↓ Skip to main content

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
Altmetric Badge

About this Attention Score

  • 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 (90th percentile)

Mentioned by

news
17 news outlets
blogs
3 blogs
twitter
1 X user
facebook
1 Facebook page

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
39 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.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 21%
Researcher 7 18%
Student > Ph. D. Student 7 18%
Student > Doctoral Student 4 10%
Student > Bachelor 2 5%
Other 5 13%
Unknown 6 15%
Readers by discipline Count As %
Medicine and Dentistry 8 21%
Nursing and Health Professions 7 18%
Social Sciences 4 10%
Business, Management and Accounting 3 8%
Mathematics 3 8%
Other 4 10%
Unknown 10 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 135. 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
#290,993
of 24,495,443 outputs
Outputs from Journal of Urban Health
#54
of 1,353 outputs
Outputs of similar age
#6,181
of 314,971 outputs
Outputs of similar age from Journal of Urban Health
#3
of 22 outputs
Altmetric has tracked 24,495,443 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 1,353 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.7. This one has done particularly well, scoring higher than 96% 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 314,971 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 22 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 90% of its contemporaries.