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Assignment Solution: Investigating Factors Influencing Abdominal Obesity

October 19, 2023
James Hartford
James Hartford
🇨🇦 Canada
Biostatistics
James Hartford, a proficient Biostatistics Assignment Helper, holds a statistics degree from McGill University. With 4 Years of experience, he excels in providing exceptional Help to students.
Key Topics
  • Problem Description:
    • Creation of 'abdominal obesity' Variable:
    • Modeling 'abdominal obesity' with 'Age' as an Independent Variable:
    • Childhood Pathogen Burden and Abdominal Obesity:
    • Logistic Regression on Childhood Pathogen Burden:
  • Conclusion:

In this in-depth exploration, we employ robust epidemiologic research methods to unravel the intricate interplay of variables. From the creation of a binary variable, 'ab_obesity,' to detailed logistic regression models, our analysis dissects the relationships between age, gender, and childhood pathogen burden. The odds ratios and confidence intervals extracted from the statistical models offer a nuanced understanding of these associations, providing valuable insights for addressing and comprehending the complexities of abdominal obesity.

Problem Description:

In this epidemiologic assignment, we explore the relationship between various factors and the presence of abdominal obesity in a dataset. The primary variables of interest are 'waistcirc,' 'gender,' 'abdominal obesity,' 'age,' and 'childhood pathogen burden.' We aim to understand how age and childhood pathogen burden contribute to the likelihood of abdominal obesity.

    Creation of 'abdominal obesity' Variable:

    1. A new binary variable, 'abdominal obesity,' is introduced based on 'waistcirc' and 'gender.' For males, a waist circumference > 102 cm is coded as 1 (abdominal obesity), and for females, > 88 cm is coded as 1.
    2. Output displays the distribution of 'abdominal obesity' and highlights a higher prevalence in men (55%) compared to women (45%).

    Modeling 'abdominal obesity' with 'Age' as an Independent Variable:

    1. 'Abdominal obesity' is modelled with 'age' using logistic regression. The parameter estimate indicates that for every 10-year increase in age, the log odds of abdominal obesity increase by 0.0153.
    2. The odds ratio for age and 'abdominal obesity' is 1.015 with a 95% CI [0.996, 1.035], suggesting a moderate increase in odds for abdominal obesity with age.
    3. The odds ratio is calculated using the exponential function of the parameter estimate.

    Childhood Pathogen Burden and Abdominal Obesity:

    The odds ratio for a childhood pathogen burden of females compared to males is 0.652 (95% CI: 0.602, 0.705). Interpretation: Individuals with a childhood pathogen burden of three have 0.652 times the odds of abdominal obesity compared to females, adjusting for other factors.

    Logistic Regression on Childhood Pathogen Burden:

    1. The odds ratio for a childhood pathogen burden of three compared to zero is 0.473 (95% CI: 0.389, 0.576). Interpretation: Individuals with a childhood pathogen burden of three have 0.473 times the odds of abdominal obesity compared to those with zero burden.
    2. The odds ratio for a childhood pathogen burden of zero compared to three is 2.114 (95% CI: 1.737, 2.572). Interpretation: Individuals with no childhood pathogen burden have 2.114 times the odds of abdominal obesity compared to those with a burden of three.

Conclusion:

This analysis provides insights into the factors influencing abdominal obesity, emphasizing the impact of age and childhood pathogen burden. The odds ratios and confidence intervals offer a robust understanding of the associations, aiding in informed interpretations and potential interventions.

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