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Diabetes, obesity, and recommended fruit and vegetable consumption in relation to food environment sub-types: a cross-sectional analysis of Behavioral Risk Factor Surveillance System, United States…

Overview of attention for article published in BMC Public Health, May 2015
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

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7 tweeters

Citations

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12 Dimensions

Readers on

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76 Mendeley
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Title
Diabetes, obesity, and recommended fruit and vegetable consumption in relation to food environment sub-types: a cross-sectional analysis of Behavioral Risk Factor Surveillance System, United States Census, and food establishment data
Published in
BMC Public Health, May 2015
DOI 10.1186/s12889-015-1819-x
Pubmed ID
Authors

Cara L Frankenfeld, Timothy F Leslie, Matthew A Makara

Abstract

Social and spatial factors are an important part of individual and community health. The objectives were to identify food establishment sub-types and evaluate prevalence of diabetes, obesity, and recommended fruit and vegetable consumption in relation to these sub-types in the Washington DC metropolitan area. A cross-sectional study design was used. A measure of retail food environment was calculated as the ratio of number of sources of unhealthier food options (fast food, convenience stores, and pharmacies) to healthier food options (grocery stores and specialty food stores). Two categories were created: ≤1.0 (healthier options) and >1.0 (unhealthier options). k-means clustering was used to identify clusters based on proportions of grocery stores, restaurants, specialty food, fast food, convenience stores, and pharmacies. Prevalence data for county-level diabetes, obesity, and consumption of five or more fruits or vegetables per day (FV5) was obtained from the Behavioral Risk Factor Surveillance System. Multiple imputation was used to predict block-group level health outcomes with US Census demographic and economic variables as the inputs. The healthier options category clustered into three sub-types: 1) specialty food, 2) grocery stores, and 3) restaurants. The unhealthier options category clustered into two sub-types: 1) convenience stores, and 2) restaurants and fast food. Within the healthier options category, diabetes prevalence in the sub-types with high restaurants (5.9 %, p = 0.002) and high specialty food (6.1 %, p = 0.002) was lower than the grocery stores sub-type (7.1 %). The high restaurants sub-type compared to the high grocery stores sub-type had significantly lower obesity prevalence (28.6 % vs. 31.2 %, p <0.001) and higher FV5 prevalence (25.2 % vs. 23.1 %, p <0.001). Within the larger unhealthier options category, there were no significant differences in diabetes, obesity, or higher FV5 prevalence across the two sub-types. However, restaurants (including fast food) sub-type was significantly associated with lower diabetes and obesity, and higher FV prevalence compared to grocery store sub-type. These results suggest that there are sub-types within larger categories of food environments that are differentially associated with adverse health outcomes. These observations support the specific food establishment composition of an area may be an important component of the food establishment-health relationship.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 76 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 22 29%
Student > Ph. D. Student 14 18%
Student > Bachelor 11 14%
Professor > Associate Professor 4 5%
Researcher 3 4%
Other 11 14%
Unknown 11 14%
Readers by discipline Count As %
Medicine and Dentistry 14 18%
Social Sciences 14 18%
Nursing and Health Professions 11 14%
Agricultural and Biological Sciences 6 8%
Psychology 3 4%
Other 10 13%
Unknown 18 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 December 2015.
All research outputs
#1,586,814
of 6,810,016 outputs
Outputs from BMC Public Health
#2,591
of 6,254 outputs
Outputs of similar age
#61,912
of 203,044 outputs
Outputs of similar age from BMC Public Health
#104
of 217 outputs
Altmetric has tracked 6,810,016 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 6,254 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has gotten more attention than average, scoring higher than 55% 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 203,044 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 217 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.