Chronic disease and falls in community-dwelling Canadians over 65 years old: a population-based study exploring associations with number and pattern of chronic conditions
Bibliographic record
Abstract
BACKGROUND: Falls and chronic disease are both important health issues in older adults. The objectives of this study were to quantify the prevalence of falls and multi-morbidity (≥2 chronic conditions) in Canadian older adults; examine associations between falls and number of chronic conditions; and explore whether certain patterns of chronic disease were associated with a greater risk of falling. METHODS: Data were derived from the Canadian Community Health Survey- Healthy Aging. Primary outcomes from 16,357 community-dwelling adults aged 65 years and over were self-reported falls in the previous 12 months and presence of 13 chronic conditions. Prevalence estimates were calculated with normalized sampling weights, and hierarchical cluster analysis was used to identify clusters based on chronic condition patterns, and tested for association to falls with logistic regression. RESULTS: Overall prevalence of falling and multi-morbidity were 19.8% and 62.0% respectively. Fall risk was significantly greater in individuals with one, two, four, five and six or more chronic conditions relative to those with none (all p < 0.05). A seven-cluster model was selected, including groups with low prevalence of chronic disease, or high prevalence of hypertension and arthritis, visual impairment, hypertension, chronic obstructive pulmonary disease (COPD), diabetes, or heart disease and hypertension. Only the hypertension cluster (Odds Ratio [OR] = 1.2) and COPD cluster (OR = 1.6) were significantly associated with increased falls relative to the low prevalence group. CONCLUSIONS: Both the number and pattern of chronic conditions were related to falls. COPD emerged as a significant predictor of falls despite affecting a smaller proportion of respondents. Continued study is warranted to verify this association and determine how to incorporate consideration of chronic disease and multi-morbidity into fall risk assessments.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".