![Location of the Ageing, Chronic Disease and Injury (ACDI) Study region. Local Government Areas (LGAs) included in the study are shaded. Data for graphic obtained from the Department of Health and Human Services, State Government of Victoria, Australia [14]. (Graphic prepared by MAS and KLH)](https://www.researchgate.net/profile/Richard-Page-2/publication/335664323/figure/fig1/AS:800184342675461@1567790222806/Location-of-the-Ageing-Chronic-Disease-and-Injury-ACDI-Study-region-Local-Government_Q320.jpg)
- Access to this full-text is provided by Springer Nature.
- Learn more
Download available
Content available from BMC Cancer
This content is subject to copyright. Terms and conditions apply.
R E S E A R C H A R T I C L E Open Access
Mapping Cancer incidence across Western
Victoria: the association with age,
accessibility, and socioeconomic status
among men and women
Stephanie P. Cowdery
1*
, Muhammad A. Sajjad
1
, Kara L. Holloway-Kew
1
, Mohammadreza Mohebbi
2,3
,
Lana J. Williams
1
, Mark A. Kotowicz
4,5
, Patricia M. Livingston
2
, Mustafa Khasraw
1,6
, Sharon Hakkennes
1
,
Trisha L. Dunning
7
, Susan Brumby
1,8
, Richard S. Page
1,5,9
, Alasdair G Sutherland
1,10
, Sharon L. Brennan-Olsen
4,11
,
Michael Berk
1,12,13
, David Campbell
5
and Julie A. Pasco
1,4,5,14
Abstract
Background: Cancer is a leading burden of disease in Australia and worldwide, with incidence rates varying with
age, sex and geographic location. As part of the Ageing, Chronic Disease and Injury study, we aimed to map the
incidence rates of primary cancer diagnoses across western Victoria and investigate the association of age,
accessibility/remoteness index of Australia (ARIA) and area-level socioeconomic status (SES) with cancer incidence.
Methods: Data on cancer incidence in the study region were extracted from the Victorian Cancer Registry (VCR) for
men and women aged 40+ years during 2010–2013, inclusive. The age-adjusted incidence rates (per 10,000
population/year), as well as specific incidence for breast, prostate, lung, bowel and melanoma cancers, were calculated
for the entire region and for the 21 Local Government Areas (LGA) that make up the whole region. The association of
aggregated age, ARIA and SES with cancer incidence rates across LGAs was determined using Poisson regression.
Results: Overall, 15,120 cancer cases were identified; 8218 (54%) men and 6902 women. For men, the age-standardised rate
of cancer incidence for the whole region was 182.1 per 10,000 population/year (95% CI 177.7–186.5) and for women, 162.2
(95% CI: 157.9–166.5). The incidence of cancer (overall) increased with increasing age for men and women. Geographical
variations in cancer incidence were also observed across the LGAs, with differences identified between men and women.
Residents of socioeconomically disadvantaged and less accessible areas had higher cancer incidence (p< 0.001).
Conclusion: Cancer incidence rates varied by age, sex, across LGAs and with ARIA. These findings not only provide an
evidence base for identifying gaps and assessing the need for services and resource allocation across this region, but also
informs policy and assists health service planning and implementation of preventative intervention strategies to reduce the
incidence of cancer across western Victoria. This study also provides a model for further research across other geographical
locations with policy and clinical practice implications, both nationally and internationally.
Keywords: Cancer incidence, Accessibility/remoteness, Socio economic status, Demographic characteristics, Age, Gender or
sex, Western Victoria
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
* Correspondence: scowde@barwonhealth.org.au
1
School of Medicine, Deakin University, Geelong, Australia
Full list of author information is available at the end of the article
Cowdery et al. BMC Cancer (2019) 19:892
https://doi.org/10.1186/s12885-019-6070-x
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Background
Cancer is a leading burden of disease in Australia and
worldwide. According to the Global Burden of Cancer
2013 report, the burden of cancer is increasing globally
and has shifted from the third leading cause of death in
1990 to become the second leading cause of death
behind cardiovascular disease, with over 8 million deaths
caused by cancer in 2013 [1].
By 2044–2045, approximately 25% of Australian resi-
dents will be aged 65 years and over, almost double the
current proportion [2]. With Australia’s ageing population
rapidly increasing, the burden of chronic disease, such as
cancer, and associated health service delivery and utilisa-
tion is also expected to rise [3] . Likewise, government
health spending is projected to increase from 5.7% of the
Gross Domestic Product in 2002–2003, to approximately
10.3% by 2044–2045 [2].
According to the Cancer Council Victoria’s‘Cancer in
Victoria: Statistics and Trends 2016 [4,5] report, projec-
tions of cancer incidence and mortality indicate an in-
creased burden of cancer in Victoria by 2027–2031; with
the annual number of all new cancer diagnoses anticipated
to increase to over 43,000 (38%) and deaths to over 13,000
(19%). When projections are analysed by sex, annual can-
cer diagnoses for Victorian men and women are forecast
to rise 25 and 52%, respectively.
The introduction of screening programs [6,7] has of-
fered the opportunity to detect cancer early, resulting in
increased treatment options and improved survival rates
overall [5]. However, the current 5 year survival from
cancer (overall) for metropolitan Melbourne residents
(69%) is higher than residents from the rest of Victoria
(65%) [4]. This observable difference has been attributed
to reduced access to screening services in rural popula-
tions, as well as lower socio economic status (SES) [8–
10] and the relocation of older individuals to more
urban areas post retirement [11]. Differences in cancer
incidence and survival in rural areas is a highly complex
issue nationally and internationally, with factors such as
ageing populations, screening and early detection, socio-
demographic and tumour characteristics, treatment
options and access to oncology services, all likely playing
a role.
Several studies have demonstrated links between socio-
economic status and incidence of malignancies such as
breast, colorectal and lung, for both men and women [12].
For instance, an American national longitudinal mortality
study showed that men and women with less than high
school education had increased lung cancer rate rates
compared to those with college education [12]. Similarly,
those with family incomes less than $12,500 had recorded
lung cancer incidence rates 1.7 times higher than the inci-
dence rates for those with incomes of greater than $50,
000 [8,12]. For Australian populations, the cross sectional
study by Baade et al. concluded that women residing in
the most socioeconomically disadvantaged areas of
Queensland were more likely than women living in more
socioeconomically advantaged areas to present with ad-
vanced breast cancer (after adjusting for individual factors
such as age, occupation, marital status and indigenous sta-
tus) [6,12].
Higher rates of advanced cancers at diagnosis have been
reported in those residing in more remote areas/areas of
low accessibility, which in turn may explain the higher
mortality rate often observed in more rural areas, despite
their overall lower incidence rate [12,13]. The systematic
review by Leung et al. [9] examined existing evidence for
differences in mammography screening service use be-
tween women in rural and metropolitan areas to investi-
gate the observed lower breast cancer survival rate for
women living in more remote areas. This review examined
data across several countries including the United States,
Korea, Croatia, Estonia, Lebanon, Northern Island and
Australia. The review concluded that women living in
rural areas were significantly less likely to have ever had a
mammogram or an up to date mammogram. This rural
disadvantage for mammography screening may contribute
to the lower incidence of breast cancer and, conversely,
the increased mortality among women living outside
metropolitan/urban areas. The review by Jemal et al. [12]
assessed cancer incidence and mortality rates for lung and
bronchus, colon and rectum, female breast, prostate,
stomach, liver, oesophageal and gynecological subtypes
among 45 select cancer registries globally. Patterns
showed that cancer rates varied by region, sex and cancer
type and that overall cancer incidence rates are increasing
in less developed and economically transitioning countries
[10]. A systematic review on colorectal cancer in Australia
by Ireland et al. [8] demonstrated that individuals with
colorectal cancer (CRC) residing in regional, rural and re-
mote areas of Australia had poorer survival rates and less
optimum clinical management. They postulated that other
factors such as age, SES and sex likely moderated this
effect. Whilst their evidence demonstrated an overall
disparity in survival for CRC, they noted that the evidence
was ‘limited and somewhat inconsistent’. Thus, to further
elucidate these effects on regional disparities, type of
region, age and SES should be assessed among men and
women to develop and implement effective interventions
aimed at improving the health and welfare of regional
Australians.
As cancer incidence is strongly related to age, with less
than 1% of tumours occuring before age 20 and 60% of
all new annual diagnoses in Australia occuring in per-
sons older than 65 years [2,5], it is vital to understand
the impact of the increasing ageing population, as well
as factors such as socioeconomic status (SES) and acces-
sibility on cancer incidence and mortality in order to
Cowdery et al. BMC Cancer (2019) 19:892 Page 2 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
inform policy and plan improved delivery of health ser-
vices. Likewise, this information will enable a profiling
model for comparison to other rural regions both
nationally and internationally.
This current study aimed to map the incidence rates
of primary cancer diagnoses and select subtypes across
the region of western Victoria by age for men and
women, and to investigate the association of age, accessi-
bility, and area-level SES with cancer incidence from
2010 to 2013.
Methods
Study design
This study forms part of the Ageing, Chronic Disease
and Injury Study (ACDI). Initiated in 2015, ACDI aims
to provide a comprehensive snapshot of the health and
wellbeing of older adults aged 40 years and over living in
western Victoria, Australia (Fig. 1)[15]. The ACDI study
aggregates information on demographic, socioeconomic
indicators and lifestyle factors obtained from health
surveys, clinical databases and government departments.
Data from registers, health and emergency services, local
community health centres and administrative databases
are collected to generate profiles on chronic disease and
injury for the study region and for sub-populations
within the region.
Study region and participants
The region of western Victoria represents close to one-
third of the state by area, comprising 21 Local Government
Areas (LGAs). Based on the 2011 Census of Population
and Housing, the 2013 Estimated Resident Population
(ERP) of the study region is 617,794, representing approxi-
mately 11% of the entire Victorian population. The three
most populous LGAs of this study region are Greater
Geelong (ERP = 221,515), Ballarat (ERP = 98,684) and
Warrnambool (ERP = 33,300) [15].
Data sources
The Victorian Cancer Registry (VCR) is a population-
based cancer registry, which provides comprehensive
information for cancer control. Notifications concerning
Fig. 1 Location of the Ageing, Chronic Disease and Injury (ACDI) Study region. Local Government Areas (LGAs) included in the study are shaded. Data for
graphic obtained from the Department of Health and Human Services, State Government of Victoria, Australia [14]. (Graphic prepared by MAS and KLH)
Cowdery et al. BMC Cancer (2019) 19:892 Page 3 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
cancer diagnoses are provided to the VCR by hospitals,
pathology laboratories and cancer screening registers.
The VCR records all invasive cancers, in-situ carcin-
omas, benign tumours and tumours of uncertain behav-
iour. Non-melanoma Basal and Squamous Cell skin
carcinomas are not recorded. For every cancer case,
demographic information such as patient name, address
and date of birth, as well as tumour details including
site, type, morphology, grade, behaviours and date of
diagnosis are recorded [16]. Data have been collected for
all cancers diagnosed in Victorian residents since 1982.
Comprehensive information concerning data quality for
the VCR have been provided elsewhere [17]. This most
recent report included three indices of data quality;
death certificate only (DCO%), histological verification
(HV%) and mortality to incidence ratio (M/I%) for spe-
cific anatomical cancer sites as well as for all malignant
tumours combined (2.0, 93 and 37 respectively).
Accessibility and Remoteness Index of Australia (ARIA)
scores are generated by assessing distance from localities
to different town categories, access to goods and services
and opportunities for social interaction [18]. ARIA scores
are grouped into 5 categories on a scale ranging from
highly accessible (ARIA score 0.00–1.84), accessible
(ARIA score > 1.84–3.51), moderately accessible (ARIA
score > 3.51–5.80), remote (ARIA score > 5.80–9.08) and
very remote (ARIA score > 9.08–12.00, 11]. The LGAs of
Hindmarsh, West Wimmera and Yarriambiack have the
highest ARIA scores (4.4, 4.1 and 3.9 respectively) in the
western Victorian region and rank within the top five
LGAs with the highest ARIA scores in the state, categoris-
ing them to the ‘moderately accessible’category. These
areas are largely agricultural based, predominantly produ-
cing grain and sheep [19]. The other LGAs in the study
region are in the ‘highly accessible’or ‘accessible’catego-
ries, with no LGAs in the ‘remote’or ‘very remote’
categories.
To assess SES, Index for Relative Socioeconomic
Advantage and Disadvantage (IRSAD) scores are gen-
erated by the Australian Bureau of Statistics (ABS)
using aggregated Census data for each LGA. The
LGAs that were identified as being in the lowest 10%
of IRSAD were the most disadvantaged, and cate-
gorised as decile 1, whilst those in the highest 10% of
IRSAD were the most advantaged, and thus cate-
gorised as decile 10 [20]. With the exception of the
sixth IRSAD decile, the study region encompasses
LGAs across all deciles of IRSAD scores.
Statistical analyses
Analyses for this study were performed utilising aggre-
gated data from 2010 to 2013 inclusive, and divided into
two parts: (i) the western Victorian region (ii) the 21
LGAs of the western Victorian region. Cancer incidence
rate data for men and women were calculated separately,
as cancer incidence differs between the sexes. For the
entire study region, incidence rates were calculated sep-
arately per age group, 40–49, 50–59, 60–69, 70–79 and
80+ years for cancer [overall] and among bowel, lung,
melanoma, prostate and breast subtypes. Bowel, lung,
melanoma, prostate and breast cancers are the five most
common cancer subtypes in Victoria, collectively
accounting for 57% of all new cancers and half of all
cancer deaths [5]. Data from the ABS 2011 Census
Community Profile Series were utilised to undertake
direct age standardisation to the 2011 Australian popula-
tion [21]. Cancer incidence was expressed as Incidence
per 10,000 population per year. Poisson regression was
used to estimate model adjusted Incidence Rate Ratios
(IRR) and their 95% confidence intervals (CIs).
For LGA level, age-adjusted incidence rates were
calculated for each LGA separately. As this study uti-
lises aggregated data on age, direct age standardisa-
tion to the 2011 Australian population was again
implemented using data from the ABS 2011 Census
Community Profile Series [21]. Cancer incidence rates
per LGA were expressed as Incidence per 10,000
population per year and 95% CIs reported after Pois-
son regression analysis.
An additional analysis was conducted to investigate as-
sociations between age [age standardisation to the 2011
Australian population,] ARIA and SES (IRSAD deciles
converted to quintiles) across the LGAs and correspond-
ing cancer incidence rates. Age-adjusted incidence rates
were calculated for the region and geocoding (Pitney
Bowes Software Pty Ltd) performed to determine SES
and ARIA codes. This analysis was performed using
Poisson regression after accounting for aggregated data
considering LGA as unit of analysis and incidence rate
ratios (IRR) were calculated.
Minitab (version 16, Minitab, State College, PA, USA)
and STATA 14 were used for analyses.
Results
Western Victorian region (whole study region)
From 2010 to 2013 inclusive, 15,120 cancer cases (2095
bowel, 1403 lung, 1359 melanoma, 2123 prostate and
1856 breast) were identified for 8218 men and 6902
women. The incidence of cancer [overall] increased with
advancing age for both men and women across the re-
gion (Fig. 2). For men, the rate ranged from 24.1 per 10,
000 population/year (95% CI 21.7–26.4) in the 40–49
years age decade to 362.2 per 10,000 population/year
(95% CI 344.7–379.7) in the 80+ years age group. For
women, the rate ranged from 37.8 per 10,000 popula-
tion/year (95% CI 34.9–40.7) in the 40–49 years age
decade to 210.3 per 10,000 population/year (95% CI
199.8–220.8) in the 80+ years age group. The male
Cowdery et al. BMC Cancer (2019) 19:892 Page 4 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
incidence rate was higher than the female rate for all age
groups except for 40–49 years, where the rate was 24.1
per 10,000 population/year and 37.8 per 10,000 popula-
tion/year for men and women, respectively (Fig. 2).
Incidence rates of bowel, lung, melanoma, prostate
and breast cancer in men and women in the ACDI Study
region from 2010 to 2013 inclusive are shown in Fig. 3.
Among each age group for women, incidence rates were
highest for breast cancer. Breast cancer rates increased
with increasing age from 16.5 per 10,000 population/year
(95% CI 14.6–18.4) in the 40–49 age decade to 32.1 per
10,000 population/year (95% CI 28.0–36.2) in the 80+
years age group. For men, prostate cancer had the
highest incidence (ranging from 2.6 per 10,000 popu-
lation/year (95% CI 1.8–3.4) in the 40–49 years age
decade to 76.7 per 10,000 population/year (95% CI
68.6–84.7) in the 80+ years age group in all age groups
except 40–49 years where melanoma had the highest
incidence rate of 4.6 per 10,000 per population/year
(95% CI 3.6–5.6).
Fig. 2 Incidence rates of all cancers for men and women in the ACDI Study region 2010–2013 inclusive. Data are presented as rates per 10,000
persons per year according to age groupings. Error bars represent 95% CIs for each age group for men and women
Fig. 3 Incidence rates of colorectal, lung, melanoma, prostate and breast cancer in men and women in the ACDI Study region 2010–2013
inclusive. Data are presented as rates per 10,000 persons per year for each age grouping
Cowdery et al. BMC Cancer (2019) 19:892 Page 5 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
21 local government areas
Figure 4shows data for age adjusted cancer incidence in
men and women, aged 40+ years for all 21 LGAs across
the study region. For men, the agriculturally based LGA
of West Wimmera recorded the highest incidence rate
of cancer overall (5.3 per 10,000 population/year 95%CI
4.1–6.5), and the lowest incidence rates occurred in
Ararat (3.7 per 10,000 population/year 95% CI 3.0–4.3)
and Pyrenees (3.5 per 10,000 population/year 95%CI
2.8–4.3). Among women, Ararat was the LGA with the
highest age adjusted incidence (3.8 per 10,000 per popu-
lation/year 95% CI 3.1–4.4). Heat maps displaying
Fig. 4 Cancer incidence rates by Local Government Area (LGA) for men and women. Configured heat maps showing age adjusted incidence rates for
men and women for aall cancers, bbowel, clung, dmelanoma, eprostate and fbreast cancer, aged 40+ years across the study region 2010–2013
inclusive. The legend shows the shading as incidence rate per 10,000 population/year. AR= Ararat, BA = Ballarat, CG = Central Goldfields, CO=Colac-
Otway, C=Corangamite, GL = Glenelg, GP = Golden Plains, GE = Greater Geelong, HP=Hepburn, HI=Hindmarsh, HS=Horsham, MR = Moorabool, MO =
Moyne, NG = Northern Grampians, PY=Pyrenees, Q = Queenscliff, SG = Southern Grampians, SC=Surf Coast, WA = Warrnambool, WW=West Wimmera
and Y=Yarriambiack
Cowdery et al. BMC Cancer (2019) 19:892 Page 6 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
cancer incidence for each of the specific cancer subtypes
(bowel, lung, melanoma, prostate and breast) across the
study region are shown in Fig. 4.
After accounting for age in multivariable Poisson
regression model, SES and ARIA were significantly as-
sociated with cancer incidence (all p< 0.001) for men
and women (Table 1). Areas with greater socio-eco-
nomic disadvantage (represented by lower IRSAD
scores) were associated with higher cancer incidence
rates. An inverse relationship was found when asses-
sing ARIA, with those in more remote areas (repre-
sented by high ARIA scores) having an overall lower
rate of cancer incidence (Fig. 5).
Discussion
This research mapped the incidence rates of primary
cancer diagnoses and select subtypes across the region
of western Victoria by age for both men and women,
and investigated the association of age, accessibility and
area-level SES on cancer incidence. In accordance with
the trends in the state of Victoria, which show approxi-
mately 75% of new cancer cases in men, and 65% in
women are diagnosed among those aged 60 years and
over [22,23], our study indicated that the incidence of
cancer [overall] increased with increasing age for men
and women across the region. Furthermore, the male in-
cidence rate was higher than the female incidence rate
for all cancers from 50 years and over, but lower for the
40–49 years age decade. This is again reflective of na-
tional figures which show significantly higher rates of
cancer incidence for men than women in those aged 55
years and over and a lower incidence for men than
women in those aged 30–49 years [22,23]. The in-
creased male incidence rate is a trend found nationally
and internationally and is attributed to several key fac-
tors including the high occurrence of prostate cancer
diagnosis largely found in western countries utilising
PSA (prostate-specific antigen) testing [12,23] as well as
increased prevalence in men in lifestyle factors known to
increase cancer risk including smoking cigarettes, in-
creased alcohol consumption, occupational exposures,
and overall poorer diet [8,23]. The high incidence of
cancer in women between the ages of 30–49 years has
been largely attributed to the high incidence of breast
cancer in this age group [23] and these trends were
reflected in our results.
Cancer incidence rates (overall and among bowel,
lung, melanoma, prostate and breast subtypes) varied be-
tween men and women across the LGAs. For men, West
Wimmera recorded the highest incidence rate of cancer
[overall] with the lowest incidence rates occurring in
Ararat and Pyrenees., Ararat was the LGA with the high-
est cancer incidence rate among women. Ararat is one
of the most socioeconomically disadvantaged LGAs in
the western Victorian region. Low IRSAD scores indicate
relatively greater socioeconomic disadvantage and a lack
of advantage overall. For example, a low score would be
reflective of an area with (among other factors) many
households with low incomes, and/or many people in
unskilled professions [24].
Results from our study further demonstrated that LGAs
with greater socio-economic disadvantage, and LGAs
which scored as less accessible and more remote, were
associated with higher cancer incidence rates. These re-
sults correlate with the current available literature for
Australian populations, which demonstrate disparities in
cancer incidence between rural and metropolitan regions
[5,6,9,25]. Whilst disparities in incidence between
metropolitan and more remote and rural areas exist, the
driving forces underpinning this association are complex.
Rural areas have higher levels of disadvantaged communi-
ties, are older, poorer and likely to have less access to
screening and treatment services which can impact will-
ingness and access to care [26,27]. Rural communities
may include agricultural workers, which have been shown
to have increased rates of some cancers [28]. Furthermore,
socioeconomic disadvantage has been associated with
lifestyle factors known to directly contribute to cancer
risk, such as increased levels of smoking [29], alcohol con-
sumption, physical inactivity and poor diet [30–32]. Thus,
it is likely that the observable disparity between urban/
metro areas and more rural and remote areas is due to
many contributing factors. Whilst these results demon-
strate an association between age, sex, accessibility and
SES on cancer incidence, any inference on causality would
need to account for confounding variables associated with
Table 1 Model adjusted Incidence Rate Ratios (IRR) for analysis
of association between age standardised cancer incidence
(cancer subtypes) rates for men and women from 2010 to 2013
in western Victoria and; SES (Index of Relative Socioeconomic
Advantage and Disadvantage) and ARIA (Accessibility/
Remoteness Index of Australia). IRRs present as mean (95%
confidence interval)
SEX Cancer Type SES ARIA
Incidence Rate
Ratios (95% CI)
Incidence Rate
Ratios (95% CI)
MEN Bowel 0.25 (0.18–0.35) 0.05 (0.02–0.10)
Lung 0.21 (0.15–0.29) 0.03 (0.02–0.07)
Melanoma 0.27 (0.20–0.37) 0.05 (0.03–0.10)
Prostate 0.25 (0.18–0.36) 0.05 (0.02–0.10)
WOMEN Bowel 0.27 (0.19–0.36) 0.05 (0.03–0.11)
Lung 0.22 (0.16–0.30) 0.03 (0.02–0.06)
Melanoma 0.16 (0.16–0.17) 0.05 (0/03–0.09)
Breast 0.34 (0.26–0.45) 0.09 (0.05–0.16)
All p< 0.001*
Cowdery et al. BMC Cancer (2019) 19:892 Page 7 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
other risk factors; such as smoking, alcohol consumption,
diet, physical inactivity and obesity, hormonal factors in
women such as hormone replacement therapy (HRT),
sunlight, radiation, occupational exposures, pollution and
genetic susceptibility [22]. Likewise, stage at diagnosis may
have also provided a more accurate description of the
observed associations.
The ACDI study aims to describe the pattern of chronic
disease and injury and its relationship with age, sex and
location for the region of western Victoria. To date, this
study has investigated several diseases and injuries includ-
ing diabetes, fracture and joint replacement [11,33–35].
The addition of comprehensive information regarding
cancer incidence not only provides a snapshot of this
disease across the region and its 21 LGAs but allows for
comparison among disease and injury categories. Out-
comes of this analyses can be utilised to produce a highly
comprehensive community profile with the potential to
improve interventions impacting cancer incidence. As
Australia’s ageing population is increasing [3], so too will
the burden of chronic diseases, such as cancer and other
comorbidities [36]. These findings have vast implications
on cancer, community and primary health services at all
points of the continuum from prevention strategies,
screening services, active treatment, survivorship and
palliation [1]. As this study comprised a large geographic
area and included populations with varying degrees of re-
moteness and socioeconomic advantage and disadvantage,
a
b
Fig. 5 Bubble plots for association between age standardized cancer rates (ASCR), and aARIA (Accessibility/Remoteness Index of Australia); bsocioeconomic
status (SES; Index for Relative Socioeconomic Advantage and Disadvantage; IRSAD) occurring during 2010–2013 (inclusive) in the region of western Victoria.
Data presented for men and women are combined. LGA populations visualized in the scale of their circular bubbles. Size of bubbles indicate LGAs
proportional size
Cowdery et al. BMC Cancer (2019) 19:892 Page 8 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
it is uniquely posited to further raise rural and remote
health disparities. An introduction to this region which
further describes its novelty has been provided elsewhere
[15].Outcomes of this study can target healthcare
utilization and management of disease locally. Import-
antly, this study can be utilised as a repeatable profiling
model in other geographical settings, where a variety of
population densities are present to identify targeted inter-
ventions to reduce disparities in cancer outcomes in re-
gional and rural communities.
Strengths and limitations
Mandatory notification of new cancer diagnoses is pro-
vided to the VCR by hospitals, pathology laboratories
and cancer screening registers across the whole of
Victoria. The VCR initiated in 1982 and is the most
comprehensive and reliable cancer registry in the state,
thus it is unlikely that any cases have been missed for
the years (2010–2013) analysed in this study. We ac-
knowledge that survival rates were not assessed in this
study. The LGAs included in this study are highly
diverse and include cities as well as regional centres and
LGAs with small populations and large areas including
agricultural lands. However, no LGAs in this study
region were in the “remote”or “very remote”ARIA cat-
egories, and this study included the LGA of Hindmarsh,
which has the highest ARIA in the state of Victoria.
Thus, results may need to be interpreted with caution
when addressing this model on highly remote areas. No
inferences can be made at an individual level as ARIA
and IRSAD were investigated on an area level, and we
utilised aggregated data for analysis. Furthermore, ARIA
scores do not account for access to oncology services
and screening centres. Nevertheless, these data can be
utilised to assist health service planning and implemen-
tation of targeted preventative and intervention strat-
egies, screening services and treatment, survivorship and
palliation procedures to ensure better service provision
across western Victoria.
Conclusion
In conclusion, results from this study identified that for
the region of western Victoria, cancer incidence rates vary
among men and women and across LGA and increase
with advancing age, greater socio-economic disadvantage
and remoteness/lower accessibility. Identifying inequalities
in rural and regional health service delivery is important
and it is anticipated that these findings will assist in imple-
menting targeted and improved services at all points of
the cancer continuum from prevention strategies, screen-
ing services, treatment, survivorship and palliation. This
study also provides a model for further understanding of
geographical locations with national and international
implications.
Abbreviations
ABS: Australian bureau of statistics; ACDI: Ageing Chronic Disease and Injury
study; ARIA: Accessibility/remoteness index of Australia; BSD: Barwon
statistical division; CI: Confidence Interval; CRC: Colorectal cancer;
ERP: Estimated resident population; IRR: Incidence rate ratios; IRSAD: Index of
relative socio-economic advantage/disadvantage; IRSD: Index of relative
socio-economic disadvantage; LGA: Local government area; PHN: Primary
Health Network; PSA: Prostate-specific antigen; SES: socioeconomic status;
VCR: Victorian cancer registry
Acknowledgements
We thank the Victorian Cancer Registry for performing the data linkage.
Authors’contributions
JAP and MAK designed the study. SPC and MAS drafted the initial paper and
analysed data regarding cancer incidence across the region and the 21 LGAs.
MAS and KLH-K generated the heat maps regarding cancer incidence. MM
and SPC performed statistical analyses (Poisson regression and bubble plots)
concerning the association of aggregated age, ARIA and SES on cancer
incidence across the LGAs. SPC drafted and lead the manuscript in entirety;
LJW, MK, SH, TLD, SB, PML, RSP, AS, SLBO, MB and DC all provided intellectual
feedback into the design of the study and reviewed the manuscript. All au-
thors read and approved the final manuscript.
Funding
The study was funded by the Western Alliance Academic Health Science
Centre, a partnership for research collaboration between Deakin University,
Federation University and 13 health service providers operating across
western Victoria. Funding is provided for research in line with the Western
Alliance’s vision, mission and principles. They provided no other role in this
study. LJW and SLB-O are supported by National Health and Medical
Research Council (NHMRC) Career Development Fellowships (1064272, and
1107510, respectively). KLH-K is supported by an Alfred Deakin Postdoctoral
Research Fellowship and SPC and MAS are supported by an IMPACT
Strategic Research Centre (Deakin University) scholarship. MB is supported by
an NHMRC Principal research Fellowship (1059660 and APP1156072).
Availability of data and materials
The data that support the findings of this study are available from the ACDI
study, but restrictions apply to the availability of these data, which were
used under license for the current study, and so are not publicly available.
Data are however available from the authors upon reasonable request and
with permission from the ACDI study Director (JAP).
Ethics approval and consent to participate
Written and/or verbal individual consent was not required as there was no
direct patient or public involvement. Aggregate data was utilised; this data
contained no personal or identifying information. All data for this region and
the 21 local government areas was obtained from existing local, national
and state registries. This study was approved by the Human Research Ethics
Committee at Barwon Health (Project 15/11).
Consent for publication
Not applicable [study does not contain data from any individual person].
Competing interests
The authors declare that they have no competing interests.
Author details
1
School of Medicine, Deakin University, Geelong, Australia.
2
Faculty of Health,
Deakin University, Geelong, Australia.
3
Faculty of Health, Biostatistics Unit,
Deakin University, Geelong, Australia.
4
Department of Medicine-Western
Health, The University of Melbourne, St Albans, Australia.
5
University Hospital
Geelong, Barwon Health, Geelong, Australia.
6
The University of Sydney,
Sydney, Australia.
7
Centre for Quality and Patient Safety Research, Barwon
Health Partnership, School of Nursing and Midwifery, Deakin University
Geelong, Hamilton, Australia.
8
National Centre for Farmer Health, Western
District Health Service, Hamilton, Australia.
9
Barwon Centre for Orthopaedic
Research and Education, Barwon Health and St John of God Hospitals,
Geelong, Australia.
10
South West Healthcare, Warrnambool, Australia.
11
Australian Institute for Musculoskeletal Science (AIMSS), St Albans, Australia.
Cowdery et al. BMC Cancer (2019) 19:892 Page 9 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
12
Orygen, The National Centre of Excellence in Youth Mental Health, Centre
for Youth Mental Health, St Albans, Australia.
13
Florey Institute for
Neuroscience and Mental Health and the Department of Psychiatry, The
University of Melbourne, Melbourne, Australia.
14
Department of
Epidemiology and Preventive Health, Monash University, Melbourne,
Australia.
Received: 6 February 2019 Accepted: 21 August 2019
References
1. Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, MacIntyre MF,
Allen C, Hansen G, Woodbrook R, Wolfe C, et al. The global burden of
Cancer 2013. JAMA Oncol. 2015;1(4):505–27.
2. Productivity Commission. Economic Implications of an Ageing Australia.
Canberra: Research Report; 2005. p. 1-428.
3. Australian Institute of Health and Welfare, Australia’s Health 2014. Australia’s
health series no. 14. Cat. no. AUS 178. Canberra: Australian Instiute of Health
and Welfare (AIHW); 2014.
4. Thursfield V, Farrugia H. Cancer in Victoria: statistics and trends 2014.
Melbourne: Cancer Council Victoria; 2015.
5. Thursfield V, Farrugia H. Cancer in Victoria: statistics and trends 2016.
Melbourne: Cancer Council Victoria; 2017.
6. Baade P, Turrell G, Aitken JF. Geographic remoteness, area-level socio-
economic disadvantage and advanced breast cancer: a cross-sectional,
multilevel study. J Epidemiol Community Health. 2011;65(11):1037–43.
7. Obertova Z, Brown C, Holmes M, Lawrenson R. Prostate cancer incidence
and mortality in rural men--a systematic review of the literature. Rural
Remote Health. 2012;12(2):2039.
8. Ireland M, March S, Crawford-Williams F, Cassimatis M, Aitken JF, Hyde MK,
Chambers SK, Sun J, Dunn J. A systematic review of geographical
differences in management and outcomes for colorectal cancer in Australia.
BMC Cancer. 2017;17(1):95.
9. Leung J, McKenzie S, Martin J, McLaughlin D. Effect of rurality on screening
for breast cancer: a systematic review and meta-analysis comparing
mammography. Rural Remote Health. 2014;14(2):2730.
10. Jemal A, Center MM, DeSantis C, Ward EM. Global patterns of cancer
incidence and mortality rates and trends. Cancer Epidemiol Biomark Prev.
2010;19(8):1893–907.
11. Holloway K, Sajjad MA, Mohebbi M, Kotowicz MA, Livingston PM, Khasraw
M, Hakkennes S, Dunning TL, Brumby S, Page RS, Pedler D, Sutherland A,
Venkatesh S, Brennan-Olsen SL, Williams LJ, Pasco JA. The epidemiology of
hip fractures across western Victoria, Australia. Bone. 2018;108:1–9.
12. Clegg L, Reichman ME, Miller BA, Hankey BF, Singh GK, Lin YD, Goodman
MT, Lynch CF, Schwartz SM, Chen VW, Bernstein L, Gomez SL, Graff JJ, Lin
CC, Johnson NJ, Edwards BK. Impact of socioeconomic status on cancer
incidence and stage at diagnosis: Selected findings from the surveillance,
epidemiology, and end results: National Longitudinal Mortality Study.
Cancer Causes Control. 2009;20:417–35.
13. Williams J, Clifford C, Hopper J, Giles G. Socioeconomic status and cancer
mortality and incidence in Melbourne. Eur J Cancer. 1991;27(7):917–21.
14. Department of Health. Victorian Population Health Survey 2011–12, survey
findings. Melbourne: State Government of Victoria; 2014.
15. Sajjad MA, Holloway KL, Kotowicz MA, Livingston PM, Khasraw M, Hakkennes S,
Dunning TL, Brumby S, Page RS, Pedler D, Sutherland A, Venkatesh S, Brennan-
Olsen SL, Williams LJ, Pasco JA. Ageing, chronic disease and injury: a study in
Western Victoria (Australia). J Public Health Res. 2016;5(2):678.
16. Cancer Council Victoria. Victorian Cancer Registry. 2014 Cited 2019. Victoria:
Cancer Epidemiology Centre. Available from: http://www.cancervic.org.au/
research/registry-statistics/vcr.
17. Thursfield V, Farrugia H, Robertson P, Giles G. Canstat: Cancer in Victoria
2009: Cancer Epidemiology Centre, Cancer Council Victoria; 2009.
18. Commonwealth Department of Health and Aged Care. Measuring
Remoteness: Accessibility/Remoteness Index of Australia (ARIA). Occasional
Papers: New Series Number 14. Canerra: ACT; 2001.
19. The Department of Economic Development Jobs Transport and Resources,
Industry Profiles, 2015.
20. Australian Bureau of Statistics (ABS). Census of population and housing: socio-
economic indexes for areas (SEIFA), Australia. Canberra: ACT: cat.no.2033.0.55.
001; 2014.
21. Australian Bureau of Statistics, Census Community Profiles. 2011.
22. Australian Institute of Heatlth and Welfare. Cancer in Australia: an overview
2010. Cancer series No 90. Cat. no. CAN 88. Canberra: AIHW; 2010.
23. Australian Institute of Health and Welfare. Cancer in Australia: an overview
2014. Cancer series No 90. Cat. no. CAN 88. Canberra: AIHW; 2014.
24. Australian Bureau of Statistics, 2049.0 Census of population and housing:
estimating homelessness. 2016.
25. Yu XQ, Luo Q, Kahn C, Grogan P, O’Connell DL, Jemale A. Contrasting temporal
trends in lung cancer incidence by socioeconomic status among women in
New South Wales, Australia, 1985–2009. Lung Cancer. 2017;108:56–61.
26. Hall S, Kaufman JS, Ricketts TC. Defining urban and rural areas in U.S.
epidemiologic studies. J Urban Health. 2006;83(2):162–75.
27. Australian Institute of Health and Welfare. Australia’s health 2016. Australia’s
health series no. 15, 2016. Cat. No. AUS 199. Canberra: AIHW; 2016.
28. Kachuri L, Harris MA, MacLeod JS, Tjepkema M, Peters PA, Demers PA.
Cancer risks in a population-based study of 70,570 agricultural workers:
results from the Canadian census health and Environment cohort
(CanCHEC). BMC Cancer. 2017;17(1):343.
29. Germain D, Durkin S, Scollo M, Wakefield M. The long-term decline of adult
tobacco use in Victoria: changes in smoking initiation and quitting over a quarter
of a century of tobacco control. Aust N Z J Public Health. 2012;36(1):17–23.
30. Poortinga W. The prevalence and clustering of four major lifestyle risk
factors in an English adult population. Prev Med. 2007;44(2):124–8.
31. Miller Y, Bauman A. Development of a chronic disease risk factor index and
identifying population subgroups at risk using New South Wales adult
Health survey 2002 data. N S W Public Health Bull. 2005;16(10):141–6.
32. Ding D, Do A, Schmidt H-M, Bauman AE. A widening gap? Changes in
multiple lifestyle risk behaviours by socioeconomic status in New South
Wales, Australia, 2002–2012. PLoS One. 2015;10(8):e0135338.
33. Sajjad M, Holloway-Kew KL, Mohebbi M, Kotowicz MA, De Abreu LLF,
Livingston PM, Khasraw M, Hakkennes S, Dunning TL, Brumby S, Page RS,
Sutherland AG, Venkatesh S, Williams LJ, Brennan-Olsen SL, Pasco JA.
Association between area-level socioeconomic status, accessibility and
diabetes-related hospitalisations: a cross-sectional analysis of data from
Western Victoria, Australia. BMJ Open. 2019;9(5):e026880.
34. Brennan-Olsen S, Vogrin S, Graves S, Holloway-Kew KL, Page RS, Sajjad MA,
Kotowicz MA, Livingston PM, Khasraw M, Hakkennes S, Dunning TL, Brumby
S, Sutherland AG, Talevski J, Green D, Kelly TL, Williams LJ, Pasco JA. Revision
joint replacement surgeries of the hip and knee across geographic region
and socioeconomic status in the western region of Victoria: a cross-
sectional multilevel analysis of registry data. BMC Musculoskelet Disord.
2019;20(1):300.
35. Brennan-Olsen S, Vogrin S, Holloway KL, Page RS, Sajjad MA, Kotowicz MA,
Livingston PM, Khasraw M, Hakkennes S, Dunning TL, Brumby S, Pedler D,
Sutherland A, Venkatesh S, Williams LJ, Duque G, Graves S, Lorimer M, Pasco
JA. Correction to: Geographic region, socioeconomic position and the
utilisation of primary total joint replacement for hip or knee osteoarthritis
across western Victoria: a cross-sectional multilevel study of the Australian
Orthopaedic Association National Joint Replacement Registry. Arch
Osteoporos. 2018;13(1):75.
36. Caughey G, Vitry AI, Gilbert AL, Roughead EE. Prevalence of comorbidity of
chronic diseases in Australia. BMC Public Health. 2008;8(8):221.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Cowdery et al. BMC Cancer (2019) 19:892 Page 10 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Content uploaded by Richard S Page
Author content
All content in this area was uploaded by Richard S Page on Sep 07, 2019
Content may be subject to copyright.
Available via license: CC BY
Content may be subject to copyright.
