↓ Skip to main content

Diet-related chronic disease in the northeastern United States: a model-based clustering approach

Overview of attention for article published in International Journal of Health Geographics, September 2015
Altmetric Badge

Mentioned by

facebook
1 Facebook page

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
63 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Diet-related chronic disease in the northeastern United States: a model-based clustering approach
Published in
International Journal of Health Geographics, September 2015
DOI 10.1186/s12942-015-0017-5
Pubmed ID
Authors

Abby Flynt, Madeleine I. G. Daepp

Abstract

Obesity and diabetes are global public health concerns. Studies indicate a relationship between socioeconomic, demographic and environmental variables and the spatial patterns of diet-related chronic disease. In this paper, we propose a methodology using model-based clustering and variable selection to predict rates of obesity and diabetes. We test this method through an application in the northeastern United States. We use model-based clustering, an unsupervised learning approach, to find latent clusters of similar US counties based on a set of socioeconomic, demographic, and environmental variables chosen through the process of variable selection. We then use Analysis of Variance and Post-hoc Tukey comparisons to examine differences in rates of obesity and diabetes for the clusters from the resulting clustering solution. We find access to supermarkets, median household income, population density and socioeconomic status to be important in clustering the counties of two northeastern states. The results of the cluster analysis can be used to identify two sets of counties with significantly lower rates of diet-related chronic disease than those observed in the other identified clusters. These relatively healthy clusters are distinguished by the large central and large fringe metropolitan areas contained in their component counties. However, the relationship of socio-demographic factors and diet-related chronic disease is more complicated than previous research would suggest. Additionally, we find evidence of low food access in two clusters of counties adjacent to large central and fringe metropolitan areas. While food access has previously been seen as a problem of inner-city or remote rural areas, this study offers preliminary evidence of declining food access in suburban areas. Model-based clustering with variable selection offers a new approach to the analysis of socioeconomic, demographic, and environmental data for diet-related chronic disease prediction. In a test application to two northeastern states, this method allows us to identify two sets of metropolitan counties with significantly lower diet-related chronic disease rates than those observed in most rural and suburban areas. Our method could be applied to larger geographic areas or other countries with comparable data sets, offering a promising method for researchers interested in the global increase in diet-related chronic disease.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 62 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 17%
Student > Bachelor 11 17%
Student > Ph. D. Student 9 14%
Researcher 8 13%
Student > Doctoral Student 5 8%
Other 7 11%
Unknown 12 19%
Readers by discipline Count As %
Nursing and Health Professions 13 21%
Medicine and Dentistry 11 17%
Agricultural and Biological Sciences 8 13%
Social Sciences 7 11%
Computer Science 3 5%
Other 10 16%
Unknown 11 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 September 2015.
All research outputs
#20,291,881
of 22,828,180 outputs
Outputs from International Journal of Health Geographics
#548
of 628 outputs
Outputs of similar age
#224,268
of 267,011 outputs
Outputs of similar age from International Journal of Health Geographics
#5
of 8 outputs
Altmetric has tracked 22,828,180 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 628 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.4. This one is in the 1st percentile – i.e., 1% 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 267,011 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.