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P78 Use of mathematical modelling for policy making: example of chronic kidney disease in chile
  1. Magdalena Walbaum1,
  2. Shaun Scholes1,
  3. Ruben Rojas2,
  4. Jennifer Mindell1,
  5. Elena Pizzo3
  1. 1Research Department of Epidemiology and Public Health, University College London, London, UK
  2. 2School of Health and Related Research, University of Sheffield, Sheffield, UK
  3. 3Applied Health Research, University College London, London, UK


Background Chronic Kidney Disease (CKD) is a leading public health problem, with substantial burden for individuals, healthcare systems and society. Predictive tools to simulate the disease in the future could help inform decision makers of potential impacts of new treatments, for effective policy making. The aim of this study was to compare the impact of introducing pre-dialysis treatment in Chile with the standard treatment using a multistate model to estimate the future cases and costs of CKD.

Methods A dynamic stock and flow model was used to simulate CKD progression in the Chilean population aged ≥40 years, to the year 2030, from the Chilean public healthcare system perspective. The model included six states replicating progression of CKD, which was assumed in 1-year cycles and was categorised as slow or fast progression. We compared the current treatment for CKD Stages 3a to End-Stage Kidney Disease (ESKD) covered in Chile with a scenario that introduced pre-dialysis treatment for CKD Stages 4 and 5. Only direct treatment costs were considered. The model was calibrated based on international evidence; 95% credibility intervals were calculated with probabilistic sensitivity analysis. We conducted a financial analysis to calculate the annual cash flow and net present value with 3% and 6% discount rates.

Results By the year 2030, there is an expected increase in cases of CKD Stages 3a to ESKD, ceteris paribus, from 452,198 (95% CI 332,760–571,636) in 2020 to 558,271 (483,997–632,545) individuals. Direct costs of CKD stages 3a to ESKD would rise from £256.2M GBP (148.8–363.6) in 2020 to £516.6M (359.5–693.6) in 2030. The introduction of pre-dialysis treatment for CKD Stages 4 and 5 would reduce the proportion of fast progressors from the 30% assumed in the baseline scenario to 20%. This intervention is estimated to decrease the number of individuals worsening to stages 5 and ESKD, and reduce the total costs of CKD healthcare by £82.6M in 2030 to £434.0M (277.1–591.0). The financial analysis showed a net present value of -£139.8M and -£117.0M with 3% and 6% discounts, respectively.

Conclusion Predictive models are a useful tool for decision-making. The inclusion of pre-dialysis treatment for CKD Stages 4 and 5 would generate savings for the healthcare system due to the reduction in progression of CKD to ESKD. These results were presented to policy makers (Health, then Treasury) in Chile, to consider including pre-dialysis treatment for Stages 4 and 5 in the funded CKD healthcare in Chile.

  • modelling
  • policy
  • chronic kidney disease

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