Optimizing hepatitis B virus screening in the United States using a simple demographics-based model.

Background And Aims: Chronic hepatitis B (CHB) affects >290 million persons globally, and only 10% have been diagnosed, presenting a severe gap that must be addressed. We developed logistic regression (LR) and machine learning (ML; random forest) models to accurately identify patients with HBV, using only easily obtained demographic data from a population-based data set.

Approach And Results: We identified participants with data on HBsAg, birth year, sex, race/ethnicity, and birthplace from 10 cycles of the National Health and Nutrition Examination Survey (1999-2018) and divided them into two cohorts: training (cycles 2, 3, 5, 6, 8, and 10; n = 39,119) and validation (cycles 1, 4, 7, and 9; n = 21,569).