<p>Association Between Multimorbid Disease Patterns and Pain Outcomes Among a Complex Chronic Care Population in Canada</p>
Bibliographic record
Abstract
Purpose: Disease multimorbidity and pain is a complex, yet common, problem for the aging population, and a significant burden on the health-care systems around the world. Despite this, disease comorbidity and the association with pain in a complex chronic care population is not well understood. This study examined the most prevalent disease combinations and their association with pain. Patients and Methods: The study initially included 139,920 residents, aged 18– 101 years, admitted to publicly funded hospital facilities for complex chronic care in Canada between the years 2006 and 2016. Data were acquired through the Canadian Institute for Health Information (CIHI) Facility-Based Continuing Care Reporting System (CCRS). Descriptive and chi-square statistics were used to summarize and compare the sample characteristics. Binary logistic regression analyses were used to examine the association between multimorbid disease categories and pain outcomes. Results: The sample consisted of 139,573 residents (57% female), mostly older (mean age = 77.32 years), married (40%), or widowed (36%). Residents took an average of 11.9 medications and 77% were using analgesic medications. On average, residents had diagnoses from 3.06 disease categories (SD = 1.43). Heart/circulation diseases were the most prevalent among the sample (73%), with neurological second (46%) and musculoskeletal third (44%). Overall, 73% of residents reported pain, with 43% reporting moderate pain severity. Residents with multiple disease categories were more likely to report the presence of pain (OR = 1.08, 95% CI: 1.07– 1.08, p < 0.001), with each additional disease category associated with an 8% increase in the odds of reporting pain. Conclusion: The findings from this study help identify common comorbid disease patterns related to pain in an institutionalized, complex chronic care population. This information contributes to both the pain and multimorbidity literature, and is invaluable for creating care plans to meet the demands of a challenging population. Keywords: complex chronic disease, comorbidity, disease categories, health, health psychology
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".