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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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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Average Attention Score compared to outputs of the same age and source

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15 X users

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49 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 27%
Student > Ph. D. Student 10 20%
Researcher 5 10%
Student > Doctoral Student 3 6%
Student > Bachelor 2 4%
Other 4 8%
Unknown 12 24%
Readers by discipline Count As %
Medicine and Dentistry 10 20%
Nursing and Health Professions 7 14%
Agricultural and Biological Sciences 4 8%
Social Sciences 3 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 6 12%
Unknown 17 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 22 February 2021.
All research outputs
#3,212,257
of 23,103,903 outputs
Outputs from BMC Medicine
#1,830
of 3,466 outputs
Outputs of similar age
#67,034
of 341,556 outputs
Outputs of similar age from BMC Medicine
#47
of 74 outputs
Altmetric has tracked 23,103,903 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,466 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.7. This one is in the 47th percentile – i.e., 47% 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 341,556 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 80% of its contemporaries.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.