Intersectionality in Digital Health Literacy and Digital Health Application Use in Germany: A MAIHDA Study by Gender, Educational Attainment, and Generation

Background/Objectives: Social determinants of health (SDOHs) are linked to inequities in digital health application (DHA) use and digital health literacy (DHL), a predictor of DHA use, but few studies have examined these inequities intersectionally. This study examined intersectional differences by gender, educational attainment, and generation. Methods: Data were obtained from an online cross-sectional survey (STELLAR; N = 1200) in Germany. Intersectionality was assessed using Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), with the intersections of gender, educational attainment, and generation defining strata for both outcomes. Results: Educational attainment was the strongest predictor of DHL and was associated with DHA use; gender was not significant for either. Generation was associated with DHA use, with higher odds for Generation Y and Z than for Traditionalists, but was not associated with DHL. Predicted DHL was highest among women with higher education from the Traditionalist or Generation Y group and lowest among men with basic education from Generation Z. Predicted DHA use was highest among women with higher education from Generation Z and lowest among men with basic education from the Baby Boomer generation. Additive main effects explained a greater share of the between-stratum variance for DHA use than for DHL, while the proportion of total variance attributable to strata remained limited for both outcomes. Conclusions: MAIHDA can identify population groups at greater risk of second-level digital divide-related inequities in DHL and DHA use, informing population-level measures to strengthen DHL and improve DHA use. Further research should clarify the underlying mechanisms.