Visual screening for malignant melanoma: a cost-effectiveness analysis.
OBJECTIVE
To evaluate the cost-effectiveness of various melanoma screening strategies proposed in the United States.


METHODS
We developed a computer simulation Markov model to evaluate alternative melanoma screening strategies.


METHODS
Hypothetical cohort of the general population and siblings of patients with melanoma. Intervention We considered the following 4 strategies: background screening only, and screening 1 time, every 2 years, and annually, all beginning at age 50 years. Prevalence, incidence, and mortality data were taken from the Surveillance, Epidemiology, and End Results Program. Sibling risk, recurrence rates, and treatment costs were taken from the literature.


METHODS
Outcomes included life expectancy, quality-adjusted life expectancy, and lifetime costs. Cost-effectiveness ratios were in dollars per quality-adjusted life year (US dollars/QALY) gained.


RESULTS
In the general population, screening 1 time, every 2 years, and annually saved 1.6, 4.4, and 5.2 QALYs per 1000 persons screened, with incremental cost-effectiveness ratios of US dollars 10,100/QALY, US dollars 80,700/QALY, and US dollars 586,800/QALY, respectively. In siblings of patients with melanoma (relative risk, 2.24 compared with the general population), 1-time, every-2-years, and annual screenings saved 3.6, 9.8, and 11.4 QALYs per 1000 persons screened, with incremental cost-effectiveness ratios of US dollars 4000/QALY, US dollars 35,500/QALY, and US dollars 257,800/QALY, respectively. In higher risk siblings of patients with melanoma (relative risk, 5.56), screening was more cost-effective. Results were most sensitive to screening cost, melanoma progression rate, and specificity of visual screening.


CONCLUSIONS
One-time melanoma screening of the general population older than 50 years is very cost-effective compared with other cancer screening programs in the United States. Screening every 2 years in siblings of patients with melanoma is also cost-effective.
Losina
Elena
E
Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. elosina@partners.org
Walensky
Rochelle P
RP
Geller
Alan
A
Beddingfield
Frederick C
FC
Wolf
Lindsey L
LL
Gilchrest
Barbara A
BA
Freedberg
Kenneth A
KA
D001900:Boston / Q000453:epidemiology; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D017048:Health Care Costs; D006801:Humans; D015994:Incidence; D008390:Markov Chains; D008403:Mass Screening / Q000191:economics*; D008545:Melanoma / Q000175:diagnosis* / Q000191:economics* / Q000453:epidemiology; D008875:Middle Aged; D015995:Prevalence; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D012680:Sensitivity and Specificity; D035781:Siblings; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000191:economics* / Q000453:epidemiology