Hierarchical modeling and other spatial analyses in prostate cancer incidence data.

BACKGROUND: 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. METHODS: 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). RESULTS: 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). CONCLUSIONS: 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.

Occurrence and distribution of Anopheles (Diptera: Culicidae) larval habitats on land cover change sites in urban Kisumu and urban Malindi, Kenya.

A multitemporal, land use land cover (LULC) classification dataset incorporating distributions of mosquito larval habitats was produced in ERDAS Imagine using the combined images from the Multispectral Thermal Imager (MTI) at 5 m spatial resolution from 2001 with Thematic Mapper-classification data at 28.5 m spatial resolution from 1987 and 1989 for Kisumu and Malindi, Kenya. Total LULC change for Kisumu over 14 yr was 30.2%. Total LULC change for Malindi over 12 yr was 30.6%. Of those areas in which change was detected, the LULC change for Kisumu was 72.5% for nonurban to urban, 21.7% urban to nonurban, 0.4% urban to water, 4.5% water to urban, and 0.9% water to nonurban. The proportion of LULC change for Malindi was 93.5% for nonurban to urban, 5.9% urban to nonurban, 0.2% urban to water, 0.3% nonurban to water, and 0.1% water to urban. A grid (270 m x 270 m cells) was overlaid over the maps stratifying grid cells based on drainage and planning. Of 84 aquatic habitats in Kisumu, 32.1% were located in LULC change sites and 67.9% were located in LULC nonchange sites. Of 170 aquatic habitats in Malindi, 26.5% were located in LULC change sites and 73.5% were located in LULC nonchange sites. The most abundant LULC change per strata with anopheline habitats was unplanned and poorly drained. Ditches and puddles in Kisumu and car tracks in Malindi displayed the highest number of anopheline larval habitats for all LULC change sites. The proportion of site positive aquatic habitats for anopheline larvae was higher in LULC change sites than for LULC nonchange sites for Kisumu. This evidence suggests LULC change can influence anopheline larval habitat distribution.