Impact of increasing Papanicolaou test sensitivity and compliance: a modeled cost and outcomes analysis.
OBJECTIVE
To model the impact of increasing screening compliance or implementing liquid-based cytology in populations with known compliance patterns and risk profiles on rates of detection of cervical precancers.


METHODS
An adaptation of a time-varying Markov model was used to follow a theoretic cohort of 100,000 women from age 20 through age 80. Separate analyses of all women, white, and black women were completed using three compliance rates (self-reported, Healthy People 2000, and Healthy People 2010 compliance) and two Papanicolaou test sensitivities (conventional Papanicolaou smear and liquid-based cytology).


RESULTS
All populations benefited from both increased compliance and liquid-based cytology use. Increasing compliance to Healthy People 2010 goals resulted in 23%, 21.7%, and 17% reductions in cervical cancer incidence for all women, white, and black women, respectively. Substituting liquid-based cytology for traditional Papanicolaou smear collection and processing with no change in compliance resulted in 32%, 32%, and 33% reductions in cervical cancer incidence for the same three subpopulations. In addition, cost-effectiveness of the liquid-based technology indirectly related to the risk profile of the population: for black women, the cost-effectiveness ratio was $10,335 per life year saved, whereas for white women, the ratio was $17,967 per life year saved.


CONCLUSIONS
Using liquid-based cytology in all populations would be cost-effective in improving outcomes from cervical cancer. In high-risk populations, this new technology may represent the most cost-effective approach to improve cervical cancer outcomes.
Montz
F J
FJ
Kelly Gynecologic Oncology Service, The Johns Hopkins Hospital and Medical Institutions, Baltimore, Maryland 21287-1248, USA. fmontz@jhmi.edu
Farber
F L
FL
Bristow
R E
RE
Cornelison
T
T
D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D005260:Female; D006801:Humans; D016014:Linear Models; D008875:Middle Aged; D018803:Models, Economic; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data*; D065006:Papanicolaou Test; D010349:Patient Compliance; D011159:Population Surveillance; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D014481:United States; D002583:Uterine Cervical Neoplasms / Q000517:prevention & control*; D014626:Vaginal Smears / Q000191:economics* / Q000706:statistics & numerical data*