Measuring spatial accessibility and within-province disparities in accessibility to county hospitals in Shaanxi Province of Western China based on web mapping navigation data

Spatial accessibility modeling to healthcare facilities in the case of health shocks of Midnapore municipality, India

This research aims to identify the accessibility of the entire population, especially the slum population to existing healthcare facilities (HCF) as well as the slum neighborhoods having low geographic accessibility, and finally, to provide an analytical model for the people living in areas that are outside the coverage range of existing healthcare facilities (HCF) across the study area. Spatial data has been collected and used based on the road network, elevation, location of HCF, municipal boundary, slum point, and satellite images from various sources. Also, non-spatial data such as socioeconomic variables are collected from questionnaires survey within a particular period. The spatial analysis tool like as near, network analysis, and predictive analysis in the ArcGIS platform was used to examine geographic accessibility. The results of the spatial analysis show that the distribution of public healthcare facility centers in the study area has not been uniformly distributed. Across 84% of areas in the study area have sound spatial accessibility with traveling time coverage is about 12 min. However, 16% of areas have a traveling time of 12 to 30 min under low accessibility with existing slum neighborhoods. Therefore, the low spatial accessibility areas are demanding new healthcare facilities in the study area. The Analytical Hierarchy Process (AHP) is employed to find the most optimal and efficient locational suitability for building new healthcare facility centers. The finding of AHP analysis for site suitability of healthcare facilities revealed five major classes as most suitable (2%), suitable (5%), moderate (35%), poor (54%), and very poor (4%) in the study area. Moreover, the realistic framework of this study helps to measure geographic accessibility and suitability in any geographical area.

In-depth look into urban and rural disparities in prehospital delay in patients with acute ST-elevation myocardial infarction and its impact on prognosis: a prospective observational study

OBJECTIVES

In line with the cardiac fast track, the 'green pathway for patients with heart attack' policy in China is implemented to reduce door-to-balloon time in patients with ST-segment elevation myocardial infarction (STEMI). However, the difference in prehospital delay between urban and rural areas of China and its impact on prognosis is unclear.

DESIGN

Prospective observational study.

SETTING

This study was conducted in a tertiary hospital, the only nationally accredited chest pain centre with percutaneous coronary intervention (PCI) capacity in Pizhou, China.

PARTICIPANTS

394 patients with STEMI without patients with in-hospital STEMI or patients lost to follow-up were included.

PRIMARY OUTCOME MEASURES

Primary outcome was major adverse cardiovascular events (MACEs), including cardiac death, non-fatal myocardial infarction and heart failure.

RESULTS

Among 394 patients enrolled, 261 (66.2%) were men, the median age was 69 years (interquartile range: 61-77 years), and 269 (68.3%) were from rural areas. Symptom-to-door (S2D) time was significantly longer for rural patients than for urban patients (p<0.001). Cox regression analyses revealed living in rural areas was independently associated with prolonged S2D time (adjusted HR 0.59; 95% CI 0.43 to 0.81; p=0.001). HR of <1 indicates that the S2D time is longer for patients in the rural group (group of interest). During 1-year follow-up, the incidence of MACEs was higher in rural patients (p=0.008). The unadjusted OR for MACEs between rural and urban patients was 2.22 (95% CI 1.22 to 4.01). Adjusting for sex did not attenuate the association (OR 2.06; 95% CI 1.13 to 3.76), but after further adjusting for age, cardiac function classification, S2D time and performance of primary PCI, we found that odds were similar for rural and urban patients (OR 1.19; 95% CI 0.59 to 2.38).

CONCLUSIONS

Rural patients with STEMI had a longer S2D time, which led to a higher incidence of MACEs. This study provides rationales for taking all the measures to avoid prehospital delay.

Facility or Transport Inequality? Decomposing Healthcare Accessibility Inequality in Shenzhen, China

Accessibility to healthcare services is crucial for residents’ wellbeing. Numerous studies have revealed significant spatial inequality in healthcare accessibility across various contexts. However, it still remains unclear whether the inequality is caused by the unbalanced spatial distribution of healthcare facilities or by unequal transport access to them. This study decomposes inequality in healthcare accessibility into facility- and transport-driven inequality by comparing scenarios of healthcare accessibility, which consider various combinations of multidimensional components of accessibility using different distance measures. Using a case study in Shenzhen, this study reveals that both facility distribution and transport access substantially contribute to spatial inequality in healthcare accessibility. Facility distribution accounts for 61.3% and 50.8% of the overall accessibility inequality for driving and transit modes, respectively. The remaining inequality is induced by imbalanced mobility provided by transport networks. Furthermore, the impact of transport component on healthcare accessibility is unevenly distributed. This study highlights that both facility- and transport-related countermeasures should be considered to improve the accessibility and equality of healthcare services. It provides transferable methods for quantitatively decomposing facility- and transport-driven inequality in accessibility to healthcare or other facilities.