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Original Article Cost-Effectiveness of Liver Cancer Screening in Adults at High Risk for Liver Cancer in the Republic of Korea
Young Hwa Lee, MPH1, Kui Son Choi, PhD1,, Jae Kwan Jun, MD, PhD1, Mina Suh, MD, PhD1, Hoo-Yeon Lee, MD, PhD2, Youn Nam Kim, MPH3, Chung Mo Nam, PhD4, Eun-Cheol Park, MD, PhD4, Woo-Hyun Cho, MD, PhD4
Cancer Research and Treatment : Official Journal of Korean Cancer Association 2014;46(3):223-233.
DOI: https://doi.org/10.4143/crt.2014.46.3.223
Published online: July 15, 2014

1National Cancer Control Institute, National Cancer Center, Goyang, Korea

2Department of Social Medicine, College of Medicine, Dankook University, Cheonan, Korea

3Department of Biostatistics, Yonsei University College of Medicine, Seoul, Korea

4Department of Preventive Medicine and Institute of Health Services Research, Yonsei University College of Medicine, Seoul, Korea

Correspondence: Kui Son Choi, PhD National Cancer Control Institute, National Cancer Center, 111 Jungbalsan-ro, Ilsandong-gu, Goyang 410-769, Korea  
Tel: 82-31-920-2912 Fax: 82-31-920-2189 E-mail: kschoi@ncc.re.kr
• Received: November 15, 2012   • Accepted: July 3, 2013

Copyright © 2014 by the Korean Cancer Association

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Purpose
    This study was conducted in order to determine the most cost-effective strategy, in terms of interval and age range, forliver cancer screening in the high-risk population of Korea.
  • Materials and Methods
    A stochastic modelwas used to simulate the cost-effectiveness ofliver cancer screening by combined ultrasonography and alpha-fetoprotein testing when varying both screening intervals and age ranges. The effectiveness of these screening strategies in the high-risk population was defined as the probability of detecting preclinical liver cancer, and costwas based on the direct cost ofthe screening and confirmative tests. Optimal cost-effectiveness was determined using the incremental cost-effectiveness ratio.
  • Results
    Among the 36 alternative strategies, one-year or two-year interval screening for men aged between 50 and 80 years, six-month or one-year interval screening for men aged between 40 and 80 years, and six-month interval screening for men aged between 30 and 80 years were identified as non-dominated strategies. For women, identified non-dominated strategies were: one-year interval screening between age 50 and 65 years, one-year or six-month interval screening between age 50 and 80 years, six-month interval screening between age 40 and 80 years, and six-month interval screening between age 30 and 80 years.
  • Conclusion
    In Korea, a one-year screening interval for men aged 50 to 80 years would be marginally cost-effective. Further studies should be conducted in order to evaluate effectiveness of liver cancer screening, and compare the cost effectiveness of different liver cancer screening programs with a final outcome indicator such as qualityadjusted life-years or disability-adjusted life-years.
  • Key words: Liver neoplasms, Screening, Cost-benefit analysis
Worldwide, liver cancer is the fifth most common cancer in men (16.0 per 100,000) and the seventh in women (6.0 per 100,000), with almost 85% of cases occurring in Asia and Africa [1]. Due to its high fatality, liver cancer is the third most common cause of cancer death worldwide [1]. Although the incidence of liver cancer in Korea has declined over the last decade, it is still the fourth most common cancer in Korean men (37.7 per 100,000) and the seventh most common cancer in Korean women (10.4 per 100,000) [2]. In addition, liver cancer is the second most common cause of cancer death in Korea [2].
In an effort to reduce liver cancer-related mortality, surveillance or screening is widely practiced and generally recommended for certain high-risk groups. Liver cancer occurs in populations with a well-defined set of risk factors and has a protracted preclinical phase, meaning that timely identification of disease can lead to appropriate treatment at a more curable stage; therefore, it is a suitable target for a surveillance program. The carcinogenic effect of chronic infection with hepatitis B and C viruses (HBV, HCV) in liver cancer development has been well demonstrated by epidemiological and experimental evidence. However, evidence on efficacy of liver cancer screening (or surveillance) programs has not yet been established. A number of screening programs have been reported since the 1970s, and tumors detected through screening were found to be smaller, resulting in increased survival [3-6]. However, in all of these studies, the duration of follow-up was limited, and lead-time bias remained. A randomized controlled trial from conducted in Shanghai using abdominal ultrasound and alpha-fetoprotein (AFP) every six months in 18,816 patients aged 35-59 years with chronic hepatitis B and other risk factors for hepatocellular carcinomas (HCC) showed a reduction in mortality by 37% [7]. While these results are promising, the confidence interval (CI) was near 1.0, intention-to-treat analysis was not used, assessment of outcome was not blinded, and generalizability to other populations is uncertain [8]. Therefore, while screening with AFP+ultrasonography (US) appears to detect significantly more HCC compared with no screening, and, despite the current recommendation to screen subjects at moderate and high risk for HCC every six months, we do not yet know with certainty whether screening can reduce all-cause mortality or HCC mortality, which modality of screening should be used (no screening, AFP, US, or AFP+US), or how frequently screening should be offered. It is possible that HCC screening may be effective, but also that harm caused by screening may outweigh any benefit [9]. The National Cancer Institute reported that liver cancer screening would not result in mortality reduction from HCC [8].
Despite the lack of concrete evidence, screening for liver cancer is widely practiced and recommended for certain at-risk groups [10,11]. In Korea, a nationwide liver cancer screening program was introduced in 2003 as part of the National Cancer Screening Program (NCSP). It was based on the HCC Surveillance Recommendations developed by the National Cancer Center and the Korean Association for the Study of the Liver in 2001 [12]. The NCSP for liver cancer in Korea provides US and AFP testing at six-month intervals to men and women aged 40 years or older with chronic HBV or HCV infection, liver cirrhosis, or chronic liver disease of any cause (i.e., high-risk population). This screening strategy is the most widely accepted and the most used in clinical practice. However, the optimal screening interval and proper age range for screening are still being debated.
A cancer screening program should be cost-effective and revised according to documented epidemiological changes of the cancer in question, and development of diagnostic technology, especially when conducted at a national level. However, few studies have investigated the cost-effectiveness of liver cancer screening by consideration of various screening intervals and age ranges. Therefore, this study was conducted in order to determine the most cost-effective strategy for men and women, in terms of interval and age range, for liver cancer screening by combined US and AFP testing in the high-risk population in Korea.
1. Model
To determine the most cost-effective interval and age range for liver cancer screening, the effectiveness and the cost of screening strategies were based on a model proposed by Lee and Zelen [13]. The optimal screening strategy was determined based on the incremental cost-effectiveness ratio (ICER), which is defined as the ratio of changes in cost and effectiveness of one screening strategy to an alternative strategy.
The effectiveness of a screening strategy was measured as the number of early-detected liver cancers found per 100,000 high-risk individuals screened. This number was derived from the estimated probability of detecting preclinical liver cancer, a state in which the disease has no symptoms but can be diagnosed. The model by Lee and Zelen [13] assumes that the natural history of a chronic disease or cancer is progressive in the manner of S0→Sp→Sc, where S0 represents Sp the cancer-free state, Sp the preclinical state, and Sc the clinical state. The sensitivity of the screening method, the distribution of the mean sojourn time (MST) in the preclinical state, and age-specific incidence rates were required for estimation of the probability of preclinical detection. MST is the duration of the pre-clinical detectable phase of the cancer, and, in the current study, it was defined as the mean time necessary for a liver tumor to change from a screening detectable size to a clinically detectable size.
We assumed that both the MST and the sensitivity of the screening method (combined US and AFP testing) were constant, as assumed by the Lee and Zelen model [13]. In the model, the time variable t refers to age, r refers to screening round, and i refers to screening interval. Assuming that screenings are performed at age t1≤t2 ＜…＜tn in a given period, the probability of detecting preclinical cancer at age tr is as follows:
Dr=β[Σi=1r-1(1-β)r-i∫ti-1tiw(x)Q(ti-x)dx+∫tr-1trw(x)Q(tr-X)dx]
(r=1, 2, ..., n), (i=1, 2, 3, ..., n)
, where w(x) denotes the probability of progression from S0 to Sp, which can be calculated by age-specific liver cancer incidence and an assumed distribution of MST in Sp: Q(t) is the survival distribution of the MST in Sp at age t with tr-1≤t＜tr, and β is the sensitivity of the modality based on the combination of US and AFP testing.
The costs were measured as the direct cost incurred when one individual underwent screening, which is reasonable for low-incidence disease [13]. Costs of screening and confirmative tests for false-positive outcomes were included. As combined US and AFP testing is used in liver cancer screening in Korea, the costs were calculated as follows:
Cost≈nKs+(1-Sp)Kd
, where n denotes the total number of screenings, Ks and Sp represent the screening cost and specificity, respectively, for the combination of US and AFP testing, and Kd the cost of confirmative testing. Costs of other adverse effects from liver cancer screening, such as discomfort from examination or recall of patients for additional imaging, were not considered in the model. The time horizon for the study was 30 to 80 years of age. We adopted the perspective of the national healthcare system.
For the current study we generated 36 possible combinations of screening intervals and age ranges for both men and women, setting screening intervals of six months, one year, or two years; an initial screening age of 30, 40, or 50 years; and a ceiling screening age of 65, 70, 75, or 80 years (Table 1). ICER was calculated for determination of the most cost-effective screening strategy for liver cancer among all those considered. Based on the calculated ICER, screening strategies were classified as non-dominated, dominated, or extended dominated. Non-dominated strategies, in which the effectiveness was higher and the costs were lower than others, were considered the most cost-effective. A dominated strategy was defined as generating worse effects and higher costs than an alternative strategy. Extended dominance occurred when a strategy was less effective and had a higher ICER than an alternative strategy.
2. Data and model assumptions
For estimation of age-specific liver cancer incidence rates for the high-risk population, the high-risk population in the NCSP database in 2008 was linked to the 2008 Korean National Cancer Incidence Database of the Korean Central Cancer Registry (KCCR). In the NCSP, the National Health Insurance (NHI) Corporation identified the high-risk group for liver cancer screening as individuals who had been tested or received medical care for HBV or HCV infection (ICD 10 code: B18, B18.0, B18.1, B18.2, Z22.5), chronic liver disease (ICD 10 code: B19, K73, K73.1, K73.2. K73.8, K73.9), or liver cirrhosis (ICD 10 code: K74, K74.1, K74.2, K74.6, K76, K70.2, K70.3, K70.9) within the past two years, using the computerized medical claims database. Incidence rates were calculated for each age group and ranged from 30 to 34 years of age to more than 85 years of age. The incidence of liver cancer in the high risk group showed a gradual increase with age until the age 80-84 years and then subsequently decreased. We assumed that individuals in the high-risk population aged 30 or over undergo liver cancer screening and are followedup until the ceiling screening age. Therefore, in the model, high-risk individuals remained in the screening cohort and underwent screening based on the set screening interval until reaching the ceiling screening age (65, 70, 75, or 80 years).
Baseline assumptions regarding MST, sensitivity and specificity, and cost of screening and confirmative tests are shown in Table 2. A previous study conducted in Taiwan reported an MST of 1.57 years (95% CI, 0.94 to 4.68 years) in cirrhotic patients and 2.66 years (95% CI, 1.68 to 6.37 years) in non-cirrhotic patients [3]. Another study conducted in Asia reported an MST of 3.2 years, regardless of the severity of cirrhosis [14]. In the current study, based on the abovementioned study conducted in Taiwan, we assumed an MST of 1.57 years for the high-risk population (i.e., chronic HBV or HCV infection, liver cirrhosis, or chronic liver disease of any cause). Sensitivity and specificity of US are known to range from 65% to 84% and from 91% to 97%, respectively [6,15]. The reported sensitivity of AFP has ranged from 39% to 69% with the standard of 20 ng/mL, and specificity from 90% to 95% [15,16]. Sensitivity increased up to 92% when US and AFP were combined [15]. However, due to differences in target populations and liver cancer screening programs, direct use of these results may be difficult.
For example, most of the aforementioned studies were hospital-based, and, in general, the sensitivity and specificity of these tests are lower in a community-based setting with an asymptomatic population. A previous study reported sensitivity and specificity of combined US and AFP testing in the NCSP ranging from 42% to 54% and from 94% to 96%, respectively [17]. Thus, this study assumed a sensitivity and specificity of combined US and AFP testing of 50% and 95%, respectively. The unit costs of US (US$41.29), AFP (US$ 10.99), and the confirmative test (magnetic resonance imaging, US$311.09) were obtained from the 2009 medical insurance costs published by the Health Insurance Review and Assessment Service [18], and combined for estimation of total costs. To assess the robustness of the proposed screening strategies, one-way sensitivity analyses were performed by changing the MST in the preclinical state, and the sensitivity and specificity of combined US and AFP testing. Alternative MSTs were 0.94, 2.66, 4.68, and 6.37 years based on the previous study [3], alternative sensitivities were 60% and 70%, and specificity was 90%. All statistical analyses were performed using MATLAB 6.1 (Mathworks Inc., Natick, MA).
Tables 3 and 4 show the 36 strategies for liver cancer screening by US and AFP in men and women in the high-risk population, as well as the number of cases found per 100,000 high-risk individuals screened, cost per 100,000 screenings, cost per preclinical case detected, incremental cases found, incremental cost, and ICER. They are listed in ascending order of cost per 100,000 screenings. The most expensive strategy, with a six-month interval and an age range of 30 to 80 years for men and women, found 5,440 and 2,212 preclinical cases per 100,000 high-risk individuals screened, respectively. The least expensive screening plan, consisting of a screening interval of two years for men and women with an age range of 50 to 65 years, detected 915 and 338 preclinical cases per 100,000 high-risk individuals screened, respectively.
Among the 36 alternative strategies for men, five (S_2_5080, S_1_5080, S_1_4080, S_0.5_4080, and S_0.5_3080) were identified as non-dominated strategies, and others were eliminated by either simple or extended dominance. The least expensive strategy was compared to a plan in which no screening was performed. The two-year or one-year interval non-dominated screening plans for the age range of 50 to 80 years had an ICER of US$40,802, US$71,020 per case detected, respectively. However, two-year non-dominated screening plans showed fewer cases per 100,000 screened, which was less than 40% of cases from the most expensive strategy (S_0.5_3080). Compared to a two-year interval plan for the 50-80 year age group, extending one-year screening for the age range of 50 to 80 (S_1_5080) was the next non-dominated strategy with an ICER of US$71,020 per one case found. The non-dominated screening plans above S_1_4080, such as S_0.5_4080 and S_0.5_3080 were not relatively cost-effective because the costs of these strategies were five times higher than the least expensive strategy (Table 3).
For women, identified non-dominated strategies were as follows: S_1_5065, S_1_5080, S_0.5_5080, S_0.5_4080, and S_0.5_3080 (Table 4). However, the ICERs of non-dominated strategies for women were at least two times higher than those for men. Compared to a one-year interval for the age range of 50 to 65 years (S_1_5065), extending one-year screening to age group 50-80 resulted in an increase in ICER from US$87,049 to US$153,976 (Table 4).
Fig. 1 illustrates our expansion path results consisting of the most cost-effective screening strategies. The expansion path graph, which plots the expected number of detected cases against the costs of each screening strategy, was illustrated based on the ICER shown in Tables 3 and 4. The graph representing the ICER shows a slow increase up to the S_1_4080 strategy for men and S_1_5080 strategy for women, but a steep increase at the S_0.5_4080 strategy for men and S_0.5_5080 strategy for women. In other words, the graph visually demonstrates that strategies above S_1_4080 for men and S1_5080 for women required large additional costs in order to achieve increasing effectiveness. Considering the ICER and the relative detection probability in the preclinical stage, the S_1_5080 and S_1_4080 strategies were chosen as the most cost-effective strategies for Korean men. For women, the ICER showed a steeper increase than for men, more than two times higher.
Sensitivity analyses of the identified cost-effective strategies for men and women were performed based on the different parameter settings (Table 5). The non-dominated strategies selected from each model for sensitivity showed consistency with those from the baseline model (data not shown). According to the various values of sensitivity parameters, relative ratio of cases to base-line model ranged from 100% to 124% of the baseline model for men and women, except the mean sojourn time. Cases found per 100,000 screened for the different values of mean sojourn time varied from 73% to 191% of the baseline model.
There is a general consensus among researchers and clinicians that liver cancer screening in high-risk groups has the potential to significantly reduce mortality [3,7]. However, due to a lack of information on its cost-effectiveness, there are limitations to introduction of liver cancer screening as a nationwide program. Optimal screening interval and the initial and ceiling screening age are the two major issues in liver cancer screening, as these factors are directly related to the detection rate of preclinical liver cancer, as well as the total cost of a program.
The optimal screening interval should be determined by tumor growth rate. An interval that is too long would allow tumors to grow to an extent that would preclude curative treatment. The mean tumor volume doubling time was estimated as 117-127 days (95% confidence interval, 80 to 203 days) [14,19]. Based on these tumor doubling times, a screening interval of 4-12 months has been suggested [14]. However, a retrospective study conducted on cirrhotic patients reported no differencein survival in patients screened at six- or 12-month intervals [20]. In addition, one study used a mathematical analysis for the tumor growth rate among HBV carriers, and showed that screening at 10-month or one-year intervals was the most cost-effective [21]. However, in studies of high-risk populations for liver cancer, screening at six-month intervals resulted in improved survival compared to one-year intervals [20,22]. Although most experts use a six-month interval, there are no clear data to suggest that it is the most-cost effective screening interval. In the current study, we explored three screening intervals: six months, one year, or two years, and the results showed that a one-year interval was the most cost-effective for both men and women.
Cost-effectiveness can be improved if screening is limited to well-defined groups of patients. In the case of liver cancer, chronic HBV and HCV infection are recognized as major risk factors for liver cancer. Cirrhosis is also a risk factor for liver cancer, irrespective of etiology, as is increasing age and male gender. However, only a limited number of studies have addressed the question of whether screening for liver cancer should or could be restricted to individuals within a certain age range. The initial screening age should be determined by the relationship between age and the incidence rate of liver cancer. In Korea, due to the high prevalence of HBV infection and related liver problems, the government introduced an HBV vaccination program in 1983; the vaccine was offered to government employees, soldiers, and students on a voluntary basis. In 1995, a national HBV vaccination program for infants and children was launched, followed by a 2002 national vaccination program directed at HBV-infected mothers for prevention of vertical transmission to their newborns. Thereafter, hepatitis B surface antigen seropositivity among members of the Korean population under 20 years of age showed a dramatic decrease to 2% [23]. Currently, most members of the Korean population under 20 years of age were born after introduction of the HBV vaccination program. Compared to this population, Koreans over 30 years of age have a higher prevalence of HBV infection. In the current study, we estimated the age-specific liver cancer incidence rates for the high-risk population between ages 30 and 80 years. Based on these incidence data, our results showed that the most cost-effective age range for liver cancer screening was from 50 to 80 for men and women.
The purpose of our study was to determine the most costeffective screening interval and age range based on the epidemiological characteristics of liver cancer in Korean men and women. In Korea, the entire population is enrolled in the mandatory NHI program, and liver cancer screening sponsored by the NHI is free of charge for most beneficiaries. Therefore, we performed the analysis from the perspective of the national healthcare system as we were primarily concerned with determining direct payments from the government. In this study, we did not measure indirect costs or lost productivity associated with liver cancer.
The effectiveness of a cancer screening program is assessed by decreases in cancer mortality rates; therefore, life-year gained (LYG), quality-adjusted life-years (QALY), or disability-adjusted life-years (DALY) are often used as the final endpoint for evaluation of screening strategies [24]. Although there is no consensus on a cost-effectiveness threshold below which an intervention would be considered unequivocally cost effective, a value of US$50,000/QALY is often cited as a benchmark [24]. On the whole, liver cancer screening in Korea might not be as cost-effective based on the threshold of US$50,000. However, we should consider that the cost effectiveness threshold is often referred to as society’s willingness topay for an additional unit of health gain (i.e., cancer detection). Thus, in the current study, interventions were classified based on the level of cost-effectiveness by convention, as described in the literature [25]: cost saving (an intervention generates a better health outcome and costs less than the comparison intervention) or cost neutral (ICER=0); very cost-effective (0＜ICER≤US$25,000); cost-effective (US$25,000＜ICER≤US$50,000); marginally cost-effective (US$50,000＜ICER≤US$100,000); or not cost-effective (US$100,000). In the current study, considering the high burden of liver cancer in Korea, we set a threshold of ICER≤US$100,000 (marginally cost-effective). In this regard, the relatively cost-effective strategies were identified in the analysis: 1) two-year interval for men in the age range 50 to 80 years (ICER, US$40,802); 2) one-year interval for men in the age range 50 to 80 years (ICER, US$71,020); 3) one-year screening interval for men in the age range 40 to 80 years (ICER, US $99,297); 4) one-year interval for women in the age range 50 to 65 years (ICER, US$87,049).
Our study has several limitations. One was the hypothesis that the rate of participation in liver cancer screening would be the same for all age ranges. In reality, different screening intervals and age ranges could affect the participation rate and in turn the cost-effectiveness analysis, depending on the strategies chosen. Conduct of afuture study will be needed in order to reflect the participation rate in the model.
In addition, we utilized the Korean National Cancer Incidence Database and NCSP database for estimation of the age-specific incidence rates of liver cancer in the high risk group. Because the voluntarily participating hospitals in KCCR could not cover all new cases in the entire country, the incidence rates could be underestimated. In addition, the high risk group for liver cancer identified from the NCSP database could not cover all high risk populations, because in the NCSP, individuals who had been tested or received medical care for HBV or HCV infection, chronic liver disease, or liver cirrhosis within the past two years were selected as the high risk group for liver cancer. However, underestimation of incidence itself does not influence the estimate of the number of examinations because the age-specific incidence rates are not unevenly underestimated over all ages.
In addition, we do not have data on the MST for liver cancer in Korea. As a result, the analysis had to be performed using reference data from Taiwan [3], which were assumed to be similar to Korean data. In the analysis, we did not distinguish between screening strategies for cirrhotic and non-cirrhotic patients, as we could not estimate liver cancer incidence rates for these groups separately. In addition, in the screening program at the national level, adoption of different screening strategies according to an individual’s underlying condition (i.e., cirrhotic or non-cirrhotic) is very difficult. The NCSP for liver cancer in Korea provides the same screening services to both cirrhotic and non-cirrhotic patients: consequently, we simply adopted a conservative MST value of 1.57 years in the baseline model for determination of the most cost-effective strategy for liver cancer screening, in terms of interval and age range for the defined high-risk population in Korea. We also performed sensitivity analyses by changing the MST in the preclinical state.
Finally, to identify the most cost-effective screening interval and target age range, we considered cases detected at the preclinical state as potential surrogate outcomes in screening strategies instead of LYG or QALY. The cost-effectiveness model using mortality reduction as an indicator of effectiveness (LYG, QALY, and DALY) mostly demands various parameters, such as progressive transition probabilities from state to state, fatality rate for each state, and medical costs related to treatment methods, such as transplantation, transarterial chemoembolization, radio frequency ablation, and so on. However, in order to obtain adequate statistical estimates, conduct of long follow-up clinical trials with a sufficiently large number of subjects would be required. As we were not able to obtain a sufficient amount of the data mentioned above on liver cancer in Korea, using the number of preclinical cases detected might be the best alternative for measuring effectiveness in this study. In addition, evidence for decreased liver cancer mortality following early detection was reported in a clinical trial of early detection [7]. However, conduct of further studies might be needed in order to evaluate the effectiveness of liver cancer screening with a final outcome indicator such as LYS, QALY, or DALY.
Despite these limitations, to the best of our knowledge, this is the first study to use a probability model for identification of cost-effective liver cancer screening strategies. The analysis used to simulate the estimated cost of liver cancer screening was based on fewer assumptions and provided more robust results when compared to the existing cost-effectiveness analysis method.
Findings of the current study demonstrate that a one-year screening interval for both men and women aged 50 to 80 years would be cost-effective. However, based on the relatively high incidence of liver cancer in Korean men, a one-year interval in the high-risk population aged 40 years or older is also acceptable. Therefore, we cautiously suggest that the initial screening age be changed from 40 to 50 years for women; a screening interval of one year would be considered a cost-effective alternative for men and women. However, due to the lack of concrete evidence regarding liver cancer screening, further studies should be conducted in order to examine the effectiveness of liver cancer screening in reduction of mortality. Also, additional research might be needed for comparison of the cost effectiveness of different liver cancer screening programs with a final outcome indicator such as QALY or DALY.

Conflict of interest relevant to this article was not reported.

Acknowledgements
This study was supported by a Grant-in-Aid for Cancer Research and Control from the National Cancer Center, Korea (grant number: 1010200) and a grant from the National R&D Program for Cancer Control, Ministry of Health and Welfare, Republic of Korea (0820420).
Fig. 1.
Expansion path graph of the most cost-effective strategies for liver cancer screening by combined ultrasonography and alpha-fetoprotein in Korean men and women.
crt-46-3-223f1.gif
Table 1.
Strategies generated for liver cancer screening by combined ultrasonography and alpha-fetoprotein testing for the cost-effectiveness analysis
Interval (yr) Initial age (yr) Ceiling age (yr) Strategy No. of screenings
0.5 30 65 S_0.5_3065 72
70 S_0.5_3070 82
75 S_0.5_3075 92
80 S_0.5_3080 102
40 65 S_0.5_4065 52
70 S_0.5_4070 62
75 S_0.5_4075 72
80 S_0.5_4080 82
50 65 S_0.5_5065 32
70 S_0.5_5070 42
75 S_0.5_5075 52
80 S_0.5_5080 62
1 30 65 S_1_3065 36
70 S_1_3070 41
75 S_1_3075 46
80 S_1_3080 51
40 65 S_1_4065 26
70 S_1_4070 31
75 S_1_4075 36
80 S_1_4080 41
50 65 S_1_5065 16
70 S_1_5070 21
75 S_1_5075 26
80 S_1_5080 31
2 30 65 S_2_3065 18
70 S_2_3070 21
75 S_2_3075 23
80 S_2_3080 26
40 65 S_2_4065 13
70 S_2_4070 16
75 S_2_4075 18
80 S_2_4080 21
50 65 S_2_5065 8
70 S_2_5070 11
75 S_2_5075 13
80 S_2_5080 16
Table 2.
Baseline assumptions and ranges tested in the sensitivity analysis
Parameters Baseline model Sensitivity analysis Reference
Mean sojourn time (yr) 1.57 0.94-4.68 7
(0.94, 2, 2.66, 4.68)
Screening testa)
 Sensitivity (%) 50 60, 70 16
 Specificity (%) 95 90 16
Unit cost (US$)
 Screening testa) 52.28b) 17
 Confirmative test 311.09c) 17

a) Combined ultrasonography and alpha-fetoprotein (AFP) testing,

b) Ultrasonography (US$41.29)+AFP (US$10.99)=52.28,

c) Magnetic resonance imaging=US$311.09.

Table 3.
Cost effectiveness of strategies for liver cancer screening by combined ultrasonography and alpha-fetoprotein testing in Korean men
Strategy Cases founda) per 100,000 screened Cost per 100,000 screenings (US$) Cost/Case detected (US$) Incremental cases foundb) Incremental costc) ICERd) Category of dominance
S2_5065 914.7 43,379,435 47,425 914.7 43,379,435 47,425 E. dominated
S2_5070 1,319.9 59,063,435 44,748 1,319.9 59,063,435 44,748 E. dominated
S2_4065 1,264.3 69,519,435 54,987 - - - Dominated
S2_5075 1,633.2 69,519,435 42,566 1,633.2 69,519,435 42,566 E. dominated
S2_4070 1,669.5 85,203,435 51,035 1,669.5 85,203,435 51,035 E. dominated
S2_5080 2,088.2 85,203,435 40,802 2,088.2 85,203,435 40,802 -
S1_5065 1,511.9 85,203,435 56,355 - - - Dominated
S2_3065 1,464.7 95,659,435 65,310 - - Dominated
S2_4075 1,982.8 95,659,435 48,245 - - - Dominated
S2_3070 1,869.9 111,343,435 59,545 - - - Dominated
S2_4080 2,437.7 111,343,435 45,676 349.5 26,140,000 74,793 E. dominated
S1_5070 2,015.8 111,343,435 55,235 - - - Dominated
S2_3075 2,183.2 121,799,435 55,789 - - - Dominated
S2_3080 2,638.2 137,483,435 52,113 550.0 52,280,000 95,055 E. dominated
S1_4065 2,038.3 137,483,435 67,450 - - - Dominated
S1_5075 2,615.6 137,483,435 52,563 - - - Dominated
S1_4070 2,542.3 163,623,435 64,360 - - - Dominated
S1_5080 3,192.4 163,623,435 51,254 1,104.2 78,420,000 71,020 -
S0.5_5065 2,035.8 168,851,435 82,941 - - - Dominated
S1_3065 2,345.7 189,763,435 80,898 - - - Dominated
S1_4075 3,142.0 189,763,435 60,396 - - - Dominated
S1_3070 2,849.6 215,903,435 75,766 - - - Dominated
S1_4080 3,718.9 215,903,435 58,056 526.5 52,280,000 99,297 -
S0.5_5070 2,707.7 221,131,435 81,668 - - - Dominated
S1_3075 3,449.4 242,043,435 70,170 - - - Dominated
S1_3080 4,026.2 268,183,435 66,610 307.3 52,280,000 170,127 E. dominated
S0.5_4065 2,740.4 273,411,435 99,771 - - - Dominated
S0.5_5075 3,512.6 273,411,435 77,837 - - - Dominated
S0.5_4070 3,412.2 325,691,435 95,449 - - - Dominated
S0.5_5080 4,284.0 325,691,435 76,025 565.1 109,788,000 194,281 E. dominated
S0.5_3065 3,151.7 377,971,435 119,926 - - - Dominated
S0.5_4075 4,217.2 377,971,435 89,626 - - - Dominated
S0.5_3070 3,823.5 430,251,435 112,528 - - Dominated
S0.5_4080 4,988.5 430,251,435 86,249 1,269.6 214,348,000 168,831 -
S0.5_3075 4,628.5 482,531,435 104,252 - - - Dominated
S0.5_3080 5,399.8 534,811,435 99,043 411.3 104,560,000 254,218 -

ICER, incremental cost-effectiveness ratio; E. dominated, extended dominated.

a) Cases found in the preclinical state per 100,000 screenings=detection probability×100,000,

b) Incremental cases found in the preclinical state compared with the next least expensive, non-dominated strategy,

c) Incremental cost compared with the next least expensive, non-dominated strategy,

d) Incremental cost/incremental cases found in preclinical state.

Table 4.
Cost effectiveness strategies for liver cancer screening by combined ultrasonography and alpha-fetoprotein testing in Korean women
Strategy Cases founda) per 100,000 screened Cost per 100,000 screenings (US$) Cost/Case detected (US$) Incremental cases foundb) Incremental costc) ICERd) Category of dominance
S2_5065 337.5 43,379,435 128,532 337.5 43,379,435 128,532 E. dominated
S2_5070 539.6 59,063,435 109,458 539.6 59,063,435 109,458 E. dominated
S2_4065 407.3 69,519,435 170,684 - - - Dominated
S2_5075 694.6 69,519,435 100,086 694.6 69,519,435 100,086 E. dominated
S2_4070 565.8 85,203,435 150,589 - - - Dominated
S2_5080 609.4 85,203,435 139,815 - - - Dominated
S1_5065 978.8 85,203,435 87,049 978.8 85,203,435 87,049 -
S2_3065 441.6 95,659,435 216,620 - - - Dominated
S2_4075 764.5 95,659,435 125,127 - - - Dominated
S2_3070 643.7 111,343,435 172,974 - - - Dominated
S2_4080 821.7 111,343,435 135,504 - - - Dominated
S1_5070 1,048.7 111,343,435 106,173 69.9 26,140,000 373,963 E. dominated
S2_3075 798.7 121,799,435 152,497 - - - Dominated
S2_3080 672.8 137,483,435 204,345 - - - Dominated
S1_4065 1,082.9 137,483,435 126,959 104.1 52,280,000 502,209 E. dominated
S1_5075 1,118.5 137,483,435 122,918 35.6 52,280,000 374,230 E. dominated
S1_4070 928.7 163,623,435 176,185 - - - Dominated
S1_5080 1,488.1 163,623,435 109,955 509.3 78,420,000 153,976 -
S0.5_5065 762.6 168,851,435 221,415 - - Dominated
S1_3065 724.9 189,763,435 261,779 - - - Dominated
S1_4075 1,225.5 189,763,435 154,846 - - - Dominated
S1_3070 980.8 215,903,435 220,130 - - - Dominated
S1_4080 1,595.1 215,903,435 135,354 107.0 52,280,000 488,598 E. dominated
S0.5_5070 1,104.7 221,131,435 200,173 - - - Dominated
S1_3075 1,277.7 242,043,435 189,437 - - - Dominated
S1_3080 1,647.3 268,183,435 162,802 159.2 104,560,000 656,784 E. dominated
S0.5_4065 905.8 273,411,435 301,845 - - - Dominated
S0.5_5075 1,502.9 273,411,435 181,923 - - - Dominated
S0.5_4070 1,248.0 325,691,435 260,971 - - - Dominated
S0.5_5080 1,999.0 325,691,435 162,927 510.9 162,068,000 317,221 -
S0.5_3065 975.6 377,971,435 387,425 - - - Dominated
S0.5_4075 1,646.1 377,971,435 229,616 - - - Dominated
S0.5_3070 1,317.7 430,251,435 326,517 - - - Dominated
S0.5_4080 2,142.2 430,251,435 200,846 143.2 104,560,000 730,168 -
S0.5_3075 1,715.9 482,531,435 281,212 - - - Dominated
S0.5_3080 2,212.0 534,811,435 241,777 69.8 104,560,000 1,497,994 -

ICER, incremental cost-effectiveness ratio; E. dominated, extended dominated.

a) Cases found in the preclinical state per 100,000 screenings=detection probability×100,000,

b) Incremental cases found in the preclinical state compared with the next least expensive, non-dominated strategy,

c) Incremental cost compared with the next least expensive, non-dominated strategy,

d) Incremental cost/incremental cases found in preclinical state.

Table 5.
Sensitivity analysis of strategies for liver cancer screening by combined ultrasonography and alpha-fetoprotein testing in Korean men and women
S1_5080
 S0.5_4080
Cases founda) per 100,000 screened (%)b) Cost per 100,000 screenings (%)b) Cases founda) per 100,000 screened (%)b) Cost per 100,000 screenings (%)b)
Men
 Baseline model 3,192.4 (100) 163,623,435 (100) 4,988.5 (100) 430,251,435 (100)
 Mean sojourn time (yr)
  0.94 2,353.9 (74) 163,623,435 (100) 4,021.4 (81) 430,251,435 (100)
  2.66 4,061.2 (127) 163,623,435 (100) 5,901.2 (118) 430,251,435 (100)
  4.68 4,921.3 (154) 163,623,435 (100) 6,808.1 (137) 430,251,435 (100)
  6.37 5,357.9 (168) 163,623,435 (100) 7,311.3 (147) 430,251,435 (100)
 Sensitivity of screening testc)
  60% 3,585.7 (112) 163,623,435 (100) 5,385.6 (108) 430,251,435 (100)
  70% 3,931.5 (123) 163,623,435 (100) 5,710.2 (115) 430,251,435 (100)
 Specificity of screening testc)
  90% 3,192.4 (100) 165,178,870 (101) 4,988.5 (100) 431,806,870 (100)
Women
 Baseline model 1,488.1 (100) 163,623,435 (100) 2,142.2 (100) 430,251,435 (100)
 Mean sojourn time (yr)
  0.94 1,078.9 (73) 163,623,435 (100) 1,706.1 (80) 430,251,435 (100)
  2.66 1,953.4 (131) 163,623,435 (100) 2,588.7 (121) 430,251,435 (100)
  4.68 2,502.0 (168) 163,623,435 (100) 3,094.7 (145) 430,251,435 (100)
  6.37 2,839.6 (191) 163,623,435 (100) 3,409.5 (159) 430,251,435 (100)
 Sensitivity of screening testc)
  60% 1,673.6 (113) 163,623,435 (100) 2,315.8 (108) 430,251,435 (100)
  70% 1,837.1 (124) 163,623,435 (100) 2,457.9 (115) 430,251,435 (100)
 Specificity of screening testc)
  90% 1,488.1 (100) 165,178,870 (101) 2,142.2 (100) 431,806,870 (100)

a) Cases found in the preclinical state per 100,000 screenings=detection probability×100,000,

b) Relative ratio compared to the baseline model,

c) Combined ultrasonography and alpha-fetoprotein testing.

  • 1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127:2893–917. ArticlePubMed
  • 2. Jung KW, Park S, Kong HJ, Won YJ, Lee JY, Seo HG, et al. Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2009. Cancer Res Treat. 2012;44:11–24. ArticlePubMedPMCPDF
  • 3. Chen TH, Chen CJ, Yen MF, Lu SN, Sun CA, Huang GT, et al. Ultrasound screening and risk factors for death from hepatocellular carcinoma in a high risk group in Taiwan. Int J Cancer. 2002;98:257–61. ArticlePubMed
  • 4. Yang B, Zhang B, Xu Y, Wang W, Shen Y, Zhang A, et al. Prospective study of early detection for primary liver cancer. J Cancer Res Clin Oncol. 1997;123:357–60. ArticlePubMed
  • 5. Yuen MF, Cheng CC, Lauder IJ, Lam SK, Ooi CG, Lai CL. Early detection of hepatocellular carcinoma increases the chance of treatment: Hong Kong experience. Hepatology. 2000;31:330–5. ArticlePubMed
  • 6. Bolondi L, Sofia S, Siringo S, Gaiani S, Casali A, Zironi G, et al. Surveillance programme of cirrhotic patients for early diagnosis and treatment of hepatocellular carcinoma: a cost effectiveness analysis. Gut. 2001;48:251–9. ArticlePubMedPMC
  • 7. Zhang BH, Yang BH, Tang ZY. Randomized controlled trial of screening for hepatocellular carcinoma. J Cancer Res Clin Oncol. 2004;130:417–22. ArticlePubMed
  • 8. National Cancer Institute. PDQ® Liver (Hepatocellular) Cancer Screening [Internet].Bethesda: National Cancer Institute; 2012. [cited 2013 Feb 28]. Available from: http://cancer.gov/cancertopics/pdq/screening/hepatocellular/HealthProfessional
  • 9. Daniele B. Alfa fetoprotein and ultrasonography screening. Hepatocellular carcinoma screening, diagnosis, and management. Bethesda: Natcher Conference Center, National Institutes of Health; 2004. p. 69–73.
  • 10. Bruix J, Sherman M, Llovet JM, Beaugrand M, Lencioni R, Burroughs AK, et al. Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. European Association for the Study of the Liver. J Hepatol. 2001;35:421–30. ArticlePubMed
  • 11. Asia-Pacific Working Party on Prevention of Hepatocellular Carcinoma. Prevention of hepatocellular carcinoma in the Asia-Pacific region: consensus statements. J Gastroenterol Hepatol. 2010;25:657–63. ArticlePubMed
  • 12. Han KH, Park JW. The usefulness and current status of the screening program for early diagnosis of hepatocellular carcinoma. J Korean Med Assoc. 2002;45:972–80. Article
  • 13. Lee SJ, Zelen M. Scheduling periodic examinations for the early detection of disease: applications to breast cancer. J Am Stat Assoc. 1998;93:1271–81. Article
  • 14. Sheu JC, Sung JL, Chen DS, Yang PM, Lai MY, Lee CS, et al. Growth rate of asymptomatic hepatocellular carcinoma and its clinical implications. Gastroenterology. 1985;89:259–66. ArticlePubMed
  • 15. Zhang B, Yang B. Combined alpha fetoprotein testing and ultrasonography as a screening test for primary liver cancer. J Med Screen. 1999;6:108–10. ArticlePubMed
  • 16. National Cancer Center. Evaluation of orgnized cancer screening program in Korea. Seoul: Ministry of Health & Welfare; 2010.
  • 17. Health Insurance Review and Assessment Services. The medical insurance cost in 2009. Seoul: Health Insurance Review and Assessment Service; 2009.
  • 18. Taouli B, Goh JS, Lu Y, Qayyum A, Yeh BM, Merriman RB, et al. Growth rate of hepatocellular carcinoma: evaluation with serial computed tomography or magnetic resonance imaging. J Comput Assist Tomogr. 2005;29:425–9. ArticlePubMed
  • 19. Trevisani F, De Notariis S, Rapaccini G, Farinati F, Benvegnu L, Zoli M, et al. Semiannual and annual surveillance of cirrhotic patients for hepatocellular carcinoma: effects on cancer stage and patient survival (Italian experience). Am J Gastroenterol. 2002;97:734–44. ArticlePubMed
  • 20. Kang JY, Lee TP, Yap I, Lun KC. Analysis of cost-effectiveness of different strategies for hepatocellular carcinoma screening in hepatitis B virus carriers. J Gastroenterol Hepatol. 1992;7:463–8. ArticlePubMed
  • 21. Thompson Coon J, Rogers G, Hewson P, Wright D, Anderson R, Jackson S, et al. Surveillance of cirrhosis for hepatocellular carcinoma: a cost-utility analysis. Br J Cancer. 2008;98:1166–75. ArticlePubMedPMC
  • 22. Santi V, Trevisani F, Gramenzi A, Grignaschi A, Mirici-Cappa F, Del Poggio P, et al. Semiannual surveillance is superior to annual surveillance for the detection of early hepatocellular carcinoma and patient survival. J Hepatol. 2010;53:291–7. ArticlePubMed
  • 23. Juon HS, Choi KS, Park EC, Kwak MS, Lee S. Hepatitis B vaccinations among Koreans: Results from 2005 Korea National Cancer Screening Survey. BMC Infect Dis. 2009;9:185.ArticlePubMedPMC
  • 24. Drummond M, O'Brien B, Stoddart G, Torrance GW. Methods for the economic evaluation of health care programmes. 2nd ed. New York: Oxford University Press; 1997.
  • 25. Grosse SD. Assessing cost-effectiveness in healthcare: history of the $50,000 per QALY threshold. Expert Rev Pharmacoecon Outcomes Res. 2008;8:165–78. ArticlePubMed

Figure & Data

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      Cost-Effectiveness of Liver Cancer Screening in Adults at High Risk for Liver Cancer in the Republic of Korea
      Cancer Res Treat. 2014;46(3):223-233.   Published online July 15, 2014
      DOI: https://doi.org/10.4143/crt.2014.46.3.223
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    Cost-Effectiveness of Liver Cancer Screening in Adults at High Risk for Liver Cancer in the Republic of Korea
    Image
    Fig. 1. Expansion path graph of the most cost-effective strategies for liver cancer screening by combined ultrasonography and alpha-fetoprotein in Korean men and women.

    Fig. 1.

    Cost-Effectiveness of Liver Cancer Screening in Adults at High Risk for Liver Cancer in the Republic of Korea
    Interval (yr) Initial age (yr) Ceiling age (yr) Strategy No. of screenings
    0.5 30 65 S_0.5_3065 72
    70 S_0.5_3070 82
    75 S_0.5_3075 92
    80 S_0.5_3080 102
    40 65 S_0.5_4065 52
    70 S_0.5_4070 62
    75 S_0.5_4075 72
    80 S_0.5_4080 82
    50 65 S_0.5_5065 32
    70 S_0.5_5070 42
    75 S_0.5_5075 52
    80 S_0.5_5080 62
    1 30 65 S_1_3065 36
    70 S_1_3070 41
    75 S_1_3075 46
    80 S_1_3080 51
    40 65 S_1_4065 26
    70 S_1_4070 31
    75 S_1_4075 36
    80 S_1_4080 41
    50 65 S_1_5065 16
    70 S_1_5070 21
    75 S_1_5075 26
    80 S_1_5080 31
    2 30 65 S_2_3065 18
    70 S_2_3070 21
    75 S_2_3075 23
    80 S_2_3080 26
    40 65 S_2_4065 13
    70 S_2_4070 16
    75 S_2_4075 18
    80 S_2_4080 21
    50 65 S_2_5065 8
    70 S_2_5070 11
    75 S_2_5075 13
    80 S_2_5080 16
    Parameters Baseline model Sensitivity analysis Reference
    Mean sojourn time (yr) 1.57 0.94-4.68 7
    (0.94, 2, 2.66, 4.68)
    Screening testa)
     Sensitivity (%) 50 60, 70 16
     Specificity (%) 95 90 16
    Unit cost (US$)
     Screening testa) 52.28b) 17
     Confirmative test 311.09c) 17
    Strategy Cases founda) per 100,000 screened Cost per 100,000 screenings (US$) Cost/Case detected (US$) Incremental cases foundb) Incremental costc) ICERd) Category of dominance
    S2_5065 914.7 43,379,435 47,425 914.7 43,379,435 47,425 E. dominated
    S2_5070 1,319.9 59,063,435 44,748 1,319.9 59,063,435 44,748 E. dominated
    S2_4065 1,264.3 69,519,435 54,987 - - - Dominated
    S2_5075 1,633.2 69,519,435 42,566 1,633.2 69,519,435 42,566 E. dominated
    S2_4070 1,669.5 85,203,435 51,035 1,669.5 85,203,435 51,035 E. dominated
    S2_5080 2,088.2 85,203,435 40,802 2,088.2 85,203,435 40,802 -
    S1_5065 1,511.9 85,203,435 56,355 - - - Dominated
    S2_3065 1,464.7 95,659,435 65,310 - - Dominated
    S2_4075 1,982.8 95,659,435 48,245 - - - Dominated
    S2_3070 1,869.9 111,343,435 59,545 - - - Dominated
    S2_4080 2,437.7 111,343,435 45,676 349.5 26,140,000 74,793 E. dominated
    S1_5070 2,015.8 111,343,435 55,235 - - - Dominated
    S2_3075 2,183.2 121,799,435 55,789 - - - Dominated
    S2_3080 2,638.2 137,483,435 52,113 550.0 52,280,000 95,055 E. dominated
    S1_4065 2,038.3 137,483,435 67,450 - - - Dominated
    S1_5075 2,615.6 137,483,435 52,563 - - - Dominated
    S1_4070 2,542.3 163,623,435 64,360 - - - Dominated
    S1_5080 3,192.4 163,623,435 51,254 1,104.2 78,420,000 71,020 -
    S0.5_5065 2,035.8 168,851,435 82,941 - - - Dominated
    S1_3065 2,345.7 189,763,435 80,898 - - - Dominated
    S1_4075 3,142.0 189,763,435 60,396 - - - Dominated
    S1_3070 2,849.6 215,903,435 75,766 - - - Dominated
    S1_4080 3,718.9 215,903,435 58,056 526.5 52,280,000 99,297 -
    S0.5_5070 2,707.7 221,131,435 81,668 - - - Dominated
    S1_3075 3,449.4 242,043,435 70,170 - - - Dominated
    S1_3080 4,026.2 268,183,435 66,610 307.3 52,280,000 170,127 E. dominated
    S0.5_4065 2,740.4 273,411,435 99,771 - - - Dominated
    S0.5_5075 3,512.6 273,411,435 77,837 - - - Dominated
    S0.5_4070 3,412.2 325,691,435 95,449 - - - Dominated
    S0.5_5080 4,284.0 325,691,435 76,025 565.1 109,788,000 194,281 E. dominated
    S0.5_3065 3,151.7 377,971,435 119,926 - - - Dominated
    S0.5_4075 4,217.2 377,971,435 89,626 - - - Dominated
    S0.5_3070 3,823.5 430,251,435 112,528 - - Dominated
    S0.5_4080 4,988.5 430,251,435 86,249 1,269.6 214,348,000 168,831 -
    S0.5_3075 4,628.5 482,531,435 104,252 - - - Dominated
    S0.5_3080 5,399.8 534,811,435 99,043 411.3 104,560,000 254,218 -
    Strategy Cases founda) per 100,000 screened Cost per 100,000 screenings (US$) Cost/Case detected (US$) Incremental cases foundb) Incremental costc) ICERd) Category of dominance
    S2_5065 337.5 43,379,435 128,532 337.5 43,379,435 128,532 E. dominated
    S2_5070 539.6 59,063,435 109,458 539.6 59,063,435 109,458 E. dominated
    S2_4065 407.3 69,519,435 170,684 - - - Dominated
    S2_5075 694.6 69,519,435 100,086 694.6 69,519,435 100,086 E. dominated
    S2_4070 565.8 85,203,435 150,589 - - - Dominated
    S2_5080 609.4 85,203,435 139,815 - - - Dominated
    S1_5065 978.8 85,203,435 87,049 978.8 85,203,435 87,049 -
    S2_3065 441.6 95,659,435 216,620 - - - Dominated
    S2_4075 764.5 95,659,435 125,127 - - - Dominated
    S2_3070 643.7 111,343,435 172,974 - - - Dominated
    S2_4080 821.7 111,343,435 135,504 - - - Dominated
    S1_5070 1,048.7 111,343,435 106,173 69.9 26,140,000 373,963 E. dominated
    S2_3075 798.7 121,799,435 152,497 - - - Dominated
    S2_3080 672.8 137,483,435 204,345 - - - Dominated
    S1_4065 1,082.9 137,483,435 126,959 104.1 52,280,000 502,209 E. dominated
    S1_5075 1,118.5 137,483,435 122,918 35.6 52,280,000 374,230 E. dominated
    S1_4070 928.7 163,623,435 176,185 - - - Dominated
    S1_5080 1,488.1 163,623,435 109,955 509.3 78,420,000 153,976 -
    S0.5_5065 762.6 168,851,435 221,415 - - Dominated
    S1_3065 724.9 189,763,435 261,779 - - - Dominated
    S1_4075 1,225.5 189,763,435 154,846 - - - Dominated
    S1_3070 980.8 215,903,435 220,130 - - - Dominated
    S1_4080 1,595.1 215,903,435 135,354 107.0 52,280,000 488,598 E. dominated
    S0.5_5070 1,104.7 221,131,435 200,173 - - - Dominated
    S1_3075 1,277.7 242,043,435 189,437 - - - Dominated
    S1_3080 1,647.3 268,183,435 162,802 159.2 104,560,000 656,784 E. dominated
    S0.5_4065 905.8 273,411,435 301,845 - - - Dominated
    S0.5_5075 1,502.9 273,411,435 181,923 - - - Dominated
    S0.5_4070 1,248.0 325,691,435 260,971 - - - Dominated
    S0.5_5080 1,999.0 325,691,435 162,927 510.9 162,068,000 317,221 -
    S0.5_3065 975.6 377,971,435 387,425 - - - Dominated
    S0.5_4075 1,646.1 377,971,435 229,616 - - - Dominated
    S0.5_3070 1,317.7 430,251,435 326,517 - - - Dominated
    S0.5_4080 2,142.2 430,251,435 200,846 143.2 104,560,000 730,168 -
    S0.5_3075 1,715.9 482,531,435 281,212 - - - Dominated
    S0.5_3080 2,212.0 534,811,435 241,777 69.8 104,560,000 1,497,994 -
    S1_5080
    		 S0.5_4080
    Cases founda) per 100,000 screened (%)b) Cost per 100,000 screenings (%)b) Cases founda) per 100,000 screened (%)b) Cost per 100,000 screenings (%)b)
    Men
     Baseline model 3,192.4 (100) 163,623,435 (100) 4,988.5 (100) 430,251,435 (100)
     Mean sojourn time (yr)
      0.94 2,353.9 (74) 163,623,435 (100) 4,021.4 (81) 430,251,435 (100)
      2.66 4,061.2 (127) 163,623,435 (100) 5,901.2 (118) 430,251,435 (100)
      4.68 4,921.3 (154) 163,623,435 (100) 6,808.1 (137) 430,251,435 (100)
      6.37 5,357.9 (168) 163,623,435 (100) 7,311.3 (147) 430,251,435 (100)
     Sensitivity of screening testc)
      60% 3,585.7 (112) 163,623,435 (100) 5,385.6 (108) 430,251,435 (100)
      70% 3,931.5 (123) 163,623,435 (100) 5,710.2 (115) 430,251,435 (100)
     Specificity of screening testc)
      90% 3,192.4 (100) 165,178,870 (101) 4,988.5 (100) 431,806,870 (100)
    Women
     Baseline model 1,488.1 (100) 163,623,435 (100) 2,142.2 (100) 430,251,435 (100)
     Mean sojourn time (yr)
      0.94 1,078.9 (73) 163,623,435 (100) 1,706.1 (80) 430,251,435 (100)
      2.66 1,953.4 (131) 163,623,435 (100) 2,588.7 (121) 430,251,435 (100)
      4.68 2,502.0 (168) 163,623,435 (100) 3,094.7 (145) 430,251,435 (100)
      6.37 2,839.6 (191) 163,623,435 (100) 3,409.5 (159) 430,251,435 (100)
     Sensitivity of screening testc)
      60% 1,673.6 (113) 163,623,435 (100) 2,315.8 (108) 430,251,435 (100)
      70% 1,837.1 (124) 163,623,435 (100) 2,457.9 (115) 430,251,435 (100)
     Specificity of screening testc)
      90% 1,488.1 (100) 165,178,870 (101) 2,142.2 (100) 431,806,870 (100)
    Table 1. Strategies generated for liver cancer screening by combined ultrasonography and alpha-fetoprotein testing for the cost-effectiveness analysis

    Table 2. Baseline assumptions and ranges tested in the sensitivity analysis

    Combined ultrasonography and alpha-fetoprotein (AFP) testing,

    Ultrasonography (US$41.29)+AFP (US$10.99)=52.28,

    Magnetic resonance imaging=US$311.09.

    Table 3. Cost effectiveness of strategies for liver cancer screening by combined ultrasonography and alpha-fetoprotein testing in Korean men

    ICER, incremental cost-effectiveness ratio; E. dominated, extended dominated.

    Cases found in the preclinical state per 100,000 screenings=detection probability×100,000,

    Incremental cases found in the preclinical state compared with the next least expensive, non-dominated strategy,

    Incremental cost compared with the next least expensive, non-dominated strategy,

    Incremental cost/incremental cases found in preclinical state.

    Table 4. Cost effectiveness strategies for liver cancer screening by combined ultrasonography and alpha-fetoprotein testing in Korean women

    ICER, incremental cost-effectiveness ratio; E. dominated, extended dominated.

    Cases found in the preclinical state per 100,000 screenings=detection probability×100,000,

    Incremental cases found in the preclinical state compared with the next least expensive, non-dominated strategy,

    Incremental cost compared with the next least expensive, non-dominated strategy,

    Incremental cost/incremental cases found in preclinical state.

    Table 5. Sensitivity analysis of strategies for liver cancer screening by combined ultrasonography and alpha-fetoprotein testing in Korean men and women

    Cases found in the preclinical state per 100,000 screenings=detection probability×100,000,

    Relative ratio compared to the baseline model,

    Combined ultrasonography and alpha-fetoprotein testing.

    Table 1.

    Table 2.

    Table 3.

    Table 4.

    Table 5.

    Cancer Res Treat : Cancer Research and Treatment
    © Korean Cancer Association.
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