C DAVIES 1,2, D KEUSKAMP 1,2, S MCDONALD 1,2,3
1Australia and New Zealand Dialysis and Transplant Registry (ANZDATA), South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia, 2Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia, 3Central Northern Adelaide Renal and Transplantation Services (CNARTS), Royal Adelaide Hospital, Adelaide, Australia
Aim: To describe incidence and mortality trends for dialysis in Australia by age and time on dialysis.
Background: Dialysis requires dedicated and costly specialised facilities with a large impact on patients’ quality of life. Predicting future prevalence and thus demand for services is essential for estimating patient burden and effective management of health care resources.
Methods: The Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry is developing Markov models to predict dialysis prevalence for the years 2021-2030, based on the data reported to ANZDATA for 2011-2019. Models will be built on probabilities for transition between four mutually exclusive states (HD, PD, Functioning Transplant, Death). Dialysis incidence (standardised to the Australian population) and mortality rates were determined for the period 2011-2019 for five age groups (0-19, 20-39, 40-59, 60-69, 70-79, 80+ years) and for dialysis-years (0, 1, 2 and 3+).
Results: Dialysis incidence was stable across most age groups, apart from a consistent positive linear trend among people aged 40-59 and 60-69 years. There was no consistent trend for mortality for dialysis patients for any age group. Mortality rate by time on dialysis also did not trend consistently over the measured time-period, however rates were lower for those on dialysis less than 3 years compared to those for 3 years or more.
Conclusion: Trends in dialysis incidence and mortality suggest that, for estimating transition probabilities in Markov modelling of future prevalence, (1) for all age groups excepting 40-69 years stable incidence rates can be used, and (2) stable mortality rates for all age groups and for two dialysis-year groups can be used.
Dr Christopher Davies is the Lead Biostatistician at the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA) within the South Australian Health and Medical Research Institute. At ANZDATA, Christopher provides statistical support for the generation of regular reports and the extraction of data for custom external requests. Christopher also conducts analyses for ANZDATA research projects and in collaboration with those in the nephrology community. Christopher completed a PhD in Statistics at the University of Adelaide, focussing on group-based trajectory modelling. His research interests include methods for comparing centres, and for longitudinal data.