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研究生： 林佳霓
Lin, Chia-Ni
論文名稱： 臺灣乳癌篩檢之成本效益分析：真實世界資料考量社會衝擊
Cost-effectiveness of mammography screening in Taiwan: Real-world data considering societal impact
指導教授： 古鯉榕
Ku, Elizabeth Li-Jung
王榮德
Wang, Jung-Der
學位類別： 博士
Doctor
系所名稱： 醫學院 - 公共衛生學系
Department of Public Health
論文出版年： 2025
畢業學年度： 113
語文別： 英文
論文頁數： 133
中文關鍵詞： 癌症篩檢 、成本效果分析 、健康餘命損失 、生產力損失 、終身醫療費用 、真實世界資料 、乳癌
外文關鍵詞： cancer screening, cost-effectiveness analysis, quality-of-life-adjusted life expectancy loss, productivity loss, lifetime medical costs, real-world data, breast cancer
ORCID： 0000-0001-5545-9714
ResearchGate： https://www.researchgate.net/profile/Chia-Ni-Lin-2
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    • 前言：乳癌篩檢已被模型分析方法證實具成本效益/效果，藉由各階段轉換機率且使用本國或國外已發表之相關參數；在已有建置完善國家級資料庫的時代，或可直接使用真實世界資料估計。
    方法：此論文從社會角度評估臺灣的乳癌篩檢；納入診斷後於追蹤期間所有動態變化，並藉此外推至終身，分期別(就業亦分5歲一組)估計終身平均餘命、終身醫療費用、健康餘命、終身就業期間與終身生產力；並利用年齡、性別、與診斷年配對之對照組計量損失，以考量目標族群間此三項因素差異；接著利用經篩檢與未經篩檢間的期別分布比例加權，考量可能因健康識能造成的選擇性偏誤，得到經篩檢發現一人可節省之損失後，計算邊際成本效果比值；分成三個層次：1. 平均餘命損失為效果，終身醫療費用為成本，並納入篩檢行政費用與可能因輻射暴露新產生乳癌之衍生成本與效果損失、2. 健康餘命損失為效果，再多納入篩檢偽陽性結果產生的費用、3. 就業期間損失與藉健保投保薪資估算之生產力損失。另外，也分別估算經/非經篩檢組的平均餘命與餘命損失，試著量化前導時間偏差，以及不同病理類型乳癌的平均餘命/損失。此論文共串聯五個國家級資料庫：癌症登記檔(2002-2017，113,169位女性)、健保資料庫-就醫檔(醫療費用，2002-2016)、健保資料庫¬承保檔(就業狀態，2002-2017)、死因統計檔(2002-2018)、乳癌篩檢資料庫(2004-2018)，並使用由成大醫院門診收集的EQ5D歐洲生活品質資料(2011-2021，1181位女性，2247次重複測量)。計量原則為終身存活函數乘上第二個函數，使用健保醫療費用、由生活品質轉換之效用值、就業人口比值與投保薪資；先藉由目標族群與利用國家生命表配對之參考族群存活率比值，滾動式外推存活函數，同時藉由與配對參考族群的關係與存活風險外推第二個函數，兩函數相乘後曲線下面積之和即為終身計量。
    結果：乳癌第I、II、III期分別有29.4/33.1、/25.0/28.5、18.2/20.1年的健康餘命/平均餘命；每經篩檢發現一位平均可節省2.9年餘命損失、美金$ 9962.5 (新臺幣297,360元)終身就醫費用損失、2.6年健康餘命損失、0.6年就業期間損失、美金$ 7168.7 (新臺幣228,682元)終身生產力損失。從付費者角度分析2010-2013年臺灣乳癌篩檢計畫並考量偽陽性結果花費後，邊際成本效果比值為每健康人年美金$ 855 (新臺幣25,520元)；加入從社會角度考量生產力損失，2010-2018年間的臺灣乳癌篩檢計畫為節省成本。將平均每發現一位乳癌之成本效果，乘上篩檢發現個案數，可計量投資之總效果；例如2024年經篩檢發現5,250位總共節省13,648年健康餘命、美金$ 5230萬 (新臺幣15億6千萬元)健保醫療費用、2,935年就業期間損失與美金$ 3760萬 (新臺幣12億元)生產力損失。
    結論：本論文以真實資料分析納入偽陽性結果費用與生產力損失後，評估台灣乳癌篩檢為節省成本；乳癌各期別(年齡層)終身結果計量結果，能協助女性醫療上共享決策，亦可協助政府估算健康政策投資之預期整體效果。

    Background: Mammography breast cancer screening (BCS) program has been found cost-effectiveness in conventional evaluation through state transitional model with parameters abstracted from domestic or international studies. In an era of comprehensive government registry datasets, one may directly utilize the real-world data directly.
    Methods: This thesis evaluated the mammography screening program in Taiwan from societal perspective; comprised all the dynamic changes after BC during follow-up period and extrapolated them to lifetime accordingly by stages (or plus five-age stratum for employment), including life expectancy (LE), lifetime medical costs (LMC), quality-adjusted life expectancy (QALE), lifetime employment duration (LED), and lifetime productivity (LP); estimated their losses by comparing them with those of age-, sex-, and calendar-year matched referents accounting for the different distributions among these covariates, and weighed them by stage proportions of BCS versus non-BCS considering the selection bias due to health literacy to obtain the incremental cost-effectiveness ratio (ICER) in three levels: 1. LE loss as effectiveness and LMC as costs including related administration costs and potential radiation-induced women with BC. 2. QALE loss as effectiveness and costs additionally including costs related to screening false-positives (FP). 3. LED loss and LP loss measured by insured salary. Additionally, this thesis estimated the LE/LE loss of BCS and non-BCS separately to depict the potential lead time bias and LE/LE loss among different pathology types. Five national datasets were linked: Taiwan Cancer Registry (2002-2017, 113,169 women with BC), National Health Insurance datasets of medical visits (2002-2016) and beneficiaries for employment status (2002-2017), National Mortality Registry (2002-2018), and National Mammography dataset (2004-2018). The quality of life was also collected using EuroQol five dimensions questionnaire (EQ5D-3L) in outpatient clinics of National Cheng Kung University Hospital (2011–2021, 1,181 women with 2,247 repeated measurements). Based on the principle of multiplying the lifetime function with a second function, e.g., medical costs, utility transformed from quality of life, employment- population ratio (EMRATIO), and insured salary, first, the survival function was extrapolated using rolling-over algorithm by the logit of survival ratio of the index cohort and the matched referents generated from national life table. Then, the second function was extrapolated relying on both the relationship of matched referents utilizing external data and the relationship with the hazards. The summation of area under the curves after multiplying was the lifetime estimates.
    Results: The QALE/LE of women with BC staged I, II, and III were 29.4/33.1, /25.0/28.5, 18.2/20.1 quality-adjusted-life years (QALYs)/years, respectively. The estimated saving per woman by BCS are 2.8 years of LE, US$ 9962.5 (NTD 297,360) of LME, 2.6 QALYs, 0.6 years of LED, and US$ 7168.7 (NTD 228,682) of LP. From payer’s perspective considering FP costs and from societal perspective, the ICER of BCS in Taiwan during 2010-2013 was US$ 855 (NTD 25,520) per QALY saved, and additionally considering productivity loss, the ICER was cost-saving during 2010-2018. Multiplying the estimated savings of BCS per woman detected with the actual numbers found can quantify the overall impact of an investment, such as 5250 cases detected in 2024 generated savings of 13,648 QALYs, US$ 52.3 million (NTD 1.56 billion) of LMC, 2935 years of LED, and 37.6 million (NTD 1.2 billion) of LP.
    Conclusion: The ICER of mammography screening program in Taiwan was cost-saving using real-world data considering FP costs and productivity loss. The stage-(age-)specific lifetime estimates may not only help the females make the medical shared-decision, but also help the government estimate the expected overall impact of the health policy investment.

    中文摘要 i Abstract iii 誌謝 vi Contents vii Contents of Tables ix Contents of Figures xi Abbreviations xiii 1 Introduction 1 1.1 Background 1 1.2 Purpose and specific aims 2 1.3 Framework 3 1.4 Thesis overview 4 2 Literature Review 6 2.1 Effectiveness estimation 6 2.2 Cost estimation 8 2.3 CUA on BCS and potential bias 9 2.4 Other considerations in cost-effectiveness evaluation on BCS 14 3 Methods 16 3.1 Fundamental Principles for lifetime quantification 16 3.1.1 LE and loss-of-LE 17 3.1.2 QoL, QALE and loss-of-QALE 20 3.1.3 LME 22 3.1.4 LED, loss-of-LED and relative loss 23 3.1.5 LP, loss-of-LP, relative loss, and presenteeism 25 3.2 Data sources, data management, and cohort establishment 27 3.2.1 Data sources 29 3.2.2 Cohort establishment 32 3.3 Measurements and variables 33 3.3.1 Outcomes 33 3.3.2 Covariates 34 3.3.3 EQ-5D-3L 34 3.4 Excess women with BC induced by radiation 35 3.5 FP’s consequences and costs 35 3.6 ICER of BC screening program 38 3.6.1 BC detected by mammography versus non-mammography 38 3.6.2 Cost-effectiveness evaluation of screening program 39 3.6.3 Variance of ICER 40 3.6.4 Variance of pooled estimates 41 3.6.5 Variance of differences of LQBCS and LQnon-BCS 41 3.7 Sensitivity analysis for cost-effectiveness evaluation 41 3.8 Justification of the semi-parametric methods 42 3.8.1 Accuracy of extrapolation of LE 42 3.8.2 Adjustment of lead time bia 42 3.9 Potential quantification of the lead time bias 43 4 Results 44 4.1 Cohort 44 4.2 LE, loss-of-LE, QALE, loss-of-QALE, and LME by stages 47 4.3 LE, loss-of-LE, LED, loss-of-LED, LP, and loss-of-LP by stages and ages 51 4.4 FP related examinations and costs 62 4.5 Savings of mammography screening 65 4.6 ICER of BCS compared to non-BCS 69 4.7 Lead time bias, relative bias, and radiation induced excess BC 74 5 Discussions 78 5.1 Main findings and strengths 78 5.2 Dialogue with previous findings 81 5.3 Limitations 84 5.4 Future implications 93 6 Conclusions 94 7 References 95 8 Appendix 105 Appendix Table 1. Temporary and preliminary summary of breast cancer screening relevant systematic review or original studies in terms of effectiveness or costs especially in Taiwan and Asia 105 Appendix Table 2. Operational definitions of the variables and estimates in the thesis 110 Appendix. List of published studies 118

    1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 2018; 68(6): 394-424.
    2. Welfare Ministry of Health and Welfare. Annual report of national cancer registry in 2018. 2020. https://www.hpa.gov.tw/EngPages/Detail.aspx?nodeid=269&pid=13498 (accessed January 14 2021).
    3. Wild C, Weiderpass E, Stewart B. World Cancer Report: Cancer Research for Cancer Prevention. Lyon, France: International Agency for Research on Cancer. Licence: CC BY-NC-ND 3.0 IGO. 2020.
    4. Hsieh HJ, Chen TH, Chang SH. Assessing chronic disease progression using non-homogeneous exponential regression Markov models: an illustration using a selective breast cancer screening in Taiwan. Stat Med 2002; 21(22): 3369-82.
    5. Wu T-Y, Chung S, Yeh M-C, Chang S-C, Hsieh H-F, Ha SJ. Understanding Breast Cancer Screening Practices in Taiwan: a Country with Universal Health Care. Asian Pacific Journal of Cancer Prevention 2012; 13(9): 4289-94.
    6. Health Promotion Administration MoHaW, Taiwan Government. Taiwan breast cancer, oral cancer and colorectal cancer screening programs. 2016. https://www.hpa.gov.tw/EngPages/Detail.aspx?nodeid=1051&pid=5957 (accessed January 14 2021).
    7. Health Promotion Administration, Ministry of Health and Welfare, Republic of China (Taiwan). Health Taiwan- Expanding cancer screening in 2025 and let the government take care of your health (in conventional Mandarin). 2024. https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=4809&pid=18712. Accessed February 2025.
    8. US Preventive Services Task Force; Nicholson WK, Silverstein M, Wong JB, Barry MJ, Chelmow D, Coker TR, Davis EM, Jaén CR, Krousel-Wood M, Lee S, Li L, Mangione CM, Rao G, Ruiz JM, Stevermer JJ, Tsevat J, Underwood SM, Wiehe S. Screening for breast cancer: US Preventive Services Task Force recommendation statement. JAMA. 2024;331(22):1918-1930.
    9. Health Promotion Administration, Ministry of Health and Welfare, Taiwan. Women utilization rate of mammography screening service. Available from: https://www.gender.ey.gov.tw/gecdb/Stat_Statistics_DetailData.aspx?sn=OR0v3mpqY4w1FJ99vq3ytw%3D%3D, 2021. [Accessed 28 April 2021].
    10. Jackson C, Stevens J, Ren S, et al. Extrapolating survival from randomized trials using external data: a review of methods. Medical Decision Making 2017; 37(4): 377-90.
    11. Hwang JS, Hu TH, Lee LJ, Wang JD. Estimating lifetime medical costs from censored claims data. Health Econ 2017; 26(12): e332-e44.
    12. Chung C-H, Hu T-H, Wang J-D, Hwang J-S. Estimation of Quality-Adjusted Life Expectancy of Oral Cancer Patients: Integration of Lifetime Survival With Repeated Quality-of-Life Measurements. Value in health regional issues 2020; 21: 59-65.
    13. Sanders GD, Neumann PJ, Basu A, et al. Recommendations for Conduct, Methodological Practices, and Reporting of Cost-effectiveness Analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA 2016; 316(10): 1093-103.
    14. Plym A, Bower H, Fredriksson I, Holmberg L, Lambert PC, Lambe M. Loss in working years after a breast cancer diagnosis. Br J Cancer 2018; 118(5): 738-43.
    15. Andersson TML, Dickman PW, Eloranta S, Lambe M, Lambert PC. Estimating the loss in expectation of life due to cancer using flexible parametric survival models. Stat Med 2013;32(30):5286–300.
    16. Oliva-Moreno J, Pena-Longobardo LM. Labour productivity loss caused by premature deaths associated with breast cancer: results from Spain over a 10-year period. Breast Cancer Res Treat 2018; 172(3): 571-6.
    17. Lyszczarz B, Nojszewska E. Productivity losses and public finance burden attributable to breast cancer in Poland, 2010-2014. BMC Cancer 2017; 17(1): 676.
    18. Lo JC. Employment pathways of cancer survivors—analysis from administrative data. The European Journal of Health Economics 2019; 20(5): 637-45.
    19. Pike J, Grosse SD. Friction cost estimates of productivity costs in cost-of-illness studies in comparison with human capital estimates: a review. Appl Health Econ Health Policy. 2018;16(6):765-778.
    20. van den Hout WB. The value of productivity: Human-capital versus friction-cost method. Ann Rheum Dis. 2010;69 Suppl 1:i89-91.
    21. Hubens K, Krol M, Coast J, Drummond MF, Brouwer WBF, Uyl-de Groot CA, Hakkaart-van Roijen L. Measurement instruments of productivity loss of paid and unpaid work: A systematic review and assessment of suitability for health economic evaluations from a societal perspective. Value Health. 2021;24(11):1686-1699.
    22. Yuasa A, Yonemoto N, LoPresti M, Ikeda S. Productivity loss/gain in cost-effectiveness analyses for vaccines: A systematic review. Expert Rev Pharmacoecon Outcomes Res. 2021;21(2):235-245.
    23. Schousboe JT, Kerlikowske K, Loh A, Cummings SR. Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness. Ann Intern Med 2011; 155(1): 10-20.
    24. Bleyer A, Welch HG. Effect of three decades of screening mammography on breast-cancer incidence. N Engl J Med 2012; 367(21): 1998-2005.
    25. Nelson HD, Fu R, Cantor A, Pappas M, Daeges M, Humphrey L. Effectiveness of breast cancer screening: systematic review and meta-analysis to update the 2009 US Preventive Services Task Force recommendation. Annals of internal medicine 2016; 164(4): 244-55.
    26. Mandrik O, Ekwunife OI, Meheus F, et al. Systematic reviews as a “lens of evidence”: Determinants of cost‐effectiveness of breast cancer screening. Cancer medicine 2019; 8(18): 7846-58.
    27. Demb J, Abraham L, Miglioretti DL, et al. Screening mammography outcomes: risk of breast cancer and mortality by comorbidity score and age. JNCI: Journal of the National Cancer Institute 2020; 112(6): 599-606.
    28. Jayasekera J, Mandelblatt JS. Systematic review of the cost effectiveness of breast cancer prevention, screening, and treatment interventions. J Clin Oncol 2020;38:332–50.
    29. Rashidian A, Barfar E, Hosseini H, Nosratnejad S, Barooti E. Cost effectiveness of breast cancer screening using mammography: A systematic review. Iran J Public Health 2013;42:347–57.
    30. Schiller-Fruhwirth IC, Jahn B, Arvandi M, Siebert U. Cost-effectiveness models in breast cancer screening in the general population: A systematic review. Appl Health Econ Health Policy 2017;15:333–51.
    31. Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–7. Med Decis Making 2012;32:733–43.
    32. Yoo KB, Kwon JA, Cho E, Kang MH, Nam JM, Choi KS, et al. Is mammography for breast cancer screening cost-effective in both Western and Asian countries? Results of a systematic review. Asian Pac J Cancer Prev 2013;14:4141–9.
    33. European Medicines Agency. Real-world evidence framework to support EU regulatory decision-making: Report on the experience gained with regulator-led studies from September 2021 to February 2023; 2023. Available from https://www.ema.europa.eu/en/news/use-real-world-evidence-regulatory-decision-making-ema-publishes-review-its-studies. Accessed November 12, 2024.
    34. Girman CJ, Ritchey ME, Re III VL. Real‐world data: Assessing electronic health records and medical claims data to support regulatory decision‐making for drug and biological products. Pharmacoepidemiol Drug Saf 2022;31:717–20.
    35. The benefits and harms of breast cancer screening: an independent review. The Lancet 2012; 380(9855): 1778-86.
    36. Myers ER, Moorman P, Gierisch JM, et al. Benefits and Harms of Breast Cancer Screening: A Systematic Review. JAMA 2015; 314(15): 1615-34.
    37. Houssami N. Overdiagnosis of breast cancer in population screening: does it make breast screening worthless? Cancer Biol Med 2017; 14(1): 1-8.
    38. Timmermans L, Bleyen L, Bacher K, et al. Screen-detected versus interval cancers: Effect of imaging modality and breast density in the Flemish Breast Cancer Screening Programme. Eur Radiol 2017; 27(9): 3810-9.
    39. Vlahiotis A, Griffin B, Stavros AT, Margolis J. Analysis of utilization patterns and associated costs of the breast imaging and diagnostic procedures after screening mammography. Clinicoecon Outcomes Res 2018; 10: 157-67.
    40. Cox B, Sneyd MJ. Bias in breast cancer research in the screening era. Breast 2013; 22(6): 1041-5.
    41. Villanti AC, Jiang Y, Abrams DB, Pyenson BS. A cost-utility analysis of lung cancer screening and the additional benefits of incorporating smoking cessation interventions. PloS one 2013; 8(8): e71379.
    42. Keen JD, Jorgensen KJ. Four Principles to Consider Before Advising Women on Screening Mammography. J Womens Health (Larchmt) 2015; 24(11): 867-74.
    43. Pyenson BS, Sander MS, Jiang Y, Kahn H, Mulshine JL. An actuarial analysis shows that offering lung cancer screening as an insurance benefit would save lives at relatively low cost. Health Affairs 2012; 31(4): 770-9.
    44. Esserman L, Shieh Y, Thompson I. Rethinking screening for breast cancer and prostate cancer. JAMA 2009; 302(15): 1685-92.
    45. Andersson TM, Rutherford MJ, Humphreys K. Assessment of lead-time bias in estimates of relative survival for breast cancer. Cancer Epidemiol 2017; 46: 50-6.
    46. Duffy SW, Nagtegaal ID, Wallis M, et al. Correcting for lead time and length bias in estimating the effect of screen detection on cancer survival. American journal of epidemiology 2008; 168(1): 98-104.
    47. Lawrence G, Wallis M, Allgood P, et al. Population estimates of survival in women with screen-detected and symptomatic breast cancer taking account of lead time and length bias. Breast Cancer Res Treat 2009; 116(1): 179-85.
    48. Jensen H, Vedsted P. Exploration of the possible effect on survival of lead-time associated with implementation of cancer patient pathways among symptomatic first-time cancer patients in Denmark. Cancer Epidemiol 2017; 49: 195-201.
    49. Lannin DR, Wang SJNEJoM. Are small breast cancers good because they are small or small because they are good? 2017; 376(23): 2286-91.
    50. Isheden G, Humphreys K. Modelling breast cancer tumour growth for a stable disease population. Stat Methods Med Res 2017: 962280217734583.
    51. Moyer VA. What we don't know can hurt our patients: physician innumeracy and overuse of screening tests. Annals of internal medicine 2012; 156(5): 392-3.
    52. Marcus PM, Prorok PC, Miller AB, DeVoto EJ, Kramer BS. Conceptualizing overdiagnosis in cancer screening. Journal of the National Cancer Institute 2015; 107(4): djv014.
    53. Etzioni R, Xia J, Hubbard R, Weiss NS, Gulati R. A reality check for overdiagnosis estimates associated with breast cancer screening. Journal of the National Cancer Institute 2014; 106(12): dju315.
    54. Black WC, Gareen IF, Soneji SS, et al. Cost-effectiveness of CT screening in the National Lung Screening Trial. N Engl J Med 2014; 371: 1793-802.
    55. Goulart BH, Bensink ME, Mummy DG, Ramsey SD. Lung cancer screening with low-dose computed tomography: costs, national expenditures, and cost-effectiveness. Journal of the National Comprehensive Cancer Network 2012; 10(2): 267-75.
    56. Román M, Hofvind S, von Euler-Chelpin M, Castells X. Long-term risk of screen-detected and interval breast cancer after false-positive results at mammography screening: joint analysis of three national cohorts. British journal of cancer 2019; 120(2): 269-75.
    57. Yen AM-F, Wu WY-Y, Tabar L, Duffy SW, Smith RA, Chen H-H. Initiators and promoters for the occurrence of screen-detected breast cancer and the progression to clinically-detected interval breast cancer. Journal of epidemiology 2017; 27(3): 98-106.
    58. Canelo-Aybar C, Ferreira DS, Ballesteros M, Posso M, Montero N, Sola I, et al. Benefits and harms of breast cancer mammography screening for women at average risk of breast cancer: A systematic review for the European Commission Initiative on breast cancer. J Med Screen 2021;28:389–404.
    59. Yaffe MJ, Mainprize JG. Risk of radiation-induced breast cancer from mammographic screening. Radiology 2011; 258(1): 98-105.
    60. Pauwels EK, Foray N, Bourguignon MH. Breast cancer induced by X-ray mammography screening? A review based on recent understanding of low-dose radiobiology. Medical Principles and Practice 2016; 25(2): 101-9.
    61. Miglioretti DL, Lange J, Van Den Broek JJ, et al. Radiation-induced breast cancer incidence and mortality from digital mammography screening: a modeling study. Annals of internal medicine 2016; 164(4): 205-14.
    62. Committee to Assess Health Risks from Exposure to Low Levels of Ionizing Radiation, National Research Council. Health risks from exposure to low levels of ionizing radiation: Biologic effects of ionizing radiation VII phase 2 report. Washington, DC: National Academies Press; 2006, p.310-311.
    63. National Development Council Taiwan. Personal Data Protection Act. Available from: https://law.moj.gov.tw/ENG/LawClass/LawAll.aspx?pcode=I0050021, 2015. [Accessed 14 January 2021].
    64. Hsieh CY, Su CC, Shao SC, Cheng TC, Chuang SY, Pan SL, et al. Taiwan's National Health Insurance Research Database: Past and future. Clin Epidemiol 2019;11:349–58.
    65. Wu TY, Chung CH, Lin CN, Hwang JS, Wang JD. Lifetime risks, loss of life expectancy, and health care expenditures for 19 types of cancer in Taiwan. Clin Epidemiol 2018; 10: 581-91.
    66. Lee H-Y, Hung M-C, Hu F-C, Chang Y-Y, Hsieh C-L, Wang J-D. Estimating quality weights for EQ-5D (EuroQol-5 dimensions) health states with the time trade-off method in Taiwan. Journal of the Formosan Medical Association 2013; 112(11): 699-706.
    67. National Health Insurance Administration MoHaW. National Health Insurance Report 2017-2018. In: National Health Insurance Administration MoHaW, editor. Taiper, Taiwan: National Health Insurance Administration, Ministry of Health and Welfare; 2017. p. 5.
    68. Organization for Economic Co-operation and Development (OECD). Employment rate (Employment-population ratio, EMRATIO). Available from https://www.oecd.org/en/data/indicators/employment-rate.html. Accessed Febrarury 2025.
    69. Wang F, Hwang JS, Huang WY, Chang YT, Wang JD. Estimation of lifetime productivity loss from patients with chronic diseases: methods and empirical evidence of end-stage kidney disease from Taiwan. Health Econ Rev. 2024;14(1):10.
    70. Wang F, Huang WY, Chang YT, Yang SC, Wang JD. Effective prevention in clinical practice may save human capital loss: Real-world evidence from Taiwan's National Health Insurance. J Formos Med Assoc. 2024;123 Suppl 3:S251-S258.
    71. Lien HM. How to construct social-economic variables from National Health Insurance data (in conventional Mandarin). J Soc Sci Philos 2011;23(3):371-398.
    72. Lai WW, Chung CH, Lin CN, Yang SC, Hwang JS, Wang JD. QALYs and medical costs saved from prevention of a cancer: Analysis of nation-wide real-world data of Taiwan with lifetime horizon. J Formos Med Assoc 2021; S0929-6646(21)00180-7.
    73. Hendrick RE, Pisano ED, Averbukh A, et al. Comparison of acquisition parameters and breast dose in digital mammography and screen-film mammography in the American College of Radiology Imaging Network digital mammographic imaging screening trial. American journal of roentgenology 2010; 194(2): 362-9.
    74. Sickles E, D’Orsi CJ, Bassett LW, et al. Report system. In: ACR BI-RADS® Atlas–Mammography. ACR BI-RADS®, Breast Imaging Reporting and Data System. 5th ed. VA: American College of Radiology, 2013:121–140. Available from https://www.acr.org/-/media/ACR/Files/RADS/BI-RADS/Mammography-Reporting.pdf. Accessed November 12 2024.
    75. Yang SC, Lai WW, Lin CC, et al. Cost-effectiveness of implementing computed tomography screening for lung cancer in Taiwan. Lung Cancer 2017; 108: 183-91.
    76. Huang CC, Lin CN, Chung CH, Hwang JS, Tsai ST, Wang JD. Cost-effectiveness analysis of the oral cancer screening program in Taiwan. Oral Oncol 2019; 89: 59-65.
    77. Adam T, Murray C. Making choices in health: WHO guide to cost-effectiveness analysis: World Health Organization; 2003.
    78. Bertram MY, Lauer JA, De Joncheere K, et al. Cost–effectiveness thresholds: pros and cons. Bulletin of the World Health Organization 2016; 94(12): 925.
    79. Statistical Bureau T. National Statstics, Taiwan- National Economics and Business Activities. 2020. https://eng.stat.gov.tw/ct.asp?xItem=41878&ctNode=6348&mp=5 (accessed January 14 2021).
    80. Oehlert GW. A note on the delta method. Am Stat 1992; 46(1): 27-29.
    81. Health Promotion Administration, Ministry of Health and Welfare. Ages of governmental mammography screening program has been expanded to range of 40–74 years old. (in conventional Mandarin). Published March 3rd, 2025. https://health99.hpa.gov.tw/news/19759. Accessed April 26th 2025
    82. ICRP. The 2007 Recommendations of the International Commission on Radiological Protection. ICRP Publication 103. Ann ICRP 2007; 37(2-4):1-332.
    83. Lin CN, Lee KT, Chang SM, Wang JD. Cost-effectiveness evaluation of mammography screening program in Taiwan: Adjusting different distributions of age and calendar year for real world data. J Formos Med Assoc. 2022;121(3):633-642.
    84. Caswell-Jin JL, Sun LP, Munoz D, Lu Y, Li Y, Huang H, Hampton JM, Song J, Jayasekera J, Schechter C, Alagoz O, Stout NK, Trentham-Dietz A, Lee SJ, Huang X, Mandelblatt JS, Berry DA, Kurian AW, Plevritis SK. Analysis of Breast Cancer Mortality in the US-1975 to 2019. JAMA. 2024;16;331(3):233-241.
    85. Katalinic A, Eisemann N, Kraywinkel K, Noftz MR, Hübner J. Breast cancer incidence and mortality before and after implementation of the German mammography screening program. Int J Cancer. 2020;147(3):709-718.
    86. Van Ourti T, O'Donnell O, Koç H, Fracheboud J, de Koning HJ. Effect of screening mammography on breast cancer mortality: Quasi-experimental evidence from rollout of the Dutch population-based program with 17-year follow-up of a cohort. Int J Cancer. 2020;146(8):2201-2208.
    87. Al Hussein Al Awamlh B, Moses KA, Whitman J, Stewart T, Kripalani S, Idrees K. Health literacy and all-cause mortality among cancer patients. Cancer. 2025;131(6):e35794.
    88. Hinchliffe SR, Rutherford MJ, Crowther MJ, Nelson CP, Lambert PC. Should relative survival be used with lung cancer data? Br J Cancer. 2012;106(11):1854-9.
    89. Talbäck M, Dickman PW. Estimating expected survival probabilities for relative survival analysis--exploring the impact of including cancer patient mortality in the calculations. Eur J Cancer. 2011;47(17):2626-32.
    90. Hinchliffe SR, Dickman PW, Lambert PC. Adjusting for the proportion of cancer deaths in the general population when using relative survival: a sensitivity analysis. Cancer Epidemiol. 2012:36(2):148-52.
    91. Chootipongchaivat S, Wong XY, Ten Haaf K, Hartman M, Tan KB, van Ravesteyn NT, et al. Cost-effectiveness analysis of breast cancer screening using mammography in Singapore: A modeling study. Cancer Epidemiol Biomarkers Prev 2021;30:653–60.
    92. Ho TH, Bissell MCS, Kerlikowske K, Hubbard RA, Sprague BL, Lee CI, et al. Cumulative probability of false-positive results after 10 years of screening with digital breast tomosynthesis vs digital mammography. JAMA Netw Open 2022;5(3):e222440.
    93. Kregting LM, Sankatsing VDV, Heijnsdijk EAM, de Koning HJ, van Ravesteyn NT. Finding the optimal mammography screening strategy: A cost-effectiveness analysis of 920 modelled strategies. Int J Cancer 2022;151:287–96.
    94. Kaur MN, Yan J, Klassen AF, David JP, Pieris D, Sharma M, et al. A systematic literature review of health utility values in breast cancer. Med Decis Making 2022;42(5):704–19.
    95. Ahern CH, Shih YC, Dong W, Parmigiani G, Shen Y. Cost-effectiveness of alternative strategies for integrating MRI into breast cancer screening for women at high risk. Br J Cancer 2014;111(8):1542–51.
    96. Brady MJ, Cella DF, Mo F, , Bonomi AE, Tulsky DS, Lloyd SR, et al. Reliability and validity of the Functional Assessment of Cancer Therapy-Breast quality-of-life instrument. J Clin Oncol 1997;15(3):974–86.
    97. Kaiser CG, Dietzel M, Vag T, Froelich MF. Cost-effectiveness of MR-mammography vs. conventional mammography in screening patients at intermediate risk of breast cancer- A model-based economic evaluation. Eur J Radiol 2021;136:109355.
    98. Stout NK, Rosenberg MA, Trentham-Dietz A, Smith MA, Robinson SM, Fryback DG. Retrospective cost-effectiveness analysis of screening mammography. J Natl Cancer Inst 2006;98(11):774–82.
    99. Mittmann N, Stout NK, Lee P, Tosteson AN, Trentham-Dietz A, Alagoz O, Yaffe MJ. Total cost-effectiveness of mammography screening strategies. Health Rep 2015;26(12):16–25.
    100. Yen AM, Tsau HS, Fann JC, Chen SL, Chiu SY, Lee YC, Pan SL, Chiu HM, Kuo WH, Chang KJ, Wu YY, Chuang SL, Hsu CY, Chang DC, Koong SL, Wu CY, Chia SL, Chen MJ, Chen HH, Chiou ST. Population-Based Breast Cancer Screening With Risk-Based and Universal Mammography Screening Compared With Clinical Breast Examination: A Propensity Score Analysis of 1 429 890 Taiwanese Women. JAMA Oncol. 2016;2(7):915-21.
    101. Yao MM, Vy VPT, Chen TH, Hsu HH, Hsu GC, Lee CS, Lin LJ, Chia SL, Wu CC, Chan WP, Yen AM. Performance measures of 8,169,869 examinations in the National Breast Cancer Screening Program in Taiwan, 2004-2020. BMC Med. 2023;21(1):497.
    102. Shen CT, Chen FM, Hsieh HM. Effect of a national population-based breast cancer screening policy on participation in mammography and stage at breast cancer diagnosis in Taiwan. Health Policy. 2020;124(4):478-485.
    103. Huang SY, Chen HM, Liao KH, Ko BS, Hsiao FY. Economic burden of cancers in Taiwan: a direct and indirect cost estimate for 2007-2017. BMJ Open. 2020;10(10):e036341.
    104. Chou YC, Lee YC. Cost effectiveness: expanding the age for breast cancer screening. Unpublished master thesis. National Yang Ming Chiao Tung University. 2015
    105. Zheng Z, Yabroff KR, Guy GP Jr, Han X, Li C, Banegas MP, Ekwueme DU, Jemal A. Annual medical expenditure and productivity loss among colorectal, female breast, and prostate cancer survivors in the United States. J Natl Cancer Inst. 2015;108(5):djv382.
    106. Ekwueme DU, Trogdon JG, Khavjou OA, Guy GP Jr. Productivity costs associated with breast cancer among survivors aged 18-44 years. Am J Prev Med. 2016;50(2):286-94.
    107. Seweryn M, Banas T, Augustynska J, Lorenc O, Kopel J, Pluta E, Skora T. The direct and indirect costs of breast cancer in Poland: Estimates for 2017-2019. Int J Environ Res Public Health. 2022;19(24):16384.
    108. Hanly P, Timmons A, Walsh PM, Sharp L. Breast and prostate cancer productivity costs: a comparison of the human capital approach and the friction cost approach. Value Health. 2012;15(3):429-36.
    109. Lo WC, Hu TH, Shih CY, Lin HH, Hwang JS. Impact of healthy lifestyle factors on life expectancy and lifetime health care expenditure: Nationwide cohort study. JMIR Public Health Surveill. 2024;10:e57045.
    110. Division of Cancer Prevention HPA. Prevention and control of breast cancer. 2019/10/16 2013. https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=205&pid=1124 (accessed January 14 2021).
    111. Ministry of Health and Welfare, Taiwan. Chapter 3 Insurance finance, Article 20. National Health Insurance Act. Amended June 28th https://law.moj.gov.tw/ENG/LawClass/LawAll.aspx?pcode=L0060001. Accessed February 2025.
    112. Goldschmidt-Clermont L. Unpaid Work in the Household: A Review of Economic Evaluation Methods. Women, Work and Development Series no. 1. 1989. Geneva: International Labor Organization.
    113. International Monetary Fund. Women, Work, and the Economy: Macroeconomic Gains from Gender Equity. IMF Staff Discussion Note, September, 2013, Washington, D.C., USA.
    114. Ortega-Ortega M, Hanly P, Pearce A, Soerjomataram I, Sharp L. Paid and unpaid productivity losses due to premature mortality from cancer in Europe in 2018. Int J Cancer. 2022;150(4):580-593.
    115. International Labour Organization. The impact of care responsibilities on women’s labour force participation. 2024. Accessed April 22nd from https://www.ilo.org/sites/default/files/2024-10/GEDI-STAT%20brief_formatted_28.10.24_final.pdf

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