Mather Frances J, Chen Vivien W, Morgan Leslie H, Correa Catherine N, Shaffer Jeffrey G, Srivastav Sudesh K, Rice Janet C, Blount George, Swalm Christopher M, Wu Xiaocheng, Scribner Richard A
Department of Biostatistics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana 70112, USA.
Am J Prev Med. 2006 Feb;30(2 Suppl):S88-100. doi: 10.1016/j.amepre.2005.09.012.
State central cancer registries are often asked to respond to questions about the spatial distribution of cancer cases. Spatial analysis methods and technology are evolving rapidly, and can be a considerable challenge to registries that do not have staff with training in this area. The purpose of this article is to describe a general methodological approach that potentially might be a starting point for many cancer registry spatial analyses at the county level.
Prostate cancer incident cases (N=31,159) from the Louisiana Tumor Registry from 1988 to 1999 were used for illustrative purposes. To explore spatio-temporal patterns, analyses focused on four time periods, each 3 years in length: 1998-1990, 1991-1993, 1994-1996, and 1997-1999. For each time period, race-specific (white and black), direct age-adjusted incidence rates and indirect standardized incidence ratios (SIRs) were calculated, smoothed using Bayesian methods, and assessed for evidence of spatial autocorrelation using global and local Moran's I. Hierarchical generalized linear models (HGLM) were fitted to identify significant covariates. Clusters of elevated and lower rates were identified using a spatial scan statistic (SaTScan).
Temporal trends in SIRs in both race groups were consistent with the introduction of prostate specific antigen (PSA) testing in Louisiana during the late 1980s and early 1990s, but possibly with a lag in black males. Clusters of lower than expected values were observed for white males in the central (p=0.001) and southeastern coastal areas (p=0.001), and to a greater extent for black males in the central (p=0.001), southwestern and southeastern coastal parishes (p=0.001).
Mapping disease occurrence by time period is an effective way to explore spatio-temporal patterns. HGLM models and software are available to control for covariates and for unstructured and spatially structured variability that may confound spatial variability patterns.
州中央癌症登记处经常被要求回答有关癌症病例空间分布的问题。空间分析方法和技术正在迅速发展，对于那些没有受过该领域培训的工作人员的登记处来说，这可能是一个相当大的挑战。本文的目的是描述一种通用的方法，这可能是许多县级癌症登记处空间分析的起点。
以1988年至1999年路易斯安那肿瘤登记处的前列腺癌发病病例（N = 31,159）为例进行说明。为了探索时空模式，分析集中在四个时间段，每个时间段为3年：1998 - 1990年、1991 - 1993年、1994 - 1996年和1997 - 1999年。对于每个时间段，计算特定种族（白人和黑人）的直接年龄调整发病率和间接标准化发病率（SIR），使用贝叶斯方法进行平滑处理，并使用全局和局部莫兰指数评估空间自相关的证据。拟合分层广义线性模型（HGLM）以识别显著的协变量。使用空间扫描统计量（SaTScan）识别发病率升高和降低的聚集区。
两个种族组的SIR时间趋势与20世纪80年代末和90年代初路易斯安那州引入前列腺特异性抗原（PSA）检测一致，但黑人男性可能存在滞后。在中部（p = 0.001）和东南部沿海地区（p = 0.001）观察到白人男性低于预期值的聚集区，在中部（p = 0.001）、西南部和东南部沿海教区（p = 0.001），黑人男性的聚集区更为明显。
按时间段绘制疾病发生情况是探索时空模式的有效方法。HGLM模型和软件可用于控制协变量以及可能混淆空间变异模式的非结构化和空间结构化变异。
