The association of physician-specialty density and melanoma prognosis in the United States, 1988 to 1993 - 08/08/11

Doi : 10.1016/j.jaad.2008.08.040

Melody J. Eide, MD, MPH ^a,^b,^e,^⁎ , Martin A. Weinstock, MD, PhD ^a,^c,^d, Melissa A. Clark, PhD ^a,^b
^a Department of Community Health, Brown University, Providence, Rhode Island
^b Center for Gerontology and Healthcare Research, Brown University, Providence, Rhode Island
^c Department of Dermatology, Brown University, Providence, Rhode Island
^d Department of Veterans Affairs Medical Center, Providence, Rhode Island
^e Department of Dermatology, Henry Ford Hospital, Detroit, Michigan

Reprint requests: Melody J. Eide, MD, MPH, Department of Dermatology, Henry Ford Hospital, 3031 W Grand Blvd, Suite 800, Detroit, MI 48202.

Abstract

Background

Studies suggest physician workforce may influence cancer outcomes.

Objective

We sought to quantify the effect of physician-specialty density on melanoma prognosis.

Methods

Data from 17,702 melanoma cases reported to the Surveillance, Epidemiology, and End Results program from 1988 to 1993 were merged with sociodemographic data (1990 US Census) and dermatologist, family practitioner, and internist density data (Area Resource File). Linear and logistic regression analyses were used to model prognosis (melanoma mortality to incidence ratio).

Results

A higher density of dermatologists was associated with better prognosis (lower mortality to incidence ratio) (β = –50 × 10⁻⁴; SE 8 × 10⁻⁴). Internist density was also a significant predictor of better prognosis whereas increased family practitioner density was associated with worse prognosis. Controlling for sociodemographics, physician density remained a significant predictor of the mortality to incidence ratio.

Limitations

Socioeconomic factors were estimated. Physician density was examined by county.

Conclusion

Controlling for sociodemographic factors, physician-specialty density predicted melanoma prognosis. This suggests that specialist health care availability may affect melanoma outcomes.

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Abbreviations used : ARF, FP, MIR, NCI, SEER, SES

Plan

Methods

Dependent measures

Independent measures

Confounding variables

Analyses

Results

Discussion

	Supported by a Dermatology Foundation award and a Surdna Foundation award (Dr Eide); grants CSP 402 from the Department of Veterans Affairs, Office of Research and Development, and CA 106592 from the National Cancer Institute (Dr Weinstock); and the Agency for Healthcare Research and Quality Institutional Training grant, HS00011-16 (Drs Clark and Eide).
	Conflicts of interest: None declared.