Differential impact of socioeconomic position across life on oral cancer risk in Kerala, India: An investigation of life‐course models under a time‐varying framework
Bibliographic record
Abstract
OBJECTIVES: The incidence of oral cancer has been rapidly increasing in India, calling for evidence contributing to a deeper understanding of its determinants. Although disadvantageous life-course socioeconomic position (SEP) is independently associated with the risk of these cancers, the explanatory mechanisms remain unclear. Possible pathways may be better understood by testing which life-course model most influences oral cancer risk. We estimated the association between life-course SEP and oral cancer risk under three life-course models: critical period, accumulation and social mobility. METHODS: We recruited incident oral cancer cases (N = 350) and controls (N = 371) frequency-matched by age and sex from two main referral hospitals in Kozhikode, Kerala, India, between 2008 and 2012. We collected information on childhood (0-16 years), early adulthood (17-30 years) and late adulthood (above 30 years) SEP and behavioural factors along the life span using interviews and a life-grid technique. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for the association between life-course SEP and oral cancer risk using inverse probability weighted marginal structural models. RESULTS: Relative to an advantageous SEP in childhood and early adulthood, a disadvantageous SEP was associated with oral cancer risk [(OR = 2.76, 95% CI: 1.99, 3.81) and (OR = 1.84, 95% CI: 1.21, 2.79), respectively]. In addition, participants who were in a disadvantageous (vs advantageous) SEP during all three periods of life had an increased oral cancer risk (OR = 4.86, 95% CI: 2.61, 9.06). The childhood to early adulthood social mobility model and overall life-course trajectories indicated strong influence of exposure to disadvantageous SEP in childhood on the risk for oral cancer. CONCLUSIONS: Using novel approaches to existing methods, our study provides empirical evidence that disadvantageous childhood SEP is critical for oral cancer risk in this population from Kerala, India.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".