Analysis of chronic kidney disease epidemiology in Kazakhstan using nationwide data for 2014–2020 and forecasting future trends of prevalence and mortality for 2030

Abstract

According to the Global Burden of Disease (GBD) study, chronic kidney disease (CKD) was prevalent in 697.5 million individuals worldwide in 2017. By 2040, it is anticipated that CKD will rank as the fifth most common cause of death. This study aims to examine the epidemiology of CKD in Kazakhstan and to project future trends in CKD prevalence and mortality by 2030. The retrospective analysis was performed on a database acquired from the Unified National Electronic Health System for 703,122 patients with CKD between 2014 and 2020. During the observation period, 444,404 women and 258,718 men were registered with CKD, 459,900 (66%) were Kazakhs and 47% were older than 50. The incidence rate notably decreased: 6365 people per million population (PMP) in 2014 and 4040 people PMP in 2020. The prevalence changed from 10,346 to 38,287 people PMP, and the mortality rate increased dramatically from 279 PMP to 916 PMP. Kazakhstan’s central regions, Turkestan and Kyzylorda were identified as the most burdensome ones. The ARIMA model projected 1,504,694 expected prevalent cases in 2030. The predicted mortality climbed from 17,068 cases in 2020 to 37,305 deaths in 2030. By 2030, the prevalence and mortality of CKD will significantly increase, according to the predicted model. A thorough action plan with effective risk factor management, enhanced screening among risk populations, and prompt treatment are required to lessen the burden of disease in Kazakhstan.

Keywords:

• Chronic kidney disease
• risk factor
• survival analysis
• prevalence forecasting
• mortality forecasting
• nationwide administrative data

Acknowledgements

We thank all staff from the Republican Center of Electronic Healthcare for providing data and consultancy.

The content of this paper was partially presented as a poster at the World Congress of Nephrology’23 (WCN’23). Congress, which was held in 2023 from March 30 to April 2.

Author contributions

All authors approved the submitted version of manuscript and agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. GZ analyzed and interpreted data and drafted the article. KM involved in interpretation of data and drafting the article. SY involved in acquisition of data and revision of the manuscript. ArG involved in interpretation of data and drafting the article. YS involved in analysis of data and article drafting. VK involved in acquisition of data and revision of the manuscript. DS involved in interpretation of data and drafting the article. AA involved in interpretation of data and drafting the article. MM, ZK, BB, and DT involved in conception and design of study and revised the article. KY involved in interpretation of data and drafting the article. AbG involved in interpretation of data and revision of the manuscript. All authors read and approved the final version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the Republican Center for Electronic Health of the Ministry of Health of the Republic of Kazakhstan but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author, Gaipov A., upon reasonable request and with permission of the Ministry of Health of the Republic of Kazakhstan.

Additional information

Funding

This study was supported by grants from the Nazarbayev University Faculty Development Research Grant Program FDCRGP 2023–2025 (Funder Project Reference: 20122022FD4104, title: In-depth epidemiology and modeling of the 10-year trends of cardiovascular diseases and their complications in Kazakhstan using aggregated big data from the Unified National Electronic Healthcare System). A.G. is a PI of the project.