Long-term monitoring of Cancer Mortality Rates in USA: A descriptive analysis using statistical process control tools
Engy Refaat Rashed , Mostafa Essam Eissa
Abstract
Background: Cancer is one of the most devastating diseases that influence humanity in the modern era. The effect is not confined to morbidity and mortality, but it is also extended to social and economic consequences despite the advancements made in the medical and healthcare fields. The present case overview will focus on the overall long-term assessment of death rates from malignancy from 1960 to 2017 as part of a global study of the mortality trend of this disease.
Methods: Statistical analysis and process control methodologies were used to study the death rate trend over this recorded period using statistical programs platform. A combination of techniques that could be used sequentially after database processing and stratification were used including distribution fitting, descriptive statistics, data fitting mathematical pattern, Gaussian Mixture Model (GMM) box plot and Individual-Moving Range (I-MR) trending chart.
Results: Two-parameter Weibull distribution (or Weibull 2) was the most appropriate that has fitted the distribution pattern of data and used for the construction of the process-behavior chart. The last two decades of the dataset showed a progressive decline in the mortality rates, which were almost linear with a higher magnitude of a negative slope than that of the initial rising pattern from 1960 until the 1990s. The hump-shaped trend showed underlying two distribution clusters: An initial distribution (I) of 81% of data of higher average mortality rates and minor one (II) that covers the last decade of monitoring record.
Discussion: A significant improvement in cancer healthcare was witnessed with a noticeable breakthrough in the last decade in the USA.
Keywords
References
1. Weatherall D, Greenwood B, Chee HL, Wasi P. Science and technology for disease control: past, present, and future. In: Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, et al, editors Disease control priorities in developing countries, 2nd ed. Washington DC: World Bank; 2006:119-38.
2. Alwan A. Global status report on noncommunicable diseases 2010. Geneva: World Health Organization; 2011.
3. Comprehensive Cancer Information [Internet]. National Cancer Institute. 2020. Available from: https://www.cancer.gov/ (accessed March 2020).
4. Who.int. 2020 [Internet]. Available from: https://www.who.int/ (accessed March 2020).
5. Gaussian mixture models [Internet]. XLSTAT, Your data analysis solution. 2020. Available from: https://www.xlstat.com/en/solutions/features/gaussian-mixture-models (accessed March 2020).
6. XLSTAT Support Center [Internet]. Help.xlstat.com. 2020. Available from: https://help.xlstat.com/s/article/gaussian-mixture-model-clustering-in-excel-tutorial?language=en_US (accessed March 2020).
7. Minitab LL. Getting Started with Minitab 17. PA, USA: Minitab Inc., State College; 2014:73.
8. Falzone L, Salomone S, Libra M. Evolution of cancer pharmacological treatments at the turn of the third millennium. Front Pharmacol 2018;9:1300. doi: 10.3389/fphar.2018.01300.
9. Zarros A, Tansey T. Pharmaceutical innovation after World War II: from rational drug discovery to biopharmaceuticals. Front Pharmacol. 2019;10:834. doi: 10.3389/fphar.2019.00834.
10. Braveman P, Gottlieb L. The social determinants of health: it's time to consider the causes of the causes. Public Health Rep. 2014;129(suppl 2):19-31. doi: 10.1177/00333549141291S206.
11. Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CMet al. Cancer treatment and survivorship statistics, 2019. CA: Cancer J Clin. 2019;69(5):363-85. doi: 10.3322/caac.21565.
12. Allart-Vorelli P, Porro B, Baguet F, Michel A, Cousson-Gélie F. Haematological cancer and quality of life: a systematic literature review. Blood Cancer J. 2015;5(4):e305. doi: 10.1038/bcj.2015.29.
13. Eissa ME, Seif M, Fares M. Assessment of purified water quality in pharmaceutical facility using six sigma tools. Int. J Pharm Qual Assur. 2015;6(2):54-72.
14. Eissa ME. Variable and attribute control charts in trend analysis of active pharmaceutical components: Process efficiency monitoring and comparative study. Experimental Medicine (EM) 2018;1(1):32-44. doi: 10.31058/j.em.2018.11003.
15. Eissa M. The attribute control charts for outbreak trends o f selected states in the USA: a brief report of the insight into the pattern. Int Med. 2019;1(1):11-4. doi: 10.5455/im.31744.
16. Sheikh K. Cancer Death Rate in U.S. Sees Sharpest One-Year Drop [Internet]. Nytimes.com. 2020. Available from: https://www.nytimes.com/2020/01/08/health/cancer-deaths-decline.html (accessed March 2020).
17. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7-30. doi: 10.3322/caac.21590.
18. American Cancer Society. Cancer mortality continues steady decline, driven by progress against lung cancer: Drop of 2.2 percent from 2016 to 2017 is largest ever-reported [Internet]. ScienceDaily. 2020. Available from: https://www.sciencedaily.com/releases/2020/01/200108074809.htm (accessed March 2020).
19. Nadler DL, Zurbenko IG. Developing a weibull model extension to estimate cancer latency. ISRN Epidemiology 2012;2013. https://doi.org/10.5402/2013/750857.
20. Arteaga CL, Adamson PC, Engelman JA, Foti M, Gaynor RB, Hilsenbeck SG, et al. AACR Cancer Progress Report 2014. Clin Cancer Res. 2014; 20(19 Suppl):S1-S112. doi: 10.1158/1078-0432.CCR-14-2123.
Submitted date:
03/28/2020
Reviewed date:
04/01/2020
Accepted date:
04/05/2020
Publication date:
04/05/2020
Facebook
Google+
Twitter
LinkedIn
Mendeley
StumbleUpon
CiteULike
Reddit
Email