Looking Beyond Income and Education
Bibliographic record
Abstract
INTRODUCTION: Healthcare spending occurs disproportionately among a very small portion of the population. Research on these high-cost users (HCUs) of health care has been overwhelmingly cross-sectional in nature and limited to the few sociodemographic and clinical characteristics available in health administrative databases. This study is the first to bridge this knowledge gap by applying a population health lens to HCUs. We investigate associations between a broad range of SES characteristics and future HCUs. METHODS: A cohort of adults from two cycles of large, nationally representative health surveys conducted in 2003 and 2005 was linked to population-based health administrative databases from a universal healthcare plan for Ontario, Canada. Comprehensive person-centered estimates of annual healthcare spending were calculated for the subsequent 5 years following interview. Baseline HCUs (top 5%) were excluded and healthcare spending for non-HCUs was analyzed. Adjusted for predisposition and need factors, the odds of future HCU status (over 5 years) were estimated according to various individual, household, and neighborhood SES factors. Analyses were conducted in 2014. RESULTS: Low income (personal and household); less than post-secondary education; and living in high-dependency neighborhoods greatly increased the odds of future HCUs. After adjustment, future HCU status was most strongly associated with food insecurity, personal income, and non-homeownership. Living in highly deprived or low ethnic concentration neighborhoods also increased the odds of becoming an HCU. CONCLUSIONS: Findings suggest that addressing social determinants of health, such as food and housing security, may be important components of interventions aiming to improve health outcomes and reduce costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 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".