The Relationship Between Obesity, Annual GDP per capita, and Life Expectancy – A Panel Analysis on 202 countries through 41 years
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».