Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health

Abstract

The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Smaller System Predictors of Satisfaction with Low-Income Paternal Involvement of Noncustodial African American Fathers

“…As long as big data tools are used to further racial inequalities instead of investigating and mitigating racial disparities, caution is suggested in using central registry systems. These central registries are susceptible to the indiscriminate misuse of predictive validity tools, which triangle multiple sources of bias reporting (Johnson & Rostain, 2020; Wesson et al, 2022). Support for African American NCFs and their multiple CPs and unmarried child dyads are essential to aiding them to navigate the myriad of providers they need to survive living with less and unprivileged.…”

Artificial Intelligence in Continuous Kidney Replacement Therapy

“…In addition to the researchers who develop and test the AI/ML models, health care professionals who interpret and clinically apply these models should recognize the existence of variables that may contribute to discrimination and inequality, including information technology resources deployed for the implementation of the tools. 11…”

“…In addition to the researchers who develop and test the AI/ML models, health care professionals who interpret and clinically apply these models should recognize the existence of variables that may contribute to discrimination and inequality, including information technology resources deployed for the implementation of the tools. 11 In conclusion, despite recent advances of AI/ML-based health care applications, limited research has focused on enhancing the provision of CKRT, the second most common extracorporeal support treatment in the intensive care unit. Potential applications of AI/ML in CKRT include systematic and dynamic risk classification, subphenotyping, quality assurance as well as augmented decision-making capacity for CKRT initiation, dose adjustments, and anticoagulation management, among others.…”

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