Factors of Hyperlipidemia Medication Adherence in a Nationwide Health Plan

The American Journal of Managed CareApril 2012
Volume 18
Issue 4

Medication adherence in hyperlipidemia remains poor on a nationally representative level. Predictors of medication adherence from a nationally representative commercial health plan are reported.


To evaluate the factors associated with nonadherence in a nationally representative sample of patients receiving lipid-lowering therapy (LLT).

Study Design:

Retrospective database analysis of treatment-naïve (1 year without LLT claim) hyperlipidemia patients evidenced by a new pharmacy claim for lipid-lowering therapy.


Pharmacy and medical claims data were analyzed for currently enrolled members receiving a new LLT from 2007 to 2008. Adherence was defi ned as percentage of days covered (PDC) and values <80% were used to categorize nonadherent patients. Independent variables included patient demographics, pharmacy utilization, and medical conditions. Stepwise logistic regression was used to predict the odds of nonadherence. Laboratory data variables were incorporated in an exploratory sub-analysis to test the robustness of the original model.


Adherence with LLT was estimated in 88,635 patients. Sixty-fi ve percent of patients were nonadherent (mean PDC = 0.33). Compared with statins, patients treated with bile acid sequestrants were 6.75 times as likely to be nonadherent (P <.001). Signifi cant (P <.05) predictors of nonadherence included age 45 to 55 years (ref: age >75 y) (odds ratio [OR]: 1.11); prior diabetes diagnosis (OR: 1.15); and unique pharmacies used (OR = 1.10). Signifi cant factors reducing nonadherence include male gender (OR: 0.77); previous heart attack (OR: 0.82); prior adherent behavior (OR: 0.89); and unique physicians seen (OR: 0.97). Compared with no copayment, patients with $5 to $30 copayments had a signifi cant reduction in the likelihood of nonadherence.


Medication adherence remains poor in patients receiving LLT. Treatment outcomes and healthcare resource use may be improved by prioritizing adherence programs in at-risk patient populations.

(Am J Manag Care. 2012;18(4):193-199)Adherence interventions may be best prioritized in populations at high risk for medication nonadherence. Compared with associated reference groups, our study suggests:

  • Women, middle-aged patients, those treated with bile acid sequestrants, patients with diabetic and neurologic comorbid conditions, and those using multiple pharmacies had worse medication adherence.

  •  Medication adherence was improved in men; patients with documented heart attacks; those with prior medication adherence; those with more routine physician use; and patients with copayments ranging from $5 to $30.

  •  Laboratory data suggested patients furthest from serum lipid goals have the highest likelihood of medication nonadherence.

Over 800,000 Americans die from cardiovascular disease (CVD) every year. Approximately 75% of fatal cardiac events are due to heart attack (acute myocardial infarction [AMI]) and stroke (cerebrovascular accident [CVA]).1 Those who survive these events have a reduction in life expectancy of up to 15 years2 and quality of life of nearly 50%.3-5 The direct and indirect societal costs attributable to CVD exceed $475 billion annually.1

A substantial body of clinical research supports lipid-lowering therapies (LLTs) as the primary therapeutic modality for reducing the risk of cardiovascular outcomes. Statins are the primary treatment modality and may confer lipid reductions of 10% to 60%.6 In the primary prevention of CVD, lipid reductions with low to medium potency statins have been shown to reduce the incidence of fatal and nonfatal cardiovascular outcomes signifi cantly (by 30% to 40%).7,8 High potency statins may deliver a larger reduction in the risk of AMI and CVA (36% to 48%).9 Depending on patient need, statins, as well as fibric acid derivatives, bile acid sequestrants, niacin preparations, cholesterol absorption inhibitors, and free fatty acid products, can be individualized to help meet clinical, economic, and humanistic goals.6

There are significant clinical data demonstrating the effi cacy of LLT in CVD; however, translating the benefi ts from clinical trials into realworld outcomes is hampered, in part, by low medication adherence. Clinical trials demonstrate that adequate medication adherence over 5 years is necessary to reduce the negative clinical outcomes associated with CVD.7,8 However, in clinical trials for LLT, full medication adherence is achieved through treatment oversight using clinical staff or by incorporating inclusion criteria that preselect patients on the basis of demonstrated medication adherence.10

Real-world adherence with LLT does not typically achieve the levels observed in clinical trials. In a study of 34,501 Medicaid patients, medication adherence with LLT dropped from 45% to 36% 3 months after LLT initiation and 79% to 56% 6 months after initiation.11 Retrospective observational studies have found that women,11,12 nonwhite race, usage of a Medicaid plan (odds ratio [OR]: 1.60), and age >75 years were associated with medication nonadherence.13 Clinically, patients with depression and dementia,13 a hospitalization in the year prior to the start of LLT,11 or treatment with anxiolytics14 were associated with nonadherence. Treatment attributes including higher daily doses of LLT15 and medication switching reduced medication adherence.12 Across studies, signifi cant heterogeneity exists in the patient populations evaluated, model variables included, definitions of medication adherence, and reporting.

The purpose of this study was to study factors associated with nonadherence in a nationally representative sample of patients receiving LLT for dyslipidemia using several new factors not included in previous studies. This study described characteristics of adherent and nonadherent patients; identifi ed factors with signifi cant impact on nonadherence; and suggested strategies to operationalize these results.



Data for this study were derived from paid pharmacy and medical claims and serum lipid laboratory data from a commercial health plan with members in 14 states from 4 regions of the United States. Data covered January 1, 2006, to April 30, 2009. Patient inclusion criteria included: receipt of LLT (“index drug”) between May 1, 2007, and April 30, 2008; 1-year continuous eligibility before and after the date of index drug receipt (“index date”); and a 1-year period without an LLT claim prior to receipt of the index LLT. Medicare and Medicaid enrollees were excluded.

Pharmacy claims included demographic and pharmacy utilization information such as medications, costs, quantities, and days of supply. Medical claims captured International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9- CM) codes and their respective diagnosis dates. Serum lipid values were retrieved for a sub-sample of patients for whom laboratory data were available. Data were de-identified and the study was approved by the Institutional Review Board at The University of Southern California.

Unit of Analysis

The unit of analysis was the patient’s first observed episode of LLT. Each patient’s pharmacy, medical claims, and laboratory data (where available) were summarized into 1 patient-level claims data set with multiple summary variables based on the index date of first LLT. The index drug was the key variable from which adherence estimates were derived.

Percentage of days covered (PDC) was used to estimate medication adherence. PDC was defi ned as the days' supply of LLT dispensed in the fi rst year following the index date divided by 365 days, multiplied by 100. The days’ supply of LLT was calculated as the total amount of medication provided divided by the frequency of administration (60 tablets, dosed twice daily equals a 30-day supply). PDC offers the advantage of measuring both medication adherence and persistence,16 both of which are important aspects of disease control in dyslipidemia. From a health plan perspective, the fixed observation period (1 year) used in PDC calculations aligns with a typical member enrollment period and payer decision-making timelines. PDC <80% was used as the cutoff for nonadherence with LLT and was the primary outcome variable.

Predictors of Suboptimal Adherence

Potential predictors of nonadherence included patient demographics; dichotomous variables based on selected ICD-9-CM codes; and variables capturing pharmacy and healthcare provider utilization in the year before the receipt of the index drug. Characteristics of the index drug such as copayment level and therapeutic class were also used as predictive covariates. Prior adherent behavior was defi ned as the ratio of total number of prescriptions filled and refi lled to the total number of unique medications used. A sub-analysis of patients with laboratory data was also performed. Evaluated here were the predictive value of baseline low-density lipoprotein (LDL) values; unique laboratory visits; and elapsed time between the most recent LDL laboratory and index date on nonadherence. This analysis was also meant to act as a sensitivity analysis to test the robustness of the full primary model.

Statistical Methods

The population was divided into adherent and nonadherent members, and descriptive statistics were compared using student t tests and χ2 tests. PDC <80% was regressed on covariates derived from demographic, pharmacy, medical, and laboratory data using stepwise logistic regression. Multilevel categorical variables such as age categories were forced into the model if any 1 age category was found to be significant. Signifi cant predictors of nonadherence were identified at the P <.05 level. All statistical analyses were performed using SAS version 9.1 (SAS Institute Inc, Cary, North Carolina).


Descriptive Statistics

Table 1

Descriptive statistics can be found in . In total, 88,635 patients were identifi ed and 65% (n = 58,074) were nonadherent with LLT over the fi rst posttreatment year. Mean PDC was significantly (P <.05) lower in nonadherent versus adherent patients (PDC = 33% vs 100%). Adherent patients were significantly older (53.7 vs 51.2 years) and male (55.6% vs 51.1%) compared with nonadherent patients. The number of comorbid conditions recorded in nonadherent patients was significantly greater than in adherent patients. Statins were the most commonly initiated treatment, and utilization was signifi cantly higher in adherent patients. Where available, baseline LDL values were significantly higher for nonadherent patients (144.5 vs 140.6 mg/dL).

Predictors of Nonadherence

Table 2

The results of the stepwise regression of the predictors of nonadherence with LLT are displayed in . This procedure resulted in 34 predictors of nonadherence with differential levels of signifi cance.

Drug Class: There were 36 drug products used to initiate treatment for dyslipidemia which were further grouped into therapeutic class variables. Patients using bile acid sequestrants were 6.7 times as likely to be nonadherent as patients treated with statins (95% confi dence interval [CI]: 5.8-7.8). Patients using niacin products, free fatty acid products, cholesterol absorption inhibitors, and combination products were all significantly more likely to be nonadherent (P <.002). The impact of treatment with fi brates on nonadherence was nonsignifi cant.

Index Copayment: Compared with patients having a $0 copayment, patients with copayments of $5 to $10, $10 to $20, and $20 to $30 (taken together, $5 to $30) had a significantly decreased likelihood of nonadherence of 12%, 12%, and 6%, respectively (all P <.01). Copayment levels of $0 to $5 and greater than $30 also decreased nonadherence, but these effects were nonsignifi cant.

Age, Sex, Region: A signifi cant gender effect was found: male patients were 23% less likely to be nonadherent versus females (P <.001). Increasing age was found to decrease nonadherence. Patients aged 25 to 35 years were 26% less likely to be nonadherent versus those under 25 years of age. Patients over 75 years of age were 67% less likely to be nonadherent than the youngest patients. Patients located in the Central and East regions had roughly a 6% and 16% increased likelihood of nonadherence versus those in the West region, respectively (P <.005).

Health Care Practitioner Utilization: Patients who visited more physicians had a signifi cantly decreased risk of nonadherence (P <.001). In contrast, each additional pharmacy used to dispense prescriptions in the pre-period increased the risk of nonadherence by 10% (P <.001).

Comorbidity Profile: Patients experiencing an AMI were 18% less likely to be nonadherent (P <.02). Patients with atherosclerotic diseases and patients with neurological disorders were both found to be 10% more likely to be nonadherent (P <.01). Further, patients with diabetes had a 15% higher likelihood of nonadherence versus those without diabetes (P <.001). A diagnosis of hypertension or coronary heart disease had no signifi cant impact on the likelihood of complying with medications for dyslipidemia.

Prior Adherent Behavior: An increase in the number of refills per medication used in the pre-period (better theoretical adherence) reduced the likelihood of nonadherence by 12% (P <.001) per unit increase in this ratio. While this proxy measure of prior adherence behavior met criteria for inclusion into the model, its components did not. Total prescriptions fi lled and total unique medications used did not meet the P <.05 level of significance.

Laboratory Data Sub-Analysis

Table 3

presents the results from the sub-analysis of nonadherence in patients with laboratory data (n = 16,400).

Baseline LDL: As described in the methods, the baseline, pre-treatment LDL value (LDL value recorded on or before LLT initiation) was used as a potential predictor of medication adherence. In general, patients with higher baseline LDL values had an increased likelihood of nonadherence. Elevated baseline LDL increased the likelihood of nonadherence significantly, by 20% to 22% (P <.003) across all LDL level categories, compared with those patients already at goal (LDL <100 mg/dL). Patients with baseline LDL greater than 180 mg/dL were found to have 32% increased probability of nonadherence versus patients at goal (P <.001).

Time Between Tests and Treatment: The absolute value of the time between most recent LDL laboratory test and index date was also found to have an infl uence on a patient’s likelihood of nonadherence. Each additional day between the final laboratory and index date decreased the likelihood of nonadherence by 0.006% (P = .0006).

Unique Laboratory Tests: The variable was estimated based on all the laboratories received for each patient, in addition to LDL, in the year prior to initiating drug therapy. It was found that each unique laboratory “visit” decreased the probability of nonadherence by 3.4% (P = .02).

Sensitivity of Results From the Primary Model to Laboratory Data Inclusion: In the laboratory data model, significant parameter estimates from the full model became nonsignificant. This was likely due to the signifi cant reduction in sample size in the sub-analysis, as none of these changes affected the direction or magnitude of the estimated effect. In contrast, some nonsignificant predictors in the full model became signifi cant in the laboratory data sub-analysis. Compared with those initiating

treatment with statins, patients treated with fi bric acid products were 29% more likely to be nonadherent (P = .0003). Similarly, a hypertension diagnosis (vs no hypertension diagnosis) in the sub-analysis was found to increase the probability of nonadherence signifi cantly, by 10% (P = .02). Lastly, index copayments over $30 decreased nonadherence by 16% (P = .01) compared with $0 index copayment. The association and direction of other copayment levels on adherence was unchanged in the sub-analysis (eg, $0 to $5, $5 to $10, $10 to $20, and $20 to $30). Although nonsignifi cant in the full model, the direction of effect of fi bric acid index prescription, hypertension diagnosis, and index copayment >$30 on nonadherence was not influenced by the addition of laboratory data.


The purpose of this study was to evaluate the factors associated with nonadherence in a nationally representative sample of patients receiving LLT for dyslipidemia. To meet this objective, 88,635 patients naïve to LLT were captured from commercial health plans across the United States and predictive models regressing nonadherence with LLT on demographic, clinical, and treatment characteristics were developed from pharmacy and medical data. The addition of laboratory data covariates were used to construct an exploratory analysis investigating the effect of laboratory data on the sensitivity of the covariates identifi ed in the primary analysis.

The results from this study raise interesting questions regarding patient- and healthcare-level factors associated with medication nonadherence. The large and signifi cant increase in nonadherence with bile acid sequestrants draws an expected link between low adherence, frequent daily dosing, and a troublesome side effect profi le. An increase in nonadherence in those using more unique pharmacies suggests that pharmacy benefi t managers may reduce nonadherence by identifying and reducing polypharmacy. Previous studies have reported that higher pharmacy copayments are associated with reductions in medication adherence; however, the present study found that modest copayments (compared with no copayment) did not adversely affect medication adherence after controlling for baseline demographic and clinical characteristics. Other data that may help explain these fi ndings, such as formulary tier placement and copayment amounts for alternatives, were not available. Future adherence research evaluating the infl uence of formulary design and cost-sharing structure would help clarify our understanding of how patient out-of-pocket costs impact medication adherence.

The study reported interesting connections between a patient’s clinical profi le and medication nonadherence. The reduced risk of nonadherence in patients seeing their physician more often can be interpreted as a blend of both patient behaviors and physician support helping to improve medication adherence outcomes. Tangible, negative health outcomes, such as an AMI, may improve medication adherence, while the opposite is true for less pronounced conditions such as atherosclerosis or diabetes. Although exploratory, we found that disease severity as measured by the distance from LDL goal increased the likelihood of nonadherence. This may indicate that initial laboratory values are both a measurement of disease severity and a proxy for poor adherence with therapeutic lifestyle modifi cations. Intuitively, as baseline LDL values increase, so does the need to proactively address medication adherence.

Some results from this study share similar themes with the primary literature, yet other results contrast with previous studies. Results agreeing with previous research include reduced nonadherence in those with prior AMI17,18 and increased nonadherence in women and those with neurological disease.11,15 Other studies showed that gender had no effect on nonadherence; older age was shown to increase nonadherence; diabetes had a nonsignifi cant effect on nonadherence; and the use of

additional unique physicians was not shown to signifi cantly impact nonadherence; all of which disagreed with this study.11,17,18

As with all studies, there are limitations to consider upon interpretation. First, Medicare and Medicaid recipients were excluded from this analysis, which may have introduced selection bias. Methodologically, PDC as a proxy for medication adherence is acceptable; however, the effect of using alternative adherence definitions on study outcomes was not evaluated. As with any retrospective analysis, the relationship between covariates and nonadherence cannot be interpreted as causal, as the association may be confounded by latent or unmeasured variables.

There are potential applications for this research in commercial health plans. These results may help target adherence interventions in patient populations such as females with neurological diseases, new to LLT. With increased utilization of electronic medical records, predictive models may help develop real-time, nonadherence risk scores and may proactively initiate conversations about medication adherence. Pharmacoeconomic models for LLT in new-start patients can utilize the reported odds ratios in product assessments of budget impact or cost-effectiveness to adjust the proportion of patients remaining on LLT at the end of 1 year. Across applications, medication adherence research may be used to more effi ci ently allocate healthcare resources by targeting drug budgets and prioritizing adherence programs in at-risk patient populations.


The authors would like to acknowledge T. Jeffrey White, PharmD, MS, for his contributions to this project.

Author Affiliations: From Department of Clinical Pharmacy and Pharmaceutical Economics and Policy (PW, JM), The University of Southern California, Los Angeles, CA; WellPoint, Inc (JW), Thousand Oaks, CA.

Funding Source: None.

Author Disclosures: Dr McCombs reports that he has received paid consultancies from Bristol-Myers Squibb and Pfi zer, grants from Bristol-Myers Squibb and Takeda, pending grants from Takeda, and has presented posters at Digestive Disease Week conference in Chicago and at The Liver meeting in Boston. The other authors (PW, JJW) report no relationship or fi nancial interest with any entity that would pose a confl ict of interest with the subject matter of this article.

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

Address correspondence to: Phillip Wiegand, PharmD, MS, 716 1/2 Second St, Annapolis, MD 21403. E-mail: phillip.wiegand@gmail.com.1. Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics—2011 update: a report from the American Heart Association. Circulation. 2011;123(4):e18-e209.

2. Peeters A, Mamun AA, Willekens F, Bonneux L. A cardiovascular life history: a life course analysis of the original Framingham Heart Study cohort. Eur Heart J. 2002;23(6):458-466.

3. Schweikert B, Hunger M, Meisinger C, et al. Quality of life several years after myocardial infarction: comparing the MONICA/KORA registry to the general population. Eur Heart J. 2009;30(4):436-443.

4. Tengs TO, Lin TH. A meta-analysis of quality-of-life estimates for stroke. Pharmacoeconomics. 2003;21(3):191-200.

5. Pignone M, Earnshaw S, Tice JA, Pletcher MJ. Aspirin, statins, or both drugs for the primary prevention of coronary heart disease events in men: a cost-utility analysis. Ann Intern Med. 2006;144(5): 326-336.

6. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) fi nal report. Circulation. 2002;106(25):3143-3421.

7. Downs JR, Clearfi eld M, Weis S, et al. Primary prevention of acute coronary events with lovastatin in men and women with average cholesterol levels: results of AFCAPS/TexCAPS. Air Force/Texas Coronary Atherosclerosis Prevention Study. JAMA. 1998;279(20):1615-1622.

8. Shepherd J, Cobbe SM, Ford I, et al; West of Scotland Coronary Prevention Study Group. Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. N Engl J Med. 1995;333(20): 1301-1307.

9. Colhoun HM, Betteridge DJ, Durrington PN, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet. 2004;364(9435):685-696.

10. Ridker PM, Danielson E, Fonseca FA, et al. Rosuvastatin to preventvascular events in men and women with elevated C-reactive protein. N Engl J Med. 2008;359(21):2195-2207.

11. Benner JS, Glynn RJ, Mogun H, et al. Long-term persistence in use of statin therapy in elderly patients. JAMA. 2002;288(4):455-461.

12. Thiebaud P, Patel BV, Nichol MB, Berenbeim DM. The effect of switching on compliance and persistence: the case of statin treatment. Am J Manag Care. 2005;11(11):670-674.

13. Blake GJ, Ridker PM, Kuntz KM. Potential cost-effectiveness of Creactive protein screening followed by targeted statin therapy for the primary prevention of cardiovascular disease among patients without overt hyperlipidemia. Am J Med. 2003;114(6):485-494.

14. Perreault S, Blais L, Lamarre D, et al. Persistence and determinants of statin therapy among middle-aged patients for primary and secondary prevention. Br J Clin Pharmacol. 2005;59(5):564-573.

15. Sung JC, Nichol MB, Venturini F, et al. Factors affecting patient compliance with antihyperlipidemic medications in an HMO population. Am J Manag Care. 1998;4 (10):1421-1430.

16. Peterson AM, Nau DP, Cramer JA, et al. A checklist for medication compliance and persistence studies using retrospective databases. Value Health. 2007;10(1):3-12.

17. Grant RW, O’Leary KM, Weilburg JB, Singer DE, Meigs JB. Impact of concurrent medication use on statin adherence and refi ll persistence. Arch Intern Med. 2004;164(21):2343-2348.

18. 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.

Related Videos
© 2023 MJH Life Sciences
All rights reserved.