{"paper_id":"3835bdc7-3c01-456e-a45c-090498c8fefb","body_text":"Cost-effectiveness of Low-Dose CT Screening for Non-smokers with a First-Degree Relative History of Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cost-effectiveness of Low-Dose CT Screening for Non-smokers with a First-Degree Relative History of Lung Cancer Yin Liu, Xiaoli Guo, Huifang Xu, Xiaoyang Wang, Hongwei Liu, Hong Wang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6244154/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 May, 2025 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Lung cancer is the leading cause of cancer-related deaths worldwide, with non-smokers in China accounting for over 40% of cases. Despite the proven efficacy of low-dose computed tomography (LDCT) in early detection and reduction of lung cancer mortality, the current paradigm of lung cancer screening, heavily focused on smoking status and age, may inadequately address the unique risk factors associated with non-smokers, particularly those with a family history of the disease. This study evaluates the cost-effectiveness of LDCT screening for non-smokers with a first-degree relative (FDR) history of lung cancer, a group at particularly high-risk. Methods We developed a state-transition Markov model to evaluate the incremental cost-effectiveness ratios (ICERs) of 16 screening strategies for a hypothetical cohort of 100,000 non-smoking individuals aged 50 with a FDR history of lung cancer, considering various starting ages (50, 55, 60, 65 years) and intervals (one-off, annual, biennial, triennial). The willingness-to-pay (WTP) threshold was set at three times China's 2022 per-capita GDP. Sensitivity analyses, scenario analyses and subgroup analysis by sex, were conducted. Results Compared to no screening, all strategies except one-off screening at age 50, were cost-effective for both sexes. Biennial LDCT starting at age 55 was found to be most effective, with an ICER of CNY 68,932/QALY for males, and CNY 80,056/QALY for females. This cost-effectiveness probability for this strategy was approximately 90% for both sexes. Sensitivity analyses indicated that annual screening at age 55 was optimal without discounting. For males, biennial at age 60 was optimal if the FDR-related odds ratio for lung cancer incidence was below 1.492. Triennial screening at age 55 was optimal for females at full adherence. Ignoring disutility from false-positive results, annual at age 55 was optimal for both sexes. Conclusions LDCT screening for non-smokers with a FDR history of lung cancer is cost-effective, especially biennial screening at 55. These findings support the development of more inclusive screening guidelines, which could enhance early detection and reduce mortality rates. Lung Cancer LDCT Screening Non-smokers Cost-effectiveness Modelling Study Figures Figure 1 Figure 2 Figure 3 Introduction Lung cancer continues to be a leading cause of the global cancer burden, with an alarming 12.4% of all new cancer cases and 18.7% of cancer-related deaths attributed to it in 2022 1 . In China, the situation is particularly dire, where lung cancer claims the primary cause of cancer mortality, with an estimated 733,291 deaths in 2022, accounting for 40.3% of the global total 2 . The burden of lung cancer in China is projected to increase mainly due to an aging population and population growth 3 . There is an urgent need for collective action to reduce this burden. Lung cancer screening based on low-dose CT (LDCT) has demonstrated significant efficacy in early detection and in reducing the mortality rate 4 , 5 . However, existing lung cancer screening guidelines focus solely on age and smoking history, potentially excluding at-risk non-smokers and overlooking a significant number of lung cancer cases 6 – 8 . It has been estimated that lung cancer in non-smokers is the seventh leading cause of cancer-related deaths worldwide, with a rising trend 9 , 10 , particularly in Asian countries like China, where over 40% of lung cancer cases occur in non-smokers 11 . The current smoking-focused screening strategy may not adequately address the distinct risk profiles and demographic characteristics of non-smokers. Consequently, there is a compelling need to develop a targeted LDCT screening strategy specifically for non-smokers. The long-term cost-effectiveness of LDCT screening must be carefully evaluated in the development of a screening strategy. While the benefits of LDCT screening in reducing lung cancer mortality, the harms of false positives, overdiagnosis, and radiation-induced cancer incidence cannot be ignored 12 . A targeted approach for high-risk populations can more effectively balance these risks with the benefits, thereby enhancing cost-effectiveness 13 , 14 . For non-smokers, a first-degree relative (FDR) history of lung cancer is a significant risk factor, highlighting the importance of screening strategies that consider genetic and environmental influences 15 , 16 . Nevertheless, the cost-effectiveness of LDCT screening for non-smokers with a FDR history of lung cancer remains largely unexplored. In response to this gap, we conducted this study to evaluate the cost-effectiveness of LDCT screening for non-smokers with a FDR history of lung cancer in China. Our analysis aimed to determine the optimal starting age and screening intervals for this specific high-risk group, thereby informing a more strategic and cost-effective approach to early lung cancer detection. Methods We conducted a model-based economic evaluation to assess the cost-effectiveness of LDCT screening for non-smokers with a FDR history of lung cancer in China, from a health-care system perspective. The FDR was defined as one’s parents, siblings or offsprings. The non-smokers were defined as those who have never smoked. The model was constructed using TreeAge Pro 2022 software and the analysis adhered to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement 17 . Markov model A state-transition Markov model was developed to simulate lung cancer progression and evaluate the costs and quality-adjusted life years (QALYs) outcomes in a lifetime horizon (until death or 78 years old, the expected life years). The model initialized with a hypothetical cohort of 100,000 individuals aged 50 years old. It was assumed that this cohort was initially lung cancer-free but had a FDR history of lung cancer at the baseline. The Markov model encapsulated both the natural history and the post-diagnosis progression of lung cancer, with a detailed description available in prior literatures 18 – 20 . In summary, the natural history consisted of six states: healthy, lung cancer stages I through IV, and death. We assumed that individuals in the healthy state would develop to lung cancer stage I at the age- and sex-specific incidence rates. Once lung cancer is developed, patients may experience progression to more advanced stages, be diagnosed through LDCT screening or standard clinical care upon symptom onset, maintain their current stage, or die. The post-diagnosis component of the model comprised five states: post-treatment lung cancer stages I through IV, and death. It was presumed that patients underwent immediate treatment upon diagnosis. Additionally, we have incorporated age- and sex-specific natural background death rates and acknowledged that individuals with lung cancer face stage-specific mortality from this disease in addition to a natural background death rate. A model cycle-length of 1-year, with half-cycle correction was assumed. Details of the Markov model was depicted in Fig. S1 . Screening Strategies We evaluated 16 LDCT screening strategies, defined by varying starting ages (50, 55, 60, or 65 years) and screening intervals (one-off, annual, biennial, or triennial). No screening approach served as the reference strategy. One-off screening was assumed to occur at each starting age. The age limits were set at 50 and 74 years, respectively, conform with Chinese screening guideline for heavy smokers, balancing life expectancy with the feasibility of screening implementation 8 . Model Input Parameters The key model input parameters were shown as Table 1 , aligning the gender distribution with China's demographic profiles as of 2022 21 . Table 1 Key input parameters of Markov model for lung cancer screening Parameters Base case (range) Distribution Reference Smoker-to-non-smoker relative risk of lung cancer ( \\(\\:{\\varvec{R}\\varvec{R}}_{\\varvec{s}\\_\\varvec{t}\\varvec{o}\\_\\varvec{N}\\varvec{S}}\\) ) 23 Male 2.41(2.18–2.65) Triangular(2.18, 2.41, 2.65) Female 2.42(2.11–2.77) Triangular(2.11, 2.42, 2.77) FDR-related odds ratio of lung cancer incidence ( \\(\\:{\\varvec{O}\\varvec{R}}_{\\varvec{F}\\varvec{D}\\varvec{R}}\\) ) Meta-analysis Male 1.88(1.01–3.49) Triangular(1.01, 1.88, 3.49) Female 2.27(1.77–2.91) Triangular(1.77, 2.27, 2.91) Transition Probabilities 25 Lung cancer stage I to stage II 0.3682 (± 50%) Beta(9.33, 16.01) Lung cancer stage I to stage III 0.0328(± 50%) Beta(14.83, 437.29) Lung cancer stage I to stage IV 0.0745(± 50%) Beta(14.15, 175.75) Lung cancer stage II to stage III 0.2260(± 50%) Beta(11.67, 39.96) Lung cancer stage II to stage IV 0.1510(± 50%) Beta(12.89, 72.50) Lung cancer stage III to stage IV 0.1455(± 50%) Beta(12.98, 76.26) Mortality Lung cancer stage I to LC death 0.1739(± 50%) Beta(12.52, 59.48) 25 Lung cancer stage II to LC death 0.2942(± 50%) Beta(10.55, 25.31) Lung cancer stage III to LC death 0.4626(± 50%) Beta(7.79, 9.06) Lung cancer stage IV to LC death 0.5880(± 50%) Beta(5.74, 4.02) After care I to death 0.089(± 50%) Beta(13.91, 142.23) 26 After care II to death 0.153(± 50%) Beta(12.86, 71.32) After care III to death 0.288(± 50%) Beta(10.65, 26.34) After care IV to death 0.353(± 50%) Beta(9.59, 17.60) Performance of LDCT Sensitivity of LDCT 0.981(0.884–0.999) Triangular (0.884, 0.981, 0.999) 28 Specificity of LDCT 0.782(0.768–0.796) Beta(2,643.78, 737.01) Overdiagnosis rate when screening 0.031(± 50%) Beta(14.86, 464.46) The additional risk of LC per screening (1/100,000) 29 Male, aged 50–54 2.1(± 50%) Triangular(1.05, 2.1, 3.15) Male, aged 55–59 1.9(± 50%) Triangular(0.95, 1.9, 2.85) Male, aged 60–64 1.7(± 50%) Triangular(0.85, 1.7, 2.55) Male, aged \\(\\:\\ge\\:\\) 65 1.4(± 50%) Triangular(0.7, 1.4, 2.1) Female, aged 50–54 5.5(± 50%) Triangular(2.75, 5.5, 8.25) Female, aged 55–59 5.1(± 50%) Triangular(2.55, 5.1, 7.65) Female, aged 60–64 4.5(± 50%) Triangular(2.25, 4.5, 6.75) Female, aged \\(\\:\\ge\\:\\) 65 3.8(± 50%) Triangular(1.9, 3.8, 5.7) Adherence to LDCT screening 54.6% (75% and 100% were assumed in scenario analysis) Diagnose rate through standard clinical care 18 Lung cancer stage I 2.46%(± 50%) Beta(14.96, 593.32) Lung cancer stage II 2.7%(± 50%) Beta(14.91, 537.48) Lung cancer stage III 51.8%(± 50%) Beta(6.89, 6.41) Lung cancer stage IV 65.8%(± 50%) Beta(4.60, 2.39) Cost (CNY) LDCT test cost 239.97(± 50%) Gamma(15.37, 0.06) 52 Biopsy diagnosis cost 1,202.9(± 50%) Gamma(15.37, 0.01) Treatment cost 25 Stage I 51,554.43(± 50%) Gamma(15.37, 0.000298) Stage II 80,568.43(± 50%) Gamma(15.37, 0.000191) Stage III 87,601.46(± 50%) Gamma(15.37, 0.000175) Stage IV 112,562.91(± 50%) Gamma(15.37,0.000136) Background medical treatment costs 5348.1(± 50%) Gamma(15.37, 0.003) 31 Utility Utility for general individuals, by sex and age 32 Male, aged 40–50 0.99(0.987–0.994) Beta(4,355.01, 43.99) Male, aged 51–60 0.984(0.980–0.988) Beta(3,872.04, 62.96) Male, aged 61–70 0.976(0.971–0.980) Beta(3,514.87, 86.43) Male, aged \\(\\:\\ge\\:\\) 71 0.947(0.936–0.958) Beta(1,514.71, 84.77) Female, aged 40–50 0.988(0.986–0.991) Beta(11,257.88, 136.74) Female, aged 51–60 0.982(0.979–0.986) Beta(7,713.61, 141.39) Female, aged 61–70 0.964(0.958–0.971) Beta(3,480.26, 129.97) Female, aged \\(\\:\\ge\\:\\) 71 0.936(0.926–0.946) Beta(2,153.09, 147.22) Utility of lung cancer by stage 33 – 35 Lung cancer stage I 0.85(0.78–0.89) Beta(136.78, 24.14) Lung cancer stage II 0.75(0.68–0.80) Beta(149.31, 49.77) Lung cancer stage III 0.69(0.56–0.79) Beta(42.18, 18.95) Lung cancer stage IV 0.69(0.38–0.70) Beta(21.46, 9.64) Disutility associated with a false-positive result 0.063(± 50%) for 3 months Beta(14.33, 213.21) 36 Discount rate 5% (0%-8%) - 53 Lung cancer incidence rate The lung cancer incidence rates among non-smokers with a FDR history of the disease ( \\(\\:{I}_{FDR}\\) ), stratified by age and sex, were estimated as \\(\\:{I}_{FDR}={I}_{N}\\times\\:{OR}_{FDR}\\) , where \\(\\:{I}_{N}\\) was the general non-smoking population's incidence rates, and \\(\\:{OR}_{FDR}\\) was FDR-related odds ratio (OR). Estimation of \\(\\:{\\varvec{I}}_{\\varvec{N}}\\) \\(\\:{I}_{N}\\:\\) was modeled as \\(\\:{{I}_{N}=I}_{G}/({RR}_{s\\_to\\_NS}\\times\\:\\text{P}+1-\\text{P})\\) , where \\(\\:{I}_{G}\\) being the overall Chinese incidence rate, from the Global Burden of Diseases (GBD) 2018 22 (Table S1 ). The variable \\(\\:{RR}_{s\\_to\\_NS}\\) denoted the smoker-to-non-smoker relative risk, with values of 2.41 (range, 2.18–2.65) for males and 2.42 (range, 2.11–2.77) for females 23 . The smoker proportion, \\(\\:P\\) , was determined from National Tobacco Use Surveillance Data between 2019 and 2020 24 (Table S2). Estimation of \\(\\:{\\varvec{O}\\varvec{R}}_{\\varvec{F}\\varvec{D}\\varvec{R}}\\) The association between a FDR history of lung cancer and lung cancer risk among non-smoking Chinese individuals varied significantly among studies. To achieve a precise assessment, we reviewed all related published studies and conducted a meta-analysis for eligible studies. We named the resulting estimate as \\(\\:{OR}_{FDR}\\) . The databases were searched from the inception of each database until June 2024 in Pubmed, Web of Science, China National Knowledge Infrastructure (CNKI) and Wanfang Data. We used the following keywords and Mesh terms in the search strategy: (‘family history’ OR ‘familial aggregation’) AND (‘lung cancer’ OR ‘lung carcinoma’ OR ‘lung neoplasm’ OR ‘lung adenocarcinoma’ OR ‘NSCLC’ OR ‘Lung Neo-plasms’ (MeSH) OR ‘Small Cell Lung Carcinoma’ (MeSH) OR ‘Carcinoma, Non-Small-Cell Lung’ (MeSH)) AND (‘China’ or ‘Chinese’) AND (‘non-smoker’ OR ‘non-smoking’ OR ‘never smoking’ OR ‘never smoker’ OR ‘who do not smoke’). For CNKI and Wanfang Data, these terms were translated into Chinese. The inclusion criteria focused on case-control or cohort studies examining the association between a FDR history of lung cancer and lung cancer risk in non-smokers, providing raw data such as odds ratios, hazard ratios, risk ratios, relative ratios, standardized incidence ratios, and their 95% confidence intervals (CIs), or sufficient data to calculate a crude odds ratio. Adjusted estimates were given precedence when both adjusted and unadjusted estimates were available. The exclusion criteria were conference proceedings, abstracts/summaries, case reports/series, reviews and repeated publications. The pooled summary estimates and 95% CIs of \\(\\:{OR}_{FDR}\\:\\) were analyzed in Stata Statistical software. Heterogeneity across studies was assessed using the I 2 statistic. A random-effects model was applied for studies with moderate heterogeneity ( I 2 > 50%); otherwise, a fixed-effects model was utilized. The data extracted from eligible studies were shown in Table S3, with the meta-analysis results featured in Fig. S2-S3. The pooled point estimates and 95% CIs served as base-case values and sensitivity analysis ranges for subsequent cost-effectiveness analysis. The final estimates were 1.88 (range, 1.01–3.49) for males and 2.27 (range, 1.77–2.91) for females. Natural background death rate The age- and sex-specific natural background death rates for non-smokers with a FDR history of lung cancer ( \\(\\:{D}_{FDR}\\) ) were assumed to mirror those of the general non-smoking population, estimated as \\(\\:{D}_{FDR}={D}_{G}-{D}_{s}\\) , with GBD 2018 data for all-cause mortality \\(\\:{D}_{G}\\) (Table S4), and smoking-attributed mortality \\(\\:{D}_{s}\\) (Table S5) 22 . Transition probabilities, stage-specific mortality and Performance of LDCT Transition probabilities and stage-specific mortality rates for lung cancer were derived from peer-reviewed studies 25 , 26 , and the sensitivity and specificity of LDCT screening were informed by a Chinese randomized controlled trial 27 . Overdiagnosis rates were sourced from the US National Lung Screening Trial 28 , and the additional radiation-induced cancer risk per LDCT screening was calculated based on an Italian LDCT screening trial 29 . Adherence, and Diagnose rate through standard clinical care The adherence rate to LDCT screening was set at 54.6%, reflecting data from Chinese National Lung Cancer Screening cohort 30 , which included 92,909 individuals with a FDR history of lung cancer. The likelihood of diagnosis through standard clinical care was extracted from the existing literature 18 . Cost and Utility Direct medical costs, including diagnosis, treatment, maintenance, and background medical treatment costs, were collected. Diagnosis-related costs included LDCT test and lung biopsy, as reported by the Cancer Screening Program in Urban China (CanSPUC). Stage-specific lung cancer treatment costs were derived from Chinese medical insurance bureaus, with maintenance costs estimated at 10% of the total treatment cost 27 . We postulated that individuals with undiagnosed lung cancer in the natural history model would seek background medical treatment for lung disease symptoms, with costs estimated from the national per capita health expenditure figures for 2022 as documented in the China Health Statistics Yearbook 31 . All costs in this study were adjusted to 2022 Chinese yuan (CNY) using the medical consumer price index. Health outcome was measured in terms of QALYs, with utility scores for healthy individuals derived from a survey of 10,056 Chinese adults 32 and lung cancer utility scores sourced from a meta-analysis 33 and epidemiological studies 34 , 35 . A disutility of 0.063 (range, 0-0.08) 36 , associated with false-positive LDCT results and lasting three months, was accounted for. Both cost estimates and QALYs were discounted at a rate of 5% (range, 0%-8%) 38 . For parameters with an unknown uncertainty range, the plausibility range was assumed to be 50% of the base value. The selection of the distribution for each parameter was informed by the characteristics of the parameters and the underlying data. Model validation Three steps were conducted to validate the model, including face validation, internal validation, and external validation. In face validation, the structure of this model had been well used, and found to be reliable, sensible and can be explained intuitively. In internal validation, two team members independently examined the model programming and calculation results, and gave a unanimous judgement. In external validation, two references were used to assess whether the model’s predictions match the observed results. First, the observed lung cancer incidence rate across the entire Chinese population served as a reference point, given the scarcity of data specific to non-smokers. We assumed a hypothetical cohort of individuals aged 50, entering the model without any screening interventions, and compared our projected sex-specific lung cancer incidence rates with those estimated in the GBD studies from 2019 to 2021 for the 50–54 age group. Our model indicated an increase in incidence rates with age, aligning well with the GBD estimates, as depicted in Fig. S4. Second, the model's projections on the distribution of lung cancer diagnoses by stage were compared against data from a comprehensive multi-center retrospective epidemiological survey conducted between 2005 and 2014 37 . Fig. S5 illustrated a strong correlation between the simulated and reported data, validating the model's external predictive accuracy. Data Analysis The model projected the expected costs and QALYs for each strategy, ranking them by QALYs gained. The incremental cost-effectiveness ratios (ICERs) were calculated for different screening strategies against no screening, and the strategy preceding it on the cost-effectiveness efficiency frontier. We identified the cost-effectiveness frontier to obtain the most cost-effective strategy. In alignment with the World Health Organization's guidance, we applied a willingness-to-pay (WTP) threshold of three times the per-capita gross domestic product (GDP) of China in 2022 (per-capita GDP, CNY 85,698) per QALY gained. Univariate sensitivity analysis was performed for the key parameters within their respective ranges to identify the main sensitive parameters. Probability sensitivity analyses based on 10,000 simulations were further conducted to determine the probability of each strategy being cost-effective compared with all other strategies. Screening adherence rate was reported to have a substantial influence on the ICERs 19 , 38 , 39 . Potential harms associated with LDCT screening, including disutility associated with false-positive results, radiation-induced lung cancer risk, and overdiagnosis rate, were challenging to quantify and lack precise evidence in China. We hence created scenario analyses as followings: (1) improving screening adherence rate to 75% and 100%; (2) disregarding false-positive disutility; (3) excluding radiation-induced lung cancer risk; (4) ruling out overdiagnosis; (5) disregarding all above potential harms. Subgroup analyses were performed to account for sex-specific differences in incidence rates and demographics. For robust results, we modeled a 100,000-person cohort, including both genders at age 50, and analyzed them separately. Results Base-Case Analysis Compared to no screening, all 16 screening strategies increased QALYs and costs, by 13 to 2,016 QALYs and CNY 13,133,000 to 293,562,000 for 100,000 individuals over a lifetime horizon; and one-off screening at age 50 (50_one-off) was not cost-effective at the given WTP threshold. The cost-effectiveness efficiency frontier included six screening strategies. Screening at age 50, regardless of intervals, did not find a place on the efficiency frontier. Annual screening starting at 55 (55_annual) maximized QALY gains but was dominated by biennial screening starting at 55 (55_biennial), which was preceding it on the efficiency frontier, with an ICER of CNY 483,260 per QALY gained. Furthermore, the 55_biennial strategy was more cost-effective than the subsequent strategy, 60_biennial strategy, with an ICER of CNY 124,230 per QALY gained. Consequently, the 55_biennial strategy emerged as the optimal approach. (Table 2 & Fig. 1 ). Table 2 Base-case cost-effectiveness results compared among different strategies in 100,000 individuals over the lifetime Strategy Cost (CNY, thousand) QALYs Incremental Cost (CNY, thousand) Incremental QALYs ICER (CNY/QALY) Vs No screening Vs the strategy preceding it on the efficiency frontier Vs No screening Vs the strategy preceding it on the efficiency frontier Vs No screening Vs the strategy preceding it on the efficiency frontier No screening 47,172 1,346,222 - - - - - - 50_one-off 74,180 1,346,235 27,008 27,008 13 13 Dominated Dominated 55_one-off 69,844 1,346,406 22,672 22,672 184 184 123,770 123,770 60_one-off 64,801 1,346,505 17,629 17,629 283 283 62,381 62,381 65_one-off a 60,305 1,346,508 13,133 13,133 286 286 45,934 45,934 65_triennial a 76,034 1,346,826 28,862 15,729 604 318 47,850 49,576 65_biennial 90,284 1,346,988 43,112 14,250 766 162 56,322 87,816 65_annual 133,424 1,347,261 86,252 57,390 1,039 435 83,024 131,721 60_triennial a 107,935 1,347,344 60,763 31,901 1,122 518 54,183 61,554 50_triennial 191,588 1,347,608 144,416 83,653 1,386 264 104,251 Dominated 55_triennial 139,067 1,347,617 91,895 31,132 1,395 273 65,904 114,062 60_biennial a 135,850 1,347,652 88,678 27,915 1,430 308 62,046 90,692 60_annual 206,314 1,347,891 159,142 70,464 1,669 239 95,397 Dominated 50_annual 429,412 1,347,963 382,240 293,562 1,741 311 219,584 Dominated 55_biennial a 175,816 1,347,973 128,644 39,966 1,751 321 73,471 124,230 50_biennial 252,412 1,347,976 205,240 76,596 1,754 3 117,031 Dominated 55_annual a 303,528 1,348,238 256,356 127,712 2,016 265 127,210 Dominated a: These strategies comprised the cost-effectiveness efficiency frontier Subgroup analysis showed similar patterns for both sexes, with 55_biennial being more cost-effective in males (Table S6-7 & Fig. S6). Sensitivity Analysis Univariate sensitivity analyses revealed that the results remained largely unchanged across parameter ranges (Fig. S7-9 in the Supplement). ICER thresholds under extreme parameter values were detailed in Table S8-13 in the Supplement. The \\(\\:{OR}_{FDR}\\) , and discount rate had a significant impact on the ICERs. Subgroup analysis demonstrated a consistency in the results. The 55_annual strategy was optimal without discounting, for both sexes. In addition, for males, the 60_biennial strategy was optimal if \\(\\:{OR}_{FDR}\\) was lower than 1.492, while 55_annual strategy would be optimal if it was 3.49. For females, the 55_biennial strategy maintained its optimal status across the full spectrum of \\(\\:{OR}_{FDR}\\) . Probabilistic sensitivity analysis indicated a 91.1% probability of 55_biennial being optimal at the WTP threshold. Below CNY 92,500 to 125,000 WTP, 60_biennial was optimal, followed by 60_triennial at lower thresholds (Fig. 2 ). Subgroup analysis showed 55_annual was optimal for males when the WTP was above CNY 380,000 (Fig. S10). Impact of improving screening adherence rate The impact of improving screening adherence rate on ICERs were shown as Fig. S11 and Fig. 3 . The results revealed that, at the given WTP threshold, except for 50_one-off and 50_annual, all screening strategies were cost-effective compared to no screening, under the assumption of 75% or 100% adherence. Higher adherence levels resulted in more QALYs gained. Probabilistic sensitivity analysis revealed that, at a 75% adherence rate, the 55_biennial strategy was most optimal at the given WTP threshold,, followed by the 55_triennial strategy; at a 100% adherence rate, the 55_ triennial strategy was most optimal, followed by the 55_biennial strategy. Subgroup analysis indicated that the 55_biennial strategy was most optimal for both males and females at a 75% adherence rate. However, at full adherence, the 55_triennial strategy emerged as the optimal strategy for females. (Fig. S12-13). Impact of disregarding potential harms from LDCT screening The impact of disregarding potential harms on ICERs were shown as Fig.S14 and Fig. 3 . The data demonstrated that more QALYs would be gained if we ignored the harms associated with LDCT screening. Notably, disregarding false-positive disutility, the 50_annual strategy maximized QALY gains, followed by the 55_annual strategy, which was identified as the most cost-effective strategy at the given WTP threshold. Conversely, when not accounting for the risk of radiation-induced lung cancer or overdiagnosis, the 55_biennial strategy, was deemed as the optimal approach under the same WTP threshold (Fig. 3 ). Subgroup analysis, as detailed in Fig. S15-16, revealed consistent patterns across different sex groups. Discussion Despite smoking being the primary lung cancer cause, diagnoses are rising among non-smokers in China and other Asian regions 9 – 11 . The present study targeted non-smokers with a FDR history of lung cancer, who were more likely to benefit from LDCT screening. The study indicated that, at a WTP threshold of three-times the 2022 per-capita GDP, LDCT screening is a cost-effective approach for non-smokers with a FDR history of lung cancer. However, this did not extend to the practice of one-off screening at the age of 50, due to the lower risk profile of this age group. The timing of starting screening is crucial, and biennial screening at age 55 was the most cost-effective strategy for both sexes. Compared to the recommendations for smokers, the optimal screening starting age and screening intervals were later and longer for non-smokers with an FDR history of lung cancer 6 – 8 . For example, the latest Chinese guideline, issued in 2021, advocated for annual LDCT screening for those who smoked at least 30 pack-years from the age of 50 8 . Our findings affirm that heavy smokers face a significantly higher risk of developing lung cancer when compared to non-smokers who have a family history of the disease among their FDRs 8 , 15 . This highlights the critical need for targeted screening initiatives that take into account the distinct risk profiles of non-smoking populations, ensuring that preventative measures are tailored to those who may be at elevated risk due to their genetic predisposition. The familial risk of lung cancer, quantified by FDR-related OR, was a key sensitivity parameter. This OR had been reported to vary with country, sex, and smoking status 40 . Our analysis, therefore, targeted Chinese non-smokers, stratified by sex. Existing literature had demonstrated that the OR was predominantly influenced by the number of affected FDRs 41 , 42 . For example, a case-control study in Anhui Province, China, reported an OR of 1.48 for individuals with one affected FDR, rising to 2.96 for those with two affected FDRs 42 . However, due to the limited data, our model did not initially account for this variation. Nevertheless, our sensitivity analysis provided valuable insights, revealing that for males, starting screening at 60 might be optimal with lower OR as 1.492, while higher OR favored annual screening from age 55. These findings show that personalizing screening based on family risk could be beneficial. More research is needed to clarify how many family members with lung cancer affects an individual's risk, helping to improve LDCT screening recommendations. The WTP threshold is pivotal in determining the most cost-effective strategy. Our research indicated that a WTP threshold between CNY 92,500 to 125,000, corresponding to 1.08 to 1.46 times the 2022 per-capita GDP, made biennial screening starting at age 60 the most optimal approach. Further lowering the WTP threshold suggested that triennial screening, beginning at either 60 or 65, could be more suitable. Thus, policy makers should weigh the cost and efficacy of screening strategies against the local economic context to select the most fitting approach. Consistent with previous studies 19 , 38 , 39 , our studies confirmed that higher adherence rates to LDCT screening were associated with greater gains in QALYs. With perfect adherence, triennial screening starting at 55 would be optimal, yielding a lower cost and a higher number of QALYs compared to biennial screening at the same age, which was the optimal strategy at a 75% adherence rate. This highlights the importance of raising public awareness of cancer screening. False-positive results impacted cost-effectiveness, without considering their disutility, more frequent screening became more cost-effective. Patients with indeterminate lung nodules experience anxiety specific to lung cancer 43 and distress while waiting for CT scan outcomes 44 . It's important to provide prompt result reporting and education to manage patient anxiety. Our subgroup analysis, aligning with a cohort study within the framework of CanSPUC, has identified that being male was a persistent risk factor for lung cancer among non-smokers 45 . In this analysis, female non-smokers might experience a slightly lower QALY gain due to a lower incidence rate. However, this difference was minimal, and screening remained cost-effective for both genders. For example, biennial screening starting at 55 could yield an additional 1,719 QALYs for females and 1,747 QALYs for males within a cohort of 100,000. This highlights the importance of gender-inclusive screening strategies in combating lung cancer. No existing studies have comprehensively evaluated the cost-effectiveness of LDCT for non-smokers in the context of lung cancer screening. A previous Chinese study did establish a risk-adapted starting age for LDCT screening 15 . This was done by taking into account a comprehensive set of risk factors, using a 10-year cumulative risk of lung cancer for heavy smokers as the threshold. The study identified that non-smokers with a FDR history of lung cancer should start annual screening at 53 for men and 55 for women. However, this study didn't consider the frequency of screening, long-term benefits, or potential risks, nor did it assess cost-effectiveness. A separate study conducted in Japan and the United States attempted to assess the cost-effectiveness of LDCT screening for non-smokers, but was constrained to a comparison of three screening strategies—LDCT, chest X-ray, and no screening—thus failing to establish the optimal starting age or the ideal screening frequency 46 . Our research, therefore, presents a timely and significant contribution to the optimization of lung cancer screening protocols in China and other nations grappling with comparable lung cancer burdens. By advocating for the inclusion of high-risk non-smokers in LDCT screening programs, we aim to bolster early detection rates and enhance overall survival prospects. This approach stands to enrich the public health arsenal, fostering a more inclusive and efficacious strategy in the battle against lung cancer. This study has several limitations. First, we had to estimate non-smoker lung cancer rates from overall rates, smoking proportions, and smoking-related risks, due to lack of direct data. Despite this systematic approach, this estimation might not fully capture non-smoker-specific nuances. Second, in our pursuit to craft universally applicable and easily executable guidance, we treated our modeled cohort as a single entity. This simplification led to a model that, while useful, oversimplified the complex realities of lung cancer progression in non-smokers. Notably, our analysis did not include lung cancer histology, a factor that could significantly alter parameters such as transition probabilities, stage-specific mortality rates, and the efficacy of LDCT screening. A 16-year evaluation of prospective cohort study conducted in China found, although significant differences in histology types were found between individuals who smoked and individuals who never smoked, the variation was slight with adenocarcinoma being the most prevalent in both groups, at 83.0% and 78.8%, respectively 47 . Consequently, without specific data on how histology affects these parameters, we relied on broader sources like the CanSPUC program and Chinese cohorts, which include both high-risk smokers and non-smokers. Additionally, due to limited data, we made the uniform assumption of cancer progression, survival rates post-diagnosis, and treatment costs across genders, which might not mirror the actual disparities. Despite these constraints, we meticulously validated our model and performed sensitivity analyses on uncertain parameters, adjusting them by ± 50% to bolster the robustness of our results. Third, we used a health-care system perspective and did not include broader economic impacts such as productivity loss or the quality of life of caregivers. Lastly, the study did not account for the increased risk of secondary cancers potentially linked to radiation exposure during screening 48 , nor did it address the implications of incidental findings that may arise from such screenings. Conclusions In conclusion, our study marks the first in-depth cost-effectiveness evaluation of LDCT screening for non-smokers with a FDR history of lung cancer. It concludes that biennial screening starting at age 55 is the most cost-effective strategy under a WTP threshold of three times the 2022 per capita GDP, for both sexes. The analysis identifies familial risk, WTP threshold, adherence rates and disutility associated with false-positive results as critical in shaping the optimal screening approach. By promoting the inclusion of high-risk non-smokers in screening programs, our research supports a more inclusive strategy for lung cancer prevention and control. Abbreviations CanSPUC cancer screening program started in urban China CHEERS Consolidated Health Economic Evaluation Reporting Standards CNY Chinese yuan FDR First-degree relative GBD Global Burden of Diseases GDP Gross domestic product ICER incremental cost-effectiveness ratio LDCT Low-dose computed tomography OR Odds Ratio QALY Quality-adjusted life year WTP Willingness-to-pay. Declarations Ethics approval and consent to participate Not applicable Funding information This work was supported by Henan Province Science and Technology Research Project (grant number 242102311158), Henan Province Medical Science and Technology Public Relations Plan Province Department joint construction project (SBGJ202403020). Competing interests The authors declare that they have no competing interests. Author’s contributions YL: Conceptualization, Methodology, Formal Analysis, Writing Original Draft; XLG, HFX, XYW, HWL, HW: Methodology, Data Curation, Validation, Review & Editing; RHK, QC, RRQ, MFZ, CC, LYZ, SZL: Validation, Review & Editing; YLQ: Project Administration, Review & Editing, Supervision; SKZ: Review & Editing, Supervision, Project Administration, Funding Acquisition; The work reported in the paper has been performed by the authors, unless clearly specified in the text. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. Global Cancer Observatory. International Agency for Research on Cancer. https://gco.iarc.fr/en . Accessed March 10, 2024. Fang Y, Li Z, Chen H, et al. Burden of lung cancer along with attributable risk factors in China from 1990 to 2019, and projections until 2030. J Cancer Res Clin Oncol. 2023;149(7):3209–18. Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395–409. de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med. 2020;382(6):503–13. Krist AH, Davidson KW, Mangione CM, et al. Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2021;325(10):962–70. Wood DE, Kazerooni EA, Aberle D, et al. NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022. J Natl Compr Canc Netw. 2022;20(7):754–64. He J, Li N, Chen WQ, et al. [China guideline for the screening and early detection of lung cancer(2021, Beijing)]. Zhonghua Zhong Liu Za Zhi. 2021;43(3):243–68. Bhopal A, Peake MD, Gilligan D, Cosford P. Lung cancer in never-smokers: a hidden disease. J R Soc Med. 2019;112(7):269–71. Toh CK, Ong WS, Lim WT, et al. A Decade of Never-smokers Among Lung Cancer Patients-Increasing Trend and Improved Survival. Clin Lung Cancer. 2018;19(5):e539–50. Shi JF, Wang L, Wu N, et al. Clinical characteristics and medical service utilization of lung cancer in China, 2005–2014: Overall design and results from a multicenter retrospective epidemiologic survey. Lung Cancer. 2019;128:91–100. Sands J, Tammemägi MC, Couraud S, et al. Lung Screening Benefits and Challenges: A Review of The Data and Outline for Implementation. J Thorac Oncol. 2021;16(1):37–53. Amicizia D, Piazza MF, Marchini F et al. Systematic Review of Lung Cancer Screening: Advancements and Strategies for Implementation. Healthcare (Basel). 2023;11(14). Lam S, Bai C, Baldwin DR, et al. Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer. J Thorac Oncol. 2024;19(1):36–51. Wang C, Dong X, Tan F, et al. Risk-Adapted Starting Age of Personalized Lung Cancer Screening: A Population-Based, Prospective Cohort Study in China. Chest. 2024;165(6):1538–54. Coté ML, Liu M, Bonassi S, et al. Increased risk of lung cancer in individuals with a family history of the disease: a pooled analysis from the International Lung Cancer Consortium. Eur J Cancer. 2012;48(13):1957–68. Husereau D, Drummond M, Petrou S, et al. Consolidated Health Economic Evaluation Reporting Standards (CHEERS)--explanation and elaboration: a report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force. Value Health. 2013;16(2):231–50. Hofer F, Kauczor HU, Stargardt T. Cost-utility analysis of a potential lung cancer screening program for a high-risk population in Germany: A modelling approach. Lung Cancer. 2018;124:189–98. Liu Y, Xu H, Lv L, et al. Risk-based lung cancer screening in heavy smokers: a benefit-harm and cost-effectiveness modeling study. BMC Med. 2024;22(1):73. Zhang T, Chen X, Li C et al. Cost-Effectiveness Analysis of Risk Factor-Based Lung Cancer Screening Program by Low-Dose Computer Tomography in Current Smokers in China. Cancers (Basel). 2023;15(18). China statistical yearbook. 2022. National Bureau of Statistics of China. https://data.stats.gov.cn/easyquery.htm?cn=C01 . Accessed Mach 10, 2024. GBD Results. Institute for Health Metrics and Evaluation. https://vizhub.healthdata.org/gbd-results/ . Accessed March 10, 2024. Chan KH, Wright N, Xiao D, et al. Tobacco smoking and risks of more than 470 diseases in China: a prospective cohort study. Lancet Public Health. 2022;7(12):e1014–26. Zhao QQ, Cong S, Fan J, et al. [Prevalence of smoking in adults aged 40 years and above in China, 2019–2020]. Zhonghua Liu Xing Bing Xue Za Zhi. 2023;44(5):735–42. Zhao Z, Wang Y, Wu W, Yang Y, Du L, Dong H. Cost-effectiveness of Low-Dose Computed Tomography With a Plasma-Based Biomarker for Lung Cancer Screening in China. JAMA Netw Open. 2022;5(5):e2213634. He Mei. LB-bDJZYZY-lWYLH-kZWWY-zZH. Clinical Characteristics and Survival of Lung Cancer Patients in Chongqing, 2001–2018. China Cancer 2020;29(11). Yang W, Qian F, Teng J, et al. Community-based lung cancer screening with low-dose CT in China: Results of the baseline screening. Lung Cancer. 2018;117:20–6. Lung Cancer Incidence. and Mortality with Extended Follow-up in the National Lung Screening Trial. J Thorac Oncol. 2019;14(10):1732–42. Rampinelli C, De Marco P, Origgi D, et al. Exposure to low dose computed tomography for lung cancer screening and risk of cancer: secondary analysis of trial data and risk-benefit analysis. BMJ. 2017;356:j347. Cao W, Tan F, Liu K, et al. Uptake of lung cancer screening with low-dose computed tomography in China: A multi-centre population-based study. EClinicalMedicine. 2022;52:101594. Commission CNH. China Health Statistics Yearbook 2022. Published 2023. Accessed. Hu W, Zhou L, Chu J, et al. Estimating population norms for the health-related quality of life of adults in southern Jiangsu Province, China. Sci Rep. 2022;12(1):9906. Blom EF, Haaf KT, de Koning HJ. Systematic Review and Meta-Analysis of Community- and Choice-Based Health State Utility Values for Lung Cancer. PharmacoEconomics. 2020;38(11):1187–200. Zeng X, Sui M, Liu B, et al. Measurement Properties of the EQ-5D-5L and EQ-5D-3L in Six Commonly Diagnosed Cancers. Patient. 2021;14(2):209–22. Zhu J, Yan XX, Liu CC, et al. Comparing EQ-5D-3L and EQ-5D-5L performance in common cancers: suggestions for instrument choosing. Qual Life Res. 2021;30(3):841–54. Mazzone PJ, Obuchowski N, Fu AZ, Phillips M, Meziane M. Quality of life and healthcare use in a randomized controlled lung cancer screening study. Ann Am Thorac Soc. 2013;10(4):324–9. Lo YL, Hsiao CF, Chang GC, et al. Risk factors for primary lung cancer among never smokers by gender in a matched case-control study. Cancer Causes Control. 2013;24(3):567–76. Han SS, Erdogan SA, Toumazis I, Leung A, Plevritis SK. Evaluating the impact of varied compliance to lung cancer screening recommendations using a microsimulation model. Cancer Causes Control. 2017;28(9):947–58. Tomonaga Y, de Nijs K, Bucher HC, de Koning H, Ten Haaf K. Cost-effectiveness of risk-based low-dose computed tomography screening for lung cancer in Switzerland. Int J Cancer. 2023. Ang L, Chan CPY, Yau WP, Seow WJ. Association between family history of lung cancer and lung cancer risk: a systematic review and meta-analysis. Lung Cancer. 2020;148:129–37. Cannon-Albright LA, Carr SR, Akerley W. Population-Based Relative Risks for Lung Cancer Based on Complete Family History of Lung Cancer. J Thorac Oncol. 2019;14(7):1184–91. Jin Y, Xu Y, Xu M, Xue S. Increased risk of cancer among relatives of patients with lung cancer in China. BMC Cancer. 2005;5:146. van den Bergh KA, Essink-Bot ML, Borsboom GJ, et al. Short-term health-related quality of life consequences in a lung cancer CT screening trial (NELSON). Br J Cancer. 2010;102(1):27–34. van den Bergh KA, Essink-Bot ML, Bunge EM, et al. Impact of computed tomography screening for lung cancer on participants in a randomized controlled trial (NELSON trial). Cancer. 2008;113(2):396–404. Guo LW, Lyu ZY, Meng QC, et al. Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China. Front Oncol. 2021;11:766939. Kowada A. Cost-effectiveness and health impact of lung cancer screening with low-dose computed tomography for never smokers in Japan and the United States: a modelling study. BMC Pulm Med. 2022;22(1):19. Tang Y, Zhao S, Zhou L, et al. A 16-year evaluation of opportunistic lung cancer screening with low-dose CT in China: comparative findings between non-smokers and smokers. BMC Cancer. 2024;24(1):1322. Adams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. Lancet. 2023;401(10374):390–408. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.pdf Cite Share Download PDF Status: Published Journal Publication published 15 May, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 07 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviews received at journal 28 Mar, 2025 Reviewers agreed at journal 21 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers invited by journal 19 Mar, 2025 Editor invited by journal 19 Mar, 2025 Editor assigned by journal 19 Mar, 2025 Submission checks completed at journal 19 Mar, 2025 First submitted to journal 17 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6244154\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":433479552,\"identity\":\"5149a816-45fb-4ae6-96da-47f0ea7b4640\",\"order_by\":0,\"name\":\"Yin Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yin\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":433479553,\"identity\":\"e74f6484-60fa-45e1-9899-a25e28478a7c\",\"order_by\":1,\"name\":\"Xiaoli Guo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaoli\",\"middleName\":\"\",\"lastName\":\"Guo\",\"suffix\":\"\"},{\"id\":433479554,\"identity\":\"f3be468e-aaa6-4f56-aa6c-4ac96e935776\",\"order_by\":2,\"name\":\"Huifang Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Huifang\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":433479557,\"identity\":\"6e05b222-72da-443c-b807-90ed11f3a37e\",\"order_by\":3,\"name\":\"Xiaoyang Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaoyang\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":433479558,\"identity\":\"dfcc3610-8d05-4d8c-98be-b4016e898f59\",\"order_by\":4,\"name\":\"Hongwei Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hongwei\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":433479560,\"identity\":\"25a67449-72ee-412d-b318-1b1619d7919b\",\"order_by\":5,\"name\":\"Hong Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hong\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":433479562,\"identity\":\"0cd3ff88-c563-4a6e-8de7-320f38324673\",\"order_by\":6,\"name\":\"Ruihua Kang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ruihua\",\"middleName\":\"\",\"lastName\":\"Kang\",\"suffix\":\"\"},{\"id\":433479564,\"identity\":\"74e10330-17ce-4e08-863c-0a10ab4cf39f\",\"order_by\":7,\"name\":\"Qiong Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qiong\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":433479565,\"identity\":\"dea56697-bc48-4afd-9285-48dbd8f923c4\",\"order_by\":8,\"name\":\"Ranran Qie\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ranran\",\"middleName\":\"\",\"lastName\":\"Qie\",\"suffix\":\"\"},{\"id\":433479566,\"identity\":\"db24468c-1bb7-4343-955a-0d8a23dd7a5c\",\"order_by\":9,\"name\":\"Mengfei Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mengfei\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":433479567,\"identity\":\"319f4c0c-28e3-4020-a733-e738d9dd678f\",\"order_by\":10,\"name\":\"Cheng Cheng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Cheng\",\"middleName\":\"\",\"lastName\":\"Cheng\",\"suffix\":\"\"},{\"id\":433479568,\"identity\":\"56584b86-0936-46f4-be30-a6104b4df1eb\",\"order_by\":11,\"name\":\"Liyang Zheng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Liyang\",\"middleName\":\"\",\"lastName\":\"Zheng\",\"suffix\":\"\"},{\"id\":433479569,\"identity\":\"ca106a5b-2b7b-4559-ac1f-c147025e6a32\",\"order_by\":12,\"name\":\"Shuzheng Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shuzheng\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":433479570,\"identity\":\"224051b7-1216-4c94-8c14-a0e39b0edf16\",\"order_by\":13,\"name\":\"Youlin Qiao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Youlin\",\"middleName\":\"\",\"lastName\":\"Qiao\",\"suffix\":\"\"},{\"id\":433479572,\"identity\":\"4774e8be-94d2-4608-ad25-c3c22f2681ee\",\"order_by\":14,\"name\":\"Shaokai Zhang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3PsQrCMBCA4SuFdAm4Xhd9hZNArCj4KnWpSxDBVUQQnIpzxZfQNxCK3cS1g4PFUYdO4iBqdXJqdRPMNxwE7ocLgKb9IgQwRtmk5yOlZvm7xAh6nvg0gVdi8jRsFxaV+ThJgoHTqlmb8Ngk0wUrXC/yEtpForqI0Kz7Xa+hiHWBe16cm6Ar7T1DRislhSLeB+QyN6kEnbO9vyGn7UkKh7A9KkogVtJeThApVuIARMVJttmvzqZIFJ+k4ZMrWNFfssOWiX8etmirRHq53sslK4zyD3vD8DU/XX8y02+2NU3T/scD/5dIZOf63WkAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"The Affiliated Cancer Hospital of Zhengzhou University \\u0026 Henan Cancer Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Shaokai\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-03-17 11:38:17\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6244154/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6244154/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12889-025-22977-w\",\"type\":\"published\",\"date\":\"2025-05-15T15:56:52+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":79575410,\"identity\":\"40770d4b-c6ed-4bed-8885-6640f065b506\",\"added_by\":\"auto\",\"created_at\":\"2025-03-31 11:14:37\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":34029,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCost-effectiveness frontiers for all 16 screening strategies. Incremental QALYs and incremental costs of intervention strategies are obtained for 100,000 individuals.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"OnlineFigure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6244154/v1/0e11dea395b6c6452bf0695c.png\"},{\"id\":79575411,\"identity\":\"fa62823c-ef35-417e-9ac9-ebc2cc357a86\",\"added_by\":\"auto\",\"created_at\":\"2025-03-31 11:14:37\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":25237,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCost-effectiveness acceptability curves for all strategies. Intervention strategies that never have the highest probability of being cost-effective within the willingness-to-pay threshold of three-times per-capita GDP are represented in grey. QALY, quality-adjusted life years.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"OnlineFigure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6244154/v1/0b1889f76a3432b8a7d31f1f.png\"},{\"id\":79575412,\"identity\":\"b0375718-d33d-429e-9881-e0df6de7555f\",\"added_by\":\"auto\",\"created_at\":\"2025-03-31 11:14:37\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":181071,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCost-effectiveness acceptability curves for all strategies at higher screening adherence rate or when disregarding potential LDCT-harms. Intervention strategies that never have the highest probability of being cost-effective within the willingness-to-pay threshold of three-times per-capita GDP are represented in grey. QALY, quality-adjusted life years.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"OnlineFigure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6244154/v1/76fc4c1663a6ee16ee8bb47d.png\"},{\"id\":83067643,\"identity\":\"91af1a4e-b4b7-4025-aab7-6d3de954a170\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 15:59:46\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1641060,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6244154/v1/54c428e9-da83-43a0-89e4-277517e951e4.pdf\"},{\"id\":79575421,\"identity\":\"34907b20-f4c2-42ef-a1ff-42c3809cf89a\",\"added_by\":\"auto\",\"created_at\":\"2025-03-31 11:14:38\",\"extension\":\"pdf\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":3763856,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Additionalfile1.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6244154/v1/1819ef2729ec0526eca00136.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Cost-effectiveness of Low-Dose CT Screening for Non-smokers with a First-Degree Relative History of Lung Cancer\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eLung cancer continues to be a leading cause of the global cancer burden, with an alarming 12.4% of all new cancer cases and 18.7% of cancer-related deaths attributed to it in 2022\\u003csup\\u003e1\\u003c/sup\\u003e. In China, the situation is particularly dire, where lung cancer claims the\\u003c/p\\u003e \\u003cp\\u003eprimary cause of cancer mortality, with an estimated 733,291 deaths in 2022, accounting for 40.3% of the global total\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e. The burden of lung cancer in China is projected to increase mainly due to an aging population and population growth\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. There is an urgent need for collective action to reduce this burden.\\u003c/p\\u003e \\u003cp\\u003eLung cancer screening based on low-dose CT (LDCT) has demonstrated significant efficacy in early detection and in reducing the mortality rate\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e. However, existing lung cancer screening guidelines focus solely on age and smoking history, potentially excluding at-risk non-smokers and overlooking a significant number of lung cancer cases\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. It has been estimated that lung cancer in non-smokers is the seventh leading cause of cancer-related deaths worldwide, with a rising trend\\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e, particularly in Asian countries like China, where over 40% of lung cancer cases occur in non-smokers\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. The current smoking-focused screening strategy may not adequately address the distinct risk profiles and demographic characteristics of non-smokers. Consequently, there is a compelling need to develop a targeted LDCT screening strategy specifically for non-smokers.\\u003c/p\\u003e \\u003cp\\u003eThe long-term cost-effectiveness of LDCT screening must be carefully evaluated in the development of a screening strategy. While the benefits of LDCT screening in reducing lung cancer mortality, the harms of false positives, overdiagnosis, and radiation-induced cancer incidence cannot be ignored\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. A targeted approach for high-risk populations can more effectively balance these risks with the benefits, thereby enhancing cost-effectiveness\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e. For non-smokers, a first-degree relative (FDR) history of lung cancer is a significant risk factor, highlighting the importance of screening strategies that consider genetic and environmental influences\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e. Nevertheless, the cost-effectiveness of LDCT screening for non-smokers with a FDR history of lung cancer remains largely unexplored.\\u003c/p\\u003e \\u003cp\\u003eIn response to this gap, we conducted this study to evaluate the cost-effectiveness of LDCT screening for non-smokers with a FDR history of lung cancer in China. Our analysis aimed to determine the optimal starting age and screening intervals for this specific high-risk group, thereby informing a more strategic and cost-effective approach to early lung cancer detection.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e We conducted a model-based economic evaluation to assess the cost-effectiveness of LDCT screening for non-smokers with a FDR history of lung cancer in China, from a health-care system perspective. The FDR was defined as one\\u0026rsquo;s parents, siblings or offsprings. The non-smokers were defined as those who have never smoked. The model was constructed using TreeAge Pro 2022 software and the analysis adhered to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement\\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMarkov model\\u003c/h2\\u003e \\u003cp\\u003eA state-transition Markov model was developed to simulate lung cancer progression and evaluate the costs and quality-adjusted life years (QALYs) outcomes in a lifetime horizon (until death or 78 years old, the expected life years). The model initialized with a hypothetical cohort of 100,000 individuals aged 50 years old. It was assumed that this cohort was initially lung cancer-free but had a FDR history of lung cancer at the baseline.\\u003c/p\\u003e \\u003cp\\u003eThe Markov model encapsulated both the natural history and the post-diagnosis progression of lung cancer, with a detailed description available in prior literatures\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e. In summary, the natural history consisted of six states: healthy, lung cancer stages I through IV, and death. We assumed that individuals in the healthy state would develop to lung cancer stage I at the age- and sex-specific incidence rates. Once lung cancer is developed, patients may experience progression to more advanced stages, be diagnosed through LDCT screening or standard clinical care upon symptom onset, maintain their current stage, or die. The post-diagnosis component of the model comprised five states: post-treatment lung cancer stages I through IV, and death. It was presumed that patients underwent immediate treatment upon diagnosis. Additionally, we have incorporated age- and sex-specific natural background death rates and acknowledged that individuals with lung cancer face stage-specific mortality from this disease in addition to a natural background death rate. A model cycle-length of 1-year, with half-cycle correction was assumed. Details of the Markov model was depicted in Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eScreening Strategies\\u003c/h3\\u003e\\n\\u003cp\\u003eWe evaluated 16 LDCT screening strategies, defined by varying starting ages (50, 55, 60, or 65 years) and screening intervals (one-off, annual, biennial, or triennial). No screening approach served as the reference strategy. One-off screening was assumed to occur at each starting age. The age limits were set at 50 and 74 years, respectively, conform with Chinese screening guideline for heavy smokers, balancing life expectancy with the feasibility of screening implementation\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003ch3\\u003eModel Input Parameters\\u003c/h3\\u003e\\n\\u003cp\\u003eThe key model input parameters were shown as Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, aligning the gender distribution with China's demographic profiles as of 2022\\u003csup\\u003e21\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eKey input parameters of Markov model for lung cancer screening\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParameters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBase case (range)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDistribution\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSmoker-to-non-smoker relative risk of lung cancer (\\u003c/b\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\varvec{R}\\\\varvec{R}}_{\\\\varvec{s}\\\\_\\\\varvec{t}\\\\varvec{o}\\\\_\\\\varvec{N}\\\\varvec{S}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.41(2.18\\u0026ndash;2.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(2.18, 2.41, 2.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.42(2.11\\u0026ndash;2.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(2.11, 2.42, 2.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFDR-related odds ratio of lung cancer incidence (\\u003c/b\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\varvec{O}\\\\varvec{R}}_{\\\\varvec{F}\\\\varvec{D}\\\\varvec{R}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMeta-analysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.88(1.01\\u0026ndash;3.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(1.01, 1.88, 3.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.27(1.77\\u0026ndash;2.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(1.77, 2.27, 2.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTransition Probabilities\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"6\\\" rowspan=\\\"7\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage I to stage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.3682 (\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(9.33, 16.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage I to stage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0328(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(14.83, 437.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage I to stage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0745(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(14.15, 175.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage II to stage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.2260(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(11.67, 39.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage II to stage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.1510(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(12.89, 72.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage III to stage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.1455(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(12.98, 76.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMortality\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage I to LC death\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.1739(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(12.52, 59.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage II to LC death\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.2942(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(10.55, 25.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage III to LC death\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.4626(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(7.79, 9.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage IV to LC death\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.5880(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(5.74, 4.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAfter care I to death\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.089(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(13.91, 142.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAfter care II to death\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.153(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(12.86, 71.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAfter care III to death\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.288(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(10.65, 26.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAfter care IV to death\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.353(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(9.59, 17.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePerformance of LDCT\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSensitivity of LDCT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.981(0.884\\u0026ndash;0.999)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular (0.884, 0.981, 0.999)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSpecificity of LDCT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.782(0.768\\u0026ndash;0.796)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(2,643.78, 737.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOverdiagnosis rate when screening\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.031(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(14.86, 464.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eThe additional risk of LC per screening (1/100,000)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale, aged 50\\u0026ndash;54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.1(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(1.05, 2.1, 3.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale, aged 55\\u0026ndash;59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.9(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(0.95, 1.9, 2.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale, aged 60\\u0026ndash;64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.7(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(0.85, 1.7, 2.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale, aged \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\ge\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.4(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(0.7, 1.4, 2.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale, aged 50\\u0026ndash;54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.5(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(2.75, 5.5, 8.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale, aged 55\\u0026ndash;59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.1(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(2.55, 5.1, 7.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale, aged 60\\u0026ndash;64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.5(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(2.25, 4.5, 6.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale, aged \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\ge\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.8(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTriangular(1.9, 3.8, 5.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAdherence to LDCT screening\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e54.6% (75% and 100% were assumed in scenario analysis)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDiagnose rate through standard clinical care\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.46%(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(14.96, 593.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.7%(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(14.91, 537.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e51.8%(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(6.89, 6.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e65.8%(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(4.60, 2.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCost (CNY)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDCT test cost\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e239.97(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGamma(15.37, 0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e52\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBiopsy diagnosis cost\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1,202.9(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGamma(15.37, 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTreatment cost\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e51,554.43(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGamma(15.37, 0.000298)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e80,568.43(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGamma(15.37, 0.000191)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e87,601.46(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGamma(15.37, 0.000175)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e112,562.91(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGamma(15.37,0.000136)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBackground medical treatment costs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5348.1(\\u0026plusmn;\\u0026thinsp;50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGamma(15.37, 0.003)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eUtility\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eUtility for general individuals, by sex and age\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"8\\\" rowspan=\\\"9\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale, aged 40\\u0026ndash;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.99(0.987\\u0026ndash;0.994)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(4,355.01, 43.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale, aged 51\\u0026ndash;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.984(0.980\\u0026ndash;0.988)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(3,872.04, 62.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale, aged 61\\u0026ndash;70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.976(0.971\\u0026ndash;0.980)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(3,514.87, 86.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale, aged \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\ge\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.947(0.936\\u0026ndash;0.958)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(1,514.71, 84.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale, aged 40\\u0026ndash;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.988(0.986\\u0026ndash;0.991)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(11,257.88, 136.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale, aged 51\\u0026ndash;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.982(0.979\\u0026ndash;0.986)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(7,713.61, 141.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale, aged 61\\u0026ndash;70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.964(0.958\\u0026ndash;0.971)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(3,480.26, 129.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale, aged \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\ge\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.936(0.926\\u0026ndash;0.946)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(2,153.09, 147.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUtility of lung cancer by stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR34\\\" citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.85(0.78\\u0026ndash;0.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(136.78, 24.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.75(0.68\\u0026ndash;0.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(149.31, 49.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.69(0.56\\u0026ndash;0.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(42.18, 18.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLung cancer stage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.69(0.38\\u0026ndash;0.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(21.46, 9.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDisutility associated with a false-positive result\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.063(\\u0026plusmn;\\u0026thinsp;50%) for 3 months\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBeta(14.33, 213.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDiscount rate\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5% (0%-8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e53\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eLung cancer incidence rate\\u003c/h3\\u003e\\n\\u003cp\\u003eThe lung cancer incidence rates among non-smokers with a FDR history of the disease (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{I}_{FDR}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), stratified by age and sex, were estimated as \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{I}_{FDR}={I}_{N}\\\\times\\\\:{OR}_{FDR}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, where \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{I}_{N}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e was the general non-smoking population's incidence rates, and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{OR}_{FDR}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e was FDR-related odds ratio (OR).\\u003c/p\\u003e\\n\\u003cdiv class=\\\"Heading\\\"\\u003eEstimation of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\varvec{I}}_{\\\\varvec{N}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/div\\u003e \\u003cp\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e \\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{I}_{N}\\\\:\\\\)\\u003c/span\\u003e \\u003c/span\\u003e was modeled as \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{{I}_{N}=I}_{G}/({RR}_{s\\\\_to\\\\_NS}\\\\times\\\\:\\\\text{P}+1-\\\\text{P})\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, where \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{I}_{G}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e being the overall Chinese incidence rate, from the Global Burden of Diseases (GBD) 2018\\u003csup\\u003e22\\u003c/sup\\u003e (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). The variable \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{RR}_{s\\\\_to\\\\_NS}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denoted the smoker-to-non-smoker relative risk, with values of 2.41 (range, 2.18\\u0026ndash;2.65) for males and 2.42 (range, 2.11\\u0026ndash;2.77) for females\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e. The smoker proportion, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:P\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, was determined from National Tobacco Use Surveillance Data between 2019 and 2020\\u003csup\\u003e24\\u003c/sup\\u003e (Table S2).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEstimation of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\varvec{O}\\\\varvec{R}}_{\\\\varvec{F}\\\\varvec{D}\\\\varvec{R}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/h2\\u003e \\u003cp\\u003eThe association between a FDR history of lung cancer and lung cancer risk among non-smoking Chinese individuals varied significantly among studies. To achieve a precise assessment, we reviewed all related published studies and conducted a meta-analysis for eligible studies. We named the resulting estimate as \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{OR}_{FDR}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe databases were searched from the inception of each database until June 2024 in Pubmed, Web of Science, China National Knowledge Infrastructure (CNKI) and Wanfang Data. We used the following keywords and Mesh terms in the search strategy: (\\u0026lsquo;family history\\u0026rsquo; OR \\u0026lsquo;familial aggregation\\u0026rsquo;) AND (\\u0026lsquo;lung cancer\\u0026rsquo; OR \\u0026lsquo;lung carcinoma\\u0026rsquo; OR \\u0026lsquo;lung neoplasm\\u0026rsquo; OR \\u0026lsquo;lung adenocarcinoma\\u0026rsquo; OR \\u0026lsquo;NSCLC\\u0026rsquo; OR \\u0026lsquo;Lung Neo-plasms\\u0026rsquo; (MeSH) OR \\u0026lsquo;Small Cell Lung Carcinoma\\u0026rsquo; (MeSH) OR \\u0026lsquo;Carcinoma, Non-Small-Cell Lung\\u0026rsquo; (MeSH)) AND (\\u0026lsquo;China\\u0026rsquo; or \\u0026lsquo;Chinese\\u0026rsquo;) AND (\\u0026lsquo;non-smoker\\u0026rsquo; OR \\u0026lsquo;non-smoking\\u0026rsquo; OR \\u0026lsquo;never smoking\\u0026rsquo; OR \\u0026lsquo;never smoker\\u0026rsquo; OR \\u0026lsquo;who do not smoke\\u0026rsquo;). For CNKI and Wanfang Data, these terms were translated into Chinese. The inclusion criteria focused on case-control or cohort studies examining the association between a FDR history of lung cancer and lung cancer risk in non-smokers, providing raw data such as odds ratios, hazard ratios, risk ratios, relative ratios, standardized incidence ratios, and their 95% confidence intervals (CIs), or sufficient data to calculate a crude odds ratio. Adjusted estimates were given precedence when both adjusted and unadjusted estimates were available. The exclusion criteria were conference proceedings, abstracts/summaries, case reports/series, reviews and repeated publications. The pooled summary estimates and 95% CIs of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{OR}_{FDR}\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003ewere analyzed in Stata Statistical software. Heterogeneity across studies was assessed using the \\u003cem\\u003eI\\u003c/em\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e statistic. A random-effects model was applied for studies with moderate heterogeneity (\\u003cem\\u003eI\\u003c/em\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;50%); otherwise, a fixed-effects model was utilized. The data extracted from eligible studies were shown in Table S3, with the meta-analysis results featured in Fig. S2-S3. The pooled point estimates and 95% CIs served as base-case values and sensitivity analysis ranges for subsequent cost-effectiveness analysis. The final estimates were 1.88 (range, 1.01\\u0026ndash;3.49) for males and 2.27 (range, 1.77\\u0026ndash;2.91) for females.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eNatural background death rate\\u003c/h3\\u003e\\n\\u003cp\\u003eThe age- and sex-specific natural background death rates for non-smokers with a FDR history of lung cancer (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{D}_{FDR}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) were assumed to mirror those of the general non-smoking population, estimated as \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{D}_{FDR}={D}_{G}-{D}_{s}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, with GBD 2018 data for all-cause mortality \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{D}_{G}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e (Table S4), and smoking-attributed mortality \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{D}_{s}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e (Table S5)\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003ch3\\u003eTransition probabilities, stage-specific mortality and Performance of LDCT\\u003c/h3\\u003e\\n\\u003cp\\u003eTransition probabilities and stage-specific mortality rates for lung cancer were derived from peer-reviewed studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e, and the sensitivity and specificity of LDCT screening were informed by a Chinese randomized controlled trial\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e. Overdiagnosis rates were sourced from the US National Lung Screening Trial\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e, and the additional radiation-induced cancer risk per LDCT screening was calculated based on an Italian LDCT screening trial\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAdherence, and Diagnose rate through standard clinical care\\u003c/h2\\u003e \\u003cp\\u003eThe adherence rate to LDCT screening was set at 54.6%, reflecting data from Chinese National Lung Cancer Screening cohort\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e, which included 92,909 individuals with a FDR history of lung cancer. The likelihood of diagnosis through standard clinical care was extracted from the existing literature\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCost and Utility\\u003c/h2\\u003e \\u003cp\\u003eDirect medical costs, including diagnosis, treatment, maintenance, and background medical treatment costs, were collected. Diagnosis-related costs included LDCT test and lung biopsy, as reported by the Cancer Screening Program in Urban China (CanSPUC). Stage-specific lung cancer treatment costs were derived from Chinese medical insurance bureaus, with maintenance costs estimated at 10% of the total treatment cost\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e. We postulated that individuals with undiagnosed lung cancer in the natural history model would seek background medical treatment for lung disease symptoms, with costs estimated from the national per capita health expenditure figures for 2022 as documented in the China Health Statistics Yearbook\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e. All costs in this study were adjusted to 2022 Chinese yuan (CNY) using the medical consumer price index.\\u003c/p\\u003e \\u003cp\\u003eHealth outcome was measured in terms of QALYs, with utility scores for healthy individuals derived from a survey of 10,056 Chinese adults\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e and lung cancer utility scores sourced from a meta-analysis\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e and epidemiological studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e. A disutility of 0.063 (range, 0-0.08)\\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e, associated with false-positive LDCT results and lasting three months, was accounted for. Both cost estimates and QALYs were discounted at a rate of 5% (range, 0%-8%)\\u003csup\\u003e38\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eFor parameters with an unknown uncertainty range, the plausibility range was assumed to be 50% of the base value. The selection of the distribution for each parameter was informed by the characteristics of the parameters and the underlying data.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eModel validation\\u003c/h2\\u003e \\u003cp\\u003eThree steps were conducted to validate the model, including face validation, internal validation, and external validation.\\u003c/p\\u003e \\u003cp\\u003eIn face validation, the structure of this model had been well used, and found to be reliable, sensible and can be explained intuitively.\\u003c/p\\u003e \\u003cp\\u003eIn internal validation, two team members independently examined the model programming and calculation results, and gave a unanimous judgement.\\u003c/p\\u003e \\u003cp\\u003eIn external validation, two references were used to assess whether the model\\u0026rsquo;s predictions match the observed results. First, the observed lung cancer incidence rate across the entire Chinese population served as a reference point, given the scarcity of data specific to non-smokers. We assumed a hypothetical cohort of individuals aged 50, entering the model without any screening interventions, and compared our projected sex-specific lung cancer incidence rates with those estimated in the GBD studies from 2019 to 2021 for the 50\\u0026ndash;54 age group. Our model indicated an increase in incidence rates with age, aligning well with the GBD estimates, as depicted in Fig. S4. Second, the model's projections on the distribution of lung cancer diagnoses by stage were compared against data from a comprehensive multi-center retrospective epidemiological survey conducted between 2005 and 2014\\u003csup\\u003e37\\u003c/sup\\u003e. Fig. S5 illustrated a strong correlation between the simulated and reported data, validating the model's external predictive accuracy.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe model projected the expected costs and QALYs for each strategy, ranking them by QALYs gained. The incremental cost-effectiveness ratios (ICERs) were calculated for different screening strategies against no screening, and the strategy preceding it on the cost-effectiveness efficiency frontier. We identified the cost-effectiveness frontier to obtain the most cost-effective strategy. In alignment with the World Health Organization's guidance, we applied a willingness-to-pay (WTP) threshold of three times the per-capita gross domestic product (GDP) of China in 2022 (per-capita GDP, CNY 85,698) per QALY gained.\\u003c/p\\u003e \\u003cp\\u003eUnivariate sensitivity analysis was performed for the key parameters within their respective ranges to identify the main sensitive parameters. Probability sensitivity analyses based on 10,000 simulations were further conducted to determine the probability of each strategy being cost-effective compared with all other strategies.\\u003c/p\\u003e \\u003cp\\u003eScreening adherence rate was reported to have a substantial influence on the ICERs\\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u003c/sup\\u003e. Potential harms associated with LDCT screening, including disutility associated with false-positive results, radiation-induced lung cancer risk, and overdiagnosis rate, were challenging to quantify and lack precise evidence in China. We hence created scenario analyses as followings: (1) improving screening adherence rate to 75% and 100%; (2) disregarding false-positive disutility; (3) excluding radiation-induced lung cancer risk; (4) ruling out overdiagnosis; (5) disregarding all above potential harms.\\u003c/p\\u003e \\u003cp\\u003eSubgroup analyses were performed to account for sex-specific differences in incidence rates and demographics. For robust results, we modeled a 100,000-person cohort, including both genders at age 50, and analyzed them separately.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBase-Case Analysis\\u003c/h2\\u003e \\u003cp\\u003eCompared to no screening, all 16 screening strategies increased QALYs and costs, by 13 to 2,016 QALYs and CNY 13,133,000 to 293,562,000 for 100,000 individuals over a lifetime horizon; and one-off screening at age 50 (50_one-off) was not cost-effective at the given WTP threshold. The cost-effectiveness efficiency frontier included six screening strategies. Screening at age 50, regardless of intervals, did not find a place on the efficiency frontier. Annual screening starting at 55 (55_annual) maximized QALY gains but was dominated by biennial screening starting at 55 (55_biennial), which was preceding it on the efficiency frontier, with an ICER of CNY 483,260 per QALY gained. Furthermore, the 55_biennial strategy was more cost-effective than the subsequent strategy, 60_biennial strategy, with an ICER of CNY 124,230 per QALY gained. Consequently, the 55_biennial strategy emerged as the optimal approach. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e \\u0026amp; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBase-case cost-effectiveness results compared among different strategies in 100,000 individuals over the lifetime\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"11\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eStrategy\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCost (CNY, thousand)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eQALYs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eIncremental Cost (CNY, thousand)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eIncremental QALYs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003eICER (CNY/QALY)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eVs No screening\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVs the strategy preceding it on the efficiency frontier\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eVs No screening\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eVs the strategy preceding it on the efficiency frontier\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eVs No screening\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eVs the strategy preceding it on the efficiency frontier\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo screening\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e47,172\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,346,222\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50_one-off\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74,180\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,346,235\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e27,008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e27,008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eDominated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eDominated\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e55_one-off\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e69,844\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,346,406\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22,672\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e22,672\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e184\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e184\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e123,770\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e123,770\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e60_one-off\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e64,801\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,346,505\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17,629\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e17,629\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e283\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e283\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e62,381\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e62,381\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e65_one-off\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e60,305\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,346,508\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13,133\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13,133\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e286\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e286\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e45,934\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e45,934\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e65_triennial\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e76,034\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,346,826\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28,862\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15,729\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e604\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e318\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e47,850\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e49,576\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e65_biennial\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e90,284\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,346,988\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e43,112\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14,250\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e766\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e162\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e56,322\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e87,816\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e65_annual\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e133,424\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,347,261\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e86,252\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e57,390\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,039\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e435\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e83,024\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e131,721\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e60_triennial\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e107,935\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,347,344\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60,763\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31,901\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,122\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e518\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e54,183\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e61,554\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50_triennial\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e191,588\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,347,608\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e144,416\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e83,653\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,386\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e264\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e104,251\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eDominated\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e55_triennial\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e139,067\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,347,617\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e91,895\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31,132\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,395\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e273\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e65,904\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e114,062\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e60_biennial\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e135,850\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,347,652\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e88,678\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e27,915\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,430\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e308\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e62,046\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e90,692\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e60_annual\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e206,314\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,347,891\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e159,142\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e70,464\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,669\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e239\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e95,397\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eDominated\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50_annual\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e429,412\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,347,963\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e382,240\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e293,562\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,741\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e219,584\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eDominated\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e55_biennial\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e175,816\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,347,973\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e128,644\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e39,966\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,751\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e321\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e73,471\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e124,230\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50_biennial\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e252,412\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,347,976\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e205,240\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e76,596\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,754\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e117,031\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eDominated\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e55_annual\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e303,528\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,348,238\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e256,356\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e127,712\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2,016\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e265\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e127,210\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eDominated\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"11\\\"\\u003ea: These strategies comprised the cost-effectiveness efficiency frontier\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSubgroup analysis showed similar patterns for both sexes, with 55_biennial being more cost-effective in males (Table S6-7 \\u0026amp; Fig. S6).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSensitivity Analysis\\u003c/h2\\u003e \\u003cp\\u003eUnivariate sensitivity analyses revealed that the results remained largely unchanged across parameter ranges (Fig. S7-9 in the Supplement). ICER thresholds under extreme parameter values were detailed in Table S8-13 in the Supplement. The \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{OR}_{FDR}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, and discount rate had a significant impact on the ICERs. Subgroup analysis demonstrated a consistency in the results. The 55_annual strategy was optimal without discounting, for both sexes. In addition, for males, the 60_biennial strategy was optimal if \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{OR}_{FDR}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e was lower than 1.492, while 55_annual strategy would be optimal if it was 3.49. For females, the 55_biennial strategy maintained its optimal status across the full spectrum of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{OR}_{FDR}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eProbabilistic sensitivity analysis indicated a 91.1% probability of 55_biennial being optimal at the WTP threshold. Below CNY 92,500 to 125,000 WTP, 60_biennial was optimal, followed by 60_triennial at lower thresholds (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Subgroup analysis showed 55_annual was optimal for males when the WTP was above CNY 380,000 (Fig. S10).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImpact of improving screening adherence rate\\u003c/h2\\u003e \\u003cp\\u003eThe impact of improving screening adherence rate on ICERs were shown as Fig. S11 and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. The results revealed that, at the given WTP threshold, except for 50_one-off and 50_annual, all screening strategies were cost-effective compared to no screening, under the assumption of 75% or 100% adherence. Higher adherence levels resulted in more QALYs gained. Probabilistic sensitivity analysis revealed that, at a 75% adherence rate, the 55_biennial strategy was most optimal at the given WTP threshold,, followed by the 55_triennial strategy; at a 100% adherence rate, the 55_ triennial strategy was most optimal, followed by the 55_biennial strategy.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSubgroup analysis indicated that the 55_biennial strategy was most optimal for both males and females at a 75% adherence rate. However, at full adherence, the 55_triennial strategy emerged as the optimal strategy for females. (Fig. S12-13).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImpact of disregarding potential harms from LDCT screening\\u003c/h2\\u003e \\u003cp\\u003eThe impact of disregarding potential harms on ICERs were shown as Fig.S14 and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. The data demonstrated that more QALYs would be gained if we ignored the harms associated with LDCT screening. Notably, disregarding false-positive disutility, the 50_annual strategy maximized QALY gains, followed by the 55_annual strategy, which was identified as the most cost-effective strategy at the given WTP threshold. Conversely, when not accounting for the risk of radiation-induced lung cancer or overdiagnosis, the 55_biennial strategy, was deemed as the optimal approach under the same WTP threshold (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSubgroup analysis, as detailed in Fig. S15-16, revealed consistent patterns across different sex groups.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eDespite smoking being the primary lung cancer cause, diagnoses are rising among non-smokers in China and other Asian regions\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR10\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. The present study targeted non-smokers with a FDR history of lung cancer, who were more likely to benefit from LDCT screening. The study indicated that, at a WTP threshold of three-times the 2022 per-capita GDP, LDCT screening is a cost-effective approach for non-smokers with a FDR history of lung cancer. However, this did not extend to the practice of one-off screening at the age of 50, due to the lower risk profile of this age group. The timing of starting screening is crucial, and biennial screening at age 55 was the most cost-effective strategy for both sexes.\\u003c/p\\u003e \\u003cp\\u003eCompared to the recommendations for smokers, the optimal screening starting age and screening intervals were later and longer for non-smokers with an FDR history of lung cancer\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. For example, the latest Chinese guideline, issued in 2021, advocated for annual LDCT screening for those who smoked at least 30 pack-years from the age of 50\\u003csup\\u003e8\\u003c/sup\\u003e. Our findings affirm that heavy smokers face a significantly higher risk of developing lung cancer when compared to non-smokers who have a family history of the disease among their FDRs\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e. This highlights the critical need for targeted screening initiatives that take into account the distinct risk profiles of non-smoking populations, ensuring that preventative measures are tailored to those who may be at elevated risk due to their genetic predisposition.\\u003c/p\\u003e \\u003cp\\u003eThe familial risk of lung cancer, quantified by FDR-related OR, was a key sensitivity parameter. This OR had been reported to vary with country, sex, and smoking status\\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e. Our analysis, therefore, targeted Chinese non-smokers, stratified by sex. Existing literature had demonstrated that the OR was predominantly influenced by the number of affected FDRs\\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e. For example, a case-control study in Anhui Province, China, reported an OR of 1.48 for individuals with one affected FDR, rising to 2.96 for those with two affected FDRs\\u003csup\\u003e\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e. However, due to the limited data, our model did not initially account for this variation. Nevertheless, our sensitivity analysis provided valuable insights, revealing that for males, starting screening at 60 might be optimal with lower OR as 1.492, while higher OR favored annual screening from age 55. These findings show that personalizing screening based on family risk could be beneficial. More research is needed to clarify how many family members with lung cancer affects an individual's risk, helping to improve LDCT screening recommendations.\\u003c/p\\u003e \\u003cp\\u003eThe WTP threshold is pivotal in determining the most cost-effective strategy. Our research indicated that a WTP threshold between CNY 92,500 to 125,000, corresponding to 1.08 to 1.46 times the 2022 per-capita GDP, made biennial screening starting at age 60 the most optimal approach. Further lowering the WTP threshold suggested that triennial screening, beginning at either 60 or 65, could be more suitable. Thus, policy makers should weigh the cost and efficacy of screening strategies against the local economic context to select the most fitting approach. Consistent with previous studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u003c/sup\\u003e, our studies confirmed that higher adherence rates to LDCT screening were associated with greater gains in QALYs. With perfect adherence, triennial screening starting at 55 would be optimal, yielding a lower cost and a higher number of QALYs compared to biennial screening at the same age, which was the optimal strategy at a 75% adherence rate. This highlights the importance of raising public awareness of cancer screening. False-positive results impacted cost-effectiveness, without considering their disutility, more frequent screening became more cost-effective. Patients with indeterminate lung nodules experience anxiety specific to lung cancer\\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e and distress while waiting for CT scan outcomes\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e. It's important to provide prompt result reporting and education to manage patient anxiety. Our subgroup analysis, aligning with a cohort study within the framework of CanSPUC, has identified that being male was a persistent risk factor for lung cancer among non-smokers\\u003csup\\u003e\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e. In this analysis, female non-smokers might experience a slightly lower QALY gain due to a lower incidence rate. However, this difference was minimal, and screening remained cost-effective for both genders. For example, biennial screening starting at 55 could yield an additional 1,719 QALYs for females and 1,747 QALYs for males within a cohort of 100,000. This highlights the importance of gender-inclusive screening strategies in combating lung cancer.\\u003c/p\\u003e \\u003cp\\u003eNo existing studies have comprehensively evaluated the cost-effectiveness of LDCT for non-smokers in the context of lung cancer screening. A previous Chinese study did establish a risk-adapted starting age for LDCT screening\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e. This was done by taking into account a comprehensive set of risk factors, using a 10-year cumulative risk of lung cancer for heavy smokers as the threshold. The study identified that non-smokers with a FDR history of lung cancer should start annual screening at 53 for men and 55 for women. However, this study didn't consider the frequency of screening, long-term benefits, or potential risks, nor did it assess cost-effectiveness. A separate study conducted in Japan and the United States attempted to assess the cost-effectiveness of LDCT screening for non-smokers, but was constrained to a comparison of three screening strategies\\u0026mdash;LDCT, chest X-ray, and no screening\\u0026mdash;thus failing to establish the optimal starting age or the ideal screening frequency\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e. Our research, therefore, presents a timely and significant contribution to the optimization of lung cancer screening protocols in China and other nations grappling with comparable lung cancer burdens. By advocating for the inclusion of high-risk non-smokers in LDCT screening programs, we aim to bolster early detection rates and enhance overall survival prospects. This approach stands to enrich the public health arsenal, fostering a more inclusive and efficacious strategy in the battle against lung cancer.\\u003c/p\\u003e \\u003cp\\u003eThis study has several limitations. First, we had to estimate non-smoker lung cancer rates from overall rates, smoking proportions, and smoking-related risks, due to lack of direct data. Despite this systematic approach, this estimation might not fully capture non-smoker-specific nuances. Second, in our pursuit to craft universally applicable and easily executable guidance, we treated our modeled cohort as a single entity. This simplification led to a model that, while useful, oversimplified the complex realities of lung cancer progression in non-smokers. Notably, our analysis did not include lung cancer histology, a factor that could significantly alter parameters such as transition probabilities, stage-specific mortality rates, and the efficacy of LDCT screening. A 16-year evaluation of prospective cohort study conducted in China found, although significant differences in histology types were found between individuals who smoked and individuals who never smoked, the variation was slight with adenocarcinoma being the most prevalent in both groups, at 83.0% and 78.8%, respectively\\u003csup\\u003e\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e. Consequently, without specific data on how histology affects these parameters, we relied on broader sources like the CanSPUC program and Chinese cohorts, which include both high-risk smokers and non-smokers. Additionally, due to limited data, we made the uniform assumption of cancer progression, survival rates post-diagnosis, and treatment costs across genders, which might not mirror the actual disparities. Despite these constraints, we meticulously validated our model and performed sensitivity analyses on uncertain parameters, adjusting them by \\u0026plusmn;\\u0026thinsp;50% to bolster the robustness of our results. Third, we used a health-care system perspective and did not include broader economic impacts such as productivity loss or the quality of life of caregivers. Lastly, the study did not account for the increased risk of secondary cancers potentially linked to radiation exposure during screening\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e, nor did it address the implications of incidental findings that may arise from such screenings.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn conclusion, our study marks the first in-depth cost-effectiveness evaluation of LDCT screening for non-smokers with a FDR history of lung cancer. It concludes that biennial screening starting at age 55 is the most cost-effective strategy under a WTP threshold of three times the 2022 per capita GDP, for both sexes. The analysis identifies familial risk, WTP threshold, adherence rates and disutility associated with false-positive results as critical in shaping the optimal screening approach. By promoting the inclusion of high-risk non-smokers in screening programs, our research supports a more inclusive strategy for lung cancer prevention and control.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCanSPUC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ecancer screening program started in urban China\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCHEERS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eConsolidated Health Economic Evaluation Reporting Standards\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCNY\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eChinese yuan\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eFDR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eFirst-degree relative\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eGBD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eGlobal Burden of Diseases\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eGDP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eGross domestic product\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eICER\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eincremental cost-effectiveness ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eLDCT\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eLow-dose computed tomography\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eOR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eOdds Ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eQALY\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eQuality-adjusted life year\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eWTP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eWillingness-to-pay.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding information\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by Henan Province Science and Technology Research Project (grant number 242102311158), Henan Province Medical Science and Technology Public Relations Plan Province Department joint construction project (SBGJ202403020).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003ch3\\u003eAuthor\\u0026rsquo;s contributions\\u003c/h3\\u003e\\n\\u003cp\\u003eYL: Conceptualization, Methodology, Formal Analysis, Writing Original Draft; XLG, HFX, XYW, HWL, HW: Methodology, Data Curation, Validation, Review \\u0026amp; Editing; RHK, QC, RRQ, MFZ, CC, LYZ, SZL: Validation, Review \\u0026amp; Editing; YLQ: Project Administration, Review \\u0026amp; Editing, Supervision; SKZ: Review \\u0026amp; Editing, Supervision, Project Administration, Funding Acquisition; The work reported in the paper has been performed by the authors, unless clearly specified in the text.\\u003c/p\\u003e\\n\\u003ch3\\u003eAvailability of data and materials\\u003c/h3\\u003e\\n\\u003cp\\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\\u0026ndash;63.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGlobal Cancer Observatory. International Agency for Research on Cancer. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://gco.iarc.fr/en\\u003c/span\\u003e\\u003cspan address=\\\"https://gco.iarc.fr/en\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. Accessed March 10, 2024.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFang Y, Li Z, Chen H, et al. Burden of lung cancer along with attributable risk factors in China from 1990 to 2019, and projections until 2030. J Cancer Res Clin Oncol. 2023;149(7):3209\\u0026ndash;18.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395\\u0026ndash;409.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ede Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med. 2020;382(6):503\\u0026ndash;13.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKrist AH, Davidson KW, Mangione CM, et al. Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2021;325(10):962\\u0026ndash;70.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWood DE, Kazerooni EA, Aberle D, et al. NCCN Guidelines\\u0026reg; Insights: Lung Cancer Screening, Version 1.2022. J Natl Compr Canc Netw. 2022;20(7):754\\u0026ndash;64.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHe J, Li N, Chen WQ, et al. [China guideline for the screening and early detection of lung cancer(2021, Beijing)]. Zhonghua Zhong Liu Za Zhi. 2021;43(3):243\\u0026ndash;68.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBhopal A, Peake MD, Gilligan D, Cosford P. Lung cancer in never-smokers: a hidden disease. J R Soc Med. 2019;112(7):269\\u0026ndash;71.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eToh CK, Ong WS, Lim WT, et al. A Decade of Never-smokers Among Lung Cancer Patients-Increasing Trend and Improved Survival. Clin Lung Cancer. 2018;19(5):e539\\u0026ndash;50.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShi JF, Wang L, Wu N, et al. Clinical characteristics and medical service utilization of lung cancer in China, 2005\\u0026ndash;2014: Overall design and results from a multicenter retrospective epidemiologic survey. Lung Cancer. 2019;128:91\\u0026ndash;100.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSands J, Tammem\\u0026auml;gi MC, Couraud S, et al. Lung Screening Benefits and Challenges: A Review of The Data and Outline for Implementation. J Thorac Oncol. 2021;16(1):37\\u0026ndash;53.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAmicizia D, Piazza MF, Marchini F et al. Systematic Review of Lung Cancer Screening: Advancements and Strategies for Implementation. \\u003cem\\u003eHealthcare (Basel).\\u003c/em\\u003e 2023;11(14).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLam S, Bai C, Baldwin DR, et al. Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer. J Thorac Oncol. 2024;19(1):36\\u0026ndash;51.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang C, Dong X, Tan F, et al. Risk-Adapted Starting Age of Personalized Lung Cancer Screening: A Population-Based, Prospective Cohort Study in China. Chest. 2024;165(6):1538\\u0026ndash;54.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCot\\u0026eacute; ML, Liu M, Bonassi S, et al. Increased risk of lung cancer in individuals with a family history of the disease: a pooled analysis from the International Lung Cancer Consortium. Eur J Cancer. 2012;48(13):1957\\u0026ndash;68.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHusereau D, Drummond M, Petrou S, et al. Consolidated Health Economic Evaluation Reporting Standards (CHEERS)--explanation and elaboration: a report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force. Value Health. 2013;16(2):231\\u0026ndash;50.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHofer F, Kauczor HU, Stargardt T. Cost-utility analysis of a potential lung cancer screening program for a high-risk population in Germany: A modelling approach. Lung Cancer. 2018;124:189\\u0026ndash;98.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu Y, Xu H, Lv L, et al. Risk-based lung cancer screening in heavy smokers: a benefit-harm and cost-effectiveness modeling study. BMC Med. 2024;22(1):73.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang T, Chen X, Li C et al. Cost-Effectiveness Analysis of Risk Factor-Based Lung Cancer Screening Program by Low-Dose Computer Tomography in Current Smokers in China. Cancers (Basel). 2023;15(18).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChina statistical yearbook. 2022. National Bureau of Statistics of China. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://data.stats.gov.cn/easyquery.htm?cn=C01\\u003c/span\\u003e\\u003cspan address=\\\"https://data.stats.gov.cn/easyquery.htm?cn=C01\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. Accessed Mach 10, 2024.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGBD Results. Institute for Health Metrics and Evaluation. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://vizhub.healthdata.org/gbd-results/\\u003c/span\\u003e\\u003cspan address=\\\"https://vizhub.healthdata.org/gbd-results/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. Accessed March 10, 2024.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChan KH, Wright N, Xiao D, et al. Tobacco smoking and risks of more than 470 diseases in China: a prospective cohort study. Lancet Public Health. 2022;7(12):e1014\\u0026ndash;26.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhao QQ, Cong S, Fan J, et al. [Prevalence of smoking in adults aged 40 years and above in China, 2019\\u0026ndash;2020]. Zhonghua Liu Xing Bing Xue Za Zhi. 2023;44(5):735\\u0026ndash;42.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhao Z, Wang Y, Wu W, Yang Y, Du L, Dong H. Cost-effectiveness of Low-Dose Computed Tomography With a Plasma-Based Biomarker for Lung Cancer Screening in China. JAMA Netw Open. 2022;5(5):e2213634.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHe Mei. LB-bDJZYZY-lWYLH-kZWWY-zZH. Clinical Characteristics and Survival of Lung Cancer Patients in Chongqing, 2001\\u0026ndash;2018. China Cancer 2020;29(11).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang W, Qian F, Teng J, et al. Community-based lung cancer screening with low-dose CT in China: Results of the baseline screening. Lung Cancer. 2018;117:20\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLung Cancer Incidence. and Mortality with Extended Follow-up in the National Lung Screening Trial. J Thorac Oncol. 2019;14(10):1732\\u0026ndash;42.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRampinelli C, De Marco P, Origgi D, et al. Exposure to low dose computed tomography for lung cancer screening and risk of cancer: secondary analysis of trial data and risk-benefit analysis. BMJ. 2017;356:j347.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCao W, Tan F, Liu K, et al. Uptake of lung cancer screening with low-dose computed tomography in China: A multi-centre population-based study. EClinicalMedicine. 2022;52:101594.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCommission CNH. China Health Statistics Yearbook 2022. Published 2023. Accessed.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHu W, Zhou L, Chu J, et al. Estimating population norms for the health-related quality of life of adults in southern Jiangsu Province, China. Sci Rep. 2022;12(1):9906.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBlom EF, Haaf KT, de Koning HJ. Systematic Review and Meta-Analysis of Community- and Choice-Based Health State Utility Values for Lung Cancer. PharmacoEconomics. 2020;38(11):1187\\u0026ndash;200.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZeng X, Sui M, Liu B, et al. Measurement Properties of the EQ-5D-5L and EQ-5D-3L in Six Commonly Diagnosed Cancers. Patient. 2021;14(2):209\\u0026ndash;22.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhu J, Yan XX, Liu CC, et al. Comparing EQ-5D-3L and EQ-5D-5L performance in common cancers: suggestions for instrument choosing. Qual Life Res. 2021;30(3):841\\u0026ndash;54.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMazzone PJ, Obuchowski N, Fu AZ, Phillips M, Meziane M. Quality of life and healthcare use in a randomized controlled lung cancer screening study. Ann Am Thorac Soc. 2013;10(4):324\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLo YL, Hsiao CF, Chang GC, et al. Risk factors for primary lung cancer among never smokers by gender in a matched case-control study. Cancer Causes Control. 2013;24(3):567\\u0026ndash;76.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHan SS, Erdogan SA, Toumazis I, Leung A, Plevritis SK. Evaluating the impact of varied compliance to lung cancer screening recommendations using a microsimulation model. Cancer Causes Control. 2017;28(9):947\\u0026ndash;58.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTomonaga Y, de Nijs K, Bucher HC, de Koning H, Ten Haaf K. Cost-effectiveness of risk-based low-dose computed tomography screening for lung cancer in Switzerland. Int J Cancer. 2023.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAng L, Chan CPY, Yau WP, Seow WJ. Association between family history of lung cancer and lung cancer risk: a systematic review and meta-analysis. Lung Cancer. 2020;148:129\\u0026ndash;37.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCannon-Albright LA, Carr SR, Akerley W. Population-Based Relative Risks for Lung Cancer Based on Complete Family History of Lung Cancer. J Thorac Oncol. 2019;14(7):1184\\u0026ndash;91.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJin Y, Xu Y, Xu M, Xue S. Increased risk of cancer among relatives of patients with lung cancer in China. BMC Cancer. 2005;5:146.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003evan den Bergh KA, Essink-Bot ML, Borsboom GJ, et al. Short-term health-related quality of life consequences in a lung cancer CT screening trial (NELSON). Br J Cancer. 2010;102(1):27\\u0026ndash;34.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003evan den Bergh KA, Essink-Bot ML, Bunge EM, et al. Impact of computed tomography screening for lung cancer on participants in a randomized controlled trial (NELSON trial). Cancer. 2008;113(2):396\\u0026ndash;404.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGuo LW, Lyu ZY, Meng QC, et al. Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China. Front Oncol. 2021;11:766939.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKowada A. Cost-effectiveness and health impact of lung cancer screening with low-dose computed tomography for never smokers in Japan and the United States: a modelling study. BMC Pulm Med. 2022;22(1):19.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTang Y, Zhao S, Zhou L, et al. A 16-year evaluation of opportunistic lung cancer screening with low-dose CT in China: comparative findings between non-smokers and smokers. BMC Cancer. 2024;24(1):1322.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAdams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. Lancet. 2023;401(10374):390\\u0026ndash;408.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pubh\",\"sideBox\":\"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pubh/default.aspx\",\"title\":\"BMC Public Health\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Lung Cancer, LDCT Screening, Non-smokers, Cost-effectiveness, Modelling Study\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6244154/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6244154/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eLung cancer is the leading cause of cancer-related deaths worldwide, with non-smokers in China accounting for over 40% of cases. Despite the proven efficacy of low-dose computed tomography (LDCT) in early detection and reduction of lung cancer mortality, the current paradigm of lung cancer screening, heavily focused on smoking status and age, may inadequately address the unique risk factors associated with non-smokers, particularly those with a family history of the disease. This study evaluates the cost-effectiveness of LDCT screening for non-smokers with a first-degree relative (FDR) history of lung cancer, a group at particularly high-risk.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe developed a state-transition Markov model to evaluate the incremental cost-effectiveness ratios (ICERs) of 16 screening strategies for a hypothetical cohort of 100,000 non-smoking individuals aged 50 with a FDR history of lung cancer, considering various starting ages (50, 55, 60, 65 years) and intervals (one-off, annual, biennial, triennial). The willingness-to-pay (WTP) threshold was set at three times China's 2022 per-capita GDP. Sensitivity analyses, scenario analyses and subgroup analysis by sex, were conducted.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eCompared to no screening, all strategies except one-off screening at age 50, were cost-effective for both sexes. Biennial LDCT starting at age 55 was found to be most effective, with an ICER of CNY 68,932/QALY for males, and CNY 80,056/QALY for females. This cost-effectiveness probability for this strategy was approximately 90% for both sexes. Sensitivity analyses indicated that annual screening at age 55 was optimal without discounting. For males, biennial at age 60 was optimal if the FDR-related odds ratio for lung cancer incidence was below 1.492. Triennial screening at age 55 was optimal for females at full adherence. Ignoring disutility from false-positive results, annual at age 55 was optimal for both sexes.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eLDCT screening for non-smokers with a FDR history of lung cancer is cost-effective, especially biennial screening at 55. These findings support the development of more inclusive screening guidelines, which could enhance early detection and reduce mortality rates.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Cost-effectiveness of Low-Dose CT Screening for Non-smokers with a First-Degree Relative History of Lung Cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-03-31 11:14:33\",\"doi\":\"10.21203/rs.3.rs-6244154/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-04-07T18:33:10+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-04-02T03:07:33+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-03-28T08:14:51+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"173632192980622416787637286470357779406\",\"date\":\"2025-03-22T02:46:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"243219801260519580607837976379880796817\",\"date\":\"2025-03-20T02:05:48+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-03-20T01:24:41+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-03-19T07:39:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-03-19T04:10:27+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-03-19T04:06:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Public Health\",\"date\":\"2025-03-17T11:27:54+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pubh\",\"sideBox\":\"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pubh/default.aspx\",\"title\":\"BMC Public Health\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"4c738763-3f45-4ba3-a9ea-3af02139897b\",\"owner\":[],\"postedDate\":\"March 31st, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-05-19T15:58:24+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6244154\",\"link\":\"https://doi.org/10.1186/s12889-025-22977-w\",\"journal\":{\"identity\":\"bmc-public-health\",\"isVorOnly\":false,\"title\":\"BMC Public Health\"},\"publishedOn\":\"2025-05-15 15:56:52\",\"publishedOnDateReadable\":\"May 15th, 2025\"},\"versionCreatedAt\":\"2025-03-31 11:14:33\",\"video\":\"\",\"vorDoi\":\"10.1186/s12889-025-22977-w\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12889-025-22977-w\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6244154\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6244154\",\"identity\":\"rs-6244154\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}