AJMC

Trends in Retail Clinic Use Among the Commercially Insured

Published Online: November 08, 2011
J. Scott Ashwood, MA; Rachel O. Reid, BA; Claude M. Setodji, PhD; Ellerie Weber, PhD; Martin Gaynor, PhD; and Ateev Mehrotra, MD, MPH

Objectives: To describe trends in retail clinic use among commercially insured patients and to identify which patient characteristics predict retail clinic use.
 

Study Design: Retrospective cohort analysis of commercial insurance claims sampled from a population of 13.3 million patients in 22 markets in 2007 to 2009.
 

Methods: 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.

 

Results: 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.
 

Conclusions: 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)

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.

METHODS

Data

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).

Population Selection

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

Statistical Analysis

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

RESULTS

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 (Figure). 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 (Table). 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.

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Issue: November 2011
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