The possible importance of income and education as covariates in cohort studies
Bibliographic record
Abstract
Background: Many cohort studies have been carried out that have provided information on the relationship between diet and health-related outcomes. Omission of important covariates during multivariate analysis may give rise to error due to residual confounding. A possibly important covariate is socioeconomic status (SES) as this is related to both diet and health. Methodology: An analysis was carried out of 76 randomly selected papers from 66 cohort studies. The papers covered many dietary variables and a wide variety of diseases/health-related outcomes. The cohort studies were carried out in many different locations and the subjects varied widely in age. Results: Approximately two-thirds of the papers (65.8%) used at least one measure of SES as a covariate. Education was used most often (60.5% of papers), followed by income (14.4%) and social class (2.6%). More than one measure of SES was used in 11.8% of papers. Conclusions: Failure to include income (or another measure of present SES, such as occupation) may therefore be a common source of error in cohort studies. Failure to include education may be particularly important as it is likely to be a weaker measure of present SES than is income. There is a need for more research on this question. SES in childhood is almost never included in multivariate analysis in cohort studies carried out on adults. This could also play a significant role in disease risk in middle age or later. Very little is known regarding whether this is also a source of residual confounding.
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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 | 0.462 | 0.714 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".