D EPARTM ENT OF HEALTH
Renal Replacement Therapy
Demand Study,
Northern Territory,
2001 to 2022
Jiqiong You
Paul D Lawton
Yuejen Zhao
Susan Poppe
Nicole Cameron
Steven Guthridge
March 2015
DEPARTMENT OF HEALTH
Acknowledgements
The 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 nonprofit 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]
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Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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Table of contents
Summary
Introduction
Methods
Data sources
Statistical analysis
Descriptive analysis
Linear regression model
Time-series model
Markov chain model
Scenario modelling
Results
v
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3
4
4
4
6
Descriptive analysis
Models and projections
Renal projection overview
Time-series model
Linear regression model
Markov chain model
Scenario analysis
Scenario 1 – changing the transplant rate
Scenario 2 – changing the proportion of the self-care dialysis
Scenario 3 – changing the incidence rate per year
Scenario 4 – changing the dialysis death rate
Scenario 5 – changing the frequency of HD treatments
Discussion
Appendix: Supplementary tables
Abbreviation & glossary
References
List of tables
List of figures
Selected Health Gains Planning publications
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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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 endstate 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.
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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DEPARTMENT OF HEALTH
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
82,185
a
The 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.
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DEPARTMENT OF HEALTH
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.
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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DEPARTMENT OF HEALTH
Methods
This 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 sources
The 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 analysis
The 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.
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Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
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
ESKD codes
(broad
definition)
ICD 10
procedure codes
or block number
Other
variables
AR-DRG
L61Z
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
N18.0
End-stage renal disease
(strict
definition)
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
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 model
In 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
NT population was modelled for the subgroups broken down by Indigenous status,
gender, and age group (under 40 years, 40 years and above).
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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DEPARTMENT OF HEALTH
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 model
The 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 timeseries. 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 model
A 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 facilitybased 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
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Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
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
ESRD
New pats
Incident
2003-12
Actual ESRD
incident rate
Incident/pop
2003-12
Tx failure
Dths Dx
Dths Tx
Tx failed rate
Dx dth rate
Tx dth rate
Prevalent
2003-12
Dx prev pats
(03-12)
Dx modality
Projection
(2013-22)
Dx prev pats
2013-18
Dialysis projection
by modality
2013-2022
Tx new pats
2013-2018
(estimation)
No. of dths
during Dx
Projected ESRD
incident rate
No. of Tx
fails to Dx
2013-22
Tx incident
rate
Rate change/year
Annual rate change
Incident/pop
Tx new
Dx prev13 ═Dx prev12┼Dx new13 ┼Tx fails to Dx13─ Dths Dx13 ─Tx new13
┼/ ─ ageing effect
Note: Dx: dialysis Tx: transplant
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
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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DEPARTMENT OF HEALTH
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 nonIndigenous 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.
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Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
Figure 2: Average interval from diagnosis to dialysis for patients with a diagnosis of
ESKD, Northern Territory public hospitals 2001-2012
Average waiting period (yrs)
1.8
1.6
1.4
Indigenous
1.2
Non-Indigenous
1.0
0.8
0.6
0.4
0.2
0.0
2001
2003
2005
2007
2009
2011
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;
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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DEPARTMENT OF HEALTH
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
Proportion registered (%)
100
90
80
70
60
50
40
30
45-64
20
<44
10
>=65
0
2001-04
2005-08
2009-12
Notes: ANZDATA= Australia and New Zealand Dialysis and Transplant Registry
End-stage kidney disease not treated with renal replacement therapy
During 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 nonIndigenous 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
Number
Non-Indigenous
Number
Indigenous
350
200
180
treated
not treated
treated
300
not treated
160
140
250
120
200
100
150
80
60
100
40
50
20
0
0
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Age group
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Age group
Notes: ANZDATA=Australia and New Zealand Dialysis and Transplant Registry; ESKD= End-stage kidney disease;
RRT= Renal replacement therapy.
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Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
Figure 5: ESKD patients treated and not-treated with RRT using “strict” definition by age
and Indigenous status, 2001-2012
Number
Number
Non-Indigenous
40
Indigenous
200
treated
35
not treated
180
treated
not treated
160
30
140
25
120
20
100
15
80
60
10
40
5
20
0
0
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Age group
Age group
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
deaths
Total 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
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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DEPARTMENT OF HEALTH
Survival rate
Over 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
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Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
Figure 7: Age adjusted survival for haemodialysis patients by four time periods - 19951999, 2000-2004, 2005-2009 and 2010-2011, Northern Territory and Australia.
1995-99
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
Proportion surviving
Proportion surviving
Survival from Start of Dialysis Treatment, by Time & Place of Start
1
NT
2
3
4
5
6
Years from Start of Treatment
7
2005-09
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
Proportion surviving
Proportion surviving
0
0
1
2
3
4
5
6
Years from Start of Treatment
2000-04
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
Aust
Aust
NT
0
1
NT
7
7
2010-11
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
Aust
2
3
4
5
6
Years from Start of Treatment
Aust
NT
0
1
2
3
4
5
6
Years from Start of Treatment
7
Adjusted for age & gender, not censored at transplant
Source: Australia and New Zealand Dialysis and Transplant Registry 2012 unpublished analysis
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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DEPARTMENT OF HEALTH
Models and projections
Renal projection overview
Table 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
Prevalence
Deaths
Transplant
During
dialysis
During
functional
transplant
68
32
2
249
67
35
81
279
73
2005
85
316
2006
76
334
2007
76
2008
New cases
Dialysis
Functional
transplant
New
transplant
Transplant
failure
2001
60
210
66
2002
60
233
10
4
12
7
2003
57
3
9
1
2004
39
4
10
4
71
42
3
7
2
73
52
2
6
4
368
78
37
2
6
2
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
Source: Australia and New Zealand Dialysis and Transplant Registry
Notes: 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.
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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
Number of treatments
120,000
100,000
80,000
60,000
Actual
40,000
Projected by regression
PProjected by time-series
20,000
Projected by static Markov Chain
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
0
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 model
Figure 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.
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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
Number of
treatments
9,000
8,000
7,000
6,000
5,000
4,000
Upper limit
3,000
Actual HD
2,000
Projected HD
1,000
Lower limit
Month & Year
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01-2022
01-2021
01-2020
01-2019
01-2018
01-2017
01-2016
01-2015
01-2014
01-2013
01-2012
01-2011
01-2010
01-2009
01-2008
01-2007
01-2006
01-2005
01-2004
01-2003
01-2002
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Figure 10: Monthly actual and projected haemodialysis treatments with 95% confidence
interval by time-series model for non-Indigenous people
Number of
treatments
800
700
600
500
400
300
200
Upper limit
Actual HD
100
Projected HD
Lower limit
01-2023
01-2022
01-2021
01-2020
01-2019
01-2018
01-2017
01-2016
01-2015
01-2014
01-2013
01-2012
01-2011
01-2010
01-2009
01-2008
01-2007
01-2006
01-2005
01-2004
01-2003
01-2002
01-2001
0
Month & Year
Figure 11: Yearly projected haemodialysis treatments by linear regression with 95%
confidence interval, 2013-2022
Number of haemodialysis
140,000
120,000
100,000
80,000
60,000
upper limit
40,000
projection
20,000
lower limit
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
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Figure 12: Actual (2001-12) and projected (2013-22) HD treatment by linear regression
model by age, gender and Indigenous status
Number of treatments
60,000
Ind Female >=40
50,000
Ind Male >=40
Non-Ind Female <40
40,000
Non-Ind Female >=40
30,000
Non-Ind Male <40
20,000
Non-Ind Male >=40
Ind Female <40
10,000
Ind Male <40
2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021
Figure 13: Projected number of haemodialysis patients by modality, Markov chain model,
2013-2022
Number of patients
1000
900
800
Transplant
700
Home HD
600
Sat HD
500
Hosp HD
400
CAPD
300
APD
200
100
0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Notes: APD= automated peritoneal dialysis; CAPD= continuous ambulatory peritoneal dialysis; HD=haemodialysis;
Hosp=hospital; Sat=satellite
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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 rate
The 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 rate
Age-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 treatments
During 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.
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Table 6: Four different scenarios in facility-based haemodialysis treatment numbers
projection
Base model
Lower projection
Higher projection
Annual 0.3%
increase to 3.9%
from 2016 onwards
Annual 0.5% decrease
to 1.3% from 2016
onwards
17% for 2013-2015
& 20% thereafter
10% (from 2013 to
2022)
(in 2012)
320
500+
1.00
0.95
1.05
Age-specific death
rate
Annual 10%
increase from the
base model
2.5%
1.
Transplant rate
based on average
rate for 2010-12
13%
Proportion of “selfcare” dialysis
patients
2.
3.
(based on average
rate for 2008-12)
420
Incidence rate for
all ages*
Annual Incidence
rate change (for
45-64 yr)
4.
Dialysis death rate
5.
Number of HD
treatments
attended
(based on average
rate for 2008-12)
Annual 10% decrease
from the base model
Maintaining 96 HD
treatments over the
years
96 HD treatments in
2013 and increasing
0.9 annually (10years average)
Increasing 1.9%
annually from
2013
Note: *per 1,000,000 population
Figure 14: Scenario 1 – the impact of facility-based haemodialysis treatments by
changing the transplant rate
Number of treatments
120,000
Actual
Base model (@2.5%, 5-years average)
Increasing @0.3% annually until 2016
Decreasing @0.3% decrease annually until 2016
100,000
80,000
60,000
40,000
20,000
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2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
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2007
2006
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Figure 15: Scenario 2 – the impact of facility-based haemodialysis treatments by
changing the proportion of self-care dialysis
Number of treatments
120,000
Actual
Base model (@0.130)
Increasing to 17% from 2013-2015 and 20% onwards
Maintaining 10% over the years
100,000
80,000
60,000
40,000
20,000
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
-
Figure 16: Scenario 3 – the impact of facility-based haemodialysis treatments by
changing the annual incidence rate for those aged 45-64 years old
Number of treatments
100,000
Actual
90,000
Base model (@0.997)
80,000
Decreasing 5% annually
Increasing 5% annually
70,000
60,000
50,000
40,000
30,000
20,000
10,000
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
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Figure 17: Scenario 4 – the impact of facility-based haemodialysis treatments by
changing the death rate
Number of treatments
120,000
Actual
Base model (@10% (10-year average))
100,000
Increasing 10% annually
Decreasing 10% annually
80,000
60,000
40,000
20,000
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
-
Figure 18: Scenario 5 – the impact of facility-based haemodialysis treatments by
changing the frequency of haemodialysis treatments
Number of treatments
120,000
Actual
Base model (@96 HD treatments in 2013 and 0.9% annual increase)
100,000
Maintaning 96 HD treatments per year
80,000
Increasing 1.9% annually
60,000
40,000
20,000
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Discussion
General summary statement
This 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 RRT
Clinically, 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 rate
An 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
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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DEPARTMENT OF HEALTH
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
to follow-up 25% for non-Indigenous vs. 11% for Indigenous), even though nonIndigenous absolute numbers are much lower than Indigenous numbers. For this study,
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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 care
Understanding 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.
Projections
Three 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.
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Scenarios based on the modifications to Markov chain model
Scenario 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 20012012. 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
decreasing in the number of treatments might be related to an increase in the uptake of
conservative care option which was stated above.
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Limitations
One 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 facilitybased 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.
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
25
DEPARTMENT OF HEALTH
Appendix: Supplementary tables
Table A1: Actual and projected facility-based haemodialysis treatments using three
statistical models, NT public hospitals 2013-2022
Year
Number of
haemodialysis
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
Projected by
Time-series
Projected by
Markov chain
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
26
Projected by linear
regression
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
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
Actual
Indigenous
Projection
Actual
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
Projection
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
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
27
DEPARTMENT OF HEALTH
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
Female
Indigenous
Male
Female
Total
Male
<40
>=40
<40
>=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
28
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
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 2
Year 1
Modality
APD
CAPD
HHD
SHD
HMHD
Death
TG
OKFR
Total
1
26
17
14
4
1
418
Count
APD
361
26
71
32
CAPD
26
314
53
6
HHD
33
31
385
874
5
147
6
11
1492
SHD
69
33
204
4258
62
281
62
9
4978
6
11
208
10
8
15
864
25
904
46
8569
HMHD
TG
Total
patients
534
243
489
404
719
5181
276
493
961
67.6
4.9
13.3
6
0.2
4.9
3.2
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
2.5
4.5
85.6
4.1
3.3
Proportion
APD
HMHD
100
100
TG
1.7 95.6
2.8
100
Note: 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
APD
CAPD
HHD
SHD
HMHD
TG
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
19
23
24
26
28
30
31
33
35
36
39
19
21
22
23
24
25
26
27
28
29
31
39
41
43
45
46
48
49
51
53
55
58
395
422
439
458
473
489
504
519
535
553
580
29
31
32
32
33
34
35
35
36
38
40
77
78
84
90
96
102
108
114
119
125
116
Total
579
616
645
674
701
727
754
780
807
836
863
Note: HHD: hospital based HD; SHD: satellite based HD; HMHD: home-based HD; TG: transplant grafted; OKFR: own
kidney function recovery
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
29
DEPARTMENT OF HEALTH
Abbreviation & glossary
ANZDATA: 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 timeseries 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 nursesupported 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.
30
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
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.
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
31
DEPARTMENT OF HEALTH
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/PastPopulationPr
ojections.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.
32
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
List of tables
Table 1: Codes applied to identify end-stage kidney disease and treatment modality... 3
Table 2: Incidence of end-stage kidney disease by definitions, Northern Territory public
hospitals 2001-2012 ..................................................................................................... 6
Table 3: Number of new patients (with ESKD) registered in ANZDATA compared with
those treated in Northern Territory public hospitals 2001-2012 ..................................... 7
Table 4: Number and proportion of patients with end-stage renal failure who died in the
care of palliative care services, 2001-2012 ................................................................... 9
Table 5: Annual number of NT residents undertaking RRT, including new and prevalent
cases, deaths and renal transplants, NT residents 2001 -2012 ...................................12
Table 6: Four different scenarios in facility-based haemodialysis treatment numbers
projection ....................................................................................................................18
Table A1: Actual and projected facility-based haemodialysis treatments using three
statistical models, NT public hospitals 2013-2022 .......................................................26
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 ......................................................................................................................27
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 ....................28
Table A4: Number and proportion of patients shifting from one modality in one year to
another one in the following year, ANZDATA 2003-2012.............................................29
Table A5: Number of patients projected by treatment modalities, 2013-2022, using
Markov chain static model ...........................................................................................29
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
33
DEPARTMENT OF HEALTH
List of figures
Figure 1: Flow chart of ESKD for dialysis projection, 2003-2022 .................................. 5
Figure 2: Average interval from diagnosis to dialysis for patients with a diagnosis of
ESKD, Northern Territory public hospitals 2001-2012 .................................................. 7
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, 20052008 and 2009-2012 .................................................................................................... 8
Figure 4: Patients with ESKD treated and not-treated with RRT using “broad” definition,
by age and Indigenous status, NT public hospitals 2001-2012 ..................................... 8
Figure 5: ESKD patients treated and not-treated with RRT using “strict” definition by
age and Indigenous status, 2001-2012......................................................................... 9
Figure 6: Crude survival for haemodialysis patients by four time periods, 1995-1999,
2000-2004, 2005-2009 and 2010-2011, Northern Territory and Australia. .................. 10
Figure 7: Age adjusted survival for haemodialysis patients by four time periods - 19951999, 2000-2004, 2005-2009 and 2010-2011, Northern Territory and Australia. ........ 11
Figure 8: Number of HD treatments from 2001-2012, and estimates for 2013-2018,
using three statistical models...................................................................................... 13
Figure 9: Monthly actual and projected haemodialysis treatments with 95% confidence
interval by time-series model for Indigenous people ................................................... 14
Figure 10: Monthly actual and projected haemodialysis treatments with 95% confidence
interval by time-series model for non-Indigenous people ............................................ 15
Figure 11: Yearly projected haemodialysis treatments by linear regression with 95%
confidence interval, 2013-2022................................................................................... 15
Figure 12: Actual (2001-12) and projected (2013-22) HD treatment by linear regression
model by age, gender and Indigenous status ............................................................. 16
Figure 13: Projected number of haemodialysis patients by modality, Markov chain
model, 2013-2022 ...................................................................................................... 16
Figure 14: Scenario 1 – the impact of facility-based haemodialysis treatments by
changing the transplant rate ....................................................................................... 18
Figure 15: Scenario 2 – the impact of facility-based haemodialysis treatments by
changing the proportion of self-care dialysis ............................................................... 19
Figure 16: Scenario 3 – the impact of facility-based haemodialysis treatments by
changing the annual incidence rate for those aged 45-64 years old ........................... 19
Figure 17: Scenario 4 – the impact of facility-based haemodialysis treatments by
changing the death rate .............................................................................................. 20
Figure 18: Scenario 5 – the impact of facility-based haemodialysis treatments by
changing the frequency of haemodialysis treatments ................................................. 20
Figure 19: Treated and not-treated ESKD patients, by Indigenous status, Australia
2001-2012 .................................................................................................................. 22
34
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
DEPARTMENT OF HEALTH
Selected Health Gains Planning
publications
Chronic diseases
Zhao 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 19941998 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 injury
Zhao 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 19941998 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 NonAboriginal 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 nonAboriginal populations in the Northern Territory. Med J Aust 2004;180(10):498-502.
Renal Replacement Therapy Demand Study, Northern Territory, 2001 to 2022
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