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The American Journal of Managed Care August 2016
Variation in US Outpatient Antibiotic Prescribing Quality Measures According to Health Plan and Geography
Rebecca M. Roberts, MS; Lauri A. Hicks, DO; and Monina Bartoces, PhD
Aligning Payment Reform and Delivery Innovation in Emergency Care
Jesse M. Pines, MD, MBA; Frank McStay, MPA; Meaghan George, MPP; Jennifer L. Wiler, MD, MBA; and Mark McClellan, MD, PhD
The Impact of Patient-Centered Medical Homes on Safety Net Clinics
Li-Hao Chu, PhD; Michael Tu, MS; Yuan-Chi Lee, MS; Jennifer N. Sayles, MD; and Neeraj Sood, PhD
The Impact of Formulary Drug Exclusion Policies on Patients and Healthcare Costs
James D. Chambers, PhD; Pallavi B. Rane, PhD; and Peter J. Neumann, ScD
Socioeconomic Disparities in Adoption of Personal Health Records Over Time
Jessica S. Ancker, PhD, MPH; Baria Hafeez, MS; and Rainu Kaushal, MD, MPH
Association of Part D Coverage Gap With COPD Medication Adherence
Yanni F. Yu, DSc, MA, MS; Larry R. Hearld, PhD; Haiyan Qu, PhD; Midge N. Ray, PhD; and Meredith L. Kilgore, PhD
The Financial Impact of Team-Based Care on Primary Care
Thomas E. Kottke, MD, MSPH; Michael V. Maciosek, PhD; Jacquelyn A. Huebsch, RN, PhD; Paul McGinnis, MD; Jolleen M. Nichols, RN; Emily D. Parker, PhD; and Juliana O. Tillema, MPA
Opinions on the Hospital Readmission Reduction Program: Results of a National Survey of Hospital Leaders
Karen E. Joynt, MD, MPH; Jose F. Figueroa, MD, MPH; E. John Orav, PhD; and Ashish K. Jha, MD, MPH
Choosing Wisely: Spine Imaging
Robert Ferrari, MD, MSc (Med), FRCPC, FACP, and Risha Gidwani, DrPH
Currently Reading
Association Among Change in Medical Costs, Level of Comorbidity, and Change in Adherence Behavior
Steven M. Kymes, PhD; Richard L. Pierce, PhD; Charmaine Girdish, MPH; Olga S. Matlin, PhD; Troyen Brennan, MD, JD, MPH; and William H. Shrank, MD, MSHS

Association Among Change in Medical Costs, Level of Comorbidity, and Change in Adherence Behavior

Steven M. Kymes, PhD; Richard L. Pierce, PhD; Charmaine Girdish, MPH; Olga S. Matlin, PhD; Troyen Brennan, MD, JD, MPH; and William H. Shrank, MD, MSHS
The authors found that comorbidity burden and the direction of behavioral change influence the relationship between adherence and medical spend. This could affect the cost-benefit considerations of medication adherence programs.

Objectives: Interventions to improve medication adherence are effective, but resource intensive. Interventions must be targeted to those who will potentially benefit most. We examined what heterogeneity exists in the value of adherence based on levels of comorbidity, and the changes in spending on medical services that followed changes in adherence behavior.

Study Design: Retrospective cohort study examining medical spending for 2 years (April 1, 2011, to March 31, 2013) in commercial insurance beneficiaries.

Methods: Multivariable linear modeling was used to adjust for differences in patient characteristics. Analyses were performed at the patient/condition level in 2 cohorts: adherent at baseline and nonadherent at baseline.

Results: We evaluated 857,041 patients, representing 1,264,797 patient therapies consisting of 40% high cholesterol, 48% hypertension, and 12% diabetes. Among those with 3 or more conditions, annual savings associated with becoming adherent were $5341, $4423, and $2081 for patients with at least diabetes, hypertension, and high cholesterol, respectively. The increased costs for patients in this group who became nonadherent were $4653, $7946, and $4008, respectively. Depending on the condition and the direction of behavior change, savings were 2 to 7 times greater than the value for individuals with fewer than 3 conditions. In most cases, the value of preventing nonadherence (ie, persistence) was greater than the value of moving people who are nonadherent to an adherent state.

Conclusions: There is important heterogeneity in the impact of medication adherence on medical spending. Clinicians and policy makers should consider this when promoting the change of adherence behavior.

Am J Manag Care. 2016;22(8):e295-e301
Take-Away Points
  • Adherence to medication in patients with chronic disease leads to improved health outcomes and lower costs. Yet, physicians, health systems, and health plans continue to face significant challenges in promoting adherence among patients. 
  • There are considerable differences in the value created by improving adherence behavior and preventing nonadherence, with respect to the baseline level of comorbidity and the direction of behavioral change (ie, adherent to nonadherent or vice versa). 
  • Clinicians and policy makers determining how to cost-effectively deploy adherence promotion programs—particularly in the setting of providers at risk for medical service spending—should consider these findings in setting population health management priorities.
There is broad consensus that improving adherence to essential medications is central to better management of chronic conditions,1,2 and there is an extensive body of literature that consistently demonstrates a strong relationship among better adherence, improved outcomes, and reduced medical costs.3,4 The focus of researchers has shifted to developing a better understanding of how to best target and deliver interventions to improve adherence behavior.

The principal challenge is that medication adherence is quite personal, and there are numerous reasons why patients fail to adhere, ranging from the complexity of therapy5 to medication costs,6 and to understanding the rationale for therapy and its appropriate administration.7 No single intervention has proven effective at addressing all of the barriers experienced by patients.

As a result, payers and risk-bearing providers increasingly seek evidence concerning the clinical benefits and cost-effectiveness of targeted interventions to improve adherence and health outcomes, and, thus, reduce costs. Due to concern that the clinical and cost benefits may not be accrued by those making the investment, churn in insurance membership can influence decisions about investing in interventions to improve adherence. Studies that help characterize the value of improving adherence within patient subpopulations are essential, particularly in the short term, so payers and risk-bearing providers can be confident that investments to improve adherence will provide a positive return on their investment.

In this study, we used a de-identified longitudinal data set to assess the effect of adherence on healthcare costs, and then characterized the value of adherence in a 1-year follow-up period. We assessed the value of adherence by several key characteristics. We studied how patient comorbidity is associated with the value of adherence to better understand how targeting patients with multiple conditions might influence both health outcomes and the return on investment in adherence promotion programs. We also explored the differences in changes in medical spending between individuals who remain adherent and those who become adherent. There has been no previous work, to our knowledge, examining this latter question; however, such evidence is critical for providers, pharmacies, and pharmacy benefits managers to develop and target interventions, and for payers and risk-bearing providers to make cost-effective investments in care improvement.


Using a nationally representative de-identified medical claims data set with claims for 24 consecutive months, we examined the impact of change in medication adherence and change in the cost of medical care for 2 groups of patients: a) those who were nonadherent in the baseline year and became adherent in the second year compared with those who remained nonadherent, and b) those who were adherent in the baseline year and became nonadherent in the second year compared with those who remained adherent. Our study was limited to patients with at least 1 of 3 diseases: diabetes, hypertension, and high cholesterol (hypercholesterolemia). To gain additional insights into the relationship between change in adherence and change in medical spending, we stratified our analyses by the number of conditions included in the patient’s claims in the baseline year. We also conducted a subanalysis examining whether there was a synergistic impact on spend when the patient is experiencing both hypertension and diabetes.

Study Sample

The study sample was drawn from a de-identified data set obtained from a national data aggregator, consisting of medical and pharmacy claims from more than 10 million commercially insured patients. No Medicare or Medicaid beneficiaries were included in the sample. The baseline period for analyses was April 1, 2011, to March 31, 2012; the follow-up period was April 1, 2012, to March 31, 2013. To be included in the cohort, we required that patients with a pharmacy or medical claim in the baseline period have benefits eligibility during the entire follow-up period and to have at least 1 of the candidate conditions—diabetes, hypertension, or hypercholesterolemia—during the baseline period. To insure that we were as sensitive as possible in case identification, we used both International Classification of Diseases, Ninth Revision, Clinical Modification codes and medication profiles to identify patients with 1 of the 3 conditions (see eAppendix, available at

The exposure of interest in this study was change in adherence status. For this purpose, adherence was measured using the medication possession ratio (MPR) calculated at the therapeutic class and averaged across the condition (eAppendix).8 MPR is a commonly used outcome metric designed to measure medication adherence. It is a proportion consisting of the total days’ supply of medication on hand divided by the elapsed time between the first fill of medication to the end of the year. Supply is prorated if the patient fills before the end of the year but won’t exhaust the fill until after year’s end. For our purposes, fill data time periods ran March to March, corresponding with eligibility data availability. Baseline MPR values spanned April 1, 2011, through March 31, 2012, and follow-up was measured between April 1, 2012, and March 31, 2013.

We created 2 cohorts based on prescription-filling behavior within the condition in the baseline period: a) patients adherent (MPR ≥0.80) to medication at the condition level, and b) patients not adherent (MPR <0.80). Adherence behavior was considered only at the condition level, with the weighted average of MPR considered when there were multiple medications taken. To gain additional insights into how the change in adherence status might affect spending on medical services, we also conducted a stratified analysis, splitting our sample into patients with a Charlson Comorbidity Index score greater than or equal to 3 and those with a score of less than 3.9

Finally, we conducted a descriptive analysis to examine whether the presence of comorbid hypertension modifies the relationship between change in adherence status and medical service utilization when diabetes is present. For this purpose, we compared the adjusted medical spend of 3 cohorts: a) patients with hypertension only, b) patients with diabetes only, and c) patients with both hypertension and diabetes. We evaluated the presence of effect modification by comparing the expected additive medical costs (ie, the spending of patients with hypertension plus the spending of patients with diabetes) with the actual observed spending of patients with diabetes and hypertension.

Outcome Measures and Model Covariates

The outcome modeled in these analyses was the spending on health services during the follow-up period. We captured allowed charges for all inpatient and outpatient services, regardless of whether they were paid by the insurer or patient. As our primary outcome was concerned with the impact of medication adherence on medical service utilization, we did not include pharmacy costs in our outcome measure.

In the multivariable regression analyses of the post period medical spending, we adjusted for medical spending in the baseline period, age, gender, comorbidity (using the Charlson Comorbidity Index9), Census region, initiator/continuer medication use status, and preventive health service use. Initiator or continuer therapy utilization status was defined at the patient/condition level, where patients without a fill for a medication within the condition classification in the 6 months prior to the baseline period were considered initiators. Preventive health service use was included to adjust for "healthy user" bias,10 and was defined as having a claim for 1 of 52 preventive services during the baseline period.11 In this way, we controlled for the healthy user bias in 2 ways: a) by using each patient as his or her own historical control, and b) by adjusting for preventive service use.

Statistical Considerations

We constructed generalized linear models to describe the total spending on health services during the follow-up period (ie, April 1, 2012, to March 31, 2013). Our unit of analyses was patient/therapy combination; thus, it was possible in our preliminary analyses for patients with multiple conditions to be considered in more than 1 disease cohort. For instance, a patient with hypertension and high cholesterol would be included in both our hypertension and high cholesterol results. In our descriptive analyses examining the additive effect of comorbidity, patients are included in only 1 cohort. Thus, the hypertension group includes only patients with hypertension, while the diabetic/hypertensive group includes individuals with both conditions.

The impact of change in adherence status was estimated by comparing the adjusted spend of those who maintained their adherence status with those whose status changed. For instance, patients who maintained adherence at baseline and follow-up were compared with those who became nonadherent at follow-up. This difference (ie, increase in spending) was considered the impact on medical service spending of becoming nonadherent. Similarly, patients who newly achieved adherence at follow-up were compared with those who were nonadherent at baseline and during follow-up. This difference (ie, reduction in spending) was considered to be the impact on medical service spending of becoming adherent.

In modeling medical spending, the use of non-Gaussian distributions is common due to the skewed nature of healthcare utilization on a population basis. However, these cautions are typically the result of a large proportion of community members with no medical spending.12 In the sample we evaluated, all patients were service utilizers, and we were modeling the change in medical spending among these patients between the 2 years. We examined this assumption empirically by testing the distribution of the change in spending in the follow-up year, and we found it to reflect a normal distribution. Therefore, we determined that ordinary least squares regression would be the most transparent and least biased approach to analysis.

All analyses were conducted using SAS Enterprise Guide 5.1 (SAS Institute, Cary, North Carolina). For this investigation, we used de-identified medical and pharmacy claims data from an external vendor; therefore, no institutional review board review for research on human subjects was required.



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