Background The purpose of this study was to identify potential health system solutions to suboptimal use of antihypertensive therapy in a diverse cohort of patients initiating treatment.
Methods Using a hypertension registry at Kaiser Permanente Northern California, we conducted a retrospective cohort study of 44 167 adults (age, ≥18 years) with hypertension who were new users of antihypertensive therapy in 2008. We used multivariate logistic regression analysis to model the relationships between race/ethnicity, specific health system factors, and early nonpersistence (failing to refill the first prescription within 90 days) and nonadherence (<80% of days covered during the 12 months following the start of treatment), respectively, controlling for sociodemographic and clinical risk factors.
Results More than 30% of patients were early nonpersistent and 1 in 5 were nonadherent to therapy. Nonwhites were more likely to exhibit both types of suboptimal medication-taking behavior compared with whites. In logistic regression models adjusted for sociodemographic, clinical, and health system factors, nonwhite race was associated with early nonpersistence (black: odds ratio, 1.56 [95% CI, 1.43-1.70]; Asian: 1.40 [1.29-1.51]; Hispanic: 1.46 [1.35-1.57]) and nonadherence (black: 1.55 [1.37-1.77]; Asian: 1.13 [1.00-1.28]; Hispanic: 1.46 [1.31-1.63]). The likelihood of early nonpersistence varied between Asians and Hispanics by choice of first-line therapy. In addition, racial and ethnic differences in nonadherence were appreciably attenuated when medication co-payment and mail-order pharmacy use were accounted for in the models.
Conclusions Racial/ethnic differences in medication-taking behavior occur early in the course of treatment. However, health system strategies designed to reduce patient co-payments, ease access to medications, and optimize the choice of initial therapy may be effective tools in narrowing persistent gaps in the use of these and other clinically effective therapies.
Heart disease is the leading cause of death in the United States and costs more than $315 billion each year in health care costs and loss in productivity.1,2 Hypertension is a major risk factor for heart disease, and modest reductions in blood pressure have been associated with significant reductions in the risk of adverse cardiovascular events such as stroke, coronary heart disease, and death.3 Despite the widespread availability of clinically efficacious medications for treating hypertension, fewer than one-third of patients with hypertension achieve recommended levels of blood pressure control.4
Some racial and ethnic subgroups may be at higher risk for inadequate blood pressure control and lower rates of antihypertensive adherence, even in settings with somewhat equal access to care.5-9 Suboptimal use of antihypertensives can occur at any stage in the process, from picking up the first prescription to making adherence a part of everyday life.4 However, because long-term adherence depends on persistence with therapy at an early stage of treatment, early nonpersistence may present a critical opportunity for identifying practices and policies with the potential to reduce disparities in cardiovascular outcomes.
The purpose of this study was to identify potential health system solutions to suboptimal adherence and persistence with antihypertensive medications at an early stage of treatment among a diverse cohort of hypertension patients with equal access to health insurance. Identifying potentially modifiable health system–level determinants of appropriate medication use across racial and ethnic groups could inform the development of interventions designed to reduce disparities in hypertension control.
Setting and study population
This retrospective cohort study was conducted at Kaiser Permanente Northern California, an integrated health care delivery system serving more than 3.3 million people. From a hypertension registry including nearly 1.3 million patients from 2000 through 2009 identified through a complex algorithm including diagnosis codes and consecutive blood pressure measurements,10 we identified 56 274 adults (age, ≥18 years) who were new users of antihypertensive therapy (defined as no evidence of antihypertensive drug dispensing during the previous 8-year period) January 1 through December 31, 2008. We excluded 7818 patients (13.9%) who were not continuously enrolled and who did not have an active drug benefit on the date that therapy was started and for at least 250 days following the therapy start date to ensure adequate follow-up. We allowed for gaps in drug coverage of no more than 60 days. We also excluded 4288 patients (7.6%) who were hospitalized at any point during that period. After excluding 1 additional patient of unknown sex, our final cohort included 44 167 patients.
The hypertension registry included clinical databases extracted from integrated electronic medical records at Kaiser Permanente Northern California. Ambulatory pharmacy data included the date of prescription of medications and refill information. In this closed pharmacy system, patients have a strong financial incentive to fill prescriptions at a health plan pharmacy, with more than 95% of the members obtaining prescription medications in-house.11,12
Definition of outcome measures
We identified 3 stages of suboptimal medication-taking behavior among patients receiving new treatment: primary nonadherence, early nonpersistence, and nonadherence. Primary nonadherence was defined as being prescribed a new antihypertensive medication but failing to fill the prescription within 2 months of the date it was ordered. Early nonpersistence was defined as filling the first prescription but failing to refill an antihypertensive medication within 90 days of the date of the first filled prescription for that medication. Patients who switched medications within the first 90 days of initiating therapy were not included in the calculation of primary nonadherence or early nonpersistence.
Nonadherence was defined as not having medication available for 20% or more of days during the 12 months following initiation of therapy. This measure was calculated only for patients continuously enrolled for at least 12 months after initiation of antihypertensive medication with at least 1 refill within the first 90 days (ie, excluding patients with early nonpersistence). Continuous gap measures are refill-based measures of nonadherence demonstrating high reliability and validity, particularly when both prescribing and dispensing data are used.13-15
As a secondary outcome, we examined systolic blood pressure (SBP), a highly reliable measure of blood pressure control across age groups,16-19 measured within 90 days before the 12-month anniversary of the date of therapy start. Good control was defined as 140 mm Hg (<130 mm Hg if diabetes mellitus or chronic kidney disease was present).
Race and ethnicity were key covariates in this analysis, and data were available from administrative sources and self-report. We classified patients as white (non-Hispanic) (37.0%), black (non-Hispanic) (6.9%), Asian (non-Hispanic) (8.8%), Hispanic (10.1%), and other or unknown (37.2%). Nearly all (97.9%) patients whose race/ethnicity was categorized as other or unknown had missing data.
We created 3 measures to represent potentially modifiable system-level determinants of suboptimal medication-taking behavior. First, we identified the level of expected co-payment for generic medications ($0-$5, $6-$10, and >$10 per prescription) for each patient. More than 95% of all prescribed antihypertensive agents were generic formulations. We also assessed voluntary enrollment in a mail-order pharmacy program, which included a financial incentive in the form of reduced co-payments for approximately 30% of the patients. Use of a mail-order pharmacy was considered a predictor of nonadherence because enrollment in the program ruled out the possibility of early nonpersistence (ie, failure to refill the first prescription).
The choice of initial antihypertensive therapy is influenced by patient and provider characteristics and preferences, as well as by health system factors, such as clinical guidelines and preferred drug lists. Therefore, we included the medication used in initiating therapy (diuretics, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, β-blockers, calcium channel blockers, and other agents, including α1-adrenergic antagonists, α2-adrenergic agonists, and peripheral vasodilators) as a third factor that is potentially modifiable through health system intervention. Patients initiating therapy with more than 1 agent (24%) were assigned to the agent listed first in the pharmacy record.
We further adjusted for factors that may be correlated with race/ethnicity, including patient age and sex. We also created categorical variables representing the median household income (<$40 000, $40 000-$74 999, or ≥$75 000 per year) and average educational attainment (<10%, 10%-19%, 20%-29%, or ≥30% with a bachelor's degree) for each patient's US Census 2000 block group of residence.
To control for severity of hypertension, we included the most recent SBP reading (<140, 140-149, 150-159, or ≥160 mm Hg) recorded before initiation of antihypertensive treatment, which was available for 93.4% of the study population. Patient-reported smoking status (yes/no) and clinically assessed body mass index (calculated as weight in kilograms divided by height in meters squared) were also included.
We also included indicators for competing health care needs,20 including several medical (cardiovascular disease, chronic kidney disease, and diabetes mellitus) and mental health (schizophrenia, bipolar disorder, anxiety, and depression) comorbidities. We used the International Classification of Diseases, Ninth Revision, Clinical Modification21 to identify conditions based on 1 inpatient or 2 outpatient diagnoses observed between 2000 and the date of initiation of antihypertensive therapy. Given challenges in identifying depression using medical records,22 we only counted depression diagnoses that occurred during the 12-month period before initiation of antihypertensive medication and used dispensing of select antidepressant classes (tricyclics, serotonin reuptake inhibitors,and norepinephrine reuptake inhibitors) to identify patients with possible depression. We further controlled for variation in the use of health services by including the number of medical office visits during the 12 months before starting antihypertensive therapy.
We used logistic regression with time-dependent covariates to estimate early nonpersistence and nonadherence, respectively, among patients with hypertension who were new users of antihypertensive therapy. We first estimated the association between race/ethnicity and the primary outcomes, controlling only for age and sex. To evaluate whether health system factors acted as potential mediators of the relationship between race/ethnicity and medication use, we sequentially added groups of covariates representing cardiovascular risk factors, socioeconomic status, medical comorbidity, psychiatric comorbidity, number of medical visits, and finally, the 3 key health system factors to the model and observed the effect on the coefficient for race/ethnicity. A relative change in the coefficient of 10% or more was considered strong evidence of a mediating or explanatory factor.
We also tested for interactions between race/ethnicity and health system factors to test for possible effect modification using the log-likelihood statistic. To facilitate the interpretation of statistically significant interactions, we used the estimates generated by the model with interaction terms to calculate predicted probabilities for each of the main outcomes for important racial/ethnic and medication subgroups. For this calculation, we set the remaining covariates to match the distribution of these factors among white patients and then graphically compared the resulting subgroups of interest. We used multiple imputation to address missing values for body mass index (28.2% missing) and baseline SBP (6.6% missing)23 and compared the model results with and without imputed values.
Finally, we visually compared changes in SBP over time, stratifying by race/ethnicity and medication persistence. All statistical analyses were conducted using commercial software (SAS, version 9.1; SAS Institute Inc).24 This study was approved by the institutional review board at Kaiser Foundation Research Institute.
At the time therapy was initiated, SBP was similar across racial/ethnic subgroups (Table 1). Black and Hispanic patients were younger, more likely to be obese, and more likely to have lower household income and educational attainment compared with other subgroups. Black and white patients were more likely to be active smokers. Rates of medical and psychiatric comorbidity were fairly similar by race/ethnicity.
Primary nonadherence (failing to fill a prescription) was generally low, affecting fewer than 5% of all patients, and was similar across racial/ethnic subgroups (data not shown). Unadjusted prevalence of early nonpersistence (Table 2) ranged between 11.3% and 42.5% and was highest for blacks. Unadjusted prevalence of nonadherence (Table 2) ranged between 17.1% and 28.1% and was also highest for black patients.
The modeling results for early nonpersistence are presented in Table 2. Adjusting only for age and sex, blacks (odds ratio [OR], 1.59; 95% CI, 1.46-1.73), Asians (1.36; 1.26-1.47), and Hispanics (1.48; 1.37-1.58) all had higher odds of early nonpersistence compared with whites. Sequential adjustment for cardiovascular risk factors, socioeconomic status, medical and psychiatric comorbidity, frequency of medical visits, and health system factors did not attenuate the associations between race/ethnicity and early nonpersistence (Table 2).
Medication co-payment and the choice of antihypertensive therapy were independently associated with early nonpersistence. A co-payment of $6 to $10 was marginally associated with higher odds of early nonpersistence relative to a co-payment less than $6 (OR, 1.06; 95% CI, 1.01-1.11). Patients initiating treatment with angiotensin II receptor blockers had a dramatically lower likelihood of early nonpersistence compared with patients initiating therapy with diuretics (OR, 0.48; 95% CI, 0.40-0.57). Tests of interactions between race/ethnicity and these health system factors revealed modification of the race/ethnicity effect by type of medication (Figure). Specifically, Asians using angiotensin-converting enzyme inhibitors had a higher likelihood of early nonpersistence (estimated proportion: 38.7%; for interaction, P = .004), as did Hispanics who initiated therapy with β-blockers (35.0%; P = .06). In contrast, Asians who initiated therapy with β-blockers (24.8%; P = .002) had lower odds of early nonpersistence with antihypertensive therapy.
Several sociodemographic and clinical factors were associated with early nonpersistence, including younger age, male sex, smoking, having a body mass index less than 25, baseline SBP between 140 and 149 mm Hg, lower annual income (<$40 000), lower educational attainment (<10% bachelor's degree), diabetes mellitus, and 3 or more medical visits during the 12 months before initiation of therapy. The results were robust to the inclusion of imputed values for body mass index and baseline SBP.
Table 2 reports the results of the models predicting antihypertensive nonadherence. Adjusting for age and sex, race/ethnicity was associated with nonadherence, with black (OR, 1.74; 95% CI, 1.53-1.97), Asian (1.20; 1.07-1.35), and Hispanic (1.67; 1.51-1.86) patients having higher odds of nonadherence compared with whites.
Medication co-payment, type of medication initiated, and enrollment in a mail-order pharmacy were all associated with nonadherence. Co-payments of $6 to $10 (OR, 1.19; 95% CI, 1.11-1.28) and $11 or more (1.35; 1.20-1.52) were associated with higher odds of nonadherence during the first year of therapy. Patients initiating therapy with angiotensin II receptor blockers (OR, 1.27; 95% CI, 1.04-1.55) and other less commonly prescribed medications (1.43; 1.18-1.73) had higher odds of nonadherence compared with patients using diuretics. Enrollment in mail-order pharmacy was associated with reduced odds of nonadherence (OR, 0.57; 95% CI, 0.53-0.61).
The association between race/ethnicity and nonadherence was attenuated by the inclusion of information about co-payment status and mail-order pharmacy use. After inclusion of these covariates, the association between race/ethnicity and nonadherence was as follows: blacks, OR 1.55 (95% CI, 1.37-1.77); Asians, 1.13 (1.00-1.28); Hispanics, 1.46 (1.31-1.63); and other/unknown, 1.01 (0.93-1.09). We found no evidence of an interaction between health system factors and race/ethnicity.
Results were robust to the inclusion of imputed body mass index and baseline SBP. After adjustment for all covariates, the estimated proportion of patients who were nonadherent by race/ethnicity was as follows: whites,16.7%; blacks, 28.0%; Asians, 20.3%; Hispanics, 26.9%; and other/unknown, 18.9%.
Twelve months after therapy was started, blood pressure control improved for all patients. Blood pressure in fewer than 20% of patients was poorly controlled compared with in more than 60% of patients at baseline. Blacks who exhibited nonpersistence with antihypertensive agents had the highest proportion (28.2%) with uncontrolled blood pressure at the end of follow-up. In contrast, Asians who were adherent to therapy had the lowest proportion with uncontrolled blood pressure after 12 months (14.1%) (data not shown).
In this setting, we found higher rates of blood pressure control and lower rates of primary nonadherence compared with previous studies.25,26 Good blood pressure control in this setting has been reported27 and may be related to early detection, as well as to payment mechanisms that financially reward physician groups based in part on the proportion of patients with good blood pressure control.
Our finding of racial/ethnic differences in both early nonpersistence and nonadherence is consistent with prior evidence.8,9,25,26 However, the interaction between race/ethnicity and medication type as predictors of early nonpersistence is novel and may reflect unmeasured clinical factors, contraindications, or treatment preferences that vary across racial/ethnic groups.28-33 Whether variation in early nonpersistence can be reduced through the use of decision tools and other strategies designed to better match patients with therapy deserves further exploration.
Our finding regarding the importance of mail-order pharmacy use as a potential policy-level mechanism for improving adherence is consistent with previous studies34-38 in this and other settings. This study provides additional evidence that exposure to a mail-order pharmacy across racial and ethnic subgroups, rather than differential response to this practice, contributes to disparities in adherence.35 However, all patients using a mail-order pharmacy in this setting also had access to telephone-based counseling from pharmacists, and approximately one-third also received a financial incentive (ie, lower co-payments) for participating in the program.35 Moreover, integrated electronic medical records may facilitate patient monitoring and continuity of care for patients receiving medications by mail. Research is needed to explore the specific mechanisms by which use of a mail-order pharmacy may influence adherence across diverse care settings.
This study has limitations that deserve consideration. First, we could not control for unmeasured physiological, behavioral, and psychosocial factors that may explain some of the observed relationships between race/ethnicity and behavior. Also, we may have misclassified some patients because of inaccurate or missing race/ethnicity data and do not have sufficient information to hypothesize about differences among patients with missing demographic data. In addition, although the use of pharmacy records to estimate adherence is well supported,13-15 we did not directly observe patient behavior. Therefore, to the extent that there is greater variation in actual vs estimated adherence, our findings may be biased. It is possible that we misclassified some patients as having hypertension; however, we believe that this possibility was reduced through the application of a complex algorithm to identify patients with hypertension to reduce any potential bias or noise relating to misdiagnosis.10
Evidence from recent disparities-focused interventions in cardiovascular disease indicates that multifaceted interventions to address barriers to self-management may be particularly effective but provide little guidance as to which barriers are most amenable to change through health system intervention.39-44 Our findings suggest that health system factors have the potential to reduce racial and ethnic differences in both early persistence with and ongoing adherence to antihypertensive therapy. Medication cost and ease of access may be universally important modifiable determinants of ongoing adherence, suggesting that medication cost assistance may be a critical component of interventions to reduce cardiovascular-related disparities. These findings suggest that the strategies implemented to reduce disparities in medication use should be tailored to the stage of treatment, recognizing that the relative importance of health system factors is likely to change as patients move from initiation of therapy to making adherence a part of their everyday lives.
We found evidence that health system factors may play important roles as mediators and modifiers of racial/ethnic differences in medication-taking behavior. Unlike socioeconomic and psychosocial determinants that can be difficult to change, medication choice, co-payment, and access are potentially modifiable through system-level intervention and have the potential to reduce nonadherence, a well-known and especially challenging aspect of hypertension management in high-risk populations.
Correspondence: Alyce S. Adams, PhD, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612 (Alyce.S.Adams@kp.org).
Accepted for Publication: August 19, 2012.
Published Online: December 10, 2012. doi:10.1001/2013.jamainternmed.955
Author Contributions: Dr Schmittdiel had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Adams, Beck, and Schmittdiel. Acquisition of data: Uratsu, Dyer, Magid, and Schmittdiel. Analysis and interpretation of data: Adams, Uratsu, Dyer, O’Connor, Beck, Butler, Ho, and Schmittdiel. Drafting of the manuscript: Adams and Beck. Critical revision of the manuscript for important intellectual content: Adams, Uratsu, Dyer, Magid, O’Connor, Beck, Butler, Ho, and Schmittdiel. Statistical analysis: Uratsu, Dyer, and Schmittdiel. Obtained funding: Magid, Beck, and Schmittdiel. Administrative, technical, and material support: Uratsu, Beck, and Schmittdiel. Study supervision: Adams and Schmittdiel.
Conflict of Interest Disclosures: None reported.
Funding/Support: This study was funded by 3U19HL091179-04S1 (National Heart, Lung, and Blood Institute) and the National Institute for Mental Health as a supplement to the Health Maintenance Organization Research Network Cardiovascular Disease Network. Additional support for Drs Adams, O’Connor, and Schmittdiel was provided by P30DK092924 (Health Delivery Systems Center for Diabetes Translational Research), funded by the National Institute for Diabetes and Digestive and Kidney Diseases.
Role of the Sponsor: The funders had no role in the design or conduct of the study; in data collection, management, analysis, or interpretation; or in the preparation, review, or approval of this manuscript.
Additional Contributions: Alan Go, MD, commented on an earlier version of this manuscript, and Karen R. Hansen, BA, assisted with the preparation of this manuscript for submission.
1.Lloyd-Jones D, Adams RJ, Brown TM,
et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2010 update: a report from the American Heart Association [published corrections appear in
Circulation. 2011;124(16):e425 and
Circulation. 2010;121(12):e260].
Circulation. 2010;121(7):e46-e21520019324
PubMedGoogle ScholarCrossref 2.Qureshi AI, Suri MF, Kirmani JF, Divani AA. Prevalence and trends of prehypertension and hypertension in United States: National Health and Nutrition Examination Surveys 1976 to 2000.
Med Sci Monit. 2005;11(9):CR403-CR40916127357
PubMedGoogle Scholar 3.Hajjar I, Kotchen TA. Trends in prevalence, awareness, treatment, and control of hypertension in the United States, 1988-2000.
JAMA. 2003;290(2):199-20612851274
PubMedGoogle ScholarCrossref 4.Krousel-Wood M, Thomas S, Muntner P, Morisky D. Medication adherence: a key factor in achieving blood pressure control and good clinical outcomes in hypertensive patients.
Curr Opin Cardiol. 2004;19(4):357-36215218396
PubMedGoogle ScholarCrossref 5.Cooper R, Cutler J, Desvigne-Nickens P,
et al. Trends and disparities in coronary heart disease, stroke, and other cardiovascular diseases in the United States: findings of the National Conference on Cardiovascular Disease Prevention.
Circulation. 2000;102(25):3137-314711120707
PubMedGoogle ScholarCrossref 6.Fiscella K, Holt K. Racial disparity in hypertension control: tallying the death toll.
Ann Fam Med. 2008;6(6):497-50219001301
PubMedGoogle ScholarCrossref 7.Adams AS, Soumerai SB, Ross-Degnan D. Use of antihypertensive drugs by Medicare enrollees: does type of drug coverage matter?
Health Aff (Millwood). 2001;20(1):276-28611194852
PubMedGoogle ScholarCrossref 8.Bagchi AD, Esposito D, Kim M, Verdier J, Bencio D. Utilization of, and adherence to, drug therapy among Medicaid beneficiaries with congestive heart failure.
Clin Ther. 2007;29(8):1771-178317919558
PubMedGoogle ScholarCrossref 9.Batson B, Belletti D, Wogen J. Effect of African American race on hypertension management: a real-world observational study among 28 US physician practices.
Ethn Dis. 2010;20(4):409-41521305830
PubMedGoogle Scholar 10.Go AS, Magid DJ, Wells B,
et al. The Cardiovascular Research Network: a new paradigm for cardiovascular quality and outcomes research.
Circ Cardiovasc Qual Outcomes. 2008;1(2):138-14720031802
PubMedGoogle ScholarCrossref 11.Selby JV, Lee J, Swain BE,
et al. Trends in time to confirmation and recognition of new-onset hypertension, 2002-2006.
Hypertension. 2010;56(4):605-61120733092
PubMedGoogle ScholarCrossref 12.Saunders KW, Davis RL, Stergachis A. Group health cooperative. In: Strom BL, ed. Pharmacoepidemiology. 4th ed. New York, NY: John Wiley & Sons Inc; 2005:223-239
13.Cramer JA, Roy A, Burrell A,
et al. Medication compliance and persistence: terminology and definitions.
Value Health. 2008;11(1):44-4718237359
PubMedGoogle ScholarCrossref 14.Hansen RA, Kim MM, Song L, Tu W, Wu J, Murray MD. Comparison of methods to assess medication adherence and classify nonadherence.
Ann Pharmacother. 2009;43(3):413-42219261962
PubMedGoogle ScholarCrossref 15.Karter AJ, Parker MM, Moffet HH, Ahmed AT, Schmittdiel JA, Selby JV. New prescription medication gaps: a comprehensive measure of adherence to new prescriptions.
Health Serv Res. 2009;44(5, pt 1):1640-166119500161
PubMedGoogle ScholarCrossref 16.Franklin SS, Gustin W IV, Wong ND,
et al. Hemodynamic patterns of age-related changes in blood pressure: the Framingham Heart Study.
Circulation. 1997;96(1):308-3159236450
PubMedGoogle ScholarCrossref 17.Wilking SV, Belanger A, Kannel WB, D’Agostino RB, Steel K. Determinants of isolated systolic hypertension.
JAMA. 1988;260(23):3451-34553210285
PubMedGoogle ScholarCrossref 18.Sagie A, Larson MG, Levy D. The natural history of borderline isolated systolic hypertension.
N Engl J Med. 1993;329(26):1912-19178247055
PubMedGoogle ScholarCrossref 19.Tate RB, Manfreda J, Krahn AD, Cuddy TE. Tracking of blood pressure over a 40-year period in the University of Manitoba Follow-up Study, 1948-1988.
Am J Epidemiol. 1995;142(9):946-9547572975
PubMedGoogle Scholar 20.Redelmeier DA, Tan SH, Booth GL. The treatment of unrelated disorders in patients with chronic medical diseases.
N Engl J Med. 1998;338(21):1516-15209593791
PubMedGoogle ScholarCrossref 21.US Department of Health and Human Services; Centers for Disease Control and Prevention; Centers for Medicare and Medicaid Services.
International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). 2005-10.http://hdl.handle.net/1902.29/CD-0177. Accessed June 12, 2008 22.Trinh NH, Youn SJ, Sousa J,
et al. Using electronic medical records to determine the diagnosis of clinical depression.
Int J Med Inform. 2011;80(7):533-54021514880
PubMedGoogle ScholarCrossref 23.Sterne JA, White IR, Carlin JB,
et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls.
BMJ. 2009;338:b239319564179
PubMedGoogle ScholarCrossref 25.Shah NR, Hirsch AG, Zacker C,
et al. Predictors of first-fill adherence for patients with hypertension.
Am J Hypertens. 2009;22(4):392-39619180061
PubMedGoogle ScholarCrossref 26.Perreault S, Lamarre D, Blais L,
et al. Persistence with treatment in newly treated middle-aged patients with essential hypertension.
Ann Pharmacother. 2005;39(9):1401-140816076920
PubMedGoogle ScholarCrossref 27.Lester H, Schmittdiel J, Selby J,
et al. The impact of removing financial incentives from clinical quality indicators: longitudinal analysis of four Kaiser Permanente indicators.
BMJ. 2010;340:c189820460330
PubMedGoogle ScholarCrossref 28.Horne R, Weinman J. Patients' beliefs about prescribed medicines and their role in adherence to treatment in chronic physical illness.
J Psychosom Res. 1999;47(6):555-56710661603
PubMedGoogle ScholarCrossref 29.Huang ES, Brown SE, Thakur N,
et al. Racial/ethnic differences in concerns about current and future medications among patients with type 2 diabetes.
Diabetes Care. 2009;32(2):311-31619017766
PubMedGoogle ScholarCrossref 30.Schüz B, Wurm S, Ziegelmann JP, Warner LM, Tesch-Römer C, Schwarzer R. Changes in functional health, changes in medication beliefs, and medication adherence.
Health Psychol. 2011;30(1):31-3921299292
PubMedGoogle ScholarCrossref 31.Lehane E, McCarthy G. Intentional and unintentional medication non-adherence: a comprehensive framework for clinical research and practice? a discussion paper.
Int J Nurs Stud. 2007;44(8):1468-147716973166
PubMedGoogle ScholarCrossref 32.Clifford S, Barber N, Horne R. Understanding different beliefs held by adherers, unintentional nonadherers, and intentional nonadherers: application of the Necessity-Concerns Framework.
J Psychosom Res. 2008;64(1):41-4618157998
PubMedGoogle ScholarCrossref 33.Schnittker J. Misgivings of medicine? African Americans' skepticism of psychiatric medication.
J Health Soc Behav. 2003;44(4):506-52415038146
PubMedGoogle ScholarCrossref 34.Duru OK, Schmittdiel JA, Dyer WT,
et al. Mail-order pharmacy use and adherence to diabetes-related medications.
Am J Manag Care. 2010;16(1):33-4020148603
PubMedGoogle Scholar 35.Schmittdiel JA, Karter AJ, Dyer W,
et al. The comparative effectiveness of mail order pharmacy use vs. local pharmacy use on LDL-C control in new statin users.
J Gen Intern Med. 2011;26(12):1396-140221773848
PubMedGoogle ScholarCrossref 36.Devine S, Vlahiotis A, Sundar H. A comparison of diabetes medication adherence and healthcare costs in patients using mail order pharmacy and retail pharmacy.
J Med Econ. 2010;13(2):203-21120345227
PubMedGoogle ScholarCrossref 37.Pittman DG, Tao Z, Chen W, Stettin GD. Antihypertensive medication adherence and subsequent healthcare utilization and costs.
Am J Manag Care. 2010;16(8):568-57620712390
PubMedGoogle Scholar 38.Zhang L, Zakharyan A, Stockl KM, Harada AS, Curtis BS, Solow BK. Mail-order pharmacy use and medication adherence among Medicare Part D beneficiaries with diabetes.
J Med Econ. 2011;14(5):562-56721728913
PubMedGoogle ScholarCrossref 39.Svarstad BL, Kotchen JM, Shireman TI,
et al. The Team Education and Adherence Monitoring (TEAM) trial: pharmacy interventions to improve hypertension control in blacks.
Circ Cardiovasc Qual Outcomes. 2009;2(3):264-27120031847
PubMedGoogle ScholarCrossref 40.Ogedegbe G, Schoenthaler A, Richardson T,
et al. An RCT of the effect of motivational interviewing on medication adherence in hypertensive African Americans: rationale and design.
Contemp Clin Trials. 2007;28(2):169-18116765100
PubMedGoogle ScholarCrossref 41.Cooper LA, Roter DL, Bone LR,
et al. A randomized controlled trial of interventions to enhance patient-physician partnership, patient adherence and high blood pressure control among ethnic minorities and poor persons: study protocol NCT00123045.
Implement Sci. 2009;4:719228414
PubMedGoogle ScholarCrossref 42.Cutrona SL, Choudhry NK, Fischer MA,
et al. Modes of delivery for interventions to improve cardiovascular medication adherence.
Am J Manag Care. 2010;16(12):929-94221348564
PubMedGoogle Scholar 43.Crook ED, Bryan NB, Hanks R,
et al. A review of interventions to reduce health disparities in cardiovascular disease in African Americans.
Ethn Dis. 2009;19(2):204-20819537234
PubMedGoogle Scholar 44.Chin MH, Walters AE, Cook SC, Huang ES. Interventions to reduce racial and ethnic disparities in health care.
Med Care Res Rev. 2007;64(5):(suppl)
7S-28S17881624
PubMedGoogle ScholarCrossref