Lung cancer mortality clusters in urban and rural areas of Shandong Province, China: A spatial scan statistical analysis

Abstract

Objective: To detect the spatial distribution and high-risk clusters of lung cancer mortality for the years 2011-2013 in Shandong Province, China. The urban-rural differences in the spatial distribution and clusters of disease mortality were also examined.Methods: Lung cancer mortality data were obtained from Shandong Death Registration System during 2011-2013, and were adjusted with the underreporting rate. The purely spatial scan statistics method was carried out using a discrete Poisson model. Results: Seven significant spatial clusters of high mortality of lung cancer were detected in Shandong Province at the county level, mainly in the eastern, northern, central, and southwestern regions. The spatial distributions were different between urban and rural populations. Population aging influenced the distribution of lung cancer clusters for the urban and rural eastern region residents. Conclusions:This study provided evidence for the presence of clusters of lung cancer mortality in Shandong, China, and found urban-rural differences in the clusters, which might be helpful in developing effective strategies to control lung cancer in different areas. K E Y W O R D Slung cancer, mortality, Shandong, spatial scan statistics

Lung Cancer: A Nationwide Study to Characterize Sex Differences, Incidence, and Spatial Patterns in Portugal

“…This analysis was not conducted for the islands. Statistical significance of clustering was based on Monte Carlo hypothesis testing by comparing the likelihood ratio statistic test from the observed dataset with the statistic test from 999 random datasets generated under the null hypothesis of no clustering (10). We used the crude number of cases per municipality because the objective was to identify real critical areas independently of modifiable or non-modifiable (age-sex populations distribution) risk factors.…”

“…The use of spatial statistical analysis is an effective method to understand geographic patterns of disease and to detect spatial clusters based on smaller units such as municipalities (10). This type of geographic information is important for planning cancer control and prevention activities that need to be adjusted to the different profiles identified.…”

“…The main strength of our study was the use of populationbased data which originated from cancer registries that combined provided nationwide coverage. Additionally, cluster identification and spatial analysis were based on smaller administrative units that can help to outline regional disparities that could be missed or disregarded if a simple urban versus rural approach was used to observe incidence rates spatially in Portugal (10).…”