Health care utilization and costs in Saskatchewan's registered Indian population with diabetes
Bibliographic record
Abstract
BACKGROUND: The prevalence of diabetes in North American is recognized to be higher in Aboriginal populations. The relative magnitude of health care utilization and expenditures between Aboriginal and non-Aboriginal populations is uncertain, however. Our objective was to compare health care utilization and per capita expenditures according to Registered Indian and diabetes status in the province of Saskatchewan. METHODS: Administrative databases from Saskatchewan Health were used to identify registered Indians and the general population diabetes cases and two controls for each diabetes case. Health care resource utilization (physician visits, hospitalizations, day surgeries and dialysis) and costs for these individuals in the 2001 calendar year were determined. The odds of having used each resource category, adjusted for age and location of residence, was assessed according to Registered Indian and diabetes status. The average number of encounters for each resource category and per capita healthcare expenditures were also determined. RESULTS: Registered Indian diabetes cases were younger than general population cases (45.7 +/- 14.5 versus 58.4 +/- 16.4 years, p < 0.001) and fewer were male (42.3% versus 53.2%, p < 0.001). Registered Indians were more likely to visit a physician, be hospitalized or receive dialysis than the general population, regardless of diabetes status. Diabetes increased the probability of having used all resource categories for both Registered Indians and the general population. Per capita health care expenditures for the diabetes subgroups were more than twice that of their respective controls and were 40% to 60% higher for registered Indians than the general population, regardless of diabetes status. CONCLUSION: Relative to individuals without the disease, both registered Indians and the general population with diabetes had substantially higher health care utilization and costs. Excess hospitalization and dialysis suggested that registered Indians with and without diabetes experienced greater morbidity than the general population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".