Adverse Childhood Experiences and Cardiovascular Risk among Young Adults: Findings from the 2019 Behavioral Risk Factor Surveillance System
Bibliographic record
Abstract
Background: Heart disease is the fourth leading cause of death for young adults aged 18–34 in the United States. Recent research suggests that adverse childhood experiences (ACEs) may shape cardiovascular health and its proximate antecedents. In the current study, we draw on a contemporary, national sample to examine the association between ACEs and cardiovascular health among young adults in the United States, as well as potential mediating pathways. Methods: The present study uses data from the 2019 Behavioral Risk Factor Surveillance System (BRFSS) to examine associations between ACEs and cardiovascular risk, as well as the role of cumulative disadvantage and poor mental health in these associations. Results: Findings indicate that young adults who have experienced a greater number of ACEs have a higher likelihood of having moderate to high cardiovascular risk compared to those who have zero or few reported ACEs. Moreover, both poor mental health and cumulative disadvantage explain a significant proportion of this association. Conclusions: The present findings suggest that young adulthood is an appropriate age for deploying prevention efforts related to cardiovascular risk, particularly for young adults reporting high levels of ACEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".