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Partitioning the population attributable fraction for a sequential chain of effects

Overview of attention for article published in Epidemiologic Perspectives & Innovations, October 2008
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Title
Partitioning the population attributable fraction for a sequential chain of effects
Published in
Epidemiologic Perspectives & Innovations, October 2008
DOI 10.1186/1742-5573-5-5
Pubmed ID
Authors

Craig A Mason, Shihfen Tu

Abstract

While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models. These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model. A method is proposed in which an overall or total PAF across multiple risk factors is partitioned into components based upon a sequential ordering of effects. This method is applied to several hypothetical data sets in order to demonstrate its application and interpretation in diverse analytic situations.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 8%
Ireland 1 4%
Unknown 22 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 20%
Professor > Associate Professor 4 16%
Researcher 4 16%
Professor 4 16%
Other 3 12%
Other 5 20%
Readers by discipline Count As %
Medicine and Dentistry 12 48%
Mathematics 2 8%
Veterinary Science and Veterinary Medicine 2 8%
Biochemistry, Genetics and Molecular Biology 1 4%
Business, Management and Accounting 1 4%
Other 4 16%
Unknown 3 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 15 February 2015.
All research outputs
#3,371,346
of 4,760,465 outputs
Outputs from Epidemiologic Perspectives & Innovations
#15
of 17 outputs
Outputs of similar age
#109,664
of 152,967 outputs
Outputs of similar age from Epidemiologic Perspectives & Innovations
#1
of 1 outputs
Altmetric has tracked 4,760,465 research outputs across all sources so far. This one is in the 16th percentile – i.e., 16% of other outputs scored the same or lower than it.
So far Altmetric has tracked 17 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one scored the same or higher as 2 of them.
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 152,967 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 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