Area Deprivation and Liver Cancer Prevalence in Shenzhen, China: A Spatial Approach Based on Social Indicators

Abstract

Cancer has become an alarming threat to human health and well-being worldwide. Examining the social determinants of cancer prevalence should effectively inform the practices and strategies on cancer treatment and prevention. However, rather few studies have conducted in this regard for developing countries. This paper attempts to characterize the association between area deprivation and liver cancer prevalence using a case of Shenzhen, China. Data from 2009 to 2011 provided by Shenzhen's Health Information Center are used to calculate the incidence rate of liver cancer at district level. An area deprivation index (ADI) at district level is established by integrating 13 indicators of 5 domains (income, employment, education, housing and demography). The weight for each indicator is determined by two typical subjective methods (AHP and fuzzy AHP) and two common objective methods (Entropy and Coefficient Variation). Quantitative comparisons indicate that the four methods are highly consistent though they assign different weight to the indicators. Spatial lag regression identifies significantly positive linear relationships between ADIs and liver cancer incidence rate from 2009 and 2010. It suggests that greater possibility of liver cancer prevalence would be expected in districts of higher social deprivation. The results also denote that the obtained relationships are insensitive to weight determination method and temporal dimension. Our study demonstrates that spatial autocorrelation should be incorporated for better understanding the association between area deprivation and liver cancer prevalence at district level. This paper provides some new insights into social indicators research.

Characterizing the inequalities in urban public green space provision in Shenzhen, China

“…Following the prior literature (Shanahan et al, 2014b;Weng et al, 2016), this paper describes the district sociodemographics from five aspects: population structure, employment, income, education, and housing arrangements. In order to guarantee low redundancy, I follow the procedure proposed by Su, Wang, Luo, Mai, and Pu (2014b) that uses the Pearson's correlation analysis and principal component analysis with 99% confidence interval.…”

“…In order to guarantee low redundancy, I follow the procedure proposed by Su, Wang, Luo, Mai, and Pu (2014b) that uses the Pearson's correlation analysis and principal component analysis with 99% confidence interval. Principal component analysis has been evidenced to be promising in reduce the redundancy indicators (Abson, Dougill, & Stringer, 2012;Weng et al, 2016;You, 2016). I finally obtain 10 variables and all the original data are provided by Shenzhen Census Bureau.…”

“…It accounts for the negative correlation between the quality index and the income variable. The float population usually reside in the outskirts with poor housing conditions in Shenzhen (Weng et al, 2016). Different from the central districts, PGSs in the outskirts are generally in the form of park, garden and forest.…”

“…However, due to the lack of fine scale observation data, the intra-urban social inequities in PGSs have been rarely reported. Shenzhen, China's first Special Economic Zone, is reputed as the pioneering city in economic reform (Weng, Pi, Tan, & Su, 2016). It bothers with Hong Kong and locates within the Guangdong Province in southern China ( Fig.…”

Spatial Evolution of Producer Service Sectors and Its Influencing Factors in Cities: A Case Study of Hangzhou, China

“…R 2 , Log likelihood and Akaike information criterion (AIC) are adopted to compare the model performances. The model with greater R 2 and log likelihood values and a lower AIC indicates better performance (Weng et al, 2016;Voss et al, 2006) [57,58], and is selected for regression analysis. If there is a conflict between the indication given by Moran's I and that given by the Lagrange multiplier test statistics (Anselin, 2005), we also select the model with best model performance for regression analysis.…”