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Neighborhood Typologies Associated with Alcohol Use among Adults in Their 30s: a Finite Mixture Modeling Approach

Overview of attention for article published in Journal of Urban Health, May 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#8 of 847)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
17 news outlets
blogs
3 blogs
twitter
2 tweeters
facebook
1 Facebook page

Citations

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4 Dimensions

Readers on

mendeley
16 Mendeley
Title
Neighborhood Typologies Associated with Alcohol Use among Adults in Their 30s: a Finite Mixture Modeling Approach
Published in
Journal of Urban Health, May 2017
DOI 10.1007/s11524-017-0161-2
Pubmed ID
Authors

Isaac C. Rhew, Rick Kosterman, Jungeun Olivia Lee

Abstract

There has been increasing interest in how neighborhood context may be associated with alcohol use. This study uses finite mixture modeling to empirically identify distinct neighborhood subtypes according to patterns of clustering of multiple neighborhood characteristics and examine whether these subtypes are associated with alcohol use. Neighborhoods were 303 census block groups in the greater Seattle, WA, area where 531 adults participating in an ongoing longitudinal study were residing in 2008. Neighborhood characteristics used to identify neighborhood subtypes included concentration of poverty, racial composition, neighborhood disorganization, and availability of on-premise alcohol outlets and off-premise hard liquor stores. Finite mixture models were used to identify latent neighborhood subtypes, and regression models with cluster robust standard errors examined associations between neighborhood subtypes and individual-level typical weekly drinking and number of past-year binge drinking episodes. Five neighborhood subtypes were identified. These subtypes could be primarily characterized as (1) high socioeconomic disadvantage, (2) moderate disadvantage, (3) low disadvantage, (4) low poverty and high disorganization, and (5) high alcohol availability. Adjusted for covariates, adults living in neighborhoods characterized by high disadvantage reported the highest levels of typical drinking and binge drinking compared to those from other neighborhood subtypes. Neighborhood subtypes derived from finite mixture models may represent meaningful categories that can help identify residential areas at elevated risk for alcohol misuse.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 31%
Student > Ph. D. Student 4 25%
Researcher 3 19%
Unspecified 2 13%
Student > Doctoral Student 1 6%
Other 1 6%
Readers by discipline Count As %
Medicine and Dentistry 4 25%
Nursing and Health Professions 4 25%
Unspecified 3 19%
Social Sciences 2 13%
Sports and Recreations 1 6%
Other 2 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 144. 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 12 June 2017.
All research outputs
#72,299
of 11,351,976 outputs
Outputs from Journal of Urban Health
#8
of 847 outputs
Outputs of similar age
#4,308
of 266,151 outputs
Outputs of similar age from Journal of Urban Health
#1
of 17 outputs
Altmetric has tracked 11,351,976 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 847 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.7. 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 266,151 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 17 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 94% of its contemporaries.