Evaluation of under- and overreporting of energy intake in the 24-hour diet recalls in the European Prospective Investigation into Cancer and Nutrition (EPIC)
Bibliographic record
Abstract
OBJECTIVE: To evaluate under- and overreporting and their determinants in the EPIC 24-hour diet recall (24-HDR) measurements collected in the European Prospective Investigation into Cancer and Nutrition (EPIC). DESIGN: Cross-sectional analysis. 24-HDR measurements were obtained by means of a standardised computerised interview program (EPIC-SOFT). The ratio of reported energy intake (EI) to estimated basal metabolic rate (BMR) was used to ascertain the magnitude, impact and determinants of misreporting. Goldberg's cut-off points were used to identify participants with physiologically extreme low or high energy intake. At the aggregate level the value of 1.55 for physical activity level (PAL) was chosen as reference. At the individual level we used multivariate statistical techniques to identify factors that could explain EI/BMR variability. Analyses were performed by adjusting for weight, height, age at recall, special diet, smoking status, day of recall (weekday vs. weekend day) and physical activity. SETTING: Twenty-seven redefined centres in the 10 countries participating in the EPIC project. SUBJECTS: In total, 35 955 men and women, aged 35-74 years, participating in the nested EPIC calibration sub-studies. RESULTS: While overreporting has only a minor impact, the percentage of subjects identified as extreme underreporters was 13.8% and 10.3% in women and men, respectively. Mean EI/BMR values in men and women were 1.44 and 1.36 including all subjects, and 1.50 and 1.44 after exclusion of misreporters. After exclusion of misreporters, adjusted EI/BMR means were consistently less than 10% different from the expected value of 1.55 for PAL (except for women in Greece and in the UK), with overall differences equal to 4.0% and 7.4% for men and women, respectively. We modelled the probability of being an underreporter in association with several individual characteristics. After adjustment for age, height, special diet, smoking status, day of recall and physical activity at work, logistic regression analyses resulted in an odds ratio (OR) of being an underreporter for the highest vs. the lowest quartile of body mass index (BMI) of 3.52 (95% confidence interval (CI) 2.91-4.26) in men and 4.80 (95% CI 4.11-5.61) in women, indicating that overweight subjects are significantly more likely to underestimate energy intake than subjects in the bottom BMI category. Older people were less likely to underestimate energy intake: ORs were 0.58 (95% CI 0.45-0.77) and 0.74 (95% CI 0.63-0.88) for age (> or =65 years vs. <50 years). Special diet and day of the week showed strong effects. CONCLUSION: EI tends to be underestimated in the vast majority of the EPIC centres, although to varying degrees; at the aggregate level most centres were below the expected reference value of 1.55. Underreporting seems to be more prevalent among women than men in the EPIC calibration sample. The hypothesis that BMI (or weight) and age are causally related to underreporting seems to be confirmed in the present work. This introduces further complexity in the within-group (centre or country) and between-group calibration of dietary questionnaire measurements to deattenuate the diet-disease relationship.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".