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Use and cumulation of evidence from modelling studies to inform policy on food taxes and subsidies: biting off more than we can chew?

Overview of attention for article published in BMC Public Health, March 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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14 X users

Citations

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16 Dimensions

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75 Mendeley
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1 CiteULike
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Title
Use and cumulation of evidence from modelling studies to inform policy on food taxes and subsidies: biting off more than we can chew?
Published in
BMC Public Health, March 2015
DOI 10.1186/s12889-015-1641-5
Pubmed ID
Authors

Ian Shemilt, Theresa M Marteau, Richard D Smith, David Ogilvie

Abstract

Food tax-subsidy policies are proposed to hold promise for helping to produce healthier patterns of food purchasing and consumption at population level. Evidence for their effects derives largely from simulation studies that explore the potential effects of untried policies using a mathematical modelling framework. This paper provides a critique first of the nature of the evidence derived from such simulation studies, and second of the challenges of cumulating that evidence to inform public health policy. Effects estimated by simulation studies of food taxes and subsidies can be expected to diverge in potentially important ways from those that would accrue in practice because these models are simplified, typically static, representations of complex adaptive systems. The level of confidence that can be placed in modelled estimates of effects is correspondingly low, and the level of associated uncertainty is high. Moreover, evidence from food tax-subsidy simulation studies cannot meaningfully be cumulated using currently available quantitative evidence synthesis methods, to reduce uncertainty about effects. Simulation studies are critical for the initial phases of an incremental research process, for drawing together diverse evidence and exploring potential longer-term effects. While simulation studies of food taxes and subsidies provide a valuable and necessary input to the formulation of public health policy in this area, they are unlikely to be sufficient, and policy makers should not place excessive reliance on evidence from such studies, either singly or cumulatively. To reflect known and unknown limitations of the models, results of such studies should be interpreted cautiously as tentative projections. Modelling studies should increasingly be integrated with more empirical studies of the effects of food tax and subsidy policies in practice.

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X Demographics

The data shown below were collected from the profiles of 14 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
New Zealand 1 1%
Unknown 73 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 16%
Student > Ph. D. Student 11 15%
Researcher 9 12%
Student > Bachelor 8 11%
Student > Postgraduate 4 5%
Other 15 20%
Unknown 16 21%
Readers by discipline Count As %
Nursing and Health Professions 16 21%
Medicine and Dentistry 11 15%
Social Sciences 9 12%
Agricultural and Biological Sciences 4 5%
Economics, Econometrics and Finance 4 5%
Other 11 15%
Unknown 20 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 13 June 2016.
All research outputs
#4,314,202
of 23,511,526 outputs
Outputs from BMC Public Health
#4,803
of 15,248 outputs
Outputs of similar age
#53,480
of 264,870 outputs
Outputs of similar age from BMC Public Health
#88
of 291 outputs
Altmetric has tracked 23,511,526 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,248 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.1. This one has gotten more attention than average, scoring higher than 68% 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 264,870 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 79% of its contemporaries.
We're also able to compare this research output to 291 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 69% of its contemporaries.