Nancy A. Garrett, PhD

To assess whether health plan members who used retail clinics chose that setting for minor conditions and continued to see other providers for more complex conditions.

Retrospective analysis of claims data in a commercially insured population.

Health plan enrollment data were used to identify and describe the analysis population. Episode Treatment Groups were used to identify members with chronic conditions and to analyze reasons for retail clinic use, complexity of retail clinic visits, and care for chronic conditions in non—retail clinic settings. Logistic regression was used to study predictors of retail clinic use.

Retail clinic users differed significantly from nonusers. The most significant predictors of retail clinic use were age, sex, and proximity to a retail clinic. Episodes of care treated in the retail clinic appeared to be less complex than similar episodes treated in other settings. Chronically ill members who used the retail clinic saw another provider for their chronic condition at rates similar to or higher than those of members who did not use the retail clinic.

Individuals may be able to identify when conditions are minor enough to be treated in a retail clinic and serious enough to be treated by a traditional provider.

Appropriate use of retail clinics depends on an individual’s ability to identify when a condition is minor enough to be treated in that setting. We used claims data from a health plan to study the patterns of care for members who use retail clinics. We found that:

Episodes seen in the retail clinic setting appeared to be less complex than similar episodes seen in an office setting. 

 Members with chronic conditions who used retail clinics sought care for their chronic conditions from non—retail clinic providers at rates similar to those of members who did not use retail clinics.

	Retail clinics, also called convenience clinics, provide preventive care and treatment for minor, acute illnesses. They are located in retail stores, supermarkets, and pharmacies, and typically are staffed by certified physician assistants or nurse practitioners. For the consumer, retail clinics have advantages compared with traditional primary care, including lower cost and the availability of services without an appointment. The first retail clinics emerged in 2000 in Minnesota; the number has continued to grow nationally from 62 in 2006 to nearly 1000 in late 2008.

 Despite this growth, a 2007 survey found that only 1.2% of households had used a retail clinic in the previous year,

 even though a recent study estimated that more than one-fourth of the US population resides within a 10-minute drive of one,

 and a 2008 survey revealed that almost half of consumers would consider using a retail clinic.

	What does increasing use of retail clinics mean for consumers and for the healthcare system? These clinics offer convenience to the consumer and lower costs to the payer.

 Their appropriate use depends on the consumer’s ability to distinguish minor acute conditions that can be treated at a retail clinic from more serious conditions that would be better addressed by traditional providers. Given research suggesting that one-third of American adults have a health literacy measured at a “basic” or “below basic” level, this ability is not self-evident.

 Retail clinics have been criticized by the American Medical Association, the American Academy of Pediatrics, and the American Academy of Family Physicians, who claim that the clinics may create a conflict of interest (because of their relationships with retail pharmacies) and promote fragmented or substandard care.

 Providers have expressed concerns about use of retail clinics and the practice of waiving the copay for retail clinic visits.

 Critics also have cited concerns about the qualifications of clinic staff and their ability to diagnose serious medical problems,

 and about the disruption of existing physician—patient relationships, especially for those with chronic conditions.

	Research to date has focused on the purpose and cost of the visit, the quality of care provided,

 and the characteristics of retail clinic users.

 This research has not substantiated the concerns voiced about retail clinics, but rather suggests that minor conditions can be treated appropriately in that setting. A question that remains unanswered is whether individuals are adept at assessing the seriousness of their own conditions and are seeking care consistent with that assessment.

	The goal of this study was to extend the research to whether members choose retail clinics for minor concerns and whether they still choose non—retail clinic providers for less minor and chronic conditions. In particular, we estimated predictors of retail clinic use, compared complexity of visits for similar episodes in retail clinic and non–retail clinic settings, and assessed the degree to which members with chronic conditions who used a retail clinic also saw other providers for their chronic conditions.

	This study was set at Blue Cross and Blue Shield of Minnesota, a national health plan headquartered in Minnesota. Health plan claims and enrollment data were analyzed for a population of commercially insured members residing in Minnesota and continuously enrolled in 2007. The analysis included only services rendered by providers contracting with the health plan and located in Minnesota. This set included 72 separate retail clinic locations. A retail clinic is a unique category for contracting purposes and is defined as a clinic located in a retail or nonmedical setting with a limited scope of services. All retail clinics are covered by the same reimbursement arrangement. The services provided at retail clinics are limited by design to address noncomplex ailments that fall within the scope of practice of the provider staff. Some health systems in Minnesota have added clinics that function as retail clinics in terms of scope of practice and reimbursement, but are staffed by physicians in addition to nurse practitioners and physician assistants. These physician-staffed clinics were classified as retail clinics for the purposes of this analysis.

	To study whether members chose retail clinics for less complex concerns, we identified members with at least 1 visit to a retail clinic in 2007. To draw a comparison with members who did not use a retail clinic, we identified members with at least 1 office visit associated with diagnoses thought to be treatable in a retail clinic (see 

). We excluded members younger than 18 months (as of January 1, 2007) because the state of Minnesota requires that retail clinic patients be at least that old. We used geocoding to identify whether each member lived within 10 miles of a retail clinic.

 We then assessed whether members with at least 1 chronic condition who used a retail clinic continued to use traditional providers for chronic conditions. Episode Treatment Group (ETG) data from the prior years 2005 and 2006 were used to identify chronic conditions in the study population. Episode Treatment Groups use proprietary algorithms to group medical and pharmacy claims into episodes of care based on clinically similar conditions.

 Individuals with a chronic condition were identified using the following ETGs: diabetes (27-30), asthma (386-389), depression (95, 96), coronary artery disease (251-254, 256-265), congestive heart failure (267, 268), chronic obstructive pulmonary disorder (390-395), hyperlipidemia (47), and hypertension (278-281). We used episodes from the prior years to exclude new diagnoses of chronic conditions in the analysis year. For the comparison group of retail clinic nonusers with a chronic condition, we did not require an office visit as an inclusion criterion.

	We also used ETGs to describe the reason for the retail clinic visit and to describe the overall health of the members. Episode Risk Groups (ERGs) aggregate ETGs of similar clinical and risk characteristics.

 A person with multiple ETGs will have 1 or more ERGs. Each member was assigned a prospective ERG risk score, a weighted combination of those ERGs that predicts utilization. We defined high health risk as those members with a score of 7 or higher. This cutoff point captured those members incurring the top 0.5% of total claims spending.

	Demographic characteristics were compared between retail clinic users and nonusers using 

2 tests to assess whether clinic use was strongly dependent on sex, age, or type of health plan product. All available data describing an individual member were included in the analysis, including sex, age, health plan product, presence of a chronic condition, residence near a retail clinic, and ERG risk score. Health plan products were categorized as preferred provider organization (PPO), health maintenance organization (HMO), or consumer-directed health plan (CDHP). The PPO plans involve a network of preferred doctors and hospitals. Generally, services incurred with providers not in the network result in higher out-of-pocket expenses (ie, deductibles, copayments, and/or coinsurance). The HMO products require members to choose a primary care provider and require a copayment for most services. The CDHP products include major medical plans with high deductibles and out-of-pocket maximums, and some form of savings vehicle (eg, Health Reimbursement Arrangement, Health Savings Account) to fund expenses that are subject to the deductible. In the time period covered by the analysis, none of these products included an incentive for members to use retail clinics.

	We assessed whether members chose the retail clinic for minor conditions using the percentage of retail clinic episodes resolved in 1 visit. For people with chronic conditions, we analyzed whether they also saw a non—retail clinic provider for that condition. This study received no external funding.

	In 2007, among commercially insured members who were continuously enrolled, 40,686 members (2.8%) had at least 1 visit to a retail clinic. This constituted an increase of 71% over the previous year (results not shown). Among members who did not have a visit to a retail clinic, 651,514 members had at least 1 office visit for an immunization or for diagnosis of a condition that was potentially treatable in a retail clinic. 

compares the demographic characteristics of retail clinic users and nonusers. They differed significantly on nearly every measure analyzed. A higher percentage of retail clinic users were female, healthier, and younger compared with retail clinic nonusers. In both populations, a majority of members were enrolled in a PPO plan. A lower percentage of retail clinic users had chronic conditions and an ERG prospective risk score of 7 or higher. More than 90% of retail clinic users lived within 10 miles of a retail clinic, but fewer than 50% of nonusers did.

	We performed a logistic regression to identify the predictors of retail clinic use (

). All of the characteristics listed in Table 2 were significant predictors. This table indicates that compared with the reference group (members aged 25-44 years), all other age groups were less likely to use the retail clinic, after controlling for other characteristics. Members enrolled in CDHP plans, members enrolled in HMOs, and women were more likely to use retail clinics, whereas sicker members and members with chronic conditions were less likely. The most significant predictor in the model was whether the member lived within 10 miles of a retail clinic.

	We next sought to understand whether members appropriately chose the retail clinic for their care by analyzing the percentage of episodes of care resolved in 1 visit. We reasoned that if the episode that prompted the retail clinic visit resolved completely in that visit, the ailment was minor and using the retail clinic for its treatment was fitting. Overall, 94.8% of episodes with a retail clinic visit began with that visit. Moreover, 89.9% of episodes involved no other providers, and 88.3% were resolved in 1 visit, suggesting that the majority of visits were for minor conditions. 

 shows the percentage of episodes resolved in 1 visit for the top 10 episode types (as described by ETG and sub-ETG) seen in the retail clinic, and compares this percentage with that for episodes seen in a non—retail clinic setting. We excluded 2 of the most frequent episode types, routine inoculation (791, 0) and screenings and immunizations incidental to other services—immunizations (901, 7). As one would expect, for both of these episode types, nearly every episode was resolved in 1 visit, in both the retail clinic and the other settings. For all but 2 episode types, a greater percentage of episodes were resolved in 1 retail clinic visit than in 1 visit to other providers. This conclusion held when we applied the Bonferroni correction. For chronic sinusitis, there was no difference between the 2 settings. For routine exams, fewer retail clinic episodes were resolved in 1 visit. This may reflect the more open-ended nature of a routine exam, which could result in referrals for follow-up. We also performed this analysis separately for those members with and without chronic conditions, and separately for children aged 2 through 11 years, and the conclusions did not change (results not shown).

	Finally, we explored how often in the study year people with chronic conditions who were seen in a retail clinic also were seen by another provider for care of their chronic condition, which reflected a member’s ability to choose a care setting appropriate for more complex conditions. We compared that with the rate at which retail clinic nonusers with chronic conditions had provider visits for their chronic condition. For this comparison, we included all commercially insured members residing in Minnesota who had at least 1 chronic condition. When comparing the retail clinic users to the nonusers, we found that they differed in the same way as in the larger population (

). We limited this comparison to members 18 years and older, yielding 8859 retail clinic users and 386,600 nonusers with 1 or more chronic conditions. Retail clinic users with diabetes were less likely to see non—retail clinic providers for the care of their chronic conditions than were nonusers, and retail clinic users with hyperlipidemia or depression were more likely (

). There were no significant differences in these measures for the other conditions studied. The number of retail clinic users with chronic obstructive pulmonary disorder was too small to make a meaningful comparison. When we applied the Bonferroni correction, the difference for members with diabetes was no longer significant, although the differences for hyperlipidemia and depression remained significant.

	The number of retail clinics has increased significantly in the last decade. With growing acknowledgment of their ability to provide quality care at lower cost, insurers have developed strategies to encourage the use of such clinics. These policies have been criticized by those who worry that retail clinics will adversely affect the care received. In this study, we compared the characteristics of people who used retail clinics with the characteristics of those who did not, and estimated the predictors of retail clinic use. We explored whether members appeared to be making appropriate choices by examining the complexity of retail clinic visits and visits to non—retail clinic providers by members with chronic conditions.

	Overall, our findings of age and sex being significant predictors reaffirm the findings of previous research.

 We also found that proximity to a retail clinic was a significant predictor of retail clinic use. The percentage of care episodes resolved in 1 visit was higher for retail clinic users than for nonusers. This finding is consistent with an earlier study that found that retail clinic users were no more likely to have an “early return” visit.

 One interpretation of this finding is that the episodes seen in the retail clinic setting were less complex and were suitably treated there. Another interpretation is that non—retail clinic providers may have been more likely to schedule follow-up visits or to refer to other providers, and that retail clinics may discourage repeat visits. Claims data did not provide adequate information to judge if those episodes (whether occurring in a retail clinic or not) that included more than 1 visit were due to the proper referral of a patient with a more serious condition to a primary care provider or specialist, unnecessary referrals, or poor quality of care provided by the originating provider. For the retail clinic users, another possible explanation for the episodes requiring additional visits may be that the consumer underestimated the complexity of the condition and chose an inappropriate setting for treatment.

	Concerns about retail clinics disrupting the patient—provider relationship have not been fully explored. When we focused on members with chronic conditions, we found that retail clinic users were at least as likely as nonusers to have had a visit with another provider for their condition. No studies to date have addressed the impact of retail clinics on coordination of care. When a patient visits a retail clinic located in a clinic system with electronic health records, that record makes it possible for the patient’s primary care provider to know about the visit. Some retail clinics provide a record of the patient’s visit to the primary care provider at the patient’s request. In the absence of either of these options, a physician may not know about a patient’s retail clinic visit, which may affect coordination of care.

	This study has several limitations. Some chronic conditions (eg, depression) may have been overidentified because of limitations of the episode grouper logic. Missing episodes and orphan records resulting from the logic may have slightly affected the results by under- or overidentifying retail clinic visits. We could not ascertain how many members attempted to use the retail clinic but were turned away because their presenting condition was not appropriate for treatment there. One study suggested 2% of retail clinic patients were triaged to an emergency department or a primary care provider.

 In our analysis of why people with chronic conditions went to the retail clinic, we did not control for clinically significant combinations of acute and chronic conditions such as asthma and bronchitis. Finally, claims data presented an incomplete picture of the decision-making process that led the member to the retail clinic. The member’s stated symptoms may not match the diagnosis eventually coded on the claim by the retail clinic provider. Furthermore, we cannot know from the claims whether additional diagnoses (beyond the presenting complaint) were uncovered during the course of the retail clinic visit and resulted in follow-up visits to other providers. These new diagnoses may have resulted in a subsequent episode of care that was not captured by this analysis.

	Previous research on retail clinics focused on the purpose of the visits, the characteristics of the patients, cost, and quality of care. In this study, we explored whether members could ascertain when the severity of their condition indicated that a retail clinic would be an appropriate treatment setting. For the most common acute and preventive conditions, the choice of a retail clinic does not appear to have been associated with problematic self-triage, with more than 88% of episodes resolved in 1 visit. Sicker members appeared to be less likely to use retail clinics. Retail clinic use was not associated with a lesser frequency of ongoing visits to other providers for the treatment of chronic conditions. The degree to which patients with chronic conditions who use the retail clinic receive high-quality care for those conditions from their regular provider should be further studied. Another area for future study is whether healthier patients using retail clinics for routine and minor acute care affect the patient mix in primary care settings.

 From Blue Cross and Blue Shield of Minnesota (ARW, XTZ, WS, HR, EPB, NAG, TJS), Eagan, MN. Dr Wilson is now with the Medical University of South Carolina, Charleston, SC.

This study was funded by Blue Cross and Blue Shield of Minnesota.

Articles

Care episodes treated in retail clinics appeared to be less complex than those treated in office settings.

Retail Clinic Versus Office Setting: Do Patients Choose Appropriate Providers?

: 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.

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.

Altering copayments for generic drugs and brand-name drugs may affect the rate of conversion to generic drugs but is unlikely in and of itself to result in complete conversion to generic drugs.

Small increases and decreases in pharmacy copayments have little effect on adherence or medical costs.

Improving adherence lowered all-cause medical costs for 3 diabetes drug classes but raised pharmacy costs for all 5 drug classes studied.

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.

 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,

 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.

 Physician practice models that incorporate aspects of a medical home

 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.

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 studies

 considered all 3 factors. Studies of cost and adherence have demonstrated the following mixed results: increasing pharmacy cost sharing had no effect on adherence,

 increasing pharmacy cost sharing decreased adherence,

 and decreasing pharmacy cost sharing increased adherence. 

 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.

 A relationship between increased adherence and fewer emergency department visits and hospitalizations has been observed.6,7,18-20 While some studies

 have shown overall medical cost savings as a result of adherence, other studies

 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.

 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.

. We used the medication possession ratio (MPR) to measure adherence.

 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. 

 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.

. 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 

) 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 

 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.

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.

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 

 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,

 in turn resulting in lower medical costs.

 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.

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.

 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.

: 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.

: 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.

: The authors (HAR, AHH, ARW, NAG, DWP) are or were previously employed by the company whose product is the subject of this analysis.

: 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).

: Holly A. Rodin, PhD, Blue Cross Blue Shield of Minnesota, 3400 Yankee Dr, R259, Eagan, MN 55122. E-mail: holly_rodin@bluecrossmn.com.

Small increases and decreases in pharmacy copayments have little influence on patients’ switching to generic drugs or on improving adherence to medication regimens.

Nancy A. Garrett, PhD

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