Small increases and decreases in pharmacy copayments have little influence on patients’ switching to generic drugs or on improving adherence to medication regimens.
: To evaluate the effect on adherence and medical care expenditures of a pharmacy benefit change that included free generic drugs and higher copayments for brand-name drugs.
: Quasi-experimental pre—post study of patients with ischemic heart disease (1286 control and 555 intervention) and patients with diabetes mellitus (4089 control and 1846 intervention).
: Medical and pharmacy claims data were analyzed for continuously enrolled members from January 1, 2005, through December 31, 2008. A generalized linear model was used to predict costs as adherence changed.
: The rate of switching from brand-name drugs to generic drugs in the intervention group was not statistically different from that in the control group. The net change in adherence was higher only for the intervention group patients taking statins who switched to generic drugs, a 6.2% increase compared with an 8.5% decrease in the control group. The estimate of medical cost savings attributable to this benefit change was significant for only the metformin class of diabetes drugs. Improved adherence independent of this benefit change was estimated to reduce all-cause medical costs for patients taking sulfonylureas, metformin, and thiazolidinediones.
: Altering copayments for pharmaceuticals may affect the rate of conversion to generic drugs but is unlikely in and of itself to result in complete conversion. However, increasing adherence can result in net savings for specific diabetic drug classes, as savings from all-cause medical costs offset the increase in pharmacy costs.
(Am J Manag Care. 2009;15(12):881-888)
A pharmacy benefit design change increasing copayments on brand-name drugs and simultaneously introducing a zero copayment on generic drugs had little influence on patients’ switching to generic drugs or on improving adherence to medication regimens.
The increasing cost of medical care in the United States is an ongoing concern, consuming more than 16.2% of the nation’s 2006 gross national product.1 Government, payers, business leaders, consumers, and healthcare providers are seeking to control healthcare costs without sacrificing quality of care. One such effort is improving the management of chronic conditions. Caring for chronic conditions in the United States consumes 78% of all healthcare dollars,2 meaning that such an effort has the potential for significant effect.
Payers and providers have implemented several approaches to improve the management of chronic conditions. Disease management programs emphasize one-on-one contact with health coaches and nurses.3 Physician practice models that incorporate aspects of a medical home4 focus on increased involvement of healthcare providers in a team approach. Using insurance benefit design to lower barriers to therapies that effectively manage chronic conditions is the focus of this analysis. Value-based plan design promotes the use of care when the benefits exceed the costs and discourages the use of care when the costs exceed the benefits.5
Value-based plan design that focuses on reducing out-of-pocket costs for prescription drugs taken by patients with chronic diseases is premised on a relationship between improved medication adherence and better condition management and between better condition management and lower costs. Most research has focused on the relationship between prescription drug out-of-pocket costs and adherence or on the relationship between adherence and medical cost or utilization, although some studies6,7 considered all 3 factors. Studies of cost and adherence have demonstrated the following mixed results: increasing pharmacy cost sharing had no effect on adherence,8,9 increasing pharmacy cost sharing decreased adherence,10,11 and decreasing pharmacy cost sharing increased adherence. 12,13 This apparent contradiction may be explained by the conditions studied, the adherence measures used, or the sample size.
Studies of the relationship between adherence and health outcomes or costs and utilization have had more conclusive findings. For example, lower adherence has been associated with worse outcomes, including mortality, in populations with diabetes mellitus or acute myocardial infarction.14-17 A relationship between increased adherence and fewer emergency department visits and hospitalizations has been observed.6,7,18-20 While some studies7,18,19 have shown overall medical cost savings as a result of adherence, other studies6,20 have not in part because the increase in pharmacy costs exceeded the medical cost savings.
This study relies on a natural experiment created when Blue Cross Blue Shield of Minnesota introduced a free generic drug benefit in July 2006 to groups with fewer than 51 employees. The new benefit eliminated the $5 copayment for generic drugs and increased by $5 the copayment for brandname and nonformulary drugs to $35 and $50, respectively. We hypothesized that adherence would increase as member costs for generic drugs decreased and that adherence would decrease as member costs for brand-name drugs increased. We also hypothesized that improved adherence would lead to better condition management and to lower medical costs. This study extends existing research by quantifying the net effect on medical and pharmacy costs of a net change in adherence due to a simultaneous increase and decrease in pharmacy copayments.
The study used a quasi-experimental pre—post design. The preperiod consisted of 24 months from January 1, 2005, through December 31, 2006. Because the benefit change had a staggered rollout beginning June 1, 2006, the intervention date was defined as January 1, 2007, halfway through the implementation year. The postperiod consisted of 24 months from January 1, 2007, to December 31, 2008.
The intervention group included 44,155 members continuously enrolled in products offered to groups with 2 to 50 employees in the study period. Members enrolled in highdeductible plans paired with federally qualified health savings accounts were not eligible for the free generic drug benefit and were excluded. The control group included 79,853 members continuously enrolled in products purchased by 175 groups with 51 or more employees. The control group experienced no change in pharmacy copayments during the study period. The control and intervention groups had access to the same provider network; consequently, discounts and fee schedules are comparable between the 2 groups.
To evaluate the effect of the copayment changes on adherence and medical costs, the analysis was narrowed to patients who had 2 chronic conditions (ischemic heart disease or diabetes mellitus) easily identified in claims data and for whom the prescribed drugs were taken specifically to treat that condition. Patients were identified in claims data using episode treatment groups (ETGs), which use proprietary algorithms to group medical and pharmacy claims into clinically homogeneous units based on complete episodes of care (Episode Treatment Groups [computer program]. Version 5.0. Symmetry Health Data Systems Inc, Phoenix, AZ). The ischemic heart disease subpopulation (1286 control and 555 intervention) included members with a cardiac episode (ETGs 251-259 and 265) and pharmacy claims indicating that a statin prescription was filled. The diabetes subpopulation (4089 control and 1846 intervention) included members with diabetes-related treatment (ETGs 27-30 and 222-224) and pharmacy claims indicating that a prescription was filled for sulfonylureas, metformin, thiazolidinediones, or insulin. These prescription drugs and their generic equivalents were identified using a proprietary drug database (Master Drug Data Base, Version 2.5. Medi-Span, Indianapolis, IN) that uses national drug codes and proprietary generic product identifiers to indicate drug groups and classes and generic equivalents.
At the time of analysis, no generic equivalents were available for insulin or for thiazolidinediones. These 2 drugs were included to assess the effect of the copayment increase on brand-name drugs. We assumed that once medication therapy commenced, it was continued to manage the condition.6,16 Using a combination of ETGs and pharmacy claims for case finding minimized the chance of including rule-out diagnoses in the analysis or of including patients taking the specified medications for conditions other than those in the analysis.
We estimated net changes in medication adherence in the preperiod and in the postperiod. This modeling estimated the percentage of patients whose adherence changed after the change in copayments. We then used generalized linear models with a gamma distribution and a log link to estimate the effect of the net change in adherence on the following 3 measures of cost: all-cause medical, condition-specific medical, and condition-specific pharmacy.6
First Stage of Measuring Adherence. We used the medication possession ratio (MPR) to measure adherence.13 The MPR was computed by dividing the number of drug days supplied by the number of days the patient had the health condition of interest. To accommodate the pre—post design, if the preperiod drug days supplied was greater than the days with the condition, excess days were added to the drug days in the postperiod. In both periods, the MPR was capped at 100%. We then categorized those with an MPR of at least 80% as being adherent and those with an MPR of less than 80% as being nonadherent. The variable of interest identified patients nonadherent in the preperiod but adherent in the postperiod and patients adherent in the preperiod but nonadherent in the postperiod. The mean of this variable indicated the net change in adherence from the preperiod to the postperiod.
The net changes in adherence from the preperiod to the postperiod were compared among patients who only took generics, patients who switched to generics, and patients who only took brand-name drugs. The copayment differentials varied among these groups. Patients who only took generics and patients who switched to generics in the preperiod had a $5 savings for each prescription filled in the postperiod. Patients who switched to generics in the postperiod had savings of $35 for formulary drugs and $50 for nonformulary drugs. Patients who took only brand-name drugs had an increase of $5 for each copayment in the postperiod. Few patients switched from generic to brand or from brand to generic and back to brand; these patients were excluded from the analysis. t Tests determined if the differences between the means were statistically significant. A regression model was not used because the value of the available control variables (eg, sex) did not change between the preperiod and the postperiod at the patient level.
Second Stage of Measuring Costs. We estimated 3 measures of cost. All-cause medical costs included all medical claims regardless of the underlying condition. Conditionspecific claims for ischemic heart disease were identified using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes 410 through 414 or procedure codes for coronary artery bypass graft or percutaneous transluminal coronary angioplasty. Condition-specific medical costs for diabetes were identified using ICD-9-CM code 250. Condition-specific pharmacy costs were identified by drug type, including statins for the ischemic heart disease subpopulation and sulfonylureas, metformin, thiazolidinediones and insulin for the diabetes subpopulation. Each of these measures represented the costs paid by the plan and the member in the preperiod and in the postperiod. The medical costs were adjusted to 2008 US dollars using the medical cost portion of the Consumer Price Index, and the pharmacy costs were adjusted to 2008 US dollars using the prescription drug cost portion of the Consumer Price Index.21
The cost model estimated how the net change in adherence affected all-cause medical costs, condition-specific medical costs, and condition-specific pharmacy costs in the postperiod. The 2 variables of interest were the net change in adherence and the interaction of that change and membership in the intervention group. Additional explanatory variables included sex, age (in years), the mean annual household income within a census block group (<$25,000, $25,000 to <$50,000, and ≥$50,000), duration of the condition, residence in a metropolitan area, and the probability of having earned a college degree and of being of white or Hispanic race/ethnicity. A log transformation of the condition-specific medical costs in the preperiod was included to be consistent with the log transformation of the dependent cost variables. The values for income, race/ethnicity, and education were imputed using census data and an age-stratified geocoding and surname analysis.22,23
We included several health-related variables specific to the conditions studied that indicated the presence of the following cardiac conditions: acute myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass graft, chronic ischemic heart disease, other ischemic heart disease, atherosclerosis, and angina. We also included variables indicating the presence of comorbidities, specifically ischemic heart disease (defined as in the ischemic heart disease subpopulation), hypertension (using ICD-9-CM codes 401-403 and 405), and diabetes (defined as in the diabetes subpopulation). These variables were constructed using claims data from the preperiod.
Characteristics of the study subpopulations are given in . Compared with the control group, the intervention group was younger, was less likely to contain female or Hispanic members, was more likely to contain white members, had lower health risks and fewer comorbidities, filled more prescriptions, and was more likely to have low income.
summarizes the switching patterns from brandname to generic drugs. Generic use was much higher in the diabetes subpopulation than in the ischemic heart disease subpopulation. Although a higher percentage of the diabetes subpopulation switched to generics, most of that switching occurred before the benefit change. However, because generic use was higher in the diabetes subpopulation, the absolute number of members switching was much fewer than in the ischemic heart disease subpopulation. In both subpopulations, there was little difference between the control group and the intervention group. The only statistically significant difference between the control group and the intervention group was the percentage of patients who continued to take brand-name metformin, with 3.0% of the control group continuing compared with 0.4% of the intervention group continuing.
Results of the adherence analysis are given in . For each drug type, the net change in adherence was tested between the control group and the intervention group for the total cohort and for patients who only took generic drugs, patients who switched to generic drugs, and patients who only took brandname drugs. Most subgroups had increases in adherence from the preperiod to the postperiod. The only statistically significant difference between the control group and the intervention group occurred in the subgroup taking statins who switched from brand-name drugs to generic drugs. This 14.7% difference reflected a 6.2% net increase in adherence among patients in the intervention group compared with an 8.5% net decrease in adherence among patients in the control group.
The model estimates for the effect of the net change in adherence on costs are given in . Summarized are the coefficients from the 3 regression models for the 2 variables of interest (the net change in adherence and a term capturing the interaction of that change and membership in the intervention group). The interaction term captures the effect of the net adherence change for the intervention group, isolating the effect of the benefit change. The change in adherence term captures the effect of the net adherence change unrelated to the intervention. The intervention was the benefit change that introduced a zero copayment on generic drugs and simultaneously increased copayments on brand-name drugs.
The change in adherence coefficients were negative and statistically significant for 3 diabetes drug classes (sulfonylureas, metformin, and thiazolidinediones), indicating that all-cause medical costs decreased as the net change in adherence increased. A similar result was seen for conditionspecific medical costs in the thiazolidinediones subgroup. As expected, the change in adherence coefficients were positive in the condition-specific pharmacy cost model, indicating that pharmacy costs increased as the net change in adherence increased. Although the magnitude of the coefficients in the condition-specific pharmacy cost model was greater than that of the coefficients in the medical cost models, the 3 models suggested overall savings, as pharmacy costs made up only a small portion of total costs.
The interaction term, which isolated the effect of the intervention, showed a decrease in all-cause medical costs and condition-specific medical costs only for the metformin subgroup. The intervention had no statistical effect on condition-specific pharmacy costs. These coefficients represent elasticities, the percentage change in costs due to a 1% change in the net change in adherence. For example, a 10% increase in the net change in adherence for the metformin intervention subgroup would result in an estimated 5.3% decrease in all-cause medical costs, a 4.2% decrease in condition-specific medical costs, and no change in condition-specific pharmacy costs.
The intent of the free generic drug benefit was to encourage switching from brand-name drugs to generic drugs. Additional expectations were that reduced out-of-pocket costs would result in improved adherence,6,11,24 in turn resulting in lower medical costs.7,18,25 This study found that lowering the cost of generic drugs and raising the price of brand-name drugs did not result in a complete transition to generic drugs for patients with ischemic heart disease or diabetes. The study also found that the $5 differential did not result in more switching to generic drugs compared with a control group that had no change in pharmacy copayment amount.
These results suggest that altering copayments for generics and brands may affect the rate of conversion to generics but is unlikely to result in complete conversion to generics. Additional initiatives to consider include the following: (1) outreach and education to members and physicians outlining alternatives that are available to lower costs without decreasing adherence, (2) a mandatory generics program, or (3) formularies that incorporate step therapy and evidence-based approaches.
While the study showed that the net change in adherence was positive for the intervention group and the control group, the change for the intervention group was statistically different from that for the control group only for patients taking statins. This indicates that there was no difference in the net change in adherence for groups facing small increases, decreases, or no change in copayments. This result is similar to earlier findings of no change in adherence even after an increase in copayment amounts.8,9
With respect to medical and pharmacy costs, the effect of the intervention was limited to the subgroup taking metformin. The finding that increasing the net change in adherence would result in all-cause medical cost savings for 3 of 5 drug classes studied is similar to results of other studies.18,25 Factors other than a change in copayments that could increase the net change in adherence include disease management programs, provider incentives that reward quality performance for chronic care, and an increase in member income. Unfortunately, data limitations prevented our controlling for these factors in this study. Furthermore, the results suggest that medical cost savings may offset increased pharmacy costs due to that increased adherence. For the other 2 drug classes studied, the finding that adherence did not significantly affect costs may be attributable in part to a postperiod that was too short to capture medical cost savings. In general, these estimates indicate that greater adherence increased condition-specific pharmacy costs. However, for the intervention herein, these estimates indicated no change in condition-specific pharmacy costs, which could be due to the design of increasing some costs and decreasing other costs simultaneously.
There are several limitations to this study. We focused on drug adherence for only 2 chronic conditions, for which the treatment drugs constitute a small subset of those covered by the free generic drug benefit. Therefore, the findings cannot be generalized to all conditions and drugs covered by the free generic drug policy. Although claims data are readily available, their use to study healthcare outcomes, treatment patterns, utilization, and costs is subject to the limitations associated with all observational administrative claims database analyses. In addition, data on race/ethnicity, education, and income are not routinely collected and were imputed. This imputation process does not capture changes in income and education that occurred during the study time period. The absence of additional unobservable characteristics from the claims data such as physician prescribing behavior and other changes in family situations could have affected the estimates.
This study examined how changing members’ out-of-pocket pharmacy costs affected adherence and medical costs. Although a decrease in the generic drug copayment to zero and a simultaneous increase in the brand-name copayment resulted in some switching from brand-name drugs to generic drugs, it was insufficient for complete transition. There was little difference in switching behavior or in the net change in adherence between the intervention group and the control group. Results of regression modeling suggested that improving adherence lowered all-cause medical costs for 3 diabetes drug classes. However, the intervention studied herein resulted in medical cost savings only for the metformin drug class.
Author Affiliations: From Blue Cross Blue Shield of Minnesota (HAR, AHH, ARW, NAG, DWP), Eagan, MN. Dr Heaton is now with UCare, Minneapolis, MN. Dr Plocher is now a vice president with Ingenix Consulting in Eden Prairie, MN.
Funding Source: This study was funded by Blue Cross Blue Shield of Minnesota, and all authors were employees of that organization at the time of the study.
Author Disclosures: The authors (HAR, AHH, ARW, NAG, DWP) are or were previously employed by the company whose product is the subject of this analysis.
Authorship Information: Concept and design (HAR, AHH, ARW, NAG); acquisition of data (HAR); analysis and interpretation of data (HAR, AHH, DWP); drafting of the manuscript (HAR, AHH, ARW, NAG); critical revision of the manuscript for important intellectual content (AHH, ARW, NAG, DWP); statistical analysis (HAR); and supervision (DWP).
Address correspondence to: Holly A. Rodin, PhD, Blue Cross Blue Shield of Minnesota, 3400 Yankee Dr, R259, Eagan, MN 55122. E-mail: firstname.lastname@example.org.
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