Adaptive risk-based pooling in public health screening

ABSTRACT

Pooled testing is commonly used in public health screening for classifying subjects in a large population as positive or negative for an infectious or genetic disease. Pooling is especially useful when screening for low-prevalence diseases under limited resources. Although pooled testing is used in various contexts (e.g., screening donated blood or for sexually transmitted diseases), a lack of understanding of how an optimal pooling scheme should be designed to maximize classification accuracy under a budget constraint hampers screening efforts.

 We propose and study an adaptive risk–based pooling scheme that considers important test and population level characteristics often over looked in the literature (e.g., dilution of pooling and heterogeneous subjects). We characterize important structural properties of optimal subject assignment policies (i.e., assignment of subjects, with different risk, to pools) and provide key insights. Our case study, on chlamydia screening, demonstrates the effectiveness of the proposed pooling scheme, with the expected number of false classifications reduced substantially over policies proposed in the literature.

KEYWORDS:

• Public health screening
• pooled testing
• risk-based screening
• dilution effect of pooling

Acknowledgments

We are grateful to Dr. Scott J. Zimmerman, Director of the North Carolina State Laboratory of Public Health, for many valuable discussions and for offering us important insights into public health screening practices and to the Associate Editor and two referees for excellent comments that improved the analysis and presentation of the article.

Additional information

Funding

This material is based upon work supported in part by the National Science Foundation under Grant No. #1055360. Any opinions findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Notes on contributors

Hrayer Aprahamian

Hrayer Aprahamian is a Ph.D. candidate in the Department of Industrial and Systems Engineering at Virginia Tech, and he will be joining the faculty of Industrial Engineering at Texas A&M in the summer of 2018. His research interests lie in applying operations research methodologies and statistical tools to address problems arising in health care systems and public policy decision-making. One of his papers was a runner-up for the 2017 INFORMS Pierskalla Award for the Best Paper in Healthcare.

Ebru K. Bish

Ebru K. Bish is an associate professor of industrial and systems engineering and an associate professor of health sciences at Virginia Tech. Her research interests lie in stochastic modeling, optimization, and decision-making under uncertainty, with applications to public health policy and health implementation science. Her specific research focuses on public health screening and surveillance of infectious diseases and genetic disorders; and on improving the safety of health care delivery. She is the recipient of the INFORMS Pierskalla Award for the Best Paper in Healthcare (first prize winner, runner-up, and a finalist), INFORMS JFIG Best Paper Award, and IIE Transactions Best Applications Paper Award. She is also the 2018–2019 Vice President /President-Elect of the INFORMS Health Applications Society.

Douglas R. Bish

Douglas R. Bish is an associate professor in the Department of Industrial and Systems Engineering and an associate professor of health sciences at Virginia Tech, with a secondary appointment at the Virginia Tech-Carilion School of Medicine. His research interests are on the application of operations research and related methodologies to solve problems in health care, emergency management, and logistics. He is the recipient of the National Science Foundation CAREER award. He is also the recipient of the INFORMS Pierskalla Award for the Best Paper in Healthcare (first prize winner, runner-up, and a finalist) and IIE Transactions Best Applications Paper Award.