Cardiovascular Disease and Mental Health in Intellectual Disabilities: A Vicious Cycle of Risk and Care Gaps
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVDs) are the primary reason for demise and disability worldwide, with a particularly severe problem in low- and middle-income nations. Among individuals with intellectual disabilities (ID), the coexistence of cognitive impairments, mental health conditions, and barriers to medical care significantly increases health risks. Objective: This review discovers the bidirectional connection between cardiovascular disease and mental health disorders in individuals with ID. It aims to classify risk factors, examine care disparities, and evaluate the need for targeted interventions. Methods: A descriptive review methodology was employed. A total of 1,182 articles were initially retrieved from PubMed, Scopus, and PsycINFO using a focused keyword strategy encompassing CVD, mental health, and intellectual disability. After applying inclusion criteria centered on disability relevance and peer-reviewed content, 914 articles were shortlisted. From these, 173 studies were selected based on quality and contextual suitability. Results: The review identified a consistent pattern of underrepresentation of individuals with ID in cardiovascular and psychiatric research despite their increased rates of congenital heart defects, lifestyle-related risk factors, and untreated mental health issues. Diagnostic overshadowing, resource constraints for caregivers, and a lack of appropriately adapted therapies further aggravate their vulnerability. Evidence suggests that caregiver-supported and community-based interventions, when customized for this population, can improve health outcomes. Conclusion: The interplay between cardiovascular disease and mental health in people with ID constitutes a complex clinical and public health challenge. A disability-sensitive research and care framework is urgently needed. Future approaches should focus on early screening, tailored interventions, integrated care models, and inclusive health policies to adequately support this marginalized population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".