Network analyses to explore multimorbidity among older adults with dementia residing in personal care homes and the community
Bibliographic record
Abstract
Background: Dementia is a progressive chronic health condition that affects an individual’s cognition and functional status. Multimorbidity, the co-occurrence of multiple chronic health conditions, is common among persons with dementia. Network analyses can describe complex profiles of chronic health conditions through graphical displays grounded in empirical data. Research Objective: Our study compares the number of chronic health conditions and patterns of multimorbidity among persons with dementia residing in personal-care homes (PCH) and outside of PCH settings. Methods: Population-based administrative data, including outpatient claims, inpatient records, pharmaceuticals, and long-term care records were obtained from the Manitoba Population Research Data Repository. This retrospective cohort consisted of persons with dementia, ages ≥67 years, who resided in Manitoba from 2015-2020. A total of 138 chronic health conditions were ascertained using a modified version of listed conditions within the Clinical Classification Software. Networks, consisting of nodes (chronic health conditions) connected by edges (cosine index, which quantifies the strength of association between pairs of chronic health conditions), were stratified by residence location (in PCH versus outside PCH). Network properties, such as: number of associations per disease (degree) and network connectivity (density), were reported. A community detection algorithm identified community clusters and calculated associations within versus between community clusters (modularity). Results: Of 19,672 persons with dementia in Manitoba, 9,609 (49%) resided in PCH. The median number of co-occurring chronic health conditions was similar among persons with dementia in PCH (median: 6, Q1-Q3: 3-10) versus outside PCH (median: 7, Q1-Q3: 4-10). Networks properties were similar for persons with dementia in PCH versus outside PCH: median degree (11 versus 12), network density (0.15 versus 0.14), and modularity (0.18 versus 0.26). Conclusions: Multimorbidity is commonly present among persons with dementia. Similar numbers and patterns of chronic health conditions were witnessed among persons with dementia residing in versus outside PCH. Unique visual and analytic techniques demonstrate that chronic health conditions among persons with dementia are often interconnected and do not form easily distinguishable patterns. These results suggest the need for comprehensive and individualized approaches for disease management among persons with dementia in all settings.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".