Healthcare resource availability and cardiovascular health in the USA
Bibliographic record
Abstract
OBJECTIVES: Cardiovascular disease (CVD) remains the leading cause of death in the USA. Reducing the population-level burden of CVD disease will require a better understanding and support of cardiovascular health (CVH) in individuals and entire communities. The objectives for this study were to examine associations between community-level healthcare resources (HCrRes) and CVH in individuals and entire communities. SETTING: This study consisted of a retrospective, cross-sectional study design, using multivariable epidemiological analyses. PARTICIPANTS: All participants in the 2011 Behavioral Risk Factor Surveillance System (BRFSS) survey were examined for eligibility. CVH, defined using the American Heart Association CVH Index (CVHI), was determined using self-reported responses to 2011 BRFSS questions. Data for determining HCrRes were obtained from the Area Health Resource File. Regression analysis was performed to examine associations between healthcare resources and CVHI in communities (linear regression) and individuals (Poisson regression). RESULTS: Mean CVHI was 3.3±0.005 and was poorer in the Southeast and Appalachian regions of the USA. Supply of primary care physicians and physician assistants were positively associated with individual and community-level CVHI, while CVD specialist supply was negatively associated with CVHI. Individuals benefiting most from increased supply of primary care providers were: middle aged; female; had non-Hispanic other race/ethnicity; those with household income <$25 000/year; and those in non-urban communities with insurance coverage. CONCLUSIONS: Our results support the importance of primary care provider supply for both individual and community CVHI, though not all sociodemographic groups benefited equally from additional primary care providers. Further research should investigate policies and factors that can effectively increase primary care provider supply and influence where they practice.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".