Type 2 diabetes mellitus and neighborhood deprivation index: A spatial analysis in Zhejiang, China

Zhang, Xin; Chen, Xiaoxiao; Gong, Weiwei

doi:10.1111/jdi.12899

Cited by 7 publications

(7 citation statements)

References 40 publications

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“…The local autocorrelation was analyzed using the Anselin local Moran's I [ 30 , 31 ] and Getis-Ord Gi* [ 32 ]. The Anselin local Moran's I index, as a local index of spatial correlation, represents the spatial correlation between a research area and its neighboring areas for the research variables, including low-low, low-high, high-low, and high-high, and not significant.…”

Section: Methodsmentioning

confidence: 99%

Epidemiological characteristics and temporal-spatial analysis of overseas imported dengue fever cases in outbreak provinces of China, 2005–2019

et al. 2022

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Background Overseas imported dengue fever is an important factor in local outbreaks of this disease in the mainland of China. To better prevent and control such local outbreaks, the epidemiological characteristics and temporal-spatial distribution of overseas imported dengue fever cases in provincial-level administrative divisions (PLADs) where dengue fever is outbreak in the mainland of China were explored. Methods Using the Chinese National Notifiable Infectious Disease Reporting Information System (CNNDS), we identified overseas imported dengue fever cases in dengue fever outbreak areas in the mainland of China from 2005 to 2019 to draw the epidemic curve and population characteristic distribution of overseas imported cases in each PLAD. Based on spatial autocorrelation analysis of ArcGIS 10.5 and temporal-spatial scanning analysis of SaTScan 9.5, we analyzed the temporal-spatial distribution of overseas imported dengue fever in dengue fever outbreak areas in the mainland of China. Results A total of 11,407 imported cases, mainly from Southeast Asia, were recorded from 2005 to 2019 in these 13 PLADs. Of which 62.1% were imported into Yunnan and Guangdong Provinces. Among the imported cases, there were more males than females, mainly from the 21–50 age group. The hot spots were concentrated in parts of Yunnan, Guangdong and Fujian Provinces. We found the cluster of infected areas were expanding northward. Conclusions Based on the analysis of overseas imported dengue cases in 13 PLADs of the mainland of China from 2005 to 2019, we obtained the epidemiological characteristics and spatial distribution of imported dengue cases. Border controls need to pay attention to key population sectors, such as 21–50 years old men and education of key populations on dengue prevention. There is a need to improve the awareness of the prevention and control of imported cases in border areas. At the same time, northern regions cannot relax their vigilance. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Epidemiological characteristics and temporal-spatial analysis of overseas imported dengue fever cases in outbreak provinces of China, 2005–2019

et al. 2022

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“…Risk factors in the built environment that have been found to be related to CMD include interaction with nature (green space, walkability, activity supportive built environment, and lack of places to exercise) [46,47] and access to a healthy food environment (distance, affordability, and availability of healthy food and high-density of fast-food restaurants) [48] (see Table 1). Social factors associated with CMD outcomes include race/ethnicity [49], neighborhood deprivation [50], safety (neighborhood violent crime and unemployment) [51], social capital [52], and rural vs. urban [53]. Other social risk factors that have been found to be associated with CMD include population density, community stressors, residential segregation, health insurance, access to health care services, lack of trust in health care providers, socio-cultural beliefs and norms (car ownership, cultural influences), and availability of social supports (see Table 1).…”

Section: Introductionmentioning

confidence: 99%

The Effects of Social, Personal, and Behavioral Risk Factors and PM2.5 on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients

Juárez

Tabatabai

Valdez

et al. 2020

IJERPH

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(1) Background: Cardio-metabolic diseases (CMD), including cardiovascular disease, stroke, and diabetes, have numerous common individual and environmental risk factors. Yet, few studies to date have considered how these multiple risk factors together affect CMD disparities between Blacks and Whites. (2) Methods: We linked daily fine particulate matter (PM2.5) measures with survey responses of participants in the Southern Community Cohort Study (SCCS). Generalized linear mixed modeling (GLMM) was used to estimate the relationship between CMD risk and social-demographic characteristics, behavioral and personal risk factors, and exposure levels of PM2.5. (3) Results: The study resulted in four key findings: (1) PM2.5 concentration level was significantly associated with reported CMD, with risk rising by 2.6% for each µg/m3 increase in PM2.5; (2) race did not predict CMD risk when clinical, lifestyle, and environmental risk factors were accounted for; (3) a significant variation of CMD risk was found among participants across states; and (4) multiple personal, clinical, and social-demographic and environmental risk factors played a role in predicting CMD occurrence. (4) Conclusions: Disparities in CMD risk among low social status populations reflect the complex interactions of exposures and cumulative risks for CMD contributed by different personal and environmental factors from natural, built, and social environments.

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“…The local autocorrelation was analyzed using Anselin Local Moran's I [30,31] and Getis-Ord Gi* [32]. Anselin Local Moran's I index, as a local index of spatial correlation, represents the spatial correlation between a research area and its neighboring areas for the research variables, including low-low, low-high, high-low, and high-high, and not signi cant.…”

Section: Discussionmentioning

confidence: 99%

Characteristics and Temporal-Spatial Analysis of Overseas Imported Dengue Fever Cases in Outbreak Provinces of China, 2005-2019

Lun

Wang

Zhao

et al. 2021

Preprint

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(1) Background: Overseas imported dengue fever is an important factor in the local epidemic of dengue fever in mainland China. Therefore, in order to effectively prevent and control the local epidemic of dengue fever in mainland China, the epidemiological characteristics and temporal-spatial distribution of overseas imported dengue fever cases in provinces where dengue fever is endemic in mainland China are explored. (2) Methods: Through the infectious disease report information management system of the Chinese Disease Prevention and Control Information System, we sorted out overseas imported dengue fever cases in local outbreaks of dengue fever in mainland China from 2005 to 2019. Using Excel 2016 to sort out the data and draw the epidemic curve and population characteristic distribution of overseas imported cases in each province. Using ArcGIS 10.7 and SaTScan 9.5, we analyzed the temporal-spatial distribution of dengue fever in provinces where dengue fever is outbreak in mainland China. (3) Results: A total of 11407 imported cases, mainly from Southeast Asia, were recorded from 2005 to 2019 in 13 provinces, of which 62.08% were imported into Yunnan and Guangdong provinces. Among the imported cases, there were more males than females, mainly from 21-50 age group. 59.18% of the cases were farmers, businessmen, housework or unemployed. The hot spots were concentrated in parts of Yunnan and Guangdong provinces. Meanwhile,we found the clustered areas were expanding northward. (4) Conclusions: Focus on the publicity and education of dengue fever prevention knowledge among men, 21-50 years old, farmers, business services, housework or unemployed professionals. Further improve the awareness of the prevention and control of imported cases in border areas and economically developed cities. At the same time, the northern region cannot relax its vigilance.

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Type 2 diabetes mellitus and neighborhood deprivation index: A spatial analysis in Zhejiang, China

Abstract: The study showed that the incidence of type 2 diabetes mellitus was higher in affluent areas than the deprived areas across the study period. It will be significant to focus preventive efforts on the least deprived areas.

Cited by 7 publications

References 40 publications

Epidemiological characteristics and temporal-spatial analysis of overseas imported dengue fever cases in outbreak provinces of China, 2005–2019

Epidemiological characteristics and temporal-spatial analysis of overseas imported dengue fever cases in outbreak provinces of China, 2005–2019

The Effects of Social, Personal, and Behavioral Risk Factors and PM2.5 on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients

Characteristics and Temporal-Spatial Analysis of Overseas Imported Dengue Fever Cases in Outbreak Provinces of China, 2005-2019

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