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)

• 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

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Source: NSW CENTRAL CANCER REGISTRY

Aged population

is at the risk of

cancer

• Percentage of aged (>50 yro) people (2011 ABS data)

Introduction - Spatial variation of population

• Population distribution, in general, is heterogeneously distributed in space

Introduction - Spatial variation of population

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• 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

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Regional Planning of Cancer Treatment services

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• 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

Regional Planning of RT services

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• 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

Data Sources

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• 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

• 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

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

Australia.

Data Sources

Proposed Methodology

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• 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

Proposed Methodology

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• 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)

Travel distance modelling - RT rates based on distance

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27% 26%24% 23%

22%20%

23%

18%

14%

0%

5%

10%

15%

20%

25%

30%

Rad

ioth

era

py u

tili

sa

tio

n

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

OSM

Setting up the software-data environment

Travel distance modelling

QGIS

osmconvert

osm2po

psql

Routable network in

PostgreSQL(ext: PostGIS/pgRouting)

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

Starting point: 1 residential land use class

Estimating population coverage

𝑅𝑇 𝐿𝐺𝐴, 𝑑𝑖𝑠𝑡𝑏𝑎𝑛𝑑 = 𝑓𝑟𝑎𝑐_𝑟𝑒𝑠𝑖𝑑(𝑑𝑖𝑠𝑡_𝑏𝑎𝑛𝑑)× 𝑅𝑇_𝑟𝑎𝑡𝑒(𝑑𝑖𝑠𝑡_𝑏𝑎𝑛𝑑) × 𝑐𝑎𝑛𝑐𝑒𝑟_𝑐𝑎𝑠𝑒𝑠(𝐿𝐺𝐴)

𝐿𝐺𝐴

𝑁

𝑑𝑖𝑠𝑡_𝑏𝑎𝑛𝑑

𝐷

𝑅𝑇(𝐿𝐺𝐴, 𝑑𝑖𝑠𝑡𝑏𝑎𝑛𝑑)

𝑓𝑟𝑎𝑐_𝑟𝑒𝑠𝑖𝑑(𝑑𝑖𝑠𝑡_𝑏𝑎𝑛𝑑) = 𝑹𝒆𝒔𝒊𝒅𝒆𝒏𝒕𝒊𝒂𝒍 𝑨𝒓𝒆𝒂𝒔(𝒅𝒊𝒔𝒕_𝒃𝒂𝒏𝒅)

𝑻𝒐𝒕𝒂𝒍 𝑨𝒓𝒆𝒂

Results – Incidence rates

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• Predicted (points) and observed (solid line) incidence rates (per 100,000)

for all cancers in males and females in Australia

Results – Population projection

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• Age structure of NSW population in years 2011 and 2026

• Overall cancer incidences in year 2011 (a) and 2026 (b) in NSW state of

Australia

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Results – Cancer incidence

2011 2026

• Constant driving distance polygons from radiotherapy centres

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Results –driving distance from RT centres

• Estimate change in access of cancer patients with the opening of new RT

centre in Shoalhaven

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Results – Scenario

Validation in Local Health District

• Comparison between actual cancer incidence dataset and predicted

results

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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

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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

Thank You

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Dr. Nagesh Shukla

SMART Infrastructure Facility

University of Wollongong

nshukla@uow.edu.au