Using functional data analysis models to estimate future time trends in age-specific breast cancer mortality for the United States and England-Wales.
BACKGROUND
Mortality/incidence predictions are used for allocating public health resources and should accurately reflect age-related changes through time. We present a new forecasting model for estimating future trends in age-related breast cancer mortality for the United States and England-Wales.


METHODS
We used functional data analysis techniques both to model breast cancer mortality-age relationships in the United States from 1950 through 2001 and England-Wales from 1950 through 2003 and to estimate 20-year predictions using a new forecasting method.


RESULTS
In the United States, trends for women aged 45 to 54 years have continued to decline since 1980. In contrast, trends in women aged 60 to 84 years increased in the 1980s and declined in the 1990s. For England-Wales, trends for women aged 45 to 74 years slightly increased before 1980, but declined thereafter. The greatest age-related changes for both regions 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 to 70 years. Forecasts suggest relatively stable trends for women older than 75 years.


CONCLUSIONS
Prediction of 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 modeling and projecting future trends of diseases for which there has been very little advancement in treatment and minimal cohort effects (eg. lethal cancers).
School of Public Health, La Trobe University, Bundoora, Melbourne, Australia. bircan.erbas@gmail.com
Bircan
B
Erbas
Muhammed
M
Akram
Dorota M
DM
Gertig
Dallas
D
English
John L
JL
Hopper
Anne M
AM
Kavanagh
Rob
R
Hyndman
D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000453:epidemiology / Q000401:mortality*; D003431:Cross-Cultural Comparison; D003627:Data Interpretation, Statistical; D004739:England / Q000453:epidemiology; D005260:Female; D005544:Forecasting / Q000379:methods*; D006801:Humans; D015994:Incidence; D008875:Middle Aged; D015233:Models, Statistical; D014481:United States / Q000453:epidemiology; D014852:Wales / Q000453:epidemiology