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ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Effect of Environmental Factors on Obesity: A Quantile Regression Approach

Anthony J Payne1, Julia A Knight2 and Taraneh Abarin1,*

1Department of Mathematics and Statistics, Memorial University, Canada

2Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada

*Corresponding Author:
Taraneh Abarin
Department of Mathematics and Statistics
Memorial University, 230 Elizabeth Ave, St. John's
NL A1B 3X9, Canada
Tel: +1(709) 864-8733
Fax: +1(709) 864-3010
E-mail: [email protected]

Received Date: March 31, 2016; Accepted Date: April 14, 2016; Published Date: April 22, 2016

Citation: Payne AJ, Knight JA, Abarin T (2016) Effect of Environmental Factors on Obesity: A Quantile Regression Approach. J Biom Biostat 7: 293. doi:10.4172/2155-6180.1000293

Copyright: © 2016 Payne AJ, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Objectives: This study explored associations of environmental factors with percent trunk fat (PTF) and body mass index (BMI), using quantile regression to explain variability in these traits at percentiles of the distributions.
Methods: Using a sample of 1695 adults from Newfoundland and Labrador, multiple and quantile regression models were used to analyse the significance of environmental factors on the average population and upper percentiles of the BMI and PTF distributions.
Results: Higher physical activity was associated with significantly lower PTF and BMI in the average population and upper percentiles, regardless of age. Both genders in percentiles closer to the median of PTF had more benefit with increased physical activity compared to higher percentiles. Interestingly, adults in higher percentiles of BMI distribution seem to benefit more with increased physical activity compared to percentiles closer to the median.
Conclusion: Using quantile regression as a robust approach toward violation of normality assumptions and outliers, variations in PTF and BMI for individuals across upper percentiles of the distributions based on some lifestyle factors were described. This method may be used to estimate the impact of certain lifestyle on different percentiles of BMI and PTF, rather than average population.

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