0
Original Investigation |

Communication and Medication Refill Adherence:  The Diabetes Study of Northern California FREE

Neda Ratanawongsa, MD, MPH; Andrew J. Karter, PhD; Melissa M. Parker, MS; Courtney R. Lyles, PhD; Michele Heisler, MD, MPA; Howard H. Moffet, MPH; Nancy Adler, PhD; E. Margaret Warton, MPH; Dean Schillinger, MD
[+] Author Affiliations

Author Affiliations: General Internal Medicine and UCSF Center for Vulnerable Populations at San Francisco General Hospital and Trauma Center, University of California, San Francisco (UCSF) (Drs Ratanawongsa, Lyles, and Schillinger); Division of Research, Kaiser Permanente, Oakland, California (Dr Karter, Mss Parker and Warton, and Mr Moffet); Department of Epidemiology, School of Public Health & Community Health, University of Washington, Seattle (Dr Karter); Center for Clinical Management Research, Health Services Research and Development (HRS&D) Center of Excellence, VA Ann Arbor Healthcare System, and Departments of Internal Medicine and Health Behavior and Health Education, University of Michigan, Ann Arbor (Dr Heisler); UCSF Center for Health and Community, San Francisco (Dr Adler); and California Diabetes Program, California Department of Public Health, Sacramento (Dr Schillinger).


JAMA Intern Med. 2013;173(3):210-218. doi:10.1001/jamainternmed.2013.1216.
Text Size: A A A
Published online

Background Poor medication refill adherence contributes to poor cardiometabolic control and diabetes outcomes. Studies linking communication between patients and health care providers to adherence often use self-reported adherence and have not explored differences across communication domains or therapeutic indications.

Methods To investigate associations between patient communication ratings and cardiometabolic medication refill adherence, we conducted a cross-sectional analysis of 9377 patients in the Diabetes Study of Northern California (DISTANCE), a race-stratified, random sample of Kaiser Permanente survey respondents. Eligible participants received 1 or more oral hypoglycemic, lipid-lowering, or antihypertensive medication in the 12 months preceding the survey. Communication was measured with a 4-item Consumer Assessment of Healthcare Providers and Systems Survey (CAHPS) score and 4 items from the Trust in Physicians and Interpersonal Processes of Care instruments. Poor adherence was classified as greater than a 20% continuous medication gap for ongoing medication therapies. Using modified least squares regression, we calculated differences in poor adherence prevalence for a 10-point decrease in CAHPS score and compared higher vs lower communication ratings on other items, adjusting for necessary sociodemographic and medical confounders derived from a directed acyclic graph.

Results In this cohort, 30% had poor cardiometabolic medication refill adherence. For each 10-point decrease in CAHPS score, the adjusted prevalence of poor adherence increased by 0.9% (P = .01). Compared with patients offering higher ratings, patients who gave health care providers lower ratings for involving patients in decisions, understanding patients' problems with treatment, and eliciting confidence and trust were more likely to have poor adherence, with absolute differences of 4% (P = .04), 5% (P = .02), and 6% (P = .03), respectively. Associations between communication and adherence were somewhat larger for hypoglycemic medications than for other medications.

Conclusions Poor communication ratings were independently associated with objectively measured inadequate cardiometabolic medication refill adherence, particularly for oral hypoglycemic medications. Future studies should investigate whether improving communication skills among clinicians with poorer patient communication ratings could improve their patients' cardiometabolic medication refill adherence and outcomes.

Figures in this Article

Persons with diabetes mellitus are at high risk for cardiovascular morbidity and mortality. Hypoglycemic, antihypertensive, and lipid-lowering medications are important tools for reducing cardiovascular risk in people with diabetes.1 Poor medication refill adherence contributes significantly to suboptimal cardiometabolic control and poor clinical outcomes.25

One proposed strategy for enhancing medication refill adherence is improving communication between patients and health care providers.6 Systematic reviews suggest that patient and provider communication behaviors affect the quality of information exchange and of primary care relationships.79 In the short term, patient-centered communication can enhance patient trust and may enable clinicians to incorporate patient preferences, needs, and values into treatment decisions.7,10 Both patient trust and shared decision making may then increase patient treatment adherence, ultimately improving patient outcomes.7 Thus, the Institute of Medicine designated patient-centeredness as a core measure for health care quality,10 and validated metrics of health care provider communication are increasingly available for individual clinicians and health systems.1113

Prior research has suggested that collaborative communication is associated with better adherence.1416 However, research using self-reported medication refill adherence measures may overestimate adherence across sociodemographic characteristics (eg, cultural differences in social desirability).1720 Also, research using self-reported measures for both communication and adherence may be affected by endogeneity bias; eg, depression could be associated with poor patient perceptions of both communication and their own adherence.2124 In addition, although shared decision making and trust may each affect adherence,7,14,25 validated instruments to measure these aspects of communication could yield insights about their relative importance. Finally, because patients' beliefs about medication benefits and adverse effects can differ across therapeutic indications, the importance of communication to patients' adherence could differ for specific types of medications.26,27

This study investigated whether patient assessments of health care provider communication were associated with objective measures of poor adherence for cardiometabolic medications using pharmacy utilization data among a diverse sample of fully insured persons with diabetes. We hypothesized that poorer patient ratings of overall communication, shared decision making, and trust would be associated with poor adherence to cardiometabolic medications.

We analyzed data from the Diabetes Study of Northern California (DISTANCE) Survey, conducted May 2005 to December 2006 among a racially and ethnically stratified sample of 20 188 Kaiser Permanente Northern California patients with diabetes, aged 30 to 75 years (response rate, 62%).28 Respondents completed the written or web survey in English or via telephone interviews offered in English, Spanish, Chinese, or Tagalog languages.28

For this analysis (Figure 1), eligible participants answered questions about patient-provider communication (not included in the Short Version of the DISTANCE survey), reported having a primary care provider, and were dispensed 1 or more oral hypoglycemic, antihypertensive, or lipid-lowering medication in the 12 months preceding the survey. We excluded subjects who changed their primary care provider, lacked continuous pharmacy benefits, or had insufficient dispensing (<2 fills) of medications to calculate adherence.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Cohort identification of patients with diabetes who completed patient-provider communication ratings survey and were prescribed at least 1 cardiometabolic medication.

This study was approved by institutional review boards of Kaiser Permanente Northern California and the University of California, San Francisco.

MEASURES

The primary exposures were key domains for patient-reported quality of patient-provider communication (Table 1):

Table Graphic Jump LocationTable 1. Patient Ratings of the Quality of Communication With Clinicians in a Cohort of 9377 Patients With Diabetes Prescribed at Least 1 Cardiometabolic Medication

  • Overall communication quality: Four items on the health care provider communication subscale of the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey.11 We modified “explain things in a way that was easy to understand” to “explain things (directly or through an interpreter) in a way you could understand” to capture the experiences of non-English–speaking patients. The Cronbach α for internal consistency of this modified scale was .80.

  • Shared decision making: Two items from the Interpersonal Processes of Care Instrument (IPC).29 We modified “did doctors ask if you would have any problems following what they recommended” to “did your personal physician seem to understand the kinds of problems you have in carrying out recommended treatments.”

  • Trust: Two items from the Trust in Physicians Scale (TIPS).30,31

Response options for both the IPC and TIPS items were modified to match the 4-point CAHPS scale options of ‘‘never,’’ ‘‘sometimes,’’ ‘‘usually,’’ and ‘‘always” during the preceding 12 months. Respondents could indicate that they had no visits or no problems for the IPC items.

We calculated a summary CAHPS score (range, 0-100, with 100 reflecting more positive experiences) by linearly transforming and then averaging CAHPS responses.3234 Because of space limitations, the survey included 4 single-item questions from the full IPC and TIPS instruments; thus, we examined these 4 items separately, dichotomized at “always”/“usually” vs “sometimes”/“never,” a common cutoff for patient communication ratings.11

The primary outcome was poor refill adherence measured by the continuous medication gap (CMG), a well-established measure of secondary adherence (adherence among ongoing users) using pharmacy utilization data.35,36 The CMG sums the proportion of days without sufficient medication supply across refill intervals between the first pharmacy dispensing during the measurement period and the last dispensing before censoring or the end of the measurement period, whichever occurs first. For patients taking more than 1 drug in the same therapeutic class, the proportion of time without medications is calculated individually for each therapeutic class, and then a summary measure is created for each drug class.35,36 We use a modified approach that accounts for stockpiling medications using a time-forward algorithm.37 Because flexible insulin dosing prohibits calculation of a fixed days' supply, we excluded insulin prescription refills from CMG calculations.

For each subject, we calculated CMG for all indications combined and separately (CMG for antihypertensive medications only, lipid-lowering medications only, and diabetes medications only).35,36 We classified respondents as poorly adherent when they had no medication supply for more than 20% of the observation time and adherent when medications were available for 80% or more of the time.5,35,36

We assessed sociodemographic and medical characteristics using survey and medical record data,28 including age, sex, self-reported race/ethnicity, educational attainment, English language proficiency,3840 functional health literacy,41,42 income, depression,43 external locus of control,44 and conscientiousness.4547 We also calculated the Deyo version of the Charlson comorbidity index using a 2-year prebaseline capture for the diagnostic and procedure codes48,49 and copayment requirements, defining higher copayments for generic drugs (>$10), brand drugs (>$30), and outpatient visits (>$20).

STATISTICAL ANALYSIS

Our modeling was guided by a directed acyclic graph, which depicts causal relationships between measured variables in the analysis (Figure 2). Directed acyclic graphs help avoid errors caused by confounding, blocking (adjustment for a variable on a causal pathway between exposure and outcome), and colliding (adjusting for variables affected by both exposure and outcome, leading to spurious associations).50,51 We reviewed existing literature and theory about causal relationships and temporal ordering among patient, health care provider, relationship, and system variables that could affect the relationship between communication and medication refill adherence.7,8,5260 We used established rules for determining the necessary covariates to estimate the direct effect of communication on medication refill adherence (Figure 2). A sensitivity analysis including number of medications for chronic conditions did not affect the point estimates for our analyses, suggesting that this variable's exclusion based on the directed acyclic graph was correct.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. Directed acyclic graph. Using established rules, adjusting only for shaded covariates was necessary to estimate the direct effect of communication on adherence. Dashed arrows indicate causal relationships blocked by adjustment. No unblocked pathways (solid arrows) remain between communication and adherence.

We weighted all multivariable analyses by the inverse of the nonproportional sampling fractions for each race/ethnic group to account for the stratified sampling design. We also addressed survey nonresponse bias using the Horvitz-Thompson approach, modeling the probability of response to the DISTANCE (The Diabetes Study of Northern California) survey and creating individual weights (reciprocal of the probability of the observed response) for all multivariable models.61 Using modified least squares regression,62 we calculated the mean absolute prevalence of poor refill adherence for respondents with CAHPS scores of 100 and the unadjusted and adjusted change in prevalence with CAHPS as a continuous predictor. For the other communication items, we calculated the mean absolute prevalence of poor refill adherence for respondents with poorer vs better communication ratings and calculated unadjusted and adjusted prevalence differences. We also calculated the unadjusted and adjusted relative risk (RR) of poor refill adherence for those with higher vs lower communication ratings using modified Poisson regression.63

PARTICIPANTS

Among 9377 eligible respondents, 7303 were prescribed hypoglycemic medications, 7052 were prescribed lipid-lowering medications, and 7967 were prescribed antihypertensive medications (Figure 1). The mean (SD) age was 59.5 (9.8) years, and 52% were women. One-quarter (27%) were white, 19% African American, 16% Latino, 12% Asian, 11% Filipino, and 11% multiracial (Table 2). Thirty-five percent earned less than $50 000 per year, 42% had high school or less educational attainment, and 38% had limited health literacy. Forty-four percent had Charlson comorbidity index scores of 2 or higher, and 45% had a hemoglobin A1c level higher than 7.0%. Patients were dispensed a mean (SD) of 5.2 (2.5) cardiometabolic medications (excluding insulin) and had seen their primary care providers for a mean (SD) of 6.2 (4.4) years.

Table Graphic Jump LocationTable 2. Characteristics of a Cohort of 9377 Patients With Diabetes Prescribed at Least 1 Cardiometabolic Medication
RATINGS OF THE QUALITY OF COMMUNICATION WITH CLINICIANS

The CAHPS scores were skewed, with 77% of respondents having the maximum score of 100. Low ratings were given by patients for health care providers involving patients in making decisions (20%), eliciting confidence and trust (8%), understanding patients' problems carrying out recommended treatments (11%), putting patients' needs first (12%), and showing respect (7%) (Table 1).

DIFFERENCES IN MEDICATION REFILL ADHERENCE

Overall, 30% of respondents had poor adherence to their cardiometabolic medication regimens (CMG >20% for regimens of ≥1 cardiometabolic medication). Poor adherence was observed in 20%, 21% and 25% of patients for antihypertensive, lipid-lowering, and oral hypoglycemic medications respectively.

The mean absolute prevalence of poor refill adherence for all cardiometabolic medications combined was 27% (95% CI, 25%-29%) for patients with CAHPS scores of 100. For each 10-point decrease in CAHPS score, the unadjusted prevalence of poor refill adherence increased by 1.6% (95% CI, 0.9%-2.3%) (P < .001). Poor refill adherence for all cardiometabolic medications combined was associated with lower patient ratings on each IPC and TIPS item (unadjusted absolute differences ranging from 8% to 11%; all P < .001) (Table 3). Compared with patients reporting higher ratings, the unadjusted RR of poor cardiometabolic refill adherence for patients with lower communication ratings ranged from 1.16 to 1.36 (all P < .001).

Table Graphic Jump LocationTable 3. Differences in Prevalence of Poor Refill Adherence for Any Cardiometabolic Medication by Ratings of Communication With Clinicians in a Cohort of 9377 Patients With Diabetes

After adjusting for potential confounders, the prevalence of poor refill adherence increased by 0.9% (95% CI, 0.2%-1.7%) (P = .01) for each 10-point decrease in CAHPS score. Compared with patients offering higher ratings, patients who gave lower ratings for health care providers' involving patients in decisions, understanding patients' problems with treatment, and eliciting confidence and trust were more likely to have poor adherence, with absolute differences of 4% (95% CI, 0%-7%) (P = .04), 5% (95% CI, 1%-10%) (P = .02), and 6% (95% CI, 1%-11%) (P = .03), respectively (Table 3). Those with lower communication ratings had higher adjusted RR of poor cardiometabolic refill adherence (adjusted RRs, 1.07-1.16; P < .05, except P = .09 for involving patients in decisions).

When examined separately by therapeutic indication, all communication items were associated with poor adherence in unadjusted analyses (data not shown). In adjusted analyses for oral hypoglycemic medications, CAHPS score and involvement in decision making were not associated with poor adherence. Low ratings for understanding problems with treatment, putting patient's needs first, and trust were associated with poor adherence for oral hypoglycemic medications, with adjusted differences of 6% (95% CI, 1%-11%) (P = .02), 5% (95% CI, 1%-11%) (P = .03), and 7% (95% CI, 1%-13%) (P = .01), respectively (data not shown). For lipid-lowering medications, only CAHPS score was associated with poor refill adherence (0.8% [95% CI, 0%-1.6%] increase in prevalence of poor adherence per 10-point decrease in CAHPS score; P = .04). None of the communication items were associated with poor refill adherence for blood pressure medications.

In this study of a racially and ethnically diverse primary care population with diabetes, patient perceptions of poorer communication with their health care providers were associated with higher prevalence of poor secondary adherence to cardiometabolic medications. These findings are consistent with prior studies about aspects of patient-provider communication and medication refill adherence in diabetes and other chronic medical conditions.1416,6466 In a cross-sectional diabetes study, older patients' evaluations of how well their physicians provided information on their illness and treatment were associated with patient self-reported medication-taking behaviors.14 A study in the Kaiser Permanente population found that a greater proportion of patients who did not initiate insulin therapy believed that their health care providers inadequately explained the risks and benefits of insulin, compared with those who initiated insulin therapy.67 Another Kaiser Permanente study found that language concordance for Spanish-speaking patients and race concordance for African Americans were associated with higher rates of cardiometabolic medication refill adherence, although it did not assess patient ratings of communication directly.64

This study adds to the literature in a number of ways. First, unlike most prior studies, we used a validated, objective measure of secondary medication refill adherence—pharmacy utilization for medication refills—to demonstrate an association with patient ratings of health care provider communication.35,36 Self-reported medication refill adherence has varying concordance with objective measures of adherence and may be subject to social desirability bias.19,68 A systematic review found that self-reported adherence was highly concordant with claims data in only 5 of 11 applicable studies.19 Also, sociodemographic characteristics such as sex and education have been associated with differences in the degree of patients' overreporting of adherence.20

Second, our findings suggest modest differences in the associations between patient ratings of communication and medication refill adherence across therapeutic indications. Oral hypoglycemic medications had both higher rates of poor refill adherence and somewhat stronger associations with patient-provider communication in adjusted analyses, compared with lipid-lowering and antihypertensive medications. The complexity, adverse effects, or perceived benefits of oral hypoglycemic medications may make patients' adherence more “sensitive” to the contributions of patient-provider communication. A focus group study of oral diabetes medication therapy initiation and intensification found that patients viewed medication therapy initiation as “evidence of personal failure and an increased burden” and viewed medication therapy intensification as increasing their risk of diabetes-related complications, preferring deescalation as their primary treatment goal.69 Similar mixed-methods studies to explore how persons with diabetes perceive different medications could offer patient-centered insights on how health beliefs influence medication refill adherence and whether their relationships with health care providers influence adherence differently.

Medication refill adherence is associated with better cardiometabolic control and reduced morbidity and mortality among those with diabetes at the highest risk for cardiovascular events.25,70 Our findings support proposed pathways from patient-centered communication, trust, and shared decision making to medication refill adherence.7 Patient-centered communication behaviors are core strategies by which clinicians engender patient trust, which enhances patients' adherence by promoting self-efficacy and moderating the negative effects of financial barriers to adherence.14,25,66,71 Patient-centered communication also allows clinicians to activate and engage patients in self-management through collaborative goal setting and action planning, which can improves diabetes self-care, medication refill adherence, and ultimately cardiometabolic control.7275

Patient-centered communication may also foster shared decision making about medications. Clinicians often fail to predict inadequate medication refill adherence,6,76,77 which may represent passive disagreement to clinicians' prescribing decisions. Patient-centered communication may allow acknowledgment and reconciliation of the different ways patients and clinicians view medication risks and benefits.67,69,7880 Skilled clinicians may also facilitate patient disclosure of nonadherence, allowing problem solving such as adjusting regimens causing adverse effects or involving patients' significant others.80

Overall, our results suggest patients' communication ratings are modestly predictive of inadequate medication refill adherence, with adjusted absolute prevalence differences of 4% to 6% and RR differences of 7% to 16%. The largest differences in adherence occurred between ratings of “usually” and “sometimes,” suggesting a conceptually meaningful difference in patients' perceptions at this cutoff. It is unclear to what extent patient-provider communication is modifiable, and if so, whether improvements in a given health care provider's communication will lead to improved adherence among that provider's patients. Cooper et al6 developed an intensive training program using personalized feedback from videotaped simulations of patient encounters to enhance clinicians' skills in patient engagement, activation, and empowerment. While the training was associated with greater improvements in patients' reports of physicians' participatory decision making and patient involvement in care, it was not associated with improvements in patients' antihypertensive medication refill adherence or blood pressure control. On the basis of our findings, it is possible that targeting clinicians with poorer patient communication ratings or focusing on specific skills related to shared decision making and trust for hypoglycemic medications may offer higher yields.

This study has limitations. First, patient ratings of health care provider communication may be subject to recall bias. Second, CMG is only 1 measure for refill adherence to medications and excludes those who are not ongoing users.5 Because discontinuation is assumed to occur after the last dispensing and stockpiled medications have been exhausted, person-time is censored and poor refill adherence after discontinuation is not captured by CMG. Also, CMG does not evaluate early stages of adherence for newly prescribed medications (primary nonadherence). However, CMG remains the most valid measure of adherence to long-term medication therapies and should have good correlation with other measures in an integrated health care delivery system that includes its own pharmacies.5,17,18,35,36 Third, owing to limitations of available pharmacy utilization data, we were unable to measure insulin therapy adherence, an important outcome given challenges with insulin therapy initiation and adherence.67,78 Fourth, the cohort excludes patients who changed health care providers, a group that may include members who rated their providers' communication more poorly. Fifth, our findings from this cross-sectional analysis may be due to unmeasured confounding or reverse causation (eg, patients' poor refill adherence to medications leading to challenging conversations with health care providers). Our analysis is strengthened by capturing and adjusting for several potential confounders from existing literature, but given the complex interrelationships between communication, adherence, medication therapy intensification, and cardiometabolic outcomes, future prospective, mixed-methods observational studies using rigorous causal analytic methods would be valuable. Sixth, the study cohort was a fully insured population receiving care in an integrated health delivery system, and findings may not be generalizable to other patient populations (eg, the uninsured). However, this population provides a reasonable model of expectations if and when the Patient Protection and Affordable Care Act is fully implemented.81 Concerns for confounding by some systemic and financial barriers to adherence are reduced in this insured population with continuous prescription medication coverage, and this study is strengthened by the study population's diversity, including 73% nonwhite minorities and 42% with high school education or less. Finally, although this study focuses on patient ratings of health care providers, interventions to promote adherence should also consider empowering patients to communicate more effectively with clinicians, eg, by disclosing their desires not to start or intensify medication therapies before they are prescribed.6,79,82

In conclusion, poor patient ratings of health care provider communication were independently associated with objectively measured, inadequate cardiometabolic medication refill adherence, particularly for oral hypoglycemic medications. Future studies should investigate whether targeting communication interventions for clinicians or health systems with poorer patient communication ratings may improve medication refill adherence and ultimately clinical outcomes.

Correspondence: Neda Ratanawongsa, MD, MPH, Division of General Internal Medicine, UCSF Center for Vulnerable Populations at San Francisco General Hospital and Trauma Center, 1001 Potrero Ave, PO Box 1364, San Francisco, CA 94110 (ratanawongsan@medsfgh.ucsf.edu).

Accepted for Publication: July 2, 2012

Published Online: December 31, 2012. doi:10.1001/jamainternmed.2013.1216

Author Contributions: Dr Ratanawongsa had full access to all of 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: Ratanawongsa, Karter, Lyles, Heisler, Warton, and Schillinger. Acquisition of data: Karter, Parker, Moffet, Adler, and Schillinger. Analysis and interpretation of data: Ratanawongsa, Karter, Parker, Lyles, Adler, and Schillinger. Drafting of the manuscript: Ratanawongsa and Warton. Critical revision of the manuscript for important intellectual content: Ratanawongsa, Karter, Parker, Lyles, Heisler, Moffet, Adler, Warton, and Schillinger. Statistical analysis: Ratanawongsa, Parker, Lyles, and Warton. Obtained funding: Karter and Schillinger. Administrative, technical, and material support: Heisler, Moffet, and Adler. Study supervision: Karter and Schillinger.

Conflict of Interest Disclosures: None reported.

Funding/Support: Funding for the DISTANCE study was provided by grants R01 DK65664, DK081796, DK080726 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and grant R01 HD046113 from the National Institute of Child Health and Human Development. Dr Ratanawongsa's mentorship by Drs Schillinger and Karter is supported by grant P30DK092924 from the NIDDK for The Health Delivery Systems–Center for Diabetes Translational Research.

Role of the Sponsors: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.

This article was corrected for errors on March 29, 2013.

American Diabetes Association.  Standards of medical care in diabetes—2012.  Diabetes Care. 2012;35:(suppl 1)  S11-S63
PubMed   |  Link to Article
Kerr EA, Zikmund-Fisher BJ, Klamerus ML, Subramanian U, Hogan MM, Hofer TP. The role of clinical uncertainty in treatment decisions for diabetic patients with uncontrolled blood pressure.  Ann Intern Med. 2008;148(10):717-727
PubMed
Heisler M, Hogan MM, Hofer TP, Schmittdiel JA, Pladevall M, Kerr EA. When more is not better: treatment intensification among hypertensive patients with poor medication adherence.  Circulation. 2008;117(22):2884-2892
PubMed   |  Link to Article
Schmittdiel JA, Uratsu CS, Karter AJ,  et al.  Why don't diabetes patients achieve recommended risk factor targets? poor adherence versus lack of treatment intensification.  J Gen Intern Med. 2008;23(5):588-594
PubMed   |  Link to Article
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):1640-1661
PubMed   |  Link to Article
Cooper LA, Roter DL, Carson KA,  et al.  A randomized trial to improve patient-centered care and hypertension control in underserved primary care patients.  J Gen Intern Med. 2011;26(11):1297-1304
PubMed   |  Link to Article
Beck RS, Daughtridge R, Sloane PD. Physician-patient communication in the primary care office: a systematic review.  J Am Board Fam Pract. 2002;15(1):25-38
PubMed
Zolnierek KB, Dimatteo MR. Physician communication and patient adherence to treatment: a meta-analysis.  Med Care. 2009;47(8):826-834
PubMed   |  Link to Article
Stewart M, Brown JB, Donner A,  et al.  The impact of patient-centered care on outcomes.  J Fam Pract. 2000;49(9):796-804
PubMed
Institute of Medicine.  Committee on Quality of Health Care in America: Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001:337
Agency for Healthcare Research and Quality.  CAHPS Clinician & Group 12-Month Survey. Updated 2011. https://www.cahps.ahrq.gov/Surveys-Guidance/CG/12-Month.aspx. Accessed January 29, 2012
Lin GA, Dudley RA. Patient-centered care: what is the best measuring stick?  Arch Intern Med. 2009;169(17):1551-1553
PubMed   |  Link to Article
Rittenhouse DR, Shortell SM. The patient-centered medical home: will it stand the test of health reform?  JAMA. 2009;301(19):2038-2040
PubMed   |  Link to Article
Heisler M, Cole I, Weir D, Kerr EA, Hayward RA. Does physician communication influence older patients' diabetes self-management and glycemic control? results from the Health and Retirement Study (HRS).  J Gerontol A Biol Sci Med Sci. 2007;62(12):1435-1442
PubMed   |  Link to Article
Naik AD, Kallen MA, Walder A, Street RL Jr. Improving hypertension control in diabetes mellitus: the effects of collaborative and proactive health communication.  Circulation. 2008;117(11):1361-1368
PubMed   |  Link to Article
Schoenthaler A, Allegrante JP, Chaplin W, Ogedegbe G. The effect of patient-provider communication on medication adherence in hypertensive black patients: does race concordance matter?  Ann Behav Med. 2012;43(3):372-382
PubMed   |  Link to Article
Gonzalez JS, Schneider HE. Methodological issues in the assessment of diabetes treatment adherence.  Curr Diab Rep. 2011;11(6):472-479
PubMed   |  Link to Article
Shi L, Liu J, Koleva Y, Fonseca V, Kalsekar A, Pawaskar M. Concordance of adherence measurement using self-reported adherence questionnaires and medication monitoring devices.  Pharmacoeconomics. 2010;28(12):1097-1107
PubMed   |  Link to Article
Garber MC, Nau DP, Erickson SR, Aikens JE, Lawrence JB. The concordance of self-report with other measures of medication adherence: a summary of the literature.  Med Care. 2004;42(7):649-652
PubMed   |  Link to Article
Rand CS, Nides M, Cowles MK, Wise RA, Connett J.The Lung Health Study Research Group.  Long-term metered-dose inhaler adherence in a clinical trial.  Am J Respir Crit Care Med. 1995;152(2):580-588
PubMed   |  Link to Article
Lustman PJ, Anderson RJ, Freedland KE, de Groot M, Carney RM, Clouse RE. Depression and poor glycemic control: a meta-analytic review of the literature.  Diabetes Care. 2000;23(7):934-942
PubMed   |  Link to Article
de Groot M, Anderson R, Freedland KE, Clouse RE, Lustman PJ. Association of depression and diabetes complications: a meta-analysis.  Psychosom Med. 2001;63(4):619-630
PubMed
Gonzalez JS, Peyrot M, McCarl LA,  et al.  Depression and diabetes treatment nonadherence: a meta-analysis.  Diabetes Care. 2008;31(12):2398-2403
PubMed   |  Link to Article
Swanson KA, Bastani R, Rubenstein LV, Meredith LS, Ford DE. Effect of mental health care and shared decision making on patient satisfaction in a community sample of patients with depression.  Med Care Res Rev. 2007;64(4):416-430
PubMed   |  Link to Article
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):1749-1755
PubMed   |  Link to Article
Rubin RR. Adherence to pharmacologic therapy in patients with type 2 diabetes mellitus.  Am J Med. 2005;118:(suppl 5A)  27S-34S
PubMed   |  Link to Article
Grant RW, Devita NG, Singer DE, Meigs JB. Polypharmacy and medication adherence in patients with type 2 diabetes.  Diabetes Care. 2003;26(5):1408-1412
PubMed   |  Link to Article
Moffet HH, Adler N, Schillinger D,  et al.  Cohort Profile: The Diabetes Study of Northern California (DISTANCE)—objectives and design of a survey follow-up study of social health disparities in a managed care population.  Int J Epidemiol. 2009;38(1):38-47
PubMed   |  Link to Article
Stewart AL, Nápoles-Springer AM, Gregorich SE, Santoyo-Olsson J. Interpersonal processes of care survey: patient-reported measures for diverse groups.  Health Serv Res. 2007;42(3, pt 1):1235-1256
PubMed   |  Link to Article
Thom DH, Ribisl KM, Stewart AL, Luke DA.The Stanford Trust Study Physicians.  Further validation and reliability testing of the Trust in Physician Scale.  Med Care. 1999;37(5):510-517
PubMed   |  Link to Article
Anderson LA, Dedrick RF. Development of the Trust in Physician scale: a measure to assess interpersonal trust in patient-physician relationships.  Psychol Rep. 1990;67(3, pt 2):1091-1100
PubMed   |  Link to Article
Morales LS, Elliott MN, Weech-Maldonado R, Spritzer KL, Hays RD. Differences in CAHPS adult survey reports and ratings by race and ethnicity: an analysis of the National CAHPS benchmarking data 1.0.  Health Serv Res. 2001;36(3):595-617
PubMed
Fongwa MN, Cunningham W, Weech-Maldonado R, Gutierrez PR, Hays RD. Reports and ratings of care: black and white Medicare enrollees.  J Health Care Poor Underserved. 2008;19(4):1136-1147
PubMed   |  Link to Article
Hays RD, Shaul JA, Williams VS,  et al.  Psychometric properties of the CAHPS 1.0 survey measures: Consumer Assessment of Health Plans Study.  Med Care. 1999;37(3):(suppl)  MS22-MS31
PubMed   |  Link to Article
Steiner JF, Prochazka AV. The assessment of refill compliance using pharmacy records: methods, validity, and applications.  J Clin Epidemiol. 1997;50(1):105-116
PubMed   |  Link to Article
Steiner JF, Koepsell TD, Fihn SD, Inui TS. A general method of compliance assessment using centralized pharmacy records: description and validation.  Med Care. 1988;26(8):814-823
PubMed   |  Link to Article
Bryson CL, Au DH, Young B, McDonell MB, Fihn SD. A refill adherence algorithm for multiple short intervals to estimate refill compliance (ReComp).  Med Care. 2007;45(6):497-504
PubMed   |  Link to Article
Fernandez A, Schillinger D, Warton EM,  et al.  Language barriers, physician-patient language concordance, and glycemic control among insured Latinos with diabetes: the Diabetes Study of Northern California (DISTANCE).  J Gen Intern Med. 2011;26(2):170-176
PubMed   |  Link to Article
Wilson E, Chen AH, Grumbach K, Wang F, Fernandez A. Effects of limited English proficiency and physician language on health care comprehension.  J Gen Intern Med. 2005;20(9):800-806
PubMed   |  Link to Article
Fernandez A, Schillinger D, Grumbach K,  et al.  Physician language ability and cultural competence: an exploratory study of communication with Spanish-speaking patients.  J Gen Intern Med. 2004;19(2):167-174
PubMed   |  Link to Article
Sarkar U, Karter AJ, Liu JY, Moffet HH, Adler NE, Schillinger D. Hypoglycemia is more common among type 2 diabetes patients with limited health literacy: the Diabetes Study of Northern California (DISTANCE).  J Gen Intern Med. 2010;25(9):962-968
PubMed   |  Link to Article
Chew LD, Griffin JM, Partin MR,  et al.  Validation of screening questions for limited health literacy in a large VA outpatient population.  J Gen Intern Med. 2008;23(5):561-566
PubMed   |  Link to Article
Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure.  J Gen Intern Med. 2001;16(9):606-613
PubMed   |  Link to Article
Peyrot M, Rubin RR. Structure and correlates of diabetes-specific locus of control.  Diabetes Care. 1994;17(9):994-1001
PubMed   |  Link to Article
Benet-Martínez V, John OP. Los Cinco Grandes across cultures and ethnic groups: multitrait multimethod analyses of the Big Five in Spanish and English.  J Pers Soc Psychol. 1998;75(3):729-750
PubMed   |  Link to Article
Srivastava S, John OP, Gosling SD, Potter J. Development of personality in early and middle adulthood: set like plaster or persistent change?  J Pers Soc Psychol. 2003;84(5):1041-1053
PubMed   |  Link to Article
Gosling SD, Rentfrow PJ, Swann WB. A very brief measure of the big-five personality domains.  J Res Pers. 2003;37(6):504-528
Link to Article
Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index.  J Clin Epidemiol. 1994;47(11):1245-1251
PubMed   |  Link to Article
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.  J Clin Epidemiol. 1992;45(6):613-619
PubMed   |  Link to Article
Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research.  Epidemiology. 1999;10(1):37-48
PubMed   |  Link to Article
Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.  Am J Epidemiol. 2002;155(2):176-184
PubMed   |  Link to Article
Osterberg L, Blaschke T. Adherence to medication.  N Engl J Med. 2005;353(5):487-497
PubMed   |  Link to Article
Kripalani S, Gatti ME, Jacobson TA. Association of age, health literacy, and medication management strategies with cardiovascular medication adherence.  Patient Educ Couns. 2010;81(2):177-181
PubMed   |  Link to Article
Yang Y, Thumula V, Pace PF, Banahan BF III, Wilkin NE, Lobb WB. Predictors of medication nonadherence among patients with diabetes in Medicare Part D programs: a retrospective cohort study.  Clin Ther. 2009;31(10):2178-2188
PubMed   |  Link to Article
Mann DM, Allegrante JP, Natarajan S, Halm EA, Charlson M. Predictors of adherence to statins for primary prevention.  Cardiovasc Drugs Ther. 2007;21(4):311-316
PubMed   |  Link to Article
Wolf MS, Davis TC, Osborn CY, Skripkauskas S, Bennett CL, Makoul G. Literacy, self-efficacy, and HIV medication adherence.  Patient Educ Couns. 2007;65(2):253-260
PubMed   |  Link to Article
Christensen AJ, Howren MB, Hillis SL,  et al.  Patient and physician beliefs about control over health: association of symmetrical beliefs with medication regimen adherence.  J Gen Intern Med. 2010;25(5):397-402
PubMed   |  Link to Article
Roumie CL, Greevy R, Wallston KA,  et al.  Patient centered primary care is associated with patient hypertension medication adherence.  J Behav Med. 2011;34(4):244-253
PubMed   |  Link to Article
DiMatteo MR, Haskard KB, Williams SL. Health beliefs, disease severity, and patient adherence: a meta-analysis.  Med Care. 2007;45(6):521-528
PubMed   |  Link to Article
DiMatteo MR. Variations in patients' adherence to medical recommendations: a quantitative review of 50 years of research.  Med Care. 2004;42(3):200-209
PubMed   |  Link to Article
Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe.  J Am Stat Assoc. 1952;47(260):663-685
Link to Article
Cheung YB. A modified least-squares regression approach to the estimation of risk difference.  Am J Epidemiol. 2007;166(11):1337-1344
PubMed   |  Link to Article
Zou G. A modified Poisson regression approach to prospective studies with binary data.  Am J Epidemiol. 2004;159(7):702-706
PubMed   |  Link to Article
Traylor AH, Schmittdiel JA, Uratsu CS, Mangione CM, Subramanian U. Adherence to cardiovascular disease medications: does patient-provider race/ethnicity and language concordance matter?  J Gen Intern Med. 2010;25(11):1172-1177
PubMed   |  Link to Article
Beach MC, Keruly J, Moore RD. Is the quality of the patient-provider relationship associated with better adherence and health outcomes for patients with HIV?  J Gen Intern Med. 2006;21(6):661-665
PubMed   |  Link to Article
Saha S, Jacobs EA, Moore RD, Beach MC. Trust in physicians and racial disparities in HIV care.  AIDS Patient Care STDS. 2010;24(7):415-420
PubMed   |  Link to Article
Karter AJ, Subramanian U, Saha C,  et al.  Barriers to insulin initiation: the translating research into action for diabetes insulin starts project.  Diabetes Care. 2010;33(4):733-735
PubMed   |  Link to Article
Farmer KC. Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice.  Clin Ther. 1999;21(6):1074-1090
PubMed   |  Link to Article
Grant RW, Pabon-Nau L, Ross KM, Youatt EJ, Pandiscio JC, Park ER. Diabetes oral medication initiation and intensification: patient views compared with current treatment guidelines.  Diabetes Educ. 2011;37(1):78-84
PubMed   |  Link to Article
Ho PM, Magid DJ, Masoudi FA, McClure DL, Rumsfeld JS. Adherence to cardioprotective medications and mortality among patients with diabetes and ischemic heart disease.  BMC Cardiovasc Disord. 2006;6:48
PubMed   |  Link to Article
Lee YY, Lin JL. The effects of trust in physician on self-efficacy, adherence and diabetes outcomes.  Soc Sci Med. 2009;68(6):1060-1068
PubMed   |  Link to Article
Gonzales R, Handley MA. Improving glycemic control when “usual” diabetes care is not enough.  Arch Intern Med. 2011;171(22):1999-2000
PubMed   |  Link to Article
Naik AD, Palmer N, Petersen NJ,  et al.  Comparative effectiveness of goal setting in diabetes mellitus group clinics: randomized clinical trial.  Arch Intern Med. 2011;171(5):453-459
PubMed   |  Link to Article
Schillinger D, Handley M, Wang F, Hammer H. Effects of self-management support on structure, process, and outcomes among vulnerable patients with diabetes: a three-arm practical clinical trial.  Diabetes Care. 2009;32(4):559-566
PubMed   |  Link to Article
Bodenheimer T, Handley MA. Goal-setting for behavior change in primary care: an exploration and status report.  Patient Educ Couns. 2009;76(2):174-180
PubMed   |  Link to Article
Bieszk N, Patel R, Heaberlin A, Wlasuk K, Zarowitz B. Detection of medication nonadherence through review of pharmacy claims data.  Am J Health Syst Pharm. 2003;60(4):360-366
PubMed
Britten N, Stevenson FA, Barry CA, Barber N, Bradley CP. Misunderstandings in prescribing decisions in general practice: qualitative study.  BMJ. 2000;320(7233):484-488
PubMed   |  Link to Article
Ratanawongsa N, Crosson JC, Schillinger D, Karter AJ, Saha CK, Marrero DG. Getting under the skin of clinical inertia in insulin initiation: the Translating Research Into Action for Diabetes (TRIAD) Insulin Starts Project.  Diabetes Educ. 2012;38(1):94-100
PubMed   |  Link to Article
Ratanawongsa N, Wright SM, Vargo EM, Carrese JA. Challenges in primary care relationships: seeing it from both sides.  Patient Educ Couns. 2011;85(1):40-45
PubMed   |  Link to Article
Bezreh T, Laws MB, Taubin T, Rifkin DE, Wilson IB. Challenges to physician-patient communication about medication use: a window into the skeptical patient's world.  Patient Prefer Adherence. 2012;6:11-18
PubMed
Kocher R, Emanuel EJ, DeParle NA. The Affordable Care Act and the future of clinical medicine: the opportunities and challenges.  Ann Intern Med. 2010;153(8):536-539
PubMed
Galliher JM, Post DM, Weiss BD,  et al.  Patients' question-asking behavior during primary care visits: a report from the AAFP National Research Network.  Ann Fam Med. 2010;8(2):151-159
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Cohort identification of patients with diabetes who completed patient-provider communication ratings survey and were prescribed at least 1 cardiometabolic medication.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. Directed acyclic graph. Using established rules, adjusting only for shaded covariates was necessary to estimate the direct effect of communication on adherence. Dashed arrows indicate causal relationships blocked by adjustment. No unblocked pathways (solid arrows) remain between communication and adherence.

Tables

Table Graphic Jump LocationTable 1. Patient Ratings of the Quality of Communication With Clinicians in a Cohort of 9377 Patients With Diabetes Prescribed at Least 1 Cardiometabolic Medication
Table Graphic Jump LocationTable 2. Characteristics of a Cohort of 9377 Patients With Diabetes Prescribed at Least 1 Cardiometabolic Medication
Table Graphic Jump LocationTable 3. Differences in Prevalence of Poor Refill Adherence for Any Cardiometabolic Medication by Ratings of Communication With Clinicians in a Cohort of 9377 Patients With Diabetes

References

American Diabetes Association.  Standards of medical care in diabetes—2012.  Diabetes Care. 2012;35:(suppl 1)  S11-S63
PubMed   |  Link to Article
Kerr EA, Zikmund-Fisher BJ, Klamerus ML, Subramanian U, Hogan MM, Hofer TP. The role of clinical uncertainty in treatment decisions for diabetic patients with uncontrolled blood pressure.  Ann Intern Med. 2008;148(10):717-727
PubMed
Heisler M, Hogan MM, Hofer TP, Schmittdiel JA, Pladevall M, Kerr EA. When more is not better: treatment intensification among hypertensive patients with poor medication adherence.  Circulation. 2008;117(22):2884-2892
PubMed   |  Link to Article
Schmittdiel JA, Uratsu CS, Karter AJ,  et al.  Why don't diabetes patients achieve recommended risk factor targets? poor adherence versus lack of treatment intensification.  J Gen Intern Med. 2008;23(5):588-594
PubMed   |  Link to Article
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):1640-1661
PubMed   |  Link to Article
Cooper LA, Roter DL, Carson KA,  et al.  A randomized trial to improve patient-centered care and hypertension control in underserved primary care patients.  J Gen Intern Med. 2011;26(11):1297-1304
PubMed   |  Link to Article
Beck RS, Daughtridge R, Sloane PD. Physician-patient communication in the primary care office: a systematic review.  J Am Board Fam Pract. 2002;15(1):25-38
PubMed
Zolnierek KB, Dimatteo MR. Physician communication and patient adherence to treatment: a meta-analysis.  Med Care. 2009;47(8):826-834
PubMed   |  Link to Article
Stewart M, Brown JB, Donner A,  et al.  The impact of patient-centered care on outcomes.  J Fam Pract. 2000;49(9):796-804
PubMed
Institute of Medicine.  Committee on Quality of Health Care in America: Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001:337
Agency for Healthcare Research and Quality.  CAHPS Clinician & Group 12-Month Survey. Updated 2011. https://www.cahps.ahrq.gov/Surveys-Guidance/CG/12-Month.aspx. Accessed January 29, 2012
Lin GA, Dudley RA. Patient-centered care: what is the best measuring stick?  Arch Intern Med. 2009;169(17):1551-1553
PubMed   |  Link to Article
Rittenhouse DR, Shortell SM. The patient-centered medical home: will it stand the test of health reform?  JAMA. 2009;301(19):2038-2040
PubMed   |  Link to Article
Heisler M, Cole I, Weir D, Kerr EA, Hayward RA. Does physician communication influence older patients' diabetes self-management and glycemic control? results from the Health and Retirement Study (HRS).  J Gerontol A Biol Sci Med Sci. 2007;62(12):1435-1442
PubMed   |  Link to Article
Naik AD, Kallen MA, Walder A, Street RL Jr. Improving hypertension control in diabetes mellitus: the effects of collaborative and proactive health communication.  Circulation. 2008;117(11):1361-1368
PubMed   |  Link to Article
Schoenthaler A, Allegrante JP, Chaplin W, Ogedegbe G. The effect of patient-provider communication on medication adherence in hypertensive black patients: does race concordance matter?  Ann Behav Med. 2012;43(3):372-382
PubMed   |  Link to Article
Gonzalez JS, Schneider HE. Methodological issues in the assessment of diabetes treatment adherence.  Curr Diab Rep. 2011;11(6):472-479
PubMed   |  Link to Article
Shi L, Liu J, Koleva Y, Fonseca V, Kalsekar A, Pawaskar M. Concordance of adherence measurement using self-reported adherence questionnaires and medication monitoring devices.  Pharmacoeconomics. 2010;28(12):1097-1107
PubMed   |  Link to Article
Garber MC, Nau DP, Erickson SR, Aikens JE, Lawrence JB. The concordance of self-report with other measures of medication adherence: a summary of the literature.  Med Care. 2004;42(7):649-652
PubMed   |  Link to Article
Rand CS, Nides M, Cowles MK, Wise RA, Connett J.The Lung Health Study Research Group.  Long-term metered-dose inhaler adherence in a clinical trial.  Am J Respir Crit Care Med. 1995;152(2):580-588
PubMed   |  Link to Article
Lustman PJ, Anderson RJ, Freedland KE, de Groot M, Carney RM, Clouse RE. Depression and poor glycemic control: a meta-analytic review of the literature.  Diabetes Care. 2000;23(7):934-942
PubMed   |  Link to Article
de Groot M, Anderson R, Freedland KE, Clouse RE, Lustman PJ. Association of depression and diabetes complications: a meta-analysis.  Psychosom Med. 2001;63(4):619-630
PubMed
Gonzalez JS, Peyrot M, McCarl LA,  et al.  Depression and diabetes treatment nonadherence: a meta-analysis.  Diabetes Care. 2008;31(12):2398-2403
PubMed   |  Link to Article
Swanson KA, Bastani R, Rubenstein LV, Meredith LS, Ford DE. Effect of mental health care and shared decision making on patient satisfaction in a community sample of patients with depression.  Med Care Res Rev. 2007;64(4):416-430
PubMed   |  Link to Article
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):1749-1755
PubMed   |  Link to Article
Rubin RR. Adherence to pharmacologic therapy in patients with type 2 diabetes mellitus.  Am J Med. 2005;118:(suppl 5A)  27S-34S
PubMed   |  Link to Article
Grant RW, Devita NG, Singer DE, Meigs JB. Polypharmacy and medication adherence in patients with type 2 diabetes.  Diabetes Care. 2003;26(5):1408-1412
PubMed   |  Link to Article
Moffet HH, Adler N, Schillinger D,  et al.  Cohort Profile: The Diabetes Study of Northern California (DISTANCE)—objectives and design of a survey follow-up study of social health disparities in a managed care population.  Int J Epidemiol. 2009;38(1):38-47
PubMed   |  Link to Article
Stewart AL, Nápoles-Springer AM, Gregorich SE, Santoyo-Olsson J. Interpersonal processes of care survey: patient-reported measures for diverse groups.  Health Serv Res. 2007;42(3, pt 1):1235-1256
PubMed   |  Link to Article
Thom DH, Ribisl KM, Stewart AL, Luke DA.The Stanford Trust Study Physicians.  Further validation and reliability testing of the Trust in Physician Scale.  Med Care. 1999;37(5):510-517
PubMed   |  Link to Article
Anderson LA, Dedrick RF. Development of the Trust in Physician scale: a measure to assess interpersonal trust in patient-physician relationships.  Psychol Rep. 1990;67(3, pt 2):1091-1100
PubMed   |  Link to Article
Morales LS, Elliott MN, Weech-Maldonado R, Spritzer KL, Hays RD. Differences in CAHPS adult survey reports and ratings by race and ethnicity: an analysis of the National CAHPS benchmarking data 1.0.  Health Serv Res. 2001;36(3):595-617
PubMed
Fongwa MN, Cunningham W, Weech-Maldonado R, Gutierrez PR, Hays RD. Reports and ratings of care: black and white Medicare enrollees.  J Health Care Poor Underserved. 2008;19(4):1136-1147
PubMed   |  Link to Article
Hays RD, Shaul JA, Williams VS,  et al.  Psychometric properties of the CAHPS 1.0 survey measures: Consumer Assessment of Health Plans Study.  Med Care. 1999;37(3):(suppl)  MS22-MS31
PubMed   |  Link to Article
Steiner JF, Prochazka AV. The assessment of refill compliance using pharmacy records: methods, validity, and applications.  J Clin Epidemiol. 1997;50(1):105-116
PubMed   |  Link to Article
Steiner JF, Koepsell TD, Fihn SD, Inui TS. A general method of compliance assessment using centralized pharmacy records: description and validation.  Med Care. 1988;26(8):814-823
PubMed   |  Link to Article
Bryson CL, Au DH, Young B, McDonell MB, Fihn SD. A refill adherence algorithm for multiple short intervals to estimate refill compliance (ReComp).  Med Care. 2007;45(6):497-504
PubMed   |  Link to Article
Fernandez A, Schillinger D, Warton EM,  et al.  Language barriers, physician-patient language concordance, and glycemic control among insured Latinos with diabetes: the Diabetes Study of Northern California (DISTANCE).  J Gen Intern Med. 2011;26(2):170-176
PubMed   |  Link to Article
Wilson E, Chen AH, Grumbach K, Wang F, Fernandez A. Effects of limited English proficiency and physician language on health care comprehension.  J Gen Intern Med. 2005;20(9):800-806
PubMed   |  Link to Article
Fernandez A, Schillinger D, Grumbach K,  et al.  Physician language ability and cultural competence: an exploratory study of communication with Spanish-speaking patients.  J Gen Intern Med. 2004;19(2):167-174
PubMed   |  Link to Article
Sarkar U, Karter AJ, Liu JY, Moffet HH, Adler NE, Schillinger D. Hypoglycemia is more common among type 2 diabetes patients with limited health literacy: the Diabetes Study of Northern California (DISTANCE).  J Gen Intern Med. 2010;25(9):962-968
PubMed   |  Link to Article
Chew LD, Griffin JM, Partin MR,  et al.  Validation of screening questions for limited health literacy in a large VA outpatient population.  J Gen Intern Med. 2008;23(5):561-566
PubMed   |  Link to Article
Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure.  J Gen Intern Med. 2001;16(9):606-613
PubMed   |  Link to Article
Peyrot M, Rubin RR. Structure and correlates of diabetes-specific locus of control.  Diabetes Care. 1994;17(9):994-1001
PubMed   |  Link to Article
Benet-Martínez V, John OP. Los Cinco Grandes across cultures and ethnic groups: multitrait multimethod analyses of the Big Five in Spanish and English.  J Pers Soc Psychol. 1998;75(3):729-750
PubMed   |  Link to Article
Srivastava S, John OP, Gosling SD, Potter J. Development of personality in early and middle adulthood: set like plaster or persistent change?  J Pers Soc Psychol. 2003;84(5):1041-1053
PubMed   |  Link to Article
Gosling SD, Rentfrow PJ, Swann WB. A very brief measure of the big-five personality domains.  J Res Pers. 2003;37(6):504-528
Link to Article
Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index.  J Clin Epidemiol. 1994;47(11):1245-1251
PubMed   |  Link to Article
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.  J Clin Epidemiol. 1992;45(6):613-619
PubMed   |  Link to Article
Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research.  Epidemiology. 1999;10(1):37-48
PubMed   |  Link to Article
Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.  Am J Epidemiol. 2002;155(2):176-184
PubMed   |  Link to Article
Osterberg L, Blaschke T. Adherence to medication.  N Engl J Med. 2005;353(5):487-497
PubMed   |  Link to Article
Kripalani S, Gatti ME, Jacobson TA. Association of age, health literacy, and medication management strategies with cardiovascular medication adherence.  Patient Educ Couns. 2010;81(2):177-181
PubMed   |  Link to Article
Yang Y, Thumula V, Pace PF, Banahan BF III, Wilkin NE, Lobb WB. Predictors of medication nonadherence among patients with diabetes in Medicare Part D programs: a retrospective cohort study.  Clin Ther. 2009;31(10):2178-2188
PubMed   |  Link to Article
Mann DM, Allegrante JP, Natarajan S, Halm EA, Charlson M. Predictors of adherence to statins for primary prevention.  Cardiovasc Drugs Ther. 2007;21(4):311-316
PubMed   |  Link to Article
Wolf MS, Davis TC, Osborn CY, Skripkauskas S, Bennett CL, Makoul G. Literacy, self-efficacy, and HIV medication adherence.  Patient Educ Couns. 2007;65(2):253-260
PubMed   |  Link to Article
Christensen AJ, Howren MB, Hillis SL,  et al.  Patient and physician beliefs about control over health: association of symmetrical beliefs with medication regimen adherence.  J Gen Intern Med. 2010;25(5):397-402
PubMed   |  Link to Article
Roumie CL, Greevy R, Wallston KA,  et al.  Patient centered primary care is associated with patient hypertension medication adherence.  J Behav Med. 2011;34(4):244-253
PubMed   |  Link to Article
DiMatteo MR, Haskard KB, Williams SL. Health beliefs, disease severity, and patient adherence: a meta-analysis.  Med Care. 2007;45(6):521-528
PubMed   |  Link to Article
DiMatteo MR. Variations in patients' adherence to medical recommendations: a quantitative review of 50 years of research.  Med Care. 2004;42(3):200-209
PubMed   |  Link to Article
Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe.  J Am Stat Assoc. 1952;47(260):663-685
Link to Article
Cheung YB. A modified least-squares regression approach to the estimation of risk difference.  Am J Epidemiol. 2007;166(11):1337-1344
PubMed   |  Link to Article
Zou G. A modified Poisson regression approach to prospective studies with binary data.  Am J Epidemiol. 2004;159(7):702-706
PubMed   |  Link to Article
Traylor AH, Schmittdiel JA, Uratsu CS, Mangione CM, Subramanian U. Adherence to cardiovascular disease medications: does patient-provider race/ethnicity and language concordance matter?  J Gen Intern Med. 2010;25(11):1172-1177
PubMed   |  Link to Article
Beach MC, Keruly J, Moore RD. Is the quality of the patient-provider relationship associated with better adherence and health outcomes for patients with HIV?  J Gen Intern Med. 2006;21(6):661-665
PubMed   |  Link to Article
Saha S, Jacobs EA, Moore RD, Beach MC. Trust in physicians and racial disparities in HIV care.  AIDS Patient Care STDS. 2010;24(7):415-420
PubMed   |  Link to Article
Karter AJ, Subramanian U, Saha C,  et al.  Barriers to insulin initiation: the translating research into action for diabetes insulin starts project.  Diabetes Care. 2010;33(4):733-735
PubMed   |  Link to Article
Farmer KC. Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice.  Clin Ther. 1999;21(6):1074-1090
PubMed   |  Link to Article
Grant RW, Pabon-Nau L, Ross KM, Youatt EJ, Pandiscio JC, Park ER. Diabetes oral medication initiation and intensification: patient views compared with current treatment guidelines.  Diabetes Educ. 2011;37(1):78-84
PubMed   |  Link to Article
Ho PM, Magid DJ, Masoudi FA, McClure DL, Rumsfeld JS. Adherence to cardioprotective medications and mortality among patients with diabetes and ischemic heart disease.  BMC Cardiovasc Disord. 2006;6:48
PubMed   |  Link to Article
Lee YY, Lin JL. The effects of trust in physician on self-efficacy, adherence and diabetes outcomes.  Soc Sci Med. 2009;68(6):1060-1068
PubMed   |  Link to Article
Gonzales R, Handley MA. Improving glycemic control when “usual” diabetes care is not enough.  Arch Intern Med. 2011;171(22):1999-2000
PubMed   |  Link to Article
Naik AD, Palmer N, Petersen NJ,  et al.  Comparative effectiveness of goal setting in diabetes mellitus group clinics: randomized clinical trial.  Arch Intern Med. 2011;171(5):453-459
PubMed   |  Link to Article
Schillinger D, Handley M, Wang F, Hammer H. Effects of self-management support on structure, process, and outcomes among vulnerable patients with diabetes: a three-arm practical clinical trial.  Diabetes Care. 2009;32(4):559-566
PubMed   |  Link to Article
Bodenheimer T, Handley MA. Goal-setting for behavior change in primary care: an exploration and status report.  Patient Educ Couns. 2009;76(2):174-180
PubMed   |  Link to Article
Bieszk N, Patel R, Heaberlin A, Wlasuk K, Zarowitz B. Detection of medication nonadherence through review of pharmacy claims data.  Am J Health Syst Pharm. 2003;60(4):360-366
PubMed
Britten N, Stevenson FA, Barry CA, Barber N, Bradley CP. Misunderstandings in prescribing decisions in general practice: qualitative study.  BMJ. 2000;320(7233):484-488
PubMed   |  Link to Article
Ratanawongsa N, Crosson JC, Schillinger D, Karter AJ, Saha CK, Marrero DG. Getting under the skin of clinical inertia in insulin initiation: the Translating Research Into Action for Diabetes (TRIAD) Insulin Starts Project.  Diabetes Educ. 2012;38(1):94-100
PubMed   |  Link to Article
Ratanawongsa N, Wright SM, Vargo EM, Carrese JA. Challenges in primary care relationships: seeing it from both sides.  Patient Educ Couns. 2011;85(1):40-45
PubMed   |  Link to Article
Bezreh T, Laws MB, Taubin T, Rifkin DE, Wilson IB. Challenges to physician-patient communication about medication use: a window into the skeptical patient's world.  Patient Prefer Adherence. 2012;6:11-18
PubMed
Kocher R, Emanuel EJ, DeParle NA. The Affordable Care Act and the future of clinical medicine: the opportunities and challenges.  Ann Intern Med. 2010;153(8):536-539
PubMed
Galliher JM, Post DM, Weiss BD,  et al.  Patients' question-asking behavior during primary care visits: a report from the AAFP National Research Network.  Ann Fam Med. 2010;8(2):151-159
PubMed   |  Link to Article

Correspondence

CME
Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).
Submit a Comment

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 8

Related Content

Customize your page view by dragging & repositioning the boxes below.

See Also...
Articles Related By Topic
Related Topics
JAMAevidence.com

Users' Guides to the Medical Literature
Clinical Resolution

Users' Guides to the Medical Literature
Clinical Scenario