Health care service utilization among the elderly: findings from the Study to Understand the Chronic Condition Experience of the Elderly and the Disabled (SUCCEED project)
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Age-related effects on health service utilization are not well understood. Most previous studies have examined only a single specific health care service or disease condition or have focused exclusively on economic variables. We aim to measure age-related change in health care utilization among the elderly. METHODS: A population-based retrospective cohort study was conducted using linked data from four administrative databases (OHIP, ODB, CIHI and RPDB). All Ontario residents over the age of 65 years and eligible for public health coverage were included in the analysis (approximately 1.6 million residents). Main outcome measures include utilization indicators for family physician visits, specialist physician visits, Emergency Department visits, drugs, lab claims, X-rays, inpatient admissions, CT scans and MRI scans. RESULTS: The mean number of utilization events for Ontarians aged 65+ years for the 1-year study period was 70 events (women = 76, men = 63). The overall absolute difference between the 65-69 age group and the 85+ age group was 155% (women = 162%, men = 130%), or 76 more events per person in the older group (women = 82, men = 61). Women averaged more events per person than men, as well as greater percentage differences by age. Drugs and diagnostics account for the majority of events. Only MRI and specialist visits were not higher among the older age groups. CONCLUSIONS: At the population level, overall health care utilization would appear to increase significantly with age. It is unclear whether increasing health care utilization prevents morbidity, decreases mortality, or improves quality of life.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".