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Figure.
Time Until Medication Fill for Fully Adherent Patients
Time Until Medication Fill for Fully Adherent Patients

Time until medication fill for fully adherent patients (patients filling all medications prescribed) for 60 days after the index visit. There was a statistically significant difference at 60 days (Wilcoxon test; P < .001).

Table 1.  
Characteristics of the Study Population by Prescription Type
Characteristics of the Study Population by Prescription Type
Table 2.  
Adherence by Patient and Prescription Characteristics
Adherence by Patient and Prescription Characteristics
Table 3.  
Number of Medications Filled According to the Number of Medications Prescribed
Number of Medications Filled According to the Number of Medications Prescribed
1.
Kaushal  R, Kern  LM, Barrón  Y, Quaresimo  J, Abramson  EL.  Electronic prescribing improves medication safety in community-based office practices.  J Gen Intern Med. 2010;25(6):530-536.PubMedGoogle ScholarCrossref
2.
Abramson  EL, Bates  DW, Jenter  C,  et al.  Ambulatory prescribing errors among community-based providers in two states.  J Am Med Inform Assoc. 2012;19(4):644-648.PubMedGoogle ScholarCrossref
3.
Solomon  MD, Majumdar  SR.  Primary non-adherence of medications: lifting the veil on prescription-filling behaviors.  J Gen Intern Med. 2010;25(4):280-281.PubMedGoogle ScholarCrossref
4.
Fernando  TJ, Nguyen  DD, Baraff  LJ.  Effect of electronically delivered prescriptions on compliance and pharmacy wait time among emergency department patients.  Acad Emerg Med. 2012;19(1):102-105.PubMedGoogle ScholarCrossref
5.
Bergeron  AR, Webb  JR, Serper  M,  et al.  Impact of electronic prescribing on medication use in ambulatory care.  Am J Manag Care. 2013;19(12):1012-1017.PubMedGoogle Scholar
6.
Shrank  WH, Choudhry  NK, Fischer  MA,  et al.  The epidemiology of prescriptions abandoned at the pharmacy.  Ann Intern Med. 2010;153(10):633-640.PubMedGoogle ScholarCrossref
7.
Fischer  MA, Choudhry  NK, Brill  G,  et al.  Trouble getting started: predictors of primary medication nonadherence.  Am J Med. 2011;124(11):1081.e9-1081.e22. PubMedGoogle ScholarCrossref
8.
Osterberg  L, Blaschke  T.  Adherence to medication.  N Engl J Med. 2005;353(5):487-497.PubMedGoogle ScholarCrossref
9.
Benner  JS, Glynn  RJ, Mogun  H, Neumann  PJ, Weinstein  MC, Avorn  J.  Long-term persistence in use of statin therapy in elderly patients.  JAMA. 2002;288(4):455-461.PubMedGoogle ScholarCrossref
10.
Chapman  RH, Benner  JS, Petrilla  AA,  et al.  Predictors of adherence with antihypertensive and lipid-lowering therapy.  Arch Intern Med. 2005;165(10):1147-1152.PubMedGoogle ScholarCrossref
11.
Siegel  D, Lopez  J, Meier  J.  Antihypertensive medication adherence in the Department of Veterans Affairs.  Am J Med. 2007;120(1):26-32.PubMedGoogle ScholarCrossref
12.
Ahn  CS, Culp  L, Huang  WW, Davis  SA, Feldman  SR.  Adherence in dermatology [published online May 15, 2016].  J Dermatolog Treat.PubMedGoogle Scholar
13.
Snyder  S, Crandell  I, Davis  SA, Feldman  SR.  Medical adherence to acne therapy: a systematic review.  Am J Clin Dermatol. 2014;15(2):87-94.PubMedGoogle ScholarCrossref
14.
Bass  AM, Anderson  KL, Feldman  SR.  Interventions to increase treatment adherence in pediatric atopic dermatitis: a systematic review.  J Clin Med. 2015;4(2):231-242.PubMedGoogle ScholarCrossref
15.
Anderson  KL, Dothard  EH, Huang  KE, Feldman  SR.  Frequency of primary nonadherence to acne treatment.  JAMA Dermatol. 2015;151(6):623-626.PubMedGoogle ScholarCrossref
16.
Richmond  NA, Lamel  SA, Braun  LR,  et al.  Primary nonadherence (failure to obtain prescribed medicines) among dermatology patients.  J Am Acad Dermatol. 2014;70(1):201-203.PubMedGoogle ScholarCrossref
17.
Storm  A, Andersen  SE, Benfeldt  E, Serup  J.  One in 3 prescriptions are never redeemed: primary nonadherence in an outpatient clinic.  J Am Acad Dermatol. 2008;59(1):27-33.PubMedGoogle ScholarCrossref
18.
Fischer  MA, Stedman  MR, Lii  J,  et al.  Primary medication non-adherence: analysis of 195,930 electronic prescriptions.  J Gen Intern Med. 2010;25(4):284-290.PubMedGoogle ScholarCrossref
19.
Tamblyn  R, Eguale  T, Huang  A, Winslade  N, Doran  P.  The incidence and determinants of primary nonadherence with prescribed medication in primary care: a cohort study.  Ann Intern Med. 2014;160(7):441-450.PubMedGoogle ScholarCrossref
20.
Hovstadius  B, Petersson  G.  Non-adherence to drug therapy and drug acquisition costs in a national population—a patient-based register study.  BMC Health Serv Res. 2011;11:326.PubMedGoogle ScholarCrossref
21.
Marcum  ZA, Gellad  WF.  Medication adherence to multidrug regimens.  Clin Geriatr Med. 2012;28(2):287-300. PubMedGoogle ScholarCrossref
22.
Pasina  L, Brucato  AL, Falcone  C,  et al.  Medication non-adherence among elderly patients newly discharged and receiving polypharmacy.  Drugs Aging. 2014;31(4):283-289. PubMedGoogle ScholarCrossref
23.
Briesacher  BA, Gurwitz  JH, Soumerai  SB.  Patients at-risk for cost-related medication nonadherence: a review of the literature.  J Gen Intern Med. 2007;22(6):864-871.PubMedGoogle ScholarCrossref
24.
Shin  J, McCombs  JS, Sanchez  RJ, Udall  M, Deminski  MC, Cheetham  TC.  Primary nonadherence to medications in an integrated healthcare setting.  Am J Manag Care. 2012;18(8):426-434.PubMedGoogle Scholar
25.
Raebel  MA, Ellis  JL, Carroll  NM,  et al.  Characteristics of patients with primary non-adherence to medications for hypertension, diabetes, and lipid disorders.  J Gen Intern Med. 2012;27(1):57-64.PubMedGoogle ScholarCrossref
26.
Khalili  H, Singh  R, Wood  M,  et al.  Premature clopidogrel discontinuation after drug-eluting stent placement in a large urban safety-net hospital.  Am J Cardiol. 2016;117(4):522-525.PubMedGoogle ScholarCrossref
27.
Blumenthal  D, Tavenner  M.  The “meaningful use” regulation for electronic health records.  N Engl J Med. 2010;363(6):501-504.PubMedGoogle ScholarCrossref
Original Investigation
January 2017

Association Between Method of Prescribing and Primary Nonadherence to Dermatologic Medication in an Urban Hospital Population

Author Affiliations
  • 1Department of Dermatology, The University of North Carolina at Chapel Hill
  • 2Department of Dermatology, University of Texas Southwestern Medical Center, Dallas
  • 3Department of Epidemiology, The University of North Carolina at Chapel Hill
  • 4Department of Biostatistics, University of Texas Southwestern Medical Center, Dallas
JAMA Dermatol. 2017;153(1):49-54. doi:10.1001/jamadermatol.2016.3491
Key Points

Question  Are patients more likely to fill and pick up medications if they receive a paper prescription or an electronic prescription?

Findings  In this record review of 2496 patients with a highly subsidized pharmaceutical benefit plan seen at the dermatology clinic of a county hospital, there was a 47% reduction in primary nonadherence if the prescription was in electronic format compared with a paper prescription.

Meaning  Patients are more likely to fill and pick up medications if they are prescribed in an electronic format.

Abstract

Importance  Prescription underuse is associated with poorer clinical outcomes. A significant proportion of underuse is owing to primary nonadherence, defined as the rate at which patients fail to fill and pick up new prescriptions. Although electronic prescribing increases coordination of care and decreases errors, its effect on primary nonadherence is less certain.

Objectives  To analyze factors associated with primary nonadherence to dermatologic medications and study whether electronic prescribing affects rates of primary nonadherence.

Design, Setting, and Participants  A retrospective review of medical records was conducted from January 1, 2011, to December 31, 2013, among a cohort of new patients prescribed dermatologic medications at a single, urban, safety-net hospital outpatient dermatology clinic.

Main Outcomes and Measures  The primary outcome was the overall rate of primary nonadherence, defined as filling and picking up all prescribed medications within a 1-year period, and the difference in primary nonadherence between patients who received electronic prescriptions and those who received paper prescriptions. Secondary outcomes included the association of primary nonadherence with sex, age, relationship status, primary language, race/ethnicity, and number of prescriptions.

Results  A total of 4318 prescriptions were written for 2496 patients (mean [SD] age, 47.7 [13.2] years; 849 men and 1647 women). The overall rate of primary nonadherence was 31.6% (n = 788). Based on multivariable analysis, the risk of primary nonadherence was 16 percentage points lower among patients given an electronic prescription (15.2%) than patients given a paper prescription (31.5%). Primary nonadherence decreased with age (<30 y, 38.9%; 30-49 y, 35.3%; and 50-69 y, 26.3%), and then increased in elderly patients 70 years and older (31.9%). Of patients who were given 1, 2, 3, 4, or 5 prescriptions, rates of primary nonadherence were 33.1%, 28.8%, 26.4%, 39.8%, and 38.1%, respectively. Primary nonadherence decreased with age but then increased in elderly patients. Patients identifying English as their primary language had the highest rate of primary nonadherence (33.9%) compared with Spanish (29%) or other speakers (20.4%).

Conclusions and Relevance  Compared with paper prescriptions, electronic prescriptions were associated with less primary nonadherence. Number of prescriptions, language, race/ethnicity, and age were associated with increased rates of primary nonadherence. Efforts must be made to understand why primary nonadherence occurs, identify patients prone to primary nonadherence, and simplify medication regimens to maximize adherence and quality of care.

Introduction

As the health care system in the United States has increasingly moved to the use of electronic medical records, electronic prescribing (e-prescribing) has become an important part of improving quality of care and the patient experience. Electronic prescribing increases coordination between pharmacist and physician and can decrease prescription errors.1,2 However, it is less certain how e-prescribing affects the rate at which patients fill (primary adherence) or do not fill (primary nonadherence) their new prescriptions.3

Although it may seem intuitive that primary adherence would increase by removing the patient from the prescription-to-pharmacy routing process, few studies have compared primary nonadherence of patients given traditional paper prescriptions vs e-prescriptions. Of these studies, there have been mixed results regarding the use of e-prescriptions, with some showing increased primary adherence, others showing decreased primary adherence, and still others showing no difference.4-7

Understanding the epidemiologic factors of prescriptions is important because underuse of prescription medications continues to be a problem. Underuse of prescription medications has been linked to poorer patient outcomes and increased health care costs.8 Most studies examining nonadherence focus on medication use patterns among patients who have already filled their prescriptions.9-14 Fewer studies focus strictly on primary nonadherence. In dermatology, only a few, mostly small studies have specifically investigated primary nonadherence.15-17

In this study, we measure primary nonadherence to dermatologic medications by examining prescription data from a large, urban county hospital system with an enclosed prescription environment. Patient and prescription characteristics were evaluated to assess factors associated with primary nonadherence.

Methods

Patients on the Parkland Health Plus (PHP) program, a taxpayer-subsidized health insurance program for uninsured, low-income residents of Dallas County, Texas, were included in this study. As part of PHP, patients receive a prescription drug benefit at considerably reduced cost if they fill their prescriptions through a closed pharmacy system. Data from the closed pharmacy system were linked with the electronic medical record system of Parkland Health and Hospital System in Dallas, Texas, for this analysis. Only new patients at PHP seen by a dermatologist in the outpatient dermatology clinic at Parkland Memorial Hospital were included. New patients were defined by not having a visit to the clinic in the prior 3 years. The index visit was defined as a visit by a new patient occurring from January 1, 2011, to December 31, 2013, during which 1 or more dermatologic medications were prescribed. Patients were excluded if they did not have a medication prescribed at their visit. Nonformulary medications were excluded from the analysis. Patients’ age, sex, self-reported race/ethnicity, relationship status (married, common law, or significant relationship [eg, domestic partnership or civil union] vs divorced, legally separated, single, or widowed), primary language spoken, number of dermatologic prescriptions, type of prescription given (electronic or paper), and date of medication pick-up were extracted from the linked electronic medical record and pharmacy record.

Primary nonadherence was defined as not filling and picking up all dermatologic prescriptions obtained during the index visit within 1 year of the prescription date. Adherence was further classified as full adherence (filling all prescriptions), some adherence (filling some but not all prescriptions), and complete nonadherence (filling none of the prescriptions). Demographic and prescription characteristics and their crude association with primary nonadherence were assessed using Mantel-Haenszel general association tests for categorical variables and analysis of variance for continuous variables. We used linear regression models (with identity and log link functions) to estimate crude and adjusted risk differences and risk ratios with 95% CIs for the risk of primary nonadherence among patients with e-prescriptions vs paper prescriptions. The variables included in the analysis were age, sex, race/ethnicity, marital status, and primary language spoken. Kaplan-Meier product limit survival curves were created for time (in days) until all prescriptions were filled. Data management and analysis was performed with SAS, version 9.3 (SAS Institute Inc).

The study was approved by the University of Texas Southwestern Medical Center Institutional Review Board. Owing to the retrospective nature of the study, patient consent was not necessary. A data use agreement was also approved for use of the deidentified data at The University of North Carolina at Chapel Hill.

Results

A total of 2496 patients met the inclusion criteria and were prescribed a total of 4318 medications for dermatologic conditions, at a mean of 1.7 prescriptions per patient. The mean (SD) age of patients was 47.7 (13.2) years, and the majority were women (1647 [66%]). Consistent with the population served by this health system, nearly half of patients (1220 [48.9%]) were Hispanic, and the rest were black (654 [26.2%]), white (443 [17.7%]), or other race/ethnicity (179 [7.2%]). The most common primary language was English (1468 [58.8%]), followed by Spanish (920 [36.9%]) (Table 1). Most encounters involved printed prescriptions (1693 [67.8%]). Overall, 3254 prescriptions (75.4%) were filled and picked up. The patient-level primary adherence rate was 68.4% (n = 1708). Of the patients who were nonadherent, 169 (6.8%) filled and picked up some of their prescriptions while 619 (24.8%) filled and picked up none.

Sex and relationship status were not associated with a difference in primary nonadherence. Rates of primary nonadherence decreased with increasing age (<30 years, 67 of 231 [29%]; 30-49 years, 295 of 1100 [26.8%]; 50-69 years, 237 of 1096 [21.6%]; and ≥70 years, 20 of 69 [29%]). Patients who did not speak English had lower rates of primary nonadherence (Spanish, 221 of 920 [24%]; other language, 20 of 108 [18.5%]) compared with those who identified English as their primary language (378 of 1468 [25.7%]). Hispanic patients had the highest full adherence rate (858 of 1220 [70.3%]) of any racial/ethnic group (Table 2).

The risk of primary nonadherence was 17 percentage points lower among patients given an e-prescription than patients given a paper prescription. This difference was 16 percentage points in the adjusted analysis. This finding represents a 47% reduction in the risk of primary nonadherence for patients who received an e-prescription vs those who received a paper prescription. Patients with paper prescriptions had a higher proportion of full adherence in the first 4 days after the prescription was issued, but after this point, patients with e-prescriptions were much more likely to be fully adherent (Figure).

Primary adherence was 66.9% (864 of 1291) when 1 prescription was given and increased to 71.2% (553 of 777) when 2 and 73.6% (201 of 273) when 3 prescriptions were given; however, primary adherence declined to 60.2% (77 of 128) when 4 prescriptions were given (Table 3).

Discussion

In this study measuring rates of primary nonadherence to medications prescribed by dermatologists, we found a 31.6% rate of primary nonadherence, defined as failing to fill and pick up all prescriptions within 1 year of receiving the prescriptions. This rate is slightly greater than the 20% to 30% rates of primary nonadherence reported in previous studies specifically investigating dermatologic medications.15-17 However, some of these studies were limited by self-reported response bias, which could have underestimated the level of primary nonadherence. In fact, 1 study showed that, although patients self-reported a rate of primary nonadherence of 6.2%, when pharmacy records were queried for external validation, rates of primary nonadherence were 45% and 25% at 2 weeks and 6 months, respectively.16 It is often difficult to compare adherence studies directly given different follow-up times, study populations, and reliability of outcomes measured (eg, surveys vs claims vs direct pharmacy data). Moreover, unlike many primary nonadherence studies, our unit of analysis for defining nonadherence was at the patient level, not the prescription level. When compared at the prescription level, our study population shows similar rates of nonadherence as reported in other studies. In our study, 24.6% of prescriptions went unfilled, which is lower than the rate of primary nonadherence to dermatological medications of 31.2% reported by Fischer et al,18 27.8% reported by Tamblyn et al,19 and 29.2% reported by Storm et al.17 This comparable level of primary nonadherence is remarkable given the low-income demographic of patients in our study. Patients with lower incomes are often more sensitive to prices, and our results are likely owing to the subsidized pharmacy benefit received by patients for their medications.

Similar to Anderson et al,15 we found a decrease in primary nonadherence to dermatologic medication with use of e-prescriptions. We also found that, during the first 4 days from the index visit, patients with paper prescriptions had a higher rate of full adherence. Although this study was not designed to establish a cause, it is possible that having a paper prescription served as a tangible reminder for patients to fill and pick up their prescription in the short term. However, in the longer term, lost or misplaced paper prescriptions could have led to a diminished likelihood of full adherence.

The effect of e-prescribing on primary nonadherence has been variable, with studies showing increased, decreased, or unchanged rates of primary nonadherence.4-7 For example, in a prospective study conducted in an emergency department, while pharmacy wait times and patient satisfaction improved, there was no difference in rates of primary nonadherence between e-prescriptions and paper prescriptions.4 In another example, a large cross-sectional cohort study examining the characteristics of abandoned prescriptions showed that e-prescriptions were more likely to be abandoned at the pharmacy.6 It is possible that the variability in primary nonadherence is owing to different populations being studied or different study designs, such as reliance on patient self-reports, for measuring rates of primary nonadherence.

A notable finding of our study is the decrease in rates of primary nonadherence when patients are given between 1 and 3 prescriptions, followed by an increase in rates of primary nonadherence when they are given more than 3 prescriptions (Table 3). The number of dispensed drugs is associated with primary nonadherence.20 Polypharmacy is a well-documented problem in patients’ compliance with complex treatment regimens.21,22 Patients on the PHP plan pay $5 per prescription, which could explain increased nonadherence beyond 3 prescriptions. Cost is a major consideration for many patients. Up to 32% of older patients take less medication than prescribed to reduce costs.23 Although patients in our study are buffered from high medication costs, multiple medications can become financially burdensome.

Similar to other studies of primary nonadherence, we did not find a sex difference in rates of primary nonadherence; however, younger patients had higher rates of primary nonadherence.15,18 In our study, Hispanic patients had among the lowest rates of primary nonadherence. This finding is in contrast to other reports that show higher rates of primary nonadherence among Hispanic patients.24,25 However, a similar finding has been reported for rates of primary nonadherence to cardiac medications within the PHP population.26 Adherence is multifactorial and complex, but it is possible that adherence of Hispanic patients is higher because Parkland Memorial Hospital has the infrastructure to accommodate the needs of its high volume of Spanish-speaking patients. Sociocultural differences of non–English-speaking patients in their trust of physician recommendations could also have positively influenced primary adherence.

Limitations

This study has some limitations. The insurance coverage environment allowed for direct study of nonadherence; however, this distinctiveness makes the study less generalizable as it exclusively encompasses a population of poor residents of Dallas County in one subspecialty clinic receiving a subsidized pharmacy benefit. Sixty-six percent of study participants were female, and the racial demographics included 48.9% Hispanic and 26.2% black patients, which may not be representative of other dermatology clinics. It is possible that there were other factors not captured in the data that could have resulted in the difference in adherence rates. Misclassification of medications as unfilled could have resulted if prescription adjustments were made by telephone after initial visit. Finally, while this study uncovered factors associated with primary nonadherence, it was not designed to understand the reasons for patient nonadherence.

Conclusions

Electronic prescribing has become one of the major criteria to evaluate meaningful use of electronic health records by health care professionals.27 In this study, we demonstrated that e-prescribing is associated with reduced rates of primary nonadherence. As the health care system transitions from paper prescriptions to directly routed e-prescriptions, it will be important to understand how that experience affects patients, particularly their likelihood of filling the prescriptions. Primary nonadherence is a common and pervasive problem. Steps should be taken to better understand why primary nonadherence happens and how it can be improved.

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Article Information

Correction: This article was corrected on November 16, 2016, to fix typographical errors in the Results sections of the Abstract and text and in the Discussion.

Accepted for Publication: July 29, 2016.

Corresponding Author: Adewole S. Adamson, MD, MPP, Department of Dermatology, The University of North Carolina at Chapel Hill, Genome Science Building, 250 Bell Tower Dr, Campus Box 7287, Chapel Hill, NC 27599 (adewole@med.unc.edu).

Published Online: October 26, 2016. doi:10.1001/jamadermatol.2016.3491

Author Contributions: Dr Adamson and Ms Gorman had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Adamson.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Adamson.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: All authors.

Obtained funding: Adamson.

Administrative, technical, or material support: Adamson.

Study supervision: Adamson.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported in part by the Department of Dermatology at the University of Texas Southwestern Medical Center and the University of North Carolina School of Medicine.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Previous Presentation: Parts of this study were presented as an abstract at the Society for Investigative Dermatology Annual Meeting; May 13, 2016; Scottsdale, Arizona.

Additional Contribution: Bhavin Patel, PharmD, MBA, Parkland Memorial Hospital Pharmacy Department, assisted with data acquisition. He was not compensated for his contribution.

References
1.
Kaushal  R, Kern  LM, Barrón  Y, Quaresimo  J, Abramson  EL.  Electronic prescribing improves medication safety in community-based office practices.  J Gen Intern Med. 2010;25(6):530-536.PubMedGoogle ScholarCrossref
2.
Abramson  EL, Bates  DW, Jenter  C,  et al.  Ambulatory prescribing errors among community-based providers in two states.  J Am Med Inform Assoc. 2012;19(4):644-648.PubMedGoogle ScholarCrossref
3.
Solomon  MD, Majumdar  SR.  Primary non-adherence of medications: lifting the veil on prescription-filling behaviors.  J Gen Intern Med. 2010;25(4):280-281.PubMedGoogle ScholarCrossref
4.
Fernando  TJ, Nguyen  DD, Baraff  LJ.  Effect of electronically delivered prescriptions on compliance and pharmacy wait time among emergency department patients.  Acad Emerg Med. 2012;19(1):102-105.PubMedGoogle ScholarCrossref
5.
Bergeron  AR, Webb  JR, Serper  M,  et al.  Impact of electronic prescribing on medication use in ambulatory care.  Am J Manag Care. 2013;19(12):1012-1017.PubMedGoogle Scholar
6.
Shrank  WH, Choudhry  NK, Fischer  MA,  et al.  The epidemiology of prescriptions abandoned at the pharmacy.  Ann Intern Med. 2010;153(10):633-640.PubMedGoogle ScholarCrossref
7.
Fischer  MA, Choudhry  NK, Brill  G,  et al.  Trouble getting started: predictors of primary medication nonadherence.  Am J Med. 2011;124(11):1081.e9-1081.e22. PubMedGoogle ScholarCrossref
8.
Osterberg  L, Blaschke  T.  Adherence to medication.  N Engl J Med. 2005;353(5):487-497.PubMedGoogle ScholarCrossref
9.
Benner  JS, Glynn  RJ, Mogun  H, Neumann  PJ, Weinstein  MC, Avorn  J.  Long-term persistence in use of statin therapy in elderly patients.  JAMA. 2002;288(4):455-461.PubMedGoogle ScholarCrossref
10.
Chapman  RH, Benner  JS, Petrilla  AA,  et al.  Predictors of adherence with antihypertensive and lipid-lowering therapy.  Arch Intern Med. 2005;165(10):1147-1152.PubMedGoogle ScholarCrossref
11.
Siegel  D, Lopez  J, Meier  J.  Antihypertensive medication adherence in the Department of Veterans Affairs.  Am J Med. 2007;120(1):26-32.PubMedGoogle ScholarCrossref
12.
Ahn  CS, Culp  L, Huang  WW, Davis  SA, Feldman  SR.  Adherence in dermatology [published online May 15, 2016].  J Dermatolog Treat.PubMedGoogle Scholar
13.
Snyder  S, Crandell  I, Davis  SA, Feldman  SR.  Medical adherence to acne therapy: a systematic review.  Am J Clin Dermatol. 2014;15(2):87-94.PubMedGoogle ScholarCrossref
14.
Bass  AM, Anderson  KL, Feldman  SR.  Interventions to increase treatment adherence in pediatric atopic dermatitis: a systematic review.  J Clin Med. 2015;4(2):231-242.PubMedGoogle ScholarCrossref
15.
Anderson  KL, Dothard  EH, Huang  KE, Feldman  SR.  Frequency of primary nonadherence to acne treatment.  JAMA Dermatol. 2015;151(6):623-626.PubMedGoogle ScholarCrossref
16.
Richmond  NA, Lamel  SA, Braun  LR,  et al.  Primary nonadherence (failure to obtain prescribed medicines) among dermatology patients.  J Am Acad Dermatol. 2014;70(1):201-203.PubMedGoogle ScholarCrossref
17.
Storm  A, Andersen  SE, Benfeldt  E, Serup  J.  One in 3 prescriptions are never redeemed: primary nonadherence in an outpatient clinic.  J Am Acad Dermatol. 2008;59(1):27-33.PubMedGoogle ScholarCrossref
18.
Fischer  MA, Stedman  MR, Lii  J,  et al.  Primary medication non-adherence: analysis of 195,930 electronic prescriptions.  J Gen Intern Med. 2010;25(4):284-290.PubMedGoogle ScholarCrossref
19.
Tamblyn  R, Eguale  T, Huang  A, Winslade  N, Doran  P.  The incidence and determinants of primary nonadherence with prescribed medication in primary care: a cohort study.  Ann Intern Med. 2014;160(7):441-450.PubMedGoogle ScholarCrossref
20.
Hovstadius  B, Petersson  G.  Non-adherence to drug therapy and drug acquisition costs in a national population—a patient-based register study.  BMC Health Serv Res. 2011;11:326.PubMedGoogle ScholarCrossref
21.
Marcum  ZA, Gellad  WF.  Medication adherence to multidrug regimens.  Clin Geriatr Med. 2012;28(2):287-300. PubMedGoogle ScholarCrossref
22.
Pasina  L, Brucato  AL, Falcone  C,  et al.  Medication non-adherence among elderly patients newly discharged and receiving polypharmacy.  Drugs Aging. 2014;31(4):283-289. PubMedGoogle ScholarCrossref
23.
Briesacher  BA, Gurwitz  JH, Soumerai  SB.  Patients at-risk for cost-related medication nonadherence: a review of the literature.  J Gen Intern Med. 2007;22(6):864-871.PubMedGoogle ScholarCrossref
24.
Shin  J, McCombs  JS, Sanchez  RJ, Udall  M, Deminski  MC, Cheetham  TC.  Primary nonadherence to medications in an integrated healthcare setting.  Am J Manag Care. 2012;18(8):426-434.PubMedGoogle Scholar
25.
Raebel  MA, Ellis  JL, Carroll  NM,  et al.  Characteristics of patients with primary non-adherence to medications for hypertension, diabetes, and lipid disorders.  J Gen Intern Med. 2012;27(1):57-64.PubMedGoogle ScholarCrossref
26.
Khalili  H, Singh  R, Wood  M,  et al.  Premature clopidogrel discontinuation after drug-eluting stent placement in a large urban safety-net hospital.  Am J Cardiol. 2016;117(4):522-525.PubMedGoogle ScholarCrossref
27.
Blumenthal  D, Tavenner  M.  The “meaningful use” regulation for electronic health records.  N Engl J Med. 2010;363(6):501-504.PubMedGoogle ScholarCrossref
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