Twenty-Year Disparity Trends in United States Stroke Death Rate by Age, Race/Ethnicity, Geography, and Socioeconomic Status
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In 2017, the Centers for Disease Control and Prevention (CDC) issued an alert that, after decades of consistent decline, the stroke death rate levelled off in 2013, particularly in younger individuals and without clear origin. The objective of this analysis was to understand whether social determinants of health have influenced trends in stroke mortality. METHODS: We performed a longitudinal analysis of county-level ischemic and hemorrhagic stroke death rate per 100,000 adults from 1999 to 2018 using a Bayesian spatiotemporally smoothed CDC dataset stratified by age (35-64 years [younger] and 65 years or older [older]) and then by county-level social determinants of health. We reported stroke death rate by county and the percentage change in stroke death rate during 2014-2018 compared with that during 2009-2013. RESULTS: < 0.001). In contrast to the younger individuals, in older individuals, the associations with increased death rates were with more traditional social determinants of health such as the social deprivation index, urban location, unemployment rate, and proportion of Black race and Hispanic ethnicity residents. DISCUSSION: Improvements in the stroke death rate in the United States are slowing and even reversing in younger individuals and many US counties. County-level increases in stroke death rate were associated with distinct social determinants of health for younger vs older individuals. These findings may inform targeted public health strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".