Research Policy and Focus


CHDS faculty and researchers use methods from decision science to address contemporary clinical and public health problems with significant health, quality of life, and/or economic consequences. The Center has a strong domestic research agenda, as well as scholarly activity focused on low, middle and other high income countries. The scope of applied analytic work ranges from prevention and treatment of communicable and chronic diseases, to cross-cutting themes such as women's health, maternal and child mortality, mental health, and health care delivery. In addition to applied work, investigators conduct research to push the field of decision science forward, by developing novel methods and identifying new applications of existing techniques. CHDS is committed to enabling the real-world impact of health decision science research to affect public health policy by actively engaging with policy communities, providing service to decision makers, enhancing technical capacity, and translating results for different stakeholders.

Our research falls into several overlapping categories:

Models and Tools

Develop and utilize a variety of models, depending on the nature of the clinical or policy problem (e.g., microsimulation, system dynamics, discrete event simulation). Develop decision support tools for the policymaker/analyst or program manager as primary user, in collaboration with health ministries and multilateral partners, for use in strategic decision making.

Applications and Analyses

Conduct analyses that comparatively evaluate the risks, benefits, and costs associated with strategies that utilize alternative technologies (e.g., health technology assessment), clinical practices (e.g., comparative effectiveness), or population-level policies (e.g., policy analysis) to address a specific health problem or set of health problems.

Theory and Methods

Push the field forward by innovating new ways to elicit preferences and measure the value individuals assign to particular health conditions, synthesize expert opinions, represent parameter and model uncertainty, empirically calibrate models, handle population heterogeneity, and address an equity objective in the course of cost-effectiveness analysis.