Distributions of late-stage melanoma diagnoses and Hispanic population by zip code, Miami–Dade County, Florida, 1997 through 2001.
Yin N, Parker DF, Hu S, Kirsner RS. Geographic Distribution of Melanoma in Miami–Dade County, FloridaOnline First. Arch Dermatol. 2011;147(5):617-618. doi:10.1001/archdermatol.2010.412
Copyright 2011 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.2011
Our research group1 has demonstrated disparities in stage at diagnosis for melanoma based on race and ethnicity and on health care delivery system. To further document these disparities, we examine herein data for Miami–Dade County, Florida.
Miami–Dade County, the most populous of Florida's 67 counties, has more than 2.5 million residents. The socioeconomic status (SES) characteristics of this county include factors that may affect health care access and utilization, such as a diverse racial and ethnic population, a large indigent population, and large percentages of foreign-born individuals.2 We sought to determine if, within the county, differences exist in stage of melanoma diagnosis and if so, what factors influence those differences.
Incidence data on melanoma as the primary site of diagnosis for the 5-year period from 1997 through 2001 were extracted from the Florida Cancer Data System (FCDS),3 Florida's statewide, population-based cancer incidence registry. In the FCDS, stage at diagnosis is coded according to the summary staging system used by the SEER (Surveillance Epidemiology and End Results) program.4 For the present analysis, in situ and local stage diagnoses (stages 0 and 1) were considered early, while regional and distant or systemic diagnoses (stages 2-4) were considered late. Cases with unknown stage were excluded.
We used zip code of residence for each case to subdivide the county. Zip codes were used rather than census tracts because they represent larger populations and numbers of melanoma cases, although population sizes of zip code areas vary slightly.5 Zip codes are also recognizable to the general public, planners, and policy makers, and there is precedence for using zip codes in similar evaluations.6- 8
Zip code population data were obtained from the US Census 20009 for population and housing, summary file 3, technical documentation 2002. Ten SES measures were extracted from this source for each of the 76 residential zip codes in Miami–Dade County (except for item 6, which was extracted from the Florida Agency for Health Care Administration10) (Table). We also developed zip code maps of Miami–Dade County using ArcMap, version 8.3 (Esri, Redlands, California) for each of the 10 SES measures.
Using data aggregated by zip code, we computed 2-tailed Pearson correlation coefficients using SPSS, version 15.0 (IBM Corporation, Somers, New York) to examine SES factors that were correlated with the percentage of melanoma late-stage diagnoses. Multiple logistic regressions were performed to further examine how the SES variables were related to stage at diagnosis, coded as either early or late. The analysis was performed on individual case data. The SES measures used were for the zip codes in which the individual lived, not for the individual patient. Ecologic analyses are commonly used when socioeconomic data for individual patients are not available.
Our study included 1009 cases of melanoma, 26% of which were diagnosed at late stage. Distribution of late-stage diagnoses for melanoma tended to be clustered by zip codes (Figure). After controlling for socioeconomic factors, we found that zip codes with higher percentages of Hispanic residents (r = 0.275) were positively correlated with late-stage diagnosis (P < .01).
Late-stage melanoma cases in Miami–Dade County showed a significant geographical clustering. This finding suggests a critical need for increased educational programs and/or medical involvement in these areas tailored to address the sociodemographic characteristics of each area. Using similar analytic techniques, other researchers12 have demonstrated similar geographic patterns for breast cancer.12
The reasons for the distribution of late-stage diagnoses found in the present study were not examined. However, our findings are consistent with previous studies in which other researchers13 found that patients with melanoma who live in areas with lower incomes, a majority nonwhite population, or a majority of residents without high school degrees have worse prognoses. These same authors also reported that local education influences melanoma prognosis.
We found that Hispanic origin was correlated with late-stage diagnosis, which suggests that Hispanic origin in Miami–Dade County could serve as a predictor of areas at risk. While we did not evaluate individual patients with late-stage melanoma diagnoses but rather zip code areas, the present results are consistent with our group's previous findings1 that Hispanics perceive themselves at lower risk of developing melanoma, even after skin cancer risk factors including skin type are controlled for. Hispanics also are less knowledgeable about melanoma, less likely to perform self-examinations, and often unaware of the clinical signs of skin cancer; they have worse outcomes. The worse outcomes are to result from issues of health care delivery, but the possibility exists that melanomas in Hispanics may be more biologically aggressive. The exact reasons underlying the correlation between geographic clustering of late-stage melanoma diagnoses and Hispanics are not yet known and would be important to investigate in future studies.
Correspondence: Dr Kirsner, Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, 1600 NW 10th Ave, RMSB, Room 2023-A, Miami, FL 33136 (firstname.lastname@example.org).
Published Online: January 17, 2011. doi:10.1001/archdermatol.2010.412
Accepted for Publication: November 8, 2010.
Author Contributions: Dr Kirsner had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Parker and Kirsner. Acquisition of data: Parker and Kirsner. Analysis and interpretation of data: Yin, Parker, Hu, and Kirsner. Drafting of the manuscript: Yin and Parker. Critical revision of the manuscript for important intellectual content: Hu and Kirsner. Obtained funding: Kirsner. Administrative, technical, and material support: Parker and Kirsner. Study supervision: Kirsner.
Financial Disclosure: None reported.
Funding/Support: This study was supported by the Centers for Disease Control and Prevention.
Role of the Sponsors: The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; or in the preparation, review, or approval of the manuscript.