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Modelling childhood growth using fractional polynomials and linear splines.

Tilling K, Macdonald-Wallis C, Lawlor DA, Hughes RA, Howe LD - Ann. Nutr. Metab. (2014)

Bottom Line: There is increasing emphasis in medical research on modelling growth across the life course and identifying factors associated with growth.Both models also showed that there was a disparity in length between manual and non-manual social class infants at birth, which decreased in magnitude until approximately 1 year of age and then increased.Each can be used to summarise individual growth trajectories and their relationships with individual-level exposures.

View Article: PubMed Central - PubMed

Affiliation: School of Social and Community Medicine and MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

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Related in: MedlinePlus

Average predicted height gain from birth to 10 years in girls of the manual and non-manual social class from the ALSPAC study predicted by the best-fitting fractional polynomial model.
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Figure 2: Average predicted height gain from birth to 10 years in girls of the manual and non-manual social class from the ALSPAC study predicted by the best-fitting fractional polynomial model.

Mentions: In order to assess the association between household social class and child growth, social class (as a binary covariate: manual vs. non-manual) was included in both the fractional polynomial and the linear spline models. For the fractional polynomials, the coefficients for the interaction between social class and the powers of age are not readily interpretable. A plot of the predicted average height gain in girls of the manual and non-manual social class (fig. 2) shows that there is a gradually widening difference: girls of the non-manual social class are slightly taller by 10 years of age. A table of predicted heights at different ages (table 3) reveals that the initial difference in height between the two groups decreases from birth to approximately 1 year of age, and then increases.


Modelling childhood growth using fractional polynomials and linear splines.

Tilling K, Macdonald-Wallis C, Lawlor DA, Hughes RA, Howe LD - Ann. Nutr. Metab. (2014)

Average predicted height gain from birth to 10 years in girls of the manual and non-manual social class from the ALSPAC study predicted by the best-fitting fractional polynomial model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4264511&req=5

Figure 2: Average predicted height gain from birth to 10 years in girls of the manual and non-manual social class from the ALSPAC study predicted by the best-fitting fractional polynomial model.
Mentions: In order to assess the association between household social class and child growth, social class (as a binary covariate: manual vs. non-manual) was included in both the fractional polynomial and the linear spline models. For the fractional polynomials, the coefficients for the interaction between social class and the powers of age are not readily interpretable. A plot of the predicted average height gain in girls of the manual and non-manual social class (fig. 2) shows that there is a gradually widening difference: girls of the non-manual social class are slightly taller by 10 years of age. A table of predicted heights at different ages (table 3) reveals that the initial difference in height between the two groups decreases from birth to approximately 1 year of age, and then increases.

Bottom Line: There is increasing emphasis in medical research on modelling growth across the life course and identifying factors associated with growth.Both models also showed that there was a disparity in length between manual and non-manual social class infants at birth, which decreased in magnitude until approximately 1 year of age and then increased.Each can be used to summarise individual growth trajectories and their relationships with individual-level exposures.

View Article: PubMed Central - PubMed

Affiliation: School of Social and Community Medicine and MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

Show MeSH
Related in: MedlinePlus