Advancing equity in healthcare systems: understanding implicit bias and infant mortality - Gran-Ruaz, Sophia M.; Mistry, Shruti; MacIntyre, M. M.; Strauss, Dana; Faber, Sonya C.; Williams, Monnica T. (Université d'Ottawa | University of Ottawa, 2025)
Abstract Using data from the Centers for Disease Control and Prevention’s Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) and Project Implicit, this study examined whether anti-Black implicit racial biases predict infant mortality for Black Americans. We examined state-level mean Black-White Implicit Association Test (BW-IAT) Bias Scores and controlled for explicit bias scores and White infant mortality rates for over 1.7 million American participants across ten different ethnoracial groups between 2018–2020. Hierarchical linear regressions determined state-level anti-Black implicit bias significantly predicted state-level Black infant mortality rates, above and beyond explicit bias and White infant mortality, in 2018 (b = .32, t(34) = 2.09, p < .05), 2019 (b = .30, t(34) = 2.09, p < .05), and 2020 (b = .32, t(34) = 2.18, p < .05). State-level anti-Black implicit bias also explained a significant proportion of variance in state-level infant mortality rates, in 2018 (R2 = 0.30, F(3, 35) = 4.89, p < 0.01), 2019 (R2 = .33, F(3, 36) = 5.95, p < .01), and 2020 (R2 = .39, F(3, 35) = 7.58, p < .001). Also, among healthcare professionals, there are similar levels of implicit biases compared to the general American population. Findings suggest that implicit racial bias is a risk factor for Black infant mortality. These findings also point to the ethical challenge implicit biases pose to equitable decision-making and patient-provider relationships in healthcare. By integrating these insights into interdisciplinary discussions, this study provides supporting data for systemic reforms and anti-bias training to create a healthcare system grounded in fairness and equity.