Advancing equity in healthcare systems: understanding implicit bias and infant mortality.

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.