Dynamics of Renal Replacement Therapy in Thailand

Dynamics of Renal Replacement Therapy in Thailand | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamics of Renal Replacement Therapy in Thailand Arinda Ma-a-lee, Tippawan Liabsuetrakul, Jutatip Thungthong, Hayato Yamana, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8044765/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Renal replacement therapy (RRT) for end-stage kidney disease (ESKD) is major public health spending in Thailand, which has been changed by policy and disease contributions. Objectives : This study aims to investigate the dynamics of RRT among Thai ESKD patients, and identify factors associated with the transition of the mode of therapy and mortality. Methods : Data were obtained from the national ESKD registry, documenting all RRT events from 2015 to 2019, and the national death registry. Baseline patient characteristics were summarized using descriptive statistics. A Markov model was used to evaluate RRT modality transition rates, while logistic regression identified factors associated with changes in health states. Results : Most patients were female (51.2%) and aged 50–69 years (57.8%) at RRT initiation. RRT use increased steadily, with CAPD being the most common initial modality (67.0%), followed by HD (31.9%) and KT (1.1%). Most had short inter-transit periods of 0–6 months (82.3%). RRT rates were highest in the northeastern region. CAPD had the highest transition rate (46.5%), followed by death (26.4%), HD (26.1%), and KT (1.1%). The probability of remaining on CAPD ranged from 63.6% to 69.6%, and for HD from 76.1% to 79.1%, while KT had the lowest transition. Multivariate analysis showed that younger patients and those who recently initiated RRT had higher transition rates to HD, with a peak in 2017 and the highest rate in Bangkok. In contrast, predictors of mortality followed the opposite pattern: older age and longer duration since RRT initiation were associated with higher mortality, while residents of Bangkok had the lowest mortality rates. Conclusion : During the study period, RRT use in Thailand increased steadily. The CAPD-first policy appears effective, as relatively few patients shifted to HD over time, though KT rates remained low. Despite limited clinical data, geographical disparities were evident, with higher HD use and lower mortality among Bangkok residents. These findings highlight the need for improved planning and implementation of RRT services in Thailand. End stage kidney disease Renal replacement theraphy Modality transition in RRT Figures Figure 1 Figure 2 Figure 3 Introduction Globally, Renal replacement therapy (RRT) for end-stage kidney disease (ESKD) is indeed a significant component of public health spending. Long-term provision of RRT, including peritoneal dialysis (PD), hemodialysis (HD) and kidney transplantation (KT), are of high costs [ 1 ]. Inequality in access to RRT is a social problem. A systematic review reported that in 2010, 9.7 million patients needed RRT but only 2.6 million could get the service [ 2 ]. Thailand is one of the Southeast Asian countries with a high prevalence of ESKD. Prior to the Universal Health Coverage (UHC) period, the annual incidence of ESKD was estimated to be 121.9 to 158.9 per million population in 2004 and 2007, respectively [ 3 ]. In October 2007, the UHC fund launched the first RRT reimbursement plan the “Peritoneal Dialysis-First” (PD First), and then extended to kidney transplantation [ 4 ]. The number of kidney replacement cases in Thailand has climbed from 21,839 in 2007 to 164,191 in 2020, while PD proportion increased from 5.5% to 21.0% in dialysis patients [ 5 ]. Assessing the rate and factors associated with health state transition at RRT is useful for healthcare planners and providers to plan the future need for an adequate dialysis modality to improve the long-term quality of life of ESKD patients. Therefore, this study aimed to investigate the baseline factors of ESKD patients at the initiated modalities, assess rates of modality transition from initiated modalities to the next health states, and identify factors associated with health state outcomes. Material and methods Study design A retrospective cohort study was conducted in Thailand using the national services RRT program database, aimed to assess the modality transition rate at RRT and its associated factors. Data source and management Two data sources the National Health Security Office (NHSO) were used in this study namely ESKD/RRT registry and death registry. The ESKD registry includes patients and RRT treatment modality during 2015 to 2019. Variables included encrypted patient's ID, gender (males and females), year of birth, 13 health regions (HR1 to HR13), starting date of dialysis, initiated CKD mode (CAPD, HD, and KT), and dates of the changes in modality and death. Altogether there were 58,077 unique cases. Data analysis The data set was cleaned. RRT modality yearly transition matrices and the patients’ age at the initation and each state of RRT state were computed. Descriptive statistics was used to describe the characteristics of the patients at RRT initiated. Markov model [ 6 – 7 ] was employed to analyse the modality transition probability matrix [ 8 ]. It assumes that a patient is always in one of a finite number of discrete health states, and all events of interest are modeled as transitions from one state to another. Four-states Markov model A Markov model is, essentially, a matrix of state transition probabilities [ 6 ]. The health status of transition in this study according to the Markov model is shown in Fig. 1 . Figure 1 shows a Markov-state diagram. There are 12 possible transitions. Death is the only absorptive state. For the reasons of completeness of the data, we confined the transition years to 2016, 2017, 2018, and 2019. Associated factors with a transitioning health states Multiple logistic regression was employed to identify factors associated with each transitioning health states. In this report, we focused on transtions of HD (the most long-term costly RRT), and death (the worst health state). A receiver operating characteristic (ROC) and its area under the curve (AUC) were computed to evaluate the model's ability to distinguish between transitioning health states and others [ 9 ]. All statistical analyses were performed using the R program [ 10 ]. Results 1. Descriptive results 1.1 The characteristics of the patients at RRT initiated The characteristics of the ESKD patients (58,077 cases) who started to receive the modality treatment at RRT from 2015 to 2019, as shown in Table 1 . Table 1: Characteristics of the patients at RRT initiated (n=58,077) Factors Frequency (%) Gender Male Female 28,315 (48.8) 29,762 (51.2) Age groups (year) 0-39 40-49 50-59 60-69 70+ 6,194 (10.7) 8,198 (14.1) 15,458 (26.6) 18,097 (31.2) 10,126(17.4) Starting year of dialysis 2015 2016 2017 2018 2019 11,566 (19.9) 11,071 (19.1) 11,324 (19.5) 12,270 (21.1) 11,846 (20.4) Starting CKD mode 1: CAPD 38,905 (67.0) 2: HD 18,522 (31.9) 3: KT 650 (1.1) Time receiving until transition (months) 1:0-6 47,795 (82.3) 2:>6-12 5,629 (9.7) 3:>12-18 3,574 (6.2) 4:>18-24 1,079 (1.9) Health region (HR)* 1:HR1(Northern) 7368(1817.1) 2:HR2(Northern) 2597(1010.0) 3:HR3(Northern) 2152(970.5) 4:HR4(Central) 5141(1487.0) 5:HR5(Central) 4687(1235.9) 6:HR6(Central) 4933(1200.0) 7:HR7(Northeasthern) 4132(1124.8) 8:HR8(Northeasthern) 4560(1076.3) 9:HR9(Northeasthern) 5351(1077.0) 10:HR10(Northeasthern) 4932(1444.2) 11:HR11(Southern) 3850(1101.0) 12:HR12(Southern) 2369(590.3) 13:HR13(BKK) 6004(1572.1) *cases per 1,000,000 population Table 1 shows the characteristics of the patients. Most of the patients were female (51.2%), starting the RRT at the age between 50 and 69 years (57.8%). The RRTs increased gradually throughout the study period. Around two thirds of patients started dialysis with CAPD (67.0%), followed by HD (31.9%), and KT (1.1%). Most of the patients had a relatively short inter-transit period of 0-6 months (82.3%). The receiving RRT rate per 1,000,000 population in different regions showed that the highest rate was found in HR1 (1,817.1) in the northern part, followed by HR13 (1,572.1) in Bangkok, HR4 (1,487.0) in the central part, and HR10 (1,444.2) in the northeastern part. The lower south region of HR12 had the lowest rate of receiving RRT (590.3). 1.2 Distribution of transitioning health states (outcome) at RRT initiated All of the patients who received modality at RRT initiatiated had a single transitioning health states as shown in table 2. Table 2: Distribution of a transitioning health states Transitioning heath states Frequency (%) 1:CAPD 26,980(46.5) 2:HD 15,141(26.1) 3:KT 629(1.1) 4:Death 15,327(26.4) Table 2 showed the health state of a single transition for the patients who initiated modalities at RRT. The highest rate of the transitioning health state was found for CAPD (46.5%), followed by death (26.4%), HD (26.1%), and KT (1.1%), respectively. 2. A single transitioning health states probability distribution at RRT The rate of initial CKD mode among ESKD patients for each starting year as presented in first column in table 3, revealing that between 2015 and 2019, CAPD initiation increased at a rate of 63.6% to 69.6%, whereas HD and KT decreased at rates of 34.8% to 29.8% and 1.6% to 0.7%, respectively. Based on Markov approach, a transition rate from initial CKD mode to the next health states within a 2-year cycle among patients at RRT was also shown in Table 3. Table 3: Observed cases and transition probability matrix with row sum equal 1 Initial CKD mode: n(%) Observed cases ( n ), next health transition (probability P* ) 1:CAPD n ( P ) 2:HD n ( P ) 3:KT n ( P ) 4:Death n ( P ) 2015-2016 1.CAPD: 7354(63.6) 4942(0.672) 119(0.016) 0(0) 2293(0.312) 2.HD: 4022(34.8) 43(0.011) 3101(0.771) 0(0) 878(0.218) 3.KT: 190(1.6) 0(0) 0(0) 186(0.979) 4(0.021) 4.Death: 0(0) 0 0 0 1 2016-2017 1.CAPD: 7372(66.6) 5034(0.683) 142(0.019) 0(0) 2196(0.298) 2.HD: 3549(32.1) 36(0.010) 2806(0.791) 1(<0.001) 706(0.199) 3.KT: 150(1.4) 0(0) 1(0.007) 143(0.953) 6(0.040) 4.Death: 0(0) 0 0 0 1 2017-2018 1.CAPD: 7605(67.2) 5208(0.685) 171(0.022) 0(0) 2226(0.293) 2.HD: 126(1.1) 49(0.014) 2816(0.784) 0(0) 728(0.203) 3.KT: 126(1.1) 0(0) 0(0) 123(0.976) 3(0.024) 4.Death: 0(0) 0 0 0 1 2018-2019 1.CAPD: 8332(67.9) 5795(0.696) 168(0.020) 1(<0.001) 2368(0.284) 2.HD: 3832(31.2) 69(0.018) 2917(0.761) 1(<0.001) 845(0.221) 3.KT: 106(0.9) 0(0) 0(0) 103(0.910) 3(0.077) 4.Death: 0(0) 0 0 0 1 2019-2020 1.CAPD: 8242(69.6) 5757(0.698) 193(0.023) 0(0) 2292(0.278) 2.HD: 3526(29.8) 46(0.013) 2707(0.768) 0(0) 773(0.219) 3.KT: 78(0.7) 1(0.013) 0(0) 71(0.910) 6(0.077) 4.Death: 0(0) 0 0 0 1 * The row sum of the probability equal 1 Table 3 showed an observed number (n) of patients at RRT and transition probabilities (P) matrix from 2015 to 2019. Cells in the diagonal line from left upper to right lower are the values of remaining in the same state during the transit period. For CAPD initation group, the percentage of remaining in CAPD range from 67.2% (2015) to 69.8% (2019). The corresponding probability for HD ranged between 76.1 and 79.1%. The KT group had the least transition rate. Transition from CAPD to HD slowly increased from 1.6% in 2015 to 2.3% in 2019. The reverse direction was in a much lower range, varying between 1.0% to 1.8%. As afored mentioned, very few CAPD (1) and HD (2) patients finally received KT. Transition probability from CAPD to death (ranging from 27.8% to 31.2% ) was generally higher than that from HD (19.9% to 22.1%). The KT group has the lowest but increasing, transition rate to death from 2.1% in 2015 to 7.7% in 2019. 3. Factors associated with a transitioning health state after initiating modalities at RRT based on logistic regression 3.1 Transition from all initiated modalities to HD state The rate of transition from all initial mordality to HD was 26.1%. Figure 2 showed that transition rate to HD was higher in young age group, having recent RRT initiation. The rate peaked in 2017 and was highest in the capital city (Bangkok). The area under the curve (AUC) is 0.945 indicating that the model performance was excellent predicted. 3.2 Transition from all initiated modality to death (absorbing state) The rate of transition from all three initial mordality to death was 26.4%. Figure 3 showed that predictor of mortality is in the opposite direction to those of transition to HD. As expected, mortality rate increased with age and duration after the initiation of RRT. Those residing in Bangkok has the lowest mortality. The area under the curve (AUC) is 0.824 indicating that the model performance was well predicted. Discussion Our results show that the RRT services steadily increased over time with somewhat decline in mortality rates. CAPD was the most commonly the starting modality of RRT throughout the study period. Yearly probability of shifting to HD was relatively small. More important is the high mortality rate in all RRT modalities. After adjustment for important confounders such as age, sex and duration since starting RRT, shifting to HD and mortality rates were geographically inequitably distributed. The residents of Bangkok has better chance to transit to HD and lower risk for mortality. Rising number of RRT over the study period may be combined effects of demographic and epidemiologic transition and the country health finance policy. Demographic transition in this case is the rapid rising of the elder population following the past increase in number of birth over the preceding half century, and the decline in age-specific mortality rates (thus prolonged life expenctancy at the middle age). Epidemiologic transition or changing in environment and lifestyle over the years has resulted in more metabolic syndrome which eventually leads to more ESKD cases. The policy to have universal coverage of RRT to all ESKD patients results in prolonged survival and increases of cumulative ESKD cases. All RRT modalities are costly. The most cost-effective modality is KT [7]. Yet the number of patients receiving this service is scanty. CAPD is basically long-term home based procedure. CAPD first policy was launched because time and financial burden on the health systems could be reduced substantially compared to HD [11]. In long-term shifting from CAPD to HD is often inevitable. The indications include mechanical and physiological failures and infection [12- 13]. As seen in our data, this shifting increased in probability as CAPD use was prolonged. Probability of shifting from CAPD to HD had no evidence of sex predilection but it was more common in young patients. The fact that the elder were less likely to be shifted to HD might be explained by differential of age preference for the young patients and/or inequality in ability to get access to HD of the elder. HD is usually an institute-based service. Yong patients can be less dependent on care-takers and easier to transport from home compared to the elderly patients. More such shifting in the capital city than in other health region after adjustment for known risk factors clearly show geographical inequity of RRT services. The number of private HD services is higher in Bangkok than in other health regions. This may be due to inequitable practices among the stakeholders. Mortality rate of the CAPD users was higher than that of the HD group after adjustment for age, sex and other available variables. This may be due to higher health risk imposed by CAPD than that by HD. On the other hand, there may be differential selection to put patients with serious comorbidity into the CAPD. Our current data had no information on this complex clinical setting and thus cannot derive a good explantion of difference in the mortality rates of different RRT modality Finally, mortality rate in Bangkok was clearly better than those in the remaining health regions. Again, this can be explained by better baseline conditions of ESKD patients and/or better general health care in the capital city. Conclusion In the study period, the use of RRT services in Thailand increase steadily. The policy to keep CAPD as priority could be considered successful due to only small percentage of shifting toward HD use over time. KT rate was however still low. Despite the lack of important clinical background information in the analysis, geographical inequity was evidenced by higher use of HD and lower mortality rate among the residents of the capital city. These problems require improvement in planning and implementation of RRT services in Thailand. Abbreviations ESKD End stage kidney disease RRT Renal replacement therapy CAPD Continuous Ambulatory Peritoneal Dialysis HD Hemodialysis KT Kidney Transplant HR Heath region UHC Universal Health Coverage NHSO National Health Security Office ROC Receiver operating characteristic AUC Area under the curve Declarations 7. Acknowledgements We extend our sincere gratitude to the Japan International Cooperation Agency for their generous support through “The Partnership Project for Global Health and Universal Health Coverage Phase 2 (GLO + UHC Phase 2).” This collaboration has played a pivotal role in enhancing data utility for the advancement of universal health coverage between Thailand and Japan. We also extend our appreciation to the Thailand Ministry of Public Health, National Health Security Office (NHSO), Prince of Songkla University, Institute for Global Health Policy Research, Bureau of International Health Cooperation, and National Center for Global Health and Medicine for their invaluable contributions to the data analysis and technical support. We would also like to express our heartfelt appreciation to Dr. Suchunya Aungkulanon from the Centers for Disease Control and Prevention for her invaluable assistance in providing socioeconomic data and super-district information to Thailand. 8. Author contributions All authors intellectually participated in the concept of the study and the plans of analysis. A.M., and J.T., was responsible for auditing, retrieving and curation processes of the data for analysis. A.M, V.C., T.L., H.Y., T.T., and H.K. played key roles in the interpretation of the results. A.M. prepared the initial draft of the manuscript and T.L, J.T., P.N., H.Y., H.K., T.T., H.I., and V.C. made signifcant contributions to the academic discussion and critically evaluated subsequent version. 9. Funding This study was supported by the Japan International Cooperation Agency via the Partnership Project for Global Health and Universal Health Coverage Phase 2 (GLO+UHC Phase 2), Institute for Global Health Policy Research, Bureau of International Health Cooperation, and National Center for Global Health and Medicine (Grant No. 20A06). 10. Availability of data and materials The data in this study are available upon request. Only users with permission from the NHSO can access this dataset. If you require access, please contact the NHSO of Thailand for permission. Ethics approval and consent to participate Patient data were encrypted and deidentifed for personalized anonymization, according to the Thai Personal Data Protection Act 2019, Thailand. Data were obtained from the NHSO with project approval granted by the Human Research Ethics Committee of the Faculty of Medicine, Prince of Songkla University (REC No. 64-584-18-1). Informed consent was not required as the data obtained did not identify any individuals. Consent for publication No applicable. Conflict of interests We have no conficts of interest to declare. Author details 1 Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani campus, Pattani, Thailand 2 Department of Epidemiology, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand 3 Policy Advocacy Unit, National Health Security Office, Nonthaburi, Thailand 4 Data Science Center, Jichi Medical University, Tochigi, Japan 5 Monitoring and Evaluation Cluster, National Health Security Office, Nonthaburi, Thailand 6 Institute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan References Abdul Manaf MR, Surendra NK, Abdul Gafor AH, Seong Hooi L, Bavanandan S. Dialysis Provision and Implications of Health Economics on Peritoneal Dialysis Utilization: A Review from a Malaysian Perspective. Int J Nephrol. 2017;2017:5819629. doi:10.1155/2017/5819629. Liyanage T, Ninomiya T, Jha V, Neal B, Harris M, Okpechi I, et al. 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Yang F, Liao M, Wang P, Liu Y. Cost-effectiveness analysis of renal replacement therapy strategies in Guangzhou city, southern China. BMJ Open. 2021;11(2):e039653. doi:10.1136/bmjopen-2020-039653. Sato RC, Zouain DM. Markov models in health care. Einstein (Sao Paulo). 2010;8:376-9 Carter JV, Pan J, Rai SN, Galandiuk S. ROC-ing along: evaluation and interpretation of receiver operating characteristic curves. Surgery. 2016;159(6):1638-45. doi:10.1016/j.surg.2015.12.029. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. Available from: https://www.R-project.org/ Kanjanabuch T, Takkavatakarn K. Global dialysis perspective: Thailand. Kidney360. 2020;1(7):671-5. doi:10.34067/KID.0000762020. Boissinot L, Landru I, Cardineau E, Zagdoun E, Ryckelynck JP, Lobbedez T. Is transition between peritoneal dialysis and hemodialysis really a gradual process? Perit Dial Int. 2013;33(4):391-7. doi:10.3747/pdi.2011.00134. Slon Roblero MF, Borman N, Bajo Rubio MA. Integrated care: enhancing transition from renal replacement therapy options to home haemodialysis. Clin Kidney J. 2020;13(1):105-10. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8044765","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544327158,"identity":"b2adf25e-6687-4e10-b16a-acad66cc4b5e","order_by":0,"name":"Arinda Ma-a-lee","email":"","orcid":"","institution":"Prince of Songkla University","correspondingAuthor":false,"prefix":"","firstName":"Arinda","middleName":"","lastName":"Ma-a-lee","suffix":""},{"id":544327159,"identity":"b21461c8-e94d-4da4-a7f2-b59a036a8a67","order_by":1,"name":"Tippawan Liabsuetrakul","email":"","orcid":"","institution":"Prince of Songkla 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11:50:15","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67965,"visible":true,"origin":"","legend":"","description":"","filename":"8668991e12214b92b5955b6cb0ede7c51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8044765/v1/8a2ac62c329b0df0ef3d1a07.xml"},{"id":96556822,"identity":"bbce2088-4ae9-4236-9ed1-aa5f7e21c44b","added_by":"auto","created_at":"2025-11-23 11:50:15","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78114,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8044765/v1/5f9307a6cf47629dae91b6e3.html"},{"id":96556824,"identity":"d247147f-4951-4f98-8def-b6db7a4b4ea7","added_by":"auto","created_at":"2025-11-23 11:50:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35987,"visible":true,"origin":"","legend":"\u003cp\u003eMarkov-states diagram for modality transition at RRT\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8044765/v1/d2cd98bbc4f5beaa7a6b6be6.png"},{"id":96556809,"identity":"38e53c20-20b6-40e6-aa6b-42ef5e97d52d","added_by":"auto","created_at":"2025-11-23 11:50:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":663119,"visible":true,"origin":"","legend":"\u003cp\u003eOdd Ratios of transition to HD among ESKD in RRT, ref is reference\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8044765/v1/7972ead5b39669e99c3fa3f8.png"},{"id":96556806,"identity":"e55bd469-6f07-4608-8eba-be36f5983a04","added_by":"auto","created_at":"2025-11-23 11:50:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":669816,"visible":true,"origin":"","legend":"\u003cp\u003eOdd Ratios of transition to death among ESKD in RRT, ref is reference\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8044765/v1/838355d47ee54b2afe1256d9.png"},{"id":96912966,"identity":"59559bed-21f9-4b2e-b479-aafcbba1d002","added_by":"auto","created_at":"2025-11-27 13:45:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2272767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8044765/v1/6f0811b2-d94f-4a48-a389-2d3418312daa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamics of Renal Replacement Therapy in Thailand","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, Renal replacement therapy (RRT) for end-stage kidney disease (ESKD) is indeed a significant component of public health spending. Long-term provision of RRT, including peritoneal dialysis (PD), hemodialysis (HD) and kidney transplantation (KT), are of high costs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Inequality in access to RRT is a social problem. A systematic review reported that in 2010, 9.7\u0026nbsp;million patients needed RRT but only 2.6\u0026nbsp;million could get the service [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThailand is one of the Southeast Asian countries with a high prevalence of ESKD. Prior to the Universal Health Coverage (UHC) period, the annual incidence of ESKD was estimated to be 121.9 to 158.9 per million population in 2004 and 2007, respectively [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In October 2007, the UHC fund launched the first RRT reimbursement plan the \u0026ldquo;Peritoneal Dialysis-First\u0026rdquo; (PD First), and then extended to kidney transplantation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The number of kidney replacement cases in Thailand has climbed from 21,839 in 2007 to 164,191 in 2020, while PD proportion increased from 5.5% to 21.0% in dialysis patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAssessing the rate and factors associated with health state transition at RRT is useful for healthcare planners and providers to plan the future need for an adequate dialysis modality to improve the long-term quality of life of ESKD patients. Therefore, this study aimed to investigate the baseline factors of ESKD patients at the initiated modalities, assess rates of modality transition from initiated modalities to the next health states, and identify factors associated with health state outcomes.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eA retrospective cohort study was conducted in Thailand using the national services RRT program database, aimed to assess the modality transition rate at RRT and its associated factors.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData source and management\u003c/h3\u003e\n\u003cp\u003eTwo data sources the National Health Security Office (NHSO) were used in this study namely ESKD/RRT registry and death registry. The ESKD registry includes patients and RRT treatment modality during 2015 to 2019. Variables included encrypted patient's ID, gender (males and females), year of birth, 13 health regions (HR1 to HR13), starting date of dialysis, initiated CKD mode (CAPD, HD, and KT), and dates of the changes in modality and death. Altogether there were 58,077 unique cases.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe data set was cleaned. RRT modality yearly transition matrices and the patients\u0026rsquo; age at the initation and each state of RRT state were computed. Descriptive statistics was used to describe the characteristics of the patients at RRT initiated.\u003c/p\u003e\u003cp\u003eMarkov model [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] was employed to analyse the modality transition probability matrix [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It assumes that a patient is always in one of a finite number of discrete health states, and all events of interest are modeled as transitions from one state to another.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFour-states Markov model\u003c/h3\u003e\n\u003cp\u003eA Markov model is, essentially, a matrix of state transition probabilities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The health status of transition in this study according to the Markov model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a Markov-state diagram. There are 12 possible transitions. Death is the only absorptive state. For the reasons of completeness of the data, we confined the transition years to 2016, 2017, 2018, and 2019.\u003c/p\u003e\n\u003ch3\u003eAssociated factors with a transitioning health states\u003c/h3\u003e\n\u003cp\u003eMultiple logistic regression was employed to identify factors associated with each transitioning health states. In this report, we focused on transtions of HD (the most long-term costly RRT), and death (the worst health state).\u003c/p\u003e\u003cp\u003eA receiver operating characteristic (ROC) and its area under the curve (AUC) were computed to evaluate the model's ability to distinguish between transitioning health states and others [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed using the R program [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e1. Descriptive results\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cem\u003e1.1 The characteristics of the patients at RRT initiated\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe characteristics of the ESKD patients (58,077 cases) who started to receive the modality treatment at RRT from 2015 to 2019, as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTable 1: Characteristics of the patients at RRT initiated (n=58,077)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eFactors \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eFrequency (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28,315 (48.8)\u003c/p\u003e\n \u003cp\u003e29,762 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eAge groups (year)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0-39\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 40-49\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 50-59\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 60-69\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6,194 (10.7)\u003c/p\u003e\n \u003cp\u003e8,198 (14.1)\u003c/p\u003e\n \u003cp\u003e15,458 (26.6)\u003c/p\u003e\n \u003cp\u003e18,097 (31.2)\u003c/p\u003e\n \u003cp\u003e10,126(17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eStarting year of dialysis\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 2016\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 2017\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 2018\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11,566 (19.9)\u003c/p\u003e\n \u003cp\u003e11,071 (19.1)\u003c/p\u003e\n \u003cp\u003e11,324 (19.5)\u003c/p\u003e\n \u003cp\u003e12,270 (21.1)\u003c/p\u003e\n \u003cp\u003e11,846 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003eStarting CKD mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e1: CAPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e38,905 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e2: HD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e18,522 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e3: KT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e650 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003eTime receiving until transition (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e1:0-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e47,795 (82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e2:\u0026gt;6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5,629 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e3:\u0026gt;12-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3,574 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e4:\u0026gt;18-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1,079 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003eHealth region (HR)*\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e1:HR1(Northern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7368(1817.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e2:HR2(Northern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2597(1010.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e3:HR3(Northern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2152(970.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e4:HR4(Central)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5141(1487.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e5:HR5(Central)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4687(1235.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e6:HR6(Central)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4933(1200.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e7:HR7(Northeasthern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4132(1124.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e8:HR8(Northeasthern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4560(1076.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e9:HR9(Northeasthern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5351(1077.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e10:HR10(Northeasthern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4932(1444.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e11:HR11(Southern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3850(1101.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e12:HR12(Southern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2369(590.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279px;\"\u003e\n \u003cp\u003e13:HR13(BKK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6004(1572.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e*cases per 1,000,000 population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the characteristics of the patients. Most of the patients were female (51.2%), starting the RRT at the age between 50 and 69 years (57.8%). The RRTs increased gradually throughout the study period. Around two thirds of patients started dialysis with CAPD (67.0%), followed by HD (31.9%), and KT (1.1%). Most of the patients had a relatively short inter-transit period of 0-6 months (82.3%). \u0026nbsp;The receiving RRT rate per 1,000,000 population in different regions showed that the highest rate was found in HR1 (1,817.1) in the northern part, followed by HR13 (1,572.1) in Bangkok, HR4 (1,487.0) in the central part, and HR10 (1,444.2) in the northeastern part. The lower south region of HR12 had the lowest rate of receiving RRT (590.3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.2 Distribution of transitioning health states (outcome) at RRT initiated\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll of the patients who received modality at RRT initiatiated\u003cem\u003e\u0026nbsp;\u003c/em\u003ehad a single transitioning health states as shown in table 2. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Distribution of a transitioning health states\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e\u003cem\u003eTransitioning heath states\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eFrequency (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1:CAPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e26,980(46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2:HD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e15,141(26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e\u0026nbsp; 3:KT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e629(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4:Death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e15,327(26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Table 2 showed the health state of a single transition for the patients who initiated modalities at RRT. The highest rate of the transitioning health state was found for CAPD (46.5%), followed by death (26.4%), HD (26.1%), and KT (1.1%), respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2. A single transitioning health states probability distribution at RRT\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rate of initial CKD mode among ESKD patients for each starting year as presented in first column in table 3, revealing that between 2015 and 2019, CAPD initiation increased at a rate of 63.6% to 69.6%, whereas HD and KT decreased at rates of 34.8% to 29.8% and 1.6% to 0.7%, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on Markov approach, a transition rate from initial CKD mode to the next health states within a 2-year cycle among patients at RRT was also shown in Table 3.\u003c/p\u003e\n\u003cp\u003eTable 3: Observed cases and transition probability matrix with row sum equal 1\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"584\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial CKD mode:\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 428px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObserved cases (\u003cem\u003en\u003c/em\u003e), next health transition (probability \u003cem\u003eP*\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1:CAPD\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2:HD\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3:KT\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4:Death\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2015-2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.CAPD: 7354(63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e4942(0.672)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e119(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2293(0.312)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2.HD:\u0026nbsp;4022(34.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e43(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3101(0.771)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e878(0.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e3.KT:\u0026nbsp;190(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e186(0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e4.Death:\u0026nbsp;0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2016-2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.CAPD:\u0026nbsp;7372(66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5034(0.683)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e142(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2196(0.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2.HD:\u0026nbsp;3549(32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e36(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2806(0.791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1(\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e706(0.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e3.KT:\u0026nbsp;150(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e143(0.953)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6(0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e4.Death:\u0026nbsp;0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2017-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.CAPD:\u0026nbsp;7605(67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5208(0.685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e171(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2226(0.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2.HD:\u0026nbsp;126(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e49(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2816(0.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e728(0.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e3.KT:\u0026nbsp;126(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e123(0.976)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e3(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e4.Death:\u0026nbsp;0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2018-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.CAPD:\u0026nbsp;8332(67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5795(0.696)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e168(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1(\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2368(0.284)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2.HD:\u0026nbsp;3832(31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e69(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2917(0.761)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1(\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e845(0.221)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;3.KT: 106(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e103(0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e3(0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e4.Death:\u0026nbsp;0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2019-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.CAPD:\u0026nbsp;8242(69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5757(0.698)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e193(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2292(0.278)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2.HD:\u0026nbsp;3526(29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e46(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2707(0.768)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e773(0.219)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e3.KT:\u0026nbsp;78(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e71(0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6(0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e4.Death:\u0026nbsp;0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* The row sum of the probability equal 1\u003c/p\u003e\n\u003cp\u003eTable 3 showed an observed number (n) of patients at RRT and transition probabilities (P) matrix from 2015 to 2019. Cells in the diagonal line from left upper to right lower are the values of remaining in the same state during the transit period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor CAPD initation group, the percentage of remaining in CAPD range from 67.2% (2015) to 69.8% (2019). The corresponding probability for HD ranged between 76.1 and 79.1%. The KT group had the least transition rate.\u003c/p\u003e\n\u003cp\u003eTransition from CAPD to HD slowly increased from 1.6% in 2015 to 2.3% in 2019. The reverse direction was in a much lower range, varying between 1.0% to 1.8%. As afored mentioned, very few CAPD (1) and HD (2) patients finally received KT. Transition probability from CAPD to death (ranging from 27.8% to 31.2% ) was generally higher than that from HD (19.9% to 22.1%). The KT group has the lowest but increasing, transition rate to death from 2.1% in 2015 to 7.7% in 2019.\u003c/p\u003e\n\u003ch3\u003e3. Factors associated with a transitioning health state after initiating modalities at RRT based on logistic regression\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1 Transition from all initiated modalities to HD state\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rate of transition from all initial mordality to HD was 26.1%. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2 showed that transition rate to HD was higher in young age group, having recent RRT initiation. The rate peaked in 2017 and was highest in the capital city (Bangkok).\u0026nbsp;The area under the curve (AUC) is 0.945 indicating that the model performance was excellent predicted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Transition from all initiated modality to death (absorbing state)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rate of transition from all three initial mordality to death was 26.4%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3 showed that predictor of mortality is in the opposite direction to those of transition to HD. As expected, mortality rate increased with age and duration after the initiation of RRT. Those residing in Bangkok has the lowest mortality. The area under the curve (AUC) is 0.824 indicating that the model performance was well predicted.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results show that the RRT services steadily increased over time with somewhat decline in mortality rates. \u0026nbsp;CAPD was the most commonly the starting modality of RRT throughout the study period. Yearly probability of shifting to HD was relatively small. More important is the high mortality rate in all RRT modalities. After adjustment for important confounders such as age, sex and duration since starting RRT, shifting to HD and mortality rates were geographically inequitably distributed. The residents of Bangkok has better chance to transit to HD and lower risk for mortality.\u003c/p\u003e\n\u003cp\u003eRising number of RRT over the study period may be combined effects of demographic and epidemiologic transition and the country health finance policy. Demographic transition in this case is the rapid rising of the elder population following the past increase in number of birth over the preceding half century, and the decline in age-specific mortality rates (thus prolonged life expenctancy at the middle age). Epidemiologic transition or changing in environment and lifestyle over the years has resulted in more metabolic syndrome which eventually leads to more ESKD cases. The policy to have universal coverage of RRT to all ESKD patients results in prolonged survival and increases of cumulative ESKD cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll RRT modalities are costly. The most cost-effective modality is KT [7]. Yet the number of patients receiving this service is scanty. CAPD is basically long-term home based procedure. CAPD first policy was launched because time and financial burden on the health systems could be reduced substantially compared to HD [11]. \u0026nbsp;In long-term shifting from CAPD to HD is often inevitable. The indications include mechanical and physiological failures and infection [12- 13]. As seen in our data, this shifting increased in probability as CAPD use was prolonged.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProbability of shifting from CAPD to HD had no\u0026nbsp;evidence of sex predilection but it was more common in young patients. The fact that the elder were less likely to be shifted to HD might be explained by differential of age preference for the young patients and/or inequality in ability to get access to HD of the elder. HD is usually an institute-based service. Yong patients can be less dependent on care-takers and easier to transport from home compared to the elderly patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore such shifting in the capital city than in other health region after adjustment for known risk factors clearly show geographical inequity of RRT services. The number of private HD services is higher in Bangkok than in other health regions. This may be due to inequitable practices among the stakeholders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMortality rate of the CAPD users was higher than that of the HD group after adjustment for age, sex and other available variables. This may be due to higher health risk imposed by CAPD than that by HD. On the other hand, there may be differential selection to put patients with serious comorbidity into the CAPD. Our current data had no information on this complex clinical setting and thus cannot derive a good explantion of difference in the mortality rates of different RRT modality\u003c/p\u003e\n\u003cp\u003eFinally, mortality rate in Bangkok was clearly better than those in the remaining health regions. Again, this can be explained by better baseline conditions of ESKD patients and/or better general health care in the capital city.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the study period, the use of RRT services in Thailand increase steadily. The policy to keep CAPD as priority could be considered successful due to only small percentage of shifting toward HD use over time. KT rate was however still low. Despite the lack of important clinical background information in the analysis, geographical inequity was evidenced by higher use of HD and lower mortality rate among the residents of the capital city. These problems require improvement in planning and implementation of RRT services in Thailand.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eESKD End stage kidney disease \u003c/p\u003e\n\u003cp\u003eRRT Renal replacement therapy\u003c/p\u003e\n\u003cp\u003eCAPD Continuous Ambulatory Peritoneal Dialysis\u003c/p\u003e\n\u003cp\u003eHD Hemodialysis\u003c/p\u003e\n\u003cp\u003eKT Kidney Transplant\u003c/p\u003e\n\u003cp\u003eHR Heath region\u003c/p\u003e\n\u003cp\u003eUHC Universal Health Coverage\u003c/p\u003e\n\u003cp\u003eNHSO National Health Security Office\u003c/p\u003e\n\u003cp\u003eROC Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC Area under the curve\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e7. Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to the Japan International Cooperation Agency for their generous support through \u0026ldquo;The Partnership Project for Global Health and Universal Health Coverage Phase 2 (GLO + UHC Phase 2).\u0026rdquo; This collaboration has played a pivotal role in enhancing data utility for the advancement of universal health coverage between Thailand and Japan. We also extend our appreciation to the Thailand Ministry of Public Health,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNational Health Security Office (NHSO), Prince of Songkla University, Institute for Global Health Policy Research, Bureau of International Health Cooperation, and National Center for Global Health and Medicine for their invaluable contributions to the data analysis and technical support. We would also like to express our heartfelt appreciation to Dr. Suchunya Aungkulanon from the Centers for Disease Control and Prevention for her invaluable assistance in providing socioeconomic data and super-district information to Thailand.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Author contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors intellectually participated in the concept of the study and the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eplans of analysis. A.M., and J.T., was responsible for auditing, retrieving and curation processes of the data for analysis. A.M, V.C., T.L., H.Y., T.T., and H.K. played key roles in the interpretation of the results. A.M. prepared the initial draft of the manuscript and T.L, J.T., P.N., H.Y., H.K., T.T., H.I., and V.C. made signifcant contributions to the academic discussion and critically evaluated subsequent version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Japan International Cooperation Agency via\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethe Partnership Project for Global Health and Universal Health Coverage Phase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2 (GLO+UHC Phase 2), Institute for Global Health Policy Research, Bureau of\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInternational Health Cooperation, and National Center for Global Health and\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMedicine (Grant No. 20A06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10. Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data in this study are available upon request. Only users with permission from the NHSO can access this dataset. If you require access, please contact the NHSO of Thailand for permission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient data were encrypted and deidentifed for personalized anonymization, according to the Thai Personal Data Protection Act 2019, Thailand. Data were obtained from the NHSO with project approval granted by the Human Research Ethics Committee of the Faculty of Medicine, Prince of Songkla University (REC No. 64-584-18-1). Informed consent was not required as the data obtained did not identify any individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of \u0026nbsp;interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have no conficts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani campus, Pattani, Thailand\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Epidemiology, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003ePolicy Advocacy Unit, National Health Security Office, Nonthaburi, Thailand\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eData Science Center, Jichi Medical University, Tochigi, Japan\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eMonitoring and Evaluation Cluster, National Health Security Office, Nonthaburi, Thailand \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e6\u003c/sup\u003eInstitute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdul Manaf MR, Surendra NK, Abdul Gafor AH, Seong Hooi L, Bavanandan S. Dialysis Provision and Implications of Health Economics on Peritoneal Dialysis Utilization: A Review from a Malaysian Perspective. Int J Nephrol. 2017;2017:5819629. doi:10.1155/2017/5819629.\u003c/li\u003e\n\u003cli\u003eLiyanage T, Ninomiya T, Jha V, Neal B, Harris M, Okpechi I, et al. Worldwide access to treatment for end-stage kidney disease: a systematic review. Lancet. 2015;385(9981):1975-82. doi:10.1016/S0140-6736(14)61601-9.\u003c/li\u003e\n\u003cli\u003eTantivess S, Werayingyong P, Chuengsaman P, Teerawattananon Y. Universal coverage of renal dialysis in Thailand: promise, progress, and prospects. BMJ. 2013;346:f462. doi:10.1136/bmj.f462.\u003c/li\u003e\n\u003cli\u003ePraditpornsilpa K, Lekhyananda S, Premasathian N, Kingwatanakul P, Lumpaopong A, Gojaseni P, et al. Prevalence trend of renal replacement therapy in Thailand: impact of health economics policy. J Med Assoc Thai. 2011;94 Suppl 4:S1-6.\u003c/li\u003e\n\u003cli\u003eChuasuwan A, Lumpaopong A. Thailand Renal Replacement Therapy Year 2020 [Internet]. Bangkok: Nephrology Society of Thailand; 2020 [cited 2024 Nov 20]. Available from: https://www.nephrothai.org/wp-content/uploads/2022/06/Final-TRT-report-2020.pdf\u003c/li\u003e\n\u003cli\u003eSchaubel DE, Morrison HI, Fenton SS. Projecting renal replacement therapy\u0026ndash;specific end-stage renal disease prevalence using registry data. Kidney Int. 2000;57:S49-54.\u003c/li\u003e\n\u003cli\u003eYang F, Liao M, Wang P, Liu Y. Cost-effectiveness analysis of renal replacement therapy strategies in Guangzhou city, southern China. BMJ Open. 2021;11(2):e039653. doi:10.1136/bmjopen-2020-039653.\u003c/li\u003e\n\u003cli\u003eSato RC, Zouain DM. Markov models in health care. Einstein (Sao Paulo). 2010;8:376-9\u003c/li\u003e\n\u003cli\u003eCarter JV, Pan J, Rai SN, Galandiuk S. ROC-ing along: evaluation and interpretation of receiver operating characteristic curves. Surgery. 2016;159(6):1638-45. doi:10.1016/j.surg.2015.12.029.\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. Available from: https://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eKanjanabuch T, Takkavatakarn K. Global dialysis perspective: Thailand. Kidney360. 2020;1(7):671-5. doi:10.34067/KID.0000762020.\u003c/li\u003e\n\u003cli\u003eBoissinot L, Landru I, Cardineau E, Zagdoun E, Ryckelynck JP, Lobbedez T. Is transition between peritoneal dialysis and hemodialysis really a gradual process? Perit Dial Int. 2013;33(4):391-7. doi:10.3747/pdi.2011.00134.\u003c/li\u003e\n\u003cli\u003eSlon Roblero MF, Borman N, Bajo Rubio MA. Integrated care: enhancing transition from renal replacement therapy options to home haemodialysis. Clin Kidney J. 2020;13(1):105-10.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"End stage kidney disease, Renal replacement theraphy, Modality transition in RRT","lastPublishedDoi":"10.21203/rs.3.rs-8044765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8044765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Renal replacement therapy (RRT) for end-stage kidney disease (ESKD) is major public health spending in Thailand, which has been changed by policy and disease contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: This study aims to investigate the dynamics of RRT among Thai ESKD patients, and identify factors associated with the transition of the mode of therapy and mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Data were obtained from the national ESKD registry, documenting all RRT events from 2015 to 2019, and the national death registry. Baseline patient characteristics were summarized using descriptive statistics. A Markov model was used to evaluate RRT modality transition rates, while logistic regression identified factors associated with changes in health states.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Most patients were female (51.2%) and aged 50–69 years (57.8%) at RRT initiation. RRT use increased steadily, with CAPD being the most common initial modality (67.0%), followed by HD (31.9%) and KT (1.1%). Most had short inter-transit periods of 0–6 months (82.3%). RRT rates were highest in the northeastern region. CAPD had the highest transition rate (46.5%), followed by death (26.4%), HD (26.1%), and KT (1.1%). The probability of remaining on CAPD ranged from 63.6% to 69.6%, and for HD from 76.1% to 79.1%, while KT had the lowest transition.\u003c/p\u003e\n\u003cp\u003eMultivariate analysis showed that younger patients and those who recently initiated RRT had higher transition rates to HD, with a peak in 2017 and the highest rate in Bangkok. In contrast, predictors of mortality followed the opposite pattern: older age and longer duration since RRT initiation were associated with higher mortality, while residents of Bangkok had the lowest mortality rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: During the study period, RRT use in Thailand increased steadily. The CAPD-first policy appears effective, as relatively few patients shifted to HD over time, though KT rates remained low. Despite limited clinical data, geographical disparities were evident, with higher HD use and lower mortality among Bangkok residents. These findings highlight the need for improved planning and implementation of RRT services in Thailand.\u003c/p\u003e","manuscriptTitle":"Dynamics of Renal Replacement Therapy in Thailand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-23 11:50:09","doi":"10.21203/rs.3.rs-8044765/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b94345ad-7f83-4ca6-b570-5fcde7cf0ef1","owner":[],"postedDate":"November 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T04:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-23 11:50:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8044765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8044765","identity":"rs-8044765","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}