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Forecasting age-related changes in breast cancer mortality among white and black US women: A functional approach

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  • Farah Yasmeen

    ()

  • Rob J Hyndman

    ()

  • Bircan Erbas

Abstract

The disparity in breast cancer mortality rates among white and black US women is widening with higher mortality rates among black women. We apply functional time series models on age-specific breast cancer mortality rates for each group of women, and forecast their mortality curves using exponential smoothing state-space models with damping. The data were obtained from the Surveillance, Epidemiology and End Results (SEER) program of the US (SEER, 2007). Mortality data were obtained from the National Centre for Health Statistics (NCHS) available on the SEER*Stat database. We use annual unadjusted breast cancer mortality rates from 1969 to 2004 in 5-year age groups (45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84). Age-specific mortality curves were obtained using nonparametric smoothing methods. The curves are then decomposed using functional principal components and we fit functional time series models with four basis functions for each population separately. The curves from each population are forecast and prediction intervals are calculated. Twenty-year forecasts indicate an over-all decline in future breast cancer mortality rates for both groups of women. This decline is steeper among white women aged 55-73 and black women aged 60-84. For black women under 55 years of age, the forecast rates are relatively stable indicating no significant change in future breast cancer mortality rates among young black women in the next 20 years.

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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 9/10.

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Length: 23 pages
Date of creation: 22 Apr 2010
Date of revision:
Handle: RePEc:msh:ebswps:2010-9

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Keywords: Breast cancer mortality; racial and ethnic disparities; screening; trends; forecasting; functional data analysis;

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  1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, October.
  2. Rob J. Hyndman & Han Lin Shang, 2008. "Rainbow plots, Bagplots and Boxplots for Functional Data," Monash Econometrics and Business Statistics Working Papers 9/08, Monash University, Department of Econometrics and Business Statistics.
  3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
  4. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
  5. Bircan Erbas & Rob J. Hyndman & Dorota M. Gertig, 2005. "Forecasting age-specific breast cancer mortality using functional data models," Monash Econometrics and Business Statistics Working Papers 3/05, Monash University, Department of Econometrics and Business Statistics.
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