Lung cancer survival by immigrant status: a population-based retrospective cohort study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Lung cancer is one of the most common cancers and causes of cancer death in Canada. Some previous literature suggests that socioeconomic inequalities in lung cancer screening, treatment and survival may exist. The objective of this study was to compare overall survival for immigrants versus long-term residents of Ontario, Canada among patients diagnosed with lung cancer. METHODS: This population-based retrospective cohort study utilized linked health administrative databases and identified all individuals (immigrants and long-term residents) aged 40 + years diagnosed with incident lung cancer between April 1, 2012 and March 31, 2017. The primary outcome was 5-year overall survival with December 31, 2019 as the end of the follow-up period. We implemented adjusted Cox proportional hazards models stratified by age at diagnosis, sex, and cancer stage at diagnosis to examine survival. RESULTS: Thirty-eight thousand seven hundred eighty-eight individuals diagnosed with lung cancer were included in our cohort including 7% who were immigrants. Immigrants were younger at diagnosis and were more likely to reside in the lowest neighbourhood income quintile (30.6% versus 24.5%) than long-term residents. After adjusting for age at diagnosis, neighbourhood income quintile, comorbidities, visits to primary care in the 6 to 30 months before diagnosis, continuity of care, cancer type and cancer stage at diagnosis, immigrant status was associated with a lower hazard of dying 5-years post-diagnosis for both females (0.7; 95% CI 0.6-0.8) and males (0.7; 95% CI 0.6-0.7) in comparison to long-term residents. This trend held in adjusted models stratified by cancer stage at diagnosis. For example, female immigrants diagnosed with early stage lung cancer had a hazard ratio of 0.5 (95% CI 0.4-0.7) in comparison to long-term residents. CONCLUSION: Overall survival post diagnosis with lung cancer was better among Ontario immigrants versus long-term residents. Additional research, potentially on the protective effects of immigrant enclave and the intersection of immigrant status with racial/ethnic identity, is needed to further explore why better overall survival for immigrants remained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".