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Remote Laboratory Management: Respiratory Virus Diagnostics
Published on: April 6, 2019
Systematic Framework for Interpreting Laboratory-Confirmed Infectious Disease Surveillance Data: A Pragmatic
Yuzo Arima1, Kazuhiko Kanou1, Hirofumi Kato1
1Yuzo Arima, PhD, Tamano Matsui, MD, PhD, and Takeshi Arashiro, MD, PhD, are Affiliate Researchers; Ayu Kasamatsu, MD, PhD, Yusuke Kobayashi, MD, PhD, and Reiko Shimbashi, MD, PhD, are Senior Researchers; Osamu Takahara, MS, and Katsuhiro Komase, PhD, are Research Scientists; Takuri Takahashi, PhD, and Taro Kamigaki, MD, PhD, are Unit Chiefs; and Motoi Suzuki, MD, PhD, is a Division Director; all at the Center for Surveillance, Immunization, and Epidemiologic Research, National Institute of Infectious Diseases, Tokyo, Japan. Takeshi Arashiro is also an Affiliate Researcher, Department of Pathology, National Institute of Infectious Diseases, Tokyo, Japan. Kazuhiko Kanou, PhD, and Yusuke Kobayashi are also Senior Researchers; Hirofumi Kato, MD, PhD, is a Research Scientist; and Yuuki Tsuchihashi, MD, PhD, is a Unit Chief; all at the Center for Field Epidemic Intelligence, Research and Professional Development, National Institute of Infectious Diseases, Tokyo, Japan.
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In infectious disease surveillance, laboratory-confirmed case counts and test positivity (proportion of tests positive for the target pathogen) are commonly used to monitor disease activity. However, these test-based data are affected not only by disease incidence but also by testing intensity, often making interpretations difficult. To help address this challenge, we present a generalizable, systematic framework that considers the number of tests performed, number of test-positive counts, and test positivity. With explicit consideration of ascertainment bias, we explain why all 3 indicators should be used to improve data interpretation, situational assessment, and communication.
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