Population-level intervention and information collection in dynamic healthcare policy

Health Care Management Science - Tập 21 - Trang 604-631 - 2017
Lauren E. Cipriano1, Thomas A. Weber2
1Ivey Business School, Western University, London, Canada
2Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

Tóm tắt

We develop a general framework for optimal health policy design in a dynamic setting. We consider a hypothetical medical intervention for a cohort of patients where one parameter varies across cohorts with imperfectly observable linear dynamics. We seek to identify the optimal time to change the current health intervention policy and the optimal time to collect decision-relevant information. We formulate this problem as a discrete-time, infinite-horizon Markov decision process and we establish structural properties in terms of first and second-order monotonicity. We demonstrate that it is generally optimal to delay information acquisition until an effect on decisions is sufficiently likely. We apply this framework to the evaluation of hepatitis C virus (HCV) screening in the general population determining which birth cohorts to screen for HCV and when to collect information about HCV prevalence.

Tài liệu tham khảo

Gold MR, Siegel JE, Russell LB, Weinstein MC (1996) Cost-Effectiveness in Health and Medicine. Oxford University Press, Oxford Drummond MF, Sculpher MJ, Torrance GW (2005) Methods for the Economic Evaluation of Health Care Programs, 3rd edn. Oxford University Press, Oxford Ades AE, Lu G, Claxton KP (2004) Expected value of sample information calculations in medical decision modeling. Med Decis Making 24(2):207–227 Claxton KP, Sculpher MJ (2006) Using value of information analysis to prioritise health research: Some lessons from recent UK experience. PharmacoEconomics 24(11):1055–1068 Eckermann S, Karnon J, Willan AR (2010) The value of value of information. PharmacoEconomics 28 (9):699–709 Philips Z, Claxton K, Palmer S (2008) The half-life of truth: what are appropriate time horizons for research decisions?. Med Decis Making 28(3):287–299 Eckermann S, Willan AR (2008) The option value of delay in health technology assessment. Med Decis Making 28(3):300–305 Juusola JL, Brandeau ML (2016) HIV treatment and prevention: a simple model to determine optimal investment. Med Decis Making 36(3):391–409 Singer ME, Younossi ZM (2001) Cost effectiveness of screening for hepatitis C virus in asymptomatic, average-risk adults. Am J Med 111(8):614–621 Chou R, Clark EC, Helfand M (2004) Screening for hepatitis C virus infection: a review of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 140(6):465–479 Rein DB, Smith BD, Wittenborn JS, Lesesne SB, Wagner LD, Roblin DW, Patel N, Ward JW, Weinbaum CM (2012) The cost-effectiveness of birth-cohort screening for hepatitis C antibody in US primary care settings. Ann Intern Med 156(4):263–270 Coffin PO, Scott JD, Golden MR, Sullivan SD (2012) Cost-effectiveness and population outcomes of general population screening for hepatitis C. Clin Infect Dis 54(9):1259–1271 McGarry LJ, Pawar VS, Panchmatia HR, Rubin JL, Davis GL, Younossi ZM, Capretta JC, O’Grady MJ, Weinstein MC (2012) Economic model of a birth cohort screening program for hepatitis C virus. Hepatology 55(5):1344–1355 Liu S, Cipriano LE, Holodniy M, Goldhaber-Fiebert JD (2013) Cost-effectiveness analysis of risk-factor guided and birth-cohort screening for chronic hepatitis C infection in the United States. PLoS One 8(3):e58975 Eckman MH, Talal AH, Gordon SC, Schiff E, Sherman KE (2013) Cost-effectiveness of screening for chronic hepatitis C infection in the United States. Clin Infect Dis 56(10):1382–1393 Smith BD, Morgan RL, Beckett GA, Falck-Ytter Y, Holtzman D, Ward JW (2012) Hepatitis C virus testing of persons born during 1945–1965: Recommendations from the Centers for Disease Control and Prevention. Ann Intern Med 157(11):817–822 Moyer VA, on behalf of the U.S. Preventive Services Task Force (2013) Screening for Hepatitis C Virus Infection in Adults: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med 159(5):349–357 Jensen R (1983) Innovation adoption and diffusion when there are competing innovations. J Econ Theory 29(1):161–171 McCardle KF (1985) Information acquisition and the adoption of new technology. Manag Sci 31(11):1372–1389 Smith JE, McCardle KF (2002) Structural properties of stochastic dynamic programs. Oper Res 50(5):796–809 Ulu C, Smith JE (2009) Uncertainty, information acquisition, and technology adoption. Oper Res 57(3):740–752 Rosenberg N (1982) Inside the black box: technology and economics. Cambridge University Press, Cambridge Bessen J (1999) Real options and the adoption of new technologies. Research on Innovation. http://www.researchoninnovation.org Kornish LJ (2006) Technology choice and timing with positive network effects. Eur J Oper Res 173(1):268–282 Chambers C, Kouvelis P (2003) Competition, learning and investment in new technology. IIE Trans 35(9):863–878 Schaefer AJ, Bailey MD, Shechter SM, Roberts MS (2005) Chapter 23: Modeling medical treatment using Markov decision processes. In: Operations Research and Health Care: A handbook of methods and applications, Springer US, volume 70 of International Series in Operations Research and Management Science. pp. 593–612 Alagoz O, Hsu H, Schaefer AJ, Roberts MS (2010) Markov decision processes: a tool for sequential decision making under uncertainty. Med Decis Making 30(4):474–483 Ahn JH, Hornberger JC (1996) Involving patients in the cadaveric kidney transplant allocation process: A decision-theoretic perspective. Manag Sci 42(5):629–641 Magni P, Quaglini S, Marchetti M, Barosi G (2000) Deciding when to intervene: a Markov decision process approach. Int J Med Inform 60(3):237–253 Hauskrecht M, Fraser H (2000) Planning treatment of ischemic heart disease with partially observable Markov decision processes. Artif Intell Med 18(3):221–244 Alagoz O, Maillart LM, Schaefer AJ, Roberts MS (2004) The optimal timing of living–donor liver transplantation. Manag Sci 50(10):1420–1430 Alagoz O, Maillart LM, Schaefer AJ, Roberts MS (2007) Choosing among cadaveric and living–donor livers. Manag Sci 53(11):1702–1715 Shechter SM, Bailey MD, Schaefer AJ, Roberts MS (2008) The optimal time to initiate HIV therapy under ordered health states. Oper Res 56(1):20–33 Shechter SM, Bailey MD, Schaefer AJ, Roberts MS (2008) A modeling framework for replacing medical therapies. IIE Trans 40(9):861–869 Kırkızlar E, Faissol DM, Griffin PM, Swann JL (2010) Timing of testing and treatment for asymptomatic diseases. Math Biosci 226(1):28–37 Kurt M, Denton B, Schaefer AJ, Shah N, Smith S (2011) The structure of optimal statin initiation policies for patients with type 2 diabetes. IIE Trans 1(1):49–65 Mason JE, Denton BT, Shah ND, Smith SA (2014) Optimizing the simultaneous management of blood pressure and cholesterol for type 2 diabetes patients. Eur J Oper Res 233(3):727–738 Zhang J, Denton BT, Balasubramanian H, Shah ND, Inman BA (2012) Optimization of prostate biopsy referral decisions. Manuf Serv Op 14(4):529–547 Ayer T, Alagoz O, Stout NK, Burnside ES (2014) Designing a new breast cancer screening program considering adherence. Manag. Sci. Forthcoming Erenay FS, Alagoz O, Said A (2014) Optimizing colonoscopy screening for colorectal cancer prevention and surveillance. Manuf Serv Op 16(3):381–400 Patrick J, Puterman ML, Queyranne M (2008) Dynamic multipriority patient scheduling for a diagnostic resource. Oper Res 56(6):1507–1525 Gocgun Y, Bresnahan BW, Ghate A, Gunn ML (2011) A Markov decision process approach to multi-category patient scheduling in a diagnostic facility. Artif Intell Med 53:73–81 Patrick J (2012) A Markov decision model for determining optimal outpatient scheduling. Health Care Manag Sci 15:91–102 Gocgun Y, Puterman ML (2014) Dynamic scheduling with due dates and time windows: an application to chemotherapy patient appointment booking. Health Care Manag Sci 17:60–76 Gupta D, Wang L (2008) Revenue management for a primary-care clinic in the presence of patient choice. Oper Res 56(3):576–592 Wang J, Fung RYK (2015) Adaptive dynamic programming algorithms for sequential appointment scheduling with patient preferences. Artif Intell Med 63:33–40 Kornish LJ, Keeney RL (2008) Repeated commit-or-defer decisions with a deadline: the influenza vaccine composition. Oper Res 56(3):527–541 Özaltın OY, Prokopyev OA, Schaefer AJ, Roberts MS (2011) Optimizing the societal benefits of the annual influenza vaccine: a stochastic programming approach. Oper Res 59(5):1131–1143 Weinstein M, Zeckhauser R (1973) Critical ratios and efficient allocation. J Public Econ 2:147–157 Culyer AJ (1989) The normative economics of health care finance and provision. Oxf Rev Econ Policy 5:34–58 Stinnett AA, Paltiel AD (1996) Mathematical programming for the efficient allocation of health care resources. J Health Econ 15:641–653 Williams I, McIver S, Moore D, Bryan S (2008) The use of economic evaluations in NHS decision-making: a review and empirical investigation. Health Technol Assess 12(7):1–175 Raiffa H, Schlaifer R (1961) Applied Statistical Decision Theory. Harvard University Press, Cambridge Weinstein MC (1983) Cost-effective priorities for cancer prevention. Science 221(4605):17–23 Hornberger JC, Brown BW, Halpern J (1995) Designing a cost-effective clinical trial. Stat Med 14 (20):2249–2259 Claxton K, Posnett J (1996) An economic approach to clinical trial design and research priority-setting. Health Econ 5(6):513–524 Claxton K (1999) The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ 18(3):341–364 Eckermann S, Willan AR (2008) Time and expected value of sample information wait for no patient. Value Health 11(3):522–526 McKenna C, Claxton K (2011) Addressing adoption and research design decisions simultaneously: the role of value of sample information analysis. Med Decis Making 31(6):853–865 Hall PS, Edlin R, Kharroubi S, Gregory W, McCabe C (2012) Expected net present value of sample information from burden to investment. Med Decis Making 32(3):E11—E21 Fenwick E, Claxton K, Sculpher M (2008) The value of implementation and the value of information: combined and uneven development. Med Decis Making 28(1):21–32 Willan AR, Eckermann S (2010) Optimal clinical trial design using value of information methods with imperfect implementation. Health Econ 19(5):549–561 Smith JE (1993) Moment methods for decision analysis. Manag Sci 39(3):340–358 Gospodinov N, Lkhagvasuren D (2014) A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains. J Appl Econom 29(5):843–859 Weber TA (2017) Optimal switching between cash-flow streams. Math. Method. Oper. Res. Forthcoming. https://doi.org/10.1007/s00186-017-0586-0 Blackwell D (1951) Comparison of experiments. In: Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. pp 93–102 Kim W (2002) The burden of hepatitis C in the United States. Hepatology 36(Suppl 1):S30—S34 Ghany MG, Strader DB, Thomas DL, Seeff LB (2009) Diagnosis, management, and treatment of hepatitis C: an update. Hepatology 49(4):1335–1374 Armstrong GL, Wasley A, Simard EP, McQuillan GM, Kuhnert WL, Alter MJ (2006) The prevalence of hepatitis C virus infection in the United States, 1999 through 2002. Ann Intern Med 144(10):705–714 Chak E, Talal AH, Sherman KE, Schiff ER, Saab S (2011) Hepatitis C virus infection in USA: an estimate of true prevalence. Liver Int 31(8):1090–1101 Denniston MM, Klevens RM, McQuillan GM, Jiles RB (2012) Awareness of infection, knowledge of hepatitis C, and medical follow-up among individuals testing positive for hepatitis C: National Health and Nutrition Examination Survey 2001-2008. Hepatology 55(6):1652–1661 Armstrong GL (2007) Injection drug users in the United States, 1979-2002: an aging population. Arch Intern Med 167(2):166–173 Armstrong GL, Alter MJ, McQuillan GM, Margolis HS (2000) The past incidence of hepatitis C virus infection: implications for the future burden of chronic liver disease in the United States. Hepatology 31(3):777–782 Joy JB, McCloskey RM, Nguyen T, Liang RH, Khudyakov Y, Olmstead A, Krajden M, Ward JW, Harrigan PR, Montaner JS, Poon AF (2016) The spread of hepatitis C virus genotype 1a in North America: a retrospective phylogenetic study. Lancet Infect Dis 16(6):698–702 Barker L Personal communication, August 2, 2016. Based on analyses using the Centers for Disease Control and Prevention, National Health and Nutrition Examination Survey (NHANES) (2005-2012) Klevens RM, Hu DJ, Jiles R, Holmberg SD (2012) Evolving epidemiology of hepatitis C virus in the United States. Clin Infect Dis Suppl 55(1):S3—9 American Association for the Study of Liver Diseases and the Infectious Diseases Society of America (AASLD-IDSA). HCV testing and linkage to care. Recommendations for testing, managing, and treating hepatitis C. Available at: http://www.hcvguidelines.org/full-report/hcv-testing-and-linkage-care. Accessed: February 23, 2017 R Development Core Team (2012) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/ US Census Bureau (2010) QT – P1: Age groups and sex: 2010. Available at: http://www.census.gov/2010census/ Mehrotra A, Zaslavsky AM, Ayanian JZ (2007) Preventive health examinations and preventive gynecological examinations in the United States. Arch Intern Med 167(17):1876 Gretch DR (1997) Diagnostic tests for hepatitis C. Hepatology 26(Suppl 3):43S–47S Hyland C, Kearns S, Young I, Battistutta D, Morgan C (1992) Predictive markers for hepatitis C antibody ELISA specificity in Australian blood donors. Transfusion Med 2(3):207–213 Center for Medicare and Medicaid Services (CMS). 2010. Medicare fee schedule. U.S. Department of Health and Human Services. http://www.cms.gov/home/medicare.asp Weinstein MC, Skinner JA (2010) Comparative effectiveness and health care spending – implications for reform. New Engl J Med 362(5):460–465 Thein HH, Yi Q, Dore GJ, Krahn MD (2008) Estimation of stage-specific fibrosis progression rates in chronic hepatitis C virus infection: A meta-analysis and meta-regression. Hepatology 48(2):418–431 Centers for Disease Control and Prevention (CDC) (2006) National Health and Nutrition Examination Survey (NHANES): analytic and reporting guidelines. http://www.cdc.gov/nchs/nhanes/nhanes2003-2004/analytical_guidelines.htm. Accessed: August 27, 2012. Last updated: September 2006 Centers for Disease Control and Prevention (CDC) (2011) Analytic note regarding 2007-2010 survey design changes and combining data across other survey cycles. http://www.cdc.gov/nchs/nhanes/nhanes2003-2004/analytical_guidelines.htm. Accessed: August 27, 2012. Last updated: September 2011 Centers for Disease Control and Prevention (CDC) (2012) National Health and Nutrition Examination Survey (NHANES) (1999–2010). http://www.cdc.gov/nchs/nhanes.htm. Accessed: August 27, 2012 Milgrom PR (1981) Good news and bad news: Representation theorems and applications. Bell J Econ 12 (12):380–391 US Bureau of Labor Statistics (2011) Consumer Price Index (CPI):1913–present. Division of consumer prices and price indexes. Available at: http://www.bls.gov/cpi/