Abstract 3225: Gross Domestic Product and Health Expenditure Associated with Incidence, 30-Day Fatality and Age at Stroke Onset: A Systematic Review
Bibliographic record
Abstract
Background: Differences in definitions of socioeconomic status (SES) and between study designs hinder their comparability across countries. We aimed to analyze the correlation of three widely used macro-SES indicators with stroke incidence and age at stroke onset. Methods: We selected population-based studies reporting incident stroke risk and/or 30-day case fatality according to pre-specified criteria. We used three macro-SES indicators that are consistently defined by international agencies: per capita gross domestic product adjusted for purchasing power parity (PPP-aGDP), total health expenditures per capita at purchasing power parity (PPP-aTHE) and unemployment rate. We used two-tailed Spearman’s test and scatter-plots for analyzing the correlation of each macro-SES indicator with incident risk of stroke, 30-day case fatality rates, proportion of hemorrhagic strokes and age at stroke onset. Results: Twenty-three manuscripts comprising 30 population-based studies fulfilled the eligibility criteria. Age-adjusted incident risk of stroke using standardized World Health Organization World population, 30-day case fatality rates, proportion of hemorrhagic strokes and age at stroke onset were associated to lower PPP-aGDP and PPP-aTHE ( Table 1 and Figures 2 and 3). There was no correlation between unemployment rates and outcome measures. Table 1. Correlation Analyses of Macro-Indicators of Socioeconomic Status Figures 1. Scatter Plots for PPP-aGDP Figures 2. Scatter Plots for PPP-aTHE Conclusions: Lower PPP-aGDP and PPP-aTHE were associated with higher incident risk of stroke, higher case fatality, greater proportion of hemorrhagic strokes and lower age at stroke onset. As a result, these macro-SES indicators may be used as proxy measures of quality of primary prevention and acute care and considered as important factors for developing strategies aimed at improving worldwide stroke care.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".