Prevalence and patterns of comorbidities in older people with type 2 diabetes in Australian primary care settings
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify the prevalence and patterns of comorbidity in community-dwelling older people with type 2 diabetes mellitus (T2DM) attending general practice settings in Australia. METHODS: This study involved a cross-sectional analysis using the Bettering the Evaluation and Care of Health (BEACH) sub-study data. In a series of sub-studies, a representative sample of general practitioners was asked to record all diagnosed chronic conditions for patients at 40 consecutive encounters using structured paper-based recording forms. The dataset was analysed with descriptive analyses, and exploratory factor analyses were applied to examine comorbidity patterns. RESULTS: Of the 14,042 patients aged 65 years or older, 2688 had a diagnosis of T2DM (19%). Of the 2688 patients with T2DM, hypertension was present in 67% (95% CI: 64.6-70.0), followed by arthritis 52% (95% CI: 48.8-54.8), hyperlipidaemia 45% (95% CI: 41.8-47.9), ischemic heart disease, 23% (95% CI: 20.7-24.9), depression 16% (95% CI: 48.8-54.8), atrial fibrillation 10% (95% CI: 8.9-11.6), congestive heart failure 7% (95% CI: 6.0-8.1), stroke/cerebrovascular accident 7% (95% CI: 5.4-8.2) and peripheral vascular disease 5% (95% CI: 4.4-6.2). We identified two comorbidity patterns among older people with T2DM. The first were psychological and musculoskeletal conditions and the second were cardiovascular conditions and chronic renal failure. CONCLUSIONS: The prevalence of cardiovascular and non-cardiovascular comorbidities in community-dwelling older people with T2DM was high. Adequate primary care strategies should be in place to support the long-term care for this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".