Disparities in Lupus and the Role of Social Determinants of Health: Current State of Knowledge and Directions for Future Research

Buie, Joy; McMillan, Euan M.; Kirby, John D.; Cardenas, Lilia H.; Eftekhari, Sanaz; Feldman, Candace H.; Gawuga, Cyrena; Knight, Andrea M.; Lim, S. Sam; McCalla, Sheryl; McClamb, Daria; Polk, B. Frank; Williams, Edith M.; Yelin, Ed; Shah, Syed Mansoor Ahmed; Costenbader, Karen H.

doi:10.1002/acr2.11590

Cited by 16 publications

(2 citation statements)

References 106 publications

(149 reference statements)

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“…These can exacerbate existing disparities in marginalised and underserved populations and violate the bioethical principles of justice. SLE is a disease that disproportionately affects racial and ethnic minorities and is therefore more sensitive to these issues (reviewed in ref 175 ). The lack of representation by minority populations in clinical research, genetic reports 176 and clinical trials 177 is a real concern.…”

Section: Future Considerationsmentioning

confidence: 99%

Systemic lupus in the era of machine learning medicine

Zhan,

Buhler,

Chen

et al. 2024

Lupus Sci Med

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Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

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Section: Future Considerationsmentioning

confidence: 99%

Systemic lupus in the era of machine learning medicine

Zhan,

Buhler,

Chen

et al. 2024

Lupus Sci Med

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“…Despite immense disparities in systemic lupus erythematosus (SLE) care for racial and ethnic minorities [ 17 ], there remains a paucity of PN programs serving those suffering from rheumatological diseases. Some studies, however, have shown efficacy of PN interventions in this population.…”

Section: Introductionmentioning

confidence: 99%

A patient-centered evaluation of a novel medical student-based patient navigation program

Wilson,

Lau,

Kristoferson

et al. 2024

Patient Education and Counseling

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The Explication of Race in Rheumatology Disparities

Lim,

Ramos,

Williams

2024

Arthritis Care & Research

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The challenge of raceThe appropriate use of race in medical research and clinical care is a topic fraught with confusion and misunderstanding. Jonathan Kahn, in his insightful book "Race in a Bottle," 1 eloquently observes that "…underlying such confusion is an even deeper set of assumptions about the nature of race and more broadly of social science in relation to the natural sciences. These assumptions are grounded in the acceptance of race as a social category that is aligned with a near-simultaneous marginalization of race as 'merely' social and hence not deserving of the same sort of care of consideration devoted to "real" natural phenomena, such as genetics." Kahn further elaborates on the distinct language of race compared with the specialized language of biomedical experts, who have spent years honing specific skills and expertise that are not accessible to the general public. In contrast, race is a concept that everyone encounters and discusses in daily life, making it appear obvious, intuitive, and seemingly simple to understand and discuss without specialized knowledge. However, this apparent simplicity is deceptive. Kahn argues that "in some contexts, this may be true; but not in the biomedical sciences," in which the use of race requires great care and expertise. The challenge lies in using race to address and understand racism and disparities without reinforcing stereotypes or incorrect and unfounded biologic assumptions. The intersection of race with various biologic, social, and environmental factors make it a powerful but potentially misleading tool if not handled with the necessary knowledge, reflection, and sensitivity. Thus, professionals in the field must navigate these waters with precision and a deep understanding of both the scientific implications and the social ramifications of their work.This complexity arises because although race is a social construct with no genetic or biologic basis, it has profound

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Disparities in Lupus and the Role of Social Determinants of Health: Current State of Knowledge and Directions for Future Research

Cited by 16 publications

References 106 publications

Systemic lupus in the era of machine learning medicine

Systemic lupus in the era of machine learning medicine

A patient-centered evaluation of a novel medical student-based patient navigation program

The Explication of Race in Rheumatology Disparities

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