The possible importance of income and education as covariates in cohort studies
Bibliographic record
Abstract
<ns4:p>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.</ns4:p> <ns4:p/> <ns4:p>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.</ns4:p> <ns4:p/> <ns4:p>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.</ns4:p> <ns4:p/> <ns4:p> 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 <ns4:italic>present</ns4:italic> 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. </ns4:p>
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".