risk (RR) of uncontrolled blood pressure (BP) among patients completing the first screening visit (n = 2505)
Controlled BPa | Uncontrolled BPa | Adjusted for age and sex | Adjusted for age, sex, |
||||||
n | Row % | N | Row % | RR | 95% CI | RR | 95% CI | ||
Total | 1266 | 50.4 | 1239 | 49.5 | |||||
Systolic BPa (mmHg), mean (SD) | 126.3 (8.4) | 151.1 (12.4) | |||||||
Diastolic BPa (mmHg), mean (SD) | 77.7 (7.2) | 89.3 (9.2) | |||||||
|
|||||||||
Female | 795 | 55.8 | 630 | 44.2 | 1.00 | Referent | 1.00 | Referent | |
Male | 471 | 43.6 | 609 | 56.4 | 1.28 | 1.18-1.38 | 1.29 | 1.19-1.40 | |
|
|||||||||
25-39 | 24 | 51.1 | 23 | 48.9 | 1.00 | 0.73-1.36 | 0.90 | 0.65-1.26 | |
40-54 | 374 | 52.3 | 341 | 47.7 | 1.00 | Referent | 1.00 | Referent | |
55-64 | 544 | 50.3 | 537 | 49.7 | 1.04 | 0.94-1.14 | 1.04 | 0.94-1.15 | |
65-75 | 324 | 48.9 | 338 | 51.1 | 1.08 | 0.97-1.20 | 1.14 | 1.02-1.27 | |
|
|||||||||
Normal (<25) | 215 | 65.0 | 116 | 35.1 | 1.00 | Referent | 1.00 | Referent | |
Overweight (25-30) | 391 | 50.9 | 377 | 49.1 | 1.36 | 1.16-1.61 | 1.34 | 1.14-1.58 | |
Obese (≥30) | 537 | 46.1 | 628 | 53.9 | 1.53 | 1.31-1.79 | 1.47 | 1.25-1.72 | |
|
|||||||||
Commercial | 950 | 50.8 | 919 | 49.2 | 1.00 | Referent | 1.00 | Referent | |
Basic health/Medicaid | 7 | 43.8 | 9 | 56.3 | 1.10 | 0.71-1.69 | 1.02 | 0.60-1.75 | |
|
|||||||||
Low | 229 | 49.5 | 234 | 50.5 | 1.00 | Referent | 1.00 | Referent | |
Medium | 814 | 50.5 | 797 | 49.5 | 0.99 | 0.90-1.10 | 0.99 | 0.89-1.11 | |
High | 218 | 51.5 | 205 | 48.5 | 0.98 | 0.86-1.12 | 0.96 | 0.84-1.11 | |
|
|||||||||
No | 664 | 48.4 | 708 | 51.6 | 1.00 | Referent | 1.00 | Referent | |
Yes | 602 | 53.1 | 531 | 46.9 | 0.92 | 0.85, 0.99 | 0.99 | 0.91, 1.07 | |
|
|||||||||
White, non-Hispanic | 1067 | 51.4 | 1011 | 48.7 | 1.00 | Referent | 1.00 | Referent | |
Black, non-Hispanic | 65 | 42.2 | 89 | 57.8 | 1.22 | 1.06-1.40 | 1.26 | 1.10-1.45 | |
Hispanic | 25 | 47.2 | 28 | 52.8 | 1.09 | 0.84-1.41 | 1.12 | 0.86-1.47 | |
Asian | 63 | 59.4 | 43 | 40.6 | 0.85 | 0.67-1.08 | 0.95 | 0.74-1.22 | |
Other | 43 | 39.8 | 65 | 60.2 | 1.22 | 1.04-1.43 | 1.18 | 1.00-1.29 | |
|
|||||||||
<HSc graduate | 10 | 52.6 | 9 | 47.4 | 0.98 | 0.59-1.64 | 0.94 | 0.53-1.64 | |
HS graduate/GEDd | 97 | 47.8 | 106 | 52.2 | 1.13 | 0.97-1.32 | 1.10 | 0.94-1.29 | |
Some post-HS | 436 | 46.8 | 496 | 53.2 | 1.15 | 1.04-1.27 | 1.10 | 0.99-1.22 | |
College graduate | 339 | 52.4 | 308 | 47.6 | 1.00 | Referent | 1.00 | Referent | |
Postgraduate | 384 | 54.6 | 320 | 45.5 | 0.94 | 0.84-1.06 | 0.95 | 0.84-1.07 | |
|
|||||||||
Full-time | 690 | 49.9 | 692 | 50.1 | 1.00 | Referent | 1.00 | Referent | |
Retired | 429 | 49.9 | 430 | 50.1 | 0.98 | 0.88-1.09 | 0.98 | 0.88-1.10 | |
Part-time | 101 | 56.4 | 78 | 43.5 | 0.91 | 0.77-1.09 | 0.94 | 0.78-1.14 | |
Other | 46 | 54.8 | 38 | 45.2 | 0.97 | 0.76-1.23 | 0.89 | 0.68-1.17 | |
|
|||||||||
Yes | 747 | 51.6 | 702 | 48.5 | 1.00 | Referent | 1.00 | Referent | |
No | 518 | 49.2 | 536 | 50.9 | 1.07 | 0.99-1.16 | 1.03 | 0.94-1.12 |
a BP and BP control measured at the first screening visit.
b BMI: body mass index.
c HS: high school.
d GED: general equivalency diploma.
Adjusted relative risk (RR) of uncontrolled blood pressure (BP) based on study recruitment guidelines requiring two measures to define uncontrolled BP (n = 2365)
Controlled BPa | Uncontrolled BPa | Adjusted for age and sex | Adjusted for age, sex, education, race, and BMIb | ||||||
n | Row % | n | Row % | RR | 95% CI | RR | 95% CI | ||
Total | 1563 | 66.1 | 802 | 33.9 | |||||
Systolic BPc (mmHg), mean (SD) | 129.9 (11.2) | 152.8 (11.7) | |||||||
Diastolic BPc (mmHg), mean (SD) | 79.4 (8.0) | 89.7 (8.7) | |||||||
|
|||||||||
Female | 966 | 71.5 | 386 | 28.6 | 1.00 | Referent | 1.00 | Referent | |
Male | 597 | 58.9 | 416 | 41.1 | 1.44 | 1.29-1.62 | 1.50 | 1.33-1.69 | |
|
|||||||||
25-39 | 27 | 67.5 | 13 | 32.5 | 0.93 | 0.59-1.48 | 0.78 | 0.48-1.29 | |
40-54 | 450 | 66.9 | 223 | 33.2 | 1.00 | Referent | 1.00 | Referent | |
55-64 | 678 | 66.3 | 345 | 33.7 | 1.01 | 0.88-1.16 | 1.04 | 0.90-1.20 | |
65-75 | 408 | 64.9 | 221 | 35.1 | 1.08 | 0.93-1.25 | 1.15 | 0.99-1.35 | |
|
|||||||||
Underweight/normal (<25) | 244 | 77.5 | 71 | 22.5 | 1.00 | Referent | 1.00 | Referent | |
Overweight (25-30) | 499 | 68.2 | 233 | 31.8 | 1.34 | 1.07-1.69 | 1.31 | 1.04-1.64 | |
Obese (≥30) | 670 | 61.1 | 427 | 38.9 | 1.67 | 1.35-2.08 | 1.60 | 1.28-2.00 | |
|
|||||||||
Commercial | 1169 | 66.3 | 594 | 33.7 | 1.00 | Referent | 1.00 | Referent | |
Basic health/Medicaid | 394 | 65.5 | 208 | 34.6 | 0.86 | 0.38-1.92 | 0.62 | 0.18-2.20 | |
|
|||||||||
Low | 290 | 66.2 | 148 | 33.8 | 1.00 | Referent | 1.00 | Referent | |
Medium | 1003 | 65.8 | 522 | 34.2 | 1.04 | 0.89-1.20 | 0.96 | 0.82-1.12 | |
High | 265 | 67.1 | 130 | 32.9 | 1.01 | 0.84-1.23 | 0.95 | 0.78-1.16 | |
|
|||||||||
No | 831 | 64.5 | 458 | 35.5 | 1.00 | Referent | 1.00 | Referent | |
Yes | 732 | 68.0 | 344 | 32.0 | 0.91 | 0.81-1.02 | 1.00 | 0.88-1.12 | |
|
|||||||||
White, non-Hispanic | 1321 | 66.9 | 654 | 33.1 | 1.00 | Referent | 1.00 | Referent | |
Black, non-Hispanic | 76 | 54.3 | 64 | 45.7 | 1.43 | 1.18-1.74 | 1.52 | 1.26-1.83 | |
Hispanic | 33 | 66.0 | 17 | 34.0 | 1.03 | 0.69-1.52 | 1.01 | 0.66-1.55 | |
Asian | 72 | 71.3 | 29 | 28.7 | 0.89 | 0.65-1.22 | 1.05 | 0.76-1.45 | |
Other | 57 | 60.6 | 37 | 39.4 | 1.17 | 0.90-1.52 | 1.15 | 0.88-1.49 | |
|
|||||||||
<HSd graduate | 13 | 72.2 | 5 | 27.8 | 0.83 | 0.38-1.84 | 0.87 | 0.40-1.88 | |
HS graduate/GEDe | 125 | 67.2 | 61 | 32.8 | 1.08 | 0.85-1.36 | 1.06 | 0.83-1.35 | |
Some post-HS | 544 | 61.8 | 336 | 38.2 | 1.23 | 1.07-1.42 | 1.17 | 1.01-1.35 | |
College graduate | 416 | 67.8 | 198 | 32.3 | 1.00 | Referent | 1.00 | Referent | |
Postgraduate | 465 | 69.7 | 202 | 30.3 | 0.92 | 0.79-1.09 | 0.95 | 0.80-1.12 | |
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Full-time | 850 | 65.3 | 452 | 34.7 | 1.00 | Referent | 1.00 | Referent | |
Retired | 539 | 66.3 | 274 | 33.7 | 0.95 | 0.81-1.11 | 0.99 | 0.84-1.16 | |
Part-time | 117 | 68.4 | 54 | 31.6 | 0.98 | 0.78-1.24 | 1.06 | 0.83-1.35 | |
Other | 57 | 73.1 | 21 | 26.9 | 0.86 | 0.59-1.24 | 0.80 | 0.53-1.21 | |
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Yes | 928 | 67.4 | 449 | 32.6 | 1.00 | Referent | 1.00 | Referent | |
No | 633 | 64.2 | 353 | 35.8 | 1.12 | 1.00-1.26 | 1.06 | 0.94-1.19 |
a BP control based on study recruitment guidelines requiring two measures (visits) to define controlled and uncontrolled BP.
b BMI: body mass index.
c BP measured at the first screening visit.
d HS: high school.
e GED: general equivalency diploma.
Patient-shared electronic health records and secure Web communications allow new opportunities for patients to be uniquely involved in their own care, including viewing their medical records, communicating asynchronously by secure email, and receiving other Web-based services. The e-BP trial demonstrated that the use of these tools and Web-based collaborative pharmacist care led to significant decreases in both systolic and diastolic BP and improved BP control.
Our recruitment efforts included contacting almost all patients with a hypertension diagnosis from 10 primary care clinics. The majority of people we contacted were interested in continuing with the recruitment process; however, one third declined. Those refusing were more likely to be from racial minority and lower socioeconomic groups. Difficulty recruiting from underserved and minority groups has been documented [
In 2005 and 2006, over 20% of the patients we attempted to recruit could not participate in a Web-based intervention because of lack of computer access. Lack of computer access was strongly related to lower levels of education, older age, and minority race and ethnicity. Adjustments for potential confounders made little difference. These groups are those typically described as being part of the “digital divide.” Multiple observational studies have documented age, race, socioeconomic, and educational disparities in the use of patient electronic health records and eHealth services [
Interestingly in our analysis, expected clinical need was not related to refusal, lack of computer access, or BP control. Others have found no or increased associations between comorbidity and health status, and Internet and use of patient electronic health records. Ralston et al [
Over half of the patients we attempted to recruit had controlled BP and did not need a pharmacist’s intervention. Using the stricter criteria of uncontrolled BP at two separate visits, 66.1% (1563/2365) of the patients had controlled BP, compared to 52.1% (1304/2505) at a single visit. After the diagnosis of hypertension is established, medication decisions are often based on measurements at a single office visit, which according to our findings might lead to misclassifying many people as having uncontrolled BP. While there is a direct relationship between increasing systolic BP and cardiovascular disease events [
Concordant with the literature, non-Hispanic blacks were more likely than other racial and ethnic groups to have uncontrolled BP [
Our analysis has several important limitations. Almost 21% of the patients we attempted to contact did not answer the survey questions, and we have no information on race, education level, self-monitoring, computer access, or BP control for this group. Additionally, almost all patients at Group Health have health insurance, few have Medicaid, and our results may not be representative of populations without health insurance. Additionally, the Pacific Northwest is known for being “wired” and potential eHealth-associated disparities may be greater in other communities [
A particular strength of our analysis is that we were able to collect administrative and electronic medical record data on the entire recruitment sample. Of those successfully contacted (8840/9298, 95.1%), over 80% (7354/8840, 83.2%) consented to answering a brief nonparticipant questionnaire. Few trials, including hypertension and eHealth studies, have access to nonparticipant data. In the Antihypertensive and Lipid-lowering Treatment to Prevent Heart Attack Trial (ALLHAT) over one third of the 33,357 participants in the hypertension trial component were black; however, because recruitment occurred by a variety of methods (radio and newspaper ads, letters, flyers, referral), the researchers were unable to characterize eligible nonparticipants. Glasgow et al [
Over 65% of adults who receive care at Group Health clinics are registered and have access to their patient-shared electronic health record and comprehensive Web services, and 30.7% of outpatient primary care encounters occur virtually, over the Web (with phone visits at 15.3% and in-person visits, 54.0%, accounting for the rest) [
Patient Web portals will likely be increasingly available in other media forms, such as cell phones. In 2008, 84% of American adults owned a cell phone, compared to 74% having access to the Internet [
Systematic reviews and meta-analyses have found strong evidence that “team-based” care for hypertension (care provided by a health professional such as a pharmacist or nurse separate from office visits) improves BP control [
In conclusion, patients unwilling or unable to participate because of lack of computer access in a Web-based intervention to improve hypertension control were more likely to be from populations that already experience disparities in health care. The majority of those willing and able to receive Web-based care had controlled BP and did not need additional Web-based pharmacist medication management. As we strive to learn how best to use patient-shared electronic health records with Web communications to improve the care of chronic conditions, specific attention will be required to insure that health disparities are minimized.
We would like to thank Annie Shaffer on her assistance in manuscript preparation and editing. This research was funded by the National Heart, Lung, and Blood Institute of the National Institutes of Health; grant R01-HL075263.
James D Ralston received grant funding from Sanofi-Aventis between 7/1/2004 and 6/30/2006. No other conflicts of interest.
Antihypertensive and Lipid-lowering Treatment to Prevent Heart Attack Trial
body mass index
blood pressure
Electronic Communications and Home Blood Pressure Monitoring
relative risk