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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Multidisciplinary Approach to Obesity Management: A Case Report
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Published on: May 30, 2025

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Global impact on human obesity - A robust non-linear panel data analysis.

Mubbasher Munir1, Zahrahtul Amani Zakaria1, Atif Amin Baig2

  • 1Faculty of Informatics and Computing, Universiti of Sultan Zainal Abidin, Terengganu, Malaysia.

Nutrition and Health
|October 5, 2022
PubMed
Summary

Human obesity rises with health and food indicators but falls with environmental and educational ones. Economic and social factors show a complex, inverted U-shaped relationship with obesity rates globally.

Keywords:
Perceived wellbeingglobal economyglobal educationglobal environmentglobal healthglobal nutritionglobesityinformation asymmetrymacroeconomic policy

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Area of Science:

  • Socio-economics
  • Global Health
  • Behavioral Economics

Background:

  • Bounded rationality suggests consumers misjudge well-being factors.
  • Rising global obesity contrasts with increased economic and social well-being.
  • Obesity is a significant deterrent to welfare, often overlooked in standard of living assessments.

Purpose of the Study:

  • To empirically investigate the socio-economic and environmental drivers of global human obesity.
  • To analyze the impact of six global indices on obesity rates across diverse countries.
  • To provide a holistic assessment of obesity's complex global determinants.

Main Methods:

  • Utilized data from 40 countries spanning 1975 to 2018.
  • Employed Panel FGLS Regression with a quadratic specification for analysis.
  • Incorporated six distinct global indices to assess various socio-economic and environmental factors.

Main Results:

  • Health and food indicators positively correlate with increased global human obesity.
  • Environmental and educational indicators demonstrate a negative correlation with global human obesity.
  • Economic and social indicators exhibit an inverted U-shaped relationship with global human obesity.

Conclusions:

  • Global obesity is influenced by a complex interplay of health, food, environmental, educational, economic, and social factors.
  • Previous research has underestimated obesity's role as a welfare deterrent.
  • This study offers a comprehensive, multi-index approach to understanding global obesity antecedents.