estimation of the prevalence of chronic kidney disease: the results of a model based estimation in kerman, iran

background: chronic kidney disease is asymptomatic until its last stages and though it is increasing globally, we are faced with paucity of a population-based model to assess this disease, particularly in developing countries. therefore, the aim of this study was to estimate the prevalence and trends of ckd according to a new estimation method.   methods: using multiplier method, we estimated the numbers of different stages of ckd based on the number of patients with end stage renal failure from 2006 to 2016. the required multipliers were extracted from a simulation of the disease in kerman following a dynamic model. the 95% uncertainty interval was computed using monte-carlo technique with 10,000 iterations.   results: the prevalence of ckda (gfr<=90ml/min/1.73m2) and ckdb (gfr less than 60ml/min/1.73m2) patients were estimated to be 7.6% (95% uncertainty interval (ui), 5.7-9.1%) and 1.1% (95% ui, 0.8-1.3%), respectively in 2011. the method revealed that the prevalence may rise up to 25.7% (95% ui, 18.2-32.5%) and 3.7% (95% ui, 2.7-4.5%) for ckda and ckdb, respectively in 2016, indicating approximately 3.3 times increase for both figures.   conclusion: this study predicted an increase in the prevalence of ckd in the future. this may be due to the increasing life expectancy of the population, the increase in the prevalence of non- communicable diseases such as hypertension and diabetes, or patients’ survival due to receiving better support. therefore, the policymakers should be concerned and well informed about this increase.