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Navigating a new era in cardiovascular disease epidemiology: big data, artificial intelligence and the imperative of
1Public Health and Tropical Medicine, College of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia.
Insights
Cardiovascular disease (CVD) research using big data and AI excludes people with disabilities (PwD). This exclusion leads to worse health outcomes and inequitable care for PwD, necessitating inclusive research frameworks.
Area of Science:
- Cardiovascular epidemiology
- Precision public health
- Health disparities research
Background:
- Cardiovascular disease (CVD) is a leading cause of global mortality and disability.
- Recent advances in big data, AI, and wearables are transforming CVD research and precision public health.
- People with disabilities (PwD) are systematically under-represented in these advancements, leading to health disparities.
Purpose of the Study:
- To analyze methodological advancements in CVD epidemiology.
- To identify reasons for the exclusion of PwD in big data and AI research.
- To propose a framework for disability-inclusive CVD research.
Main Methods:
- Review of methodological innovations in CVD epidemiology (biobanks, deep learning, polygenic risk scores, wearables, causal inference).
- Analysis of under-representation of PwD in clinical trials, biobanks, EHRs, and AI models.
- Development of a framework for inclusive big-data and AI-enabled CVD research.
Main Results:
- Innovations in CVD research have not equitably benefited PwD.
- PwD face worse cardiovascular health and less precise characterization due to exclusion.
- Current big data and AI approaches in CVD research lack generalizability and ethical legitimacy without disability inclusion.
Conclusions:
- Disability inclusion is essential for the validity and ethical integrity of precision cardiovascular research.
- A layered framework is proposed to ensure PwD are included in big data and AI-driven CVD research.
- Addressing the exclusion of PwD is critical for achieving equitable cardiovascular health outcomes.
Abstract:
Cardiovascular disease (CVD) remains the leading cause of premature global mortality and one of the largest contributors to disability-adjusted life years lost. Over the past decade, the field has been transformed by the convergence of population biobanks, deep learning applied to imaging and electrocardiography, polygenic risk scores, wearable biosensors, and methodological advances in causal inference and target trial emulation. These innovations are reshaping precision public health for the general population. Yet the gains have not been equitably distributed. People with disability (PwD), comprising approximately 16 per cent of the global population and recognised by the United States National Institute on Minority Health and Health Disparities as a population experiencing health disparities are systematically under-represented in clinical trials, biobanks, electronic health records and the artificial-intelligence (AI) models trained upon them. Their cardiovascular health is therefore both worse and less precisely characterised than that of the general population. This article maps the key methodological vectors of change in CVD epidemiology, explains why each has so far failed to reach PwD, and presents a layered, defendable framework for disability-inclusive big-data and AI-enabled CVD research. It argues that disability inclusion is not a peripheral equity concern but a stress-test for the validity, generalisability and ethical legitimacy of the entire precision-cardiovascular enterprise.
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