Gillian Gorham1, Kirsten Howard2, Joan Cunningham3, Federica Barzi3, Paul Lawton3, Alan Cass3. 1. Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, 0810, Darwin, Australia. gillian.gorham@menzies.edu.au. 2. Sydney School of Public Health, The University of Sydney, Sydney, Australia. 3. Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, 0810, Darwin, Australia.
Abstract
BACKGROUND: Rates of end-stage kidney disease in Australia are highest in the Northern Territory (NT), with the burden of disease heaviest in remote areas. However, the high cost of delivering dialysis services in remote areas has resulted in centralisation, requiring many people to relocate for treatment. Patients argue that treatment closer to home improves health outcomes and reduces downstream healthcare use. Existing dialysis cost studies have not compared total health care costs associated with treatment in different locations. OBJECTIVE: To estimate and compare, from a payer perspective, the observed health service costs (all cause hospital admissions, emergency department presentations and maintenance dialysis) associated with different dialysis models in urban, rural and remote locations. METHODS: Using cost weights attributed to diagnostic codes in the NT Department of Health's hospital admission data set (2008-2014), we calculated the mean (SD) total annual health service costs by dialysis model for 995 dialysis patients. Generalized linear modeling with bootstrapping tested the marginal cost differences between different explanatory variables to estimate 'best casemix'/'worst casemix' cost scenarios. RESULTS: The mean annual patient hospital expenditure was highest for urban models at $97 928 (SD $21 261) and $43 440 (SD $5 048) and lowest for remote at $19 584 (SD $4 394). When combined with the observed maintenance dialysis costs, expenditure was the highest for urban models at $148 510 (SD $19 774). The incremental cost increase of dialysing in an urban area, compared with a rural area, for a relocated person from a remote area, was $5 648 more and increased further for those from remote and very remote areas to $10 785 and $15 118 respectively. CONCLUSIONS: This study demonstrates that dialysis treatment in urban areas for relocated people has health and cost implications that maybe greater than the cost of remote service delivery. The study emphasises the importance of considering all health service costs and cost consequences of service delivery models. KEY POINTS FOR DECISION MAKERS: Relocation for dialysis treatment has serious health and economic consequences. Relocated people have low dialysis attendance and high hospital costs in urban areas. While remote dialysis service models are more expensive than urban models, the comparative cost differences are significantly reduced when all health service costs are included. The delivery of equitable and accessible dialysis service models requires a holistic approach that incorporates the needs of the patient; hence dialysis cost studies must consider the full range of cost impacts beyond the dialysis treatments alone.
BACKGROUND: Rates of end-stage kidney disease in Australia are highest in the Northern Territory (NT), with the burden of disease heaviest in remote areas. However, the high cost of delivering dialysis services in remote areas has resulted in centralisation, requiring many people to relocate for treatment. Patients argue that treatment closer to home improves health outcomes and reduces downstream healthcare use. Existing dialysis cost studies have not compared total health care costs associated with treatment in different locations. OBJECTIVE: To estimate and compare, from a payer perspective, the observed health service costs (all cause hospital admissions, emergency department presentations and maintenance dialysis) associated with different dialysis models in urban, rural and remote locations. METHODS: Using cost weights attributed to diagnostic codes in the NT Department of Health's hospital admission data set (2008-2014), we calculated the mean (SD) total annual health service costs by dialysis model for 995 dialysis patients. Generalized linear modeling with bootstrapping tested the marginal cost differences between different explanatory variables to estimate 'best casemix'/'worst casemix' cost scenarios. RESULTS: The mean annual patient hospital expenditure was highest for urban models at $97 928 (SD $21 261) and $43 440 (SD $5 048) and lowest for remote at $19 584 (SD $4 394). When combined with the observed maintenance dialysis costs, expenditure was the highest for urban models at $148 510 (SD $19 774). The incremental cost increase of dialysing in an urban area, compared with a rural area, for a relocated person from a remote area, was $5 648 more and increased further for those from remote and very remote areas to $10 785 and $15 118 respectively. CONCLUSIONS: This study demonstrates that dialysis treatment in urban areas for relocated people has health and cost implications that maybe greater than the cost of remote service delivery. The study emphasises the importance of considering all health service costs and cost consequences of service delivery models. KEY POINTS FOR DECISION MAKERS: Relocation for dialysis treatment has serious health and economic consequences. Relocated people have low dialysis attendance and high hospital costs in urban areas. While remote dialysis service models are more expensive than urban models, the comparative cost differences are significantly reduced when all health service costs are included. The delivery of equitable and accessible dialysis service models requires a holistic approach that incorporates the needs of the patient; hence dialysis cost studies must consider the full range of cost impacts beyond the dialysis treatments alone.
Authors: Carlos K H Wong; Julie Chen; Samuel K S Fung; Maggie M Y Mok; Yuk Lun Cheng; Irene Kong; Wai Kei Lo; Sing Leung Lui; Tak Mao Chan; Cindy L K Lam Journal: Nephrol Dial Transplant Date: 2019-09-01 Impact factor: 5.992
Authors: G Gorham; K Howard; Y Zhao; A M S Ahmed; P D Lawton; C Sajiv; S W Majoni; P Wood; T Conlon; S Signal; S L Robinson; S Brown; A Cass Journal: BMC Nephrol Date: 2019-06-25 Impact factor: 2.388
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