Spatial inequalities of COVID-19 mortality rate in relation to socioeconomic and environmental factors across England

In this study, we aimed to examine spatial inequalities of COVID-19 mortality rate in relation to spatial inequalities of socioeconomic and environmental factors across England. Specifically, we first explored spatial patterns of COVID-19 mortality rate in comparison to non-COVID-19 mortality rate. Subsequently, we established models to investigate contributions of socioeconomic and environmental factors to spatial variations of COVID-19 mortality rate across England (N = 317). Two newly developed specifications of spatial regression models were established successfully to estimate COVID-19 mortality rate (R² = 0.49 and R² = 0.793). The level of spatial inequalities of COVID-19 mortality is higher than that of non-COVID-19 mortality in England. Although global spatial association of COVID-19 mortality and non-COVID-19 mortality is positive, local spatial association of COVID-19 mortality and non-COVID-19 mortality is negative in some areas. Expectedly, hospital accessibility is negatively related to COVID-19 mortality rate. Percent of Asians, percent of Blacks, and unemployment rate are positively related to COVID-19 mortality rate. More importantly, relative humidity is negatively related to COVID-19 mortality rate. Moreover, among the spatial models estimated, the 'random effects specification of eigenvector spatial filtering model' outperforms the 'matrix exponential spatial specification of spatial autoregressive model'.

[Satellite data for recording health-relevant environmental conditions: examples and interdisciplinary potential]

Environmental conditions influence human health and interact with other factors such as DNA, lifestyle, or the social environment. Earth observations from space provide data on the most diverse manifestations of these environmental conditions and make it possible to quantify them spatially. Using two examples - the availability of open and recreational space and the spatial distribution of air pollution - this article presents the potential of Earth observations for health studies. In addition, possible applications for health-related issues are discussed. To this end, we try to outline key points for an interdisciplinary approach that meets the conceptual, data technology, and ethical challenges.

Understanding temporal change of spatial accessibility to healthcare: An analytic framework for local factor impacts

Population demand, health service supply, and the linkages between them (e.g., transport infrastructure) are important factors that determine spatial accessibility to healthcare at a place. These three factors vary differently over time and location, leading to temporal changes and spatial disparities in access to healthcare. Few analytic methods have been developed to measure local impacts of these factors on healthcare accessibility over time, which are essential to alleviating health disparities and evaluating intervention programs. We propose a spatially explicit analytic framework to measure local factor impacts over time by adopting a chain substitution method from economics. The analysis is illustrated by a case study of spatial accessibility to physicians in Florida, USA, from 1990 to 2010. For each census block group, the results show the impact of local population change, physician relocation, and road-network expansion on the loss and gain of healthcare accessibility over time. The leading impact factor are identified for each census block group through comparison, and spatial clusters of factor impacts are discovered. To the literature of healthcare accessibility, this article presents a promising start of factor impact analysis and offers new perspectives in exploring spatial processes underlying people's access to healthcare.

Clinic access and teenage birth rates: Racial/ethnic and spatial disparities in Houston, TX

Teenage motherhood is a pressing issue in the United States, and one that is disproportionately affecting racial/ethnic minorities. In this research, we examine the relationship between the distance to the nearest reproductive health clinic and teenage birth rates across all zip codes in Houston, Texas. Our primary data come from the Texas Department of State Health Services. We use spatial regression analysis techniques to examine the link between clinic proximity and local teenage birth rates for all females aged 15 to 19, and separately by maternal race/ethnicity. We find, overall, limited support for a connection between clinic distance and local teenage birth rates. However, clinics seem to matter most for explaining non-Hispanic white teenage birth rates, particularly in high-poverty zip codes. The racial/ethnic and economic variation in the importance of clinic distance suggests tailoring clinic outreach to more effectively serve a wider range of teenage populations. We argue social accessibility should be considered in addition to geographic accessibility in order for clinics to help prevent teenage pregnancy.