TY - JOUR
T1 - Tuberculosis diagnosis and treatment under uncertainty
AU - Cassidy, Rachel
AU - Manski, Charles F.
N1 - Funding Information:
ACKNOWLEDGMENTS. R.C.’s work on this project was performed while she was a Postdoctoral Fellow at the Institute for Fiscal Studies (IFS) and was funded by the Economic and Social Research Council Centre for the Microeconomic Analysis of Public Policy at the IFS. We thank Kalipso Chalkidou, Michael Gmeiner, Rein Houben, and seminar audiences at the Centre for Microdata Methods and Practice at University College London and the Institute for Policy Research at Northwestern University for valuable comments.
Funding Information:
R.C.’s work on this project was performed while she was a Postdoctoral Fellow at the Institute for Fiscal Studies (IFS) and was funded by the Economic and Social Research Council Centre for the Micro-economic Analysis of Public Policy at the IFS. We thank Kalipso Chalkidou, Michael Gmeiner, Rein Houben, and seminar audiences at the Centre for Microdata Methods and Practice at University College London and the Institute for Policy Research at Northwestern University for valuable comments.
Publisher Copyright:
© 2019 National Academy of Sciences. All rights reserved.
PY - 2019/11/12
Y1 - 2019/11/12
N2 - In 2017, 1.6 million people worldwide died from tuberculosis (TB). A new TB diagnostic test—Xpert MTB/RIF from Cepheid—was endorsed by the World Health Organization in 2010. Trials demonstrated that Xpert is faster and has greater sensitivity and specificity than smear microscopy—the most common sputum-based diagnostic test. However, subsequent trials found no impact of introducing Xpert on morbidity and mortality. We present a decision-theoretic model of how a clinician might decide whether to order Xpert or other tests for TB, and whether to treat a patient, with or without test results. Our first result characterizes the conditions under which it is optimal to perform empirical treatment; that is, treatment without diagnostic testing. We then examine the implications for decision making of partial knowledge of TB prevalence or test accuracy. This partial knowledge generates ambiguity, also known as deep uncertainty, about the best testing and treatment policy. In the presence of such ambiguity, we show the usefulness of diversification of testing and treatment.
AB - In 2017, 1.6 million people worldwide died from tuberculosis (TB). A new TB diagnostic test—Xpert MTB/RIF from Cepheid—was endorsed by the World Health Organization in 2010. Trials demonstrated that Xpert is faster and has greater sensitivity and specificity than smear microscopy—the most common sputum-based diagnostic test. However, subsequent trials found no impact of introducing Xpert on morbidity and mortality. We present a decision-theoretic model of how a clinician might decide whether to order Xpert or other tests for TB, and whether to treat a patient, with or without test results. Our first result characterizes the conditions under which it is optimal to perform empirical treatment; that is, treatment without diagnostic testing. We then examine the implications for decision making of partial knowledge of TB prevalence or test accuracy. This partial knowledge generates ambiguity, also known as deep uncertainty, about the best testing and treatment policy. In the presence of such ambiguity, we show the usefulness of diversification of testing and treatment.
KW - Decision under ambiguity
KW - Diagnosis and treatment
KW - Medical decision making
KW - Public health
KW - Tuberculosis
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U2 - 10.1073/pnas.1912091116
DO - 10.1073/pnas.1912091116
M3 - Article
C2 - 31662476
AN - SCOPUS:85074887254
SN - 0027-8424
VL - 116
SP - 22990
EP - 22997
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 46
ER -
