Nasopharyngeal carcinoma (NPC) exhibits a distinctive ethnic and geographical distribution, with high incidence rates in Southern China, Southeast Asia and the Middle East/North Africa (1–4). According to World Health Organization estimates, China accounted for nearly half of the global NPC burden in 2020 (2–4). Currently, the Chinese Ministry of Health recommends using anti-EBV IgA (VCA-IgA and EBNA1-IgA) serological tests for NPC screening in individuals aged 30 to 69 years in NPC endemic areas (5). While this age-based screening strategy has shown improvements in early diagnosis rate from 22.4 to 79.0%, and may reduce NPC mortality, it is limited by low positive predictive value (PPV) of only about 4%, meaning that more than 95% of individuals who test positive do not have NPC (6–8). This leads to excessive laboratory tests, endoscopic examinations, and imaging, thereby increasing both screening costs and medical visits (8, 9). Consequently, there is a need to improve the performance of NPC screening by reducing unnecessary clinical examinations while maintaining cost-effectiveness.

Recent large-scale population studies have highlighted the potential of using a polygenic risk score (PRS), which incorporates the effects of SNPs (Single Nucleotide Polymorphism), to identify individuals at high risk of cancer (10–13). This approach has emerged as a promising tool for personalized screening in breast cancers and prostate cancers, and has also demonstrated to improve the cost-effectiveness and the benefit-to-harm ratio (14–20). Given the regional distribution disparities, familial clustering, and high heritability of NPC, genetic factors are believed to play an important role in its etiology (4, 21–25). This makes NPC an ideal candidate for developing a PRS for risk stratification, especially in high-risk regions of Southern China. Our previous study conducted a multi-center large-scale genome-wide association study of NPC and developed a PRS comprising 12 SNPs (26). By using this PRS to select high-risk individuals for EBV serological screening, we observed a substantial improvement in PPV, particularly in the top 20 and 5% of the PRS, with the PPV reaching 7.99 and 11.91%, respectively (26).

With the declining costs of sequencing, the detection cost of PRS has become more affordable, indicating its potential economic viability for large-scale screening. As highlighted by the 2021 Polygenic Risk Score Task Force of the International Common Disease Alliance, the lack of economic evidence regarding the large-scale screening application of PRS underscores an urgent need for research (13). Therefore, further evidence and evaluation are required for the implementation of PRS into NPC screening programs and its cost-effectiveness. It is essential to assess how a polygenic risk-stratified approach can be integrated into existing EBV serological screening strategies, including considerations such as the selection of high-risk individuals for screening and determining the appropriate screening frequency.

In light of this, we conducted a comprehensive cost-effectiveness analysis to compare the age-based screening strategy with the polygenic risk-stratified screening strategy. By evaluating and comparing the cost-effectiveness of these two screening strategies with different risk thresholds and screening frequencies, we aim to provide valuable insights for decision-making and resource allocation in NPC screening.

Our study highlights the potential benefits of incorporating polygenic data into population-level screening for nasopharyngeal carcinoma in endemic areas of China. The polygenic risk-stratified screening strategies proved to be more cost-effective than age-based screening strategies, particularly for the population aged 30–54 years. The polygenic risk-stratified screening strategies not only reduce the numbers of EBV serological tests and nasopharyngoscopies but also maintained most of the early diagnoses and averted deaths achieved by age-based screening strategies. Almost all strategies on the cost-effective efficiency frontiers were polygenic risk-stratified screening strategies, indicating their superior performance in terms of cost-effectiveness. For men, the polygenic risk-stratified screening strategy with a 0.7% or 0.8% 10-year AR threshold and a 1-year screening frequency was found to be the most cost-effective. Similarly, for women, the strategy with a 0.3% risk threshold and a 1-year screening frequency was the most cost-effective. These strategies yielded more QALYs at a lower cost level, making them attractive choices for targeted screening. Overall, our findings may offer valuable insights for personalized NPC screening, potentially leading to better outcomes and resource allocation in endemic areas of China.

Recently, polygenic risk scores have shown promise in predicting an individual’s genetic susceptibility to cancer and have the potential to improve the efficiency of population-level screening (13, 37–39). However, the economic feasibility of incorporating polygenic data into population-level screening is a critical consideration (13, 40, 41). Several studies have focused on cancers like breast, prostate, and colorectal cancer, using various model to evaluate the use of PRS in cost-effectiveness analyses of cancer screening. These studies have suggested that using polygenic risk data would be cost-effective (14, 16, 17, 42–46). Our study aligns with these findings and suggests that polygenic risk-stratified screening strategies could be more cost-effective for NPC, particularly for the population aged 30–54 years. This is because, firstly, polygenic risk stratification allows for a more precise estimation of an individual’s risk for NPC, thus enabling tailored screening strategies. Secondly, targeting screening efforts toward individuals with a high genetic predisposition to NPC allows for more efficient allocation of healthcare resources. This maximizes the benefits of screening while minimizing unnecessary interventions and associated costs for individuals at low genetic risk. Finally, since the SNPs composing PRS only require one-time testing in a lifetime, the cost of assessing genetic risk for all participants is lower than the cumulative cost of EBV antibody testing for low-risk individuals.

Implementing this risk-stratified screening strategy involves prioritizing more frequent screenings for individuals identified as high risk, with the goal of maximizing the identification of NPC patients within this high-risk population. While individual PRS was innate and constant, the 10-year AR value we calculate by combining age and PRS are dynamic and effectively pinpoint individuals at high risk across age groups. By setting a higher predefined 10-year AR threshold, the higher PPV of EBV serological screening for fewer high-risk individuals leads to a higher yield of QALYs per EBV serological test. However, it failed to identify potential early-stage NPC patients in the low-risk population, leading to their progression to the advanced stage before diagnosis, which incurs additional economic costs. To address it, we have developed a set of risk threshold gradients to determine the ideal balance between benefits and costs of screening. In more detail, for men aged 30–54 years, the strategies with a 10-year risk threshold of 0.7% or greater were found to be cost-effective. The strategy with a 0.7% risk threshold and a screening frequency of 1 year (AR-0.7%-1) was most cost-effective, except for the age group of 40–44 where a 0.8% AR threshold (AR-0.8%-1) was most cost-effective. For women aged 30–54 years, the strategy with a 0.3% risk threshold and a 1-year screening frequency (AR-0.3%-1) was the most cost-effective among the considered strategies on the efficiency frontiers. However, for individuals aged 55 and above, the age-based screening strategies were found to be more cost-effective than the polygenic risk-stratified screening strategies. This implies that the polygenic risk-stratified screening strategy may be more beneficial for younger individuals compared to the age-based strategy. Initiating age-based screening for the younger population often results in many unnecessary screening tests and clinical examinations owing to their low incidence rate. But the polygenic risk-stratified screening strategy offers a solution by targeting high-risk individuals to undergo screenings. This approach can effectively offset the additional cost of evaluating the genetic risks of all participants.

In addition to evaluating the cost-effectiveness of polygenic-risk stratified screening strategies, we have also supplemented the existing screening strategies for population with difference age group. In previous health economic evaluations of NPC screening strategies, the focus has primarily been on one-time lifetime screening for individuals aged 50 years (30, 31, 47). However, there is limited understanding of both the optimal screening frequency and the potential benefits of screening individuals in different age groups (6). In this study, we conducted a comprehensive evaluation of the age-based screening strategies in different age groups and explored the optimal screening frequency for each age group. Under a WTP threshold of twice the per capita GDP, the optimal screening frequency for men aged 30–59 was found to be every 3 years, while for individuals aged 60 and above, the screening frequency increased to every 4–5 years. It was observed that for younger age groups, more frequent screening led to the detection of a greater number of young patients, yielding higher benefits. For women aged 30–54, the optimal screening frequency was determined to be every 5 years, considering their lower incidence rate. For women aged 55 and above, it was not considered cost-effective to use aged-based screening strategies with a frequency of every 1–5 years. It should be noted that these results were based on a screening strategy set at every 1–5 years. Further study should consider exploring lower frequency screenings.

In sensitivity analysis, the probability sensitivity analysis consistently showed that the optimal screening strategies for each age group had the highest probability of being cost-effective with the WTP threshold, and supported the effectiveness of the proposed screening strategies. Univariate sensitivity analysis identified several model parameters that were sensitive to the results, such as the discount rate, compliance, utility of stage I, and cost of testing anti-EBV IgA antibodies, which were also reported as sensitivity parameters in previous studies (30, 31). Furthermore, we have also assessed the influence of the cost of PRS on ICER in the polygenic risk-stratified strategies, taking into account the lack of accuracy data on per-individual costs of polygenic risk stratification for implementation in large-scale screening programs before. We found the cost of PRS was a crucial sensitive parameter, and its change had a more pronounced impact on women because the cost of PRS for all participants accounts for a larger proportion of the total cost of screening. Additionally, endoscopic compliance is also a sensitive parameter, emphasizing the importance of raising health awareness and promoting screening compliance among the population.

According to the latest expert consensus, the current recommendations for NPC screening involve either male-only or sex-neutral middle-aged adults (6). Our study findings supported that when implementing the same screening strategy, the ICER was generally lower for men due to their higher incidence rate. This indicated better cost-effectiveness for men, which aligns with the previous study (30, 31). However, if screening was conducted for the general population without considering gender and utilizing a unified screening strategy, there were certain challenges in terms of cost-effectiveness. Therefore, our study constructed multi screening Markov models with different risk threshold criteria and screening frequencies based on the disease risk among different genders and ages. Our results showed that the risk threshold for women was lower than that for men in the optimal polygenic risk-stratified strategies, and the screening frequency for women was also lower than that for men in the optimal age-based strategies. It was essential to utilize different screening criteria tailored to specific gender populations. These proposed screening strategies in our study offered more flexible options for NPC screening in areas with resource disparities, with the aim of achieving more effective personalized screening.

Our study also has several limitations that should be acknowledged. Firstly, we acknowledged that our research model is tailored for the high-risk endemic areas, especially for the Southern China region. This aligned with the expert consensus recommending mass screening exclusively in endemic areas (6). Therefore, our Markov models and conclusions may not be directly applicable to non-endemic area with different cancer incidence rate, genetic backgrounds and healthcare system. Secondly, in comparison to existing screening guidelines that focus solely on age in endemic areas (5), we have integrated sex and genetic factors to refine the precision of screening. However, other factors associated with NPC risk, such as smoking, should be taken into account in further research. Then, our age-based screening strategy did not consider the past EBV serological results, as the unpredictable nature of anti-EBV IgA changes in lifetime, making it challenging to accurately simulate the existing screening protocol (48, 49). Moreover, incorporating compliance into modeling is indeed complex, as it can vary based on age, sex, education, socioeconomic status and other factors (8). We assumed a 100% compliance rate for further nasopharyngeal endoscopy, which may have led to an overestimation of the health benefits and costs associated with NPC screening. We conducted univariate sensitivity analyses using assumptions of 70% for compliance (7). Finally, our model did not consider the impact of contemporary treatment modalities, such as the increasingly widespread use of immunotherapy (50–52).

In conclusion, our study provides compelling evidence supporting the superiority of polygenic risk-based NPC screening strategies over age-based strategies for individuals aged 30–54, from a health economics perspective. By tailoring the screening frequency and risk stratification based on age, sex and polygenic risk score, we can optimize the use of resources and maximize the health benefits for NPC screening.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

The study was approved by the ethics committee of the Sun Yat-sen University Cancer Center. The patients/participants provided their written informed consent to participate in this study.

D-WY: Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing, Formal analysis, Software. JM: Methodology, Writing – review & editing, Supervision. W-QX: Writing – review & editing, Data curation, Resources. MT: Data curation, Writing – review & editing. LL: Data curation, Writing – review & editing. YZ: Writing – review & editing, Data curation. HD: Resources, Writing – review & editing. T-MW: Resources, Writing – review & editing. YL: Resources, Writing – review & editing. Y-XW: Resources, Writing – review & editing. X-HZ: Resources, Writing – review & editing. TZ: Resources, Writing – review & editing. X-ZL: Resources, Writing – review & editing. P-FZ: Resources, Writing – review & editing. X-YC: Resources, Writing – review & editing. XY: Resources, Writing – review & editing. FL: Resources, Writing – review & editing. MJ: Resources, Writing – review & editing. YS: Resources, Writing – review & editing. Y-QH: Resources, Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing. W-HJ: Conceptualization, Data curation, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing, Funding acquisition, Investigation, Visualization.