LDCT Screening Cost-Effectiveness for Familial Lung Cancer Risk in Taiwan

ABSTRACT

Objectives: Most economic evaluations of low-dose CT (LDCT) screening focus on heavy smokers, with limited evidence for individuals with a family history of lung cancer (LCFH). This study evaluated the cost-effectiveness of LDCT screening for individuals with LCFH using Taiwan epidemiologic data and cost data derived from the Taiwan National Health Insurance program.

Study Design: A Markov model simulation evaluated the cost-effectiveness of LDCT screening vs nonscreening using 10,000 iterations in the probabilistic sensitivity analysis, a 1-year cycle, a 20-year time horizon, and a 3% annual discount rate.

Methods: The analysis adopted the perspective of the Taiwan health care payer and assumed annual LDCT screening with full adherence. The willingness-to-pay (WTP) threshold was set at $32,440, corresponding to Taiwan’s 2024 gross domestic product per capita. We used incremental costs and quality-adjusted life years (QALYs) to calculate the incremental cost-utility ratio (ICUR). Deterministic and probabilistic sensitivity analyses were conducted to evaluate parameter uncertainty.

Results: At the specified WTP threshold, LDCT screening yielded an ICUR of $23,786/QALYs, which was below the prespecified WTP threshold. In the simulated cohort, 158,375 LDCT examinations resulted in 609 lung cancer diagnoses over 20 years. Probabilistic sensitivity analysis demonstrated a 61.22% probability of cost-effectiveness. Results were most sensitive to lung cancer incidence in the nonscreening cohort, LDCT positivity rate, and positive predictive value.

Conclusions: LDCT screening for individuals with LCFH was cost-effective at the selected threshold under modeled assumptions. Targeted implementation in high-risk populations may enhance value-based resource allocation; however, real-world adherence, evolving treatment costs, and program scalability should be considered in coverage and reimbursement decisions.

Am J Manag Care. 2026;32(10):e364-e371

doi:10.37765/ajmc.2026.90032

Takeaway Points

To our knowledge, this is the first economic evaluation of low-dose CT (LDCT) screening for individuals with a family history of lung cancer, a population largely absent from prior cost-effectiveness research, which has focused on heavy smokers.

• LDCT screening was cost-effective for individuals with a family history of lung cancer, with an incremental cost-utility ratio below Taiwan’s willingness-to-pay threshold.
• Taiwan National Health Insurance cost and epidemiologic data supported extending LDCT screening eligibility to this high-risk group.
• Lung cancer incidence in the nonscreening group, LDCT positivity rate, and positive predictive value were key drivers of cost-effectiveness.

Lung cancer remains the leading cause of cancer-related mortality worldwide, accounting for approximately 1.8 million deaths in 2022.¹ Prognosis is strongly stage dependent, with a substantially improved survival when the disease is detected early.^2,3 Low-dose CT (LDCT) is currently the only evidence-based screening modality shown to reduce lung cancer mortality.^4-7 The phase 3 National Lung Screening Trial (NLST) (NCT00047385) and the NELSON trial (ISRCTN63545820) demonstrated mortality reductions of 20% and 24%, respectively,^4,5 leading to widespread recommendations for LDCT screening among heavy smokers and high-risk individuals.^8,9

Although the cost-effectiveness of LDCT screening for heavy smokers is well established, limited economic evidence exists for individuals with a family history of lung cancer (LCFH).^10-14 Multiple studies identify LCFH as an independent risk factor,^15-18 with a pooled measure of association of 1.85.¹⁹ Approximately 12.6% of the general population has LCFH, indicating a sizeable high-risk subgroup.²⁰ This is particularly relevant in East Asia, where a substantial proportion of lung cancers occur in never-smokers.²¹ Since 2022, the Taiwan Early Detection Program for Lung Cancer has incorporated LCFH as an eligibility criterion for LDCT screening.²² However, cost-effectiveness analyses specifically evaluating this population remain scarce.

We therefore developed a Markov model using Taiwan epidemiologic and cost data derived from the Taiwan National Health Insurance (NHI) program to evaluate the cost-effectiveness of LDCT screening among individuals 55 years and older with LCFH.

METHODS

Study Design and Markov Model

We constructed a state-transition Markov model to simulate the natural history of lung cancer and downstream clinical and economic outcomes, with and without annual LDCT screening (Figure 1). The model was implemented in TreeAge Pro 2021 (TreeAge Software, LLC). The analysis adopted the Taiwan health care payer perspective and included direct medical costs of screening, diagnostic evaluation, and stage treatment.

The simulated cohort consisted of individuals 55 years and older with at least 1 first-degree relative diagnosed with lung cancer. The model used a 1-year cycle length and a 20-year time horizon. A 20-year horizon was selected to capture long-term survival and cost consequences of stage shift while limiting uncertainty associated with very long-term extrapolation in an older cohort. Costs and quality-adjusted life years (QALYs) were discounted at 3% annually, consistent with commonly used economic evaluation practice.²³

The model included 11 mutually exclusive health states: LDCT screening, nonscreening, stage I through IV lung cancer (year 1 and year ≥ 2 for each stage), and death. Stage stratification by year was used to reflect differences in survival and costs during the initial year after diagnosis vs subsequent years. Lung cancer stage definitions followed the American Joint Committee on Cancer classification criteria.²⁴ Death was modeled as an absorbing state. Individuals entered either the screening or nonscreening state at model initiation. Screening and nonscreening pathways, including possible outcomes at each screening round, are described below (see LDCT Screening Pathway). Full adherence to annual screening and recommended follow-up was assumed. This assumption was used to estimate intrinsic cost-effectiveness under ideal implementation conditions; real-world adherence may be lower and would be expected to attenuate effectiveness and potentially reduce cost-effectiveness.

This study was reviewed and approved by Yuan’s General Hospital Institutional Review Board (YGHIRB-20231016B). Informed consent was not deemed necessary due to the theoretical nature of this modeling study.

LDCT Screening Pathway

The upper panel (eAppendix Figure 1 [eAppendix available at ajmc.com]) illustrates the LDCT screening strategy for individuals 55 years and older with LCFH. The model was simulated over a 20-year horizon with annual cycles, permitting up to 20 screening rounds conditional on survival. After each screening, individuals could experience a positive, false-negative, or true-negative result or death. Individuals with positive results underwent diagnostic evaluation and transitioned to cancer stage states if malignancy was confirmed; otherwise, they returned to the screening state. Individuals with false-negative results transitioned to cancer stage states in subsequent cycles, consistent with the natural history observed in the nonscreening arm. Individuals with true-negative results returned to screening. The lower panel represents the nonscreening strategy, in which individuals either remained cancer free or developed lung cancer and transitioned directly to stage states. Death from any cause was modeled as an absorbing state in both strategies.

Transition Probabilities

Wang et al reported the cumulative incidence of lung cancer in the LDCT screening cohort over a median follow-up period of 12 years.¹⁶ To incorporate these data into the Markov model, the reported incidence rate was converted into a cycle transition probability using the standard exponential transformation: P = 1 − e^{(−IR × t)}, where IR denotes the incidence rate and t is the cycle length. This approach assumes a constant hazard within each cycle and is commonly used when adapting long-term incidence rates to annual-cycle models.²⁵

Additional screening performance inputs (positivity rate, positive predictive value [PPV], and false-negative rate) and the stage distribution at diagnosis in the screening arm were also obtained from Wang et al.¹⁶ In that study, a positive LDCT result was defined as solid or part-solid nodules of 5 mm or larger or nonsolid nodules of 8 mm or larger.¹⁶ We used the positive rate to estimate the proportion of individuals screening positive and PPV to estimate confirmed cancer among those screening positive. The overall incidence in the screening arm was reconstructed as Incidence = (Positive rate × PPV) + (False-negative/total cases). This formulation preserves internal consistency between test characteristics and modeled lung cancer detection. For the nonscreening cohort, lung cancer incidence was estimated using Taiwan cancer registry data and the relative risk associated with LCFH derived from published literature.^19,26 Stage distribution at diagnosis for nonscreening was derived from published sources.²⁷ All baseline parameters and distributions used for probabilistic sensitivity analysis are summarized in Table 1.

Within each cancer stage, annual survival probabilities were modeled assuming a constant hazard (exponential survival). Although this is a simplification of potentially time-varying real-world hazards, it is a conventional approach in state-transition models when detailed time-to-event functions are unavailable. Survival parameters were stage specific and applied differently for the first year after diagnosis and subsequent years (Table 1).^18,26

Costs

Direct medical costs included LDCT examinations, follow-up evaluations after positive screening, diagnostic procedures (eg, CT-guided biopsy), and stage treatment costs (year 1 and year ≥ 2).^23,29 The costs related to the diagnostic workup and management of positive nodules, including imaging studies, biopsy procedures, and subsequent treatment of lung cancer, were primarily derived from Yang et al.²³ In that study, cost estimates were obtained using payment standards from the Taiwan NHI Research Database. Costs were inflation adjusted to 2024 US$ using the Taiwan Consumer Price Index and converted using a fixed exchange rate of 30 Taiwanese new dollars per 1 US$, consistent with model assumptions.^30,31 All costs are reported in 2024 US$.

Health Utilities

QALYs were calculated by applying health utility weights to time spent in each health state. Stage-specific utilities (Table 1) were obtained from Yang et al,²³ who estimated them using EQ-5D and Taiwan scoring functions.³² Individuals without lung cancer were assigned a baseline utility of 1.0. This structural assumption simplifies modeling but may overestimate the QALYs gained from screening in older populations, whose baseline utility is typically below perfect health; this limitation is considered in the interpretation.

Outcomes

Primary outcomes were total discounted costs, total discounted QALYs, incremental costs, incremental QALYs, and the incremental cost-utility ratio (ICUR). The willingness-to-pay (WTP) threshold was set at $32,440, corresponding to Taiwan’s 2024 gross domestic product per capita.³³

Sensitivity Analysis

A 1-way sensitivity analysis was performed to identify the parameters that significantly influence model output, thereby highlighting key path variables. This analysis used a tornado diagram, with each parameter varied by plus or minus 20% from its baseline value in the Markov model. Parameters were ranked according to their impact on the ICURs. In addition, a probabilistic sensitivity analysis (PSA) was conducted in which model variables followed specific probability distributions. We employed Monte Carlo simulations to randomly sample these parameters and evaluate the impact of variability, iterating the simulation 10,000 times. The distribution of cost-effectiveness outcomes was analyzed using a scatter plot across different quadrants.

RESULTS

In the LDCT screening cohort simulated over 20 annual cycles, individuals spent a mean of 15.84 years in the LDCT screening state and 3.76 years in the death state, with smaller proportions of time distributed across cancer stage states. In a simulated cohort of 10,000 individuals undergoing annual LDCT screening, the model projected 158,375 LDCT examinations and 609 lung cancer diagnoses over 20 years.

Base-case discounted costs were $4544 for LDCT screening and $1852 for nonscreening, yielding an incremental cost of $2692 (Table 2). Discounted effectiveness was 16.17 QALYs for LDCT screening and 16.06 QALYs for nonscreening, yielding an incremental effectiveness of 0.11 QALYs. The resulting ICUR was $23,786/QALY, which was below the WTP threshold of $32,440.

In PSA, LDCT screening had a 61.22% probability of being cost-effective at the WTP threshold (eAppendix Figure 2), indicating moderate decision uncertainty. Net monetary benefit increased with WTP for both strategies; at a WTP of $32,440, LDCT screening yielded a small net monetary benefit advantage compared with nonscreening (eAppendix Figure 3).

The incremental cost-effectiveness scatter plot showed that most simulations fell in the top (northeast) quadrant, consistent with higher costs and higher effectiveness for screening, with 61.22% of simulated outcomes below the WTP threshold (Figure 2). One-way sensitivity analysis identified 3 parameters with the greatest impact on the ICUR: baseline lung cancer incidence in the nonscreening cohort, LDCT positivity rate, and PPV (eAppendix Figure 4). Screening became less favorable when baseline incidence was lower and when test performance parameters implied greater false-positive burden.

DISCUSSION

Summary of Findings

To our knowledge, this study is the first to evaluate the cost-effectiveness of LDCT screening among individuals at elevated risk due to LCFH. LCFH has been recognized as an important risk factor, particularly among never-smokers, individuals with a multiplex family history, and those with affected maternal relatives.^16,18,34 In Taiwan, LCFH has been incorporated as an eligibility criterion in the national Early Detection Program for Lung Cancer. Although the mortality benefit of LDCT screening is well established, economic evaluation is essential to inform efficient resource allocation in population-based screening programs.^16,35 Using Taiwan epidemiologic and NHI cost data, this simulation study provides an economic framework to support policy decisions regarding LDCT screening in individuals with LCFH.

Comparison of Cost-Effectiveness With Previous Literature

The modeled annual lung cancer incidence in the LDCT screening cohort, derived from Wang CL et al,¹⁶ was 0.39% compared with 0.35% in the nonscreening cohort. Theoretically, screening should not alter the underlying biological incidence of disease; rather, this difference likely reflects earlier detection and potential overdiagnosis associated with screening. Although radiation-induced malignancy from LDCT is minimal, prior analyses estimate that repeat exposure creates a small excess lifetime risk.³⁶ More importantly, overdiagnosis, defined as the detection of indolent cancers that would not have become clinically significant, has been documented in major screening trials. The NLST reported an overdiagnosis rate of approximately 18.5%,³⁷ whereas extended follow-up from the NELSON trial demonstrated a reduction in overdiagnosis from 19.7% to 8.9% after 11 years of follow-up.⁵ Given the 12-year median follow-up in the Wang et al study,¹⁶ the modest excess incidence observed in our modeled screening arm is broadly consistent with long-term follow-up data from randomized trials.

The Markov model projected a higher proportion of early-stage lung cancers in the screening arm, reflecting stage shift attributable to screening. In Taiwan, early-stage lung cancer may incur substantial cumulative treatment costs due to surgical intervention and longer survival duration.²³ Consequently, increased early-stage detection, together with recurring screening and diagnostic expenditures, contributed to higher total costs for the LDCT strategy, resulting in an incremental cost of $2692. Nevertheless, when health gains were incorporated, LDCT screening remained cost-effective over the 20-year horizon under the selected WTP threshold.

Deterministic sensitivity analysis identified baseline lung cancer incidence in the nonscreening cohort as the most influential determinant of the ICUR. Higher baseline incidence improves the economic value of screening, whereas lower incidence diminishes cost-effectiveness. In our model, screening was no longer economically favorable when annual incidence fell below approximately 0.30%, underscoring the importance of risk-based targeting. Screening performance characteristics were also influential. Higher positivity rates increased follow-up and diagnostic costs due to false-positive findings, thereby increasing the ICUR. Conversely, improvements in PPV reduced unnecessary diagnostic procedures and enhanced cost-effectiveness. These findings highlight the importance of both appropriate risk stratification and optimized screening quality in maximizing the economic value of LDCT programs.

Implications for Clinical Practice and Policy

Published cost-effectiveness analyses of LDCT screening among heavy smokers (Table 3) report ICURs in Western countries generally ranging from approximately CAD $52,000 per QALY to €28,000 to €115,000 per life-year gained.^10,38-40 In East Asian settings, reported ICURs typically range from $13,057 to $25,383 per QALY, with studies adopting a societal perspective often yielding higher ICURs than those conducted from a health care payer perspective.^23,41,42 Overall, all of these published studies consistently demonstrated that LDCT screening is cost-effective. In the present analysis, the ICUR of $23,786/QALY falls within the upper range reported in East Asia and below many Western estimates. For comparison, Yang et al estimated an ICUR of $19,683/QALY for LDCT screening among heavy smokers with a 30 or greater pack-year history in Taiwan.²³ This difference likely reflects variation in baseline risk: Heavy smokers generally have higher underlying lung cancer incidence than individuals selected solely by family history. Consequently, LDCT screening may yield greater economic value in higher-risk smoking populations than in those defined by LCFH. Given the differences in modeling assumptions, analytic perspectives, cost structures, and screening eligibility criteria across studies, these comparisons should be interpreted cautiously. Further research incorporating risk-stratified approaches and real-world implementation data is warranted to better define the relative economic value of LDCT screening across high-risk subgroups.

Strengths and Limitations

This study applied model parameters derived from governmental sources, peer-reviewed literature, and evidence-informed assumptions to evaluate the cost-effectiveness of LDCT screening among individuals with LCFH. To capture clinically meaningful differences in prognosis and resource utilization, each lung cancer stage was stratified into 2 temporal phases: the first year following diagnosis and subsequent years. Lastly, a 20-year state-transition Markov model was implemented to simulate long-term health outcomes and costs, facilitating estimation of lifetime economic consequences within a defined time horizon.

Several limitations also warrant consideration. First, the model assumed full adherence to annual LDCT screening and recommended diagnostic evaluation. In practice, participation and follow-up completion may be suboptimal, potentially attenuating effectiveness and altering cost-effectiveness estimates. The assumption of complete adherence was adopted to evaluate intrinsic economic value under ideal implementation conditions; incorporation of real-world adherence data would yield more pragmatic estimates. Second, in situ lung cancers were not explicitly modeled due to limited data availability, restricting the analysis to invasive disease and potentially underestimating the full clinical impact of screening. Third, key inputs, including transition probabilities and cost parameters, were derived from secondary data sources and assumptions, which may introduce uncertainty and limit external validity. In particular, some treatment cost estimates were based on data predating widespread use of targeted therapies and immunotherapy. If contemporary late-stage treatment costs are higher than modeled, the economic value of earlier detection may be underestimated. Fourth, survival within each cancer stage was modeled under a constant-hazard assumption, consistent with an exponential distribution. Although commonly used in state-transition models, real-world lung cancer mortality often exhibits time-varying hazards. Direct validation of early-stage survival was limited due to interstage transitions in our model; however, stage IV survival, modeled without allowing stage transitions, was estimated at 46%, 29%, 18%, 11%, and 7% from years 1 through 5, respectively, and closely approximated Taiwan NHI registry estimates of 46%, 25%, 12%, 6%, and 3%,⁴⁴ suggesting reasonable alignment for advanced disease. Nevertheless, time-dependent survival dynamics may not be fully captured. Fifth, deterministic sensitivity analyses applied a uniform plus or minus 20% variation due to the absence of consistently reported variance estimates in the source literature.⁴⁵ This approach does not fully reflect parameter statistical uncertainty. Lung cancer incidence in the screening and nonscreening cohorts was also varied independently to assess the influence of the parameter, although these incidences are epidemiologically interdependent in real-world settings. Therefore, results from the tornado analysis should be interpreted as exploratory assessments of parameter influence rather than simulations of correlated epidemiologic changes.⁴⁵ Finally, reliance on aggregated secondary data limited stratification by age, smoking exposure, and degree of familial risk, constraining subgroup analyses. Generalizability to other health care systems may therefore be influenced by differences in epidemiology, costs, and screening implementation. Future studies incorporating real-world program data, updated treatment costs, and more flexible survival modeling approaches would further refine these economic estimates.

CONCLUSIONS

This study demonstrates that LDCT lung cancer screening for individuals with LCFH was more cost-effective than nonscreening at the chosen WTP threshold. Because data parameters were primarily derived from Taiwan-specific research literature and government sources, these findings offer contextually relevant evidence to inform community-based LDCT screening policy in Taiwan. Real-world adherence, evolving treatment costs, and program scalability should also be considered in coverage and policy decisions. Future research should incorporate primary data from the Taiwan Early Detection Program for Lung Cancer into this model to yield more precise estimates, and extending this approach to additional subgroups would offer more comprehensive information for policy formulation and resource allocation.

Author Affiliations: Department of Radiology, Yuan’s General Hospital (FCH, CHL), Kaohsiung, Taiwan; Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University (FCH, CHL, HYS), Kaohsiung, Taiwan; Department of Electronic Engineering, National Kaohsiung University of Science and Technology (CHL), Kaohsiung, Taiwan; Master of Health Care Management Program, Department of Business Management, National Sun Yat-sen University (HYS), Kaohsiung, Taiwan; Department of Medical Research, Kaohsiung Medical University Hospital (HYS), Kaohsiung, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University (HYS), Taichung, Taiwan.

Source of Funding: This work was supported by grants from Taiwan’s Ministry of Science and Technology (MOST 111-2410-H-037-002-MY3) and National Science and Technology Council (NSTC 114-2410-H-037-009-SS3).

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (FCH, CHL, HYS); acquisition of data (FCH); analysis and interpretation of data (FCH, CHL, HYS); drafting of the manuscript (FCH, HYS); critical revision of the manuscript for important intellectual content (FCH, HYS); statistical analysis (FCH, HYS); provision of patients or study materials (FCH, CHL); obtaining funding (FCH, CHL, HYS); and administrative, technical, or logistic support (FCH).

Address Correspondence to: Hon-Yi Shi, DrPH, MS, Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, 100 Zihyou 1st Road, Kaohsiung 80708, Taiwan. Email: hshi@kmu.edu.tw.

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