Purpose · Web viewI132Hypertensive heart & renal dis w heart renal fail ESKD codes (strict definition) N18.0End-stage renal disease N18.5Chronic kidney disease, stage 5 I120Hypertensive

DEPARTMENT OF HEALTH

Renal Replacement Therapy Demand Study,

Northern Territory, 2001 to 2022

Jiqiong YouPaul D Lawton

Yuejen ZhaoSusan Poppe

Nicole CameronSteven Guthridge

March 2015


AcknowledgementsThe authors are grateful to the many people, who have assisted in the production of this report, including:

- Professor Stephen McDonald for his continuing support and for providing survival data from the Australia and New Zealand Dialysis and Transplant Registry;

- Staff from acute care information services and data warehouse for providing hospital data

- Jenny Cleary and Ian Pollock who sponsored this project and provided management support.

© Department of Health, Northern Territory 2015

This publication is copyright. The information in this report may be freely copied and distributed for non-profit purposes such as study, research, health service management and public information subject to the inclusion of an acknowledgement of the source. Reproduction for other purposes requires the written permission of the Chief Executive of the Department of Health, Northern Territory.

Suggested citation

You JQ, Lawton P, Zhao Y, Poppe S, Cameron N, Guthridge S. Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022, Department of Health, Darwin, 2015

ISBN 978-0-9757203-3-2

An electronic version is available at:

http://www.health.nt.gov.au/Health_Gains/Publications/index.aspx

General enquiries about this publication should be directed to:

Director, Health Gains Planning Branch

Department of Health

PO Box 40596, Casuarina, NT 0811

Phone: (08) 8985 8074

Email: [email protected]

2 Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022

http://www.health.nt.gov.au/Health_Gains/Publications/index.aspx


Table of contents Summary vIntroduction 1Methods 2

Data sources 2Statistical analysis 2

Descriptive analysis 2Linear regression model 3Time-series model 4Markov chain model 4Scenario modelling 4

Results 6Descriptive analysis 6Models and projections 12

Renal projection overview 12Time-series model 13Linear regression model 13Markov chain model 14

Scenario analysis 17Scenario 1 – changing the transplant rate 17Scenario 2 – changing the proportion of the self-care dialysis17Scenario 3 – changing the incidence rate per year 17Scenario 4 – changing the dialysis death rate 17Scenario 5 – changing the frequency of HD treatments 17

Discussion 21Appendix: Supplementary tables 26Abbreviation & glossary 30References 32List of tables 33List of figures 34Selected Health Gains Planning publications 35

Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022 3




Summary Across Australia, demand for renal replacement therapy (RRT) services has been growing at a significant rate over the last decade. Health service funders are faced with increasing service delivery costs and investment requirements. In the five-year period from 2007 to 2011, the number of dialysis patients increased by 27% in the Northern Territory (NT). Same day haemodialysis (HD) now comprises close to 50% of total NT public hospital admissions and in recent years the number of NT patients with end-state kidney disease (ESKD) using palliative care has doubled.

This report provides an overview of the projected demand for RRT in the NT in the next ten years. The projections require consideration of four parameters: the number of patients developing ESKD, the proportion of those with ESKD who progress to RRT, the length of time that patients continue using RRT (survival), and the type of RRT. Three separate methods were used for the projections - linear regression, an autoregressive integrated moving average time-series model, and a static Markov chain model.

The number of patients receiving RRT was available from NT hospital data, and around 75% of these patients had been notified to the Australian and New Zealand Dialysis and Transplant Registry (ANZDATA). The ANZDATA registry is used for compiling information about the incidence, prevalence and quality of RRT in Australia and New Zealand. The NT survival rate for dialysis patients has improved substantially in the last ten years. The unadjusted NT median survival improved from 4.5 years in 1995-99 to 6.0 years in 2005-09, which exceeded the recent national median survival (5.0 years). After adjustment for differences in age distribution between NT and Australian populations, the age-adjusted median survival time for the NT of 5.3 years still compared favourably with the national median survival time.

From 2001 to 2012, the number of HD treatments in the NT increased, on average, by around 3,200 per year. The projections for the number of facility-based HD treatments estimate a further increase of between 41% and 70% from 2013 to 2022. The projected average annual increase of HD treatments through this period ranged from 2,700 using the Markov chain model, through 3,300 using the time-series model to 4,600 using the linear regression model (Figure ES1 and Table ES1).

A benefit of the Markov chain model is that it allows adjustments of the various parameters within the model to assess the impacts of various assumptions. Five scenarios demonstrated that there may be considerable variations in future demand for facility-based HD treatments depending on changes to policy and clinical practice. The Markov chain model can be utilised for future and ongoing assessment of the demand for renal dialysis.



Figure ES1: Demand projections for facility-based haemodialysis treatments, using three statistical models, Northern Territory 2013 to 2022

Table ES1: Demand projections for facility-based haemodialysis treatments, using three statistical models, Northern Territory 2013 to 2022

Year Linear regression Time-series Markov chain

2012 55,650 55,650 55,117a

2013 59,070 58,798 58,260

2014 63,048 62,062 60,967

2015 67,117 65,325 63,719

2016 71,315 68,588 66,154

2017 75,487 71,851 68,541

2018 79,812 75,115 70,969

2019 84,379 78,378 73,323

2020 89,360 81,641 75,704

2021 94,776 84,904 78,262

2022 100,367 88,168 82,185aThe Markov chain model estimated the number of haemodialysis treatments in 2012 based on number of clients in 2012 but number of treatments in the previous years.



Introduction Across Australia, demand for renal replacement therapy (RRT) services has been growing at a significant rate over the last decade (1, 2). Health service funders for RRT are faced with increasing service delivery costs and investment requirements (3). The Northern Territory (NT) is experiencing growing demand for dialysis services. In 2009, the registered incidence of treated end-state kidney disease (ESKD) was 32 per 100,000 population (4). The number of patients on dialysis increased by 27% over the five year period from 2007 to 2011 (2). Same day haemodialysis (HD) now comprises close to 50% of total hospital admissions in NT public hospitals (5).

Although much could be done to slow the increase through targeted prevention and early intervention strategies, many health service funders have invested in greater capacity and new facilities only to find that actual demand significantly exceeded forward demand estimates and supply. New facilities that were thought to be sufficient for 20 years are now overflowing. The challenge for dialysis service planning is to manage priorities of equitable access with increasing demand for services in a financially constrained environment. Accurate projection of future demand is crucial for government in planning and providing adequate, effective and efficient RRT services for Territorians.

The objective of this study is to improve the understanding of projected demand and related service requirements to ensure future service sustainability through clinical service planning for dialysis services, where strategies could be developed to align available resources with demand.

This report includes:

provision of a summary of ESKD for NT residents including the incidence of ESKD and treatments;

projection of demands for RRT in the next 10 years including the application of different models and using different sources of data; and

projection of demand for facility-based HD under several scenarios which model various assumptions that could arise from changes in policy and clinical practice.



MethodsThis study provides an estimate of NT RRT service provision from 2001 to 2012 and projects future service demand from 2013 to 2022. Baseline data was gathered about the numbers of patients with ESKD in the NT, including patients treated and not treated with RRT. Three different models were applied separately (time-series, linear regression and Markov chain) to project future facility-based dialysis demand.

Data sourcesThe study used four data sources:

NT public hospital data: hospital admissions from 2001 to 2012; The Australia and New Zealand Dialysis and Transplant Registry (ANZDATA):

NT data from 2003 to 2012; NT population data: NT population data 2003-2012 was used to estimate the

ESKD incidence, prevalence and RRT modality; and NT population projection 2013-2020: The NT population projection data was

sourced from Department of Treasury and Finance (6).

Statistical analysis Descriptive analysisThe descriptive analysis provided in this study included the prevalence of ESKD and the proportion of those treated with RRT, compared to national averages. The ANZDATA registration rate was also estimated. The registration rate was used to adjust for the actual number of facility-based HD patients in projection models.

In this study, we first identified all ESKD patients from the NT public hospital data from 2001 to 2012. Two alternative definitions were used to identify patients with ESKD (Table 1). A “broad” definition followed the Australian Institute of Health and Welfare (AIHW) definition, which included both “specified”, and “unspecified” chronic renal failure (7). A “strict” definition excluded “unspecified” chronic renal failure, and focused on those with a clearly coded diagnosis of ESKD. Hospital data with all 49 diagnosis fields were used to identify cases with ESKD. Before the sixth edition of ICD-10-AM, introduced in 2008, earlier stages of chronic kidney disease (CKD) were not specifically recognised and as a result early or less severe CKD cases were coded as unspecified chronic renal failure. Both strict and broad definitions are presented in the report, as the former is more precise, while the latter allows comparison with national data published by AIHW.

We then estimated the proportion of ESKD patients receiving any type of RRT. Patients recorded with a diagnosis of ESKD were checked with procedure and diagnosis codes in the hospital data (Table 1). The date of the initial RRT and modality were identified. We compared the number of ESKD patients without recorded RRT to those with RRT in the NT, NT to the national rate, and Indigenous with non-Indigenous rates.



Palliative care provides conservative treatment for ESKD, rather than renal dialysis or transplant. We estimated the proportion of ESKD patients who died in palliative care in NT and compared this proportion with the national average.

Finally, survival curves for different RRT inception cohorts for both the NT and Australia were generated using the national ANZDATA data for the period from 1995 to 2011. The ESKD survival information was applied to subsequent modelling for the total RRT and satellite HD demand projections.

Table 1: Codes applied to identify end-stage kidney disease and treatment modality

ICD 10 diagnosis codes & description ICD 10 procedure codes or block number

Other variables

ESKD codes (broad definition)

N18.0 End-stage renal disease

N18.8 Other chronic renal failure

N18.9 Chronic kidney disease, unspecified

N18.90 Unspecified chronic renal failure

N18.91 Chronic renal impairment

N19 Unspecified renal failure

N18.5 Chronic kidney disease, stage 5

I120 Hypertensive renal dis w renal failure

I131 Hypertensive heart & renal dis w renal failure

I132 Hypertensive heart & renal dis w heart renal fail

ESKD codes

(strict definition)

N18.0 End-stage renal disease

N18.5 Chronic kidney disease, stage 5

I120 Hypertensive renal dis w renal failure

I131 H/T heart & renal dis w renal failure

I132 H/T heart & renal dis w heart renal fail

HD codes Z49.1 Extracorporeal dialysis 13100-00 AR-DRG L61Z

PD codes Z49.2 Other dialysis Block: 1061, 1062

Kidney transplant

Z94.0 Kidney transplant status

Palliation Z51.5 Palliative care Care type

Notes: AR-DRG= Australian Refined Diagnosis Related Group; ESKD=end-stage kidney disease; HD=haemodialysis; ICD= International Classification of Diseases; PD=peritoneal dialysis

Linear regression modelIn this study, the linear regression analysis was based on historical HD treatment utilisation rate, assuming that the rate increased with a linear relationship to the independent variables (e.g. age, gender and Indigenous status). Using yearly data from 2001 to 2012 from the NT public hospital separations dataset, the utilisation rate for the


DEPARTMENT OF HEALTHNT population was modelled for the subgroups broken down by Indigenous status, gender, and age group (under 40 years, 40 years and above).

For each group, the linear regression model was projected to 2022, taking into account projected NT population changes. In this study, we also reported 95% confidence interval to represent the upper and lower limits of statistical confidence.

Time-series modelThe Auto Regression Integrated Moving Average (ARIMA) time-series model was also used to project demand for HD treatment. This method has been widely used in medical research, epidemiology and health economics, and has been shown to be useful in estimating future values when there is a significant random component. It is a form of regression analysis that seeks to project future movements by examining the differences and connections between the actual data and estimated values in the time-series. The advantage of the ARIMA model is that the related projections accommodate seasonal variation, errors and stationary periods within a dataset.

The model followed the same methods developed within the Department of Health in 2002 (8), which used monthly data on the number of HD treatments from NT public hospitals and expanded the model to project demand for facility-based HD treatments for Indigenous and non-Indigenous Territorians separately.

Markov chain modelA Markov chain model was also constructed as an alternative method for estimating RRT demand in the NT. The model was based on the general structure (including some assumptions) of an earlier model used to estimate RRT demand at a national level (3). The model follows a cohort of existing patients with ESKD and receiving RRT, along with the addition of patients starting RRT each year. For each cohort of future patients, the model assigned probabilities which were based on historical ANZDATA data from 2003 to 2012. The length of each ‘treatment’ cycle in the model was one year. The structure of the model is shown in detail in Figure 1, which presents the pathway for dialysis patients as they cycle through the different RRT modalities. The model was stratified by the age groups: 25-44, 45-64, 65-74, and 75 years and older. The Markov chain model used ANZDATA rather than NT public hospitals data.

The annual incidence of ESKD treated with RRT and the prevalence of ESKD treated with different RRT modalities in the NT were projected up to 2022 using historical data from 2003-2012. The “base” projection was predicated on the assumption that the observed probabilities of changes to patient outcomes from 2003 to 2012 would be maintained to 2022. The number of facility-based HD patients was inflated by a factor of 1.33, because of a 75% registration rate in the ANZDATA for NT residents receiving RRT over the five-year from 2008-2012. At this stage, we are unable to report 95% confidence interval for the Markov chain model as the summary data used in the model does not contain sufficient information to estimate random variation. Technically the Markov chain model in this study is described as “static” Markov chain model as the probabilities of the various events, based on historical data, have been assumed to remain constant through time.



Scenario modelling Unlike other regression methods, the Markov chain model is based on assumptions that can be varied, which allows sensitivity analysis. We modelled the subset of facility-based HD treatment in five scenarios, each with different assumptions (listed below), to assess the impact on future HD demand.

Scenario 1 – changing the kidney transplant rate

Scenario 2 – changing the proportion of patients undergoing self-care dialysis, whether peritoneal dialysis (PD) or HD

Scenario 3 – changing the annual incidence of ESKD

Scenario 4 – changing the death rate

Scenario 5 – changing the dialysis attendance rate.

Figure 1: Flow chart of ESKD for dialysis projection, 2003-2022

Actual ESRD incident rate

Tx failed rate Tx dth rateDx dth rate Tx incident rate

ESRD New pats Tx newTx failure Dths TxDths Dx

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Incident2003-12

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Dx prev13 ═Dx prev12┼Dx new13 ┼Tx fails to Dx13─ Dths Dx13 ─Tx new13┼/ ─ ageing effect

Dialysis projectionby modality2013-2022

Dx

modality

Notes: Data based on dialysis clients registered with ANZDATA.ANZDATA=Australia and New Zealand Dialysis and Transplant Registry; Dths=Deaths; Dx=Dialysis; ESKD=End-state kidney disease; pats=Patients; prev=Prevalent; Tx=Transplant


Note: Dx: dialysis Tx: transplant


Results Descriptive analysis Number of patients with end-stage kidney disease Using NT public hospital data, between 2001 and 2012, at least 1,344 patients were newly diagnosed with ESKD using the “strict” definition and 3,391 patients were diagnosed using the “broad” definition (Table 2). Using the “strict” definition, 954 (71%) patients were treated with RRT; using the “broad” definition, 1247 ESKD patients (37%) were treated with RRT (Table 2).

Table 2: Incidence of end-stage kidney disease by definitions, Northern Territory public hospitals 2001-2012

“Strict” definition “Broad” definition

All ESKD Treated with RRT All ESKD Treated with RRT

2001 122 53 274 74

2002 122 60 321 78

2003 119 70 315 89

2004 130 69 399 154

2005 159 93 436 110

2006 118 82 362 99

2007 128 90 431 101

2008 83 93 232 108

2009 58 79 113 97

2010 77 75 141 101

2011 105 94 164 113

2012 123 96 203 123

Total 1344 954 3391 1247

Notes: ESKD= End-stage kidney disease; RRT= Renal replacement therapy.

The average time from the first diagnosis of ESKD to the initial dialysis treatment in an NT public hospital has decreased dramatically for both Indigenous and non-Indigenous people (based on the broader definition) (Figure 2). For Indigenous people, the period decreased from 1.6 years in 2001-2002 to 2.4 months in 2012-2013. For non-Indigenous people, it also dropped, from around one year before 2006 to one month in 2012.

Among 1,247 ESKD patients treated with dialysis in NT public hospitals, 72% were recorded in ANZDATA during the 12-year period (Table 3). The average registration rate has been about 75% in the last five years. Figure 3 shows that patients aged 45-64 years old were more likely to be registered in the ANZDATA compared to the other age groups.



Figure 2: Average interval from diagnosis to dialysis for patients with a diagnosis of ESKD, Northern Territory public hospitals 2001-2012

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Table 3: Number of new patients (with ESKD) registered in ANZDATA compared with those treated in Northern Territory public hospitals 2001-2012

New ESKD patients registered

New ESKD patients treated Registration rate (%)

2001 60 74 81.1

2002 60 78 76.9

2003 57 89 64.0

2004 81 154 52.6

2005 85 110 77.3

2006 76 99 76.8

2007 76 100 76.0

2008 90 109 82.6

2009 72 97 74.2

2010 65 101 64.4

2011 83 112 74.1

2012 98 124 79.0

2001-2012 Total 903 1247 72.4

2008-2012 Total 408 543 75.1

Notes: ANZDATA= Australia and New Zealand Dialysis and Transplant Registry; ESKD= End-stage kidney disease;



Figure 3: Trend in the proportion of all hospital patients receiving dialysis for ESKD who are registered in ANZDATA, by age group and three time periods; 2001-2004, 2005-2008 and 2009-2012

2001-04 2005-08 2009-120

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Notes: ANZDATA= Australia and New Zealand Dialysis and Transplant Registry

End-stage kidney disease not treated with renal replacement therapyDuring 2001-2012, there were 1.6 untreated ESKD patients in addition to every treated ESKD patient when using the “broad” definition (Figure 4). Using the “strict” definition, there were 0.4 untreated patients for every treated patient (Figure 5). Like national figures, NT Indigenous people had higher rates of RRT for ESKD compared to non-Indigenous people (2).

Figure 4: Patients with ESKD treated and not-treated with RRT using “broad” definition, by age and Indigenous status, NT public hospitals 2001-2012

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Notes: ANZDATA=Australia and New Zealand Dialysis and Transplant Registry; ESKD= End-stage kidney disease; RRT= Renal replacement therapy.

Figure 5: ESKD patients treated and not-treated with RRT using “strict” definition by age and Indigenous status, 2001-2012

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Notes: ANZDATA=Australia and New Zealand Dialysis and Transplant Registry; ESKD= End-stage kidney disease; RRT= Renal replacement therapy.

Conservative care for patients with end-stage kidney disease Over the years of study, there have been substantial increases in the number of ESKD patients receiving conservative care services including palliative care. The number of ESKD deaths who were admitted under palliative care service doubled between 2005 and 2012, in part related to the opening, in 2005, of the hospice located on the Royal Darwin Hospital campus (Table 4). In 2012, 47% of patients who died in hospital with ESKD were under the care of palliative care services. This can be compared with only 22% nationally (9). The number of ESKD patients who received conservative care was likely to be under-reported as conservative care provided in communities was not available for inclusion in this study.

Table 4: Number and proportion of patients with end-stage renal failure who died in the care of palliative care services, 2001-2012

Based on all diagnoses Based on principal diagnosis

Total ESKD patient deaths

Palliative care patient deaths

% of all palliative care patient

deathsTotal ESKD

patient deaths

Palliative care patient

deaths

% of all palliative

care patient deaths

2001 81 6 7.4 6 1 16.7

2002 88 6 6.8 5 0 0.0

2003 113 18 15.9 4 2 50.0

2004 104 6 5.8 4 0 0.0

2005 156 26 16.7 5 2 40.0

006 129 37 28.7 3 1 33.3

2007 152 51 33.6 5 5 100.0

2008 166 42 25.3 2 1 50.0

2009 125 58 46.4 7 5 71.4

2010 143 55 38.5 8 6 75.0

2011 139 63 45.3 17 12 70.6

2012 131 61 46.6 11 9 81.8



Total 1527 429 28.1 77 44 57.1

Note: ESKD= End-stage kidney disease, using codes of N17-N19 following AIHW definition

Survival rateOver the last ten years there has been considerable improvement in the five-year survival of ESKD patients receiving dialysis in the NT, with median survival rates increasing from 4.5 years in 1995-99 to 6.0 years in 2005-2009. This improvement has outstripped the average national improvement, from 4.5 years in 1995-99 to 5.0 years in 2005-2009 (Figure 6). When the younger age of the dialysis population in the NT is taken into account, five-year survival in the NT (5.3 years) still compares favourably with the national survival (Figure 7). The impact of future changes in death rates was indirectly modelled in the fourth scenario of this study.

Figure 6: Crude survival for haemodialysis patients by four time periods, 1995-1999, 2000-2004, 2005-2009 and 2010-2011, Northern Territory and Australia.

Source: Australia and New Zealand Dialysis and Transplant Registry 2012 unpublished analysis



Figure 7: Age adjusted survival for haemodialysis patients by four time periods - 1995-1999, 2000-2004, 2005-2009 and 2010-2011, Northern Territory and Australia.

0.100.200.300.400.500.600.700.800.901.00

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Adjusted for age & gender, not censored at transplant

Survival from Start of Dialysis Treatment, by Time & Place of Start

Source: Australia and New Zealand Dialysis and Transplant Registry 2012 unpublished analysis



Models and projections Renal projection overviewTable 5 displays the stock and flow of NT residents with ESKD for the period from 2001 to 2012. During the twelve-year period there was an average of about 500 RRT registered patients in the NT. Among them, 15% were new patients (75), 70% existing dialysis patients (351) and 15% existing functional transplant patients (71).

The renal transplant rate is defined as the number of new transplants in a specific year as a proportion of the number of prevalent dialysis patients in the previous year (see abbreviations & glossary). The transplant failure rate is defined as the number of transplant failures as a proportion of functional transplants, considering the number of deaths during functional transplant in a specific year. Death rate is the number of deaths occurring in a specific calendar year as a proportion of all prevalent dialysis and functional kidney transplant cases for that year. Death rates were further classified as death during dialysis and death with a functional kidney transplant.

Table 5: Annual number of NT residents undertaking RRT, including new and prevalent cases, deaths and renal transplants, NT residents 2001 -2012

New cases

Prevalence Deaths Transplant

Dialysis Functionaltransplant

Duringdialysis

Duringfunctional transplant

Newtransplant

Transplantfailure

2001 60 210 66 10 4

2002 60 233 68 32 2 12 7

2003 57 249 67 35 3 9 1

2004 81 279 73 39 4 10 4

2005 85 316 71 42 3 7 2

2006 76 334 73 52 2 6 4

2007 76 368 78 37 2 6 2

2008 90 398 74 57 3 4 2

2009 72 417 67 45 4 5 6

2010 65 441 69 43 1 5 1

2011 83 464 68 49 4 12 3

2012 98 502 77 47 3 16 3

Total 903 4211 851 478 31 102 39

average

75 351 71

40 3 9 3Source: Australia and New Zealand Dialysis and Transplant RegistryNotes: 1) new renal transplant rate in 2012 = 16/(464) =3.4% 2) transplant failure rate in 2012= transplant failure / (prevalent functional transplant – deaths during functionary transplant) = 3/(77-3) =3.9%3) death rate in 2012= (deaths during dialysis + deaths during functional transplant ) / total prevalent = (47+3)/(502+77)=8.6%

In 2012, the renal transplant rate was 3.4%, indicating for every 100 dialysis patients, 3.4 patients would undergo renal transplant. The renal transplant failure rate of 4.1% indicated of 100 functional transplant patients, 4 patients would develop renal failure.



The death rate was 8.6%, suggesting that for every 100 ESKD with treated RRT, 8.6 would die either during dialysis or with a functional transplant.

Figure 8 is a summary of the number of facility-based HD treatments in the NT using historical data and the three different methods of projection. From 2001 to 2012, the annual HD treatments increased by 3,200 per year (Table A1). The projected annual increase of HD treatments from 2013 to 2022 ranged from 2,700 using the Markov chain model to 3,300 by the time-series and 3,700 by the linear regression model.

Figure 8: Number of HD treatments from 2001-2012, and estimates for 2013-2018, using three statistical models

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Time-series model The projected numbers of HD treatments from the ARIMA time-series model are displayed for NT Indigenous people (Figure 9) and NT non-Indigenous people (Figure 10). The annual projected HD treatments using the ARIMA model, from 2013 to 2022, is an increase of around 3,300 and 180 for NT Indigenous and non-Indigenous people respectively (Table A2). The total annual projected HD treatment increase is about 4.5% for both Indigenous and non-Indigenous people.

Linear regression modelFigure 11 shows the projected annual total number of HD treatment together with 95% confidence intervals. The projected annual increase based on the linear regression model is 6.03% (95%CI: 6.02-6.03%) which was higher than the estimate based on the ARIMA model. Figure 12 and Table A3 display the actual and projected number of HD patients by gender, age group and Indigenous status.



Markov chain model The Markov chain model is based on a cohort of existing patients with ESKD receiving RRT, along with the addition of new patients starting RRT each year. It also uses exit information of patients receiving RRT, with either death during treatment or a change in the modality. An average of 75 patients started RRT every year, and the average number of prevalent RRT cases was 422 (83% were dialysis patients and 17% were functional renal transplant patients) (Table 5). The ANZDATA information of patient flow is listed in Table 5 and the number of patients changing from one modality to another modality in the following year is summarised and reported in TableA4. For the projection, we projected the number of patients receiving RRT modalities by using a static Markov chain model. The results were reported in Figure 13 and TableA5. The summary Figure 8 was only part of the full Markov chain model which only included the facility-based HD treatments. The number of facility-based HD patients was inflated by a factor of 1.33, due to a 75% registration rate in the ANZDATA registry of NT residents receiving RRT over the five-year from 2008-2012 (Table 3). The average number of HD treatments per patient per calendar year was based on historical NT public hospitals data using a linear regression projection.

Figure 9: Monthly actual and projected haemodialysis treatments with 95% confidence interval by time-series model for Indigenous people

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Figure 10: Monthly actual and projected haemodialysis treatments with 95% confidence interval by time-series model for non-Indigenous people

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023

0

100

200

300

400

500

600

700

800

Upper limitActual HDProjected HDLower limit

Month & Year


Figure 11: Yearly projected haemodialysis treatments by linear regression with 95% confidence interval, 2013-2022

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

upper limit

projection

lower limit

Number of haemodialysis



Figure 12: Actual (2001-12) and projected (2013-22) HD treatment by linear regression model by age, gender and Indigenous status

-

10,000

20,000

30,000

40,000

50,000

60,000

Ind Female >=40

Ind Male >=40

Non-Ind Female <40

Non-Ind Female >=40

Non-Ind Male <40

Non-Ind Male >=40

Ind Female <40

Ind Male <40


Figure 13: Projected number of haemodialysis patients by modality, Markov chain model, 2013-2022

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 20220

100

200

300

400

500

600

700

800

900

1000

Transplant

Home HD

Sat HD

Hosp HD

CAPD

APD

Number of patients

Notes: APD= automated peritoneal dialysis; CAPD= continuous ambulatory peritoneal dialysis; HD=haemodialysis; Hosp=hospital; Sat=satellite



Scenario analysis Five different scenarios were applied to the projections in the Markov chain model which unlike other methods, allows a variation of the underlying parameters and assessment of the impact of those variations on future demand for haemodialysis. These scenarios and the changes to underlying assumptions were based on realistic variations in clinical practice.

Scenario 1 – changing the transplant rateThe average transplant rate was 2.5% of all NT dialysis patients, from 2010 to 2012, which was used in the base model and resulted in a small yearly increase in absolute numbers from 2013 to 2022. The assumption for a lower projection was that the transplant rate increased 0.3% annually from 2013 and plateaued at 3.9% from 2016 onwards. The assumption for higher projection was that the transplant rate decreased 0.3% annually from 2013 and plateaued at 1.3% from 2016 onwards (Figure 14).

Scenario 2 – changing the proportion of the self-care dialysis The average proportion of self-care dialysis (both peritoneal dialysis and self-care HD) was 13% during the five-year period from 2008 to 2012. The assumption for the lower projection was that self-care dialysis increased to 17% between 2013 and 2015, and 20% thereafter. The assumption of the higher projection was to maintain the self-care dialysis proportion at 10% (Figure 15).

Scenario 3 – changing the incidence rate per year Patients in the age group of 45-64 years accounted for almost 60% of all patients receiving RRT in the NT. The annual rate of change was no increase or decrease from 2003 to 2012. The assumption for the lower projection was an a 5% annual decrease in rate and the assumption for the higher projection was 5% annual increase for those aged 45-64 years old (Figure 16).

Scenario 4 – changing the dialysis death rateAge-specific death rates were calculated for 2008-2012. The assumption for the lower projection was that the death rate increased 10% annually and the assumption of the higher projection was the death rate decreased 10% annually, from 2013 onwards (Figure 17).

Scenario 5 – changing the frequency of HD treatmentsDuring 2001-2012, the average number of HD treatments per patient increased by 0.9% annually. In 2013, the average number of HD treatments per patient was 96. The assumption for the lower projection was that the average number of treatments was maintained at 96 over future years and the higher projection assumption was the number of treatments increased at 1.9% annually from 2013 (Figure 18).

Table 6 lists the varying assumptions underlying the five scenarios; for each of the five scenarios, all other assumptions reverted to the base model. The lower projection is defined as any scenario which results in a projection of fewer facility-based HD treatments than the base model, while the higher projection is defined as any scenario which results a projection of more HD treatments than the base model.



Table 6: Four different scenarios in facility-based haemodialysis treatment numbers projection

Base model Lower projection Higher projection

1. Transplant rate2.5%

based on average rate for 2010-12

Annual 0.3% increase to 3.9%

from 2016 onwards

Annual 0.5% decrease to 1.3% from 2016

onwards

2. Proportion of “self-care” dialysis patients

13%

(based on average rate for 2008-12)

17% for 2013-2015 & 20% thereafter

10% (from 2013 to 2022)

3. Incidence rate for all ages*

420

(in 2012) 320 500+Annual Incidence rate change (for 45-64 yr) 1.00 0.95 1.05

4. Dialysis death rate

Age-specific death rate

(based on average rate for 2008-12)

Annual 10% increase from the

base modelAnnual 10% decrease

from the base model

5. Number of HD treatments attended

96 HD treatments in 2013 and increasing

0.9 annually (10-years average)

Maintaining 96 HD treatments over the

years Increasing 1.9% annually from

2013Note: *per 1,000,000 population

Figure 14: Scenario 1 – the impact of facility-based haemodialysis treatments by changing the transplant rate

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

-

20,000

40,000

60,000

80,000

100,000

120,000 ActualBase model (@2.5%, 5-years average)Increasing @0.3% annually until 2016Decreasing @0.3% decrease annually until 2016




Figure 15: Scenario 2 – the impact of facility-based haemodialysis treatments by changing the proportion of self-care dialysis

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

-

20,000

40,000

60,000

80,000

100,000

120,000 ActualBase model (@0.130)Increasing to 17% from 2013-2015 and 20% onwardsMaintaining 10% over the years


Figure 16: Scenario 3 – the impact of facility-based haemodialysis treatments by changing the annual incidence rate for those aged 45-64 years old

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

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2022

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90,000

100,000 ActualBase model (@0.997)Decreasing 5% annuallyIncreasing 5% annually




Figure 17: Scenario 4 – the impact of facility-based haemodialysis treatments by changing the death rate

2001

2002

2003

2004

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2006

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120,000 ActualBase model (@10% (10-year average))Increasing 10% annuallyDecreasing 10% annually


Figure 18: Scenario 5 – the impact of facility-based haemodialysis treatments by changing the frequency of haemodialysis treatments

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

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2022

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60,000

80,000

100,000

120,000 Actual

Base model (@96 HD treatments in 2013 and 0.9% annual increase)

Maintaning 96 HD treatments per year

Increasing 1.9% annually






Discussion General summary statementThis report describes analysis of de-identified patient-level unlinked data from two main sources, the ANZDATA registry and NT public hospitals admitted dataset, to estimate future demand for dialysis. The estimations, using three statistical models and two data sources, have reached similar projections for renal service and dialysis demand. The estimates between the highest projection method (linear regression) and the lowest projection method (Markov chain) varied from 1% of middle estimate (58,800) in 2013 to 21% of middle estimate (88,168) in 2022. There has been no similar multi-faceted modelling exercise reported nationally or internationally.

Total numbers of patients with ESKD in the NT The starting point for assessing demand is to determine the number of patients who are developing ESKD and the expected proportions who will progress to either conservative treatment or to RRT. This initial step is problematic as there is no reliable source for determining the incidence of ESKD in the NT population. In a recent AIHW report, the “broad” definition of ESKD using death data included those specified, other and unspecified chronic renal failure together with hypertensive heart disease accompanied by renal failure. We followed the AIHW definition in our report, so that comparisons could be made between NT and national figures. We also applied a revised “strict” definition, which focused on the specified chronic renal failure (stage 5) and ESKD.

Our study showed that by applying the AIHW definition, there are currently many more NT patients diagnosed with ESKD than are currently treated by the different forms of RRT. Awareness of CKD in the NT amongst clinicians and clinical coders is high, which is likely to lead to greater diagnosis of CKD than in other jurisdictions. However, applying the second and more plausible definition of ESKD to the NT public hospitalisation dataset suggests that there is not a significant level of “unmet need” for RRT in the NT.

Decreasing time between diagnosis of ESKD in hospital data and commencement of RRTClinically, it is meaningful to estimate the duration from the first ESKD diagnosis to the commencement of RRT. This information would be useful in projecting the new intake of the RRT patients. Primary care or laboratory data are required to accurately define the date when the first ESKD diagnosis was established. In this study we were unable to access these two datasets and therefore the first date of ESKD diagnosis was unknown. Figure 2 provided only a proxy estimate from the hospital data, which showed a reduction in time between the first diagnosis of ESKD in hospital data and commencement of RRT. The reduction was seen in both “strict” and “broad” definition. This measure does not necessarily reflect changes in the natural history of CKD/ESKD or clinical practice.

Registration rateAn average of 75% of ESKD diagnoses treated with RRT identified in the NT public hospital dataset using the “broad” definition were apparent in ANZDATA numbers. Part of this difference may be the result of CKD/ESKD coding changes noted above, as the



“strict” definition showed a much smaller discrepancy between the two sources of data. Other possible reasons include: inclusion of “holiday” HD patients from interstate or overseas, mis-coding of patients receiving HD for acute kidney injury rather than for ESKD and the ANZDATA registry’s criteria of 3-months ongoing modality treatments. More study is needed to investigate the demographics and clinical profile for those not registered in the ANZDATA. This further study would require linked data which is beyond the scope of this study.

Since the Markov chain model used ANZDATA rather than NT public hospital data, a 1.33 inflation factor was applied to patient numbers used in this model (due to 75% registration rate) to enable comparison with the other models based solely on NT public hospitals data.

“Unmet needs?”Examining the proportion of patients with ESKD who are not treated with RRT may be an indicator of the unmet needs of the population, particularly in younger age groups (less than 65 years old).

Nationally, there were similar numbers of treated (10,949) and untreated (10,421) cases during 2003-2007 (using ANZDATA and death registration data rather than hospitalisation data). Treatment rates, however, were highly influenced by age. For those aged under 65 years, more than 90% of cases received RRT. Treatment rates fell substantially in the older age groups, with only about one-tenth of those aged 80 years or over receiving RRT (Figure 19).

Figure 19: Treated and not-treated ESKD patients, by Indigenous status, Australia 2001-2012

Source: Australian Institute of Health and Welfare 2011. End-stage kidney disease in Australia: total incidence, 2003–2007. Cat. no. PHE 143. Canberra: AIHW.

In the NT, it is not clear if there is much scope for an increase in uptake of RRT treatment for people aged 40-64 years old with identified ESKD (Figure 5). As previously noted, diagnosis coding changes within the study period create the differences between Figure 4 and Figure 5.

The number of NT non-Indigenous clients with ESKD who do not receive RRT treatment may be overestimated in this report. Historically non-Indigenous people from the NT have higher rates of migration interstate than any other jurisdictions: the migration rate increases with advancing age, possibly to seek medical treatment or family support. From the limited data sources the NT Department of Health hold there are suggestions that the higher proportion of non-Indigenous patients with ESKD who do not receive RRT in the NT might be due to interstate migration after diagnosis (loss


DEPARTMENT OF HEALTHto follow-up 25% for non-Indigenous vs. 11% for Indigenous), even though non-Indigenous absolute numbers are much lower than Indigenous numbers. For this study, we were unable to track interstate treatments among those who migrated. Therefore, it would be hard to estimate the true RRT for non-Indigenous people.

Conservative careUnderstanding the change of ESKD patients receiving palliative care services is important in renal demand modelling process as it might impact on the number of HD treatments per patient as well as the death rate among dialysis patients. Not only did the number of ESKD patients in palliative care services double from 2005 to 2012, the NT rate of ESKD who died in the palliative care was more than two times the national average. Though the absolute number was small, the recorded number of ESKD patients receiving palliative care may have been underreported, as information of the service delivered within remote communities was not validated and used in this study.

ProjectionsThree models (linear regression, time-series ARIMA and Markov chain) estimated similar numbers of facility-based HD treatments in the future. From 2001 to 2012, the annual average number of HD treatments increased by around 3,200 per year. The projected annual increase of HD treatments ranged from 2,700 by Markov chain model to 3,300 by time-series and 4,600 by linear regression model (Figure 8 and Table A1). The relatively high degree of concordance between models is more noteworthy than the absolute variation of their projections, as the models used different methodology and datasets to arrive at similar numbers.

The projection indicates that the number of NT residents commencing RRT will lie in the range of 593-639 in 2013 and 660-1117 in 2022 according to the Markov chain model. These projections are based upon observed age-specific incidence rate for treated ESKD. An increasing demand for RRT could be driven by an increasing incidence of CKD, and increase of population projections.

The time-series ARIMA model used historical data of the number of HD treatments over 144 monthly points. The advantage of the ARIMA modelling is it can take into account trends, seasonality, cycles, errors and non-stationary aspects of a data set when making projections. None of other models are able to pick up precisely of these changes. The actual number of HD treatments embeds background changes in population, including incidence, prevalence, participation rate, death rates and capacity of renal services; but none of these are explicitly taken into account in the time-series model and its projection.

The linear regression model takes into consideration both the actual yearly change in numbers of HD treatments as well as projected yearly changes to the population. As constructed for this report, this model is more flexible than the time-series model in that it can model and project different sub-population groups separately. The linear regression relies on the assumptions that the HD treatment rate maintains the linear relationship to the dependent variable. The assumption, however, might not be validated in future due to the changing policy and clinical practice.

The Markov chain model projected the most conservative estimate among the three models. Based on ANZDATA rather than NT public hospitals admitted data, its strength is the ability to model changes to the numbers and movements of patients (“stock and flow”) according to assumption that can be varied (if, for example, there was a change to clinical practice). Neither of the other two models is able to do this. The projection



from Markov chain is lowest among the three models even though the number of HD treatments was inflated by a factor of 1.33, due to a 75% registration rate.

Scenarios based on the modifications to Markov chain modelScenario 1 – changing the transplant rate

Future variations in the rate of kidney transplantation in the NT modelled are plausible as there are widely differing clinical opinions about its role for Indigenous Territorians. Some believe that transplant rates have been inequitably low and should be increased dramatically, whereas others are concerned about the high mortality and rates of kidney failure after transplant and argue for more caution.

Scenario 2 – changing the proportion of “self-care” dialysis patients

Proportionally, the self-care dialysis modality in the NT has been quite stable over the last decade. It is highly unlikely that a future large rise of the proportion of self-care treatments occurs among a remote Indigenous population with low levels of school education. The increase of self-care dialysis is also constrained by lack of the additional resources required. It is plausible that smaller increases in proportion will occur; equally it is also plausible that the proportion will decline slightly (whilst absolute numbers continue to increase) as the burden of a high incidence of ESKD continues.

Scenario 3 – changing the incidence rate of RRT

The annual incidence rate for ESKD commencing RRT has remained stable at approximately four time national rates for the last ten years. Both a continuation at the same rate or a small but sustained decrease (due to the improved delivery and maintenance of strategies to identify and delay the progression of earlier stages of chronic kidney disease in primary care in the NT) are plausible. As the “strict’ definition for ESKD in the NT public hospitalisation data is more clinically plausible, there does not appear to be much “unmet need” for RRT in the NT.

Scenario 4 – changing the dialysis death rate

The base model scenario (projecting continuation of the 2008-2012 dialysis survival rate) is more likely than either increases or decreases. Further improvements beyond those of the last ten years would require better age-adjusted survival than national figures. Deterioration (a rise in dialysis death rates i.e. due to decrease in survival rate) is also unlikely as improvements appear to have been sustained. Conversely an increase in the uptake of conservative (palliative) care options is more likely due to the ongoing utilisation of the renal palliative care pathway and continued resourcing. Therefore survival rates could be negatively influenced by an increase in the utilisation of renal conservative care options. Conservative care is a viable option for the management of chronic diseases.

Survival rates, death rates and renal palliative care admissions and related deaths should continue to be followed closely. The modelled changes to the base model assume 10% continued year-on-year improvements in survival rates, which are less likely than smaller variations or one-off changes.

Scenario 5 –changing the number of HD treatments attended

The base model scenario identifies small sustained rises in HD attendance from 2001-2012. Projections are more likely to realise either a large increase in HD attendance or a static picture. However both of these options have been modelled. It is pertinent to note that there is scope for substantial increases in the number of HD treatments in the NT regardless of any changes in the number of treated patients. The possibility of


DEPARTMENT OF HEALTHdecreasing in the number of treatments might be related to an increase in the uptake of conservative care option which was stated above.

LimitationsOne method to project future incidence of ESKD receiving RRT services involves understanding the incidence of CKD at later stages (stage 4 & 5), the CKD progression rate, the uptake rate of RRT amongst those known to have CKD and the uptake of RRT amongst those not previously known to have CKD. Currently, our study is limited by a lack of reliable linked information on CKD, and is focused on the modelling from ESKD diagnosis onwards. Nevertheless, we estimated future demand for dialysis by assuming different scenarios to reflect the potential change of CKD incidence and progression rate on RRT demand.

The data used in this report is subject to potential variation and possible bias, relying as it does on a chain of interpretation from a doctor’s diagnosis, to a clinical coder’s arbitration, to data entry and finally to a data analyst’s definition. This is inherent in any analysis of administrative data. Of the methods available to check the quality of administrative data, the most accessible and realistic is data linkage (although this also has methodological challenges). The data used in this report were not linked and this represents an opportunity for future work.

This report does use data from different sources that are able to be cross-referenced, uses three different models that create similar projections of the number of facility-based HD treatments and develops tools that allow sensitivity of total numbers of facility-based HD treatments to different clinical assumptions and demographic changes to be tested.

The projection from Markov chain is lowest among the three models even though the number of HD treatments was inflated by a factor of 1.33, due to a 75% registration rate. The current static Markov chain model assumes that observed probabilities of changes to patient outcome using 2003 to 2012 data would be maintained to 2022. This might not be true in the recent years when the renal service has undergone substantial changes in practise and policy. At this stage, we were unable to report the 95% confidence interval for Markov chain model as the “static” Markov chain model does not provide the technique for simulation. This requires a more advanced method – the “dynamic” Markov chain model, which simulate disease progressions and modality switches under influence of random variations for all key assumptions. The simulation will then be able to provide the upper and lower limit of 95% confidence interval when the key assumptions vary simultaneously.

Recommendations To conduct further investigation in the counting of dialysis separations for

satellite and home based therapies and identify and close any counting gaps. This will, over time, improve the validity and reliability of future renal projections;

To expand the static Markov chain model to a dynamic Markov chain model to provide more accurate estimation of the random variations of the key assumptions;

To improve the Markov chain model by including additional parameters such as yearly progression rate from CKD at Stage 4 and 5 to ESKD, the current rate of patients with ESKD and the ESKD patients not progressing to RRT;



To investigate the impact of the ANZDATA three month registration stand-down period on dialysis data available for planning and benchmarking.



Appendix: Supplementary tablesTable A1: Actual and projected facility-based haemodialysis treatments using three statistical models, NT public hospitals 2013-2022

YearNumber of

haemodialysisProjected by linear

regression

Projected by

Time-seriesProjected by

Markov chain

2001 20,549

2002 24,342

2003 25,881

2004 29,692

2005 33,516

2006 35,437

2007 37,048

2008 41,440

2009 45,052

2010 46,232

2011 50,177

2012 55,650 55,650 55,650 55,117

2013 59,070 58,798 58,260

2014 63,048 62,062 60,967

2015 67,117 65,325 63,719

2016 71,315 68,588 66,154

2017 75,487 71,851 68,541

2018 79,812 75,115 70,969

2019 84,379 78,378 73,323

2020 89,360 81,641 75,704

2021 94,776 84,904 78,262

2022 100,367 88,168 82,185



Table A2: The actual number of HD treatments (2001-2012) and projected number of HD treatments (2013-2023) using time-series model by Indigenous status, NT public hospitals

Non-Indigenous Indigenous

Actual Projection Actual Projection

2001 2,189 18,368

2002 3,270 21,154

2003 3,419 22,515

2004 3,105 26,587

2005 3,210 30,306

2006 2,743 32,694

2007 2,373 34,675

2008 3,034 38,406

2009 3,302 41,750

2010 3,212 43,020

2011 3,361 46,816

2012 4,246 51,404

2013 4,617 54,182

2014 4,796 57,266

2015 4,975 60,350

2016 5,154 63,434

2017 5,333 66,518

2018 5,512 69,602

2019 5,691 72,686

2020 5,871 75,771

2021 6,050 78,855

2022 6,229 81,939



Table A3: The actual number of HD patients (2001-2012) and projected number of HD patients (2013-2023) using linear regression model, NT public hospitals

Non-Indigenous Indigenous Total

Female Male Female Male

<40 >=40<4

0 >=40 <40 >=40 <40 >=40

2001 1 22 7 24 21 89 19 75 258

2002 2 18 5 33 22 106 19 78 283

2003 3 26 5 35 24 128 19 79 319

2004 20 25 10 45 42 141 29 103 415

2005 4 29 7 37 26 163 26 113 405

2006 1 21 6 21 20 173 27 131 400

2007 2 26 7 30 26 180 25 136 432

2008 3 21 6 29 29 204 25 148 465

2009 6 19 3 32 32 212 24 147 475

2010 5 32 6 33 31 221 25 164 517

2011 5 19 5 39 38 240 27 172 545

2012 6 24 2 44 42 261 30 175 584

2013 6 18 4 36 34 300 26 212 637

2014 6 15 4 37 37 320 27 223 669

2015 7 12 4 37 39 340 27 234 701

2016 8 9 5 38 41 360 28 246 734

2017 8 7 5 39 43 380 28 257 766

2018 9 4 5 39 46 400 28 268 800

2019 10 0 5 40 48 422 29 280 835

2020 10 0 5 40 51 446 29 293 876

2021 11 0 5 41 53 472 30 308 921

2022 12 0 6 42 55 499 30 323 967

2023 13 0 6 43 58 526 31 338 1015



Table A4: Number and proportion of patients shifting from one modality in one year to another one in the following year, ANZDATA 2003-2012

Year 1 Year 2

Modality APD CAPD HHD SHD HMHD Death TG OKFR Total

Count

APD 361 26 71 32 1 26 17 534

CAPD 26 314 53 6 14 4 1 418

HHD 33 31 385 874 5 147 6 11 1492

SHD 69 33 204 4258 62 281 62 9 4978

HMHD 6 11 208 10 8 243

TG 15 864 25 904Total patients 489 404 719 5181 276 493 961 46 8569Proportion

APD 67.6 4.9 13.3 6 0.2 4.9 3.2 100

CAPD 6.2 75.1 12.7 1.4 - 3.3 1 0.2 100

HHD 2.2 2.1 25.8 58.6 0.3 9.9 0.4 0.7 100

SHD 1.4 0.7 4.1 85.5 1.2 5.6 1.2 0.2 100

HMHD 2.5 4.5 85.6 4.1 3.3 100

TG 1.7 95.6 2.8 100Note: numbers refer to the numbers of modality shifts per year (including continuing the same modality from one year to the next) ; HHD: hospital based HD; SHD: satellite based HD; HMHD: home-based HD; TG: transplant grafted; OKFR: own kidney function recovery

Table A5: Number of patients projected by treatment modalities, 2013-2022, using Markov chain static model

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

APD19 23 24 26 28 30 31 33 35 36 39

CAPD19 21 22 23 24 25 26 27 28 29 31

HHD39 41 43 45 46 48 49 51 53 55 58

SHD395 422 439 458 473 489 504 519 535 553 580

HMHD29 31 32 32 33 34 35 35 36 38 40

TG77 78 84 90 96 102 108 114 119 125 116

Total 579 616 645 674 701 727 754 780 807 836 863Note: HHD: hospital based HD; SHD: satellite based HD; HMHD: home-based HD; TG: transplant grafted; OKFR: own kidney function recovery



Abbreviation & glossaryANZDATA: Australian and New Zealand Dialysis and Transplant Registry, which compiles information about the incidence, prevalence and quality of RRT nationally and for each jurisdiction based on data sent to it by every treating renal unit.

ANZDATA registration rate: Number of ESKD registered in ANZDATA as a proportion of all patients identified with ESKD (see below glossary) with treatment in the NT hospitals.

APD: See the definition in PD

ARIMA: Autoregressive Integrated Moving Average, a form of analysis suited to time-series data where there is a significant random component. It is a form of regression analysis that seeks to project future movements by examining the differences between values in the series instead of using the actual data values. ARIMA modelling can take into account trends, seasonality, cycles, errors and non-stationary aspects of a data set when making projections.

CKD: Chronic kidney disease, which is present for more than three months and is characterised by varying changes to kidney function that can be measured and divided into five stages.

Death rate: In this report, the number of deaths occurring in a specific calendar year as a proportion of all prevalent dialysis and functional kidney transplant cases for that year. It was further classified as death during dialysis and death with a functional kidney transplant.

Dialysis: the process of purification of extracellular fluid in the body that involves osmosis and diffusion across a semi-permeable membrane. In this report, refers to both haemodialysis and peritoneal dialysis.

ESKD: End stage kidney disease, is the final stage of kidney failure (as that resulting from diabetes, chronic hypertension, or glomerulonephritis) that is marked by the complete or nearly complete irreversible loss of renal function. ESKD may or may not be treated with RRT. In this report, we applied two definitions of ESKD. One is a “broad” definition which followed the AIHW definition which includes specific and unspecified chronic renal failures, and a “strict” definition which excluded unspecified ”chronic renal failure” codes, and focused on specified chronic renal failure.

Facility-based haemodialysis: In a regional town or urban area, this is usually nurse-supported in a larger facility (a “satellite” such as at Nightcliff or Flynn Drive, or in a major hospital). In a remote community, this could be “self-care” in a primary health care clinic (a “renal-ready room”) or a demountable HD facility, but in certain cases are nurse-supported.

HD, haemodialysis, which involves removal of blood from the body and purification of this by a machine. Can be done in the home, in a community-based facility, in an urban or regional facility or in a hospital. Can also be self-care or nurse-supported.

HD utilisation rate: Number of HD treatments divided by the total NT population in a specific year.

Linear regression: a statistical process that describes the relationship between two continuous variables. In this report, refers to a model using the yearly rate of HD treatments per population stratified in various ways to project the yearly rate in future years.



Markov chain: a mathematical system that undergoes transitions from one finite state to others. In this report, refers to a static discrete-time model where probabilities of changes from one RRT modality to another (including death) are assigned and used for projection.

Palliative care: AIHW defined palliative care patients from the two hospital data components: "Palliative Care" from hospital care type information and “palliative care” (ICD-10 codes Z51.5) from diagnosis fields.

PD, peritoneal dialysis, which involves putting special sterile fluid into the space surrounding the stomach and intestines (the peritoneal space) for periods of time. Can be done during the day (CAPD: continuous ambulatory peritoneal dialysis) or overnight (APD: automated peritoneal dialysis). PD is almost always a self-care dialysis technique (see below).

RRT, renal replacement therapy, which refers to any or all of peritoneal dialysis, HD or kidney transplantation.

Renal transplant failure rate: Transplant failure rate is defined as the number of transplant failure as a proportion of functional transplants, given the number of deaths during functional transplant in a specific year.

Self-care: Self-care is dialysis is that is not nurse-supported, but is performed by the patient or a family member.

Time-series model: a general term that refers to any statistical model that uses data repeated over time. The ARIMA model is one particular type.

Transplant rate: Number of new kidney transplants in a specific calendar year as a proportion of the total number of dialysis patients in the previous year.



References

1. McDonald S, Excell L, Livingston B. ANZDATA Registry Report 2008. Adelaide, South Australia. Australia and New Zealand Dialysis and Transplant Registry, 2008.

2. McDonald S, Clayton P, Hurst K. ANZDATA Registry Report 2012. Adelaide, South Australia. Australia and New Zealand Dialysis and Transplant Registry, 2012.

3. Kidney Health Australia. The Economic Impact of End-stage Kidney Disease in Australia, Projections to 2020. 2010.

4. Australian Institute of Health and Welfare. Projections of the Incidence of Treated End-stage Kidney Disease in Australia. Canberra: AIHW, 2011.

5. Department of Health. Annual Report. Darwin, NT: 2013.

6. Department of Treasury and Finance. Population Projections Interim - Update (2013 Release). NT Government. 2013. Available from: http://www.treasury.nt.gov.au/Economy/populationprojections/Pages/PastPopulationProjections.aspx.

7. Australian Institute of Health and Welfare. End-stage Kidney Disease in Australia: Total Incidence, 2003–2007. Canberra: AIHW, 2011.

8. You J, Hoy W, Zhao Y, Beaver C, Eagar K. End-stage renal disease in the Northern Territory: current and future treatment costs. The Medical Journal of Australia. 2002;176(10):461-5.

9. Australian Institute of Health and Welfare. Trends in Palliative Care in Australian Hospitals. Canberra: AIHW, 2011.

10. Sparke C, Moon L, Green F, Mathew T, Cass A, Chadban S, et al. Estimating the total incidence of kidney failure in Australia including individuals who are not treated by dialysis or transplantation. American Journal of Kidney Diseases; 2013;61(3):413-9.



List of tables Table 1: Codes applied to identify end-stage kidney disease and treatment modality....3Table 2: Incidence of end-stage kidney disease by definitions, Northern Territory public hospitals 2001-2012........................................................................................................6Table 3: Number of new patients (with ESKD) registered in ANZDATA compared with those treated in Northern Territory public hospitals 2001-2012.......................................7Table 4: Number and proportion of patients with end-stage renal failure who died in the care of palliative care services, 2001-2012.....................................................................9Table 5: Annual number of NT residents undertaking RRT, including new and prevalent cases, deaths and renal transplants, NT residents 2001 -2012....................................12Table 6: Four different scenarios in facility-based haemodialysis treatment numbers projection.......................................................................................................................18Table A1: Actual and projected facility-based haemodialysis treatments using three statistical models, NT public hospitals 2013-2022.........................................................26Table A2: The actual number of HD treatments (2001-2012) and projected number of HD treatments (2013-2023) using time-series model by Indigenous status, NT public hospitals.........................................................................................................................27Table A3: The actual number of HD patients (2001-2012) and projected number of HD patients (2013-2023) using linear regression model, NT public hospitals.....................28Table A4: Number and proportion of patients shifting from one modality in one year to another one in the following year, ANZDATA 2003-2012..............................................29Table A5: Number of patients projected by treatment modalities, 2013-2022, using Markov chain static model.............................................................................................29



List of figuresFigure 1: Flow chart of ESKD for dialysis projection, 2003-2022....................................5Figure 2: Average interval from diagnosis to dialysis for patients with a diagnosis of ESKD, Northern Territory public hospitals 2001-2012.....................................................7Figure 3: Trend in the proportion of all hospital patients receiving dialysis for ESKD who are registered in ANZDATA, by age group and three time periods; 2001-2004, 2005-2008 and 2009-2012.......................................................................................................8Figure 4: Patients with ESKD treated and not-treated with RRT using “broad” definition, by age and Indigenous status, NT public hospitals 2001-2012.......................................8Figure 5: ESKD patients treated and not-treated with RRT using “strict” definition by age and Indigenous status, 2001-2012...........................................................................9Figure 6: Crude survival for haemodialysis patients by four time periods, 1995-1999, 2000-2004, 2005-2009 and 2010-2011, Northern Territory and Australia....................10Figure 7: Age adjusted survival for haemodialysis patients by four time periods - 1995-1999, 2000-2004, 2005-2009 and 2010-2011, Northern Territory and Australia..........11Figure 8: Number of HD treatments from 2001-2012, and estimates for 2013-2018, using three statistical models........................................................................................13Figure 9: Monthly actual and projected haemodialysis treatments with 95% confidence interval by time-series model for Indigenous people.....................................................14Figure 10: Monthly actual and projected haemodialysis treatments with 95% confidence interval by time-series model for non-Indigenous people..............................................15Figure 11: Yearly projected haemodialysis treatments by linear regression with 95% confidence interval, 2013-2022.....................................................................................15Figure 12: Actual (2001-12) and projected (2013-22) HD treatment by linear regression model by age, gender and Indigenous status................................................................16Figure 13: Projected number of haemodialysis patients by modality, Markov chain model, 2013-2022..........................................................................................................16Figure 14: Scenario 1 – the impact of facility-based haemodialysis treatments by changing the transplant rate..........................................................................................18Figure 15: Scenario 2 – the impact of facility-based haemodialysis treatments by changing the proportion of self-care dialysis.................................................................19Figure 16: Scenario 3 – the impact of facility-based haemodialysis treatments by changing the annual incidence rate for those aged 45-64 years old.............................19Figure 17: Scenario 4 – the impact of facility-based haemodialysis treatments by changing the death rate.................................................................................................20Figure 18: Scenario 5 – the impact of facility-based haemodialysis treatments by changing the frequency of haemodialysis treatments...................................................20Figure 19: Treated and not-treated ESKD patients, by Indigenous status, Australia 2001-2012



Selected Health Gains Planning publications Chronic diseasesZhao Y, Condon J, You J, Guthridge S, He V. Assessing improvements in survival for stroke patients in the Northern Territory 1992-2013: a marginal structural analysis. Australian Health Review, 2015.

You J, Condon JR, Zhao Y, Guthridge SL. Stroke incidence and case-fatality among Indigenous and non-Indigenous populations in the Northern Territory of Australia, 1999-2011. International Journal of Stroke, 2015.

Lawton PD, Cunningham J, Zhao Y, Gray NA, Chatfield MD, Baade PD, et al. Survival of Indigenous Australians receiving renal replacement therapy: closing the gap? Med J Aust 2015;202(4):200-4.

Li SQ, Guthridge SL, Aratchige PE, Lowe M, Wang Z, Zhao Y, Krause V. Dementia prevalence and incidence among the Indigenous and non-Indigenous populations of the Northern Territory, using capture-recapture methods Med J Aust 2014; 200(8):465-469.

Condon J, You J, McDonnell J. Performance of comorbidity indices in measuring outcomes after acute myocardial infarction in Australian Indigenous and non-Indigenous patients. Intern Med J 2012;42(7):e165-73.

Zhao Y, Condon JR, Guthridge S, You J. Living longer with a greater health burden--changes in the burden of disease and injury in the Northern Territory Indigenous population between 1994-1998 and 1999-2003. Aust N Z J Public Health. 2010;34 Suppl 1:S93-8.

You J, Condon J, Zhao Y, Guthridge S. Incidence and survival after acute myocardial infarction in Indigenous and non-Indigenous people in the Northern Territory, 1992-2004. Med J Aust 2009;190(6):298-302.

Fearnley E, Li S, Guthridge S. Trends in chronic disease mortality in the Northern Territory Aboriginal population, 1997-2004: Using underlying and multiple causes of death. Aust N Z J Public Health 2009;33(6):551-5.

Zhao Y, Connors C, Wright J, Guthridge S, Bailie R. Estimating chronic disease prevalence among the remote Aboriginal population of the Northern Territory using multiple data sources. Aust N Z J Public Health 2008;32(4):307-13.

Burden of disease and injuryZhao Y, Condon J, Guthridge S, You J. Living longer with a greater health burden: changes in the burden of disease and injury in the Northern Territory Indigenous population between 1994-1998 and 1999-2003. Aust N Z J Public Health 2010;34 Suppl 1:S93-8.

Zhao Y, Malyon R. Life years at risk: A population health measure from a prevention perspective. Int J Environ Res Public Health 2009;6(9):2387-96.

Zhao Y. Disease burdens and disability-adjusted life years (DALYS) in Aboriginal and Non-Aboriginal populations. In: Preedy V, Watson R, eds. Handbook of Disease Burdens and Quality of Life Measures Springer, 2009;603-27.

Zhao Y, Guthridge S, Magnus A, Vos T. Burden of disease and injury in Aboriginal and non-Aboriginal populations in the Northern Territory. Med J Aust 2004;180(10):498-502.


Documents

Purpose · Web viewI132Hypertensive heart & renal dis w heart renal fail ESKD codes (strict definition) N18.0End-stage renal disease N18.5Chronic kidney disease, stage 5 I120Hypertensive