Department of Economics, School of Economics, Minzu University of China, Beijing, China.
Research Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China.
Front Public Health. 2022 Oct 4;10:954247. doi: 10.3389/fpubh.2022.954247. eCollection 2022.
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.
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.
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.
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.
目前，乳腺癌（BC）在全球恶性肿瘤中排名靠前，引起了广泛关注。与传统的 BC 生物学决定因素分析相比，本研究关注宏观因素，包括夜间光照（LAN）、PM2.5、人均消费支出、经济密度、人口密度和医疗床位数量，为政府实施 BC 干预提供目标。
本研究选取了 2013 年至 2016 年中国的 182 个地级市作为样本。采用时空加权回归（GTWR）模型描述 BC 规模与宏观因素之间的时空相关性。
结果表明，GTWR 模型可以更好地揭示时空变化。在时间维度上，各变量回归系数的波动具有显著意义。在空间维度上，LAN、人均消费支出、人口密度和医疗床位数量的正影响从西向东逐渐增加，PM2.5 的正系数从北向南逐渐增加。经济密度的负影响从西向东逐渐增加。
各变量随时间波动的程度表明政府需要持续关注 BC 预防。空间异质性特征也促使政府关注中国东部和西部或中国南部和北部的不同宏观指标。换句话说，我们的研究有助于推动政府关注关键地区，并采取有针对性的措施来遏制 BC 的快速增长。
