Abstract
Background
U.S. adolescents and young adults are using indoor tanning at high rates, even though it has been linked to both melanoma and squamous cell cancer. Because the availability of commercial indoor tanning facilities may influence use, data are needed on the number and density of such facilities.
Methods
In March 2006, commercial indoor tanning facilities in 116 large U.S. cities were identified, and the number and density (per 100,000 population) were computed for each city. Bivariate and multivariate analyses conducted in 2008 tested the association between tanning-facility density and selected geographic, climatologic, demographic, and legislative variables.
Results
Mean facility number and density across cities were 41.8 (SD=30.8) and 11.8 (SD=6.0), respectively. In multivariate analysis, cities with higher percentages of whites and lower ultraviolet (UV)index scores had significantly higher facility densities than those with lower percentages of whites and higher UV index scores.
Conclusions
These data indicate that commercial indoor tanning is widely available in the urban U.S., and this availability may help explain the high usage of indoor tanning.
Introduction
Use of tanning lamps is associated with both melanoma and squamous cell cancer.1 Approximately 20% of U.S. adults aged 18–29 years used indoor tanning in the past 12 months,2 and rates are also high among adolescents,3,4 with estimates for older teen girls as high as 40%.4
Based on built environment–related research for other health-related behaviors such as alcohol and tobacco,5–7 the availability of commercial indoor tanning likely influences indoor tanning use. Therefore, accurately measuring the availability of indoor tanning facilities and identifying correlates of availability are important. CITY100 (Correlates of Indoor Tanning in Youth) is a multicomponent project focusing on potential correlates of adolescents’ use of indoor tanning.8–10 In this tanning-facility–availability component, updated estimates from a 1998 study11 are provided with the following methodologic improvements: (1) higher-quality procedures for identifying indoor tanning facilities, (2) more accurate identification of city borders, and (3) the inclusion of a larger number of cities (N=116 vs N=80).
Methods
Cities
The sample consisted of the 100 most populous U.S. cities, which were located in 34 states, plus the most populous city in each of the remaining 16 states.12
For each of the 116 cities, geographic boundaries for the city proper were created with GIS.13 Buffer zones of 1, 2, and 3 miles around the boundary of each city were created, because the residents living in the city proper likely travel beyond the formal boundaries.
Outcome Variable
The outcome variable was tanning-facility density, computed by dividing the number of indoor tanning facilities in the city plus those in the 3-mile buffer zone by the city proper’s total population12 and then multiplying the result by 100,000. Inclusion criteria for facilities were (1) must offer ultraviolet radiation indoor tanning and (2) must be open to the public and not require a membership (thereby being more accessible to adolescents).
In March 2006, with tanning salons as the key word, eligible commercial establishments in each city and its buffer zones were identified using two Internet business listings: Reference USA. com was the primary search engine and SuperPages.com was the secondary search engine. Businesses were entered into a database file and geocoded to the city-specific map. For 30 of the cities (25.8%), two research assistants independently counted the number of facilities.
Predictor Variables
Geographic variables included the region of country,12 the latitude, longitude, and elevation,14 and whether the city is on an ocean coast. Climatologic data consisted of the annual average temperature (°F), the mean days that the temperature was ≥90°F, the mean days that the temperature was ≤32°F, the mean days that precipitation was at least 0.01 inches, the average percentage of possible sunshine,15 and the average daily ultraviolet (UV) index.16 Demographic data consisted of the percentage of whites in the total city population, the percentage aged 15–19 years, the percentage with at least some college, and median family income.12 Legislative data included whether the state had any indoor tanning law, whether the state had a law restricting youth access to indoor tanning, and the stringency score of the general law, using data collected in a previous study.10 The range of possible stringency scores was 0–100, and cities in states without an indoor tanning law were given a score of zero. Additionally, whether tanning facilities in states with indoor tanning laws were inspected at least annually was included.9
Contextual Data
Placing the number of indoor tanning facilities in a socio-culturally meaningful context was desirable. Therefore, the numbers of Starbucks and McDonald’s for each of the cities were estimated by systematically searching Superpages.com.17
Statistical Analysis
Analyses, performed in Spring 2008, included examining bivariate relationships between facility density and potential predictors using independent t-tests, ANOVA, or zero-order correlations. With facility density as the dependent variable, multiple linear regression was conducted with those variables found to have significant associations with density in the bivariate tests. Due to multicol-linearity between several study variables, only five variables were included in the multivariate model: coastal/noncoastal status, annual average temperature, UV index, the percentage of whites, and the percentage with some college or more. Variables were selected based on what was thought to more directly influence indoor tanning availability (e.g., temperature and UV index versus latitude were selected).
Results
A total of 4561 facilities were identified. The raters had exact agreement on the number of indoor tanning facilities for 22 (73.3%) of the 30 cities; for the remaining eight cities, they differed by only one facility. The mean number of facilities for the 116 cities was 41.8 (SD=30.8), and the mean density was 11.8 (SD=6.0). The number and density of indoor tanning facilities for each city are provided in Table 1. The mean numbers of Starbucks and McDonald’s per city were 19 (SD=25.2) and 29.6 (SD=22.5), respectively.
Table 1.
City population size, facility number, and facility density
| City | Populationa | Number of facilitiesb | Facility density (per 100,000 population)c |
|---|---|---|---|
| Northeast | |||
| Pittsburgh PA | 334,563 | 93 | 27.8 |
| Portland ME | 64,249 | 16 | 24.9 |
| Providence RI | 173,618 | 41 | 23.6 |
| Burlington VT | 22,876 | 7 | 18.0 |
| Manchester NH | 107,006 | 19 | 17.8 |
| Rochester NY | 219,773 | 38 | 17.3 |
| Boston MA | 589,141 | 90 | 15.3 |
| Yonkers NY | 196,086 | 23 | 11.7 |
| Newark NJ | 273,546 | 30 | 11.0 |
| Jersey City NJ | 240,055 | 25 | 10.4 |
| Buffalo NY | 292,648 | 21 | 7.2 |
| Bridgeport CT | 139,529 | 8 | 5.7 |
| Philadelphia PA | 1,517,550 | 83 | 5.5 |
| New York NY | 8,008,278 | 183 | 2.3 |
| South | |||
| Charleston WV | 53,421 | 18 | 33.7 |
| Columbia SC | 116,278 | 28 | 24.1 |
| Plano TX | 222,030 | 52 | 23.4 |
| Birmingham AL | 242,820 | 49 | 20.2 |
| Chesapeake VA | 199,184 | 40 | 20.1 |
| Fort Worth TX | 534,694 | 90 | 16.8 |
| Tulsa OK | 393,049 | 65 | 16.5 |
| Jackson MS | 184,256 | 30 | 16.3 |
| Mobile AL | 198,915 | 32 | 16.1 |
| Louisville KY | 256,231 | 41 | 16.0 |
| Oklahoma City OK | 506,132 | 79 | 15.6 |
| Baton Rouge LA | 227,818 | 33 | 14.5 |
| Shreveport LA | 200,145 | 29 | 14.5 |
| Augusta GA | 195,182 | 28 | 14.0 |
| Tampa FL | 303,447 | 41 | 13.5 |
| Richmond VA | 197,790 | 26 | 13.2 |
| Garland TX | 215,768 | 28 | 13.0 |
| Arlington TX | 332,969 | 41 | 12.3 |
| St. Petersburg FL | 248,232 | 30 | 12.1 |
| Greensboro NC | 223,891 | 27 | 12.1 |
| Raleigh NC | 276,093 | 33 | 12.0 |
| Virginia Beach VA | 425,257 | 50 | 11.8 |
| Norfolk VA | 234,403 | 26 | 11.1 |
| Irving TX | 191,615 | 21 | 11.0 |
| Lexington KY | 260,512 | 28 | 10.8 |
| Charlotte NC | 540,828 | 56 | 10.4 |
| Lubbock TX | 199,564 | 20 | 10.0 |
| Jacksonville FL | 735,617 | 73 | 9.9 |
| Wilmington DE | 72,664 | 7 | 9.6 |
| Atlanta GA | 416,474 | 40 | 9.6 |
| Dallas TX | 1,188,580 | 111 | 9.3 |
| Austin TX | 656,562 | 58 | 8.8 |
| Nashville TN | 545,524 | 50 | 8.8 |
| Montgomery AL | 201,568 | 17 | 8.4 |
| Little Rock AR | 183,133 | 15 | 8.2 |
| Houston TX | 1,953,631 | 137 | 7.0 |
| Baltimore MD | 651,154 | 41 | 6.3 |
| New Orleans LA | 484,674 | 28 | 5.8 |
| Memphis TN | 650,100 | 37 | 5.7 |
| Miami FL | 362,470 | 19 | 5.2 |
| San Antonio TX | 1,144,646 | 39 | 3.4 |
| Corpus Christi TX | 277,454 | 9 | 3.2 |
| Washington DC | 572,059 | 12 | 2.1 |
| El Paso TX | 563,662 | 9 | 1.6 |
| Hialeah FL | 226,419 | 3 | 1.3 |
| Midwest | |||
| Akron OH | 217,074 | 57 | 26.3 |
| Grand Rapids MI | 197,800 | 41 | 20.7 |
| Des Moines IA | 198,682 | 39 | 19.6 |
| Fargo ND | 90,599 | 15 | 16.6 |
| Fort Wayne IN | 205,727 | 34 | 16.5 |
| Columbus OH | 711,470 | 116 | 16.3 |
| Toledo OH | 313,619 | 47 | 15.0 |
| Madison WI | 208,054 | 31 | 14.9 |
| Kansas City MO | 441,545 | 65 | 14.7 |
| St. Paul MN | 287,151 | 38 | 13.2 |
| Lincoln NE | 225,581 | 29 | 12.9 |
| Omaha NE | 390,007 | 50 | 12.8 |
| Cleveland OH | 478,403 | 60 | 12.5 |
| Indianapolis IN | 781,870 | 98 | 12.4 |
| Wichita KS | 344,284 | 41 | 11.9 |
| Milwaukee WI | 596,974 | 68 | 11.4 |
| Cincinnati OH | 331,285 | 36 | 10.9 |
| Sioux Falls SD | 123,975 | 13 | 10.5 |
| Minneapolis MN | 382,618 | 38 | 9.9 |
| St Louis MO | 348,189 | 19 | 5.5 |
| Detroit MI | 951,270 | 49 | 5.2 |
| Chicago IL | 2,896,016 | 135 | 4.7 |
| West | |||
| Scottsdale AZ | 202,705 | 44 | 21.7 |
| Tacoma WA | 193,556 | 40 | 20.7 |
| Cheyenne WY | 53,011 | 9 | 17.0 |
| Glendale AZ | 218,812 | 32 | 14.6 |
| Boise ID | 185,787 | 27 | 14.5 |
| Billings MT | 89,847 | 13 | 14.5 |
| Bakersfield CA | 247,057 | 34 | 13.8 |
| Anaheim CA | 328,014 | 43 | 13.1 |
| Denver CO | 554,636 | 71 | 12.8 |
| Spokane WA | 195,629 | 25 | 12.8 |
| Mesa AZ | 396,375 | 47 | 11.9 |
| Las Vegas NV | 478,434 | 56 | 11.7 |
| Aurora CO | 276,393 | 32 | 11.6 |
| Salt Lake City UT | 181,743 | 21 | 11.6 |
| Portland OR | 529,121 | 60 | 11.3 |
| Anchorage AK | 260,283 | 29 | 11.1 |
| Colorado Springs CO | 360,890 | 38 | 10.5 |
| Sacramento CA | 407,018 | 41 | 10.1 |
| Riverside CA | 255,166 | 25 | 9.8 |
| Seattle WA | 563,374 | 54 | 9.6 |
| Santa Ana CA | 337,977 | 31 | 9.2 |
| Glendale CA | 194,973 | 17 | 8.7 |
| Phoenix AZ | 1,321,045 | 99 | 7.5 |
| San Diego CA | 1,223,400 | 90 | 7.4 |
| Fresno CA | 427,652 | 31 | 7.3 |
| Long Beach CA | 461,522 | 31 | 6.7 |
| Albuquerque NM | 448,607 | 27 | 6.0 |
| Tucson AZ | 486,699 | 26 | 5.3 |
| San Jose CA | 894,943 | 32 | 3.6 |
| Honolulu HI | 371,657 | 13 | 3.5 |
| Los Angeles CA | 3,694,820 | 128 | 3.5 |
| Fremont CA | 203,413 | 7 | 3.4 |
| Oakland CA | 399,484 | 11 | 2.8 |
| Stockton CA | 243,771 | 6 | 2.5 |
| San Francisco CA | 776,733 | 18 | 2.3 |
Based on U.S. Census 2000 data for the city proper
Within a 3-mile buffer zone surrounding the city proper
Based on the population and number of facilities listed in the adjacent columns
Table 2 shows results from bivariate analyses. The multivariate model accounted for 23.8% of the variance in facility density and was a good fit (F=6.56; p<0.001). Cities with higher percentages of white residents (β=0.29, p=0.004) and cities with a lower UV index (β=−0.46, p=0.02) had significantly higher facility density. Coastal status, annual average temperature, and the percentage of residents having some college or more were no longer significantly associated with facility density.
Table 2.
Bivariate associations between study variables and facility density
| M density | SD | Test statistica | |
|---|---|---|---|
| Geographic variables | |||
| Region | — | — | F=2.58 |
| Northeast | 14.2 | 7.8 | — |
| Midwest | 13.4 | 5.1 | — |
| South | 11.8 | 6.3 | — |
| West | 9.8 | 4.9 | — |
| Coastal city status | — | — | t=2.34* |
| No | 12.4 | 6.0 | — |
| Yes | 9.2 | 5.4 | — |
| Northern latitude | — | — | r=0.27** |
| Western longitude | — | — | r=−0.28** |
| Elevation (feet) | — | — | r =0.04 |
| Climatologic variables | |||
| Annual average temperature in 2005 (°F) | — | — | r=−0.28** |
| M # days maximum temperature ≥90° | — | — | r=−0.12 |
| M # days minimum temperature ≤32° | — | — | r=0.33*** |
| M # days precipitation ≥0.01 inch | — | — | r=0.31*** |
| Annual sunshine (mean % of possible) | — | — | r=−0.26* |
| Ultraviolet (UV) index (cloudy) | — | — | r=−0.32*** |
| Demographic variables | |||
| % white | — | — | r=0.38*** |
| % teens | — | — | r=0.08 |
| % some college or more | — | — | r=0.2* |
| Median family income (dollars) | — | — | r=0.12 |
| Legislation variables | |||
| State indoor tanning law | — | — | t=0.98 |
| No | 12.7 | 6.1 | — |
| Yes | 11.4 | 6.0 | — |
| Youth indoor tanning law | — | — | t=1.37 |
| No | 12.7 | 6.0 | — |
| Yes | 11.1 | 6.0 | — |
| Annual inspection of facilities | — | — | t=−1.19 |
| No | 11.0 | 5.9 | — |
| Yes | 12.8 | 6.0 | — |
| Law stringency | — | — | r=−0.07 |
Significant bivariate associations are shown in boldfaced type.
p<0.05
p≤0.01
p≤0.001
Discussion
High numbers of indoor tanning facilities and high facility density were found in many of the cities, and the number of facilities exceeded the numbers of two ubiquitous institutions—Starbucks and McDonald’s. Facility density was higher in cities with a larger percentage of whites and with a lower UV index. The percentage of the white population also had predicted facility density in two previous studies.11,18 Given that whites are more likely to use indoor tanning facilities,8 the finding regarding an association between the percentage of whites and facility density may reflect the business plan of indoor tanning facilities to be located in areas with higher demand. The association between a lower UV index and higher facility density may be due to residents’ desires to seek warmth, tanned skin, or both when natural sunlight is less available.
Methodologic limitations included the study’s cross-sectional design, the inclusion of only large cities, and not validating the sources used to identify the indoor tanning facilities, Starbucks, and McDonald’s. Therefore, causality between density and its correlates should not be inferred, and the findings may not generalize to smaller cities or rural areas. These estimates provide a snapshot of facility availability; it was outside of the study’s scope to evaluate facility stability. Thus, some facilities may subsequently have gone out of business, while new businesses may not have been listed yet in the directories. Strengths included carefully identifying facilities using precise geographic boundaries, including a broad range of potential predictor variables, and using a sample that represented the largest U.S. cities and all 50 states.
In conclusion, this study documents the wide availability of indoor tanning facilities in the U.S. In other areas of public health, the availability of built-environment resources has been linked with both health-risk and health-protective behaviors.5–7,19,20 Likewise, the availability of commercial tanning may be partly responsible for the high rates of indoor tanning among young adults and teens.2–4,8,21–23 Future research should systematically assess whether tanning-facility availability is associated with indoor tanning and skin-cancer incidence.24
Acknowledgments
The authors wish to thank Dr. George Belch, Dr. Jean Forster, Dr. Todd Gilmer, Dr. Martin Weinstock, Ami Hurd, Latrice Pichon, Justin Shepard, and Debra Rubio for their help with this study. This study was funded by the NIH National Cancer Institute (R01CA093532 and K05CA100051).
Footnotes
No financial disclosures were reported by the authors of this paper.
References
- 1.The International Agency for Research on Cancer. The association of use of sunbeds with cutaneous malignant melanoma and other skin cancers: a systematic review. Int J Cancer. 2007;120:1116–1122. doi: 10.1002/ijc.22453. [DOI] [PubMed] [Google Scholar]
- 2.Heckman CJ, Coups EJ, Manne SL. Prevalence and correlates of indoor tanning among U.S. adults. J Am Acad Dermatol. 2008;58:769–780. doi: 10.1016/j.jaad.2008.01.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Cokkinides VE, Weinstock MA, O’Connell MC, Thun MJ. Use of indoor tanning sunlamps by U.S. youth, ages 11–18 years, and by their parent or guardian caregivers: prevalence and correlates. Pediatrics. 2002;109:1124–1130. doi: 10.1542/peds.109.6.1124. [DOI] [PubMed] [Google Scholar]
- 4.Geller AC, Coldit G, Oliveria S, et al. Use of sunscreen, sunburning rates, and tanning bed use among more than 10,000 U.S. children and adolescents. Pediatrics. 2002;109:1009–1014. doi: 10.1542/peds.109.6.1009. [DOI] [PubMed] [Google Scholar]
- 5.Alaniz ML. Alcohol availability and targeted advertising in racial/ethnic minority communities. Alcohol Health Res World. 1998;22:286–289. [PMC free article] [PubMed] [Google Scholar]
- 6.Leatherdale ST, Strath JM. Tobacco retailer density surrounding schools and cigarette access behaviors among underage smoking students. Ann Behav Med. 2007;33:105–111. doi: 10.1207/s15324796abm3301_12. [DOI] [PubMed] [Google Scholar]
- 7.Novak SP, Reardon SF, Raudenbush SW, Buka SL. Retail tobacco outlet density and youth cigarette smoking: a propensity-modeling approach. Am J Public Health. 2006;96:670–676. doi: 10.2105/AJPH.2004.061622. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hoerster KD, Mayer JA, Woodruff SI, Malcarne V, Roesch SC, Clapp E. The influence of parents and peers on adolescent indoor tanning behavior: findings from a multi-city sample. J Am Acad Dermatol. 2007;57:990–997. doi: 10.1016/j.jaad.2007.06.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Mayer J, Hoerster K, Pichon L, Rubio D, Woodruff S, Forster J. Enforcement of state indoor tanning laws in the U.S. Prev Chronic Dis. 2008;5:A125. [PMC free article] [PubMed] [Google Scholar]
- 10.Woodruff SI, Pichon LC, Hoerster KD, Forster JL, Gilmer T, Mayer JA. Measuring the stringency of states’ indoor tanning regulations: instrument development and outcomes. J Am Acad Dermatol. 2007;56:774–780. doi: 10.1016/j.jaad.2006.12.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Palmer RC, Mayer JA, Woodruff SI, Eckhardt L, Sallis JF. Indoor tanning facility density in eighty U.S. cities. J Community Health. 2002;27:191–202. doi: 10.1023/a:1015202023512. [DOI] [PubMed] [Google Scholar]
- 12.U.S. Census Bureau. U.S. census. 2000 www.census.gov/main/www/cen2000.html.
- 13.U.S. Census Bureau. Census Bureau geography. www.census.gov/geo/www/cob/bdy_files.html.
- 14.Geographic Names Information System. Query form for the U.S. and its territories. geonames.usgs.gov/pls/gnispublic/f?p=105:1:1937699091005919.
- 15.National Oceanic and Atmospheric Administration. National Oceanic and Atmospheric Administration (NOAA) ols.nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00095-PUB-A0001.
- 16.National Oceanic and Atmospheric Administration/National Weather Service. UV index: information. www.cpc.ncep.noaa.gov/products/stratosphere/uv_index/uv_what.shtml.
- 17.Superpages. Yellow pages: superpages. www.superpages.com/
- 18.Patel MR, Mayer JA, Slymen DJ, Weeks JR, Hurd AL. Correlates of tanning facility prevalence within San Diego County, California census tracts. J Community Health. 2007;32:391–400. doi: 10.1007/s10900-007-9061-9. [DOI] [PubMed] [Google Scholar]
- 19.Diez Roux AV, Evenson KR, McGinn AP, et al. Availability of recreational resources and physical activity in adults. Am J Public Health. 2007;97:493–499. doi: 10.2105/AJPH.2006.087734. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Teach SJ, Guagliardo MF, Crain EF, et al. Spatial accessibility of primary care pediatric services in an urban environment: association with asthma management and outcome. Pediatrics. 2006;117:S78–S85. doi: 10.1542/peds.2005-2000E. [DOI] [PubMed] [Google Scholar]
- 21.Demko CA, Borawski EA, Debanne SM, Cooper KD, Stange KC. Use of indoor tanning facilities by white adolescents in the U.S. Arch Pediatr Adolesc Med. 2003;157:854–860. doi: 10.1001/archpedi.157.9.854. [DOI] [PubMed] [Google Scholar]
- 22.CDC. Quick stats: percentage of teens aged 14–17 years who used indoor tanning devices during the preceeding 12 months, by sex and age—U.S., 2005. MMWR Morb Mort Wkly Rep. 2006;55:1101. [Google Scholar]
- 23.Stryker JE, Yaroch AL, Moser RP, Atienza A, Glanz K. Prevalence of sunless tanning product use and related behaviors among adults in the U.S.: results from a national survey. J Am Acad Dermatol. 2007;56:387–390. doi: 10.1016/j.jaad.2006.08.051. [DOI] [PubMed] [Google Scholar]
- 24.Purdue MP, Freeman LE, Anderson WF, Tucker MA. Recent trends in incidence of cutaneous melanoma among U.S. Caucasian young adults. J Invest Dermatol. 2008;128:2905–2908. doi: 10.1038/jid.2008.159. [DOI] [PMC free article] [PubMed] [Google Scholar]