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

ABSTRACT

BACKGROUND: 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. MATERIALS AND METHODS: The data were obtained from the Surveillance, Epidemiology and End Results (SEER) program of the US [1]. 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. RESULTS: Twenty-year forecasts indicate an overall decline in future breast cancer mortality rates for both groups of women. This decline appears to be 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 there is no significant change in future breast cancer mortality rates among young black women in the next 20 years. CONCLUSION: White women have smooth and consistent patterns in breast cancer mortality rates for all age-groups whereas the mortality rates for black women are much more variable. The projections suggest, for some age groups, black American women may not benefit equally from the overall decline in breast cancer mortality in the United States.