Regional Patterns of Multimorbidity and Hospitalization in Saskatchewan’s Aging Population
Bibliographic record
Abstract
Background: Multimorbidity (the co-occurrence of two or more chronic conditions) is increasingly common among older adults and contributes to diminished well-being and greater healthcare use. While national data highlight regional variation, few studies have examined how multimorbidity is patterned within provinces like Saskatchewan or how it relates to access and acute care use. Objective: To describe sociodemographic and geographic patterns of multimorbidity among older adults in Saskatchewan and examine its association with healthcare access, unmet needs, and recent emergency department (ED) visits and hospitalizations. Methods: We conducted a secondary analysis of a population-based telephone survey of 1,093 adults aged 65+ across Saskatchewan. Respondents were categorized by chronic disease burden (none, one, or multimorbidity). Descriptive statistics and postal code–level mapping explored health status, access, and utilization. Results: Multimorbidity (10.6%) was more prevalent among older adults aged 75+, Indigenous respondents, and those with lower education. It was associated with poorer self-rated health, greater unmet needs, and higher ED visits (20.7%) and hospitalizations (12.1%) compared to those without chronic conditions. Northern regions had proportionally higher multimorbidity, despite smaller populations. Conclusions: Findings highlight social and spatial disparities in chronic disease burden and underscore the need for equity-focused strategies in Saskatchewan’s rural and northern communities.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".