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The American Journal of Managed Care October 2013
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Dispensing Channel and Medication Adherence: Evidence Across 3 Therapy Classes
Reethi Iyengar, PhD, MBA, MHM; Rochelle Henderson, PhD, MPA; Jay Visaria, PhD, MPH; and Sharon Glave Frazee, PhD, MPH
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Jacob D. Langley, MS-HSM; Tricia J. Johnson, PhD; Samuel F. Hohmann, PhD, MS-HSM; Steve J. Meurer, PhD, MBA, MHS; and Andy N. Garman, PsyD
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Vaishali Patel, PhD, MPH; Matthew J. Swain, MPH; Jennifer King, PhD; and Michael F. Furukawa, PhD
Physician Assistants in American Medicine: The Half-Century Mark
James F. Cawley, MPH, PA-C; and Roderick S. Hooker, PhD, PA
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David T. Liss, PhD; Paul A. Fishman, PhD; Carolyn M. Rutter, PhD; David Grembowski, PhD; Tyler R. Ross, MA; Eric A. Johnson, MS; and Robert J. Reid, MD, PhD
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Erin R. Giovannetti, PhD; Sydney Dy, MD; Bruce Leff, MD; Christine Weston, PhD; Karen Adams, PhD, MT; Tom B. Valuck, MD, JD; Aisha T. Pittman, MPH; Caroline S. Blaum, MD; Barbara A. McCann, MSW; and Cynthia M. Boyd, MD, MPH

Dispensing Channel and Medication Adherence: Evidence Across 3 Therapy Classes

Reethi Iyengar, PhD, MBA, MHM; Rochelle Henderson, PhD, MPA; Jay Visaria, PhD, MPH; and Sharon Glave Frazee, PhD, MPH
Findings indicate that patients using mail order pharmacies had significantly better adherence to antidiabetics, antihypertensives, and antihyperlipidemics than patients who used the retail dispensing channel.
Additionally, an analysis on a subsample of patients filling exclusively 90-day prescriptions evaluated the impact of channel on adherence. Inclusion was limited to patients who filled all their prescriptions through 1 channel only (either retail or mail order). The final analytical subanalysis sample consisted of data for 7253 diabetes, 35,797 hypertension, and 32,391 high blood cholesterol patients. All analyses were conducted using SAS version 9.3 (SAS Institute Inc, Cary, North Carolina).


Descriptive Findings

Generally, patients using mail order tended to be older and included a lower proportion of females than retail or mixed channels. Mail order patients had lower OOP costs per 30-day adjusted prescriptions, more average days of supply per claim, lower average disease burden, and marginally higher mean severity of illness (for diabetes and hypertension) than retailpatients. However, patients in the mixed group were more severely ill compared with mail order or retail patients. Across all 3 classes, prior adherence was significantly higher in mail order than in retail or mixed channels, reflecting the importance of using a proxy measure to control for PAB in the model in order to obtain less biased estimates of the effect of dispensing channel on adherence (eAppendix C, available at

On average, mail order had 18.2% more adherent patients than retail and 21.6% more adherent patients than mixed channels across the 3 therapy classes. Unadjusted adherence rates for those using mail order were consistently higher than those for patients using retail, with percentage point differences ranging from 16.9% for high blood cholesterol medications to 20.3% for antidiabetic agents (Table 1).

Subanalysis comparing 90-day retail patients with 90-day mail order patients also indicated that adherence rates were significantly higher in mail order compared with retail for users of hypertension (80.9% vs 76.3%; P <.001) and high blood cholesterol medication (76.9% vs 73.9%; P <.001). There was no significant difference in adherence rates for diabetes medication users among mail order and retail channel users (69.0% vs 67.3%; P = .1996).

Multivariate Findings

The results presented in Table 2 indicate the significance of controlling for the PAB effect. Model 1 presents the odds of a patient being adherent without controlling for prior adherence behavior. Model 2 presented the odds of a patient being adherent controlling for the PAB effect. Prior adherence behavior had a significant impact on the results, as observed from the corresponding odds ratio (OR). It was the strongest contributor to the odds of a patient being adherent across all 3 therapy classes. ORs ranged from 5.87 to 9.49.

After controlling for relevant population differences (age, sex, and patient OOP cost per 30-day adjusted prescription) and key covariates (PAB, average days of supply per claim, disease burden, severity of illness, and urbanicity), the differences follow the same pattern as shown in model 1 (Table 2, model 2). In 2011, the likelihood of a mail order patient being adherent was approximately 1.15 times higher than that of a retail patient for antidiabetics, 1.11 times higher for antihypertensives, and 1.19 times higher for antihyperlipidemics. The odds of being adherent for a patient using mixed channels were approximately 20% to 30% lower than the odds for a retail patient for antidiabetics, antihypertensives, and antihyperlipidemics.

On average, mail order had 3.2% more adherent patients than retail and 10.2% more adherent patients than mixed channels across the 3 therapy classes. Adjusted adherence rates for those using mail order were consistently higher than those for patients using retail, with percentage point differences ranging from 2.3 for high blood pressure medications to 3.8 for antihyperlipidemics. The adjusted adherence rate for antidiabetics was 3.5% higher for patients using the mail order channel compared with patients using the retail channel.

Additional sensitivity analysis examining patients filling only 90-day prescriptions concluded that the adjusted odds of being adherent were significantly higher in mail order compared with retail for hypertension patients (OR = 1.19, 95% CI, 1.12-1.26) and high blood cholesterol patients (OR = 1.10, 95% CI, 1.03-1.18), indicating that mail order patients using antihypertensives and antihyperlipidemics were almost 19% and 10% more likely, respectively, to be adherent than their respective retail counterparts, even after equalizing the days of supply differential. There was no significant difference in the adjusted odds of being adherent between dispensing channels for diabetes patients (OR = 1.02, 95% CI, 0.90-1.16).


Mail order patients were significantly more adherent than their retail counterparts after controlling for demographics, drug use patterns, differences in days of supply, and the PAB effect. Although sensitivity analysis revealed no statistically significant difference for patients on diabetes medications, the findings from the main model were consistent across the 3 studied therapy classes. For hypertension and high blood cholesterol, patients classified as using a mixed channel had significantly lower adherence rates compared with patients who received 66.7% or more of their prescriptions from either retail or mail order. Controlling for the PAB effect, adjusting for the difference in days of supply in all models, and including only those patients who could exercise their choice of channel, the results offer strong evidence that mail order leads to greater odds of being adherent. The multivariate adjusted coefficients on age, sex, and OOP costs are consistent with previous research.13 As this study used prior adherence to identify and control for the PAB effect, estimates of adherence behavior with respect to dispensing channel are less likely to be biased than those in any of the previous channel adherence studies.

This study makes an important methodologic contribution in the area by controlling for an important confounder—the PAB effect—which no previously published channel adherence studies took into account. Therefore, some results might have misattributed the impact of channel on adherence. Although 1 study on statins did address adherence and used prior adherence to identify healthy adherers in the context of outcomes, the study did not examine dispensing channel.22 To our knowledge, this study is the first to  address the concern of researchers who stressed the need to find a measure that controls for the confounding of patients’  predisposition to be adherent and to caution researchers to interpret the results of previous studies in light of this important limitation.21,22,28


The results of this study should be interpreted in the context of its limitations. First, the study analyzed pharmacy administrative claims data, using medication possession as a proxy for medication-taking behavior. It assumed that a pill in hand is a pill taken, which is not always the case. However, the use of medication possession as a proxy for medication use has been well documented in previous studies.13,23,26,28 Further, claims data cannot help distinguish between nonadherence and prescriber-recommended discontinuation. Second, because the study sample was limited to commercially insured patients aged 18 to 64 years, the results of the analyses may not be generalizable to other populations. Third, our ability to control for the confounding effects of covariates in the relationship between dispensing channel and adherence was limited by the reliability and validity of these proxy measures. We had access only to pharmaceutical claims data, and lack of medical information is a limitation of this study. Further studies should

look into medical covariates to strengthen the proxy variables. Lastly, we also were limited by our study design, because we included patients on medication for at least 2 years. Thus, patients who did not fill at least 1 prescription in each of the distinct time periods of the study were excluded from the analysis.


We found that patients receiving their medications via mail order had greater adherence than those receiving the medications via retail, even after accounting for differences in days of supply. Until now, to our best knowledge, channel adherence evaluations neither addressed the effect of prior adherence behaviors nor controlled for the difference in days of supply between channels. Thus, this study provides  empirically sound evidence that mail order is an important alternative to retail pharmacies for helping patients reach optimal adherence. By accounting for the biases resulting from the effect of prior adherence behavior and differential days of supply, this study demonstrates that the mail order channel is associated with improved adherence rates that are not simply a by-product of self-selection or increased days of supply.

Our study evaluated the overall impact of dispensing channel on adherence to medications in 3 widely used chronic therapy classes. Although 45% of US adults have at least 1 of the 3 studied conditions,29 studies across additional maintenance therapy classes would create a more comprehensive perspective on the relationship between channel and adherence. Future research should use a similar approach (using past measures as a proxy for future effects) to control for individual-specific characteristics. Also intriguing was the finding that adherence was better in both the mail order and retail cohorts compared with the mixed cohort. Further studies could examine the mixed cohort in greater detail, including medical claims data wherever possible. Our study presents mail order as a viable alternative strategy to address medication nonadherence and provides evidence to health plan sponsors and managed care organizations who want to encourage use of more cost-effective and efficient dispensing channels.

Author Affiliations: Express Scripts (RI, RH, JV, SGF), St. Louis, MO. Funding Source: Funding for the study was provided by Express Scripts.

Author Disclosure: The authors report employment with Express Scripts.

Authorship Information: Concept and design (RI, RH, JV); acquisition of data (RI); analysis and interpretation of data (RI, RH, JV); drafting of the manuscript (RI, RH, SF); critical revision of the manuscript for important intellectual content (RI, RH, SGF, JV); statistical analysis (RI, RH); obtaining funding (RI, RH, JV, SGF); and supervision (RI, RH, SGF).

Address correspondence to: Reethi Iyengar, PhD, MBA, MHM, Research and New Solutions, Express Scripts, 4600 N Hanley Rd, PTIC08, St Louis, MO 63134. E-mail:
1. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005;353(5):487-497.

2. Brunton SA. Improving medication adherence in chronic disease management. J Fam Pract. 2011;60(4)(suppl improving):S1-S8.

3. Albert NM. Improving medication adherence in chronic cardiovascular disease. Crit Care Nurse. 2008;28(5):54-64.

4. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and health care cost. Med Care. 2005;43(6):521-530.

5. Stuart BC, Simoni-Wastila L, Zhao L, Lloyd JT, Doshi JA. Increased persistency in medication use by U.S. Medicare beneficiaries with diabetes is associated with lower hospitalization rates and cost savings. Diabetes Care. 2009;32(4):647-649.

6. DiMatteo MR, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes: a meta-analysis. Med Care. 2002; 40(9):794-811.

7. McDermott MM, Schmitt B, Wallner E. Impact of medication nonadherence on coronary heart disease outcomes: a critical review. Arch Intern Med. 1997;157(17):1921-1929.

8. Nasseh K, Frazee SG, Visaria J, Vlahiotis A, Tian Y. Cost of medication nonadherence associated with diabetes, hypertension, and dyslipidemia. Am J Pharm Benefits. 2012;4(2):e41-e47.

9. Zhang L, Zakharyan A, Stockl KM, Harada AS, Curtis BS, Solow BK. Mail-order pharmacy use and medication adherence among Medicare Part D beneficiaries with diabetes. J Med Econ. 2011;14(5):562-567.

10. Duru OK, Schmittdiel JA, Dyer WT, et al. Mail-order pharmacy use and adherence to diabetes-related medications. Am J Manag Care. 2010;16(1):33-40.

11. Sharma KP, Taylor TN. Pharmacy effect on adherence to antidiabetic medications. Med Care. 2012;50(8):685-691.

12. Carroll NV, Brusilovsky I, York B, Oscar R. Comparison of costs of community and mail service pharmacy. J Am Pharm Assoc (2003). 2005;45(3):336-343.

13. Visaria J, Frazee SG, Devine ST. Asthma controller adherence in mail order pharmacy compared to retail pharmacy. Am J Pharm Benefits. 2012;4(3):e73-e80.

14. Khandelwal N, Duncan I, Rubinstein E, et al. Medication adherence for 90-day quantities of medication dispensed through retail and mail order pharmacies. Am J Manag Care.

15. Liberman JN, Girdish C. Recent trends in the dispensing of 90-daysupply prescriptions at retail pharmacies: implications for improved convenience and access. Am Health Drug Benefits. 2011;4(2):95-100.

16. Brennan TA, Dollear TJ, Hu M, et al. An integrated pharmacy-based program improved medication prescription and adherence rates in diabetes patients. Health Aff (Millwood). 2012;31(1):120-129.

17. Samaras K, Campbell LV, Chisholm DJ. Can the effectiveness of physical activity programs be improved? response to Clark. Diabetes Care. 1998;21(1):195-196.

18. Giannetti V. Adherence to therapeutic regimens. Res Social Adm Pharm. 2005;1(3):375-377.

19. Shay LE. A concept analysis: adherence and weight loss. Nurs Forum. 2008;43(1):42-52.

20. Krumholz HM. Outcomes research: generating evidence for best practice and policies. Circulation. 2008;118(3):309-318.

21. Brookhart MA, Stürmer T, Glynn RJ, Rassen J, Schneeweiss S. Confounding control in healthcare database research: challenges and potential approaches. Med Care. 2010;48(6) suppl):S114-S120.

22. Patrick AR, Shrank WH, Glynn RJ, et al. The association between statin use and outcomes potentially attributable to an unhealthy lifestyle in older adults. Value Health. 2011;14(4):513-520.

23. Pittman DG, Chen W, Bowlin S, Foody JM. Adherence to statins, subsequent healthcare costs, and cardiovascular hospitalizations. Am J Cardiol. 2011;107(11):1662-1666.

24. Janis IB, Nock MK. Behavioral forecasts do not improve the prediction of future behavior: a prospective study of self-Injury. J Clin Psychol. 2008;64(10):1-11.

25. Karve S, Cleves MA, Helm M, Hudson TJ, West DS, Martin BC. An empirical basis for standardizing adherence measures derived from administrative claims data among diabetic patients. Med Care. 2008;46(11):1125-1133.

26. Devine S, Vlahiotis A, Sundar H. A comparison of diabetes medication adherence and healthcare costs in patients using mail order pharmacy and retail pharmacy. J Med Econ. 2010;13(2):203-211.

27. US Census Bureau. Metropolitan and micropolitan statistical areas main. Revised May 24, 2012. Accessed August 21, 2012.

28. Brookhart MA, Patrick AR, Dormuth C, et al. Adherence to lipidlowering therapy and the use of preventive health services: an investigation of the healthy user effect. Am J Epidemiol. 2007;166(3):348-354.

29. Fryar CD, Hirsch R, Eberhardt MS, Yoon SS, Wright JD. Hypertension, high serum total cholesterol, and diabetes: racial and ethnic prevalence differences in U.S. adults, 1999-2006. NCHS Data Brief. 2010;(36):1-8.
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