Evaluation of different methods to handle misreporting in obesity research: evidence from the Canadian national nutrition survey
Bibliographic record
Abstract
The association of dietary exposures with health outcomes may be attenuated or reversed as a result of energy intake (EI) misreporting. This study evaluated several methods for dealing with implausible recalls when analysing the association between dietary factors and obesity. We examined data from 16,187 Canadians aged ≥12 years in the nationally representative Canadian Community Health Survey 2.2. Under- and over-reporting were defined as the ratio of EI:estimated energy requirement <0·7 and >1·42, respectively. Multinomial logistic regression-generalised logit model was conducted to test the utility of different methods for handling misreporting, including (a) adjusting for variables related to misreporting, (b) excluding misreported recalls, (c) adjusting for reporting groups (under-, plausible and over-reporters), (d) adjusting for propensity score and (e) stratifying the analyses by reporting groups. In the basic model, EI showed a negative association with overweight (OR 0·988; 95% CI 0·979, 0·998) and obesity (OR 0·989; 95% CI 0·977, 0·999). Similarly, the association between total energy density and overweight (OR 0·670; 95% CI 0·487, 0·923) and obesity (OR 0·709; 95% CI 0·495, 1·016) was inverse. Among all methods of handling misreporting, adjusting for the reporting status revealed the most satisfactory results, where a positive association between EI and overweight (OR 1·037; 95% CI 1·019, 1·055) and obesity (OR 1·109; 95% CI 1·082, 1·137) was observed (P<0·0001), as well as direct positive associations between energy density and percentage energy from solid fats and added sugars with obesity (P<0·05). The results of this study can help advance knowledge about the relationship between dietary variables and obesity and demonstrate to researchers and nutrition policy makers the importance of adjusting for recall plausibility in obesity research, which is highly relevant in light of global obesity epidemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".