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Regression models for linking patterns of growth to a later outcome: infant growth and childhood overweight

Overview of attention for article published in BMC Medical Research Methodology, April 2016
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3 tweeters

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Title
Regression models for linking patterns of growth to a later outcome: infant growth and childhood overweight
Published in
BMC Medical Research Methodology, April 2016
DOI 10.1186/s12874-016-0143-1
Pubmed ID
Authors

Andrew K. Wills, Bjørn Heine Strand, Kari Glavin, Richard J. Silverwood, Ragnhild Hovengen

Abstract

Regression models are widely used to link serial measures of anthropometric size or changes in size to a later outcome. Different parameterisations of these models enable one to target different questions about the effect of growth, however, their interpretation can be challenging. Our objective was to formulate and classify several sets of parameterisations by their underlying growth pattern contrast, and to discuss their utility using an expository example. We describe and classify five sets of model parameterisations in accordance with their underlying growth pattern contrast (conditional growth; being bigger v being smaller; becoming bigger and staying bigger; growing faster v being bigger; becoming and staying bigger versus being bigger). The contrasts are estimated by including different sets of repeated measures of size and changes in size in a regression model. We illustrate these models in the setting of linking infant growth (measured on 6 occasions: birth, 6 weeks, 3, 6, 12 and 24 months) in weight-for-height-for-age z-scores to later childhood overweight at 8y using complete cases from the Norwegian Childhood Growth study (n = 900). In our expository example, conditional growth during all periods, becoming bigger in any interval and staying bigger through infancy, and being bigger from birth were all associated with higher odds of later overweight. The highest odds of later overweight occurred for individuals who experienced high conditional growth or became bigger in the 3 to 6 month period and stayed bigger, and those who were bigger from birth to 24 months. Comparisons between periods and between growth patterns require large sample sizes and need to consider how to scale associations to make comparisons fair; with respect to the latter, we show one approach. Studies interested in detrimental growth patterns may gain extra insight from reporting several sets of growth pattern contrasts, and hence an approach that incorporates several sets of model parameterisations. Co-efficients from these models require careful interpretation, taking account of the other variables that are conditioned on.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 20%
Student > Master 4 20%
Student > Bachelor 3 15%
Student > Doctoral Student 2 10%
Librarian 2 10%
Other 3 15%
Unknown 2 10%
Readers by discipline Count As %
Medicine and Dentistry 6 30%
Nursing and Health Professions 5 25%
Agricultural and Biological Sciences 1 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Mathematics 1 5%
Other 4 20%
Unknown 2 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 April 2016.
All research outputs
#3,890,947
of 7,534,762 outputs
Outputs from BMC Medical Research Methodology
#518
of 774 outputs
Outputs of similar age
#147,526
of 271,506 outputs
Outputs of similar age from BMC Medical Research Methodology
#21
of 29 outputs
Altmetric has tracked 7,534,762 research outputs across all sources so far. This one is in the 27th percentile – i.e., 27% of other outputs scored the same or lower than it.
So far Altmetric has tracked 774 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 271,506 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.