Record Details

Title Predicting US- and state-level cancer counts for the current calendar year
Author Zhu L,
Secondary Authors LW Pickle, K Ghosh, D Naishadham, K Portier, HS Chen, HJ Kim, Z Zou, J Cucinelli, B Kohler, BK Edwards, J King, EJ Feuer, A Jemal
Publication Type (Help) article
Journal Cancer
Month Feb 15
Year 2012
Pages 1100-9
Volume 118
Number 4
Publisher
Address
Note doi: 10.1002/cncr.27405
URL
PubMed ID 22228583
NCI Id
EPub Date 2012 Jan 06
Citation Zhu L , LW Pickle, K Ghosh, D Naishadham, K Portier, HS Chen, HJ Kim, Z Zou, J Cucinelli, B Kohler, BK Edwards, J King, EJ Feuer, A Jemal. Predicting US- and state-level cancer counts for the current calendar year. Cancer. 2012 Feb 15;118(4):1100-9. EPub 2012 Jan 06. PMID 22228583.
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Abstract

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. 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. 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. 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.



Keywords

Keyword
calendar year
cancer
counts
predicting