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Medication Adherence and Measures of Health Plan Quality
Seth A. Seabury, PhD; Darius N. Lakdawalla, PhD; J. Samantha Dougherty, PhD; Jeff Sullivan, MS; and Dana P. Goldman, PhD
Stimulating Comprehensive Medication Reviews Among Medicare Part D Beneficiaries
William R. Doucette, PhD; Jane F. Pendergast, PhD; Yiran Zhang, MS, BS Pharm; Grant Brown, PhD; Elizabeth A. Chrischilles, PhD; Karen B. Farris, PhD; and Jessica Frank, PharmD
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Helaine E. Resnick, PhD, MPH; and Michael E. Chernew, PhD

Medication Adherence and Measures of Health Plan Quality

Seth A. Seabury, PhD; Darius N. Lakdawalla, PhD; J. Samantha Dougherty, PhD; Jeff Sullivan, MS; and Dana P. Goldman, PhD
This study examines the association between plan-level measures of health outcomes and medication adherence to assess the viability of adherence as a measure of plan performance.
ABSTRACT

Objectives: Medication adherence is increasingly being considered as a measure for performance-based reimbursement contracts in healthcare systems. However, the association between health outcomes and adherence at the plan level is unknown.

Study Design: Retrospective analysis of medical and pharmacy claims from a large private sector claims database from 2000 to 2009.

Methods: We compared plan-level measures of medication adherence and health outcomes for patients with diabetes and congestive heart failure (CHF). Plan performance was based on average rates of disease complications. Medication adherence was calculated as the percent of patients having 80% of days covered for medications treating diabetes or CHF. Both adherence and outcomes were adjusted for patient differences using multivariate regression. Plans were stratified into low, moderate, and high adherence, based on adherence in the bottom quartile, middle 2 quartiles, and top quartile, respectively.

Results: Average adherence varied significantly across plans. Plans with low adherence to diabetes medications had adjusted rates of uncontrolled diabetes admissions of 13.2 per 1000 patients, compared with 11.2 in moderate adherence plans and 8.3 in high adherence plans (P <.001). The adjusted rate of CHF-related hospitalization was 15.3% in low adherence plans, compared with 12.4% in moderate adherence plans and 12.2% in high adherence plans (P <.001). These patterns were consistent across different types of complications for both diabetes and CHF.

Conclusions: Private health plans vary considerably in average adherence to medications treating chronic diseases. Plans with higher average adherence had lower rates of disease complications, suggesting that medication adherence measures are potentially useful tools for improving the performance of health plans.

Am J Manag Care. 2015;21(6):e379-e389

Take-Away Points

This study examines the association between plan-level measures of health outcomes and medication adherence to assess the viability of adherence as a measure of plan performance.
  • We found that plan-level averages of medication adherence were associated with significantly lower rates of disease-related complications for both diabetes and congestive heart failure.
  • These findings suggest that medication adherence measures are potentially useful tools for improving the performance of health plans.
  • More needs to be done to understand how and why providers and plans can improve medication adherence.
 
Measuring the quality of care provided to patients by health plans and providers is an increasingly common element of evaluating and improving healthcare delivery. Efforts to increase value in healthcare often include incorporating measures of quality into provider compensation or plan ratings.1-3 This is reshaping how health plans evaluate themselves, how employers choose plans, and how consumers decide who provides their care. At present, many healthcare organizations participate in quality reporting, whether for internal quality improvement or for public reporting. New payment models are emerging that directly incorporate these quality data into coverage and reimbursement decisions.

Many quality measures have been developed to evaluate the appropriateness of the prescribing, monitoring, and use of prescription medicines in the treatment of both acute and chronic conditions. Medication adherence is a natural candidate for quality measurement, as individuals with higher adherence consistently demonstrate better clinical outcomes as well as lower use of and spending on other medical services.4-7 For example, a recent study found that poor adherence can lead to higher costs of up to $702 per month for Medicare beneficiaries with diabetes and up to $840 for those with heart failure.8 This persistent empirical relationship was reflected recently in the decision by the Congressional Budget Office to incorporate a reduction in spending on medical services to reflect the health benefits associated with medication use among Medicare beneficiaries.9

However, there is little understanding of the ways in which explicitly measuring and tracking medication adherence and outcomes at the plan level will incentivize health plans to implement measures that improve upon those process and outcome metrics. In particular, it is not known whether plans with higher average adherence exhibit better health outcomes or lower health spending. There is considerable research demonstrating that medication adherence is associated with improved clinical outcomes and reduced spending for individuals,9 although this does not necessarily mean that average medication adherence rates can provide useful signals of health plan performance. There are many drivers of patient outcomes and plan performance, and it is possible that medication adherence does not vary enough across plans, or that the patient populations differ too much, to isolate a clear relationship between plan-level adherence and outcomes. To better understand the implications and possible value of using medication adherence as a measure of health plan performance, we need to understand the link between adherence and outcomes at the plan level after controlling for patient differences across plans.

We used a large database of claims from private sector health plans to compute commonly used measures of plan performance on outcomes and plan-level rates of medication adherence for patients with diabetes and congestive heart failure (CHF). We selected these 2 conditions because they are relatively common and lead to significant social costs, and also because there is ample evidence supporting a relationship between adherence to guideline-recommended medications and improved clinical outcomes for these patients on an individual level.1,6,10-15 We tested the relationship between plan-level quality measures of medication adherence and health outcomes to assess whether better adherence was associated with lower disease complication rates and spending.

METHODS

We estimated the relationship between plan-level adherence and outcomes according to a 2-step process. We first computed average rates of adherence and health complications related to diabetes and CHF at the plan level. We computed unadjusted averages, and we also used multivariate regression to adjust for patient characteristics and to predict the adherence and complication rates that would result if every plan had patients with the same observable characteristics. In the second stage, we estimated the association between adherence and disease complication rates in order to test whether higher adherence was associated with better outcomes. Here we describe these procedures and the data used in greater detail.

Data and Study Sample

We used a database of medical and pharmacy claims data for individuals enrolled in health plans of large private employers from 2000 to 2009. These data have been used extensively to study medication adherence and patient outcomes in past work.16-22 The database contained medical claims or encounter data from all available healthcare sites, including both inpatient and outpatient care. The billing data were broken down by payer (eg, out-of-pocket or health plan) and provided information on up to 4 associated diagnosis codes and 1 associated procedure code. The database also included pharmacy claims for all outpatient pharmaceutical purchases; each claim included unique drug identifiers, the number of days of medication supplied, and total payments by all payers. Enrollment records provided information on basic demographics, including age, gender, and 3-digit zip code of residence. These claims data were rolled up to a patient level to identify individual diagnoses, characteristics, adherence, and hospitalizations, and then aggregated at the plan level for analysis.

Study Sample

We restricted the sample to individuals aged 18 to 64 years who were diagnosed with either diabetes or CHF. We excluded individuals 65 years or older to focus on private health plans and to exclude patients covered by Medicare. Patients with diabetes were identified based on an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis code of 250.xx listed as the primary or secondary diagnosis in 1 inpatient claim, or at least 2 outpatient claims within a 30-day window over 1 calendar year. We required at least 2 outpatient codes to eliminate cases where the ICD-9-CM code was listed on a claim as a possible diagnosis but was later ruled out. CHF was identified the same way, using ICD-9-CM diagnosis codes of 428.xx.

Using these inclusion criteria, we created separate cohorts of individuals with diabetes and with CHF. Any individual diagnosed with both diseases in the same year was included separately in both samples. Once identified as being in either or both samples, individuals were grouped into health plans according to their enrollment records. Individuals were included from the year of initial observed diagnosis for as long as they remained enrolled. We required that individuals be continuously enrolled over a full calendar year to ensure that we observed all their utilization throughout the year. When aggregating the information to the plan level, we also required that plans have at least 100 patients to be included in the analysis.

Plan Performance Measures

Many quality measures have been developed to evaluate plan performance, and a full accounting of the relationship between all quality measures tracking medication adherence and health outcomes is beyond the scope of this study. We evaluated a set of quality metrics specific to medication adherence and health outcomes that were endorsed by the National Quality Forum (NQF), relevant for diabetes or CHF, and observable in retrospective claims analysis.23 The NQF is a nonprofit organization whose mission is to build consensus around approved quality measures for health plan performance. The NQF measures are used by CMS in quality reporting programs for physicians, plans, and hospitals.

Medication adherence. We measured medication adherence using the percentage of days covered (PDC) over a 1-year period. PDC is an NQF-endorsed quality measure for adherence that is commonly used by researchers to evaluate patient adherence.23 Therapeutic classes were selected based on treatment guidelines for diabetes and CHF. Diabetes medications included beta-blockers, angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), calcium channel blockers, oral diabetes medications, and statins. For CHF, we included beta-blockers, ACE inhibitors, ARBs, and diuretics. Within each health plan, we identified all patients who were prescribed each type of medication at least once and computed the PDC for the calendar year after the date of initiation. Patients who met the PDC threshold of 80% or greater over the year were considered to have “good” adherence. The 80% PDC threshold is often used as an NQF-endorsed measure of quality; for example, CMS uses it for certain drug classes in its performance measurement system for Medicare Part D plans.24

Patient outcomes. We measured patient outcomes using quality measures endorsed by NQF that reflect disease complications generally considered preventable for the 2 relevant diseases. For diabetes, this includes the rate of inpatient admissions for uncontrolled diabetes, emergent care for hypoglycemia or hyperglycemia, rates of short-term diabetes complications, and rates of long-term diabetes complications. Given that some outcomes are relatively uncommon, we reported them in terms of rates per 1000 patients. CHF outcome measures included hospitalizations or emergent care visits for CHF or other comorbid conditions, including diabetes, coronary artery disease, or hypertension. As these were comparatively more common than the diabetes complications, they were computed in terms of percent of patients per year. We also computed the average annual total medical expenditures for patients in each sample, normalized to 2009 dollars using the Consumer Price Index.

Statistical Analysis

Regression adjustment. The goal of our analysis was to describe how variation in medication adherence across plans correlated with variation in plan-level patient outcomes. However, this correlation could be confounded by the presence of differences across plans; for example, plans with sicker patients will on average tend to have patients using more medications and more medical services, regardless of their adherence patterns. To address for potential bias driven by systematic differences in patients across health plans, we adjusted for patient characteristics using multivariate regression. We then aggregated adjusted adherence and outcome values up to the plan level to construct our key analytic plan-level quality measures of disease complications, spending, and adherence. In the eAppendix (available at www.ajmc.com), we expand on this and provide a more detailed discussion of our empirical strategy.

 
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