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Related Concept Videos

Obesity01:24

Obesity

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 adipocytes...
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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

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Mapping the obesity in iran by bayesian spatial model.

Maryam Farhadian1, Abbas Moghimbeigi, Mohsen Aliabadi

  • 1Dept. Of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Science, Hamadan, Iran.

Iranian Journal of Public Health
|August 23, 2013
PubMed
Summary
This summary is machine-generated.

This study mapped obesity patterns in Iran using Bayesian spatial models. Mazandaran province showed the highest obesity rates for both men and women, highlighting regional disparities in public health.

Keywords:
Bayesian Spatial ModelIranMappingObesity

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Multidisciplinary Approach to Obesity Management: A Case Report
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Multidisciplinary Approach to Obesity Management: A Case Report
05:10

Multidisciplinary Approach to Obesity Management: A Case Report

Published on: May 30, 2025

Area of Science:

  • Epidemiology
  • Spatial Analysis
  • Public Health

Background:

  • Geographical mapping is crucial for analyzing disease patterns and identifying high-risk areas.
  • Understanding disease distribution aids in targeted therapeutic interventions and resource allocation.
  • Obesity prevalence and its geographical distribution in Iran require detailed investigation.

Purpose of the Study:

  • To investigate the geographical pattern of obesity in Iran.
  • To utilize Bayesian spatial models for a precise understanding of obesity distribution.
  • To identify high-risk regions for obesity within Iran.

Main Methods:

  • Data on obese individuals by sex across 30 Iranian provinces in 2007 were analyzed.
  • Bayesian spatial models were employed for data analysis.
  • Software R, Open BUGS, and GeoBUGS were used for data analysis and adjacency matrix generation.

Main Results:

  • Mazandaran province exhibited the highest obesity percentages for men (17.8%) and women (29.9%).
  • Sistan and Baluchestan had the lowest male obesity rate (4.9%), while Hormozgan had the lowest female obesity rate (11.9%).
  • Mazandaran showed the greatest odds ratio for obesity in both sexes.

Conclusions:

  • Identifying geographical obesity distribution is essential for health system management and planning in Iran.
  • The findings support targeted resource allocation to high-risk regions.
  • Spatial analysis of obesity provides a foundation for effective public health interventions.