Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
권호 표지
DOI QR코드

DOI QR Code

Modeling Age-specific Cancer Incidences Using Logistic Growth Equations: Implications for Data Collection

  • Shen, Xing-Rong (School of Health Service Management, Anhui Medical University) ;
  • Feng, Rui (Department of Literature Review and Analysis, Library of Anhui Medical University) ;
  • Chai, Jing (School of Health Service Management, Anhui Medical University) ;
  • Cheng, Jing (School of Health Service Management, Anhui Medical University) ;
  • Wang, De-Bin (School of Health Service Management, Anhui Medical University)
  • Published : 2014.12.18
Download PDF
⟨ Previous Next ⟩

Abstract

Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.

Keywords

References

  1. Baker K, Rath T, Flak MB, et al (2013). Neonatal Fc receptor expression in dendritic cells mediates protective immunity against colorectal cancer. Immunity, 39, 1095-107. https://doi.org/10.1016/j.immuni.2013.11.003
  2. Bouchbika Z, Haddad H, Benchakroun N, et al (2013). Cancer incidence in Morocco: report from Casablanca registry 2005-2007. Pan Afr Med J, 16, 31.
  3. Chen PL, Zhao T, Feng R, et al (2014). Patterns and trends with cancer incidence and mortality rates reported by the China National Cancer Registry. Asian Pac J Cancer Prev, 15, 6327-32. https://doi.org/10.7314/APJCP.2014.15.15.6327
  4. Chockalingam K, Vedhachalam C, Rangasamy S (2013). Prevalence of tobacco use in urban, semi urban and rural areas in and around Chennai City, India. PLoS One, 8, 76005. https://doi.org/10.1371/journal.pone.0076005
  5. China Ministry of Health (2008). China third national death cause survey. China Cancer, 5, 344.
  6. Dyzmann-Sroka A, Malicki J (2014). Cancer incidence and mortality in the greater poland region-analysis of the year 2010 and future trends. Rep Pract Oncol Radiother, 19, 296-300. https://doi.org/10.1016/j.rpor.2014.04.001
  7. Du LB, Li HZ, Wang XH, et al (2014). Analysis of cancer incidence in Zhejiang cancer registry in China during 2000 to 2009. Asian Pac J Cancer Prev, 15, 5839-43. https://doi.org/10.7314/APJCP.2014.15.14.5839
  8. Dexter TA, Kowalewski M (2013). Jackknife-corrected parametric bootstrap estimates of growth rates in bivalve mollusks using nearest living relatives. Theor Popul Biol, 90, 36-48. https://doi.org/10.1016/j.tpb.2013.09.008
  9. Fory's U, Marciniak CA (2003). Logistic equations in tumor growth modeling. Int J Appl Math Comput Sci, 13, 317-25.
  10. Goss PE, Strasser-Weippl K, Lee-Bychkovsky BL, et al (2014). Challenges to effective cancer control in China, India, and Russia. Lancet Oncol, 15, 489-538. https://doi.org/10.1016/S1470-2045(14)70029-4
  11. He J, Chen WQ (2012). Chinese cancer registry annual report. Chin J Cancer Res, 24, 171-80. https://doi.org/10.1007/s11670-012-0171-2
  12. Hutchison C, Roffers S, Fritz A (1997). Cancer registry management: principles and practice. lenexa, kan: kendall/ hunt publishing Co.
  13. Izquierdo JN, Schoenbach VJ (2000). The potential and limitations of data from population-based state cancer registries. Am J Public Health, 90, 695-8. https://doi.org/10.2105/AJPH.90.5.695
  14. Jurgens V, Ess S, Cerny T, Vounatsou P (2014). A Bayesian generalized age-period-cohort power model for cancer projections. Stat Med, 33, 4627-36. https://doi.org/10.1002/sim.6248
  15. Katulanda P, Ranasinghe C, Rathnapala A, et al (2014). Prevalence, patterns and correlates of alcohol consumption and its' association with tobacco smoking among Sri Lankan adults: a cross-sectional study. BMC Public Health, 14, 612. https://doi.org/10.1186/1471-2458-14-612
  16. Knorr-Held L, Rainer E (2001). Projections of lung cancer mortality in West Germany: a case study in Bayesian prediction. Biostatistics, 2, 109-29. https://doi.org/10.1093/biostatistics/2.1.109
  17. Kim HJ, Fay MP, Feuer EJ, Midthune DN (2000). Permutation tests for join point regression with applications to cancer rates. Stat Med, 19, 335-51. https://doi.org/10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-Z
  18. Lee TC, Dean CB, Semenciw R (2011). Short-term cancer mortality projections: a comparative study of prediction methods. Stat Med, 30, 3387-402. https://doi.org/10.1002/sim.4373
  19. Leung GM, Woo PP, McGhee SM, et al (2006). Age-periodcohort analysis of cervical cancer incidence in Hong Kong from 1972 to 2001 using maximum likelihood and Bayesian methods. J Epidemiol Community Health, 60, 712-20. https://doi.org/10.1136/jech.2005.042275
  20. Moller B (2004). Prediction of cancer incidence-methodological considerations and trends in the Nordic countries 1958-2022. phd thesis, faculty of medicine, university of oslo, unipuc AS, oslo.
  21. Meira KC, Silva GA, Silva CM, Valente JG (2013). Age-periodcohort effect on mortality from cervical cancer. Rev Saude Publica, 47, 274-82. https://doi.org/10.1590/S0034-8910.2013047004253
  22. Ma X, Yu H (2006). Global burden of cancer. Yale J Biol Med, 79, 85-94.
  23. Ocana-Riola R, Mayoral-Cortes JM, Blanco-Reina E (2013). Age-period-cohort effect on lung cancer mortality in southern Spain. Eur J Cancer Prev, 22, 549-57. https://doi.org/10.1097/CEJ.0b013e3283656366
  24. Parkin DM, Bray F, Ferlay J, Pisani P (2005).Global cancer statistics, 2002, CA Cancer J Clin, 55, 74-108. https://doi.org/10.3322/canjclin.55.2.74
  25. Parkin DM, Bray F, Ferlay J, Pisani P (2001).Estimating the world cancer burden: GLOBOACN 2000. Int J Cancer, 94, 153-6. https://doi.org/10.1002/ijc.1440
  26. Parkin DM (2001). Global cancer statistics in the year 2000. Lancet Oncol, 2, 533-43. https://doi.org/10.1016/S1470-2045(01)00486-7
  27. Shaukat U, Ismail M, Mehmood N (2013). Epidemiology, major risk factors and genetic predisposition for breast cancer in the Pakistani population. Asian Pac J Cancer Prev, 14, 5625-9. https://doi.org/10.7314/APJCP.2013.14.10.5625
  28. Tyson MD, Humphreys MR, Parker AS, et al (2013). Ageperiod- cohort analysis of renal cell carcinoma in United States adults. Urology, 82, 43-7. https://doi.org/10.1016/j.urology.2013.02.065
  29. Tangka F, Subramanian S, Beebe MC, Trebino D, Michaud F (2010). Economic assessment of central cancer registry operations, Part III: Results from 5 programs. J Registry Manag, 37, 152-5.
  30. Ullrich A, Miller A (2014). Global response to the burden of cancer: the WHO approach. Am Soc Clin Oncol Educ Book, 311-5.
  31. Wang P, Xu C, Yu C (2014). Age-period-cohort analysis on the cancer mortality in rural China: 1990-2010. Int J Equity Health, 13, 1. https://doi.org/10.1186/1475-9276-13-1
  32. Wei KR, Yu X, Zheng RS, et al (2014). Incidence and mortality of liver cancer in China, 2010. Chin J Cancer, 33, 388-94.
  33. Wu J, Li W, Liu Z, et al (2012). Ageing-associated changes in cellular immunity based on the SENIEUR protocol. Scand J Immunol, 75, 641-6. https://doi.org/10.1111/j.1365-3083.2012.02698.x
  34. Wei QL (2009). Malignant disease burden research. MD thesis. Xiamen university.
  35. World Health Organization. World health statistics 2006. Geneva WHO.
  36. Wingo PA, Landis S, Parker S, et al (1998). Using cancer registry and vital statistics data to estimate the number of new cancer cases and deaths in the United States for the upcoming year. J Registry Management, 25, 43-51.
  37. Yu LY, Chen ZZ, Zheng FQ, et al (2013). Demographic analysis, a comparison of the jackknife and bootstrap methods, and predation projection: a case study of Chrysopa pallens (Neuroptera: Chrysopidae). J Econ Entomol, 106,1-9. https://doi.org/10.1603/EC12200

Cited by

  1. Pap Smear Combined with HPV Testing: A Reasonable Tool for Women with High-grade Cervical Intraepithelial Neoplasia Treated by LEEP vol.16, pp.10, 2015, https://doi.org/10.7314/APJCP.2015.16.10.4297
  2. Utrecht Interstitial Applicator Shifts and DVH Parameter Changes in 3D CT-based HDR Brachytherapy of Cervical Cancer vol.16, pp.9, 2015, https://doi.org/10.7314/APJCP.2015.16.9.3945