Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors

Abstract

Background: Currently, breast cancer (BC) is ranked among the top malignant tumors in the world, and has attracted widespread attention. Compared with the traditional analysis on biological determinants of BC, this study focused on macro factors, including light at night (LAN), PM2.5, per capita consumption expenditure, economic density, population density, and number of medical beds, to provide targets for the government to implement BC interventions.

Methods: A total of 182 prefecture-level cities in China from 2013 to 2016 were selected as the sample of the study. The geographically and temporally weighted regression (GTWR) model was adopted to describe the spatiotemporal correlation between the scale of BC and macro factors.

Results: The results showed that the GTWR model can better reveal the spatiotemporal variation. In the temporal dimension, the fluctuations of the regression coefficients of each variable were significant. In the spatial dimension, the positive impacts of LAN, per capita consumption expenditure, population density and number of medical beds gradually increased from west to east, and the positive coefficient of PM2.5 gradually increased from north to south. The negative impact of economic density gradually increased from west to east.

Conclusion: The fact that the degree of effect of each variable fluctuates over time reminds the government to pay continuous attention to BC prevention. The spatial heterogeneity features also urge the government to focus on different macro indicators in eastern and western China or southern and northern China. In other words, our research helps drive the government to center on key regions and take targeted measures to curb the rapid growth of BC.

Keywords: breast cancer scale; geographically and temporally weighted regression model; light at night; macro factors; temporal and spatial heterogeneity.

Figure 1

The scale of breast cancer in 182 Chinese prefectural-level units during 2013–2016.

Figure 2

Temporal variation in the estimated coefficients. (A–F) represent the coefficient variations of LAN, PM2.5, per capita consumption expenditure, economic density, population density, and number of medical beds, respectively.

Figure 3

Spatial distribution of the average coefficients for LAN.

Figure 4

Spatial distribution of the average coefficients for other explanatory variables. (A–E) represent the spatial distribution of the average coefficients of PM2.5, per capita consumption expenditure, economic density, population density, and number of medical beds, respectively.