Optimal information collection for dynamic health care policy
- Lauren Elizabeth Cipriano.
- June 2013.
- Physical description
- online resource (xx, 246 pages) : illustrations (some color)
- Cipriano, Lauren Elizabeth.
- Brandeau, Margaret L. thesis advisor (primary).
- Owens, Douglas K. thesis advisor.
- Weber, Thomas A., 1969- thesis advisor (primary).
- Stanford University. Department of Management Science and Engineering.
- Stanford University. Committee on Graduate Studies. degree grantor.
- Includes bibliographical references (p. 190-246). 406 refs.
- Mathematical models of health systems and disease can provide important information to decision makers when direct experimentation is impossible, impractical, or unethical, and when there are many possible decision alternatives. When costs or benefits of a medical or health policy decision are in the distant future (perhaps decades away) or are borne by other members of society, mathematical models may be the only practical way to fully evaluate the costs and consequences of policy alternatives. Through modeling, the decision maker can develop a deeper understanding of which factors have the greatest impact on the outcome of interest and explicitly explore decision uncertainty. This dissertation applies and extends the use of mathematical models in the application area of health policy -- specifically to applications of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) screening in injection drug users (IDUs) and the general population.
- Drug Users > statistics & numerical data
- HIV Infections > economics
- Hepacivirus > isolation & purification
- Hepatitis C > economics
- Decision Making
- Mass Screening > economics
- Models, Economic
- RNA, Viral > analysis
- Publication date
- Submitted to the Department of Management Science and Engineering and the Committee on Graduate Studies of Stanford University.
- Thesis (Ph.D.)--Stanford University, 2013.