Network Analyses to Explore Comorbidities Among Older Adults Living With Dementia
Bibliographic record
Abstract
BACKGROUND: Older persons living with dementia (PLWD) often have multiple other chronic health conditions (i.e., comorbidities). Network analyses can describe complex profiles of chronic health conditions through graphical displays grounded in empirical data. Our study compared patterns of chronic health conditions among PLWD residing in and outside of long-term care (LTC) settings. METHODS: Population-based administrative data, including outpatient physician claims, inpatient records, pharmaceutical records, and LTC records, for the study were from the Canadian province of Manitoba. We included PLWD, ages ≥ 67 years, with two or more other chronic health conditions, who resided in Manitoba from 2017 to 2020. A total of 138 chronic health conditions were ascertained using a modification of the open-source Clinical Classification Software. Networks defined by nodes (health conditions) and edges (associations between nodes) were stratified by residence location (in versus outside LTC). Network properties were described, including: density (ratio of number of edges to number of potential edges), and modularity (associations between and within clusters of health conditions), and the median and interquartile range (IQR) for node degree (number of associations per node). RESULTS: The population comprised 19,672 PLWD, of which 17,534 (89.1%) had two or more chronic health conditions. The median number of co-occurring conditions was similar among PLWD in LTC (median: 6, IQR: 3-10) versus outside LTC (median: 7, IQR: 4-10). Network properties were similar for PLWD and multiple comorbidities residing in versus outside LTC, including node degree (median 11 vs. 12), density (0.15 vs. 0.14), and modularity (0.18 vs. 0.26). CONCLUSIONS: Multiple chronic diseases characterize PLWD residing in and outside of LTC. Using network analyses, chronic diseases among PLWD do not form easily distinguishable groups or patterns. This suggests the need for comprehensive clinical assessments, individualized approaches for disease management, and highlights the importance of person-specific care.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".