Universal Drug Coverage and Socioeconomic Disparities in Health Care Costs Among Persons With Diabetes
Bibliographic record
Abstract
OBJECTIVE: To examine whether neighborhood socioeconomic status (SES) is a predictor of non-drug-related health care costs among Canadian adults with diabetes and, if so, whether SES disparities in costs are reduced after age 65 years, when universal drug coverage commences as an insurable benefit. RESEARCH DESIGN AND METHODS: Administrative health databases were used to examine publicly funded health care expenditures among 698,113 younger (20-64 years) and older (≥65 years) adults with diabetes in Ontario from April 2004 to March 2014. Generalized linear models were constructed to examine relative and absolute differences in health care costs (total and non-drug-related costs) across neighborhood SES quintiles, by age, with adjustment for differences in age, sex, diabetes duration, and comorbidity. RESULTS: Unadjusted costs per person-year in the lowest SES quintile (Q1) versus the highest (Q5) were 39% higher among younger adults ($5,954 vs. $4,270 [Canadian dollars]) but only 9% higher among older adults ($10,917 vs. $9,993). Adjusted non-drug costs (primarily for hospitalizations and physician visits) were $1,569 per person-year higher among younger adults in Q1 vs. Q5 (modeled relative cost difference: 35.7% higher) and $139.3 million per year among all individuals in Q1. Scenarios in which these excess costs per person-year were decreased by ≥10% or matched the relative difference among seniors suggested a potential for savings in the range of $26.0-$128.2 million per year among all lower-SES adults under age 65 years (Q1-Q4). CONCLUSIONS: SES is a predictor of diabetes-related health care costs in our setting, more so among adults under age 65 years, a group that lacks universal drug coverage under Ontario's health care system. Non-drug-related health care costs were more than one-third higher in younger, lower-SES adults, translating to >$1 billion more in health care expenditures over 10 years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".