| Title |
County-level contextual factors associated with diabetes incidence in the United States
|
|---|---|
| Published in |
Annals of Epidemiology, November 2017
|
| DOI | 10.1016/j.annepidem.2017.11.002 |
| Pubmed ID | |
| Authors |
Solveig A. Cunningham, Shivani A. Patel, Gloria L. Beckles, Linda S. Geiss, Neil Mehta, Hui Xie, Giuseppina Imperatore |
| Abstract |
Health and administrative systems are facing spatial clustering in chronic diseases such as diabetes. This study explores how geographic distribution of diabetes in the United States is associated with socioeconomic and built environment characteristics and health-relevant policies. We compiled nationally representative county-level data from multiple data sources. We standardized characteristics to a mean = 0 and a SD = 1 and modeled county-level age-adjusted diagnosed diabetes incidence in 2013 using 2-level hierarchical linear regression. Incidence of age-standardized diagnosed diabetes in 2013 varied across U.S. counties (n = 3109), ranging from 310 to 2190 new cases/100,000, with an average of 856.4/100,000. Socioeconomic and health-related characteristics explained ∼42% of the variation in diabetes incidence across counties. After accounting for other characteristics, counties with higher unemployment, higher poverty, and longer commutes had higher incidence rates than counties with lower levels. Counties with more exercise opportunities, access to healthy food, and primary care physicians had fewer diabetes cases. Features of the socioeconomic and built environment were associated with diabetes incidence; identifying the salient modifiable features of counties can inform targeted policies to reduce diabetes incidence. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 69 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 12% |
| Researcher | 7 | 10% |
| Student > Doctoral Student | 7 | 10% |
| Unspecified | 6 | 9% |
| Student > Ph. D. Student | 5 | 7% |
| Other | 10 | 14% |
| Unknown | 26 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 14% |
| Nursing and Health Professions | 9 | 13% |
| Unspecified | 6 | 9% |
| Social Sciences | 4 | 6% |
| Environmental Science | 2 | 3% |
| Other | 4 | 6% |
| Unknown | 34 | 49% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 July 2019.
All research outputs
#2,864,961
of 25,382,440 outputs
Outputs from Annals of Epidemiology
#358
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Outputs of similar age
#60,679
of 445,887 outputs
Outputs of similar age from Annals of Epidemiology
#12
of 27 outputs
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