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Breaking Barriers with Data Equity: The Essential Role of Data Disaggregation in Achieving Health Equity.

Annu Rev Public Health

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1Center for Health Policy Research, University of California, Los Angeles, California, USA; email:

Achieving health equity necessitates high-quality data to address disparities that have remained stagnant or even worsened over time despite public health interventions. Data disaggregation, the breakdown of data into detailed subcategories, is crucial in health disparities research. It reveals and contextualizes hidden trends and patterns about marginalized populations and guides resource allocation and program development for specific needs in these populations.

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Determining the level of consciousness in patients with brain injury-and more fundamentally, establishing what they can experience-is ethically and clinically impactful. Patient behaviors may unreliably reflect their level of consciousness: a subset of unresponsive patients demonstrate covert consciousness by willfully modulating their brain activity to commands through fMRI or EEG. However, current paradigms for assessing covert consciousness remain fundamentally limited because they are insensitive, rely on imperfect assumptions of functional neuroanatomy, and do not reflect the spectrum of conscious experience.

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