Association of rurality status with all-cause and cancer-specific survival: a systematic review and meta-analysis adjusting for clinical factors, demographics, and geographical remoteness
Bibliographic record
Abstract
The association of rurality status with cancer survival has not been consistently reported. In people diagnosed with cancer, this review aims to determine the association of rural and remote living with survival as compared to urban living, and to determine the modifying effects of geographical, medical, demographic, and socioeconomic factors on cancer survival. A systematic review with meta-analysis and meta-regression was conducted, searching four databases in August 2024. Observational cohort studies were eligible if they reported all-cause or cancer-specific survival according to rurality status in Organisation for Economic Co-operation and Development (OECD) countries. All ages, sexes, and cancer types were eligible. Risk of bias was assessed using the Newcastle-Ottawa Scale and pooled models were evaluated using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE). Meta-analyses and meta-regressions were performed using R statistical environment. 37 studies reporting on 110 comparator groups were included. People with cancer in rural areas were at a survival disadvantage compared to people in urban areas, with 15% lower odds of all-cause survival (OR 0.85 [95% CI 0.74, 0.97]) and 10% lower odds of cancer-specific survival (OR: 0.90 [95% CI 0.86, 0.95]). Cancer type and degree of geographical remoteness were consistent modifiers of survival in univariable and multivariable regression. Increasing degree of geographical remoteness was associated with lower odds of all-cause survival (OR 0.28 [95% CI 0.12-0.67]). People living in rural areas diagnosed with cancer have lower odds of all-cause and cancer-specific survival which worsened with increasing geographical remoteness. Type of cancer was consistently found to be a modifying factor of cancer survival. Increased recognition of people living in rural areas as a priority population group in health and cancer policies is needed to improve cancer equity. Funding: Commonwealth of Australia's Medical Research Future Fund (MRF2030313).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".