Geographic remoteness and risk of advanced colorectal cancer at diagnosis in Queensland: a multilevel study.
ABSTRACT We examine the relationships between geographic remoteness, area disadvantage and risk of advanced colorectal cancer.
Multilevel models were used to assess the area- and individual-level contributions to the risk of advanced disease among people aged 20-79 years diagnosed with colorectal cancer in Queensland, Australia between 1997 and 2007 (n=18,561).
Multilevel analysis showed that colorectal cancer patients living in inner regional (OR=1.09, 1.01-1.19) and outer regional (OR=1.11, 1.01-1.22) areas were significantly more likely to be diagnosed with advanced cancer than those in major cities (P=0.045) after adjusting for individual-level variables. The best-fitting final model did not include area disadvantage. Stratified analysis suggested this remoteness effect was limited to people diagnosed with colon cancer (P=0.048) and not significant for rectal cancer patients (P=0.873).
Given the relationship between stage and survival outcomes, it is imperative that the reasons for these rurality inequities in advanced disease be identified and addressed.
- SourceAvailable from: Craig Sinclair[show abstract] [hide abstract]
ABSTRACT: BACKGROUND: Previous studies have focused on the treatment received by rural cancer patients and have not examined their diagnostic pathways as reasons for poorer outcomes in rural Australia. OBJECTIVES: To compare and explore symptom appraisal and help-seeking behaviour in patients with breast, lung, prostate or colorectal cancer from rural Western Australia (WA). METHODS: A mixed-methods study of people recently diagnosed with breast, lung, prostate or colorectal cancer from rural WA. The time from first symptom to diagnosis (i.e. total diagnostic interval, TDI) was calculated from interviews and medical records. RESULTS: Sixty-six participants were recruited (24 breast, 20 colorectal, 14 prostate and 8 lung cancer patients). There was a highly significant difference in time from symptom onset to seeking help between cancers (P = 0.006). Geometric mean symptom appraisal for colorectal cancer was significantly longer than that for breast and lung cancers [geometric mean differences: 2.58 (95% confidence interval, CI: 0.64-4.53), P = 0.01; 3.97 (1.63-6.30), P = 0.001, respectively]. There was a significant overall difference in arithmetic mean TDI (P = 0.046); breast cancer TDI was significantly shorter than colorectal or prostate cancer TDI [mean difference : 266.3 days (95% CI: 45.9-486.8), P = 0.019; 277.0 days, (32.1-521.9), P = 0.027, respectively]. These differences were explained by the nature and personal interpretation of symptoms, perceived as well as real problems of access to health care, optimism, stoicism, machismo, fear, embarrassment and competing demands. CONCLUSIONS: Longer symptom appraisal was observed for colorectal cancer. Participants defined core characteristics of rural Australians as optimism, stoicism and machismo. These features, as well as access to health care, contribute to later presentation of cancer.Family Practice 01/2013; · 1.83 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: BACKGROUND: Previous studies have focused on the treatment received by rural cancer patients and have not examined their diagnostic pathways as reasons for poorer outcomes in rural Australia. OBJECTIVES: To compare and explore diagnostic pathways and diagnostic intervals in patients with breast, lung, prostate or colorectal cancer from rural Western Australia (WA) to inform future interventions aimed at reducing time to cancer diagnosis. METHODS: Mixed methods study of people recently diagnosed with breast, lung, prostate or colorectal cancer from the Goldfields and Great Southern Regions of WA. Qualitative interviews explored participants' diagnostic pathways and factors underlying differences observed between individuals and cancers. Data were extracted from general practice and hospital records to calculate intervals from first presentation in general practice to final diagnosis. RESULTS: Sixty-six participants were recruited (43 Goldfields and 23 Great Southern region; 24 breast, 20 colorectal, 14 prostate and 8 lung cancers). There were significant overall differences between cancers in time from presentation in general practice to referral (P = 0.045), from referral to seeing a specialist (P = 0.010) and from specialist appointment to cancer diagnosis (P ≤ 0.001). These differences were due to the nature of presenting symptoms, access to diagnostic tests and multiple visits to specialists. Breast cancer was diagnosed more quickly because its symptoms are more specific and due to better access to diagnostic tests and specialist one-stop clinics. CONCLUSIONS: Interventions to improve cancer diagnosis in rural Australia should focus on better case selection in general practice and better access to diagnostic tests, especially for prostate and colorectal cancers.Family Practice 05/2013; · 1.83 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: BACKGROUND: This longitudinal study describes the five year trajectories of health-related quality of life(HR-QOL) and life satisfaction in long term colorectal cancer survivors.Patients and methodsA population-based sample of 1966 colorectal cancer survivors were surveyed at six timepoints from five months to five years post-diagnosis. Predictor variables were: sociodemographicvariables, optimism; cancer threat appraisal; perceived social support. Qualityof life was assessed with the Functional Assessment of Cancer Therapy-Colorectal (HRQOL);and the Satisfaction with Life Scale. Growth mixture models were applied to identifytrajectory classes and their predictors. RESULTS: Distinct adjustment trajectories were identified for HR-QOL and life satisfaction. Loweroptimism, poorer social support, a more negative cognitive appraisal, and younger age wereassociated with poorer life satisfaction, while survivors with less than 8 years of educationhad higher life satisfaction. This pattern was similar for overall HR-QOL except thateducational level was not a significant predictor and later stage disease and female genderemerged as related to poorer outcomes. One in five survivors reported poorer constant HRQOL(19.2%) and a small group had poor life satisfaction (7.2%); 26.2% reported constanthigh HR-QOL and 48.8% had high constant life satisfaction. Socioeconomic disadvantageand remoteness of residence uniquely predicted poorer outcomes in the colorectal cancerspecific HR-QOL sub domain. CONCLUSION: Although HR-QOL and subjective cognitive QOL share similar antecedents their trajectorypatterns suggested they are distinct adjustment outcomes; with life satisfaction emerging astemporally stable phenomenon. Unique patterns of risk support suggest the need to accountfor heterogeneity in adjustment in longitudinal QOL studies with cancer survivors.Health and Quality of Life Outcomes 03/2013; 11(1):46. · 2.27 Impact Factor
Geographic remoteness and risk of advanced colorectal cancer
at diagnosis in Queensland: a multilevel study
PD Baade*,1,2,3, P Dasgupta1, J Aitken1,3,4and G Turrell2
1Viertel Centre for Research in Cancer Control, Cancer Council Queensland, Spring Hill, Brisbane, QLD 4004, Australia;2School of Public Health,
Queensland University of Technology, Brisbane, QLD 4059, Australia;3Griffith Health Institute, Griffith University, Gold Coast, QLD 4222, Australia;
4School of Population Health, University of Queensland, Brisbane, QLD 4061, Australia
BACKGROUND: We examine the relationships between geographic remoteness, area disadvantage and risk of advanced colorectal
METHODS: Multilevel models were used to assess the area- and individual-level contributions to the risk of advanced disease among
people aged 20–79 years diagnosed with colorectal cancer in Queensland, Australia between 1997 and 2007 (n¼18561).
RESULTS: Multilevel analysis showed that colorectal cancer patients living in inner regional (OR¼1.09, 1.01–1.19) and outer regional
(OR¼1.11, 1.01–1.22) areas were significantly more likely to be diagnosed with advanced cancer than those in major cities
(P¼0.045) after adjusting for individual-level variables. The best-fitting final model did not include area disadvantage. Stratified analysis
suggested this remoteness effect was limited to people diagnosed with colon cancer (P¼0.048) and not significant for rectal cancer
CONCLUSION: Given the relationship between stage and survival outcomes, it is imperative that the reasons for these rurality inequities
in advanced disease be identified and addressed.
British Journal of Cancer (2011) 105, 1039–1041. doi:10.1038/bjc.2011.356
Published online 6 September 2011
& 2011 Cancer Research UK
Keywords: colorectal cancer; stage; health inequality; rural health; socio-economic status
While colorectal (CRC) survival rates in Australia are among the
highest in the world (Coleman et al, 2011), people living outside
major cities or in disadvantaged areas have poorer prognosis (Yu
et al, 2005; Kelsall et al, 2009). Since stage at diagnosis is a major
predictor of long-term CRC outcomes (Altekruse et al, 2010), its
relationship to factors such as socio-economic status (SES) and
geographic remoteness is of particular relevance for cancer
control. With major medical centres being concentrated in densely
populated urban centres, it has been suggested that rural and
remote locations may be associated with poorer access to
screening and specialised health care (Parikh-Patel et al, 2006;
Heathcote and Armstrong, 2007). However ecological studies have
presented a mixed picture in terms of SES (Parikh-Patel et al, 2006;
Frederiksen et al, 2008; Henry et al, 2009; Booth et al, 2010) and
geographic remoteness (Fazio et al, 2005; McLafferty and Wang,
2009; Sankaranarayanan et al, 2009) in stage at diagnosis.
Most of the evidence is based on ecological studies and these are
not able to separate the area-level or individual-level influences
(Baade et al, 2010), limiting our understanding about area-level
health inequalities. To date, no Australian study has employed
multilevel methods to investigate links between geographic
individual-level factors and
MATERIALS AND METHODS
Ethical approval to conduct this study was obtained from the
University of Queensland and Queensland Health. Data for
individuals aged 20–79 years diagnosed with invasive stage
1–IV CRC (ICD-O3 codes C18 to C20, C21.8) in Queensland
between 1 January 1996 and 31 December 2007 (inclusive)
(n¼18561) with complete address information were extracted
from the Queensland Cancer Registry (QCR).
Information extracted from pathology forms (Krnjacki et al,
2008) was used to determine stage at diagnosis according to TNM
system (Sobin and Wittlekind, 2002) as described previously
(Baade et al, 2011). For multivariate analysis, localised cases
(Stages I–II) were considered as early stage (Parikh-Patel et al,
2006; Henry et al, 2009) while regional and distant cases were
categorised as ‘advanced’ based on their lower survival rates
(Altekruse et al, 2010).
Information was obtained from QCR on individual-level
variables: year and age of diagnosis, cancer site, gender, marital
status, occupation (Turrell et al, 2007) and indigenous status (see
Table 2 for categories).
Statistical Local Areas (SLAs), which are typically based on
local governments and councils and thus likely to be socio-
economically relevant to their residents, were used as the
geographical definition for area-level analysis (Baade et al,
2010). Remoteness of residence was defined using the Accessi-
bility/Remoteness Index of Australia (ARIAþ) classification
measured using theIndex
Received 6 July 2011; revised 18 July 2011; accepted 13 August 2011;
published online 6 September 2011
*Correspondence: Professor PD Baade; E-mail: email@example.com
British Journal of Cancer (2011) 105, 1039–1041
& 2011 Cancer Research UK All rights reserved 0007– 0920/11
Disadvantage (IRSD) (Australian Bureau of Statistics, 2006) which
categorises SLAs into five quintiles of increasing advantage from
Multilevel logistic modelling (MLwiN 2.21) using Markov Chain
Monte Carlo (Browne, 2009) approaches in MLwiN version 2.21
(University of Bristol, UK) was used. Chain convergence was
checked using Raftery-Lewis diagnostics. Models were compared
Spiegelhalter et al, 2002), with smaller values indicating better fit.
Analyses were conducted in three steps: (1) a null model
comprising individuals (Level 1) nested in SLAs (Level 2) with
no fixed effects; (2) extending to include individual-level factors
as fixed effects (Model 2); and (3) geographic remoteness
(Model 3) and neighbourhood disadvantage (Model 4) were
included separately as fixed effects to quantify how much area
variation in stage was due to these factors independent of
compositional effects, and then in combination (Model 5). Fixed
effects results are reported as odds ratios (95% CI) (Merlo et al,
2001; Eikemo et al, 2008). Significance of individual coefficients was
tested using Z test.
Overall, 57.1% of patients were male with 67.2% having colon
cancer. The mean age at diagnosis was 65 years (median¼66
years). About 44.8% were diagnosed with advanced CRC. Just over
half of the patients (57.6%) lived in major cities and around 36.5%
were in the two most affluent SES quintiles.
In the multivariable logistic regression analyses, the null model
(Model 1) indicated significant (P¼0.041) between area variations
across the SLAs (Table 1).
Based on the DIC statistic (Table 1), the model fit improved
substantially by including individual-level characteristics (Model 2)
and then geographic remoteness (Model 3). Adding area
disadvantage (Model 4) to Model 2 did not improve the fit.
Similarly, the full model (Model 5) provided a poor fit to the data
than Model 3, suggesting that Model 3 was the best-fitting model
for these data. There was no evidence for area-level interaction
(results not shown).
In this final model (Model 3), and independent of individual
factors, geographic remoteness was associated with cancer
stage (Table 2). At individual level sex, occupation, indigenous
status and anatomic site were independent predictors (Po0.001)
of advanced CRC (Table 2). Independent of area effects the
likelihood of advanced CRC was significantly higher for females
than for males; blue-collar workers vs professionals; individuals
with known indigenous status compared with unknown and
patients with colon rather than rectal cancer (Table 2).
Analyses stratified by cancer site (Model 3) (results not shown)
showed that area remoteness was significant for colon cancer
(P¼0.048) but not for rectal cancer (P¼0.873).
Table 1Random effects
Model 1 Model 2 Model 3Model 4 Model 5
Area variance and standard error
P-value for area variance
Percentage reduction in area variance from the null model
Abbreviation: DIC¼Deviance Information Criterion. Model 1: no fixed effects. Model 2: adjusted for sex, age, year, marital and indigenous status, occupation and cancer site.
Model 3: adjusted for sex, age, year, marital and indigenous status, occupation, cancer site and remoteness. Model 4: adjusted for sex, age, year, marital and indigenous status,
occupation, cancer site and area disadvantage. Model 5: adjusted for sex, age, year, marital and indigenous status, occupation, cancer site, remoteness and area disadvantage.
advanced stage colorectal cancer, Queensland, 1996–2007
Final fixed effect factors on the probability of experiencing
Fixed effects OR95% CI
Area-remoteness index of Australia
Year of diagnosis
Not in the labour force
Abbreviations: OR¼odds ratio; CI¼confidence interval.
Determinants of colorectal cancer stage
PD Baade et al
British Journal of Cancer (2011) 105(7), 1039–1041
& 2011 Cancer Research UK
This study is one of the first to consider geographical variations in
CRC stage at diagnosis after adjusting for both area- and
individual-level factors. We found significant evidence that a
person’s risk of being diagnosed with advanced CRC depends on
where they live, specifically for those diagnosed with colon cancer,
independently of the individual characteristics of the patient
themselves. The impact of geographical location, however, was
limited to rurality with no evidence that area disadvantage was
associated with stage at diagnosis.
Given the nature of our data, any discussion of the possible
reasons for the remoteness differential can only be speculative; but
these may include a relative shortage of experienced medical staff
in regional areas and greater difficulty of accessing diagnostic
Significantly higher risks of late-stage diagnosis were seen for
patients with colon vs rectal cancer, consistent with international
studies (Frederiksen et al, 2008; Sankaranarayanan et al, 2009). We
also found that the risk of advanced disease was higher in more
regional areas compared with major cities for colon cancers only.
A contributing reason for both these observations may be that
compared with colon cancer rectal cancer often presents with more
visible symptoms (Majumdar et al, 1999), thereby making patients
more likely to seek medical care and be diagnosed earlier.
The strengths of this study include the use of staged CRC cases
from a large, unselected, state-wide population-based registry.
Approximately 84% of records in our initial cohort had sufficient
information to be staged similar to that reported elsewhere (Yu
et al, 2008). We were limited to the individual-level SES variable of
occupation, since the QCR does not collect information about
education (Frederiksen et al, 2008), income (Frederiksen et al,
2008) or private insurance status (Halpern et al, 2009) known to be
associated with advanced CRC. In addition, due to the high
prevalence of advanced CRC the odds ratios may reflect an
overestimation of the relative risk.
Given the relationship between stage at diagnosis and survival
outcomes, it is imperative that the reasons for the geographical
inequities in advanced disease be identified and addressed.
This study was supported by a grant from the (Australian)
National Health and Medical Research Council (NHMRC)
(ID561700). Associate Professor Peter Baade is supported by an
NHMRC Career Development Fellowship (ID1005334); Professor
Gavin Turrell is supported by an NHMRC Senior Research
Fellowship (ID 1003710).
Conflict of interest
The authors declare no conflict of interest.
AIHW (2004) Rural, Regional and Remote Health: A Guide to Remoteness
Classifications. Australian Institute of Health and Welfare AIHW Cat.
No. PHE 53: Canberra
Altekruse S, Kosary C, Krapcho M, Neyman N, Aminou R, Waldron W, Ruh
LJ, Howlade RN, Tatalovich Z, Cho H, Mariotto A, Eisner M, Lewis D,
Cronin K, Chen H, Feuer E, Stinchcomb D, Edwards BE (2010) SEER
Cancer Statistics Review, 1975–2007. In http://seer.cancer.gov/csr/
1975_2007/ based on November 2009 SEER data submission, posted to
the SEER web site, 2010
Australian Bureau of Statistics (2006) Census of Population and Housing:
Socio-Economic Indexes for Areas (SEIFA), Australia, 2006. ABS: Canberra
Baade PD, Dasgupta P, Turrell G, Aitken JF (2011) Association between
survival and distance to closest radiation treatment facilities for people
diagnosed with rectal cancer in Queensland, Australia. Med J Aust;
e-pub ahead of print 19 September 2011, doi:10.5694/mja10.11204
Baade PD, Turrell G, Aitken JF (2010) A multilevel study of the determinants
of area-level inequalities in colorectal cancer survival. BMC Cancer 10: 24
Booth CM, Li G, Zhang-Salomons J, Mackillop WJ (2010) The impact of
socioeconomic status on stage of cancer at diagnosis and survival.
Cancer 116: 4160–4167
Browne WJ (2009) MCMC estimation in MLwin v2.1. Bristol, UK.
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 (2011)
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 377: 127–138
Eikemo TA, Bambra C, Judge K, Ringdal K (2008) Welfare state regimes
and differences in self-perceived health in Europe: a multilevel analysis.
Soc Sci Med 66: 2281–2295
Fazio L, Cotterchio M, Manno M, McLaughlin J, Gallinger S (2005)
Association between colonic screening, subject characteristics, and stage
of colorectal cancer. Am J Gastroenterol 100: 2531–2539
Frederiksen BL, Osler M, Harling H, Jorgensen T (2008) Social inequalities
in stage at diagnosis of rectal but not in colonic cancer: a nationwide
study. Br J Cancer 98: 668–673
Halpern MT, Pavluck AL, Ko CY, Ward EM (2009) Factors associated with
colon cancer stage at diagnosis. Dig Dis Sci 54: 2680–2693
Heathcote K, Armstrong BK (2007) Disparities in cancer outcomes in
regional and rural Australia. Cancer Forum 31: 70–74
HenryKA,Sherman R,RocheLM(2009)Colorectal cancerstageat diagnosisand
area socioeconomic characteristics in New Jersey. Health Place 15: 505–513
Kelsall HL, Baglietto L, Muller D, Haydon AM, English DR, Giles GG (2009)
The effect of socioeconomic status on survival from colorectal cancer in
the Melbourne Collaborative Cohort Study. Soc Sci Med 68: 290–297
Krnjacki LJ, Baade PD, Lynch BM, Aitken JF (2008) Reliability of collecting
colorectal cancer stage information from pathology reports and general
practitioners in Queensland. Aust NZJ Public Health 32: 378–382
MajumdarSR, Fletcher RH, Evans AT(1999)Howdoescolorectal cancerpresent?
Symptoms, duration, and clues to location. Am J Gastroenterol 94: 3039–3045
McLafferty S, Wang F (2009) Rural reversal? Rural-urban disparities in
late-stage cancer risk in Illinois. Cancer 115: 2755–2764
Merlo J, Ostergren PO, Broms K, Bjorck-Linne A, Liedholm H (2001)
Survival after initial hospitalisation for heart failure: a multilevel analysis
of patients in Swedish acute care hospitals. J Epidemiol Community
Health 55: 323–329
Parikh-Patel A, Bates JH, Campleman S (2006) Colorectal cancer stage
at diagnosis by socioeconomic and urban/rural status in California,
1988–2000. Cancer 107: 1189–1195
Sankaranarayanan J, Watanabe-Galloway S, Sun J, Qiu F, Boilesen E,
Thorson AG (2009) Rurality and other determinants of early colorectal
cancer diagnosis in Nebraska: a 6-year cancer registry study, 1998–2003.
J Rural Health 25: 358–365
Sobin LH, Wittlekind C (2002) TNM Classification of Malignant Tumours
6th edn. John Wiley and Sons: New York
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures
of model complexity and fit. J R Stat Soc Ser B (Stat Meth) 64: 583–639
Turrell G, Kavanagh A, Draper G, Subramanian SV (2007) Do places affect
the probability of death in Australia? A multilevel study of area-level
disadvantage, individual-level socioeconomic position and all-cause
mortality, 1998–2000. J Epidemiol Community Health 61: 13–19
Yu XQ, O’Connell DL, Gibberd RW, Armstrong BK (2005) A population-
based study from New South Wales, Australia 1996–2001: area variation
in survival from colorectal cancer. Eur J Cancer 41: 2715–2721
Yu XQ, O’Connell DL, Gibberd RW, Abrahamowicz M, Armstrong BK
(2008) Misclassification of colorectal cancer stage and area variation in
survival. Int J Cancer 122: 398–402
Determinants of colorectal cancer stage
PD Baade et al
British Journal of Cancer (2011) 105(7), 1039–1041
& 2011 Cancer Research UK