Does the edge effect impact the healthcare equity? An examination of the equity in hospitals accessibility in the edge city in multi-scale

Gu, Zongni; Luo, Xiaolong; Tang, Mi; Liu, Xiaoman

doi:10.1016/j.jtrangeo.2022.103513

Cited by 14 publications

(7 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of health outcomes, a uent neighborhoods tend to fare better due to access to healthcare resources, recreational facilities, and healthier living conditions. Lower-income neighborhoods may face health disparities due to limited healthcare access and exposure to environmental risks (Gu et al 2023).…”

Section: Relationship Between Income Level and Neighborhoodsmentioning

confidence: 99%

Predictors of income level: A sociodemographic, social and contextual analysis

Leite,

Silva,

Dias

2024

Preprint

View full text Add to dashboard Cite

The purpose of this study is to investigate the impact of income levels on different facets of everyday life, encompassing aspects such as food accessibility, housing, living conditions, healthcare services. Additionally, the research intends to grasp the factors that contribute to defining the financial worth of income. The public database used in this study was retrieved from the World Values Survey (WVS): Wave 7 that includes several items related to demographics, social and neighborhood variables. Persistent gender income gaps, favoring men and younger individuals in higher brackets, result from historical norms, pay gaps, and age-related factors. Education correlates with income, notably among upper-middle and upper-class individuals. Neighborhood dynamics affect incomes positively through diverse interactions and negatively through health challenges. Income disparities are intricate and influenced by a combination of demographic, social, and personal factors. Addressing these economic hurdles and fostering diverse interactions contribute to high incomes.

show abstract

Section: Relationship Between Income Level and Neighborhoodsmentioning

confidence: 99%

Predictors of income level: A sociodemographic, social and contextual analysis

Leite,

Silva,

Dias

2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: Data Source and Processingmentioning

confidence: 99%

“…Due to the confidentiality of data, it is difficult to obtain refined population data. Some recent studies have begun to obtain the number of households in the residential communities from the real estate rental service platform and speculate on the population data [45,46]. Therefore, we crawled the POI data of the residential communities within the 3rd ring road in 2021 from the well-known real estate rental service platform "Anjuke" in China (https://wuhan.anjuke.com/ (accessed on 4 March 2023)), including the name of the residential communities, latitude and longitude coordinates, and the total number of households.…”

Section: Data Source and Processingmentioning

confidence: 99%

Spatial Optimization of Park Green Spaces by an Improved Two-Step Optimization Model from the Perspective of Maximizing Accessibility Equity

Xing

Jing

et al. 2023

Land

View full text Add to dashboard Cite

As a type of public land, park green spaces (PGSs) carry the daily recreation and social communication of urban residents, and the inequity of their space accessibility has been widely confirmed. However, the optimized suggestions based on evaluations of accessibility and equity in previous studies cannot guide actual planning effectively because the reasonable locations and scales of construction of PGSs were difficult to accurately identify. This study first constructed two equity objective functions: the minimum standard deviation (SD) and the minimum Gini coefficient (GC) in accessibility; then an improved two-step optimization method considering location-allocation was adopted to maximal accessibility equity (MAE) for optimizing the spatial layout of PGSs and doing comparisons of these two objectives for further analysis. The results showed that the improved method based on covering the accessibility blind area and preserving the existing PGSs could optimize the location selection and rationally determine the area. The two objective functions were both effective for optimization, but the GC minimization is more advantageous than the SD for achieving the MAE. The accessibility value increased significantly in a higher proportion of regions, and the overall accessibility median increased by 0.0445. It is worth mentioning that the MAE optimization would lead to a new imbalance between supply and demand in some regions. This indicated that the strictly restricted area standard may lead to the oversupply of PGSs in some areas, while the improvement of equity might not mean the improvement of accessibility. The proposed optimization framework could achieve the optimal layout of PGSs on the goal of MAE. Our findings also could provide inspiration for the equity allocation of other types of public facility lands and support decision-making for government departments regarding management and planning.

show abstract

“…To be consistent with previous studies [26,28,[30][31][32][33][34][35][36][37], four categories of variables were used in this research: (i) location characteristics reflecting the geographical advantages; (ii) neighborhood characteristics representing the surrounding environment; (iii) income characteristics affected by regional development and the strength of the economy; and (iv) population characteristics considering the health needs of various groups. The specific indicators are summarized in Table 1.…”

Section: Identification Of Determinants To Healthcare Accessibilitymentioning

confidence: 99%

“…The disparities in access to public services are affected by a variety of spatial (e.g., geographic location or surrounding environment) and non-spatial factors (e.g., socioeconomic and demographic characteristics) that are likely to limit healthcare opportunities, thereby leading to disparities in the equitable allocation [24][25][26][27][28][29][30]. The travel distance to public services depends on their geographic location and has a negative correlation with the accessibility [31].…”

Section: Introductionmentioning

confidence: 99%

Exploring Equity in a Hierarchical Medical Treatment System: A Focus on Determinants of Spatial Accessibility

Du,

Liu,

Luo

2023

IJGI

View full text Add to dashboard Cite

It is essential to understand the spatial equity of healthcare services to achieve the Sustainable Development Goals. Spatial and non-spatial factors affect access to healthcare, resulting in inequality in the hierarchical medical treatment system. Thus, to provide a comprehensive equity evaluation, it is indispensable to investigate the extent to which spatial accessibility to healthcare services varies due to various factors. This study attempted to analyze the determinants of healthcare accessibility under multi-trip modes and integrate them into Theil index, as a demand index to evaluate spatial equity in the system. The results reveal an inadequate and inequitable distribution of healthcare resources. While access to primary hospitals is limited (47.37% of residential locations cannot access them on foot), 96.58% of residential locations can access general and tertiary hospitals via public transport or driving. Furthermore, inequitable access to the three-tiered medical system was evaluated on a more granular scale, with primary hospitals being closest to achieving equity (inequitable for only 48.83% of residential locations), followed by general and tertiary hospitals (82.01% and 89.20%, respectively). The unequal residential locations brought on by an abundance of medical resources are far from those with a shortage of resources (66.86% > 5.34%). It is thus suggested that services be expanded or resources be transferred to move toward a more equitable system. Our findings provide policymakers with insights into how to increase accessibility to public health.

show abstract

Does the edge effect impact the healthcare equity? An examination of the equity in hospitals accessibility in the edge city in multi-scale

Cited by 14 publications

References 67 publications

Predictors of income level: A sociodemographic, social and contextual analysis

Predictors of income level: A sociodemographic, social and contextual analysis

Spatial Optimization of Park Green Spaces by an Improved Two-Step Optimization Model from the Perspective of Maximizing Accessibility Equity

Exploring Equity in a Hierarchical Medical Treatment System: A Focus on Determinants of Spatial Accessibility

Contact Info

Product

Resources

About