Bircan Erbas¹, Muhammad Akram², Dorota M Gertig³, Dallas English^4,5, John L. Hopper⁵, Anne M Kavanagh⁶ and Rob J Hyndman²

Journal of Epidemiology (2010), 20(2), 159–165.

1. School of Public Health, La Trobe University, Bundoora, 3086 Australia
2. Business and Economic Forecasting Unit, Monash University, Clayton, 3800, Australia.
3. Victoria Cytology Service Inc, Carlton, 3053 Australia.
4. Cancer Epidemiology Centre, The Cancer Council Victoria, Carlton 3053 Australia.
5. Centre for MEGA Epidemiology, The University of Melbourne, Parkville 3053 Australia.
6. Key Centre for Women’s Health in Society, School of Population Health, The University of Melbourne, Parkville, 3053 Australia.

ABSTRACT

Background: Mortality/​incidence predictions are used for planning public health resources and need to accurately reflect age-​​related changes through time. We present a new forecasting model to estimate future trends in age-​​related breast cancer mortality for the United States and England-​​Wales.

Material and methods: We use functional data analysis techniques to model breast cancer mortality-​​age relationships in the United States from 1950 to 2001 and England-​​Wales from 1950 to 2003, and estimate 20-​​year predictions using a new forecasting method.

Results: In the United States, trends for women aged 45–54 years continued to decline since 1980. In contrast, trends in women aged 60 — 84 years increased in the 1980s and declined in the 1990s. For England-​​Wales, trends for women aged 45 to 74 years slightly increased prior to 1980, but declined thereafter. The greatest age-​​related changes for both countries were during the 1990s. For both the United States and England-​​Wales, trends are expected to decline and then stabilize with the greatest decline in women aged 60 — 70 years. Forecasts suggest relatively stable trends for women over 75 years.

Conclusions: Predicting age related changes in mortality/​incidence can be used for planning and targeting programs for specific age groups. Currently, these models are being extended to incorporate other variables that may influence age-​​related changes in mortality/​incidence trends. In their current form, these models will be most useful for modelling and projecting future trends of diseases where there has been very little advancement in treatment and minimal cohort effects such as lethal cancers.

Key words: breast cancer, forecasting, functional-​​data-​​analysis models, mortality trends

Online paper