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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
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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
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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.
The 4 cohorts analyzed are described in Table 1. The 4 cohorts analyzed are described in Table 1. The combined sample of patients with at least 1 of the candidate conditions totaled 857,041 distinct patients and 1,264,797 patient therapies. Of these, 61% had only 1 condition: high cholesterol (23.7%), hypertension (34.1%), or diabetes (3.5%). The balance (39%) had multiple conditions: high cholesterol/hypertension (24.6%), diabetes/hypertension (2.8%), diabetes/high cholesterol (2.3%), or all 3 conditions (8.9%). Nonadherent patients who became adherent were more likely to be male, be a medication continuer, and have hypertension; on average, they were older and had higher baseline medical service cost than those who remained nonadherent. Adherent patients who became nonadherent were more likely to be female, live in the South, have point-of-service coverage, engage in fewer health-seeking behaviors, have high cholesterol, and be a medication initiator; on average, they were younger and had higher baseline medical service costs than those who remained adherent.

The results of the multivariable analyses are presented in the Figure. The number of conditions a patient had in the baseline period dramatically influenced the magnitude of change in spending when adherence behavior changed (Table 2). Patients having 3 or more conditions at baseline had up to 7 times greater savings (or increased spend) than patients with 1 or 2 conditions. Specifically, patients with 1 to 2 conditions who became adherent had a modest reduction in spending ranging from a reduction of $757 per year (diabetes) to an increase of $365 per year (high cholesterol). In contrast, the group that had 3 or more conditions had savings ranging from $2081 for high cholesterol to $5341 for diabetes. Among the adherent patients who became nonadherent, those with 1 or 2 conditions had increased spend ranging from $1045 for patients with high cholesterol to $1706 for those with hypertension; those with 3 or more conditions had increase spend ranging of $4008 for patients with high cholesterol to $7946 for those with hypertension. With the exception of high cholesterol in the overall sample, and hypertension among those with 1 to 2 conditions (both in the nonadherent to adherent cohort), all of these differences were statistically significant at the P <.001 level.

It is notable in the Figure that the increase in spend among patients who were adherent and became nonadherent was larger than the decrease in spending for patients who were nonadherent and became adherent. After adjustment, patients with diabetes who became adherent spent $2495 less on health services in the year they became adherent than those who remained nonadherent. In contrast, patients with diabetes who were nonadherent after being adherent had an increased spend of $2763. This relationship was more pronounced in patients with hypertension and high cholesterol. Among patients who were nonadherent who became adherent and had hypertension, the spending was $766 less per year than those who remained nonadherent, while those with high cholesterol spent $26 less. However, among those who were adherent who became nonadherent, patients with hypertension spent $2663 more and patients with high cholesterol spent $1526 more.

In Table 3, we present the results of the descriptive analyses examining the synergistic effect of hypertension and diabetes on medical service spending, and, here again, we see the dramatic impact of the presence of 3 or more conditions. Among patients with 2 conditions (ie, hypertension and diabetes only), there was modest impact above the expected contribution of the individual conditions alone. However, among those with 3 or more conditions, there were substantial differences between the expected (additive) spend and actual spend. For patients with 3 or more conditions who became adherent, the savings were $2418 greater than the expected. For patients with 3 or more conditions who became nonadherent, the increased spend was $1261 greater than the expected. In all cases, except for those patients with 1 to 2 conditions who became adherent, the difference between the additive (expected) and actual was significantly different at the P <.001 level.


In this study, we used a rigorous design to demonstrate that short-term changes in adherence have a meaningful and immediate impact on changes in healthcare spending. It is not necessarily surprising to learn that patients with multiple conditions have higher adherence-related savings potential than those with fewer conditions, but the magnitude of this difference demonstrates that the relationship is likely to be nonlinear. These results have key implications for targeting adherence interventions. In the Figure, we can clearly see that the change in spending associated with change in adherence behavior is much higher when the patient has 3 or more conditions. This would indicate that priority should be given to patients with multiple conditions when implementing adherence programs in resource-constrained settings.

Our findings have important implications in an era when we see new payment models moving risk for patient health status closer to physicians and health systems. If the adherence behavior and results from our analyses were applied to a sample of 100,000 members with each disease, the plan savings would range from $38 million for patients with high cholesterol to $63 million for those with diabetes. Given the increasingly limited budgets that are available for providing medical services, it is essential that spending by payers and providers implementing healthcare programs represent an efficient use of resources. A key finding of our study was that a disproportionate share of these savings came from patients with 3 or more conditions. In this cohort, 17% of the patients would have 3 or more conditions, yet 31% of the savings among patients with diabetes, 53% of savings for patients with high cholesterol, and 60% of savings for patients with hypertension would come from patients with greater comorbidity.

Similarly, the findings point to the importance of persistence in programs promoting adherence. In our example of 100,000 patients with each condition, we found that between 50% and 80% of our savings, depending on condition and level of comorbidity, come from keeping people adherent rather than promoting adherence among those who are nonadherent. This is a nuance that has not been previously reported in the literature, but has profound implications in the targeting of adherence resources. It may be easier and more efficient to prevent nonadherence in a patient who is currently adherent to their medication than it is to treat nonadherence in a patient who is nonadherent. It follows that there will be a high return on investment from programs promoting persistency and in implementation of targeting algorithms that identify patients who are currently adherent but at risk of nonadherence, so that a successful early intervention can be made.13

Finally, the finding concerning the synergistic impact of comorbid hypertension and diabetes points to a particular case of the finding we made that surrounds the importance of 3 or more conditions. In this we see, not surprisingly, the importance of adherence in patients with diabetes regardless of the level of comorbidity or persistence status. The impact of comorbidity on the change in medical spend in patients with diabetes and hypertension is well in excess of that seen when we combine the impact of each condition standing on its own. This would indicate—as has long been recognized—that it is critical to take aggressive steps to ensure adherence among patients with diabetes, particularly among those with hypertension.

Taken together, our findings have important implications for insurers and provider groups, such as accountable care organizations, who are at risk for the cost of medical services. Although a number of studies have demonstrated convincingly that patients who are adherent to their medications incur lower costs, this study helps establish that “all adherence is not equal.” Changing a patient’s behavior is not a costless effort. This is particularly true in the field of adherence where the cost of interventions might range substantially based on the nature and comprehensiveness of the intervention. Our findings provide evidence that a carefully nuanced targeting program that tailors interventions to patients based upon their adherence history and comorbidities would result in greater benefit from these programs than would a program with a less focused approach.


Our study has several limitations. First, in a cohort study such as ours, it is impossible to control for all potential sources of confounding. Despite our efforts to address this in multivariable analyses, there are likely to be several unknown factors separate from adherence that might account for the impact we have identified. Second, the presence of the healthy user bias challenges the validity of many studies examining the value of medication adherence. Some authors have made recommendations to resolve this through the inclusion of covariates identifying patients who engage in good health practices; however, we found that more than 80% of our cohort members engaged in preventive health services, thus limiting the usefulness of this as a method to adjust for this bias. Nevertheless, our study design addresses this bias by stratifying on baseline adherence behavior and examining the impact of behavior change within the cohort members. In this manner, each patient acts as their own control, limiting the impact of the healthy user effect. Third, we did not consider the cost of medications in our estimation of the impact of the change in adherence behavior on the cost of medical care. It was not our intention to conduct a cost-benefit study of the impact of pharmacy care in these conditions; that study has been conducted previously, and it demonstrated a strong return on investment associated with pharmacy care.4 Our intention was to examine the impact of heterogeneity in change in adherence behavior on medical spend, and we believe that we have clearly demonstrated the potential importance of heterogeneity in response to those considering implementation of adherence programs. Additional studies of the cost-benefit of pharmaceutical interventions may be the subject of future investigations.

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