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Identifying and characterizing hepatitis C virus hotspots in Massachusetts: a spatial epidemiological approach

Overview of attention for article published in BMC Infectious Diseases, April 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#47 of 6,153)
  • High Attention Score compared to outputs of the same age (96th percentile)

Mentioned by

news
11 news outlets
twitter
5 tweeters

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
69 Mendeley
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Title
Identifying and characterizing hepatitis C virus hotspots in Massachusetts: a spatial epidemiological approach
Published in
BMC Infectious Diseases, April 2017
DOI 10.1186/s12879-017-2400-2
Pubmed ID
Authors

Thomas J. Stopka, Michael A. Goulart, David J. Meyers, Marga Hutcheson, Kerri Barton, Shauna Onofrey, Daniel Church, Ashley Donahue, Kenneth K. H. Chui

Abstract

Hepatitis C virus (HCV) infections have increased during the past decade but little is known about geographic clustering patterns. We used a unique analytical approach, combining geographic information systems (GIS), spatial epidemiology, and statistical modeling to identify and characterize HCV hotspots, statistically significant clusters of census tracts with elevated HCV counts and rates. We compiled sociodemographic and HCV surveillance data (n = 99,780 cases) for Massachusetts census tracts (n = 1464) from 2002 to 2013. We used a five-step spatial epidemiological approach, calculating incremental spatial autocorrelations and Getis-Ord Gi* statistics to identify clusters. We conducted logistic regression analyses to determine factors associated with the HCV hotspots. We identified nine HCV clusters, with the largest in Boston, New Bedford/Fall River, Worcester, and Springfield (p < 0.05). In multivariable analyses, we found that HCV hotspots were independently and positively associated with the percent of the population that was Hispanic (adjusted odds ratio [AOR]: 1.07; 95% confidence interval [CI]: 1.04, 1.09) and the percent of households receiving food stamps (AOR: 1.83; 95% CI: 1.22, 2.74). HCV hotspots were independently and negatively associated with the percent of the population that were high school graduates or higher (AOR: 0.91; 95% CI: 0.89, 0.93) and the percent of the population in the "other" race/ethnicity category (AOR: 0.88; 95% CI: 0.85, 0.91). We identified locations where HCV clusters were a concern, and where enhanced HCV prevention, treatment, and care can help combat the HCV epidemic in Massachusetts. GIS, spatial epidemiological and statistical analyses provided a rigorous approach to identify hotspot clusters of disease, which can inform public health policy and intervention targeting. Further studies that incorporate spatiotemporal cluster analyses, Bayesian spatial and geostatistical models, spatially weighted regression analyses, and assessment of associations between HCV clustering and the built environment are needed to expand upon our combined spatial epidemiological and statistical methods.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 19%
Student > Ph. D. Student 10 14%
Researcher 7 10%
Student > Bachelor 7 10%
Professor > Associate Professor 6 9%
Other 11 16%
Unknown 15 22%
Readers by discipline Count As %
Medicine and Dentistry 12 17%
Social Sciences 8 12%
Nursing and Health Professions 8 12%
Mathematics 5 7%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 16 23%
Unknown 18 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 87. 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 26 September 2017.
All research outputs
#287,966
of 17,375,479 outputs
Outputs from BMC Infectious Diseases
#47
of 6,153 outputs
Outputs of similar age
#8,703
of 273,059 outputs
Outputs of similar age from BMC Infectious Diseases
#1
of 1 outputs
Altmetric has tracked 17,375,479 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 6,153 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.5. This one has done particularly well, scoring higher than 99% 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 273,059 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 96% 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