Cent Eur J Public Health 2017, 25(Suppl 2):S72-S79 | DOI: 10.21101/cejph.a5053

Influence of demographic determinants on the number of deaths caused by circulatory system diseases in comparison to the number of deaths caused by neoplasms in Slovak regions from 1996-2014

Ján Fedačko1, Daniel Pella1, Beáta Gavurová2, Samuel Koróny3
1 1st Department of Internal Medicine, Louis Pasteur University Hospital, Pavol Jozef Šafárik University in Košice, Košice, Slovak Republic
2 Faculty of Economics, Technical University of Košice, Košice, Slovak Republic
3 Research and Innovation Centre, Faculty of Economics, Matej Bel University, Banská Bystrica, Slovak Republic

Objectives: The objective of our study was to evaluate the influence of available demographic determinants on the number of deaths caused by circulatory system diseases as compared to deaths caused by neoplasms in Slovakia in 1996-2014.

Methods: Mortality data were kindly provided by the National Health Information Centre in Slovakia. The first method was trend curve fitting of death ratios caused by circulatory system diseases (Chapter IX) and of deaths caused by neoplasms (Chapter II) as a function of age for both sexes. The second method comprised a decision tree for classification between deaths caused by Chapter IX and Chapter II diseases. Input variables were available demographic indicators: age, sex, marital status, region, and calendar year of death. Statistical data analyses were performed by IBM SPSS version 19 statistical software.

Results: We found that the odds ratios of deaths caused by circulatory system diseases (Chapter IX) in comparison with deaths caused by neoplasms (Chapter II) were non-decreasing. At first, the values of odds ratios are constant until they reach a critical sex-dependent value with a subsequent steady increase. In the case of men the odds ratio was greater than in the 60 years age-group where the odds ratio value increased slowly (from 1.14 at age 60 to 7.25 at age 90 years). The relative increase was 6.36 (7.25/1.14). The odds ratio in the women group was smaller but increased more rapidly (from 0.81 at age 60 to 12.27 at age 90 years). The relative increase was 15.15 in women (12.27/0.81). Hence, the odds ratio of death caused by Chapter IX diseases vs. Chapter II was greater in the older women group (i.e. higher age values). Utilizing the decision tree model, we have found that the most significant demographic determinant of death counts in both ICD Chapters was the age of the deceased, followed by marital status and finally gender. The last two predictors (year and region) were relatively negligible though formally significant.

Conclusions: The proposed method could be useful for prognostic classification of patients and primarily beneficial for hospitals in human or financial resources planning.

Keywords: mortality, diseases of the circulatory system, neoplasms, decision tree

Received: January 26, 2017; Revised: December 19, 2017; Published: December 30, 2017  Show citation

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Fedačko J, Pella D, Gavurová B, Koróny S. Influence of demographic determinants on the number of deaths caused by circulatory system diseases in comparison to the number of deaths caused by neoplasms in Slovak regions from 1996-2014. Cent Eur J Public Health. 2017;25(Supplement 2):S72-79. doi: 10.21101/cejph.a5053. PubMed PMID: 29524373.
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    References

    1. Ibrahim NA, Abdul Kudus A, Daud I, Abu Bakar MR. Decision tree for competing risks survival probability in breast cancer study. IJMBR. 2008;3(1):25-9.
    2. Dudoit S, Fridlyand J, Speed TP. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc. 2002;97(457):77-87. Go to original source...
    3. Loh WY, He X, Man MA. Regression tree approach to identifying subgroups with differential treatment effects. Stat Med. 2015;34(11):1818-33. Go to original source... Go to PubMed...
    4. Jazbec J, Todorovski L, Jereb B. Classification tree analysis of second neoplasms in survivors of childhood cancer. BMC Cancer. 2007 Feb 2;7:27. doi.org/10.1186/1471-2407-7-27. Go to original source... Go to PubMed...
    5. Spitz MR, Hong WK, Amos CI, Wu X, Schabath MB, Dong Q, et al. A risk model for prediction of lung cancer. J Natl Cancer Inst. 2007;99(9):715-26. Go to original source... Go to PubMed...
    6. Etzel CJ, Kachroo S, Liu M, D'Amelio A, Dong Q, Cote ML, et al. Development and validation of a lung cancer risk prediction model for African-Americans. Cancer Prev Res. 2008;1(4):255-65. Go to original source... Go to PubMed...
    7. Tsien CL, Fraser HS, Long WJ, Kennedy RL. Using classification tree and logistic regression methods to diagnose myocardial infarction. Stud Health Technol Inform. 1998;52(1):493-7. Go to PubMed...
    8. Solinger AB, Rothman SI. Risks of mortality associated with common laboratory tests: a novel, simple and meaningful way to set decision limits from data available in the electronic medical record. Clin Chem Lab Med. 2013;51(9):1803-13. Go to original source... Go to PubMed...
    9. Pittman J, Huang E, Dressman H, Horng CF, Cheng SH, Tsou MH, et al. Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc Natl Acad Sci USA. 2004;101(22):8431-6. Go to original source... Go to PubMed...
    10. Gülkesen KH, Köksal İT, Özden S, Saka O. Prediction of prostate cancer using decision tree algorithm. Turk J Med Sci. 2010;40(5):681-6. Go to original source...
    11. Ignatius TS, Chiu YL, Wong TW, Tang JL. Deaths from nasopharyngeal cancer among waiters and waitresses in Chinese restaurants. Int Arch Occup Environ Health. 2004;77(7):499-504. Go to original source... Go to PubMed...
    12. Clary T, Ritz B. Pancreatic cancer mortality and organochlorine pesticide exposure in California, 1989-1996. Am J Ind Med. 2003;43(3):306-13. Go to original source... Go to PubMed...
    13. Lee LJ, Chung CW, Ma YC, Wang GS, Chen PC, Hwang YH, et al. Increased mortality odds ratio of male liver cancer in a community contaminated by chlorinated hydrocarbons in groundwater. Occup Environ Med. 2003;60(5):364-9. Go to original source... Go to PubMed...
    14. MacArthur AC, Le ND, Abanto ZU, Gallagher RP. Occupational female breast and reproductive cancer mortality in British Columbia, Canada, 1950-94. Occ Med. 2007;57(4):246-53. Go to original source... Go to PubMed...
    15. Pol L, Thomas R. The Demography of health and healthcare. Dordrecht: Springer; 2013. Go to original source...
    16. Siegel KJ, Swanson DA. The Methods and materials of demography. Dordrecht: Springer; 2004.
    17. Miettinen OS. Theoretical epidemiology: principles of occurrence research in medicine. New York: John Wiley & Sons; 1985.
    18. Van Belle G, Fisher LD, Heagerty PJ, Lumley T. Biostatistics: a methodology for the health sciences. 2nd ed. Hoboken: John Wiley & Sons; 2004. Go to original source...
    19. Armitage P, Berry G, Matthews JNS. Statistical methods in medical research. Oxford: Blackwell Science; 2002. Go to original source...
    20. Breiman L. Classification and regression trees. New York: Chapman & Hall; 1984.
    21. Šoltés V, Gavurová B. The possibilities of day surgery system development within the health policy in Slovakia. Health Econ Rev. 2014;4:35. doi:10.1186/s13561-014-0035-1. Go to original source... Go to PubMed...
    22. Šoltés V, Gavurová B. The Functionality comparison of the health care systems by the analytical hierarchy process method. E M Ekon Manag. 2014;17(3):100-17. Go to original source...
    23. Gavurová B, Vagašová T. Regional differences of standardised mortality rates for ischemic heart diseases in the Slovak Republic for the period 1996-2013 in the context of income inequality. Health Econ Rev. 2016;6:21. doi:10.1186/s13561-016-0099-1. Go to original source... Go to PubMed...
    24. Kamenský G, Dvoranová I, Murín J, Dlesk A. The national program for the prevention of diseases of the heart and blood vessels [Internet]. 2011 [cited 2016 Nov 2]. Available from: http://www.tvojesrdce.sk/files/documents/31.pdf. (In Slovak.)

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