Does Overweight and Obesity Impact on Self‐Rated Health? Evidence Using Instrumental Variables Ordered Probit Models
Bibliographic record
Abstract
This paper, for the first time, presents estimates of the causal impact of overweight and obesity on self-rated health (SRH) using instrumental variables (IV) econometric methods. While a number of previous studies have sought to better understand the determinants of SRH, there is no consensus in relation to the impact of overweight and obesity. Using data from a large nationally representative sample of Irish parents and their children, we estimate a range of ordered probit models to isolate the causal effect of overweight and obesity on SRH. Our data includes independently and objectively recorded weight and height measures for parents and their children and we instrument for parental body mass index (BMI) status using the BMI of a biological child. After controlling for a range of individual, socioeconomic, health and lifestyle related variables, we find that being overweight has a negligible impact on SRH, while being obese has a practically and statistically significant negative impact on SRH, with these effects most pronounced for those who are most obese. We find only minor differences in these effects across gender. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.021 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".