https://doi.org/10.4081/gh.2023.1175
Spatial analysis of the relationship between out-of-pocket expenditure and socioeconomic status in South Korea
Submitted: 25 November 2022
Accepted: 15 March 2023
Published: 25 May 2023
Accepted: 15 March 2023
Abstract Views: 764
PDF: 435
HTML: 21
HTML: 21
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The rapid increase in out-of-pocket expenditures regressively raises the issue of equity in medical access opportunities according to income class and negatively affects public health. Factors related to out-of-pocket expenses have been analyzed in previous studies using an ordinary regression model (Ordinary Least Squares [OLS]). However, as OLS assumes equal error variance, it does not consider spatial variation due to spatial heterogeneity and dependence. Accordingly, this study presents a spatial analysis of outpatient out-of-pocket expenses from 2015 to 2020, targeting 237 local governments nationwide, excluding islands and island regions. R (version 4.1.1) was used for statistical analysis, and QGIS (version 3.10.9), GWR4 (version 4.0.9), and Geoda (version 1.20.0.10) were used for the spatial analysis. As a result, in OLS, it was found that the aging rate and number of general hospitals, clinics, public health centers, and beds had a positive (+) significant effect on outpatient out-of-pocket expenses. The Geographically Weighted Regression (GWR) suggests regional differences exist concerning out-of-pocket payments. As a result of comparing the OLS and GWR models through the Adj. R² and Akaike’s Information Criterion indices, the GWR model showed a higher fit. This study provides public health professionals and policymakers with insights that could inform effective regional strategies for appropriate out-of-pocket cost management.
Akaike H, 1974. A new look at the statistical model identification. IEEE Trans. Automat. Contr. AC-19:716-23.
DOI: https://doi.org/10.1109/TAC.1974.1100705
An BK, 2011. Factors affecting cost-sharing charges for in-patients. Health Policy Manag 22:451-65.
DOI: https://doi.org/10.4332/KJHPA.2012.22.3.451
Anselin L, 1995. Local Indicators of Spatial Association (LISA). Geogr Anal 27:03-115.
DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin L, Bera AK, 1998. Spatial dependence in linear regression models with an introduction to spatial econometrics. Statistics Textbooks and Monographs 155:237-90.
Asante A, Price J, Hayen A, Jan S, Wiseman V, 2016. Equity in health care financing in low-and middle-income countries: a systematic review of evidence from studies using benefit and financing incidence analyses. PloS One 11:e0152866.
DOI: https://doi.org/10.1371/journal.pone.0152866
Bai P, Tang Y, Zhang W, Zeng M, 2021. Does economic policy uncertainty matter for healthcare expenditure in China? a spatial econometric analysis. Front Public Health 9:673778.
DOI: https://doi.org/10.3389/fpubh.2021.673778
Beck N, Gleditsch KS, Beardsley K, 2005. Space is more thangeography: Using spatial econometrics in the study of political economy.Int Stud Q 50:27-44.
DOI: https://doi.org/10.1111/j.1468-2478.2006.00391.x
Bose S, 2014. Determinants of per capita state-level health expenditures in the United States: a spatial panel approach. J Reg Anal Policy Forthcoming 45:93-107.
Cho I, 2010. A study on spatial effects in MAUP: with a focus on scale and zoning effects. Master Thesis, Korea National University of Education. Korea, 144.
Chou WL, Wang Z, 2009. Regional inequality in China’s health care expenditures. Health Econ 18:S137-S146.
DOI: https://doi.org/10.1002/hec.1511
Fotheringham AS, Oshan TM, 2016. Geographically weighted regression and multicollinearity: dispelling the myth. J Geo Syst 18:303-29.
DOI: https://doi.org/10.1007/s10109-016-0239-5
Hutcheson GD, 2011. Ordinary least-squares regression. In The SAGE Dictionary of Quantitative Management Research. L Moutinho and GD Hutcheson Eds; 224-8.
DOI: https://doi.org/10.4135/9781446251119.n67
Jarque CM, Bera AK, 1981. Efficient tests for normality, homoscedasticity and serial independence of regression residuals: Monte Carlo evidence. Econ Lett 7:313–318.
DOI: https://doi.org/10.1016/0165-1765(81)90035-5
Jo E-K, Lee K-S, 2014. Analysis on the regional variation of the rate of inpatient medical costs in local-out: Geographically weighted regression approach. Korean J Health Serv Manag 8:11-22.
DOI: https://doi.org/10.12811/kshsm.2014.8.2.011
Joo Y, Lee HY, 2013. Exploratory study of the relationship between regional environmental characteristics and regional mortality rates. J Korean Reg Sci Assoc 29:99-121.
Kim J-S, 2011. Increase in medical expenses for the elderly and effective management plan. Public Health Iss Focus 114:1-8.
Kim K, Shine E, Baek S, Choi Y, Jung K, 2011. A study on catastrophic user-paid expenditure attributes for patients in national health insurance. Korean J Health Econ Policy 17:75-99.
Kim S-G, Park W-S, Chung W-J, Yu S-H, 2005. Out-of-pocket health expenditures by non-elderly and elderly persons in Korea. J Prev Med Public Health 38:408-14.
Koenker R, Bassett Jr., G, 1978. Regression Quantiles.; Econometrica 46:33-50.
DOI: https://doi.org/10.2307/1913643
Laba TL, Usherwood T, Leeder S, Yusuf F, Gillespie J, Perkovic V, Wilson A, Jan S, Essue B, 2014. Co-payments for health care: what is their real cost? Aust Health Rev 39:33-36.
DOI: https://doi.org/10.1071/AH14087
Łyszczarz B, Abdi Z, 2021. Factors associated with out-of-pocket health expenditure in Polish Regions. Healthcare (Basel) 9:1750.
DOI: https://doi.org/10.3390/healthcare9121750
Malik AM, Syed SIA, 2012. Socio-economic determinants of household out-of-pocket payments on healthcare in Pakistan. Int J Equity Health 11:1-7.
DOI: https://doi.org/10.1186/1475-9276-11-51
Moran PA, 1950, Notes on continuous stochastic phenomena. Biometrika 37:17-23.
DOI: https://doi.org/10.1093/biomet/37.1-2.17
Mwale ML, Mchenga M, Chirwa GC, 2022. A spatial analysis of out-of-pocket payments for healthcare in Malawi. J Health Policy Plan 37:65-72.
DOI: https://doi.org/10.1093/heapol/czab090
Nakaya T, Fotheringham S, Charlton M, Brunsdon C, 2009. Semiparametric geographically weighted generalised linear modelling in GWR 4.0.
OECD. OECD Health Statistics. 2022.Health expenditure and financing (SHA). Available from: fromhttp://stats.oecd.org/Index.aspx?DataSetCode=SHA
Roh YH, 2013, Analysis on factors relating to external medical service use of health insurance patients using spatial regression analysis. Health Policy and Management 23:387-96.
DOI: https://doi.org/10.4332/KJHPA.2013.23.4.387
Schwartz SH, 1994. Are there universal aspects in the structure and contents of human values? J Soc Iss 50:19-45.
DOI: https://doi.org/10.1111/j.1540-4560.1994.tb01196.x
Tang X, Xie X, Rao Z, Zheng Z, Hu C, Li S, Hu Z, 2021. Spatial analysis and comparison of the economic burden of common diseases: Aninvestigation of 5.7 million rural elderly inpatients in Southeast China, 2010–2016. Front Public Health 17:774342.
DOI: https://doi.org/10.3389/fpubh.2021.774342
Yang J-S, 2007. Expenditure on medical care and ratio of medical care spending to consumption expenditure in elderly households. J Fam Better Life 25:1-13.
Yang S, Wang D, Xu L, Wang C, Yang X, Lo K, 2021. Private healthcare expenditure in China: aregional comparative analysis. Healthcare (Basel) 9:1374.
DOI: https://doi.org/10.3390/healthcare9101374
Zhang R, Li J, Du X, Ma T, Zhang L, Zhang Q, Xia F, 2019. What has driven the spatial spillover of China’s out-of-pocket payments? BMC Health Serv Res 19:1-12.
DOI: https://doi.org/10.1186/s12913-019-4451-0
How to Cite
Kwon, Y.-G., & Choi, M.-K. (2023). Spatial analysis of the relationship between out-of-pocket expenditure and socioeconomic status in South Korea. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1175
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Rita Roquette, Marco Painho, Baltazar Nunes, Spatial epidemiology of cancer: a review of data sources, methods and risk factors , Geospatial Health: Vol. 12 No. 1 (2017)
- Carlos Mena, Cesar Sepúlveda, Eduardo Fuentes, Yony Ormazábal, Iván Palomo, Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search , Geospatial Health: Vol. 13 No. 1 (2018)
- André Oliveira, Antònio J.R. Cabral, Jorge M. Mendes, Maria R.O. Martins, Pedro Cabral, Spatiotemporal analysis of the relationship between socioeconomic factors and stroke in the Portuguese mainland population under 65 years old , Geospatial Health: Vol. 10 No. 2 (2015)
- Addisu Jember Zeleke, Rossella Miglio, Pierpaolo Palumbo, Paolo Tubertini, Lorenzo Chiari, Bologna MODELS4COVID Study Group of the University of Bologna and the National Institute for Nuclear Physics (INFN), Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy , Geospatial Health: Vol. 17 No. 2 (2022)
- Ei Sandar U, Wongsa Laohasiriwong, Kittipong Sornlorm, Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand , Geospatial Health: Vol. 18 No. 1 (2023)
- Rutendo Birri Makota, Eustasius Musenge, Spatial heterogeneity in relationship between district patterns of HIV incidence and covariates in Zimbabwe: a multi-scale geographically weighted regression analysis , Geospatial Health: Vol. 18 No. 2 (2023)
- Amanda G. Carvalho, Carolina Lorraine H. Dias, David J. Blok, Eliane Ignotti, João Gabriel G. Luz, Intra-urban differences underlying leprosy spatial distribution in central Brazil: geospatial techniques as potential tools for surveillance , Geospatial Health: Vol. 18 No. 2 (2023)
- Gilbert Nduwayezu, Pengxiang Zhao, Clarisse Kagoyire, Lina Eklund, Jean Pierre Bizimana, Petter Pilesjo, Ali Mansourian, Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda , Geospatial Health: Vol. 18 No. 1 (2023)
- Zhi-Min Hong, Hu-Hu Wang, Yan-Juan Wang, Wen-Rui Wang, Spatiotemporal analysis of hand, foot and mouth disease data using time-lag geographically-weighted regression , Geospatial Health: Vol. 15 No. 2 (2020)
- Sue C. Grady, April N. Frake, Qiong Zhang, Matlhogonolo Bene, Demetrice R. Jordan, Joshua Vertalka, Thania C. Dossantos, Ameen Kadhim, Judith Namanya, Lisa-Marie Pierre, Yi Fan, Peiling Zhou, Fatoumata B. Barry, Libbey Kutch, Neonatal mortality in East Africa and West Africa: a geographic analysis of district-level demographic and health survey data , Geospatial Health: Vol. 12 No. 1 (2017)
You may also start an advanced similarity search for this article.
