2.1. Study Design
In this study, we conduct cost-effectiveness analysis from sociological perspective. A state-transition Markov model is developed to simulate the natural history of lung cancer development. In a Markov model, clinical situations are described according to the discrete health states, ‘Markov states,’ which individuals can be in. All individuals are always in one of these states, and all events of interest are modeled as a transition from one state to another[18]. Based on Markov model, we simulate screening and non-screening strategies to obtain the costs and effectiveness. The parameters required for the model simulation include cost-related parameters (screening, diagnosis, treatment), effect-related parameters (quality-adjusted life years), and parameters related to transition probability (morbidity, mortality, clinical-stage distribution of cancers, etc.), which are derived from NLCSIP & CanSPUC, published literatures, and cancer registry data. First, the study conducts a baseline analysis. Second, sensitivity analysis is carried out to assess the robust of the results using reasonable ranges of uncertain parameters.
2.2. Study population and screening strategies
Five screening strategies and their corresponding non-screening strategies for lung cancer are compared in cost and effectiveness under a decision analytic model based on a state-transition Markov process, respectively. A hypothetical static cohort of 100,000 high risk smokers (>20 pack years) from Chinese urban areas entered the model and their health histories are simulated by sex until 76 years old. The proportion of males of this research is set according to the 2015 annual report of Chinese people and employment statistics[19]. All health states are modelled as Markov states with one-year cycle. The initial screening ages for the five screening strategies and five corresponding unscreened strategies are the age of 40 years, 45 years, 50 years, 55 years, and 60 years. We compare the cost-effectiveness of screening and their corresponding non-screening strategies at the same initial age, respectively.
2.3. Markov Model and its Transition Probabilities
No-screened Cohort. In the non-screening model, the natural history of lung cancer development is simulated as a transition from health to lung cancer (Based on data from NLCSIP program, we divide the cases of detection into early (stage I) and non-early stage lung cancer), and ultimately to death (death from cancer or death from other causes)(Figure-1a)[15]. The probability from health to death comes from China's demographic census [20]. The probability of lung cancer-specific death comes from published literature [21].The disease progression parameters from health to lung cancer are calculated on the basis of the lung cancer incidence of smokers in China [17]. The incidence of smokers(>20 pack years) is calculated as follows:
First, age and sex specific lung cancer incidences in the common Chinese urban population (IG) are collected from the China Cancer Registry Annual Report 2017 (urban data) at age intervals of 5 years [17]. Next, the lung cancer incidences of smokers (IS) in each age and gender group are calculated using the following formula 1 [22]:
IS =OR × IG /(1 + (OR − 1) ×R) formula 1
Where OR (2.85 for men and 2.33 for women) is the odds ratio from published literature [21]. R is the age- and sex-specific rate of smoking reported in the Global Adult Tobacco Survey (GATS) China 2010 Country Report [23]. Further, we calculate the incidence of non-smokers (IN) by the incidence of smokers (IS), the proportion of smokers, and the overall population morbidity (I), according to formula 2.
I=IS×R+IN× (1-R) formula 2
Then, we use the lung cancer relative risk of smokers (>20 pack years) versus non-smokers to calculate the lung cancer incidence(I20) of smokers(>20 pack years) in China using the following formula 3. The relative risk (RR)of lung cancer (>20 pack years)attributable to smoking is derived from published literature [24].
I20=I×RR formula 3
Screened Cohort. Adherent smokers in the screened cohort receive annual LDCT testing. Smokers with positive results in LDCT screen are required to make additional diagnostic biopsy test. The sensitivity and specificity of LDCT for lung cancer are set according to the data from published study [22]. In addition, screening is superimposed on the lung cancer natural history module, resulting in early detection as determined by LDCT screening performance characteristics(Figure-1b).The clinical stage distribution of lung cancer patients in the non-screening cohort is from a multicenter retrospective epidemiologic survey from CanSPUC program [25].
The clinical-stage distribution of lung cancer patients in the screened cohort is from opportunistic screening data of multi-center in NLCSIP program. A total of 21,397 asymptomatic individuals were screened by the NLCSIP program, and 199 patients were diagnosed with lung cancer, of which 85.6% were stage I lung cancer patients.
People who are screened may be diagnosed with lung cancer that does not cause clinical disease (over-diagnosis bias), and many of them may not be diagnosed under non-screening conditions. Some studies set the over-diagnosis rate at 0% at baseline [26, 27]; Manser et.al set the over-diagnosis rate at 12% to 20 % based on early autopsy reports for lung cancer in Australia [28]. In this study, the over-diagnosis rate is set as 0% in the baseline, and we set the over-diagnosis rate as 0%-20% in the sensitivity analysis.
Screening can also create lead time bias. Lead time,being interpreted as the extended survival time due to screening,is the difference between the time of screening diagnosis and the time of clinical diagnosis. An average 1-year lead time for screening detected lung cancers is incorporated in the study[15].
Model Assumptions
(1) People who participate in screening do not increase their cancer risk due to radiation from LDCT. (2) In the screening group, the compliance of the population for screening is 100%, and those people who participate in the screening will continue to participate in the next annual lung cancer screening until the age of 76 years. (3) The clinical-stage distribution of false-negative lung cancer in LDCT screening strategy is the same as that of unscreened individuals.
2.4. Costs
The cost includes the screening cost, biopsy diagnosis cost, and lung cancer treatment cost. The LDCT screening cost comes from CanSPUC program, and the cost of biopsy diagnosis comes from the price of medical service in the hospital of CanSPUC program hospital. The lung cancer treatment cost comes from multi-center retrospective survey in the 13 provinces of CanSPUC program, including a total of 15,437 people. The inpatients whose most treatment cost occurred in the investigated hospitals and discharged for the last time between January 1, 2002 and December 31, 2011 were included. All the costs in this study are expressed in USD and are discounted to the price level of 2018 at a discount rate of 3% [29] (Table-1).
2.5. Quality of life
QALYs take into account both survival and quality of life determined by the progression and severity of lung cancer. We obtain utility scores of quality of life for the health states from a current meta-analysis [30]. Utility scores are 0.823 for the early stage lung cancer, 0.573 for the non-early stage lung cancer, and 1 for the health state (Table-1).
Table 1. Parameters used for the modeling of lung cancer screening protocols.
|
Variable
|
Values (range)
|
Reference
|
Lung cancer probabilities(%)
Proportion of early-stage cancer among lung cancers detected with no screening
|
19.00
|
[25]
|
Proportion of early-stage cancer among lung cancers detected with LDCT
|
85.60
|
NLCSIP
|
RR(>20 pack-years)
|
3.87
|
[24]
|
Sensitivity of LDCT(%)
|
87.70(71.80-100)
|
[22]
|
Specificity of LDCT(%)
|
90.60(86.30-91.10)
|
[22]
|
Mortality (%)
Early-stage lung cancer
|
11.12
|
[21]
|
Non-early-stage lung cancer
|
35.34
|
|
Discount rate
|
3%
|
[29]
|
General population smoking rate (%)
Men
40-44
45-64
65-76
Women
40-44
45-64
65-76
|
59.30
63.00
40.20
1.60
3.20
6.70
|
[23]
|
Cost(USD)
Screening
|
68.00
|
Canspuc
|
Treatment
Early-stage lung cancer
|
7984.30
|
|
Non-early-stage lung cancer
|
8158.39
|
|
Pre-diagnosis cost
Diagnostic cost
|
91.11
178.70
|
|
Utility
|
|
[30]
|
Early-stage lung cancer
|
0.825
|
|
Non-early-stage lung cancer
|
0.573
|
|
2.6. Effectiveness of Lung cancer screening
The effectiveness of screening is measured by comparing the difference in lung cancer specific deaths and QALYs. The lung cancer specific deaths and QALYs gained under each screening strategy equals to the difference between the screening strategy and its corresponding non-screening strategy.
Primary outcome of the cost-effectiveness analysis is the ICER (Incremental Cost-Effectiveness Ratio) which is calculated by dividing the incremental cost by the incremental QALYs gained for each screening strategy compared to its corresponding non-screening strategy. In this study, the ceiling ratio is defined to the threshold recommend by the World health organization (WHO) [31]. When ICER is less than three times the GDP per capita, the strategy is cost-effectiveness. Conversely, the strategy is no cost-effectiveness. China's per capita GDP in 2018 was 9768.78 USD, thus 29306.34 USD (9768.78×3) as the ceiling ratio was given in our study.
2.7. Sensitivity analysis
In one way sensitivity analysis, we test the impact of the parameters such as sensitivity, specificity, and over-diagnosis rate on the robustness of the cost-effectiveness. The ranges of parameter variations are set as: 71.8% to 100% for sensitivity,86.3% to 91.1% for specificity and 0% to 20% for over-diagnosis rate. The worst scenario of LDCT screening is estimated by the combinations of the lowest values of three parameters above.