Occupation as a measure of life course socioeconomic position and the risk of oral cancers in India
Bibliographic record
Abstract
OBJECTIVES: Evidence suggests that different indicators of socioeconomic position (SEP) contribute to oral cancer risk. Occupational status, as a measure of SEP, may be able to capture aspects of social hierarchy in societies in which employment is highly correlated with other social structures such as caste systems. Often in such societies, the life course of an individual is also influenced by this hierarchy. However, the influence of life course occupational status on the risk of oral cancer is not well understood. This study aims to identify the life course model that is best supported by the data using life course SEP-as represented by occupation-on oral cancer risk in a population in the South of India. METHODS: Data from the HeNCe Life study, Indian site were used. Incident oral cancer cases (N = 350) were recruited from two major referral hospitals in Kozhikode, Kerala, South India, from 2008 to 2012. Controls (N = 371), frequency-matched by age (5 years) and sex were recruited from the outpatient clinics at the same hospitals as the cases. Life grid-based structured interviews collected information on an array of exposures throughout the life course of the participant. Occupation was coded with the 1988 International Standard Classification of Occupations, transformed to the simplified European Socioeconomic Classification, and further dichotomized into advantageous and disadvantageous SEP at three different life periods (childhood, early adulthood and late adulthood). The analysis was conducted using the Bayesian relevant life course exposure model with a Dirichlet noninformative prior and a weakly informative Cauchy prior to the overall lifetime effect and confounders. RESULTS: Participants in disadvantaged SEP throughout their life had 3.6 times higher risk of oral cancer than those in advantaged SEP (OR = 3.6; 95% CrI = 1.6-7.2), after adjusting for potential confounders. While the crude and sex- and age-adjusted models showed a clear childhood sensitive period for this risk, the model further adjusted for behavioural factors could not distinguish the specific life course period best explained by data. CONCLUSION: Occupation status alone could provide a similar overarching risk estimate for oral cancer to those obtained from more complex measures of SEP.
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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 | 0.003 | 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".