Epidemiology of physical–mental multimorbidity and its impact among Aboriginal and Torres Strait Islander in Australia: a cross-sectional analysis of a nationally representative sample
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the differences in multimorbidity between Aboriginal and Torres Strait Islander people and non-Indigenous Australians, and the effect of multimorbidity on health service use and work productivity. SETTING: Cross-sectional sample of the Household, Income and Labour Dynamics in Australia wave 17. PARTICIPANTS: A nationally representative sample of 16 749 respondents aged 18 years and above. OUTCOME MEASURES: Multimorbidity prevalence and pattern, self-reported health, health service use and employment productivity by Indigenous status. RESULTS: Aboriginal respondents reported a higher prevalence of multimorbidity (24.2%) compared with non-Indigenous Australians (20.7%), and the prevalence of mental-physical multimorbidity was almost twice as high (16.1% vs 8.1%). Multimorbidity pattern varies significantly among the Aboriginal and non-Indigenous Australians. Multimorbidity was associated with higher health service use (any overnight admission: adjusted OR=1.52, 95% CI=1.46 to 1.58), reduced employment productivity (days of sick leave: coefficient=0.25, 95% CI=0.19 to 0.31) and lower perceived health status (SF6D score: coefficient=-0.04, 95% CI=-0.05 to -0.04). These associations were found to be comparable in both Aboriginal and non-Indigenous populations. CONCLUSIONS: Multimorbidity prevalence was significantly greater among Aboriginal and Torres Strait Islanders compared with the non-Indigenous population, especially mental-physical multimorbidity. Strategies are required for better prevention and management of multimorbidity for the aboriginal population to reduce health inequalities in Australia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".