The Impact of Social Determinants of Health on Treatment Received in Patients with Stage I Lung Cancer in Ontario: A Population-Based Analysis
Bibliographic record
Abstract
Surgical resection is recommended for operable stage I non-small-cell lung cancer (NSCLC), while radiotherapy reserved for inoperable patients. Very comorbid patients may receive no treatment at all. Social determinants of health (SDOHs) may influence access to these treatments. We examined how SDOHs affect treatment modality among these patients using a population-based retrospective cohort study using ICES data including adults with stage I NSCLC diagnosed between 2007 and 2023. Multivariable logistic regression assessed associations between SDOH and treatment received. Of 19,179 patients, 54.4% received only surgery, 15.8% received only radiotherapy, 27.5% received no treatment, and 2.3% received surgery and radiotherapy. Surgery was less likely in patients aged >80 versus <50 (OR 0.07, p < 0.001), patients with frailty (OR 0.38, p < 0.001), patients with ≥5 comorbidities (OR 0.21, p < 0.001), or those who were not rostered with a family physician (OR 0.59, p < 0.001). Recent immigrants were more likely to undergo surgery (OR 1.23, p = 0.035), as well as those in the highest neighbourhood income quintile (OR 1.45, p < 0.001). Surgery was less likely for those living 50–100 km from a cancer centre (OR 0.85, p = 0.004). Radiotherapy was more likely in patients aged >80 (OR 9.86, p < 0.001), those with ≥5 comorbidities (OR 2.23, p < 0.001), or those in the lowest household income quintile (OR 1.27, p = 0.009). Recent immigrants were less likely to receive radiotherapy (OR 0.69, p = 0.005). SDOHs independently influence treatment type for stage I NSCLC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".