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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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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
23 tweeters

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
181 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.

Twitter Demographics

The data shown below were collected from the profiles of 23 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 181 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 178 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 21%
Student > Ph. D. Student 32 18%
Student > Master 25 14%
Other 12 7%
Student > Doctoral Student 10 6%
Other 32 18%
Unknown 32 18%
Readers by discipline Count As %
Medicine and Dentistry 29 16%
Social Sciences 19 10%
Computer Science 17 9%
Nursing and Health Professions 16 9%
Psychology 16 9%
Other 38 21%
Unknown 46 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 20 December 2019.
All research outputs
#1,463,025
of 16,470,846 outputs
Outputs from American Journal of Preventive Medicine
#1,180
of 4,044 outputs
Outputs of similar age
#38,257
of 297,132 outputs
Outputs of similar age from American Journal of Preventive Medicine
#34
of 98 outputs
Altmetric has tracked 16,470,846 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,044 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 34.7. This one has gotten more attention than average, scoring higher than 70% 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 297,132 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 87% of its contemporaries.
We're also able to compare this research output to 98 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 65% of its contemporaries.