Differences in Myocardial Infarction and Stroke Knowledge and Awareness Among US‐ and Foreign‐Born Individuals: Potential Causes and Implications
Bibliographic record
Abstract
n this issue of the Journal of the American Heart Association (JAHA), Mannoh et al examined disparities in awareness of myocardial infarction (MI) and stroke symptoms among US-and foreign-born adults.Their most important finding, that there are differences in awareness of MI and stroke symptoms by region of birth, 1 confirms and expands upon prior research 2 in several ways. See Article by Mannoh et al.The authors used data from the National Health Interview Survey (NHIS) from 2014 and 2017 to evaluate self-reported knowledge and awareness of MI and stroke by region of birth.Region of birth was categorized as Mexico/Central America/Caribbean, South America, Europe, Russia, Africa, the Middle East, the Indian subcontinent, Asia, Southeast Asia, and the United States.The authors also controlled for a range of potential confounders, including age, health insurance status, and access to a regular source of medical care.In addition, they considered the participants' educational and socioeconomic status, as well as their atherosclerotic cardiovascular disease (ASCVD) risk factors and sex.This study provides several important insights, raises new questions, and hints at potential interventions that could aid in increasing knowledge and awareness of MI and stroke in certain foreign-born populations.The study's primary finding is a disparity (ie, otherwise unexplained difference) in MI and stroke knowledge and awareness for US-born and foreign-born individuals.These differences were observed in both unadjusted and adjusted analyses in sociodemographic, educational attainment, and cardiovascular risk factors.More specifically, awareness of both MI and stroke symptoms were highest among individuals born in the United States, slightly lower for individuals born in Europe and Russia, and lowest in individuals born in Asia for MI and in individuals born in the Indian subcontinent for stroke.What might explain these differences in awareness?The differences might reflect underlying differences in the prevalence of MI and stroke in each racial and ethnic group and geographic region.For example, a study by Hastings et al 3 found that the leading cause of death for Asian Americans from 2003 to 2011 was cancer (accounting for 28.6% of total causes of death) with heart disease being second (accounting for 23.5% of the cause of death).Alternatively, the leading cause of death in both Russia and Europe is cardiovascular disease as The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".