Radiotherapy and time to initial treatment following a cancer diagnosis among First Nations Australians: results from a population-based analysis
Bibliographic record
Abstract
BACKGROUND: First Nations Australians experience poorer cancer outcomes than other Australians. This is attributable to multidimensional factors, including disparities in access to cancer services and treatments. Radiotherapy (RT) is an important component of cancer treatment yet evidence of its utilisation among First Nations Australians is limited. We aimed to examine RT utilisation, time to the treatment, and associated factors in First Nations Peoples diagnosed with cancer in Queensland, Australia. METHODS: First Nations Queenslanders (N = 1884) and other Queenslanders (N = 104,204) diagnosed with any cancer between 1st July 2011 and 30th June 2015 and their RT details between 1st July 2011 and 30th June 2018 were identified using the Cancer CostMod dataset, comprising Queensland Cancer Registry data linked with Queensland Hospital Admitted Patient Data Collection (QHAPDC) and Medicare Benefits Schedule (MBS). Analysis was limited to those with non-missing Indigenous status who linked to MBS and/or QHAPDC records (N = 105, 983). Differences in RT utilisation and waiting times by First Nations status were tested using logistic regressions, non-parametric tests, and quantile regression. RESULTS: Among 105, 983 people with a cancer diagnosis in Queensland, 28.6% had RT, with external Beam RT(EBRT) as the predominant type (n = 29,387, 96.9%). One-third (33.5%) of First Nations cancer patients received RT at least once, compared to 28.5% of other Queensland cancer patients (P < 0.001). After adjustment for covariates, First Nations cancer patients had a greater likelihood of RT utilisation than other Queenslanders (adjusted odds ratio(aOR): 1.15; 95% (confidence-interval (CI): 1.04-1.27) and more pronounced within the first year after diagnosis (aOR: 1.23: 95% CI:1.11-1.37). Among those receiving any RT, the median time from cancer diagnosis to first RT was 118 days (Interquartile-range (IQR): 55-232) for First Nations and 132 days (IQR: 59-258) for other Queenslanders (P = 0.034). CONCLUSIONS: A higher proportion of First Nations cancer patients received RT than other Queensland cancer patients, particularly in the first year following diagnosis. However, RT utilisation for all cancer patients was notably lower (28.6%) than the national optimal RT(EBRT) rate of 48%. This finding highlights the need for RT to align with optimal care standards, which is crucial for improving cancer outcomes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".