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Primary Nonadherence to Medications in an Integrated Healthcare Setting
Janet Shin, PharmD; Jeffrey S. McCombs, PhD; Robert J. Sanchez, RPh, PhD; Margarita Udall, MPH; Michael C. Deminski, MS, RPh; and T. Craig Cheetham, PharmD, MS
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Primary Nonadherence to Medications in an Integrated Healthcare Setting

Janet Shin, PharmD; Jeffrey S. McCombs, PhD; Robert J. Sanchez, RPh, PhD; Margarita Udall, MPH; Michael C. Deminski, MS, RPh; and T. Craig Cheetham, PharmD, MS
This retrospective study measures primary nonadherence rates for 10 drug groups and identifies important factors of primary nonadherence for chronic and acute medications.
The overall PNA rate across all 10 drug groups was 9.8%. PNA varied by therapeutic drug group, ranging from 2.9% for anti-infectives to 22.4% for antiosteoporosis medications (Figure 2). Varying the definition of PNA from 14 days to 90 days only decreased the overall PNA rate to 8.0%. PNA rates were highest for antiosteoporosis medications, antihyperlipidemics, and analgesics. The predicted probability of a primary nonadherent patient for the baseline case (ie, reference group for each variable) was 34% for chronic and 17% for acute therapy. Of the 569,095 prescriptions included in the study, 437,940 (77%) prescriptions were filled on the same day as they were written and 525,449 (92%) prescriptions were eventually filled within 6 months of the index date.

An unadjusted comparison between primary adherent and nonadherent prescriptions revealed several small but significant differences in patient, prescriber, and prescription characteristics (eAppendices B-D). PNA was more common for minority race/ethnicities, lower household incomes, and patients prescribed a greater number of prescriptions on the index date. A significantly greater proportion of primary nonadherent prescriptions were written by younger prescribers with less than 10 years of Kaiser experience. Prescriptions written for brand name medications and treatments for asymptomatic diseases were more likely to be primary nonadherent.

Results from the preliminary multivariable logistic regressions revealed significant interaction effects between the acute versus chronic therapy variable and other important variables in the model. Thus, the multivariable logistic regression results were stratified by chronic or acute therapy. The adjusted OR and 95% confidence intervals (CIs) resulting from the logistic regression are presented separately in Tables 1 to 3 for patient, prescriber, and prescription characteristics, respectively.

Patient Characteristics (Table 1)

The effects of patient gender and age differed depending on whether the treatment was acute or chronic. However, when analgesics were excluded as part of a sensitivity analysis, the trend in effect of patient age was similar between acute and chronic treatments. Patient race had a consistent effect across medications where blacks (acute: OR = 1.30 [1.25-1.36], chronic: OR = 1.26 [1.18-1.33]) and Hispanics (acute: OR = 1.19 [1.16-1.23], chronic: OR = 1.06 [1.01-1.11]) were more likely to be primary nonadherent compared with whites. Baseline patient comorbidities such as hyperlipidemia, diabetes without complications, paraplegia, and peripheral vascular disease increased the risk of PNA. However, cancer and renal disease lowered the risk.

Patients with dual insurance or Medicaid coverage (forchronic drugs only) were less likely to be primary nonadherent. Patients who filled at least 1 prescription in the pre-index period were also less likely to be primary nonadherent (acute: OR = 0.06 [0.06-0.07], chronic: OR = 0.11 [0.10-0.12]). Treatment-naïve patients were more likely to be primary nonadherent, especially to acute medications (acute: OR = 2.52 [2.36-2.70], chronic: 1.07 [1.03-1.12]). However, the effect of treatment-naïve patients for acute therapy becomes statistically insignificant when analgesics are excluded from the analysis. Lastly, patients with prior ED visits had mixed effects (acute: OR = 1.19 [1.16-1.22], chronic: 0.88 [0.85- 0.92]), while prior hospitalization lowered the risk of PNA to acute medications (OR = 0.96 [0.93-0.99]).

Prescriber Characteristics (Table 2)

Patients given acute medications were more likely to be primary nonadherent if the prescriptions were written by younger providers or by providers of minority race/ethnicity. Patients given chronic medications were more likely to be primary nonadherent if they were prescribed by females (OR = 1.11 [1.07- 1.15]) or blacks (OR=1.10 [1.03-1.19]). Patients who received prescriptions written by emergency medicine, obstetrics and gynecology, pediatrics, or urgent care prescribers were less likely to be primary nonadherent. Patient–provider concordance in terms of gender was not significant, but patients who were of the same race/ethnicity as the prescriber were more likely to be primary nonadherent to acute medications (OR = 1.05 [1.02-1.08]).

Prescription Characteristics (Table 3)

Patients given acute medications with brand names (OR = 1.49 [1.42-1.56]) or a higher number of other medications prescribed on the index date (OR = 1.26 [1.25-1.28]) were more likely to be primary nonadherent. For patients given chronic medications, having a higher number of medications prescribed on the index date (OR = 0.83 [0.81-0.85]) or symptomatic disease (OR = 0.51 [0.48-0.53]) decreased the risk of PNA. Prescriptions written on the weekend were more likely to be primary nonadherent for chronic medications (OR = 1.16 [1.09-1.24]) but less likely for acute medications (OR = 0.70 [0.68-0.73]).


Although the overall PNA rate across all 10 drug groups was only 9.8%, rates for certain therapeutic drug groups such as antiosteoporosis medications, antihyperlipidemics, and analn gesics were higher. For these drug groups, approximately 1 in 5 patients did not pick up their medications after 14 days. This causes concern, especially since medications for osteoporosis and hyperlipidemia have been shown to decrease morbidity and mortality.11,12 In particular, statin and bisphosphonate therapy have also been demonstrated as cost-effective.13,14 The availability of over-the-counter (OTC) analgesics may contribute to the relatively higher PNA rate associated with analgesics.

The PNA rates found in this study are somewhat higher than those reported in recent studies set in integrated healthcare settings.7,8 These differences could be attributed to the criteria used to define PNA. Some studies defined PNA as the failure to fill prescriptions within 30 or 60 days. This study used 14 days to define PNA since most prescriptions were filled within 2 weeks and it would be unreasonable from a policy perspective for clinicians to wait 30 days or more to contact patients for intervention. Changing our definition from 14 to 30 days produced PNA rates closer to those of previous studies.

The analysis revealed that some factors consistently increased or decreased the likelihood of PNA across all drug groups. Black patients were more likely to be nonadherent, which is consistent with prior research.15 However, the effect of race (and income) should be cautiously interpreted since these socioeconomic variables were geocoded. Patients with baseline comorbidities (except for cancer and renal disease) were less likely to fill their prescription, which is also consistent with findings from prior studies.8,16-18 Patients with multiple comorbidities are likely taking several medications, and adding another medication to their current regimen is likely to increase pill burden, the complexity of the medication therapy, and the risk of drug interactions. However, if the prescription was used to treat symptomatic disease, the patient was more likely to fill the prescription.

Similar to a recent study by Liberman and colleagues (2010), we found that patients with prior fills were more likely to fill their current prescription.9 Our study suggests that prior prescription fill history is a strong indicator of the patient’s compliant behavior and willingness to take medications in the future. Treatment-naïve patients were more likely to be primary nonadherent to medications for chronic conditions. These patients may be at the early stage of their disease and elect to postpone filling their prescriptions in order to try alternative methods like diet and exercise or OTC products. Alternatively, prescriptions written by emergency medicine and urgent care prescribers may indicate increased severity and acuteness of the patient’s illness, and thus were associated with a decreased likelihood of PNA.

The effects of some covariates differed based on whether the treatment was acute or chronic. Younger patients were more likely to fill their acute medications and less likely to fill their chronic medications relative to older patients. The effects of brand name medications and a greater prescription and copay burden on the index date only affected acute medications, suggesting that patients may be more sensitive to the cost of the drug when the disease is acute. Patients were less likely to pick up prescriptions prescribed on a weekend for chronic medications and more likely for acute medications, suggesting that patients may feel less urgency in treating chronic diseases compared with acute diseases.

Similar to Raebel and colleagues (2012),8 only a few variables were strongly associated with PNA, such as prior prescription fill and being treatment-naïve. Such results highlight the difficulty of quantifying complex human medicationtaking behavior using administrative data. Patient surveys have identified that the most common reasons for PNA are those related to patient concerns regarding the medication, such as potential side effects and the perceived need for the medication.6 Capturing these effects can be difficult when using quantitative administrative data.

This is one of the first studies to examine PNA in both chronic and acute medications in an integrated healthcare setting. Strengths of study design include requiring membership and prescription benefits and the use of e-prescribing and eMR data, which allowed direct linkage of prescription orders to dispensing information and minimized the risk of outcome misclassification. Use of uniquely rich eMR data also allowed the inclusion of many more patient, prescriber, and prescription characteristics in the logistic regression model than in most other studies. In addition, the sample size for this study is very large (n = 398,025), which improves the robustness of our results. In comparison, Karter and colleagues (2009)7 examined 27,329 patients and Raebel and colleagues (2012)8 included 16,173 patients. A limitation of this study is the limited generalizability of our results to patient populations in nonintegrated healthcare systems, as well as Medicaid or uninsured patients, who were not well represented in this study.

The results of this study help clinicians and healthcare decision makers to understand the rate of PNA and how it varies by therapeutic drug group in an integrated healthcare setting. In addition, these results may assist clinicians and payers in making informed decisions when designing and implementing cost-effective patient interventions to improve adherence to chronic and acute medications, which may be one way to improve patient health outcomes while decreasing healthcare costs. Future research should focus on measuring the effect of PNA on patient health outcomes and cost.

The authors would like to acknowledge Southida S. Vansomphone, PharmD, for her valuable help in developing and validating the accuracy of the data used in this study.

Author Affiliations: Pharmacy Analytical Services (JS, TCC), Kaiser Permanente Southern California, Downey, CA; Pharmaceutical Economics and Policy (JS, JSM), University of Southern California School of Pharmacy, Los Angeles, CA; Pfizer Inc (RJS, MU, MCD), New York, NY.

Funding Source: This study was funded through an unrestricted graduate fellowship grant by Pfizer Inc.

Author Disclosures: Dr Sanchez and Ms Udall report employment with Pfizer Inc, the funder of the study. The other authors (JS, JSM, MCD, TCC) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JS, RJS, JSM, MU, MCD, TCC); acquisition of data (JS); analysis and interpretation of data (JS, RJS, JSM, MU, MCD, TCC); drafting of the manuscript (JS, RJS, JSM, MCD); critical revision of the manuscript for important intellectual content (JS, RJS, JSM, MU, MCD, TCC); statistical analysis (JS); provision of study materials or patients (JSM); obtaining funding (JSM); administrative, technical, or logistic support (RJS); and supervision (RJS).

Address correspondence to: Janet Shin, PharmD, 12254 Bellflower Blvd, Downey, CA 90242. E-mail:
1. Cramer JA, Roy A, Burrell A, et al. Medication compliance and persistence: terminology and definitions. Value Health. 2008;11(1):44-47.

2. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medicationadherence on hospitalization risk and healthcare cost. Med Care. 2005;43(6):521-530.

3. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005; 353(5):487-497.

4. Roebuck MC, Liberman JN, Gemmill-Toyama M, Brennan TA. Medication adherence leads to lower health care use and costs despite increased drug spending. Health Aff (Millwood). 2011;30(1):91-99.

5. Beardon PH, McGilchrist MM, McKendrick AD, McDevitt DG, MacDonald TM. Primary non-compliance with prescribed medication in primary care. BMJ. 1993;307(6908):846-848.

6. Gadkari AS, McHorney CA. Medication nonfulfillment rates and reasons: narrative systematic review. Curr Med Res Opin. 2010;26(3): 683-705.

7. Karter AJ, Parker MM, Moffet HH, et al. New prescription medication gaps: a comprehensive measure of adherence to new prescriptions. Health Serv Res. 2009;44(5, pt 1):1640-1661.

8. 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.

9. Liberman JN, Hutchins DS, Popiel RG, et al. Determinants of primary nonadherence in asthma-controller and dyslipidemia pharmacotherapy. Am J Pharm Benefits. 2010;2(2):111-118.

10. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40(5):373-383.

11. Ross S, Samuels E, Gairy K, et al. A meta-analysis of osteoporotic fracture risk with medication nonadherence. Value Health. 2011;14(4): 571-581.

12. Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20,536 highrisk individuals: a randomised placebo-controlled trial. Lancet. 2002; 360(9326):7-22.

13. Lazar LD, Pletcher MJ, Coxson PG, Bibbins-Domingo K, Goldman L. Cost-effectiveness of statin therapy for primary prevention in a lowcost statin era. Circulation. 2011;124(2):146-153.

14. Pham AN, Datta SK, Weber TJ, Walter LC, Colón-Emeric CS. Cost-effectiveness of oral bisphosphonates for osteoporosis at different ages and levels of life expectancy. J Am Geriatr Soc. 2011;59(9):1642-1649.

15. Wroth TH, Pathman DE. Primary medication adherence in a rural population: the role of the patient-physician relationship and satisfaction with care. J Am Board Fam Med. 19(5):478-486.

16. Kennedy J, Tuleu I, Mackay K. Unfilled prescriptions of medicare beneficiaries: prevalence, reasons, and types of medicines prescribed. J Manag Care Pharm. 2008;14(6):553-560.

17. Shah NR, Hirsch AG, Zacker C, et al. Factors associated with first-fill adherence rates for diabetic medications: a cohort study. J Gen Intern Med. 2009;24(2):233-237.

18. Shah NR, Hirsch AG, Zacker C, et al. Predictors of first-fill adherence for patients with hypertension. Am J Hypertens. 2009;22(4):392-396.
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