Title page
Temporal trends show improved breast cancer survival in Australia but
widening urban–rural differences
Xue Qin Yu,1,2 Qingwei Luo,1,2 Clare Kahn,1 Dianne L O’Connell,1,2,3,4 Nehmat Houssami2
1.
2.
3.
4.
Cancer Research Division, Cancer Council New South Wales, Sydney, Australia
Sydney School of Public Health, University of Sydney, Sydney, Australia
School of Public Health and Community Medicine, University of NSW, Sydney, Australia
School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
Corresponding author and address:
Dr Xue Qin Yu
Cancer Research Division
Cancer Council NSW
P.O. Box 572
Kings Cross NSW 1340
Australia
Tel: +61 2 9334 1851
Fax: +61 2 8302 3550
Email: xueqiny@nswcc.org.au
1
Main text
Abstract
We examined geographic patterns in breast cancer survival over time using populationbased data for breast cancer diagnosed between 1987 and 2007 in New South Wales,
Australia. We found that five-year relative survival increased during the entire study period.
Multivariable analysis indicated that there was little geographic variation in 1992-1996, but
in 1997-2001 and 2002-2007 geographic variation was statistically significant (P < 0.01),
with women living in rural areas having higher risk of death from breast cancer. The
underlying reasons for this widening survival disparity must be identified so that
appropriately targeted interventions can be implemented and the disparity reduced.
Key words
breast cancer, geography, inequality, temporal trends, survival, population health
Introduction
Breast cancer is the most commonly diagnosed cancer in Australian women [1]. As in
most developed countries [2], prognosis for breast cancer patients has improved over the
past 20 years in Australia [3, 4]. These survival benefits were not however uniformly
experienced by all population subgroups, with patients living in socioeconomic
disadvantaged or geographically remote areas having poorer survival [5-8]. Factors that
may mediate these disparities include differences in the stage at diagnosis, access to and
quality of care received, and other correlates of geographic or socioeconomic
disadvantage. While disparities in cancer survival according to place of residence are well
established in Australia [5-8], few studies have looked at the temporal trends in these
disparities.
The aim of this study was to describe recent geographic patterns in breast cancer survival
in the Australian state of New South Wales (NSW), and investigate temporal trends in
these geographic variations adjusted for confounders.
Methods
Data were obtained from the NSW Central Cancer Registry for all first primary breast
cancers (ICD-O3: C50) [9] diagnosed in women aged 18–84 years from January 1987 to
December 2007 that were prevalent cases between 1992 and 2007. Cases were excluded
if they were reported through death certificate only or first identified post-mortem. Ethics
approval was obtained from the NSW Population and Health Service Research Ethics
Committee (ref: 2009/03/139).
The outcome variable was all-cause survival after a diagnosis of breast cancer. Survival
status was obtained through record linkage of the cancer cases in the cancer registry with
the death records from the NSW Register of Births, Deaths and Marriages and the
National Death Index. All eligible patients were followed up until 31 December 2007, the
most recent data available.
Two area-based measures were used: geographic remoteness and socioeconomic status
(SES) of local government areas of residence at diagnosis. Geographic remoteness of
residence was categorised into major cities, inner regional, rural (including outer regional,
remote and very remote areas) using the Australian Standard Geographic Classification
Remoteness Structure [10]. The socioeconomic disadvantage tertiles were defined using
the Index of Relative Socio-economic Disadvantage derived from the 2001 Census [11].
Additional variables included were age at diagnosis (18–49, 50–59, 60–69 and 70–84
years), and disease stage at diagnosis (localised, regional, distant and unknown).
2
Main text
Statistical analysis
The methods used have been described in detail previously [12]. Five-year relative
survival was calculated for each geographic region using the period approach [13], with
cancer cases under observation in each of three “at-risk” periods: 1992–1996, 1997–2001
and 2002–2007. The period approach was used because it provides reliable predictions of
5-year cohort survival when sufficient follow-up is not available for recent diagnosed
patients, such as for those diagnosed in the most recent period (2002–2007) [14]. We then
used a Poisson regression model [15] to calculate the relative excess risk (major cities as
reference category) of death (RER) within 5 years of diagnosis, adjusting for age group,
disease stage at diagnosis, and SES tertiles stratified by at risk period. We fitted two
models: one including SES and another without. To support interpretation of results, we
repeated the above analysis stratified by disease stage (localised vs non-localised). Finally,
we added an interaction term to the model between the geographic location and at-risk
period to allow the effect of geographic remoteness to change over time and then
assessed if this interaction was statistically significant. A two-sided, log-likelihood ratio test
with a P value <0.05 indicated statistical significance. Further analysis was undertaken to
investigate the possible impact of lead-time bias on survival due to potential urban–rural
differences in the intensity of mammographic screening. We investigated this possibility by
estimating the age-standardised mortality ratio during the first five years after breast
cancer diagnosis by geographic location over the three at risk periods. Analyses were
performed using Stata statistical software, version 13.1 (StataCorp).
Results
Of the 63,757 eligible women diagnosed with breast cancer, 72.8% were resident in major
cities and 20.6% were resident in inner regional areas. Characteristics of the study
population can be found in online Appendix 1.
The 5-year relative survival for women diagnosed with breast cancer increased during the
entire study period, from 81.5% in 1992–1996 and 86.7% in 1997–2001 to 89.6% in 2002–
2007. The improvement in survival over time was also observed across categories of
geographic remoteness (Fig 1), but survival was consistently lower for women living in
rural areas across the whole study period 1992-2007.
'At risk' period
Relative survival %
1992-1996
1997-2001
2002-2007
100
100
100
90
90
90
80
80
80
70
70
0
1
2
3
4
5
70
0
1
2
3
4
5
0
1
2
3
4
5
Years since diagnosis
Major cities
Inner regional
Rural
Fig 1. Relative survival (95% confidence interval: CI) for breast cancer in NSW, Australia,
by geographic remoteness for each of the three at-risk periods, 1992–2007
3
Main text
The results of our multivariable analysis (Table 1) show that after adjustment for all
prognostic factors there was little geographic variation in 1992-1996. During 1997-2001,
the adjusted RER was significantly higher for patients living in inner regional and rural
areas than for those in major cities, although the RER for inner regional became nonsignificant after further adjustment for SES. In the most recent period (2002-2007), the
RER for rural areas was larger and statistically significant, while the RER for inner regional
areas was not statistically significant. The interaction between geographic remoteness and
at-risk period was significant (P=0.037), indicating that the urban–rural differential had
widened over time. Stratified analyses (online Appendix 2) found that the urban–rural
disparities were only observed for non-localised cancer (p=0.001).
Table 1 Relative excess risk of death during the first 5 years after breast cancer diagnosis
in NSW, Australia, by geographic remoteness, and over time for the three at-risk periods,
1992–2007
Geographic location
‘At risk’ period 1992-1996
Major cities
Inner regional
Rural
‘At risk’ period 1997-2001
Major cities
Inner regional
Rural
‘At risk’ period 2002-2007
Major cities
Inner regional
Rural
p-value for interaction of geographic
location and at risk period
Relative Excess Risk (95% CI)
Not adjusted for SES*
Adjusted for SES
1.00
1.00
1.06
(0.89 – 1.12)
(0.89 – 1.26)
1.00
0.96
1.00
(0.85 – 1.07)
(0.84 – 1.20)
1.00
1.13
1.28
(1.01 – 1.27)
(1.07 – 1.53)
1.00
1.08
1.21
(0.96 – 1.21)
(1.01 – 1.44)
1.00
0.95
1.39
(0.84 – 1.06)
(1.18 – 1.64)
1.00
0.90
1.31
(0.80 – 1.01)
(1.11 – 1.55)
0.037
0.036
* SES – socioeconomic status. These relative excess risk estimates were adjusted for age and
disease stage at diagnosis.
Further analysis suggested that similar geographic patterns were observed in mortality
over time (online Appendix 3). The risk of dying in the first 5 years after diagnosis was
more than 10% higher for women living in rural areas in the most recent period, although
this did not reach statistical significance due to the small number of women in this group.
The main reason for the differences between the survival and mortality results may be
because the length of survival was not considered in the mortality rates calculation (a
death that occurred in the first year after diagnosis was treated exactly the same as one
that occurred in the fifth year after diagnosis) while this was accounted for in the survival
model.
Discussion
The results of this study demonstrate that although the overall survival outlook for women
diagnosed with breast cancer in NSW has improved over time, there is an emerging
inequality by place of residence, mainly in rural areas compared to major cities. This
urban-rural survival inequality was found only among women diagnosed with non-localised
breast cancer (inclusive of those with nodal disease, and locally advanced or metastatic
breast cancer).
4
Main text
Given that these results have taken into account disease stage at diagnosis, it may be that
differences in the application of treatments of known effectiveness, such as systemic
treatment for non-localised disease, are contributing to variations in outcomes. A potential
explanation for this may be that women living in rural and remote areas were more likely to
have limited access to health services and had to travel long distances to oncology
treatment centres [5, 16], including radiotherapy facilities [17]. Addressing these issues of
access and providing rural patients with levels of multi-disciplinary treatment equal to
those in major cities is a significant challenge in Australia due to the large distances
involved and the low population density outside the metropolitan areas [6, 16].
Outcomes for breast cancer patients are better when treated by surgeons with higher case
volumes and specialist expertise [17, 18], so it’s possible that variable experience in breast
cancer treatment of rural surgeons may have been a contributing factor [19]. However,
given that urban-rural inequality in survival was evident only among women diagnosed
with non-localised breast cancer it seems more likely that not receiving stage-appropriate
therapy may be the underlying reason for these findings. Again this may be related to
geographic distances and oncology service location [20].
In conclusion, we found that geographic disparities in breast cancer survival in NSW
became evident from the later 1990s and continued in the most recent period. It is
important that the underlying reasons for this disparity are identified so that appropriately
targeted interventions can be implemented and the disparity reduced.
Acknowledgments
We would like to thank the NSW Central Cancer Registry for providing the data for the
study. Xue Qin Yu was supported by an Australian National Health & Medical Research
Council Training Fellowship (Ref: 550002). Nehmat Houssami is supported by a National
Breast Cancer Foundation (NBCF Australia) Practitioner Fellowship.
Competing interests
The authors declare that they have no competing interests.
References
1.
Australian Institute of Health and Welfare & Cancer Australia: Breast cancer in Australia: an
overview. Canberra: Australian Institute of Health and Welfare, 2012.
2.
Coleman MP, Forman D, Bryant H, Butler J, Rachet B, Maringe C, Nur U, Tracey E, Coory M,
Hatcher J, McGahan CE, Turner D, Marrett L, Gjerstorff ML, Johannesen TB, Adolfsson J, Lambe M,
Lawrence G, Meechan D, Morris EJ, Middleton R, Steward J, Richards MA. Cancer survival in
Australia, Canada, Denmark, Norway, Sweden, and the UK, 1995-2007 (the International Cancer
Benchmarking Partnership): an analysis of population-based cancer registry data. Lancet 2011;377:
127-138.
3.
Yu X, Baade P, O'Connell DL. Conditional survival of cancer patients: an Australian perspective.
BMC Cancer 2012;12: 460.
4.
Yu XQ, O'Connell DL, Gibberd RW, Coates AS, Armstrong BK. Trends in survival and excess risk of
death after diagnosis of cancer in 1980-1996 in New South Wales, Australia. Int J Cancer 2006;119:
894-900.
5.
Jong KE, Smith DP, Yu XQ, O'Connell DL, Goldstein D, Armstrong BK. Remoteness of residence
and survival from cancer in New South Wales. Med J Aust 2004;180: 618-622.
6.
Yu XQ, O'Connell D L, Gibberd RW, Armstrong BK. A population-based study from New South
Wales, Australia 1996-2001: area variation in survival from colorectal cancer. Eur J Cancer 2005;41:
2715-2721.
5
Main text
7.
Yu XQ, O'Connell DL, Gibberd RW, Armstrong BK. Assessing the impact of socio-economic status
on cancer survival in New South Wales, Australia 1996-2001. Cancer Causes Control 2008;19:
1383-1390.
8.
Yu XQ, O'Connell DL, Gibberd RW, Smith DP, Dickman PW, Armstrong BK. Estimating regional
variation in cancer survival: a tool for improving cancer care. Cancer Causes Control 2004;15: 611618.
9.
Fritz A, Percy C, Jack A, Shanmugaratnam K, Sobin L, Parkin D, Whelan S (eds.). International
Classification of Diseases for Oncology. Geneva, Switzerland: World Health Organisation; 2000.
10.
Australian Bureau of Statistics. ASGC Remoteness Classification: Purpose and Use. Canberra:
Commonwealth of Australia, 2003.
11.
Australian Bureau of Statistics. Information paper: An Introduction to Socio-Economic Indexes for
Areas (SEIFA), 2006. Canberra: Australian Bureau of Statistics, 2008.
12.
Yu XQ, Luo Q, Smith DP, O'Connell DL, Baade PD. Geographic variation in prostate cancer survival
in New South Wales. Med J Aust 2014;200: 586-590.
13.
Brenner H. Long-term survival rates of cancer patients achieved by the end of the 20th century: a
period analysis. Lancet 2002;360: 1131-1135.
14.
Brenner H, Hakulinen T. Advanced detection of time trends in long-term cancer patient survival:
experience from 50 years of cancer registration in Finland. Am J Epidemiol 2002;156: 566-577.
15.
Dickman PW, Sloggett A, Hills M, Hakulinen T. Regression models for relative survival. Stat Med
2004;23: 51-64.
16.
Cramb SM, Mengersen KL, Turrell G, Baade PD. Spatial inequalities in colorectal and breast cancer
survival: premature deaths and associated factors. Health Place 2012;18: 1412-1421.
17.
Mitchell KJ, Fritschi L, Reid A, McEvoy SP, Ingram DM, Jamrozik K, Clayforth C, Byrne MJ. Ruralurban differences in the presentation, management and survival of breast cancer in Western
Australia. Breast 2006;15: 769-776.
18.
Chowdhury MM, Dagash H, Pierro A. A systematic review of the impact of volume of surgery and
specialization on patient outcome. Br J Surg 2007;94: 145-161.
19.
Craft PS, Buckingham JM, Dahlstrom JE, Beckmann KR, Zhang Y, Stuart-Harris R, Jacob G, Roder
D, Tait N. Variation in the management of early breast cancer in rural and metropolitan centres:
implications for the organisation of rural cancer services. Breast 2010;19: 396-401.
20.
Underhill C, Bartel R, Goldstein D, Snodgrass H, Begbie S, Yates P, White K, Jong K, Grogan P.
Mapping oncology services in regional and rural Australia. Aust J Rural Health 2009;17: 321-329.
6
Appendices
Appendices
Appendix 1: Characteristics of breast cancer patients diagnosed in 1987-2007 in New South Wales, Australia, by geographic
remoteness
Geographic location
Characteristic
Number of cases
Total (%)
N=63757
Major
cities
n=46421
Inner
regional
n=13160
Rural
n=4176
Age at diagnosis (year)
<0.0001
18-50
17,646
(27.7)
75.8
18.2
6.1
50-59
16,472
(25.8)
73.3
20.1
6.7
60-69
15,163
(23.8)
70.2
22.6
7.3
14,476
(22.7)
71.5
22.3
6.2
70-84
Year of diagnosis
<0.0001
1987-1991
9,988
(15.7)
74.6
18.8
6.6
1992-1996
14,664
(23.0)
73.2
20.2
6.6
1997-2001
16,849
(26.4)
72.8
20.6
6.6
22,256
(34.9)
71.8
21.8
6.5
Localised
33,685
(52.8)
72.6
20.9
6.5
Regional
21,534
(33.8)
74.5
19.4
6.2
2,589
(4.1)
71.9
21.2
7.0
Unknown
Socioeconomic status
5,949
(9.3)
68.3
23.8
7.8
Least disadvantaged
23,457
(36.8)
93.6
5.9
0.5
Middle group
20,000
(31.4)
62.7
29.5
7.9
Most disadvantaged
20,300
(31.8)
58.8
29.0
12.3
2002-2007
Stage of disease
Distant
p-value
<0.0001
<0.0001
7
Appendices
Appendix 2: Relative excess risk of death during the first 5 years after breast cancer diagnosis in NSW, Australia, by disease stage
at diagnosis, 1992–2007
Relative Excess Risk (95% CI)
Localised stage†
Geographic region
Not adjusted for SES*
Adjusted for SES
Not adjusted for SES
p=0.8
p=0.1
p<0.0001
Major cities
1.00
Inner regional
0.94
Rural
‘At risk' period
1.00
(0.78 - 1.14)
0.92 (0.68 - 1.26)
p<0.0001
1992-1996
1.00
1997-2001
0.54
2002-2007
Age at diagnosis (year)
Non-localised stage†
0.83
1.00
(0.68 - 1.01)
0.78 (0.57 - 1.08)
p<0.0001
1.00
(0.46 - 0.64)
0.30 (0.25 - 0.37)
p<0.0001
0.54
1.06
1.34 (1.19 - 1.50)
p<0.0001
0.30 (0.25 - 0.37)
p<0.0001
1.00
(0.92 - 1.09)
1.25 (1.11 - 1.41)
p<0.0001
1.00
(0.73 - 0.85)
0.57 (0.53 - 0.62)
p<0.0001
1.00
0.79
(0.73 - 0.85)
0.57 (0.53 - 0.62)
p<0.0001
18-49
1.00
50-59
0.70
(0.59 - 0.83)
0.70
(0.59 - 0.83)
1.03
(0.94 - 1.11)
1.03
(0.95 - 1.12)
60-69
0.55
(0.45 - 0.69)
0.55
(0.44 - 0.68)
1.19
(1.09 - 1.30)
1.19
(1.09 - 1.30)
0.65
(0.48 - 0.87)
0.66 (0.50 - 0.89)
p=0.0002
1.74
(1.60 - 1.91)
1.75 (1.60 - 1.91)
p<0.0001
70-84
Socioeconomic status
1.00
0.79
p=0.001
1.00
(0.97 - 1.14)
1.00
(0.46 - 0.64)
Adjusted for SES
1.00
Least disadvantaged
1.00
1.00
Middle group
1.27
(1.05 - 1.54)
1.18
(1.09 - 1.28)
Most disadvantaged
1.47
(1.22 - 1.77)
1.18
(1.09 - 1.28)
†
A summary stage was used: Localised (stage I - cancer contained entirely in the breast); Non-localised including locally advanced (stage II - spread to
adjacent organs or tissues), regional (stage III - spread to regional lymph nodes), distant (stage IV - distant metastases). Less than 10% of the cases were
excluded in this analysis because of being coded as ‘unknown’ (where information in the notifications was insufficient to assign stage).
SES – socioeconomic status.
8
Appendices
Appendix 3: Age-standardised all-cause mortality ratios (95% CI) during the first five years after diagnosis for breast cancer
patients by geographic location from 1992 to 2007, NSW Australia
Geographic location
Major cities*
Inner regional
Rural
1992-1996
100.0
91.3
95.8
(84.2-98.9)
(83.4-109.4)
p-value
0.02
0.53
1997-2001
100.0
102.6
105.6
(95.0-110.8)
(92.0-120.6)
p-value
0.52
0.47
2002-2007
100.0
99.6
110.2
(92.5-107.0)
(96.9-124.9)
p-value
0.91
0.15
* Major cities was used as the standard population.
9