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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
JAMA Intern Med. Author manuscript; available in PMC Mar 27, 2013.
Published in final edited form as:
PMCID: PMC3609434
NIHMSID: NIHMS433915
Communication and Medication Adherence: The Diabetes Study of Northern California
Neda Ratanawongsa, MD, MPH,1 Andrew J. Karter, PhD,2,3 Melissa M. Parker, MS,2 Courtney R. Lyles, PhD,1 Michele Heisler, MD, MPH,4 Howard H. Moffet, MPH,2 Nancy Adler, PhD,5 E. Margaret Warton, MPH,2 and Dean Schillinger, MD1,6
1General Internal Medicine and UCSF Center for Vulnerable Populations at San Francisco General Hospital and Trauma Center, University of California, San Francisco, CA
2Division of Research, Kaiser Permanente, Oakland, CA
3Department of Epidemiology, School of Public Health & Community Health, University of Washington, Seattle, WA
4University of Michigan and Ann Arbor Veterans Affairs Health Care System, Ann Arbor, MI
5UCSF Center for Health and Community, San Francisco, CA, USA
6California Diabetes Program, California Department of Public Health, Sacramento, CA
Corresponding Author and Reprint Requests: Neda Ratanawongsa, MD, MPH, Assistant Professor of Medicine, Division of General Internal Medicine, UCSF Center for Vulnerable Populations at San Francisco General Hospital and Trauma Center 1001 Potrero Avenue, Box 1364, San Francisco CA 94110, Phone: 415-206-3188, Fax: 415-206-5586, ratanawongsan/at/medsfgh.ucsf.edu
Background
Poor medication adherence contributes to poor cardiometabolic control and diabetes outcomes. Studies linking patient-provider communication 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 adherence, we conducted a cross-sectional analysis of 9,377 patients in the Diabetes Study of Northern California (DISTANCE), a race-stratified, random sample of Kaiser Permanente survey respondents. Eligible participants received ≥1 oral hypoglycemic, lipid-lowering, or anti-hypertensive 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 >20% continuous medication gap for ongoing medications. Using modified least squares regression, we calculated differences in poor adherence prevalence for a 10-point decrease in CAHPS score and comparing 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 adherence. For each 10-point decrease in CAHPS score, the adjusted prevalence of poor adherence increased by 0.9% (p=0.01). Compared with patients offering higher ratings, patients who gave lower ratings for 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% (p=0.04), 5% (p=0.02) and 6% (p=0.03), respectively. Associations between communication and adherence were somewhat larger for hypoglycemic medications than other medications.
Conclusions
Poor communication ratings were independently associated with objectively measured inadequate cardiometabolic medication 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 adherence and outcomes.
Persons with diabetes are at high risk for cardiovascular morbidity and mortality. Hypoglycemic, anti-hypertensive, and lipid-lowering medications are important tools for reducing cardiovascular risk in people with diabetes.1 Poor adherence to medications contributes significantly to suboptimal cardiometabolic control and poor clinical outcomes.25
One proposed strategy for enhancing medication adherence is improving patient-provider communication.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 IOM designated patient-centeredness as a core measure for health care quality,10 and validated metrics of 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 adherence measures may overestimate adherence across sociodemographic characteristics (e.g., cultural differences in social desirability).1720 Also, research using self-report measures for both communication and adherence may be affected by endogeneity bias; e.g., 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 side effects can differ across therapeutic indications, the importance of communication to patient adherence could differ for specific types of medications.26,27
This study investigated whether patient assessments of 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 30–75 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.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 (PCP), and were dispensed ≥1 oral hypoglycemic, anti-hypertensive, or lipid-lowering medications in the 12 months preceding the survey. We excluded subjects who changed PCP, lacked continuous pharmacy benefits, or had insufficient dispensing (<2 fills) of medications to calculate adherence.
Figure 1
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 2):
Table 2
Table 2
Patient ratings of the quality of communication with clinicians in a cohort of patients with diabetes prescribed at least one cardiometabolic medication (N=9,377)
  • Overall communication quality: 4 items on the 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’s alpha for internal consistency of this modified scale was 0.80.
  • Shared decision-making: 2 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: 2 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 Due to space limitations, the survey included four 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 cut-off 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 data.35,36 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, if that occurs first. For patients taking > 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 timeforward algorithm.37 Because pharmacy utilization data did not provide insulin fixed days supply based on prescribed dosing, we excluded insulin prescription refills.
For each subject, we calculated CMG for all indications combined and separately (CMG for antihypertensives only, lipid-lowering meds only, and diabetes meds only).35,36 We classified respondents as poorly adherent when they had no medication supply >20% of the observation time and adherent when medications were available for 80% or more of the time.5,35,36
We assessed socio-demographic and medical characteristics using survey and medical record data,28 including: age, sex, self-reported race/ethnicity, educational attainment, English 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 pre-baseline capture for the diagnostic and procedure codes48,49 and co-payment requirements, defining higher co-payments for generic drugs (> $10), brand drugs (> $30), and outpatient visits (>$20).
Statistical Analysis
Our modeling was guided by a directed acyclic graph (DAG), which depicts causal relationships between measured variables in the analysis (Figure 2). DAGs 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, provider, relationship, and system variables that could affect the relationship between communication and medication adherence.7,8,5260 We used established rules for determining the necessary covariates to estimate the direct effect of communication on medication 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 DAG was correct.
Figure 2
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 (more ...)
We weighted all multivariable analyses by the inverse of the non-proportional sampling fractions for each race/ethnic group to account for the stratified sampling design. We also addressed survey non-response bias using the Horvitz–Thompson approach, modeling the probability of response to the DISTANCE 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 9,377 eligible respondents, 7,303 were prescribed hypoglycemic medications, 7,052 were prescribed lipid-lowering medications, and 7,967 were prescribed anti-hypertensives (Figure 1). The average age was 59.5 years (SD 9.8), and 52% were women. One quarter (27%) were Caucasian, 19% African-American, 16% Latino, 12% Asian, 11% Filipino, and 11% multiracial (Table 1). Thirty-five percent earned <$50,000 per year, 42% had high school or less educational attainment, and 38% had limited health literacy. Forty-four percent had Charlson index scores ≥2, and 45% had hemoglobin A1c >7.0%. Patients were dispensed an average of 5.2 (SD 2.5) cardiometabolic medications (excluding insulin) and had seen their PCPs for 6.2 years on average (SD 4.4).
Table 1
Table 1
Characteristics of a cohort of patients with diabetes prescribed at least one cardiometabolic medication (N=9,377)
Ratings of the Quality of Communication with Clinicians
CAHPS scores were skewed, with 77% of respondents having the maximum score of 100. Low ratings were given by patients for providers involving patients in making decisions (20%), eliciting confidence and trust (11%), understanding patients’ problems carrying out recommended treatments (11%), putting patients’ needs first (12%), and showing respect (7%) (Table 2).
Differences in Medication 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 anti-hypertensive, 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%). Poor adherence for all cardiometabolic medications combined was associated with lower patient ratings on each IPC and TIPS item (unadjusted absolute differences ranging 8%–11%, all p<0.01) (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<0.01).
Table 3
Table 3
Differences in prevalence of poor refill adherence for any cardiometabolic medication, by ratings of communication with clinicians in a cohort of patients with diabetes (n=9,377)
After adjusting for potential confounders, the prevalence of poor refill adherence increased by 0.9% (95% CI 0.2%–1.7%) for each 10-point decrease in CAHPS score. Compared with patients offering higher ratings, patients who gave lower ratings for 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=0.04), 5% (95% CI 1%–10%, p=0.02) and 6% (95% CI 1%–11%, p=0.03), respectively. Those with lower communication ratings had higher adjusted RR of poor cardiometabolic refill adherence (adjusted RR 1.07 – 1.16, p<0.05 except p=0.09 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=0.02), 5% (95% CI 1%–11%, p= 0.03) and 7% (95% CI 1%–13%, p=0.01), respectively (data not shown). For lipid-lowering medications, only CAHPS score was associated with poor adherence (0.8% increase in prevalence of poor adherence per 10-point decrease in CAHPS score, 95% CI 0%–1.6%, p=0.04). 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 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 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 failed to initiate insulin felt that their health care providers inadequately explained the risks and benefits of insulin, compared to those who initiated insulin. 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 adherence, although it did not assess patient ratings of communication directly.64
This study adds to this literature in a number of ways. First, unlike most prior studies, we used a validated, objective measure of secondary medication adherence – pharmacy utilization for medication refills – to demonstrate an association with patient ratings of provider communication.35,36 Self-reported medication 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 out of 11 applicable studies.19 Also, sociodemographic characteristics such as gender and education have been associated with differences in the degree of patient over-reporting of adherence.20
Second, our findings suggest modest differences in the associations between patient ratings of communication and medication 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, side effects, or perceived benefits of oral hypoglycemics may make patient adherence more “sensitive” to the contributions of patient-provider communication. A focus group study of oral diabetes medication initiation and intensification found that patients viewed medication initiation as “evidence of personal failure and an increased burden” and viewed medication intensification as increasing their risk of diabetes-related complications, preferring de-escalation 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 health beliefs influence medication adherence and whether their relationships with providers influence adherence differently.
Medication adherence is associated with better cardiometabolic control and reduced morbidity and mortality among those with diabetes at highest risk for cardiovascular events.25,70 Our findings support proposed pathways from patient-centered communication, trust, and shared decision-making to medication adherence.7 Patient-centered communication behaviors are core strategies by which clinicians engender patient trust, which enhances patient 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 adherence, and ultimately cardiometabolic control.7275
Patient-centered communication may also foster shared decision-making about medications. Clinicians often fail to predict inadequate medication adherence,6,76,77 which may represent passive disagreement to clinicians’ prescribing decisions. Patient-centered communication may allow acknowledgement 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 non-adherence, allowing problem-solving such as adjusting regimens causing side effects or involving patients’ significant others.80
Overall, our results suggest patients’ communication ratings are modestly predictive of inadequate medication adherence, with adjusted absolute prevalence differences of 4%–6% and relative risk differences of 7%–16%. The largest differences in adherence occurred between ratings of “usually” and “sometimes,” suggesting a conceptually meaningful difference in patients’ perceptions at this cut-off. It is unclear to what extent patient-provider communication is modifiable, and if so, whether improvements in a given provider’s communication will lead to improved adherence among that provider’s patients. Cooper et al 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 patient report of physicians’ participatory decision-making and patient involvement in care, it was not associated with improvements in patient anti-hypertensive medication adherence or blood pressure control.6 Based on 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 provider communication may be subject to recall bias. Second, CMG is only one measure for 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. CMG also does not evaluate early stages of adherence for newly prescribed medications (primary nonadherence). However, CMG remains the most valid measure of adherence to chronic medications 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, due to limitations of available pharmacy data, we were unable to measure insulin adherence, an important outcome given challenges with insulin initiation and adherence.67,78 Fourth, the cohort excludes patients who changed providers, a group which 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 (e.g., poor patient adherence to medications leading to challenging conversations with 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 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 (e.g., the uninsured). However this population provides a reasonable model of expectations if and when the 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% non-white minorities and 42% with high school education or less. Finally, although this study focuses on patient ratings of providers, interventions to promote adherence should also consider empowering patients to communicate more effectively with clinicians, e.g., by disclosing their desires not to start or intensify medications before they are prescribed.6,79,82
In summary, poor patient ratings of provider communication were independently associated with objectively measured, inadequate cardiometabolic medication 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 adherence and ultimately clinical outcomes.
Acknowledgments
Funding for the DISTANCE study was provided by the National Institute of Diabetes, Digestive and Kidney Diseases R01 DK65664, DK081796, DK080726 and National Institute of Child Health and Human Development R01 HD046113. NR’s mentorship by DS and AK are supported by the National Institute of Diabetes and Digestive and Kidney Diseases for The Health Delivery Systems-Center for Diabetes Translational Research (CDTR) (P30DK092924).
Footnotes
Conflicts of Interest and Financial Disclosure: The authors have no conflicts of interest to disclose.
None of the authors had conflicts of interest, and the funders had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.
All authors contributed to the conception and design, and drafting and critical revision of the manuscript, including final approval of the version to be published. Neda Ratanawongsa, MD, MPH, led the conception and design and analysis and interpretation of data; drafted the article and revised it critically for important intellectual content; and had final approval of the version to be published. Andrew J. Karter, PhD, assisted with the conception and design and acquisition of data and analysis and interpretation of data; revised it critically for important intellectual content; and had final approval of the version to be published. Melissa M. Parker, MS assisted with the conception and design and analysis and interpretation of data; revised it critically for important intellectual content; and had final approval of the version to be published. Courtney R. Lyles, PhD, assisted with the conception and design and analysis and interpretation of data; revised it critically for important intellectualvcontent; and had final approval of the version to be published. Michele Heisler, MD, MPH, assisted with the conception and design and analysis and interpretation of data; revised it critically for important intellectual content; and had final approval of the version to be published. Howard H. Moffet, MPH, assisted with the conception and design and acquisition of data and analysis and interpretation of data; revised it critically for important intellectual content; and had final approval of the version to be published. Nancy Adler, PhD, assisted with the conception and design and acquisition of data and analysis and interpretation of data; revised it critically for important intellectual content; and had final approval of the version to be published. E. Margaret Warton, MPH, assisted with the conception and design and acquisition of data and analysis and interpretation of data; revised it critically for important intellectual content; and had final approval of the version to be published. Dean Schillinger MD, assisted with the conception and design and acquisition of data and analysis and interpretation of data; revised it critically for important intellectual content; and had final approval of the version to be published. Neda Ratanawongsa, MD, MPH, 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.
1. Standards of medical care in diabetes--2012. Diabetes Care. 2012;35(Suppl 1):S11–63. [PMC free article] [PubMed]
2. 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]
3. 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]
4. 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. [PMC free article] [PubMed]
5. 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. [PMC free article] [PubMed]
6. 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. [PMC free article] [PubMed]
7. 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]
8. Zolnierek KB, Dimatteo MR. Physician communication and patient adherence to treatment: A meta-analysis. Med Care. 2009;47(8):826–834. [PMC free article] [PubMed]
9. 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]
10. Institute of Medicine. Crossing the quality chasm: A new health system for the 21st century. Washington, D.C: National Academy Press; 2001. Committee on Quality of Health Care in America; p. 337.
11. Agency for Healthcare Research and Quality. [Accessed January 29, 2012.];CAHPS Clinician & Group 12-Month Survey. CAHPS Clinician & Group 12-Month Survey Website. https://www.cahps.ahrq.gov/Surveys-Guidance/CG/12-Month.aspx. Updated 2011.
12. Lin GA, Dudley RA. Patient-centered care: What is the best measuring stick? Arch Intern Med. 2009;169(17):1551–1553. [PubMed]
13. Rittenhouse DR, Shortell SM. The patient-centered medical home: Will it stand the test of health reform? JAMA. 2009;301(19):2038–2040. [PubMed]
14. 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]
15. 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]
16. 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 [PMC free article] [PubMed]
17. Gonzalez JS, Schneider HE. Methodological issues in the assessment of diabetes treatment adherence. Curr Diab Rep. 2011;11(6):472–479. [PubMed]
18. 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]
19. 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]
20. Rand CS, Nides M, Cowles MK, Wise RA, Connett J. Long-term metered-dose inhaler adherence in a clinical trial. The Lung Health Study Research Group. Am J Respir Crit Care Med. 1995;152(2):580–588. [PubMed]
21. 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]
22. 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]
23. Gonzalez JS, Peyrot M, McCarl LA, et al. Depression and diabetes treatment nonadherence: A meta-analysis. Diabetes Care. 2008;31(12):2398–2403. [PMC free article] [PubMed]
24. 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]
25. 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]
26. Rubin RR. Adherence to pharmacologic therapy in patients with type 2 diabetes mellitus. Am J Med. 2005;118(Suppl 5A):27S–34S. [PubMed]
27. 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]
28. 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. [PMC free article] [PubMed]
29. Stewart AL, Napoles-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. [PMC free article] [PubMed]
30. Thom DH, Ribisl KM, Stewart AL, Luke DA. Further validation and reliability testing of the Trust In Physician Scale. The Stanford Trust Study Physicians. Med Care. 1999;37(5):510–517. [PubMed]
31. 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]
32. 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. [PMC free article] [PubMed]
33. 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]
34. 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):MS22–31. [PubMed]
35. 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]
36. 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]
37. 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]
38. 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. [PMC free article] [PubMed]
39. 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. [PMC free article] [PubMed]
40. 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. [PMC free article] [PubMed]
41. 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. [PMC free article] [PubMed]
42. 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. [PMC free article] [PubMed]
43. 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. [PMC free article] [PubMed]
44. Peyrot M, Rubin RR. Structure and correlates of diabetes-specific locus of control. Diabetes Care. 1994;17(9):994–1001. [PubMed]
45. Benet-Martinez 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]
46. 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]
47. Gosling SD, Rentfrow PJ, Swann WB. A very brief measure of the big-five personality domains. J Res Pers. 2003;37:504-504–528.
48. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251. [PubMed]
49. 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]
50. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37–48. [PubMed]
51. Hernan MA, Hernandez-Diaz 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]
52. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005;353(5):487–497. [PubMed]
53. 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]
54. Yang Y, Thumula V, Pace PF, Banahan BF, 3rd, 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):2178–88. discussion 2150–1. [PubMed]
55. 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]
56. 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]
57. 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. [PMC free article] [PubMed]
58. 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]
59. DiMatteo MR, Haskard KB, Williams SL. Health beliefs, disease severity, and patient adherence: A meta-analysis. Med Care. 2007;45(6):521–528. [PubMed]
60. 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]
61. Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. J Amer Statist Assoc. 1952;47(260):663–685.
62. Cheung YB. A modified least-squares regression approach to the estimation of risk difference. Am J Epidemiol. 2007;166(11):1337–1344. [PubMed]
63. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–706. [PubMed]
64. 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. [PMC free article] [PubMed]
65. 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. [PMC free article] [PubMed]
66. 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. [PMC free article] [PubMed]
67. Karter AJ, Subramanian U, Saha C, et al. Barriers to insulin initiation: The translating research into action for diabetes (TRIAD) insulin starts project. Diabetes Care. 2010;33(4):733–5. [PMC free article] [PubMed]
68. Farmer KC. Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice. Clin Ther. 1999;21(6):1074–90. discussion 1073. [PubMed]
69. 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. [PMC free article] [PubMed]
70. 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. [PMC free article] [PubMed]
71. 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]
72. Gonzales R, Handley MA. Improving glycemic control when “usual” diabetes care is not enough: Comment on “the effect of a structured behavioral intervention on poorly controlled diabetes. Arch Intern Med. 2011;171(22):1999–2000. [PubMed]
73. 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. [PMC free article] [PubMed]
74. 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. [PMC free article] [PubMed]
75. 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]
76. 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]
77. 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. [PMC free article] [PubMed]
78. 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 [PMC free article] [PubMed]
79. 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]
80. 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. [PMC free article] [PubMed]
81. 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]
82. 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]