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Designing and Undertaking a Health Economics Study of Digital Health Interventions

Overview of attention for article published in American Journal of Preventive Medicine, November 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

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2 policy sources
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21 X users

Citations

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64 Dimensions

Readers on

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262 Mendeley
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Title
Designing and Undertaking a Health Economics Study of Digital Health Interventions
Published in
American Journal of Preventive Medicine, November 2016
DOI 10.1016/j.amepre.2016.05.007
Pubmed ID
Authors

Paul McNamee, Elizabeth Murray, Michael P. Kelly, Laura Bojke, Jim Chilcott, Alastair Fischer, Robert West, Lucy Yardley

Abstract

This paper introduces and discusses key issues in the economic evaluation of digital health interventions. The purpose is to stimulate debate so that existing economic techniques may be refined or new methods developed. The paper does not seek to provide definitive guidance on appropriate methods of economic analysis for digital health interventions. This paper describes existing guides and analytic frameworks that have been suggested for the economic evaluation of healthcare interventions. Using selected examples of digital health interventions, it assesses how well existing guides and frameworks align to digital health interventions. It shows that digital health interventions may be best characterized as complex interventions in complex systems. Key features of complexity relate to intervention complexity, outcome complexity, and causal pathway complexity, with much of this driven by iterative intervention development over time and uncertainty regarding likely reach of the interventions among the relevant population. These characteristics imply that more-complex methods of economic evaluation are likely to be better able to capture fully the impact of the intervention on costs and benefits over the appropriate time horizon. This complexity includes wider measurement of costs and benefits, and a modeling framework that is able to capture dynamic interactions among the intervention, the population of interest, and the environment. The authors recommend that future research should develop and apply more-flexible modeling techniques to allow better prediction of the interdependency between interventions and important environmental influences.

X Demographics

X Demographics

The data shown below were collected from the profiles of 21 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 262 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Norway 1 <1%
Switzerland 1 <1%
Unknown 259 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 18%
Researcher 40 15%
Student > Master 35 13%
Other 13 5%
Student > Doctoral Student 13 5%
Other 48 18%
Unknown 66 25%
Readers by discipline Count As %
Medicine and Dentistry 36 14%
Social Sciences 26 10%
Nursing and Health Professions 23 9%
Computer Science 21 8%
Psychology 17 6%
Other 57 22%
Unknown 82 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 19 April 2023.
All research outputs
#1,891,648
of 25,371,288 outputs
Outputs from American Journal of Preventive Medicine
#1,344
of 5,270 outputs
Outputs of similar age
#32,915
of 317,794 outputs
Outputs of similar age from American Journal of Preventive Medicine
#31
of 96 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,270 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 41.1. This one has gotten more attention than average, scoring higher than 74% 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 317,794 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 89% of its contemporaries.
We're also able to compare this research output to 96 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 67% of its contemporaries.