Related Experiment Video
Updated: Feb 13, 2026
Analysis and Specification of Starch Granule Size Distributions
Published on: March 4, 2021
Change in BMI Distribution over a 24-Year Period and Associated Socioeconomic Gradients: A Quantile Regression
Mekdes K Gebremariam1,2, Onyebuchi A Arah1,3,4, Nanna Lien2
1Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, USA.
Objective:
This study assessed the change in body mass index (BMI) distribution among 18- or 19-year-olds over 24 years. It also investigated parallel changes in the distribution of birth weight and in the association between birth weight and later risk of overweight and/or obesity. Parental educational variations in the trends and associations were explored.
Methods:
The study used data on 606,832 male military conscripts enlisted between 1985 and 2008. Quantile regression was used to assess the temporal change in BMI and birth weight distribution. The association between birth weight and overweight and/or obesity at age 18 or 19 years was quantified by using logistic regression.
Results:
Increases in BMI over time were found namely in the 90th, 95th, 97th, and 99th percentiles. Socioeconomic differences in this increase were documented in the 75th to 97th percentiles. The distribution of birth weight and the association between birth weight and the risk of overweight and/or obesity at age 18 or 19 years remained stable over time.
Conclusions:
The difference in the increase in BMI between low and high percentiles indicates the limited role of mean BMI in reflecting population changes. The results suggest a need to focus on those with low socioeconomic position in the upper ends of the BMI distribution to combat increasing disparities in obesity-related outcomes.
Related Concept Videos
01:52Regression Toward the Mean
01:11Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
01:18Microsoft Excel: Regression Analysis
To perform regression...
01:25Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
01:26What is an Electrochemical Gradient?
The chemical gradient relies on differences in the abundance of a substance on the outside versus the inside of a cell and flows from areas of high to low ion concentration. In contrast, the electrical gradient revolves around an...
01:50Global Climate Change