Reducing ovarian cancer mortality through screening: Is it possible, and can we afford it?
OBJECTIVE
Ovarian cancer is a leading cause of cancer-related deaths among women. Given the low prevalence of this disease, the effectiveness of screening strategies has not been established. We wished to estimate the clinical impact and cost-effectiveness of potential screening strategies for ovarian cancer using population-specific data.


METHODS
A Markov state transition model to simulate the natural history of ovarian cancer in a cohort of women age 20 to 100. Age-specific incidence and mortality rates were obtained from SEER. Base-case characteristics of a potential screening test were sensitivity 85%, specificity 95%, and cost $50. Outcome measures were mortality reduction, lifetime number of false positive screening tests, positive predictive value, years of life saved (YLS), lifetime costs in US dollars, and incremental cost-effectiveness ratios (ICER, in cost/YLS).


RESULTS
Model-predicted lifetime risk of ovarian cancer (1.38%), lifetime risk of death from ovarian cancer (0.95%), and stage distribution (stage I-19%; stage II-7%; stage III, IV, or unstaged - 74%) closely approximated SEER data. Annual screening resulted in 43% reduction in ovarian cancer mortality, with ICER of $73,469/YLS (base case) and $36,025/YLS (high-risk population) compared to no screening. In the base case, the average lifetime number of false positive tests is 1.06. Cost-effectiveness of screening is most sensitive to test frequency, specificity and cost.


CONCLUSIONS
Annual screening for ovarian cancer has the potential to be cost effective, particularly in high-risk populations. Clinically acceptable positive predictive values are achieved if specificity exceeds 99%. Mortality reduction above 50% may not be achievable without screening intervals less than 12 months.
Havrilesky
Laura J
LJ
Division of Gynecologic Oncology, Duke University Medical Center, Durham, NC 27710, USA.
Sanders
Gillian D
GD
Kulasingam
Shalini
S
Myers
Evan R
ER
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D005260:Female; D006801:Humans; D008390:Markov Chains; D008403:Mass Screening / Q000191:economics / Q000379:methods; D008875:Middle Aged; D015233:Models, Statistical; D009367:Neoplasm Staging; D010051:Ovarian Neoplasms / Q000191:economics / Q000401:mortality* / Q000473:pathology / Q000517:prevention & control*; D011237:Predictive Value of Tests; D018426:SEER Program; D014481:United States / Q000453:epidemiology; D055815:Young Adult