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Modeling optimal combination of breast and cervical cancer screening strategies in China

BMC Women's Health volume 25, Article number: 56 (2025) Cite this article

Abstract

Background

Breast and cervical cancers are the commonest cancers among women. Secondary prevention of cancer through screening minimizes disease burden and improves survival outcomes. Optimizing screening strategies for breast and cervical cancers is a challenge in resource-limited settings with a high population density such as China. Therefore, we aimed at assessing the efficiency of different combined screening strategies for breast and cervical cancers under different budgets in China.

Methods

Markov cohort model was used to evaluate the cost-effectiveness of 36 strategy combinations for breast and cervical cancer screening with varying screening modality and intervals. The results were used as inputs in the Integer Programming (IP) model to determine the combination of the different screening options under different budgets.

Results

The optimal combination strategy was biennial breast ultrasonography (BUS) and mammography (MAM) in parallel screening and quinquennial human papillomavirus (HPV) for breast and cervical cancer screening under the threshold of the annual per capita social cost investment (PCSCI) (18.80 USD) in China. Using this strategy, the total investment cost for 100,000 females was 1,877,984.50 USD, and the incremental life-years compared with no screening was 3,122 life-years. The optimal combination strategy included annual clinical breast examination (CBE), BUS and MAM in series screening, and biennial thin-layer liquid-based cytology (TCT) and HPV in series screening with the annual PCSCI reaching 37.60 USD. Thereafter, as the cost input continued to increase, the optimal combination strategy remained unchanged, and the sum of incremental life-years and actual input costs did not increase.

Conclusions

From a social cost–benefit perspective, biennial BUS and MAM in parallel screening, and quinquennial HPV screening is the most efficient combination strategy with limited budget, while annual CBE, BUS and MAM in series screening and biennial TCT and HPV in series screening are the most efficient combination strategy with sufficient budget.

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Background

Breast cancer is the most common cancer in women, with an age-standardized incidence rate of 39.10 per 100,000 and an age-standardized mortality rate of 9.96 per 100,000 in China in 2020 [1]. Meanwhile, the age-standardized incidence and mortality rates of cervical cancer is 10.70 per 100,000 and 5.27 per 100,000, respectively [1]. Cancer screening is associated with early detection and high cure rates, whereas failure to detect precancerous lesions increases the risk to developing cancer and premature death [2, 3]. Breast self-examination (BSE) and mammography (MAM) are the commonest screening modalities of breast cancer worldwide, especially in developed countries [4]. Meanwhile, the screening strategy (Breast cancer screening consists of clinical breast examination (CBE) and breast ultrasonography (BUS), and cervical cancer screening mainly adopts HPV tests with genotyping with HPV-positive persons need further TCT based on positivity) currently used in China is already a combined model [5]. Cytology and HPV testing were commonly used for cervical cancer screening worldwide [6,7,8]. Healthy China Initiative (2019–2030) showed that the overall screening rate for breast and cervical cancers in 2019 was 52.6% [9], which is far from the World Health Organization's (WHO) goal of achieving a 70.0% screening rate to eliminate cervical cancer. Data from 17 cancer registries in China showed that the 5-year survival rate for breast cancer increased from 73.1% to 82.0% from 2003 to 2015, but it is still below the goal of WHO Global Breast Cancer Initiative (90%) [10].

Many studies have demonstrated the cost-effectiveness of various strategies for breast and cervical cancer screening. Biennial and triennial MAM screening were more cost-effective than non-screening scenarios in the United States and Netherlands [11, 12], biennial CBE combined with MAM screening was most cost-effective among women aged 40–49 years in Japan [13], BUS is the most effective screening intervention, and biennial CBE and BUS in parallel screening was most the cost-effective among 40–64 years old women in China [14, 15]. A Markov model-study found that quinquennial high-risk human papillomavirus (HPV) testing was cost-effective for cervical cancer screening among Brazilian women aged 25–64 years [16]. Quinquennial and decennial HPV screening among Swedish women aged 23–50 years and beyond 50 years was cost-effective [17]. Quinquennial liquid-based cytology (LBC), triennial LBC and HPV typing, and annual LBC screening were dominant strategies for urban women [18], and quinquennial HPV testing was cost-effective for rural areas in China [19]. Compared with no screening, quinquennial HPV testing was more cost-effective for Chinese women aged 30–59 years [20].

The Integer Programming (IP) model is used to maximize the total benefit generated by resources within a limited budget of health care finances (a type of optimization model) [21,22,23,24,25,26,27]. Combination of preventive strategies for cervical cancer (screening and/or vaccination against HPV) achieves maximum reduction in cancer cases within a fixed budget using the IP model [21, 23]. A study presented the specific example as a shift from the assessing costs and benefits of interventions for reducing mortality and morbidity attributable to cervical cancer to exploring what synergies might be achievable by thinking across disease burdens [24].

Breast and cervical cancer screenings were performed by community health service centers (CHSC) according to a section of the National Public Health Services package since 2009 targeting women aged 35 years and above in China [9]. Considering that breast and cervical cancer screenings are conducted by CHSC and the funds are distributed in a bundle, we aimed at identifying the most efficient strategy combinations of breast and cervical cancer screening under different budgets from the societal perspective in this study. This study aimed to evaluate the costs and health benefits of different screening strategies in China and to propose measures for optimizing screening programs from a cost-effectiveness perspective. These findings might be applied to other upper-middle-income countries with similar development levels to China.

Methods

Markov cohort model was used to estimate the costs and incremental life-years of different combination strategies for breast and cervical cancer screening. The IP model was used to determine the combination of the different screening options under different budgets. The outcome of cost-effectiveness analysis from the Markov model was entered as input data for the IP model.

Evaluation model-Markov model

Screening models were constructed using the main breast and cervical cancer screening strategy studied in China. Our study aims to find the optimal combination strategy for cervical and breast cancer screening when funds are limited from the societal perspective. We simulated disease progression among 100,000 women aged 35—64 years over a 50-year period, with a 1-year cycle length to describe the natural history of breast and cervical cancers. The screening modality of breast cancer mainly included CBE, breast ultrasonography (BUS) and MAM, with the screening intervals of 1 and 2 years, while cervical cancer screening mainly included thin-layer liquid-based cytology (TCT) and HPV testing, at intervals of 2, 3 and 5 years. Joint tests included independent test (only a single test is carried out); parallel test (if the result of any one of the two or three tests is positive, it is defined as positive; if the results of the two or three tests are all negative, it is defined as negative); serial test (if the results of the two or three tests are all positive, it is defined as positive; if any one of the results of the two or three tests is negative, it is defined as negative). We analyzed 36 combinations of strategies for breast and cervical cancer screening by different screening modality and intervals were analyzed using the Markov model (Fig. 1).

Fig. 1
figure 1

Combination of breast cancer (A) and cervical cancer (B) screening strategies. “|”, serial test; “&”, parallel test; CBE Clinical breast examination, BUS Breast ultrasonography, MAM Mammography, HPV Human papillomavirus, TCT Thin-layer liquid-based cytology

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The Markov natural history model for breast cancer consisted of 9 health states: healthy, ductal carcinoma insitu (DCIS), breast cancer stage 1, breast cancer stage 2, breast cancer stage 3, breast cancer stage 4, treatment-related death from breast cancer within 1 year, death from breast cancer, and death from other causes (Fig. 2A). The natural history model for cervical cancer included 8 health states: healthy, infection with oncogenic HPV, cervical intraepithelial neoplasia (CIN) grade 1, CIN grade 2, CIN grade 3, cervical cancer, death from cervical cancer, and death from other causes (Fig. 2B). The model assumed that CIN3 is originates from CIN1/CIN2/CIN3, and all grades of CIN have the probability of regression. We constructed the Markov models of breast and cervical cancer screening using TreeAge Pro 2011 software.

Fig. 2
figure 2

Markov natural history model flow diagram of breast cancer (A) and cervical cancer (B). Source: [7]. DCIS, Ductal carcinoma in situ; BC1/2/3/4, Breast cancer stage 1/2/3/4; CIN1/2/3, Cervical intraepithelial neoplasia grade 1/2/3; HPV, Human papillomavirus

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Transition probability parameters (incidence, mortality, prevalence, and all-cause mortality reported in Global Burden of Disease Study (GBD) 2019 [28]), initial probabilities of natural history model, the sensitivity and specificity of the screening strategies, treatment-related parameters (Table s1-s7), and cost parameters (the aggregate of screening tests costs, diagnosis costs, treatment costs, program management costs, and transportation costs) was input in the Markov model (Table 1). The incremental cost-effectiveness ratio (ICER) was used to evaluate the health economics benefits through the following equation:

$$ICER=\frac{{C}_{B}-{C}_{A}}{{E}_{B}-{E}_{A}}$$

where CB denotes the cost of implementing screening strategy; CA denotes the cost of no screening; EB denotes the life years saved due to implementation of screening; EA denotes the life years of no screening. The ICER values were compared with the gross domestic product (GDP) per capita. When the ICER < 1 GDP per capita, the screening strategy is considered very cost-effective. When the ICER is 1–3 GDP per capita, the screening strategy is considered cost-effective. When the ICER > 3 GDP per capita, the screening strategy is not cost-effective. All prices were shown in USD (US Dollar), and 1USD = 6.45CNY (Chinese Yuan). The threshold value used in this study was 11,136.12 USD, which is the GDP per capita of China in 2020.

Table 1 Cost parameters of Markov model
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A discount rate (the ratio of discounting the cash flow at a certain point in the future to the present moment) of 3% was used in this study [29], with the following discount rate formula:

$${C}_{0}=\frac{{C}_{t}}{{(1+{r}_{d})}^{t}}$$

where, C0 represents the current cost value, Ct represents the cost value after time t, and rd represents the discount rate. We explored the factors influencing the cost-effectiveness of screening strategies for breast and cervical cancer using tornado analysis. Combined with the cost parameters of each strategy, we simulated the model to calculate the incremental life years and the annual per capita social cost investment (PCSCI) for each strategies. To validate our model, we performed internal validation using the life expectancy of the breast and cervical cancer cohort, ensuring that our simulated outcomes aligned with observed data from the life expectancy of the Chinese population in 2019, as published by the National Health and Health Commission. Additionally, we conducted sensitivity analyses to examine the impact of uncertainty in screening test performance on our results. The results of health economic evaluation of the screening strategies were used as input parameters to the IP model.

CBE Clinical breast examination, BUS Breast ultrasonography, MAM Mammography, DCIS Ductal carcinoma in situ, HPV Human papillomavirus, TCT Thin-layer liquid-based cytology, “|”, serial test; “&”, parallel test.

The IP model

PCSCI and life-years saved from Markov model were input parameters in the IP model. The IP model was also called the 0–1 model. The 0–1 decision variable Xij was introduced to indicate relevance of a screening strategy in that cancer screening program. If Xij = 1, then the strategy is invested; if Xij = 0, then the strategy is not invested. eij is the social health effect obtained by the implementation of the jth screening strategy for the ith cancer screening program {E11, E12, …, Eij}. The IP model is quantitatively described as n cancer screenings for Pi women {i1, i2…, in}, with k screening strategies for each screening program, denoted as {j1, j2 … jk}. Then the optimal allocation resource model with the max Z (the social health utility output) is detailed below.

$$\mathit{max }Z=\sum_{i=1}^{n}\sum_{j=1}^{k}{E}_{ij}{X}_{ij}{P}_{i}$$
$$s.t. \sum_{i=1}^{n}\sum_{j=1}^{k}{C}_{ij}{X}_{ij}{P}_{i}\le B{*P}_{i} ,i\forall \in n;\forall j\in k (a)$$
$$\sum_{j=1}^{k}{X}_{ij}=1, \forall i\in n (b)$$
$${X}_{ij}\in \left\{0,1\right\}, \forall i\in n;\forall j\in k (c)$$

For screening programs, i = 1,2, where 1 is breast cancer screening and 2 is cervical cancer screening. We denoted 18 screening strategies as j = 1…18. P1 = P2 = 100,000 people:the number of participants for breast and cervical cancer screening is 100,000. B in constraint (a) denotes the upper limit of per capita health cost spent on cancer prevention and control per year, and Cij represents the cost per person per year. The cost of malignant tumor prevention and control in China in 2020 accounts for 1.70% of the GDP [34], and that of prevention and control of breast and cervical cancers in China in 2018 accounts for 9.93% of all malignant tumors [35]. Thus, the initial value of B was set as 11,136.12 (GDP per capita in 2020) * 1.70% * 9.93% = 18.80 USD, and the thresholds for B values were set to stack 10% each time. We constructed an IP model to simulate a combined dominant strategy for breast and cervical cancer screening at constant annual per capita costs using the LINGO 11.0 software.

Results

Cost-effectiveness of individual screening

Eighteen breast cancer screening strategies had an ICER less than 1 GDP per capita. Both annual and biennial breast cancer screening were dominant over non-screening scenarios. The ICER for 6 screening strategies of cervical cancer were less than 1 GDP per capita, and biennial screening for cervical cancer was dominant over annual and triennial screening (Table 2).

Table 2 Cost-effectiveness of breast and cervical cancer screening strategies
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The life expectancy of the Markov model cohorts for breast and cervical cancer was 77.67 and 77.94 years, respectively, which is similar to the life expectancy of 77.3 years for the Chinese population in 2019, as published by the National Health and Health Commission.

Annual BUS and MAM in parallel screening, CBE and BUS in series screening, BUS and MAM in series screening, and CBE, BUS and MAM in series screening were dominant strategies (7,11,13,15 for breast cancer on Table 2 were on the cost-effectiveness advantage line) for breast cancer (Fig. 3A). The dominant strategies of cervical cancer were biennial, triennial and quinquennial HPV testing; biennial, triennial and quinquennial TCT triage after primary HPV screening; and biennial TCT and HPV in series screening (4,5,6,7,16,17,18 for cervical cancer on Table 2 were on the cost-effectiveness advantage line) (Fig. 3B). The ranking of ICER for each screening strategy was maintained constant by restricting the discount rate to 1–5%, indicating that the model is stable and the output is relatively reasonable (Table s8, Table s9, and Fig s1).

Fig. 3
figure 3

Cost-effectiveness curve of breast cancer (A) and cervical cancer (B) screening strategies. CBE Clinical breast examination, BUS Breast ultrasonography, MAM Mammography, HPV Human papillomavirus, TCT Thin-layer liquid-based cytology, “|”, serial test; “&”, parallel test. Yellow pentagrams represent the situation when no screening is performed. When there are multiple strategies to choose from, the screening strategies are plotted in order of cost from smallest to largest in the cost-effectiveness graph, and the scattered points in the bottom right of the graph are connected into a line, which is the cost-effectiveness advantage curve, then the strategy at the point on the line costs less and gains more life years than other strategies

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Combination of the different screening options

Annual CBE and BUS in series screening was usually selected when annual PCSCI threshold was less than 28.20USD, while annual CBE, BUS and MAM in series screening was optimal for higher PCSCI. The commonest optimal combination strategies of cervical cancer included biennial, triennial and quinquennial HPV testing when PCSCI threshold was less than 35.72USD. HPV testing was highly recommended despite the frequency. TCT was recommended with biennial HPV when PCSCI was greater than 37.60 USD (Table 3).

Table 3 Optimal combination strategies under different annual PCSCI
Full size table

When the annual PCSCI was set at 18.80 USD, we selected biennial BUS and MAM in parallel screening and quinquennial HPV testing as the optimal combination. The total investment cost for 100,000 people was 1,877,984.50 USD, and the incremental life years was 3,122 life-years compared with no screening (Fig. 4). When annual PCSCI was set at 37.60 USD; annual CBE, BUS and MAM in series screening; and biennial TCT and HPV in series screening was selected as the optimal combination strategy. This was maintained, and the sum of incremental life years and investment costs did not increase.

Fig. 4
figure 4

The sum of the total cost and life-years under different annual PCSCI

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Discussion

Breast and cervical cancer screenings are conducted jointly in China, with combined funding assigned for both. This study aimed to find the optimal combination strategy for cervical and breast cancer screening from the societal perspective when funds are limited. The cost-effectiveness of 18 breast cancer screening and 18 cervical cancer screening strategies was evaluated using the Markov model. The cost per person per year derived from the model was input into the IP model to determine the optimal combination strategy of breast and cervical cancer screening. Biennial BUS and MAM in parallel screening, and quinquennial HPV testing was recommended as the optimal combination strategy at 18.80 USD of annual PCSCI (limited budget). Moreover, annual CBE, BUS and MAM in series screening combined with biennial TCT and HPV in series screening was recommended as the optimal combination strategy at 37.60 USD of annual PCSCI (sufficient budget).

Our findings showed that all 18 screening strategies for breast cancer were cost-effective under a threshold of 1 GDP, indicating that breast cancer screening is justified in China. Annual screening strategies of BUS and MAM in parallel screening, CBE and BUS in series screening, BUS and MAM in series screening, and CBE, BUS and MAM in series screening were dominant strategies. The cost of annual screening of CBE, BUS and MAM in series screening was higher than that of others, but was most effective as it demonstrated the highest life-years saved from annual screening. Our results were similar to that of a study that compared the cost-effectiveness of 132 screening strategies for breast cancer; and found that biennial/triennial BUS and MAM in parallel screening, and biennial CBE, BUS and MAM in series screening were the most dominant among women aged 35–59 years [36]. Another study showed that biennial CBE combined with concurrent BUS and MAM in individuals with breast cancer was very cost-effective, and was also recommended in Shanghai [37]. We found that biennial, triennial and quinquennial HPV testing, TCT triage after primary HPV screening, and biennial TCT and HPV in series screening were the dominant screening strategies for cervical cancer. Triennial TCT triage after primary HPV screening was the preferred screening strategy for cervical cancer in China when 1 GDP was used as the cost effectiveness threshold. Biennial HPV was the preferred strategy when 2 GDPs was used as the threshold. The 2012 American Cancer Society guidelines and several studies have shown that cytology combined with HPV high-risk typing test triage is the recommended screening test for cervical cancer [30, 38]. In addition, some studies have indicated that HPV testing alone is also a feasible screening modality for cervical cancer [19, 39]. Most of these findings were based on model simulations, the parameters were mostly from experimental studies, and their effects in the real world need further verification.

Among the optimal combination strategies with different annual PCSCI, the most frequent strategies of breast cancer screening included: CBE and BUS in series screening; and CBE, BUS and MAM in series screening. HPV testing was strongly recommended for cervical cancer which was in line with the dominant strategies for breast and cervical cancers screening of health in this study. When the annual PCSCI reaches 37.60 USD, the optimal combination strategy is annual CBE, BUS and MAM in series screening, and biennial TCT and HPV in series screening. Thus, the series model of breast and cervical cancer screening, and the complementary MAM and TCT screening modality are suitable with resource sufficient budget.

The outcomes of the Markov model aligned with observed data from the life expectancy of the Chinese population in 2019, as published by the National Health and Health Commission. The results of the sensitivity analyses found that the ordering of the incremental cost-effectiveness ratios for each screening strategy remained unchanged, indicating that the model was stable. This study also has limitations. First, the key parameters used in the model were obtained from settings in China and could provide insights into the selection of breast and cervical cancer screening strategies in other upper-middle-income countries similar to China. However, generalization of our conclusions to other countries and regions should be made with caution. Second, quality-adjusted life years of health outcome, the effect of different breast densities and the treatment situations of different stages of cervical cancer on screening results were not considered in the health economics evaluation, and should be added in the future. Third, the arbitrary values set (18.80 USD and 37.60 USD) as budget were also one-sided. Finally, we did not consider indirect costs (productivity loss of patient and family caused by cancer, disability and premature death), which minimizes the potential of ICER metric making in formulating relevant strategies.

Conclusions

From a social cost–benefit perspective, biennial BUS and MAM in parallel screening, and quinquennial HPV screening is the most efficient combination strategy with limited budget, while annual CBE, BUS and MAM in series screening and biennial TCT and HPV in series screening are the most efficient combination strategy with sufficient budget.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

IP:

Inter programming

BUS:

Breast ultrasound

HPV:

Human papillomavirus

MAM:

Mammography

PCSCI:

Per capita social cost investment

TCT:

Thinprep cytology test

CBE:

Clinical breast examination

LBC:

Liquid-based cytology

CHSC:

Community health service centers

DCIS:

Ductal carcinoma in situ

CIN:

Cervical intraepithelial neoplasia

ICER:

Incremental cost-effectiveness ratio

GDP:

Gross domestic product

LYs:

Life years

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Acknowledgements

We thank and appreciate the cooperation of all the participants in this study who gave their time and described their experiences, without whom our work would not have been possible.

Footnotes

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This study was supported by the National Natural Science Foundation of China, grant number: 720741666. The funding organization had no role in the design or conduct of this research.

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Authors and Affiliations

  1. Department of Epidemiology and Health Statistics, School of Public Health, Tianjin Medical University, No.22, Qixiangtai Road, Tianjin, 300070, China

    Xuewei Liu, Yanbo Wang, Bei Gao, Xinlin Lu, Yuan Wang & Wenli Lu

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  1. Xuewei Liu
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  2. Yanbo Wang
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  3. Bei Gao
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  4. Xinlin Lu
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  5. Yuan Wang
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  6. Wenli Lu
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Contributions

XL made contributions to the conception and design of the article, drafting the article, and revising it. YW and BG made contribution to analysis and interpretation of data and revision of the manuscript. XL made contributions to data analysis and drafting of the manuscript. YW and WL made contribution to interpretation of data and drafting the article. All authors contributed to the interpretation of the results and article and approved the submitted version.

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Correspondence to Wenli Lu.

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Liu, X., Wang, Y., Gao, B. et al. Modeling optimal combination of breast and cervical cancer screening strategies in China. BMC Women's Health 25, 56 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12905-025-03573-x

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Keywords

BMC Women's Health

ISSN: 1472-6874

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