The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Distance to Diagnosing Provider as a Measure of Access for Patients With Melanoma
Open accessAbstract
OBJECTIVE: To examine the effect of travel distance and other sociodemographic factors on access to a diagnosing provider for patients with melanoma. DESIGN: Analysis was performed of all incident cases of melanoma in 2000 from 42 North Carolina counties. SETTING: Academic research. PARTICIPANTS: Patients and providers from 42 North Carolina counties were geocoded to street address. MAIN OUTCOME MEASURES: Associations between Breslow thickness and clinical and sociodemographic factors (age, sex, poverty rate, rurality, provider supply, and distance to diagnosing provider) were examined. RESULTS: Of 643 eligible cases, 4.4% were excluded because of missing data. The median Breslow thickness was 0.6 mm (range, 0.1-20.0 mm). The median distance to diagnosing provider was 8 miles (range, 0-386 miles). For each 1-mile increase in distance, Breslow thickness increased by 0.6% (P =.003). For each 1% increase in poverty rate, Breslow thickness increased by 1% (P =.04). Breslow thickness was 19% greater for patients aged 51 to 80 years than for those aged 0 to 50 years (P =.02) and was 109% greater for patients older than 80 years than for those aged 0 to 50 years (P < .001). Sex, rurality, and supply of dermatologists were not associated with Breslow thickness. CONCLUSIONS: For patients with melanoma, distance to the diagnosing provider is a meaningful measure of access that captures different information than community-level measures of rurality, provider supply, and socioeconomic status. Future work should be targeted at identifying factors that may affect distance to diagnosing provider and serve as barriers to melanoma care.
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“However, various authors have reported difficulties in obtaining an appointment with a dermatologist [12,14-17].”
From Use of Smartphones for Early Detection of Melanoma: Systematic Review (2018) · cites this paper as [16]
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Cites
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Access to Health Care in America
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Screening for Cutaneous Melanoma by Skin Self-Examination
Berwick, M., Begg, C. B., Fine, J. A., Roush, G. C., & Barnhill, R. L. (1996) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/88.1.17 · Cited by 430
Self-citationRace, socioeconomic status and stage at diagnosis for five common malignancies
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Correlation Between Prognostic Factors and Increasing Age in Melanoma
Chao, C., Martin, R. C. G., Ross, M. I., Reintgen, D. S., Edwards, M. J., Noyes, R. D., Hagendoorn, L. J., Stromberg, A. J., & McMasters, K. M. (2004) · Annals of Surgical Oncology · doi:10.1245/aso.2004.04.015 · Cited by 219
Lifetime Risk of Melanoma in CDKN2A Mutation Carriers in a Population-Based Sample
Begg, C. B., Orlow, I., Hummer, A., Armstrong, B. K., Kricker, A., Marrett, L. D., Millikan, R. C., Gruber, S. B., Anton‐Culver, H., Zanetti, R., et al. (2005) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/dji312 · Cited by 214
Self-citationTransportation as a barrier to cancer treatment.
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Comparing Alternative Methods of Measuring Geographic Access to Health Services
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Distance as a Barrier to Cancer Diagnosis and Treatment: Review of the Literature
Ambroggi, M., Biasini, C., Giovane, C. D., Fornari, F., & Cavanna, L. (2015) · The Oncologist · doi:10.1634/theoncologist.2015-0110 · Cited by 551
Rural–Urban Differences in Cancer Incidence and Trends in the United States
Zahnd, W. E., James, A. S., Jenkins, W. D., Izadi, S. R., Fogleman, A. J., Steward, D. E., Colditz, G. A., & Brard, L. (2017) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-17-0430 · Cited by 443
The US dermatology workforce: a specialty remains in shortage.
Kimball, A. B., & Resneck, J. S. (2008) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2008.06.037 · Cited by 257
Rural Residence and Cancer Outcomes in the United States: Issues and Challenges
Meilleur, A., Subramanian, S. V., Plascak, J. J., Fisher, J. L., Paskett, E. D., & Lamont, E. B. (2013) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-13-0404 · Cited by 247
Comparison of Dermatologist Density Between Urban and Rural Counties in the United States
Feng, H., Berk-Krauss, J., Feng, P. W., & Stein, J. (2018) · JAMA Dermatology · doi:10.1001/jamadermatol.2018.3022 · Cited by 211
Metadata
- Authors
- Stitzenberg, K. B., Thomas, N. E., Dalton, K., Brier, S. E., Ollila, D. W., Berwick, M., Mattingly, D., & Millikan, R. C.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of New Mexico (US)
- Venue
- Archives of Dermatology
- Publisher
- American Medical Association (AMA)
- Published
- 2007-08-01
- Volume
- 143
- Issue
- 8
- Pages
- 991-8
- ISSN
- 0003-987X (ISSN-L)
- PMC
- PMC3629703
- OpenAlex
- W2054994397
- Cited by
- 119
- References
- 36
- Citations by year
- 2026: 12 · 2025: 7 · 2024: 10 · 2023: 9 · 2022: 10 · 2021: 13 · 2020: 8 · 2019: 7 · 2018: 8 · 2017: 5 · 2016: 5 · 2015: 2 · 2014: 4 · 2013: 1 · 2012: 4
- FWCI
- 1.68
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Archives of Dermatology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Agency for Healthcare Research and Quality (T32HS000032, T32-HS00032)
- National Cancer Institute (K07CA102096, R01CA112243, U01CA083180)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- melanoma, health care access, travel distance, Breslow thickness, sociodemographic factors, poverty, age, rurality, health disparities, North Carolina
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Female, Health Services Accessibility, Humans, Male, Melanoma, Middle Aged, Neoplasm Staging, North Carolina, Retrospective Studies, Skin Neoplasms, Socioeconomic Factors, Incidence, Linear Models, Databases, Factual
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