Predicting US- and state-level cancer counts for the current calendar year: Part I: evaluation of temporal projection methods for mortality.
BACKGROUND
A study was undertaken to evaluate the temporal projection methods that are applied by the American Cancer Society to predict 4-year-ahead projections.


METHODS
Cancer mortality data recorded in each year from 1969 through 2007 for the United States overall and for each state from the National Center for Health Statistics was obtained. Based on the mortality data through 2000, 2001, 2002, and 2003, Projections were made 4 years ahead to estimate the expected number of cancer deaths in 2004, 2005, 2006, 2007, respectively, in the United States and in each state, using 5 projection methods. These predictive estimates were compared to the observed number of deaths that occurred for all cancers combined and 47 cancer sites at the national level, and 21 cancer sites at the state level.


RESULTS
Among the models that were compared, the joinpoint regression model with modified Bayesian information criterion selection produced estimates that are closest to the actual number of deaths. Overall, results show the 4-year-ahead projection has larger error than 3-year-ahead projection of death counts when the same method is used. However, 4-year-ahead projection from the new method performed better than the 3-year-ahead projection from the current state-space method.


CONCLUSIONS
The Joinpoint method with modified Bayesian information criterion model has the smallest error of all the models considered for 4-year-ahead projection of cancer deaths to the current year for the United States overall and for each state. This method will be used by the American Cancer Society to project the number of cancer deaths starting in 2012.
Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. Huann-Sheng.Chen@nih.gov
Huann-Sheng
HS
Chen
Kenneth
K
Portier
Kaushik
K
Ghosh
Deepa
D
Naishadham
Hyune-Ju
HJ
Kim
Li
L
Zhu
Linda W
LW
Pickle
Martin
M
Krapcho
Steve
S
Scoppa
Ahmedin
A
Jemal
Eric J
EJ
Feuer
D000570:American Cancer Society; D001499:Bayes Theorem; D005544:Forecasting / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology* / Q000401:mortality*; D012189:Retrospective Studies; D014481:United States / Q000453:epidemiology