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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Am Board Fam Med. Author manuscript; available in PMC 2010 April 4.
Published in final edited form as:
PMCID: PMC2848989
NIHMSID: NIHMS180473

The SocioDemographic Characteristics of the Communities Served by Retail Clinics

Abstract

PURPOSE

As a rapidly growing new health care delivery model in the United States, retail clinics have been the subject of much debate and controversy. Located physically within a retail store, retail clinics provide simple acute and preventive services for a fixed price and without an appointment. Some hope that retail clinics can be a new safety-net provider for the poor and those without a primary care physician. To better understand the potential for retail clinics to achieve this goal, we describe the socio-demographic characteristics of the communities in which they operate.

METHODS

We created an inventory of all retail clinics in the United States and determined the proportion that are in Health Profession Shortage Area (HPSA). We defined each retail clinic’s catchment area as all census blocks that were less than a five-minute driving distance from the clinic. We compared the socio-demographic characteristics of the population within and outside of these retail clinic catchment areas.

RESULTS

Of the 982 clinics in 32 states, 88.4% were in an urban area and 12.5% were in a HPSA (20.9% of the US population lives within a HPSA). Compared to the rest of the urban population, the population living within a retail clinic catchment area has a higher median household income ($52,849 vs. $46,080), is better educated (32.6% vs. 24.9% with a college degree), and is as likely to be uninsured (17.7% vs. 17.0%). In a multivariate model, the census block’s median household income had the strongest association with whether the census block was in a retail clinic catchment area (OR 3.63 (95% CI 3.26–4.05) median income ≥$54,779 vs. median income –003C;$30,781)

CONCLUSIONS

We find that relatively few retail clinics are located in HPSAs and compared to the rest of the urban population, the population living in close proximity to a retail clinic has a higher income.

Offering a novel method of health care delivery, retail clinics have garnered significant interest from patients, politicians, physicians, and health plans. They are called retail clinics because they are physically located in retail stores such as grocery stores, drugstores, or “big box” stores such as Wal-Mart. Retail clinics provide walk-in care for a limited number of acute illnesses and preventative care services for a fixed price and visits are usually covered by most health insurance plans.(1) Generally staffed by nurse-practitioners, retail clinics focus on patient convenience by requiring no appointment and offering night and weekend hours. The number of retail clinics, most of which are operated for profit, has rapidly increased and it is estimated that there will be three million patient visits to retail clinics by 2008.(2)

As the number of retail clinics has increased, several physician associations have raised concerns about the safety of the retail clinic model. The concern is that retail clinics can possibly increase fragmentation of care, provide inferior care, and adversely impact delivery of preventative care.(39) Most retail clinics are owned by for-profit drugstore chains and some have worried that in an effort to increase pharmacy sales, retail clinic providers will overprescribe medications.(45) However, policymakers including California Governor Schwarzenegger,(10) Pennsylvania Governor Rendell, (11) and Senator John McCain (12) have supported the growth of retail clinics and included them in their health reform proposals. Potential benefits of clinics cited include improving access to care and providing affordable care for the poor and underserved.(13,14)

Despite the interest and controversy generated by retail clinics, there has been little empirical evaluation of their impact. (1, 1521) The goals of this study were to describe the location of retail clinics, determine whether they are disproportionately located in areas with a scarcity of health providers, and describe the socio-demographic characteristics of the population which lives close to a retail clinic.

METHODS

Inventory of Retail Clinics

We compiled a list of retail clinic companies based on several sources: recent industry reports, analysis from a clinic consulting company (merchantmedicine.com), and general web searches.(22, 23) To our knowledge there have been no hard definitions of retail clinics in previous literature. We therefore chose to define retail clinics based on the following three criteria: (1) location within a retail store, (2) staffing by nurse practitioners or physician assistants, and (3) a limited menu of services with pre-specified fixed prices. We did include some companies whose clinics did not satisfy all these criteria but which embodied the character of the model. For example, we included QuickHealth clinics which are located in retail stores such as Wal-Mart, use a menu of services, but are staffed by physicians. We chose to include two clinic operators that target transient populations: AeroClinic, which operates in airports and Roadside Med, which operates in Pilot Travel centers which are located next to highways. These clinics serve employees of their respective retail hubs in addition to travelers. We verified the list of companies with two experts on the retail clinic industry: MaryKate Scott, an independent consultant, and Caroline Ridgeway, from the Convenient Care Association, which is a lobbying organization for the retail clinic industry. We obtained the addresses of all clinics from the retail clinic companies’ websites between June and August 2008.

Mapping of Retail Clinics

Using Geospatial Imaging Software (GIS: ArcInfo v9.3 ESRI Inc., Redlands CA), we mapped the location of each clinic. Many retail clinics have opened in newly built neighborhoods that do not map directly to an identified street in the ESRI street database which is several years old. For 28% of clinics, we utilized Google maps to correct misspelled addresses, identify alternate city names or zip codes, or isolate the closest address of a map-able location. If we utilized the closest map-able address, it was within 1–2 blocks of the actual retail clinic location.

Creating Retail Clinic Catchment Areas

We defined the catchment area around each retail clinic to be a five-minute driving distance in each direction. The catchment algorithm relies on road-based travel to define the point-boundaries in every direction and then makes linear connections between these point-boundaries to create a boundary loop. The travel time calculated incorporates average driving speed on the type of road (e.g. faster speeds on highways), but does not incorporate typical levels of traffic. We chose a five-minute driving distance to define the minimum size of catchment areas because previous research estimates 5-minutes as the time persons are willing to travel to some retail stores.(24) We looked at a 10-minute driving distance catchment area, as well, to test the sensitivity of our analyses.

Defining Population Within and Outside Retail Clinics Catchment Areas

We compared the socio-demographic characteristics of the population within and outside the retail clinic catchment areas. Because the vast majority of retail clinics were in urban areas, our analyses focus just on the US urban population. Urban areas were defined by the Census Bureau(25) and included regions with a population greater than 500–1000 people per square mile or adjoining areas. Using ArcGIS we selected all urban census blocks whose centroids lay within a retail clinic catchment area. Census blocks are the smallest increment of census data and there were approximately 8.5 million census blocks in the 2000 census.(25) They contain limited aggregated information from a 100% survey of the population and average 33 people per block. Blocks are aggregated into block groups, which are aggregated into census tracts, which are aggregated into counties, which are aggregated into states. After evaluating several possible aerial interpolation methods, we felt the census block centroid analysis to be the most logical for our purposes given the granularity of the data.

We acquired US Census data at the block level on race, ethnicity, age, and gender from 2000 (the most recent data available). Since income, education, and urban population data are not available only at the block level, we imputed these data down from the census tract. For example, if 22% of the tract had a college education, then we assumed that 22% of the population of each block in that tract had a college education. On average 120 blocks make up census tracts, which contain an average of 4,000 people. To our knowledge, the most granular health insurance data is available at the county level. We obtained data on health insurance rates at the county level from the Census Bureau’s Small Area Health Insurance Estimates division from 2005 and imputed this data to the block level.

We created a multivariate model to determine characteristics of a census block that were associated with the census block being within a 5 minute retail clinic catchment area. The unit of analysis was the census block (n=2,379,547). We set the outcome variable as a binary “within” or “outside of” a retail clinic catchment area and used census block characteristics listed above (e.g. percentage of census block that was non-white race) as predictor variables. We limited our analysis to urban census blocks with more than ≥10 residents to ensure a stable estimate of census characteristics. Because of collinearity with median income, we did not include two variables in the model, percentage of residents with a college education and below the poverty line. Insurance rates were not included because they were imputed from the county level. Due to a non-linear relationship between predictor and outcome variables, we chose to divide the census block characteristics into quartiles. Many of the predictor variables were collected at the level of the census tract so we accounted for clustering using proc surveylogistic (SAS version 9.1, SAS Institute, Cary, NC). We conducted a sensitivity analysis using a binary outcome variable of “within” or “outside of” a 10 minute catchment area. The results were substantively the same.

We used GIS software to also determine the fraction of retail clinics located in primary care Health Profession Shortage Areas (HPSA). HPSA geographic boundaries are defined by the Health Resources and Services Administration.(26)

RESULTS

As of August 2008, 42 different clinic companies operated 982 retail clinics in the US (Table 1). Though over 40 operators run retail clinics, the five largest (MinuteClinic, Take-Care, Little Clinic, TargetClinic, and Redi-Clinic) run 82% of the clinics. Clinics were located most frequently in urban areas (88.2% of clinics) and 99.1% of the population that lives within 5 minutes of a retail clinic lives in an urban area. Across the nation 12.5% of the clinics are located in a HPSA while 20.9% of the general US population lives in a HPSA.(27)

Table 1
Retail Clinic Companies Operating Clinics as of August 2008

Compared to other urban residents, the population that lives within a 5-minute retail clinic catchment area has a higher median household income ($52,943 vs. $46,080), is better educated (32.6% vs. 24.9% with a college education), is less likely to live below the poverty line (10.2% vs. 12.6%), and has a higher proportion of the population that is Hispanic or Latino (17.6% vs. 15.4%) (Table 2). They have similar uninsurance rates (17.7% vs. 17.0%), and there were no notable differences in age or gender. The demographic comparison was substantively the same when we used a 10-minute catchment area.

Table 2
Socio-Demographic Characteristics of Urban Population Within and Outside of a Retail Clinic Catchment Area

We created a multivariate model to determine characteristics of a census block that were associated with being within a 5-minute retail clinic catchment area. The characteristic with the strongest association was median household income. Compared to census blocks with a median household income <$31,781, census blocks where the median income was ≥$54,779 were more likely (OR 3.63 95% CI 3.26–4.05) to be within a 5 minute catchment area (Table 3). Because of the large number of census blocks (>2 million) all the other variables had statistically significant associations, but the magnitude of the associations was relatively smaller (OR < 2).

Table 3
Census Tract Characteristics Associated With Being in a Retail Clinic Catchment Area (Multivariate Model)

DISCUSSION

Supporters of the retail clinic model have argued that its growth could improve access to care in general, but in particular for the underserved including the poor and those with little access to primary care physicians. We find that retail clinics are not preferentially located in communities with these demographics. A relatively small fraction of clinics are located in a HPSA and the income of census blocks within a 5-minute of a retail clinic was higher than the rest of the urban population.

How can these findings influence the continuing debate about retail clinics? First, it should dispel the notion that retail clinic companies are specifically targeting the underserved. Rather, except for the differences in income and education, the urban residents that live within a retail clinic catchment area are relatively similar to the urban population as a whole. This may not be surprising. Retail clinics are most commonly run by for-profit companies who want to reach as broad a segment of the population as a whole. It is also important to note that primary care physician offices are often preferentially located in higher income areas.(28) Second, the vast majority of retail clinics are in urban areas. Again this might make sense from a business perspective to reach as many possible clients as possible, but rural areas are most in need of new care options.

There are several key limitations to our analyses. The decision on where retail clinic companies place a retail clinic is also likely influenced by variables outside our analysis such as available partner retail stores (e.g. availability of a Walgreens in the area), suitable store layout for a retail clinic (e.g. a corner of the store with available plumbing), and store foot traffic. We utilized a five-minute and ten-minute driving distance to define retail clinic catchments areas, but this was not informed by previous access to care literature. This literature has generally focused on other types of situations such as how distance to a radiation oncologist might impact decision to undergo breast conserving surgery and therefore was not applicable to retail clinics.(29) We therefore used data on typical travel times to a grocery store as a proxy.(24) We recognize that patients vary in the distance they are willing to travel and other factors such as physical geography and personal driving patterns will influence who is willing to drive to a clinic. For example, patients in rural areas are likely willing to drive a longer distance. As noted above, we looked at the entire population within a catchment area recognizing that this population is the “possible clientele” rather than the “probable clientele” of the retail clinic. For example, an elderly patient with a strong relationship with her PCP may live in the catchment area but is unlikely to come to the retail clinic.

There were several analytic issues related to using Census data that introduced some error in our results. Several socio-demographic characteristics in our analysis were not available at the census block. For example, health insurance data is available only at the county level. In order to include these data, we chose to impute available county data on insurance rates to the census block level. Because counties can encompass areas much larger than a typical retail clinic catchment area, we have limited ability to detect differences in insurance rates of those within and outside a retail clinic catchment area. Therefore, insurance-related results should be interpreted with caution. Previous research has found that retail clinics attract patients who are less likely to use insurance to pay for a visit than patients who visit a physician.(1) It might be that those without insurance might be preferentially seeking retail clinics for care. Lastly, by necessity we used census data from 2000 while clinic addresses and HPSA boundaries were from 2008. All of these census-related methodological issues introduce some error in our results, but whether they bias our findings is unclear.

Retail clinics are a rapidly growing segment of the US health care system that has garnered significant interest and controversy. We believe our study represents the first study in the peer-reviewed literature to describe on a national scale who can visit a retail clinic. The communities surrounding retail clinics are on average wealthier and better educated than the rest of the nation. These results can help inform the ongoing policy debate about the potential harms and benefits of the retail clinic model.

Acknowledgments

Grant Support: This work was supported by a grant from the California Health Care Foundation and a career development award from the National Center for Research Resources, a component of the National Institutes of Health. The funders had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; or the preparation, review, or approval of the manuscript.

Footnotes

Conflict of Interest:

None of the authors have a financial interest in or a financial conflict with the subject matter or materials discussed in this manuscript.

Contributor Information

Rena Rudavsky, RAND Health, Arlington, VA.

Ateev Mehrotra, RAND Health, Pittsburgh, PA; University of Pittsburgh School of Medicine.

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