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Assessing the value of screening tools: reviewing the challenges and opportunities of cost-effectiveness analysis

Overview of attention for article published in Public Health Reviews, July 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#28 of 141)
  • High Attention Score compared to outputs of the same age (85th percentile)

Mentioned by

blogs
1 blog
twitter
15 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
42 Mendeley
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Title
Assessing the value of screening tools: reviewing the challenges and opportunities of cost-effectiveness analysis
Published in
Public Health Reviews, July 2018
DOI 10.1186/s40985-018-0093-8
Pubmed ID
Authors

Nicolas Iragorri, Eldon Spackman

Abstract

Screening is an important part of preventive medicine. Ideally, screening tools identify patients early enough to provide treatment and avoid or reduce symptoms and other consequences, improving health outcomes of the population at a reasonable cost. Cost-effectiveness analyses combine the expected benefits and costs of interventions and can be used to assess the value of screening tools. This review seeks to evaluate the latest cost-effectiveness analyses on screening tools to identify the current challenges encountered and potential methods to overcome them. A systematic literature search of EMBASE and MEDLINE identified cost-effectiveness analyses of screening tools published in 2017. Data extracted included the population, disease, screening tools, comparators, perspective, time horizon, discounting, and outcomes. Challenges and methodological suggestions were narratively synthesized. Four key categories were identified: screening pathways, pre-symptomatic disease, treatment outcomes, and non-health benefits. Not all studies included treatment outcomes; 15 studies (22%) did not include treatment following diagnosis. Quality-adjusted life years were used by 35 (51.4%) as the main outcome. Studies that undertook a societal perspective did not report non-health benefits and costs consistently. Two important challenges identified were (i) estimating the sojourn time, i.e., the time between when a patient can be identified by screening tests and when they would have been identified due to symptoms, and (ii) estimating the treatment effect and progression rates of patients identified early. To capture all important costs and outcomes of a screening tool, screening pathways should be modeled including patient treatment. Also, false positive and false negative patients are likely to have important costs and consequences and should be included in the analysis. As these patients are difficult to identify in regular data sources, common treatment patterns should be used to determine how these patients are likely to be treated. It is important that assumptions are clearly indicated and that the consequences of these assumptions are tested in sensitivity analyses, particularly the assumptions of independence of consecutive tests and the level of patient and provider compliance to guidelines and sojourn times. As data is rarely available regarding the progression of undiagnosed patients, extrapolation from diagnosed patients may be necessary.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 26%
Researcher 9 21%
Student > Ph. D. Student 5 12%
Student > Bachelor 5 12%
Unspecified 4 10%
Other 8 19%
Readers by discipline Count As %
Medicine and Dentistry 16 38%
Unspecified 8 19%
Nursing and Health Professions 5 12%
Social Sciences 4 10%
Arts and Humanities 2 5%
Other 7 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 24 June 2019.
All research outputs
#1,133,749
of 13,538,736 outputs
Outputs from Public Health Reviews
#28
of 141 outputs
Outputs of similar age
#38,705
of 264,547 outputs
Outputs of similar age from Public Health Reviews
#1
of 2 outputs
Altmetric has tracked 13,538,736 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 141 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 80% 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 264,547 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 85% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them