2023_Development Of Robust Global Indexes Associated With Global Human Obesity Using Prognosis Statistical Modelling
| Format: | General Document |
|---|
| _version_ | 1860798149515280384 |
|---|---|
| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2023-10-04 |
| format | General Document |
| id | 16186 |
| institution | UniSZA |
| originalfilename | 16186_b03cc38afb564d7.pdf |
| person | Mubbasher Munir |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16186 |
| sourcemedia | Server storage Scanned document |
| spelling | 16186 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16186 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Informatics & Computing English application/pdf 1.5 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access UNIVERSITI SULTAN ZAINAL ABIDIN SAMBox 3.0.10; modified using iTextSharp™ 5.5.10 ©2000-2016 iText Group NV (AGPL-version) Copyright©PWB2025 283 2023-10-04 16186_b03cc38afb564d7.pdf Obesity—Statistics Obesity—Statistics Body Mass Index (BMI) 2023_Development Of Robust Global Indexes Associated With Global Human Obesity Using Prognosis Statistical Modelling The World Health Organization (WHO) has estimated that non-communicable diseases are responsible for 60% of the world's annual mortality, and globesity (globesity) is one of them. Therefore, development of globesity indexes is very crucial especially for global policymakers, scientists, and practitioners in investigating the actual effect on human health in a global context. To fill the research gap, principal axis factoring (PAF) has been used to develop social, education, economic, environment, food & nutrition, and health indexes. The study aims to give a solution to several statistical model violations, such as heteroskedasticity, outliers, normality, model biases and linearity. Globesity is expected to become the next pandemic in violation of several United Nations’ Sustainable Development Goals. There are no common databases of targeted global indexes and generalized models which have been developed in the literature. A total of 78 global databases were collected from WHO, Global Burden of Diseases, OurWorldinData.org, KOF Globalization Index, World Economic Forum, Food and Agricultural Organization and World Development Indicators including 183 countries. For robust modelling, several statistical techniques such as Second-Generation Panel Cointegration Test, Feasible Generalized Least Square, Quantile, Quadratic, Ridge, and Lasso models have been developed to investigate the correlation of six global indexes with globesity. The analysis starts from the ordinary least square regression model and is modified to robust models by giving the solution. Based on the developed indexes, results showed that health and food & nutrition indexes had increased globesity while environmental and education indexes decreased globesity. Economic and social indexes have followed an inverted U-shaped (downwards and then upwards) pattern with globesity. There is a strong negative correlation between global education and globesity in medium, high, and very high Human Development Level (HDI) countries, but it shows a weak correlation in countries with low HDI. Quantile, Quadratic and Panel models have shown a significant impact of newly developed indexes on globesity. For Ridge model, education and environmental indexes have significant indirect effect while economic, food & nutrition and health indexes have direct effect in decreasing globesity. For Lasso model, only education index has an insignificant effect on globesity. Low political globalization tends to increase globesity and resources towards them and vice versa. Several robust statistical models have been developed for global policymaking. It is concluded that globesity is affected by several global indexes which are social, education, economic, environmental, health, globalization, food & nutrition. Higher globesity is affecting the nation's productivity with a lack of education, economic instability, massive climate change, social engagement, health hazards and unequal food supply change across the countries. All the proposed models have significantly contributed to tackling global issues in reducing globesity. Mubbasher Munir Dissertations, Academic Global Obesity Trends Thesis |
| spellingShingle | 2023_Development Of Robust Global Indexes Associated With Global Human Obesity Using Prognosis Statistical Modelling |
| state | Terengganu |
| subject | Obesity—Statistics Dissertations, Academic |
| summary | The World Health Organization (WHO) has estimated that non-communicable diseases are responsible for 60% of the world's annual mortality, and globesity (globesity) is one of them. Therefore, development of globesity indexes is very crucial especially for global policymakers, scientists, and practitioners in investigating the actual effect on human health in a global context. To fill the research gap, principal axis factoring (PAF) has been used to develop social, education, economic, environment, food & nutrition, and health indexes. The study aims to give a solution to several statistical model violations, such as heteroskedasticity, outliers, normality, model biases and linearity. Globesity is expected to become the next pandemic in violation of several United Nations’ Sustainable Development Goals. There are no common databases of targeted global indexes and generalized models which have been developed in the literature. A total of 78 global databases were collected from WHO, Global Burden of Diseases, OurWorldinData.org, KOF Globalization Index, World Economic Forum, Food and Agricultural Organization and World Development Indicators including 183 countries. For robust modelling, several statistical techniques such as Second-Generation Panel Cointegration Test, Feasible Generalized Least Square, Quantile, Quadratic, Ridge, and Lasso models have been developed to investigate the correlation of six global indexes with globesity. The analysis starts from the ordinary least square regression model and is modified to robust models by giving the solution. Based on the developed indexes, results showed that health and food & nutrition indexes had increased globesity while environmental and education indexes decreased globesity. Economic and social indexes have followed an inverted U-shaped (downwards and then upwards) pattern with globesity. There is a strong negative correlation between global education and globesity in medium, high, and very high Human Development Level (HDI) countries, but it shows a weak correlation in countries with low HDI. Quantile, Quadratic and Panel models have shown a significant impact of newly developed indexes on globesity. For Ridge model, education and environmental indexes have significant indirect effect while economic, food & nutrition and health indexes have direct effect in decreasing globesity. For Lasso model, only education index has an insignificant effect on globesity. Low political globalization tends to increase globesity and resources towards them and vice versa. Several robust statistical models have been developed for global policymaking. It is concluded that globesity is affected by several global indexes which are social, education, economic, environmental, health, globalization, food & nutrition. Higher globesity is affecting the nation's productivity with a lack of education, economic instability, massive climate change, social engagement, health hazards and unequal food supply change across the countries. All the proposed models have significantly contributed to tackling global issues in reducing globesity. |
| title | 2023_Development Of Robust Global Indexes Associated With Global Human Obesity Using Prognosis Statistical Modelling |
| title_full | 2023_Development Of Robust Global Indexes Associated With Global Human Obesity Using Prognosis Statistical Modelling |
| title_fullStr | 2023_Development Of Robust Global Indexes Associated With Global Human Obesity Using Prognosis Statistical Modelling |
| title_full_unstemmed | 2023_Development Of Robust Global Indexes Associated With Global Human Obesity Using Prognosis Statistical Modelling |
| title_short | 2023_Development Of Robust Global Indexes Associated With Global Human Obesity Using Prognosis Statistical Modelling |
| title_sort | 2023_development of robust global indexes associated with global human obesity using prognosis statistical modelling |