Reassessing access to intensive care using an estimate of the population incidence of critical illness
Bibliographic record
Abstract
BACKGROUND: The consistently observed male predominance of patients in intensive care units (ICUs) has raised concerns about gender-based disparities in ICU access. Comparing rates of ICU admission requires choosing a normalizing factor (denominator), and the denominator usually used to compare such rates between subpopulations is the size of those subpopulations. However, the appropriate denominator is the number of people whose medical condition warranted ICU care. We devised an estimate of the number of critically ill people in the general population, and used it to compare rates of ICU admission by gender and income. METHODS: This population-based, retrospective analysis included all adults in the Canadian province of Manitoba, 2004-2015. We created an estimate for the number of critically ill people who warrant ICU care, and used it as the denominator to generate critical illness-normalized rates of ICU admission. These were compared to the usual population-normalized rates of ICU care. RESULTS: Men outnumbered women in ICUs for all age groups; population-normalized male:female rate ratios significantly exceed 0 for every age group, ranging from 1.15 to 2.10. Using critical-illness normalized rates, this male predominance largely disappeared; critically ill men and women aged 45-74 years were admitted in equivalent proportions (critical-illness normalized rate ratios 0.96-1.01). While population-normalized rates of ICU care were higher in lower income strata (p < 0.001), the gradient for critical illness-based rates was reversed (p < 0.001). CONCLUSIONS: Across a 30-year adult age span, the male predominance of ICU patients was accounted for by higher estimated rates of critical illness among men. People in lower income strata had lower critical-illness normalized rates of ICU admission. Our methods highlight that correct inferences about access to healthcare require calculating rates using denominators appropriate for this purpose.
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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 | 0.003 |
| 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".