Evaluating the Ascertainment Bias of SARS-CoV-2 Using Spatial Backcalculation of Airport Screening Data in Japan

Background: Despite global efforts to strengthen surveillance systems, ascertainment bias persisted. We evaluated the extent of this bias by comparing Japan’s airport entry screening data with local surveillance data. Methods: Entry screening data of non-Japanese passengers arriving from 25 countries were analyzed, and countries were categorized by income level: high-income countries (HICs), upper-middle-income countries (UMICs), and lower-middle-income countries (LMICs). A statistical model was developed to describe entry screening positivity using surveillance data of the country of origin. Results: Ascertainment bias was lowest in HICs (positivity = 1.76 times that of local surveillance data, 95% confidence interval [CI]: 1.42, 2.13). Bias estimates in the remaining income levels were substantially higher: positivity was 25.71 times (95% CI: 21.72, 30.09) and 24.38 times (95% CI: 21.29, 27.72) than that of surveillance data in UMICs and LMICs, respectively. The denial of new entry visas was associated with a relative risk reduction of 0.78 (95% CI: 0.72, 0.83) in UMICs. However, no significant effect was observed in LMICs (−0.13; 95% CI: −0.33, 0.03). Vaccination did not substantially alter post-arrival positivity. Conclusions: A comparison of entry screening data with local surveillance data indicated that ascertainment bias depends on income level.