The Relationship Between Obesity, Annual GDP per capita, and Life Expectancy – A Panel Analysis on 202 countries through 41 years
Bibliographic record
Abstract
Background: Obesity levels have increased significantly around the world. Earlier studies showed that obesity was a disease of the socioeconomic elite-those who were wealthier, had easier access to more food, who in the process consumer high calories, leading to obesity. In contrast, recent studies show a negative correlation between high socioeconomic conditions and obesity levels. A limitation with these studies is that they rely on a small sample of countries. Aims: In this study we determine the effects of a countries’ income and life expectancy rates on obesity rates for both men and women. Material and Methods: We ran a fixed effect panel regression analysis on a sample of 202 countries over 41 years. Results and Significances: We find, that if a country’s GDP per capita increased by a $1,000, the number of women who are obese would decrease by .02%. Interestingly, for men, the findings did not match: an increase in GDP per capita increased obesity rates among men. We also find that as the obesity rate of a given country increases, its life expectancy decreases, however, this affect is twice as strong for men than for women. These results shed light on the fact that our current approaches to reducing obesity may work for women but may not be working for men. Future policies to tackle obesity should take in to behavioral differences across gender Biography: Ayush Malhotra is a grade 8 student at Centennial Public School in Waterloo, Ontario. Over the last year he has worked on this research project and had the pleasure of presenting his work at the Annual Canadian Wide Science Fair (CWSF) held in New Brunswick. Shavin Malhotra helped guide Ayush on this project and Ayush hopes to continue expanding this line of research in future. Speaker Publications: 1. American Medical Association AMA Adopts New Policies on Second Day of Voting at Annual Meeting [Internet] 2013. 2. Stevens GA, Singh GM, Lu Y, Danaei G, Lin JK, Finucane MM, et al. National, regional, and global trends in adult overweight and obesity prevalences. Popul Health Metr. 2012;10(1):22. 3. Hu FB. Obesity epidemiology. Oxford University Press; Oxford; New York: 2008. p. 498. 4. Hill JO, Wyatt HR, Peters JC. Energy Balance and Obesity. Circulation. 2012 Jul 3;126(1):126–32. 5. 2008 Physical Activity Guidelines for Americans [Internet] [cited 2014 Apr 21]. 5th World Congress on Public Health and Nutrition; London, UK- February 24-25, 2020. Abstract Citation: Ayush Malhotra, The relationship between obesity, annual GDP per capita, and life expectancy – A panel analysis on 202 countries through 41 years, Public Health 2020, 5th World Congress on Public Health and Nutrition; London, UK- February 24-25, 2020 (https://publichealth.healthconferences.org/abstract/2020/the-relationship-between-obesity-annual-gdp-per-capita-and-life-expectancy-a-panel-analysis-on-202-countries-through-41-years)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".