Multimorbidity Frameworks Impact Prevalence and Relationships with Patient‐Important Outcomes
Bibliographic record
Abstract
OBJECTIVES: To explore how different frameworks and categories of chronic conditions impact multimorbidity (defined as two or more chronic conditions) prevalence estimates and associations with patient-important functional outcomes. DESIGN: Baseline data from a population-based cohort study. SETTING: National sample of Canadians. PARTICIPANTS: A total of 51 338 community-living adults, aged 45 to 85 years. MAIN OUTCOME MEASURES: Chronic conditions from three commonly recognized frameworks were categorized as: (1) diseases, (2) risk factors, or (3) symptoms. Estimates of multimorbidity prevalence were compared among frameworks by age and sex. Separate weighted logistic regression models were used to explore the impact of the different frameworks and categories of chronic conditions on odds ratios (ORs) for multimorbidity for four patient-important functional outcomes: disability, social participation restriction, and self-rated physical and mental health. RESULTS: One framework included diseases and risk factors, and two frameworks included diseases, risk factors, and symptoms. The prevalence of multimorbidity differed among the frameworks, ranging from 33.5% to 60.6% having two or more chronic conditions. Including risk factors in frameworks increased prevalence estimates, while including symptoms increased prevalence estimates and associations with most patient-important outcomes. The two frameworks that included symptoms had the largest ORs for associations with disability, social participation restriction, and self-rated physical health but not self-rated mental health. Similar results were found when we compared ORs for patient-important outcome for multimorbidity based on three subframeworks: one including diseases only, one including diseases and risk factors, and one including diseases, risk factors, and symptoms. CONCLUSIONS: Including risk factors appeared to increase only the prevalence of multimorbidity without significantly altering relationships to outcomes. The inclusion of symptoms increased prevalence and associations with patient-important outcomes. These findings underscore the importance of considering not only the number, but also the category, of conditions included in multimorbidity frameworks, as simply counting the number of diagnoses may reduce sensitivity to outcomes that are important to individuals. J Am Geriatr Soc 67:1632-1640, 2019.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".