Disparity in the spatial distribution of clinics within a metropolitan city

Lee, Kwang-Soo

doi:10.4081/gh.2013.80

Cited by 11 publications

(11 citation statements)

References 21 publications

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“…For example, the spatial disparity between physicians in hospitals and clinics and the population in Japan was estimated by Lorenz curves and Gini coefficients [5]. Disparity in the spatial distribution of clinics within the city of Daejeon was analysed by hot-spot analysis [6]. Geographic distribution of healthcare resources in China was estimated by dynamic convergence model [7].…”

Section: Introductionmentioning

confidence: 99%

“…Socioeconomic factors such as the economy [7], healthcare investment [12], education [13], government policies [1,14], urbanisation [15] and demographic characteristics [16] are considered important determinants of healthcare resource distribution. For example, Lee pointed out that the population proportion aged over 65 years, the number of businesses and employees contribute to heterogeneity in the spatial distribution of clinics [6]. Qin and Hsieh found that GDP per capita has a significant and non-linear impact on the convergence rate of healthcare resources [7].…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

Song

Wei

Deng

et al. 2019

IJERPH

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In China, upper-level healthcare (ULHC) and lower-level healthcare (LLHC) provide different public medical and health services. Only when these two levels of healthcare resources are distributed equally and synergistically can the public’s demands for healthcare be met fairly. Despite a number of previous studies having analysed the spatial distribution of healthcare and its determinants, few have evaluated the differences in spatial equity between ULHC and LLHC and investigated their institutional, geographical and socioeconomic influences and spillover effects. This study aims to bridge this gap by analysing panel data on the two levels of healthcare resources in 31 Chinese provinces covering the period 2003–2015 using Moran’s I models and dynamic spatial Durbin panel models (DSDMs). The results indicate that, over the study period, although both levels of healthcare resources improved considerably in all regions, spatial disparities were large. The spatio-temporal characteristics of ULHC and LLHC differed, although both levels were relatively low to the north-west of the Hu Huanyong Line. DSDM analysis revealed direct and indirect effects at both short-and long-term scales for both levels of healthcare resources. Meanwhile, the influencing factors had different impacts on the different levels of healthcare resources. In general, long-term effects were greater for ULHC and short-term effects were greater for LLHC. The spillover effects of ULHC were more significant than those of LLHC. More specifically, industrial structure, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of ULHC, while industrial structure, urbanisation, topography, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of LLHC. These findings have important implications for policymakers seeking to optimize the availability of the two levels of healthcare resources.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

Song

Wei

Deng

et al. 2019

IJERPH

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“…The geographical accessibility of hospitals is an important issue affecting the use of hospital services (Love and Lindquist, 1995;Lee, 2013). Traffic convenience has been considered in decisions about the location of dialysis facilities (Eben-Chaime and Pliskin, 1992) and Graham (1984) considers labour force and its mobility important factors in planning activities.…”

Section: Mobilitymentioning

confidence: 99%

“…Mobility was measured by four variables: "number of subway entrances", "road area" (m 2 ) and two road types, i.e. "highways and major arterial roads" along with "minor arterials and side roads" since a higher number of subway entrances near a hospital and larger road areas are known to increase accessibility (Lee, 2013). The housing factor was measured using the area in m 2 of three building variables: "apartment", "row house" and "detached house".…”

Section: Analytical Modelmentioning

confidence: 99%

Hospital distribution in a metropolitan city: assessment by a geographical information system grid modelling approach

Lee

Moon

2014

Geospat Health

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Abstract. Grid models were used to assess urban hospital distribution in Seoul, the capital of South Korea. A geographical information system (GIS) based analytical model was developed and applied to assess the situation in a metropolitan area with a population exceeding 10 million. Secondary data for this analysis were obtained from multiple sources: the Korean Statistical Information Service, the Korean Hospital Association and the Statistical Geographical Information System. A grid of cells measuring 1 × 1 km was superimposed on the city map and a set of variables related to population, economy, mobility and housing were identified and measured for each cell. Socio-demographic variables were included to reflect the characteristics of each area. Analytical models were then developed using GIS software with the number of hospitals as the dependent variable. Applying multiple linear regression and geographically weighted regression models, three factors (highway and major arterial road areas; number of subway entrances; and row house areas) were statistically significant in explaining the variance of hospital distribution for each cell. The overall results show that GIS is a useful tool for analysing and understanding location strategies. This approach appears a useful source of information for decision-makers concerned with the distribution of hospitals and other health care centres in a city.

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“…This result could be explained as follows. First, in South Korea, large hospitals and healthcare facilities are concentrated in the capital area 32. Therefore, other areas have relatively insufficient facilities and thus offer a relatively lower quality of care.…”

Section: Discussionmentioning

confidence: 99%

Patient and Hospital Factors associated with 30-Day Unplanned Readmission in Patients with Stroke

Lee

Park

Shin

et al. 2019

Journal of Investigative Medicine

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Stroke is frequently associated with readmission; moreover, readmission is regarded as an important indicator of the quality of stroke care. Thus, we investigated factors associated with 30-day readmission in patients with stroke in South Korea. We used claims data from 2013 for stroke (I60-I62) patients (n=44 729) in 94 hospitals and classified unplanned readmission according to the Centers for Medicare and Medicaid guidelines. We used multilevel models to investigate patient (age, gender, type of insurance, admission via emergency room, length of stay, type of stroke, Elixhauser Index Score) and hospital (stroke care quality grade, location of hospital, type of hospital, number of doctors and nurses per 100 beds) factors associated with readmission within 30 days of discharge. Among the 44 729 patients admitted due to stroke, 9.2% (n=4124) were readmitted to hospital and 7.6% (n=3379) had unplanned readmissions. Regarding patient characteristics, medical aid and longer hospital stay were associated with 30-day readmission rate. Among hospital factors, patients admitted to a low-grade hospital or a non-capital area hospital were more likely to be readmitted within 30 days of discharge. We identified patient and hospital factors associated with 30-day readmission among stroke patients. In particular, patients admitted to hospitals with higher quality stroke care showed lower readmission rates.

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Disparity in the spatial distribution of clinics within a metropolitan city

Cited by 11 publications

References 21 publications

Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

Hospital distribution in a metropolitan city: assessment by a geographical information system grid modelling approach

Patient and Hospital Factors associated with 30-Day Unplanned Readmission in Patients with Stroke

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