Are social isolation, lack of social support or loneliness risk factors for cardiovascular disease in Australia and New Zealand? A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: An international systematic review concluded that individuals with poor social health (social isolation, lack of social support or loneliness) are 30% more likely to develop coronary heart disease (CHD) and stroke. Notably, the two included Australian papers reported no association between social health and CHD or stroke. OBJECTIVE: We undertook a systematic review and meta-analysis to investigate the association between social isolation, lack of social support and loneliness and cardiovascular disease (CVD) incidence among people living in Australia and New Zealand. METHODS: Four electronic databases were systematically searched for longitudinal studies published until June 2020. Two reviewers undertook title/abstract screen and one reviewer undertook full-text screen and data extraction. Quality was assessed using the Newcastle - Ottawa Quality Assessment Scale. RESULTS: Of the 725 unique records retrieved, five papers met our inclusion criteria. These papers reported data from three Australian longitudinal datasets, with a total of 2137 CHD and 590 stroke events recorded over follow-up periods ranging from 3 to 16 years. Reports of two CHD and two stroke outcomes were suitable for meta-analysis. The included papers reported no association between social health and incidence of CVD in all fully adjusted models and most unadjusted models. CONCLUSIONS: Our systematic review is inconclusive as it identified only a few studies, which relied heavily on self-reported CVD. Further studies using medical diagnosis of CVD, and assessing the potential influence of residential remoteness, are needed to better understand the relationship between social health and CVD incidence in Australia and New Zealand.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".