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The impact of individual-level heterogeneity on estimated infectious disease burden: a simulation study

Overview of attention for article published in Population Health Metrics, December 2016
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3 tweeters

Citations

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1 Dimensions

Readers on

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17 Mendeley
Title
The impact of individual-level heterogeneity on estimated infectious disease burden: a simulation study
Published in
Population Health Metrics, December 2016
DOI 10.1186/s12963-016-0116-y
Pubmed ID
Authors

Scott A. McDonald, Brecht Devleesschauwer, Jacco Wallinga

Abstract

Disease burden is not evenly distributed within a population; this uneven distribution can be due to individual heterogeneity in progression rates between disease stages. Composite measures of disease burden that are based on disease progression models, such as the disability-adjusted life year (DALY), are widely used to quantify the current and future burden of infectious diseases. Our goal was to investigate to what extent ignoring the presence of heterogeneity could bias DALY computation. Simulations using individual-based models for hypothetical infectious diseases with short and long natural histories were run assuming either "population-averaged" progression probabilities between disease stages, or progression probabilities that were influenced by an a priori defined individual-level frailty (i.e., heterogeneity in disease risk) distribution, and DALYs were calculated. Under the assumption of heterogeneity in transition rates and increasing frailty with age, the short natural history disease model predicted 14% fewer DALYs compared with the homogenous population assumption. Simulations of a long natural history disease indicated that assuming homogeneity in transition rates when heterogeneity was present could overestimate total DALYs, in the present case by 4% (95% quantile interval: 1-8%). The consequences of ignoring population heterogeneity should be considered when defining transition parameters for natural history models and when interpreting the resulting disease burden estimates.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ecuador 1 6%
Unknown 16 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 24%
Unspecified 3 18%
Lecturer > Senior Lecturer 2 12%
Professor 2 12%
Student > Postgraduate 2 12%
Other 4 24%
Readers by discipline Count As %
Unspecified 3 18%
Social Sciences 3 18%
Medicine and Dentistry 3 18%
Neuroscience 2 12%
Economics, Econometrics and Finance 1 6%
Other 5 29%

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 17 February 2017.
All research outputs
#7,057,587
of 11,333,579 outputs
Outputs from Population Health Metrics
#181
of 263 outputs
Outputs of similar age
#173,898
of 319,551 outputs
Outputs of similar age from Population Health Metrics
#7
of 13 outputs
Altmetric has tracked 11,333,579 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 263 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. 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 319,551 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.