Can't escape it: the out‐of‐pocket cost of health care in Australia
Bibliographic record
Abstract
OBJECTIVE: To analyse the annual out-of-pocket (OOP) expenditure on health care as directly reported by Australian households grouped into older households (those with a reference person aged ≥ 65 years) and younger households (those with a reference person aged < 65 years). DESIGN: Descriptive analysis of statutory data collected by the Australian Bureau of Statistics. SETTING AND PARTICIPANTS: Probability sample of 9774 households across all states and territories. MAIN OUTCOME MEASURES: OOP expenditure on health care. RESULTS: The mean annual OOP expenditure on health care among the older households was estimated as $3585 ± $686 (9.4% of the total expenditure on all goods and services), and among the younger households, it was $3377 ± $83 (4.7% of the total expenditure on all goods and services). Cost of medicines (mainly non-prescription drugs and to a lesser extent the copayments for Pharmaceutical Benefits Scheme scripts) was the biggest item of expenditure for the older households, and the cost of private health insurance (PHI) was the most expensive item for the younger households. Overall, the OOP expenditure, as reported by the Australian households, was $28.7 ± $1.3 billion compared with $21.2 billion as reported by the Australian Institute of Health and Welfare. Unlike our estimate, the Institute's figure was based on statutory data collections and did not include the cost of PHI premiums. CONCLUSIONS: OOP expenses account for almost a quarter (22%) of the total health care costs in Australia. The mean annual OOP expenditure was slightly higher for the older households compared with the younger households, despite the fact that the older households had significantly lower income and had greater access to health care cards, which were used to defray additional health care costs associated with age.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".