Spatial analysis of cancers in Taiwan
Date Issued
2007
Date
2007
Author(s)
Hu, Li-Chun
DOI
zh-TW
Abstract
Cancer is the leading causes of death in Taiwan. In epidemiology, disease maps are often used to explore the causes of diseases. Visual interpretation of these maps, however, usually leads to concerns of imprecision. This research seeks to take advantage of recent developments in spatial statistics to improve researchers’ exploration of the spatial characteristics and patterns revealed by data.
The case in discussion is whether the distribution of female and male cancers exhibit spatial clustering by Moran’s I and Local Indicator of Spatial Association (LISA, local Moran) and to further understand the spatial variations. The dataset used in this study is from the statistical data of electric atlas of cancer mortality and incidence published by Taiwan’s Department of Health in 2003, which included age-standardized incidence and mortality rates. As shown in the results, the five main incidence and mortality rates of cancers over the past years demonstrated a spatial clustering characteristic. Spatially, areas of high incidence rates are usually of high mortality rate. The incidence and mortality rate of both male and female with stomach cancer (ICD151) are clustered in the east, the incidence rate cluster and mortality rate of male and female with colon, rectum, rectosigmoid junction & anus cancer (ICD153-154) tend to be relatively close, while the incidence and mortality rate of the population with lip, oral cavity (ICD140,141,143-146,148-149) will vary according to sex.
The characteristic of spatial cluster can reveal whether the pattern correlates with socioeconomic differences in the area. Using the incidence of female with colorectal cancers and mortality of male with stomach cancer as examples, these cases show a clear characteristic of spatial clustering relevant to the socioeconomic factors of the area. Utilizing ordinary least squares model and spatial error regression model, this research found that other than socioeconomic factors, integrating spatial dependency is also important in tracing the causes of spatial clusters. The spatial error regression model also took into consideration of the fact that factors effecting cancer distribution are spatially dependent. The result has proven the model to be much more robust and effective in parameter estimations.
Subjects
空間分析
空間自相關
空間迴歸分析
醫學地理
癌症地圖
spatial analysis
spatial autocorrelation
spatial regression analysis
medical geography
cancer map
SDGs
Type
thesis
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