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      Inequity in dialysis related practices and outcomes in Aotearoa/New Zealand: a Kaupapa Māori analysis

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          In Aotearoa/New Zealand, Māori, as the indigenous people, experience chronic kidney disease at three times the rate of non-Māori, non-Pacific New Zealanders. Māori commence dialysis treatment for end-stage kidney disease at three times the rate of New Zealand European adults. To examine for evidence of inequity in dialysis-related incidence, treatment practices, and survival according to indigeneity in Aotearoa/New Zealand, utilising a Kaupapa Māori approach.


          We conducted a retrospective cohort study involving adults who commenced treatment for end-stage kidney disease in Aotearoa/New Zealand between 2002 and 2011. We extracted data from the Australian and New Zealand Dialysis and Transplant Registry (ANZDATA) linked to the New Zealand National Health Index (NHI). Propensity score methods were used to assemble a cohort of 1039 Māori patients matched 1:1 on clinical and socio-demographic characteristics with a cohort of 1026 non-Māori patients. We compared incidence of end-stage kidney disease and treatment practices. Differences in the risks of all-cause mortality during treatment between propensity-matched cohorts were estimated using Cox proportional hazards and generalised linear models.


          Non-Māori patients were older, more frequently lived in urban areas (83% versus 67% [standardised difference 0.38]) and bore less socioeconomic deprivation (36% living in highest decile areas versus 14% [0.53]). Fewer non-Māori patients had diabetes (35% versus 69%, [− 0.72]) as a cause of kidney failure. Non-Māori patients were more frequently treated with peritoneal dialysis (34% versus 29% [0.11]), received a pre-emptive kidney transplant (4% vs 1% [0.19]), and were referred to specialist care < 3 months before treatment (25% vs 19% [0.15]) than Māori patients. Fewer non-Māori started dialysis with a non-tunnelled dialysis vascular catheter (43% versus 47% [− 0.08]). The indigenous-age standardised incidence rate ratio for non-Māori commencing renal replacement therapy in 2011 was 0.50 (95% CI, 0.40–0.61) compared with Māori.

          Propensity score matching generated cohorts with similar characteristics, although non-Māori less frequently started dialysis with a non-tunnelled venous catheter (30% versus 47% [− 0.35]) or lived remotely (3% versus 14% [− 0.50]). In matched cohorts, non-Māori experienced lower all-cause mortality at 5 yr. after commencement of treatment (risk ratio 0.78, 95% CI 0.72–0.84). New Zealand European patients experienced lower mortality than Māori patients in indigenous age-standardised analyses (age-standardised mortality rate ratio 0.58, 95% CI 0.51–0.67).


          Non-Māori patients are treated with temporary dialysis vascular access less often than Māori, and experience longer life expectancy with dialysis, even when socioeconomic, demographic, and geographical factors are equivalent. Based on these disparities, health services should monitor and address inequitable treatment practices and outcomes in end-stage kidney disease care.

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          Most cited references 28

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          The central role of the propensity score in observational studies for causal effects

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            An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

            The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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              Chronic kidney disease: global dimension and perspectives.

              Chronic kidney disease is defined as a reduced glomerular filtration rate, increased urinary albumin excretion, or both, and is an increasing public health issue. Prevalence is estimated to be 8-16% worldwide. Complications include increased all-cause and cardiovascular mortality, kidney-disease progression, acute kidney injury, cognitive decline, anaemia, mineral and bone disorders, and fractures. Worldwide, diabetes mellitus is the most common cause of chronic kidney disease, but in some regions other causes, such as herbal and environmental toxins, are more common. The poorest populations are at the highest risk. Screening and intervention can prevent chronic kidney disease, and where management strategies have been implemented the incidence of end-stage kidney disease has been reduced. Awareness of the disorder, however, remains low in many communities and among many physicians. Strategies to reduce burden and costs related to chronic kidney disease need to be included in national programmes for non-communicable diseases. Copyright © 2013 Elsevier Ltd. All rights reserved.

                Author and article information

                Int J Equity Health
                Int J Equity Health
                International Journal for Equity in Health
                BioMed Central (London )
                20 February 2018
                20 February 2018
                : 17
                [1 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Māori and Indigenous Health Institute, , University of Otago Christchurch, ; 2 Riccarton Ave, Christchurch, 8140 New Zealand
                [2 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Department of Medicine, , University of Otago Christchurch, ; Christchurch, New Zealand
                [3 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Department of Population Health, , University of Otago Christchurch, ; Christchurch, New Zealand
                © The Author(s). 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

                Funded by: FundRef, Health Research Council of New Zealand;
                Award ID: 15/413
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                © The Author(s) 2018

                Health & Social care

                dialysis, indigenous, māori, disparities, equity


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