↓ Skip to main content

Finding big shots: small-area mapping and spatial modelling of obesity among Swiss male conscripts

Overview of attention for article published in BMC Obesity, February 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#45 of 179)
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

twitter
9 X users
facebook
2 Facebook pages
q&a
1 Q&A thread

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
35 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Finding big shots: small-area mapping and spatial modelling of obesity among Swiss male conscripts
Published in
BMC Obesity, February 2016
DOI 10.1186/s40608-016-0092-6
Pubmed ID
Authors

Radoslaw Panczak, Leonhard Held, André Moser, Philip A. Jones, Frank J. Rühli, Kaspar Staub

Abstract

In Switzerland, as in other developed countries, the prevalence of overweight and obesity has increased substantially since the early 1990s. Most of the analyses so far have been based on sporadic surveys or self-reported data and did not offer potential for small-area analyses. The goal of this study was to investigate spatial variation and determinants of obesity among young Swiss men using recent conscription data. A complete, anonymized dataset of conscription records for the 2010-2012 period were provided by Swiss Armed Forces. We used a series of Bayesian hierarchical logistic regression models to investigate the spatial pattern of obesity across 3,187 postcodes, varying them by type of random effects (spatially unstructured and structured), level of adjustment by individual (age and professional status) and area-based [urbanicity and index of socio-economic position (SEP)] characteristics. The analysed dataset consisted of 100,919 conscripts, out of which 5,892 (5.8 %) were obese. Crude obesity prevalence increased with age among conscripts of lower individual and area-based SEP and varied greatly over postcodes. Best model's estimates of adjusted odds ratios of obesity on postcode level ranged from 0.61 to 1.93 and showed a strong spatial pattern of obesity risk across the country. Odds ratios above 1 concentrated in central and north Switzerland. Smaller pockets of elevated obesity risk also emerged around cities of Geneva, Fribourg and Lausanne. Lower estimates were observed in North-East and East as well as south of the Alps. Importantly, small regional outliers were observed and patterning did not follow administrative boundaries. Similarly as with crude obesity prevalence, the best fitting model confirmed increasing risk of obesity with age and among conscripts of lower professional status. The risk decreased with higher area-based SEP and, to a lesser degree - in rural areas. In Switzerland, there is a substantial spatial variation in obesity risk among young Swiss men. Small-area estimates of obesity risk derived from conscripts records contribute to its understanding and could be used to design further studies and interventions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Switzerland 1 3%
Unknown 33 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 29%
Student > Doctoral Student 8 23%
Student > Ph. D. Student 3 9%
Student > Master 3 9%
Professor 1 3%
Other 3 9%
Unknown 7 20%
Readers by discipline Count As %
Medicine and Dentistry 9 26%
Nursing and Health Professions 2 6%
Economics, Econometrics and Finance 2 6%
Environmental Science 2 6%
Social Sciences 2 6%
Other 9 26%
Unknown 9 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 04 February 2021.
All research outputs
#3,393,943
of 23,881,329 outputs
Outputs from BMC Obesity
#45
of 179 outputs
Outputs of similar age
#53,911
of 300,485 outputs
Outputs of similar age from BMC Obesity
#9
of 17 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 179 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.5. This one has done well, scoring higher than 78% of its peers.
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 300,485 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.