61. Improving the Equity and Efficiency of Cystic Fibrosis Screening
Invited abstract in session MB-13: Medical services and applications, stream OR in Healthcare (ORAHS).
Monday, 10:30-12:00Room: Clarendon SR 1.01
Authors (first author is the speaker)
| 1. | Hussein El Hajj
|
| Information Systems and Analytics, Santa Clara University | |
| 2. | Ebru Bish
|
| ISM, The University of Alabama | |
| 3. | Douglas Bish
|
| ISM, University of Alabama |
Abstract
Newborn screening (NBS) plays a key role in detecting life-threatening genetic disorders, with cystic fibrosis (CF) being a prominent example. CF stems from pathogenic variants in the CFTR gene, detectable through genetic testing, and is often associated with elevated immunoreactive trypsinogen (IRT) enzyme levels. With over 700 known CF pathogenic variant types, CF genetic testing is expensive and capacity-constrained. Consequently, across all US states, CF NBS begins with an inexpensive biochemical IRT test; genetic testing is reserved for a small fraction of newborns with elevated IRT levels, based on an IRT threshold. However, challenges arise in setting an optimal IRT threshold that balances sensitivity with overall screening cost and genetic testing capacity, exacerbated by variations in IRT levels and CF prevalences among different racial groups. This paper introduces an innovative screening approach, integrating an inexpensive small-panel genetic test (DNA) with IRT test, to develop a strategy that can customize IRT thresholds for the newborns. This approach leads to novel optimization problems, involving variant selection for the small-panel DNA, and IRT threshold customization based on the number of variants detected by the small-panel DNA. We establish key structural properties of optimal IRT thresholds, and develop Pareto frontiers that inform the decision-maker of the efficiency versus equity trade-off.
Keywords
- Health Care
- Combinatorial Optimization
- OR/MS and the Public Sector
Status: accepted
Back to the list of papers