臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：控制組的設計與健保政策效果之評估
作者：林育宏 引用關係
作者(外文)：Yu-Hung Lin
校院名稱：國立高雄第一科技大學
系所名稱：管理研究所
指導教授：許碩芬
學位類別：博士
出版日期：2012
主題關鍵詞：分量迴歸；部分負擔制度；差異中之差異；調整後臨床群組病例組合系統；總額支付制度；Adjusted Clinical Group Case-Mix System；Difference-in-Differences；Global Budget Payment System；Quantile Regression；Copayment system
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:33
健保制度改革對於醫療利用雖有一定的控制效果，但其影響程度及層面均相當分歧。例如部分負擔制度雖普遍可降低被保險人的道德風險，然而在不同的醫療服務項目、被保險人疾病程度及收入所得不同的情形下，其費用控制效果並無一致的結論。又例如總額預算支付制度對於醫療費用控制效果，眾多文獻亦呈現分歧的實證結果。本文認為產生此種分歧的原因可能有二：一來自於醫療利用資料的非線性特徵，二為控制組的品質不良，導致控制組本身亦受到制度變動的影響，而無法適切評估制度效果。
本文主要分為兩大部分：第一部份探討健保需求面的部分負擔制度效果。首先利用調整後臨床群組病例組合系統(Adjusted Clinical Groups, ACG)建立被保險人的罹病風險指標，以反映其健康程度。並據此指標設計控制組，以排除制度變動之外，影響醫療利用的其他無法觀察的因素。其次比較本研究設計之ACG控制組與自然控制組對於制度的評估效果，並分析在不同年齡層以及疾病程度下制度效果的差異。第二部分探討西醫基層總額預算制度在供給面的費用控制效果。由於醫療利用乃病患與醫師兩者共同決策的產物，因此首先以Heckman兩階段估計法以及工具變數的設計，以控制病患的決策對於醫療費用的影響。其次應用第一部份所建立的ACG控制組探討總額預算制度的成效。另外，由於醫療利用資料存在相當程度的偏斜性，因此本文以分量迴歸探討部分負擔以及西醫基層總額預算的制度效果，在不同醫療利用分位量所呈現的差異。
本文實證結果主要如下：首先，各項迴歸模式的評比指標顯示，本文設計之ACG控制組優於自然控制組，且以前者所評估的部分負擔制度效果大於後者。分量迴歸的實證結果顯示，部分負擔制度對於中高醫療利用者具有較大的費用控制效果。
其次，在總額預算支付制度的效果方面，分量迴歸顯示中、高醫療利用者之制度效果較大，但高醫療利用且為重大傷病患者對制度較無彈性。值得注意的是，由於醫療服務的需求會隨著年齡及疾病程度的增加而顯著增加，因此高齡者/重大傷病患者之醫療需求彈性相對較小，在制度變動後減少的醫療費用應較少。然而本文實證結果卻顯示65歲以下之低醫療利用者/非重大傷病之中醫療利用者所減少的醫療費用，卻反而比高齡者/重大傷病患者為少，因此隱含醫療機構誘發醫療需求之可能。
According to literature reports, health care system reform over medical utilization has provided a certain degree of control, but the extent and level of influence are relatively variable. For example, copayment can reduce the moral hazard of the insured. No unanimous agreement exists regarding the effect on control costs of required medical services that are dependent on the severity of the insured’s disease or other health condition. Another example is that empirical results from the global budget payment system for effectiveness of medical cost controls also show differences. These differences may be due to two reasons: Fist, medical utilization data are nonlinear. Second, the control group is of poor quality; if the control group is itself affected by the health system changes leading to reform effects, then this invalidates the assessment to some extent.
This study comprises two parts: In the first part, we explore the reform effects of the copayment system on health care demand. In this study, we use the Adjusted Clinical Groups (ACG) to establish insured people’s morbidity risk indicators to provide an index for the insured patients’ health. According to the index, we designed the control group to exclude the impact of medical utilization of factors other than changes to the system. By assessing the empirical results from the ACG control group and the natural control group, we analyzed the differences in the effect of reform by age group and by extent of disease.
In the second part, we explore the cost control effect of the global budget payment system of health care supply because both patient and physician decide on medical utilization. First, we employ the Heckman two-stage estimation and design of instrumental variables to control the influence of medical expenses on the patients’ decision-making. We then employ the ACG control group to investigate the effectiveness of the global budget payment system. Additionally, this paper adopts the quantile regression estimator approach to assess the effects of the global budget payment system across the entire conditional distribution, and to provide a more complete picture of this relationship.
Our findings indicate the following: First, the ACG control group in this study design is superior to the natural control group according to the assessment indicators of some regression models; the ACG group has a greater control effect over the copayment system than does the natural control group. The empirical results from quantile regression analysis show that the cost control effect of the copayment system is greatest for insured parties with medium and high utilization of medical resources.
Second, the empirical results from quantile regression analysis indicate that the medical expenses control effect over the global budget payment system is greater for insured parties with medium and high medical utilization, but high medical utilization by patients with a catastrophic illness is a lack of flexibility for health care reform.
The demand for medical care increases significantly with increasing age and severity-aggravated illness, the relatively sensitive elasticity of demands by elderly and by people with catastrophic illness is smaller, and the reduction in their medical expenses should be less after changing the health care system. However, empirical results indicate that low medical utilization insured under the age of 65 years, and medium medical utilization insured without catastrophic illness result in medical expenses decreased to less than those of elderly and those with catastrophic illness. In conclusion, medical institutions may induce demand on medical resources.
一、中文參考文獻
1.中央健康保險局網站：http://www.nhi.gov.tw/。new window
2.林兆欣、許碩芬、黃玉珂，2007年，“台灣健保醫療費用成長因素與供需誘因機制之費用控制成效”，風險管理學報，第9卷，第3期，197-219。new window
3.許碩芬、楊雅玲，2007年，“醫療提供者之行為策略-賽局理論之應用”，管理學報，第24卷，第6期，657-670。
4.許碩芬、楊雅玲、陳和全，2007年，“社會困境？─全民健保總額預算制下醫療提供者策略的均衡分析”，管理學報，第24卷，第2期，155-166。new window

二、英文參考文獻
1.Ashenfelter, O. and D. Card, 1985, “Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs”, Review of Economics and Statistics, 67 (4), 648-660.new window
2.Beck, N. and J. N. Katz, 1995, “What To Do (and Not To Do) with Times-Series Cross-Section Data”, American Political Science Review, 89, 634-647.
3.Bishop J. A., J. P. Formby, and L. A. Zeager, 1996, “Relative undernutrition in puerto rico under alternative food assistance programmes”, Applied Economics, 28(8), 1009-1017.
4.Carr-Hill, R. A., 1994, “Efficiency and equity implications of the health care reforms”, Social Science and Medicine, 39(9), 1189-1201.
5.Chen, F., J. Laditka, S. Laditka, and S. Xirasagar, 2007, “Provider’s responses to global budgeting in Taiwan: what were the initial effects?” Health Services Management Research, 20, 113-120.
6.Cheng, SH., CC. Chen, and WL. Chang, 2009, “Hospital response to a global budget program under universal health insurance in Taiwan”, Health Policy, 92, 158-164.
7.Cheng, SH. and TL. Chiang, 1997, “The effect of universal health insurance on health care utilization in Taiwan”, Journal of American Medical Association, 278, 89-93.
8.Cherkin, D. C., L. Grothaus, and E. H. Wagner, 1990, “The effect of office visit copayments on preventive care services in an HMO”, Inquiry, 27, 24-38.
9.Chiappori, P. A., F. Durand, and P. Y. Geoffard, 1998, “Moral hazard and the demand for physician services: first lessons from a French natural experiment”, European Ecnomic Review, 42, 499–511.
10.Chu, D. K., 1992, “Global budgeting of hospital in Hong Kong”, Social Science & Medicine, 35, 857-868.
11.Cockx, B. and C. Brasseur, 2003, “The demand for physician services: Evidence from a natural experiment”, Journal of Health Economics, 22(6), 881-913.
12.Ellis, R. P. and T. G. McGuire, 1990, “Optimal payment systems for health services”, Journal of Health Economics, 9, 375-396.
13.Ellis, R. P. and T. G. McGuire, 1993, “Supply-side and demand-side cost sharing in health care”, Journal Economics Perspectives, 7(4), 135-151.
14.Fowler, E. J. and G. F. Anderson, 1996, “Capitation Adjustment for Pediatric Populations”, PEDIATRICS, 98 (1), 10 -17.new window
15.Gerdtham, U. G., 1997, “Equity in health care utilization: further tests based on hurdle models and Swedish micro data”, Health Economics, 6, 303-319.
16.Grogger, J. T. and R. T. Carson, 1991, “Models for trybcated counts”, Journal of Applied Econometrics, 6(3), 225-238.
17.Grossman, M., 1972, “On the concept of health capital and the demand for health”, Journal of Political Economy, 80, 223-255.
18.Gurmu, S., 1997, “Semi-parametric estimation of hurdle regression models with an application to medical utilization”, Journal of Applied Econometrics, 12, 225-242.
19.Heckman, J., 1979, “Sample selection bias as a specification error”, Econometrica, 47, 153-161.
20.Heckman, J., H. Ichimura, and P. Todd, 1997, “Matching As An Econometric Evaluation Estimtor: Evidence from Evaluating a Job Training Program”, Review of Economic Studies, 64, 605-665.
21.Hirano, K. and G. W. Imbens, 2001, “Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization”, Health Services & Outcomes Research Methodology, 2, 259–278.
22.Hurley, J., J. Lomas, and L. J. Goldsmith, 1997, “Physician responses to global physician expenditure budgets in Canada, a common property perspective”, Milbank Quarterly, 75(3), 343-64.
23.Johns Hopkins University Bloomberg School of Public Health, 2010, The Johns Hopkins ACG Case-Mix System: Documentation and Application Manual for PC (DOS/WIN/NT) and Unix Version 9.0, Baltimore, MD: Johns Hopkins University.
24.Johnson, R. E., M. J. Goodman, M. C. Hornbrook, and M. B. Eldredge, 1997, “The impact of increasing patient prescription drug cost sharing on therapeutic classes of drugs received and on the health status of elderly HMO members”, Health Services Research, 32(1), 103-122.new window
25.Jones, A. M, 2007, “Panel Data Methods and Applications to Health Economics”, In: T. C. Mills and K. Patterson (eds.), The Palgrave Handbook of Econometrics Volume II: Applied Econometrics, 557-631, Basingstoke: Palgrave MacMillan.
26.Jones, A. M. and N. Rice, 2011, “Econometric Evaluation of Health Policies”. In: S. Glied and P. C. Smith (eds.), The Oxford Handbook of Health Economics, 890-923, Oxford University Press.
27.Keeler, E. B. and J. E. Rolph, 1988, “The demand for episodes of treatment in the health insurance experiment”, Journal of Health Economics, 7(4), 337-367.
28.Koenker, R. and G. Bassett, 1978, “Regression quantile”, Econometrica, 46, 33-50.
29.Koenker, R. and K. F. Hallock, 2001, “Quantile regression: An introduction”, Journal of Economic Perspective, 51, 143-156.
30.Machado, J. A. F. and J. M. C. Santos Silva, 2005, “Quantiles for counts”, Journal of the American Statistical Association, 100, 1226-1237.
31.Manning, W. G., J. P. Newhouse, and N. Duan, 1987, “Health insurance and the demand for medical care: evidence from a randomized experiment”, American Economic Review, 77(3), 251-277.
32.Mougeot, M. and F. Naegelen, 2005, “Hospital price regulation and expenditure policy”, Journal of Health Economics, 24, 55-72.
33.Newhouse, J. P., M. B. Buntin, and W. G. Manning, 1993, “Risk Adjustment for a children’s capitation rate”, Health Care Financing Review, 15(1), 39-54.new window
34.Norton, E. C., C. H. Van Hountven, R. C. Lindorooth, S. L. Normand, and B. Dickey, 2002, “Does prospective payment reduce inpatient length of stay?”, Health Economics, 11, 377-387.
35.Pohlmeier, W. and V. Ulrich, 1995, “An econometric model of the two-part decisionmaking process in the demand for health care”, The Journal of Human Resources, 30, 339-361.
36.Puhani P. A. and K. Sonderhof, 2010, “The effects of a sick pay reform on absence and on health-related outcomes”, Journal of Health Economics, 29, 285–302.
37.Redmon D. P. and P. J. Yakoboski, 1995, “The nominal and real effects of hospital global budgets in France”. Inquiry, 32, 174-183.
38.Rosen, B., S. B. Greenberg, R. Gross, and R. Feldman, 2010, “When co-payments for physician visits can affect supply as well as demand: findings from a natural experiment in Israel’s national health insurance system”, International Journal of Health Planning and Management, 26, 21-37.
39.Rosenbaum, P. R. and D. B. Rubin, 1983, “The Central Role of Propensity Score in Observational Studies for Casual Effects”, Biometrika, 70, 41-55.
40.Rossett, R. N. and L. F. Huang, 1973, “The effect of health insurance on the demand for medical care”, Journal of Political Economy, 81, 281-305.
41.Schmidt, L., 2007, “Effects of infertility insurance mandates on fertility”, Journal of Health Economics, 26, 431–446.
42.Selby, Joe V., B. H. Fireman, and B. E. Swain, 1996, “Effect of a copayment on use of the emergency department in a health maintenance organization”, New England Journal of Medicine, 334(10), 635-641.
43.Stoller, S. D., R. Willis, and Y. Zhou, 1996, “Effects of copayment on office visits in a large group model HMO”, AHSR-FHSR Annual Meeting.
44.Van de Ven, W. P. M. M. and R. P. Ellis, 2000, “Risk adjustment in competitive health plan markets”, In: Newhouse, J. P., Culyer, A. J. (Eds.), Handbook of Health Economics, North-Holland, Elsevier, Amsterdam.
45.Van de Voorde, C., E. Van Doorslaer, and E. Schokkaert, 2001, “Effects of cost sharing on physician utilisation under favourable conditions for supplier-induced demand”, Health Economics, 10, 457-471.
46.Weil, T. P., 1990, “Living with NHI”, Health Progress, 71(3), 44-48.
47.Winkelmann R., 2004a, “Health care reform and the number of doctor visits - An econometric analysis”, Journal of Applied Econometrics, 19(4), 455–472.
48.Winkelmann, R., 2004b, “Co-payments for prescription drugs and the demand for doctor visits—evidence from a natural experiment”, Health Economics, 13, 1081–1089.
49.Winkelmann, R., 2006, “Reforming health care: Evidence from quantile regression for counts”, Journal of Health Economics, 25, 131-145.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：675（臺灣：667，外國：8） ／ 本年度總使用人次：1520404 ／ 訪客人次（自102年09月）：67315156
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上