Prevalence of surgery in Indigenous people with cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: As cancer incidence increases globally, so does the prevalence of cancer among Indigenous peoples. Indigenous peoples face significant barriers to healthcare, including access to and uptake of surgery. To date, the synthesis of access to and uptake of surgery for Indigenous peoples living with cancer has not yet been reported. Methods: We conducted a systematic literature review and meta-analysis of access to and uptake of surgery for Indigenous peoples in Canada, Australia, New Zealand, and the United States. Five databases were searched to identify studies of Indigenous adults with cancer and those who received surgery. The Joanna Briggs Institute critical appraisal tools were used to assess the quality and inclusion of articles. Random effect meta-analyses were conducted to estimate the pooled prevalence of surgery in Indigenous people with cancer. Findings: Of the 52 studies in the systematic review, 38 were included in the meta-analysis. The pooled prevalence of surgery in Indigenous people with cancer was 56.2% (95% confidence interval (CI): 45.4-66.7%), including 42.8% (95% CI: 36.3-49.5%) in the Native Hawaiian population, 44.5% (95% CI: 38.7-50.3%) in the Inuit and 51.5% (95%CI: 36.8-65.9%) in Aboriginal and Torres Strait Islander people. Overall, Indigenous people received marginally less cancer surgery than non-Indigenous people (3%, 95% CI: 0-6%). Indigenous people were 15% (95% CI: 6-23%) less likely to receive surgery than non-Indigenous people for respiratory cancers. Remoteness, travel distance, financial barriers, and long waiting times to receive surgery were factors cited as contributing to lower access to surgery for Indigenous people compared to non-Indigenous people. Interpretation: Efforts to improve access and use of cancer services and surgery for Indigenous peoples should be multilevel to address individual factors, health services and systems, and structural barriers. These determinants need to be addressed to expedite optimal care for Indigenous peoples, especially those living in outer metropolitan areas. Funding: The Research Alliance for Urban Goori Health (RAUGH) funded this project. GG was funded by an NHMRC Investigator Grant (#1176651).
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".