Presented by SMART Research Fellow dr Nagesh Shukla to Euro Mini Conference from 30 March to 1 April 2015.

1. 1
Population Accessibility to Radiotherapy
Services in NSW Region of Australia: a
methodological contribution
Presented by: Dr. Nagesh Shukla
SMART Infrastructure Facility, University of Wollongong,
NSW, Australia 2500
Dr. R Wickramasuriya (SMART, Uni-Wollongong, Australia)
Prof. Andrew Miller (Illawarra Cancer Care Centre, ISLHD)
Prof. Pascal Perez (SMART, Uni-Wollongong, Australia)

2. • Cancer is estimated to be the leading cause of burden of disease in Australia in 2010,
accounting for 19% of the total burden.
• Cancer incidences increase with age and varies with gender
Introduction
2
Source: NSW CENTRAL CANCER REGISTRY
Aged population
is at the risk of
cancer

3. • Percentage of aged (>50 yro) people (2011 ABS data)
Introduction - Spatial variation of population

4. • Population distribution, in general, is heterogeneously distributed in space
Introduction - Spatial variation of population
4

5. • Population evolution happens in space and time
• Growth rates
• Immigration
• Cancer rates for different types of cancer varies overtime
Space-time effects on cancer incidences
5

6. Regional Planning of Cancer Treatment services
6
• As life expectancy continues to grow; the proportion of elderly people in the
population will steadily increase over the next decades
– it is expected that the number of cancer cases will continue to grow
• Thus, the pressure on specialised treatment services will increase as well,
calling for better planning and allocation of healthcare resources
• Radiotherapy (RT) is an essential mode of cancer treatment and contributes
to the cure of many cancer patients.
– Evidence suggests that 52.3% of all diagnosed cancer cases in Australia would benefit from
RT
– However, only 38% of cancer sufferers receive radiotherapy at some point after the initial
detection
– This is largely due to the travel distance/access factors to RT centres

7. Regional Planning of RT services
7
• This research study proposes a methodology for location planning for RT
services with the help of:
– Population projections
– Cancer incidence rates estimation/prediction
– Road distance based accessibility to treatment centres
– Future RT demand estimation

8. Data Sources
8
• Cancer incidence dataset (AIHW):
– age group and sex specific cancer rates for all
and specific cancer types in Australia
– incidence, trends, projections, survival, and
prevalence
• ABS population tables:
– Census community profiles
– Population projections

9. • Road network data from OpenStreetMap
– It is a crowd-sourced initiative to collect and map roads, trails, and points of interest, with an
ultimate aim of building a geographic database
Data Sources

10. • Existing RT centres in NSW
– The data about the existing RT treatment facilities is accessed from Department of Health,
Australia.
Data Sources

11. Proposed Methodology
11
• Age-sex specific rate (ASR) for cancer incidence modelling
– Linear regression is used to model the past trend of cancer incidences
– Models have been developed for each age-sex group
– Cancer incidences data for years 2000 to 2009 have been used
• Assumptions:
– incidence is homogeneous across different local government areas (LGAs)
– ages were grouped in 5 year interval assumes that each age group is
homogeneous
– it is assumed that the past trends will continue in future

12. Proposed Methodology
12
• Population projections
– These projections are based on the past trends (over a decade) of
• fertility,
• mortality,
• and migration trends
– the base population is projected into the future year annually by estimating the
effect of births, deaths and migration within each age-sex group
𝑐𝑎𝑛𝑐𝑒𝑟_𝑐𝑎𝑠𝑒𝑠(𝐿𝐺𝐴, 𝑡)=Population(LGA, t) × ASR(t)

13. Travel distance modelling - RT rates based on distance
13
27% 26%
24% 23%
22%
20%
23%
18%
14%
0%
5%
10%
15%
20%
25%
30%
Radiotherapyutilisation
Distance in kilometres
Proportion of patients who received radiotherapy by distance from patient's residence to
the nearest radiotherapy facility
NSW & ACT 2004-06
Gabriel et al. (2013)
Radiotherapy utilisation in
NSW & ACT 2004-06 - A Data
Linkage and a GIS experience

14. OSM
Setting up the software-data environment
Travel distance modelling
QGIS
osmconvert
osm2po
psql
Routable network in
PostgreSQL(ext: PostGIS/pgRouting)

15. Generating constant driving distance polygons
Travel distance modelling
Routable Network in
PostgreSQL
+
Origin (RT Centre) *
+
Distance (e.g. 50km) *
pgRouting
pgr_drivingdistance
Reachable nodes
Isochrone
* loop

16. Starting point: 1 residential land use class
Estimating population coverage
𝑅𝑇 𝐿𝐺𝐴, 𝑑𝑖𝑠𝑡 𝑏𝑎𝑛𝑑 = 𝑓𝑟𝑎𝑐_𝑟𝑒𝑠𝑖𝑑(𝑑𝑖𝑠𝑡_𝑏𝑎𝑛𝑑)× 𝑅𝑇_𝑟𝑎𝑡𝑒(𝑑𝑖𝑠𝑡_𝑏𝑎𝑛𝑑) × 𝑐𝑎𝑛𝑐𝑒𝑟_𝑐𝑎𝑠𝑒𝑠(𝐿𝐺𝐴)
𝐿𝐺𝐴
𝑁
𝑑𝑖𝑠𝑡_𝑏𝑎𝑛𝑑
𝐷
𝑅𝑇(𝐿𝐺𝐴, 𝑑𝑖𝑠𝑡 𝑏𝑎𝑛𝑑)
𝑓𝑟𝑎𝑐_𝑟𝑒𝑠𝑖𝑑(𝑑𝑖𝑠𝑡_𝑏𝑎𝑛𝑑) = 𝑹𝒆𝒔𝒊𝒅𝒆𝒏𝒕𝒊𝒂𝒍 𝑨𝒓𝒆𝒂𝒔(𝒅𝒊𝒔𝒕_𝒃𝒂𝒏𝒅)
𝑻𝒐𝒕𝒂𝒍 𝑨𝒓𝒆𝒂

17. Results – Incidence rates
17
• Predicted (points) and observed (solid line) incidence rates (per 100,000)
for all cancers in males and females in Australia

18. Results – Population projection
18
• Age structure of NSW population in years 2011 and 2026

19. • Overall cancer incidences in year 2011 (a) and 2026 (b) in NSW state of
Australia
19
Results – Cancer incidence
2011 2026

20. • Constant driving distance polygons from radiotherapy centres
20
Results –driving distance from RT centres

21. • Estimate change in access of cancer patients with the opening of new RT
centre in Shoalhaven
21
Results – Scenario

22. Validation in Local Health District
• Comparison between actual cancer incidence dataset and predicted
results
22
Local Government
Area
Actual Cancer Count
(2004- 2008)
Average Actual
Cancer
Count/year
Predicted Count
(2011)
Predicted Count
(2011-2015)
Kiama 627 125 151 804
Shellharbour 1,475 295 371 1999
Shoalhaven 3,481 696 771 4038
Wollongong 5,223 1,045 1,228 6,515
NSW 177,519 33,504 41,424 219,812

23. 23
Conclusion and Future work
• The proposed methodology takes into account –
– Varying cancer incidence rates
– Population evolution
– Accessibility to RT centres
• Tools developed in this work are open source
– R
– PostgreSQL
– Python
– QGIS
• Future work
– Modelling for different types of cancer
– Residential land use changes over time
– Use of synthetic population methodology for population evolution

24. Thank You 
24
Dr. Nagesh Shukla
SMART Infrastructure Facility
University of Wollongong
nshukla@uow.edu.au