A study of the breast cancer dynamics in North Carolina.
This work is concerned with the study of breast cancer incidence in the State of North Carolina. Methodologically, the current analysis illustrates the importance of spatiotemporal random field modelling and introduces a mode of reasoning that is based on a combination of inductive and deductive processes. The composite space/time analysis utilizes the variability characteristics of incidence and the mathematical features of the random field model to fit it to the data. The analysis is significantly general and can efficiently represent non-homogeneous and non-stationary characteristics of breast cancer variation. Incidence predictions are produced using data at the same time period as well as data from other time periods and disease registries. The random field provides a rigorous and systematic method for generating detailed maps, which offer a quantitative description of the incidence variation from place to place and from time to time, together with a measure of the accuracy of the incidence maps. Spatiotemporal mapping accounts for the geographical locations and the time instants of the incidence observations, which is not usually the case with most empirical Bayes methods. It is also more accurate than purely spatial statistics methods, and can offer valuable information about the breast cancer risk and dynamics in North Carolina. Field studies could be initialized in high-rate areas identified by the maps in an effort to uncover environmental or life-style factors that might be responsible for the high risk rates. Also, the incidence maps can help elucidate causal mechanisms, explain disease occurrences at a certain scale, and offer guidance in health management and administration.
Christakos
G
G
Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill 27599-7400, USA.
Lai
J J
JJ
D001943:Breast Neoplasms / Q000453:epidemiology*; D015984:Causality; D003430:Cross-Sectional Studies; D001330:Electronic Data Processing; D005260:Female; D005544:Forecasting; D006801:Humans; D015994:Incidence; D008137:Longitudinal Studies; D015233:Models, Statistical; D009657:North Carolina / Q000453:epidemiology; D012042:Registries / Q000706:statistics & numerical data; D013029:Space-Time Clustering; D013269:Stochastic Processes; D013997:Time Factors; D019371:Topography, Medical / Q000379:methods*