Using Correction Equations Based on Measured Height and Weight Weakens Associations between Obesity Based on Self-Reports and Chronic Diseases
Bibliographic record
Abstract
Objective. Researchers have established a preponderance of height overestimation among men and weight underestimation among women in self-reported anthropometric data, which skews obesity prevalence data and obscures obesity-chronic disease relationships. The objective of this study was to reevaluate associations between obesity and chronic diseases using body mass index (BMI) correction equations derived from measured data. Methods. Measured height and weight (MHW) data were collected on a subsample of the 17,126 Atlantic Canadians who participated in the 2007-2008 Canadian Community Health Survey (CCHS). To obtain corrected BMI estimates for the 17,126 adults, correction equations were developed in the MHW subsample and multiple regression procedures were used to model BMI. To test obesity-chronic disease relationships, logistic regression models were utilized. Results. The correction procedure eliminated statistically significant relations ( P<0.05 ) between obesity and chronic bronchitis and obesity and stroke. Also, correction attenuated many relationships between adiposity and chronic disease. For example, among obese adults, there was a 13%, 12%, and 7% reduction in the adjusted odds ratios for asthma, urinary incontinence, and cardiovascular disease, respectively. Conclusion. Further research is needed to fully understand how the usage of self-reported data alters our understanding of the relationships between overweight or obesity and chronic diseases.
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 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 | 0.020 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".