Journal Browser

► Journal Browser

Special Issue Editors

Dr. Kosuke Tamura

E-Mail Website
Guest Editor

Neighborhood Social & Geospatial Determinants of Health Disparities Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD 20892, USA
Interests: neighborhood social environment; cardiovascular disease risk; psychosocial factors; biomarkers of stress; wearables; mHealth/eHealth; spatial epidemiology; geographic information system; health disparities; ecological momentary assessment

Dr. Tiffany M. Powell-Wiley

E-Mail Website
Guest Editor

1. Social Determinants of Obesity and Cardiovascular Risk Laboratory, Division of Intramural Research, National Heart, Lung and Blood Institute, NIH, 10 Center Drive, Room 5-5332, MSC 1454, Bethesda, MD 20892, USA
2. Intramural Research Program, National Institute on Minority and Health Disparities, NIH, 10 Center Drive, Room 5-5332, MSC 1454, Bethesda, MD 20892, USA
Interests: neighborhood social environment and cardiovascular risk factors; neighborhood socioeconomic deprivation and obesity; systems science approaches in development of multilevel physical activity interventions; biologic markers of chronic stress from adverse environmental conditions

Special Issue Information

Dear Colleagues,

Emerging public health crises (e.g., the coronavirus disease 2019 (COVID-19) pandemic) disproportionately impact certain populations, such as racial/ethnic minority groups and individuals having comorbidities (e.g., obesity, diabetes, and heart diseases). Recent studies emphasize that neighborhood social environment plays a key role shaping disparities in emerging and chronic diseases in the US and around the globe. Specifically, neighborhood research focusing on neighborhood characteristics include built or physical environment, neighborhood socioeconomic status (SES), neighborhood segregation, poverty, and violence. There is a small but growing interest in applying geospatial methods, such as geographic information systems (GIS) and global positioning systems (GPS) to quantify exposure to neighborhood characteristics, and their links to emerging and chronic diseases. Analyses using geospatial methods (e.g., geographically weighted regression (GWR) and spatial scan statistics) allow researchers to identify certain geographic areas where emerging and chronic diseases originate and cluster. Thus, research applying geospatial methods can also be beneficial for public health policy makers and practitioners to take advantage of these methods. Findings of such analyses can help to facilitate and develop more geospatially oriented screenings and prevention activities.

This Special Issue emphasizes specific research topics including the application of geospatial methods, such as GIS/GPS technologies and GWR analyses, as well as traditional objective and subjective (perceived) exposure assessment on neighborhood social contexts, such as neighborhood SES, poverty, neighborhood racial composition, built or physical environment, and social cohesion. This issue focuses on both emerging (COVID-19) and chronic diseases (e.g., obesity, diabetes, cardiovascular disease, cancers). Especially, COVID-19 can serve as an outcome in relation to neighborhood social environment, or a confounder to investigate how exposure to neighborhood social environment impacts chronic diseases. We encourage longitudinal studies examining cause–effect; however, we also accept studies with cross-sectional design as well as mediation analyses with rigorous statistical methods.

Dr. Kosuke Tamura

Dr. Tiffany M. Powell-Wiley
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

geographic information system
coronavirus disease 2019
chronic diseases
perceived and objective neighborhood environment
spatial epidemiology
heart disease
diabetes
physical activity
obesity
sleep
neighborhood segregation
longitudinal and cross-sectional design
mediation analyses

Published Papers (2 papers)

Download All Papers

Research

22 pages, 7921 KiB

Open AccessArticle

Spatial Clustering of County-Level COVID-19 Rates in the U.S.

by Marcus R. Andrews, Kosuke Tamura, Janae N. Best, Joniqua N. Ceasar, Kaylin G. Batey, Troy A. Kearse, Jr., Lavell V. Allen III, Yvonne Baumer, Billy S. Collins, Valerie M. Mitchell and Tiffany M. Powell-Wiley

Int. J. Environ. Res. Public Health 2021, 18(22), 12170; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182212170 - 19 Nov 2021

Cited by 13 | Viewed by 2933

Abstract

Despite the widespread prevalence of cases associated with the coronavirus disease 2019 (COVID-19) pandemic, little is known about the spatial clustering of COVID-19 in the United States. Data on COVID-19 cases were used to identify U.S. counties that have both high and low COVID-19 incident proportions and clusters. Our results suggest that there are a variety of sociodemographic variables that are associated with the severity of COVID-19 county-level incident proportions. As the pandemic evolved, communities of color were disproportionately impacted. Subsequently, it shifted from communities of color and metropolitan areas to rural areas in the U.S. Our final period showed limited differences in county characteristics, suggesting that COVID-19 infections were more widespread. The findings might address the systemic barriers and health disparities that may result in high incident proportions of COVID-19 clusters. Full article

(This article belongs to the Special Issue Emerging and Chronic Diseases: Application of Geospatial Approach)

► Show Figures

Figure 1

14 pages, 2189 KiB

Open AccessArticle

Social Determinants of Diabetes-Related Preventable Hospitalization in Taiwan: A Spatial Analysis

by Chung-Yi Li, Yung-Chung Chuang, Pei-Chun Chen, Michael S. Chen, Miaw-Chwen Lee, Li-Jung Elizabeth Ku and Chiachi Bonnie Lee

Int. J. Environ. Res. Public Health 2021, 18(4), 2146; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18042146 - 22 Feb 2021

Cited by 3 | Viewed by 2624

Abstract

Diabetes-Related Preventable Hospitalization (DRPH) has been identified as an important indicator of efficiency and quality of the health system and can be modified by social determinants. However, the spatial disparities, clustering, and relationships between DRPH and social determinants have rarely been investigated. Accordingly, this study examined the association of DRPH with area deprivation, densities of certificated diabetes health-promoting clinics (DHPC) and hospitals (DHPH), and the presence of elderly social services (ESS) using both statistical and spatial analyses. Data were obtained from the 2010–2016 National Health Insurance Research Database (NHIRD) and government open data. Township-level ordinary least squares (OSL) and geographically weighted regression (GWR) were conducted. DRPH rates were found to be negatively associated with densities of DHPC (β = −66.36, p = 0.029; 40.3% of all townships) and ESS (β = −1.85, p = 0.027; 28.4% of all townships) but positively associated with area deprivation (β = 2.96, p = 0.002; 25.6% of all townships) in both OLS and GWR models. Significant relationships were found in varying areas in the GWR model. DRPH rates are high in townships of Taiwan that have lower DHPC densities, lower ESS densities, and greater socioeconomic deprivation. Spatial analysis could identify areas of concern for potential intervention. Full article

(This article belongs to the Special Issue Emerging and Chronic Diseases: Application of Geospatial Approach)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Emerging and Chronic Diseases: Application of Geospatial Approach

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (2 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI