The study of diabetes prevalence and related risk factors in Fuyang, a Chinese county under rapid urbanization

Yang, Fei; He, Yong; Sun, Liqin; Chen, Jinyun; Lou, Qinhua; Bao, Lianfang; Cha, Jun

doi:10.1007/s13410-015-0405-1

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Several leading factors can cause heart diseases such as alcohol use (Vogel, 2019), smoking (Barengo et al 2017), household income (Xiang et al 2018), urbanization rate (Allender et al 2008), blood pressure and depression (Brunström andCarlberg 2018, Li et al 2020), as well as obesity and physical inactivity (Arsenault, 2010). Many of these factors such as urbanization rate (Fei et al 2016), obesity, physical inactivity (Eaton and Eaton 2017), household income (Maty et al 2005), smoking (Akter et al 2017) and drinking habits (Holst et al 2017) can also lead to diabetes. However, the existing research does not consider the spatiotemporal variation of those risk factors associated with heart disease or type 2 diabetes.…”

Section: Introductionmentioning

confidence: 99%

Analyzing spatial variations of heart disease and type-2 diabetes: A multi-scale geographically weighted regression approach

Cui

Zhang

et al. 2022

Comput.Urban Sci.

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Heart disease is the leading cause of death in the United States. A person who has type-2 diabetes is twice as likely to have heart disease than someone who doesn’t have diabetes. Therefore, analyzing factors associated with both diseases and their interrelationships is essential for cardiovascular disease control and public health. In this article, we propose a Multi-scale Geographically Weighted Regression (MGWR) approach to observe spatial variations of environmental and demographic risk factors such as alcohol consumption behavior, lack of physical activity, obesity rate, urbanization rate, and income from 2005 to 2015 in the United States. The MGWR model has applied to eight census divisions of the United States at the county level: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, and Mountain. Results illustrate that there are notable differences in the spatial variation of the risk factors behind these two diseases. In particular, obesity has been a leading factor that associate with diabetes in the east, south-central, and south Atlantic regions of the U.S. On the other hand, smoking and alcohol consumption was the primary concern in the northern part of the U.S., in 2005. In 2015, alcohol consumption levels decreased, but the smoking level remained the same in those regions, which showed a significant impact on diabetes in the neighboring regions. Between 2005 and 2015, lack of physical exercise has become a significant risk factor associated with diabetes in the Northeast and West parts of the U.S. The proposed MGWR produced high goodness to fit (R2) for most of the areas in the United States.

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Section: Introductionmentioning

confidence: 99%

Analyzing spatial variations of heart disease and type-2 diabetes: A multi-scale geographically weighted regression approach

Cui

Zhang

et al. 2022

Comput.Urban Sci.

View full text Add to dashboard Cite

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“…Academics and government officials alike have been increasing their exhortations with respect to the public health crises that have emerged amidst the breathtaking pace of recent economic and urban development, most notably in South Asia (e.g., India) and China [1][2][3]. Both the United Nations and the World Health Organization have launched initiatives to address the alarming rise of chronic medical conditions as agrarian landscapes cede to urban sprawl [4].…”

Section: Introductionmentioning

confidence: 99%

Exploring Differences in the Rate of Type 2 Diabetes Among American Cities: How Urbanization Continues to Challenge the Traditional Epidemiological View

Young¹

2019

Urban Science

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As the world’s largest urban regions continue to expand, a concomitant rise in non-communicable diseases, particularly type 2 diabetes, poses an increasingly ominous challenge to experts in the field of public health. Given that the majority of the world’s population (54%) resides in urban areas, a figure likely to reach two-thirds by 2050, this issue presents serious implications for medical practitioners as well as policymakers seeking to manage long-term healthcare costs while sustaining historic increases in life expectancy. To explore how these trends are continuing to affect the United States, a multiple regression analysis was conducted using data provided by the Centers for Disease Control and Prevention (CDC) through their initiative, 500 Cities: Local Data for Better Health. The regression models revealed that larger cities reported significantly higher rates of type 2 diabetes even after controlling for variables that have been perennially linked to disease onset (e.g., levels of obesity, sedentary behavior). Implications are discussed, most notably the argument for moving beyond the ‘food desert’ paradigm when identifying and explaining which characteristics of larger cities place their residents at increased risk. This approach could help reveal opportunities for intervention that may not have garnered sufficient attention in the extant literature.

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The study of diabetes prevalence and related risk factors in Fuyang, a Chinese county under rapid urbanization

Cited by 2 publications

References 13 publications

Analyzing spatial variations of heart disease and type-2 diabetes: A multi-scale geographically weighted regression approach

Analyzing spatial variations of heart disease and type-2 diabetes: A multi-scale geographically weighted regression approach

Exploring Differences in the Rate of Type 2 Diabetes Among American Cities: How Urbanization Continues to Challenge the Traditional Epidemiological View

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