Fig 1.
Screening and Clinical Assessment Pathway for the (CBE + BUS) +MAM Strategy.
Fig 2.
Simplified flowchart.
Table 1.
Key Transition and Risk Parameters in the Markov Model for Breast Cancer Screening.
Table 2.
Age-specific incidence of breast cancer.
Table 3.
State transition probability parameters of Markov model for breast cancer screening, Transition probabilities of breast cancer.
Table 4.
Stage distribution probability parameter table of Markov model for breast cancer screening.
Table 5.
Cost parameter estimates for screening program.
Table 6.
Medical costs (CNY).
Table 7.
Health utility value.
Table 8.
Sensitivity and specificity parameters of breast cancer screening protocols.
Table 9.
Detection of different stages of breast cancer in different age groups (per 100, 000 women).
Fig 3.
Cost-utility scatter plot of breast cancer screening programs.
This figure presents the cost-utility scatter plot of the 27 simulated screening strategies. The vertical axis represents the total cost per person, and the horizontal axis represents the effectiveness in Quality-Adjusted Life Years (QALYs) per person. Strategies that are less effective and more costly than a combination of other strategies (i.e., dominated strategies) are plotted primarily in the top-left area of the graph relative to the cost-effectiveness frontier. The cost-effectiveness frontier, formed by the undominated strategies, runs from the bottom-left to the top-right. The undominated strategies are explicitly labeled in the figure and are, in order of increasing effectiveness: (CBE+BUS)+MAM/3year/45_65, (CBE+BUS)+MAM/1year/40_65, (CBE+BUS)+MAM/1year/35_65, (CBE+BUS)+MAM/1year/35_69, and (CBE+BUS)+MAM/1year/35_74. Their respective Incremental Cost-Utility Ratios (ICURs) are presented in the text.
Fig 4.
Tornado diagram of one-way sensitivity analysis for (CBE + BUS) +MAM/2year/35-65 breast cancer screening vs no screening.
Fig 5.
Tornado diagram of one-way sensitivity analysis for (CBE + BUS) +MAM/3year/45_65 breast cancer screening vs no screening.
Fig 6.
Tornado diagram of one-way sensitivity analysis for (CBE + BUS) +MAM/3year/45_65 breast cancer screening vs (CBE + BUS)+MAM/2year/35-65.
Fig 7.
A. Incremental cost-utilities scatter plot of probabilistic sensitivity analysis for (CBE + BUS)+MAM/2year/35_65 breast cancer screening vs no screening; B, Incremental cost-utilities scatter plot of probabilistic sensitivity analysis for (CBE + BUS)+MAM/3year/45_65 breast cancer screening vs no screening.
Each point in the scatter plot represents the result of one iteration from a second-order Monte Carlo simulation (1,000 iterations in total). Green points: iterations where the incremental cost-utility ratio (ICUR) falls below the willingness-to-pay (WTP) threshold of 537,000 CNY/QALY. The 95% confidence ellipse represents the joint uncertainty of the results, with its center at the mean incremental cost and QALYs, and its area encompassing approximately 95% of the simulated points.
Fig 8.
Incremental cost-utilities scatter plot of probabilistic sensitivity analysis for (CBE + BUS)+MAM/2year/35_65 breast cancer screening vs (CBE + BUS)+MAM/3year/45_65.
Each point in the scatter plot represents the result of one iteration from a second-order Monte Carlo simulation (1,000 iterations in total), showing the incremental cost (ΔCost) and incremental quality-adjusted life years (ΔQALYs) of the (CBE + BUS) +MAM/2year/35_65 compared to (CBE + BUS)+MAM/3year/45_65. Green points: iterations where the incremental cost-utility ratio (ICUR) falls below the willingness-to-pay (WTP) threshold of 537,000 CNY/QALY. Red points: iterations where the ICUR exceeds the WTP threshold. The 95% confidence ellipse represents the joint uncertainty of the results, with its center at the mean incremental cost and QALYs, and its area encompassing approximately 95% of the simulated points.