Potentially preventable dementia in a First Nations population in the Torres Strait and Northern Peninsula Area of North Queensland, Australia: a cross sectional analysis using population attributable fractions
Bibliographic record
Abstract
Background Dementia is highly prevalent among Australia's First Nations peoples, including Torres Strait Islander and Aboriginal peoples in Far North Queensland (FNQ). It is likely that historically recent exposure to modifiable risk factors underlies these rates, and a large proportion of dementia may be potentially preventable. Methods Data from two adult community health checks (2015–2018) were analyzed to determine the prevalence of 11 modifiable dementia risk factors among the First Nations residents of the Torres Strait and Northern Peninsula Area of FNQ. Population attributable fractions (PAF%) for dementia were calculated using age-standardized prevalence estimates derived from these health checks and relative risks obtained from previous meta-analyses in other populations. PAF% estimates were weighted for communality to account for overlap of risk factors. Findings A third (34.9%) of the dementia burden in this population may be attributed to 11 potentially modifiable risk factors. Hypertension (6.3%), diabetes mellitus (6.0%), obesity (5.3%), and smoking (3.6%) were the highest contributing risk factors. The contribution of depression (1.3%) and alcohol (0.2%) was lower than other global and national estimates. While the adjusted PAF% for social isolation was low based on the adult community health check data (1.1%), it was higher (2.8%) when official census data were analyzed. Interpretation These results suggest that a substantial proportion of dementia in FNQ First Nations peoples could potentially be prevented. Government investment in preventative health now is essential to reduce the future burden of dementia. Funding National Health and Medical Research Council (NHMRC, GNT1107140, GNT1191144, GNT1106175, GNT0631947).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".