Cover: Robust Decision Making in Health Policy

Robust Decision Making in Health Policy

Applications to COVID-19 and Colorectal Cancer

Published Dec 22, 2022

by Pedro Nascimento de Lima

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The COVID-19 pandemic demonstrated the value of modeling to inform health policy. Models were used to provide situational awareness and inform mitigation policies. However, uncertainty surrounding the longevity of vaccine and infection-induced immunity, the emergence and characteristics of SARS‐CoV‐2 variant strains, and behavioral responses to policy interventions prevent modelers from providing more than a few weeks of model-based foresight. Under those conditions, policymakers have options to control the pandemic, but deep uncertainties deny the prediction of their long-term effects. Robust Decision Making (RDM) is a set of methods and tools designed to inform decisions under conditions of deep uncertainty. This dissertation presents three papers exploring the utility of RDM for supporting health policy decisions. The first paper discusses how public health decision-makers may benefit from RDM, using pandemic response policies as a motivating example. The second paper presents a stress test of California's COVID-19 reopening strategy, demonstrating that adaptive reopening plans are superior to non-adaptive ones. The third evaluates the robustness of colorectal cancer screening strategies to uncertainties surrounding the natural history of the disease. Finally, this dissertation reflects on the broader applicability of RDM to health policy decision-analytic problems and reflects on future research directions.

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This document was submitted as a dissertation in September 2022 in partial fulfillment of the requirements of the doctoral degree in Public Policy Analysis at the Pardee RAND Graduate School. The faculty committee that supervised and approved the dissertation consisted of Robert Lempert (Chair), Carolyn Rutter, Jeanne Ringel, Raffaele Vardavas, and Jonathan Ozik.

This dissertation was supported by two Rothenberg Dissertation Awards and by the National Cancer Institute (NCI) as part of the Cancer Intervention and Surveillance Modeling Network (CISNET) through grant U01-CA253913. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE- AC02-06CH11357. I would like to thank the Argonne Leadership Computing Facility staff for their timely and critical support. This research was completed with resources provided by the Laboratory Computing Resource Center at Argonne National Laboratory. The content is solely the author's responsibility and does not necessarily represent the official views of any sponsor.

This publication is part of the RAND dissertation series. Pardee RAND dissertations are produced by graduate fellows of the Pardee RAND Graduate School, the world's leading producer of Ph.D.'s in policy analysis. The dissertations are supervised, reviewed, and approved by a Pardee RAND faculty committee overseeing each dissertation.

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