Educational Status, Cancer Stage, and Survival in South India: A Population-Based Study
Bibliographic record
Abstract
PURPOSE Lower socioeconomic status is associated with more advanced cancer at the time of cancer diagnosis. It is unknown whether this leads to inferior survival in low- and middle-income countries. Here, we explore the association between educational level and survival in South India. METHODS The Trivandrum Cancer Registry (3.3 million population) was used to identify all cases of breast and cervical cancer (women) and oral cavity (OC) and lung cancer (men) diagnosed during 2012-2014. Educational level was classified as illiterate/primary school, middle school, and secondary school and above. Survival was measured from date of diagnosis using the Kaplan-Meier method. Cox proportional hazards regression modeling was used to describe the associations among education, stage of cancer at diagnosis, and survival. RESULTS The study population included 3,640 patients with breast (n = 1,727), cervical (n = 425), OC (n = 702), and lung (n = 786) cancer. Educational level was 27%, 23%, and 32% for illiterate/primary, middle, and secondary school and above, respectively. The 5-year survival rate for breast cancer was 59%, 68%, and 73% ( P = .001); for cervical cancer, 51%, 52%, and 60% ( P = .146); and for OC cancer, 42%, 35%, and 48% ( P = .012) for illiterate/primary, middle school, and secondary school and above, respectively. The survival gradient across social groups was substantially attenuated when stage was added to the multivariable model. There was no observed difference in survival across educational groups for lung cancer (2%, 4%, and 3%; P = .224). CONCLUSION Data from this population-based study in South India demonstrate that patients from a lower educational background have inferior survival and that this is at least partially explained by having more advanced disease at the time of diagnosis. Public health efforts are needed to facilitate timely diagnosis and reduce disparities in cancer outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".