研究生: |
吳芷螢 Zh-Ying Wu |
---|---|
論文名稱: |
高齡社會下之血液供需缺口分析及因應策略之效益評估 Evaluation of the Impacts of Aging Society on Future Blood Supply and the Effectiveness of Mitigation Strategies |
指導教授: |
林希偉
Shi-Woei Lin 李強笙 Chiang-Sheng Lee |
口試委員: |
吳崇光
Chung-Guang Wu 李強笙 Chiang-Sheng Lee 林希偉 Shi-Woei Lin |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 工業管理系 Department of Industrial Management |
論文出版年: | 2017 |
畢業學年度: | 105 |
語文別: | 中文 |
論文頁數: | 82 |
中文關鍵詞: | 血液缺口 、人口結構改變 、專家判斷 、Cooke 古典模型 、蒙地卡羅模擬 |
外文關鍵詞: | Gap in blood supply, Demographic structure, Expert judgement, Cook’s classic model, Monte Carlo simulation |
相關次數: | 點閱:288 下載:0 |
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台灣社會的人口老化問題日益嚴重,生育率下降及少子化情況使得年輕捐血族群比率下降,而老齡化社會與老年人口增加則加速用血量增加的趨勢,未來可能因人口結構之消長,產生血液供需的嚴重缺口。本研究以人口結構的趨勢變化為基礎,結合健保資料庫中的用血資訊,推估未來血液的供給與需求,本研究同時考量人口結構外之質化因子如醫療科技與政策的改變以及文化變遷等,透過Cooke 古典模型整合專家意見,並在搭配實證數據後,以蒙地卡羅模擬方法求得更合理的預測。研究指出加入非人口結構因素後,血液供需之缺口將可下修約四分之一,但 2033 年的缺口仍有百分之九十的機率落在170,712與463,299單位間。在血液管理方案的效益評估方面,本研究指出寬放捐血年齡無法明顯消弭供應缺口,但提供額外捐血誘因或者改變醫療給付政策則有較大效益,研究結果可提供相關單位決策之參考。
Population aging is currently shifting the distribution of Taiwan’s population towards older ages. The changes in demographic structure seriously affect the blood supply because eligible blood donor population consists primarily of young and healthy individuals while the patients requiring blood transfusions belong to the older age groups. Thus, this population-based study aims to estimate the future supply and demand of blood based on changes in demographic structure and the data of blood donation and transfusion in the national health insurance database. In particular, for taking factors other than demographics into consideration, this research also elicited expert judgments on the effects of the advances in medical technologies, policy changes, and cultural changes on the supply and demand of the blood product. Cooke’s classic model was used to aggregate the probability distribution judgments provided by different experts and the Monte Carlo simulation method was implemented to obtain a predicted distribution of the gap in blood supply (in year 2033). The research pointed out that after adding non-demographic factors, the average gap in blood supply and demand in 2033 will be reduced by about one quarter. The simulated results also showed that that there will be 90% probability that the gap will fall between 170,712 and 463,299 units. In terms of the effectiveness of mitigation strategies, this research showed that a more lenient age restriction could not significantly eliminate the gap while providing additional incentives to blood donors or changing the medical treatment polices should be more effective.
Armstrong, J. S. (2001). Extrapolation for time-series and cross-sectional data. In Principles of forecasting (pp. 217-243). Springer US.
Anderson, S. A., M. Menis, K. O’Connell, and D. R. Burwen (2007) Blood use by inpatient elderly population in the United States. Transfusion, 47(4), 582–592.
Aspinall, W. (2008). Expert judgment elicitation using the classical model and Excalibur. Seventh Session of the Statistics and Risk Assessment Section’s International Expert Advisory Group on Risk Modeling: Iterative Risk Assessment Processes for Policy Development Under Conditions of Uncertainty I Emerging Infectious Diseases: Round IV, 1-22.
Ali, A., Auvinen, M. K., & Rautonen, J. (2010). Blood donors and blood collection: The aging population poses a global challenge for blood services. Transfusion, 50(3), 584-588.
Aspinall, W. (2010). A route to more tractable expert advice. Nature, 463(7279), 294-295.
Brockhoff, K. (1975). The performance of forecasting groups in computer dialogue and face-to-face discussion. The Delphi method: Techniques and Applications, 291-321.
Bier, V. M. (2004), “Implications of the Research on Expert Overconfidence and Dependence”, Reliability Engineering and System Safety, 85, 321 – 329.
Borkent‐Raven, B. A., Janssen, M. P., & Van Der Poel, C. L. (2010). Demographic changes and predicting blood supply and demand in the Netherlands. Transfusion, 50(11), 2455-2460.
Benjamin, R. J., and B. I. Whitaker. (2011) Boom or bust? Estimating blood demand and supply as the baby boomers age. Transfusion, 51, 670–3.
Bolger, F. and Rowe, R. (2015) The Aggregation of Expert Judgment: Do Good Things Come to Those Who Weight? Risk Analysis, 35(1), 5 – 11.
Cooke, R. (1991). Experts in uncertainty: opinion and subjective probability in science. Oxford University Press on Demand.
Cooke, R. M., & Solomatine, D. (1992). EXCALIBR Integrated System for Processing Expert Judgements version 3.0. Delft University of Technology and SoLogic Delft, Delft.
Clemen, R. T., & Winkler, R. L. (1999). Combining probability distributions from experts in risk analysis. Risk Analysis, 19(2), 187-203.
Cullen, A. C., & Frey, H. C. (1999). Probabilistic techniques in exposure assessment: a handbook for dealing with variability and uncertainty in models and inputs. Springer Science & Business Media.
Cooke, R. M., & Goossens, L. H. J. (2000). Procedures guide for structural expert judgement in accident consequence modelling. Radiation Protection Dosimetry, 90(3), 303-309.
Currie, C. J., T. C. Patel, P. McEwan, and S. Dixon (2004) Evaluation of the future supply and demand for blood products in the United Kingdom National Health Service. Transfusion Medicine, 14, 19–24.
Cooke, R. M., & Goossens, L. L. (2008). TU Delft expert judgment data base. Reliability Engineering & System Safety, 93(5), 657-674.
Clemen, R. T. (2008). Comment on Cooke's classical method. Reliability Engineering & System Safety, 93(5), 760-765.
Clemen, R. T., & Reilly, T. (2013). Making hard decisions with DecisionTools. Cengage Learning.
Dalkey, N. (1969). An experimental study of group opinion: the Delphi method. Futures, 1(5), 408-426.
Dalkey, N. C., Brown, B. B., & Cochran, S. W. (1970). The delphi method, IV: Effect of percentile feedback and feed-in of relevant facts. Rand Corporation.
Desalvo, F., Verlicchi, F., & Tomasini, I. (2011). An estimate of future transfusion needs in the province of Ravenna made on the basis of Italian national statistics and past consumption. Blood Transfusion, 9(4), 413.
Drackley, A., Newbold, K. B., Paez, A., & Heddle, N. (2012). Forecasting Ontario's blood supply and demand. Transfusion, 52(2), 366-374.
Eggstaff, J. W., Mazzuchi, T. A., & Sarkani, S. (2014). The effect of the number of seed variables on the performance of Cooke’s classical model. Reliability Engineering & System Safety, 121, 72-82.
Fan, W., Yi, Q. L., Xi, G., Goldman, M., Germain, M., & O'brien, S. F. (2012). The impact of increasing the upper age limit of donation on the eligible blood donor population in Canada. Transfusion Medicine, 22(6), 395-403.
Gillespie, T. W., & Hillyer, C. D. (2002). Blood donors and factors impacting the blood donation decision. Transfusion Medicine Reviews, 16(2), 115-130.
Greinacher, A., Fendrich, K., Alpen, U., & Hoffmann, W. (2007). Impact of demographic changes on the blood supply: Mecklenburg‐West Pomerania as a model region for Europe. Transfusion, 47(3), 395-401.
Greinacher, A., K. Fendrich, and W. Hoffmann (2010) Demographic changes: the impact for safe blood supply. Transfusion Medicine and Hemotherapy, 37(3), 141–148.
Greinacher, A., K. Fendrich, R. Brzenska, V. Kiefel, and W. Hoffmann (2011) Implications of demographics on future blood supply: a population-based cross-sectional study. Transfusion, 51(4), 702–709.
Jóhannsdóttir, V., Gudmundsson, S., Möller, E., Aspelund, T., & Zoëga, H. (2016). Blood donors in Iceland: a nationwide population‐based study from 2005 to 2013. Transfusion, 56(6pt2), 1654-1661.
Keefer, D. L., & Bodily, S. E. (1983). Three-point approximations for continuous random variables. Management Science, 29(5), 595-609.
Kahneman, D., & Riepe, M. W. (1998). Aspects of investor psychology. The Journal of Portfolio Management, 24(4), 52-65.
Lichtenstein, S., & Fischhoff, B. (1977). Do those who know more also know more about how much they know?. Organizational Behavior and Human Performance, 20(2), 159-183.
Lichtenstein, S., Fischhoff, B., & Phillips, L. D. (1982). Calibration of probabilities: the state of the art to 1980.
Lee, C. K., Hong, J., & Hung, A. T. F. (2008). An update of blood donor recruitment and retention in Hong Kong. Asian Journal of Transfusion Science, 2(2), 47.
Leahy, M. F., & Mukhtar, S. A. (2012). From blood transfusion to patient blood management: a new paradigm for patient care and cost assessment of blood transfusion practice. Internal Medicine Journal, 42(3), 332-338.
Lacetera, N., Macis, M., & Slonim, R. (2013). Economic rewards to motivate blood donations. Science, 340(6135), 927-928.
Lau, E. H., He, X. Q., Lee, C. K., & Wu, J. T. (2013). Predicting future blood demand from thalassemia major patients in Hong Kong. PloS One, 8(12), e81846.
Lattimore, S., Wickenden, C., & Brailsford, S. R. (2015). Blood donors in England and North Wales: demography and patterns of donation. Transfusion, 55(1), 91-99.
Morgan, M. G., Henrion, M., & Small, M. (1992). Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge university press.
Murry, J. W., & Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423.
Misje, A. H., V. Bosnes, and H. E. Heier (2008) Recruiting and retaining young people as voluntary blood donors. Vox Songuinis, 94, 119–124.
Murphy, E. L., Shaz, B., Hillyer, C. D., Carey, P., Custer, B. S., Hirschler, N., Fang, J., & Schreiber, G. B. (2009). Minority and foreign‐born representation among US blood donors: demographics and donation frequency for 2006. Transfusion, 49(10), 2221-2228.
Morgan, M. G. (2015). Our Knowledge of the World is Often Not Simple: Policymakers Should Not Duck that Fact, But Should Deal with It. Risk Analysis, 35(1), 19-20.
Ownby, H. E., Kong, F., Watanabe, K., Tu, Y., & Nass, C. C. (1999). Analysis of donor return behavior. Transfusion, 39(10), 1128-1135.
O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., ... & Rakow, T. Eliciting and Fitting a Parametric Distribution. Uncertain Judgements: Eliciting Experts' Probabilities, 121-151.
Plous, S. (1993). The psychology of judgment and decision making. Mcgraw-Hill Book Company.
Putnam, R. D. (2001) Bowling Alone: The Collapse and Revival American Community. New York, NY, Simon & Schuster.
Palo, R. (2013). Epidemiology of blood component use in Finland.
Russo, J. E., & Schoemaker, P. J. (1992). Managing overconfidence. Sloan Management Review, 33(2), 7.
Royse, D., & Doochin, K. E. (1995). Multi‐gallon blood donors: who are they?. Transfusion, 35(10), 826-831.
Riley, W., Schwei, M., & McCullough, J. (2007). The United States' potential blood donor pool: estimating the prevalence of donor‐exclusion factors on the pool of potential donors. Transfusion, 47(7), 1180-1188.
Rogers, M. A., Blumberg, N., Heal, J. M., & Langa, K. M. (2011). Utilization of blood transfusion among older adults in the United States. Transfusion, 51(4), 710-718.
Stone, M. (1961). The opinion pool. The Annals of Mathematical Statistics, 32(4), 1339-1342.
Shlyakhter, A. I., Kammen, D. M., Broido, C. L., & Wilson, R. (1994). Quantifying the credibility of energy projections from trends in past data: The US energy sector. Energy Policy, 22(2), 119-130.
Soll, J. B., & Klayman, J. (2004). Overconfidence in interval estimates. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(2), 299.
Schreiber, G. B., U. K. Sharma, D. J. Wright, S. A. Glynn, H. E. Ownby, Y. Tu, G. Garratty, J. Piliavin, T. Zuck, and R. Gilcher (2005) First year donation pattern predict long-term commitment for first-time donors. Vox Songuinis, 88, 114–121.
Schlumpf, K. S., Glynn, S. A., Schreiber, G. B., Wright, D. J., Randolph Steele, W., Tu, Y., Hermansen, S., Higgins, M. J., Garratty, G., & Murphy, E. L. (2008). Factors influencing donor return. Transfusion, 48(2), 264-272.
Seifried, E., Klueter, H., Weidmann, C., Staudenmaier, T., Schrezenmeier, H., Henschler, R., ... & Mueller, M. M. (2011). How much blood is needed?. Vox sanguinis, 100(1), 10-21.
Sutherland, W. J., & Burgman, M. (2015). Policy advice: Use experts wisely. Nature, 526(7573), 317.
von Neumann, J., & Ulam, S. (1949). Monte carlo method. National Bureau of Standards Applied Mathematics Series, 12, 36.
Wells, A. W., P. J. Mounter, C. E. Chapman, D. Stainsby, and J. P. Wallis (2002) Where does blood go? Prospective observational study of red cell transfusion in north England. British Medical Journal, 325, 803–4.
Wallis, J. P., & Dzik, S. (2004). Is fresh frozen plasma overtransfused in the United States?. Transfusion, 44(11), 1674-1675.
Wells, A. W., Llewelyn, C. A., Casbard, A., Johnson, A. J., Amin, M., Ballard, S., ... & Williamson, L. M. (2009). The EASTR Study: indications for transfusion and estimates of transfusion recipient numbers in hospitals supplied by the National Blood Service. Transfusion Medicine, 19(6), 315-328.
Whitsett, C., Vaglio, S., & Grazzini, G. (2012). Alternative blood products and clinical needs in transfusion medicine. Stem Cells International, 2012.
Wevers, A., Wigboldus, D. H., de Kort, W. L., van Baaren, R., & Veldhuizen, I. J. (2014). Characteristics of donors who do or do not return to give blood and barriers to their return. Blood Transfusion, 12(1), 1-37.
Winkler, R. L. (2015). Equal Versus Differential Weighting in Combining Forecasts. Risk Analysis, 35(1), 16.
Yu, P. L. H., K. H. Chung, C. K. Lin, J. S. Chan, and C. K. Lee (2007) Predicting potential drop-out and future commitment for first-time donors based on first 1.5 year donation patterns: the case in Hong Kong Chinese donors. Vox Sanguinis, 93, 57–63.
Zou, S., F. Musavi, E. P. Notari, and C. T. Fang (2008) Changing age distribution of the blood donor population in the United States. Transfusion, 48, 251–257.
Zeiler, T., Lander‐Kox, J., Eichler, H., Alt, T., & Bux, J. (2011). The safety of blood donation by elderly blood donors. Vox Sanguinis, 101(4), 313-319.
中文文獻
林誠謙. (1989). 蒙地卡羅法簡介. 物理雙月刊, 11(2), 163-176.
洪啟民. (2006). 醫學中心醫師對使用新鮮冷凍血漿認知, 態度之探討. 臺灣大學醫療機構管理研究所學位論文, 1-149.
謝佩翰. (2011). 台灣醫療用血資源耗用分析之研究. 中臺科技大學健康產業管理研究所學位論文, (2011 年), 1-127.
網路文獻
國家發展委員會。中華民國人口推估(105至150年)。取自http://www.ndc.gov.tw/Content_List.aspx?n=84223C65B6F94D72
醫療財團法人台灣血液基金會(2013)。102年報。取自http://intra.blood.org.tw/upload/3f9ab620-cd06-472b-b500-7d637ae77e18.pdf
醫療財團法人台灣血液基金會(2014)。103年報。取自http://intra.blood.org.tw/upload/5df216ff-95db-4062-ab16-6b5c81fc4c52.pdf
內政部統計處。內政統計查詢網。取自
http://statis.moi.gov.tw/micst/stmain.jsp?sys=100
PracticalForecasting.com(2007)。Forecast Structure.。取自http://www.practicalforecasting.com/forecast-structure.html