Retail clinic use increased dramatically over 3 years, especially among young, healthy, higher income enrollees living close to retail clinics.
To describe trends in retail clinic use among commercially insured patients and to identify which patient characteristics predict retail clinic use.
Retrospective cohort analysis of commercial insurance claims sampled from a population of 13.3 million patients in 22 markets in 2007 to 2009.
We identified 11 simple acute conditions that can be managed at a retail clinic and described trends in retail clinic utilization for these conditions. We used multiple logistic regressions to identify predictors of retail clinic versus another care site for these conditions and assessed whether those predictors changed over time.
Retail clinic use increased 10-fold from 2007 to 2009. By 2009, 6.9% of all visits for the 11 conditions were to a retail clinic. Proximity to a retail clinic was the strongest predictor of use. Patients living within 1 mile of a retail clinic were 7.5% more likely to use one than those living 10 to 20 miles away (P <.001). Women ( 0.9%, P<.001), young adults ( 1.6%, P <.001), patients without a chronic condition ( 0.9%, P <.001), and patients with high incomes ( 2.6%, P <.001) were more likely to use retail clinics. All these associations became stronger over time. There was no association between primary care physician availability and retail clinic use.
If these trends continue, health plans will see a dramatic increase in retail clinic utilization. While use is increasing on average, it is particularly increasing among young, healthy, and higher income patients living close to retail clinics.
(Am J Manag Care. 2011;17(11):e443-e448)
We examined trends in retail clinic use among a commercially insured population and examined the predictors of retail clinic use.
Retail clinics are a new model for delivering ambulatory care with a focus on patient convenience. Located in pharmacies, grocery stores, or “big box stores” such as Walmart or Target, retail clinics focus on the treatment of a limited set of simple acute conditions or preventive care.1-4 As the number of retail clinics has grown over the last 5 years, there has been interest among payers in who will visit a retail clinic and for what reason.1,5,6 Initial studies found that the first users of retail clinics were primarily young, healthy patients who do not have a primary care provider.2,7 In this article we describe (1) the trends in retail clinic use in a large commercially insured population, (2) patient characteristics that predict use of a retail clinic versus another care site, (3) whether the demographic profile of retail clinic users has changed, and (4) whether retail clinic use is higher in communities with a shortage of primary care.
We used 2007 to 2009 claims and enrollment data provided by Aetna for their 13.3 million enrollees in 22 markets in which there are retail clinics. Aetna covered retail clinic visits throughout the 3 years. The copayment varied based on the employer, but in general was similar to the copayment for a physician visit. Aetna provided data on all enrollees who had at least 1 visit to a retail clinic (n =367,448), as well as a random sample of enrollees from the same markets who did not visit a retail clinic (n = 1,010,910). In our analyses we weighted all enrollees by the inverse of their likelihood of selection for our sample.
Focus on Acute Care Visits
We identified outpatient visits for a set of 11 acute conditions most commonly seen at retail clinics. We identified outpatient visits using the following Current Procedural Terminology8 codes: 99201-99205 and 99211-99215. The 11 “retail clinic—sensitive” conditions (associated 3-digit International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM]9 diagnosis codes) were upper respiratory infections (460, 465), sinusitis (461, 473), bronchitis (490, 466), pharyngitis (462, 463, 034), otitis media (381, 382), otitis externa (380), conjunctivitis (372), urinary tract infections (599, 595), allergic rhinitis (477), influenza (487), and unspecified viral infection (079). Together they accounted for 88% of acute care visits to retail clinics. We did not evaluate utilization trends for preventive care such as immunizations. Though immunizations accounted for 40% of visits to retail clinics, they are inconsistently recorded in nonretail clinic settings and patients also frequently receive immunizations at sites where no claim is issued (eg, work sites).
We limited our population to enrollees under 65 years of age who lived within 20 miles of a retail clinic. We excluded enrollees over age 65 because they were likely to have coinsurance with Medicare; therefore, all claims might not have been captured in our data. We computed the geodesic distance from the center of each enrollee’s zip code to the center of each retail clinic’s zip code and dropped enrollees who lived more than 20 miles from any retail clinic. Our goal was to examine utilization trends in the market areas for retail clinics, and the vast majority (97.6%) of the enrollees who visited a retail clinic lived fewer than 20 miles from a retail clinic.
Predictors of Retail Clinic Use
Our predictors of retail clinic use were sex, age, distance to retail clinic, health status, income level, and access to primary care physicians. We included distance to a retail clinic because in prior studies proximity to providers was an important driver of use.10,11 To control for health status, we divided enrollees into 3 groups: no chronic conditions, 1 chronic condition, and 2 or more chronic conditions. The chronic conditions identified were 27 pediatric and adult chronic conditions used in prior work on risk adjustment.12,13
We used 2000 ZIP Code Tabulation Area median household income from the US Census Bureau as a proxy for enrollee income divided into 3 groups: low income (<2 times the federal poverty level), medium income (>2 times the federal poverty level to <$59,000), and high income (>$59,000). We chose $59,000 as a cutoff because it is the 90th percentile of US ZIP Code Tabulation Area median household income.
To control for the availability of alternatives to retail clinics, we included fixed effects for each of the 22 healthcare markets. We also controlled for whether an enrollee resided in a zip code in which the majority of the population lived in a federally designated primary care Health Professional Shortage Area.14
In our first analysis, we tracked monthly retail clinic utilization for retail clinic—sensitive conditions in the entire study population. In our second analysis, we modeled the choice to use a retail clinic versus other providers for a retail clinic–sensitive condition. Our predictor variables were those listed above. The population of enrollees consisted of those that had a visit to any care site for a retail clinic–sensitive condition in 2007 to 2009. We ran a logistic regression predicting the likelihood of a patient visiting a retail clinic versus other care sites. In our third analysis, we evaluated changes in our predictors over time. We hypothesized that as the number of retail clinic visitors increased, they would become less distinct from the general population. We estimated separate prediction models for each year with the predictor variables listed above. In order to test the significance of the change in the effect of each of our predictors between 2007 and 2009, we conducted an analysis combining the 2007 and 2009 observations that included interactions with 2009 for all of our predictors. Statistical significance of the interaction terms would indicate statistically significant changes in the predictors between 2007 and 2009.
For all of our logistic models, we used the Surveylogistic procedure in SAS version 9.22 (SAS Institute Inc, Cary, North Carolina). We weighted the enrollees by the inverse of the likelihood of selection and clustered our standard errors by zip code. To estimate the marginal effect of each predictor on the likelihood of retail clinic use, we used the method of predictive margins, also called recycled prediction.15
Among all 13.3 million enrollees in our study population, retail clinic use increased between January 2007 and December 2009 from a monthly rate of 0.3 visits per 1000 enrollees to 2.7 visits per 1000. Among the 3.8 million enrollees who had at least 1 retail clinic—sensitive visit to any site in 2007 to 2009, visits to retail clinics increased from a monthly rate of 0.6 visits per 1000 enrollees in January 2007 to 6.5 visits per 1000 enrollees in December 2009 (igure). There was a cleaseasonal pattern with spikes in the winter months and troughs in the summer months.
Of all enrollees who had a retail clinic—sensitive condition visit between 2007 and 2009, 210,763 (5.5%) visited a retail clinic. That fraction was 1.5%, 4.5%, and 6.9% in 2007, 2008, and 2009, respectively.
Enrollee Predictors of Retail Clinic Use
In our model of retail clinic use at any time in 2007 to 2009, the strongest predictors were distance, age, chronic illness, income, and sex (). Enrollees who lived less than 1 mile from a retail clinic were 7.5% (95% confi dence interval [CI] 6.6%-8.4%) more likely to visit a retail clinic than those who lived 10 to 20 miles away. Adults aged 18 to 44 years were more likely to visit a retail clinic than other age groups, and enrollees with no chronic conditions were 0.9% (95% CI 0.8%-1.0%) more likely to visit retail clinics than enrollees with 2 or more chronic conditions. We found that enrollees in zip codes with higher median household incomes were 2.6% (95% CI 2.1%-3.1%) more likely to use a retail clinic than those from zip codes with low median incomes. Women were 0.9% (95% CI 0.84%-0.98%) more likely to use retail clinics. We found no difference in the likelihood to use retail clinics for those who lived in Health Professional Shortage Areas.
Change Over Time in Patient Predictors
The Figure shows the monthly rate of retail clinic use broken down by the key population characteristics identifi ed by our model: proximity, age, health status, sex, and income (panels 2-6). In each panel except panel 4 (health status) and panel 6 (income), the distance between the top line and the other lines in the panel generally increases, indicating that the effect of that characteristic was getting stronger over time. For example, the rate of visits per 1000 enrollees from January 2007 to December 2009 for those who live within 1 mile of a retail clinic (panel 2) increased from 1.2 visits per 1000 enrollees in January 2007 to 10.5 visits in December 2009, while for patients 10 to 20 miles away it increased from 0.1 to 2.0 per 1000 enrollees.
In our models we tested for a change in predictors over time by estimating separate multivariable logistic models for each year. The marginal effects became stronger over time. For example, the marginal effect for enrollees who lived within 1 mile of a retail clinic increased from 2.5% in 2007 to 11.3% in 2009 (Appendix). The difference was statistically significant. The marginal effects for age, health status, and sex all increased significantly from 2007 to 2009. However, while the marginal effects of these predictors increased, the associated odds ratios remained steady (). This implies that the relative importance of the predictor did not change because retail clinic use was increasing across the population.
In a large commercially insured population, we saw a striking increase in retail clinic use between 2007 and 2009. In January 2007, shortly after retail clinics entered these markets, few enrollees visited them (less than 1 visit per 1000 enrollees). By December 2009, retail clinic use reached an average of almost 6 visits per 1000 enrollees for those who sought care for acute conditions. It is clear that enrollees are “voting with their feet” and that retail clinics are meeting an unmet need for simple acute care and/or addressing a shortage of traditional healthcare providers.
Our results do not provide a definitive explanation of why enrollees are choosing retail clinics over other sources of care, but they provide some clues. Consistent with prior research,16 our results suggest that convenience is the strongest predictor of retail clinic use. Enrollees who lived close to a retail clinic were much more likely to use one. Controlling for distance to a retail clinic, we also found that enrollees in high-income zip codes were more likely to use a retail clinic. This is possibly consistent with prior studies that have shown that higher income patients place a higher value on their time.17 Our results are not entirely consistent on whether primary care relationships drive retail clinic use. We found that healthier, young adults were the most common users of retail clinics. This group is less likely to have a primary care provider and therefore might visit a retail clinic because they have no alternative. However, we found no association between primary care availability and use of retail clinics. This is contrary to arguments that retail clinics fulfill unmet demand in areas of primary care shortage.1
After rapid growth in the number of retail clinics in 2007, there has been no significant net change in the number of retail clinics since mid-2008.18 Despite concerns about the profitability and long-term viability of retail clinics,19 we believe it is notable that the utilization of retail clinics among at least the commercially insured is growing rapidly.
If these trends continue, payers that offer coverage of retail clinic visits can expect to see continued and rapid growth in the use of these clinics. Care initiated at retail clinics is 30% to 40% cheaper than similar care at physician offices and approximately 80% cheaper than similar care at emergency departments.3 If the growth in retail clinic visits we observed represents substitution for other sources of care, then the increase in retail clinic use could lead to lower costs. However, if these visits represented new utilization (ie, patients seeking care when previously they would have stayed at home), then costs could increase. Future work should explore whether retail clinic visits represent new utilization or replacement of visits to physicians or emergency departments.
The authors gratefully acknowledge the Robert Wood Johnson Foundation’s Changes in Health Care Financing and Organization Initiative for financial support of this work and Aetna for providing access to their claims data. Ms Reid’s contributions to this work occurred prior to her employment at the Centers for Medicare & Medicaid Services.
Author Affiliations: From RAND Corporation (JSA, CMS, EW, AM), Pittsburgh, PA; H. John Heinz III College, Carnegie Mellon University (JSA, MG), Pittsburgh, PA; University of Pittsburgh School of Medicine (ROR, EW, AM), Pittsburgh, PA.
Funding Source: Funding for this study was provided by the Robert Wood Johnson Foundation.
Author Disclosures: The authors (JSA, ROR, CMS, EW, MG, AM) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (JSA, ROR, CMS, EW, MG, AM); acquisition of data (AM); analysis and interpretation of data (JSA, ROR, CMS, EW, MG); drafting of the manuscript (JSA, ROR); critical revision of the manuscript for important intellectual content (JSA, ROR, CMS, EW, MG, AM); statistical analysis (JSA, ROR, CMS); and obtaining funding (CMS, AM).
Address correspondence to: J. Scott Ashwood, MA, RAND Corporation, 4570 Fifth Ave, Ste 600, Pittsburgh, PA 15213. E-mail: firstname.lastname@example.org.
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