Forecasting age-related changes in breast cancer mortality among white and black US women: A functional approach
Farah Yasmeen (),
Rob Hyndman () and
No 9/10, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
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.
Keywords: Breast cancer mortality; racial and ethnic disparities; screening; trends; forecasting; functional data analysis (search for similar items in EconPapers)
JEL-codes: C14 C23 J11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-hea
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