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Quantifying the impact of social groups and vaccination on inequalities in infectious diseases using a mathematical model

Overview of attention for article published in BMC Medicine, September 2018
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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 (81st percentile)

Mentioned by

twitter
15 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
28 Mendeley
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Title
Quantifying the impact of social groups and vaccination on inequalities in infectious diseases using a mathematical model
Published in
BMC Medicine, September 2018
DOI 10.1186/s12916-018-1152-1
Pubmed ID
Authors

James D. Munday, Albert Jan van Hoek, W. John Edmunds, Katherine E. Atkins

Abstract

Social and cultural disparities in infectious disease burden are caused by systematic differences between communities. Some differences have a direct and proportional impact on disease burden, such as health-seeking behaviour and severity of infection. Other differences-such as contact rates and susceptibility-affect the risk of transmission, where the impact on disease burden is indirect and remains unclear. Furthermore, the concomitant impact of vaccination on such inequalities is not well understood. To quantify the role of differences in transmission on inequalities and the subsequent impact of vaccination, we developed a novel mathematical framework that integrates a mechanistic model of disease transmission with a demographic model of social structure, calibrated to epidemiologic and empirical social contact data. Our model suggests realistic differences in two key factors contributing to the rates of transmission-contact rate and susceptibility-between two social groups can lead to twice the risk of infection in the high-risk population group relative to the low-risk population group. The more isolated the high-risk group, the greater this disease inequality. Vaccination amplified this inequality further: equal vaccine uptake across the two population groups led to up to seven times the risk of infection in the high-risk group. To mitigate these inequalities, the high-risk population group would require disproportionately high vaccination uptake. Our results suggest that differences in contact rate and susceptibility can play an important role in explaining observed inequalities in infectious diseases. Importantly, we demonstrate that, contrary to social policy intentions, promoting an equal vaccine uptake across population groups may magnify inequalities in infectious disease risk.

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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 29%
Student > Master 6 21%
Researcher 5 18%
Student > Doctoral Student 3 11%
Librarian 1 4%
Other 2 7%
Unknown 3 11%
Readers by discipline Count As %
Medicine and Dentistry 7 25%
Nursing and Health Professions 4 14%
Agricultural and Biological Sciences 3 11%
Social Sciences 3 11%
Psychology 1 4%
Other 3 11%
Unknown 7 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 27 September 2018.
All research outputs
#2,034,439
of 16,606,190 outputs
Outputs from BMC Medicine
#1,393
of 2,624 outputs
Outputs of similar age
#52,344
of 280,407 outputs
Outputs of similar age from BMC Medicine
#1
of 1 outputs
Altmetric has tracked 16,606,190 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,624 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.2. This one is in the 46th percentile – i.e., 46% 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 280,407 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 81% of its contemporaries.
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