Spatial associations between chronic kidney disease and socio-economic factors in Thailand

Submitted: 21 October 2023
Accepted: 14 January 2024
Published: 30 January 2024
Abstract Views: 4442
PDF: 273
HTML: 31
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.


Chronic kidney disease (CKD) is a persistent, progressive condition characterized by gradual decline of kidney functions leading to a range of health issues. This research used recent data from the Ministry of Public Health in Thailand and applied spatial regression and local indicators of spatial association (LISA) to examine the spatial associations with night-time light, Internet access and the local number of health personnel per population. Univariate Moran’s I scatter plot for CKD in Thailand’s provinces revealed a significant positive spatial autocorrelation with a value of 0.393. High-High (HH) CKD clusters were found to be predominantly located in the North, with Low-Low (LL) ones in the South. The LISA analysis identified one HH and one LL with regard to Internet access, 15 HH and five LL clusters related to night-time light and eight HH and five LL clusters associated with the number of health personnel in the area. Spatial regression unveiled significant and meaningful connections between various factors and CKD in Thailand. Night-time light displayed a positive association with CKD in both the spatial error model (SEM) and the spatial lag model (SLM), with coefficients of 3.356 and 2.999, respectively. Conversely, Internet access exhibited corresponding negative CKD associations with a SEM coefficient of - 0.035 and a SLM one of -0.039. Similarly, the health staff/population ratio also demonstrated negative associations with SEM and SLM, with coefficients of -0.033 and -0.068, respectively. SEM emerged as the most suitable spatial regression model with 54.8% according to R2. Also, the Akaike information criterion (AIC) test indicated a better performance for this model, resulting in 697.148 and 698.198 for SEM and SLM, respectively. These findings emphasize the complex interconnection between factors contributing to the prevalence of CKD in Thailand and suggest that socioeconomic and health service factors are significant contributing factors. Addressing this issue will necessitate concentrated efforts to enhance access to health services, especially in urban areas experiencing rapid economic growth.



PlumX Metrics


Download data is not yet available.


Akaike H, 1981. Likelihood of a model and information criteria. J Econom 16:3-14. DOI:
Anselin L, 1995. Local Indicators of Spatial Association—LISA. Geogr Anal 27:93-115. DOI:
Anselin L, 2001. Spatial Effects in Econometric Practice in Environmental and Resource Economics. Am J Agric Econ 83:705-710. DOI:
Anselin L, 2022. Spatial econometrics: Chapter 6, pp. 101-122, in Handbook of Spatial Analysis in the Social Sciences. Edward Elgar Publishing, Cheltenham, UK. DOI:
Bello AK, Peters J, Rigby J, Rahman AA, El Nahas M. 2008. Socioeconomic status and chronic kidney disease at presentation to a renal service in the United Kingdom. Clin J Am Soc Nephro 3:1316-23. DOI:
Bonner A, Gillespie K, Campbell KL, Corones-Watkins K, Hayes B, Harvie B, Kelly JT, Havas K, 2018. Evaluating the prevalence and opportunity for technology use in chronic kidney disease patients: a cross-sectional study. BMC Nephrol 19:28. DOI:
Cha'on U, Tippayawat P, Sae-Ung N, Pinlaor P, Sirithanaphol W, Theeranut A, Tungsanga K, Chowchuen P, Sharma A, Boonlakron S, Anutrakulchai S, 2022. High prevalence of chronic kidney disease and its related risk factors in rural areas of Northeast Thailand. Scic Rep 12:18188. DOI:
Elhorst J, 2010. Applied spatial econometrics: raising the bar. Spat Econ Anal 5:9-28. DOI:
Ghelichi-Ghojogh M, Fararouei M, Seif M, Pakfetrat M, 2022. Chronic kidney disease and its health-related factors: a case-control study. BMC Nephrol 23:24. DOI:
Griffith DA. 1983. Book review: Cliff, A. D. and Ord, J. K. 1981: Spatial processes - models and applications. London: Pion. Progress in Human Geography, 7(1), 149-150. DOI:
Kazancioğlu R, 2013. Risk factors for chronic kidney disease: an update. Kidney Int Suppl 3:368-371. DOI:
Ke C, Liang J, Liu M, Liu S, Wang C, 2022. Burden of chronic kidney disease and its risk-attributable burden in 137 low-and middle-income countries, 1990–2019: results from the global burden of disease study 2019. BMC Nephrol 23:17. DOI:
Kernbach ME, Hall RJ, Burkett-Cadena ND, Unnasch TR, Martin LB, 2018. Dim light at night: physiological effects and ecological consequences for infectious disease. Integr Comp Biol 58:995-1007. DOI:
Kovesdy CP, 2022. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl 12:7-11. DOI:
Lee LF, Yu J, 2015. Estimation of fixed effects panel regression models with separable and nonseparable space–time filters. J Econom 184:174-192. DOI:
Ministry of Public Health, 2021. Mortality rate of patients with severe septicemia community-acquired. Available from:
Ministry of Public Health, 2023. 2. Health Archives. Usage of public health services. Accessed: 28 Nov 2023. Available from:
Muleta S, Melaku T, Chelkeba L, Assefa D, 2017. Blood pressure control and its determinants among diabetes mellitus co-morbid hypertensive patients at Jimma University medical center, South West Ethiopia. Clin Hypertens 23:29. DOI:
Noels H, Jankowski J, 2023. Increased Risk of Cardiovascular Complications in Chronic Kidney Disease: Introduction to a Compendium. Circ Res 132:899-901. DOI:
Pacheco AI, Tyrrell TJ, 2002. Testing spatial patterns and growth spillover effects in clusters of cities. J Geogr Syst 4:275-85. DOI:
Pollock C, James G, Garcia Sanchez JJ, Carrero JJ, Arnold M, Lam CSP, Chen HT, Nolan S, Pecoits-Filho R, Wheeler DC, 2022. Healthcare resource utilisation and related costs of patients with CKD from the UK: a report from the DISCOVER CKD retrospective cohort. Clin Kidney J 15:2124-34. DOI:
Radford J, Kitsos A, Stankovich J, Castelino R, Khanam M, Jose M, Peterson G, Saunder T, Wimmer B, Razizaidi T, 2019. Epidemiology of chronic kidney disease in Australian general practice: National Prescribing Service MedicineWise MedicineInsight dataset. Nephrology (Carlton) 24:1017-25. DOI:
Ramakrishnan C, Tan NC, Yoon S, Hwang SJ, Foo MWY, Paulpandi M, Gun SY, Lee JY, Chang ZY, Jafar TH, 2022. Healthcare professionals' perspectives on facilitators of and barriers to CKD management in primary care: a qualitative study in Singapore clinics. BMC Health Serv Res 22:560. DOI:
Sun H, Qin K, Zou C, Wang HH, Lu C, Chen W, Guo VY, 2021. The association of nighttime sleep duration and quality with chronic kidney disease in middle-aged and older Chinese: a cohort study. Sleep Med 86:25-31. DOI:
Thailand Board of Investment, 2022. Thailand in brief. Accessed: 24Nov2023. Available from:
Thailand National Statistical Office, 2023.Thailand in brief. Accessed:4 Aug2023. Available from:
Tsai M-C, Lojanapiwat B, Chang C-C, Noppakun K, Khumrin P, Li S-H, Lee C-Y, Lee H-C, Khwanngern K, 2023. Risk Prediction Model for Chronic Kidney Disease in Thailand Using Artificial Intelligence and SHAP. Diagnostics (Basel) 13:3548. DOI:
Vallianou NG, Mitesh S, Gkogkou A, Geladari E, 2019. Chronic Kidney Disease and Cardiovascular Disease: Is there Any Relationship? Curr Cardiol Rev 15:55-63. DOI:
Viton PA, 2010. Notes on spatial econometric models. City Reg Plan 870.03.
Zeng X, Liu J, Tao S, Hong HG, Li Y, Fu P, 2018. Associations between socioeconomic status and chronic kidney disease: a meta-analysis. J Epidemiol Community Health 72:270-9. DOI:

How to Cite

Sansuk, J., & Sornlorm, K. (2024). Spatial associations between chronic kidney disease and socio-economic factors in Thailand. Geospatial Health, 19(1).