The link between out-of-pocket costs and inequality in specialist care in Australia
Bibliographic record
Abstract
Objective Out-of-pocket (OOP) costs could act as a potential barrier to accessing specialist services, particularly among low-income patients. The aim of this study is to examine the link between OOP costs and socioeconomic inequality in specialist services in Australia. Methods This study is based on population-level data from the Medicare Benefits Schedule of Australia in 2014-15. Three outcomes of specialist care were used: all visits, visits without OOP costs (bulk-billed services), and visits with OOP costs. Logistic and zero-inflated negative binomial regression models were used to examine the association between outcome variables and area-level socioeconomic status after controlling for age, sex, state of residence, and geographic remoteness. The concentration index was used to quantify the extent of inequality. Results Our results indicate that the distribution of specialist visits favoured the people living in wealthier areas of Australia. There was a pro-rich inequality in specialist visits associated with OOP costs. However, the distribution of the visits incurring zero OOP cost was slightly favourable to the people living in lower socioeconomic areas. The pro-poor distribution of visits with zero OOP cost was insufficient to offset the pro-rich distribution among the visits with OOP costs. Conclusions OOP costs for specialist care might partly undermine the equity principle of Medicare in Australia. This presents a challenge to the government on how best to influence the rate and distribution of specialists' services.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".