Do socioeconomic measures improve prediction of cardiovascular disease hospitalization?
Bibliographic record
Abstract
Background: Risk prediction models for cardiovascular disease (CVD) hospitalization have improved prediction accuracy when individual-level measures of socioeconomic status (SES) are considered. However, it is unclear how validated, area-level SES measures, available at the population-level in Canada, might improve prediction of CVD hospitalization. Objectives: The research objectives were to (1) test the incremental predictive value of area-level SES measures and (2) compare the incremental predictive performance of different area-level SES measures on time to hospitalization for CVD. Methods: A retrospective cohort design used Manitoba administrative health records from 2014 to 2020 and area-level SES measures from 2016 Statistics Canada Census data. Individuals 40+ years as of April 1st, 2016 were followed until an acute myocardial infarction (AMI) or stroke hospitalization or loss to follow-up. Covariates included age, sex, comorbid conditions, prior healthcare use, AMI/stroke-related prescription medications, and one or more area-level SES measures, including the Socioeconomic Factor Index - Version 2 (SEFI-2), Material Deprivation Index, Social Deprivation Index, and the Canadian Index of Multiple Deprivation (CIMD). Cox proportional hazards models were assessed for model accuracy (area under the curve; AUC), discrimination (net reclassification improvement; NRI and integrated discrimination improvement; IDI) and calibration (Brier score). Results: Overall predictive performance of models containing one or more SES measures (fully-adjusted model; AUC = 0.753 – 0.757) was similar to model containing all other covariates (partially-adjusted model; AUC = 0.753). Discrimination performance was poor or statistically non-significant (NRI = -0.158 – 0.019; IDI = < 0.000). Prediction error was low for all models (Brier score = 0.022). Conclusion: Area-level SES measures did not add predictive value to CVD hospitalization risk models. Risk factors available in administrative health data, like demographics and comorbid conditions, already provide a similar amount of information in terms of predictive ability. Area-level SES measures are useful for characterizing and describing populations but may not have strong predictive value to individual-level outcomes. Further studies are recommended to explore their use in prediction of other health conditions and jurisdictions, and comparisons with individual-level SES measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".