Acute healthcare resource utilization by age: A cohort study
Bibliographic record
Abstract
BACKGROUND: Granular data related to the likelihood of individuals of different ages accessing acute and critical care services over time is lacking. METHODS: We used population-based, administrative data from Ontario to identify residents of specific ages (20, 30, 40, etc. to 100) on January 1st every year from 1995-2019. We assessed rates of emergency department (ED) visits (2003-19), hospitalizations, intensive care unit (ICU) admissions (2003-19), and mechanical ventilation. FINDINGS: Overall the 25-year study period, ED were the most common acute healthcare encounter with 100-year-olds having the lowest rate (138.7/1,000) and 90-year-olds the highest (378.5/1,000). Rates of hospitalization ranged from 24.2/1,000 for those age 20 up to 224.9/1,000 for those age 90. Rates of ICU admission and mechanical ventilation were lowest for those age 20 (1.0 and 0.4/1,000), more than tripled by age 50 (3.3 and 1.7/1,000) and peaked at age 80 (20.3 and 10.1/1,000). Over time rates of ED visits increased (164.3 /1,000 in 2003 vs 199.1 /1,000 in 2019) as did rates of invasive mechanical ventilation (2.0/1,000 in 1995 vs 2.9/1,000 in 2019), whereas rates of ICU admission remained stable (4.8/1,000 in 2003 vs 4.9/1,000 in 2019) and hospitalization declined (66.8/1,000 in 1995 vs 51.5/1,000 in 2019). Age stratified analysis demonstrated that rates of ED presentation increased for those age 70 and younger while hospitalization decreased for all age groups; ICU admission and mechanical ventilation rates changed variably by age, with increasing rates demonstrated primarily among people under the age of 50. INTERPRETATION: Rates of hospitalizations have decreased over time across all age groups, whereas rates of ED presentation, ICU admissions, and mechanical ventilation have increased, primarily driven by younger adults. These findings suggest that although the delivery of healthcare may be moving away from inpatient medicine, there is a growing population of young adults requiring significant healthcare resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".