CB 399: Public Health 101: Introduction to decision analysis and cost-effectiveness analysis for public health and clinical decision making Spring 2015

Public Health 101: Introduction to decision analysis and cost-effectiveness analysis for public health and clinical decision making

Course Director: Eric Rubin, MD, PhD
Course Instructors: Christian Suharlim, MD, MPH; Emily A. Burger, MPhil, PhD; Stephen Sy, SM’15

Curriculum Fellow: Zofia Gajdos, Zofia_Gajdos@hms.harvard.edu

Day 1 Interest Form: http://goo.gl/forms/5IlZ2cy8Et

Decision science is the study of how people make decisions and how people can make better decisions in the presence of uncertainty, complexity and competing values. Decision-analytic methods utilize an interdisciplinary approach that provides a structured and systematic method to inform complex decisions by enumerating the tradeoffs that accompany any particular action or inaction. Decision science has been applied in several fields, including: business, military, clinical, and public health, including healthcare and the environment.

All countries face resource constraints, either economic (e.g., money) or physical (e.g., time), that require stakeholders to make difficult but necessary decisions. Worldwide, countries are increasingly using value for money and efficiency arguments associated with new interventions and pharmaceuticals as a specific criterion on which to allocate new health technologies. Cost-effectiveness analyses can use decision-analytic methods to inform policies and practices in healthcare by systematically integrating scientific evidence with explicit consideration of individual and societal values for outcomes including mortality, morbidity (e.g., quality of life), resource use and monetary costs. This course encompasses introductory analytic approaches such as decision tree modeling and cost-effectiveness analysis.

Course objectives

This course is designed to provide an introduction to the methods and applications of decision analysis and cost-effectiveness analysis. Upon completion of this Nanocourse, participants are expected to be able to:

- Understand the importance (and limitations) of decision analysis in clinical and public health decision making

- Identify elements of a decision problem and the information required for decision analysis in clinical and public health decision making

- Apply decision tree techniques to aid clinical and public health decision making

- Incorporate diagnostic test information and enumerate the health and economic consequences of alternative health interventions

- Understand the basic concepts of economic evaluation and the importance of cost-effectiveness analysis

- Identify components of a cost-effectiveness analysis

 

Course Outline

First Session: Introduction, structuring, and evaluating a decision problem

- Introduction to decision science, concepts and common applications

- Structuring a decision problem and identify decision problem components

- Building a decision tree model and calculating expected value

- Incorporating test information to decision tree model

- Incorporating quality of life to decision tree model

- Sensitivity analysis and threshold analysis

- Background on economic evaluation and resource allocation

- Identifying components of a cost-effectiveness analysis

- Incorporating economic consequences into a decision tree problem

- Hand out case example

 

Second Session: Cost-effectiveness analysis using decision-analytic software (Treeage™ Software (v2015))

- Shopping spree vs competing choice (hands on exercise)

- Calculating the cost-effectiveness of a program

- Societal willingness-to-pay (WTP) threshold and interpreting CEA results

- Introduce case example II

- Lab session using Decision science software: structuring decision trees, calculating expected value, conducting one-way sensitivity analysis and cost-effectiveness analysis

(Note: students to bring laptop installed with Treeage™: trial version )

- Brief introduction to advanced modeling methods and available courses in decision science at Harvard TH Chan School of Public Health

 

Suggested Reading / Assignments (due before class)

First Session:

- Goldie S, Corso P. Chapter 7, Decision Analysis in Haddix AC, et al. Prevention Effectiveness: A Guide to Decision Analysis and Economic Evaluation.

- Hunink MGM, Glasziou PP, et al. Decision Making in Health and Medicine, Chapters 2, 3, 5 (Section 5.1), and 6 (Sections 6.1-6.2)

- Case example 1: Structuring decision trees and calculating expected values

- Hunink MGM, Glasziou PP, et al. Decision Making in Health and Medicine, Chapter 9

- (optional) Laxminarayan et al. Intervention Cost-Effectiveness: Overview of Main Messages. Disease Control Priorities in Developing Countries 2nd Edition. 2006. http://www.ncbi.nlm.nih.gov/books/NBK11784/pdf/ch2.pdf

- (optional) Siebert U. When should decision-analytic modeling be used in the economic evaluation of health care? European Journal of Health Economics 2003; 4; 143-150. http://www.jstor.org.ezp-prod1.hul.harvard.edu/stable/3570079

Second Session:

- Case example 2: Structuring decision trees, calculating expected values, and conducting cost-effectiveness analysis

- Briggs et al. Probabilistic Analysis of Cost-Effectiveness Models: Choosing between Treatment Strategies for Gastroesophagial Reflux Disease. Med Decis Making. 2002 Jul-Aug;22(4):290-308. (read p290-294) http://mdm.sagepub.com.ezp-prod1.hul.harvard.edu/content/22/4/290.full.pdf

- (optional) Masucci et al. Cost-effectiveness of the Carbon-13 Urea Breath Test for the Detection of Helicobacter Pylori: an economic analysis. Ont Health Technol Assess Ser. 2013; 13(20): 1–28. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3817923/

Books are on reserve at Countway Library. Links to articles are accessible with Harvard PIN. Case examples will be distributed in class

Schedule:

First Session: Thursday April 23, 1:30 – 4:30 PM  
Location: Armenise Amphitheater

Second Session: Thursday April 30, 1:30 – 4:30 PM  
Location: TMEC Amphitheater

 

AUDITORS (Post-Docs, Faculty, or Staff) PLEASE SIGN THE INTEREST FORM TO ATTEND THE 1st SESSION. http://goo.gl/forms/5IlZ2cy8Et

THOSE INTERESTED TO TAKE THE NANOCOURSE FOR ACADEMIC CREDIT PLEASE ENROLL THROUGH THIS WEBSITE (LOGIN AND ENROLL BELOW). DROP DEADLINE TUESDAY APRIL 21, 2015

Instructor Biosketches

Christian Suharlim, MD, MPH is a postdoctoral research and teaching fellow at the Center for Health Decision Science. His research at the center include investigating cost-effectiveness of DOT-HAART for HIV patients and the cost of immunization programs in resource-poor settings. He is also working to implement MDR-TB diagnosis improvement in Indonesia. Prior to HSPH, he practiced medicine at a community health center in the rural area of Indonesia and worked as an officer in the Indonesian Ministry of Health. He received his MD from Universitas Indonesia and his MPH from Harvard T.H. Chan School of Public Health

Emily Burger, MPhil, PhD is a postdoctoral research fellow at the Center for Health Decision Science. Her current research uses static and dynamic disease simulation models to estimate the cost-effectiveness of primary and secondary prevention strategies for HPV-related conditions in Norway. In addition, she is eliciting preferences of Norwegian women with respect to new cervical cancer screening technologies that are currently under consideration by the Norwegian government. She received her MPhil and PhD in Health Economics and Policy from the University of Oslo.

Stephen Sy, SM’15 is a programmer who is developing and maintaining various disease simulation and cost-effective analysis models associated with cervical cancer prevention and screening, HPV vaccination, and cardiovascular disease policy, each of which is currently being applied to several global settings. He received a BS degree in Mathematics from the University of North Carolina and worked many years as an analyst in the defense industry before switching to a career in public health and decision science. He will be receiving his SM in health Policy and Management from Harvard T.H. Chan School of Public Health.