How Views of the Organization of Primary Care Among Patients with Hypertension Vary by Race or Ethnicity

How Views of the Organization of Primary Care Among Patients with Hypertension Vary by Race or... Abstract Introduction: We assessed potential racial or ethnic differences in the degree to which veterans with pharmaceutically treated hypertension report experiences with their primary care system that are consistent with optimal chronic illness care as suggested by Wagner’s Chronic Care Model (CCM). Materials and Methods: A cross-sectional analysis of the results of the Patient Assessment of Chronic Illness Care (PACIC), which measured components of the care system suggested by the CCM and was completed at baseline by participants in a hypertension disease management clinical trial. Participants had a recent history of uncontrolled systolic blood pressure. Results: Among 377 patients, non-Hispanic African American veterans had almost twice the odds of indicating that their primary care experience is consistent with CCM features when compared with non-Hispanic White patients (odds ratio (OR) = 1.86; 95% confidence interval (CI) = 1.16–2.98). Similar statistically significant associations were observed for follow-up care (OR = 2.59; 95% CI = 1.49–4.50), patient activation (OR = 1.80; 95% CI = 1.13–2.87), goal setting (OR = 1.65; 95% CI = 1.03–2.64), and help with problem solving (OR = 1.62; 95% CI = 1.00–2.60). Conclusions: Non-Hispanic African Americans with pharmaceutically treated hypertension report that the primary care system more closely approximates the Wagner CCM than non-Hispanic White patients. INTRODUCTION Approximately a third of adults in the United States have hypertension, including more than two-thirds of people over age 65 yr.1 Despite significant evidence that controlling hypertension through diet, exercise, and/or medications improves cardiovascular and renal outcomes, fewer than half of Americans with hypertension have their blood pressure under control.1,2 The Wagner Chronic Care Model (CCM) postulates that chronic illness management is optimized when health care systems have (1) established links to the community, (2) capacity to provide effective self-management support through delivery systems organized around teams, (3) clinical decision support, and (4) robust information technology systems.3,4 Having CCM elements in primary care and disease management programs has been associated with better chronic illness process and outcomes,5–14 including blood pressure and cardiovascular disease risk.15–18 The CCM serves as a basis for other robust models for organizing and delivering primary care, including the patient-centered medical home (PCMH).19 Over the past two decades, the Veterans Affairs (VA) health care system has evolved with specific links to the CCM and PCMH models,20,21 including the introduction of the patient-aligned care team (PACT) in 2010 as the basis of organizing primary care.22–24 The goal of the PACT is to improve the quality of care, including satisfaction, health care outcomes, and costs. Despite the fact that PACTs were not designed to specifically address racial or ethnic disparities in care and there has not been a specific emphasis on PACT implementation in sites of care with larger proportions of racial or ethnic minorities,25 the VA does have a programmatic and research emphasis on reducing health disparities.26–28 The reorganization of VA care has been accompanied by significant improvements in hypertension control. For example, control rates increased from 46% to 76% between 2000 and 2010. Despite improvements in control among both non-Hispanic Whites and African Americans, control rates consistently remained lower among African American compared with non-Hispanic White patients.29 To fully reap the potential benefits of the CCM and related models, systems must reduce pervasive racial/ethnic disparities in the process and outcomes of care, including hypertension.1,30–32 One potential explanation for observed disparities is that racial and socioeconomic groups have different perceptions of, and interactions with, primary care.33 Thus, we examined whether, among veterans with hypertension, racial/ethnic groups differ in their reported experiences with elements of the CCM. METHODS Overview We conducted this study with eligible patients receiving primary care from three clinics associated with one VAMC. We utilized baseline data from patients enrolled between November 6, 2012 and April 9, 2015. The study was approved by the Durham VA Medical Center’s institutional review board (IRB). Eligible Patients The parent study is a randomized trial that examines the impact of a disease management program that titrates the intensity of resources to patients’ level of hypertension control (clinicaltrials.gov registration no. NCT01390272). Overall trial methods have been previously described.34 Briefly, study subjects were English-speaking adult patients living in the community with access to a telephone, had been seen at a study clinic in the last year, reported that they consider their primary care provider to be at the VA, reported that they receive the majority of their health care from the VA, and had a history of pharmaceutically treated hypertension with uncontrolled systolic blood pressure during the year before identification of patients to approach for potential study enrollment. The definition of control was based on the Seventh Report of the Joint National Commission on Prevention, Detection, Evaluation and Treatment of High Blood Pressure (JNC-7) (≥140 mmHg for patients without diabetes and ≥130 mmHg for patients with diabetes),35 which was the version of the guideline in place at the beginning of study enrollment. Patients were excluded if they were enrolled in an ongoing clinical trial or clinical program that would be expected to impact blood pressure control, had type 1 diabetes, class IV congestive heart failure, end-stage renal disease, metastatic cancer, a history of solid organ or bone marrow transplantation, or a diagnosis of active psychosis at baseline. Women who reported being pregnant or planning to become pregnant over the next 18 mo were also excluded. Measures At study baseline (i.e., before the trial intervention began), we administered the validated 2005 version of the Patient Assessment of Chronic Illness Care (PACIC).36 Although not hypertension specific, the PACIC measures patients’ perceptions of their chronic illness care system that are mapped to the CCM.3,4,36,37 PACIC scores have been positively associated with patient-reported self-management resources and behaviors, medication adherence, quality of life, and satisfaction with health care.38,39 For a series of statements related to their regular doctor or provider and affiliated clinicians who care for their chronic illness, patients were asked how often during the previous 6 mo time they had various experiences related to their care. Responses were on a 5-point Likert scale anchored by 1 (almost never) to 5 (almost always). The PACIC produced the following subscale scores: (1) patient activation, (2) delivery system design (care teams)/decision support, (3) collaborative goal setting, (4) collaborative problem solving/contextual counseling, and (5) follow-up/coordination. In addition, an overall PACIC summary score (i.e., overall fidelity to the CCM) was calculated. Scores range from 1 to 5 with higher scales indicating greater fidelity to the CCM. The primary explanatory variable of interest was race or ethnicity [separate variables for non-Hispanic Black or African American and other race or ethnicity vs. non-Hispanic White or Caucasian (referent)]. Logistic regression models utilized for this study include the following variables/measures: (1) education level (high school or less vs. greater than high school), (2) gender (female vs. male), (3) age (continuous variable), (4) pharmaceutically treated diabetes vs. not having pharmaceutically treated diabetes, (5) whether the individual has a person with whom the veteran is close (proxy for social support) (have person whom the person is close vs. not having such a person), and (6) mean of three baseline study visit systolic blood pressure measurements taken 30 s apart in a seated position after waiting 5 min (continuous variable serving as an indication of level of disease control). Data Analysis For our primary analyses, PACIC scores >3.5 on each scale were considered to represent “presence of” CCM components. This cutoff was chosen because it represents the top quarter of the scale range and has been used previously to examine factors associated with presence of CCM components utilizing the PACIC.40–43 Sensitivity analyses were also done using scores of ≥3 and ≥4 as the cutoffs. Separate logistic regression models were fit for each PACIC subscale and the overall PACIC score. The primary explanatory variable of interest was race or ethnicity (variables described above). Simple (unadjusted) and multivariable (adjusted) logistic regression models were fit with multivariable models including race and variables covering the following topics (measures described in detail above): (1) education level, (2) gender, (3) age, (4) diabetes status, (5) social support, and (6) baseline systolic blood pressure. We report odds ratios (OR) and 95% confidence intervals (CI). Analyses were done using SAS version 9.4 (SAS Institute, Inc., Cary, NC). Results The analysis sample included 377 of 385 (98%) enrolled patients with complete baseline data on outcomes and explanatory variables. Reflecting the patient population seen in the VA,44 patients were predominantly older (mean age 63.6 yr) men (92.3%) (Table I). The majority of patients were non-Hispanic African Americans (61.0%). Non-Hispanic Whites and patients of other racial or ethnic backgrounds made up 32.1% and 6.9% of patients included in the analysis, respectively. Table I. Characteristics of Patients Included in the Final Logistic Regression Models, n = 377 Characteristic  Mean (SD) or Percent  Race/ethnicity   White or Caucasian (non-Hispanic)  32.1%   Black or African American (non-Hispanic)  61.0%   Other race or ethnicity  6.9%  High school education or less  34.7%  Male  92.3%  Age (yr)  63.6 (8.8)  Diabetes (pharmaceutically treated)  57.0%  Patients without a close personal relationship  10.1%  Systolic blood pressure (mmHg)a  143.8 (17.6)  Characteristic  Mean (SD) or Percent  Race/ethnicity   White or Caucasian (non-Hispanic)  32.1%   Black or African American (non-Hispanic)  61.0%   Other race or ethnicity  6.9%  High school education or less  34.7%  Male  92.3%  Age (yr)  63.6 (8.8)  Diabetes (pharmaceutically treated)  57.0%  Patients without a close personal relationship  10.1%  Systolic blood pressure (mmHg)a  143.8 (17.6)  SD, standard deviation. aMean of three baseline site visit systolic blood pressures taken in a seated position 5 min apart. Table I. Characteristics of Patients Included in the Final Logistic Regression Models, n = 377 Characteristic  Mean (SD) or Percent  Race/ethnicity   White or Caucasian (non-Hispanic)  32.1%   Black or African American (non-Hispanic)  61.0%   Other race or ethnicity  6.9%  High school education or less  34.7%  Male  92.3%  Age (yr)  63.6 (8.8)  Diabetes (pharmaceutically treated)  57.0%  Patients without a close personal relationship  10.1%  Systolic blood pressure (mmHg)a  143.8 (17.6)  Characteristic  Mean (SD) or Percent  Race/ethnicity   White or Caucasian (non-Hispanic)  32.1%   Black or African American (non-Hispanic)  61.0%   Other race or ethnicity  6.9%  High school education or less  34.7%  Male  92.3%  Age (yr)  63.6 (8.8)  Diabetes (pharmaceutically treated)  57.0%  Patients without a close personal relationship  10.1%  Systolic blood pressure (mmHg)a  143.8 (17.6)  SD, standard deviation. aMean of three baseline site visit systolic blood pressures taken in a seated position 5 min apart. The mean PACIC summary score was 3.4 (SD=0.9); 48.3% of patients indicated implementation of the CCM (Table II). The mean PACIC subscale scores (Table II) ranged from 2.9 (follow-up) to 3.9 (delivery system design/care teams); the percentage of patients reporting CCM reflection ranged from 29.7% (follow-up) to 69.2% (delivery system design/care teams). These scores reflect a modest level of CCM implementation. Table II. PACIC Results, n = 377 PACIC Scale  Scale Mean (SD) (Possible Range=1–5)  % of Patients Indicating CCM Implementation (Scale Score ≥ 3.5)  Patient activation  3.4 (1.1)  50.1  Delivery system design/care teams  3.9 (0.9)  69.2  Goal setting  3.4 (1.1)  49.1  Problem solving  3.6 (1.1)  58.4  Follow-up  2.9 (1.1)  29.7  PACIC summary score  3.4 (0.9)  48.3  PACIC Scale  Scale Mean (SD) (Possible Range=1–5)  % of Patients Indicating CCM Implementation (Scale Score ≥ 3.5)  Patient activation  3.4 (1.1)  50.1  Delivery system design/care teams  3.9 (0.9)  69.2  Goal setting  3.4 (1.1)  49.1  Problem solving  3.6 (1.1)  58.4  Follow-up  2.9 (1.1)  29.7  PACIC summary score  3.4 (0.9)  48.3  CCM, chronic care model; PACIC, patient assessment of chronic illness care; SD, standard deviation. Table II. PACIC Results, n = 377 PACIC Scale  Scale Mean (SD) (Possible Range=1–5)  % of Patients Indicating CCM Implementation (Scale Score ≥ 3.5)  Patient activation  3.4 (1.1)  50.1  Delivery system design/care teams  3.9 (0.9)  69.2  Goal setting  3.4 (1.1)  49.1  Problem solving  3.6 (1.1)  58.4  Follow-up  2.9 (1.1)  29.7  PACIC summary score  3.4 (0.9)  48.3  PACIC Scale  Scale Mean (SD) (Possible Range=1–5)  % of Patients Indicating CCM Implementation (Scale Score ≥ 3.5)  Patient activation  3.4 (1.1)  50.1  Delivery system design/care teams  3.9 (0.9)  69.2  Goal setting  3.4 (1.1)  49.1  Problem solving  3.6 (1.1)  58.4  Follow-up  2.9 (1.1)  29.7  PACIC summary score  3.4 (0.9)  48.3  CCM, chronic care model; PACIC, patient assessment of chronic illness care; SD, standard deviation. The unadjusted relationship between race or ethnicity and PACIC results are detailed in Table III. Non-Hispanic African Americans had higher odds of indicating that the chronic illness care system at the VA is in line with the CCM compared with non-Hispanic Whites (OR=1.75; 95% CI = 1.12–2.73). For all subscales, non-Hispanic African Americans had greater odds of reporting aspects of the care system being in line with the CCM. However, the associations did not reach statistical significance for delivery system design/care teams and assistance with problem solving. Significant associations were not observed when comparing the experience of individuals of other race or ethnicity with non-Hispanic White patients. Table III. Logistic Regress Results – Association Between Chronic Care Model (CCM) Concordance and Race or Ethnicity, n = 377 PACIC Scale Implementeda  Non-Hispanic African American Vs. Non-Hispanic White  Other Race or Ethnicity Vs. Non-Hispanic White  Unadjusted  Adjustedb,c  Unadjusted  Adjustedb,c  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Patient activation  1.78 (1.14–2.78)*  1.80 (1.13–2.87)*  1.71 (0.73–4.02)  1.74 (0.73–4.16)  Delivery system design/care teams  1.42 (0.89–2.27)  1.55 (0.94–2.55)  1.90 (0.71–5.10)  2.14 (0.78–5.86)  Goal setting  1.60 (1.03–2.50)*  1.65 (1.03–2.64)*  1.42 (0.61–3.32)  1.39 (0.58–3.33)  Problem solving  1.49 (0.96–2.33)  1.62 (1.00–2.60)*  0.76 (0.33–1.79)  0.86 (0.36–2.07)  Follow-up  2.45 (1.45–4.15)*  2.59 (1.49–4.50)*  1.01 (0.35–2.98)  0.93 (0.30–2.81)  PACIC summary score  1.75 (1.12–2.73)*  1.86 (1.16–2.98)*  1.12 (0.47–2.63)  1.14 (0.47–2.76)  PACIC Scale Implementeda  Non-Hispanic African American Vs. Non-Hispanic White  Other Race or Ethnicity Vs. Non-Hispanic White  Unadjusted  Adjustedb,c  Unadjusted  Adjustedb,c  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Patient activation  1.78 (1.14–2.78)*  1.80 (1.13–2.87)*  1.71 (0.73–4.02)  1.74 (0.73–4.16)  Delivery system design/care teams  1.42 (0.89–2.27)  1.55 (0.94–2.55)  1.90 (0.71–5.10)  2.14 (0.78–5.86)  Goal setting  1.60 (1.03–2.50)*  1.65 (1.03–2.64)*  1.42 (0.61–3.32)  1.39 (0.58–3.33)  Problem solving  1.49 (0.96–2.33)  1.62 (1.00–2.60)*  0.76 (0.33–1.79)  0.86 (0.36–2.07)  Follow-up  2.45 (1.45–4.15)*  2.59 (1.49–4.50)*  1.01 (0.35–2.98)  0.93 (0.30–2.81)  PACIC summary score  1.75 (1.12–2.73)*  1.86 (1.16–2.98)*  1.12 (0.47–2.63)  1.14 (0.47–2.76)  *p<0.05. CI, confidence interval; PACIC, Patient Assessment of Chronic Illness Care. aCCM implementation=PACIC score ≥ 3.5. bSeparate adjusted models included the primary explanatory variable of interest, which was race or ethnicity [separate variables for non-Hispanic Black or African American and other race or ethnicity vs. non-Hispanic White or Caucasian (referent)]. Results were also adjusted for (1) education level, (2) gender, (3) age, (4) diabetes status, (5) social support, and (6) baseline systolic blood pressure. cc-Statistics for adjusted models range from 0.60 (patient activation) to 0.70 (follow-up). Table III. Logistic Regress Results – Association Between Chronic Care Model (CCM) Concordance and Race or Ethnicity, n = 377 PACIC Scale Implementeda  Non-Hispanic African American Vs. Non-Hispanic White  Other Race or Ethnicity Vs. Non-Hispanic White  Unadjusted  Adjustedb,c  Unadjusted  Adjustedb,c  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Patient activation  1.78 (1.14–2.78)*  1.80 (1.13–2.87)*  1.71 (0.73–4.02)  1.74 (0.73–4.16)  Delivery system design/care teams  1.42 (0.89–2.27)  1.55 (0.94–2.55)  1.90 (0.71–5.10)  2.14 (0.78–5.86)  Goal setting  1.60 (1.03–2.50)*  1.65 (1.03–2.64)*  1.42 (0.61–3.32)  1.39 (0.58–3.33)  Problem solving  1.49 (0.96–2.33)  1.62 (1.00–2.60)*  0.76 (0.33–1.79)  0.86 (0.36–2.07)  Follow-up  2.45 (1.45–4.15)*  2.59 (1.49–4.50)*  1.01 (0.35–2.98)  0.93 (0.30–2.81)  PACIC summary score  1.75 (1.12–2.73)*  1.86 (1.16–2.98)*  1.12 (0.47–2.63)  1.14 (0.47–2.76)  PACIC Scale Implementeda  Non-Hispanic African American Vs. Non-Hispanic White  Other Race or Ethnicity Vs. Non-Hispanic White  Unadjusted  Adjustedb,c  Unadjusted  Adjustedb,c  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Patient activation  1.78 (1.14–2.78)*  1.80 (1.13–2.87)*  1.71 (0.73–4.02)  1.74 (0.73–4.16)  Delivery system design/care teams  1.42 (0.89–2.27)  1.55 (0.94–2.55)  1.90 (0.71–5.10)  2.14 (0.78–5.86)  Goal setting  1.60 (1.03–2.50)*  1.65 (1.03–2.64)*  1.42 (0.61–3.32)  1.39 (0.58–3.33)  Problem solving  1.49 (0.96–2.33)  1.62 (1.00–2.60)*  0.76 (0.33–1.79)  0.86 (0.36–2.07)  Follow-up  2.45 (1.45–4.15)*  2.59 (1.49–4.50)*  1.01 (0.35–2.98)  0.93 (0.30–2.81)  PACIC summary score  1.75 (1.12–2.73)*  1.86 (1.16–2.98)*  1.12 (0.47–2.63)  1.14 (0.47–2.76)  *p<0.05. CI, confidence interval; PACIC, Patient Assessment of Chronic Illness Care. aCCM implementation=PACIC score ≥ 3.5. bSeparate adjusted models included the primary explanatory variable of interest, which was race or ethnicity [separate variables for non-Hispanic Black or African American and other race or ethnicity vs. non-Hispanic White or Caucasian (referent)]. Results were also adjusted for (1) education level, (2) gender, (3) age, (4) diabetes status, (5) social support, and (6) baseline systolic blood pressure. cc-Statistics for adjusted models range from 0.60 (patient activation) to 0.70 (follow-up). In adjusted models, non-Hispanic African American patients with hypertension had almost twice the odds of indicating that their experience with their regular VA health care team was consistent with the CCM (OR=1.86; 95% CI = 1.16–2.98) compared with non-Hispanic Whites (see Table III). Similar associations were seen for PACIC subscales comparing non-Hispanic African Americans with non-Hispanics Whites, including follow-up care (OR=2.59; 95% CI = 1.49–4.50), patient activation (OR=1.80; 95% CI = 1.13–2.87), goal setting (OR=1.65; 95% CI = 1.03–2.64), and help with problem solving (OR=1.62; 95% CI = 1.00–2.60). Similar patterns were found with both unadjusted and adjusted models (see Table III). Although the association for delivery system design/care teams did not reach statistical significance, the odds ratio estimate was in a similar direction (OR=1.55; 95% CI = 0.94–2.55). Comparisons of concordance with CCM between other race or ethnicity and non-Hispanic Whites were not statistically significant (Table III). The sensitivity analyses using PACIC score cutoffs of ≥3 or ≥4 (rather than 3.5) to indicate concordance generally produced similar results (results not shown). With a cutoff of ≥3, associations indicating greater odds of reporting CCM concordance among non-Hispanic African Americans compared with non-Hispanic Whites were statistically significant for all PACIC scales. With a cutoff of ≥4, associations indicating greater odds of reporting CCM concordance among non-Hispanic African Americans compared with non-Hispanic Whites were in the same direction for all PACIC scales; however, statistical significance was only achieved for patient activation, follow-up, and the overall summary score. DISCUSSION Compared with non-Hispanic Whites, non-Hispanic African American patients had nearly twice the odds of reporting that the chronic illness care system at the VA is consistent with the CCM. Significantly greater odds were reported for primary care supporting patient follow-up, activation, goal setting, and problem solving; the relationship was in the same direction for delivery systems being designed around teams but did not achieve statistical significance. These results are robust to sensitivity analyses based on different concordance cutoff scores and are adjusted for measures of age, gender, education, social support, diabetes status, and blood pressure. This study is comparable with the one we conducted before the implementation of PACT. In 2006–2007, we surveyed a random sample of patients with diabetes treated at the same VA medical center. When comparing non-White (predominantly African American) and White patients, non-White patients had more than twice the odds of reporting that the chronic illness care system was in line with the CCM (OR=2.28).40 Additionally, a 2013 study of VA patients with multiple chronic conditions from around the VA system conducted by Balbale et al found that non-White patients were more likely than White patients to have a PACIC score of ≥3.5. However, the estimated odds ratio in that study was modest (OR=1.19).43 PACIC scores were modestly higher in the present study than in the study from 2006 to 2007. The mean PACIC summary score in the present study was 3.4, and 48.3% of patients had a score of ≥3.5. By contrast, the equivalent numbers in the previous study were 3.1 and 43.9%, respectively. A larger percentage of patients in the present study reported scores ≥3.5 on the delivery system organized around teams (8.3 percentage points more in the present study), follow-up care (5.4 percentage points more in the present study), goal setting (3.6 percentage points more in the present study), and problem solving (2.8 percentage points more in the present study). There was essentially no difference in the percentage of patients reporting patient activation. The larger percentage of patients reporting concordance with CCM especially on the delivery system design/care teams domain may be reflective of an impact of the VA efforts to reorganize into patient-aligned care teams (PACTs).22 There are multiple possible explanations for our findings concerning the association between non-Hispanic African American race/ethnicity and PACIC sores. First, the VA primary care clinics may pay additional attention to the needs of African American patients with hypertension. This possibility is supported by evidence from a separate clinical trial conducted at the same medical center indicating that African Americans were more likely than Whites to consider hypertension to be a serious condition and to have the related symptom of increased urination. This may either result in increased attention on the part of providers or be the result of such attention to hypertension.45 Further, results for a different VA tertiary care medical center indicated that providers were more involved in advising African American than White patients concerning hypertension and medication adherence.46 Second, the VA has made significant attempts to transform the primary care system according to the PCMH model22–24 and has generally had lower levels of racial disparities in diabetes-related care processes than the rest of the US health care system.47 This may be reflected in the fact that PACIC results at this medical center indicated a greater degree of organization around teams than was seen among diabetes patients before the transition to the PCMH model.40 Third, it is possible that lower levels of trust in the health care system48,49 and health research in general50 or perceived discrimination by the health care system51 may have led to a response bias in which non-White patients were not as trusting that answers would remain confidential or not impact their care. However, this does not appear likely because these patients consented to be enrolled in a clinical trial focused on hypertension disease management. This study has some important limitations and considerations. The study was conducted among patients of clinics associated with a single tertiary care VAMC, which may limit generalizability. However, an association has been observed between race and PACIC sores among VA patients in other settings.43 Second, even if representative of VAMCs, findings may not be generalizable beyond the VA, a highly integrated delivery system that serves predominantly older men who are socioeconomically vulnerable.52 Third, the PACIC is not hypertension specific. As a result, experiences related to conditions other than hypertension may have impact PACIC results. Finally, our sample included patients reporting having a regular primary care provider. Thus, the results may not generalize to patients with poor access to or high dissatisfaction with VA health care. COnclusion Non-Hispanic African Americans with pharmaceutically treated hypertension report that the primary care system more closely approximates the Wagner CCM than non-Hispanic White patients. This finding is consistent with results from a study among patients with diabetes at the same VA medical center almost a decade earlier. Future research should focus on identifying the elements of health care organizations that allow these organizations to successfully offer all patients, including traditionally disadvantaged patients such as minorities and those with low socioeconomic status, the type of integrated primary care that will allow them to address their individual self-management needs. Funding This research was funded by the Health Services Research & Development (HSR&D) Service of the United States Department of Veterans Affairs (VA) [IIR 10–383]. Dr. Bosworth is supported by Senior Research Career Scientist award from the VA HSR&D Service (RCS 08-027). The work was supported by the Center of Innovation for Health Services Research in Primary Care (CIN 13-410) at the Durham Veterans Affairs Medical Center. References 1 Mozaffarian D, Benjamin EJ, Go AS, et al.  : Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation  2016; 133( 4): e38– e360. Google Scholar CrossRef Search ADS PubMed  2 Chobanian AV: Shattuck Lecture. The hypertension paradox – more uncontrolled disease despite improved therapy. N Engl J Med  2009; 361( 9): 878– 87. Google Scholar CrossRef Search ADS PubMed  3 Wagner EH: Chronic disease management: what will it take to improve care for chronic illness? Eff Clin Pract  1998; 1( 1): 2– 4. Google Scholar PubMed  4 Bodenheimer T, Wagner EH, Grumbach K: Improving primary care for patients with chronic illness. JAMA  2002; 288( 14): 1775– 9. Google Scholar CrossRef Search ADS PubMed  5 Fleming B, Silver A, Ocepek-Welikson K, Keller D: The relationship between organizational systems and clinical quality in diabetes care. Am J Manag Care  2004; 10( 12): 934– 44. Google Scholar PubMed  6 Jackson GL, Yano EM, Edelman D, et al.  : Veterans affairs primary care organizational characteristics associated with better diabetes control. Am J Manag Care  2005; 11( 4): 225– 37. Google Scholar PubMed  7 Nutting PA, Dickinson WP, Dickinson LM, et al.  : Use of chronic care model elements is associated with higher-quality care for diabetes. Ann Fam Med  2007; 5( 1): 14– 20. Google Scholar CrossRef Search ADS PubMed  8 Bodenheimer T, Wagner EH, Grumbach K: Improving primary care for patients with chronic illness: the chronic care model, Part 2. JAMA  2002; 288( 15): 1909– 14. Google Scholar CrossRef Search ADS PubMed  9 Coleman K, Austin BT, Brach C, Wagner EH: Evidence on the Chronic Care Model in the new millennium. Health Aff (Millwood)  2009; 28( 1): 75– 85. Google Scholar CrossRef Search ADS PubMed  10 Tsai AC, Morton SC, Mangione CM, Keeler EB: A meta-analysis of interventions to improve care for chronic illnesses. Am J Manag Care  2005; 11( 8): 478– 88. Google Scholar PubMed  11 Adams SG, Smith PK, Allan PF, Anzueto A, Pugh JA, Cornell JE: Systematic review of the chronic care model in chronic obstructive pulmonary disease prevention and management. Arch Intern Med  2007; 167( 6): 551– 61. Google Scholar CrossRef Search ADS PubMed  12 Jacobson D, Gance-Cleveland B: A systematic review of primary healthcare provider education and training using the Chronic Care Model for childhood obesity. Obes Rev  2011; 12( 5): e244– 256. Google Scholar CrossRef Search ADS PubMed  13 Woltmann E, Grogan-Kaylor A, Perron B, Georges H, Kilbourne AM, Bauer MS: Comparative effectiveness of collaborative chronic care models for mental health conditions across primary, specialty, and behavioral health care settings: systematic review and meta-analysis. Am J Psychiatry  2012; 169( 8): 790– 804. Google Scholar CrossRef Search ADS PubMed  14 Stellefson M, Dipnarine K, Stopka C: The chronic care model and diabetes management in US primary care settings: a systematic review. Prev Chronic Dis  2013; 10: E26. Google Scholar CrossRef Search ADS PubMed  15 Green BB, Cook AJ, Ralston JD, et al.  : Effectiveness of home blood pressure monitoring, web communication, and pharmacist care on hypertension control: a randomized controlled trial. JAMA  2008; 299( 24): 2857– 67. Google Scholar CrossRef Search ADS PubMed  16 Margolis KL, Asche SE, Bergdall AR, et al.  : Effect of home blood pressure telemonitoring and pharmacist management on blood pressure control: a cluster randomized clinical trial. JAMA  2013; 310( 1): 46– 56. Google Scholar CrossRef Search ADS PubMed  17 Carter BL, Bosworth HB, Green BB: The hypertension team: the role of the pharmacist, nurse, and teamwork in hypertension therapy. J Clin Hypertens (Greenwich)  2012; 14( 1): 51– 65. Google Scholar CrossRef Search ADS PubMed  18 Vargas RB, Mangione CM, Asch S, et al.  : Can a chronic care model collaborative reduce heart disease risk in patients with diabetes? J Gen Intern Med  2007; 22( 2): 215– 22. Google Scholar CrossRef Search ADS PubMed  19 Jackson GL, Powers BJ, Chatterjee R, et al.  : The patient centered medical home. A systematic review. Ann Intern Med  2013; 158( 3): 169– 78. Google Scholar CrossRef Search ADS PubMed  20 Jackson GL, Weinberger M: A decade with the chronic care model: some progress and opportunity for more. Med Care  2009; 47( 9): 929– 31. Google Scholar CrossRef Search ADS PubMed  21 Kizer KW, Dudley RA: Extreme makeover: transformation of the veterans health care system. Annu Rev Public Health  2009; 30: 313– 39. Google Scholar CrossRef Search ADS PubMed  22 Rosland AM, Nelson K, Sun H, et al.  : The patient-centered medical home in the Veterans Health Administration. Am J Manag Care  2013; 19( 7): e263– 272. Google Scholar PubMed  23 Nelson KM, Helfrich C, Sun H, et al.  : Implementation of the Patient-Centered Medical Home in the Veterans Health Administration: Associations With Patient Satisfaction, Quality of Care, Staff Burnout, and Hospital and Emergency Department Use. JAMA Intern Med  2014; 174( 8): 1350– 8. Google Scholar CrossRef Search ADS PubMed  24 Bidassie B, Davies ML, Stark R, Boushon B: VA Experience in Implementing Patient-Centered Medical Home Using a Breakthrough Series Collaborative. J Gen Intern Med  2014; 29( Suppl 2): 563– 71. Google Scholar CrossRef Search ADS PubMed  25 Hernandez SE, Taylor L, Grembowski D, et al.  : A First Look at PCMH Implementation for Minority Veterans: Room for Improvement. Med Care  2016; 54( 3): 253– 61. Google Scholar CrossRef Search ADS PubMed  26 Uchendu US: Institutional journey in pursuit of health equity: Veterans Health Administration’s Office of Health Equity. Am J Public Health  2014; 104( Suppl 4): S511– 513. Google Scholar CrossRef Search ADS PubMed  27 Clancy CM, Uchendu US, Jones KT: Excellence and equality in health care. Am J Public Health  2014; 104( Suppl 4): S527– 528. Google Scholar CrossRef Search ADS PubMed  28 Atkins D, Kilbourne A, Lipson L: Health equity research in the Veterans Health Administration: we’ve come far but aren’t there yet. Am J Public Health  2014; 104( Suppl 4): S525– 526. Google Scholar CrossRef Search ADS PubMed  29 Fletcher RD, Amdur RL, Kolodner R, et al.  : Blood pressure control among US veterans: a large multiyear analysis of blood pressure data from the Veterans Administration health data repository. Circulation  2012; 125( 20): 2462– 8. Google Scholar CrossRef Search ADS PubMed  30 Hertz RP, Unger AN, Cornell JA, Saunders E: Racial disparities in hypertension prevalence, awareness, and management. Arch Intern Med  2005; 165( 18): 2098– 2104. Google Scholar CrossRef Search ADS PubMed  31 Morenoff JD, House JS, Hansen BB, Williams DR, Kaplan GA, Hunte HE: Understanding social disparities in hypertension prevalence, awareness, treatment, and control: the role of neighborhood context. Soc Sci Med  2007; 65( 9): 1853– 66. Google Scholar CrossRef Search ADS PubMed  32 Mueller M, Purnell TS, Mensah GA, Cooper LA: Reducing racial and ethnic disparities in hypertension prevention and control: what will it take to translate research into practice and policy? Am J Hypertens  2015; 28( 6): 699– 716. Google Scholar CrossRef Search ADS PubMed  33 Saha S, Freeman M, Toure J, Tippens KM, Weeks C, Ibrahim S: Racial and ethnic disparities in the VA health care system: a systematic review. J Gen Intern Med  2008; 23( 5): 654– 71. Google Scholar CrossRef Search ADS PubMed  34 Jackson GL, Weinberger M, Kirshner MA, et al.  : Open-label randomized trial of titrated disease management for patients with hypertension: study design and baseline sample characteristics. Contemp Clin Trials  2016; 50: 5– 15. Google Scholar CrossRef Search ADS PubMed  35 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( 19): 2560– 72. Google Scholar CrossRef Search ADS PubMed  36 Glasgow RE, Wagner EH, Schaefer J, Mahoney LD, Reid RJ, Greene SM: Development and validation of the Patient Assessment of Chronic Illness Care (PACIC). Med Care  2005; 43( 5): 436– 44. Google Scholar CrossRef Search ADS PubMed  37 Glasgow RE, Whitesides H, Nelson CC, King DK: Use of the Patient Assessment of Chronic Illness Care (PACIC) with diabetic patients: relationship to patient characteristics, receipt of care, and self-management. Diabetes Care  2005; 28( 11): 2655– 61. Google Scholar CrossRef Search ADS PubMed  38 Schmittdiel J, Mosen DM, Glasgow RE, Hibbard J, Remmers C, Bellows J: Patient Assessment of Chronic Illness Care (PACIC) and improved patient-centered outcomes for chronic conditions. J Gen Intern Med  2008; 23( 1): 77– 80. Google Scholar CrossRef Search ADS PubMed  39 Randell RL, Long MD, Martin CF, et al.  : Patient perception of chronic illness care in a large inflammatory bowel disease cohort. Inflamm Bowel Dis  2013; 19( 7): 1428– 33. Google Scholar CrossRef Search ADS PubMed  40 Jackson GL, Weinberger M, Hamilton NS, Edelman D: Racial/ethnic and educational-level differences in diabetes care experiences in primary care. Prim Care Diabetes  2008; 2( 1): 39– 44. Google Scholar CrossRef Search ADS PubMed  41 Houle J, Beaulieu MD, Lussier MT, et al.  : Patients’ experience of chronic illness care in a network of teaching settings. Can Fam Physician  2012; 58( 12): 1366– 73. Google Scholar PubMed  42 Wu CX, Tan WS, See RC, et al.  : A matched-group study protocol to evaluate the implementation of an Integrated Care Pathway programme for chronic obstructive pulmonary disease in Singapore. BMJ open  2015; 5( 1): e005655. Google Scholar CrossRef Search ADS PubMed  43 Balbale SN, Etingen B, Malhiot A, Miskevics S, LaVela SL: Perceptions of Chronic Illness Care Among Veterans With Multiple Chronic Conditions. Mil Med  2016; 181( 5): 439– 44. Google Scholar CrossRef Search ADS PubMed  44 Hausmann LR, Gao S, Mor MK, Schaefer JH Jr., Fine MJ: Understanding racial and ethnic differences in patient experiences with outpatient health care in Veterans Affairs Medical Centers. Med Care  2013; 51( 6): 532– 9. Google Scholar CrossRef Search ADS PubMed  45 Bosworth HB, Dudley T, Olsen MK, et al.  : Racial differences in blood pressure control: potential explanatory factors. Am J Med  2006; 119( 1): 70. Google Scholar CrossRef Search ADS PubMed  46 Kressin NR, Wang F, Long J, et al.  : Hypertensive patients’ race, health beliefs, process of care, and medication adherence. J Gen Intern Med  2007; 22( 6): 768– 74. Google Scholar CrossRef Search ADS PubMed  47 Saha S, Freeman M, Toure J, Tippens KM, Weeks C: Racial and Ethnic Disparities in the VA Healthcare System: A Systematic Review . Washington, DC, Department of Veterans Affairs, Veterans Health Administration, Health Services Research & Development Service, 2007 June. 48 Doescher MP, Saver BG, Franks P, Fiscella K: Racial and ethnic disparities in perceptions of physician style and trust. Arch Fam Med  2000; 9( 10): 1156– 63. Google Scholar CrossRef Search ADS PubMed  49 Piette JD, Heisler M, Krein S, Kerr EA: The role of patient–physician trust in moderating medication nonadherence due to cost pressures. Arch Intern Med  2005; 165( 15): 1749– 55. Google Scholar CrossRef Search ADS PubMed  50 Corbie-Smith G, Thomas SB, St George DM: Distrust, race, and research. Arch Intern Med  2002; 162( 21): 2458– 63. Nov 25. Google Scholar CrossRef Search ADS PubMed  51 Van Houtven CH, Voils CI, Oddone EZ, et al.  : Perceived discrimination and reported delay of pharmacy prescriptions and medical tests. J Gen Intern Med  2005; 20( 7): 578– 83. Google Scholar CrossRef Search ADS PubMed  52 Morgan RO, Teal CR, Reddy SG, Ford ME, Ashton CM: Measurement in Veterans Affairs Health Services Research: veterans as a special population. Health Serv Res  2005; 40( 5 Pt 2): 1573– 83. Google Scholar CrossRef Search ADS PubMed  Author notes The views expressed in this article are those of the authors and do not represent the position or policy of the Department of Veterans Affairs, United States Government, or Duke University. Published by Oxford University Press on behalf of Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Military Medicine Oxford University Press

How Views of the Organization of Primary Care Among Patients with Hypertension Vary by Race or Ethnicity

Loading next page...
 
/lp/ou_press/how-views-of-the-organization-of-primary-care-among-patients-with-AQ1XKDm64w
Publisher
Association of Military Surgeons of the United States
Copyright
Published by Oxford University Press on behalf of Association of Military Surgeons of the United States 2018.
ISSN
0026-4075
eISSN
1930-613X
D.O.I.
10.1093/milmed/usx111
Publisher site
See Article on Publisher Site

Abstract

Abstract Introduction: We assessed potential racial or ethnic differences in the degree to which veterans with pharmaceutically treated hypertension report experiences with their primary care system that are consistent with optimal chronic illness care as suggested by Wagner’s Chronic Care Model (CCM). Materials and Methods: A cross-sectional analysis of the results of the Patient Assessment of Chronic Illness Care (PACIC), which measured components of the care system suggested by the CCM and was completed at baseline by participants in a hypertension disease management clinical trial. Participants had a recent history of uncontrolled systolic blood pressure. Results: Among 377 patients, non-Hispanic African American veterans had almost twice the odds of indicating that their primary care experience is consistent with CCM features when compared with non-Hispanic White patients (odds ratio (OR) = 1.86; 95% confidence interval (CI) = 1.16–2.98). Similar statistically significant associations were observed for follow-up care (OR = 2.59; 95% CI = 1.49–4.50), patient activation (OR = 1.80; 95% CI = 1.13–2.87), goal setting (OR = 1.65; 95% CI = 1.03–2.64), and help with problem solving (OR = 1.62; 95% CI = 1.00–2.60). Conclusions: Non-Hispanic African Americans with pharmaceutically treated hypertension report that the primary care system more closely approximates the Wagner CCM than non-Hispanic White patients. INTRODUCTION Approximately a third of adults in the United States have hypertension, including more than two-thirds of people over age 65 yr.1 Despite significant evidence that controlling hypertension through diet, exercise, and/or medications improves cardiovascular and renal outcomes, fewer than half of Americans with hypertension have their blood pressure under control.1,2 The Wagner Chronic Care Model (CCM) postulates that chronic illness management is optimized when health care systems have (1) established links to the community, (2) capacity to provide effective self-management support through delivery systems organized around teams, (3) clinical decision support, and (4) robust information technology systems.3,4 Having CCM elements in primary care and disease management programs has been associated with better chronic illness process and outcomes,5–14 including blood pressure and cardiovascular disease risk.15–18 The CCM serves as a basis for other robust models for organizing and delivering primary care, including the patient-centered medical home (PCMH).19 Over the past two decades, the Veterans Affairs (VA) health care system has evolved with specific links to the CCM and PCMH models,20,21 including the introduction of the patient-aligned care team (PACT) in 2010 as the basis of organizing primary care.22–24 The goal of the PACT is to improve the quality of care, including satisfaction, health care outcomes, and costs. Despite the fact that PACTs were not designed to specifically address racial or ethnic disparities in care and there has not been a specific emphasis on PACT implementation in sites of care with larger proportions of racial or ethnic minorities,25 the VA does have a programmatic and research emphasis on reducing health disparities.26–28 The reorganization of VA care has been accompanied by significant improvements in hypertension control. For example, control rates increased from 46% to 76% between 2000 and 2010. Despite improvements in control among both non-Hispanic Whites and African Americans, control rates consistently remained lower among African American compared with non-Hispanic White patients.29 To fully reap the potential benefits of the CCM and related models, systems must reduce pervasive racial/ethnic disparities in the process and outcomes of care, including hypertension.1,30–32 One potential explanation for observed disparities is that racial and socioeconomic groups have different perceptions of, and interactions with, primary care.33 Thus, we examined whether, among veterans with hypertension, racial/ethnic groups differ in their reported experiences with elements of the CCM. METHODS Overview We conducted this study with eligible patients receiving primary care from three clinics associated with one VAMC. We utilized baseline data from patients enrolled between November 6, 2012 and April 9, 2015. The study was approved by the Durham VA Medical Center’s institutional review board (IRB). Eligible Patients The parent study is a randomized trial that examines the impact of a disease management program that titrates the intensity of resources to patients’ level of hypertension control (clinicaltrials.gov registration no. NCT01390272). Overall trial methods have been previously described.34 Briefly, study subjects were English-speaking adult patients living in the community with access to a telephone, had been seen at a study clinic in the last year, reported that they consider their primary care provider to be at the VA, reported that they receive the majority of their health care from the VA, and had a history of pharmaceutically treated hypertension with uncontrolled systolic blood pressure during the year before identification of patients to approach for potential study enrollment. The definition of control was based on the Seventh Report of the Joint National Commission on Prevention, Detection, Evaluation and Treatment of High Blood Pressure (JNC-7) (≥140 mmHg for patients without diabetes and ≥130 mmHg for patients with diabetes),35 which was the version of the guideline in place at the beginning of study enrollment. Patients were excluded if they were enrolled in an ongoing clinical trial or clinical program that would be expected to impact blood pressure control, had type 1 diabetes, class IV congestive heart failure, end-stage renal disease, metastatic cancer, a history of solid organ or bone marrow transplantation, or a diagnosis of active psychosis at baseline. Women who reported being pregnant or planning to become pregnant over the next 18 mo were also excluded. Measures At study baseline (i.e., before the trial intervention began), we administered the validated 2005 version of the Patient Assessment of Chronic Illness Care (PACIC).36 Although not hypertension specific, the PACIC measures patients’ perceptions of their chronic illness care system that are mapped to the CCM.3,4,36,37 PACIC scores have been positively associated with patient-reported self-management resources and behaviors, medication adherence, quality of life, and satisfaction with health care.38,39 For a series of statements related to their regular doctor or provider and affiliated clinicians who care for their chronic illness, patients were asked how often during the previous 6 mo time they had various experiences related to their care. Responses were on a 5-point Likert scale anchored by 1 (almost never) to 5 (almost always). The PACIC produced the following subscale scores: (1) patient activation, (2) delivery system design (care teams)/decision support, (3) collaborative goal setting, (4) collaborative problem solving/contextual counseling, and (5) follow-up/coordination. In addition, an overall PACIC summary score (i.e., overall fidelity to the CCM) was calculated. Scores range from 1 to 5 with higher scales indicating greater fidelity to the CCM. The primary explanatory variable of interest was race or ethnicity [separate variables for non-Hispanic Black or African American and other race or ethnicity vs. non-Hispanic White or Caucasian (referent)]. Logistic regression models utilized for this study include the following variables/measures: (1) education level (high school or less vs. greater than high school), (2) gender (female vs. male), (3) age (continuous variable), (4) pharmaceutically treated diabetes vs. not having pharmaceutically treated diabetes, (5) whether the individual has a person with whom the veteran is close (proxy for social support) (have person whom the person is close vs. not having such a person), and (6) mean of three baseline study visit systolic blood pressure measurements taken 30 s apart in a seated position after waiting 5 min (continuous variable serving as an indication of level of disease control). Data Analysis For our primary analyses, PACIC scores >3.5 on each scale were considered to represent “presence of” CCM components. This cutoff was chosen because it represents the top quarter of the scale range and has been used previously to examine factors associated with presence of CCM components utilizing the PACIC.40–43 Sensitivity analyses were also done using scores of ≥3 and ≥4 as the cutoffs. Separate logistic regression models were fit for each PACIC subscale and the overall PACIC score. The primary explanatory variable of interest was race or ethnicity (variables described above). Simple (unadjusted) and multivariable (adjusted) logistic regression models were fit with multivariable models including race and variables covering the following topics (measures described in detail above): (1) education level, (2) gender, (3) age, (4) diabetes status, (5) social support, and (6) baseline systolic blood pressure. We report odds ratios (OR) and 95% confidence intervals (CI). Analyses were done using SAS version 9.4 (SAS Institute, Inc., Cary, NC). Results The analysis sample included 377 of 385 (98%) enrolled patients with complete baseline data on outcomes and explanatory variables. Reflecting the patient population seen in the VA,44 patients were predominantly older (mean age 63.6 yr) men (92.3%) (Table I). The majority of patients were non-Hispanic African Americans (61.0%). Non-Hispanic Whites and patients of other racial or ethnic backgrounds made up 32.1% and 6.9% of patients included in the analysis, respectively. Table I. Characteristics of Patients Included in the Final Logistic Regression Models, n = 377 Characteristic  Mean (SD) or Percent  Race/ethnicity   White or Caucasian (non-Hispanic)  32.1%   Black or African American (non-Hispanic)  61.0%   Other race or ethnicity  6.9%  High school education or less  34.7%  Male  92.3%  Age (yr)  63.6 (8.8)  Diabetes (pharmaceutically treated)  57.0%  Patients without a close personal relationship  10.1%  Systolic blood pressure (mmHg)a  143.8 (17.6)  Characteristic  Mean (SD) or Percent  Race/ethnicity   White or Caucasian (non-Hispanic)  32.1%   Black or African American (non-Hispanic)  61.0%   Other race or ethnicity  6.9%  High school education or less  34.7%  Male  92.3%  Age (yr)  63.6 (8.8)  Diabetes (pharmaceutically treated)  57.0%  Patients without a close personal relationship  10.1%  Systolic blood pressure (mmHg)a  143.8 (17.6)  SD, standard deviation. aMean of three baseline site visit systolic blood pressures taken in a seated position 5 min apart. Table I. Characteristics of Patients Included in the Final Logistic Regression Models, n = 377 Characteristic  Mean (SD) or Percent  Race/ethnicity   White or Caucasian (non-Hispanic)  32.1%   Black or African American (non-Hispanic)  61.0%   Other race or ethnicity  6.9%  High school education or less  34.7%  Male  92.3%  Age (yr)  63.6 (8.8)  Diabetes (pharmaceutically treated)  57.0%  Patients without a close personal relationship  10.1%  Systolic blood pressure (mmHg)a  143.8 (17.6)  Characteristic  Mean (SD) or Percent  Race/ethnicity   White or Caucasian (non-Hispanic)  32.1%   Black or African American (non-Hispanic)  61.0%   Other race or ethnicity  6.9%  High school education or less  34.7%  Male  92.3%  Age (yr)  63.6 (8.8)  Diabetes (pharmaceutically treated)  57.0%  Patients without a close personal relationship  10.1%  Systolic blood pressure (mmHg)a  143.8 (17.6)  SD, standard deviation. aMean of three baseline site visit systolic blood pressures taken in a seated position 5 min apart. The mean PACIC summary score was 3.4 (SD=0.9); 48.3% of patients indicated implementation of the CCM (Table II). The mean PACIC subscale scores (Table II) ranged from 2.9 (follow-up) to 3.9 (delivery system design/care teams); the percentage of patients reporting CCM reflection ranged from 29.7% (follow-up) to 69.2% (delivery system design/care teams). These scores reflect a modest level of CCM implementation. Table II. PACIC Results, n = 377 PACIC Scale  Scale Mean (SD) (Possible Range=1–5)  % of Patients Indicating CCM Implementation (Scale Score ≥ 3.5)  Patient activation  3.4 (1.1)  50.1  Delivery system design/care teams  3.9 (0.9)  69.2  Goal setting  3.4 (1.1)  49.1  Problem solving  3.6 (1.1)  58.4  Follow-up  2.9 (1.1)  29.7  PACIC summary score  3.4 (0.9)  48.3  PACIC Scale  Scale Mean (SD) (Possible Range=1–5)  % of Patients Indicating CCM Implementation (Scale Score ≥ 3.5)  Patient activation  3.4 (1.1)  50.1  Delivery system design/care teams  3.9 (0.9)  69.2  Goal setting  3.4 (1.1)  49.1  Problem solving  3.6 (1.1)  58.4  Follow-up  2.9 (1.1)  29.7  PACIC summary score  3.4 (0.9)  48.3  CCM, chronic care model; PACIC, patient assessment of chronic illness care; SD, standard deviation. Table II. PACIC Results, n = 377 PACIC Scale  Scale Mean (SD) (Possible Range=1–5)  % of Patients Indicating CCM Implementation (Scale Score ≥ 3.5)  Patient activation  3.4 (1.1)  50.1  Delivery system design/care teams  3.9 (0.9)  69.2  Goal setting  3.4 (1.1)  49.1  Problem solving  3.6 (1.1)  58.4  Follow-up  2.9 (1.1)  29.7  PACIC summary score  3.4 (0.9)  48.3  PACIC Scale  Scale Mean (SD) (Possible Range=1–5)  % of Patients Indicating CCM Implementation (Scale Score ≥ 3.5)  Patient activation  3.4 (1.1)  50.1  Delivery system design/care teams  3.9 (0.9)  69.2  Goal setting  3.4 (1.1)  49.1  Problem solving  3.6 (1.1)  58.4  Follow-up  2.9 (1.1)  29.7  PACIC summary score  3.4 (0.9)  48.3  CCM, chronic care model; PACIC, patient assessment of chronic illness care; SD, standard deviation. The unadjusted relationship between race or ethnicity and PACIC results are detailed in Table III. Non-Hispanic African Americans had higher odds of indicating that the chronic illness care system at the VA is in line with the CCM compared with non-Hispanic Whites (OR=1.75; 95% CI = 1.12–2.73). For all subscales, non-Hispanic African Americans had greater odds of reporting aspects of the care system being in line with the CCM. However, the associations did not reach statistical significance for delivery system design/care teams and assistance with problem solving. Significant associations were not observed when comparing the experience of individuals of other race or ethnicity with non-Hispanic White patients. Table III. Logistic Regress Results – Association Between Chronic Care Model (CCM) Concordance and Race or Ethnicity, n = 377 PACIC Scale Implementeda  Non-Hispanic African American Vs. Non-Hispanic White  Other Race or Ethnicity Vs. Non-Hispanic White  Unadjusted  Adjustedb,c  Unadjusted  Adjustedb,c  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Patient activation  1.78 (1.14–2.78)*  1.80 (1.13–2.87)*  1.71 (0.73–4.02)  1.74 (0.73–4.16)  Delivery system design/care teams  1.42 (0.89–2.27)  1.55 (0.94–2.55)  1.90 (0.71–5.10)  2.14 (0.78–5.86)  Goal setting  1.60 (1.03–2.50)*  1.65 (1.03–2.64)*  1.42 (0.61–3.32)  1.39 (0.58–3.33)  Problem solving  1.49 (0.96–2.33)  1.62 (1.00–2.60)*  0.76 (0.33–1.79)  0.86 (0.36–2.07)  Follow-up  2.45 (1.45–4.15)*  2.59 (1.49–4.50)*  1.01 (0.35–2.98)  0.93 (0.30–2.81)  PACIC summary score  1.75 (1.12–2.73)*  1.86 (1.16–2.98)*  1.12 (0.47–2.63)  1.14 (0.47–2.76)  PACIC Scale Implementeda  Non-Hispanic African American Vs. Non-Hispanic White  Other Race or Ethnicity Vs. Non-Hispanic White  Unadjusted  Adjustedb,c  Unadjusted  Adjustedb,c  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Patient activation  1.78 (1.14–2.78)*  1.80 (1.13–2.87)*  1.71 (0.73–4.02)  1.74 (0.73–4.16)  Delivery system design/care teams  1.42 (0.89–2.27)  1.55 (0.94–2.55)  1.90 (0.71–5.10)  2.14 (0.78–5.86)  Goal setting  1.60 (1.03–2.50)*  1.65 (1.03–2.64)*  1.42 (0.61–3.32)  1.39 (0.58–3.33)  Problem solving  1.49 (0.96–2.33)  1.62 (1.00–2.60)*  0.76 (0.33–1.79)  0.86 (0.36–2.07)  Follow-up  2.45 (1.45–4.15)*  2.59 (1.49–4.50)*  1.01 (0.35–2.98)  0.93 (0.30–2.81)  PACIC summary score  1.75 (1.12–2.73)*  1.86 (1.16–2.98)*  1.12 (0.47–2.63)  1.14 (0.47–2.76)  *p<0.05. CI, confidence interval; PACIC, Patient Assessment of Chronic Illness Care. aCCM implementation=PACIC score ≥ 3.5. bSeparate adjusted models included the primary explanatory variable of interest, which was race or ethnicity [separate variables for non-Hispanic Black or African American and other race or ethnicity vs. non-Hispanic White or Caucasian (referent)]. Results were also adjusted for (1) education level, (2) gender, (3) age, (4) diabetes status, (5) social support, and (6) baseline systolic blood pressure. cc-Statistics for adjusted models range from 0.60 (patient activation) to 0.70 (follow-up). Table III. Logistic Regress Results – Association Between Chronic Care Model (CCM) Concordance and Race or Ethnicity, n = 377 PACIC Scale Implementeda  Non-Hispanic African American Vs. Non-Hispanic White  Other Race or Ethnicity Vs. Non-Hispanic White  Unadjusted  Adjustedb,c  Unadjusted  Adjustedb,c  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Patient activation  1.78 (1.14–2.78)*  1.80 (1.13–2.87)*  1.71 (0.73–4.02)  1.74 (0.73–4.16)  Delivery system design/care teams  1.42 (0.89–2.27)  1.55 (0.94–2.55)  1.90 (0.71–5.10)  2.14 (0.78–5.86)  Goal setting  1.60 (1.03–2.50)*  1.65 (1.03–2.64)*  1.42 (0.61–3.32)  1.39 (0.58–3.33)  Problem solving  1.49 (0.96–2.33)  1.62 (1.00–2.60)*  0.76 (0.33–1.79)  0.86 (0.36–2.07)  Follow-up  2.45 (1.45–4.15)*  2.59 (1.49–4.50)*  1.01 (0.35–2.98)  0.93 (0.30–2.81)  PACIC summary score  1.75 (1.12–2.73)*  1.86 (1.16–2.98)*  1.12 (0.47–2.63)  1.14 (0.47–2.76)  PACIC Scale Implementeda  Non-Hispanic African American Vs. Non-Hispanic White  Other Race or Ethnicity Vs. Non-Hispanic White  Unadjusted  Adjustedb,c  Unadjusted  Adjustedb,c  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Odds Ratio (95% CI)  Patient activation  1.78 (1.14–2.78)*  1.80 (1.13–2.87)*  1.71 (0.73–4.02)  1.74 (0.73–4.16)  Delivery system design/care teams  1.42 (0.89–2.27)  1.55 (0.94–2.55)  1.90 (0.71–5.10)  2.14 (0.78–5.86)  Goal setting  1.60 (1.03–2.50)*  1.65 (1.03–2.64)*  1.42 (0.61–3.32)  1.39 (0.58–3.33)  Problem solving  1.49 (0.96–2.33)  1.62 (1.00–2.60)*  0.76 (0.33–1.79)  0.86 (0.36–2.07)  Follow-up  2.45 (1.45–4.15)*  2.59 (1.49–4.50)*  1.01 (0.35–2.98)  0.93 (0.30–2.81)  PACIC summary score  1.75 (1.12–2.73)*  1.86 (1.16–2.98)*  1.12 (0.47–2.63)  1.14 (0.47–2.76)  *p<0.05. CI, confidence interval; PACIC, Patient Assessment of Chronic Illness Care. aCCM implementation=PACIC score ≥ 3.5. bSeparate adjusted models included the primary explanatory variable of interest, which was race or ethnicity [separate variables for non-Hispanic Black or African American and other race or ethnicity vs. non-Hispanic White or Caucasian (referent)]. Results were also adjusted for (1) education level, (2) gender, (3) age, (4) diabetes status, (5) social support, and (6) baseline systolic blood pressure. cc-Statistics for adjusted models range from 0.60 (patient activation) to 0.70 (follow-up). In adjusted models, non-Hispanic African American patients with hypertension had almost twice the odds of indicating that their experience with their regular VA health care team was consistent with the CCM (OR=1.86; 95% CI = 1.16–2.98) compared with non-Hispanic Whites (see Table III). Similar associations were seen for PACIC subscales comparing non-Hispanic African Americans with non-Hispanics Whites, including follow-up care (OR=2.59; 95% CI = 1.49–4.50), patient activation (OR=1.80; 95% CI = 1.13–2.87), goal setting (OR=1.65; 95% CI = 1.03–2.64), and help with problem solving (OR=1.62; 95% CI = 1.00–2.60). Similar patterns were found with both unadjusted and adjusted models (see Table III). Although the association for delivery system design/care teams did not reach statistical significance, the odds ratio estimate was in a similar direction (OR=1.55; 95% CI = 0.94–2.55). Comparisons of concordance with CCM between other race or ethnicity and non-Hispanic Whites were not statistically significant (Table III). The sensitivity analyses using PACIC score cutoffs of ≥3 or ≥4 (rather than 3.5) to indicate concordance generally produced similar results (results not shown). With a cutoff of ≥3, associations indicating greater odds of reporting CCM concordance among non-Hispanic African Americans compared with non-Hispanic Whites were statistically significant for all PACIC scales. With a cutoff of ≥4, associations indicating greater odds of reporting CCM concordance among non-Hispanic African Americans compared with non-Hispanic Whites were in the same direction for all PACIC scales; however, statistical significance was only achieved for patient activation, follow-up, and the overall summary score. DISCUSSION Compared with non-Hispanic Whites, non-Hispanic African American patients had nearly twice the odds of reporting that the chronic illness care system at the VA is consistent with the CCM. Significantly greater odds were reported for primary care supporting patient follow-up, activation, goal setting, and problem solving; the relationship was in the same direction for delivery systems being designed around teams but did not achieve statistical significance. These results are robust to sensitivity analyses based on different concordance cutoff scores and are adjusted for measures of age, gender, education, social support, diabetes status, and blood pressure. This study is comparable with the one we conducted before the implementation of PACT. In 2006–2007, we surveyed a random sample of patients with diabetes treated at the same VA medical center. When comparing non-White (predominantly African American) and White patients, non-White patients had more than twice the odds of reporting that the chronic illness care system was in line with the CCM (OR=2.28).40 Additionally, a 2013 study of VA patients with multiple chronic conditions from around the VA system conducted by Balbale et al found that non-White patients were more likely than White patients to have a PACIC score of ≥3.5. However, the estimated odds ratio in that study was modest (OR=1.19).43 PACIC scores were modestly higher in the present study than in the study from 2006 to 2007. The mean PACIC summary score in the present study was 3.4, and 48.3% of patients had a score of ≥3.5. By contrast, the equivalent numbers in the previous study were 3.1 and 43.9%, respectively. A larger percentage of patients in the present study reported scores ≥3.5 on the delivery system organized around teams (8.3 percentage points more in the present study), follow-up care (5.4 percentage points more in the present study), goal setting (3.6 percentage points more in the present study), and problem solving (2.8 percentage points more in the present study). There was essentially no difference in the percentage of patients reporting patient activation. The larger percentage of patients reporting concordance with CCM especially on the delivery system design/care teams domain may be reflective of an impact of the VA efforts to reorganize into patient-aligned care teams (PACTs).22 There are multiple possible explanations for our findings concerning the association between non-Hispanic African American race/ethnicity and PACIC sores. First, the VA primary care clinics may pay additional attention to the needs of African American patients with hypertension. This possibility is supported by evidence from a separate clinical trial conducted at the same medical center indicating that African Americans were more likely than Whites to consider hypertension to be a serious condition and to have the related symptom of increased urination. This may either result in increased attention on the part of providers or be the result of such attention to hypertension.45 Further, results for a different VA tertiary care medical center indicated that providers were more involved in advising African American than White patients concerning hypertension and medication adherence.46 Second, the VA has made significant attempts to transform the primary care system according to the PCMH model22–24 and has generally had lower levels of racial disparities in diabetes-related care processes than the rest of the US health care system.47 This may be reflected in the fact that PACIC results at this medical center indicated a greater degree of organization around teams than was seen among diabetes patients before the transition to the PCMH model.40 Third, it is possible that lower levels of trust in the health care system48,49 and health research in general50 or perceived discrimination by the health care system51 may have led to a response bias in which non-White patients were not as trusting that answers would remain confidential or not impact their care. However, this does not appear likely because these patients consented to be enrolled in a clinical trial focused on hypertension disease management. This study has some important limitations and considerations. The study was conducted among patients of clinics associated with a single tertiary care VAMC, which may limit generalizability. However, an association has been observed between race and PACIC sores among VA patients in other settings.43 Second, even if representative of VAMCs, findings may not be generalizable beyond the VA, a highly integrated delivery system that serves predominantly older men who are socioeconomically vulnerable.52 Third, the PACIC is not hypertension specific. As a result, experiences related to conditions other than hypertension may have impact PACIC results. Finally, our sample included patients reporting having a regular primary care provider. Thus, the results may not generalize to patients with poor access to or high dissatisfaction with VA health care. COnclusion Non-Hispanic African Americans with pharmaceutically treated hypertension report that the primary care system more closely approximates the Wagner CCM than non-Hispanic White patients. This finding is consistent with results from a study among patients with diabetes at the same VA medical center almost a decade earlier. Future research should focus on identifying the elements of health care organizations that allow these organizations to successfully offer all patients, including traditionally disadvantaged patients such as minorities and those with low socioeconomic status, the type of integrated primary care that will allow them to address their individual self-management needs. Funding This research was funded by the Health Services Research & Development (HSR&D) Service of the United States Department of Veterans Affairs (VA) [IIR 10–383]. Dr. Bosworth is supported by Senior Research Career Scientist award from the VA HSR&D Service (RCS 08-027). The work was supported by the Center of Innovation for Health Services Research in Primary Care (CIN 13-410) at the Durham Veterans Affairs Medical Center. References 1 Mozaffarian D, Benjamin EJ, Go AS, et al.  : Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation  2016; 133( 4): e38– e360. Google Scholar CrossRef Search ADS PubMed  2 Chobanian AV: Shattuck Lecture. The hypertension paradox – more uncontrolled disease despite improved therapy. N Engl J Med  2009; 361( 9): 878– 87. Google Scholar CrossRef Search ADS PubMed  3 Wagner EH: Chronic disease management: what will it take to improve care for chronic illness? Eff Clin Pract  1998; 1( 1): 2– 4. Google Scholar PubMed  4 Bodenheimer T, Wagner EH, Grumbach K: Improving primary care for patients with chronic illness. JAMA  2002; 288( 14): 1775– 9. Google Scholar CrossRef Search ADS PubMed  5 Fleming B, Silver A, Ocepek-Welikson K, Keller D: The relationship between organizational systems and clinical quality in diabetes care. Am J Manag Care  2004; 10( 12): 934– 44. Google Scholar PubMed  6 Jackson GL, Yano EM, Edelman D, et al.  : Veterans affairs primary care organizational characteristics associated with better diabetes control. Am J Manag Care  2005; 11( 4): 225– 37. Google Scholar PubMed  7 Nutting PA, Dickinson WP, Dickinson LM, et al.  : Use of chronic care model elements is associated with higher-quality care for diabetes. Ann Fam Med  2007; 5( 1): 14– 20. Google Scholar CrossRef Search ADS PubMed  8 Bodenheimer T, Wagner EH, Grumbach K: Improving primary care for patients with chronic illness: the chronic care model, Part 2. JAMA  2002; 288( 15): 1909– 14. Google Scholar CrossRef Search ADS PubMed  9 Coleman K, Austin BT, Brach C, Wagner EH: Evidence on the Chronic Care Model in the new millennium. Health Aff (Millwood)  2009; 28( 1): 75– 85. Google Scholar CrossRef Search ADS PubMed  10 Tsai AC, Morton SC, Mangione CM, Keeler EB: A meta-analysis of interventions to improve care for chronic illnesses. Am J Manag Care  2005; 11( 8): 478– 88. Google Scholar PubMed  11 Adams SG, Smith PK, Allan PF, Anzueto A, Pugh JA, Cornell JE: Systematic review of the chronic care model in chronic obstructive pulmonary disease prevention and management. Arch Intern Med  2007; 167( 6): 551– 61. Google Scholar CrossRef Search ADS PubMed  12 Jacobson D, Gance-Cleveland B: A systematic review of primary healthcare provider education and training using the Chronic Care Model for childhood obesity. Obes Rev  2011; 12( 5): e244– 256. Google Scholar CrossRef Search ADS PubMed  13 Woltmann E, Grogan-Kaylor A, Perron B, Georges H, Kilbourne AM, Bauer MS: Comparative effectiveness of collaborative chronic care models for mental health conditions across primary, specialty, and behavioral health care settings: systematic review and meta-analysis. Am J Psychiatry  2012; 169( 8): 790– 804. Google Scholar CrossRef Search ADS PubMed  14 Stellefson M, Dipnarine K, Stopka C: The chronic care model and diabetes management in US primary care settings: a systematic review. Prev Chronic Dis  2013; 10: E26. Google Scholar CrossRef Search ADS PubMed  15 Green BB, Cook AJ, Ralston JD, et al.  : Effectiveness of home blood pressure monitoring, web communication, and pharmacist care on hypertension control: a randomized controlled trial. JAMA  2008; 299( 24): 2857– 67. Google Scholar CrossRef Search ADS PubMed  16 Margolis KL, Asche SE, Bergdall AR, et al.  : Effect of home blood pressure telemonitoring and pharmacist management on blood pressure control: a cluster randomized clinical trial. JAMA  2013; 310( 1): 46– 56. Google Scholar CrossRef Search ADS PubMed  17 Carter BL, Bosworth HB, Green BB: The hypertension team: the role of the pharmacist, nurse, and teamwork in hypertension therapy. J Clin Hypertens (Greenwich)  2012; 14( 1): 51– 65. Google Scholar CrossRef Search ADS PubMed  18 Vargas RB, Mangione CM, Asch S, et al.  : Can a chronic care model collaborative reduce heart disease risk in patients with diabetes? J Gen Intern Med  2007; 22( 2): 215– 22. Google Scholar CrossRef Search ADS PubMed  19 Jackson GL, Powers BJ, Chatterjee R, et al.  : The patient centered medical home. A systematic review. Ann Intern Med  2013; 158( 3): 169– 78. Google Scholar CrossRef Search ADS PubMed  20 Jackson GL, Weinberger M: A decade with the chronic care model: some progress and opportunity for more. Med Care  2009; 47( 9): 929– 31. Google Scholar CrossRef Search ADS PubMed  21 Kizer KW, Dudley RA: Extreme makeover: transformation of the veterans health care system. Annu Rev Public Health  2009; 30: 313– 39. Google Scholar CrossRef Search ADS PubMed  22 Rosland AM, Nelson K, Sun H, et al.  : The patient-centered medical home in the Veterans Health Administration. Am J Manag Care  2013; 19( 7): e263– 272. Google Scholar PubMed  23 Nelson KM, Helfrich C, Sun H, et al.  : Implementation of the Patient-Centered Medical Home in the Veterans Health Administration: Associations With Patient Satisfaction, Quality of Care, Staff Burnout, and Hospital and Emergency Department Use. JAMA Intern Med  2014; 174( 8): 1350– 8. Google Scholar CrossRef Search ADS PubMed  24 Bidassie B, Davies ML, Stark R, Boushon B: VA Experience in Implementing Patient-Centered Medical Home Using a Breakthrough Series Collaborative. J Gen Intern Med  2014; 29( Suppl 2): 563– 71. Google Scholar CrossRef Search ADS PubMed  25 Hernandez SE, Taylor L, Grembowski D, et al.  : A First Look at PCMH Implementation for Minority Veterans: Room for Improvement. Med Care  2016; 54( 3): 253– 61. Google Scholar CrossRef Search ADS PubMed  26 Uchendu US: Institutional journey in pursuit of health equity: Veterans Health Administration’s Office of Health Equity. Am J Public Health  2014; 104( Suppl 4): S511– 513. Google Scholar CrossRef Search ADS PubMed  27 Clancy CM, Uchendu US, Jones KT: Excellence and equality in health care. Am J Public Health  2014; 104( Suppl 4): S527– 528. Google Scholar CrossRef Search ADS PubMed  28 Atkins D, Kilbourne A, Lipson L: Health equity research in the Veterans Health Administration: we’ve come far but aren’t there yet. Am J Public Health  2014; 104( Suppl 4): S525– 526. Google Scholar CrossRef Search ADS PubMed  29 Fletcher RD, Amdur RL, Kolodner R, et al.  : Blood pressure control among US veterans: a large multiyear analysis of blood pressure data from the Veterans Administration health data repository. Circulation  2012; 125( 20): 2462– 8. Google Scholar CrossRef Search ADS PubMed  30 Hertz RP, Unger AN, Cornell JA, Saunders E: Racial disparities in hypertension prevalence, awareness, and management. Arch Intern Med  2005; 165( 18): 2098– 2104. Google Scholar CrossRef Search ADS PubMed  31 Morenoff JD, House JS, Hansen BB, Williams DR, Kaplan GA, Hunte HE: Understanding social disparities in hypertension prevalence, awareness, treatment, and control: the role of neighborhood context. Soc Sci Med  2007; 65( 9): 1853– 66. Google Scholar CrossRef Search ADS PubMed  32 Mueller M, Purnell TS, Mensah GA, Cooper LA: Reducing racial and ethnic disparities in hypertension prevention and control: what will it take to translate research into practice and policy? Am J Hypertens  2015; 28( 6): 699– 716. Google Scholar CrossRef Search ADS PubMed  33 Saha S, Freeman M, Toure J, Tippens KM, Weeks C, Ibrahim S: Racial and ethnic disparities in the VA health care system: a systematic review. J Gen Intern Med  2008; 23( 5): 654– 71. Google Scholar CrossRef Search ADS PubMed  34 Jackson GL, Weinberger M, Kirshner MA, et al.  : Open-label randomized trial of titrated disease management for patients with hypertension: study design and baseline sample characteristics. Contemp Clin Trials  2016; 50: 5– 15. Google Scholar CrossRef Search ADS PubMed  35 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( 19): 2560– 72. Google Scholar CrossRef Search ADS PubMed  36 Glasgow RE, Wagner EH, Schaefer J, Mahoney LD, Reid RJ, Greene SM: Development and validation of the Patient Assessment of Chronic Illness Care (PACIC). Med Care  2005; 43( 5): 436– 44. Google Scholar CrossRef Search ADS PubMed  37 Glasgow RE, Whitesides H, Nelson CC, King DK: Use of the Patient Assessment of Chronic Illness Care (PACIC) with diabetic patients: relationship to patient characteristics, receipt of care, and self-management. Diabetes Care  2005; 28( 11): 2655– 61. Google Scholar CrossRef Search ADS PubMed  38 Schmittdiel J, Mosen DM, Glasgow RE, Hibbard J, Remmers C, Bellows J: Patient Assessment of Chronic Illness Care (PACIC) and improved patient-centered outcomes for chronic conditions. J Gen Intern Med  2008; 23( 1): 77– 80. Google Scholar CrossRef Search ADS PubMed  39 Randell RL, Long MD, Martin CF, et al.  : Patient perception of chronic illness care in a large inflammatory bowel disease cohort. Inflamm Bowel Dis  2013; 19( 7): 1428– 33. Google Scholar CrossRef Search ADS PubMed  40 Jackson GL, Weinberger M, Hamilton NS, Edelman D: Racial/ethnic and educational-level differences in diabetes care experiences in primary care. Prim Care Diabetes  2008; 2( 1): 39– 44. Google Scholar CrossRef Search ADS PubMed  41 Houle J, Beaulieu MD, Lussier MT, et al.  : Patients’ experience of chronic illness care in a network of teaching settings. Can Fam Physician  2012; 58( 12): 1366– 73. Google Scholar PubMed  42 Wu CX, Tan WS, See RC, et al.  : A matched-group study protocol to evaluate the implementation of an Integrated Care Pathway programme for chronic obstructive pulmonary disease in Singapore. BMJ open  2015; 5( 1): e005655. Google Scholar CrossRef Search ADS PubMed  43 Balbale SN, Etingen B, Malhiot A, Miskevics S, LaVela SL: Perceptions of Chronic Illness Care Among Veterans With Multiple Chronic Conditions. Mil Med  2016; 181( 5): 439– 44. Google Scholar CrossRef Search ADS PubMed  44 Hausmann LR, Gao S, Mor MK, Schaefer JH Jr., Fine MJ: Understanding racial and ethnic differences in patient experiences with outpatient health care in Veterans Affairs Medical Centers. Med Care  2013; 51( 6): 532– 9. Google Scholar CrossRef Search ADS PubMed  45 Bosworth HB, Dudley T, Olsen MK, et al.  : Racial differences in blood pressure control: potential explanatory factors. Am J Med  2006; 119( 1): 70. Google Scholar CrossRef Search ADS PubMed  46 Kressin NR, Wang F, Long J, et al.  : Hypertensive patients’ race, health beliefs, process of care, and medication adherence. J Gen Intern Med  2007; 22( 6): 768– 74. Google Scholar CrossRef Search ADS PubMed  47 Saha S, Freeman M, Toure J, Tippens KM, Weeks C: Racial and Ethnic Disparities in the VA Healthcare System: A Systematic Review . Washington, DC, Department of Veterans Affairs, Veterans Health Administration, Health Services Research & Development Service, 2007 June. 48 Doescher MP, Saver BG, Franks P, Fiscella K: Racial and ethnic disparities in perceptions of physician style and trust. Arch Fam Med  2000; 9( 10): 1156– 63. Google Scholar CrossRef Search ADS PubMed  49 Piette JD, Heisler M, Krein S, Kerr EA: The role of patient–physician trust in moderating medication nonadherence due to cost pressures. Arch Intern Med  2005; 165( 15): 1749– 55. Google Scholar CrossRef Search ADS PubMed  50 Corbie-Smith G, Thomas SB, St George DM: Distrust, race, and research. Arch Intern Med  2002; 162( 21): 2458– 63. Nov 25. Google Scholar CrossRef Search ADS PubMed  51 Van Houtven CH, Voils CI, Oddone EZ, et al.  : Perceived discrimination and reported delay of pharmacy prescriptions and medical tests. J Gen Intern Med  2005; 20( 7): 578– 83. Google Scholar CrossRef Search ADS PubMed  52 Morgan RO, Teal CR, Reddy SG, Ford ME, Ashton CM: Measurement in Veterans Affairs Health Services Research: veterans as a special population. Health Serv Res  2005; 40( 5 Pt 2): 1573– 83. Google Scholar CrossRef Search ADS PubMed  Author notes The views expressed in this article are those of the authors and do not represent the position or policy of the Department of Veterans Affairs, United States Government, or Duke University. Published by Oxford University Press on behalf of Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Journal

Military MedicineOxford University Press

Published: Apr 17, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off