Navigating a new era in cardiovascular disease epidemiology: big data, artificial intelligence and the imperative of disability inclusion - Report - MDSpire
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Navigating a new era in cardiovascular disease epidemiology: big data, artificial intelligence and the imperative of disability inclusion
Clinical Report: Exploring Big Data and AI in Cardiovascular Disease Epidemiology
Background
Cardiovascular disease is the leading cause of premature mortality globally and significantly contributes to disability-adjusted life years lost. Recent methodological advancements in CVD epidemiology, including the integration of multi-omic data and machine learning, have the potential to enhance precision public health. However, the lack of representation of PwD in these innovations raises concerns about the generalizability and validity of findings.
Data Highlights
No specific numerical data or trial data provided in the source material.
Key Findings
Cardiovascular disease (CVD) is the leading cause of premature mortality and a major contributor to disability-adjusted life years lost.
People with disabilities (PwD) experience a disproportionate burden of CVD, with prevalence rates of heart disease and stroke significantly higher than those without disabilities.
PwD are systematically underrepresented in clinical trials and datasets, which threatens the generalizability of precision CVD research.
Recent methodological advancements in CVD epidemiology include the use of deep learning and population biobanks, yet these have not reached PwD effectively.
The United States National Institute on Minority Health and Health Disparities has recognized PwD as a population experiencing health disparities.
A layered framework for disability-inclusive big-data and AI-enabled CVD research is proposed to address these gaps.
Clinical Implications
The underrepresentation of PwD in CVD research necessitates a reevaluation of clinical trial eligibility criteria to ensure equitable health outcomes. Incorporating disability inclusion in big data and AI initiatives is essential for advancing precision public health.
Conclusion
Addressing the methodological gaps in CVD epidemiology concerning PwD is crucial for improving health equity and ensuring the validity of research outcomes.
