RESEARCH PAPER
Increasing deaths from colorectal cancer in Poland – Insights for optimising colorectal cancer screening in society and space
 
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Maria Skłodowska-Curie Institute – Oncology Centre, Warsaw School of Economics, Warsaw, Poland
 
 
Corresponding author
Krzysztof Czaderny   

Maria Skłodowska-Curie Institute – Oncology Centre; Warsaw School of Economics, Madalińskiego 6/8, 02-513 Warszawa, Poland
 
 
Ann Agric Environ Med. 2019;26(1):125-132
 
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ABSTRACT
Introduction and objective:
With respect to the increasing numbers of deaths due to colorectal cancer in Poland, the aim of the study was to investigate socio-demographic characteristics which influence colorectal cancer screening acceptance and to assess spatial variation of colorectal cancer mortality.

Material and methods:
An age-period-cohort model was estimated to assess mortality trends in colorectal cancer in Poland. A geographical analysis was performed by spatial regression. Factors influencing participation in colorectal cancer screening were identified using structural equation modelling.

Results:
In 2014 in Poland, 6.4 thousand men and 5.0 thousand women died due to colorectal cancer. In total, by 2030 this number is expected to rise to nearly 14.4 thousand. Observed spatial clustering of age-adjusted colorectal cancer mortality is associated with spatial variation in tobacco use, employment in industry, and consumption of red meat. Patient-physician communication, advanced age, and healthy diet are the most important predictors of colorectal cancer screening acceptance. Tobacco and alcohol users are not more likely to participate in colorectal cancer screening, adjusting for other variables.

Conclusions:
Self-selection of patients who follow healthy diet means that individuals at higher risk of colorectal cancer are less likely to participate in colorectal cancer screening. Therefore, screening should be more targeted. According to the structural equation modelling results, the phenomenon of ‘no-show’ for screening can be mitigated by patient-physician communication. The inhabitants of the Greater Poland region are at the highest risk of dying due to colorectal cancer, which may have public health policy implications.

ACKNOWLEDGEMENTS
This work was supported by the Maria Skłodowska-Curie Institute – Oncology Centre (grant no. GW35KC).
REFERENCES (32)
1.
Johnson CM, Wei C, Ensor JE, Smolenski DJ, Amos CI, Levin B, et al. Meta-analyses of colorectal cancer risk factors. Cancer Causes Control. 2013; 24(6): 1207–1222.
 
2.
Sherman RL, Henry KA, Tannenbaum SL, Feaster DJ, Kobetz E, Lee DJ. Applying spatial analysis tools in public health: an example using SaTScan to detect geographic targets for colorectal cancer screening interventions. Prev Chronic Dis. 2014; 11: E41.
 
3.
Huang JL, Fang Y, Liang M, Li ST, Ng SK, Hui ZS, et al. Approaching the hard-to-reach in organized colorectal cancer screening: an overview of individual, provider and system level coping strategies. AIMS Public Health. 2017; 4(3): 289–300.
 
4.
Clayton D, Schifflers E. Models for temporal variation in cancer rates. II: Age-period-cohort models. Stat Med. 1987; 6(4): 469–481.
 
5.
Hobcraft J, Menken J, Preston S. Age, period, and cohort effects in demography: a review. Popul Index. 1982; 48(1): 4–43.
 
6.
Currie ID. On fitting generalized linear and non-linear models of mortality. Scand Actuar J. 2016; 2016(4): 356–383.
 
7.
Villegas AM, Millossovich P, Kaishev VK. StMoMo: An R package for stochastic mortality modelling. R package 2017. Accessed August 1st, 2018 at: https://CRAN.R-project.org/pac....
 
8.
Haberman S, Renshaw A. On age-period-cohort parametric mortality rate projections. Insur Math Econ. 2009; 45(2): 255–270.
 
9.
Central Statistical Office of Poland. Population projection 2014–2050. Warsaw, 2014.
 
10.
Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Stat Med. 1995; 14(8): 799–810.
 
11.
Anselin L. Spatial econometrics: Methods and models. 1st ed. Dordrecht: Kluwer Academic Publishers, 1988.
 
12.
Muthén B, Du Toit SHC, Spisic D. Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes, 1997. Accessed August 1st, 2018 at: http://www.statmodel.com/downl....
 
13.
McDonald RP. Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum, 1999.
 
14.
Browne MW, Cudeck R. Alternative ways of assessing model fit. In: Bollen KA, Long JS, editors. Testing structural equation models. Vol. 154. Newbury Park: Sage, 1993: 136–162.
 
15.
Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling. 1999; 6(1): 1–55.
 
16.
Nussbeck F, Eid M, Lischetzke T. Analysing multitrait–multimethod data with structural equation models for ordinal variables applying the WLSMV estimator: What sample size is needed for valid results? Br J Math Stat Psychol. 2006; 59(Pt 1): 195–213.
 
17.
Bosetti C, Levi F, Rosato V, Bertuccio P, Lucchini F, Negri E, et al. Recent trends in colorectal cancer mortality in Europe. Int J Cancer. 2011; 129(1): 180–191.
 
18.
Zavoral M, Suchanek S, Zavada F, Dusek L, Muzik J, Seifert B, et al. Colorectal cancer screening in Europe. World J Gastroenterol. 2009; 15(47): 5907–5915.
 
19.
Navarro M, Nicolas A, Ferrandez A, Lanas A. Colorectal cancer population screening programs worldwide in 2016: An update. World J Gastroenterol. 2017; 23(20): 3632–3642.
 
20.
Vernon SW. Participation in colorectal cancer screening: a review. J Natl Cancer Inst. 1997; 89(19): 1406–1422.
 
21.
Czaderny K. High prostate cancer mortality in Poland. A spatial, temporal and structural analysis. Przegl Epidemiol. 2018; 72(2): 235–246.
 
22.
McGregor LM, von Wagner C, Atkin W, Kralj-Hans I, Halloran SP, Handley G et al. Reducing the social gradient in uptake of the NHS Colorectal Cancer Screening Programme using a narrative-based information leaflet: A cluster-randomised trial. Gastroenterol Res Pract. 2016; 2016: 3670150.
 
23.
Arnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017; 66(4): 683–691.
 
24.
McCaffery K, Wardle J, Nadel M, Atkin W. Socioeconomic variation in participation in colorectal cancer screening. J Med Screen. 2002; 9(3): 104–108.
 
25.
Deding U, Henig AS, Salling A, Torp-Pedersen C, Bøggild H. Sociodemographic predictors of participation in colorectal cancer screening. J Colorectal Dis. 2017; 32(8): 1117–1124.
 
26.
Khosravi Shadmani F, Ayubi E, Khazaei S, Sani M, Mansouri Hanis S, Khazaei S, et al. Geographic distribution of the incidence of colorectal cancer in Iran: a population-based study. Epidemiol Health. 2017; 39: e2017020.
 
27.
Lai SM, Zhang KB, Uhler RJ, Harrison JN, Clutter GG, Williams MA. Geographic variation in the incidence of colorectal cancer in the United States, 1998–2001. Cancer. 2006; 107 (5 Suppl): 1172–1180.
 
28.
Liang PS, Chen T-Y, Giovannucci E. Cigarette smoking and colorectal cancer incidence and mortality: Systematic review and meta-analysis. Int J Cancer. 2009; 124(10): 2406–2415.
 
29.
Wu S, Feng B, LI K, Zhu X, Liang S, Liu X, et al. Fish consumption and colorectal cancer risk in humans: A systematic review and meta-analysis. Am J Med. 2012; 125(6): 551–559.e5.
 
30.
Oddone E, Modonesi C, Gatta G. Occupational exposures and colorectal cancers: A quantitative overview of epidemiological evidence. World J Gastroenterol. 2014; 20(35): 12431–12444.
 
31.
Alexander DD, Weed DL, Cushing CA, Lowe KA. Meta-analysis of prospective studies of red meat consumption and colorectal cancer. Eur J Cancer Prev. 2011; 20(4): 293–307.
 
32.
Bardou M, Barkun AN, Martel M. Obesity and colorectal cancer. Gut. 2013; 62(6): 933–947.
 
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ISSN:1232-1966
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