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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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  • Above-average Attention Score compared to outputs of the same age (55th percentile)
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Mentioned by

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7 X users

Citations

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23 Dimensions

Readers on

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118 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.

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X Demographics

The data shown below were collected from the profiles of 7 X users 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 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 118 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 31 26%
Student > Ph. D. Student 15 13%
Student > Bachelor 14 12%
Researcher 6 5%
Student > Doctoral Student 5 4%
Other 17 14%
Unknown 30 25%
Readers by discipline Count As %
Nursing and Health Professions 19 16%
Medicine and Dentistry 19 16%
Social Sciences 17 14%
Agricultural and Biological Sciences 7 6%
Psychology 4 3%
Other 14 12%
Unknown 38 32%
Attention Score in Context

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
#7,953,894
of 23,940,793 outputs
Outputs from BMC Public Health
#8,383
of 15,743 outputs
Outputs of similar age
#92,685
of 267,428 outputs
Outputs of similar age from BMC Public Health
#124
of 232 outputs
Altmetric has tracked 23,940,793 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 15,743 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.3. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 267,428 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 55% of its contemporaries.
We're also able to compare this research output to 232 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.