Currently Viewing:
The American Journal of Managed Care August 2012
Racial Disparities in African Americans With Diabetes: Process and Outcome Mismatch
John B. Bulger, DO; Jay H. Shubrook, DO; and Richard Snow, DO, MPH
Cardiac Risk Is Not Associated With Hypertension Treatment Intensification
Jeremy B. Sussman, MD, MS; Donna M. Zulman, MD, MS; Rodney Hayward, MD; Timothy P. Hofer, MD, MS; and Eve A. Kerr, MD, MPH
A Technology Solution for the High-Tech Diagnostic Imaging Conundrum
Leif I. Solberg, MD; Cally Vinz, MBA, RN; and Jim E. Trevis, BA
Currently Reading
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
Patient Medical Group Continuity and Healthcare Utilization
Louise H. Anderson, PhD; Thomas J. Flottemesch, PhD; Patricia Fontaine, MD, MS; Leif I. Solberg, MD; and Stephen E. Asche, MA
Cost-Sharing and Initiation of Disease-Modifying Therapy for Multiple Sclerosis
John Romley, PhD; Dana Goldman, PhD; Michael Eber, BSE; Homa Dastani, PhD; Edward Kim, MD, MBA; and Swetha Raparla, BPharm, MS
Which Components of Health Information Technology Will Drive Financial Value?
Lisa M. Kern, MD, MPH; Adam Wilcox, PhD; Jason Shapiro, MD; Rina V. Dhopeshwarkar, MPH; and Rainu Kaushal, MD, MPH
Preventable Hospitalizations and Medicare Managed Care: A Small Area Analysis
Jayasree Basu, PhD
Home Care Program for Patients at High Risk of Hospitalization
Stuart Levine, MD, MHA; Bernard A. Steinman, PhD; Karol Attaway, MHA; Tyler Jung, MD; and Susan Enguidanos, PhD
Questioning the Widely Publicized Savings Reported for North Carolina Medicaid
Al Lewis, JD
Measuring Migraine-Related Quality of Care Across 10 Health Plans
Valerie P. Pracilio, MPH; Stephen Silberstein, MD; Joseph Couto, PharmD, MBA; Jon Bumbaugh, MA; Mary Hopkins, RN; Daisy Ng-Mak, PhD; Cary Sennett, MD, PhD; and Neil I. Goldfarb, BA
Impact of Point-of-Care Case Management on Readmissions and Costs
Andrew Kolbasovsky, PsyD, MBA; Joseph Zeitlin, MD; and William Gillespie, MD
Impact of a Program Encouraging the Use of Generic Antipsychotics
Eric D. A. Hermes, MD; Michael Sernyak, MD; and Robert Rosenheck, MD

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.
Objectives: To measure primary nonadherence (PNA) rates for 10 therapeutic drug groups and identify factors associated with PNA to chronic and acute medications.

Study Design: Retrospective cohort study.

Methods: New prescriptions written in an integrated healthcare system for study drugs were identified between December 1, 2009, and February 28, 2010. PNA was defined as the failure to fill a prescription within 14 days of when it was written. PNA rates were calculated by drug group and descriptive statistics were performed. Multivariable logistic regression was used to identify significant patient, provider, and prescription characteristics associated with PNA. Results were stratified by acute versus chronic treatment.

Results: A total of 569,095 new prescriptions were written during the 3-month period. Across all drug groups, the PNA rate was 9.8%. PNA rates for individual drug groups varied and were highest for osteoporosis medications (22.4%) and antihyperlipidemics (22.3%). Patients who filled at least 1 prescription in the prior year (odds ratio [OR], 95% confidence interval [CI] for acute = 0.06 [0.06-0.07], for chronic = 0.11 [0.10-0.12]) or had a prescription for a symptomatic disease (OR = 0.51 [0.48-0.53]) were more likely to fill their prescription. Patients were more likely to be primary nonadherent if they were black (OR acute = 1.30 [1.25-1.36], chronic = 1.26 [1.18-1.33]) or treatment-naïve to therapy (OR acute = 2.52 [2.36-2.7], chronic=1.07 [1.03-1.12]).

Conclusions: Overall PNA was 9.8% but individual PNA rates varied by therapeutic drug group. Factors of PNA were mostly consistent across drug groups, but some depended on whether the treatment was acute or chronic.

(Am J Manag Care. 2012;18(8):426-434)
This retrospective cohort study of 569,095 new prescriptions in an integrated healthcare setting measures primary nonadherence (PNA) rates for 10 drug groups and examines patient, prescriber, and prescription factors associated with primary adherence to chronic and acute medications.

  • PNA rates vary by drug group and are highest for antiosteoporosis medications and antihyperlipidemics.

  • Patients who filled at least 1 prescription in the prior year or had a prescription for symptomatic disease were more likely to fill their prescription.

  •  Patients who were treatment-naïve to disease therapy, black or Hispanic, or had certain baseline comorbidities were less likely to fill their prescription.
Healthcare professionals and payers are constantly looking for ways to improve patient health outcomes while decreasing costs. One possible approach to this challenge is to improve medication adherence, which refers to the extent to which patients take their medications as prescribed by their provider.1 Prior research has suggested that improved adherence is associated with better health outcomes and lower overall health costs for certain chronic diseases such as diabetes, hypertension, hypercholesterolemia, and congestive heart failure.2-4

Most adherence research has focused on “secondary nonadherence,” which occurs when patients do not refill their prescriptions on time or they discontinue their medications altogether. A less-studied form of medication nonadherence is called “primary nonadherence” (PNA), which occurs when patients fail to pick up a newly prescribed prescription from the pharmacy.5 Incidence, causes, and outcomes of PNA are relatively unknown compared with those of secondary nonadherence. Since most chronic and acute diseases in the United States are often managed by prescription medications, PNA could potentially be a significant factor in determining healthcare outcomes and costs, especially if the rate of PNA is high.

PNA rates vary widely in the literature, ranging from 0.5% to 57.1% depending on the study setting, therapeutic drug group, and methodological factors such as the way PNA is defined.6 PNA studies in integrated healthcare delivery systems have usually been limited to medications used to treat chronic diseases such as diabetes, hyperlipidemia, and hypertension, with PNA rates ranging from 3.2% to 13% depending on the drug therapeutic group.7,8 Examining PNA to both chronic and acute medications provides a more comprehensive understanding. Identifying risk factors associated with PNA may help clinicians target patients who are at risk.

In this study, we examined the rates and risk factors of PNA in an integrated healthcare setting across 10 drug groups which included both chronic and acute medications. Study objectives were 1) to measure PNA rates for 10 therapeutic drug groups, and 2) to identify patient, prescriber, and prescription risk factors for PNA to chronic and acute medications.



This retrospective cohort study was conducted at Kaiser Permanente Southern California (KPSC) in Downey, California, and was approved by the KPSC Institutional Review Board. KPSC is a large managed care organization providing comprehensive healthcare to an estimated 3.3 million members at 14 medical centers. Patient information on demographics and healthcare encounters (diagnoses, procedures, laboratory results, and prescriptions) are captured in the Kaiser electronic medical record (eMR) system. KPSC members receive the majority of their healthcare and prescriptions at Kaiser Permanente facilities. Medical centers vary in population size from Kern County—serving only 92,745 patients, to San Diego—serving 490,154 members. All prescribers enter all prescriptions in the eMR system and the information is electronically sent to the Kaiser pharmacy of the patient’s choice. Copay information is only available for sold prescriptions.

Inclusion and Exclusion Criteria

A total of 10 therapeutic drug groups were selected based on disease prevalence, clinical interest, or the potential impact of PNA on healthcare outcomes: anti-infectives, analgesics, migraine medications, antidiabetics, osteoporosis medications, cardiovascular agents, antihyperlipidemics, antiasthmatics, antidepressants, and anticoagulants. The first 3 drug groups were categorized as acute therapy and the remaining were categorized as chronic therapy. Each therapeutic drug group consists of several drug classes (eAppendix A, available at For example, the antidiabetics therapeutic drug group includes drug classes such as insulin and sulfonylureas. A total of 874 individual drug products were included in the study.

New prescriptions for a study drug prescribed between December 1, 2009, and February 28, 2010, were included. A new prescription was defined as a drug with no prior dispensing in the same drug class during the 12 months before the date the prescription was ordered (index date). For example, a sulfonylurea prescription would not be considered new if a prescription for a drug in the sulfonylurea class was dispensed during the 12 months prior to the index date. This minimized the effect of preexisting drug supplies on current filling and also excluded prescriptions used as augmentation therapy or those episodes where drugs were switched within a drug class.

Patients were required to have continuous membership and drug benefits for 12 months before and after the index date. The post-index drug benefit criterion was designed to maximize the likelihood that patients filled their medications at Kaiser rather than at outside pharmacies. The 12-month pre-index period was used to identify baseline patient characteristics. Prescriptions that were renewed, transferred from an outside pharmacy, verbally ordered by the prescriber, printed out in the doctor’s office, or were hard copy prescriptions from outside providers were excluded from the study.

Prescriptions that were switched to a different drug or later cancelled by the prescriber before being picked up by the patient were also excluded. In addition, prescriptions that had missing patient demographic information or that were filled at a Kaiser pharmacy located outside of Southern California were excluded. Lastly, since pregnancy complicates medication therapy and may result in drug discontinuation, prescriptions written for female patients who had become pregnant (based on gestation date) during the study period were excluded.

Study Outcome

The primary study outcome was PNA, which was defined as the failure to fill a prescription within 14 days of the index date. Previous studies have demonstrated that most patients fill their medications within the first 2 weeks of the index date.7,9 Sensitivity analysis was performed to examine changes in PNA rates when the definition of PNA varied from 14 to 30 and 90 days.

Patient, Prescriber, and Prescription Characteristics

Patient characteristics included patient age at index date and gender as recorded in the eMR. Patient race was geocoded based on 2010 census tract data. The patient’s zip code was linked to US Census 2000 data to assign median household income. The 12-month pre-index period was used to identify baseline comorbidities based on occurrence of at least 1 International Classification of Diseases, Ninth Revision, Clinical Modification code. Selected disease comorbidites included the 17 standard diseases used to calculate the Charlson Comorbidity Index10 and 5 additional disease states (Alzheimer’s disease, hyperlipidemia, migraine, depression, and osteoporosis) corresponding to the therapeutic drug groups examined in the study.

The pre-index period was also used to calculate baseline healthcare utilization, which included the number of prior clinic visits, emergency department (ED) visits, or hospitalizations. Patients who did not have any prior use of prescriptions in the same therapeutic drug group were flagged as treatment-naïve to disease therapy. For example, diabetic patients who were treatment- naïve did not use sulfonylureas or any other classes of antidiabetic medications, such as insulin or biguanides, during the pre-index period. A flag was created to indicate if the patient had filled at least 1 prescription in the pre-index period as a measure of the patient’s compliant medication behavior. Pharmacy benefits (dual insurance, primary/dependent, and plan type: Medicare, Medicaid, commercial) were also captured.

Prescriber characteristics included prescriber age and gender, as well as prescriber race/ethnicity, years of experience practicing at KPSC, and specialty. Dummy variables were used to indicate if the patient and physician were of the same gender or race/ethnicity in order to assess whether having a prescriber of the same gender or race/ethnicity would influence the patient-prescriber relationship and result in improved primary adherence.

Characteristics of the index prescription were identified from pharmacy records and included acute versus chronic therapy, generic versus brand, pharmacy regional location, month prescribed, and weekday versus weekend prescribing. Since copay information was unavailable for prescriptions that were not filled, it was not included in the analysis. The total number of prescriptions written for study drugs on the index date was examined to investigate if patients given multiple medications and likely facing an increased pill and copay burden are more likely to be primary nonadherent. Lastly, dummy variables were used to indicate if the prescription was used to treat symptomatic disease, and included any medication in the following drug groups: antimigraine, analgesics, anti-infectives, antiasthmatics, and antidepressants.

Statistical Analyses

This analysis was performed at the prescription level, so any patient may have had more than 1 observation in the data set. PNA rates were calculated overall and by drug group. The frequency distribution when new prescriptions were filled relative to the index date was examined. Descriptive statistics using t tests and χ2 tests were used to compare unadjusted differences in characteristics of filled prescriptions with those of unfilled prescriptions.

Multivariable logistic regression was used to calculate odds ratios (ORs) and identify significant factors associated with PNA to study drugs when adjusting for other study variables. All patient, prescriber, and prescription characteristics were considered for inclusion in the model. Interaction terms with the acute versus chronic variable and patient characteristics were tested. The final inclusion of variables into the model was based on statistical significance or theoretical plausibility. A significance level of less than 0.05 was considered statistically significant. All statistical analyses were performed using the SAS statistical package version 9.1 (SAS Institute, Cary, North Carolina).


A total of 569,095 new prescriptions were written for 398,025 patients during the 3-month period that satisfied study inclusion and exclusion criteria (Figure 1). Most prescriptions were written for anti-infectives (43.5%) or analgesics (24.6%), followed by antiasthmatics (9.7%), cardiovascular (8.6%), antidepressants (4.8%), antihyperlipidemics (3.9%), antidiabetics (2.5%), antimigraine (1.0%), antiosteoporosis (0.9%), and anticoagulants (0.3%). The average (standard deviation [SD]) patient copay for sold prescriptions was $9.89 (14.06).

Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up