Abstract P210: The Social Distribution Of Ideal Cardiovascular Health: A Global Systematic Review
Bibliographic record
Abstract
Introduction: Numerous studies have examined the social determinants of ideal cardiovascular health (ICVH) around the world, but no work has summarized evidence to date. This study aimed to systematically review findings on the social distribution of ICVH globally, and to compare trends in high-income countries (HICs) vs. low/middle-income countries (LMICs). Methods: In November 2019, we systematically searched PubMed, Embase, and LILACS for observational studies published after the American Heart Association (AHA) defined ICVH as a combination of health factors and behaviors in 2010. Search terms included ICVH/Life’s Simple 7 and a pre-defined set of social determinants of health (i.e., education, income/wealth, socioeconomic status (SES), employment, occupation, and race/ethnicity). Each abstract was reviewed by two independent researchers. Studies were included if associations between a composite measure of ICVH and a social determinant of health was quantified using statistical methods. We evaluated risk of bias using an adapted version of the Newcastle-Ottawa Quality Assessment Scale. Overall findings and comparisons between HICs and LMICs (defined by World Bank guidelines) were summarized narratively. Results: A total of 33 studies met inclusion criteria. Only 8 studies were from LMICs (n=4 from China), while 25 were from HICs (n=19 from the US). The most commonly assessed social determinants were education (n=18) and income/wealth (n=17). In both HICs and LMICs, few studies examined occupation or area-level measures, like rurality/urbanicity. Most studies were cross-sectional (n=27). Two thirds of studies and had a moderate (n=14, 43%) or high (n=8, 24%) risk of bias, but no systematic differences were noted by country setting. Nearly half of studies used composite ICVH measures that were of moderate or poor quality (i.e., based on only self-reported data and/or unvalidated instruments), and only 15% of studies (n=5) assessed each ICVH component using the exact criteria defined by the AHA. Despite substantial heterogeneity in how ICVH measures were derived and analyzed (e.g., as a binary, categorical, or count variable), fairly consistent associations were observed between higher levels of ICVH and higher social status (higher education, income/wealth, racial/ethnic majority status) across both HICs and LMICs. Studies of occupation (n=6, all from HICs) and area-level measures (n=4, 3 from LMICs) were less conclusive. Conclusion: Associations between higher social status and ICVH were noted in both HICs and LMICs, but most evidence was based on correlational data from cross-sectional studies in the US, primarily in relation to education and income. Important gaps in the literature include studies from LMICs, longitudinal designs to improve causal inference, and investigations of occupation, rurality/urbanicity, and race/ethnicity in non-US settings.
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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 | 0.022 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".