Geographical disparities and temporal trends of end stage kidney disease incidence: a systematic review

While individual studies address the geographical and temporal variations contributing to end-stage kidney disease (ESKD) incidence, there is a notable lack of comprehensive research consolidating these findings. This study systematically reviewed and analyzed original articles reporting the incidence of ESKD across different geographical locations and time over two decades worldwide. The study focused on individuals with ESKD, aged 18 and older, regardless of whether they received kidney replacement therapy. Original studies published between 01 Jan 2003 and 23 March 2026 focusing on ESKD incidence and reporting geographical variations and/or temporal trends were included. Literature screening and data extraction were performed using Covidence. Geographical variations in ESKD incidence were assessed across countries, major sub-divisions, and small areas, while temporal trends were evaluated both annually and seasonally. Among forty-one papers reviewed, 18 (43.9%) reported geographical variation, 17 (41.5%) reported temporal trends, and 6(14.6%) reported both. Of those examining geographical variations, 16.7% assessed ESKD incidence across countries, 33.3% across major sub-divisions, and 50.0% across small areas. ESKD incidence varies widely across regions, ranging from 100 per million population in Nepal to over 450 in Taiwan, with higher rates in high-income settings and notable within-country disparities in areas such as the southern United States, southern Taiwan, and parts of Japan. Incidence has increased over time, though some regions remain stable, with higher rates observed in winter than summer. Geographical variation in ESKD incidence was observed across countries, regions, and small areas, with notable increases over time. Factors impacting geographical variations include angiotensin converting enzyme inhibitor use, income, erythropoietin use, obesity, and paraquat exposure, while race, country of residence, diabetes type, hypertension, cardiovascular disease, and calendar year affect temporal variations. Both age and gender contribute to geographical and temporal variations.