Decomposing body mass index gaps between Mediterranean countries: A counterfactual quantile regression analysis

Costa‐Font, Joan; Fabbri, Daniele; Gil, Joan

doi:10.1016/j.ehb.2009.08.003

Cited by 32 publications

(33 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Examples of parametric quantile modelling include CostaFont et al 25 , who studied the cross-country gap in BMI between Italy and Spain in 2003 by applying a decomposition methodology to the entire BMI distribution, by…”

Section: Parametric Quantile Regressionmentioning

confidence: 99%

Statistical methods for body mass index: A selective review

Liu

Alhamzawi

et al. 2016

Stat Methods Med Res

View full text Add to dashboard Cite

Abstract:Obesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index (BMI), is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. To guide effective public health action, we need to understand the complex system of intercorrelated influences on BMI. This paper, based on all eligible articles searched from Global health, Medline and Web of Science databases, reviews both classical and modern statistical methods for BMI analysis. We give a description of each of these methods, exploring the classification, links and differences between them and the reasons for choosing one over the others in different settings. We aim to provide a key resource and statistical library for researchers in public health and medicine to deal with obesity and BMI data analysis. AbstractObesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index (BMI), is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. To guide effective public health action, we need to understand the complex system of intercorrelated influences on BMI. This paper, based on all eligible articles searched from Global health, Medline and Web of Science databases, reviews both classical and modern statistical methods for BMI analysis. We give a description of each of these methods, exploring the classification, links and differences between them and the reasons for choosing one over the others in different settings. We aim to provide a key resource and statistical library for researchers in public health and medicine to deal with obesity and BMI data analysis.

show abstract

Section: Parametric Quantile Regressionmentioning

confidence: 99%

Statistical methods for body mass index: A selective review

Liu

Alhamzawi

et al. 2016

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“…Economic growth caused the population to undergo profound transformations in reducing health risks. However, it also saw the advance of endemic health problems, such as diabetes mellitus, asthma and respiratory, cardiovascular and psychological diseases, particularly in the juvenile phase; correlated with this evidence was the nation-wide increase in overweight (Costa-Font et al, 2009). Significant increases in BMI between the two Italian cohorts has led to the postulation of a change of factors that drive weight gains.…”

Section: Introductionmentioning

confidence: 98%

Health and Body Mass Index Changes Among Italian Conscripts Born in 1951 and 1980

Lanari

2014

J. Biosoc. Sci.

View full text Add to dashboard Cite

This paper investigates changes in BMI in the second half of the twentieth century, focusing on two cohorts of Italian conscripts born in 1951 and 1980, and examines how the correlates of high and low BMI have changed over time. Data from conscript cohorts taken from the archives of Italian military districts show that younger conscripts exposed to the rising prevalence of immune-allergological and psychological diseases and metabolic dysfunctions underwent substantial increases in weight. These results are evidenced by quantile regression models, the largest BMI gains being found in diabetic conscripts at the 75th and 90th percentiles.

show abstract

“…The majority of decomposition efforts focus their attention on developed nations (Michaud et al 2007, Costa-Font and Gil 2008, Costa-Font et al 2009, 2010. Case and Menedez (2009) decompose obesity disparities between males and females in South Africa, but there is little work decomposing differences across ethnic groups in developing countries.…”

Section: Introductionmentioning

confidence: 99%

“…Second, the importance of the exposure effect relative to the response effect may vary at different points of the BMI distribution. This method has previously been used by Costa-Font et al (2009, 2010 to decompose obesity differences between Spain and Italy.…”

Section: Introductionmentioning

confidence: 99%