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Is population structure sufficient to generate area-level inequalities in influenza rates? An examination using agent-based models

Overview of attention for article published in BMC Public Health, September 2015
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

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17 news outlets
blogs
2 blogs
twitter
2 tweeters
facebook
2 Facebook pages

Citations

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29 Dimensions

Readers on

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87 Mendeley
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Title
Is population structure sufficient to generate area-level inequalities in influenza rates? An examination using agent-based models
Published in
BMC Public Health, September 2015
DOI 10.1186/s12889-015-2284-2
Pubmed ID
Authors

Supriya Kumar, Kaitlin Piper, David D. Galloway, James L. Hadler, John J. Grefenstette

Abstract

In New Haven County, CT (NHC), influenza hospitalization rates have been shown to increase with census tract poverty in multiple influenza seasons. Though multiple factors have been hypothesized to cause these inequalities, including population structure, differential vaccine uptake, and differential access to healthcare, the impact of each in generating observed inequalities remains unknown. We can design interventions targeting factors with the greatest explanatory power if we quantify the proportion of observed inequalities that hypothesized factors are able to generate. Here, we ask if population structure is sufficient to generate the observed area-level inequalities in NHC. To our knowledge, this is the first use of simulation models to examine the causes of differential poverty-related influenza rates. Using agent-based models with a census-informed, realistic representation of household size, age-structure, population density in NHC census tracts, and contact rates in workplaces, schools, households, and neighborhoods, we measured poverty-related differential influenza attack rates over the course of an epidemic with a 23 % overall clinical attack rate. We examined the role of asthma prevalence rates as well as individual contact rates and infection susceptibility in generating observed area-level influenza inequalities. Simulated attack rates (AR) among adults increased with census tract poverty level (F = 30.5; P < 0.001) in an epidemic caused by a virus similar to A (H1N1) pdm09. We detected a steeper, earlier influenza rate increase in high-poverty census tracts-a finding that we corroborate with a temporal analysis of NHC surveillance data during the 2009 H1N1 pandemic. The ratio of the simulated adult AR in the highest- to lowest-poverty tracts was 33 % of the ratio observed in surveillance data. Increasing individual contact rates in the neighborhood did not increase simulated area-level inequalities. When we modified individual susceptibility such that it was inversely proportional to household income, inequalities in AR between high- and low-poverty census tracts were comparable to those observed in reality. To our knowledge, this is the first study to use simulations to probe the causes of observed inequalities in influenza disease patterns. Knowledge of the causes and their relative explanatory power will allow us to design interventions that have the greatest impact on reducing inequalities. Differential exposure due to population structure in our realistic simulation model explains a third of the observed inequality. Differential susceptibility to disease due to prevailing chronic conditions, vaccine uptake, and smoking should be considered in future models in order to quantify the role of additional factors in generating influenza inequalities.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Australia 1 1%
Unknown 83 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 18%
Researcher 16 18%
Student > Master 14 16%
Student > Doctoral Student 5 6%
Student > Bachelor 5 6%
Other 13 15%
Unknown 18 21%
Readers by discipline Count As %
Medicine and Dentistry 19 22%
Social Sciences 6 7%
Nursing and Health Professions 6 7%
Agricultural and Biological Sciences 5 6%
Computer Science 5 6%
Other 16 18%
Unknown 30 34%

Attention Score in Context

This research output has an Altmetric Attention Score of 148. 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 23 February 2021.
All research outputs
#231,872
of 22,829,083 outputs
Outputs from BMC Public Health
#196
of 14,871 outputs
Outputs of similar age
#3,340
of 274,809 outputs
Outputs of similar age from BMC Public Health
#2
of 275 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,871 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one has done particularly well, scoring higher than 98% of its peers.
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 274,809 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 275 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.