Affiliations of authors: M. E. van den Akker-van Marle, M. van Ballegooijen, G. J. van Oortmarssen, J. D. F. Habbema, Department of Public Health, Faculty of Medicine and Health Sciences, Erasmus University Rotterdam, The Netherlands; R. Boer, Department of Public Health, Faculty of Medicine and Health Sciences, Erasmus University Rotterdam, and RAND Health, Santa Monica, CA.
Correspondence to: M. E. van den Akker-van Marle, MSc, Department of Public Health, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands (e-mail: vanmarle{at}mgz.fgg.eur.nl).
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ABSTRACT |
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INTRODUCTION |
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How much the differences in the recommendations alter the cost effectiveness of the screening policies is unclear. The method of choice for the evaluation and comparison of different health care policies is cost-effectiveness analysis, which, for cervical cancer screening, involves a comparison of different screening policies that consider screening costs, possible savings in treatment, and potential health effects, such as life-years gained and cervical cancer deaths prevented. Such a comparison of policies would lead to the identification of efficient policies for which no alternative policies currently exist that result in more life-years gained for lower costs. In the rational decision-making process for making cervical cancer-screening recommendations, a policy maker can compare the incremental and/or average costs per life-year gained of the efficient policies with the maximum allowed values or thresholds for the incremental and/or average costs per life-year gained and identify the most efficient screening policy given the available resources.
In this study, the microsimulation screening analysis (MISCAN) model (6,7) for cervical cancer screening was used to evaluate and compare almost 500 screening policies that differed with respect to the recommended number of screenings, screening intervals, and targeted age ranges. These screening policies consist of fictitious screening policies, policies used in countries with a cervical screening program or in which screening was recommended in national guidelines (5,813), policies recommended in the literature (14), and policies found to be cost-effective in other studies (2,1521). We estimated the life-years gained and the costs of the policies and identified efficient screening policies. We determined the best policy for different thresholds for the incremental costs per life-year gained. The results were compared with existing policies and recommendations and with policies that have emerged from other cost-effectiveness analyses (2,1521). Our analysis uses demographic, epidemiologic, screening, and treatment characteristics from The Netherlands. Because these characteristics may be different for other countries, we investigated the extent to which differences in demographic, epidemiologic, screening, and treatment characteristics result in differences in screening recommendations.
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METHODS |
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The fictitious screening policies considered in this cost-effectiveness analysis are listed in Supplementary Table 1 (available at the Journal's Web site http://jnci.oupjournals.org). We also included screening policies used in countries with cervical screening programs or recommended in national guidelines (5,813) and screening policies considered in other cost-effectiveness analyses (2,1521).
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Model Specifications: Demography and Epidemiology
The Dutch population at risk for cervical cancer was simulated from demographic data (22) and hysterectomy (for reasons other than cervical cancer) rates that were obtained from the National Hospital Admission Registration (23).
The background risk, i.e., the risk of dying of cervical cancer in a situation without screening, of cervical cancer-related mortality was derived from an ageperiod cohort analysis (24). For our analyses, we assumed that the lifetime background risk of developing cervical cancer (or its progressive precursors) was proportional to the estimated relative level of cervical cancer mortality for each birth cohort. Furthermore, we assumed that there was a fixed ratio in each birth cohort between the lifetime risks of preinvasive disease that will spontaneously regress and preinvasive disease that will progress to cervical cancer. The cumulative incidence of progressive preinvasive cervical cancer by birth cohort was 0.0229 for those born from 1889 through 1918, 0.0235 for those born from 1919 through 1928, 0.0128 for those born from 1929 through 1938, 0.0106 for those born from 1939 through 1948, and 0.0148 for those born from 1949 through 2000.
For our analyses, we considered the reported negative association between attendance to the screening program and risk of cervical cancer (2527) by subdividing the simulated population into two risk strata: 90% of the women were assumed to be potential attenders and were assumed to have a low risk of developing cervical cancer, and the remaining 10% of the population was assumed to be persistent nonattenders. On the basis of results from British Columbia in which the risk of attenders was estimated at 0.74 of the average risk (27), we assumed that the persistent nonattenders have a risk of cervical cancer three times higher than that of the attenders.
The age distribution of the incidence of progressive preinvasive neoplasia was determined with the use of the age components of the mortality derived from the ageperiod cohort analysis (24), the distribution of the duration of the preclinical stages of the disease, and the duration between clinical diagnosis and death combined with the age-specific lethality from cervical cancer. The age distribution of the incidence of regressive preinvasive neoplasia was calibrated by calculating the difference between observed cervical intraepithelial neoplasia (CIN) detection rates in The Netherlands (derived from the Dutch Network and National Database for Pathology [PALGA] data for the year 1992) and the detection rates of progressive CIN predicted by MISCAN. The resulting age distributions of preinvasive incidence of regressive and progressive disease, respectively, are shown in Supplementary Table 2 (available at the Journal's Web site http://jnci.oupjournals.org).
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The simulated screening policies were assumed to start in 1993 and to continue for 27 years until 2020. Screening practices before 1993, however, will influence the effectiveness of the screening program after 1993; therefore, this practice has been included in the simulation of the Dutch situation.
Information on the screening activities before 1993 was obtained from survey data (24,33,34). The attendance rate from 1993 onward was assumed to be 80% until age 50 years and to decrease by 0.5% per year thereafter. Because we assumed that 10% of the population will never attend the screening program, we calculated a probability of 88.9% for the potential attenders, which constituted 90% of the population, to actually respond to a scheduled screening examination. After age 50 years, the attendance rate was assumed to decrease by 0.5% per year. This is in accordance with the percentage of women in The Netherlands who had a Pap smear from 1990 through 1994.
The sensitivity of the Pap smear for different disease stages is 80% for preinvasive CIN (27), 85% for preclinical invasive stages IA and IB, and 90% for preclinical invasive stage II+.
False-positive test results indicate the specificity of the Pap smear. We assumed that 0.06% of screening attenders were referred for a colposcopy and a biopsy after which no cervical neoplasia was found and that 6.2% of screening attenders will, on average, have 1.8 repeat smears because of borderline test results after their primary smear before they return to the regular screening schedule (PALGA 1992).
For the simulation model, the percentage of women surviving after a clinical diagnosis of cancer was assumed to be age dependent and stage dependent on the basis of Dutch incidence and mortality figures from the prescreening period in The Netherlands (24). Cancers clinically detected in stage IB have a more favorable prognosis than cancers detected in stage II+, and women aged 3050 years who are diagnosed with stage II+ disease have a higher probability of surviving than women diagnosed with the same disease when younger than 30 years or older than 50 years (Supplementary Table 3 available at the Journal's Web site http://jnci.oupjournals.org).
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Model Specifications: Costs
The costs of a screening program (Supplementary Table 4 available at the Journal's Web site http://jnci.oupjounals.org) were divided into fixed costs and variable costs. Fixed costs are associated with coordinating and evaluating a cervical cancer-screening program. Variable costs are divided into invitation costs and screening costs. Screening costs include time and travel costs for the woman, costs of smear taking, costs of cytologic evaluation, costs of registration in the PALGA, and the costs of 5.3% (estimated from PALGA data for the year 1992) of the smears that are repeated because of inadequate smears or smears without endocervical cells (36,37).
The costs of diagnostic and treatment procedures for the different disease stages and the costs of treatment and palliative care for advanced cervical cancer in the last phase before dying of cervical cancer were derived from cost studies in The Netherlands (24,36,38,39).
Cost-Effectiveness Analysis
In this study, MISCAN was used to predict costs and effects for organized screening programs for a 27-year period. We assumed a hypothetic situation in which the organized program is the only screening program and in which no opportunistic screening (i.e., spontaneous screening for other than medical reasons) occurs. The simulated effects are accounted for until all simulated women who could have benefited from the program have died. The costs are presented in U.S. dollars (1 U.S. dollar = 2 Dutch florins). The effects are presented in days of life gained per woman per year of the screening program. The cost-effectiveness calculations are conducted from the societal perspective.
To identify efficient screening policies, we compared the simulated costs of and life-years gained from each policy. A policy was considered to be efficient when there was no alternative policy resulting in more life-years gained for the same or lower costs (simple dominance) and when there was no combination of two other screening policies that gained more life-years for the same costs (extended dominance) (40,41).
The effects per woman during her lifetime were derived by multiplying the number of days gained per woman per year of the screening program by the average life expectancy of a woman in The Netherlands, which is 80 years (42). In the calculation of incremental and/or average cost-effectiveness ratios, both costs and effects were discounted at a rate of 3% to convert future costs and health effects to their present value [i.e., dollars expended or health effects experienced n years in the future are discounted by a factor of 1/(1.03)n (41)], as recommended by the Panel on Cost Effectiveness in Health and Medicine.
Because of the nature of microsimulations, estimates for costs and effects are affected by random fluctuation. We calculated this fluctuation to be less than 2% of the estimated value of the cost-effectiveness ratio and up to 35% for the incremental cost-effectiveness ratio. Therefore, to reduce the influence of random fluctuation, the incremental cost-effectiveness ratio was estimated by enlarging the simulated population 10 times to 400 million women.
Sensitivity Analysis
A one-way sensitivity analysis was performed on background incidence, attendance, sensitivity (proportion of false-negative tests) and specificity of the screening test, and costs (fixed costs, screening costs, and assessment and treatment costs). The background incidence and fixed costs, screening costs, and treatment costs were halved and doubled to obtain the low values and high values, respectively, used in the sensitivity analysis. To determine attendance, the lack of attendance values were halved and doubled to obtain the high estimates and low estimates, respectively. To determine the sensitivity of the screening test, we halved and doubled the proportion of false-negative results for all stages to obtain the high estimates and low estimates, respectively. To determine the specificity of the screening test, we obtained the high values by halving the percentage of repeat smears because of borderline test results and the proportion of referrals for biopsy after which no cervical neoplasia was found, and the low values were obtained by doubling the baseline estimates for these parameters.
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RESULTS |
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From nearly 500 screening policies, there was a broad range of combinations of predicted costs and effects as measured by life-years gained (Fig. 1). Per 1 000 000 women of the general population, the costs varied between 0.5 million and 9.5 million U.S. dollars, and the effects ranged from 50 life-years to 350 life-years gained per year of the screening program.
Next, after deleting those that were not efficient, we obtained the efficient screening policies for which no alternative policy exists that result in more life-years gained for lower costs. There were 15 efficient screening policies, and together they represented the efficient frontier (Fig. 2). The age range of efficient screening policies increased from age 4052 years for policies that recommend two examinations during a woman's lifetime to age 2080 years for those that recommend more than 20 examinations. In general, a more intensive screening policy was one that recommended that screening start at a younger age, end at an older age, and have a shorter interval between examinations (Table 1
). For the efficient policies, the effects of the total screening program on life expectancy ranged from 11.6 days for those that recommend two scheduled examinations during a woman's lifetime to 32.4 days for those that recommend 40 scheduled examinations. We estimated that total elimination of cervical cancer would yield a gain in life expectancy of 46 days.
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Sensitivity Analysis
Differences in demographic, epidemiologic, and screening characteristics, such as background incidence, attendance, sensitivity and specificity of the screening test, and cost, may lead to different choices in efficient screening policies. The influences of these differences were investigated in a sensitivity analysis (Table 3). A higher background incidence, i.e., the incidence of invasive cancer in the hypothetic situation where there has never been screening, led to higher effects of a screening policy because the effects were proportional to the incidence of cervical cancer (see Table 3
). This resulted in a more favorable incremental cost-effectiveness ratio, and consequently, more intensive screening policies were feasible given a threshold value for the incremental cost-effectiveness ratio.
Differences in either the percentage of women who will attend a recommended screening examination (screening attendance) or sensitivity of the screening test will not only affect the choice of the number of screening examinations to be offered per woman but will also affect the choice of the age range and time interval between the scheduled examinations (43). Higher attendance and/or sensitivity will make longer intervals between screenings and, simultaneously, broader age ranges more favorable in terms of cost-effectiveness because the role for a subsequent screening to detect abnormalities previously missed would be less important (Table 3).
A lower specificity will increase the incremental costs per life-year gained and, therefore, lead to a lower number of scheduled examinations that would be offered per woman to achieve the same incremental cost per life-year gained compared with the baseline situation in which Dutch characteristics are incorporated. The choice of the number of scheduled examinations depends on the costs of medical procedures (including screening itself) that are generated or prevented by screening. If the costs of Pap smears, assessment, and treatment of false-positive results and CIN generated by screening are higher than those assumed in the baseline situation, then the cost-effectiveness ratio of screening will be unfavorably influenced. In contrast, higher costs for treatment of invasive cancers and advanced disease, some of which are prevented by screening, will lower the incremental and/or average cost-effectiveness ratio. The fixed costs for coordinating and evaluating a cervical screening program do not influence the incremental costs per life-year gained. However, the average costs per life-year gained will increase if the fixed costs are higher.
International Comparison
We next compared the screening policies from countries with cervical cancer-screening programs or national guidelines, with the assumption that their demographic, epidemiologic, screening, and treatment characteristics were similar to those in The Netherlands. As shown in Fig. 3, several of the screening policies are remarkably close to the efficient frontier. However, for several screening policies, such as those from Sweden, Denmark, the U.K. (16 scheduled examinations), the United States, and Australia, alternative policies could be recommended to reduce costs for the same amount of life-years gained or to improve effectiveness while keeping the costs the same. These alternative policies are situated in the upper-left quadrant of the marking for a screening policy of a country in Fig. 3
. For example, the area in which more cost-effective screening policies are situated for the United States is identified in Fig. 3
by a broken line. It can be seen in Fig. 3
that the policy for screening every 4 years between ages 22 and 78 years with 15 examinations has the same effects for much lower cost (yearly almost $1 million less) than the recommendations issued by the U.S. Preventive Task Force (10) for screening every 3 years between ages 18 and 66 years with 17 scheduled examinations. If, however, a more intensive policy is committed to (the U.S. Preventive Task Force policy is conservative compared with recommendations from other U.S. authorities and current U.S. practice that recommends annual screening), the efficient screening policies with 2030 examinations and an interval of 23 years starting after age 20 years are more cost effective than current practice.
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We obtained the background incidence of cervical cancer for each country (4446). We calculated the price levels for each country by dividing the health care-specific purchasing power parities (47), which adjust the exchange rates for different countries to the health care-specific price levels by the current exchange rates. By comparing the incidence and the price levels to those of The Netherlands, we obtained relative incidence and relative price levels, respectively. These values were then plotted (Fig. 4, A). The solid line represents the situation in which the relative incidence is equal to the relative price level. For the countries that fell above the line (Denmark and the United States, assuming a high background incidence to be representative), the cost-effectiveness estimates of a policy will be more favorable than those estimates in The Netherlands. Consequently, more intensive screening policies will stay below a certain threshold value of the incremental and/or average cost-effectiveness ratio. For the countries that fell below the line (Australia, the U.K., Iceland, Finland, Sweden, and the United States, assuming a low background incidence), having a relatively low incidence and/or a higher price level, the cost-effectiveness estimates will be less favorable than those in The Netherlands.
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Finally, we calculated the costs and effects of screening policies evaluated in other cost-effectiveness analyses (2,1521) with our MISCAN model (Fig. 5). Most policies appeared to be close to our efficient frontier (Fig. 5
). The screening policies with intervals between screening examinations varying by age that were found to be efficient by Gustafsson and Adami (16) are close to our efficient frontier, as were those with fixed intervals (screening every 7 years between ages 30 and 58 years). Eddy (15) investigated screening every 3 years between ages 20 and 74 years, alternative ages to start screening (17, 23, or 26 years), and alternative screening intervals (every 2 or 4 years) and concluded that a minimal screening policy of every 3 years between ages 20 and 65 years was cost efficient. However, this minimal policy and screening every 3 years between ages 29 and 74 years are less than efficient according to our model (Fig. 5
). For this number of scheduled examinations (16 in both cases), an interval of screening every 4 years would be more efficient (see Fig. 2
). McCrory et al. (19) calculated the costs and effects for three screening policies based on conventional Pap smears that started at age 18 years and had a screening interval of every 1, 2, or 3 years. The screening policies with a screening interval of every 2 or 3 years, which we included in our analysis, appeared to be close to the efficient frontier. The screening policy with a 1-year interval was omitted, as no screening policies with more than 40 scheduled examinations were included in our analyses. Although the screening policy also may have appeared to be quite close to the efficient frontier, the incremental and/or average cost-effectiveness will be far outside the range that we considered to be acceptable. The screening policies considered by Waugh (21) (screening every 3 years between ages 20 and 59 years and screening every 5 years between ages 20 and 60 years) and Sherlaw-Johnson (20) (screening every 3 years between ages 18 and 64 years) and some of the screening policies considered optimal by Gyrd-Hansen (17) (varying from five scheduled examinations between ages 30 and 50 years to 28 scheduled examinations between ages 25 and 69 years) were not efficient according to our model.
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DISCUSSION |
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With the use of the MISCAN program, we determined a predicted gain in life expectancy of 46 days if cervical cancer is eliminated. This gain is small compared with the predicted increase in life expectancy of other diseases, such as the approximately 1.5 years if coronary heart disease were eliminated in women (48), and is directly related to the relatively low mortality rate from cervical cancer. However, it is more relevant to compare the gain in life expectancies with different health interventions because most health interventions can only partly eliminate the disease. Also, elimination or near elimination of cervical cancer through screening does not seem possible considering the persistent level of nonattendance of women at high risk for cervical cancer. Because we based our model on the Dutch cervical cancer-screening figures, we assumed an attendance of about 80%. Half of the remaining 20% are persistent nonattenders. The nonattenders were assumed to be at high risk for cervical cancer and accounted for 25% of the cervical cancer mortality, putting the upper limit of attainable gain in life expectancy by cervical cancer screening at 75% of 46 days or a total of 34 days.
Our predictions show that the efficient screening policies that range from two to 40 scheduled examinations result in a gain in life expectancy from 12 to 32 days. Wright and Weinstein (49) reviewed gains in life expectancy from a variety of health interventions and found estimates on a gain in life expectancy of 0.8 months for women aged 5060 years who are offered biennial mammography and of 8 months for women aged 35 years who quit cigarette smoking. Our estimates for the effects of cervical cancer screening are at the lower side of this range. However, in addition to the effects, costs also must be considered when evaluating diverse health interventions. Cost-effectiveness ratios as estimated in this study express the trade-off between costs and effects of interventions (50).
There are several limitations associated with cost-effectiveness analyses, including random fluctuation and outcome uncertainty (41). Random fluctuation complicates the determination of the efficient screening policies because repeat estimations of costs and effects may yield different estimates for the costs and effects that result in small differences in screening policies determined to be efficient. This was illustrated by the screening policy including seven scheduled examinations between ages 27 and 68 years, which was found to be efficient in our initial predictions but not after enlarging the simulated population.
Outcome uncertainty is related to both parameter and model uncertainty. Parameter uncertainty is the uncertainty about the true values of the input parameters, whereas model uncertainty involves the way these parameters are modeled. An example of model uncertainty is that we made the assumption that costs for coordinating and evaluating a cervical screening program were fixed and thus that these costs were independent of the number of scheduled examinations. Increasing the coordinating and evaluating costs with the number of scheduled examinations will decrease the incremental cost-effectiveness ratio for screening policies with a small number of examinations but will increase the incremental cost-effectiveness ratio for more intensive screening policies.
In addition to the study design limitations, our results would be influenced if quality-adjusted life-years gained were used instead of life-years gained to include any side effects of the intervention. The negative side effects of screening, including those on quality of life, are largely proportional to the number of screening examinations. By contrast, the favorable effects of screening follow the law of diminishing returns. Combining the negative side effects and the favorable effects of screening in terms of quality-adjusted life-years will result in a rapid decrease in the number of incremental quality-adjusted life-years gained for screening policies with an increase in the number of examinations (24), and eventually any additional intensifying screening will decrease the net health effects. Uncertainty analysis and quality-of-life considerations are both subjects of ongoing research.
The present cost-effectiveness estimates are obtained for a model that aimed to be representative of cervical cancer screening in The Netherlands. Different demographic, epidemiologic, and screening characteristics led to changes in the choice of the number of Pap smears offered per woman, the choice of the age range to be screened, and the time period between the scheduled number of Pap smears. However, we found (Fig. 4, B) that the diversity in the number of scheduled examinations in currently used or recommended screening policies, which varied from seven to 27 examinations, cannot be explained by differences in the incidence of or price level in the countries involved. A factor that may influence the age range is the age-specific incidence of invasive cervical cancer, which reflects the age-specific incidence of progressive CIN. By comparing the age-specific incidence among different populations, Gustafsson et al. (44) found that, in addition to differences in the level of cervical cancer incidence, there were two patterns of age-specific incidence. In the first pattern, illustrated by some European countries, including The Netherlands, the peak age-specific incidence of invasive cervical cancer occurs at a younger age and declines rapidly thereafter. In the second pattern, illustrated by the United States, New Zealand, and Asian and African countries, the peak age-specific incidence of invasive cervical cancer occurs at an older age and declines slowly thereafter. Therefore, in countries where the initial peak in age-specific incidence occurs at an older age, there will be a shift in the estimated optimal screening starting age, moving upward, to an older age, and/or to lengthening the screening interval. Thus, when considering the incidence of invasive cervical cancer in the United States and Australia, it is unclear why screening policies that have short screening intervals and that start at a young age are recommended. Possible differences among countries in the implicit threshold values of the acceptable incremental and/or average cost-effectiveness ratio provide no plausible explanation for the diversity in screening policies. The diversity, therefore, originates from other sources, including, for example, the rationality of the recommendation process, the data and evidence used in choosing among policies, or the methods used in evaluating policies. The latter is illustrated by the fact that even though policies evaluated in other cost-effectiveness studies (24) were close to our efficient frontier (Fig. 5
), the estimated incremental and/or average cost-effectiveness ratios differed considerably among studies (24) and may, subsequently, have led to different elected screening policies.
Moreover, our model considers features that were not considered in other cost-effectiveness analyses (24); this may have contributed to the different cost-effectiveness estimates. First, in our model both costs and effects were discounted to the start of screening at a rate of 3% (41). Second, we assumed that nonattendance is associated with an increased risk of cervical cancer (2527). Third, we accounted for the fact that the population is already screened to a certain extent. Our assumption leads to a lower prevalence of preclinical disease and, consequently, to a lower baseline risk for cervical cancer at the start of the screening program. Therefore, our cost-effectiveness estimates will be less favorable.
The current cost-effectiveness analyses concern high-income countries. However, in low-income countries in Southern America, Africa, and Asia, the incidence and cancer-related death rate from cervical cancer is much greater than the "high" incidence selected in our sensitivity analyses. Although the incidence of cervical cancer can be reduced by a Pap smear-based screening program, such a program is often not feasible in low-income countries because it requires a high degree of organization with cytologic laboratories and personnel. An alternative for Pap smear screening in developing countries may be aided visual inspection of the cervix, which has a sensitivity similar to Pap smears but a lower specificity (51,52). A specific cost-effectiveness analysis to investigate the possibility of a screening program based on aided visual inspection in low-income countries is warranted.
Although the present analyses are based on Pap smear screening, which is the conventional method for detection of cervical lesions in large-scale settings, there are new methods for the detection of cervical cancer; for example, screening for the presence of oncogenic variants of the human papillomavirus. The cost-effectiveness of screening for human papillomavirus is not yet known (53). Other developments involve new diagnostic technologies in cytopathology, such as liquid-based cytology and computer-aided imaging. In the future, these or other new developments may lead to improvements in test characteristics and/or changes in costs, which would require reconsidering the optimal screening policies. Because women who are regularly screened at the appropriate ages already have a reduced risk of cervical cancer, the gain in cost-effectiveness of cervical cancer screening must arise from reducing the overall costs and simplifying the screening process by reducing the number of false-positive results. The great breakthrough in the latter has to come from methods that are able to distinguish progressive and regressive disease.
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Manuscript received May 30, 2001; revised November 8, 2001; accepted November 30, 2001.
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