Sociodemographic Factors and Stage of Cancer at Diagnosis: A Population-Based Study in South India
Bibliographic record
Abstract
PURPOSE Lower socioeconomic status is associated with inferior cancer survival in high-income countries, but whether this applies to low- and middle-income countries is not well described. Here, we use a population-based cancer registry to explore the association between educational level and stage of cancer at diagnosis in South India. METHODS We used the Trivandrum District population-based cancer registry to identify all cases of breast and cervical cancer (women) and oral cavity (OC) and lung cancer (men) who were diagnosed from 2012 to 2014. Educational status—classified as illiterate/primary school, middle school, or secondary school or higher—was the primary exposure of interest. Primary outcome was the proportion of patients with advanced stage disease at diagnosis defined as stage III and IV (breast, cervix, or OC) or regional/metastatic (lung). RESULTS The study population included 4,547 patients with breast (n = 2,283), cervix (n = 481), OC (n = 797), and lung (n = 986) cancer. Educational status was 22%, 19%, and 26% for illiterate/primary, middle, and secondary school or higher, respectively. Educational status was missing for 33% of patients. The proportion of all patients with advanced stage disease was 37% (breast), 39% (cervix), 67% (OC), and 88% (lung). Patients with illiterate/primary school educational status were considerably more likely to have advanced breast cancer (50% v 39% v 36%; P < .001), cervix cancer (46% v 43% v 24%; P = .002), and OC cancer (77% v 76% v 59%; P < .001) compared with patients with higher educational levels. The proportion of patients with advanced lung cancer did not vary across educational levels (89% v 84% v 88%; P = .350). CONCLUSION A substantial proportion of patients in South India have advanced cancer at the time of diagnosis. This is particularly true among those with the lowest levels of education. Future health awareness and preventive interventions must target less-educated communities to reduce delays in seeking medical care for cancer.
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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 | 0.001 |
| 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".