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Measuring causes of death in populations: a new metric that corrects cause-specific mortality fractions for chance

Overview of attention for article published in Population Health Metrics, October 2015
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3 X users

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25 Dimensions

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37 Mendeley
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Title
Measuring causes of death in populations: a new metric that corrects cause-specific mortality fractions for chance
Published in
Population Health Metrics, October 2015
DOI 10.1186/s12963-015-0061-1
Pubmed ID
Authors

Abraham D. Flaxman, Peter T. Serina, Bernardo Hernandez, Christopher J. L. Murray, Ian Riley, Alan D. Lopez

Abstract

Verbal autopsy is gaining increasing acceptance as a method for determining the underlying cause of death when the cause of death given on death certificates is unavailable or unreliable, and there are now a number of alternative approaches for mapping from verbal autopsy interviews to the underlying cause of death. For public health applications, the population-level aggregates of the underlying causes are of primary interest, expressed as the cause-specific mortality fractions (CSMFs) for a mutually exclusive, collectively exhaustive cause list. Until now, CSMF Accuracy is the primary metric that has been used for measuring the quality of CSMF estimation methods. Although it allows for relative comparisons of alternative methods, CSMF Accuracy provides misleading numbers in absolute terms, because even random allocation of underlying causes yields relatively high CSMF accuracy. Therefore, the objective of this study was to develop and test a measure of CSMF that corrects this problem. We developed a baseline approach of random allocation and measured its performance analytically and through Monte Carlo simulation. We used this to develop a new metric of population-level estimation accuracy, the Chance Corrected CSMF Accuracy (CCCSMF Accuracy), which has value near zero for random guessing, and negative quality values for estimation methods that are worse than random at the population level. The CCCSMF Accuracy formula was found to be CCSMF Accuracy = (CSMF Accuracy - 0.632) / (1 - 0.632), which indicates that, at the population-level, some existing and commonly used VA methods perform worse than random guessing. CCCSMF Accuracy should be used instead of CSMF Accuracy when assessing VA estimation methods because it provides a more easily interpreted measure of the quality of population-level estimates.

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The data shown below were collected from the profiles of 3 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Bangladesh 1 3%
Unknown 36 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 16%
Student > Bachelor 5 14%
Student > Master 4 11%
Researcher 3 8%
Student > Postgraduate 3 8%
Other 6 16%
Unknown 10 27%
Readers by discipline Count As %
Medicine and Dentistry 12 32%
Computer Science 3 8%
Social Sciences 3 8%
Agricultural and Biological Sciences 2 5%
Psychology 2 5%
Other 4 11%
Unknown 11 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 25 March 2023.
All research outputs
#15,072,355
of 23,937,746 outputs
Outputs from Population Health Metrics
#287
of 396 outputs
Outputs of similar age
#148,265
of 282,597 outputs
Outputs of similar age from Population Health Metrics
#7
of 10 outputs
Altmetric has tracked 23,937,746 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 396 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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 282,597 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.