Prevalence of psychiatric disorders for Indigenous Australians: a population-based birth cohort study
Bibliographic record
Abstract
AIMS: Limited information exists about the prevalence of psychiatric illness for Indigenous Australians. This study examines the prevalence of diagnosed psychiatric disorders in Indigenous Australians and compares this to non-Indigenous Australians. The aims were to: (1) determine prevalence rates for psychiatric diagnoses for Indigenous Australians admitted to hospital; and (2) examine whether the profile of psychiatric diagnoses for Indigenous Australians was different compared with non-Indigenous Australians. METHODS: A birth cohort design was adopted, with the population consisting of 45 141 individuals born in the Australian State of Queensland in 1990 (6.3% Indigenous). Linked administrative data from Queensland Health hospital admissions were used to identify psychiatric diagnoses from age 4/5 to 23/24 years. Crude lifetime prevalence rates of psychiatric diagnoses for Indigenous and non-Indigenous individuals were derived from the hospital admissions data. The cumulative incidence of psychiatric diagnoses was modelled separately for Indigenous and non-Indigenous individuals. Logistic regression was used to model differences between Indigenous and non-Indigenous psychiatric presentations while controlling for sociodemographic characteristics. RESULTS: There were 2783 (6.2%) individuals in the cohort with a diagnosed psychiatric disorder from a hospital admission. The prevalence of any psychiatric diagnosis at age 23/24 years was 17.2% (491) for Indigenous Australians compared with 5.4% (2292) for non-Indigenous Australians. Indigenous individuals were diagnosed earlier, with overrepresentation in psychiatric illness becoming more pronounced with age. Indigenous individuals were overrepresented in almost all categories of psychiatric disorder and this was most pronounced for substance use disorders (SUDs) (12.2 v. 2.6% of Indigenous and non-Indigenous individuals, respectively). Differences between Indigenous and non-Indigenous Australians in the likelihood of psychiatric disorders were not statistically significant after controlling for sociodemographic characteristics, except for SUDs. CONCLUSIONS: There is significant inequality in psychiatric morbidity between Indigenous and non-Indigenous Australians across most forms of psychiatric illness that is evident from an early age and becomes more pronounced with age. SUDs are particularly prevalent, highlighting the importance of appropriate interventions to prevent and address these problems. Inequalities in mental health may be driven by socioeconomic disadvantage experienced by Indigenous individuals.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".