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The American Journal of Managed Care June 2015
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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
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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
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 estimated separate regression models for each medication class and each health outcome. All regressions included controls for patient age, gender, and number of comorbidities, as well as fixed effects for state of residence and year. Note that while our data identify plans consistently within a year, the coding of plans is not fully consistent within an employer over time. Thus, a plan in our analysis really refers to a plan-year, which is our primary unit of analysis, and we were unable to use plan-fixed effects. In the eAppendix, we discuss the results of a sensitivity analysis in which we included employer fixed effects and found that our results were largely unchanged. Comorbidities were identified from the medical claims and included ulcer, depression, allergic rhinitis, migraine, osteoarthritis, chronic sinusitis, anxiety or tension disorder, epilepsy, gastric acid disorder, glaucoma, irritable bowel syndrome, malignancies, psychotic illness, thyroid disorder, rheumatoid arthritis, tuberculosis, HIV, anemia, and chronic obstructive pulmonary disease. Other common conditions that were related to cardiovascular health (eg, acute myocardial infarction or hypertension) were excluded because the onset of claims could be endogenously related to individuals’ medication use or other healthcare related to their diabetes or CHF.

We calculated adjusted plan-level adherence and health outcomes as follows. For medication adherence, we regressed the percent of individuals with PDC greater than 80% on these covariates using a linear probability model to obtain predicted PDC values for each individual. We then generated predicted values for adjusted adherence holding other factors fixed at their mean values, so that the remaining variation would come from fixed differences across plan-years and not from differences in observable patient characteristics. We replicated this procedure for health outcomes and spending, leaving us with a set of adjusted, plan-level averages for medication adherence and outcomes. The same process was conducted separately for CHF and diabetes.

This regression adjustment eliminates observed heterogeneity in the patient population across plans but does not necessarily address unobserved heterogeneity across plans that could be correlated with both medication adherence and health outcomes. For example, significant changes in the plan population in a given year (if, say, the plan benefits changed in such a way as to attract sicker patients with poorer adherence) could drive a correlation between adherence and outcomes. Thus, the association between plan-level adherence and outcomes are not necessarily causal, and the findings should be interpreted accordingly.

An index of medication adherence. We computed the average level of medication adherence across plans separately for beta-blockers, ACE inhibitors, ARBs, calcium channel blockers, oral diabetes medications, and statins in the diabetes sample, and beta-blockers, ACE inhibitors, ARBs, and diuretics in the CHF sample. We then combined the PDC measures into a single index of medication adherence for each condition. Based on the value of the index, we grouped health plans into categories of high, moderate, and low levels of adherence.

Specifically, to construct the index we applied the following procedure separately in both disease cohorts. First, for each health plan we computed the average share of patients with at least an 80% PDC separately for each medication class. Then, for each plan, we calculated the weighted average of adherence across drugs for each plan-year, where adherence was weighted by the number of patients in the plan taking each drug. Using this weighted average, we identified the upper and lower quartile values of adherence across plans (ie, the values of adherence that 25% of plans were above and below, respectively). Finally, we stratified plans into 3 categories of adherence—high, moderate, or low—based on whether their weighted average was above the 75th percentile, between the 75th and 25th percentile, or below the 25th percentile across plans, respectively. We focused our analysis on the top and bottom quartiles of plans because we wanted to emphasize the impact of plans that did particularly well or particularly poorly—the “outlier” plans.

We used the same process to categorize plans into different adherence categories using both the adjusted and unadjusted values of the PDC, and in both disease cohorts. For our main analysis, we regressed the adjusted health outcomes against the medication adherence variables as the independent variables using ordinary least squares regression. Note that in a 2-step estimation procedure, where we use regression to adjust for health plan characteristics in the first step and regress adjusted outcomes against adjusted adherence in the second step, the variance estimates are biased in the second step.25 To statistically compare differences across the low, moderate, and high adherence plans, we used a bootstrap procedure. Specifically, we resampled individuals with replacement and computed P values for differences across low, moderate, and high adherence plans using bootstrap variance estimates based on 200 draws.

RESULTS 

Table 1 presents the share of individuals within plans who show evidence of good adherence, as identified by a PDC of 80% or better. Only adherence to medications used in plan quality measures (as specified above) are computed for each sample; for example, we do not report adherence to antidiabetes medications in the CHF sample, even though CHF patients with diabetes may take them. For patients with diabetes, 54% to 56% of patients within a plan exhibit good adherence to therapy, on average, except in the case of statin medications, for which just 45.2% exhibit good adherence. Adherence to ACE inhibitors and ARBs is generally lower for CHF patients within a plan, while adherence to diuretics is particularly low, at 37%. Individuals taking diuretics have been shown in past research to exhibit lower adherence levels than other agents for treating cardiovascular disease.26,27 As a result, the weighted average of plan-level adherence across therapies is significantly lower for the CHF sample (46.8%, compared with 52.9% for diabetes).

We report plan-level characteristics in Table 2. The table reports the mean values of adherence and other characteristics, including health outcomes, plan size, and average total medical expenditures, for different plan years. The table shows that medication adherence is slightly higher in the diabetes sample (approximately 53% compared with 47%). Also, as we would expect, there are many more diabetes patients per plan, while patients with CHF are sicker on average, demonstrated by their higher rates of complications and higher total healthcare expenditures. Note that because we required at least 100 patients in a plan to be included in the analysis, the number of plans included is much smaller in the CHF sample than the diabetes sample (195 vs 919).

The thresholds below and above which plans are defined as having low or moderate adherence, respectively, were defined using the 25th and 75th percentiles of the adherence across plans. We classified plans with adherence below the 25th percentile as having low adherence, those above the 75th percentile as having high adherence, and those with adherence between these levels as having moderate adherence. We found that there was substantial variability in average medication adherence across plans. In the diabetes sample, the threshold for low plan-level adherence is 48.3%, meaning that less than half of plan enrollees with diabetes are considered to have good adherence. The threshold for high plan-level adherence, the 75th percentile, is 58.6%. The significant differences in adherence across plans are important, because they suggest that there is heterogeneity in adherence across plans that can potentially be targeted for intervention by policy makers. For CHF patients, adherence ranges from 43.5% at the 25th percentile to 50.9% at the 75th percentile. In the eAppendix, we provide some additional detail on the sample and the variation in key variables across plans.

In Table 3, we compare the variation in plan-level health outcomes to plan-level adherence in the diabetes sample. Specifically, we report the average health outcomes for patients in the low, moderate, and high adherence plans. The top panel reports the association between unadjusted outcomes and unadjusted adherence, while the bottom panel reports the association between adjusted outcomes and adjusted adherence. P values are reported in parentheses reflecting statistically significant differences between the moderate and high adherence plans and the poor adherence plans.

There is a positive association between poor adherence and adverse outcomes for diabetes patients. In the top “unadjusted” panel, low adherence plans exhibit significantly more uncontrolled diabetes, emergent care for glycemic events, and long-term complications, compared with the high adherence plans. The patterns for diabetes complications are less clear, but adjusting for differences in patient characteristics across plans clarifies the associations.

After adjusting for the characteristics of individual patients, patients in low adherence plans exhibit statistically worse outcomes compared either with those in high adherence plans, or with those in moderate adherence plans. Significance is achieved at the 1% level for all the high adherence comparisons, and at the 5% or 10% level for the moderate adherence comparisons.

Table 4 reports the associations between plan adherence measures and outcome measures for CHF patients. As with the previous table, the top panel reports unadjusted values and the bottom panel reports adjusted outcomes, with bootstrap P values in parentheses.

For the unadjusted values, low adherence plans have statistically higher (P <.001) rates of hospitalizations and emergency department (ED) visits for CHF itself and all the co-morbidities considered in quality measurement, compared with high adherence plans. The same is true when comparing low adherence plans to moderate adherence plans, where the highest P value is .01. These findings are largely unchanged by the regression adjustment, as shown in the bottom panel. Low adherence plans perform significantly worse than those with moderate or high adherence. For example, rates of CHF hospitalization are 3.1 percentage points higher in low versus high adherence plans, a 25% difference. Rates of CHF ED visits for low adherence plans are 3.9 percentage points higher, nearly double those for plans with moderate or high adherence. The adjusted comparison of adherence and coronary artery disease hospitalizations are not significantly different for high adherence versus low adherence plans.

 
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