Predicting US- and state-level cancer counts for the current calendar year: Part II: evaluation of spatiotemporal projection methods for incidence.
BACKGROUND
The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases.


METHODS
Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age-specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay-adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted.


RESULTS
The differences between the spatiotemporal model-estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method.


CONCLUSIONS
The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts.
Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. li.zhu@nih.gov
Li
L
Zhu
Linda W
LW
Pickle
Kaushik
K
Ghosh
Deepa
D
Naishadham
Kenneth
K
Portier
Huann-Sheng
HS
Chen
Hyune-Ju
HJ
Kim
Zhaohui
Z
Zou
James
J
Cucinelli
Betsy
B
Kohler
Brenda K
BK
Edwards
Jessica
J
King
Eric J
EJ
Feuer
Ahmedin
A
Jemal
D000570:American Cancer Society; D005260:Female; D005544:Forecasting / Q000379:methods*; D006801:Humans; D015994:Incidence; D008297:Male; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology*; D012189:Retrospective Studies; D012727:Sex Characteristics; D014481:United States / Q000453:epidemiology