title: Predicting county-level cancer incidence rates and counts in the USA.
abstract: Many countries, including the USA, publish predicted numbers of cancer incidence and death in current and future years for the whole country. These predictions provide important information on the cancer burden for cancer control planners, policymakers and the general public. Based on evidence from several empirical studies, the joinpoint (segmented-line linear regression) model (JPM) has been adopted by the American Cancer Society to estimate the number of new cancer cases in the USA and in individual states since 2007. Recently, cancer incidence in smaller geographic regions such as counties, and local policy makers are increasingly interested with Federal Information Processing Standard code regions. The natural extension is to directly apply the JPM to county-level cancer incidence data. The direct application has several drawbacks and its performance has not been evaluated. To address the concerns, we developed a spatial random-effects JPM for county-level cancer incidence data. The proposed model was used to predict both cancer incidence rates and counts at the county level. The standard JPM and the proposed method were compared through a validation study. The proposed method outperformed the standard JPM for almost all cancer sites, especially for moderate or rare cancer sites and for counties with small population sizes. As an application, we predicted county-level prostate cancer incidence rates and counts for the year 2011 in Connecticut.
authors.affiliation: Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, MA, 20892, U.S.A.
authors.forename: Binbing
authors.initials: B
authors.lastname: Yu
mesh_terms: D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000453:epidemiology*; D018426:SEER Program; D014481:United States / Q000453:epidemiology
keywords: SEER; ZIP model; cancer incidence; joinpoint model; spatial correlation